DETAILED ACTION
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 Application
This action is in reply to the correspondence received through January 16, 2026.
Claims 3, 5-9, 11, 15, and 17-20 are amended.
Claims 21-24 are canceled.
Claims 1-20 are pending.
Information Disclosure Statement
The information disclosure statement submitted January 16, 2026 and its contents have been considered.
Claim Rejections - 35 U.S.C. § 101
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.
Claims 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to non-statutory subject matter. Claims 1-20 are directed to an abstract idea without significantly more as required by the Alice test as discussed below.
Step 1
Claims 1-20 are directed to a process, machine, manufacture, or composition of matter.
Step 2A
Claims 1-20 are directed to abstract ideas, as explained below.
Prong one of the Step 2A analysis requires identifying the specific limitation(s) in the claim under examination that the examiner believes recites an abstract idea; and determining whether the identified limitation(s) falls within at least one of the groupings of abstract ideas of mathematical concepts, mental processes, and certain methods of organizing human activity.
The claims recite the following limitations that are directed to abstract ideas. Claim 1 recites obtaining a Drug-Drug Interaction (DDI) embedding value, representing occurrence of DDIs between a substance of interest and one or more drugs selected from a plurality of baseline drugs, in a DDI embedding space; receiving a chemical structure data element, representing a chemical structure of the substance of interest; and predicting efficacy of the substance of interest in treatment of the predetermined medical condition based on (i) the DDI embedding value and (ii) the structure data element. Claim 13 recites similar features as claim 1. Claims 2-12 and 14-20 further specify features of the algorithms recited in the independent claims or characteristics of the data used thereby.
These limitations describe abstract ideas that correspond to concepts identified as abstract ideas by the courts as mathematical concepts—such as mathematical relationships, mathematical formulas or equations, and mathematical calculations—because the claimed features directed toward storing values (e.g., as vectors) and using these values to predict an efficacy value are mathematical relationships, mathematical formulas or equations, and mathematical calculations.
These limitations describe abstract ideas that correspond to concepts identified as abstract ideas by the courts as mental processes—such as concepts performed in the human mind (including an observation, evaluation, judgment, or opinion)—because the claimed features identified above are concepts performed in the human mind (including an observation, evaluation, judgment, or opinion).
These limitations describe abstract ideas that correspond to concepts identified as abstract ideas by the courts as certain methods of organizing human activity—such as fundamental economic principles or practices (including hedging, insurance, mitigating risk), commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations), managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)—because the claimed features identified above manage personal behavior or relationships or interactions between people including following rules or instructions.
Thus, the concepts set forth in claims 1-20 recite abstract ideas.
Prong two of the Step 2A requires identifying whether there are any additional elements recited in the claim beyond the judicial exception(s), and evaluating those additional elements to determine whether they integrate the exception into a practical application of the exception. “Integration into a practical application” requires an additional element or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception. Further, “integration into a practical application” uses the considerations laid out by the Supreme Court and the Federal Circuit to evaluate whether the judicial exception is integrated into a practical application, such as considerations discussed in M.P.E.P. § 2106.05(a)-(h).
The claims recite the following additional elements beyond those identified above as being directed to an abstract idea. Claim 1 recite recites that its method is performed by a processor. Claim 13 recites a non-transitory memory device, modules of instruction code, and at least one processor. Some of the dependent claims recite providing an output.
The identified judicial exception(s) are not integrated into a practical application for the following reasons.
First, evaluated individually, the additional elements do not integrate the identified abstract ideas into a practical application. The additional computer elements identified above—the processors, modules, and non-transitory memory device—are recited at a high level of generality. Inclusion of these elements amounts to mere instructions to implement the identified abstract ideas on a computer. See M.P.E.P. § 2106.05(f). The use of conventional computer elements to provide an output is the insignificant, extra-solution activity of mere data gathering or outputting in conjunction with a law of nature or abstract idea. See M.P.E.P. § 2106.05(g). To the extent that the claims transform data, the mere manipulation of data is not a transformation. See M.P.E.P. § 2106.05(c). Inclusion of the computing system in the claims amounts to generally linking the use of the judicial exception to a particular technological environment or field of use. See M.P.E.P. § 2106.05(h). Thus, taken alone, the additional elements do not amount to significantly more than a judicial exception.
Second, evaluating the claim limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. See M.P.E.P. § 2106.05(a). Their collective functions merely provide an implementation of the identified abstract ideas on a computer system in the general field of use of predicting efficacy of treatment. See M.P.E.P. § 2106.05(h).
Thus, claims 1-20 recite mathematical concepts, mental processes, or certain methods of organizing human activity without including additional elements that integrate the exception into a practical application of the exception.
Accordingly, claims 1-20 are directed to abstract ideas.
Step 2B
Claims 1-20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, when considered both individually and as an ordered combination, do not amount to significantly more than the abstract idea.
The analysis above describes how the claims recite the additional elements beyond those identified above as being directed to an abstract idea, as well as why identified judicial exception(s) are not integrated into a practical application. These findings are hereby incorporated into the analysis of the additional elements when considered both individually and in combination. Additional features of these analyses are discussed below.
Evaluated individually, the additional elements do not amount to significantly more than a judicial exception. In addition to the factors discussed regarding Step 2A, prong two, these additional computer elements also provide conventional computer functions that do not add meaningful limits to practicing the abstract idea. Generic computer components recited as performing generic computer functions that are well-understood, routine and conventional activities amount to no more than implementing the abstract idea with a computerized system. The use of generic computer components to provide an output is the well-understood, routine, and conventional computer functions of receiving or transmitting data over a network, e.g., the Internet, and does not impose any meaningful limit on the computer implementation of the identified abstract ideas. See M.P.E.P. § 2106.05(d)(II). Thus, taken alone, the additional elements do not amount to significantly more than a judicial exception.
Evaluating the claim limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. In addition to the factors discussed regarding Step 2A, prong two, there is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely amount to mere instructions to implement the identified abstract ideas on a computer.
Thus, claims 1-20, taken individually and as an ordered combination of elements, are not directed to eligible subject matter since they are directed to an abstract idea without significantly more.
Statement Regarding the Prior Art
Claims 1 and 13 recite features for, inter alia, obtaining a Drug-Drug Interaction (DDI) embedding value, representing occurrence of DDIs between a substance of interest and one or more drugs selected from a plurality of baseline drugs, in a DDI embedding space; receiving a chemical structure data element, representing a chemical structure of the substance of interest; and predicting efficacy of the substance of interest in treatment of the predetermined medical condition based on (i) the DDI embedding value and (ii) the structure data element.
Celebi et al. (“Evaluation of knowledge graph embedding approaches for drug-drug interaction prediction in realistic settings.” BMC bioinformatics 20.1 (2019): 726) teaches to accurately assess the performance for predicting drug-drug interactions (DDIs). The settings for disjoint cross-validation produced lower performance scores, as expected, but still were good at predicting the drug interactions. Celebi had applied Logistic Regression, Naive Bayes and Random Forest on DrugBank knowledge graph with the 10-fold traditional cross validation using RDF2Vec, TransE and TransD. RDF2Vec with Skip-Gram generally surpasses other embedding methods. Celebi also tested RDF2Vec on various drug knowledge graphs such as DrugBank, PharmGKB and KEGG to predict unknown drug-drug interactions. The performance was not enhanced significantly when an integrated knowledge graph including these three datasets was used. However, Celebi does not disclose all of the features as arranged in the independent claims of the present application.
Sarshogh et al. (U.S. Pub. No. 2023/0230662 A1) teaches techniques for generating a training dataset for training a molecule embedding module using contrastive learning, wherein the definition of similarity is based on molecular scaffold similarity. For example, systems access a molecular dataset and separate the molecular dataset into positive samples and negative samples. Systems then generate a training dataset comprising the positive samples and negative samples. Systems and methods are also provided for using the trained molecule embedding module to generate molecule embeddings and for building an end-to-end machine learning model configured to perform molecular embedding analysis and molecular property prediction, the model comprising the trained molecule embedding module and a property prediction module. However, Sarshogh does not teach all of the features as arranged in the independent claims of the present application.
Guo et al. (U.S. Pub. No. 2023/0067528 A1) teaches methods for building and training machine learning models configured to generate in-domain embeddings and perform multimodal analysis inside the same domain. The models include a first encoder trained to receive input from one or more entities represented in a first modality and to encode the one or more entities in the first modality, such that the first encoder is configured to output a first set of embeddings. The models also include a second encoder trained to receive input from one or more entities represented in the second modality and to encode the one or more entities in the second modality, such that the second encoder is configured to output a second set of embeddings. The models also include a projection layer configured to project the first set of embeddings and the second set of embeddings to a shared contrastive space. Guo also teaches using these techniques to perform molecular similarity, drug similarity and drug interaction prediction However, Guo does not teach all of the features as arranged in the independent claims of the present application.
Bajpai et al. (U.S. Pub. No. 2022/0101972 A1) teaches systems that leverage artificial intelligence and machine learning to identify molecules or compounds for use in pharmaceuticals. In aspects, one or more machine learning (ML) models may be trained to identify molecules based on pharmaceutical data that indicates properties of previously-identified pharmaceutical molecules, such as physiochemical structure, side effects, toxicity, solubility, and the like. The ML models may include generative models, such as generative adversarial networks or variational autoencoders. The trained ML models may be used to identify new (e.g., previously-unidentified) molecules, or the trained ML models may be provided to client devices for use in molecule identification (e.g., drug discovery). However, Bajpai does not teach all of the features as arranged in the independent claims of the present application.
The following references have been cited to further show the state of the art with respect to predicting efficacy of treatments:
Yuan et al. (U.S. Pub. No. 2021/0065913 A1) (artificial intelligence-based methods for early drug discovery and related training methods); and
Bucher et al. (U.S. Pub. No. 2022/0358373A1) (latent space optimization of generative machine learning models).
For these reasons, the closest art of record, including the combination of the references mentioned above, does not teach, suggest, or render obvious each and every element of the independent claims. Further, one of ordinary skill in the art at the time of invention would not look to combine these references, or the closest art of record, to arrive at the present claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Christopher Tokarczyk, whose telephone number is 571-272-9594. The examiner can normally be reached Monday-Thursday between 6:00 AM and 4:00 PM Eastern.
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/CHRISTOPHER B TOKARCZYK/Primary Examiner, Art Unit 3687