Log in

Patent Information Users Group, Inc.

The International Society for Patent Information Professionals

Log in

Webinar 1: The Linear Law of Patent Analysis, Patent Family Reductions and Assignee Cleanups

  • 24 Jan 2019
  • 11:00 AM - 12:00 PM
  • Webinar
  • 96


Registration is closed

Preparing Patent Analytics Projects and Patent Landscape Reports- Hands-On Lessons

PIUG presents an opportunity to learn valuable practical skills from Tony Trippe, a leading expert with many years of experience in Patinformatics. Join us for a series of webinars providing details on some of the stages required for performing patent analytics, and for the preparation of a Patent Landscape Report (PLR).

Patent analytics and PLRs support informed decision-making and are designed to efficiently address the concerns associated with making high stakes decisions in technologically advanced areas with a maximum degree of confidence. For many years’ decision-makers operated based on personal networks and intuition. With the institution of patent analytics, and PLRs, it is possible for these critical decisions to be made with data-driven approaches that deliver informed choices, and lower risk profiles. Working with patent data can be complicated due to the nature of the subject matter, and the lack of standard in the way it is delivered from the offices that generate it.

This four part series of webinars provides specific steps for the production of patent analysis projects and PLRs including step-by-step instructions for performing these operations:

  • 1.       The Linear Law of Patent Analysis, Patent Family Reductions and Assignee Cleanups
    • The Linear Law of Patent Analysis was proposed as a framework for performing patent analysis projects in 2002. It was developed to assist practitioners in understanding the importance of starting an analysis by investigating the needs of the customer for the analytics, as opposed to simply jumping in with an analysis tool. A brief overview of this concept and methodology for performing patent analytics will be discussed.

      Due to the nuances of the worldwide patent system a situation exists where a single invention might have many individual patent documents associated with it, depending on the number of countries the applicant sought protection in. The situation becomes even more complicated as patent documents are published at different stages throughout the prosecution cycle. In order to clarify the number of inventions produced compared to the number of patent documents published, the concept of a patent family was created. There are a number of different types of patent families, and methods for reducing the number of documents to represent a family. An examination of these different methods will be provided.

      Data cleanup and grouping are processes for the manual, or automatic standardization of terms or items, within a data field, to correct errors or inconsistencies, or to group synonymous entries. It is required by patent analysts in order to produce statistically relevant results. It is necessary since raw patent data is notoriously "messy" and requires cleanup or standardization to produce accurate results. Misspellings, for instance, are a common occurrence within certain fields, and require correction. There are also many terms with the same or similar meanings, within the English language, and these should be grouped together when analyzing concepts. Specific examples and tools for cleaning and standardizing patent data for generating a data table will be covered.

Time listed is in US Eastern Time Zone

© 2022 The Patent Information Users Group, Inc.   

Mailing Address:  40 E. Main St., #1438

Newark, DE  19711

Phone: +1 (302) 660-3275   Fax: +1 (302) 660-3276



Follow PIUG:  


Go to PIUG TwitterGo to PIUG LinkedIn

Notice on use of PIUG name and logo:  

No one may use the PIUG name or logo for any promotional or commercial purpose or any other purpose without the prior written consent of the PIUG Board of Directors.  

Powered by Wild Apricot Membership Software