DETAILED ACTION
The Applicant’s response, received 31 March 2026, has been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
Claims 1-16, 18, 20-37, 39-42, 44-51, 53-76, and 81-112 are pending.
Claims 37, 58-76, 81-84, and 86-111 are withdrawn.
Claims 1-16, 18, 20-36, 39-42, 44-51, 53-57, 85, and 112 are rejected.
Priority
This application claims benefit of 63/053,307, filed 17 July 2020.
Therefore, the effective filing date of the claimed invention is 17 July 2020.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 31 March 2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the examiner.
Claim Objections
The objection to claim 24 in the Office action mailed 01 October 2025 has been withdrawn in view of the amendment received 31 March 2026.
Claim Rejections - 35 USC § 112
The rejection of claims 1-16, 18, 20-36, 39-42, 44-51, 53-57, 85, and 112 under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, in the Office action mailed 01 October 2025 has been withdrawn in view of the amendment received 31 March 2026.
Claim Rejections - 35 USC § 101
The amendment received 31 March 2026 has been fully considered, however after further consideration, the rejection of claims 1-16, 18, 20-36, 39-42, 44-51, 53-57, 85, and 112 under 35 U.S.C. 101 in the Office action mailed 01 October 2025 is maintained with modification in view of the amendment, as noted below.
The rejection has been updated to incorporate the amended claim language in independent claims 1, 85, and 112.
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 1-16, 18, 20-36, 39-42, 44-51, 53-57, 85, and 112 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea and a law of nature without significantly more. The claims recite: (a) mathematical concepts (e.g., mathematical relationships, formulas or equations, mathematical calculations); (b) mental processes, i.e., concepts performed in the human mind, (e.g., observation, evaluation, judgement, opinion); and (c) a law of nature (e.g., naturally occurring relationships).
Claim Interpretations
Claim 1 recites the limitation “an immunoprotein complex (IPC) of the subject, wherein the IPC comprises a major histocompatibility complex (MHC).” This limitation is interpreted to mean data of an immunogenic peptide-MHC complex, i.e., binding between a peptide and an MHC molecule.
Claim 1 recites the limitations “accessing a set of peptide sequences…having been identified by processing a disease sample from a subject” and “accessing an immunoprotein complex (IPC) sequence.” These limitations are interpreted as reciting a product-by-process limitation of accessing data, with the product being the data, and not requiring the processing steps of producing the data being accessed (e.g., performing steps of collecting a disease sample from a subject; obtaining genomic material from the disease sample; and/or using a genomic sequencing machine to obtain sequence read data).
Claim 85 recites the limitations “inputting a plurality of variant-coding sequences…each variant-coding sequence of the plurality of variant-coding sequences having been identified by processing a disease sample from a subject” and “inputting an immunoprotein complex (IPC) sequence identified for an immunoprotein complex (IPC) of the subject.” These limitations are interpreted as reciting a product-by-process limitation of inputting data, with the product being the data, and not requiring the processing steps of producing the data being inputted (e.g., performing steps of collecting a disease sample from a subject; obtaining genomic material from the disease sample; and/or using a genomic sequencing machine to obtain sequence read data).
Claim 112 recites the limitations “access a set of peptide sequences…having been identified by processing a disease sample from a subject” and “access an immunoprotein complex (IPC) sequence identified for an immunoprotein complex (IPC) of the subject.” These limitations are interpreted as reciting a product-by-process limitation of accessing data, with the product being the data, and not requiring the processing steps of producing the data being accessed (e.g., performing steps of collecting a disease sample from a subject; obtaining genomic material from the disease sample; and/or using a genomic sequencing machine to obtain sequence read data).
Claims 1, 85, and 112 recite the limitation “obtaining a plurality of positive peptide sequences.” This limitation is interpreted as reciting a product-by-process limitation of obtaining data, with the product being the data, and not requiring the processing steps of producing the data being obtained (e.g., obtaining genomic material; and/or using a genomic sequencing machine to obtain sequence read data).
Claims 1, 3-5, 7-11, 14-16, 23, 29, 85, and 112 recite the limitation “attention block.” The term “attention” is interpreted to mean a mechanism that allows machine learning models to dynamically focus on pertinent parts of input data, e.g., by assigning numerical weighted values (e.g., Specification, ¶ [0112], [0149] & [0228]), and the term “block” is interpreted to mean the combination of mathematical algorithms used in producing the attention values in a particular attention layer (e.g., Specification, ¶ [0055]), e.g., matrix multiplication of linear data layers, dot product of vectors, normalization, or transforming score values into probabilities.
Dependent claim 20 recites the limitation “an experiment-based result identifying an interaction affinity indication…wherein the interaction affinity indication was detected using an assay or biosensor-based methodology.” This limitation is interpreted as reciting a product-by-process limitation of selecting data, with the product being the data, and not requiring the processing steps of producing the data that is selected (e.g., using an assay or biosensor-based methodology).
Dependent claim 21 recites the limitation “an experiment-based result including an interaction indication…wherein at least one of immunoprecipitation or mass spectrometry was used to determine the interaction indication.” This limitation is interpreted as reciting a product-by-process limitation of selecting data, with the product being the data, and not requiring the processing steps of producing the data that is selected (e.g., performing immunoprecipitation or mass spectrometry techniques).
Claim 30 recites the limitation “transformer encoders.” The term “transformer” is interpreted to mean neural networks trained to process sequential input data (e.g., natural language text, or genomic sequences), using a self-attention mechanism that allows the network to weigh the importance of different input parts in generating the internal representation. The term “encoder” is interpreted to mean a mathematical model designed to learn embeddings that can be used for predictive modeling tasks, e.g., classification (e.g., Specification, ¶¶ [0140], [0226] & [0260]).
Claim 32 recites the limitation “wherein the IPC sequence is identified using the disease sample.” This limitation is interpreted as reciting a product-by-process limitation of analyzing data, with the product being the data, and not requiring the processing steps of producing the data (e.g., obtaining genomic material from a disease sample, and using a genomic sequencing machine to obtain sequence read data representing the disease sample).
Claim 33 recites the limitation “wherein the IPC sequence is identified using a biological sample from the subject.” This limitation is interpreted as reciting a product-by-process limitation of analyzing data, with the product being the data, and not requiring the processing steps of producing the data (e.g., obtaining genomic material from a biological sample from a subject, and using a genomic sequencing machine to obtain sequence read data representing the biological sample).
Claim 34 recites the limitation “wherein the disease sample includes cancer cells.” This limitation is interpreted as reciting a product-by-process limitation of analyzing data of a disease sample, with the product being the data, and not requiring the processing steps of producing the data (e.g., obtaining genomic material from a disease sample that includes cancer cells, and using a genomic sequencing machine to obtain sequence read data representing the disease sample).
Claim 39 recites the limitation “wherein the disease sample includes tissue.” This limitation is interpreted as reciting a product-by-process limitation of analyzing data of a disease sample, with the product being the data, and not requiring the processing steps of producing the data (e.g., obtaining genomic material from a disease sample that includes tissue, and using a genomic sequencing machine to obtain sequence read data representing the disease sample).
Claim 41 recites the limitation “wherein at least one peptide sequence of the set of peptide sequences is a genomic sequence derived from the disease sample.” This limitation is interpreted as reciting a product-by-process limitation of analyzing data of a disease sample, with the product being the data, and not requiring the processing steps of producing the data (e.g., obtaining genomic material from a disease sample that includes tissue, and using a genomic sequencing machine to obtain sequence read data representing the disease sample).
Claim 42 recites the limitation “wherein each of at least one of the set of variant-coding sequences is based on RNA sequences of the disease sample.” This limitation is interpreted as reciting a product-by-process limitation of analyzing data of a disease sample, with the product being the data, and not requiring the processing steps of producing the data (e.g., obtaining genomic material from a disease sample that includes tissue, and using a genomic sequencing machine to obtain sequence read data representing the disease sample).
Claim 46 recites the limitation “initiating an action that facilitates manufacture of the individualized vaccine that includes the set of treatment peptides.” This limitation is interpreted to encompass transmitting data (e.g., Specification, ¶¶ [0017], [0202], & [0472]).
Claim 47 recites the limitation “generating an alert that triggers a computerized process involved in the manufacture of the individualized vaccine.” This limitation is interpreted to mean outputting and transmitting data (e.g., Specification, ¶ [0075]).
Claims 1, 3-6, 10-12, 14, 15, 23, 28, 29, 35, 48, 85, and 112 recite the limitation “representation(s).” This limitation is interpreted to mean a numerical representation of sequence data, e.g., a vector (e.g., Specification, ¶ [0385]).
Response to Arguments
The Applicant’s arguments/remarks received 31 March 2026 have been fully considered, but are not persuasive.
The Applicant states on page 46 (para. 4) of the Remarks (as originally numbered) that the Examiner alleges that several limitations are interpreted as reciting product-by-process limitations, and the Applicant further states that under MPEP 2173.05(p), a product-by-process claim is a product claim that defines the claimed product in terms of the process by which it is made, and accordingly, none of the pending claims are product-by-process claims, e.g., claim 1 is a method claim and contains limitations for performing the method. The Applicant further states that under a broadest reasonable interpretation (BRI), words of the claim must be given their plain meaning, unless such meaning is inconsistent with the specification (MPEP 2111.01), and therefore the Applicant does not concede the Examiner’s interpretation of any limitations.
These arguments are not persuasive, because as noted in the MPEP at 2113 subsection I., even though product-by-process claims are limited by and defined by the process, determination of patentability is based on the product itself (i.e., the data, as in the case of the instant claims), and does not depend on its method of production. In the instant claims, a machine-learning model and/or neural network uses previously generated data (e.g., obtaining a plurality of peptide sequences) that are recited as product-by-process limitations that are embedded within the claims. Furthermore, the MPEP does not exclude ‘data’ as a product, and the various examples provided in MPEP 2113.I are merely examples and are not limiting of the subject matter than can be considered as a product-by-process limitation.
Subject matter eligibility evaluation in accordance with MPEP 2106.
Eligibility Step 1: Step 1 of the eligibility analysis asks: Is the claim to a process, machine, manufacture or composition of matter?
Claims 1-16, 18, 20-36, 39-42, 44-51, 53-57, and 85 are directed to a method (i.e., a process); and claim 112 is directed to a system comprising one or more data processors and a non-transitory computer-readable storage medium (i.e., a machine or a manufacture).
Therefore, these claims are encompassed by the categories of statutory subject matter, and thus satisfy the subject matter eligibility requirements under step 1.
[Step 1: YES]
Eligibility Step 2A: First it is determined in Prong One whether a claim recites a judicial exception, and if so, then it is determined in Prong Two whether the recited judicial exception is integrated into a practical application of that exception.
Eligibility Step 2A Prong One: In determining whether a claim is directed to a judicial exception, examination is performed that analyzes whether the claim recites a judicial exception, i.e., whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim.
Independent claim 1 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
processing a set of peptide representations that represents the set of peptide sequences using a first attention block in an initial attention subsystem of an attention-based machine-learning model and an immunoprotein complex (IPC) representation that represents the IPC sequence using a second attention block in the initial attention subsystem to generate an output (i.e., mental processes and mathematical concepts),
wherein the attention-based machine learning model is trained to reduce the number of false negatives in outputs predicted by the attention-based machine learning model by applying a negative set switching training technique during the training phase of the attention-based machine-learning model (i.e., mental processes and mathematical concepts), wherein the negative set switching training technique comprises:
generating a plurality of negative peptide sequences based on the plurality of positive peptide sequences (i.e., mental processes); and
training the attention-based machine-learning model in a plurality of epochs, wherein the attention-based machine-learning model is trained using the same plurality of positive peptide sequences and a different subset of the negative training dataset in each epoch of the plurality of epochs (i.e. mathematical concepts);
wherein the output includes at least one of an interaction prediction or an interaction affinity prediction for a corresponding peptide-IPC combination (i.e., mental processes), and wherein:
the corresponding peptide-IPC combination includes a peptide of the set of peptides and the MHC (i.e., mental processes);
the interaction prediction for the corresponding peptide-IPC combination predicts whether the MHC will present the peptide at a cell surface (i.e., mental processes and mathematical concepts); and
the interaction affinity prediction for the corresponding peptide-IPC combination predicts a binding affinity between the peptide and the MHC (i.e., mental processes and mathematical concepts);
selecting, based on the output, a subset of the set of peptide sequences based on one or more relative or absolute threshold affinity values (i.e., mental processes); and
identifying at least one peptide of the subset of the set of peptides as a target for an immunotherapy (i.e., mental processes).
Independent claim 85 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the attention-based machine-learning model is configured to process a plurality of variant representations that represents the plurality of variant-coding sequences using a first attention block in an initial attention subsystem of an attention-based machine-learning model and an immunoprotein complex (IPC) representation that represents the IPC sequence using a second attention block in the initial attention subsystem to generate an output (i.e., mental processes and mathematical concepts),
wherein the attention-based machine-learning model is trained to reduce the number of false negatives in outputs predicted by the attention-based machine learning model by applying a negative set switching training technique during the training phase of the attention-based machine-learning model (i.e., mental processes mathematical concepts),
where the negative set switching training technique comprises:
generating a plurality of negative peptide sequences based on the plurality of positive peptide sequences (i.e., mental processes); and
training the attention-based machine-learning model in a plurality of epochs, wherein the attention-based machine-learning model is trained using the same plurality of positive peptide sequences and a different subset of the negative training dataset in each epoch of the plurality of epochs (i.e., mathematical concepts);
wherein the output includes at least one of an interaction prediction or an interaction affinity prediction for a corresponding peptide-IPC combination (i.e., mental processes), and wherein:
the corresponding peptide-IPC combination includes a peptide of the set of peptides and the MHC (i.e., mental processes);
the interaction prediction for the corresponding peptide-IPC combination predicts whether the MHC will present the peptide at a cell surface (i.e., mental processes and mathematical concepts); and
the interaction affinity prediction for the corresponding peptide-IPC combination predicts a binding affinity between the peptide and the MHC (i.e., mental processes and mathematical concepts); and
selecting, based on the output, a subset of the plurality of mutant peptides based on one or more relative or absolute threshold affinity values (i.e., mental processes); and
identifying at least one peptide of the subset of the set of peptides as a target for an immunotherapy (i.e., mental processes).
Independent claim 112 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
process a set of peptide representations that represents the set of peptide sequences using a first attention block in an initial attention subsystem of an attention-based machine-learning model and an immunoprotein complex (IPC) representation that represents the IPC sequence using a second attention block in the initial attention subsystem to generate an output (i.e., mental processes and mathematical concepts),
wherein the attention-based machine-learning model is trained to reduce the number of false negatives in outputs predicted by the attention-based machine learning model by applying a negative set switching training technique during the training phase of the attention-based machine-learning model (i.e., mathematical concepts),
wherein the negative set switching training technique comprises:
generating a plurality of negative peptide sequences based on the plurality of positive peptide sequences (i.e., mental processes); and
training the attention-based machine-learning model in a plurality of epochs, wherein the attention-based machine-learning model is trained using the same plurality of positive peptide sequences and a different subset of the negative training dataset in each epoch of the plurality of epochs (i.e., mathematical concepts);
wherein the output includes at least one of an interaction prediction or an interaction affinity prediction for a corresponding peptide-IPC combination (i.e., mental processes), and wherein:
the corresponding peptide-IPC combination includes a peptide of the set of peptides and the MHC (i.e., mental processes);
the interaction prediction for the corresponding peptide-IPC combination predicts whether the MHC will present the peptide at a cell surface (i.e., mental processes and mathematical concepts); and
the interaction affinity prediction for the corresponding peptide-IPC combination predicts a binding affinity between the peptide and the MHC (i.e., mental processes and mathematical concepts);
selecting, based on the output, a subset of the set of peptide sequences based on one or more relative or absolute threshold affinity values (i.e., mental processes);
identifying at least one peptide of the subset of the set of peptides as a target for an immunotherapy (i.e., mental processes); and
identifying the personalized composition including the at least one peptide, a plurality of nucleic acids that encode the at least one peptide, or a plurality of cells expressing the at least one peptide (i.e., mental processes).
Independent claims 1, 85, and 112, and those claims dependent therefrom, further recite a law of nature by associating genomic data (properties of peptide-immunoprotein complex combinations) with phenotypes (immunogenicity for a corresponding peptide-immunoprotein complex combination), i.e., a genotype-phenotype correlation (MPEP 2106.04(b)).
Dependent claims 2-16, 18, 20-36, 39-42, 44-46, 48, 49, and 53-57 recite the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
Dependent claim 2 further recites:
wherein at least one peptide sequence of the set of peptide sequences comprises a variant-coding sequence that includes a variant with respect to a corresponding reference sequence (i.e., mental processes).
Dependent claim 3 further recites:
transforming the peptide representation via the first attention block into a transformed peptide representation, wherein the first attention block includes a set of attention sub-blocks in which each attention sub-block of the set of attention sub-blocks includes a self-attention layer (i.e., mental processes and mathematical concepts).
Dependent claim 4 further recites:
transforming the IPC representation via the second attention block into a transformed IPC representation, wherein the second attention block includes a set of attention sub-blocks in which each attention sub-block of the set of attention sub-blocks includes a self-attention layer (i.e., mental processes and mathematical concepts).
Dependent claim 5 further recites:
wherein at least a portion of the peptide representation corresponds to a monomer in the peptide sequence and at least a portion of the IPC representation corresponds to a monomer in the IPC sequence (i.e., mental processes); and wherein the processing comprises:
generating a transformed peptide representation based on the peptide representation using the first attention block and a first set of weights (i.e., mental processes and mathematical concepts);
generating a transformed IPC representation based on the IPC representation using the second attention block and a second set of weights (i.e., mental processes and mathematical concepts); and
generating a composite representation using the transformed peptide representation and the transformed MHC representation (i.e., mental processes and mathematical concepts).
Dependent claim 6 further recites:
embedding a peptide sequence of the set of peptide sequences to generate an embedded peptide representation for the peptide sequence (i.e., mental processes and mathematical concepts); and
encoding, positionally, the embedded peptide representation for the peptide sequence to generate a peptide representation of the set of peptide representations that represents the peptide sequence (i.e., mental processes and mathematical concepts).
Dependent claim 7 further recites:
the first attention block comprises a set of attention sub-blocks (i.e., mathematical concepts); and
each attention sub-block of the set of attention sub-blocks includes a neural network that comprises at least one self-attention layer (i.e., mathematical concepts).
Dependent claim 8 further recites:
the second attention block comprises a set of attention sub-blocks (i.e., mathematical concepts); and
each attention sub-block of the set of attention sub-blocks includes a neural network that comprises at least one self-attention layer (i.e., mathematical concepts).
Dependent claim 9 further recites:
the first attention block comprises a first plurality of attention sub-blocks (i.e., mathematical concepts);
the second attention block comprises a first plurality of attention sub-blocks (i.e., mathematical concepts); and
each attention sub-block of the first set of attention sub-blocks and the second set of attention sub-blocks includes a neural network that comprises at least one self-attention layer (i.e., mathematical concepts).
Dependent claim 10 further recites:
wherein a peptide representation of the set of peptide representations forms a first portion of an aggregate representation processed using the first attention block (i.e., mental processes and mathematical concepts); and
a second portion of the aggregate representation represents at least one of an N-flank sequence or a C-flank sequence (i.e., mental processes).
Dependent claim 11 further recites:
a peptide sequence of the set of peptide sequences forms a first portion of an aggregate sequence (i.e., mental processes);
a second portion of the aggregate sequence includes at least one of an N-flank sequence or a C-flank sequence (i.e., mental processes); and
the attention-based machine learning model includes a representation block that receives and processes the aggregate sequence to form an aggregate representation that includes a peptide representation of the set of peptide representations corresponding to the peptide sequence, wherein the aggregate representation is processed by the first attention block (i.e., mathematical concepts).
Dependent claim 12 further recites:
embedding the IPC sequence to generate an embedded IPC representation of the IPC sequence (i.e., mental processes and mathematical concepts); and
encoding, positionally, the embedded IPC representation of the IPC sequence to generate the IPC representation (i.e., mental processes and mathematical concepts).
Dependent claim 13 further recites:
wherein the attention-based machine-learning model includes a plurality of self-attention layers and for each of the plurality of self-attention layers, a corresponding downstream feedforward neural network (i.e., mathematical concepts).
Dependent claim 14 further recites:
the first attention block includes a first neural network configured to receive and process a peptide representation of the set of peptide representations to generate a transformed peptide representation (i.e., mathematical concepts);
the second attention block includes a second neural network configured to receive and process the IPC representation to generate a transformed IPC representation (i.e., mathematical concepts);
wherein each of the first neural network and the second neural network includes at least one self-attention layer (i.e., mathematical concepts); and
wherein the attention-based machine-learning model is configured to generate a composite representation using the transformed peptide representation and the transformed IPC representation (i.e., mathematical concepts).
Dependent claim 15 further recites:
wherein the attention-based machine-learning model further includes:
a composite attention block that includes a neural network configured to receive and process the composite representation, wherein the neural network includes a self-attention layer (i.e., mathematical concepts).
Dependent claim 16 further recites:
wherein the attention-based machine-learning model further includes:
a composite attention block that includes a set of attention sub-blocks, wherein each attention sub-block of the set of attention sub-blocks includes a neural network that comprises at least one self-attention layer (i.e., mathematical concepts).
Dependent claim 18 further recites:
wherein the attention-based machine-learning model is trained using a training data set that includes at least one of experimental interaction affinity data or experimental interaction data for a plurality of training peptide sequences and a set of training MHC sequences (i.e., mathematical concepts).
Dependent claim 20 further recites:
wherein the training data set includes a plurality of training data elements, at least one training data element of the plurality of training data elements comprises at least one of:
a training peptide sequence characterizing a training peptide not included in the set of peptides (i.e., mental processes);
a training IPC sequence characterizing a training IPC that is different from the IPC (i.e., mental processes); and
an experiment-based result identifying an interaction affinity indication between the training peptide and the training IPC, wherein the interaction affinity indication was detected using an assay or biosensor-based methodology (i.e., mental processes).
Dependent claim 21 further recites:
wherein the training data set includes a plurality of training data elements, at least one training data element of the plurality of training data elements comprises at least one of:
a training peptide sequence characterizing a training peptide not included in the set of peptides (i.e., mental processes);
a training MHC sequence characterizing a training MHC that is different from the IPC (i.e., mental processes); and
an experiment-based result including an interaction indication that identifies whether the training peptide was presented by the training MHC at a cell surface, wherein at least one of immunoprecipitation or mass spectrometry was used to determine the interaction indication (i.e., mental processes).
Dependent claim 22 further recites:
training the attention-based machine-learning model, prior to the processing step, using a training data set that includes at least one of binding affinities, interaction indications, or immunogenicity indications for a plurality of peptide-IPC combinations, wherein the training data set includes a plurality of training peptide sequences and at least one of a plurality of training major histocompatibility complex (MHC) sequences (i.e., mathematical concepts).
Dependent claim 23 further recites:
processing the set of peptide representations using the first attention block and the IPC representation using the second attention block to generate a set of composite representations for a set of peptide-IPC combinations (i.e., mental processes and mathematical concepts);
processing the set of composite representations to generate a set of results (i.e., mental processes and mathematical concepts);
selecting a subset of the set of peptide-IPC combinations, wherein a set of selected interactions is more likely to occur with each peptide-IPC combination of the subset as compared to a remaining subset of the set of peptide-IPC combinations (i.e., mental processes); and
wherein a report generated based on the output identifies each peptide within the subset (i.e., mental processes).
Dependent claim 24 further recites:
wherein the output includes an immunogenicity prediction for a corresponding peptide-IPC combination, wherein
each peptide of the set of peptides is used to form a set of peptide-IPC combinations (i.e., mental processes); and
the attention-based machine-learning model is configured to generate the immunogenicity prediction for each peptide-IPC combination of the set of peptide-IPC combinations, the immunogenicity prediction for a peptide-IPC combination of the set of peptide-IPC combinations being a prediction of tumor-specific immunogenicity of a peptide in the peptide-IPC combination (i.e., mental processes and mathematical concepts).
Dependent claim 25 further recites:
wherein a report generated based on the output identifies a subset of peptides from the set of peptides having increased tumor-specific immunogenicity relative to a remaining portion of the set of peptides (i.e., mental processes).
Dependent claim 26 further recites:
wherein the IPC is a major histocompatibility complex (MHC) (i.e., mental processes);
wherein each peptide of the set of peptides is used to form a set of peptide-MHC combinations (i.e., mental processes); and
wherein the attention-based machine-learning model is configured to generate the interaction prediction for each peptide-MHC combination of the set of peptide-MHC combinations, the interaction prediction for a peptide-MHC combination of the set of peptide-MHC combinations being a prediction of whether a peptide in the peptide-MHC combination is presented by the MHC at a cell surface (i.e., mental processes and mathematical concepts).
Dependent claim 27 further recites:
wherein a report generated based on the output identifies a subset of peptides from the set of peptides having an increased likelihood of presentation by the MHC relative to a remaining portion of the set of peptides (i.e., mental processes).
Dependent claim 28 further recites:
wherein a peptide sequence of the set of peptide sequences is a variant-coding sequence characterizing a mutant peptide, the variant-coding sequence comprising:
a first part identifying a sequence at an N-terminus of the mutant peptide (i.e., mental processes); and
a second part identifying a sequence of an epitope of the mutant peptide (i.e., mental processes); and the processing comprises:
processing a first representation of the first part of the variant-coding sequence using a first self-attention layer of the initial attention subsystem (i.e., mental processes and mathematical concepts); and
processing a second representation of the second part of the variant-coding sequence using a second self-attention layer of the initial attention subsystem (i.e., mental processes and mathematical concepts).
Dependent claim 29 further recites:
wherein the first representation and the second representation are processed within the first attention block (i.e., mental processes and mathematical concepts).
Dependent claim 30 further recites:
wherein the attention-based machine-learning model includes one or more transformer encoders, wherein each of the one or more transformer encoders includes a self-attention layer (i.e., mathematical concepts).
Dependent claim 31 further recites:
wherein the IPC sequence and each of the set of peptide sequences includes an ordered set of amino-acid identifiers (i.e., mental processes).
Dependent claim 32 further recites:
wherein the IPC sequence is identified using the disease sample (i.e., mental processes).
Dependent claim 33 further recites:
wherein the IPC sequence is identified using a biological sample from the subject (i.e., mental processes).
Dependent claim 34 further recites:
wherein the disease sample includes cancer cells (i.e., mental processes).
Dependent claim 35 further recites:
wherein the IPC of the subject includes a major histocompatibility complex (MHC) (i.e., mental processes);
wherein the IPC sequence includes an MHC sequence (i.e., mental processes); and
wherein the IPC representation includes an MHC representation (i.e., mental processes).
Dependent claim 36 further recites:
wherein the MHC includes an MHC class-I molecule (i.e., mental processes).
Dependent claim 39 further recites:
wherein the disease sample includes tissue (i.e., mental processes).
Dependent claim 40 further recites:
wherein at least one peptide of the set of peptides is a neoantigen (i.e., mental processes).
Dependent claim 41 further recites:
wherein at least one peptide sequence of the set of peptide sequences is a genomic sequence derived from the disease sample (i.e., mental processes).
Dependent claim 42 further recites:
wherein each of at least one of the set of variant-coding sequences is based on RNA sequences of the disease sample (i.e., mental processes).
Dependent claim 44 further recites:
wherein the report identifies a subset of peptides from the set of peptides to include in an individualized vaccine to treat a medical condition of the subject (i.e., mental processes).
Dependent claim 45 further recites:
generating a treatment recommendation to the subject that includes the individualized vaccine (i.e., mental processes).
Dependent claim 46 further recites:
determining a set of treatment peptides for inclusion in an individualized vaccine based on the report (i.e., mental processes); and
initiating an action that facilitates manufacture of the individualized vaccine that includes the set of treatment peptides (i.e., organizing human activity).
Dependent claim 48 further recites:
receiving, from an embedding block in the attention-based machine-learning model, a representation that comprises a plurality of elements, wherein the representation is either a peptide representation of the set of peptide representations that represents a peptide sequence in the set of peptide sequences or the IPC representation representing the IPC sequence; and wherein each element in the multi-element data set corresponds to a monomer in either the peptide sequence or the IPC sequence (i.e., mental processes and mathematical concepts);
determining, for each element of the plurality of elements, a key vector, a value vector, and a query vector based on a set of key weights, a set of value weights, and a set of query weights, respectively, associated with a self-attention layer of the attention-based machine learning model (i.e., mental processes and mathematical concepts);
performing a transformation of the plurality of elements to form a plurality of modified elements, wherein the transformation is performed using attention scores generated for the plurality of elements and the value vector determined for each of the plurality of elements (i.e., mental processes and mathematical concepts); and
generating the output based on the plurality of modified elements (i.e., mental processes and mathematical concepts).
Dependent claim 49 further recites:
determining an attention score of the selected element using the key vector and the query vector of the element, wherein a remaining portion of the plurality of elements other than the selected element forms a set of remaining elements (i.e., mental processes and mathematical concepts);
determining an additional attention score for each remaining element of the set of remaining elements using a key vector of the remaining element and the query vector of the selected element to form a set of additional attention scores (i.e., mental processes and mathematical concepts); and
generating a modified element using the attention score, the set of additional attention scores, and the value vector of each element of the plurality of elements (i.e., mental processes and mathematical concepts).
Dependent claim 53 further recites:
wherein the immunotherapy is selected from a group consisting of a T cell therapy, a personalized cancer therapy, an antigen-specific immunotherapy, an antigen-dependent immunotherapy, a vaccine, and a natural killer (NK) cell therapy (i.e., mental processes).
Dependent claim 54 further recites:
determining to exclude at least one peptide of the set of peptides as a target for an immunotherapy based on the report (i.e., mental processes).
Dependent claim 55 further recites:
wherein the immunotherapy is selected from a group consisting of a T cell therapy, a personalized cancer therapy, an antigen-specific immunotherapy, an antigen-dependent immunotherapy, a vaccine, and a natural killer (NK) cell therapy (i.e., mental processes).
Dependent claim 56 further recites:
wherein the IPC is a human leukocyte antigen (HLA) molecule (i.e., mental processes).
Dependent claim 57 further recites:
defining the set of peptide sequences based on the sequencing of the disease sample from the subject (i.e., mental processes);
identifying, based on a report generated based on the output, a subset of the set of peptide sequences (i.e., mental processes).
The abstract ideas recited in the claims are evaluated under the broadest reasonable interpretation (BRI) of the claim limitations when read in light of and consistent with the specification. As noted in the foregoing section, the claims are determined to contain limitations that can practically be performed in the human mind with the aid of a pen and paper (e.g., selecting, based on the output, a subset of the set of peptide sequences based on one or more relative or absolute threshold affinity values; and identifying at least one peptide of the subset of the set of peptides as a target for an immunotherapy), and therefore recite judicial exceptions from the mental process grouping of abstract ideas. Additionally, the recited limitations that are identified as judicial exceptions from the mathematical concepts grouping of abstract ideas (e.g., attention-based machine learning models are considered mathematical concepts under the BRI at least because, an attention-based machine-learning model comprises an attention mechanism, also referred to as scaled dot-product attention, wherein when generating predictions, it enables the model to assign varying weights to distinct units within a sequence, and thus, the attention mechanism weighs sequence units according to how relevant they are to the sequence unit being considered by the model at any given point in the algorithm. The attention mechanism itself is mathematical, as each unit in a sequence has three vectors associated with it: Query (Q), Key (K), and Value (V), and by taking the dot product of one unit’s query and another unit’s key, and dividing the result by the square root of the key vector’s dimensionality, one can calculate the attention score between two units. The weighted sum is the self-attention mechanism’s output, and the scores that follow are used to with the Values.) are abstract ideas irrespective of whether or not the limitations are practical to perform in the human mind. Furthermore, a law of nature correlating a genotype-phenotype association is identified at Eligibility Step 2A: Prong One.
Therefore, claims 1-16, 18, 20-36, 39-42, 44-51, 53-57, 85, and 112 recite an abstract idea and a law of nature.
[Step 2A Prong One: YES]
Eligibility Step 2A Prong Two: In determining whether a claim is directed to a judicial exception, further examination is performed that analyzes if the claim recites additional elements that when examined as a whole integrates the judicial exception(s) into a practical application (MPEP 2106.04(d)). A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. The claimed additional elements are analyzed to determine if the abstract idea is integrated into a practical application (MPEP 2106.04(d)(I); MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the abstract idea, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d)(III)).
The judicial exceptions identified in Eligibility Step 2A Prong One are not integrated into a practical application because of the reasons noted below.
Dependent claims 2, 5-16, 18, 20-36, 39-42, 45, 48, 49, and 53-56 do not recite any elements in addition to the judicial exception, and thus are part of the judicial exception.
The additional elements in independent claim 1 include:
accessing a set of peptide sequences characterizing a set of peptides, each peptide sequence of the set of peptide sequences having been identified by processing a disease sample from the subject (i.e., accessing data);
accessing an immunoprotein complex (IPC) sequence identified for an immunoprotein complex (IPC) of the subject, wherein the IPC comprises a major histocompatibility complex (MHC) (i.e., accessing data);
obtaining a plurality of positive peptide sequences (i.e., obtaining data); and
creating the personalized composition including the at least one peptide, a plurality of nucleic acids that encode the at least one peptide, or a plurality of cells expressing the at least one peptide.
The additional elements in independent claim 85 include:
inputting a plurality of variant-coding sequences characterizing a plurality of mutant peptides into an attention-based machine-learning model, each variant-coding sequence of the plurality of variant-coding sequences having been identified by processing a disease sample from the subject (i.e., inputting data);
inputting an immunoprotein complex (IPC) sequence identified for an immunoprotein complex (IPC) of the subject into the attention-based machine-learning model, wherein the IPC comprises a major histocompatibility complex (MHC) (i.e., inputting data);
obtaining a plurality of positive peptide sequences (i.e., obtaining data); and
creating the personalized composition including the at least one peptide, a plurality of nucleic acids that encode the at least one peptide, or a plurality of cells expressing the at least one peptide.
The additional elements in independent claim 112 include:
one or more data processors;
a non-transitory computer readable storage medium;
access a set of peptide sequences characterizing a set of peptides, each peptide sequence of the set of peptide sequences having been identified by processing a disease sample from a subject (i.e., accessing data);
access an immunoprotein complex (IPC) sequence identified for an immunoprotein complex (IPC) of the subject, wherein the IPC comprises a major histocompatibility complex (MHC) (i.e., accessing data); and
obtaining a plurality of positive peptide sequences (i.e., obtaining data).
The additional elements in dependent claims 3, 4, 44, 46, 47, 50, 51, and 57 include:
receiving a peptide representation of the set of peptide representations for a corresponding peptide sequence of the set of peptide sequences (i.e., receiving data) (claim 3);
receiving the IPC representation (i.e., receiving data) (claim 4);
receiving input data entered by a user, the input data corresponding to the subject (i.e., receiving data) (claims 44 and 46);
wherein the set of peptide sequences and the IPC sequence are accessed, in response to receiving the input data, via retrieval from a data store (i.e., accessing data) (claims 44 and 46);
generating an alert that triggers a computerized process involved in the manufacture of the individualized vaccine (claim 47);
displaying a report generated based on the output on a graphical user interface on a display system (i.e., displaying data) (claim 50);
a first computing platform (claim 51);
sending a report generated based on the output to a second computing platform over a set of communications links that includes at least one of a wired communications link or a wireless communications link (i.e., transmitting data) (claim 51);
sequencing the disease sample from the subject (claim 57);
synthesizing mRNA that codes for at least one peptide included in the subset of the set of peptides (claim 57);
complexing the mRNA with lipids to produce a mRNA-lipoplex treatment (claim 57); and
administering the mRNA-lipoplex treatment to the subject (claim 57).
The additional elements of a computer (claim 47); first and second computing platforms (claim 51); one or more data processors (claim 112); a non-transitory computer readable storage medium (claim 112); and displaying a report generated based on the output on a graphical user interface on a display system (claim 50); invoke a computer and/or computer-related components merely as tools for use in the claimed process, such that they amount to no more than mere instructions to apply the exceptions using a generic computer (MPEP 2106.05(f)), and therefore are not an improvement to computer functionality itself, or an improvement to any other technology or technical field, and thus, do not integrate the judicial exceptions into a practical application (MPEP 2106.04(d)(1)).
The additional elements of accessing data (claims 1 and 112); obtaining data (claims 1, 85, and 112); receiving data (claims 3 and 4); inputting data (claim 85); receiving input data entered by a user (claims 44 and 46); and accessing data, in response to receiving the input data, via retrieval from a data store (claims 44 and 46); are merely pre-solution and/or post-solution activities – nominal additions to the claims that do not meaningfully limit the claims, and therefore do not add more than insignificant extra-solution activity to the judicial exceptions (MPEP 2106.05(g)).
The additional elements of generating an alert that triggers a computerized process involved in the manufacture of the individualized vaccine (claim 47); and sending data over a set of communications links that includes at least one of a wired communications link or a wireless communications link (claim 51); are merely post-solution steps of transmitting data output – nominal or tangential addition to the claims that do not meaningfully limit the claims, and therefore do not add more than insignificant extra-solution activity to the judicial exceptions (MPEP 2106.05(g)).
The additional element of sequencing the disease sample from the subject (claim 57) is merely a pre-solution activity of gathering data for use in the claimed process – a nominal or tangential addition to the claims that does not meaningfully limit the claims, and therefore does not add more than insignificant extra-solution activity to the judicial exceptions (MPEP 2106.05(g)).
The additional element of creating the personalized composition including the at least one peptide, a plurality of nucleic acids that encode the at least one peptide, or a plurality of cells expressing the at least one peptide (claims 1 and 85); amounts to mere instructions to apply an exception, because this type of recitation is equivalent to the words “apply it”. The claim limitation attempts to cover any solution to the identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, because the limitation of the personalized composition could include any peptide, or any of a plurality of nucleic acids, or any of a plurality of cells, since there is not a restriction, limit, or indication as to what the constituents of the personalized composition actually are. Furthermore, this additional element does not effect a particular treatment or prophylaxis for a disease or medical conditions (MPEP 2106.04(d)(2)), and therefore, does not integrate the judicial exceptions into a practical application (MPEP 2106.05(f)).
The additional elements of synthesizing mRNA (claim 57); complexing the mRNA with lipids to produce a mRNA-lipoplex treatment (claim 57); and administering the mRNA-lipoplex treatment to the subject (claim 57); do not recite an action that effects a particular treatment or prophylaxis for a disease or medical condition, at least because a treatment or prophylaxis limitation must be “particular,” i.e., specifically identified (e.g., see factor (a.) at MPEP 2106.04(d)(2)) and also because a treatment or prophylaxis limitation must have more than a nominal or insignificant relationship to the claim (e.g., see factor (b.) at MPEP 2106.04(d)(2)). Furthermore, the “identifying” step in claim 57 is based on the output, which is mere instructions to apply the exception (MPEP 2106.05(f)), and therefore these additional elements do not integrate the judicial exceptions into a practical application.
Thus, the additionally recited elements merely invoke a computer as a tool, and/or amount to insignificant extra-solution data gathering activity, and/or amount to mere instructions to apply an exception, and/or do not effect a particular treatment or prophylaxis, and as such, when all limitations in claims 1-16, 18, 20-36, 39-42, 44-51, 53-57, 85, and 112 have been considered as a whole (i.e., the analysis takes into consideration all the claim limitations and how those limitations interact and impact each other when evaluating whether the exception is integrated into a practical application), the claims are deemed to not recite any additional elements that would integrate a judicial exception into a practical application, and therefore claims 1-16, 18, 20-36, 39-42, 44-51, 53-57, 85, and 112 are directed to an abstract idea (MPEP 2106.04(d)).
[Step 2A Prong Two: NO]
Eligibility Step 2B: Because the claims recite an abstract idea, and do not integrate that abstract idea into a practical application, the claims are probed for a specific inventive concept. 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 amount to significantly more than the judicial exception (MPEP 2106.05A i-vi).
The claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception(s) because of the reasons noted below.
Dependent claims 2, 5-16, 18, 20-36, 39-42, 45, 48, 49, and 53-56 do not recite any elements in addition to the judicial exception(s).
The additional elements recited in independent claims 1, 85, and 112 and dependent claims 3, 4, 44, 46, 47, 50, 51, and 57 are identified above, and carried over from Step 2A: Prong Two along with their conclusions for analysis at Step 2B. Any additional element or combination of elements that was considered to be insignificant extra-solution activity at Step 2A: Prong Two was re-evaluated at Step 2B, because if such re-evaluation finds that the element is unconventional or otherwise more than what is well-understood, routine, conventional activity in the field, this finding may indicate that the additional element is no longer considered to be insignificant; and all additional elements and combination of elements were evaluated to determine whether any additional elements or combination of elements are other than what is well-understood, routine, conventional activity in the field, or simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, per MPEP 2106.05(d).
The additional elements of a computer (claim 47); first and second computing platforms (claim 51); one or more data processors (claim 112); a non-transitory computer readable storage medium (claim 112); displaying a report generated based on the output on a graphical user interface on a display system (claim 50); accessing data (claims 1 and 112); obtaining data (claims 1, 85, and 112); receiving data (claims 3 and 4); inputting data (claim 85); receiving input data entered by a user (claims 44 and 46); accessing data, in response to receiving the input data, via retrieval from a data store (claims 44 and 46); and transmitting data (claims 47 and 51); are conventional computer components and/or functions (see MPEP at 2106.05(b) and 2106.05(d)(II) regarding conventionality of computer components and computer processes).
The additional element of sequencing a disease sample from a subject (claim 57) is conventional. Evidence for the conventionality is shown by:
Cieslik et al. (“Cancer transcriptome profiling at the juncture of clinical translation.” Nature Reviews: Genetics, 2018, Vol. 19, pp. 93-109, as cited in the Office action mailed 06 September 2024).
Cieslik et al. reviews applications of next-generation sequencing to cancer transcriptome profiling (Title) and shows that using RNA sequencing (RNA-seq), it has now become possible to sequence and quantify the transcriptional outputs of individual cells or thousands of samples (Abstract). Cieslik et al. further shows transcriptomic protocols adapted for a wide range of input materials, including cell cultures, body fluids, and solid tissues; and clinically relevant samples such as formalin fixed-paraffin embedded (FFPE) tumor tissues (page 98, column 1, para. 3). Cieslik et al. further shows practical considerations for clinical RNA sequencing, such as transcriptomic platform technologies, RNA-seq protocols, and depth of sequencing (page 99, Box 1).
The additional elements of creating a personalized composition including at least one peptide, a plurality of nucleic acids that encode the at least one peptide, or a plurality of cells expressing at least one peptide (claims 1 and 85); synthesizing mRNA (claim 57); complexing the mRNA with lipids to produce a mRNA-lipoplex treatment (claim 57); and administering the mRNA-lipoplex treatment to the subject (claim 57); are conventional. Evidence for the conventionality is shown by:
Zhu et al. (“Efficient Nanovaccine Delivery in Cancer Immunotherapy.” ACS Nano, 2017, Volume 11, pp. 2387-2392, as cited in the Office action mailed 06 September 2024).
Zhu et al. reviews efficient nanovaccine delivery in cancer immunotherapy (Title; and Abstract), and discusses complexation of liposome and neoantigen-based mRNA for the development of mRNA-lipoplex (RNA-LPX; page 2390, column 2, para. 2). Zhu et al. further shows that the intravenous injected RNA-LPX resulted in efficient mRNA delivery to lymphoid dendritic cells (DCs), leading to subsequent induction of potent neoantigen-specific T-cell responses in mice and in human melanoma patients (Ibid.). Zhu et al. further shows that simply by gene engineering, one mRNA can be synthesized to encode multiepitope cancer-specific neoantigens (Ibid.). Zhu et al further shows that technological advancements in high-throughput exosome sequencing, mass spectrometry, bioinformatics, and peptide manufacturing have facilitated the identification of cancer neoantigens and the production of synthetic neoantigen peptides within a reasonable time course, making it possible to use synthetic neoantigen as a cancer vaccine (page 2388, col. 1); and further shows that simply be gene engineering, one mRNA can be synthesized to encode multiepitope cancer-specific neoantigens, thus achieving a broad spectrum of antitumor T-cell responses upon intracellular neoantigen translation (page 2390, col. 2, para. 2).
Therefore, when taken alone (i.e., individually), all additional elements in claims 1-16, 18, 20-36, 39-42, 44-51, 53-57, 85, and 112 do not amount to significantly more than the above-identified judicial exception(s). Even when evaluated as an ordered combination, the additional elements fail to transform the exception(s) into a patent-eligible application of that exception. Thus, claims 1-16, 18, 20-36, 39-42, 44-51, 53-57, 85, and 112 are deemed to not contribute an inventive concept, i.e., amount to significantly more than the judicial exception(s) (MPEP 2106.05(II)).
[Step 2B: NO]
Response to Arguments
The Applicant’s arguments/remarks received 31 March 2026 have been fully considered, but are not persuasive.
The Applicant provides a summary of aspects of MPEP 2106.04(a)(2) on page 31 (para. 2) and a summary of the August 4, 2025 Memorandum (“Reminders on Evaluating Subject Matter Eligibility of Claims under 35 U.S.C. 101.”) at paragraph 3 on page 31 (Remarks), and states (para. 4) that here, amended claim 1 is not directed to a mathematical concept because it does not recite mathematical relationships, mathematical formulas or equations, or mathematical calculations. The Applicant further states that the Examiner asserts that independent claim 1 recites mathematical concepts because it involves training an attention-based machine-learning model, processing representations, and generating predictions, and further states on page 32 (para. 1), that in particular, the Examiner’s analysis rests on the premise that training datasets inherently comprise a mathematical relationship and training a machine-learning model is inherently a mathematical process and that this inherent mathematical nature is sufficient to establish that the claim recites a mathematical concept. The Applicant further states (para. 2) that under the standard set forth in MPEP 2106.04(a)(2) and the August Memorandum, the relevant inquire is whether the claim recites a mathematical concepts, not whether mathematics may be used to implement the claimed steps. The Applicant further states that USPTO guidance expressly cautions against treating machine-learning claims as abstract merely because they involve mathematical concepts, and that this principle is illustrated by USPTO Eligibility Example 39, which addresses a computer-implemented method of training a neural network. The Applicant further states (para. 3) that according to Example 39, the claim does not recite any mathematical relationships, formulas, or calculations, and while some of the limitations may be based on mathematical concepts, the mathematical concepts are not recited in the claims. The Applicant further states that as further explained in the August memorandum, even though training the neural network involves a broad array of techniques and/activities that may involve or rely upon mathematical concepts, the limitation does not set forth or describe any mathematical relationships, calculations, formulas, or equations using words or mathematical symbols. The Applicant further states on page 33 (para. 2) that here, claim 1 is analogous, and further states that the claim 1 limitations provided in the argument do not set forth or recite any mathematical relationship, formula, equation, or calculation in the claim language, and accordingly, consistent with MPEP 2106.04(a)(2), the August Memorandum, and USPTO Example 39, claim 1 does not recite a mathematical concept, but instead merely involves mathematics.
These arguments are not persuasive, because first, with regard to the Applicant’s attempt to analogize the instant claims with Example 39, and using the August memorandum for support of the Applicant’s argument, it is noted that the fact pattern of training a neural network for facial recognition is not analogous to the fact pattern of the instant claims (e.g., training and using a neural network model to use sequence data derived from a subject’s disease sample to generate an interaction prediction for a corresponding peptide-IPC combination that predicts whether the MHC will present a peptide at a cell surface). Second, Example 39 considers a hypothetical method for training a neural network for facial detection, and is only intended to be illustrative of the claim analysis performed using MPEP 2106, and of the particular issues noted in the Example. Example 39 should be interpreted based on the fact patterns set forth in the Example's claim, as other fact patterns may have different eligibility outcomes, as evidenced in the rejection of the instant claims above. In particular, Example 39 comprises steps of applying one or more transformations to each digital facial image (e.g., mirroring, rotating, smoothing, or contrast reduction) to create a modified set of digital facial images; creating a first training set comprising the collected set of digital facial images, the modified set of digital facial images, and a set of digital non-facial images; training the neural network in a first stage using the first training set; creating a second training set for a second stage of training comprising the first training set and digital non-facial images that are incorrectly detected as facial images after the first stage of training; and training the neural network in a second stage using the second training set. While the exemplified analysis of Example 39 concludes that the claim does not recite any judicial exceptions (e.g., mental processes or mathematical concepts), this conclusion is determined from the particular fact pattern of the hypothetical claimed process, and should not be generalized as an axiom that claim limitations reciting training a neural network do not recite judicial exceptions. Third, as evidence of how other fact patterns may have different eligibility outcomes, see Ex parte Guillaume Desjardins, Appeal 2024-000567, in which the Board identified steps of training a machine learning model as broadly, but reasonably, encompassing a mathematical algorithm computing mathematical calculations and manipulating particular information, i.e., values of certain parameters to train a machine learning model (pages 20-21). It is further noted that the Appeals Review Panel (“ARP”) that was convened to review the Board’s Decision on Appeal did not disturb the Board’s finding at Prong One, and proceeded to the analysis at Step 2A Prong Two. Fourth, regarding the August 4, 2025 memorandum regarding reminders on evaluating subject matter eligibility of claims under 35 U.S.C. 101, the memorandum clearly states (page 1) that it is not intended to announce any new USPTO practice or procedure and is meant to be consistent with existing USPTO guidance, and further states that Examiners should consult the specific MPEP sections for more thorough information. Accordingly, the rejection above and the rejections of record have been raised in accordance with the eligibility analysis framework provided at MPEP 2106. Fifth, regarding the Applicant’s argument that the claims do not set forth or recite any mathematical relationship, formula, equation, or calculation in the claim language, and accordingly, consistent with MPEP 2106.04(a)(2), the August Memorandum, and USPTO Example 39, claim 1 does not recite a mathematical concept, but instead merely involves mathematics, it is noted in response that the MPEP at 2106.04 II.A.1. is instructive, describing an example of a claim that merely involves, or is based on, an exception is a claim to "A teeter-totter comprising an elongated member pivotably attached to a base member, having seats and handles attached at opposing sides of the elongated member." This claim is based on the concept of a lever pivoting on a fulcrum, which involves the natural principles of mechanical advantage and the law of the lever. However, this claim does not recite (i.e., set forth or describe) these natural principles and therefore is not directed to a judicial exception. In contrast, a claim that recites (i.e., sets forth or describes) training steps for training an attention-based machine-learning model in a plurality of epochs (i.e., iterations) recites a mathematical concept because a key aspect of model training is weight adjustment, i.e., the training necessitates updating the weights and biases to reduce loss, which is fundamental to learning.
The Applicant states on page 33 (bottom) of the Remarks that claim 1 is not directed to a mathematical relationship, and further states that the Examiner asserts that claim 1 recites a mathematical relationship because training datasets inherently comprise a mathematical relationship and because training a machine-learning model involves learning relationships between inputs and outputs. The Applicant further states that this assertion improperly expands the meaning of mathematical relationship beyond the scope set forth in MPEP 2106.04(a)(2). The Applicant further states on page 34 (para. 1) that under MPEP 2106.04(a)(2), a mathematical relationship is a relationship between variables or numbers, and that mathematical relationship may be expressed in words or using mathematical symbols. The Applicant provides (para. 2) examples of mathematical relationships as provided at MPEP2106.04(a)(2), and further states (para. 3) that in particular, the fact that a machine-learning model may learn relationships during training does not mean that the claim recites a mathematical relationship, and further states that as reflected in the examples cited in MPEP 2106.04(a)(2) as well as Example 39, a mathematical relationship is recited only where the relationship itself is set forth in the claim language, either expressly or in words describing such a dependency. The Applicant further states that, specifically, claim 1 does not recite a relationship between variables or numbers – claim 1 does not recite a relationship between physical properties, a conversion, or a mathematical relationship. The Applicant further states on page 35 (para. 1) that claim 1 is also not directed to organizing information and manipulating information through mathematical correlations. The Applicant points to Digitech Image Techs., LLC v. Electronics for Imaging, Inc., and states that the court indicated that a claim reciting generating second data for describing a device dependent transformation of spatial information content of the image in said device independent color space through use of spatial stimuli and device response characteristic functions as directed to an abstract idea because it described a process of organizing information through mathematical correlations, however, here, claim 1 does not recite any mathematical functions and is thus distinct from the ineligible claims of Digitech. The Applicant further states (para. 2) that claim 1 is also distinct from the ineligible claim 2 of Example 47, because, unlike claim 2 of Example 47, claim 1 does not recite any mathematical algorithm, e.g., a backpropagation algorithm, a gradient descent algorithm, nor does claim 1 recite organizing information and manipulating information through mathematical correlations, e.g., discretizing continuous data. The Applicant further states that instead, claim 1 recites techniques for training a machine-learning model using positive and negative training data in a specific manner across a plurality of epochs, and further states that these techniques are not mathematical algorithms, and accordingly, claim 1 is not directed to a mathematical relationship.
These arguments are not persuasive, because first, the examples of mathematical relationships provided under MPEP 2106.04(a)(2) are non-limiting examples, and furthermore, the MPEP at 2106.04(a)(2) states that a mathematical relationship may be expressed in words. Second, it is noted that training a machine-learning model relies on mathematical relationships to learn patterns from data and make predictions, and requires using mathematical algorithms (e.g., gradient descent) to adjust weights and biases (which are mathematical parameters) by calculating and minimizing a ‘cost function’ which is a measure of the error between the model’s predictions and the actual desired outputs, with the goal of training being to minimize this function. Third, while the terms "set forth" and "described" are thus both equated with "recite", their different language is intended to indicate that there are two ways in which an exception can be recited in a claim. For instance, the claims in Diehr, 450 U.S. at 178 n. 2, 179 n.5, 191-92, 209 USPQ at 4-5 (1981), clearly stated a mathematical equation in the repetitively calculating step, and the claims in Mayo, 566 U.S. 66, 75-77, 101 USPQ2d 1961, 1967-68 (2012), clearly stated laws of nature in the wherein clause, such that the claims "set forth" an identifiable judicial exception. Alternatively, the claims in Alice Corp., 573 U.S. at 218, 110 USPQ2d at 1982, described the concept of intermediated settlement without ever explicitly using the words "intermediated" or "settlement." Fourth, it is important to note that a mathematical concept need not be expressed in mathematical symbols, because words used in a claim operating on data to solve a problem can serve the same purpose as a formula or equation (MPEP 2106.04(a)(2)(I)), and therefore, a claim that recites a mathematical concept, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. As noted in the rejection above, when claim 1 is evaluated at Eligibility Step 2A: Prong One, the claim is determined to describe (i.e., recite) judicial exceptions from the mathematical concepts grouping of abstract ideas, as opposed to merely being based on or involving a mathematical concept, e.g., at least the claim 1 limitation “training the attention-based machine-learning model in a plurality of epochs, wherein the attention-based machine-learning model is trained using the same plurality of positive peptide sequences and a different subset of the negative training dataset in each epoch of the plurality of epochs” recites a mathematical relationship between the two different types of data, i.e., the positive dataset and the negative dataset, because the datasets are converted to numerical values before being input to the model, and those numerical values represent the relationship between the characteristics of the biological sequence data. Fifth, claim 1 in the instant application is not analogous to claim 2 of Example 47, not least because claim 2 of Example 47 is directed to a system for the detection of malicious network packets that enhances security by acting in real time to proactively prevent network intrusions, whereas the instant claim 1 is broadly directed to training and using a neural network model to use sequence data derived from a subject’s disease sample to generate an interaction prediction for a corresponding peptide-IPC combination that predicts whether the MHC will present a peptide at a cell surface, thus, the Examples should be interpreted based on the fact patterns set forth in a particular Example, as other fact patterns may have different eligibility outcomes, as evidenced in the rejection of the instant claims above. Sixth, regarding Digitech, the court explained that such claims were directed to an abstract idea because they described a process of organizing information through mathematical correlations, like Flook's method of calculating using a mathematical formula, however, the instant claims have been interpreted based on the fact patterns set forth in a particular instant claim, as noted in the above rejection.
The Applicant states on page 36 (para. 2) of the Remarks that claim 1 is not directed to mathematical formulas or equations, and that the Examiner takes the position that claim 1 is directed to mathematical formulas or equations because attention-based machine learning models are implemented using mathematical formulas and equations. The Applicant further states that MPEP 2106.04(a)(2) defines this category by what the claim recites, not be what may be used to implement the claim. The Applicant further states (para. 3) that under MPEP 2106.04(a)(2), a claim that recites a numerical formula or equation will be considered as falling within the mathematical concepts grouping, and provides examples of mathematical equations or formulas from MPEP 2106.04(a)(2), and further states (para. 4) that claim 1 is distinct from these examples, and unlike the examples, claim 1 does not recited any mathematical formulas or equations, in numeric format or textual format. The Applicant further states (para. 5) that claim 1 is also distinct from the ineligible claim 1 of Eligibility Example 48, and further states on page 37 that unlike claim 1 of Eligibility Example 48, instant claim 1 does not recite the conversion of signals using a mathematical operation, nor does claim 1 recite a mathematical formula or equation, and accordingly, claim 1 is not directed to a mathematical formula or equation.
These arguments are not persuasive, because first, the examples of mathematical equations or formulas recited in a claim at MPEP 2106.04(a)(2) are non-limiting examples, and furthermore, the MPEP at 2106.04(a)(2) states that there are instances where a formula or equation is written in text format and should be considered as falling within this grouping. Second, and importantly, the above rejection does not actually identify any of the claim limitations as reciting mathematical equations or formulas. Third, regarding the Applicant’s attempt to distinguish the instant claims from claim 1 of Example 48, it is reiterated that the Examples should be interpreted based on the fact patterns set forth in a particular Example, as other fact patterns may have different eligibility outcomes, as evidenced in the rejection of the instant claims above.
The Applicant states on page 37 (para. 2) of the Remarks that claim 1 is not directed to mathematical calculations, and further states that the Examiner asserts that claim 1 recites mathematical calculations because training an attention-based machine-learning model necessarily involves mathematical calculations, such as calculating error values, adjusting weights, or minimizing a cost function. The Applicant further states that, however, under MPEP 2106.04(a)(2), the relevant inquiry is whether the claim language itself recites a mathematical operation or an act of calculating a value. The Applicant further states that none of the examples provided under MPEP 2106.04(a)(2) treat the mere fact that calculations occur during execution as sufficient, rather, each involves a claim that expressly requires performing a calculation to determine a numerical value. The Applicant further states (para. 3) that under MPEP 2106.04(a)(2), a mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation, and further states on page 38 that amended claim 1 is distinct from the provided examples, and unlike the examples, claim 1 does not recite a statistical analysis or calculation of a value based on mathematical formulas, and accordingly, amended claim 1 does not recite any mathematical operations.
These arguments are not persuasive, because first, the above rejection does not actually identify any limitations as reciting a mathematical calculation, but rather identifies particular limitations as encompassing mathematical concepts, however it is noted that claim 1 recites “wherein the output [from the model] includes at least one of an interaction prediction or an interaction affinity prediction…” where one of skill in the art would understand that a machine learning model generates a prediction based broadly on a calculation involving multiplying inputs by weights and adding in the biases and converting raw scores into decimals that add up to 1.0. Second, the examples provided under MPEP 2106.04(a)(2) are non-limiting examples, and as discussed in the foregoing responses to arguments and reiterated here, the instant claims have been interpreted based on the fact patterns set forth in a particular instant claim, as noted in the above rejection.
The Applicant states on page 38 (Section II) that claim 1 is not directed to a mental process, and further states that in the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence, the USPTO reiterated that claims do not recite a mental process when they contain limitations that cannot practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitations. The Applicant points to SRI Int’l, Inc. v. Cisco Systems, Inc., and states that the court declined to identify the claimed collection and analysis of network data as abstract because the human mind is not equipped to detect suspicious activity by using network monitors and analyzing network packets as recited by the claims. The Applicant further states that claim 1 does not recite a mental process because it contains limitations that cannot practically be performed in the human mind, e.g., claim 1 recites how the attention-based machine-learning model can be trained in a plurality of epochs, and further states on page 39 (top) that the human mind is not equipped to train a machine-learning model in the recited manner in a plurality of epochs, and further states that claim 1 recites creating the personalized composition including the at least one peptide, a plurality of nucleic acids that encode the at least one peptide, or a plurality of cells expressing the at least one peptide, and that this is a step that cannot be practically performed in the human mind. The Applicant provides examples of claims that recite mental processes at MPEP 2106.04(a)(2), and states that amended claim 1 is distinct from all of these examples, and unlike these examples, claim 1 does not recite data analysis steps that are recited at a high level of generality such that they could practically be performed in the human mind, and further states that claim 1 does not recite simple steps of data comparison, nor does claim 1 recite observations, evaluations, judgements, and opinions. The Applicant further states on page 40 (para. 1) that the Applicant recognizes that a claim that requires a computer may still recite a mental process, and further states that the MPEP provides several Federal Circuit cases in which a mental process performed on a generic computer is deemed abstract, e.g., Voter Verified, Inc. v. Election Systems & Software, LLC; also e.g., Intellectual Ventures I LLC v. Symantec Corp.; and also, e.g., Mortgage Grader, Inc. v. First Choice Loan Servs. Inc.; however, the Applicant states that instant claim 1 is distinct from all of these cases because claim 1 goes beyond simply requiring a computer. The Applicant further states that claim 1 does not recite a mental process such as vote verification, email distribution, misuse detection, or shopping for loan packages, but instead, claim 1 recites limitations that not only require a computer system, but also can only be performed by a computer system because it is impractical for a human mind to carry out these steps. The Applicant further points to USPTO Eligibility Example 39, and further states that this Example further supports this conclusion, because in Example 39, the USPTO expressly determined that a claim directed to generating training data and training a neural network using the training data (analogous to claim 1) did not recite a mental process because the steps are not practically performed in the human mind.
These arguments are not persuasive, because first, with regard to the Applicant’s attempt to analogize the instant claims to SRI Int’l, Inc. v. Cisco Systems, Inc., and distinguish the instant claims from Voter Verified, Inc. v. Election Systems & Software, LLC; Intellectual Ventures I LLC v. Symantec Corp.; and Mortgage Grader, Inc. v. First Choice Loan Servs. Inc.; it is noted that the instant claims have been interpreted based on the fact patterns set forth in each of the instant claims, and therefore these fact patterns result in an eligibility determination that is particular to the instant claims under examination, as noted in the above rejection. Second, the Applicant’s arguments present limitations that are not actually identified as mental processes in the above rejection, e.g., steps to train a machine-learning model in a plurality of epochs (i.e., iterations), and creating the personalized composition including the at least one peptide, a plurality of nucleic acids that encode the at least one peptide, or a plurality of cells expressing the at least one peptide, are limitations that are not identified as mental processes in the above rejection, contrary to the Applicant’s arguments.
The Applicant states on page 41 that claim 1 is not directed to a law of nature, and further states that the MPEP states that laws of nature and natural phenomena include naturally occurring principles/relations and nature-based products that are naturally occurring or that do not have markedly different characteristics compared to what occurs in nature. The Applicant further points to a passage from the MPEP at 2106.04(b), and states that claim 1 is not focuses on merely observing or detecting a natural relationship, rather, claim 1 recites specific machine-learning techniques to identify peptides as a target for an immunotherapy. The Applicant further states that even to the extent the Examiner characterizes the claim as involving an association between genomic data and immunogenicity, that association is not itself claimed as a natural rule or principle, but is instead used as part of a recited process for selecting peptides and creating a personalized composition. The Applicant further states that these machine-learning techniques, including specific ways to train an attention-based machine-learning model and specific ways to use the machine-learning models, go far beyond merely observing or detecting a natural relationship. The Applicant further states that claim 1 recites creating the personalized composition including the at least one peptide, a plurality of nucleic acids that encode the at least one peptide, or a plurality of cells expressing the at least one peptide, and that similar to Tilghman, claim 1 is not focused on a natural correlation, but instead recites steps for identifying peptides and creating a personalized composition.
These arguments are not persuasive, because first, the fact pattern of Tilghman (i.e., claims reciting process steps for manufacturing fatty acids and glycerol by hydrolyzing fat at high temperature and pressure) is not found to be analogous to the fact pattern of the instant claims (e.g., training and using a neural network model to use sequence data derived from a subject’s disease sample to generate an interaction prediction for a corresponding peptide-IPC combination that predicts whether the MHC will present a peptide at a cell surface). Second, it is noted that claim 1 at least recites a method for identifying at least one peptide from a subset of a set of peptides as a target for an immunotherapy. With regard to whether the claim recites a law of nature, it is further noted that the relation between a target peptide and an immunological response is a consequence of whether an individual subject's major histocompatibility complex (MHC) molecules will present a particular peptide at a cell surface - an entirely natural process. Thus, the instant claims recite a law of nature.
The Applicant states on page 42 (para. 2) that even assuming arguendo that claim 1 recites a judicial exception, the claim covers a technical solution to a technical problem, and thus as a whole integrates the recited judicial exception into a practical application of the exception. The Applicant further states that as explained in MPEP 2106.04(d), subsection III, the Step 2A Prong Two analysis considers the claim as a whole, that is, the limitations containing the judicial exception as well as the additional elements in the claim besides the judicial exception need to be evaluated together to determine whether the claim integrates the judicial exception into a practical application. The Applicant further point to the 2019 PEG for exemplary considerations for integration into a practical application, further states that more specifically, in the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence, the USPTO states that it is important to evaluate whether the claim covers a particular solution to a problem. The Applicant further states on page 43 that claim 1, considered as a whole, covers a particular solution to a problem of training machine-learning models that both reduce a complexity of the training and improve training performance, and that specifically, claim 1 recites steps for solving a technical problem of false negatives in outputs, reciting that the attention-based machine-learning model is trained to reduce the number of false negatives in outputs predicted by the attention-based machine learning model by applying a negative set switching training technique during the training phase of the attention-based machine-learning model. The Applicant points to paragraph [0057] in the Applicant’s specification for an explanation for why training a model for neoantigen analysis can be a technical challenge, and further states that to address these technical challenges in model training, amended claim 1 recites specific training techniques that improve the performance of the training process, e.g., the system obtains a plurality of positive peptide sequences and generates a plurality of negative peptide sequences based on the plurality of positive peptide sequences, and further, the system trains the attention-based machine-learning model in a plurality of epochs, where the attention-based machine-learning model is trained using the same plurality of positive peptide sequences and a different subset of the negative training dataset in each epoch of the plurality of epochs, and that this way, the model can be trained in an efficient manner but also the performance of the training is enhanced. The Applicant further points to paragraphs [0185] & [0186] in the Applicant’s specification and further states that claim 1 covers a technical solution to a technical problem in that it recites the attention-based machine-learning model is trained to reduce the number of false negative in outputs predicted by the attention-based machine learning model by applying a negative set switching training technique during the training phase of the attention-based machine-learning model, and further recites the specific steps in the negative set switching training technique, providing overall robustness to the training and improving the performance. The Applicant further states on page 45 (para. 1) that accordingly, even assuming arguendo if amended claim 1 is directed to a judicial exception, amended claim 1 integrates the alleged judicial exception into a practical application because it recites elements that reflect an improvement to a technology or technical field. The Applicant further states (para. 2) amended claim 1 also integrates the alleged judicial exception into a practical application because it recites a specific ordered combination of steps and specific data structures and training techniques such that it goes beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. The Applicant further states (para. 3) that under the recent precedential decision in Ex parte Guillaume Desjardins (Appeal Docket No. 2024-000567), the Appeals Review Panel held that a computer-implemented method of training a machine learning model is subject-matter eligible when the specification identifies improvements in training the machine learning model itself and the claims recite that disclosed improvement. The Applicant further states that under Desjardins, machine learning features should not be overgeneralized as judicial exceptions nor remaining additional elements as mere generic computer components, with the Desjardins Panel cautioning that Examiners and panels should not evaluate claims at such a high level of generality. The Applicant further states (para. 4) that in Desjardins, although the claims were found at Step 2A, Prong One to recite an abstract idea, the Appeals Review Panel determined at Step 2A, Prong Two that the claims integrated the abstract idea into a practical application because they recited a specific training technique that improved how the machine learning model itself operated – namely, training the model to learn new tasks while preserving performance on prior tasks and avoiding catastrophic forgetting. The Applicant further states that the Panel relied on the fact that the claims, when evaluated as a whole, reflected the disclosed technical improvement to the training process. The Applicant further states (para. 5) that instant claim 1 is analogous, and as discussed above, the specification identifies a technical problem in training machine-learning models for peptide-MHC interaction analysis, including the high risk of false negatives, and further states that claim 1 recites a specific training technique (i.e., negative set switching across epochs using the same positive set and different negative subsets) to reduce the number of false negatives in outputs predicted by the attention-based machine-learning model. The Applicant further states on page 46 (para. 1) that as in Desjardins, the claim does not merely apply a judicial exception on a computer, but instead recites a concrete improvement to how the machine-learning model is trained and how it performs, and further states (para. 2) that accordingly, consistent with Desjardins and MPEP 2106.05, claim 1 integrates any alleged judicial exception into a practical application at Step 2A Prong Two because it reflects a specific, technology-focused improvement to machine-learning training and performance, and further states (para. 3) that for the above reasons, claim 1 is directed to eligible subject matter.
These arguments are not persuasive, because first, training a machine-learning model using negative set switching is an algorithmic training strategy that toggles between using positive sample data sets (e.g., representative of “self” or acceptable, normal conditions) and negative sample data sets (e.g., anomalies or intrusions), and therefore comprise limitations identified as judicial exceptions at Step 2A Prong One. Second, regarding the Applicant’s argument that “the model can be trained in an efficient manner but also the performance of the training is enhanced” using negative set switching, and that “the specific steps in the negative set switching training technique[] provid[e] overall robustness to the training and improving the performance,” it is noted that these purported improvements appear to be improvements to abstract idea (i.e., training and using a machine learning model to generate a data ouput), and not improvements to the functioning of a computer itself, or to another technology or technical area. Third, with regard to the Applicant’s argument that “amended claim 1 also integrates the alleged judicial exception into a practical application because it recites a specific ordered combination of steps and specific data structures and training techniques,” it appears that the Applicant is attempting to analogize instant claim 1 with McRO and Enfish, respectively. Regarding the Applicant’s apparent attempt at analogizing the instant claims to Enfish, the instant claims are not analogous to the claims in Enfish, because the instant claims broadly recite steps of training and using a neural network model to use sequence data derived from a subject’s disease sample to generate an interaction prediction for a corresponding peptide-IPC combination that predicts whether the MHC will present a peptide at a cell surface, whereas in contrast, the improvement recited in Enfish is found in a data structure that corresponds to a storage and retrieval structure configured in a computer memory comprising a self-referential table that is designed to improve the way a computer stores and retrieves data in memory, and thus is an improvement to computer functionality itself. Stated a different way, the improvement was found in the structure of the table itself (e.g., relationships between rows and columns) as arranged (i.e., configured) in a physical memory device, irrespective of any particular data being stored or searched. With regard to the Applicant’s apparent attempt at analogizing the instant claims with the eligibility determination in McRO, it is noted that in McRO, when looked at as a whole, claim 1 is directed to a patentable, technological improvement over the existing, manual 3-D animation techniques, i.e., the claim recited “a specific asserted improvement in computer animation” that was directed to the creation of something physical – namely, the display of lip synchronization and facial expressions of animated characters on screens for viewing by human eyes, and therefore was determined to not be directed to an unpatentable abstract idea at Eligibility Step 2A (i.e., Alice step one). Unlike the technological improvement found in McRO, the instant claimed purported improvement to training and using a neural network model in order to use sequence data derived from a subject’s disease sample to generate an interaction prediction for a corresponding peptide-IPC combination to predicts whether the MHC will present a peptide at a cell surface, is a purported improvement to the abstract idea (data analysis), and not an improvement to computer functionality itself, or an improvement to another technology or technical field. Fourth, with regard to the Applicant’s attempt to analogize the instant claims to the claims in Desjardins, these arguments are not persuasive at least because the fact patterns differ between the claims at issue in Desjardins and the instant claims, not least in that the “ARP” in Desjardins notes that the Federal Circuit held that the eligibility determination should turn on whether “the claims are directed to an improvement to computer functionality versus being directed to an abstract idea” (citing Enfish), prior to the “ARP” finding that the improvement to how the machine learning model operates allows artificial intelligence (AI) systems to use less of their storage capacity and enables reduced system complexity, as supported by the Specification – i.e., the improvement to the model provided an improvement to computer functionality itself. This fact pattern contrasts with the fact pattern of the training steps recited in the instant claims, which are steps of training and using a neural network model in order to use sequence data derived from a subject’s disease sample to generate an interaction prediction for a corresponding peptide-IPC combination to predicts whether the MHC will present a peptide at a cell surface.
Conclusion
No claims are allowed.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/S.W.B./Examiner, Art Unit 1687
/Joseph Woitach/Primary Examiner, Art Unit 1687
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