Patent Assignment Data Restructure

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.

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

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(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.

For example,

                city                  | postcode_city
NEW YORK, NY 10022-3201               | 10022-3201
BEAVERTON, OREGON 97005-6453          | 97005-6453
SANTA BARBARA, CA 93130-3003          | 93130-3003
NEW YORK NY 10022-3201                | 10022-3201
SUNNYVALE, CA 94088-3453              | 94088-3453
94088-3470                            | 94088-3470
CS 46510-35065 RENNES CEDEX           | 46510-3506
NEW YORK, NY 10013-2412               | 10013-2412
OALKLAND, CA 94612-3550               | 94612-3550
OXFORD CT 06483-1011                  | 06483-1011

The extracted post code records are stored in table ptoassigneend_missus_final.

SQL code is in:

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

3. State (U.S.)

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

For example,

                city                  |  state_city
NEW YORK, NY 10022-3201               | NY
BEAVERTON, OREGON 97005-6453          | OREGON
SANTA BARBARA, CA 93130-3003          | CA
SUNNYVALE, CA 94088-3453              | CA
NEW YORK, NY 10013-2412               | NY
OALKLAND, CA 94612-3550               | CA
SANTA CLARA, CA 95052-8090            | CA
PEORIA, IL  61629-6490                | IL
MIDVALE, UTAH 84047-1408              | UTAH
OAKLAND, CA 94612-3550                | CA
HARRISBURG, PA 17105-3608             | PA
ROCHESTER, NY  14650-2201             | NY
NEW YORK, NY  10013-2412              | NY
HOUSTON, TEXAS 77256-6571             | TEXAS
BROOKINGS, SOUTH DAKOTA 57006-0128    | SOUTH DAKOTA

The extracted state records are stored in table ptoassigneend_missus_final.

SQL code is in:

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

4. City (U.S.)

There are some patterns that can be used to extract city information. They are in:

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

A list of samples extracted from addrline1 are used to summarize the patterns. The samples are in

Z:/PatentAddress/citysample.txt

5. Issues

• The 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'.

• Both state name and its abbreviation exist.

• Some records are 'A CORP. OF CA.' or 'A CORP. OF NY' or 'A CORPORATION OF CA', which may be another method to extract state information.