Artificial Intelligence Accelerates Decision-Making in Patent Portfolio Management

By Christopher Young
December 24, 2019

“Decisions acting at the level of individual patents, where tens or even hundreds of thousands of individual patents are potentially available as inputs, would be good candidates for the application of artificial intelligence.”

Contemporary AI technology of the kind one has increasingly heard about in recent years is based on machine learning and deep learning methodologies. These use large amounts of computing power to crunch thousands of sample input-output pairs to train adaptable data structure models. Eventually, they are able to produce their own correct outputs when presented with an nth + 1 input. These can be thought of as questions and answers. If an AI model is given, say, 10,000 sample questions with correct answers, it will be able to correctly answer the 10,001st question by itself. Once trained, computing requirements are low.

Due to the nature of the methodology, AI is appropriate for situations that involve repetitive decision-making processes. For one thing, many existing examples of correct decisions must be available during the training. Further, after the training phase, a system is applied to similar situations over and over again. Because of this, the application space for AI is sometimes overblown. However, once understood, this limitation usefully directs our attention to instances of decision-making that can be automated or made more efficient using AI.

Employing AI for Patent Portfolio Management

If we consider patent portfolio management in terms of constituent decision-making processes, we might be able to identify which of them are appropriate for the application of AI. Patent portfolio management typically includes tasks like full portfolio assessments, estimates of the potential value of individual patents, identification of weaker areas of technical coverage in a portfolio, identification of patents to cull, the evaluation of external portfolios, comparisons of such portfolios to internal portfolios, the decision to enforce IP with respect to external organizations, to acquire external portfolios, or to sell a portion of a portfolio, etc. Which of these processes are likely to benefit from AI?

Understanding the applicability of AI to repetitive processes in need of large amounts of previously existing sample inputs for training, we can narrow the field of action significantly. Strategic decisions acting at the level of portfolios as a whole, such as the decision to enforce IP with a given competitor, are poor candidates because they happen very rarely and are highly context-sensitive. Processes with low numbers of practically attainable input samples are in general difficult to automate. However, decisions acting at the level of individual patents, where tens or even hundreds of thousands of individual patents are potentially available as inputs, would be good candidates. Examples of this include which patents to cull, which patents have potentially high value, and automatically detecting the technology category of a patent. It should be remarked that properly applied automation at the level of the individual patent can ultimately also help decision-making at higher strategic levels, but by this time the decision-making is still made by human beings, albeit based on a richer data set provided by AI.

Training Samples and Subject Matter Experts

Another practical limitation impacts on the choice of applications: generation of training samples. The AI model in training requires thousands of appropriately formatted input samples. The simple existence of previously made decisions of a certain type is not enough. They must be available to the training team, properly vetted (i.e. considered to be correct), and then formatted for input. In the case of decisions made on patents, these are more or less always made by highly-trained technical staff. To generate samples into an AI for patent decision-making, one must have access to such technical experts during the sample-generation phase and have them dedicate their time to generating samples. In practice this means that Subject Matter Experts are asked to engage in an evaluation activity in the same way they would for a real-world project, but with targets selected to generate the best training samples possible.

Once enough decisions have been generated by human Subject Matter Experts, they can be used as a training set for an AI. After the compute-heavy training phase, the AI is then available to very rapidly answer further samples with no human intervention.

Through this process, decades of knowledge, as it exists in the minds of the Subject Matter Experts that are called upon during the training phase, are distilled into a computerized process which can then significantly accelerate decision-making in patent portfolio management.

 

The Author

Christopher Young

Christopher Young is a business development consultant and currently serves as Vice President, Business Development for ICmasters, a technical services company based in Ottawa, Canada, serving the global semiconductor industry with advanced reverse engineering and Intellectual Property support services. His career and academic interests have always spanned technology and the humanities, and are expressed through an avid enthusiasm for helping technology companies successfully commercialize their innovations in the global marketplace.

For more information or to contact Christopher, please visit his Firm Profile Page.

Warning & Disclaimer: The pages, articles and comments on IPWatchdog.com do not constitute legal advice, nor do they create any attorney-client relationship. The articles published express the personal opinion and views of the author and should not be attributed to the author’s employer, clients or the sponsors of IPWatchdog.com. Read more.

Discuss this

There are currently 8 Comments comments. Join the discussion.

  1. Julie Austin December 24, 2019 12:38 pm

    Nobody’s job is safe from robots!

  2. Anon December 24, 2019 3:35 pm

    Julie,

    Shall we merely prepare to throw our sabots into the (AI) machine?

  3. Benny December 25, 2019 8:58 am

    At one company I know, you would need to develop artificial stupidity to take over role of management.

  4. Anon December 25, 2019 10:57 am

    Benny,

    Just one…?

    See (generally) Dilbert – based in substance in that writer’s experience with management.

  5. TR December 25, 2019 2:38 pm

    1. I feel like patent valuation is somewhat difficult with AI because of the constantly changing licensing landscape. I have worked with PatSnap and found their model for valuation accurate in terms of order but not magnitude.

    2. One area where I feel AI could be great is in looking at file wrappers to determine what claim language gets nixed by examiners and how to steer apps to certain practice groups.

    3. I feel like what we are really talking about with all of this patent AI stuff is actually a subset of AI–Machine Learning?

  6. Valery Polyakov, Ph.D., Esq. December 25, 2019 8:06 pm

    Given a large enough sample size and a right method, you do not need manual curation! From your paper, I am not sure what are you trying to predict and what is your sample dataset. Could you elaborate?

  7. Paul Morgan December 26, 2019 10:39 am

    Interesting, but I wonder how well AI could accurately determine portfolio patents needing culling to avoid maintenance fees, or to try to license, including having claims that are two narrow or too broad, claims covering only now technically obsolescent products, expensive alternatives to cheaper solutions for the same problem, missed prior art or 112 issues, etc., etc. Some prior computer patent analysis tests, like counting later patent citations of a patent to try to ID “pioneer” patents, involve serious misunderstandings of how prior art patents to cite are selected by examiners.

  8. Anon December 28, 2019 10:51 am

    I have to agree with Mr. Morgan as to the difficulty ANY training set could possibly provide to an AI/Machine Learning algorithm any sense of meaningful portfolio management capability.

    Perhaps AI may be used as a t001 to identify items that need to be reviewed for any number of identified reasons, but the sum total analysis and actual evaluation — especially when it comes to evaluating on a relative scale of what else may be present in a portfolio, what may be nascent (developing) in a portfolio pipeline, and the necessary juggling of portfolio holdings in view of any number of possible legal developments is just not something that one can (at this stage) leave to a computing “intelligence” to decide.

Post a Comment

Respectfully add to the discussion.

Name *
Email *
Website