publications-on-drone-detection-analysis.ipynb

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   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "In this notebook I will analyze the amount of publications on the topic of \"***drone detection***\" in a public dataset called \"***Google Patents***\" and make a graph of that data. \n",
    "\n",
    "It goes without saying, that by using simple SQL queries I can only achieve certain levels of *accuracy* in finding the right publications. But what I could try to do is make shifts in how strict my queries are in sorting out the right contents. By using more keywords I could get more results for the plot and many of them would be relevant, but the accuracy will definitely decrease. Or I could remove most keywords and get a near certain accuracy, but I would get very few points for my graph, making it less convincing and thus less valuable.\n",
    "\n",
    "So, as the first step I'm importing a module called BigQueryHelper as was the task:"
   ]
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    {
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     "text": [
      "Using Kaggle's public dataset BigQuery integration.\n"
     ]
    }
   ],
   "source": [
    "# Start by importing the bq_helper module and calling on the specific active_project and dataset_name for the BigQuery dataset.\n",
    "\n",
    "import bq_helper\n",
    "from bq_helper import BigQueryHelper\n",
    "# https://www.kaggle.com/sohier/introduction-to-the-bq-helper-package\n",
    "\n",
    "patents = bq_helper.BigQueryHelper(active_project=\"patents-public-data\",\n",
    "                                   dataset_name=\"patents\")"
   ]
  },
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   "source": [
    "Next are the tables in the given data table. We will be working with 'publications', since it contains all the data combined."
   ]
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     "text": [
      "Using Kaggle's public dataset BigQuery integration.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['publications',\n",
       " 'publications_201710',\n",
       " 'publications_201802',\n",
       " 'publications_201809',\n",
       " 'publications_201903',\n",
       " 'publications_201909',\n",
       " 'publications_201912',\n",
       " 'publications_202004',\n",
       " 'publications_202007',\n",
       " 'publications_202101',\n",
       " 'publications_202105',\n",
       " 'publications_202111',\n",
       " 'publications_202204',\n",
       " 'publications_202208',\n",
       " 'publications_202212']"
      ]
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     "metadata": {},
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   ],
   "source": [
    "# View table names under the patents data table\n",
    "bq_assistant = BigQueryHelper(\"patents-public-data\", \"patents\")\n",
    "bq_assistant.list_tables()"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "To demonstrate how the original data looks, I will request for a few publications from there. I'll also remove restrictions beforehand, because Jupyter Notebook will restrict it to only 20 columns otherwise."
   ]
  },
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       "                                                 cpc  fi fterm locarno  \\\n",
       "0  [{'code': 'H04L63/205', 'inventive': False, 'f...  []    []      []   \n",
       "1  [{'code': 'A61P9/00', 'inventive': True, 'firs...  []    []      []   \n",
       "2  [{'code': 'B23K35/404', 'inventive': True, 'fi...  []    []      []   \n",
       "\n",
       "                                            citation parent  \\\n",
       "0  [{'publication_number': 'US-5634122-A', 'appli...     []   \n",
       "1  [{'publication_number': 'EP-0017438-A1', 'appl...     []   \n",
       "2  [{'publication_number': 'GB-502823-A', 'applic...     []   \n",
       "\n",
       "                                               child entity_status art_unit  \n",
       "0                                                 []         large     2493  \n",
       "1  [{'publication_number': '', 'application_numbe...         large     1624  \n",
       "2                                                 []         large     3742  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_rows', None)\n",
    "pd.set_option('display.max_columns', None)\n",
    "# View the first three rows of the publications data table\n",
    "bq_assistant.head(\"publications\", num_rows=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b15aa84",
   "metadata": {
    "papermill": {
     "duration": 0.008338,
     "end_time": "2023-04-21T07:04:24.287394",
     "exception": false,
     "start_time": "2023-04-21T07:04:24.279056",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "As a sidenote I'll demonstrate all the columns in the database. Altough it's important, it doesn't really affect the topic of my research."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "569567d8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-21T07:04:24.308743Z",
     "iopub.status.busy": "2023-04-21T07:04:24.307946Z",
     "iopub.status.idle": "2023-04-21T07:04:24.435441Z",
     "shell.execute_reply": "2023-04-21T07:04:24.434034Z"
    },
    "papermill": {
     "duration": 0.141382,
     "end_time": "2023-04-21T07:04:24.438029",
     "exception": false,
     "start_time": "2023-04-21T07:04:24.296647",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/src/bq-helper/bq_helper.py:63: FutureWarning: The pandas.np module is deprecated and will be removed from pandas in a future version. Import numpy directly instead\n",
      "  'fields': pd.np.nan,\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>type</th>\n",
       "      <th>mode</th>\n",
       "      <th>description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>publication_number</td>\n",
       "      <td>STRING</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>Patent publication number (DOCDB compatible), ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>application_number</td>\n",
       "      <td>STRING</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>Patent application number (DOCDB compatible), ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>country_code</td>\n",
       "      <td>STRING</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>Country code, eg: 'US', 'EP', etc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>kind_code</td>\n",
       "      <td>STRING</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>Kind code, indicating application, grant, sear...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>application_kind</td>\n",
       "      <td>STRING</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>High-level kind of the application: A=patent; ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>application_number_formatted</td>\n",
       "      <td>STRING</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>Application number, formatted to the patent of...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>pct_number</td>\n",
       "      <td>STRING</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>PCT number for this application if it was part...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>family_id</td>\n",
       "      <td>STRING</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>Family ID (simple family). Grouping on family ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>spif_publication_number</td>\n",
       "      <td>STRING</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>SPIF standard (spif.group) publication number,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>spif_application_number</td>\n",
       "      <td>STRING</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>SPIF standard (spif.group) application number,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>title_localized</td>\n",
       "      <td>RECORD</td>\n",
       "      <td>REPEATED</td>\n",
       "      <td>The publication titles in different languages</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>title_localized.text</td>\n",
       "      <td>STRING</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>Localized text</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>title_localized.language</td>\n",
       "      <td>STRING</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>Two-letter language code for this text</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>title_localized.truncated</td>\n",
       "      <td>BOOLEAN</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>Is this text truncated?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>abstract_localized</td>\n",
       "      <td>RECORD</td>\n",
       "      <td>REPEATED</td>\n",
       "      <td>The publication abstracts in different languages</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>abstract_localized.text</td>\n",
       "      <td>STRING</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>Localized text</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>abstract_localized.language</td>\n",
       "      <td>STRING</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>Two-letter language code for this text</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>abstract_localized.truncated</td>\n",
       "      <td>BOOLEAN</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>Is this text truncated?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>claims_localized</td>\n",
       "      <td>RECORD</td>\n",
       "      <td>REPEATED</td>\n",
       "      <td>For US publications only, the claims in plain ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>claims_localized.text</td>\n",
       "      <td>STRING</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>Localized text</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>claims_localized.language</td>\n",
       "      <td>STRING</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>Two-letter language code for this text</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>claims_localized.truncated</td>\n",
       "      <td>BOOLEAN</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>Is this text truncated?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>claims_localized_html</td>\n",
       "      <td>RECORD</td>\n",
       "      <td>REPEATED</td>\n",
       "      <td>For US publications only, the claims in HTML</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>claims_localized_html.text</td>\n",
       "      <td>STRING</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>Localized text</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>claims_localized_html.language</td>\n",
       "      <td>STRING</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>Two-letter language code for this text</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>claims_localized_html.truncated</td>\n",
       "      <td>BOOLEAN</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>Is this text truncated?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>description_localized</td>\n",
       "      <td>RECORD</td>\n",
       "      <td>REPEATED</td>\n",
       "      <td>For US publications only, the description in p...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>description_localized.text</td>\n",
       "      <td>STRING</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>Localized text</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>description_localized.language</td>\n",
       "      <td>STRING</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>Two-letter language code for this text</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>description_localized.truncated</td>\n",
       "      <td>BOOLEAN</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>Is this text truncated?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>description_localized_html</td>\n",
       "      <td>RECORD</td>\n",
       "      <td>REPEATED</td>\n",
       "      <td>For US publications only, the description in H...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>description_localized_html.text</td>\n",
       "      <td>STRING</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>Localized text</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>description_localized_html.language</td>\n",
       "      <td>STRING</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>Two-letter language code for this text</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>description_localized_html.truncated</td>\n",
       "      <td>BOOLEAN</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>Is this text truncated?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>publication_date</td>\n",
       "      <td>INTEGER</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>The publication date.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>filing_date</td>\n",
       "      <td>INTEGER</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>The filing date.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>grant_date</td>\n",
       "      <td>INTEGER</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>The grant date, or 0 if not granted.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>priority_date</td>\n",
       "      <td>INTEGER</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>The earliest priority date from the priority c...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>priority_claim</td>\n",
       "      <td>RECORD</td>\n",
       "      <td>REPEATED</td>\n",
       "      <td>The application numbers of the priority claims...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>priority_claim.publication_number</td>\n",
       "      <td>STRING</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>Same as [publication_number]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>priority_claim.application_number</td>\n",
       "      <td>STRING</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>Same as [application_number]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>priority_claim.npl_text</td>\n",
       "      <td>STRING</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>Free-text citation (non-patent literature, etc).</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>priority_claim.type</td>\n",
       "      <td>STRING</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>The type of reference (see parent field for va...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>priority_claim.category</td>\n",
       "      <td>STRING</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>The category of reference (see parent field fo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>priority_claim.filing_date</td>\n",
       "      <td>INTEGER</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>The filing date.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>inventor</td>\n",
       "      <td>STRING</td>\n",
       "      <td>REPEATED</td>\n",
       "      <td>The inventors.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>inventor_harmonized</td>\n",
       "      <td>RECORD</td>\n",
       "      <td>REPEATED</td>\n",
       "      <td>The harmonized inventors and their countries.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>inventor_harmonized.name</td>\n",
       "      <td>STRING</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>Name</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>inventor_harmonized.country_code</td>\n",
       "      <td>STRING</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>The two-letter country code</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>assignee</td>\n",
       "      <td>STRING</td>\n",
       "      <td>REPEATED</td>\n",
       "      <td>The assignees/applicants.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>assignee_harmonized</td>\n",
       "      <td>RECORD</td>\n",
       "      <td>REPEATED</td>\n",
       "      <td>The harmonized assignees and their countries.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>assignee_harmonized.name</td>\n",
       "      <td>STRING</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>Name</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>assignee_harmonized.country_code</td>\n",
       "      <td>STRING</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>The two-letter country code</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>examiner</td>\n",
       "      <td>RECORD</td>\n",
       "      <td>REPEATED</td>\n",
       "      <td>The examiner of this publication and their cou...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>examiner.name</td>\n",
       "      <td>STRING</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>Name</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>examiner.department</td>\n",
       "      <td>STRING</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>The examiner's department</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>examiner.level</td>\n",
       "      <td>STRING</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>The examiner's level</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>uspc</td>\n",
       "      <td>RECORD</td>\n",
       "      <td>REPEATED</td>\n",
       "      <td>The US Patent Classification (USPC) codes.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>uspc.code</td>\n",
       "      <td>STRING</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>Classification code</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>uspc.inventive</td>\n",
       "      <td>BOOLEAN</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>Is this classification inventive/main?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>uspc.first</td>\n",
       "      <td>BOOLEAN</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>Is this classification the first/primary?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>uspc.tree</td>\n",
       "      <td>STRING</td>\n",
       "      <td>REPEATED</td>\n",
       "      <td>The full classification tree from the root to ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>ipc</td>\n",
       "      <td>RECORD</td>\n",
       "      <td>REPEATED</td>\n",
       "      <td>The International Patent Classification (IPC) ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>ipc.code</td>\n",
       "      <td>STRING</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>Classification code</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>ipc.inventive</td>\n",
       "      <td>BOOLEAN</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>Is this classification inventive/main?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>ipc.first</td>\n",
       "      <td>BOOLEAN</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>Is this classification the first/primary?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>ipc.tree</td>\n",
       "      <td>STRING</td>\n",
       "      <td>REPEATED</td>\n",
       "      <td>The full classification tree from the root to ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>cpc</td>\n",
       "      <td>RECORD</td>\n",
       "      <td>REPEATED</td>\n",
       "      <td>The Cooperative Patent Classification (CPC) co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>cpc.code</td>\n",
       "      <td>STRING</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>Classification code</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>cpc.inventive</td>\n",
       "      <td>BOOLEAN</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>Is this classification inventive/main?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>cpc.first</td>\n",
       "      <td>BOOLEAN</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>Is this classification the first/primary?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>cpc.tree</td>\n",
       "      <td>STRING</td>\n",
       "      <td>REPEATED</td>\n",
       "      <td>The full classification tree from the root to ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>fi</td>\n",
       "      <td>RECORD</td>\n",
       "      <td>REPEATED</td>\n",
       "      <td>The FI classification codes.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>fi.code</td>\n",
       "      <td>STRING</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>Classification code</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>fi.inventive</td>\n",
       "      <td>BOOLEAN</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>Is this classification inventive/main?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>fi.first</td>\n",
       "      <td>BOOLEAN</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>Is this classification the first/primary?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>fi.tree</td>\n",
       "      <td>STRING</td>\n",
       "      <td>REPEATED</td>\n",
       "      <td>The full classification tree from the root to ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>fterm</td>\n",
       "      <td>RECORD</td>\n",
       "      <td>REPEATED</td>\n",
       "      <td>The F-term classification codes.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>fterm.code</td>\n",
       "      <td>STRING</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>Classification code</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>fterm.inventive</td>\n",
       "      <td>BOOLEAN</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>Is this classification inventive/main?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>fterm.first</td>\n",
       "      <td>BOOLEAN</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>Is this classification the first/primary?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>fterm.tree</td>\n",
       "      <td>STRING</td>\n",
       "      <td>REPEATED</td>\n",
       "      <td>The full classification tree from the root to ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>locarno</td>\n",
       "      <td>RECORD</td>\n",
       "      <td>REPEATED</td>\n",
       "      <td>The Locarno classification codes.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>locarno.code</td>\n",
       "      <td>STRING</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>Classification code</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>locarno.inventive</td>\n",
       "      <td>BOOLEAN</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>Is this classification inventive/main?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>locarno.first</td>\n",
       "      <td>BOOLEAN</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>Is this classification the first/primary?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>locarno.tree</td>\n",
       "      <td>STRING</td>\n",
       "      <td>REPEATED</td>\n",
       "      <td>The full classification tree from the root to ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>citation</td>\n",
       "      <td>RECORD</td>\n",
       "      <td>REPEATED</td>\n",
       "      <td>The citations of this publication. Category is...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>citation.publication_number</td>\n",
       "      <td>STRING</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>Same as [publication_number]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>citation.application_number</td>\n",
       "      <td>STRING</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>Same as [application_number]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>citation.npl_text</td>\n",
       "      <td>STRING</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>Free-text citation (non-patent literature, etc).</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>citation.type</td>\n",
       "      <td>STRING</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>The type of reference (see parent field for va...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>citation.category</td>\n",
       "      <td>STRING</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>The category of reference (see parent field fo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>citation.filing_date</td>\n",
       "      <td>INTEGER</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>The filing date.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>parent</td>\n",
       "      <td>RECORD</td>\n",
       "      <td>REPEATED</td>\n",
       "      <td>The parent applications (continuations, etc). ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>parent.publication_number</td>\n",
       "      <td>STRING</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>Same as [publication_number]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>parent.application_number</td>\n",
       "      <td>STRING</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>Same as [application_number]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>parent.npl_text</td>\n",
       "      <td>STRING</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>Free-text citation (non-patent literature, etc).</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>parent.type</td>\n",
       "      <td>STRING</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>The type of reference (see parent field for va...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>parent.category</td>\n",
       "      <td>STRING</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>The category of reference (see parent field fo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>parent.filing_date</td>\n",
       "      <td>INTEGER</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>The filing date.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>child</td>\n",
       "      <td>RECORD</td>\n",
       "      <td>REPEATED</td>\n",
       "      <td>The child applications (continuations, etc). C...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>child.publication_number</td>\n",
       "      <td>STRING</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>Same as [publication_number]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>child.application_number</td>\n",
       "      <td>STRING</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>Same as [application_number]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>child.npl_text</td>\n",
       "      <td>STRING</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>Free-text citation (non-patent literature, etc).</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>child.type</td>\n",
       "      <td>STRING</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>The type of reference (see parent field for va...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>child.category</td>\n",
       "      <td>STRING</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>The category of reference (see parent field fo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>child.filing_date</td>\n",
       "      <td>INTEGER</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>The filing date.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>entity_status</td>\n",
       "      <td>STRING</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>The USPTO entity status (large, small).</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>art_unit</td>\n",
       "      <td>STRING</td>\n",
       "      <td>NULLABLE</td>\n",
       "      <td>The USPTO art unit performing the examination ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     name     type      mode  \\\n",
       "0                      publication_number   STRING  NULLABLE   \n",
       "1                      application_number   STRING  NULLABLE   \n",
       "2                            country_code   STRING  NULLABLE   \n",
       "3                               kind_code   STRING  NULLABLE   \n",
       "4                        application_kind   STRING  NULLABLE   \n",
       "5            application_number_formatted   STRING  NULLABLE   \n",
       "6                              pct_number   STRING  NULLABLE   \n",
       "7                               family_id   STRING  NULLABLE   \n",
       "8                 spif_publication_number   STRING  NULLABLE   \n",
       "9                 spif_application_number   STRING  NULLABLE   \n",
       "10                        title_localized   RECORD  REPEATED   \n",
       "11                   title_localized.text   STRING  NULLABLE   \n",
       "12               title_localized.language   STRING  NULLABLE   \n",
       "13              title_localized.truncated  BOOLEAN  NULLABLE   \n",
       "14                     abstract_localized   RECORD  REPEATED   \n",
       "15                abstract_localized.text   STRING  NULLABLE   \n",
       "16            abstract_localized.language   STRING  NULLABLE   \n",
       "17           abstract_localized.truncated  BOOLEAN  NULLABLE   \n",
       "18                       claims_localized   RECORD  REPEATED   \n",
       "19                  claims_localized.text   STRING  NULLABLE   \n",
       "20              claims_localized.language   STRING  NULLABLE   \n",
       "21             claims_localized.truncated  BOOLEAN  NULLABLE   \n",
       "22                  claims_localized_html   RECORD  REPEATED   \n",
       "23             claims_localized_html.text   STRING  NULLABLE   \n",
       "24         claims_localized_html.language   STRING  NULLABLE   \n",
       "25        claims_localized_html.truncated  BOOLEAN  NULLABLE   \n",
       "26                  description_localized   RECORD  REPEATED   \n",
       "27             description_localized.text   STRING  NULLABLE   \n",
       "28         description_localized.language   STRING  NULLABLE   \n",
       "29        description_localized.truncated  BOOLEAN  NULLABLE   \n",
       "30             description_localized_html   RECORD  REPEATED   \n",
       "31        description_localized_html.text   STRING  NULLABLE   \n",
       "32    description_localized_html.language   STRING  NULLABLE   \n",
       "33   description_localized_html.truncated  BOOLEAN  NULLABLE   \n",
       "34                       publication_date  INTEGER  NULLABLE   \n",
       "35                            filing_date  INTEGER  NULLABLE   \n",
       "36                             grant_date  INTEGER  NULLABLE   \n",
       "37                          priority_date  INTEGER  NULLABLE   \n",
       "38                         priority_claim   RECORD  REPEATED   \n",
       "39      priority_claim.publication_number   STRING  NULLABLE   \n",
       "40      priority_claim.application_number   STRING  NULLABLE   \n",
       "41                priority_claim.npl_text   STRING  NULLABLE   \n",
       "42                    priority_claim.type   STRING  NULLABLE   \n",
       "43                priority_claim.category   STRING  NULLABLE   \n",
       "44             priority_claim.filing_date  INTEGER  NULLABLE   \n",
       "45                               inventor   STRING  REPEATED   \n",
       "46                    inventor_harmonized   RECORD  REPEATED   \n",
       "47               inventor_harmonized.name   STRING  NULLABLE   \n",
       "48       inventor_harmonized.country_code   STRING  NULLABLE   \n",
       "49                               assignee   STRING  REPEATED   \n",
       "50                    assignee_harmonized   RECORD  REPEATED   \n",
       "51               assignee_harmonized.name   STRING  NULLABLE   \n",
       "52       assignee_harmonized.country_code   STRING  NULLABLE   \n",
       "53                               examiner   RECORD  REPEATED   \n",
       "54                          examiner.name   STRING  NULLABLE   \n",
       "55                    examiner.department   STRING  NULLABLE   \n",
       "56                         examiner.level   STRING  NULLABLE   \n",
       "57                                   uspc   RECORD  REPEATED   \n",
       "58                              uspc.code   STRING  NULLABLE   \n",
       "59                         uspc.inventive  BOOLEAN  NULLABLE   \n",
       "60                             uspc.first  BOOLEAN  NULLABLE   \n",
       "61                              uspc.tree   STRING  REPEATED   \n",
       "62                                    ipc   RECORD  REPEATED   \n",
       "63                               ipc.code   STRING  NULLABLE   \n",
       "64                          ipc.inventive  BOOLEAN  NULLABLE   \n",
       "65                              ipc.first  BOOLEAN  NULLABLE   \n",
       "66                               ipc.tree   STRING  REPEATED   \n",
       "67                                    cpc   RECORD  REPEATED   \n",
       "68                               cpc.code   STRING  NULLABLE   \n",
       "69                          cpc.inventive  BOOLEAN  NULLABLE   \n",
       "70                              cpc.first  BOOLEAN  NULLABLE   \n",
       "71                               cpc.tree   STRING  REPEATED   \n",
       "72                                     fi   RECORD  REPEATED   \n",
       "73                                fi.code   STRING  NULLABLE   \n",
       "74                           fi.inventive  BOOLEAN  NULLABLE   \n",
       "75                               fi.first  BOOLEAN  NULLABLE   \n",
       "76                                fi.tree   STRING  REPEATED   \n",
       "77                                  fterm   RECORD  REPEATED   \n",
       "78                             fterm.code   STRING  NULLABLE   \n",
       "79                        fterm.inventive  BOOLEAN  NULLABLE   \n",
       "80                            fterm.first  BOOLEAN  NULLABLE   \n",
       "81                             fterm.tree   STRING  REPEATED   \n",
       "82                                locarno   RECORD  REPEATED   \n",
       "83                           locarno.code   STRING  NULLABLE   \n",
       "84                      locarno.inventive  BOOLEAN  NULLABLE   \n",
       "85                          locarno.first  BOOLEAN  NULLABLE   \n",
       "86                           locarno.tree   STRING  REPEATED   \n",
       "87                               citation   RECORD  REPEATED   \n",
       "88            citation.publication_number   STRING  NULLABLE   \n",
       "89            citation.application_number   STRING  NULLABLE   \n",
       "90                      citation.npl_text   STRING  NULLABLE   \n",
       "91                          citation.type   STRING  NULLABLE   \n",
       "92                      citation.category   STRING  NULLABLE   \n",
       "93                   citation.filing_date  INTEGER  NULLABLE   \n",
       "94                                 parent   RECORD  REPEATED   \n",
       "95              parent.publication_number   STRING  NULLABLE   \n",
       "96              parent.application_number   STRING  NULLABLE   \n",
       "97                        parent.npl_text   STRING  NULLABLE   \n",
       "98                            parent.type   STRING  NULLABLE   \n",
       "99                        parent.category   STRING  NULLABLE   \n",
       "100                    parent.filing_date  INTEGER  NULLABLE   \n",
       "101                                 child   RECORD  REPEATED   \n",
       "102              child.publication_number   STRING  NULLABLE   \n",
       "103              child.application_number   STRING  NULLABLE   \n",
       "104                        child.npl_text   STRING  NULLABLE   \n",
       "105                            child.type   STRING  NULLABLE   \n",
       "106                        child.category   STRING  NULLABLE   \n",
       "107                     child.filing_date  INTEGER  NULLABLE   \n",
       "108                         entity_status   STRING  NULLABLE   \n",
       "109                              art_unit   STRING  NULLABLE   \n",
       "\n",
       "                                           description  \n",
       "0    Patent publication number (DOCDB compatible), ...  \n",
       "1    Patent application number (DOCDB compatible), ...  \n",
       "2                    Country code, eg: 'US', 'EP', etc  \n",
       "3    Kind code, indicating application, grant, sear...  \n",
       "4    High-level kind of the application: A=patent; ...  \n",
       "5    Application number, formatted to the patent of...  \n",
       "6    PCT number for this application if it was part...  \n",
       "7    Family ID (simple family). Grouping on family ...  \n",
       "8    SPIF standard (spif.group) publication number,...  \n",
       "9    SPIF standard (spif.group) application number,...  \n",
       "10       The publication titles in different languages  \n",
       "11                                      Localized text  \n",
       "12              Two-letter language code for this text  \n",
       "13                             Is this text truncated?  \n",
       "14    The publication abstracts in different languages  \n",
       "15                                      Localized text  \n",
       "16              Two-letter language code for this text  \n",
       "17                             Is this text truncated?  \n",
       "18   For US publications only, the claims in plain ...  \n",
       "19                                      Localized text  \n",
       "20              Two-letter language code for this text  \n",
       "21                             Is this text truncated?  \n",
       "22        For US publications only, the claims in HTML  \n",
       "23                                      Localized text  \n",
       "24              Two-letter language code for this text  \n",
       "25                             Is this text truncated?  \n",
       "26   For US publications only, the description in p...  \n",
       "27                                      Localized text  \n",
       "28              Two-letter language code for this text  \n",
       "29                             Is this text truncated?  \n",
       "30   For US publications only, the description in H...  \n",
       "31                                      Localized text  \n",
       "32              Two-letter language code for this text  \n",
       "33                             Is this text truncated?  \n",
       "34                               The publication date.  \n",
       "35                                    The filing date.  \n",
       "36                The grant date, or 0 if not granted.  \n",
       "37   The earliest priority date from the priority c...  \n",
       "38   The application numbers of the priority claims...  \n",
       "39                        Same as [publication_number]  \n",
       "40                        Same as [application_number]  \n",
       "41    Free-text citation (non-patent literature, etc).  \n",
       "42   The type of reference (see parent field for va...  \n",
       "43   The category of reference (see parent field fo...  \n",
       "44                                    The filing date.  \n",
       "45                                      The inventors.  \n",
       "46       The harmonized inventors and their countries.  \n",
       "47                                                Name  \n",
       "48                         The two-letter country code  \n",
       "49                           The assignees/applicants.  \n",
       "50       The harmonized assignees and their countries.  \n",
       "51                                                Name  \n",
       "52                         The two-letter country code  \n",
       "53   The examiner of this publication and their cou...  \n",
       "54                                                Name  \n",
       "55                           The examiner's department  \n",
       "56                                The examiner's level  \n",
       "57          The US Patent Classification (USPC) codes.  \n",
       "58                                 Classification code  \n",
       "59              Is this classification inventive/main?  \n",
       "60           Is this classification the first/primary?  \n",
       "61   The full classification tree from the root to ...  \n",
       "62   The International Patent Classification (IPC) ...  \n",
       "63                                 Classification code  \n",
       "64              Is this classification inventive/main?  \n",
       "65           Is this classification the first/primary?  \n",
       "66   The full classification tree from the root to ...  \n",
       "67   The Cooperative Patent Classification (CPC) co...  \n",
       "68                                 Classification code  \n",
       "69              Is this classification inventive/main?  \n",
       "70           Is this classification the first/primary?  \n",
       "71   The full classification tree from the root to ...  \n",
       "72                        The FI classification codes.  \n",
       "73                                 Classification code  \n",
       "74              Is this classification inventive/main?  \n",
       "75           Is this classification the first/primary?  \n",
       "76   The full classification tree from the root to ...  \n",
       "77                    The F-term classification codes.  \n",
       "78                                 Classification code  \n",
       "79              Is this classification inventive/main?  \n",
       "80           Is this classification the first/primary?  \n",
       "81   The full classification tree from the root to ...  \n",
       "82                   The Locarno classification codes.  \n",
       "83                                 Classification code  \n",
       "84              Is this classification inventive/main?  \n",
       "85           Is this classification the first/primary?  \n",
       "86   The full classification tree from the root to ...  \n",
       "87   The citations of this publication. Category is...  \n",
       "88                        Same as [publication_number]  \n",
       "89                        Same as [application_number]  \n",
       "90    Free-text citation (non-patent literature, etc).  \n",
       "91   The type of reference (see parent field for va...  \n",
       "92   The category of reference (see parent field fo...  \n",
       "93                                    The filing date.  \n",
       "94   The parent applications (continuations, etc). ...  \n",
       "95                        Same as [publication_number]  \n",
       "96                        Same as [application_number]  \n",
       "97    Free-text citation (non-patent literature, etc).  \n",
       "98   The type of reference (see parent field for va...  \n",
       "99   The category of reference (see parent field fo...  \n",
       "100                                   The filing date.  \n",
       "101  The child applications (continuations, etc). C...  \n",
       "102                       Same as [publication_number]  \n",
       "103                       Same as [application_number]  \n",
       "104   Free-text citation (non-patent literature, etc).  \n",
       "105  The type of reference (see parent field for va...  \n",
       "106  The category of reference (see parent field fo...  \n",
       "107                                   The filing date.  \n",
       "108            The USPTO entity status (large, small).  \n",
       "109  The USPTO art unit performing the examination ...  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View information on all columns in the publications data table\n",
    "bq_assistant.table_schema(\"publications\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b72c8f79",
   "metadata": {
    "papermill": {
     "duration": 0.009724,
     "end_time": "2023-04-21T07:04:24.458013",
     "exception": false,
     "start_time": "2023-04-21T07:04:24.448289",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Then I will write a SQL query which will return publications with the required keywords in them. I used two groups of synonyms, one group for each word in \"drone detection\", so a combination like \"unmanned aircraft identification\" would be found by the query. The more keywords I add, the more results it will give, but at the cost of there being wrong publications mixed in. I could also add more places to be searched, but in this particular query I'm only going through the titles and abstracts of the publications. It's only logical that if nothing about drone detection is remotely mentioned in the title and brief description, then it's highly unlikely that the article is on that topic.\n",
    "\n",
    "The things I'm requesting are self-explanatory. For the purposes of estimating accuracy I'm requesting the \"title_localized\" field, which is a \"ARRAY<STRUCT<text string, language string, truncated boolean>>\" type. Basically, an array of structures, each of them containing a different localization of the publication's title - could be in English, Chinese or any other language. It could also be called a Nested Table, which is why I'm using the UNNEST command. It makes a table from a given array, then I'm selecting every localized text there was in this column and checking it for the keywords that I've set up. I could also make another table for the keywords, but there isn't much time left to hand in the task. I could change it upon a request, though. So, if there is a localized text that fits the bill, the publication proceeds to the local dataframe which I subsequently name \"dataframe\". I know, I'm very creative. Then I'm doing exactly the same thing to the \"abstract_localized\" field, which, I should mention, has exactly the same structure.\n",
    "\n",
    "After that I'm making the actual SQL query using the aforementioned bq_helper module and showing 10 rows from the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "91d05d36",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-21T07:04:24.480376Z",
     "iopub.status.busy": "2023-04-21T07:04:24.479875Z",
     "iopub.status.idle": "2023-04-21T07:04:34.678958Z",
     "shell.execute_reply": "2023-04-21T07:04:34.677881Z"
    },
    "papermill": {
     "duration": 10.213216,
     "end_time": "2023-04-21T07:04:34.681427",
     "exception": false,
     "start_time": "2023-04-21T07:04:24.468211",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/google/cloud/bigquery/client.py:440: UserWarning: Cannot create BigQuery Storage client, the dependency google-cloud-bigquery-storage is not installed.\n",
      "  \"Cannot create BigQuery Storage client, the dependency \"\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>publication_number</th>\n",
       "      <th>title_localized</th>\n",
       "      <th>publication_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>WO-2022033098-A1</td>\n",
       "      <td>[{'text': '一种基于无人机的水稻冠层温度检测方法', 'language': 'z...</td>\n",
       "      <td>20220217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>WO-2018046124-A1</td>\n",
       "      <td>[{'text': 'Véhicule doté d&amp;#39;une fonctionnal...</td>\n",
       "      <td>20180315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>WO-2018121247-A1</td>\n",
       "      <td>[{'text': '控制无人机飞行的方法、装置以及无人机', 'language': 'z...</td>\n",
       "      <td>20180705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>WO-2013111319-A1</td>\n",
       "      <td>[{'text': '無人電気所遠隔監視制御装置用再立上げ装置および方法', 'langua...</td>\n",
       "      <td>20130801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>WO-2012077911-A3</td>\n",
       "      <td>[{'text': '공작기계의 공구 손상 탐지장치 및 공구손상 탐지방법', 'lan...</td>\n",
       "      <td>20120823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45698</th>\n",
       "      <td>CN-202824031-U</td>\n",
       "      <td>[{'text': '宝特瓶粉碎机的清洗结构', 'language': 'zh', 'tr...</td>\n",
       "      <td>20130327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45699</th>\n",
       "      <td>CN-203720977-U</td>\n",
       "      <td>[{'text': '无人值守综合探测系统', 'language': 'zh', 'tru...</td>\n",
       "      <td>20140716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45700</th>\n",
       "      <td>CN-203767529-U</td>\n",
       "      <td>[{'text': '原料厂多料仓小车卸料自动控制系统', 'language': 'zh'...</td>\n",
       "      <td>20140813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45701</th>\n",
       "      <td>CN-205450697-U</td>\n",
       "      <td>[{'text': '一种异物入侵安全防控装置', 'language': 'zh', 't...</td>\n",
       "      <td>20160810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45702</th>\n",
       "      <td>CN-205565677-U</td>\n",
       "      <td>[{'text': '一种电力设备系统电压保护装置', 'language': 'zh', ...</td>\n",
       "      <td>20160907</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>45703 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      publication_number                                    title_localized  \\\n",
       "0       WO-2022033098-A1  [{'text': '一种基于无人机的水稻冠层温度检测方法', 'language': 'z...   \n",
       "1       WO-2018046124-A1  [{'text': 'Véhicule doté d&#39;une fonctionnal...   \n",
       "2       WO-2018121247-A1  [{'text': '控制无人机飞行的方法、装置以及无人机', 'language': 'z...   \n",
       "3       WO-2013111319-A1  [{'text': '無人電気所遠隔監視制御装置用再立上げ装置および方法', 'langua...   \n",
       "4       WO-2012077911-A3  [{'text': '공작기계의 공구 손상 탐지장치 및 공구손상 탐지방법', 'lan...   \n",
       "...                  ...                                                ...   \n",
       "45698     CN-202824031-U  [{'text': '宝特瓶粉碎机的清洗结构', 'language': 'zh', 'tr...   \n",
       "45699     CN-203720977-U  [{'text': '无人值守综合探测系统', 'language': 'zh', 'tru...   \n",
       "45700     CN-203767529-U  [{'text': '原料厂多料仓小车卸料自动控制系统', 'language': 'zh'...   \n",
       "45701     CN-205450697-U  [{'text': '一种异物入侵安全防控装置', 'language': 'zh', 't...   \n",
       "45702     CN-205565677-U  [{'text': '一种电力设备系统电压保护装置', 'language': 'zh', ...   \n",
       "\n",
       "       publication_date  \n",
       "0              20220217  \n",
       "1              20180315  \n",
       "2              20180705  \n",
       "3              20130801  \n",
       "4              20120823  \n",
       "...                 ...  \n",
       "45698          20130327  \n",
       "45699          20140716  \n",
       "45700          20140813  \n",
       "45701          20160810  \n",
       "45702          20160907  \n",
       "\n",
       "[45703 rows x 3 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_rows', 10)\n",
    "pd.set_option('display.max_columns', 10)\n",
    "query = \"\"\"\n",
    "SELECT publication_number,\n",
    "title_localized,\n",
    "publication_date,\n",
    "FROM\n",
    "  `patents-public-data.patents.publications`\n",
    "WHERE EXISTS(\n",
    "    SELECT text\n",
    "    FROM UNNEST(title_localized)\n",
    "    WHERE (\n",
    "      (text LIKE '% drone%' OR\n",
    "      text LIKE '%Drone%' OR\n",
    "      text LIKE '%unmanned%' OR\n",
    "      text LIKE '%Unmanned%') AND\n",
    "      (text LIKE '%detect%' OR\n",
    "      text LIKE '%Detect%' OR\n",
    "      text LIKE '%identif%' OR\n",
    "      text LIKE '%Identif%' OR\n",
    "      text LIKE '%Spot%' OR\n",
    "      text LIKE '%spot%')\n",
    "    )\n",
    ") OR EXISTS(\n",
    "    SELECT text\n",
    "    FROM UNNEST(abstract_localized)\n",
    "    WHERE (\n",
    "      (text LIKE '% drone%' OR\n",
    "      text LIKE '%Drone%' OR\n",
    "      text LIKE '%unmanned%' OR\n",
    "      text LIKE '%Unmanned%') AND\n",
    "      (text LIKE '%detect%' OR\n",
    "      text LIKE '%Detect%' OR\n",
    "      text LIKE '%identif%' OR\n",
    "      text LIKE '%Identif%' OR\n",
    "      text LIKE '%Spot%' OR\n",
    "      text LIKE '%spot%')\n",
    "    )\n",
    ");\n",
    "        \"\"\"\n",
    "dataframe = patents.query_to_pandas_safe(query, max_gb_scanned=170)\n",
    "dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac7a1f09",
   "metadata": {
    "papermill": {
     "duration": 0.010044,
     "end_time": "2023-04-21T07:04:34.702132",
     "exception": false,
     "start_time": "2023-04-21T07:04:34.692088",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Interesting results. 9 of the 10 displayed results are in Chinese, Korean or something along those lines. 1 of them is French. Apparently, they all still have english localizations. It would be nice if the results were accurate, but, unfortunately, they aren't. There are many patents about drones being used to do something remotely and since all drones have the function of identifying what's around them, having the keyword \"identifying\" is detrimental to overall accuracy. But that is only in the case of checking the abstract from the publication. It would probably be much better to only check the titles of these publications. The results would most likely be more accurate, but also more scarce. We won't know if it is better until we try it, but that will be a bit later - for now I will demonstrate how to make a quarterly graph out of the resulting dataframe."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f03cc4b7",
   "metadata": {
    "papermill": {
     "duration": 0.010356,
     "end_time": "2023-04-21T07:04:34.722920",
     "exception": false,
     "start_time": "2023-04-21T07:04:34.712564",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "First, I'm converting original data to a different format, so that Pandas can use it in it's functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0d5108f7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-21T07:04:34.746050Z",
     "iopub.status.busy": "2023-04-21T07:04:34.745566Z",
     "iopub.status.idle": "2023-04-21T07:04:34.803691Z",
     "shell.execute_reply": "2023-04-21T07:04:34.802254Z"
    },
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     "duration": 0.073351,
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     "exception": false,
     "start_time": "2023-04-21T07:04:34.733218",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       2022-02-17\n",
       "1       2018-03-15\n",
       "2       2018-07-05\n",
       "3       2013-08-01\n",
       "4       2012-08-23\n",
       "           ...    \n",
       "45698   2013-03-27\n",
       "45699   2014-07-16\n",
       "45700   2014-08-13\n",
       "45701   2016-08-10\n",
       "45702   2016-09-07\n",
       "Name: publication_date, Length: 45702, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe.drop(dataframe[dataframe['publication_date'] == 0].index, inplace = True)\n",
    "# Just fixing an error in the database. One of the publications somehow had \"0\" as a publication date.\n",
    "# Even though the number of lines didn't change, this fixed my issue.\n",
    "dataframe['publication_date'] = pd.to_datetime(dataframe['publication_date'], format = '%Y%m%d')\n",
    "dataframe['publication_date']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cc59696",
   "metadata": {
    "papermill": {
     "duration": 0.01062,
     "end_time": "2023-04-21T07:04:34.828613",
     "exception": false,
     "start_time": "2023-04-21T07:04:34.817993",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Then I'm running lots of tedious Python code. This is one of the main parts of the task, so I'll cover it, but I'm not proud of the code I've written. I just had to do it quickly, so I even imported a module, because it would otherwise take me possibly several hours to find a fitting solution within already connected modules. \n",
    "\n",
    "So, firstly, it converts the timedate type \"publication_date\" data into quarterly dates, all saved in a new column called \"quarter\". Then I'm making a new column called \"quarters\" converting the quarterly dates from the previous column into strings. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "afb36320",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-21T07:04:34.853300Z",
     "iopub.status.busy": "2023-04-21T07:04:34.852850Z",
     "iopub.status.idle": "2023-04-21T07:04:35.126670Z",
     "shell.execute_reply": "2023-04-21T07:04:35.125327Z"
    },
    "papermill": {
     "duration": 0.289754,
     "end_time": "2023-04-21T07:04:35.129769",
     "exception": false,
     "start_time": "2023-04-21T07:04:34.840015",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>publication_number</th>\n",
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       "      <th>quarters</th>\n",
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       "      <th>0</th>\n",
       "      <td>WO-2022033098-A1</td>\n",
       "      <td>[{'text': '一种基于无人机的水稻冠层温度检测方法', 'language': 'z...</td>\n",
       "      <td>2022-02-17</td>\n",
       "      <td>2022Q1</td>\n",
       "      <td>2022Q1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>WO-2018046124-A1</td>\n",
       "      <td>[{'text': 'Véhicule doté d&amp;#39;une fonctionnal...</td>\n",
       "      <td>2018-03-15</td>\n",
       "      <td>2018Q1</td>\n",
       "      <td>2018Q1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>WO-2018121247-A1</td>\n",
       "      <td>[{'text': '控制无人机飞行的方法、装置以及无人机', 'language': 'z...</td>\n",
       "      <td>2018-07-05</td>\n",
       "      <td>2018Q3</td>\n",
       "      <td>2018Q3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>WO-2013111319-A1</td>\n",
       "      <td>[{'text': '無人電気所遠隔監視制御装置用再立上げ装置および方法', 'langua...</td>\n",
       "      <td>2013-08-01</td>\n",
       "      <td>2013Q3</td>\n",
       "      <td>2013Q3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>WO-2012077911-A3</td>\n",
       "      <td>[{'text': '공작기계의 공구 손상 탐지장치 및 공구손상 탐지방법', 'lan...</td>\n",
       "      <td>2012-08-23</td>\n",
       "      <td>2012Q3</td>\n",
       "      <td>2012Q3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45698</th>\n",
       "      <td>CN-202824031-U</td>\n",
       "      <td>[{'text': '宝特瓶粉碎机的清洗结构', 'language': 'zh', 'tr...</td>\n",
       "      <td>2013-03-27</td>\n",
       "      <td>2013Q1</td>\n",
       "      <td>2013Q1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45699</th>\n",
       "      <td>CN-203720977-U</td>\n",
       "      <td>[{'text': '无人值守综合探测系统', 'language': 'zh', 'tru...</td>\n",
       "      <td>2014-07-16</td>\n",
       "      <td>2014Q3</td>\n",
       "      <td>2014Q3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45700</th>\n",
       "      <td>CN-203767529-U</td>\n",
       "      <td>[{'text': '原料厂多料仓小车卸料自动控制系统', 'language': 'zh'...</td>\n",
       "      <td>2014-08-13</td>\n",
       "      <td>2014Q3</td>\n",
       "      <td>2014Q3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45701</th>\n",
       "      <td>CN-205450697-U</td>\n",
       "      <td>[{'text': '一种异物入侵安全防控装置', 'language': 'zh', 't...</td>\n",
       "      <td>2016-08-10</td>\n",
       "      <td>2016Q3</td>\n",
       "      <td>2016Q3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45702</th>\n",
       "      <td>CN-205565677-U</td>\n",
       "      <td>[{'text': '一种电力设备系统电压保护装置', 'language': 'zh', ...</td>\n",
       "      <td>2016-09-07</td>\n",
       "      <td>2016Q3</td>\n",
       "      <td>2016Q3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>45702 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      publication_number                                    title_localized  \\\n",
       "0       WO-2022033098-A1  [{'text': '一种基于无人机的水稻冠层温度检测方法', 'language': 'z...   \n",
       "1       WO-2018046124-A1  [{'text': 'Véhicule doté d&#39;une fonctionnal...   \n",
       "2       WO-2018121247-A1  [{'text': '控制无人机飞行的方法、装置以及无人机', 'language': 'z...   \n",
       "3       WO-2013111319-A1  [{'text': '無人電気所遠隔監視制御装置用再立上げ装置および方法', 'langua...   \n",
       "4       WO-2012077911-A3  [{'text': '공작기계의 공구 손상 탐지장치 및 공구손상 탐지방법', 'lan...   \n",
       "...                  ...                                                ...   \n",
       "45698     CN-202824031-U  [{'text': '宝特瓶粉碎机的清洗结构', 'language': 'zh', 'tr...   \n",
       "45699     CN-203720977-U  [{'text': '无人值守综合探测系统', 'language': 'zh', 'tru...   \n",
       "45700     CN-203767529-U  [{'text': '原料厂多料仓小车卸料自动控制系统', 'language': 'zh'...   \n",
       "45701     CN-205450697-U  [{'text': '一种异物入侵安全防控装置', 'language': 'zh', 't...   \n",
       "45702     CN-205565677-U  [{'text': '一种电力设备系统电压保护装置', 'language': 'zh', ...   \n",
       "\n",
       "      publication_date quarter quarters  \n",
       "0           2022-02-17  2022Q1   2022Q1  \n",
       "1           2018-03-15  2018Q1   2018Q1  \n",
       "2           2018-07-05  2018Q3   2018Q3  \n",
       "3           2013-08-01  2013Q3   2013Q3  \n",
       "4           2012-08-23  2012Q3   2012Q3  \n",
       "...                ...     ...      ...  \n",
       "45698       2013-03-27  2013Q1   2013Q1  \n",
       "45699       2014-07-16  2014Q3   2014Q3  \n",
       "45700       2014-08-13  2014Q3   2014Q3  \n",
       "45701       2016-08-10  2016Q3   2016Q3  \n",
       "45702       2016-09-07  2016Q3   2016Q3  \n",
       "\n",
       "[45702 rows x 5 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe['quarter'] = dataframe['publication_date'].dt.to_period('Q')\n",
    "dataframe['quarters'] = dataframe['quarter'].astype(str)\n",
    "dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9215dccf",
   "metadata": {
    "papermill": {
     "duration": 0.010581,
     "end_time": "2023-04-21T07:04:35.151307",
     "exception": false,
     "start_time": "2023-04-21T07:04:35.140726",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "After that I import \"Counter\" module, so that I could easily count the amount of times each quarter is encountered in the \"publications_date\" column. Then, I get the data into arrays, which are then sorted with sorted() and zip() function.(both are built-in for python) Finally, I'm converting the data into datetime format from Pandas. It's used to get a good plot - otherwise the dates on the x axis would be just normal string and would overlap with each other, making it impossible to read anything."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c2a6b9fb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-21T07:04:35.174808Z",
     "iopub.status.busy": "2023-04-21T07:04:35.174368Z",
     "iopub.status.idle": "2023-04-21T07:04:35.194997Z",
     "shell.execute_reply": "2023-04-21T07:04:35.193335Z"
    },
    "papermill": {
     "duration": 0.036017,
     "end_time": "2023-04-21T07:04:35.198127",
     "exception": false,
     "start_time": "2023-04-21T07:04:35.162110",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 193 quarters with patents.\n",
      "('1964Q2', '1970Q2', '1971Q2', '1972Q1', '1973Q2', '1973Q3', '1975Q2', '1975Q3', '1976Q1', '1976Q3', '1977Q1', '1977Q2', '1977Q4', '1978Q1', '1978Q3', '1978Q4', '1979Q1', '1979Q2', '1979Q3', '1979Q4', '1980Q1', '1980Q2', '1980Q3', '1980Q4', '1981Q1', '1981Q2', '1981Q3', '1981Q4', '1982Q1', '1982Q2', '1982Q3', '1982Q4', '1983Q1', '1983Q2', '1983Q3', '1983Q4', '1984Q1', '1984Q2', '1984Q3', '1984Q4', '1985Q1', '1985Q2', '1985Q3', '1985Q4', '1986Q1', '1986Q2', '1986Q3', '1986Q4', '1987Q1', '1987Q2', '1987Q3', '1987Q4', '1988Q1', '1988Q2', '1988Q3', '1988Q4', '1989Q1', '1989Q2', '1989Q3', '1989Q4', '1990Q1', '1990Q2', '1990Q3', '1990Q4', '1991Q1', '1991Q2', '1991Q3', '1991Q4', '1992Q1', '1992Q2', '1992Q3', '1992Q4', '1993Q1', '1993Q2', '1993Q3', '1993Q4', '1994Q1', '1994Q2', '1994Q3', '1994Q4', '1995Q1', '1995Q2', '1995Q3', '1995Q4', '1996Q1', '1996Q2', '1996Q3', '1996Q4', '1997Q1', '1997Q2', '1997Q3', '1997Q4', '1998Q1', '1998Q2', '1998Q3', '1998Q4', '1999Q1', '1999Q2', '1999Q3', '1999Q4', '2000Q1', '2000Q2', '2000Q3', '2000Q4', '2001Q1', '2001Q2', '2001Q3', '2001Q4', '2002Q1', '2002Q2', '2002Q3', '2002Q4', '2003Q1', '2003Q2', '2003Q3', '2003Q4', '2004Q1', '2004Q2', '2004Q3', '2004Q4', '2005Q1', '2005Q2', '2005Q3', '2005Q4', '2006Q1', '2006Q2', '2006Q3', '2006Q4', '2007Q1', '2007Q2', '2007Q3', '2007Q4', '2008Q1', '2008Q2', '2008Q3', '2008Q4', '2009Q1', '2009Q2', '2009Q3', '2009Q4', '2010Q1', '2010Q2', '2010Q3', '2010Q4', '2011Q1', '2011Q2', '2011Q3', '2011Q4', '2012Q1', '2012Q2', '2012Q3', '2012Q4', '2013Q1', '2013Q2', '2013Q3', '2013Q4', '2014Q1', '2014Q2', '2014Q3', '2014Q4', '2015Q1', '2015Q2', '2015Q3', '2015Q4', '2016Q1', '2016Q2', '2016Q3', '2016Q4', '2017Q1', '2017Q2', '2017Q3', '2017Q4', '2018Q1', '2018Q2', '2018Q3', '2018Q4', '2019Q1', '2019Q2', '2019Q3', '2019Q4', '2020Q1', '2020Q2', '2020Q3', '2020Q4', '2021Q1', '2021Q2', '2021Q3', '2021Q4', '2022Q1', '2022Q2', '2022Q3', '2022Q4', '2023Q1')\n",
      "(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 3, 5, 2, 1, 5, 3, 11, 2, 7, 2, 3, 4, 4, 8, 6, 8, 7, 10, 5, 7, 8, 7, 8, 6, 16, 37, 28, 34, 22, 19, 27, 28, 23, 31, 43, 57, 45, 39, 40, 34, 35, 33, 36, 33, 41, 36, 55, 46, 44, 28, 46, 28, 30, 25, 34, 37, 39, 25, 31, 53, 25, 34, 28, 50, 30, 35, 50, 25, 28, 23, 20, 26, 34, 39, 27, 18, 21, 33, 39, 18, 26, 30, 16, 20, 13, 12, 18, 18, 19, 20, 23, 23, 15, 20, 32, 28, 24, 27, 23, 26, 37, 19, 18, 17, 11, 16, 21, 30, 30, 20, 19, 13, 22, 18, 20, 21, 25, 14, 37, 27, 24, 26, 23, 37, 45, 36, 36, 34, 42, 56, 32, 55, 56, 54, 42, 50, 57, 69, 86, 98, 105, 86, 119, 96, 91, 124, 143, 172, 157, 204, 269, 334, 361, 479, 552, 551, 659, 789, 796, 871, 1005, 1135, 1191, 1447, 1368, 1405, 1328, 1306, 1243, 1726, 1715, 1902, 1837, 2121, 2039, 2251, 2032, 2215, 2208, 2013, 1423)\n"
     ]
    }
   ],
   "source": [
    "from collections import Counter\n",
    "result = Counter(dataframe['quarters'])\n",
    "\n",
    "print(\"There are\", len(result), \"quarters with patents.\")\n",
    "\n",
    "time = []\n",
    "amount = []\n",
    "for item in result:\n",
    "    time.append(item)\n",
    "    amount.append(result[item])\n",
    "time, amount = zip(*sorted(zip(time, amount)))\n",
    "print(time)\n",
    "print(amount)\n",
    "date = []\n",
    "for i in range(len(time)):\n",
    "    date += [time[i][:4] + \"-\" + str((int(time[i][5]))*3-2) + \"-1\"]\n",
    "dates = pd.to_datetime(date)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c84047b",
   "metadata": {
    "papermill": {
     "duration": 0.010783,
     "end_time": "2023-04-21T07:04:35.219975",
     "exception": false,
     "start_time": "2023-04-21T07:04:35.209192",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Finally, after processing all that data, everything is ready for the resulting plot. I won't get into details on how to use MatPlotLib, since this plot is just a few lines of code. Each point on the graph represents a quarter when there was *at least one* publication on our topic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "60ded155",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-21T07:04:35.243971Z",
     "iopub.status.busy": "2023-04-21T07:04:35.243565Z",
     "iopub.status.idle": "2023-04-21T07:04:35.535750Z",
     "shell.execute_reply": "2023-04-21T07:04:35.534333Z"
    },
    "papermill": {
     "duration": 0.30773,
     "end_time": "2023-04-21T07:04:35.538612",
     "exception": false,
     "start_time": "2023-04-21T07:04:35.230882",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AQIsIC3FrnCfHJzstjKMdAgAAAIFmcHxkt2NSE6M8Oj45hEowAAAAApmrPYLbMgwpe/YYj45PDrW07QkOzEpwz48CAQAAQL9X02aP4JSESFnL6x2fpyZGKXv2GGVlpHh0T6ee4JbArASTBAMAAASxqtrWJNgwpLuvOllF+ytUUWNTQkyE0kbEe1QBtmubBAdqTzBJMAAAQBCrqW9NgqMjwxRisSjj6MRe3zOULdIAAAAQyOxHJscOCvPaPdkiDQAAAAGt+nA7RHSU95Jg58MyArMdgiQYAAAgiNkrwTGRVIIBAAAQJKoP9wTH+KgdIlAXxpEEAwAABDFHJdib7RAWFsYBAAAgQJmm6egJ9mYSHBbadp9gKsEAAAAIIPW2ZjUfTlKDrRLMPsEAAABBxDRN7fqmTKUV9Wp7DIY3k2DDMBRiMdTcYgZsTzBJMAAAQJD4eFex1m/Zre9Lazo85s0kWGpdHNfc0qzGAK0E0w4BAAAQBHLzrXroxc9cJsBSa4Jsmt6r2tpPjWsmCQYAAIA/mKap9VuK1FWO+2buft2c84Fy861eec2Qw9ukcVgGAAAA/KJgX7mKy+u6HVdcXqc1G3d4JRG2V4IDdWEcSTAAAMAAV15tc3usaUrr3y7qdWtEqKU1zQzUhXEkwQAAAANcQky4R+OLy+pUuL+iV68Zeniv4KYWKsEAAADwg/SRCUpNiPLoOeXVDb16zVAL7RAAAADwI8MwlD1njAyj+7F2CTERvXpNFsYBAADA77IyUnTd+ScoJSGy27GpiVFKGxHfq9djYRwAAAACwoljU7XsipO7HGMYUvbsMTI8KRu7EBoS2AvjODEOAAAgiLRtT0iMjVBZ1ZHe39TEKGXPHqOsjJRev05IgFeCSYIBAACCSGPTkaT0mvnj1djUoooamxJiIpQ2Ir7XFWA7+xZpJMEAAADwO1tTs+Pj8NAQHTu8d72/nTmyRVpgtkPQEwwAABBE2laC7YmqL9gXxjUHaCWYJBgAACCINLapBIeF+DAJtrBFGgAAAAJEX1eCA7UnmCQYAAAgiLRNgsNCvLMIzpUjh2WQBAMAAMDPbG2T4D6pBNMOAQAAAD9r2xMc4sueYMdhGYFZCfbKFmkff/yxvvrqK2VlZenYY4/1xi0BAADgA42NrUlpaIghi5f2BHYlxBLYlWCPk+AbbrhB4eHhWrFihSTp2Wef1e233y5JCg8PV05OjqZNm+bdWQIAAMArGpvtSbBvGwLCHPsEB2Yl2ON3n5ubq1mzZjk+f+yxx5Sdna3c3FydeeaZWr16tVcnCAAAAO+xL4zzdRIcYrG3QwRmJdjjd3/o0CGlpqZKkgoLC/X9999r4cKFio6O1vz581VQUOD1SQIAAMA7bI2tPcG+XBQntTkso8VUixl4ibDH7z4hIUHffvutJOmdd95RSkqK0tLSJEnNzc1qCdCSNwAAAKSmw5VgXx6UITlXmgNxcZzHPcGnnHKK7rvvPu3atUsbNmzQueee63issLBQI0aM8OoEAQAA4D32LdJ8XQkOabMHcVOzqTCvbMfgPR5P57e//a2am5v17rvv6tRTT9V1113neOyNN95w6hcGAABAYLFvkebrnuC29w/EAzM8ToJjY2MdO0O09+yzz/Z6QgAAAPAdx8K4UN9tjya1T4IHQE8wAAAA+q/GPusJPpJkD4ie4Pr6eq1Zs0avvfaaDhw4IJvN1mHMl19+6ZXJAQAAwLuOVIL7sB2iJfAqwR4nwbfffrs2bdqks846S+edd57CwsJ8MS8AAAD4gO1wT7DPK8GWtgvjBkAleMuWLbrxxhu1YMECX8wHAAAAPtTYZ7tDtN0iLfAqwR6/+5CQEP3gBz/wxVwAAADgY311Ylzb+zcGYCXY43d/4YUX6uWXX/bFXAAAAOBj9i3S+urEOGmALIyLiopSbm6uLrjgAk2fPl1xcXFOjxuGocsuu8xb8wMAAIAX9VUlOCTAt0jzOAm+7777JEnfffedtm/f3uFxkmAAAIDAZWvsm57g0JABtjBu165dvpgHAAAA+kBfVYLDArwSzGEZAAAAQaSp2b5Fmm9PjHPaHaJlAFSCJamsrExPP/20cnNzVVFRofj4eJ144om6+OKLlZiY6O05AgAAwEuOtEOE+PR1Ar0dwuNK8N69e3XOOefo0UcfVVNTk0aNGqWmpiY98sgjOuecc7R3715fzBMAAAC91NJiqvnw6W2hPq4Eh1oGWDvEypUrFRsbq9dff13r1q3Tn/70J61bt05vvPGG4uLitGrVKo/ut23bNi1evFgzZ85URkaG3nzzTafHb7rpJmVkZDj999Of/tRpjM1m0/LlyzV16lRNmjRJixcv1oEDB5zGVFRUaOnSpcrKylJWVpaWLl2qyspKT98+AABAv9V2v17fH5YxwCrBH374oX75y19q+PDhTteHDRuma6+9Vh988IFH96utrVVGRoaWLVvW6ZhZs2bp3Xffdfz32GOPOT1+11136Y033tD999+vZ555RrW1tbr66qvVfLjnRZJuuOEG7dq1S48//rgef/xx7dq1SzfeeKNHcwUAAOjPmpqOJKM+3yLN6djkwKsEe9wT3NLSotBQ108LDQ1Vi4eNz6eeeqpOPfXULseEh4crJSXF5WNVVVV68cUXdc8992j69OmSpHvvvVezZ8/We++9p1mzZmn37t1655139Pzzz2vixImSpOXLl+uCCy7Qnj17dOyxx3o0ZwAAgP6oLyvBhmEoNMRQU7M5MA7LmDx5stasWaMTTzxRCQkJjusVFRV65JFHlJWV5c35SZI++ugjTZs2TXFxcZoyZYp+/etfa/DgwZKknTt3qrGxUTNmzHCMHzJkiNLS0pSXl6dZs2YpLy9PsbGxjgRYkiZNmqTY2Fjl5eV5lARbLIYsFt/20AQD+4rREB//FArXiL9/EX//Iv7+Rfz9q6VNQTYiPEShPt8r2KKm5ma1mKbPX8tTHifBN910ky655BLNmTNHJ598slJSUlRSUqL3339f4eHhHvcEd+eUU07RWWedpeHDh2v//v168MEH9bOf/UwvvfSSwsPDVVJSorCwMMXHxzs9Lzk5WSUlJZKkkpISR9Lc1uDBgx1j3JWUFC3DIAn2lri4KH9PIagRf/8i/v5F/P2L+PtHTeORimxiwiAlJkb79PXCQkNUb2tWaHioz1/LUx4nwWlpafrnP/+ptWvXKjc3V7t371Z8fLwuuOACXXbZZRo6dKhXJ/ijH/3I8XF6errGjx+vuXPn6u2339YZZ5zR6fNMs/veE9M0PU5oDx2qoRLsBSEhFsXFRamysi4gf0Uy0BF//yL+/kX8/Yv4+1dZeZ3j44Y6m8rKanz6evaCf3VNg89fqy13Eu4e7RM8dOhQ3XzzzT15aq+lpqZq+PDh+vrrryW1VnwbGxsd+xXblZaWKjMz0zGmtLS0w70OHTrkskLclZYWUy0tgdfc3V81N7c4NemjbxF//yL+/kX8/Yv4+0eDrcnxsWEYPv8zCDm8TVpjY+D9eQdWc4YbysrK9P333ys1NVWSNH78eIWFhWnr1q2OMcXFxSosLHQkwZmZmaqqqtJnn33mGLN9+3ZVVVU5xgAAAAx0bbcqC+uDvmz7XsSNAVj1d6sS/OMf/1h//OMflZ6erh//+MddjjUMQ//85z/dnkBNTY3TARv79+/Xl19+qfj4eMXHx2v16tU644wzlJKSom+//Vb333+/EhMTdfrpp0uSYmNjdf7552vVqlVKTExUfHy8Vq1apfT0dMduEaNHj9asWbN066236o477pAk3XbbbZozZw47QwAAgH7JNE0V7CtXebVNCTHhSh+Z0G2bZ2PbLdL6YKGafRu2QGx9cSsJHj9+vKKiWhvYx40b59WFYTt37tTChQsdn69YsUKSNH/+fP3hD39QQUGBNm7cqKqqKqWkpGjq1Km6//77FRMT43jOLbfcotDQUF1//fWqr6/XtGnTtHLlSoWEHDkO8L777tOdd96pRYsWSZLmzp3b5d7EAAAAgSo336r1W4pU3KbHNzUhStlzxigrw/W2slK7LdJ8fGKcdOTAjEDcJ9gw3VlBBgertcrfUxgQQkMtSkyMVllZTcD1CAUD4u9fxN+/iL9/Ef/ey823as3GHXKVwRmGtGTehE4T4U8KrVr94g5J0h+vmaHE2AhfTlV3Pvmx9nxXqZOOS9Xic8f79LXaSkmJ7XaMx3Xw1atX6+DBgy4fKy4u1urVqz29JQAAANxgmqbWbylymQC3Pi6tf7uo012ynE+M830l+Eg7RODVXD1Ogh9++OEuk+CHH36415MCAABARwX7yp1aIFwpLqtT4f4Kl4+1bYfw9bHJ0pEt0qzldcrfW+bWFrZ9xeMt0rqavNVqVVxcXK8mBAAAANfKq21ujmtweb3twjhfH5ucm2/V7m8rJUl7i6u16pk8t/qW+4pbSfCmTZu0adMmSa27P6xatUqxsc69FjabTTt37tTkyZO9P0sAAAAoISbczXGue33tW6QZkkJ8ePhXZ33LxeV1WrNxR5d9y33FrR8BGhsbVVNTo5qaGpmmqbq6Osfn9v8Mw9C5556r22+/3ddzBgAACErpIxOUmtD1kdMJMeE6VFnvsv3AXgkOC7V4dbevtnrbt9xX3KoEz58/X/Pnz5ckLViwQH/4wx80evRon04MAAAAzgzDUPacMZ3uDiG1tkw89q8vJHXcNs2eBPtyj2BP+pbTRyb4bB7d8TgC69atIwEGAADwk6yMFC2ZN0ERYSHdjrW3H+TmWyUd2a/Xl6fF9bZvua94vDBOklpaWvTBBx/oq6++ks3m/EYNw9Bll13mjbkBAADAhayMFL39aZw+/6qs27H29oPJ6clqbGqW5NtFcb3tW+4rHifBVqtVl156qb755hsZhuHo52jbV0ISDAAA4FsNNvcPG7G3HzQergT7cns0e99yVy0RqYlRShsR77M5uMPjCKxcuVKJiYn673//K9M09fzzz2vz5s361a9+pWOOOUavvfaaL+YJAACANupsTR6NL69u6JNKsL1vubN1d4YhZc8e47OFee7yOALbtm3TokWLlJJyZFuL4cOHa/HixTr33HN1xx13eHWCAAAA6Ki+wbMkOCEmQk1Nh3uCfbxHsL1vOTXReSeL1MSogNgeTepBO0RVVZWSkpJksVgUExOj0tJSx2OTJk3SY4895tUJAgAAoKO6hma3x9rbD97K3S/Jtwvj7LIyUjQ5PVkF+8pVUWNTQkyE0kbE+70CbOdxEjxixAgVFxdLksaMGaOXX35Zc+bMkSS9+eabSkhI8OoEAQAA4Mw0TbfbIdq2H9j6YIs059c2lHF0Yp+8lqc8ToJnz56trVu36kc/+pF+8Ytf6JprrtG0adMUGhqqkpIS/b//9/98MU8AAAAcZmts6XSf4LZSE6OUPfvIPsH2E+N8uTCuv/A4Cb7hhhscH5966ql69tln9eabb6q+vl7Tp0/Xqaee6tUJAgAAwFltQ2O3Y0Ishu6+cqosliMJr/2wjPA+qgQHsh7tE9zWhAkTNGHCBG/MBQAAAG748pvu9wdubjFVsL9CY9u0I1AJPsLjCLz//vt68cUXXT720ksv6YMPPuj1pAAAANC50gr3Tlvbd7DK6XN7JdjXu0P0Bx5H4IEHHnDaEaKtQ4cO6YEHHujtnAAAANCF8DD3Urii/RXK31vmONyssY8XxgUyjyNQWFio8ePHu3xs3LhxKioq6vWkAAAA0LnBce4dObwt36pVz+Tp5pwPlJtvdbRDhIUExjZl/uRxT7BhGKqqqnL5WEVFhZqb3d+zDgAAAJ6r9+DIZEkqLq/Tmo07FBXemvqFhYb4Ylr9iseV4IkTJ+rpp592lNXtTNPUM888o4kTJ3ptcgAAAOio7R7BqQlRXYw8wjSPPC+USrDnleDrrrtOCxcu1DnnnKP58+crJSVFxcXF2rhxo77++mutW7fOF/MEAADAYfYjkyPCQ7Ti6pNVsK9cX35Tpn9u/brL59lrmFSCe5AEZ2Zm6u9//7vuvfde3XfffWppaZHFYtGkSZP097//XZMmTfLBNAEAAGBXZ2ttP40KD3GcylZebXP7+WGhVIJ7tE9wVlaW/vGPf6i+vl4VFRWKi4tTVJR7pXgAAAD0jr0SHBVxJJVLiAl3+/nfldR4fU79Ta/2x4iMjNSQIUNIgAEAAPpQrYskOH1kgtv9wZ/kWzus7wo2blWC77zzTi1atEjDhw/XnXfe2e34W2+9tdcTAwAAgGv1bdoh7AzDUPacMXp4w45un19Z26jC/RVKH5ngqykGPLeS4M2bN+snP/mJhg8frs2bN3c51jAMkmAAAAAfqjtcCY6McE7lsjJSdMaUkXp9275u71Fe7d6pcwOV20mwq48BAADQ9+oa7JXgjqlcZlqyW0lwQox7B24MVJyZBwAA0M/U2+yV4I5bnbnTG5wQE6G0EfE+mVt/4VYl+PPPP/fopuPGjevRZAAAANA9ezuEq0qwvTd4zcYd6mzt22knHiXDCO5t0txKgs8//3y3AmWapgzD0JdfftnriQEAAKAj0zSPLIyLcJ3KZWWkaMm8CVr/dpGKy+o6PD7+B4N9Osf+wK0k+Mknn/T1PAAAAOCGxqYWNbe0lnijXLRD2GVlpGhyerIK9pVr/du7tee7SsdjYaF0xLqVBJ900km+ngcAAADcYG+FkDqvBNvZT5M76bghTknwvuJqDUsaFNQtET06MU6SqqqqlJ+fL6vVqpSUFGVkZCg2NtabcwMAAEA79iOTJSnSRU+wK43NzU6f57z8uTb8d4+y54xRVkaKV+fXX3icBLe0tOiBBx7QunXrVFd3pMckKipKl156qa6//nqFhHRemgcAAEDPOVeCu8+5cvOteum/ezpcLy6v05qNO7Rk3oSgTIQ9ToLvuecePfXUU7ryyit1xhlnKCkpSYcOHdJrr72mxx9/XI2Njbrpppt8MVcAAICgV982Ce6mEmyaptZvKep0lwjTlNa/XaTJ6clB1xrhcRK8YcMG/fKXv9RVV13luDZkyBAdd9xxioqK0t/+9jeSYAAAAB9xaofophJcsK9cxeUdd4doq7isLiiPUPZ4aWBzc3On+wCPGzdOze16TgAAAOA9niyMK6+2uXXPYDxC2eMk+Mwzz9Qrr7zi8rFXXnlFP/zhD3s9KQAAALhW36YS3F07REJMuFv3DMYjlN1qh3j99dcdH0+ZMkX333+/FixYoNNPP12DBw9WaWmp3nzzTe3du1e//vWvfTZZAACAYGevBEeEhchi6bqP136EclctEamJUUF5hLJbSfAvf/nLDtcOHjyobdu2dbh+8803a968eb2eGAAAADqyJ8Hd9QNL3R+hbBhS9uwxQbcoTnIzCX7rrbd8PQ8AAAB0wzRNfX+oVpIUYhgyTbPbBLazI5RTE6OUPZt9grt01FFH+XoeAAAA6EJuvlXrtxQ5WhsOVTXo5pwP3Drwwn6E8u7vKtVkGgqzmDp2WFxQVoDtPN4i7bvvvut2zPDhw3s0GQAAAHSUm2912dLgyYEXhmFo7DGJSkyMVllZjZqaWnw448DncRI8d+7cbn9q+PLLL3s8IQAAABzBgRe+4XES/OCDD3a4VlFRoXfffVc7d+7U9ddf7415AQAAQBx44SseJ8Fnnnmmy+s//elPdffdd+uTTz7ROeec0+uJAQAAgAMvfMXjwzK6Mnv2bP373//25i0BAACCGgde+IZXk+BPPvlE4eHu/UEBAACge/YDL7oSrAde9IbH7RB33nlnh2s2m0179uxRbm6uFi1a5JWJAQAAgAMvfMXjJHjz5s0drkVERGjo0KH6/e9/r+zsbK9MDAAAAK0mpyfr7GnH6F/vfeN0PdgPvOgNryTBAAAA8I32h2RI0qCIUJ150tE6e/oxVIB7yKs9wQAAAPAe+yEZ7bdIq21o0sZ39+iTghI/zaz/87gSLElFRUV69NFHtX37dlmtVqWkpGjixIm6+uqrlZaW5u05AgAABB0OyfAtjyvBb7/9tubNm6e8vDzNnj1bixcv1uzZs5WXl6f58+fr7bff9sE0AQAAgosnh2TAcx5Xgu+55x7NmjVLDz/8sCyWIzn0zTffrCVLluiee+7R7NmzvTlHAACAoMMhGb7lcSV4//79uuiii5wSYEmyWCy6+OKLtX//fq9NDgAAIFhxSIZveZwEZ2RkdJro7t+/n55gAAAAL+CQDN/yOAletmyZ1q5dqw0bNqiqqkqSVFVVpZdeeklr167V73//e69PEgAAINjYD8nobM0bh2T0jmGana05dC0zM1NNTU1qamqSJIWGhjp9HBYWduTmhqHc3FwvTtf/rNYqf09hQAgNtSgxMVplZTVqamrx93SCDvH3L+LvX8Tfv4i/53LzrXp80xdqaGx2XOvpIRnBEv+UlNhux3i8MG7RokX8xAEAANBHsjJS9OpH0dr9baVGDY3VhaelKW1EPPlYL3mcBF933XW+mAcAAAA6UXF4p4hjh8cpfWSCfyczQHBiHAAAQAAzTdOxDVpiLDtBeAtJMAAAQACrqmtUU3PrEi62Q/MekmAAAIAAVl515DAMKsHeQxIMAAAQwMpIgn3CrSR4xYoV+v777yVJ27ZtU01NjU8nBQAAgFZlbY5Fph3Ce9xKgp988klZrVZJ0sKFC7V7926fTgoAAACt7O0QkeEhiorweGMvdMKtJHjw4MHavn27pNYVigAAAOgb9nYIWiG8y60fJ8455xzddddduvvuu2UYhi644IJOxxqGoS+++MJrEwQAAAhm9nYIWiG8y60k+MYbb9T06dNVVFSklStX6tJLL9Xw4cN9PTcAAICgZ2+HSKIS7FVuN5bMnDlTM2fO1FtvvaWLLrpIo0eP9uW8AAAAoCPtEAkkwV7lcXf1unXrHB+bpqmamhpFR0dzfjUAAICX2RqbVVPfJImeYG/r0RLDjz76SKtXr1ZeXp6ampoUGhqqyZMn67rrrtOJJ57o7TkCAAAEpUNV9Y6Pa+ubZJomhUcv8TgJ3rp1q6666iqNGjVKV199tZKTk2W1WvXaa6/psssu02OPPabp06f7Yq4AAABBIzffqqffyHd8/tL/9ujdz75X9pwxyspI8ePMBgaPT4x74IEHdMopp2jTpk269tprdeGFF+q6667Tv/71L82aNUsPPPCAR/fbtm2bFi9erJkzZyojI0Nvvvmm0+Omaeqhhx7SzJkzdcIJJ2jBggUqLCx0GmOz2bR8+XJNnTpVkyZN0uLFi3XgwAGnMRUVFVq6dKmysrKUlZWlpUuXqrKy0tO3DwAA4HO5+Vat2bhD5dU2p+vF5XVas3GHcvOtfprZwOFxElxQUKCLLrqoQyneMAxddNFFys/P7+SZrtXW1iojI0PLli1z+fhf/vIXrV27VsuWLdMLL7yg5ORkXX755aqurnaMueuuu/TGG2/o/vvv1zPPPKPa2lpdffXVam5udoy54YYbtGvXLj3++ON6/PHHtWvXLt14440ezRUAAMDXTNPU+i1F6uxoBtOU1r9dxNkNveRxEjxo0CAdPHjQ5WMHDx7UoEGDPLrfqaeeql//+tc644wzOjxmmqaefPJJLV68WGeccYbS09O1atUq1dfXa9OmTZKkqqoqvfjii7rppps0ffp0HX/88br33ntVUFCg9957T5K0e/duvfPOO7rzzjuVmZmpzMxMLV++XFu2bNGePXs8jAAAAIDvFOwrV3F5XZdjisvqVLi/oo9mNDB53BM8d+5c/fGPf9TQoUM1a9Ysx/V3331X999/v0477TSvTW7//v2yWq2aOXOm41p4eLimTJmivLw8XXjhhdq5c6caGxs1Y8YMx5ghQ4YoLS1NeXl5mjVrlvLy8hQbG6uJEyc6xkyaNEmxsbHKy8vTscce6/acLBZDFgsN6b0VEmJx+j/6FvH3L+LvX8Tfv4h/96rqGt0aV1lrU2ioZ3Ek/kd4nATfeOONKigo0JVXXqmYmBgNHjxYpaWlqqmp0YQJE7zaYmC1tva7DB482Ol6cnKyvvvuO0lSSUmJwsLCFB8f32FMSUmJY0z7e9jvax/jrqQktoPzpri4KH9PIagRf/8i/v5F/P2L+Hdu5LD47gdJOnp4ghITo3v0GsS/B0lwfHy8nnvuOW3ZskW5ubmqrKxUfHy8srKyNHv2bFks3v/Jon3S6U4PjLtjPE1oDx2qoRLsBSEhFsXFRamysk7NzS3+nk7QIf7+Rfz9i/j7F/Hv3vDESKUmRqm4rPOWiCGJURqWEKGyshqP7h0s8Xfnh4Me7RNssVh02mmnebX1wZWUlNbtP0pKSpSamuq4XlpaquTkZEmtFd/GxkZVVFQ4VYNLS0uVmZnpGFNaWtrh/ocOHXJZIe5KS4uplhYa0b2lublFTU0D95sw0BF//yL+/kX8/Yv4dy179hit2bjD5eI4w5B+MnuMmptNST3LSYh/DxbG9aURI0YoJSVFW7dudVyz2Wzatm2bI8EdP368wsLCnMYUFxersLDQMSYzM1NVVVX67LPPHGO2b9+uqqoqxxgAAIBAkZWRosv+b2yH66mJUVoybwL7BHtBjyrB3lRTU6O9e/c6Pt+/f7++/PJLxcfHa/jw4Vq4cKFycnI0atQoHXPMMcrJyVFkZKTOPvtsSVJsbKzOP/98rVq1SomJiYqPj9eqVauUnp7uOLRj9OjRmjVrlm699VbdcccdkqTbbrtNc+bM8WhRHAAAQF85OjXW8fGPp4/SuB8kKW1EPGuTvMTvSfDOnTu1cOFCx+crVqyQJM2fP18rV67UlVdeqYaGBt1+++2qqKjQxIkT9be//U0xMTGO59xyyy0KDQ3V9ddfr/r6ek2bNk0rV65USEiIY8x9992nO++8U4sWLZLUustFZ3sTAwAA+FvbI5Onjx+qIUmebUOLrhkmOy17xGqt8vcUBoTQUIsSE6NVVlYT9D1J/kD8/Yv4+xfx9y/i774tn+zXutcLJEmP3HCqIsJCunlG94Il/ikpsd2O8agnuKGhQcuXL3fqrQUAAID3lVU3SJIGRYR6JQGGM4+S4IiICL300kuqr6/vfjAAAAB6rKyqNQlOjI3w80wGJo93h8jMzNT27dt9MRcAAAAcVn44CU4gCfYJjxfG/fKXv9TSpUsVGhqqU045RUlJSR1WKSYkJHhrfgAAAEGprNomSUqMIQn2BY+T4AsvvFCStGrVKt1zzz0ux3z55Ze9mxUAAECQK6MS7FMeJ8F33303+9MBAAD4UIOtWXUNTZKkJJJgn/A4CT7vvPN8MQ8AAAAcZt8ZQqIS7Cs9Pja5oqJCH3/8sf71r3+poqJCUusWai0tA3fPOQAAgL5gb4WQ6An2FY8rwc3NzXrwwQe1bt061dXVyTAMvfDCC4qPj9e1116riRMn6tprr/XFXAEAAIJCedskmEqwT3hcCf7zn/+sp556SkuXLtUrr7yitgfOzZ07V5s3b/bqBAEAAIKNvR0ixGIoZlCYn2czMHlcCd6wYYN+85vf6OKLL1Zzc7PTY0cffbT27dvntckBAAAEG9M0tefbSklSdGSY2I7ANzyuBJeXl2v06NEuH2tpaVFTU1OvJwUAABCMcvOtujnnA31SaJUkVdbadHPOB8rNt/p5ZgOPx0nwqFGjtHXrVpePffjhh0pLS+v1pAAAAIJNbr5VazbuUHF5ndP14vI6rdm4g0TYyzxuh7jssst02223KTQ0VGeddZYk6cCBA/r000+1bt06rVixwuuTBAAAGMhM09T6LUVqs9Sq3ePS+reLNDk9mfMavKRH+wRXVFTooYceUk5OjiTpmmuuUVRUlK6//nr96Ec/8vokAQAABrKCfeUdKsDtFZfVqXB/hdJHJvTNpAY4j5NgSbr88sv105/+VJ988onKy8sVHx+vyZMnKyYmxtvzAwAAGPDKq21ujmvofhDc0qMkWJKio6M1a9Ysb84FAAAgKCXEhLs5jj2DvaVHSfChQ4f0xBNPaPv27bJarUpJSdHEiRP1s5/9TElJSd6eIwAAwICWPjJBqQlRXbZEpCZGKW1EfB/OamDzeHeI7du368wzz9S6des0aNAgTZ48WYMGDdK6dev0wx/+UNu3b/fFPAEAAAYswzCUPWeMOlvzZhhS9uwxLIrzIsM0O1uH6Np5552niIgIPfbYY4qNjXVcr6qq0pVXXqnGxka9+OKLXp9ooLBaq/w9hQEhNNSixMRolZXVqKmpxd/TCTrE37+Iv38Rf/8i/l3LzS/Wmo07nXaJSE2MUvbsMcrKSOn1/YMl/ikpsd2O8bgdoqioSA8++KBTAixJsbGxuvLKK/XrX//a01sCAABA0tFDYh0J8GlZIzRlbKrSRsRTAfYBj5PgY445RpWVlS4fq6qq0siRI3s9KQAAgGC09+CR3zjPnXyUhg2O9uNsBjaPe4KXLl2qhx56SB999JHT9Q8//FCrV6/WjTfe6LXJAQAABAvTNJVXUCJJCg0xlJoQ5ecZDWxuVYJ//OMfO31eVVWln/3sZ4qNjVViYqLKyspUVVWluLg43XfffTr11FN9MlkAAICBKDffqvVbihy7QzQ1m/rdXz5U9hzv9AKjI7eS4HHjxtGLAgAA4GWmaWrTe19r4ztfqf1OBcXldVqzcYeWzJtAIuwDbiXBK1eu9PU8AAAAgkpuvlXPby6UtaK+0zGmKa1/u0iT05MpSHqZxz3BAAAA6J3cfKvWbNzRZQJsV1xWp8L9FX0wq+DSoxPjPv30U7366qv6/vvv1dDgfIa1YRh65JFHvDI5AACAgcY0Ta3fUiRPTmoor27ofhA84nES/MQTT2jFihUaPHiwRo4cqbCwMF/MCwAAYEAq2Ffe5fHIriTERPhoNsHL4yT4b3/7my699FLdcsstsljopgAAAPBEebXNo/GpiVFKGxHvo9kEL4+z2Pr6ep122mkkwAAAAD2QEBPu9ljDkLJnj2FRnA94nMmeddZZ+t///ueLuQAAAAx46SMT3DoIIzUxiu3RfMjjdojf/e53uuWWW3TDDTdo2rRpiouL6zDmjDPO8MrkAAAABhrDMJQ9Z4zWbNzR6eK4+bOO1dnTj6EC7EMeJ8FFRUX65JNP9N133+mVV17p8LhhGPryyy+9MjkAAICBKCsjRUvmTdBTr+erouZIj3BqYpSyZ3NKXF/oUSU4Ojpajz76qEaNGsXuEAAAAD2QlZGikoo6Pbe5SJK09KJJGnt0ItXfPuJxErxnzx499NBDOuWUU3wxHwAAgKBRcXiniLjocB13TJKfZxNcPF4Yl5GRoUOHDvliLgAAAEGl7PAhGImx7APc1zxOgn//+9/r73//u7Zu3aqmpiZfzAkAACAolFW2HpucyGEYfc7jdohLL71UTU1N+vnPfy6LxaKICOc/NMMwlJub67UJAgAADFRUgv3H4yR40aJFNGwDAAD0kmmaKqtq7QlOIAnucx4nwdddd50v5gEAABBUauqb1NTcIklKIgnuc5x9DAAA4AdlVQ2Oj6kE9z2PK8ELFy7sdsyTTz7Zo8kAAAAEi7KqesfHLIzrex4nwVFRUR16gsvLy5Wfn6+4uDgdd9xxXpscAADAQNW2EszCuL7ncRKck5Pj8nppaal+8Ytf6Nxzz+31pAAAAAY6exIcGR6iqAiPUzL0ktd6ggcPHqwrr7xSDzzwgLduCQAAMGCVsz2aX3l1YVxLS4usVqs3bwkAADDgmKapfcXVkqTwUItM0/TzjIKPx7X3zz//vMO1xsZG7d69Ww8//LBOOOEEr0wMAABgIMrNt2r9liIVl9dJkr45WK2bcz5Q9pwxyspI8fPsgofHSfD555/fYWGc/aeXiRMnavny5d6ZGQAAwACTm2/Vmo071L7wW1xepzUbd2jJvAkkwn3E4yTY1fZnERERGjp0qIYMGeKVSQEAAAw0pmlq/ZaiDgnwkcel9W8XaXJ6Mqfz9gGPk+CTTjrJF/MAAAAY0Ar2lTtaIDpTXFanwv0VSh+Z0DeTCmKcGAcAANAHyqttbo5r6H4Qes2tSnBmZqbbZXnDMJSbm9urSQEAAAw0CTHhbo5jy7S+4FYSvGjRom6T4NzcXL3//vv0sAAAALiQPjJBqQlRXbZEpCZGKW1EfB/OKni5lQRfd911nT6Wm5ur1atX6/3331dGRoauueYar00OAABgoDAMQ9lzxrjcHaL1cSl79hgKin2kx2f0ffzxx1q9erU+/PBDjR07VqtXr9bpp5/uzbkBAAAMKFkZKVoyb4Jy/vm5mppbHNdTE6OUPZt9gvuSx0nwtm3b9NBDD+mjjz7S8ccfr9WrV+u0007zxdwAAAAGnKyMFEWEWdTU3KJJYwbrrKnHKG1EPBXgPuZ2Evzhhx9q9erV2rZtmyZMmKBHH31Us2fP9uHUAAAABp66hibV1DdJkiaMTmY7ND9xKwlesGCBPv74Y02cOFGPPfaYTjnlFF/PCwAAYEAqqah3fJwSH+nHmQQ3t5Lgbdu2SZLy8/N1/fXXdzmWLdIAAAA6V9Jmd4iUhCg/ziS4uZUEX3vttb6eBwAAQFCwHk6CDUlJcVSC/YUkGAAAoA9ZD7dDJMZFKCyUw3v9hcgDAAD0IXs7RHI8rRD+RBIMAADQh+yV4JQEWiH8qceHZQAAAKB7pmmqYF+5yqttiosO08FDNZJae4JN02R/YD8hCQYAAPCR3Hyr1m8pUnGbHSHs3t1xQAX7KpQ9h5Pi/IF2CAAAAB/IzbdqzcYdLhNgu+LyOq3ZuEO5+dY+nBkkkmAAAACvM01T67cUyTTdGSutf7tIpjuD4TUkwQAAAF5WsK+8ywpwe8VldSrcX+HDGaE9kmAAAAAvK6+29eA5DT6YCTpDEgwAAOBlCTHhPXhOhA9mgs6QBAMAAHhZ+sgEpSa4fxhGamKU0kbE+3BGaI8kGAAAwMsMw1D2nDFyZwtgw5CyZ49hv+A+RhIMAADgA1kZKVoyb4JCLJ0nt6mJUVoybwL7BPtBwB+W8dBDD2n16tVO15KTk7V161ZJrVuQrF69Ws8995wqKys1ceJELVu2TGlpaY7xNptNq1at0qZNm9TQ0KCTTz5Zf/jDHzR06NA+fS8AACC4HD8qUc0trVufTR8/VLNOGCZTUmWNTQkxEUobEU8F2E8CPgmWpLS0NK1du9bxeUhIiOPjv/zlL1q7dq1WrlypUaNG6ZFHHtHll1+uV199VTExMZKku+66S1u2bNH999+vhIQErVy5UldffbVeeuklp3sBAAB4077iasfHs04YpoyjE/04G7TVL9ohQkJClJKS4vgvKSlJUmsV+Mknn9TixYt1xhlnKD09XatWrVJ9fb02bdokSaqqqtKLL76om266SdOnT9fxxx+ve++9VwUFBXrvvff8+bYAAMAAt/dglePjkamxfpwJ2usXleBvvvlGM2fOVHh4uCZOnKjf/OY3GjlypPbv3y+r1aqZM2c6xoaHh2vKlCnKy8vThRdeqJ07d6qxsVEzZsxwjBkyZIjS0tKUl5enWbNmeTQXi8WQpYveHrgnJMTi9H/0LeLvX8Tfv4i/fwVT/E3T1M6vSiW1bpkWGx3m99aHYIp/dwI+CT7hhBO0atUqjRo1SqWlpXrkkUd04YUXatOmTbJaW8/ZHjx4sNNzkpOT9d1330mSSkpKFBYWpvj4+A5jSkpKPJ5PUlK037+AB5K4OPe3j4H3EX//Iv7+Rfz9a6DH//0d32ntv77Q96U1kloPz7jp0Q90+Y+P17QJw/08u4Eff3cEfBJ86qmnOn0+adIk/fCHP9TGjRs1ceJESeqQlLpz9nZPz+c+dKiGSrAXhIRYFBcXpcrKOjU3t/h7OkGH+PsX8fcv4u9fwRD/j3cV66EXP1P7VOP70hqteGKbrjv/BJ04NtUvcwuG+EtSYmJ0t2MCPglub9CgQUpPT9fXX3+t008/XVJrtTc19cgXU2lpqZKTkyW1VnwbGxtVUVHhVA0uLS1VZmamx6/f0mKqpaVnCTQ6am5uUVPTwP0mDHTE37+Iv38Rf/8aqPE3TVP/eLOwQwJ85HHpH28VauLowX79zfJAjb8n+l1DiM1m0+7du5WSkqIRI0YoJSXFsV2a/fFt27Y5Etzx48crLCzMaUxxcbEKCwt7lAQDAAB0pmBfuYrL67ocU1xWp8L9FX00I3Qm4CvBq1at0pw5czRs2DAdOnRIjzzyiKqrqzV//nwZhqGFCxcqJydHo0aN0jHHHKOcnBxFRkbq7LPPliTFxsbq/PPP16pVq5SYmKj4+HitWrVK6enpmj59up/fHQAAGEjKq21ujmvw8UzQnYBPgg8cOKDf/OY3Ki8vV2JioiZNmqTnn39eRx11lCTpyiuvVENDg26//XZVVFRo4sSJ+tvf/ubYI1iSbrnlFoWGhur6669XfX29pk2bppUrV7JHMAAA8KqEmHA3x0X4eCbojmH2dIVYkLJaq7ofhG6FhlqUmBitsrKaoO9J8gfi71/E37+Iv38N9Pibpqmbcz7osiUiNTFKK6462S89wQM9/nYpKd3vydzveoIBAAAClWEYyp4zRp3lt4YhZc8ew3arAYAkGAAAwIuyMlL0i3njO1xPTYzSknkTlJWR4odZob2A7wkGAADob9JGJDg+njv5KJ103BCljYinAhxASIIBAAC8rLSi3vHxlLGpSh+Z4L/JwCXaIQAAALzsUOWRJHhwfKQfZ4LOkAQDAAB4WenhJNgw2A4tUJEEAwAAeJm9HSIhJkKhIaRbgYg/FQAAAC+zV4JphQhcJMEAAABedqiy9VjkwXEkwYGKJBgAAMDL7JXgpDj6gQMVSTAAAIAXNdiaVV3XKElKphIcsEiCAQAAvOhQ1ZHt0ZJIggMWSTAAAICXmKap7UUljs+TYmmHCFScGAcAAOAFuflWrd9SpOLyOse1h17aoQvmpikrI8WPM4MrVIIBAAB6KTffqjUbdzglwJJUUlGvNRt3KDff6qeZoTMkwQAAAL1gmqbWbymSaXb2uLT+7SKZnQ2AX5AEAwAA9ELBvvIOFeD2isvqVLi/oo9mBHeQBAMAAPRCXmFJ94MklVc3+Hgm8ARJMAAAQA/l5lv1+rZ9bo1NiGGniEBCEgwAANAD9l5gd6QmRiltRLyPZwRPkAQDAAD0gDu9wJJkGFL27DEyDKMPZgV3kQQDAAD0QHm1za1xZ5w4kn2CAxBJMAAAQA8kxIS7NS4znQQ4EJEEAwAA9ED6yASlJkR1OYZe4MBFEgwAANADhmEoe84YddbqSy9wYCMJBgAA6KGsjBRddtbYDtdTE6O0ZN4EeoEDWKi/JwAAANCfxUSFOT7Onj1ao4+KV9qIeCrAAY4kGAAAoBd2f1cpSYoIC9GZJx0ti4Xktz+gHQIAAKCHTNPUzj2lkqQhiVGd9gcj8JAEAwAA9EBuvlU35XygvcXVkqS9xdW6OecD5eZb/TwzuIMkGAAAwEO5+Vat2bhD1nYnxhWX12nNxh0kwv0ASTAAAIAHTNPU+i1FMs3OHpfWv10ks7MBCAgkwQAAAB4o2Feu4nYV4PaKy+pUuL+ij2aEniAJBgAA8EB5tc3NcQ0+ngl6gyQYAADAAwkx4W6Oi/DxTNAbJMEAAAAeSB+ZoNSEqC7HpCZGKW1EfB/NCD1BEgwAAOABwzCUPWdMF49L2bPHcGJcgCMJBgAA8FBWRop+MCy2w/XUxCgtmTdBWRkpfpgVPMGxyQAAAB4yTVOlla0L3zLTkjX1+CFKiIlQ2oh4KsD9BEkwAACAB0zT1LZdxaqsad0lIjMtWScdN8TPs4KnSIIBAADclJtv1fotRU77BG989ytFRYTRAtHP0BMMAADgBvtRye0PyjhU2cBRyf0QSTAAAEA3OCp54CEJBgAA6AZHJQ889AQDAAB0wjRNFewr18dutjpwVHL/QRIMAADggqtFcN3hqOT+gyQYAACgHfsiOE9afDkquX+hJxgAAKCN7hbBucJRyf0PlWAAAIA23FkE11ZqYpSyZ49hn+B+hkowAABAG+XVNo/G/+TU0STA/RBJMAAAQBvFZe5XgSXphf/uZn/gfogkGAAA4LDcfKs2vLPHo+ewP3D/RBIMAACgIwvieoL9gfsfFsYBAICgYT/8orzapoSYcKWPTHDs6LDpvW88WhDXFvsD9z8kwQAAICi4OvwiNSFK2XPGSJLHbRCOe7A/cL9EEgwAAAa8zg6/KC6v08MbdiguOrxH92V/4P6LJBgAAAxYpmkqf2+Z1r2W3+XhF5U1nm2LJrE/cH9HEgwAAAYUe99vXmGJPvryoMf7/nZl/qxjNSQpSgkxEUobEU8FuB8jCQYAAAOGq75fb5l/yrH68fRRXr8v/IMkGAAADAi5+cVas3Fnl20PPZWSEKmzpx3j/RvDb0iCAQBAv/fxrmLl/PNznyTAhiH9dE4arQ8DDEkwAADo11p3ftjpk3uz+G3gIgkGAAD9VktLi55+I99n97/8/8Yq4+hEn90f/kMSDAAA+qXcfKuefiPfq7s/tFfRg63T0D+QBAMIOl0dm9ofXwfob0zT1K5vylRaUe/43pCkgn3lKqtqUHVdo2KiwpQQEy4ZhipcfA/5chFcWxyHPHCRBAMIKl0dm+rNnr+uXmfquCFee53OuJuAdzWOJB6+8PGuYq3fslvfl9Y4rsUPCpdpdH9gRWpClH4ye7S+K6nWy1u/9nkCzHHIA5thmr7+EhpYrNYqf09hQAgNtSgxMVplZTVqamrx93SCTjDG3zRNbXrva2185yt19pfeknnjdeLY1F6/VmfHs9qdMnGYTho/XOEWafTwOEnyarLpbqLf1ThJffLDgj8E49e/r7n7A1NfVW+9wTCkJfMm9Puv9/aC5es/JSW22zEkwR4iCfaOYPkmDFSBHn9vVyBz8616fnOhrBX1XY6zGNJpWUcpJWGQYqLClBgb4XYFNW1EvAr3V6isqkHPbylyu0fRVQWsu2TT/tptf21sn+snBSWdJuBt/1HvLlE3JJc/LHiaGHj6Z9kX1ee+/vrvTxX1nvx5bXrva72V+60qa498DSfEhGvK2FSlJEQ5vj6ram3K+ecXam7p+7Sjs69nSZp63BB9ubfM+XtwAO8IEeh//3uLO0kw7RAAAkpn1cmfzB6t2EFhHicSnlSeWkzpjY+/dboWNyhMcyePUPrRCSo/nHRay+u0bVexU6JrGOpRdauitmOyXFxepzUbdzglm+4cA5sSH6mGppZO52Ga0tNv5MvW2KT1b+/ucr6dPWSa0vq3izQ5PblHyVFnCb6n4/taTxPZvmq/8QZP55qbb9VTr+W7/Bour7bpjY/3+3S+7kiMjdDFp6dLav26LS5r897aJLr2P9+KGhvHIQcRKsEeohLsHcHyk2igCtT4d1edbKvtP86dVUb9WXnyhviYcP109mjt2luu7UUlqqxt9PeUHH48fZSOH5Xosoc4r7BE7+74XrX1TZ0+v23rSVfJlN38WccqJSGyQ+W7J4mKp1//rpLDhJhwnXTcEGWmJTst6mr7m4FX3v+m0/abnlbUXVX/e5qstU3si8tqtfHdrzr93lsyb7yyMlKcxm9456sevW5fSYgJ131LpstisUgSie5hgfr3v7fRDuEDJMHeESzfhIEqEONvmqZuzvnAKdFwx8TRg/XNwSqfbpGEztkr5TKkze2quF0JsRi6+pzj9X1pz5OpnvyGwDRN7f6uUrYWUwes1RoUEerYgaDcZXuJtdvfJESFh8hiMVTTRdLvSkpCpFZePa3T+bpT/W/7m4q2OyhIHZPywv0VKq+2KT46TAX7Kzz68zIkRUaEqK6h2aP36C8DtZ/XGwLx739fIAn2AZJg7wiWb8JAFYjx37W3TPc8k+fvaaCfixsUptOyRurs6cdIOpII9iTxi4oIUX1Dc6etId4wc8JQTZ8wrEMC66o1xF2ukvKetuv0RyEWQ4vPHaesjN4vch2IAvHvf18gCfYBkmDvCJZvQn9z1ccoSbu/q1SjKYUZrbsTeLpQSep+P8/2laf2lba244vLavVW7v6A+nU/+rfwMItCLBbVNXhWnfW3qPAQtZimGhr5e7GnvLXLy0AVLP/+sjCuH+tPq4l7wzRN5e8t6/J9enMf07bj7YmZq03Y3Z17V/eSOv91pKsx7e/hKol0dX97IhodGaqa+iZHgumq6jUoMlSmKafEwNXCr67u4e6vfoOp8oTAY2tskdT//oGvs/WPdoNAFB8drkvPyKAFAm6jEuyhvqgE96fVxD0VGmrRrv0Vevzlnc6rddu9T2/uY+rqXm15EuPu7uVq26sQi+G0QKu7zeHbj+/u/gAw0Mw6YZje3fF9lz9Qx0WH6bTJrS0wA7FY5G1Ugo8IuiT46aef1l//+ldZrValpaXplltu0Yknnuj2832dBHe1On4gNfp/WlSi1S9+Jlc5nv19SvLaPqbu7jrgTow92cEAgctiSLGDwlXBDxJAQEpNjNKKq07WJwUlHbY3s+/OMTk9JWh3eegpkuAjgqod4t///rdWrFih3//+95o8ebL+8Y9/6Morr9Qrr7yi4cOH+3t6Mk1T67cUdbnHpzv7cwY60zT1j7cKXSbArY9Lz28plCHDK/uYdhfXrp7rau7u3guBbfG5R7Z8+rSwRB/tKlZZVYNPXis1MUo/OXW0YqJC9ejLn9P7jAErIsyi0BBL9+1SkqaOG6IPvzjYadEne/YYGYahrIwUTU5PZnszeF1QVYKzs7N1/PHH6/bbb3dc+7//+z+dfvrpuuGGG9y6hy8rwfl7y7TKjdXxR6fGaFBk//35pba+SXuLq33+OvY49eT1OotxX80dvtPZyvEjPd4N+uLrMm3fXdptu8mgyFDJlGrb9lhHh2luZmuPdaWLf7C99ZuE6MhQTR8/VPW2Zr372fc+3cEAcId9X17DMLr8XhqSGKUrzh2vsSPi9eHnB7s8xALeRyX4iP6bSXnIZrPp888/11VXXeV0fcaMGcrLc39bJovFkMXim58+q+rcqw6RhLmnN3EixgPXkvnjNeW4IS4fG3fsYEnSjBOGH1602foPeUJMuEzJsXAwdlC4Yx9ZSY5x7hxeMHXcEIWFWfTc5iJ9X1LjuB4fHS4ZUkU3+x3HR4fp9BOP1jkzRzleJzM9Rc+9VaiDbRIJ+7jhyYP0/OYip8cGRYTIsFhU0+bvnPY96Ikx4Zo6bqiyMlJUWWPrcA93REeFatYJwxUdFap3Pv1OxeVdH1uN/sswpIVnjVV4eGta0dn3UmJshI4blaT4+EGqrKzT1HFDdNLxqR59D6F3QkIsTv8PZkFTCT548KBOOeUUPfvss5o8ebLj+qOPPqoNGzbotddec+s+pmn67Jtz5+4S3bxma7fjjhuVpLjocJ/MoS9U1tj05deHfP469jj15PU6i3FfzR1HXHrWWA1PjlFSfKQqqhv091e+cEoe7ZLiIzVr4jANGRyjg6U1enf7dyqtOJJ0DUuO1uVnH69pE/zf+iS1/l3y+Z5SlVU2KCk+Usf/IEmSHNcS4yJkmlJZZb0qam2Kjw7X4PgoHf+DpE5bddrfr+0uKl29VlJ8pI4blagvvjrk8vlt73Gool6fFlm17YuDKu+kfSQhNkJnz/yBfnpautMcnn+zQE+/tqvTKnhibITmnjhSG94u6rRdamJasj4rKvFbS1JibIQWnzdB+w5Wa9PWr5xiEDMoTKZpqqbuyG8G7LEYmRqrR1/6rFctNzFRYTptygjVNjR3GX9vMCRdctZYVdc26p3t3zp9L1kMQy1t/gAC7XsLcFfQJcH/+Mc/lJmZ6bj+yCOP6OWXX9arr77q1n1KS6t9Vgk2TVNL17zn9Guh9oYkRumew79u6q9M09SNa97rsqqUmhApGUaXsehK2zi5E9fOnutq7p7cCz0XYjFcVm3tVaWyqvoOVdn2SVugVpdCQiyKi4tSZWWdmpv7568jXVXKK6pt3cb6413FnVat7dVtV2OGJEbpgtPSdOLYVJePWwx1mji7Eh8Trsy0ZB0/KqlDpd9aXq8PvzjolLC6qsC7+hqTOv/NgGma+ue7X+nNj/d7tCCzu9dOiAnXrr3leqvdfaMPb4nYtl0nPrr1MJGMY1pPmDtYVqt3t3/faaxdvc+0EfEq2FfR4++tgfD1358FS/wTE6O7HRM0SbDNZtOkSZP04IMP6oc//KHj+p133qldu3bpqaeecus+7A7hHewOge4M5A3vg6UnrzP2/uuuFjl1N6b942OOilPh/gqXixzb92kPjo/U1BOOUnl5bafxd2eO3nj/8dHhyt9Xrs2ffOvUN9uT3Q9czVlSr2PtbcH+9e9vwRJ/tkhrJzs7W+PGjdMf/vAHx7Uf/ehHOu200wJiYZxdbr51wC8UsO8T/NeXdzpVINq/z65iIcmjOLm6V1uexLi7e8UfbqWo6GqfYBdj2upyn+BuntteVESILIbzARftf6XZnfaVtoSYcE0Zm6qUhCjFDgpTQkyEy3/M2yYgFdUNqqpt7HL8QPtadyVY/hHyl+6SukCMf18nov4UiPEPJsESf5Lgdv7973/rxhtv1B/+8AdlZmbqueee0/r167Vp0yYdddRRbt2jr45NHuh/Idq/CQ8dqtYXXx3qcZXC0zi1r8CYkssV/O7o7l6SXFaqOqvStL9H2/Fd3b/8cGIZExWq6romR4LZ1XwGx0fqpAnD9eFn3+lQZb3j/u2T1M7m01W8evNnMhC/1l0Jln+EAhXx9y/i71/BEn+SYBfsh2UUFxcrPT1dN998s6ZMmeL28/sqCR7oguWbMFARf/8i/v5F/P2L+PtXsMSfLdJcuOSSS3TJJZf4exoAAADwIzaJAwAAQNAhCQYAAEDQIQkGAABA0CEJBgAAQNAhCQYAAEDQIQkGAABA0CEJBgAAQNAhCQYAAEDQIQkGAABA0CEJBgAAQNAhCQYAAEDQIQkGAABA0DFM0zT9PQkAAACgL1EJBgAAQNAhCQYAAEDQIQkGAABA0CEJBgAAQNAhCQYAAEDQIQkGAABA0CEJBgAAQNAhCQYAAEDQIQkGAABA0CEJBgAAQNAhCUaPbdu2TYsXL9bMmTOVkZGhN9980+nxkpIS3XTTTZo5c6YmTpyoK664Ql9//bXj8f379ysjI8Plf//5z38c4yoqKrR06VJlZWUpKytLS5cuVWVlZV+9zYDV2/hLktVq1dKlSzVjxgxNmjRJ8+fP16uvvuo0hvi75o347927V9dcc41OPvlkTZ48Wb/61a9UUlLiNIb4d5STk6Pzzz9fmZmZmjZtmpYsWaI9e/Y4jTFNUw899JBmzpypE044QQsWLFBhYaHTGJvNpuXLl2vq1KmaNGmSFi9erAMHDjiNIf4deSv+zz33nBYsWKDJkycrIyPDZVyJf0feiH95ebmWL1+uM888UxMnTtTs2bN15513qqqqyuk+Az3+JMHosdraWmVkZGjZsmUdHjNNU9dcc4327dunNWvWaMOGDTrqqKN0+eWXq7a2VpI0bNgwvfvuu07/XXfddRo0aJBOOeUUx71uuOEG7dq1S48//rgef/xx7dq1SzfeeGOfvc9A1dv4S9KNN96or776So888oj+9a9/6Yc//KF+/etf64svvnCMIf6u9Tb+tbW1WrRokQzD0BNPPKFnn31WjY2NWrx4sVpaWhz3Iv4dffTRR7rkkkv0/PPPa+3atWpubtYVV1zh9LX9l7/8RWvXrtWyZcv0wgsvKDk5WZdffrmqq6sdY+666y698cYbuv/++/XMM8+otrZWV199tZqbmx1jiH9H3op/XV2dZs2apcWLF3f6WsS/I2/Ev7i4WMXFxfrtb3+rf/3rX1qxYoXeeecd/e53v3N6rQEffxPwgvT0dPONN95wfL5nzx4zPT3dLCgocFxramoyTzrpJPP555/v9D7nnnuuefPNNzs+LyoqMtPT081PP/3UcS0vL89MT083d+/e7eV30X/1NP6TJk0yN2zY4HSvtmOIv3t6Ev933nnHHDt2rFlVVeUYU15ebqanp5tbt241TZP4u6u0tNRMT083P/roI9M0TbOlpcWcMWOGmZOT4xjT0NBgZmVlmc8++6xpmqZZWVlpjhs3znzllVccYw4cOGCOHTvW/N///meaJvF3V0/i39YHH3xgpqenmxUVFU7Xib97eht/u3//+9/muHHjzMbGRtM0gyP+VILhEzabTZIUERHhuBYSEqKwsDDl5ua6fM7OnTv15Zdf6ic/+YnjWl5enmJjYzVx4kTHtUmTJik2NlZ5eXk+mn3/5278J0+erP/85z8qLy9XS0uLXnnlFdlsNk2dOlUS8e8pd+Jvs9lkGIbCw8MdYyIiImSxWBxjiL977L/CjY+Pl9TaamW1WjVz5kzHmPDwcE2ZMsURt507d6qxsVEzZsxwjBkyZIjS0tIcY4i/e3oSf3cQf/d4K/7V1dWKiYlRaGiopOCIP0kwfOLYY4/VUUcdpT/+8Y+qqKiQzWbTY489JqvVKqvV6vI5L7zwgkaPHq3Jkyc7rpWUlGjw4MEdxg4ePLhD7ySOcDf+DzzwgJqamjR16lRNmDBBy5Yt0+rVq3X00UdLIv495U78J02apKioKN17772qq6tTbW2t7rnnHrW0tDjGEP/umaapFStWKCsrS+np6ZLkiF/72CUnJzviVlJSorCwMEfi0NkY4t+1nsbfHcS/e96Kf1lZmdasWaMLLrjAcS0Y4k8SDJ8ICwvTn//8Z3399dc66aSTNGnSJH344Yc65ZRTZLF0/LKrr6/Xpk2bnKrAXTFNU4ZheHvaA4a78X/ggQdUWVmpv//973rxxRd1+eWX61e/+pXy8/O7vD/x75o78U9KStKDDz6oLVu2KDMzUyeeeKKqqqo0btw4l98jbRH/I+644w4VFBToT3/6U4fH2sfINM1u7+fuGOLfytvxdwfxP8Ib8a+urtbVV1+t0aNH69prr+32NQdS/EP9PQEMXOPHj9fLL7+sqqoqNTY2KikpSdnZ2Ro/fnyHsa+++qrq6+s1b948p+vJyckqLS3tMP7QoUMuf0LFEd3Ff+/evXrqqae0adMmpaWlSZLGjh2rjz/+WE8//bTuuOMO4t8L7nz9z5w5U2+++aYOHTqk0NBQxcXFacaMGRoxYoQkvv67s3z5cm3evFlPPfWUhg4d6riekpIiqbWSlZqa6rheWlqq5ORkSa2xbWxsVEVFhVM1uLS0VJmZmY4xxL9zvYm/O4h/17wR/+rqav385z/XoEGD9PDDDyssLMzxWDDEn0owfC42NlZJSUn6+uuvtXPnTp122mkdxrz44ouaO3eukpKSnK5nZmaqqqpKn332mePa9u3bVVVV5fiHCl3rLP51dXWS1KHqGBIS4qgYEP/ec+frPykpSXFxcXr//fdVWlqquXPnSiL+nTFNU3fccYdef/11PfHEExo5cqTT4yNGjFBKSoq2bt3quGaz2bRt2zZH3MaPH6+wsDCnMcXFxSosLHSMIf6ueSP+7iD+rnkr/tXV1briiisUFhamRx55xGkNgxQc8acSjB6rqanR3r17HZ/v379fX375peLj4zV8+HD95z//UVJSkoYPH678/HzdfffdOv30052a9SXpm2++0bZt2/TYY491eI3Ro0dr1qxZuvXWW3XHHXdIkm677TbNmTNHxx57rG/fYIDrbfyPPfZYHXPMMVq2bJl++9vfKiEhQW+++aa2bt2qnJwcScS/K974+n/xxRc1evRoJSUlKS8vT3fffbcuu+wyR2yJv2u33367Nm3apDVr1ig6OtrRAxkbG6vIyEgZhqGFCxcqJydHo0aN0jHHHKOcnBxFRkbq7LPPdow9//zztWrVKiUmJio+Pl6rVq1Senq6pk+fLon4d8Yb8Zdae1dLSkoc30cFBQWKjo7WsGHDlJCQQPw74Y34V1dXa9GiRaqrq9O9996r6upqx/ZpSUlJCgkJCYr4G6a3mnQQdD788EMtXLiww/X58+dr5cqVevLJJ/XXv/5VpaWlSklJ0bnnnqslS5Y4rYaXpD/96U96+eWXtWXLFpe9kOXl5brzzju1efNmSdLcuXO1bNkyxcXF+eaN9RPeiP/XX3+tP/7xj8rNzVVtba2OPvpoLVq0yKkthfi75o3433fffdqwYYMqKip01FFH6cILL9Rll13m1G9H/DvKyMhweX3FihU677zzJLVWy1avXq3nnntOFRUVmjhxopYtW+ZYPCRJDQ0Nuueee7Rp0ybV19dr2rRp+v3vf69hw4Y5xhD/jrwV/4ceekirV6/u8j7EvyNvxL+zv78k6a233nK0ZA30+JMEAwAAIOjQEwwAAICgQxIMAACAoEMSDAAAgKBDEgwAAICgQxIMAACAoEMSDAAAgKBDEgwAAICgQxIMAACAoMOxyQDQj7U9dcswDEVHR2v48OGaMmWKLrnkEo0ePdqj+3344YfKy8vT4sWLfTFdAAgYVIIBoJ+LjIzUc889p3/84x/685//rPPOO0/vvfeezj33XL388sse3eujjz5STk6Oj2YKAIGDSjAA9HMWi0WTJk1yfD5jxgxdfPHFuuqqq/S73/1OkydP1siRI/03QQAIQFSCAWAAioiI0G233abGxkatX79ekrRx40ZddNFFOumkkzRlyhQtWLBAn332meM59taK2tpaZWRkKCMjQwsWLHA8vnv3bv3iF79QVlaWJk2apKuuukp79+7t8/cGAN5AJRgABqgxY8ZoyJAhysvLkyTt379f8+bN09FHHy2bzaZNmzbpkksu0T//+U/94Ac/UHZ2tg4cOKBNmzbpiSeekCTFxMRIkvbt26cLL7xQaWlpWrlypQzD0KOPPqrLLrtMr776qsLDw/32PgGgJ0iCAWAAGzZsmEpKSiRJ1157reN6S0uLZsyYoR07dmjDhg36zW9+o6FDh2ro0KEd2iskafXq1YqLi9PatWsVEREhSZo8ebJOO+00rV+/XpdcckmfvScA8AaSYAAYwEzTlGEYklrbGf70pz8pLy9PpaWljjFff/11t/fZunWrfvSjHykkJERNTU2SpLi4OGVkZGjnzp0+mTsA+BJJMAAMYAcOHNCoUaNUXV2tRYsWKSkpSTfddJOGDx+uiIgI3XrrrWpoaOj2PmVlZXriiSccbRJtRUZG+mLqAOBTJMEAMEAVFhbq4MGDmj9/vj799FMdOHBAOTk5Gjt2rGNMVVWVhg4d2u294uPjdeqpp+riiy/u8Fh0dLRX5w0AfYEkGAAGoIaGBi1fvlzh4eHKzs7Wrl27JElhYWGOMZ988om+/fZbpaWlOa6FhYXJZrN1uN+0adNUWFio448/XiEhIb5/AwDgYyTBANDPtbS06NNPP5Uk1dbWqqCgQM8995z27dunlStXasSIEYqMjNSgQYN0++2366qrrtLBgwe1evVqDRkyxOleo0ePVlNTk5544gllZmYqJiZGxx57rH75y1/qJz/5ia644gr99Kc/VXJyskpKSvTRRx/pxBNP1Nlnn+2Hdw4APWeYpmn6exIAgJ5pf2zyoEGDdNRRR7k8Nvl///uf7rnnHu3du1ejRo3SDTfcoMcff1yDBg1ynBLX1NSku+66S6+//rpKS0s1ZcoUrVu3TlLrAroHHnhA77//vmpra5WSkqIpU6bo5z//uVM1GQD6A5JgAAAABB1OjAMAAEDQIQkGAABA0CEJBgAAQNAhCQYAAEDQIQkGAABA0CEJBgAAQNAhCQYAAEDQIQkGAABA0CEJBgAAQNAhCQYAAEDQIQkGAABA0Pn/HSiFBl1a0dYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from datetime import datetime, timedelta\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib import dates as mpl_dates\n",
    "import matplotlib.pyplot as plt # plotting plots\n",
    "plt.style.use('seaborn')\n",
    "plt.plot_date(dates, amount, linestyle = 'solid')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Number of publications')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a33357d5",
   "metadata": {
    "papermill": {
     "duration": 0.011213,
     "end_time": "2023-04-21T07:04:35.562866",
     "exception": false,
     "start_time": "2023-04-21T07:04:35.551653",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "It's pretty easy to look closer at the latest trend, let's do just that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9e37054d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-21T07:04:35.589601Z",
     "iopub.status.busy": "2023-04-21T07:04:35.589012Z",
     "iopub.status.idle": "2023-04-21T07:04:35.850303Z",
     "shell.execute_reply": "2023-04-21T07:04:35.848945Z"
    },
    "papermill": {
     "duration": 0.278766,
     "end_time": "2023-04-21T07:04:35.853086",
     "exception": false,
     "start_time": "2023-04-21T07:04:35.574320",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot_date(dates[140:], amount[140:], linestyle = 'solid')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Number of publications')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50e807e0",
   "metadata": {
    "papermill": {
     "duration": 0.011928,
     "end_time": "2023-04-21T07:04:35.877484",
     "exception": false,
     "start_time": "2023-04-21T07:04:35.865556",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "We can see exponential growth on the graph. We could look more closely, actually."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4b591adf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-21T07:04:35.903991Z",
     "iopub.status.busy": "2023-04-21T07:04:35.903530Z",
     "iopub.status.idle": "2023-04-21T07:04:36.164768Z",
     "shell.execute_reply": "2023-04-21T07:04:36.163414Z"
    },
    "papermill": {
     "duration": 0.278095,
     "end_time": "2023-04-21T07:04:36.167811",
     "exception": false,
     "start_time": "2023-04-21T07:04:35.889716",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot_date(dates[185:], amount[185:], linestyle = 'solid')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Number of publications')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0cf49ed",
   "metadata": {
    "papermill": {
     "duration": 0.012758,
     "end_time": "2023-04-21T07:04:36.195188",
     "exception": false,
     "start_time": "2023-04-21T07:04:36.182430",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Here it becomes clear that my data is actually split quarterly. Of course, the last quarter can't be taken too seriously - maybe some data can't be added instantly or the \"Google Patents\" database isn't updated that often. Actually, the graph is going down lately, no exponential growth and not even linear growth. I wonder if it's because of the recent tensions between China and the US, seeing how a lot of publications are from China."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41ac04de",
   "metadata": {
    "papermill": {
     "duration": 0.013477,
     "end_time": "2023-04-21T07:04:36.222960",
     "exception": false,
     "start_time": "2023-04-21T07:04:36.209483",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "These were the graphs for our more lenient search terms. Here I will do the same code with a more strict SQL query to see if checking only titles will have an improvement to overall accuracy and if the graph will change or not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cf252a4b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-21T07:04:36.251959Z",
     "iopub.status.busy": "2023-04-21T07:04:36.251454Z",
     "iopub.status.idle": "2023-04-21T07:04:39.507186Z",
     "shell.execute_reply": "2023-04-21T07:04:39.505319Z"
    },
    "papermill": {
     "duration": 3.275826,
     "end_time": "2023-04-21T07:04:39.512092",
     "exception": false,
     "start_time": "2023-04-21T07:04:36.236266",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/google/cloud/bigquery/client.py:440: UserWarning: Cannot create BigQuery Storage client, the dependency google-cloud-bigquery-storage is not installed.\n",
      "  \"Cannot create BigQuery Storage client, the dependency \"\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>publication_number</th>\n",
       "      <th>title_localized</th>\n",
       "      <th>publication_date</th>\n",
       "      <th>quarter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>WO-2017097596-A2</td>\n",
       "      <td>[{'text': 'Verfahren und steuergerät zum erken...</td>\n",
       "      <td>2017-06-15</td>\n",
       "      <td>2017Q2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>WO-2020082366-A1</td>\n",
       "      <td>[{'text': '电力线路检测方法、装置、无人机及计算机可读存储介质', 'langua...</td>\n",
       "      <td>2020-04-30</td>\n",
       "      <td>2020Q2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>WO-2019174255-A1</td>\n",
       "      <td>[{'text': 'Method and apparatus for detecting ...</td>\n",
       "      <td>2019-09-19</td>\n",
       "      <td>2019Q3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>WO-2018020965-A1</td>\n",
       "      <td>[{'text': '無人飛行体検知システム及び無人飛行体検知方法', 'language'...</td>\n",
       "      <td>2018-02-01</td>\n",
       "      <td>2018Q1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>RU-2745658-C2</td>\n",
       "      <td>[{'text': 'Method and device for detecting rad...</td>\n",
       "      <td>2021-03-30</td>\n",
       "      <td>2021Q1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7444</th>\n",
       "      <td>WO-2020191937-A1</td>\n",
       "      <td>[{'text': '用于机械结构件检测的无人机', 'language': 'zh', '...</td>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>2020Q4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7445</th>\n",
       "      <td>WO-2022082843-A1</td>\n",
       "      <td>[{'text': '一种多传感器融合的无人车探测避障系统及避障方法', 'language...</td>\n",
       "      <td>2022-04-28</td>\n",
       "      <td>2022Q2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7446</th>\n",
       "      <td>WO-2022021028-A1</td>\n",
       "      <td>[{'text': '目标检测方法、装置、无人机及计算机可读存储介质', 'language...</td>\n",
       "      <td>2022-02-03</td>\n",
       "      <td>2022Q1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7447</th>\n",
       "      <td>WO-2017219469-A1</td>\n",
       "      <td>[{'text': '无人船船体中裂缝扩展的超声探测器', 'language': 'zh'...</td>\n",
       "      <td>2017-12-28</td>\n",
       "      <td>2017Q4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7448</th>\n",
       "      <td>WO-2022133676-A1</td>\n",
       "      <td>[{'text': '无人机机臂状态的检测方法、无人机的控制装置及无人机', 'langua...</td>\n",
       "      <td>2022-06-30</td>\n",
       "      <td>2022Q2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7449 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     publication_number                                    title_localized  \\\n",
       "0      WO-2017097596-A2  [{'text': 'Verfahren und steuergerät zum erken...   \n",
       "1      WO-2020082366-A1  [{'text': '电力线路检测方法、装置、无人机及计算机可读存储介质', 'langua...   \n",
       "2      WO-2019174255-A1  [{'text': 'Method and apparatus for detecting ...   \n",
       "3      WO-2018020965-A1  [{'text': '無人飛行体検知システム及び無人飛行体検知方法', 'language'...   \n",
       "4         RU-2745658-C2  [{'text': 'Method and device for detecting rad...   \n",
       "...                 ...                                                ...   \n",
       "7444   WO-2020191937-A1  [{'text': '用于机械结构件检测的无人机', 'language': 'zh', '...   \n",
       "7445   WO-2022082843-A1  [{'text': '一种多传感器融合的无人车探测避障系统及避障方法', 'language...   \n",
       "7446   WO-2022021028-A1  [{'text': '目标检测方法、装置、无人机及计算机可读存储介质', 'language...   \n",
       "7447   WO-2017219469-A1  [{'text': '无人船船体中裂缝扩展的超声探测器', 'language': 'zh'...   \n",
       "7448   WO-2022133676-A1  [{'text': '无人机机臂状态的检测方法、无人机的控制装置及无人机', 'langua...   \n",
       "\n",
       "     publication_date quarter  \n",
       "0          2017-06-15  2017Q2  \n",
       "1          2020-04-30  2020Q2  \n",
       "2          2019-09-19  2019Q3  \n",
       "3          2018-02-01  2018Q1  \n",
       "4          2021-03-30  2021Q1  \n",
       "...               ...     ...  \n",
       "7444       2020-10-01  2020Q4  \n",
       "7445       2022-04-28  2022Q2  \n",
       "7446       2022-02-03  2022Q1  \n",
       "7447       2017-12-28  2017Q4  \n",
       "7448       2022-06-30  2022Q2  \n",
       "\n",
       "[7449 rows x 4 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_rows', 10)\n",
    "pd.set_option('display.max_columns', 10)\n",
    "query2 = \"\"\"\n",
    "SELECT publication_number,\n",
    "title_localized,\n",
    "publication_date,\n",
    "FROM\n",
    "  `patents-public-data.patents.publications`\n",
    "WHERE EXISTS(\n",
    "    SELECT text\n",
    "    FROM UNNEST(title_localized)\n",
    "    WHERE (\n",
    "      (text LIKE '% drone%' OR\n",
    "      text LIKE '%Drone%' OR\n",
    "      text LIKE '%unmanned%' OR\n",
    "      text LIKE '%Unmanned%') AND\n",
    "      (text LIKE '%detect%' OR\n",
    "      text LIKE '%Detect%' OR\n",
    "      text LIKE '%identif%' OR\n",
    "      text LIKE '%Identif%' OR\n",
    "      text LIKE '%Spot%' OR\n",
    "      text LIKE '%spot%')\n",
    "    )\n",
    ");\n",
    "        \"\"\"\n",
    "dataframe2 = patents.query_to_pandas_safe(query2, max_gb_scanned=17)\n",
    "dataframe2['publication_date'] = pd.to_datetime(dataframe2['publication_date'], format = '%Y%m%d')\n",
    "dataframe2['quarter'] = dataframe2['publication_date'].dt.to_period('Q')\n",
    "dataframe2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7a40f821",
   "metadata": {
    "collapsed": true,
    "execution": {
     "iopub.execute_input": "2023-04-21T07:04:39.545476Z",
     "iopub.status.busy": "2023-04-21T07:04:39.544933Z",
     "iopub.status.idle": "2023-04-21T07:04:39.601730Z",
     "shell.execute_reply": "2023-04-21T07:04:39.600155Z"
    },
    "jupyter": {
     "outputs_hidden": true
    },
    "papermill": {
     "duration": 0.0778,
     "end_time": "2023-04-21T07:04:39.605627",
     "exception": false,
     "start_time": "2023-04-21T07:04:39.527827",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 142 quarters with patents.\n",
      "('1977Q1', '1977Q2', '1978Q1', '1978Q4', '1979Q1', '1979Q2', '1979Q3', '1980Q1', '1981Q1', '1982Q2', '1983Q1', '1983Q2', '1983Q3', '1984Q2', '1984Q3', '1984Q4', '1985Q1', '1985Q2', '1985Q3', '1985Q4', '1986Q1', '1986Q2', '1986Q3', '1986Q4', '1987Q1', '1987Q2', '1987Q3', '1987Q4', '1988Q2', '1988Q3', '1988Q4', '1989Q1', '1989Q2', '1989Q3', '1989Q4', '1990Q1', '1990Q2', '1990Q3', '1990Q4', '1991Q1', '1991Q3', '1991Q4', '1992Q1', '1992Q3', '1992Q4', '1993Q2', '1993Q3', '1993Q4', '1994Q1', '1994Q2', '1994Q3', '1994Q4', '1995Q1', '1995Q2', '1995Q4', '1996Q1', '1996Q2', '1996Q3', '1997Q2', '1997Q3', '1997Q4', '1998Q1', '1998Q2', '1999Q2', '1999Q3', '2000Q1', '2000Q2', '2000Q3', '2000Q4', '2001Q1', '2001Q3', '2001Q4', '2002Q1', '2002Q2', '2002Q3', '2003Q1', '2003Q2', '2003Q3', '2003Q4', '2004Q3', '2005Q4', '2006Q3', '2007Q3', '2007Q4', '2008Q2', '2008Q3', '2008Q4', '2009Q1', '2009Q3', '2010Q1', '2010Q2', '2010Q3', '2010Q4', '2011Q1', '2011Q2', '2011Q3', '2011Q4', '2012Q1', '2012Q2', '2012Q3', '2012Q4', '2013Q1', '2013Q2', '2013Q3', '2013Q4', '2014Q1', '2014Q2', '2014Q3', '2014Q4', '2015Q1', '2015Q2', '2015Q3', '2015Q4', '2016Q1', '2016Q2', '2016Q3', '2016Q4', '2017Q1', '2017Q2', '2017Q3', '2017Q4', '2018Q1', '2018Q2', '2018Q3', '2018Q4', '2019Q1', '2019Q2', '2019Q3', '2019Q4', '2020Q1', '2020Q2', '2020Q3', '2020Q4', '2021Q1', '2021Q2', '2021Q3', '2021Q4', '2022Q1', '2022Q2', '2022Q3', '2022Q4', '2023Q1')\n",
      "(1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 6, 4, 3, 1, 4, 2, 1, 4, 5, 5, 16, 5, 1, 2, 3, 5, 4, 5, 5, 7, 8, 9, 2, 2, 6, 4, 4, 3, 2, 6, 3, 4, 2, 2, 5, 1, 1, 2, 1, 3, 1, 3, 3, 1, 2, 1, 4, 1, 2, 2, 1, 3, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 3, 2, 4, 3, 4, 2, 2, 8, 3, 3, 7, 5, 1, 6, 6, 10, 6, 4, 7, 6, 9, 9, 19, 20, 24, 29, 37, 39, 54, 84, 72, 78, 126, 102, 114, 135, 187, 187, 188, 191, 222, 214, 207, 233, 313, 313, 341, 338, 382, 371, 441, 409, 482, 433, 445, 287)\n"
     ]
    }
   ],
   "source": [
    "dataframe2['quarters'] = dataframe2['quarter'].astype(str)\n",
    "result2 = Counter(dataframe2['quarters'])\n",
    "\n",
    "print(\"There are\", len(result2), \"quarters with patents.\")\n",
    "\n",
    "time2 = []\n",
    "amount2 = []\n",
    "for item in result2:\n",
    "    time2.append(item)\n",
    "    amount2.append(result2[item])\n",
    "time2, amount2 = zip(*sorted(zip(time2, amount2)))\n",
    "print(time2)\n",
    "print(amount2)\n",
    "date2 = []\n",
    "for i in range(len(time2)):\n",
    "    date2 += [time2[i][:4] + \"-\" + str((int(time2[i][5]))*3-2) + \"-1\"]\n",
    "dates2 = pd.to_datetime(date2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "12bc7e53",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-21T07:04:39.636329Z",
     "iopub.status.busy": "2023-04-21T07:04:39.635782Z",
     "iopub.status.idle": "2023-04-21T07:04:39.948827Z",
     "shell.execute_reply": "2023-04-21T07:04:39.946926Z"
    },
    "papermill": {
     "duration": 0.333016,
     "end_time": "2023-04-21T07:04:39.952711",
     "exception": false,
     "start_time": "2023-04-21T07:04:39.619695",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot_date(dates2, amount2, linestyle = 'solid')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Number of publications')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21c29772",
   "metadata": {
    "papermill": {
     "duration": 0.016843,
     "end_time": "2023-04-21T07:04:39.986557",
     "exception": false,
     "start_time": "2023-04-21T07:04:39.969714",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "So far, it looks like it's not as random as the previous one. Let's take a look from about 2012 when the quick growth just begins."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c806e8b3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-21T07:04:40.019429Z",
     "iopub.status.busy": "2023-04-21T07:04:40.018962Z",
     "iopub.status.idle": "2023-04-21T07:04:40.294829Z",
     "shell.execute_reply": "2023-04-21T07:04:40.293699Z"
    },
    "papermill": {
     "duration": 0.295809,
     "end_time": "2023-04-21T07:04:40.297451",
     "exception": false,
     "start_time": "2023-04-21T07:04:40.001642",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot_date(dates2[90:], amount2[90:], linestyle = 'solid')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Number of publications')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "029994ad",
   "metadata": {
    "papermill": {
     "duration": 0.014453,
     "end_time": "2023-04-21T07:04:40.326636",
     "exception": false,
     "start_time": "2023-04-21T07:04:40.312183",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "This graph is much steadier than for the other query. A beautiful exponential growth. Except for the last quarter, obviously."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d444b0d3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-21T07:04:40.358531Z",
     "iopub.status.busy": "2023-04-21T07:04:40.357899Z",
     "iopub.status.idle": "2023-04-21T07:04:40.784442Z",
     "shell.execute_reply": "2023-04-21T07:04:40.783361Z"
    },
    "papermill": {
     "duration": 0.445613,
     "end_time": "2023-04-21T07:04:40.786982",
     "exception": false,
     "start_time": "2023-04-21T07:04:40.341369",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot_date(dates2[110:], amount2[110:], linestyle = 'solid')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Number of publications')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bbd029f",
   "metadata": {
    "papermill": {
     "duration": 0.016007,
     "end_time": "2023-04-21T07:04:40.818965",
     "exception": false,
     "start_time": "2023-04-21T07:04:40.802958",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "From 2015 growth start to look linear - maybe that means we picked a rather short time period, even though it was the most meaningful for the growth of our topic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ccaef356",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-21T07:04:40.854193Z",
     "iopub.status.busy": "2023-04-21T07:04:40.853123Z",
     "iopub.status.idle": "2023-04-21T07:04:41.175475Z",
     "shell.execute_reply": "2023-04-21T07:04:41.173886Z"
    },
    "papermill": {
     "duration": 0.344112,
     "end_time": "2023-04-21T07:04:41.178612",
     "exception": false,
     "start_time": "2023-04-21T07:04:40.834500",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot_date(dates2[132:], amount2[132:], linestyle = 'solid')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Number of publications')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39268e70",
   "metadata": {
    "papermill": {
     "duration": 0.018443,
     "end_time": "2023-04-21T07:04:41.213542",
     "exception": false,
     "start_time": "2023-04-21T07:04:41.195099",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Here we can observe the latest trend of drone detection publications."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "921b98e5",
   "metadata": {
    "papermill": {
     "duration": 0.017607,
     "end_time": "2023-04-21T07:04:41.252590",
     "exception": false,
     "start_time": "2023-04-21T07:04:41.234983",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "As a conclusion, I'd like to point out that the latest trend for \"higher-accuracy search terms\" results is a steady and rather fast growth while the result for the other query was stagnation. This actually shows that accuracy of the search terms could mean a lot - we got a very different result just from that. So, if we were asked to learn whether this topic is growing in relevance or not the answers would have been opposites to each other."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.12"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 32.113101,
   "end_time": "2023-04-21T07:04:41.994679",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2023-04-21T07:04:09.881578",
   "version": "2.4.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}