Pro se appellant overturns Section 101 Abstract Idea rejection

Applicants typically rely on outside counsel who use their expertise to navigate the response strategy. And for good reason. These outside counsel have developed years of experience in advancing client goals while complying with the numerous patent laws and rules. Here, however, we write about an ex parte appeal where the Board agreed with a pro se applicant in overturning the Section 101 rejection.

The decision Ex parte Yemmela Appeal No. 2018-005814 (Oct. 25, 2019). The art unit, 2659, handles technology related to Linguistics, Speech Processing and Audio Compression.

Before getting to the substance of the rejections, the Board appears to show some level of sympathy with the appellant in a footnote: “We recognized that Appellant may be unfamiliar with patent prosecution procedure. While Appellant may prosecute the application (unless the application is assigned to a juristic entity; see 37 C.F.R. § 1.31), lack of skill in the field of patent prosecution usually acts as a liability in affording maximum protection for the invention disclosed. If Appellant chooses to request continued prosecution of the application, Appellant is advised to secure the services of a registered patent attorney or agent to prosecute the application, since the value of a patent is largely dependent upon skilled preparation and prosecution.”

It can be readily seen that the claim language is not smooth:

That being said, the Board (as it usually does) assessed each rejection independently. It laid out the framework for Section 101 judicial exceptions:

The U.S. Patent and Trademark Office (USPTO) recently published revised guidance on the application of the two-part analysis. USPTO, 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 (January 7, 2019) (“Recent Guidance”); see also USPTO, October 2019 Update: Subject Matter Eligibility, available at default/files/documents/peg_oct_2019_update.pdf (Oct. 17, 2019).

Under that guidance, the Board rejected the Examiner’s assertion that the claims “recite” an abstract idea, under prong 1 of Step 2A. The Examiner had not provided sufficient determinations for this “sweeping” determination. Specifically, the Board agreed with the appellant that the claim recitations represent “a specific technique that computers can use to solve [the disclosed] problem in a way that is different from the way that [the] problem is solved by humans.” Without analysis of whether the specific claim recitations are part of the purported abstract idea or whether they represent additional recitations to be analyzed separately, the rejection fell.

The Board then proceeded to analyze the claims under prong 2 of Step 2A. The Board found that the Examiner made similarly deficient determinations as it relates to the claims “being directed to” an abstract idea. The Board found the Examiner broadly characterizes the recitations of the claim as representing “functions that would be performed by a skilled human in this field.” However, this characterization lacked “analysis regarding whether the specific claim recitations are part of the purported abstract idea or whether they represent additional recitations to be analyzed separately. ” The Section 101 rejection thus did not have legs at this point.

The Board proceeded to sustain others of the Examiner’s rejections (including Section 112 and 103 rejections).

A take home lesson from this decision is that a compelling argument under prong 1 is to argue that the claim addresses a problem that is solved by computers in a specific technique computers that humans cannot solve. A second lesson is that if an Examiner a broad characterization of what the asserted abstract idea is, it can be successfully pushed back. Especially if the analysis lacks specificity. The Examiner cannot have their cake and assert that the abstract idea is a broad concept, but ignore specific “additional” elements outside of that broad concept.

A final take home is that outside counsel can help deliver a winning strategy. The Section 112 rejection could have easily been cleaned up before appeal. But as is shown here, sometimes even pro se appellants can overturn even very tricky rejections.

AIPLA 2019 Annual Meeting

If you’re at the 2019 AIPLA Annual Meeting in DC, please stop by our booth. We would love to talk to you about your interests in patent data and how we might work together or help your needs. We’d also love to show you our new module “Office Action Answers”. @aipla #aiplaam2019 #annualmeeting

Patenting Machine Learning Tech at USPTO vs EPO

Artificial intelligence technology has been around for a long time, but has recently made advances prompting recognition as the transformative force that it truly is. While applicants have successfully patented artificial intelligence inventions for many years, the US has been more favorable than Europe for some types of AI. Here we focus on one area of difference between patent-eligibility of NLP inventions in the USPTO versus the EPO. We use board decisions for distinguishing the two jurisdictions. 

As some background, machine learning is the AI technique most frequently disclosed in patents, and is included in more than a third of all AI-related patent applications.


Photo credit Aglaé Bassens et al., “Deep Learning Illustrated: A Visual, Interactive Guide to Artificial Intelligence” (August 5, 2019). 

Machine learning is an AI technique ever-growing in dominance. Deep learning and neural networks are fastest growing of the lot. This is at least partially represented in patent filings, where filings of machine learning patent applications have grown annually on average by 28% from 2013 to 2016. This is notably higher than the average annual growth across all new areas of technology, which was 10% during the same time. Within this category, deep learning, used for example in speech recognition, had a 175% average annual growth rate from 2013 to 2016. 

Among functional applications, computer vision is the most popular, and is mentioned in almost half of AI-related patents. The next hottest area in functional applications is natural language processing. Examples of NLP in industry include classifying documents; machine translation; search engine optimization; speech recognition; and chatbots. We focus here on patent-eligibility of NLP in this blog post. 

Source: WIPO Technology Trends 2019, “Artificial Intelligence,” at 14 and 31.

In the US, machine learning applications have generally fared well for patentability purposes. Even though many of these machine learning inventions are rooted in software, and presumably vulnerable to Alice-type eligibility rejections, allowance rates have been substantially higher than other software classes. For example, comparing class 706 (Artificial Intelligence: Data Processing) to class 705 (Financial, Business Practice, Management, or Cost/Price Determination: Data Processing), we see a big difference. 


One reason why these allowance rates are much lower may depend on the art unit differences. Machine learning inventions typically get assigned to the 2121 or 2122 art units whereas business method inventions get assigned to the 3620s, 3680s and 3690s. The 3600 art units are well known for applying knee-jerk Section 101 patent-ineligible rejections whereas AI art units are not as preoccupied with Section 101. Often times, Examiners in these machine learning art units see the cutting edge technology of machine learning in these applications and generally quickly grant the patents for these inventions. 

But not all AI inventions are as easy to get allowed, especially depending on the jurisdiction. Take Europe, for example. The standard for patent-eligibility at the EPO is somewhat different than the US in that it requires a sufficiently technical nature (i.e., the claim must have a technical implementation or technical application). For image processing and speech recognition, this technical nature can be easily shown. But other types of machine learning tech, such as NLP, have not been so recognized as having a technical purpose.  

As pointed out in this Marks & Clerk blog post, there is a historical context to difficulties in patenting some NLP technologies. 

In T 52/85, the Board considered a system for automatically generating a list of expressions whose meaning was related to an input linguistic expression.  The Board held that the relationship between the input and output expressions was not of a technical nature, but was instead a matter of their “abstract linguistic information content”.  The Board consequently found that the claimed subject-matter was unpatentable.

In another relatively old decision, T 1177/97, the technology at issue related to machine translation.  The Board again found the claimed subject-matter to be unpatentable, stating “Features or aspects of the method which reflect only peculiarities of the field of linguistics, however, must be ignored in assessing inventive step.”  This statement is often quoted by examiners when applying the Comvik approach to inventions in the field of natural language processing. Although the Board in T 1177/97 also held that “information and methods related to linguistics may in principle assume technical character if they are used in a computer system and form part of a technical problem solution”, it is hard in practice to convince the EPO that a technical problem is solved by the linguistic aspects of an invention.

The US has been more favorable about the patentability of NLP technology. For example, the PTAB has recently reversed patent-eligibility rejections in a large proportion of AI applications. Results will follow. For example, one PTAB panel recently overturned a NLP-related invention as passing the two-step Alice framework. In Ex parte BAUGHMAN et al., Appeal No. 2019-000665 (PTAB Sept. 25, 2019), the PTAB overturned an Examiner’s rejection for the claims being directed to an abstract idea. 


The Board, under step 2A prong 2, reasoned that the claim recites additional elements, which are outside the abstract idea, that include: “receiving, by the question answering system, a function call comprising an input question and a set of non-local context evidence in closure form.” The Board explained that the recited use of a “function call” and the use of “closure form” are particular (non-generic) software technology limitations. Specifically, the “function closure”-related software limitations recited in claim 1’s first step are integrated with the limitations that describe the abstract idea for generating answers to a question. 

The Board viewed the claim holistically by stating that “[t]aken as a whole, claim 1 recites a set of steps for a particular query- and hypothesis-based processing sequence and set of rules, executed by a QA system.” Then citing McRO, the Board held that this amounts to “us[ing] the limited rules in a process specifically designed to achieve an improved technological result in conventional industry practice,” i.e., to improve the technology of QA systems.” After coming to this determination, the Board found that the claim imposes meaningful limits on the application of the recited judicial exception for generating candidate answers to a question and thus are not directed to a patent-ineligible abstract idea. 

AI will continue to transform all sectors of industry and patentability standards across jurisdictions will continue to change. Patentability standards across jurisdictions should continue to evolve to balance the growing impact of AI on society. As it does so, it is important to anticipate prosecution strategy internationally with the best patent data. 

Ex parte Decisions have been Updated on Anticipat

We have been analyzing ex parte decisions at the PTAB for many years now. So for every day, we can see the decisions that have been imported from the USPTO. This came in handy a few months ago when USPTO personnel told us that they completed a migration of all ex parte PTAB decisions to a modernized webpage. While we were excited for this new functionality (including a RESTful API), we started noticing abnormalities in the data.

For example, July 2019 (the month when the transition to the new page took place) only had 150 decisions. That was a much lower number of appeal decisions than we were used to seeing. The next month, August, had even fewer with 109 such decisions. By contrast, June 2019 (the month before the transition took effect), had 732 ex parte decisions. This is in line with prior months, even though it is not uncommon for busy end-of-quarter months to exceed 1000 decisions.
But just to give you the fairest comparison, the prior year of the same month, July 2018, had 771 such decisions and June 2018 had 766 decisions. So to have only 150 decisions for July 2019 and even fewer decisions for August seemed strange to us. With such a dramatic decrease of the historical volume of these decisions, it seemed highly unlikely the cause would be from a sudden drop in output by the PTAB. 
We reached out to the USPTO personnel with our findings and they confirmed that there was a glitch that they would resolve. Several weeks later–in fact last Friday–the missing decisions for the last few months were replenished on the USPTO page. Our importer was hungrily back to action.
With so many decisions to process in one business day, our daily recap email came out for this Monday in an abnormal way. But by Tuesday, we were back to our normal daily email, showing that the USPTO published 66 decisions in one day.
Get this fresh recap of PTAB decisions delivered straight to your inbox by signing up for an Anticipat membership.

Now that we have an updated list of decisions for the past several months, we will continue posting trends and insights about appealed decisions. If you are interested in trying out the Anticipat Research database for yourself, sign up for a 14-day free trial here: