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 .
Claims Status
Claims 1-20 are pending.
Claims 1-20 are examined on the merits.
Priority
The instant application claims priority to US provisional Application No. 63/308,083, filed 02/09/2022. Therefore, the Effective Filing Date (EFD) assigned to each of the claims 1-20 is the provisional filing date of Application No. 63/308,083, filed 02/09/2022.
Information Disclosure Statement
The Information Disclosure Statements filed 01/09/2023 is in compliance with the provisions of 37 CFR 1.97 and has therefore been considered. A signed copy of the IDS document is included with this Office Action.
It is noted that certain references lack appropriate page numbers as is required under 37 CFR 1.97. The Examiner has annotated the references herein. Applicant is kindly reminded to provide proper citations in compliance with 37 CFR 1.97 in all future submission to the office.
Drawings
The drawings filed 01/09/2023 are accepted.
Claim Interpretation
With respect to claims 1, 8, and 15, the step of “extracting peptides to identify a virus or tumor cells” is interpreted as described in paragraph [00063] of the Specification, which discloses that an off-the-shelf peptide processing pipeline to extract some peptides that can uniquely identify the virus or tumor cells. Thus, the step is interpreted as a step implemented using software and not a laboratory step of extracting peptides.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
With respect to claims 1, 8, and 15, the claims recite the limitation of “extracting peptides to identify a virus or tumor cells”. The claims are indefinite because it is unclear if “to identify a virus or tumor cells” is an active step in the claims or instead an intended effect of extracting peptides.
With respect to claims 6, 13, and 20, the claims recite the limitation of “TCRs difficult to optimize”. The claims are indefinite because “difficult” is a relative term which renders the claim indefinite. The term is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
Claim Rejections - 35 USC § 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 inventions are directed to an abstract idea of mental steps, mathematic concepts, or a natural law without significantly more.
The MPEP at MPEP 2106.03 sets forth steps for identifying eligible subject matter:
(1) Are the claims directed to a process, machine, manufacture or composition of
matter?
(2A)(1) Are the claims directed to a judicially recognized exception, i.e. a law of nature,
a natural phenomenon, or an abstract idea?
(2A)(2) If the claims are directed to a judicial exception under Prong One, then is the
judicial exception integrated into a practical application?
(2B) If the claims are directed to a judicial exception and do not integrate the judicial
exception, do the claims provide an inventive concept?
With respect to step (1): Yes, the claims are directed to a method, a non-transitory computer-readable medium, and a system.
With respect to step (2A)(1): The claims are directed to abstract ideas of mental processes and mathematical concepts.
“Claims directed to nothing more than abstract ideas (such as a mathematical formula or equation), natural phenomena, and laws of nature are not eligible for patent protection” (MPEP 2106.04). Abstract ideas include mathematical concepts (mathematical formulas or equations, mathematical relationships and mathematical calculations), certain methods of organizing human activity, and mental processes (procedures for observing, evaluating, analyzing/judging and organizing information (MPEP 2106.04(a)(2)). Laws of nature or natural phenomena include naturally occurring principles/relations that are naturally occurring or that do not have markedly different characteristics compared to what occurs in nature (MPEP 2106(b)).
Mental processes recited in claims 1, 8, and 15:
randomly sampling batches of TCRs and following a policy network to mutate the TCRs
outputting the mutated TCRs
ranking the outputted TCRs to utilize top-ranked TCR candidates to target the virus or the tumor cells for immunotherapy
Mathematical concepts recited in claims 1, 8, and 15:
predicting, by a deep neural network, interaction scores between the extracted peptides and the TCRs from the target patients
developing a deep reinforcement learning (DRL) framework with TCR mutation policies to generate TCRs with maximum binding scores
defining reward functions based on a reconstruction-based score and a density estimation-based score
Dependent claims 2-7, 9-14, and 16-20 recite additional steps that either are directed to abstract ideas or further limit the judicial exceptions in independent claims 1, 8, and 15, and as such, are further directed to abstract ideas. Hence, the claims explicitly recite numerous elements that individually and in combination constitute abstract ideas. The relevant recitations are:
Claims 2, 9, and 16: “wherein the reward functions measure both a likelihood of mutated sequences being valid TCRs and probabilities of the TCRs recognizing peptides”
Claims 3, 10, and 17: “wherein the measurement of the likelihood of the mutated sequences being valid TCRs is enabled by a TCR autoencoder (TCR-AE) trained only by TCRs”
Claims 4, 11, and 18: “wherein density estimation over a latent space within the TCR-AE is evaluated by using a Gaussian Mixture Model (GMM)”
Claims 5, 12, and 19: “wherein the TCR-AE uses a bidirectional long short-term memory (LSTM) to encode an input sequence into a hidden vector by concatenating last hidden vectors from two LSTM directions”
Claims 6, 13, and 20: “wherein a buffering and re-optimizing framework including a buffer is employed to handle TCRs difficult to optimize and to generalize optimization capacity to more diverse TCRs”
Claims 7 and 14: “wherein the TCRs and the extracted peptides are encoded by a TCR-AE in a distributed embedding space, and a mapping is learnt between the embedding space and the TCR mutation policies”
The abstract ideas in the claims are evaluated under Broadest Reasonable Interpretation (BRI) and determined herein to each cover mental processes and mathematic concepts because the claims recite no more than using machine learning, a mathematical concept, to predict properties of a TCR sequence, and then using mental processes to generate sequences and rank sequences based on the results of the mathematical concepts. The claims broadly recite peptides and TCR sequences, and thus one of ordinary skill can perform mental processes of sampling and ranking.
With respect to step (2A)(2): The claims must therefore be examined further to determine whether they integrate that abstract idea into a practical application (MPEP 2106.04(d)). The claimed additional elements are analyzed alone or in combination to determine if the judicial exception is integrated into a practical application (MPEP 2106.04(d).I.; MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the judicial exception, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d).III).
Claims 1, 8, and 15 recite the following additional elements that are not abstract ideas:
extract peptides to identify a virus or tumor cells
collect a library of TCRs from target patients
a system comprising a memory and one or more processor in communication with the memory
a non-transitory computer-readable storage medium comprising a computer-readable program
The steps of extracting peptides and collecting a library of TCRs generates the data on which the judicial exceptions are performed and is thus considered data gathering. Data gathering does not impose any meaningful limitation on the abstract idea, or how the abstract idea is performed. Data gathering steps are not sufficient to integrate an abstract idea into a practical application (MPEP 2106.05(g)). The elements of a system comprising a memory and one or more processors, and a non-transitory computer-readable storage medium are directed to elements of a generic computer. The courts have weighed in and consistently maintained that when, for example, a memory, display, processor, machine, etc. ... are recited so generically (i.e., no details are provided) that they represent no more than mere instructions to apply the judicial exception on a computer, and these limitations may be viewed as nothing more than generally linking the use of the judicial exception to the technological environment of a computer (see MPEP 2106.05(f)).
None of the dependent claims recite additional elements, alone or in combination, which would integrate a judicial exception into a practical application.
Lastly, the claims have been evaluated with respect to step (2B): Because the claims recite an abstract idea, and do not integrate that abstract idea into a practical application, the claims lack a specific inventive concept. Under said analysis, Applicant is reminded that the judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05). Identifying whether the additional elements beyond the abstract idea amount to such an inventive concept requires considering the additional elements individually and in combination to determine if they provide significantly more than the judicial exception (MPEP 2106.05.A i-vi).
With respect to the instant claims, the additional elements described above do not rise to the level of significantly more than the judicial exception. As set forth in the MPEP at 2106.05(d).I, determinations of whether or not additional elements (or a combination of additional elements) may provide significantly more and/or an inventive concept rests in whether or not the additional elements (or combination of elements) represents well-understood, routine, conventional activity. Said assessment is made by a factual determination stemming from a conclusion that an element (or combination of elements) is widely prevalent or in common use in the relevant industry, which is determined by either a citation to an express statement in the specification or to a statement made by an applicant during prosecution that demonstrates a well-understood, routine or conventional nature of the additional element(s); a citation to one or more of the court decisions as discussed in MPEP 2106(d)(II) as noting the well-understood, routine, conventional nature of the additional element(s); a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s); and/or a statement that the examiner is taking official notice with respect to the well-understood, routine, conventional nature of the additional element(s).
With respect to claims 1, 8, and 15: The additional elements of extracting peptides, collecting a library of TCRs from target patients, a system comprising a memory and one or more processors in communication with the memory, and a non-transitory computer-readable storage medium comprising a computer-readable program do not rise to the level of significantly more than the judicial exception. As exemplified in the MPEP at 2106.05(d).II. with respect to Genetic Techs. Ltd., 818 F.3d at 1377 and 118 USPQ2d at 1546, analyzing DNA to provide sequence information or detect allelic variants is a well-understood, routine, and conventional activity. Furthermore, with respect to extracting peptides, the Specification at paragraph [00063] discloses utilizing off-the-shelf peptide processing pipelines. Furthermore, as exemplified in the MPEP at 2106.05(f) with reference to Alice Corp. 573 US at 223, 110 USPQ2d at 1983 “claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible”. Therefore, the device constitutes no more than a general link to a technological environment, which is insufficient to constitute an inventive concept that would render the claims significantly more than the abstract idea (see MPEP 2105(b)I-III). As such, it is recognized that these additional limitations are routine, well understood, and conventional in the art. These limitations do not improve the functioning of a computer, or comprise an improvement to any other technical field, they do not require or set forth a particular machine, they do not affect a transformation of matter, nor do they provide a non-conventional or unconventional step. As such, these limitations fail to rise to the level of significantly more.
The claims have all been examined to identify the presence of one or more judicial exceptions. Each additional limitation in the claims has been addressed, alone and in combination, to determine whether the additional limitations integrate the judicial exception into a practical application. Each additional limitation in the claims has been addressed, alone and in combination, to determine whether those additional limitations provide an inventive concept which provides significantly more than those exceptions. Individually, the limitations of the claims and the claims as a whole have been found lacking.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 7, 8, 14, and 15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Davidsen et al. (“Deep generative models for T cell receptor protein sequences”, eLIFE, published September 2019).
Regarding claims 1, 8, and 15, Davidsen et al. teaches a method comprising:
extracting peptides to identify a virus or tumor cells: Davidsen et al. teaches an ensemble of protein sequences summarizing each individual’s previous immune exposures and these proteins being a sample from a probability distribution (page 1, Section Introduction) and prior art extracting peptides to identify TCR sequences (page 10, Section Data);
collecting a library of TCRs from target patients (page 10, Section Data);
predicting, by a deep neural network, interaction scores between the extracted peptides and the TCRs from the target patients (page 2, Section Methods overview, paragraph 2; page 5, Section VAE models learn the rules of VDJ recombination);
developing a deep reinforcement learning (DRL) framework with TCR mutation policies to generate TCRs with maximum binding scores: Davidsen et al. teaches that in the clonal selection mechanism of immune memory, T cells that bind antigen increase in frequency and thus increasing the frequency of their corresponding TCR sequences (page 1, Section Introduction) and teaches generating sequences with cohort frequency, cohort frequency thus being an indicator for antigen binding (page 4, Section VAE models predict cohort frequency; page 5, Section VAE models learn the rules of VDJ recombination);
defining reward functions based on a reconstruction-based score (page 5, Section VAE models learn the rules of VDJ recombination, paragraph 2; page 11, Section Models) and a density estimation-based score (Figure 8; page 10, paragraph 1);
randomly sampling batches of TCRs and following a policy network to mutate the TCRs (page 2, Section Methods overview, paragraph 3);
outputting mutated TCRs (page 11, Section Models, paragraph 2); and
ranking the outputted TCRs to utilize top-ranked TCR candidates to target the virus or the tumor cells for immunotherapy (page 11, Section Models, paragraph 4).
Furthermore, Davidsen et al. teaches a computer-implemented method, as evidenced by the use of probabilistic models (Abstract), and thus inherently teaches a computer comprising a non-transitory computer readable medium and a processor.
Regarding claims 7 and 14, the claims are directed to the TCRs and the extracted peptides being encoded by a TCR-AE in a distributed embedding space, and a mapping being learnt between the embedding space and the TCR mutation policies. Davidsen et al. teaches the method of claim 1 and the computer-readable storage medium of claim 8. Davidsen et al. also teaches fitting a variational autoencoder model parameterized by deep neural networks to T cell receptor repertoires (Abstract), and teaches embedding the sequences encoded by the variational autoencoder in a latent space (page 7, Section “The latent space embedding”). Davidsen et al. teaches a mapping being learnt between the embedding space the TCR V and J gene mutation policies (page 7, Section “The latent space embedding”).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 2, 3, 5, 9, 10, 12, 16, 17, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Davidsen et al., as applied to claims 1, 7, 8, 14, and 15 in the 102 rejection above, in view of Bi et al. (“An Attention Based Bidirectional LSTM Method to Predict the Binding of TCR and Epitope”, published September 2021).
Regarding claims 2, 9, and 16, the claims are directed to the rewards functions measuring both a likelihood of mutated sequences being valid TCRs and probabilities of the TCRs recognizing peptides. Davidsen et al. teaches the method of claim 1, the computer-readable storage medium of claim 8, and the system of claim 15. Davidsen et al. also teaches functions measuring likelihoods of the generated sequences being valid TCR sequences comparable to that of real sequences (page 5, Section VAE models learn the rules of VDJ recombination, paragraph 2).
Davidsen et al. does not teach the claim element of measuring probabilities of the TCRs recognizing peptides.
However, Bi et al. teaches an attention based bidirectional long short-term memory network method to predict the binding of TCR and epitope. Bi et al. teaches the prior art exploring recognition patterns of clusters of TCR sequences and their target epitopes (page 3273, column 1, last paragraph). Bi et al. teaches that studying the specific recognition between TCR and peptide-major histocompatibility complex can help us between understand immune mechanisms (Abstract), and using attention networks to perform predictions of amino acids that have a significant impact on binding results (page 3274, Section 2.3) and the probabilities (page 3275, column 1, paragraph 2).
Regarding claims 3, 10, and 17, the claims are directed to the measurement of the likelihood of the mutated sequences being valid TCRs being enabled by a TCR autoencoder (TCR-AE) trained only by TCRs. Davidsen et al. teaches the method of claim 2, the computer-readable storage medium of claim 9, and the system of claim 16 in view of Bi et al. Davidsen et al. also teaches fitting a variational autoencoder model parameterized by deep neural networks to T cell receptor repertoires (Abstract), and teaches embedding the sequences encoded by the variational autoencoder in a latent space (page 7, Section “The latent space embedding”). Davidsen et al. teaches the variation autoencoder only being trained on a collection of observed TCR sequences (page 2, Section Methods overview, paragraph 3).
Regarding claims 5, 12, and 19, the claims are directed to the TCR-AE using a bidirectional long short-term memory (LSTM) to encode an input sequence into a hidden vector by concatenating last hidden vectors from two LSTM directions. Davidsen et al. teaches the method of claim 3, the computer-readable storage medium of claim 10, and the system of claim 17 in view of Bi et al. Davidsen et al. teaches the variational autoencoder encoding the TCR sequences using one-hot encoding and concatenating the vectors (page 11, Section “Encoding the TCR sequences”).
Davidsen et al. does not teach the claim elements of using a bidirectional long short-term memory (LSTM) to encode an input sequence into a hidden vector by concatenating last hidden vectors from two LSTM directions.
However, Bi et al. teaches a bidirectional LSTM that can identify the binding of TCRs to epitopes (Abstract). Bi et al. teaches using the bidirectional LSTM network to encode input amino acid sequences into hidden vectors by concatenating the final vectors from two LSTM directions (page 3274, column 2, paragraph 2).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art to have incorporated using a bidirectional LSTM and binding predictions of Bi et al. to the method of Davidsen et al. because Davidsen et al. is directed to a model for generated TCR sequences for targeting immune sequences (Abstract) and Bi et al. is directed to a model for predicting binding of TCRs and epitopes (Abstract). Thus, one of ordinary skill in the art would have a reasonable expectation of success of generating TCR sequences using a model that includes a bidirectional LSTM network and would be motivated to do so in order to predict the binding of TCRs and epitopes using the LSTM network.
Claims 4, 11, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Davidsen et al. in view of Bi et al., as applied to claims 2, 3, 5, 9, 10, 12, 16, 17, and 19 above, and further in view of Dilokthanakul et al. (“Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders”, January 2017).
Regarding claims 4, 11, and 18, the claims are directed to density estimation over a latent space within the TCR-AE being evaluated by using a Gaussian Mixture Model (GMM). Davidsen et al. teaches the method of claim 3, the computer-readable storage medium of claim 10, and the system of claim 17 in view of Bi et al.
Davidsen et al. does not teach the claim element of density estimation over a latent space within the TCR-AE being evaluated by using a GMM.
Bi et al. teaches a framework using a Gaussian process to make predictions (page 3273, column 1, paragraph 2).
Bi et al. does not teach the claim element of density estimation over a latent space within the TCR-AE being evaluated by using a GMM.
However, Dilokthanakul et al. teaches deep unsupervised clustering with Gaussian mixture variational autoencoders. Dilokthanakul et al. teaches a variant of a variational autoencoder model with a Gaussian mixture as a prior distribution (Abstract). Dilokthanakul et al. teaches using a mixture of Gaussian as a prior over a latent space within the variational autoencoder (page 3, paragraph 2).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the Gaussian mixture model of Dilokthanakul et al. to the method of Davidsen et al. in view of Bi et al. because Davidsen et al. is directed to a model for generated TCR sequences for targeting immune sequences (Abstract) and Dilokthanakul et al. is directed to deep unsupervised clustering with gaussian mixture variational autoencoders (Abstract). Davidsen et al. teaches fitting variational autoencoder models parameterized by deep neural networks to T cell receptor repertoires (Abstract). Thus, one of ordinary skill in the art would have a reasonable expectation of success of performing clustering of TCR sequences and generating TCR sequences by combining the prior art references, and would be motivated to do so for improved unsupervised clustering using deep generative models.
Claims 6, 13, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Davidsen et al., as applied to claims 1, 7, 8, 14, and 15 in the 102 rejection above, in view of Yates et al. (“Theories and quantification of thymic selection”, Frontiers in immunology, published February 2014).
Regarding claims 6, 13, and 20, the claims are directed to a buffering and re-optimizing framework including a buffer being employed to handle TCRs difficult to optimize and to generalize optimization capacity to more diverse TCRs. Davidsen et al. teaches the method of claim 1, the computer-readable storage medium of claim 8, and the system of claim 15. Davidsen et al. teaches optimization of sequences (page 11, Section Models, paragraph 4) and that in order to generate diverse and function TCRs, T cells combine a stochastic process for choosing from a pool of V, D, and J genes with a processor selecting for expression and MHC-recognition (page 1, Section Introduction).
Davidsen et al. does not teach the claim element of a buffering framework.
However, Yates et al. teaches a quantitative T cell receptor modeling approach (Abstract). Yates et al. teaches a framework that allows for generalized TCR optimization through a buffering mechanism wherein sequences are able to withstand multiple substitutions in the peptide sequence to which they bind most strongly, and so are more resistant to negative selection than those TCRs with strongly binding residues (page 8, column 1, paragraph 4).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated a buffering framework to the method of Davidsen et al. because Davidsen et al. is directed to a model for generated TCR sequences for targeting immune sequences (Abstract) and Yates et al. is directed to quantitative modeling approaches of the selection process for diverse T cell repertoires (Abstract). Thus, one of ordinary skill in the art would have a reasonable expectation of success of generating TCR sequences with a buffering mechanism and would be motivated to do so in order to generate diversely recognized TCRs.
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).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of copending Application No. 18/174,799. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of Application ‘799 encompass the instant claims.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented
Instant Claims
Application ‘799
Claim(s)
Limitations
Claim(s)
Limitations
1
A method for implementing deep reinforcement learning with T-cell receptor (TCR) mutation policies to generate binding TCRs recognizing target peptides for immunotherapy, the method comprising: extracting peptides to identify a virus or tumor cells; collecting a library of TCRs from target patients; predicting, by a deep neural network, interaction scores between the extracted peptides and the TCRs from the target patients; developing a deep reinforcement learning (DRL) framework with TCR mutation policies to generate TCRs with maximum binding scores; defining reward functions based on a reconstruction-based score and a density estimation-based score; randomly sampling batches of TCRs and following a policy network to mutate the TCRs; outputting mutated TCRs; and ranking the outputted TCRs to utilize top-ranked TCR candidates to target the virus or the tumor cells for immunotherapy.
1
A method for implementing deep reinforcement learning with T-cell receptor (TCR) mutation policies to generate binding TCRs recognizing target peptides for immunotherapy, the method comprising: extracting peptides to identify a virus or tumor cells; collecting a library of TCRs from target patients; predicting, by a deep neural network, interaction scores between the extracted peptides and the TCRs from the target patients; developing a deep reinforcement learning (DRL) framework with TCR mutation policies to generate TCRs with maximum binding scores; defining reward functions based on a reconstruction-based score and a density estimation-based score; randomly sampling batches of TCRs and following a policy network to mutate the TCRs; outputting mutated TCRs; ranking the outputted TCRs to utilize top-ranked TCR candidates to target the virus or the tumor cells for immunotherapy; and for each top-ranked TCR candidate, repeatedly identifying a set of self-peptides that the top-ranked TCR candidate binds to and further optimizing the top-ranked TCR candidate greedily by maximizing a sum of its interaction scores with a given set of peptide antigens while minimizing a sum of its interaction scores with the set of self-peptides until one or more stopping criteria of efficacy and safety are met.
8
A non-transitory computer-readable storage medium comprising a computer-readable program for implementing deep reinforcement learning with T-cell receptor (TCR) mutation policies to generate binding TCRs recognizing target peptides for immunotherapy, wherein the computer-readable program when executed on a computer causes the computer to perform the steps of: extracting peptides to identify a virus or tumor cells; collecting a library of TCRs from target patients; predicting, by a deep neural network, interaction scores between the extracted peptides and the TCRs from the target patients; developing a deep reinforcement learning (DRL) framework with TCR mutation policies to generate TCRs with maximum binding scores; defining reward functions based on a reconstruction-based score and a density estimation-based score; randomly sampling batches of TCRs and following a policy network to mutate the TCRs; outputting mutated TCRs; and ranking the outputted TCRs to utilize top-ranked TCR candidates to target the virus or the tumor cells for immunotherapy.
8
A non-transitory computer-readable storage medium comprising a computer-readable program for implementing deep reinforcement learning with T-cell receptor (TCR) mutation policies to generate binding TCRs recognizing target peptides for immunotherapy, wherein the computer-readable program when executed on a computer causes the computer to perform the steps of: extracting peptides to identify a virus or tumor cells; collecting a library of TCRs from target patients; predicting, by a deep neural network, interaction scores between the extracted peptides and the TCRs from the target patients; developing a deep reinforcement learning (DRL) framework with TCR mutation policies to generate TCRs with maximum binding scores; defining reward functions based on a reconstruction-based score and a density estimation-based score; randomly sampling batches of TCRs and following a policy network to mutate the TCRs; outputting mutated TCRs; ranking the outputted TCRs to utilize top-ranked TCR candidates to target the virus or the tumor cells for immunotherapy; and for each top-ranked TCR candidate, repeatedly identifying a set of self-peptides that the top-ranked TCR candidate binds to and further optimizing the top-ranked TCR candidate greedily by maximizing a sum of its interaction scores with a given set of peptide antigens while minimizing a sum of its interaction scores with the set of self-peptides until one or more stopping criteria of efficacy and safety are met.
15
A system for implementing deep reinforcement learning with T-cell receptor (TCR) mutation policies to generate binding TCRs recognizing target peptides for immunotherapy, the system comprising: a memory; and one or more processors in communication with the memory configured to: extract peptides to identify a virus or tumor cells; collect a library of TCRs from target patients; predict, by a deep neural network, interaction scores between the extracted peptides and the TCRs from the target patients; develop a deep reinforcement learning (DRL) framework with TCR mutation policies to generate TCRs with maximum binding scores; define reward functions based on a reconstruction-based score and a density estimation-based score; randomly sample batches of TCRs and following a policy network to mutate the TCRs; output mutated TCRs; and rank the outputted TCRs to utilize top-ranked TCR candidates to target the virus or the tumor cells for immunotherapy.
15
A system for implementing deep reinforcement learning with T-cell receptor (TCR) mutation policies to generate binding TCRs recognizing target peptides for immunotherapy, the system comprising: a memory; and one or more processors in communication with the memory configured to: extract peptides to identify a virus or tumor cells; collect a library of TCRs from target patients; predict, by a deep neural network, interaction scores between the extracted peptides and the TCRs from the target patients; develop a deep reinforcement learning (DRL) framework with TCR mutation policies to generate TCRs with maximum binding scores; define reward functions based on a reconstruction-based score and a density estimation-based score; randomly sample batches of TCRs and following a policy network to mutate the TCRs; output mutated TCRs; rank the outputted TCRs to utilize top-ranked TCR candidates to target the virus or the tumor cells for immunotherapy; and for each top-ranked TCR candidate, repeatedly identify a set of self-peptides that the top-ranked TCR candidate binds to and further optimize the top-ranked TCR candidate greedily by maximizing a sum of its interaction scores with a given set of peptide antigens while minimizing a sum of its interaction scores with the set of self-peptides until one or more stopping criteria of efficacy and safety are met.
2, 9, and 16
wherein the reward functions measure both a likelihood of mutated sequences being valid TCRs and probabilities of the TCRs recognizing peptides
2, 9, and 16
wherein the reward functions measure both a likelihood of mutated sequences being valid TCRs and probabilities of the TCRs recognizing peptides
3, 10, and 17
wherein the measurement of the likelihood of the mutated sequences being valid TCRs is enabled by a TCR autoencoder (TCR-AE) trained only by TCRs
3, 10, and 17
wherein the measurement of the likelihood of the mutated sequences being valid TCRs is enabled by a TCR autoencoder (TCR-AE) trained only by TCRs
4, 11, and 18
wherein density estimation over a latent space within the TCR-AE is evaluated by using a Gaussian Mixture Model (GMM)
4, 11, and 18
wherein density estimation over a latent space within the TCR-AE is evaluated by using a Gaussian Mixture Model (GMM)
5, 12, and 19
wherein the TCR-AE uses a bidirectional long short-term memory (LSTM) to encode an input sequence into a hidden vector by concatenating last hidden vectors from two LSTM directions
5, 12 and 19
wherein the TCR-AE uses a bidirectional long short-term memory (LSTM) to encode an input sequence into a hidden vector by concatenating last hidden vectors from two LSTM directions
6, 13, and 20
wherein a buffering and re-optimizing framework including a buffer is employed to handle TCRs difficult to optimize and to generalize optimization capacity to more diverse TCRs
6, 13, and 20
wherein a buffering and re-optimizing framework including a buffer is employed to handle TCRs difficult to optimize and to generalize optimization capacity to more diverse TCRs
7 and 14
wherein the TCRs and the extracted peptides are encoded by a TCR-AE in a distributed embedding space, and a mapping is learnt between the embedding space and the TCR mutation policies
7 and 14
wherein the TCRs and the extracted peptides are encoded by a TCR-AE in a distributed embedding space, and a mapping is learnt between the embedding space and the TCR mutation policies
Claims 1-20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of copending Application No. 18/414,670. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of Application ‘670 encompass the instant claims.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Instant Claims
Application ‘670
Claim(s)
Limitations
Claim(s)
Limitations
1
A method for implementing deep reinforcement learning with T-cell receptor (TCR) mutation policies to generate binding TCRs recognizing target peptides for immunotherapy, the method comprising: extracting peptides to identify a virus or tumor cells; collecting a library of TCRs from target patients; predicting, by a deep neural network, interaction scores between the extracted peptides and the TCRs from the target patients; developing a deep reinforcement learning (DRL) framework with TCR mutation policies to generate TCRs with maximum binding scores; defining reward functions based on a reconstruction-based score and a density estimation-based score; randomly sampling batches of TCRs and following a policy network to mutate the TCRs; outputting mutated TCRs; and ranking the outputted TCRs to utilize top-ranked TCR candidates to target the virus or the tumor cells for immunotherapy.
1
A method for implementing deep reinforcement learning with T-cell receptor (TCR) mutation policies to generate binding TCRs recognizing target peptides for immunotherapy, the method comprising: extracting peptides to identify a virus or tumor cells; collecting a library of TCRs from target patients; predicting, by a deep neural network, interaction scores between the extracted peptides and the TCRs from the target patients; developing a deep reinforcement learning (DRL) framework with TCR mutation policies to generate TCRs with maximum binding scores, wherein the TCRs and the extracted peptides are encoded in a distributed embedding space, and a mapping is learnt between the embedding space and the TCR mutation policies by mutating one amino acid of the TCRs at a step; defining reward functions based on a reconstruction-based score and a density estimation-based score; randomly sampling batches of TCRs and following a policy network to mutate the TCRs; outputting mutated TCRs; and ranking the outputted TCRs to utilize top-ranked TCR candidates to target the virus or the tumor cells for immunotherapy.
8
A non-transitory computer-readable storage medium comprising a computer-readable program for implementing deep reinforcement learning with T-cell receptor (TCR) mutation policies to generate binding TCRs recognizing target peptides for immunotherapy, wherein the computer-readable program when executed on a computer causes the computer to perform the steps of: extracting peptides to identify a virus or tumor cells; collecting a library of TCRs from target patients; predicting, by a deep neural network, interaction scores between the extracted peptides and the TCRs from the target patients; developing a deep reinforcement learning (DRL) framework with TCR mutation policies to generate TCRs with maximum binding scores; defining reward functions based on a reconstruction-based score and a density estimation-based score; randomly sampling batches of TCRs and following a policy network to mutate the TCRs; outputting mutated TCRs; and ranking the outputted TCRs to utilize top-ranked TCR candidates to target the virus or the tumor cells for immunotherapy.
8
A non-transitory computer-readable storage medium comprising a computer-readable program for implementing deep reinforcement learning with T-cell receptor (TCR) mutation policies to generate binding TCRs recognizing target peptides for immunotherapy, wherein the computer-readable program when executed on a computer causes the computer to perform the steps of: extracting peptides to identify a virus or tumor cells; collecting a library of TCRs from target patients; predicting, by a deep neural network, interaction scores between the extracted peptides and the TCRs from the target patients; developing a deep reinforcement learning (DRL) framework with TCR mutation policies to generate TCRs with maximum binding scores, wherein the TCRs and the extracted peptides are encoded in a distributed embedding space, and a mapping is learnt between the embedding space and the TCR mutation policies by mutating one amino acid of the TCRs at a step; defining reward functions based on a reconstruction-based score and a density estimation-based score; randomly sampling batches of TCRs and following a policy network to mutate the TCRs; outputting mutated TCRs; and ranking the outputted TCRs to utilize top-ranked TCR candidates to target the virus or the tumor cells for immunotherapy.
15
A system for implementing deep reinforcement learning with T-cell receptor (TCR) mutation policies to generate binding TCRs recognizing target peptides for immunotherapy, the system comprising: a memory; and one or more processors in communication with the memory configured to: extract peptides to identify a virus or tumor cells; collect a library of TCRs from target patients; predict, by a deep neural network, interaction scores between the extracted peptides and the TCRs from the target patients; develop a deep reinforcement learning (DRL) framework with TCR mutation policies to generate TCRs with maximum binding scores; define reward functions based on a reconstruction-based score and a density estimation-based score; randomly sample batches of TCRs and following a policy network to mutate the TCRs; output mutated TCRs; and rank the outputted TCRs to utilize top-ranked TCR candidates to target the virus or the tumor cells for immunotherapy.
15
A system for implementing deep reinforcement learning with T-cell receptor (TCR) mutation policies to generate binding TCRs recognizing target peptides for immunotherapy, the system comprising: a memory; and one or more processors in communication with the memory configured to: extract peptides to identify a virus or tumor cells; collect a library of TCRs from target patients; predict, by a deep neural network, interaction scores between the extracted peptides and the TCRs from the target patients; develop a deep reinforcement learning (DRL) framework with TCR mutation policies to generate TCRs with maximum binding scores, wherein the TCRs and the extracted peptides are encoded in a distributed embedding space, and a mapping is learnt between the embedding space and the TCR mutation policies by mutating one amino acid of the TCRs at a step; define reward functions based on a reconstruction-based score and a density estimation-based score; randomly sample batches of TCRs and following a policy network to mutate the TCRs; output mutated TCRs; and rank the outputted TCRs to utilize top-ranked TCR candidates to target the virus or the tumor cells for immunotherapy.
2, 9, and 16
wherein the reward functions measure both a likelihood of mutated sequences being valid TCRs and probabilities of the TCRs recognizing peptides
2, 9, and 16
wherein the reward functions measure both a likelihood of mutated sequences being valid TCRs and probabilities of the TCRs recognizing peptides
3, 10, and 17
wherein the measurement of the likelihood of the mutated sequences being valid TCRs is enabled by a TCR autoencoder (TCR-AE) trained only by TCRs
3, 10, and 17
wherein the measurement of the likelihood of the mutated sequences being valid TCRs is enabled by a TCR autoencoder (TCR-AE) trained only by TCRs
4, 11, and 18
wherein density estimation over a latent space within the TCR-AE is evaluated by using a Gaussian Mixture Model (GMM)
4, 11, and 18
wherein density estimation over a latent space within the TCR-AE is evaluated by using a Gaussian Mixture Model (GMM)
5, 12, and 19
wherein the TCR-AE uses a bidirectional long short-term memory (LSTM) to encode an input sequence into a hidden vector by concatenating last hidden vectors from two LSTM directions
5, 12 and 19
wherein the TCR-AE uses a bidirectional long short-term memory (LSTM) to encode an input sequence into a hidden vector by concatenating last hidden vectors from two LSTM directions
6, 13, and 20
wherein a buffering and re-optimizing framework including a buffer is employed to handle TCRs difficult to optimize and to generalize optimization capacity to more diverse TCRs
6, 13, and 20
wherein a buffering and re-optimizing framework including a buffer is employed to handle TCRs difficult to optimize and to generalize optimization capacity to more diverse TCRs
7 and 14
wherein the TCRs and the extracted peptides are encoded by a TCR-AE in a distributed embedding space, and a mapping is learnt between the embedding space and the TCR mutation policies
7 and 14
wherein the TCRs and the extracted peptides are encoded by a TCR-AE in a distributed embedding space
Claims 1-20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of copending Application No. 18/414,645. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of Application ‘645 encompass the instant claims.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Instant Claims
Application ‘645
Claim(s)
Limitations
Claim(s)
Limitations
1
A method for implementing deep reinforcement learning with T-cell receptor (TCR) mutation policies to generate binding TCRs recognizing target peptides for immunotherapy, the method comprising: extracting peptides to identify a virus or tumor cells; collecting a library of TCRs from target patients; predicting, by a deep neural network, interaction scores between the extracted peptides and the TCRs from the target patients; developing a deep reinforcement learning (DRL) framework with TCR mutation policies to generate TCRs with maximum binding scores; defining reward functions based on a reconstruction-based score and a density estimation-based score; randomly sampling batches of TCRs and following a policy network to mutate the TCRs; outputting mutated TCRs; and ranking the outputted TCRs to utilize top-ranked TCR candidates to target the virus or the tumor cells for immunotherapy.
1
A method for implementing deep reinforcement learning with T-cell receptor (TCR) mutation policies to generate binding TCRs recognizing target peptides for immunotherapy, the method comprising: extracting peptides to identify a virus or tumor cells; collecting a library of TCRs from target patients; predicting, by a deep neural network, interaction scores between the extracted peptides and the TCRs from the target patients; developing a deep reinforcement learning (DRL) framework with TCR mutation policies to generate TCRs with maximum binding scores; defining reward functions based on a reconstruction-based score and a density estimation-based score; optimizing a policy network using proximal policy optimization; randomly sampling batches of TCRs and following the policy network to mutate the TCRs; outputting mutated TCRs; and ranking the outputted TCRs to utilize top-ranked TCR candidates to target the virus or the tumor cells for immunotherapy.
8
A non-transitory computer-readable storage medium comprising a computer-readable program for implementing deep reinforcement learning with T-cell receptor (TCR) mutation policies to generate binding TCRs recognizing target peptides for immunotherapy, wherein the computer-readable program when executed on a computer causes the computer to perform the steps of: extracting peptides to identify a virus or tumor cells; collecting a library of TCRs from target patients; predicting, by a deep neural network, interaction scores between the extracted peptides and the TCRs from the target patients; developing a deep reinforcement learning (DRL) framework with TCR mutation policies to generate TCRs with maximum binding scores; defining reward functions based on a reconstruction-based score and a density estimation-based score; randomly sampling batches of TCRs and following a policy network to mutate the TCRs; outputting mutated TCRs; and ranking the outputted TCRs to utilize top-ranked TCR candidates to target the virus or the tumor cells for immunotherapy.
8
A non-transitory computer-readable storage medium comprising a computer-readable program for implementing deep reinforcement learning with T-cell receptor (TCR) mutation policies to generate binding TCRs recognizing target peptides for immunotherapy, wherein the computer-readable program when executed on a computer causes the computer to perform the steps of: extracting peptides to identify a virus or tumor cells; collecting a library of TCRs from target patients; predicting, by a deep neural network, interaction scores between the extracted peptides and the TCRs from the target patients; developing a deep reinforcement learning (DRL) framework with TCR mutation policies to generate TCRs with maximum binding scores; defining reward functions based on a reconstruction-based score and a density estimation-based score; optimizing a policy network using proximal policy optimization; randomly sampling batches of TCRs and following the policy network to mutate the TCRs; outputting mutated TCRs; and ranking the outputted TCRs to utilize top-ranked TCR candidates to target the virus or the tumor cells for immunotherapy.
15
A system for implementing deep reinforcement learning with T-cell receptor (TCR) mutation policies to generate binding TCRs recognizing target peptides for immunotherapy, the system comprising: a memory; and one or more processors in communication with the memory configured to: extract peptides to identify a virus or tumor cells; collect a library of TCRs from target patients; predict, by a deep neural network, interaction scores between the extracted peptides and the TCRs from the target patients; develop a deep reinforcement learning (DRL) framework with TCR mutation policies to generate TCRs with maximum binding scores; define reward functions based on a reconstruction-based score and a density estimation-based score; randomly sample batches of TCRs and following a policy network to mutate the TCRs; output mutated TCRs; and rank the outputted TCRs to utilize top-ranked TCR candidates to target the virus or the tumor cells for immunotherapy.
15
A system for implementing deep reinforcement learning with T-cell receptor (TCR) mutation policies to generate binding TCRs recognizing target peptides for immunotherapy, the system comprising: a memory; and one or more processors in communication with the memory configured to: extract peptides to identify a virus or tumor cells; collect a library of TCRs from target patients; predict, by a deep neural network, interaction scores between the extracted peptides and the TCRs from the target patients; develop a deep reinforcement learning (DRL) framework with TCR mutation policies to generate TCRs with maximum binding scores; define reward functions based on a reconstruction-based score and a density estimation-based score; optimize a policy network using proximal policy optimization; randomly sample batches of TCRs and following the policy network to mutate the TCRs; output mutated TCRs; and rank the outputted TCRs to utilize top-ranked TCR candidates to target the virus or the tumor cells for immunotherapy.
2, 9, and 16
wherein the reward functions measure both a likelihood of mutated sequences being valid TCRs and probabilities of the TCRs recognizing peptides
2, 9, and 16
wherein the reward functions measure both a likelihood of mutated sequences being valid TCRs and probabilities of the TCRs recognizing peptides
3, 10, and 17
wherein the measurement of the likelihood of the mutated sequences being valid TCRs is enabled by a TCR autoencoder (TCR-AE) trained only by TCRs
3, 10, and 17
wherein the measurement of the likelihood of the mutated sequences being valid TCRs is enabled by a TCR autoencoder (TCR-AE) trained only by TCRs
4, 11, and 18
wherein density estimation over a latent space within the TCR-AE is evaluated by using a Gaussian Mixture Model (GMM)
4, 11, and 18
wherein density estimation over a latent space within the TCR-AE is evaluated by using a Gaussian Mixture Model (GMM)
5, 12, and 19
wherein the TCR-AE uses a bidirectional long short-term memory (LSTM) to encode an input sequence into a hidden vector by concatenating last hidden vectors from two LSTM directions
5, 12, and 19
wherein the TCR-AE uses a bidirectional long short-term memory (LSTM) to encode an input sequence into a hidden vector by concatenating last hidden vectors from two LSTM directions
6, 13, and 20
wherein a buffering and re-optimizing framework including a buffer is employed to handle TCRs difficult to optimize and to generalize optimization capacity to more diverse TCRs
6, 13, and 20
wherein a buffering and re-optimizing framework including a buffer is employed to handle TCRs difficult to optimize and to generalize optimization capacity to more diverse TCRs
7 and 14
wherein the TCRs and the extracted peptides are encoded by a TCR-AE in a distributed embedding space, and a mapping is learnt between the embedding space and the TCR mutation policies
7 and 14
wherein the TCRs and the extracted peptides are encoded by a TCR-AE in a distributed embedding space, and a mapping is learnt between the embedding space and the TCR mutation policies
Claims 1-20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of copending Application No. 18/414,687. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of Application ‘670 encompass the instant claims.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Instant Claims
Application ‘687
Claim(s)
Limitations
Claim(s)
Limitations
1
A method for implementing deep reinforcement learning with T-cell receptor (TCR) mutation policies to generate binding TCRs recognizing target peptides for immunotherapy, the method comprising: extracting peptides to identify a virus or tumor cells; collecting a library of TCRs from target patients; predicting, by a deep neural network, interaction scores between the extracted peptides and the TCRs from the target patients; developing a deep reinforcement learning (DRL) framework with TCR mutation policies to generate TCRs with maximum binding scores; defining reward functions based on a reconstruction-based score and a density estimation-based score; randomly sampling batches of TCRs and following a policy network to mutate the TCRs; outputting mutated TCRs; and ranking the outputted TCRs to utilize top-ranked TCR candidates to target the virus or the tumor cells for immunotherapy.
1
A method for implementing deep reinforcement learning with T-cell receptor (TCR) mutation policies to generate binding TCRs recognizing target peptides for immunotherapy, the method comprising: extracting peptides to identify a virus or tumor cells; collecting a library of TCRs from target patients; predicting, by a deep neural network, interaction scores between the extracted peptides and the TCRs from the target patients; developing a deep reinforcement learning (DRL) framework with TCR mutation policies to generate TCRs with maximum binding scores; defining reward functions based on a reconstruction-based score and a density estimation-based score; randomly sampling batches of TCRs and following a policy network to mutate the TCRs; outputting mutated TCRs; and ranking the outputted TCRs to utilize top-ranked TCR candidates to target the virus or the tumor cells for immunotherapy, wherein the measurement of the likelihood of the mutated sequences being valid TCRs is enabled by a TCR autoencoder (TCR-AE), wherein a cross entropy loss is applied during training of TCR-AE to ensure that a decoded TCR has the same length as a TCR input.
8
A non-transitory computer-readable storage medium comprising a computer-readable program for implementing deep reinforcement learning with T-cell receptor (TCR) mutation policies to generate binding TCRs recognizing target peptides for immunotherapy, wherein the computer-readable program when executed on a computer causes the computer to perform the steps of: extracting peptides to identify a virus or tumor cells; collecting a library of TCRs from target patients; predicting, by a deep neural network, interaction scores between the extracted peptides and the TCRs from the target patients; developing a deep reinforcement learning (DRL) framework with TCR mutation policies to generate TCRs with maximum binding scores; defining reward functions based on a reconstruction-based score and a density estimation-based score; randomly sampling batches of TCRs and following a policy network to mutate the TCRs; outputting mutated TCRs; and ranking the outputted TCRs to utilize top-ranked TCR candidates to target the virus or the tumor cells for immunotherapy.
8
A non-transitory computer-readable storage medium comprising a computer-readable program for implementing deep reinforcement learning with T-cell receptor (TCR) mutation policies to generate binding TCRs recognizing target peptides for immunotherapy, wherein the computer-readable program when executed on a computer causes the computer to perform the steps of: extracting peptides to identify a virus or tumor cells; collecting a library of TCRs from target patients; predicting, by a deep neural network, interaction scores between the extracted peptides and the TCRs from the target patients; developing a deep reinforcement learning (DRL) framework with TCR mutation policies to generate TCRs with maximum binding scores; defining reward functions based on a reconstruction-based score and a density estimation-based score; randomly sampling batches of TCRs and following a policy network to mutate the TCRs; outputting mutated TCRs; and ranking the outputted TCRs to utilize top-ranked TCR candidates to target the virus or the tumor cells for immunotherapy, wherein the measurement of the likelihood of the mutated sequences being valid TCRs is enabled by a TCR autoencoder (TCR-AE), wherein a cross entropy loss is applied during training of TCR-AE to ensure that a decoded TCR has the same length as a TCR input.
15
A system for implementing deep reinforcement learning with T-cell receptor (TCR) mutation policies to generate binding TCRs recognizing target peptides for immunotherapy, the system comprising: a memory; and one or more processors in communication with the memory configured to: extract peptides to identify a virus or tumor cells; collect a library of TCRs from target patients; predict, by a deep neural network, interaction scores between the extracted peptides and the TCRs from the target patients; develop a deep reinforcement learning (DRL) framework with TCR mutation policies to generate TCRs with maximum binding scores; define reward functions based on a reconstruction-based score and a density estimation-based score; randomly sample batches of TCRs and following a policy network to mutate the TCRs; output mutated TCRs; and rank the outputted TCRs to utilize top-ranked TCR candidates to target the virus or the tumor cells for immunotherapy.
15
A system for implementing deep reinforcement learning with T-cell receptor (TCR) mutation policies to generate binding TCRs recognizing target peptides for immunotherapy, the system comprising: a memory; and one or more processors in communication with the memory configured to: extract peptides to identify a virus or tumor cells; collect a library of TCRs from target patients; predict, by a deep neural network, interaction scores between the extracted peptides and the TCRs from the target patients; develop a deep reinforcement learning (DRL) framework with TCR mutation policies to generate TCRs with maximum binding scores; define reward functions based on a reconstruction-based score and a density estimation-based score; randomly sample batches of TCRs and following a policy network to mutate the TCRs; output mutated TCRs; and rank the outputted TCRs to utilize top-ranked TCR candidates to target the virus or the tumor cells for immunotherapy, wherein the measurement of the likelihood of the mutated sequences being valid TCRs is enabled by a TCR autoencoder (TCR-AE), wherein a cross entropy loss is applied during training of TCR-AE to ensure that a decoded TCR has the same length as a TCR input.
2, 9, and 16
wherein the reward functions measure both a likelihood of mutated sequences being valid TCRs and probabilities of the TCRs recognizing peptides
2, 9, and 16
wherein the reward functions measure both a likelihood of mutated sequences being valid TCRs and probabilities of the TCRs recognizing peptides
3, 10, and 17
wherein the measurement of the likelihood of the mutated sequences being valid TCRs is enabled by a TCR autoencoder (TCR-AE) trained only by TCRs
3, 10, and 17
wherein the TCR-AE is trained only by TCRs
4, 11, and 18
wherein density estimation over a latent space within the TCR-AE is evaluated by using a Gaussian Mixture Model (GMM)
4, 11, and 18
wherein density estimation over a latent space within the TCR-AE is evaluated by using a Gaussian Mixture Model (GMM)
5, 12, and 19
wherein the TCR-AE uses a bidirectional long short-term memory (LSTM) to encode an input sequence into a hidden vector by concatenating last hidden vectors from two LSTM directions
5, 12, and 19
wherein the TCR-AE uses a bidirectional long short-term memory (LSTM) to encode an input sequence into a hidden vector by concatenating last hidden vectors from two LSTM directions
6, 13, and 20
wherein a buffering and re-optimizing framework including a buffer is employed to handle TCRs difficult to optimize and to generalize optimization capacity to more diverse TCRs
6, 13, and 20
wherein a buffering and re-optimizing framework including a buffer is employed to handle TCRs difficult to optimize and to generalize optimization capacity to more diverse TCRs
7 and 14
wherein the TCRs and the extracted peptides are encoded by a TCR-AE in a distributed embedding space, and a mapping is learnt between the embedding space and the TCR mutation policies
7 and 14
wherein the TCRs and the extracted peptides are encoded by a TCR-AE in a distributed embedding space, and a mapping is learnt between the embedding space and the TCR mutation policies
Conclusion
No claims are allowed.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Emilie A Smith whose telephone number is (571)272-7543. The examiner can normally be reached 9am - 5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Larry D Riggs can be reached at (571)270-3062. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/E.A.S./Examiner, Art Unit 1686
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685
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