5th International PatentSight Summit

Use Cases and Best Practices in IP Analytics
- Online Sessions -

Watch the recordings →

Subscribe to News Service

Global Innovation Ranking: Top 100 World's Most Innovative Companies

Webinar - Open Access
 

Watch the recording

Common issues affecting open source patent data and the PatentSight® approach to data cleaning

A Handbook for Patent Data Quality

Common issues affecting open source patent data and the LexisNexis® PatentSight® approach to data cleaning

To download this document, please fill out the form below and click Submit

Download the Report

High Quality Data is a Prerequisite for Reliable Patent Analytics

Many issues affect the quality of patent data that is available from open-source patent databases. One of the most important data points contained in a patent document is the field containing the “applicant name”. Mistakes in this field can cause patents to be assigned to the wrong commercial entity and lead to incorrect corporate decision making, costing companies valuable resources.

As pioneers in the field, and based on years of extensive collaborative research, LexisNexis® PatentSight® has developed a unique and industry-trusted approach toward ensuring consistent high data quality. Read our "Handbook for Patent Data Quality" to find out how we curate our patent database to enable our users to extract reliable and actionable insights.

Major topics covered in this document:

  • Common issues found in patent data
  • Consequences of relying on bad data
  • PatentSight’s unique approach toward data cleaning

Watch this video to get a glimpse of how PatentSight enhances patent data to provide high-quality data for analysis on our business intelligence platform.

patentsight_data_quality_Final(subs) (12)