DETAILED ACTION
Applicant’s response filed 07/01/2026 has been fully considered. Rejections and/or objections not reiterated from previous Office Actions are hereby withdrawn. The following rejections and/or objections are either reiterated or newly applied.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
Claims 1-11, 13, 15, 17-19, and 23-29 are pending and under consideration in this action.
Claims 12, 14, 16, 20-22, and 30-39 were previously canceled.
Claims 1-11, 13, 15, 17-19, and 23-28 are allowed.
Claim 29 is rejected.
Priority
The instant application is 371 of PCT/US21/13451, filed 1/14/2021, which claims priority to U.S. Provisional Application number 62/961,112, filed 1/14/2020, as reflected in the filing receipt mailed 9/14/2023. The claim for domestic benefit for claims 1-11, 13, 15, 17-19, and 23-29 is acknowledged. As such, the effective filing date of claims 1-11, 13, 15, 17-19, and 23-29 is 1/14/2020.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 07/01/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the IDS has been considered by the examiner.
It is noted that certain references lack appropriate titles, issue number, article numbers, years, and/or page numbers (NPL # A14, A19, A22, and A23). The Examiner has annotated those references herein. Applicant is kindly reminded to provide proper citations in compliance with 37 CFR 1.97 in all future submissions to the office.
Specification
The objection to the title is withdrawn in view of Applicant’s amendment to the title filed 07/01/2026 (Applicant’s Remarks, Pg. 14-15).
The objection to the Specification is withdrawn in view of Applicant’s amendments to the Specification filed 07/01/2026 and submission of the IDS dated 07/01/2026 (Applicant’s Remarks, Pg. 14-15).
Claim Objections
The objections to claims 18-19 and 25 are withdrawn in view of Applicant’s amendments to the claims filed 07/01/2026 (Applicant’s Remarks, Pg. 15).
Claim Rejections - 35 USC § 112(b)
The rejection of claims 3-5, 19, 25 and 29 under 35 U.S.C. 112(b) as being indefinite is withdrawn in view of Applicant’s amendments to the claims filed 07/01/2026 (Applicant’s Remarks, Pg. 15-16).
Claim Rejections - 35 USC § 101
Withdrawn Rejections
The rejection of claims 1-11, 13, 15, 17-19, and 23-28 under 35 U.S.C. 101 as being directed towards an abstract idea without significantly more is withdrawn in view of Applicant’s amendments to the claims filed 07/01/2026 and Applicant’s Remarks were found persuasive (Applicant’s Remarks, Pg. 16-21). Claims 1-11, 13, 15, 17-19 and 23-28 were analyzed under 35 U.S.C. 101 and it was found that the limitations of jointly training an untrained or partially untrained neural network encoder and an untrained or partially untrained classifier (step (B) of claims 1, 27, and 28); jointly updating the first plurality of weights and the second plurality of weights by comparing the classification of the predicted biological property ... to the one or more biological properties (step (B)(ii) of claims 1, 27 and 28); training an untrained or partially untrained decoder (step (D) of claims 1, 27 and 28); and updating the third plurality of weights by comparing the chemical structure of each respective compound outputted by the untrained or partially untrained decoder to the actual chemical structure of the respective compound from the second training dataset (step (D)(ii) of claims 1, 27 and 28) recite a technical improvement by providing a method for training models that allows for the generation of compounds with a target property. Additionally, analogous to Desjardins, the above limitations recite an improvement in the training and operation of the machine learning model under Step 2A, Prong Two.
Maintained Rejections
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 29 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite both (1) mathematical concepts (mathematical relationships, formulas or equations, or mathematical calculations) and (2) mental processes, i.e., concepts performed in the human mind (including observations, evaluations, judgements or opinions) (see MPEP § 2106.04(a)).
Any newly recited portion is necessitated by claim amendment.
Framework with which to evaluate Subject Matter Eligibility as outlined in MPEP § 2106:
Step 1: Are the claims directed to a process, machine, manufacture or composition of matter;
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e., a law of nature, a natural phenomenon, or an abstract idea;
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application (Prong Two); and
Step 2B: If the claims do not integrate the judicial exception, do the claims provide an inventive concept.
Framework as it pertains to the instant claims:
Step 1:
In the instant application, claim 29 is directed towards a method, which falls into one of the categories of statutory subject matter (Step 1: YES).
Step 2A, Prong One:
In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A, Prong One). The following instant claims recite limitations that equate to one or more categories of judicial exceptions:
Claim 29 recites a mathematical concept (i.e., generating a projected representation) in "wherein the first projected representation has N dimensions", "wherein N is an integer between 20 and 80", and "wherein the corresponding projected representation has N dimensions"; a mental process (i.e., an evaluation of the N dimensions) in “wherein the N dimensions of the first projected representation comprise a representation of information about the first biological property”; a mathematical concept (i.e., sampling vectors from the projected representation; see specification Para. [00171]) in "using the first projected representation to obtain one or more candidate projections"; a mental process (i.e., an evaluation of the candidate compounds for the presence of a compound) in "wherein the first compound is not present in the plurality of candidate compounds"; and a mathematical concept (i.e., using the classifier to determine if the compound has the targeted property; the classifier can be a logistic regression classifier, k-nearest neighbor classifier, a decision tree classifier as disclosed in specification Para. [00141]) in "obtaining a classification of the respective candidate compound by inputting the corresponding projected representation of the respective candidate compound into the trained classifier, wherein, when the trained classifier indicates that the corresponding projected representation of the respective candidate compound has the first biological property, the respective candidate compound is deemed to have the first biological property”.
These recitations are similar to the concepts of collecting information, and displaying certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), comparing information regarding a sample or test to a control or target data in Univ. of Utah Research Found. v. Ambry Genetics Corp. (774 F.3d 755, 113 U.S.P.Q.2d 1241 (Fed. Cir. 2014)) and Association for Molecular Pathology v. USPTO (689 F.3d 1303, 103 U.S.P.Q.2d 1681 (Fed. Cir. 2012)), and organizing and manipulating information through mathematical correlations in Digitech Image Techs., LLC v Electronics for Imaging, Inc. (758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)) that the courts have identified as concepts that can be practically performed in the human mind or mathematical relationships.
The abstract ideas recited in the claims are evaluated under the broadest reasonable interpretation (BRI) of the claim limitations when read in light of and consistent with the specification, and are determined to be directed to mental processes that in the simplest embodiments are not too complex to practically perform in the human mind. Additionally, the recited limitations that are identified as judicial exceptions from the mathematical concepts grouping of abstract ideas are abstract ideas irrespective of whether or not the limitations are practical to perform in the human mind.
Specifically, claim 29 involves nothing more than generating a projected representation, obtaining candidate projections, and generating a compound classification. The steps reciting generating a projected representation, obtaining candidate projections, and generating a compound classification are, under the BRI, performed using mathematical operations. The instant Specification (see Para. [0009], [0073], and [00123]) discloses that the projected representation is an N-dimensional vector representation of the compound. The instant Specification (see Para. [00171]) also discloses that obtaining the candidate projections is performed by sampling vectors from the projected representation. The instant Specification (see Para. [00141]) also discloses that the classifier is a logistic regression classifier, a k-nearest neighbor classifier, a deep neural network classifier, a support vector machine classifier, a decision tree classifier, or a naive Bayes classifier. Therefore, the claimed steps are not further defined beyond something that reads on performing calculations using a computer as a tool. As such, said steps are directed to judicial exceptions. The instant claims must therefore be examined further to determine whether they integrate the abstract idea into a practical application (Step 2A, Prong One: YES).
Step 2A, Prong Two:
In determining whether a claim is directed to a judicial exception, further examination is performed that analyzes if the claim recites additional elements that when examined as a whole integrates the judicial exception(s) into a practical application (MPEP § 2106.04(d)). A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. The claimed additional elements are analyzed to determine if the abstract idea is integrated into a practical application (MPEP § 2106.04(d)(I)). If the claim contains no additional elements beyond the abstract idea, the claim fails to integrate the abstract idea into a practical application (MPEP § 2106.04(d)(III)). The following independent claims recite limitations that equate to additional elements:
Claim 29 recites “at a computer system comprising at least one processor and a memory storing at least one program for execution by the at least one processor, the at least one program comprising instructions”; “obtaining a first projected representation of a first compound that is assigned the first biological property by inputting a chemical structure of the first compound into a trained neural network encoder”; “inputting each candidate projection in the one or more candidate projections into a trained decoder thereby obtaining a plurality of candidate compounds”; and “obtaining a corresponding projected representation for the respective candidate compound by inputting a chemical structure of the candidate compound into the trained neural network encoder”.
Regarding the above cited limitation in claim 29 of (i) at a computer system comprising at least one processor and a memory storing at least one program for execution by the at least one processor, the at least one program comprising instructions. This limitation requires only a generic computer component, which does not improve computer technology. Therefore, this limitation equates to mere instructions to implement an abstract idea on a generic computer, which the courts have established does not render an abstract idea eligible in Alice Corp. 573 U.S. at 223, 110 USPQ2d at 1983.
Regarding the above cited limitations in claim 29 of (ii) obtaining a first projected representation of a first compound that is assigned the first biological property by inputting a chemical structure of the first compound into a trained neural network encoder; (iii) inputting each candidate projection in the one or more candidate projections into a trained decoder thereby obtaining a plurality of candidate compounds; and (iv) obtaining a corresponding projected representation for the respective candidate compound by inputting a chemical structure of the candidate compound into the trained neural network encoder. These limitations equate to insignificant, extra-solution activity of mere data gathering because these limitations gather data before or after the recited judicial exceptions of generating a projected representation, obtaining candidate projections, and generating a compound classification (see MPEP § 2106.04(d)). As such, claim 29 is directed to an abstract idea (Step 2A, Prong Two: NO).
Step 2B:
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that equate to well-understood, routine and conventional (WURC) limitations (MPEP § 2106.05(d)). The instant independent claims recite same additional elements described in Step 2A, Prong Two above.
Regarding the above cited limitation in claim 29 of (i) at a computer system comprising at least one processor and a memory storing at least one program for execution by the at least one processor, the at least one program comprising instructions. This limitation equates to instructions to implement an abstract idea on a generic computing environment, which the courts have established does not provide an inventive concept (see MPEP § 2106.05(d) and MPEP § 2106.05(f)).
Regarding the above cited limitations in claims 1 and 29 of (ii) obtaining a first projected representation of a first compound that is assigned the first biological property by inputting a chemical structure of the first compound into a trained neural network encoder; (iii) inputting each candidate projection in the one or more candidate projections into a trained decoder thereby obtaining a plurality of candidate compounds; and (iv) obtaining a corresponding projected representation for the respective candidate compound by inputting a chemical structure of the candidate compound into the trained neural network encoder. These limitations when viewed individually and in combination, are well-understood, routine and conventional (WURC) limitations as taught by Oono et al. (U.S. Patent Application Publication US 2017/0161635 A1; previously cited). Oono et al. discloses a generative model used to generate chemical compounds that have desired characteristics, e.g., activity against a selected target (Abstract). Oono et al. further discloses the generation of latent representations of compounds, which are passed through one or more layers of an encoder (limitations (ii) and (iv)) (Para. [0058], [0078], and [0110]). Oono et al. further discloses inputting a latent representation of a compound into the decoder and that the during training, the decoder may learn to regenerate original compound representations from latent representations (limitation (iii)) (Para. [0078] and [0136]).
These additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the instant claims do not amount to significantly more than the judicial exception itself (Step 2B: NO). As such, claim 29 is not patent eligible.
Response to Arguments under 35 U.S.C. 101
Applicant’s arguments filed 07/01/2026 have been fully considered but they are not persuasive.
1. Applicant argues that claim 29 includes all the limitations of claim 1, and thus, are patent eligible for at least the same reasons (Applicant’s Remarks, Pg. 21).
It is respectfully submitted that this is not persuasive for the following reasons:
Claim 29 does not contain all the training limitations for the encoder, classifier, and decoder, as recited in claim 1, nor do the limitations in claim 29 improve the functioning of the computer, as described in detail below.
With regards to argument (I) for claim 1 (Applicant’s Remarks, Pg. 16-18), reciting in part that “claim 1 provides a technical solution to the above technical problem by providing a method for training models that allows for the generation of novel compounds that have a target biological property”. Claim 29 does not require the same training steps, and instead only requires the use of the trained model. The training methodology (e.g., at least the limitations of jointly training an untrained or partially untrained neural network encoder and an untrained or partially untrained classifier (step (B) of claim 1); jointly updating the first plurality of weights and the second plurality of weights by comparing the classification of the predicted biological property ... to the one or more biological properties (step (B)(ii) of claim 1); and training an untrained or partially untrained decoder (step (D) of claim 1)), which provide the technical improvement, are not required by the limitations recited in claim 29.
With regards to argument (II) for claim 1 (Applicant’s Remarks, Pg. 18-19), which recites in part “by coupling the training of the classifier with the training of the neural network encoder, claim 1 preserves the computer system's ability to perform the two tasks of: (i) generating improved projected representations of molecules that incorporate target biological properties and (ii) classifying biological properties of projected representations” and “adjusting the third plurality of weights passes the constraints to the decoder such that it generates novel compounds that similarly contain target biological properties, based on projected representations obtained from the trained encoder. In this way, analogous to the claim at issue in Desjardins, Applicant's claim 1 imposes constraints that protects the performance of a first and second machine learning task, while optimizing performance of a third machine learning model task”. Analogous to argument (I) for claim 1, claim 29 does not require the same training limitations as claim 1, as claim 29 does not require joint training of the encoder and classifier nor does it require updating weights in the training process. Therefore, the recited training limitations are not commensurate in scope with limitations recited in claim 29, and the argument for improvement to how the machine learning model operates, analogous to Desjardins, is not persuasive.
With regards to argument (III) for claim 1 (Applicant’s Remarks, Pg. 19-21), reciting in part “the constrained representational learning of claim 1 acts like the self-referential data structure of Enfish in the sense that they cause the computer to operate more efficiently or effectively” and “Here, the claimed constraints enable the computer to generate projected representations of compounds that incorporate biological properties without relying on prior knowledge or external data relating to compound function or activity”. No evidence has been presented to suggest that the computer itself has been altered in any way, i.e., by changing the functioning of a processor or by changing the way in which it stores or accesses memory. Nothing about the physical components of the computer nor the way the computer operates is changed by the limitations in claim 29. While the claimed methodology may reduce the use of computational resources, this does not equate to changing the function of physical components of the computer. Therefore, the use of a computer to perform the limitations in claim 29 invokes a computer as a tool (see MPEP 2106.05(a)(I)).
Therefore, since claim 29 does not recite specific training limitations, analogous to claim 1, nor do the limitations in claim 29 improve the functioning of the computer itself, claim 29 is not patent eligible. This argument is thus not persuasive.
Claim Rejections - 35 USC § 103
Withdrawn Rejections
The rejection of claims 1-9 and 27-28 under 35 U.S.C. 103 as being unpatentable over Oono et al. in view of Hernandez et al. is withdrawn in view of Applicant’s amendments to the claims filed 07/01/2026 (Applicant’s Remarks, Pg. 21-24). Specifically, while Oono et al. discloses the concurrent training of the generative model (encoder) and predictor (classifier) (Para. [0008], [0067], and [0069]), Oono et al. does not teach the joint updating of the weights by comparing the classification of the predicted biological property to the respective compound in the training dataset, as disclosed in amended independent claims 1, 27, and 28. Hernandez et al. also does not teach the joint training of an encoder and a classifier, and therefore also does not teach the joint updating of the weights by comparing the classification of the predicted biological property to the respective compound in the training dataset.
The rejection of claims 10-11, 13, and 17 under 35 U.S.C. 103 as being unpatentable over Oono et al. in view of Hernandez et al. and Torng et al. is withdrawn in view of Applicant’s amendments to the claims filed 07/01/2026 (Applicant’s Remarks, Pg. 24-25). Specifically, Torng et al. also does not teach the joint training of an encoder and a classifier, and therefore also does not teach the joint updating of the weights by comparing the classification of the predicted biological property to the respective compound in the training dataset.
The rejection of claim 15 under 35 U.S.C. 103 as being unpatentable over Oono et al. in view of Hernandez et al. and Knyazev et al. is withdrawn in view of Applicant’s amendments to the claims filed 07/01/2026 (Applicant’s Remarks, Pg. 25). Specifically, Knyazev et al. also does not teach the joint training of an encoder and a classifier, and therefore also does not teach the joint updating of the weights by comparing the classification of the predicted biological property to the respective compound in the training dataset.
The rejection of claims 18-19 and 23-26 under 35 U.S.C. 103 as being unpatentable over Oono et al. in view of Hernandez et al. and Costello et al. is withdrawn in view of Applicant’s amendments to the claims filed 07/01/2026 (Applicant’s Remarks, Pg. 25). Specifically, Costello et al. also does not teach the joint training of an encoder and a classifier, and therefore also does not teach the joint updating of the weights by comparing the classification of the predicted biological property to the respective compound in the training dataset.
The rejection of claim 29 under 35 U.S.C. 103 as being unpatentable over Oono et al. in view of Torng et al. is withdrawn in view of Applicant’s amendments to the claims filed 07/01/2026 (Applicant’s Remarks, Pg. 25-26). Specifically, neither Oono et al. nor Torng et al. teaches obtaining a projected representation from the encoder with N dimensions of information about the biological property, with N being an integer between 20 and 80.
Conclusion
Claims 1-11, 13, 15, 17-19, and 23-28 are allowed.
Claim 29 is rejected.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Claims 1-11, 13, 15, 17-19, and 23-28 appear to be free from the prior art because the prior art does not fairly suggest or teach the joint updating of the weights, during training of the encoder and classifier, by comparing the classification of the predicted biological property to the respective compound in the training dataset. The closest prior art is Oono et al. (U.S. Patent Application Publication US 2017/0161635 A1; previously cited). Oono et al. discloses a generative model to generate chemical compounds with desired characteristics, including the training of an encoder, a classifier, and a decoder (Abstract and Para. [0009],[0054], [0110]). Oono et al. further discloses the use of the trained model to generate a chemical compound, which is not in the training set, with a specific property (Para. [0009] and [0050]). While Oono et al. discloses the concurrent training of the generative model (encoder) and predictor (classifier) (Para. [0008], [0067], and [0069]), Oono et al. does not disclose the joint updating of the weights by comparing the classification of the predicted biological property to the respective compound in the training dataset, as disclosed in amended independent claims 1, 27, and 28. Claims 2-11, 13, 15, 17-19, and 23-26 appear to be free from the prior art due to their dependency on claim 1.
Claim 29 appears to be free from the prior art because the prior art does not fairly suggest or teach obtaining a projected representation from the encoder with N dimensions of information about the biological property, with N being an integer between 20 and 80. The closest prior art is Oono et al. (U.S. Patent Application Publication US 2017/0161635 A1; previously cited). Oono et al. discloses a generative model to generate chemical compounds with desired characteristics (Abstract). Oono et al. further discloses the use of an autoencoder to generate a representation, e.g. a fingerprint, matching a property (Para. [0110] and [0112]). Oono et al. further discloses the use of a trained decoder to generate candidate compounds and trained classifier to classify and label a compound with a specific property (Para. [0050], [0077]-[0078], and [0099]). However, Oono et al. does not teach obtaining a projected representation from the encoder with N dimensions of information about the biological property, with N being an integer between 20 and 80, as disclosed in instant claim 29.
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/D.P.S./Examiner, Art Unit 1687
/Lori A. Clow/Primary Examiner, Art Unit 1687
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