DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of 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Claims 37, 58-76, 81-84, and 86-111 are withdrawn.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
Claims 1-16, 18, 20-37, 39-42, 44-51, 53-76, and 81-112 are given the benefit of priority to Provisional Application No. 63/053,307, filed 17 July 2020.
Claim Interpretation
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 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 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.” 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 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.” 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 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 (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 (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 (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 mean transmitting data (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 (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 (Specification, ¶ [0385]).
Claim Objections
The objection to claims 9, 57, and 78 in the Office action mailed 06 September 2024 is withdrawn in view of the amendment received 04 December 2024.
Claim Rejections - 35 USC § 112
The rejection of claims 1-36, 38-57, 77-80, 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 06 September 2024 is withdrawn in view of the amendment received 04 December 2024.
Claim Rejections - 35 USC § 101
The amendment received 04 December 2024 has been fully considered, however after further consideration the rejection of claims 1-36, 38-57, 77-80, 85, and 112 under 35 U.S.C. 101 in the Office action mailed 06 September 2024 is maintained with modification in view of the amendment.
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).
Following the flowchart in MPEP 2106:
Eligibility Step 1
Claims 1-16, 18, 20-36, 39-42, 44-51, 53-57, and 85 are directed to a method (process) of using an attention-based machine-learning model to predict peptide binding, presentation, and immunogenicity; and claim 112 is directed to a computer system (machine or manufacture) configured to perform a method (process) of using an attention-based machine-learning model to predict peptide binding, presentation, and immunogenicity. Therefore, these claims are encompassed by the categories of statutory subject matter, and thus satisfy the subject matter eligibility requirements under step 1.
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 a mental process of considering data of 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; a mental process of considering data of an immunoprotein complex (IPC) sequence identified for an immunoprotein complex (IPC) of the subject, wherein the IPC comprises a major histocompatibility complex (MHC); a mental process and a mathematical concept of considering data for 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; a mental process and a mathematical concept of considering data for processing an immunoprotein complex (IPC) representation that represents the IPC sequence using a second attention block in the initial attention subsystem; a mental process and a mathematical concept of considering data for using the attention-based machine-learning model to generate an output, (i.e., data), wherein the output data includes at least one of an interaction prediction, an interaction affinity prediction, or an immunogenicity prediction for a corresponding peptide-IPC combination; and a mental process of considering data wherein the corresponding peptide-IPC combination includes a peptide of the set of peptides and the MHC; a mental process of considering data wherein the interaction prediction for the corresponding peptide-IPC combination predicts whether the MHC will present the peptide at a cell surface; and a mental process of considering data wherein the interaction affinity prediction for the corresponding peptide-IPC combination predicts a binding affinity between the peptide and the MHC; and a mental process of considering data for identifying at least one peptide of the set of peptides as a target for an immunotherapy based on the generated data.
Independent claim 85 recites a mental process of considering data of a plurality of variant-coding sequences characterizing a plurality of mutant peptides, each variant-coding sequence of the plurality of variant-coding sequences having been identified by processing a disease sample from a subject; a mental process of considering data of an immunoprotein complex (IPC) sequence identified for an immunoprotein complex (IPC) of the subject, wherein the IPC comprises a major histocompatibility complex (MHC); a mental process and a mathematical concept of considering data wherein an 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 a mental process and a mathematical concept of considering data to process 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., data), wherein the output data includes at least one of an interaction prediction, an interaction affinity prediction, or an immunogenicity prediction for a corresponding mutant peptide-IPC combination; and a mental process of considering data wherein the corresponding peptide-IPC combination includes a peptide of the set of peptides and the MHC; a mental process of considering data wherein the interaction prediction for the corresponding peptide-IPC combination predicts whether the MHC will present the peptide at a cell surface; and a mental process of considering data wherein the interaction affinity prediction for the corresponding peptide-IPC combination predicts a binding affinity between the peptide and the MHC; and a mental process of considering data for selecting, based on the output, a subset of the plurality of mutant peptides to use in a treatment for the subject.
Independent claim 112 recites a mental process of considering data of 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; a mental process of considering data of an immunoprotein complex (IPC) sequence identified for an immunoprotein complex (IPC) of the subject, wherein the IPC comprises a major histocompatibility complex (MHC); a mental process and a mathematical concept of considering data to 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; a mental process and a mathematical concept of considering data to process an immunoprotein complex (IPC) representation that represents the IPC sequence using a second attention block in the initial attention subsystem; a mental process and a mathematical concept of considering data for using an attention-based machine-learning model to generate an output, (i.e., data), wherein the output data includes at least one of an interaction prediction, an interaction affinity prediction, or an immunogenicity prediction for a corresponding peptide-IPC combination; and a mental process of considering data wherein the corresponding peptide-IPC combination includes a peptide of the set of peptides and the MHC; a mental process of considering data wherein the interaction prediction for the corresponding peptide-IPC combination predicts whether the MHC will present the peptide at a cell surface; and a mental process of considering data wherein the interaction affinity prediction for the corresponding peptide-IPC combination predicts a binding affinity between the peptide and the MHC; and a mental process of considering data for identifying at least one peptide of the set of peptides as a target for an immunotherapy based on the generated data.
Independent claims 1, 85, and 112, and those claims dependent therefrom, further recite a law of nature by correlating 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 claim 2 further recites a mental process of considering data 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.
Dependent claim 3 further recites a mental process of considering data of a peptide representation of the set of peptide representations for a corresponding peptide sequence of the set of peptide sequences; and a mental process and a mathematical concept of considering data for 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.
Dependent claim 4 further recites a mental process of considering data of the IPC representation; and a mental process and a mathematical concept of considering data for 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.
Dependent claim 5 further recites a mental process and a mathematical concept of considering data 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; and wherein the processing comprises: a mental process and a mathematical concept of considering data for generating a transformed peptide representation based on the peptide representation using the first attention block and a first set of weights; a mental process and a mathematical concept of considering data for generating a transformed IPC representation based on the IPC representation using the second attention block and a second set of weights; and a mental process and a mathematical concept of considering data for generating a composite representation using the transformed peptide representation and the transformed MHC representation.
Dependent claim 6 further recites a mental process and a mathematical concept of considering data for embedding a peptide sequence of the set of peptide sequences to generate an embedded peptide representation for the peptide sequence; and a mental process and a mathematical concept of considering data for 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.
Dependent claim 7 further recites a mental process and a mathematical concept of considering the first attention block comprises a set of attention sub-blocks; and a mental process and a mathematical concept of considering that each attention sub-block of the set of attention sub-blocks includes a neural network that comprises at least one self-attention layer.
Dependent claim 8 further recites a mental process and a mathematical concept of considering the second attention block comprises a set of attention sub-blocks; and a mental process and a mathematical concept of considering each attention sub-block of the set of attention sub-blocks includes a neural network that comprises at least one self-attention layer.
Dependent claim 9 further recites a mental process and a mathematical concept of considering the first attention block comprises a first plurality of attention sub-blocks; a mental process and a mathematical concept of considering the second attention block comprises a first plurality of attention sub-blocks; and a mental process and a mathematical concept of considering 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.
Dependent claim 10 further recites a mental process and a mathematical concept of considering data wherein a peptide representation of the set of peptide representations forms a first portion of an aggregate representation processed using the first attention block; and a mental process and a mathematical concept of considering data wherein a second portion of the aggregate representation represents at least one of an N-flank sequence or a C-flank sequence.
Dependent claim 11 further recites a mental process of considering data wherein a peptide sequence of the set of peptide sequences forms a first portion of an aggregate sequence; a mental process of considering data wherein a second portion of the aggregate sequence includes at least one of an N-flank sequence or a C-flank sequence; and a mental process and a mathematical concept of considering data wherein 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.
Dependent claim 12 further recites a mental process and a mathematical concept of considering data for embedding the IPC sequence to generate an embedded IPC representation of the IPC sequence; and a mental process and a mathematical concept of considering data for encoding, positionally, the embedded IPC representation of the IPC sequence to generate the IPC representation.
Dependent claim 13 further recites a mental process and a mathematical concept of considering 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.
Dependent claim 14 further recites a mental process and a mathematical concept of considering 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; a mental process and a mathematical concept of considering the second attention block includes a second neural network configured to receive and process the IPC representation to generate a transformed IPC representation; and a mental process and a mathematical concept of considering wherein each of the first neural network and the second neural network includes at least one self-attention layer; and a mental process and a mathematical concept of considering wherein the attention-based machine-learning model is configured to generate a composite representation using the transformed peptide representation and the transformed IPC representation.
Dependent claim 15 further recites a mental process and a mathematical concept of considering 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.
Dependent claim 16 further recites a mental process and a mathematical concept of considering 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.
Dependent claim 18 further recites a mental process and a mathematical concept of considering data 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.
Dependent claim 20 further recites a mental process of considering data 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; a training IPC sequence characterizing a training IPC that is different from the IPC; 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.
Dependent claim 21 further recites a mental process of considering data 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; a training MHC sequence characterizing a training MHC that is different from the IPC; 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.
Dependent claim 22 further recites a mental process and a mathematical concept of considering data for 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.
Dependent claim 23 further recites a mental process and a mathematical concept of considering data for 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; a mental process and a mathematical concept of considering data for processing the set of composite representations to generate a set of results; a mental process and a mathematical concept of considering data for 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; and a mental process of considering data wherein a report generated based on the output identifies each peptide within the subset.
Dependent claim 24 further recites a mental process of considering data wherein each peptide of the set of peptides is used to form a set of peptide-IPC combinations; and a mental process and a mathematical concept of considering 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.
Dependent claim 25 further recites a mental process of considering data 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.
Dependent claim 26 further recites a mental process of considering data wherein the IPC is a major histocompatibility complex (MHC); a mental process of considering data wherein each peptide of the set of peptides is used to form a set of peptide-MHC combinations; and a mental process and a mathematical concept of considering 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.
Dependent claim 27 further recites a mental process of considering data 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.
Dependent claim 28 further recites a mental process of considering data 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; and a second part identifying a sequence of an epitope of the mutant peptide; and the processing comprises: a mental process and a mathematical concept of considering data for processing a first representation of the first part of the variant-coding sequence using a first self-attention layer of the initial attention subsystem; and a mental process and a mathematical concept of considering data for processing a second representation of the second part of the variant-coding sequence using a second self-attention layer of the initial attention subsystem.
Dependent claim 29 further recites a mental process and a mathematical concept of considering data wherein the first representation and the second representation are processed within the first attention block.
Dependent claim 30 further recites a mental process and a mathematical concept of considering 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.
Dependent claim 31 further recites a mental process and a mathematical concept of considering data wherein the IPC sequence and each of the set of peptide sequences includes an ordered set of amino-acid identifiers.
Dependent claim 32 further recites a mental process of considering data wherein the IPC sequence is identified using the disease sample.
Dependent claim 33 further recites a mental process of considering data wherein the IPC sequence is identified using a biological sample from the subject.
Dependent claim 34 further recites a mental process of considering data wherein the disease sample includes cancer cells.
Dependent claim 35 further recites a mental process of considering data wherein the IPC of the subject includes a major histocompatibility complex (MHC); a mental process of considering data wherein the IPC sequence includes an MHC sequence; and a mental process of considering data wherein the IPC representation includes an MHC representation.
Dependent claim 36 further recites a mental process of considering data wherein the MHC includes an MHC class-I molecule.
Dependent claim 39 further recites a mental process of considering data wherein the disease sample includes tissue.
Dependent claim 40 further recites a mental process of considering data wherein at least one peptide of the set of peptides is a neoantigen.
Dependent claim 41 further recites a mental process of considering data wherein at least one peptide sequence of the set of peptide sequences is a genomic sequence derived from the disease sample.
Dependent claim 42 further recites a mental process of considering data wherein each of at least one of the set of variant-coding sequences is based on RNA sequences of the disease sample.
Dependent claim 44 further recites a mental process of considering data entered by a user, the input data corresponding to the subject; a mental process of considering data from a data store of the set of peptide sequences and the IPC sequence; and a mental process of considering data 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.
Dependent claim 45 further recites a mental process of considering data for generating a treatment recommendation to the subject that includes the individualized vaccine.
Dependent claim 46 further recites a mental process of considering data entered by a user, the input data corresponding to the subject; a mental process of considering data from a data store of the set of peptide sequences and the IPC sequence; a mental process of considering data for determining a set of treatment peptides for inclusion in an individualized vaccine based on the report; and a mental process of considering data of the set of treatment peptides for the manufacture of the individualized vaccine.
Dependent claim 47 further recites a mental process of considering data for generating an alert.
Dependent claim 48 further recites a mental process of considering data of a representation from an embedding block in the attention-based machine-learning model, wherein the representation comprises a plurality of elements; a mental process of considering data 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 a mental process of considering data wherein each element in the multi-element data set corresponds to a monomer in either the peptide sequence or the IPC sequence; a mental process and a mathematical concept of considering data for 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; a mental process and a mathematical concept of considering data for 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; and a mental process of considering data for generating data based on the plurality of modified elements.
Dependent claim 49 further recites a mental process and a mathematical concept of considering data for 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; a mental process and a mathematical concept of considering data for 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; and a mental process and a mathematical concept of considering data for 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.
Dependent claim 50 further recites a mental process of considering data for a report.
Dependent claim 51 further recites a mental process of considering data for processing; and a mental process of considering data for a report.
Dependent claim 53 further recites a mental process of considering data 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.
Dependent claim 54 further recites a mental process of considering data for determining to exclude at least one peptide of the set of peptides as a target for an immunotherapy based on the report.
Dependent claim 55 further recites a mental process of considering data 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.
Dependent claim 56 further recites a mental process of considering data wherein the IPC is a human leukocyte antigen (HLA) molecule.
Dependent claim 57 further recites a mental process of considering data for defining the set of peptide sequences based on the sequencing of the disease sample from the subject; a mental process of considering data for identifying, based on a report generated based on the output, a subset of the set of peptide sequences; and a mental process of considering data for mRNA that codes for at least one peptide included in the subset of the set of peptides.
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, and are determined to be directed to the analysis of genomic sequence data that in the simplest embodiments are mental processes that are not too complex to be performed in the human mind with the aid of a pencil and paper. Additionally, the recited limitations that are identified as judicial exceptions from the mathematical concepts grouping of abstract ideas are abstract ideas irrespective of whether or not the limitations are practical to perform in the human mind (e.g., Specification ¶¶ [0112], [0145], [0149], & [0236] – [0238]). Furthermore, a law of nature correlating a genotype-phenotype association is identified at Eligibility Step 2A: Prong One.
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 noted above are not integrated into a practical application because the additional elements of a computer in independent claims 1, 85, and 112, and dependent claims 2-16, 18, 20-36, 39-42, 44-51, and 53-57; one or more data processors and a non-transitory computer-readable storage medium in independent claim 112; first and second computing platforms in dependent claim 51; and a graphical user interface on a display system in dependent claim 50; do not result in an improvement to computer functionality itself, and therefore do not integrate the judicial exceptions into a practical application. The additional elements of accessing data in independent claims 1 and 112; receiving input data entered by a user and accessing and retrieving data in dependent claims 44 and 46; and receiving data in dependent claims 3, 4, 11, and 48; are insignificant extra-solution activities, and therefore do not integrate the judicial exceptions into a practical application. The additional elements of inputting data in independent claim 85; and outputting data in independent claims 1, 85, and 112, and dependent claim 48; are insignificant extra-solution activities, and therefore do not integrate the judicial exceptions into a practical application. The additional elements of initiating an action that facilitates manufacture of the individualized vaccine that includes the set of treatment peptides in dependent claim 46, wherein the initiating the action comprises the additional element of generating an alert that triggers a computerized process involved in the manufacture of the individualized vaccine in dependent claim 47, are merely post-solution steps with an intended result of outputting and transmitting data – a nominal addition to the claims that does not meaningfully limit the judicial exceptions, and therefore are insignificant extra-solution activities that do not integrate the recited judicial exceptions into a practical application (MPEP 2106.05(g)). The additional element of transmitting data over a set of communications links that includes at least one of a wired communications link or a wireless communications link in dependent claim 51 is an insignificant extra-solution activity, and therefore does not integrate the recited judicial exceptions into a practical application (MPEP 2106.05(g)). The additional element of displaying data in dependent claim 50 is insignificant extra-solution activity, and therefore does not integrate the recited judicial exceptions into a practical application (MPEP 2106.05(g)). The additional element of sequencing a disease sample from a subject in dependent claim 57 is a data gathering step used in the recited judicial exceptions, and therefore does not integrate the recited judicial exceptions into a practical application. The additional element of synthesizing mRNA in dependent claim 57 does not recite an action that effects a particular transformation or reduction of a particular article to a different state or thing, and therefore does not integrate the judicial exceptions into a practical application (MPEP 2106.05(c)). The additional element of complexing the mRNA with lipids to produce a mRNA-lipoplex treatment in dependent claim 57 does not recite an action that effects a particular transformation or reduction of a particular article to a different state or thing, and therefore does not integrate the judicial exceptions into a practical application (MPEP 2106.05(c)). The additional element of administering the mRNA-lipoplex treatment to the subject in dependent claim 57 does not recite an action that effects a particular treatment or prophylaxis for a disease or medical condition, and therefore does not integrate the judicial exceptions into a practical application (MPEP 2106.04(d)(2)).
All limitations in claims 1-16, 18, 20-36, 39-42, 44-51, 53-57, 85, and 112 have been considered as a whole, and are deemed to not recite any additional elements that would integrate a judicial exception into a practical application (MPEP 2106.04(d)).
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 additional elements that are sufficient to amount to significantly more than the judicial exception(s) because the additional elements of a computer in independent claims 1, 85, and 112, and dependent claims 2-16, 18, 20-36, 39-42, 44-51, and 53-57; one or more data processors and a non-transitory computer-readable storage medium in independent claim 112; first and second computing platforms in dependent claim 51; a graphical user interface on a display system in dependent claim 50; accessing data in independent claims 1 and 112; receiving input data entered by a user and accessing and retrieving data in dependent claims 44 and 46; receiving data in dependent claims 3, 4, 11, and 48; inputting data in independent claim 85; outputting data in independent claims 1, 85, and 112, and dependent claim 48; outputting and transmitting data in dependent claims 46 and 47; transmitting data over a set of communications links that includes at least one of a wired communications link or a wireless communications link in dependent claim 51; and displaying data in dependent claim 50; are conventional (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 in dependent claim 57 is conventional. Evidence for the conventionality is shown by Cieslik et al. (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 synthesizing mRNA; complexing the mRNA with lipids to produce a mRNA-lipoplex treatment; and administering the mRNA-lipoplex treatment to the subject in dependent claim 57 are conventional. Evidence for the conventionality is shown by Zhu et al. (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.).
Furthermore, all additional elements in claims 1-16, 18, 20-36, 39-42, 44-51, 53-57, 85, and 112 have been evaluated individually and in combination, and are deemed to not contribute an inventive concept, i.e., amount to significantly more than the judicial exceptions (MPEP 2106.05(II)).
Response to Arguments
The Applicant’s arguments/remarks received 04 December 2024 have been fully considered, but are not persuasive.
The Applicant summarizes the 101 rejection at Step 2A Prong One of the Eligibility Analysis on pages 31 and 32 of the Remarks (paras. 7 and 1, respectively) by stating that the Office asserts that claims 1, 85, and 112 are directed to mental processes, e.g., considering data for peptide sequences and an immunoprotein complex (IPC) sequence; to mathematical concepts through the use of, e.g., peptide and IPC representations for the processing of sequence data by a machine learning model; and to a law of nature by associating genomic data (properties of peptide and immunoprotein complex sequence data) with phenotypes (immunogenicity for a corresponding peptide-immunoprotein complex combination). The Applicant further states on page 32 (para. 2) that claims 1 and 85 have been amended to read “computer-implemented method” to further emphasize that the claimed methods require the use of computer processors, and are not methods that can be performed simply as a mental exercise or by using pencil and paper, and further that the inclusion of the limitation that an “attention-based machine-learning model” is used to process the sequence data, or representations thereof.
This argument is not persuasive, because first, claims can recite a mental process even if they are claimed as being performed on a computer (MPEP 2106.04(a)(2)(III)(C)), and second, machine-learning is fundamentally about creating and implementing algorithms that facilitate making decisions and predictions based on data, wherein the model itself is constructed using mathematics, and wherein the data that is used as input to the model, e.g., genomic sequence data, is first converted or transformed into numerical representations prior to being used as input to the model. Third, 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. Fourth, although the amount of data and the number of calculations may be considered to be significantly large and take considerable time and effort to process manually, the use of a general-purpose computer to perform the claimed method at a rate and accuracy that can far outstrip the mental performance of a skilled artisan does not change the nature of the activity being performed (i.e., an abstract idea), and therefore does not materially alter the patent eligibility of the claimed subject matter.
The Applicant states on page 32 (para. 3) that the limitations reciting “accessing a set of peptide sequences” and “accessing an immunoprotein complex (IPC) sequence” would be understood by one of ordinary skill in the art to refer to a physical transfer of sequence data (i.e., receipt of the sequence data by one or more computer processors) rather than mere mental processes of considering data.
This argument is persuasive in part, to the extent that the rejection above and the rejection in the Office action mailed 06 September 2024 identify the limitation reciting the step of “accessing” data as an additional element at Eligibility Step 2A: Prong Two. However, this argument is not persuasive in part, to the extent that the data itself is comprised of information, and information is inherently abstract, and able to be received, considered, observed, and evaluated in the human mind, with or without the aid of pencil and paper, and although the amount of information may be considered to be significantly large and take considerable time and effort to process manually, the information itself is an abstract idea.
The Applicant states on pages 32 and 33 (paras. 4 and 1, respectively) that claim 1 recites the limitations “processing a set of peptide representations…and an immunoprotein complex (IPC) representation” using “a first attention block in an initial attention subsystem of an attention-based machine-learning model” and “a second attention block in the initial attention subsystem” of the attention-based machine learning model to generate an output comprising “at least one of an interaction prediction, an interaction affinity prediction, or an immunogenicity prediction for a corresponding peptide-IPC combination,” and that these limitations cannot be considered mere mental processes of considering data, as the claim explicitly requires the use of a machine learning model to perform the processing, and the problem of predicting binding interactions and binding affinities between peptides and proteins, as well as of predicting the immunogenicity of a peptide presented on a cell surface by an MHC protein, is enormously complex, i.e., too complex to be performed simply as a mental exercise or by using pencil and paper.
These arguments are not persuasive, because as noted in the foregoing responses to arguments, first, claims can recite a mental process even if they are claimed as being performed on a computer (MPEP 2106.04(a)(2)(III)(C)), and second, machine-learning is fundamentally about creating and implementing algorithms that facilitate making decisions and predictions based on data, wherein the model itself is constructed using mathematics, and wherein the data that is used as input to the model, e.g., genomic sequence data, is first converted or transformed into numerical representations prior to being used as input to the model. Third, 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. Fourth, although the amount of data and the number of calculations may be considered to be significantly large and take considerable time and effort to process manually, the use of a general-purpose computer to perform the claimed method at a rate and accuracy that can far outstrip the mental performance of a skilled artisan does not change the nature of the activity being performed (i.e., an abstract idea), and therefore does not materially alter the patent eligibility of the claimed subject matter. Fifth, the data itself is comprised of information, and information is inherently abstract, and able to be received, considered, observed, and evaluated in the human mind, with or without the aid of pencil and paper, and although the amount of information may be considered to be significantly large and take considerable time and effort to process manually, the information itself is an abstract idea.
The Applicant states on page 33 (para. 2) that while peptide and protein sequence data specifies the linear, one-dimensional string of amino acid resides that make up a given peptide or protein, binding interactions between peptides and protein molecules are critically dependent on the three-dimensional structure of both the peptide and the protein, and further that the prediction of the three-dimensional structure of a peptide or protein molecule – let alone the prediction of binding interactions between them – is enormously complex due to the large number of degrees of freedom that arise from, e.g., stretching, bending, and rotation around peptide bonds, etc., and the different intramolecular forces (electrostatic, hydrogen bonding, van der Waals interactions, etc.), intermolecular forces (electrostatic, hydrogen bonding, van der Waals interactions, etc.), and environmental factors (e.g., solvent polarity, etc.) that influence the three dimensional structure of a peptide or protein molecule.
These arguments are not persuasive, because as noted in the foregoing responses to arguments, the claim limitations identified as judicial exceptions from the mental process grouping of abstract ideas at Eligibility Step 2A: Prong One in the rejection above are directed to the analysis of data that in the simplest embodiments are able to be performed mentally, or with paper and pencil, and although the amount of data may be considered to be significantly large, and the calculations may be considered to be complex and take considerable time and effort to process manually, the use of a general-purpose computer to perform the claimed method at a rate and accuracy that can far outstrip the mental performance of a skilled artisan does not change the nature of the activity being performed (i.e., an abstract idea), and therefore does not materially alter the patent eligibility of the claimed subject matter.
The Applicant states on page 33 (para. 3) that as noted in the MPEP at 2106.04(a)(2) and the recent Guidance Update on Subject Matter Eligibility, Including on Artificial Intelligence, released by the USPTO on July 17, 2024 [hereafter, the “USPTO Guidance Update”], 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 further states that for the same reason, claim 1 is not directed to a law of nature, as there is no direct 1-to-1 correlation between peptide and protein sequences and a prediction of whether or not they interact.
These arguments are not persuasive, because first, and as noted in the foregoing responses to arguments, the claim limitations identified as judicial exceptions from the mental process grouping of abstract ideas at Eligibility Step 2A: Prong One in the rejection above are directed to the analysis of data that in the simplest embodiments are able to be performed mentally, or with paper and pencil, and although the amount of data may be considered to be significantly large, and the calculations may be considered to be complex and take considerable time and effort to process manually, the use of a general-purpose computer to perform the claimed method at a rate and accuracy that can far outstrip the mental performance of a skilled artisan does not change the nature of the activity being performed (i.e., an abstract idea), and therefore does not materially alter the patent eligibility of the claimed subject matter. Second, claim 1 is identified as reciting a law of nature at Eligibility Step 2A: Prong One because the claim recites limitations correlating 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)).
The Applicant states on page 34 (para. 1) that as indicated in claim 1, the peptide sequence data and the MHC sequence data are transformed into peptide and MHC representations (e.g., feature vectors) prior to being provided as input to the attention-based machine learning model, and that this is a common step in machine learning-based methods that facilitates downstream processing and improves prediction accuracy. The Applicant further states that although the transformation process is a mathematical process, the claim is not directed to the mathematical process itself, and as indicated in MPEP 2106.04(a)(2) subsection I and the recent USPTO Guidance Update, “a claim does not recite a mathematical concept (i.e., the claim limitations do not fall within the mathematical concept grouping) if it is only based on or involves a mathematical concept.”
This argument is not persuasive, because first, at Eligibility Step 2A: Prong One, examiners evaluate 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. 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." Second, 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.
The Applicant states on page 34 (paras. 2, 4, and 5) and page 35 (para. 2) that the independent claims are both novel and non-obvious, and directly tied to a practical application, i.e., that the predictions output by the machine learning model are used to identify at least one peptide of the set of peptides as a target for an immunotherapy.
These arguments are not persuasive, because first, evaluating whether judicial exceptions are integrated into a practical application comprises: (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception(s); and (2) evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application, using one or more of the considerations introduced in subsection I at 2106.04(d) of the MPEP, and discussed in more detail in MPEP §§ 2106.04(d)(1), 2106.04(d)(2), 2106.05(a) through (c) and 2106.05(e) through (h). As noted in the rejection above, 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, they are deemed to not recite any additional elements that would integrate a judicial exception into a practical application (MPEP 2106.04(d)). Furthermore, the instant claimed practical application of using a machine learning model to output predictions that are used to identify at least one peptide of the set of peptides as a target for an immunotherapy 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. Second, evaluating whether a judicial exception has been integrated into a practical application is different from an anticipation analysis under 35 U.S.C. 102 or an obviousness analysis under 35 U.S.C. 103, and because there are separate and distinct requirements from eligibility, patentability of the claimed invention under 35 U.S.C. 102 and 103 with respect to the prior art is neither required for, nor a guarantee of, patent eligibility under 35 U.S.C. 101 (MPEP 2106.05(I)).
The Applicant states on page 35 of the Remarks (para. 1) that the Patent Office asserts that the claims do not include additional elements that are sufficient to amount to significantly more than the alleged judicial exceptions at Eligibility Step 2B, and further states that the Applicant disagrees (para. 2) because the independent claims are not directed to a judicial exception, and that furthermore, independent claim 1, for example, is both novel and non-obvious, and is directly tied to a practical application, i.e., that the predictions output by the machine learning model are used to identify at least one peptide of the set of peptides as a target for an immunotherapy.
These arguments are not persuasive, because first, a conclusion of whether a claim is eligible at Step 2B requires that all relevant considerations be evaluated, which comprises steps of: (1) carrying over the identification of any additional element(s) in the claim from Step 2A Prong Two; (2) carrying over the conclusions from Step 2A Prong Two on the considerations discussed in MPEP §§ 2106.05(a) - (c), (e) (f) and (h); (3) re-evaluating any additional element or combination of elements that was considered to be insignificant extra-solution activity per MPEP § 2106.05(g), 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 (4) evaluating whether any additional element 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). As noted in the rejection above, when all additional elements in claims 1-16, 18, 20-36, 39-42, 44-51, 53-57, 85, and 112 have been evaluated individually and in combination at Eligibility Step 2B, they are deemed to not contribute an inventive concept, i.e., amount to significantly more than the judicial exceptions (MPEP 2106.05(II)). Second, the instant claimed method of using a machine learning model to output predictions that are used to identify at least one peptide of the set of peptides as a target for an immunotherapy 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. Third, the search for an inventive concept is different from an anticipation analysis under 35 U.S.C. 102 or an obviousness analysis under 35 U.S.C. 103, and because there are separate and distinct requirements from eligibility, patentability of the claimed invention under 35 U.S.C. 102 and 103 with respect to the prior art is neither required for, nor a guarantee of, patent eligibility under 35 U.S.C. 101 (MPEP 2106.05(I)).
Conclusion
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 extension fee 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
/Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687
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