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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 7/29/2026 has been entered.
Applicants Amendments
Applicants’ amendment filed 7/29/2026 has been received and entered. Claims 1, 20, 21 have been amended.
Claims 1-21 are pending.
Priority
This application filed 1/21/2022 is a continuation of 16/403331 filed now US Patent 11,264117 which a continuation of PCT/US18/55283 filed 10/10/2018 which claims benefit to US provisional application 62/570569 filed 10/101/2017.
Applicants do comment on the summary of priority.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 7/29/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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).
Response to Applicants comments
Applicants request the rejections be held in abeyance.
In response, as noted in previous office actions, a rejection and response cannot be held in abeyance. In view of no specific arguments, the rejection is maintained for the reasons of record.
The rejection of record have been updated to include the amendments to the claims and are provided below.
Claims 1-21 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims of U.S. Patent No. 11264117 (application 16/403331 which is the parent of this continuation). Although the claims at issue are not identical, they are not patentably distinct from each other because bot the claim sets overlap in the specific method steps used to analyze potential peptides ability to bind to MHC using a neural network model.
A copy of claim 1 of ‘117 is provided for clarity of the record and comparison of pending claims of the instant application.
1. (Currently Amended) A method for identifying one or more neoantigens from one or more tumor cells of a subject that are likely to be presented on a surface of the tumor cells, the method comprising the steps of:
(a) obtaining at least one of exome, transcriptome, or whole genome nucleotide sequencing data from the tumor cells and normal cells of the subject, wherein the nucleotide sequencing data is used to obtain data representing peptide sequences of each of a set of neoantigens identified by comparing the nucleotide sequencing data from the tumor cells and the nucleotide sequencing data from the normal cells, wherein the peptide sequence of each neoantigen comprises at least one alteration that makes it distinct from the corresponding wild-type peptide sequence identified from the normal cells of the subject;
(b) encoding the peptide sequence of each of the neoantigens into a corresponding numerical vector, each numerical vector including information regarding a plurality of amino acids that make up the peptide sequence and a set of positions of the amino acids in the peptide sequence;
(c) associating the peptide sequence of each of the neoantigens with one or more k- mer blocks of a plurality of k-mer blocks of the nucleotide sequencing data of the subject;
(d) determining, using a neural network model stored in a non-transitory computer readable medium of one or more computing devices a set of presentation likelihoods for the set of neoantigens, each presentation likelihood in the set representing the likelihood that a corresponding neoantigen is presented by one or more MHC alleles on the surface of the tumor cells of the subject, the neural network model comprising:
(i) two or more layers comprising a first layer and a second layer, each layer comprising one or more nodes, wherein said nodes comprise a memory location for one or more input values;
(ii) a plurality of connections between nodes of said first layer and one or more nodes of said second layer;
(iii) optimized parameters stored in memory locations, wherein the optimized parameters transform input values of nodes of the first layer into input values for nodes of the second layer connected to the nodes of the first layer,
(iv) wherein the optimized parameters are identified using at least a training data set comprising:
(A) training peptide sequences encoded as numerical vectors including information regarding a plurality of amino acids that make up the peptide sequences and a set of positions of the amino acids in the peptide sequences;
(B) at least one MHC allele associated with the training peptide sequences;
(C) for each of the training peptide sequences, one or more labels indicating associations between the training peptide sequence and one or more k-mer blocks of a plurality of k-mer blocks of the nucleotide sequencing data of the training peptide sequences; and
(D) for each of the training peptide sequences, a label indicating whether the training peptide was presented by the at least one MHC allele, wherein a subset of the optimized parameters represent a presence or absence of a presentation hotspot for the one or more k-mer blocks; and
(e) wherein said determining comprises:
(i) forward feeding the numerical vectors, using a computer processor, through nodes of the first layer and the second layer of the neural network model, said forward feeding comprising transforming values of the numerical vectors as they are fed from nodes of the first layer to nodes of the second layer using the optimized parameters;
(ii) generating, using a computer processor, the set of presentation likelihoods for the set of antigens from the transformed numerical vectors, each presentation likelihood in the set representing the likelihood that a corresponding antigen is presented by one or more class I MHC alleles on the surface of the tumor cells of the subject;
(f) selecting a subset of the set of neoantigens based on the set of presentation likelihoods to generate a set of selected neoantigens; and
(g) returning the set of selected neoantigens.
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.
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.
Claim 21 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 is withdrawn.
The amendment to the claim to delete the limitation of ‘for achieving an improvement to a computer’ which is accomplished ‘by improving a neural network model’ has addressed the basis of the rejection. The steps of the claim and broader outcome of the analysis steps provide a clear basis of what is practiced.
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-21 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement is withdrawn.
In view of the amendments to provide for a more generic indication of what is represented by the ‘value’ required of the claims for determining, it is agreed that there is adequate support for the claims as a whole. Each of the steps provide for steps which can be practiced and applied broadly with any input data, where the data or input would drive any specific outcome or possible neoantigens based on protein sequences that might be presented by MHC,
Claim Rejections - 35 USC § 101
101 Rejection
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 1-21 are rejected under 35 USC § 101 because the claimed invention is directed to non-statutory subject matter.
Claim analysis
Claim 1 has been amended and still is generally directed to the method that assess whether a peptide will bind and be presented by a MHC allele. Specifically, claim 1 as an example of independent claims has been amended to indicate that a presentation hotspot ‘for one or more k-mer blocks was previously identified’ which provides the basis for the optimized parameters to use in evaluating MHC and peptide k-mer presentation as possible antigens. The steps of the claims set forth providing data of peptides serving as possible antigens and values that they are likely presented, then creating a neural network model which represents this data and using the model to assess other proteins. The claims set forth ‘using a neural network’ but no specific nor any empirical steps for determining or optimizing or any specific training steps or data analysis for what is required for any of the data, and only data is provided and processed to provide information about the protein evaluated.
For step 1 of the 101 analysis, the claims are found to be directed to a statutory category of a method.
For step 2A of the 101 analysis, the judicial exception of the claims are the steps of accessing sequence data for malicious sequences. The step of the method under the broadest most reasonable interpretation, the instant claims recite judicial exceptions that are an abstract idea of the type that is in the grouping of “mental process”, such as procedures for evaluating, analyzing or organizing information, forming judgement or an opinion. Steps drawn to a mental process recited in the claims include encoding peptide sequences into vectors, associating peptide sequences with K-mer blocks of nucleotide sequencing data, inputting vectors and K-mer blocks into a model, determining presentation likelihood, determining relation between the presentation likelihood and vectors and K-mers, selecting a subset of antigens. Further, the step of generating presentation likelihood by a machine-learned model addresses determining presentation likelihood using a plurality of parameters and a function, which is able to be performed in the mind, but for the recitation of the computer system (“machine learned process”). Other than reciting “by a machine-learned model”, nothing in the claim element precludes the step from practically being performed in the human mind. There are no specifics in the claims of the steps that are performed by the machine-learned model that are clearly rooted in computer technology and are not able to be performed in human mind. The mere nominal recitation of a generic “machine-learned model” does not take the claim limitation out of the mental processes grouping. Thus, the claim recites steps drawn to a mental process.
Recent guidance from the office requires that the judicial exception be evaluated under a second prong to determine whether the judicial exception is practically applied. In the instant case, the claims do not have an additional element. In review of the claim requirements, the judicial exception is not integrated into a practical application because the claim can encompass additional elements which are data gathering, using a generic computer component, and/or insignificant pre-solution activity. Obtaining sequence data is a pre-solution activity directed to aspects of the information being analyzed
To the extent the additional element of using a processor to utilize “machine-learned model” amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim does not describe any specific computational steps by which the computer performs or carries out the abstract idea, nor do they provide any details of how specific structures of the computer are used to implement these functions. The claim states nothing more than that a generic computer performs the functions that constitute the abstract idea. Hence, these are mere instructions to apply the abstract idea using a computer, and therefore the claim does not integrate the abstract idea into a practical application. Claims that are directed to abstract ideas must be examined further to determine whether the additional elements besides the abstract idea render the claims significantly more than the abstract idea.
It is noted that in explaining the Alice framework, the Court wrote that "[i]n cases involving software innovations, [the step one] inquiry often turns on whether the claims focus on the specific asserted improvement in computer capabilities or, instead, on a process that qualifies as an abstract idea for which computers are invoked merely as a tool." The Court further noted that "[s]ince Alice, we have found software inventions to be patent-eligible where they have made non-abstract improvements to existing technological processes and computer technology." Moreover, these improvements must be specific -- "[a]n improved result, without more stated in the claim, is not enough to confer eligibility to an otherwise abstract idea . . . [t]o be patent-eligible, the claims must recite a specific means or method that solves a problem in an existing technological process." None of the dependent claims recite any additional non-abstract elements; they are all directed to further aspects of the information being analyzed, the manner in which that analysis is performed, or the mathematical operations performed on the information. Because the claims recite an abstract idea, and do not integrate that abstract idea into a practical application, the claims are directed to that abstract idea.
For step 2B of the 101 analysis, each of the independent claims recites additional elements and are found to be the steps of obtaining sequence data. As such, the claims do not provide for any additional element to consider under step 2B.
Response to Applicants arguments
Applicants provide an overview of case law applying to 101 analysis and note the amendment of step c) for determining by forward feeding the data into a neural network. Applicants argue that previous models did not consider ‘hotspot parameters’ and note experiments that show a difference of 0.82 to 0.77 between with and without hotspot information respectively.
In response, the claims provide for high level generic values, any type of training data, data ‘previously identified’ and no specific elements for optimization that it does not appear that the argument is consistent with the breadth of the claims, and thus necessarily flows from practicing the steps as claimed. More generally, if data about a peptide being presented more likely than others was known, it appears that it would follow that the peptides more likely to bind and be presented as antigens by MHC would be identified, and does not appear to provide an improvement or unique model structure or function of a neural network. Given the generic nature of what is required, there does not appear to be an improvement as argued. Importantly, providing data about ‘hotspots’ that was previously identified appears to be another piece of data one can evaluate mentally in considering or determining a possible antigen and falls into the judicial exception of the claims.
Under prong 2, applicants argue that forward feeding the data into a neural network requires using a neural network and with an better model, provides an improvement and is more than a mere instruction.
In response, ‘using a neural network’ is very generic and all tha tis provided by the claims are generic requirements of high-level types of data to provide or forward feed into the model for analysis. There is no specific model created, nor are there any specific or particular rules one how it is created or structured. Unlike the fact pattern of Desjardins, the present claims provide for no specific steps of training, nor any basis of improvement and given the breadth of the claims encompass the broad use of a neural network to analyze data for MHC and peptide binding, and requires that all the data be provided by others to implement the analysis required of the claimed method.
Under Step 2B, applicants argue that additional elements of ‘forward feeding…’ are unconventional and non-routine.
In response, these elements appear to be generally descriptive of how the data is considered. The term forward feeding is simply providing the data to the model, and the steps for ‘transforming the value’ appear to provide instructions to correlate any hotspot and possible peptide when analyzing the peptide/k-mer for binding to MHC. There does not appear to be any transformation which appears to make the claim patent eligible, and only appears that the transformation is consistent with evaluating or providing values to represent other data, here how a hotspot correlates to any given peptide k-mer. Importantly, there is no specific data required, and the claims are directed and comprise method steps which are to be performed. Focusing on what is required of each of the steps, each are found to be instructions to analyze data and provide a final evaluation of whether any k-mer peptide which was known to bind to MHC could serve as a neoantigen. Without any specificity to how this is performed and only high level indication of obtaining data, there is no necessary outcome that this provides any improvement, nor any real world application of an identified peptide as broadly claimed. In prosecution, the specific teachings of the specification as exemplification have been noted. For example, the information in the figures was noted in the analysis, has considered generally teachings for example at [0010] which teaches ‘These deep learning models also utilize parameters describing the presence or absence of presentation hotspots in k-mer blocks associated with peptide sequences in determining presentation likelihood of the peptides.’ In review of the specification, it is noted that ‘hotspot’ is not defined and only at page 66 of the specification is quoted but not defined after providing a means of assessing possible antigenicity across different tissue types, possible loss of function and generally that it can be determined through appropriate analysis methods, which generally appear to rely on knowledge of MHC alleles and possible presentation, and generally that in the analysis a peptide or variant peptides ability to be presented based on calculated interactions with every and all possible MHC alleles (see section starting at VIII.b on page 60). Importantly, this is not a limiting requirement of the claims, but even so the guidance provides evidence that the ‘hotspot’ parameter is simply another form of analysis, or if provided a value used in analysis which does not move the claims from data analysis to a patent eligible category under Prong 2A. It is generally acknowledged that different analysis methods provide different results, and the some may be viewed to have greater predictive ability, however in the analysis of 101 increased predictive ability still fall into the category of analysis and abstract concepts. Further, the analysis attempts to provide a correlation that may exist in nature, for example the ability of peptide to be presented by MHC once it is altered. Given the claims as a whole and the guidance of the specification, while there is some indication in the figures that a specific method performs differently when a ‘hotspot’ parameter is used, it does not appear to be an improvement given that a hotspot value would represent the ability of a peptide be presented or bind to MHC, and the general data suggests that a majority of the peptides identified without it are sufficient to predict that it may be a neoantigen given the figures. Accordingly, for the reasons above and of record the rejection is maintained.
As noted previously, one way to overcome a rejection for non-patent-eligible subject matter is to persuasively argue that the claimed subject matter is not directed to a judicial exception. Another way for the applicants to overcome the rejection is to persuasively argue that the claims contain elements in addition to the judicial exception that either individually or as an ordered combination are not well understood, routine, or conventional. Another way for the applicants to overcome the rejection is to persuasively argue that the claims as a whole result in an improvement to a technology. Persuasive evidence for an improvement to a technology could be a comparison of results of the claimed subject matter with results of the prior art, or arguments based on scientific reasoning that the claimed subject matter inherently results an improvement over the prior art. The applicants should show why the claims require the improvement in all embodiments.
Conclusion
No claim is allowed.
The art of record demonstrates identification of peptides presented by MHC molecules was known. In the new IDS, Burden et al. is noted for providing neural network models that provide peptides that bind to MHC molecules, and depending on the source of the protein data could serve as the basis of a possible neoantigen. Also, sequences for MHC and peptides that bind to them were also known as possible sources of data that could be used to train a model, for example as provided by Huczko et al and more generally by Sette et al. Other models such as that provided by Stranzl et al. (Immunogenetics, 2010, 62:357–368) provide identifying presentation likelihoods for antigens presented by MHC 1 alleles with a method that comprises generating MHC class I presentation likelihood score by combining predictions of proteasomal cleavage, TAP transport efficiency, and MHC binding, the combining being viewed as generating presentation likelihood by applying a presentation model. It is well known that peptides can serve as antigens for MHC, as exemplified by Soon-Shiong et al. (20170028044) which provides a method for identifying cancer-related patient-specific neoantigens and teach that to resolve the problem of similarity between HLA alleles, nucleic sequences are read into relatively small k-mers (typically having a length of between 10-20 bases), and by implementing a weighted vote process in which each patient sequence read provides a vote and Deniger et al. (Molecular Therapy Volume 24, Supplement 1, May 2016, S155) teach that mutations expressed by the tumor can serve as neoantigens for autologous T cells, and that, while neoantigens are usually unique to each patient, i.e. no overlapping mutations between patients, identifying ‘hotspot’ mutations existing in all tumor types or among many patients opens opportunities for treatment of other cancer patients.
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/Joseph Woitach/Primary Examiner, Art Unit 1687
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