Examining Art Units To Avoid Subject Matter Eligibility Challenges For Bioinformatics And AI-related Patents – Intellectual Property

Codi Saxon

United States: Examining Art Units To Avoid Subject Matter Eligibility Challenges For Bioinformatics And AI-related Patents To print this article, all you need is to be registered or login on Mondaq.com. Overview of Subject Matter Eligibility Challenges Computer-based inventions – especially in […]


United States:

Examining Art Units To Avoid Subject Matter Eligibility Challenges For Bioinformatics And AI-related Patents


To print this article, all you need is to be registered or login on Mondaq.com.

Overview of Subject Matter Eligibility Challenges

Computer-based inventions – especially in the machine learning
(ML), bioinformatics, and artificial intelligence (AI) fields – are
susceptible to subject matter eligibility challenges. Subject
matter eligibility challenges may prevent a patent application from
being granted by the United States Patent and Trademark Office
(USPTO) and may even be asserted to invalidate a patent post-grant.
Subject matter eligibility challenges include categorizing the
computer-related invention as an abstract idea, which includes
mental processes (concepts capable of being performed on pen and
paper), methods of organizing human activity (such as managing
interactions between persons), and mathematical concepts.

In recent years, the Federal Circuit has implemented a
multi-step test to determine whether patent claims would survive a
subject matter eligibility challenge. In some cases, the Federal
Circuit has upheld the validity of claims reciting an abstract idea
when the claims integrate the abstract idea into a practical
application or recite additional elements that amount to
significantly more than the abstract idea. Similarly, the USPTO
implements a similar multi-step test with various guidelines to
determine whether patent claims in an application will survive a
subject matter eligibility rejection. Nevertheless, 101
jurisprudence remains unsettled in regards to the key determining
criteria and is continually evolving with new guidelines and
emerging case law. As a result, the fate of bioinformatics and
machine learning-based patents and patent applications remains
uncertain.

As we previously discussed in Patenting Considerations for Artificial
Intelligence in Biotech and Synthetic Biology Part 1,
computer-based applications for inventions covering a gamut of life
science disciplines – from sequencing and functional genomics to
drug design, discovery, and testing – have realized tremendous
benefits due to the use of machine learning and AI. But the patent
rights protecting these advanced inventions are susceptible to the
same subject matter eligibility vulnerabilities mentioned
above.

Subject Matter Eligibility Challenges in AI and
Bioinformatics

Many ML, bioinformatics, and AI patents face an uphill battle
for patentability due to the use of computer systems and
algorithms, and the rapidly evolving law surrounding subject matter
eligibility. These computation-heavy areas face subject matter
eligibility challenges, especially for their ML features or
proximity to mathematical calculations.

For example, the Federal Circuit issued a decision that
manifests the troubling eligibility landscape in the fields of
machine learning and bioinformatics. In In re Board of Trustees
of the Leland Stanford Junior Univ.
, No. 2020-1288 (Fed. Cir.
Mar. 25, 2021), the Federal Circuit affirmed the rejection of the
patentability of claims directed to computerized methods to
generate genetic data. Here, Stanford sought to patent claims
directed to a “computerized method for inferring haplotype
phase in a collection of unrelated individuals” that created
“new data” with the use of specific rules and machine
learning techniques. These machine learning techniques included
steps such as building a data structure describing a Hidden Markov
Model, repeatedly randomly modifying at least one of the imputed
initial haplotype phases, and automatically replacing an imputed
haplotype phase. Id. at 4-5.

The Federal Circuit held that the claims did not “involve
practical, technological improvements extending beyond improving
the accuracy of a mathematically calculated statistical
prediction.” Id. at 10. Additionally, the Federal
Circuit found that “the recited steps of receiving,
extracting, and storing data amount to well-known, routine, and
conventional steps taken when executing a mathematical algorithm on
a regular computer,” and the claims recite generic computer
components that were in no way “specialized.”
Id. at 12. As such, Stanford’s patent application for
its computerized method for inferring haplotype phase did not get
granted as a patent. At the time of publication, the court
proceedings have terminated for this patent application.

As previously discussed here and here, there are a number of ways to mitigate
the likelihood of a subject matter eligibility rejection. For
example, an effective drafting technique includes adding explicit
descriptions in the specification that identifies specific
industries or applications where the AI may be particularly useful
and explaining AI’s advantages over existing systems and
processes.

In this article, we discuss another technique that includes
drafting a patent application for placement into a specific art
unit, which is possibly one of the most important drafting
considerations that few patent practitioners think about. This
article provides insight to USPTO art units to reduce the
likelihood of abstract idea attacks in the first place for your ML,
bioinformatics, AI, and computational patent.

Art Units Covering Technologies Related to AI and
Bioinformatics

The USPTO assigns each U.S. patent application to one of many
art units, which are organizational units of technology subclasses.
Some art units at the USPTO may behave more aggressively than
others in asserting subject matter eligibility challenges.
Let’s look at art units 1631 and 2129 to compare the
aggressiveness of different art units and their assertiveness
relating to subject matter eligibility challenges.

1631 Art Unit

Field: Molecular Biology, Bioinformatics,
Nucleic Acids, Recombinant DNA and RNA, Gene Regulation

At a glance:

  • 59.7% allowance rate

  • 718 allowed patent applications in past 5
    years

  • 485 abandoned applications in past 5
    years

  • 80.7% of abandoned
    applications
    within the past 5 years had a 101
    rejection
    at the final office action

Rejections in Final Office Action of Abandoned Applications in
Art Unit 1631

1133844a.jpg

2129 Art Unit

Field: Artificial Intelligence &
Miscellaneous Computer Applications

At a glance:

  • 82.5% allowance rate

  • 832 allowed patent applications in past 5
    years

  • 176 abandoned applications in past 5
    years

  • 39.8% of abandoned
    applications
    within the past 5 years had a 101
    rejection
    at the final office action

Rejections in Final Office Action of Abandoned Applications in
Art Unit 2129

1133844b.jpg

Art unit 1631 tends to cover technologies related to molecular
biology, bioinformatics, and gene regulation. Art unit 2129 covers
artificial intelligence and miscellaneous computer technologies. As
such, it is possible that the higher number of 101 rejections in
the 1631 art unit is attributable to art unit 1631 encountering
many applications directed toward a naturally occurring substance
and/or a law of nature. Nevertheless, comparing the aggressiveness
of the two art units, art unit 1631 appears to be more aggressive
in asserting 101 rejections with 80.7% of
abandoned applications within the past five years having a 101
rejection at the final office action. In contrast, art unit 2129
tends to be less aggressive at asserting 101 rejections with
39.8% of abandoned applications within the past
five years having a 101 rejection at the final office action.
Additionally, art unit 2129 has a much higher allowance rate than
art unit 1631. The foregoing data suggests a ML-centric or
bioinformatics patent is more likely to go abandoned for subject
matter ineligibility in art unit 1631 than art unit 2129.

The comparison between art unit 1631 and art unit 2129 is
salient to patent practitioners for a number of reasons. First, the
historical examination outcomes for these art units reveal which
one is friendlier towards AI-based applications. The number of
applications in each art unit that were abandoned due to a 101
rejection is indicative of the difficulty of overcoming a 101
rejection in each art unit. Second, prior to filing a patent
application, patent practitioners may wish to adjust their claim
term usage, title, and abstract in the specification in an effort
to direct the patent application to a more favorable art unit.
Although there is little transparency in how the USPTO sorts patent
applications into different art units, the technical field,
abstract, and claim language of a patent application are likely
factors considered during this process. Preemptive efforts to route
a patent application to a more favorable art unit may be especially
worthwhile because once a patent application is assigned to an art
unit, the USPTO offers no recourse for reassigning the application
to a different art unit. As such, identifying examination outcomes
in each art unit before drafting a patent is knowledge to be
leveraged as a practiced patent practitioner drafts a ML,
bioinformatics, or AI-related patent application.

Perhaps the many roadblocks Stanford’s patent application
faced through prosecution may be tied to the application’s
assignment to art unit 1631. In the case of Stanford’s patent
application for its computerized method for generating genetic
data, the application was likely to have been assigned to art unit
1631 due to language such as “the field of computer
diagnostics” and “methods for analyzing a genome.”
Furthermore, the USPTO may have taken into account language in the
claims’ preambles including “computerized method for
interpreting genetic data” and “processing unit to
interpret genetic data” in its decision to assign
Stanford’s application to the 1631 art unit. Additionally, the
abstract is focused on how an algorithm performs optimization on
haplotypes. As such, there were multiple areas (e.g., the field of
invention, claims, and abstract) that contained language for
computational analysis as being involved in analyzing genomes and
genetic data.

Takeaways

Subject matter eligibility challenges are here to stay,
especially for computer-based bioinformatics and AI-related patent
applications. The risks of receiving subject matter eligibility
challenges can be mitigated by comparing examination outcomes in
each art unit and then drafting patent applications to target
favorable art units. Drafting techniques include modifying the
claims, abstract, and title to target a favorable art unit.
Drafting an application for placement into a specific art unit can
be an important consideration in patent preparation. Once an art
unit is assigned to your patent application, you cannot have the
application reassigned to a different art unit. Practitioners
should therefore be mindful while drafting the specification to
consider an art unit to avoid prosecution landmines down the road,
thereby enhancing the likelihood of a patent grant.

The content of this article is intended to provide a general
guide to the subject matter. Specialist advice should be sought
about your specific circumstances.

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