Sponsored by ClaimHit
Webinar: AI for Patent Licensing – Beyond Statistical Triage to Evidence-Grounded Product Analysis
Determining which companies and products may be practicing patented claims, and therefore warrant licensing, partnership, or enforcement analysis, has traditionally been a slow, manual, and incomplete process. AI has the potential to change that, but speed does not guarantee reliability. Systems that merely prompt a general-purpose language model and present its output as fact can produce confident but unverifiable – and sometimes fabricated – infringement conclusions.
Join us on Thursday, October 1, at 12 PM ET, for an in-depth conversation about what rigorous AI-assisted infringement discovery and patent licensing analysis require, where current approaches fall short, and how practitioners can distinguish credible findings from unsupported model output. This will be an educational, practitioner-focused program examining methodology and analysis rather than a product presentation or demonstration.
The conversation will begin with the challenge of identifying licensing opportunities across an entire patent portfolio. Many portfolio owners rely on statistical indicators such as citation counts, family size, claim length, and forward-citation velocity to determine which patents merit further investigation. Those indicators can help identify prominent or potentially valuable assets, but they do not answer the central licensing question: whether a company or product may actually be practicing the patented claims.
Panelists will then examine the feasibility of a portfolio-wide product analysis that evaluates patented claims against real-world companies and products. Rather than merely ranking patents according to statistical characteristics, this approach seeks to provide a comprehensive view of how a portfolio may map to potential licensees, and to identify where the available evidence supports, contradicts, or is insufficient to establish a potential claim match.
The discussion will address the difficult issues that determine whether this analysis can be trusted, including multichannel discovery, source quality, evidence verification, transparent negative and inconclusive findings, and the continuing need for experienced legal and technical review. Panelists will also examine the specific challenges presented by standard-essential patent analysis, including mapping claims to 3GPP, IEEE, and ITU specifications; accounting for different releases and versions; and evaluating claims whose limitations may be distributed across multiple specifications.
