Guest Lecture on "AI Patent Quality: Avoiding Flaws Inherent In Human-Performed Work" at Stanford Law

I would like to thank Jason Du Mont and his students for inviting me to speak today at Stanford Law on "AI Patent Quality: Avoiding Flaws Inherent In Human-Performed Work," as part of the Law, Science, and Technology LLM Colloquium. I was impressed by their thoughtful and challenging questions.

To summarize the presentation, most of what patent attorneys do today can be done better by machines using existing technology as applied to patent law. Because AI can optimize strategies based on tens of thousands of data points simultaneously, I believe AI will make better decisions, on average, than the human patent attorney counterparts. The underlying algorithms and technology might be ready to take us there, but the patent-specific tools are not yet fully developed. In the meantime, we can make use of as much data and stats-based analytics as possible. Based on wisdom gained from studies outside of patent law, I think we should defer decisions to AI or stats-based analytics when the algorithms or analytics themselves are shown to be predictive, withstand scrutiny, and are otherwise well-founded. That said, they don't need to be perfect to be better than us.

Most law schools don't teach their students to trust well-founded AI to make decisions. Instead, law schools focus on exposing the students to enough data points so the students themselves can get better gut feelings. To achieve measurably better outcomes that take advantage of the information available from half a million U.S. patent filings each year and thousands of recent patent cases, we need to unlearn some of that more traditional training so the AI can be trusted despite our gut feelings.

The full slide deck (with fancy animations) is available for download here or in video form below. In case you're interested, the suggested readings for the course follow the video. In addition to the suggested readings, I suggest "Thinking, Fast and Slow," by Daniel Kahneman, which is cited in the McAfee articles.

As usual, I would love to hear your thoughts. Although none of the students said this out loud, "Eric, you're crazy and this will never happen" is a valid thought (even though AI is already solving problems of greater complexity than patent work).

Non-Patent Readings

(required) McAfee, Andrew. “Big Data’s Biggest Challenge? Convincing People NOT to Trust Their Judgment” & “When Human Judgment Works Well, and When it Doesn’t” Harvard Bus. Rev. 2013-14.

(optional) Logg, Jennifer; Minson, Julia; Moore, Don. “Algorithm Appreciation: People Prefer Algorithmic To Human Judgment.” Harvard Business School NOM Unit Working Paper No. 17-086. Mar. 28, 2017

(optional) Kleinberg, Jon; Lakkaraju, Himabindu; Leskovec, Jure; Ludwig, Jens; Mullainathan, Sendhil. “Human Decisions And Machine Predictions.” Quarterly Journal of Economics, 2017.

Patent Readings

(required) Sutton, Eric. “Pursuit of Extremely Short Patent Claims.” IPWatchdog. May 17, 2016.

(required) Sutton, Eric. “Do You Know What Your Provisional Application Did Last Summer?” Patently-O. Nov. 9, 2017.

(required) Sutton, Eric. “Navigating Art Unit 3689, If You Must” & (optional) “Are Applicants ALWAYS Able To Survive Art Unit 2138?” (optional) Patnotechnic. May 21, 2018.

(optional) Patent Information Users Group, Inc. “Patent Analysis, Mapping, and Visualization Tools.” Last Updated Feb. 9, 2017.

(optional, if background needed) Quinn, Gene; Benson, Michael. “Understanding U.S. Patent Prosecution.” Posted June 30, 2018.

(optional marketing video) Innography. “Innography’s True Semantic Search.” Posted Jan. 31, 2017.

(optional marketing video) LexisNexis IP. “What Color Is Your Examiner Red, Yellow or Green Customizing Your Prosecution Strategies.” Posted Apr. 19, 2018.