Using AI to Reduce Transactional Costs of Patent Validity and Infringement Determinations

The United States has a clear need for patent reform, but does our legislature understand how to implement that reform?

For decades, a shortcoming to our approach to patent reform has been misidentifying the problem as “patent trolls” (more generally, bad actors). See here. Based on a misidentified problem, we have implemented a decades-long policy to systematically weaken patent rights, in an attempt to deter this archetypal bad actor. See here.

If we instead use economic principles to address patent reform, we would understand the root problem to our patent system—exorbitantly bloated transactional costs. See here for a deeper dive into economics, transactional costs, and our patent system.

Of the three main transactional costs, the focus of this article is on informational costs; specifically, the costs to determine (1) patent validity and (2) patent scope, i.e., whether a patent covers a given product or service.

The Three Main Transactional Costs to Determine Patent Validity

In essence, the three main costs associated with determining patent validity are time, capital and the resources needed to determine whether a patent is valid. The process to determine a patent’s validity is typically rooted in one or more of litigation and inter partes review (IPR). In litigation, the cost can range from $1 million to $4 million per party and can take several years, while an IPR could range from $100,000 to $700,000 per party and take 18 months. If there is an appeal, the time, cost and resources to determine validity goes up.

But if a patent issued by the United States Patent and Trademark Office (USPTO) comes with a “strong” presumption of validity, why does it cost so much to reassess validity?

This cost is driven in part by the success rate of defendants attempting to invalidate patent claims. To illustrate, in a white paper titled “United States Patent Invalidity Study 2012”, of the 283 district court cases that made patent validity determinations between 2007 and 2011, 243 cases held patent claims to be invalid in those cases. That’s an 85.8% invalidity rate at the district court level of at least one patent claim.

And from Lex Machina 2017 data relating to IPRs, in cases where IPRs were instituted (approximately 53% of the time when IPR petitions are filed), for the cases that go to final decision, only 5% of those cases have all the claims upheld.

Why are validity rates so low in adversarial proceedings?

The problem lies in the stark difference in resources allocated to examining a patent application during prosecution and resources allocated to invaliding a patent in an adversarial proceeding (e.g., litigation, IPR).

In prosecution, a primary examiner is tasked with reviewing the claims and conducting prior art searches. But this examiner is under time pressure to analyze the application and make a relatively quick determination whether to allow or reject the claims.

Often, this results on an examination that relies on a limited universe of prior art the examiner happens to find and is able to analyze in the allotted time.

During litigation or an IPR, however, parties with an interest in the outcome typically allocate substantially more resources to invalidate or defend a patent, typically due to the higher stakes involved in such adversarial proceedings. Typically, entire teams and search firms are deployed to find prior art that could invalidate asserted patents.

Not surprisingly, a substantial percentage of all litigation and IPR validity decisions involves prior art that was not even considered by an examiner during prosecution. This, in practicality, makes validity a de novo review during such adversarial proceedings.

This stark differential in resource allocation between prosecution and adversarial proceedings leads to low validity rates, which in turn lead to higher transactional costs to determine validity during such adversarial proceedings.

Hence, to reduce transactional costs to determine validity we must decrease this differential in resource allocation between prosecution and adversarial proceedings, to effectively increase the reliability of validity determinations during patent prosecution.

Using AI to Augment USPTO Validity Decisions

To decrease this differential, this writer strongly urges the development and application of robust artificial intelligence (AI) to help make more reliable validity determinations during the patent prosecution process.

For example, Carnegie Mellon’s Center for AI and Patent Analysis is developing AI algorithms that combine the capabilities of AI, machine learning, and natural language processing (NLP) to examine patent applications. The Center’s Patent Examination Initiative is directed to enabling patent examiners to “deliver high quality and timely examination,” by “deliver[ing] the most pertinent information and analysis for robust examination.”

While the Center for AI and Patent Analysis program is directed to assist decision makers, such as patent examiners, can we go a step further and develop an AI engine to actually examine patent applications, and make extensive searches and exhaustive analyses to provide highly reliable determinations on patent validity?

Could this AI determination of patent validity be robust enough that that determination could hold a high degree of reliability during later adversarial proceedings?

The Transactional Costs to Determine Patent Scope 

When it comes to determining whether a product or service infringes a patent, the informational costs are the time, capital and resources to make such a determination. This cost is rooted in litigation and takes a combination of one or more motions for summary judgment, a jury verdict and an appellate ruling.

The problem is that this process could take years and costs millions of dollars and could easily exceed the cost of the value of the patent itself.

For example, according to AIPLA data, when a litigation involves less than a million dollars of exposure to an alleged patent infringer, each party will expend approximately $700,000 – or a total of $1.4 million dollars to fight the patent lawsuit. Hence, in “low exposure” cases, the total cost of patent litigation exceeds the expected return on patent damages.

And when a litigation involves more than $25 million of exposure to an alleged patent infringer, AIPLA data estimates each party to expend approximately $4 million, totaling $8 million. But according to PwC Patent Litigation Study of 2018, the median patent damages award from 2013 to 2017 was just $6 million. In “high exposure” cases, the patent holder typically expects a damages award well above the median, but nonetheless still risks a damages award that does not justify the time and expense of the litigation itself.

For the vast majority of patents, the informational cost to determine patent scope in litigation exceeds the value of the patent itself. Ironically, this highly skewed transactional cost is the economic driver for “patent trolling” or “nuisance litigation.”

To truly reform our patent system, we must find a way to more efficiently determine patent scope, i.e., significantly lower the transactional costs to determine whether a patent reads on a given product or service.

Giving AI the Gavel?

Today’s methodology to make such a determination is done by humans, e.g., a judge, appellate panel, jury. If humans can do it, why can’t we implement artificial intelligence (AI) to more efficiently make this determination? Or at least provide a recommendation with a high degree of certainty?

Now, this doesn’t mean we would be replacing a patent holder’s right to a jury trial. Instead, can AI be used to more efficiently determine whether a patent reads on a given product or service? AI technology, if effectively designed and implemented, could eliminate years of litigation and save millions of dollars in wasteful transactional costs.

What would it entail to create a patent system where AI makes determinations or recommendations on patent infringement?  At a high level, this would involve (i) evaluating the scope of the patent claims and (ii) determining whether the claims map to an allegedly infringing process or system.

Regarding (1), AI algorithms would likely need to be combined with NLP and machine learning.

NLP helps us understand text in context rather than in a vacuum. NLP implements sentiment analysis (identifying underlying sentiment such as positive, negative, or neutral), intent analysis, and contextual semantic search (i.e., to give context to terms and phrases).

Machine learning is a branch of AI that takes known data to ideally make increasingly accurate predictions over time, as more data is inputted into the system.

In patent infringement litigation, AI, NLP and Machine Learning could be used to understand and analyze patent claim scope. To determine patent scope, AI could apply NLP tools such as intent analysis to show the purpose of the patent claim and contextual semantic search to relate the language in the claims to field-specific terms.  As more patents are analyzed over time, this  could help improve accuracy.

As noted above, Carnegie Mellon’s Center for AI and Patent Analysis is developing AI algorithms to address this very analysis. Notably, their Patent Initiative Model operates by creating “layers” of algorithms that extract and analyze text from patents. The lower layers are designed to directly process “text of patents, applications, Offices Action, Patent Trial and Appeal Board (PTAB) opinions, and other documents,” while the higher layers are designed to manipulate data, analyze, and make decisions based on analysis. This type of analysis could be used to construe claims.

Regarding (ii), to enable AI to operate in the realm of patent infringement analysis, it would need to go a step further to frame what a product or service does, and then make an assessment on infringement based on a claim mapping. In essence, this would involve the AI becoming fluent in two languages—the language in the patent claims scope and the language specific to the technologies alleged to infringe.

To become fluent in the language of a product or service, the AI would need to be able to take as input, e.g., source code, diagrams, schematics, and other specifications, and determine what aspect of the technology is relevant to the claims, and how the relevant aspect of the technology operates.

After the AI is fluent in the language of the patent claims and alleged product, it would then need to perform a claim mapping to determine a likelihood of infringement.

Ideally, the AI engine would be robust and powerful enough where its determinations and recommendations on patent infringement would be highly reliable.

Two Big Steps

Of course, we are a long way from AI reaching this level of competency and proficiency.  But if we could develop an AI engine to significantly reduce transactional costs to determine the informational costs of validity and to make patent scope / infringement determinations, this could modernize the patent system and enable highly efficient patent transactions. These would be two important steps in the right direction to improve our patent system.

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