Patenting AI Tools of Drug Discovery

Drug Development Pipeline

“Jump first and then think as much as you want!”

PrimateAI Case Study

“Few things are harder to put up with than the annoyance of a good example.”

Product-By-Process Claims

“Sometimes it’s damned hard to tell the dancer from the dance.”

Process that Defines the Product

The purpose of product-by-process patent claims is to allow inventors to claim an otherwise patentable product that resists definition by other than the process by which it is made.

Distinctive Structural Characteristics

The structure implied by the process steps of a product-by-process limitation should be considered especially (i) where the product can only be defined by the process steps, or (ii) where the manufacturing process steps would be expected to impart distinctive structural characteristics to the final product. See M.P.E.P. § 2113.

Validity v/s Infringement

The asymmetry regarding validity and infringement of a product-by-process claim is typically to the disadvantage of the patentee; claimed products can be anticipated by products made by a different process, but the claimed products are infringed only by those products made by the same process. See Amgen v. F. Hoffmann-La Roche, 580 F.3d 1340.

PrimateAI has many novelties like its non-human primate species training data, deep residual model architectures, bootstrapped training techniques, etc.

We select PrimateAI’s non-human primate training data as the source that imparts distinctive empirical structure to its output, i.e., the PrimateAI Scores, and craft the following claim.

A system, comprising:

memory storing respective benign-eliminated pathogenicity scores for respective variants in a genome, wherein the benign-eliminated pathogenicity scores are generated by:

(a) eliminating from a pathogenic set of variants those common missense variants that are observed in non-human primates but are clinically benign in humans;

(b) including in a benign set of variants the common missense variants that are observed in the non-human primates and common missense variants that are observed in humans;

(c) assigning a pathogenic label to first protein sequences containing the pathogenic set, and a benign label to second protein sequences containing the benign set; and

(d) training a classifier to process the first and second protein sequences as inputs, and to generate the benign-eliminated pathogenicity scores as outputs.

Our p-b-p claim uses the phrase “benign-eliminated pathogenicity scores” to cover the PrimateAI Scores as a novel product.

Steps (a) to (d) form a novel process without the AI intricacies.

The claim language is comprehensively supported by PrimateAI’s patent disclosure.

Dependent claims can add further novelty to the “benign-eliminated pathogenicity scores” by using other novel features of PrimateAI like “protein structure-based,” “evolutionary conversation-based,” and “ensembled.”

Product Claims

“Let’s dance.”

Product-by-process claims are more vulnerable to patentability/validity attacks. An alleged infringer may escape the scope of a product-by-process claim by using a different, non-equivalent process.

Therefore, we now craft a “product claim” based on the patentable characterization of the PrimateAI Scores as “benign-eliminated pathogenicity scores.”

Our product claim is inspired by Enfish and McRo, which conferred patentability on novel data structures that improved the accuracy and effect provided by a computer. Of course, our Specification should provide evidence of improved accuracy and effect (which is the case with PrimateAI’s patent disclosure).

A system for performing pathogenicity analysis, comprising:

memory configured with a pathogenicity table, the pathogenicity table including respective benign-eliminated pathogenicity scores for respective variants in a genome, wherein the benign-eliminated pathogenicity scores improve accuracy of the pathogenicity analysis by identifying as benign those common missense variants that are observed in non-human primates but are clinically benign in humans; and

means for retrieving the benign-eliminated pathogenicity scores in the pathogenicity table to perform the pathogenicity analysis with the improved accuracy.

Our product claim is now ripe for use in our downstream claims.

Compared to AI process claims, AI product claims are easier to enforce. Products created by a patented process are protected from unauthorized importation into the U.S., but information created by a patented process is not. This is Federal Circuit’s ongoing interpretation of Section 271(g) and referred to as the Bayer Loophole.

Target Discovery Claims

“Please don’t stop the music.”

PrimateAI discovered fourteen candidate genes in intellectual disability, which had previously not reached the genome-wide significance threshold.

Therefore, we now craft a “process claim” for target discovery based on the patentable characterization of the PrimateAI Scores as “benign-eliminated pathogenicity scores.”

A method of identifying disease-causing biomarkers, including:

accessing biomarker data for a cohort of individuals diagnosed with a disease;

accessing a pathogenicity table that identifies respective benign-eliminated pathogenicity scores for respective mutations located in the biomarker data;

based on the benign-eliminated pathogenicity scores, selecting pathogenic mutations from the mutations, wherein the benign-eliminated pathogenicity scores improve selection of the pathogenic mutations by eliminating as benign mutations those common missense mutations that are observed in non-human primates but are clinically benign in humans;

identifying regions of the biomarker data that contain a threshold number of the pathogenic mutations across multiple individuals in the cohort; and

inferring a causal relationship between the identified regions and the disease.

Let’s also craft a “product claim” for a target discovered based on the patentable characterization of the PrimateAI Scores as “benign-eliminated pathogenicity scores.”

A benign-eliminated biomarker comprising a plurality of pathogenic mutations having respective benign-eliminated pathogenic scores that locate the benign-eliminated biomarker based on eliminating as benign mutations those common missense mutations that are observed in non-human primates but are clinically benign in humans.

The reader will appreciate that we can combine the above two claims to get back to where we started, i.e., a product-by-process claim.

Lead Discovery Claims

“Icarus flew too close to the sun, but at least he flew.”

Our reluctance towards product-by-process claims makes way for “use claims.”

A process limitation describing how a product is used, rather than how the product is made, does not result in a product-by-process claim. See Mentor Corp. v. Coloplast, Inc., 998 F.2d 992 (Fed. Cir. 1993).

We now craft a “process claim” for producing a lead discovered based on the patentable characterization of the PrimateAI Scores as “benign-eliminated pathogenicity scores.”

A method of producing an inhibitor or activator substance as a therapeutic agent for modulating activity of a benign-eliminated biomarker, including:

synthesizing the inhibitor or activator substance as the therapeutic agent for in vivo modulation of the benign-eliminated biomarker, for therapeutic intervention in treatment of a disease state or condition associated with the benign-eliminated biomarker,

wherein the benign-eliminated biomarker comprises a plurality of pathogenic mutations having respective benign-eliminated pathogenic scores that locate the benign-eliminated biomarker based on eliminating as benign mutations those common missense mutations that are observed in non-human primates but are clinically benign in humans.

Conclusion

“This is just the beginning.”

AI patents are best crafted when they are drafted two levels deeper than what is the norm for patent applications.

Efforts should be made to draft claims that (1) directly protect the drug design royalty stream, (2) increase the potential licensing royalty base and litigation damages, and (3) have a high infringement detection quotient that makes enforcement easier.

Don’t let the AI blackbox box you in. Think outside the box!