Patent Assignment Data Restructure

In order to restructure the current patent dataset, the data requires rigorous cleaning. The primary areas for improvement are:

McNair Project

Patent Assignment Data Restructure
Project Information
Project Title	Patent Data Restructure
Owner	Marcela Interiano, Sonia Zhang
Start Date	201701
Deadline	201705
Keywords	Patent
Primary Billing
Notes
Has project status	Active
Subsumes:	Patent Data (Wiki Page)
Copyright © 2016 edegan.com. All Rights Reserved.

1. Clean ptoassignment table to unique keys.

2. Clean ptoproperties to remove nonutility patents. The patent numbers currently include:

• 7 digit patent numbers
• application numbers
• unknown numbers that cannot be matched to patent numbers in the patent table

20090108066

20100007288

20090108066

20100110022

• Design and Reissue patents ('%D%' or '%RE%')
• alphanumeric character strings

3. Restructure address information in ptoassignee table to extract meaningful information

4. Verify that cleaned patent documentids correspond to patent numbers or application numbers in the patent table

5. Restructure address information in ptoassignment table

6. Transform structure of the dataset

Semester Plan

The final deliverable for the semester is a table with the following structure:

Reel No | Frame No | Invention Title | Filing Date | Patent No | Application No | Publication No | Match By

The schema of the table will be:

 Column          |          Type         | Modifiers
-----------------+-----------------------+-----------
Reel No          |        integer        |
Frame No         |        integer        |
Invention Title  | character varying(500)|
Filing Date      |          date         |
Patent No        |        integer        |
Application No   |        integer        |
Publication No   |        integer        |
Match By         |        integer        |

Currently the pto tables contain varying identifiers for one invention title that is involved in a reassignment. The purpose of the table is to have each identifier for an invention title listed in a single row, making it easy to track the invention through various transactions. The Match By column will inform the user which identifier should be used to match to other tables in the patent database.

Data Cleanup Progress

Patent Number Cleanup

The goal is to only have assignment records on utility patents. The patents in ptoproperty include alphanumerics which represent reissue and design patents as well as mistakes in the data input. Additionally, the documentids include application numbers or ids and publication numbers. The ptoproperty table stores the patent ids as character strings.

First the duplicates were dropped from the ptoproperty table creating ptoproperty_cleaned.

SELECT COUNT(*) FROM (SELECT DISTINCT * FROM ptoproperty) As T; 
--27266638
 
SELECT COUNT(*) FROM ptoproperty_cleaned;
--27266638

Next, the Reissue and Design patents were removed.

SELECT COUNT(*) FROM ptoproperty WHERE documentid LIKE 'RE%';
--38512

SELECT COUNT(*) FROM ptoproperty WHERE documentid LIKE 'D%';
--1128247

Restructure Address Information

The dbase is patent.

The table is ptoassigneend.

SQL code and other things are in:

E:/McNair/Projects/PatentAddress

IO files are on the dbase server in:

Z:/PatentAddress

1. Introduction

• Five features (addrline1, addrline2, city, country, postcode) in the table contain address information.
• Features addrline1, addrline2 and city are not cleaned. They have city, country and postcode information.
• The object of this project is to extract city, country and postcode information from the three features above.
• By now, we only focus on cleaning American patents.

2. Postcode and State (U.S.)

U.S. post code follows the pattern [five digits - four digits]. In this way, U.S. patents can be extracted by searching for post code with regular expression.

The state and post code are always together, separated by a space. So we can also extract state information with regular expression too.

SQL code are in:

E:/McNair/Projects/PatentAddress/RxPostcode.sql

The extracted records are stored in table ptoassigneend_missus.

3. City (U.S.)

4. Issues

• The ultimate post code and zip regex for other countries besides U.S. can be found here:

http://stackoverflow.com/questions/578406/what-is-the-ultimate-postal-code-and-zip-regex

• The city feature needs to be standardized. For example, 'GRAND CAYMAN, CAYMAN ISLAND' and 'GRAND CAYMAN' indicate the same city.

• Some state and country features don't match. For example, 'Beijing' - 'UNITED STATES, 10022'.