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 Claims
Claims 2-21 are pending in the present application with claims 2, 9, and 16 being independent, as set forth in the Preliminary Amendment dated September 3, 2024.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
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Claims 2-21 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-3, 5, 8, 9, 12, 15, 16, 19, and 20 of U.S. Patent No. 11,967,436 ("the '436 Patent") in view of NPL " A Computational-Based Method for Predicting Drug–Target Interactions by Using Stacked Autoencoder Deep Neural Network" to Wang et al. ("Wang"). Although the claims at issue are not identical, they are not patentably distinct from each other:
Claims 1-3, 5, 8, 9, 12, 15, 16, 19, and 20 of the '436 Patent disclose all the limitations of claims 2-21 of the present application except for the predicted "new disease indication" for a drug (including the training data including "representations of diseases in a second modality," projecting the representations of "diseases" into the common representation space to obtain "disease vectors," etc., specifically being in relation to a "protein target" for the drug as recited in claims 2-21 of the present application. Stated differently, claims 1-3, 5, 8, 9, 12, 15, 16, 19, and 20 of the '436 Patent are silent regarding predicting a "protein target" for the drug, the training data including "representations of proteins in a second modality," projecting the representations of "proteins" into the common representation space to obtain "protein vectors," etc. For reference, a comparison between claim 2 of the present application and claim 1 of the '436 Patent are presented below:
Claim 2 of Present Application
Claim 1 of the '436 Patent
2. A method of predicting a protein target for a drug, the method comprising: using at least one computer processor to perform:
1. A method for predicting a new disease indication for a given drug, the method comprising using at least one processor to perform:
training a statistical model using training data comprising representations of drugs in a first modality and representations of proteins in a second modality, the statistical model comprising a drug encoder, a protein encoder, a common representation space, a drug decoder and a protein decoder, wherein the training comprises:
training a statistical model by applying a self-supervised learning technique to training data to obtain a trained statistical model, the training data comprising representations of drugs in a first modality and representations of diseases in a second modality, the statistical model comprising a drug encoder, a disease encoder, a common representation space, a drug decoder and a disease decoder, wherein the training comprises:
projecting (1) the representations of the drugs into the common representation space using the drug encoder to obtain drug vectors and (2) the representations of the proteins into the common representation space using the protein encoder to obtain protein vectors;
projecting the representations of the drugs and diseases into the common representation space using the drug encoder to obtain drug vectors; projecting the representations of the diseases into the common representation space using the disease encoder to obtain disease vectors;
combining the drug vectors with the protein vectors to obtain a plurality of joint vectors;
combining the drug vectors with the disease vectors to obtain a plurality of joint drug-disease vectors;
providing the plurality of joint vectors as input to the drug decoder and/or the protein decoder to obtain decoded output vectors; and
providing the joint drug-disease vectors as input to the drug decoder and/or the disease decoder to obtain decoded output vectors; determining a difference between the decoded output vectors, and at least some of the representations of drugs and diseases; and
updating parameters of the statistical model using the decoded output vectors to obtain the trained statistical model;
updating parameters of the statistical model based on the difference between the decoded output vectors and the at least some representations of drugs and diseases;
obtaining (1) a representation of the drug comprising data from the first modality and (2) representations of a plurality of proteins comprising data from the second modality; and
obtaining a representation of the given drug comprising data from the first modality; obtaining representations of a plurality of diseases comprising data from the second modality; and
identifying the protein target for the drug from the plurality of proteins at least in part by processing, using the trained statistical model, the representation of the given drug and the representations of the plurality of proteins.
predicting the new disease indication for the given drug using the trained statistical model, the trained statistical model comprising learned parameters for projecting data from the first and second modalities into the common representation space in which data from the first and second modalities can be compared, the learned parameters including parameters of the drug encoder trained to project drug representations into the common representation space and parameters of the disease encoder trained to project disease representations into the common representation space, the predicting comprising: projecting the representation of the given drug into the common representation space using the learned parameters of the trained drug encoder to obtain a first vector in the common representation space representing the given drug; projecting the representations of the plurality of diseases into the common representation space using the learned parameters of the trained disease encoder to obtain a plurality of vectors in the common representation space representing respective ones of the plurality of diseases; determining a measure of similarity between the first vector representing the given drug and each of the plurality of vectors representing the plurality of diseases; and identifying at least one disease of the plurality of diseases as the new disease indication based on the measure of similarity between the first vector representing the given drug and each of the plurality of vectors representing the plurality of diseases.
Nevertheless, Wang teaches (Abstract on page 361 and beginning of Introduction on page 362) that it was known in the healthcare informatics art that part of the process of identifying disease indications for drugs is identifying protein targets for drugs such as via a network graph where drugs and proteins are nodes and interactions between them are edges (page 363) and ML techniques (e.g., autoencoders, etc.) are used to predict target proteins (Figs. 1-3 on pages 364-366 and Conclusion on page 371). This arrangement advantageously facilitates mining of hidden interactions between drugs and protein targets thereby promoting the research and development of drugs for purposes of treating diseases (Conclusion on page 371).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for the prediction of the new disease indication for a given drug in claims 1-3, 5, 8, 9, 12, 15, 16, 19, and 20 of the '436 Patent to include prediction of a protein target for the drug as taught by Wang (including the training data including "representations of proteins in a second modality," projecting the representations of "proteins" into the common representation space to obtain "protein vectors," etc.) to advantageously facilitate mining of hidden interactions between drugs and protein targets thereby promoting the research and development of drugs for purposes of treating diseases. A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so. KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id.
Statement Regarding Subject-Matter Eligibility
When currently pending claims 2-21 are considered in view of the 2019 Revised Patent Subject Matter Eligibility Guidance (which collectively includes the guidance in the January 7, 2019 Federal Register notice and the October 2019 update issued by the USPTO as now incorporated into the MPEP, and as supported by relevant case law), the claims are patent eligible under 35 USC 101.
While independent claims 2, 9, and 16 recite certain limitations that include a “mental process” abstract idea (e.g., combining drug and protein vectors to obtain joint vectors, obtaining drug and protein representations, identifying a protein target for a drug by processing the drug and protein representations) because they can be practically performed in the human mind (e.g., with pen and paper), the claims recite additional limitations that amount to a “practical application” of the abstract idea and/or are “significantly more” than the abstract idea.
For instance, at least the additional limitations of training a statistical model using training data comprising representations of drugs in a first modality and representations of proteins in a second modality, the statistical model comprising a drug encoder, a protein encoder, a common representation space, a drug decoder and a protein decoder, wherein the training comprises: i) projecting (1) the representations of the drugs into the common representation space using the drug encoder to obtain drug vectors and (2) the representations of the proteins into the common representation space using the protein encoder to obtain protein vectors; ii) providing the plurality of joint vectors as input to the drug decoder and/or the protein decoder to obtain decoded output vectors; and iii) updating parameters of the statistical model using the decoded output vectors to obtain the trained statistical model; and using the trained statistical model to identify the protein target for the drug from the plurality of proteins at least in part by processing the representation of the given drug and the representations of the plurality of proteins amount to other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment. MPEP 2106.04(d)(I); 2106.05(e). Specifically, such limitations are meaningful “because they integrate the results of the analysis into a specific and tangible method that results in the method moving from abstract scientific principles to specific application.” Id.
Furthermore, the Examiner asserts that the additional limitations provide “significantly more” than the at least one abstract idea because they recite a particular manner of training the statistical model to identify the protein target rather than just the end result of doing so. See Electric Power, 830 F.3d at 1356, 119 USPQ2d at 1743 (see MPEP § 2106.05(f)(1)); See McRO, 837 F.3d at 1314-15, 120 USPQ2d at 1102-03. MPEP 2106.05(f).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO-892.
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/JONATHON A. SZUMNY/Primary Examiner, Art Unit 3686