4 - Examiner Monitor.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Examiner Monitor\n",
    "\n",
    "This notebook shows how patent_client can be used to examine what is on a particular examiner's docket, and some simple statistics for a particular examiner.\n",
    "\n",
    "The input to this will just be an application that we know the examiner is handling - like for example a case we just got an Office Action from."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "app_no = '13629597'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we'll do our standard imports, query the USApplication object for that application, and retreive the examiner's name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The examiner for 13629597 is FOSTER, GERRAD A\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from patent_client_plus import USApplication\n",
    "\n",
    "app = USApplication.objects.get(app_no)\n",
    "examiner = app.app_exam_name\n",
    "print(f'The examiner for {app_no} is {examiner}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we'll go and retreive all the cases that this examiner has ever handled by searching USApplication for all applications listing the same examiner, and then populate a subset of that data into a Pandas DataFrame.\n",
    "\n",
    "*NOTE:* Because the USApplication object is lazy, this might take a minute - patent_client is now retrieving the application data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>appl_id</th>\n",
       "      <th>app_filing_date</th>\n",
       "      <th>patent_title</th>\n",
       "      <th>patent_number</th>\n",
       "      <th>app_status</th>\n",
       "      <th>app_status_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13046650</td>\n",
       "      <td>2011-03-11</td>\n",
       "      <td>LEARNING ROAD FEATURE DELAY TIMES BASED ON AGG...</td>\n",
       "      <td>9257042</td>\n",
       "      <td>Patented Case</td>\n",
       "      <td>2016-01-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13256177</td>\n",
       "      <td>2011-09-12</td>\n",
       "      <td>Method For Optimizing The Power Consumption Of...</td>\n",
       "      <td>None</td>\n",
       "      <td>Abandoned -- Failure to Respond to an Office A...</td>\n",
       "      <td>2014-08-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13273818</td>\n",
       "      <td>2011-10-14</td>\n",
       "      <td>METHOD AND APPARATUS FOR DETERMINING THE RELAT...</td>\n",
       "      <td>8990002</td>\n",
       "      <td>Patented Case</td>\n",
       "      <td>2015-03-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13280377</td>\n",
       "      <td>2011-10-25</td>\n",
       "      <td>RANGE DISPLAY APPARATUS</td>\n",
       "      <td>None</td>\n",
       "      <td>Abandoned -- Failure to Respond to an Office A...</td>\n",
       "      <td>2015-05-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13323676</td>\n",
       "      <td>2011-12-12</td>\n",
       "      <td>RELAY FUSION DETECTING TECHNIQUE FOR HIGH VOLT...</td>\n",
       "      <td>None</td>\n",
       "      <td>Abandoned -- Failure to Respond to an Office A...</td>\n",
       "      <td>2014-06-23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    appl_id app_filing_date  \\\n",
       "0  13046650      2011-03-11   \n",
       "1  13256177      2011-09-12   \n",
       "2  13273818      2011-10-14   \n",
       "3  13280377      2011-10-25   \n",
       "4  13323676      2011-12-12   \n",
       "\n",
       "                                        patent_title patent_number  \\\n",
       "0  LEARNING ROAD FEATURE DELAY TIMES BASED ON AGG...       9257042   \n",
       "1  Method For Optimizing The Power Consumption Of...          None   \n",
       "2  METHOD AND APPARATUS FOR DETERMINING THE RELAT...       8990002   \n",
       "3                            RANGE DISPLAY APPARATUS          None   \n",
       "4  RELAY FUSION DETECTING TECHNIQUE FOR HIGH VOLT...          None   \n",
       "\n",
       "                                          app_status app_status_date  \n",
       "0                                      Patented Case      2016-01-20  \n",
       "1  Abandoned -- Failure to Respond to an Office A...      2014-08-21  \n",
       "2                                      Patented Case      2015-03-04  \n",
       "3  Abandoned -- Failure to Respond to an Office A...      2015-05-31  \n",
       "4  Abandoned -- Failure to Respond to an Office A...      2014-06-23  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "examiners_applications = USApplication.objects.filter(app_exam_name=examiner)\n",
    "fields = ['appl_id', 'app_filing_date', 'patent_title', 'patent_number', 'app_status', 'app_status_date']\n",
    "exam_df = pd.DataFrame.from_records(examiners_applications.values(*fields))\n",
    "exam_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simple Lookups\n",
    "\n",
    "Now that we have all the Examiner's cases, we want to take a peek at what the Examiner is working on. To do that, we'll make a list of application status names that we're interested in - those that indicate that the Examiner is still working on the case and the case isn't abandoned, allowed, or awaiting a first office action"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>appl_id</th>\n",
       "      <th>app_filing_date</th>\n",
       "      <th>patent_title</th>\n",
       "      <th>patent_number</th>\n",
       "      <th>app_status</th>\n",
       "      <th>app_status_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>14260279</td>\n",
       "      <td>2014-04-23</td>\n",
       "      <td>SYSTEMS AND METHODS FOR TRANSFORMING SENSORY M...</td>\n",
       "      <td>None</td>\n",
       "      <td>Final Rejection Mailed</td>\n",
       "      <td>2018-11-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>14534144</td>\n",
       "      <td>2014-11-05</td>\n",
       "      <td>APPARATUS FOR PERFORMING SPECIFIC TASK OF VEHI...</td>\n",
       "      <td>None</td>\n",
       "      <td>Advisory Action Mailed</td>\n",
       "      <td>2018-12-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>14608014</td>\n",
       "      <td>2015-01-28</td>\n",
       "      <td>Unmanned Aerial Vehicle</td>\n",
       "      <td>None</td>\n",
       "      <td>Response to Non-Final Office Action Entered an...</td>\n",
       "      <td>2016-12-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>14721299</td>\n",
       "      <td>2015-05-26</td>\n",
       "      <td>CONTROL METHOD FOR VEHICLE</td>\n",
       "      <td>None</td>\n",
       "      <td>Response to Non-Final Office Action Entered an...</td>\n",
       "      <td>2019-01-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>14812102</td>\n",
       "      <td>2015-07-29</td>\n",
       "      <td>METHOD AND SYSTEM FOR PERFORMING VEHICLE INSPE...</td>\n",
       "      <td>None</td>\n",
       "      <td>Response to Non-Final Office Action Entered an...</td>\n",
       "      <td>2018-11-15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      appl_id app_filing_date  \\\n",
       "79   14260279      2014-04-23   \n",
       "93   14534144      2014-11-05   \n",
       "104  14608014      2015-01-28   \n",
       "114  14721299      2015-05-26   \n",
       "116  14812102      2015-07-29   \n",
       "\n",
       "                                          patent_title patent_number  \\\n",
       "79   SYSTEMS AND METHODS FOR TRANSFORMING SENSORY M...          None   \n",
       "93   APPARATUS FOR PERFORMING SPECIFIC TASK OF VEHI...          None   \n",
       "104                            Unmanned Aerial Vehicle          None   \n",
       "114                         CONTROL METHOD FOR VEHICLE          None   \n",
       "116  METHOD AND SYSTEM FOR PERFORMING VEHICLE INSPE...          None   \n",
       "\n",
       "                                            app_status app_status_date  \n",
       "79                              Final Rejection Mailed      2018-11-28  \n",
       "93                              Advisory Action Mailed      2018-12-04  \n",
       "104  Response to Non-Final Office Action Entered an...      2016-12-10  \n",
       "114  Response to Non-Final Office Action Entered an...      2019-01-02  \n",
       "116  Response to Non-Final Office Action Entered an...      2018-11-15  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "active_pros_statuses = [\n",
    "    'Advisory Action Mailed',\n",
    "    'Board of Appeals Decision Rendered',\n",
    "    'Non Final Action Mailed',\n",
    "    'Response after Final Action Forwarded to Examiner',\n",
    "    'Response to Non-Final Office Action Entered and Forwarded to Examiner',\n",
    "    'Final Rejection Mailed',\n",
    "]\n",
    "\n",
    "active_df = exam_df[exam_df['app_status'].isin(active_pros_statuses)]\n",
    "active_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Maybe we're trying to predict when an Examiner might get to a particular case. To do that, we can use a Pandas filter to select all the cases that have the status \"Docketed New Case - Ready for Examination\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>appl_id</th>\n",
       "      <th>app_filing_date</th>\n",
       "      <th>patent_title</th>\n",
       "      <th>patent_number</th>\n",
       "      <th>app_status</th>\n",
       "      <th>app_status_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>15370028</td>\n",
       "      <td>2016-12-06</td>\n",
       "      <td>Method for Controlling Position of Vehicle</td>\n",
       "      <td>None</td>\n",
       "      <td>Docketed New Case - Ready for Examination</td>\n",
       "      <td>2017-01-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>15375749</td>\n",
       "      <td>2016-12-12</td>\n",
       "      <td>APPARATUS AND METHOD OF REDUCING VIBRATIONS OF...</td>\n",
       "      <td>None</td>\n",
       "      <td>Docketed New Case - Ready for Examination</td>\n",
       "      <td>2017-03-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>15378628</td>\n",
       "      <td>2016-12-14</td>\n",
       "      <td>CONTROL UNIT FOR VEHICLE AND CONTROL METHOD FO...</td>\n",
       "      <td>None</td>\n",
       "      <td>Docketed New Case - Ready for Examination</td>\n",
       "      <td>2017-03-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>15320717</td>\n",
       "      <td>2016-12-20</td>\n",
       "      <td>MOTOR CONTROL DEVICE, MOTOR UNIT INCLUDING SAI...</td>\n",
       "      <td>None</td>\n",
       "      <td>Docketed New Case - Ready for Examination</td>\n",
       "      <td>2017-03-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>15409787</td>\n",
       "      <td>2017-01-19</td>\n",
       "      <td>V2V Collaborative Relative Positioning System</td>\n",
       "      <td>None</td>\n",
       "      <td>Docketed New Case - Ready for Examination</td>\n",
       "      <td>2017-03-02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      appl_id app_filing_date  \\\n",
       "171  15370028      2016-12-06   \n",
       "173  15375749      2016-12-12   \n",
       "174  15378628      2016-12-14   \n",
       "176  15320717      2016-12-20   \n",
       "178  15409787      2017-01-19   \n",
       "\n",
       "                                          patent_title patent_number  \\\n",
       "171         Method for Controlling Position of Vehicle          None   \n",
       "173  APPARATUS AND METHOD OF REDUCING VIBRATIONS OF...          None   \n",
       "174  CONTROL UNIT FOR VEHICLE AND CONTROL METHOD FO...          None   \n",
       "176  MOTOR CONTROL DEVICE, MOTOR UNIT INCLUDING SAI...          None   \n",
       "178      V2V Collaborative Relative Positioning System          None   \n",
       "\n",
       "                                    app_status app_status_date  \n",
       "171  Docketed New Case - Ready for Examination      2017-01-10  \n",
       "173  Docketed New Case - Ready for Examination      2017-03-02  \n",
       "174  Docketed New Case - Ready for Examination      2017-03-02  \n",
       "176  Docketed New Case - Ready for Examination      2017-03-02  \n",
       "178  Docketed New Case - Ready for Examination      2017-03-02  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "docket_df = exam_df[exam_df['app_status'] == 'Docketed New Case - Ready for Examination']\n",
    "docket_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we can use a simple function to count how many cases are ahead of ours, first by sorting by filing date, then counting how many cases down our application is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 47 cases ahead of 13629597 on Examiner FOSTER, GERRAD A's Docket\n"
     ]
    }
   ],
   "source": [
    "docket_df = docket_df.sort_values('app_filing_date', ascending=True)\n",
    "\n",
    "counter = 0\n",
    "\n",
    "for i, row in docket_df.iterrows():\n",
    "    if row['appl_id'] == app_no:\n",
    "        break\n",
    "    counter += 1\n",
    "\n",
    "print(f\"There are {counter} cases ahead of {app_no} on Examiner {examiner}'s Docket\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Statistical Analysis\n",
    "\n",
    "Maybe we want some more detailed information. Things like average pendency, overall case load, etc. Let's do some of those statistics now.\n",
    "\n",
    "First, we'll take a look at the examiner's entire docket, bucketed by years, to get a general sense for the Examiner's workflow and case dispositions. To start, we'll add a column that will classify a case as either \"Patented\", \"Abandoned\", or \"Pending\". To do that, we'll use the Pandas \"apply\" function. If the case has a patent number, we'll categorize it as \"Patented.\" This is better than looking at status, because the case might have lapsed for failure to pay maintenace fees. Then we'll look at the status and see if the word \"abandoned\" appears in it. If so, we can be reasonably sure the case has been abandoned. If it doesn't fit in either category, then the case is still pending."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>appl_id</th>\n",
       "      <th>app_filing_date</th>\n",
       "      <th>patent_title</th>\n",
       "      <th>patent_number</th>\n",
       "      <th>app_status</th>\n",
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       "      <th>0</th>\n",
       "      <td>13046650</td>\n",
       "      <td>2011-03-11</td>\n",
       "      <td>LEARNING ROAD FEATURE DELAY TIMES BASED ON AGG...</td>\n",
       "      <td>9257042</td>\n",
       "      <td>Patented Case</td>\n",
       "      <td>2016-01-20</td>\n",
       "      <td>2011</td>\n",
       "      <td>Patented</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13256177</td>\n",
       "      <td>2011-09-12</td>\n",
       "      <td>Method For Optimizing The Power Consumption Of...</td>\n",
       "      <td>None</td>\n",
       "      <td>Abandoned -- Failure to Respond to an Office A...</td>\n",
       "      <td>2014-08-21</td>\n",
       "      <td>2011</td>\n",
       "      <td>Abandoned</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13273818</td>\n",
       "      <td>2011-10-14</td>\n",
       "      <td>METHOD AND APPARATUS FOR DETERMINING THE RELAT...</td>\n",
       "      <td>8990002</td>\n",
       "      <td>Patented Case</td>\n",
       "      <td>2015-03-04</td>\n",
       "      <td>2011</td>\n",
       "      <td>Patented</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13280377</td>\n",
       "      <td>2011-10-25</td>\n",
       "      <td>RANGE DISPLAY APPARATUS</td>\n",
       "      <td>None</td>\n",
       "      <td>Abandoned -- Failure to Respond to an Office A...</td>\n",
       "      <td>2015-05-31</td>\n",
       "      <td>2011</td>\n",
       "      <td>Abandoned</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13323676</td>\n",
       "      <td>2011-12-12</td>\n",
       "      <td>RELAY FUSION DETECTING TECHNIQUE FOR HIGH VOLT...</td>\n",
       "      <td>None</td>\n",
       "      <td>Abandoned -- Failure to Respond to an Office A...</td>\n",
       "      <td>2014-06-23</td>\n",
       "      <td>2011</td>\n",
       "      <td>Abandoned</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    appl_id app_filing_date  \\\n",
       "0  13046650      2011-03-11   \n",
       "1  13256177      2011-09-12   \n",
       "2  13273818      2011-10-14   \n",
       "3  13280377      2011-10-25   \n",
       "4  13323676      2011-12-12   \n",
       "\n",
       "                                        patent_title patent_number  \\\n",
       "0  LEARNING ROAD FEATURE DELAY TIMES BASED ON AGG...       9257042   \n",
       "1  Method For Optimizing The Power Consumption Of...          None   \n",
       "2  METHOD AND APPARATUS FOR DETERMINING THE RELAT...       8990002   \n",
       "3                            RANGE DISPLAY APPARATUS          None   \n",
       "4  RELAY FUSION DETECTING TECHNIQUE FOR HIGH VOLT...          None   \n",
       "\n",
       "                                          app_status app_status_date  year  \\\n",
       "0                                      Patented Case      2016-01-20  2011   \n",
       "1  Abandoned -- Failure to Respond to an Office A...      2014-08-21  2011   \n",
       "2                                      Patented Case      2015-03-04  2011   \n",
       "3  Abandoned -- Failure to Respond to an Office A...      2015-05-31  2011   \n",
       "4  Abandoned -- Failure to Respond to an Office A...      2014-06-23  2011   \n",
       "\n",
       "       final  \n",
       "0   Patented  \n",
       "1  Abandoned  \n",
       "2   Patented  \n",
       "3  Abandoned  \n",
       "4  Abandoned  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def final_disposition(row):\n",
    "    row['year'] = row['app_filing_date'].year\n",
    "    if row['patent_number']:\n",
    "        row['final'] = 'Patented'\n",
    "    elif 'abandon' in str(row['app_status']).lower():\n",
    "        row['final'] = 'Abandoned'\n",
    "    else:\n",
    "        row['final'] = 'Pending'\n",
    "    return row\n",
    "        \n",
    "final_df = exam_df[~exam_df['appl_id'].str.contains('PCT')].apply(final_disposition, axis=1)\n",
    "\n",
    "final_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we need to transform the Pandas dataframe into something we can plot. What we would like to have is a dataframe that looks like this:\n",
    "\n",
    "| Year | Pending | Patented | Expired |\n",
    "|------|---------|----------|---------|\n",
    "| 2015 | 13      | 12       | 1       |\n",
    "| 2016 | 14      | 15       | 15      |\n",
    "\n",
    "To do that, we're going to need to do an aggregation by year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "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>final</th>\n",
       "      <th>Abandoned</th>\n",
       "      <th>Patented</th>\n",
       "      <th>Pending</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>19.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>3.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>3.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "final  Abandoned  Patented  Pending\n",
       "year                               \n",
       "2011         3.0       2.0      NaN\n",
       "2012         6.0      11.0      NaN\n",
       "2013        19.0      27.0      NaN\n",
       "2014         3.0      26.0      4.0\n",
       "2015         3.0      24.0      6.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "def patented(series):\n",
    "    return len(list(filter(lambda x: x == '')))\n",
    "\n",
    "# Group by year, and by final, then create a count of each\n",
    "result = final_df.groupby(['year', 'final']).agg({'final': 'count'}).rename(columns={'final': 'count'})\n",
    "result = result.reset_index() # Convert indexes into columns\n",
    "result = result.pivot(index='year', columns='final', values='count')\n",
    "\n",
    "result.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And now we plot it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x119c919b0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "result.plot.bar(figsize=(10,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Average Time to First Action\n",
    "\n",
    "Maybe we'd like to know how long the Examiner takes from docketing until a first Office Action. To do that, we'll use the transaction information stored on each case. For example, from our original app, the transactions look like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "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>code</th>\n",
       "      <th>description</th>\n",
       "      <th>record_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M1551</td>\n",
       "      <td>Payment of Maintenance Fee, 4th Year, Large En...</td>\n",
       "      <td>2018-07-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PGM/</td>\n",
       "      <td>Recordation of Patent Grant Mailed</td>\n",
       "      <td>2015-01-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EML_NTR</td>\n",
       "      <td>Email Notification</td>\n",
       "      <td>2014-12-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>WPIR</td>\n",
       "      <td>Issue Notification Mailed</td>\n",
       "      <td>2014-12-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PTAC</td>\n",
       "      <td>Patent Issue Date Used in PTA Calculation</td>\n",
       "      <td>2015-01-13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      code                                        description record_date\n",
       "0    M1551  Payment of Maintenance Fee, 4th Year, Large En...  2018-07-06\n",
       "1     PGM/                 Recordation of Patent Grant Mailed  2015-01-13\n",
       "2  EML_NTR                                 Email Notification  2014-12-25\n",
       "3     WPIR                          Issue Notification Mailed  2014-12-23\n",
       "4     PTAC          Patent Issue Date Used in PTA Calculation  2015-01-13"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame.from_records(app.transactions).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we need to know all the possible transaction codes. To do that, we can take every case in the examiner's stack, and create a set of code-description tuples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 239 entries, 0 to 238\n",
      "Data columns (total 2 columns):\n",
      "code           239 non-null object\n",
      "description    239 non-null object\n",
      "dtypes: object(2)\n",
      "memory usage: 3.8+ KB\n"
     ]
    }
   ],
   "source": [
    "exam_codes = list(set((r['code'], r['description']) for a in examiners_applications for r in a.transactions))\n",
    "exam_codes = [{'code': r[0], 'description': r[1]} for r in exam_codes]\n",
    "codes_df = pd.DataFrame.from_records(exam_codes)\n",
    "codes_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Obviously there are entirely too many possible codes / descriptions to easily pick through in a notebook, so we'll export the spreadsheet to excel for review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "codes_df.to_excel('codes.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After review, it looks like all the relevant codes for a first action by the examiner begin one one of three prefixes - MFA, CT, or MC. And the case is initially docketed with a code 'DOCK'. So let's use this to calculate a time to first office action for each case. Since we're digging deeper into each case, we'll put the applications themselves as a column of the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "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>appl_id</th>\n",
       "      <th>obj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13046650</td>\n",
       "      <td>&lt;USApplication(appl_id=13046650)&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13256177</td>\n",
       "      <td>&lt;USApplication(appl_id=13256177)&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13273818</td>\n",
       "      <td>&lt;USApplication(appl_id=13273818)&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13280377</td>\n",
       "      <td>&lt;USApplication(appl_id=13280377)&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13323676</td>\n",
       "      <td>&lt;USApplication(appl_id=13323676)&gt;</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    appl_id                                obj\n",
       "0  13046650  <USApplication(appl_id=13046650)>\n",
       "1  13256177  <USApplication(appl_id=13256177)>\n",
       "2  13273818  <USApplication(appl_id=13273818)>\n",
       "3  13280377  <USApplication(appl_id=13280377)>\n",
       "4  13323676  <USApplication(appl_id=13323676)>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "app_df = pd.DataFrame.from_records({'appl_id': a.appl_id, 'obj': a} for a in examiners_applications if 'PCT' not in a.appl_id)\n",
    "app_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we'll write a function that takes an application, and returns the time to first Examiner's action, measured as days from DOCK to an action by the examiner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "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>appl_id</th>\n",
       "      <th>docketed_date</th>\n",
       "      <th>first_action</th>\n",
       "      <th>first_action_date</th>\n",
       "      <th>obj</th>\n",
       "      <th>time_to_action</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13046650</td>\n",
       "      <td>2011-07-05</td>\n",
       "      <td>Non-Final Rejection</td>\n",
       "      <td>2013-03-02</td>\n",
       "      <td>&lt;USApplication(appl_id=13046650)&gt;</td>\n",
       "      <td>606.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13256177</td>\n",
       "      <td>2011-10-31</td>\n",
       "      <td>Non-Final Rejection</td>\n",
       "      <td>2013-09-16</td>\n",
       "      <td>&lt;USApplication(appl_id=13256177)&gt;</td>\n",
       "      <td>686.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13273818</td>\n",
       "      <td>2012-02-03</td>\n",
       "      <td>Non-Final Rejection</td>\n",
       "      <td>2014-04-07</td>\n",
       "      <td>&lt;USApplication(appl_id=13273818)&gt;</td>\n",
       "      <td>794.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13280377</td>\n",
       "      <td>2011-12-06</td>\n",
       "      <td>Non-Final Rejection</td>\n",
       "      <td>2013-11-13</td>\n",
       "      <td>&lt;USApplication(appl_id=13280377)&gt;</td>\n",
       "      <td>708.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13323676</td>\n",
       "      <td>2012-02-03</td>\n",
       "      <td>Non-Final Rejection</td>\n",
       "      <td>2013-11-18</td>\n",
       "      <td>&lt;USApplication(appl_id=13323676)&gt;</td>\n",
       "      <td>654.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    appl_id docketed_date         first_action first_action_date  \\\n",
       "0  13046650    2011-07-05  Non-Final Rejection        2013-03-02   \n",
       "1  13256177    2011-10-31  Non-Final Rejection        2013-09-16   \n",
       "2  13273818    2012-02-03  Non-Final Rejection        2014-04-07   \n",
       "3  13280377    2011-12-06  Non-Final Rejection        2013-11-13   \n",
       "4  13323676    2012-02-03  Non-Final Rejection        2013-11-18   \n",
       "\n",
       "                                 obj  time_to_action  \n",
       "0  <USApplication(appl_id=13046650)>           606.0  \n",
       "1  <USApplication(appl_id=13256177)>           686.0  \n",
       "2  <USApplication(appl_id=13273818)>           794.0  \n",
       "3  <USApplication(appl_id=13280377)>           708.0  \n",
       "4  <USApplication(appl_id=13323676)>           654.0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import re\n",
    "from datetime import datetime\n",
    "\n",
    "first_action_re=re.compile('^(MFA|CT|MC)')\n",
    "\n",
    "def time_to_action(row):\n",
    "    #print(obj.transactions)\n",
    "    obj = row['obj']\n",
    "    transactions = sorted(obj.transaction_history, key=lambda x: x.date)\n",
    "    docketing_date = next(r for r in transactions if r.code == 'DOCK').date\n",
    "    try:\n",
    "        first_action = next(r for r in transactions if first_action_re.search(r.code))\n",
    "    except StopIteration:\n",
    "        return row\n",
    "    row['first_action'] = first_action.description\n",
    "    row['first_action_date'] = first_action.date\n",
    "    row['docketed_date'] = docketing_date\n",
    "    \n",
    "    row['time_to_action'] = (\n",
    "        first_action.date\n",
    "        - docketing_date\n",
    "    ).days\n",
    "    return row\n",
    "\n",
    "\n",
    "app_df= app_df.apply(time_to_action, axis=1)\n",
    "app_df = app_df.astype({'docketed_date': datetime, 'first_action_date': datetime})\n",
    "app_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And now we can look at the aggregate stats for time to first office action:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time_to_action</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>191.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>355.476440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>199.396532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>201.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>373.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>520.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>794.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       time_to_action\n",
       "count      191.000000\n",
       "mean       355.476440\n",
       "std        199.396532\n",
       "min          0.000000\n",
       "25%        201.500000\n",
       "50%        373.000000\n",
       "75%        520.500000\n",
       "max        794.000000"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "app_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also look at the average time by what the response type is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">time_to_action</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first_action</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mail First Action Interview Office Action</th>\n",
       "      <td>1.0</td>\n",
       "      <td>672.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>672.0</td>\n",
       "      <td>672.0</td>\n",
       "      <td>672.0</td>\n",
       "      <td>672.0</td>\n",
       "      <td>672.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Non-Final Rejection</th>\n",
       "      <td>179.0</td>\n",
       "      <td>352.737430</td>\n",
       "      <td>199.089566</td>\n",
       "      <td>0.0</td>\n",
       "      <td>201.5</td>\n",
       "      <td>370.0</td>\n",
       "      <td>515.5</td>\n",
       "      <td>794.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Restriction/Election Requirement</th>\n",
       "      <td>11.0</td>\n",
       "      <td>371.272727</td>\n",
       "      <td>198.646969</td>\n",
       "      <td>24.0</td>\n",
       "      <td>219.5</td>\n",
       "      <td>422.0</td>\n",
       "      <td>514.0</td>\n",
       "      <td>584.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          time_to_action              \\\n",
       "                                                   count        mean   \n",
       "first_action                                                           \n",
       "Mail First Action Interview Office Action            1.0  672.000000   \n",
       "Non-Final Rejection                                179.0  352.737430   \n",
       "Restriction/Election Requirement                    11.0  371.272727   \n",
       "\n",
       "                                                                            \\\n",
       "                                                  std    min    25%    50%   \n",
       "first_action                                                                 \n",
       "Mail First Action Interview Office Action         NaN  672.0  672.0  672.0   \n",
       "Non-Final Rejection                        199.089566    0.0  201.5  370.0   \n",
       "Restriction/Election Requirement           198.646969   24.0  219.5  422.0   \n",
       "\n",
       "                                                         \n",
       "                                             75%    max  \n",
       "first_action                                             \n",
       "Mail First Action Interview Office Action  672.0  672.0  \n",
       "Non-Final Rejection                        515.5  794.0  \n",
       "Restriction/Election Requirement           514.0  584.0  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "app_df.groupby('first_action').describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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