Measuring the accuracy of AI for classifying patents – what’s the Gold Standard?

WEBINAR on Tuesday 30th June 2020

Supervised machine learning applied to strategic patent intelligence is now embedded in the workflows of the world’s leading patents organisations. The case for AI in driving material efficiency gains and enabling strategic analysis from global patent data is proven with companies now chasing the competitive advantage of the early adopters.

In March this year, Cipher published in World Patent Information its article titled Construction and Evaluation of Gold Standards [1] to provide an objective and reliable way for both developers and users of patent classification software to better understand and test the accuracy of automated patent technology mapping.

Join our webinar on Tuesday 30th June 2020 at 3.30pm BST.

The webinar Measuring the accuracy of AI for classifying patents? will address:

• How a Gold Standard helps test the accuracy of machine learning
• The approach to building a Gold Standard that can be trusted
• Testing Cipher against the Gold Standard

We look forward to hosting you on our webinar where we will discuss best practice for testing the accuracy of machine learning algorithms in the patent world and anticipate a lively discussion and debate.

Speakers: Steve Harris, CTO, Cipher;

Tony Trippe, Managing Director, Patinformatics LLC

Moderator: Nigel Swycher, CEO, Cipher

A registration link [for a webinar recording at GotoWebinar.com]

Source: Cipher website

About Cipher: “Cipher is recognised as the leading provider of strategic patent intelligence to major patent owning organisations.”. “Cipher’s mission is to deliver patent intelligence to the teams responsible for strategic decisions to enable evidence-based decisions. We use AI and machine learning to improve accuracy and efficiency, while at the same time reducing cost and promoting the importance of intangible assets both internally and externally.”

[1] Harris, S., Trippe, A., Challis, D., Swycher, N., 2020. Construction and evaluation of gold standards for patent classification—A case study on quantum computing. World Patent Information 61, 101961. https://doi.org/10.1016/j.wpi.2020.101961 (an open access article)