DETAILED ACTION
Applicant’s response filed on 30 October 2025 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. It is noted that the Examiner of record has changed since the previous Office Action. It is now examiner Peter Nguyen of Art Unit 1685.
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 .
Claim Status
Claims 1-8, 12-22, 26-32, and 35-38 are currently pending and under examination herein.
Claims 1-8, 12-22, 26-32, and 35-38 are rejected.
Priority
The instant application also claims benefit to U.S. provisional application No. 62/814801 filed on 03/06/2019. Domestic benefit is acknowledged. As such, the effective filing date of claims 1-8, 12-22, 26-32 and 35-38 is 03/06/2019.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 10/30/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. A signed copy of a list of references cited from each IDS is included in this Office Action.
Claim Rejections - 35 USC § 101
Response to Arguments
The previous rejection to claim(s) 1-8, 12-22, 26-32, and 35-38 under 35 U.S.C. 101 is withdrawn. The limitations of cloning and infusing the expanded T-cells integrate into a practical application as a particular transformation because the judicial exception is used to transfer T-cells via genetic engineering (see MPEP 2106.05(c)). Therefore amended claims 1-8, 12-22, 26-32, and 35-38 are patent eligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
The present rejection(s) reference specific passages from cited prior art. However,
Applicant is advised that the rejections are based on the entirety of each cited prior art. That is,
each cited prior art reference “must be considered in its entirety”. (See MPEP 2141.02(VI))
Therefore, Applicant is advised to review all portions of the cited prior art if traversing a
rejection based on the cited prior art.
Response to Arguments
Applicant’s arguments, see pages 9-10 under “Rejections under 25. U.S.C. 103” filed on 10/30/2025, with respect to claim(s) 1-8, 12-22, 26-32, and 35-38 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground of rejection is made in view of Yelensky, Kato et al., and Hu et al. as necessitated by amendment.
Claim(s) 1-8, 12-22, 26-27, 31-32, 35, 37, and 38 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yelensky (US20170212984A1) in view of Kato et al. (Effective screening of T cells recognizing neoantigens and construction of T-cell receptor-engineered T cells. Oncotarget. 2018 Jan 13;9(13):11009-11019.). This rejection is newly recited and necessitated by claim amendment.
Regarding claim 1, Yelensky teaches:
A method for identifying one or more T-cells that are antigen-specific for at least one neoantigen that are likely to be presented by one or more class II MHC alleles on a surface of cells (identifying neoantigens from tumor cells; see paragraph [0093] and claim 1), the method comprising the steps of:
obtaining data representing peptide sequences of each of a set of neoantigens (obtain sequencing data from tumor cells; see paragraph [0093]; see claim 1);
encoding the peptide sequences of each of the neoantigens into a corresponding numerical vector (see encoding of peptide sequence of the neoantigen into a numerical vector recited in claim 2);
inputting the numerical vectors, using a computer processor, into a machine-learned presentation model to generate a set of presentation likelihoods for the set of neoantigens (applying one or more presentation models to numerically encoded peptide sequence generate the corresponding presentation likelihood; see claim 2; see also [0093] wherein peptide sequence is based on presentation models to generate a numerical likelihood), each presentation likelihood in the set representing the likelihood that a corresponding neoantigen is presented by the one or more class II MHC alleles on the surface of cells of a subject (explicitly recited in claim 2; see also [0093]), the machine-learned presentation model (machine learned presentation model described in [0094]) comprising:
a plurality of parameters identified at least based on a training data set (reference data comprising a set of corresponding labels in [0094]) comprising:
for each sample in a plurality of samples, a label obtained by mass spectrometry measuring presence of peptides bound to at least one class II MHC allele in a set of class II MHC alleles identified as present in the sample (reference data comprised of mass spectrometry data in [0094]);
and for each of one or more of the samples, training peptide sequences encoded as numerical vectors including information regarding a plurality of amino acids that make up the peptides and a set of positions of the amino acids in the peptides (see details on the encoding module 314 in paragraph [0365]) used to generate the presentation model described in [0094].)
a function representing a relation between the numerical vector received as input and the presentation likelihood generated as output based on the numerical vector and the parameters (see [0402] for the estimated presentation likelihood modeled by training module 316);
wherein the machine-learned presentation model is designed to identify positions of amino acids of a binding core or a binding anchor of peptide sequences (encoding process for generation of presentation models indicate particular amino acids are at which positions; see claim 2; see also details of the encoding module at [0365] where amino acids are identified at the j-th position; identified positions are recited as anchor residues in [0037] and Fig. 13F tied to an presentation model configuration);
selecting a subset of the set of neoantigens based on the set of presentation likelihoods to generate a set of selected neoantigens (subset of neoantigens selected based on the numerical likelihoods to generate a set as disclosed in [0093]);
and identifying one or more T-cells that are antigen-specific for at least one of the neoantigens in the subset (generated set of selected neoantigens described in [0093]; see [0478] for presentation models that contains peptide sequences that were presented by MHC alleles on the cell surface, and recognized by T-cells).
Although Yelensky teaches a method for identifying one or more T-cells that are antigen-specific for at least one neoantigen, he does not explicitly teach the subsequent steps of genetically engineering a plurality of T-cells to express at least one of the one or more identified T-cell receptors, and culturing the plurality of T-cells under conditions that expand the plurality of T-cells. Kato teaches genetically engineering a plurality of T-cells to express at least one of the one or more identified T-cell receptors (see explicit recitation of constructed TCR-engineered t-cells via transfer of cancer-specific T cell receptor genes in T cells that can be isolated; see page 11010) and culturing the plurality of T-cells under conditions that expand the plurality of T-cells (expanding T-cell clones is a known alternative method as recited in “Abstract”; see page 11009). Therefore, it would have been obvious to one of ordinary skill in the art to incorporate Kato’s genetically engineered T-cells into Yelensky’s antigen-specific identification pipeline to bypass suboptimal conditions which impede the reactivation of existing T cells or the priming of neoantigen-specific T cells in a patient as evidenced by Kato (“Abstract”; see page 11009). There would have been a reasonable expectation of success as identification of T-cells that are antigen-specific would allow for the generation of TCR-engineered T cells.
Regarding claim 2, Yelensky teaches:
The method of claim 1, wherein inputting the numerical vector into the machine-learned presentation model comprises: applying the machine-learned presentation model to the peptide sequence of the neoantigen to generate a dependency score for each of the one or more class II MHC alleles indicating whether the class II MHC allele will present the neoantigen based on the particular amino acids at the particular positions of the peptide sequence (see [0101] for explicit recitation; paragraphs [0402]-[0403] for details on the application of the machine-learned presentation model).
Regarding claim 3, Yelensky teaches:
The method of claim 2, wherein inputting the numerical vector into the machine-learned presentation model further comprises: transforming the dependency scores to generate a corresponding per-allele likelihood for each class II MHC allele indicating a likelihood that the corresponding class II MHC allele will present the corresponding neoantigen; and combining the per-allele likelihoods to generate the presentation likelihood of the neoantigen (see paragraph [0102]).
Regarding claim 4, Yelensky teaches:
The method of claim 3, wherein the transforming the dependency scores models the presentation of the neoantigen as mutually exclusive across the one or more class II MHC alleles (see paragraph [0103]).
Regarding claim 5, Yelensky teaches:
The method of claim 2, wherein inputting the numerical vector into the machine-learned presentation model further comprises: transforming a combination of the dependency scores to generate the presentation likelihood (see paragraph [0104]), wherein transforming the combination of the dependency scores models the presentation of the neoantigen as interfering between the one or more class II MHC alleles (see paragraph [0105]).
Regarding claim 6, Yelensky teaches:
The method of claim 2, wherein the set of presentation likelihoods are further identified by at least one or more allele noninteracting features, and further comprising: applying the machine-learned presentation model to the allele noninteracting features to generate a dependency score for the allele noninteracting features indicating whether the peptide sequence of the corresponding neoantigen will be presented based on the allele noninteracting features (paragraph [0106]).
Regarding claim 7, Yelensky teaches:
The method of claim 6, further comprising: combining the dependency score for each class II MHC allele in the one or more class II MHC alleles with the dependency score for the allele noninteracting features; transforming the combined dependency scores for each class II MHC allele to generate a per-allele likelihood for each class II MHC allele indicating a likelihood that the corresponding class II MHC allele will present the corresponding neoantigen; and combining the per-allele likelihoods to generate the presentation likelihood (see paragraph [0107]).
Regarding claim 8, Yelensky teaches:
The method of claim 6, further comprising: combining the dependency scores for each of the class II MHC alleles and the dependency score for the allele noninteracting features; and transforming the combined dependency scores to generate the presentation likelihood (paragraph [0108]).
Regarding claim 12, Yelensky teaches:
The method of claim 1, wherein encoding the peptide sequence comprises encoding the peptide sequence using a one-hot encoding scheme (paragraph [0127]).
Regarding claim 13, Yelensky teaches:
The method of claim 1, wherein the plurality of samples comprise at least one of: (a) one or more cell lines engineered to express a single class II MHC allele; (b) one or more cell lines engineered to express a plurality of class II MHC alleles; (c) one or more human cell lines obtained or derived from a plurality of patients; (d) fresh or frozen tumor samples obtained from a plurality of patients; and (e) fresh or frozen tissue samples obtained from a plurality of patients (see [0110]-[0114] and claim 14).
Regarding claim 14, Yelensky teaches:
The method of claim 1, wherein the training data set further comprises at least one of: (a) data associated with peptide-MHC binding affinity measurements for at least one of the peptides (paragraph [0122]); and (b) data associated with peptide-MHC binding stability measurements for at least one of the peptides (paragraph [0123]).
Regarding claim 15, Yelensky teaches:
The method of claim 1, wherein the set of presentation likelihoods are further identified by at least expression levels of the one or more class II MHC alleles in the subject, as measured by RNA-seq or mass spectrometry (presentation information is comprised of two parts recited as allele-interacting and allele-noninteracting as disclosed in paragraph [0308]); allele-interacting information expression levels measured in paragraph [0319])
Regarding claim 16, Yelensky teaches:
The method of claim 1, wherein the set of presentation likelihoods are further identified by features comprising at least one of: (a) predicted affinity between a neoantigen in the set of neoantigens and the one or more class II MHC alleles; and (b) predicted stability of the neoantigen encoded peptide-MHC complex (see paragraph [0145]).
Regarding claim 17, Yelensky teaches:
The method of claim 1, wherein the set of numerical likelihoods are further identified by features comprising at least one of: (a) the C-terminal sequences flanking the neoantigen encoded peptide sequence within its source protein sequence; and (b) the N-terminal sequences flanking the neoantigen encoded peptide sequence within its source protein sequence (C-terminal and N-terminal features described with the allele-noninteracting component of the likelihoods recited in paragraphs [0324] and [0326]).
Regarding claim 18, Yelensky teaches:
The method of claim 1, wherein selecting the set of selected neoantigens comprises selecting neoantigens that have an increased likelihood of being presented on the cell surface relative to unselected neoantigens based on the machine-learned presentation model (see paragraph [0175]).
Regarding claim 19, Yelensky teaches:
The method of claim 1, wherein selecting the set of selected neoantigens comprises selecting neoantigens that have an increased likelihood of being capable of inducing a tumor-specific immune response in the subject relative to unselected neoantigens based on the machine-learned presentation model (see paragraph [0176]).
Regarding claim 20, Yelensky teaches:
The method of claim 1, wherein selecting the set of selected neoantigens comprises selecting neoantigens that have an increased likelihood of being capable of being presented to naive T-cells by professional antigen presenting cells (APCs) relative to unselected neoantigens based on the presentation model, optionally wherein the APC is a dendritic cell (DC) (paragraph [0177] and claim 21).
Regarding claim 21, Yelensky teaches:
The method of claim 1, wherein selecting the set of selected neoantigens comprises selecting neoantigens that have a decreased likelihood of being subject to inhibition via central or peripheral tolerance relative to unselected neoantigens based on the machine-learned presentation model (see paragraph [0141]).
Regarding claim 22, Yelensky teaches:
The method of claim 1, wherein selecting the set of selected neoantigens comprises selecting neoantigens that have a decreased likelihood of being capable of inducing an autoimmune response to normal tissue in the subject relative to unselected neoantigens based on the machine-learned presentation model (see paragraph [0142]).
Regarding claim 26, Yelensky teaches:
The method of claim 1, wherein the machine-learned presentation model is a neural network model (paragraph [0406]-[0408]).
Regarding claim 27, Yelensky teaches:
The method of claim 26, wherein the neural network model includes a plurality of network models for the class II MHC alleles, each network model assigned to a corresponding class II MHC allele of the class II MHC alleles and including a series of nodes arranged in one or more layers (paragraph [0407]).
Regarding claim 31, Kato teaches:
The method of antigen-specific T-cell identification, he does not explicitly teach the method of claim 1, wherein identifying the one or more T-cells comprises co-culturing the one or more T-cells with one or more of the neoantigens in the subset under conditions that expand the one or more T-cells (identification of HLA-A-restricted TCRs specific for neoantigens completed during the T-cell expansion process; see “Abstract”; page 11009).
Regarding claim 32, Kato teaches:
The method of claim 1, wherein identifying the one or more T-cells comprises contacting the one or more T-cells with an MHC multimer comprising one or more of the neoantigens in the subset under conditions that allow binding between the T- cells and the MHC multimer (genetically engineered T-cells bind with specific dextramers that were loaded with the condition of having a mutant as described on page 11012)
Regarding claim 35, Kato teaches:
An isolated T-cell that is antigen-specific for at least one selected neoantigen in the subset of claim 1 (transfer of cancer-specific T cell receptor gene into T cells to be isolated is a known method; see page 11010 in “Introduction”).
Regarding claim 37, Kato teaches:
The method of claim 1, wherein genetically engineering the plurality of T-cells to express at least one of the one or more identified T-cell receptors comprises: cloning the T-cell receptor sequences of the one or more identified T-cells into an expression vector (expanding T cell clones is a known technique as recited in “Abstract” on page 11009); and transfecting each of the plurality of T-cells with the expression vector (transfer of cancer-specific T cell receptor gene into T cells to be isolated is a known method; see page 11010 in “Introduction”).
Regarding claim 38, Kato teaches:
The method of claim 1, further comprising: infusing the expanded T-cells into the subject (induction of neoantigen-specific T cells recited as a known method in “Abstract”; page 11009).
Claim(s) 28-30 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yelensky (US20170212984A1) and Kato et al. (Effective screening of T cells recognizing neoantigens and construction of T-cell receptor-engineered T cells. Oncotarget. 2018 Jan 13;9(13):11009-11019.), as applied to claims 1 and 26-27 above, in view of Hu et al. (Hu, J., & Liu, Z. (2017). DeepMHC: Deep Convolutional Neural Networks for High-performance peptide-MHC Binding Affinity Prediction. bioRxiv.)
Regarding claim 28,
Yelensky and Kato teaches the claimed invention substantially as claimed above.
Yelensky as modified does not explicitly teach the architecture in the method of claim 27, wherein each network model further includes one or more convolutional neural networks, each of the one or more convolutional neural networks including a series of nodes arranged in one or more layers and having a filter of a different size, the filter of each of the one or more convolutional neural networks sized to identify the positions of the amino acids in the peptide sequence of each neoantigen that comprise a binding core or a binding anchor of the peptide sequence. Hu teaches a deep convolutional neural network-based protein-peptide binding prediction algorithm (The base DeepMHC model is comprised of convolution layers with filters of size 1x2 and 1x3 and descriptions of neuron architecture in “Convolutional Neural Network Model” on pages 4-5; see also Fig. 4 which explores different DeepMHC model network structures via hyperparameters with a 9-length sequence is encoded into a dimension input tensor wherein each row encodes an amino acid position to be fed into convolutional layers). Therefore, it would have been obvious to one of ordinary skill in the art to incorporate Hu’s layered convolutional neural network model into Yelensky as modified’s antigen-specific identification pipeline in order to outperform conventional methods in DNA-protein binding specificity prediction as evidenced by Hu et al. (“Abstract”; page 1). There would have been a reasonable expectation of success as Yelensky as modified’s pipeline already includes a machine-learned presentation model and operates in the same field of endeavor.
Regarding claim 29, Yelensky teaches:
The method of claim 27, wherein the neural network model is trained by updating the parameters of the neural network model, and wherein the parameters of at least two network models are jointly updated for at least one training iteration (see paragraph [0399] and [0400] for loss function updating for instances of data; minimizing the loss function with respect to θh and θw which are parameters optimized with respect to the same loss function in paragraph [0440]).
Regarding claim 30, Yelensky teaches:
The method of claim 26, wherein the machine-learned presentation model is a deep learning model that includes one or more layers of nodes (see paragraph [0407] where a network function is represented by a network model having a series of nodes arranged in one or more layers).
Double Patenting
Response to Arguments
Applicant’s arguments, see page 11 under “Double Patenting Rejection” filed on 10/30/2025, with respect to with respect to claim(s) 1-8, 12-22, 26-32, and 35-38 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground of rejection is made in view of Kato et al.
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-8, 12-22, 27, and 29-30 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent Number 11264117B2 in view of Kato et al. This rejection is newly recited and necessitated by claim amendment.
Claim 1 recites a method of obtaining data representing peptide sequences of each of a set of neoantigens, encoding the peptide sequence of each of the neoantigens into a numerical vector, training a neural network using numerical vectors to determine a set of presentation likelihoods wherein a plurality of parameters is identified, which will return a desired data set (e.g. neoantigens or antigen-specific t-cells) which corresponds to claim 1 of US Patent No. 11264117.
Claim 2 recites a method of generating the set of presentation likelihoods/model by generating a dependency score per MHC allele which corresponds to claim 2 of US Patent No. 11264117.
Claim 3 recites the transformation of the dependency scores to generate a per-allele likelihood and a subsequent combination step which corresponds to claim 3 of US Patent No. 11264117.
Claim 4 recites the transformation of dependency scores models the presentation of the neoantigen as mutually exclusive across the one or more MHC alleles which corresponds to claim 4 of US Patent No. 11264117.
Claim 5 recites transforming the dependency scores to generate the presentation likelihood which corresponds to claim 5 of US Patent No. 11264117.
Claim 6 recites identifying one or more allele noninteracting features and further comprising applying the machine-learned presentation model to the allele noninteracting features to generate a dependency score for the allele noninteracting features indicating whether the peptide sequence of the corresponding neoantigen will be presented based on the allele noninteracting features which corresponds to claim 6 of US Patent No. 11264117.
Claim 7 recites combining the dependency score for each class II MHC allele in the one or more class II MHC alleles with the dependency score for the allele noninteracting features; transforming the combined dependency scores for each class II MHC allele to generate a per-allele likelihood for each class II MHC allele indicating a likelihood that the corresponding class II MHC allele will present the corresponding neoantigen; and combining the per-allele likelihoods to generate the presentation likelihood which corresponds to claim 7 of US Patent No. 11264117.
Claim 8 recites combining the dependency scores for each of the class II MHC alleles and the dependency score for the allele noninteracting features; and transforming the combined dependency scores to generate the presentation likelihood which corresponds to claim 8 of US Patent No. 11264117.
Claim 12 recites the limitation wherein encoding the peptide sequence comprises encoding the peptide sequence using a one-hot encoding scheme which corresponds to claim 12 of US Patent No. 11264117.
Claim 13 recites wherein the plurality of samples comprise at least one of: (a) one or more cell lines engineered to express a single class II MHC allele; (b) one or more cell lines engineered to express a plurality of class II MHC alleles; (c) one or more human cell lines obtained or derived from a plurality of patients; (d) fresh or frozen tumor samples obtained from a plurality of patients; and (e) fresh or frozen tissue samples obtained from a plurality of patients which corresponds to claim 13 of US Patent No. 11264117.
Claim 14 recites wherein the training data set further comprises at least one of: (a) data associated with peptide-MHC binding affinity measurements for at least one of the peptides; and (b) data associated with peptide-MHC binding stability measurements for at least one of the peptides which corresponds to claim 14 of US Patent No. 11264117.
Claim 15 recites wherein the set of presentation likelihoods are further identified by at least expression levels of the one or more class II MHC alleles in the subject, as measured by RNA-seq or mass spectrometry which corresponds to claim 15 of US Patent No. 11264117.
Claim 16 recites wherein the set of presentation likelihoods are further identified by features comprising at least one of: (a) predicted affinity between a neoantigen in the set of neoantigens and the one or more class II MHC alleles; and (b) predicted stability of the neoantigen encoded peptide-MHC complex which corresponds to claim 16 of US Patent No. 11264117.
Claim 17 recites the method wherein the set of numerical likelihoods are further identified by features comprising at least one of: (a) the C-terminal sequences flanking the neoantigen encoded peptide sequence within its source protein sequence; and (b) the N-terminal sequences flanking the neoantigen encoded peptide sequence within its source protein sequence which corresponds to claim 17 of US Patent No. 11264117.
Claim 18 recites wherein selecting the set of selected neoantigens comprises selecting neoantigens that have an increased likelihood of being presented on the cell surface relative to unselected neoantigens based on the machine-learned presentation model which corresponds to claims 1 (for the machine-learned presentation model) and 18 of US Patent No. 11264117.
Claim 19 recites wherein selecting the set of selected neoantigens comprises selecting neoantigens that have an increased likelihood of being capable of inducing a tumor-specific immune response in the subject relative to unselected neoantigens based on the machine-learned presentation model which corresponds to claim 1 (for the machine-learned presentation model) and 19 of US Patent No. 11264117.
Claim 20 recites wherein selecting the set of selected neoantigens comprises selecting neoantigens that have an increased likelihood of being capable of being presented to naive T-cells by professional antigen presenting cells (APCs) relative to unselected neoantigens based on the presentation model, optionally wherein the APC is a dendritic cell (DC) which corresponds to claim 1 (for the machine-learned presentation model) and 20 of US Patent No. 11264117.
Claim 21 recites wherein selecting the set of selected neoantigens comprises selecting neoantigens that have a decreased likelihood of being subject to inhibition via central or peripheral tolerance relative to unselected neoantigens based on the machine-learned presentation model which corresponds to claim 1 (for the machine-learned presentation model) and 21 of US Patent No. 11264117.
Claim 22 recites wherein selecting the set of selected neoantigens comprises selecting neoantigens that have a decreased likelihood of being capable of inducing an autoimmune response to normal tissue in the subject relative to unselected neoantigens based on the machine-learned presentation model which corresponds to claim 1 (for the machine-learned presentation model) and 22 of US Patent No. 11264117.
Claim 27 recites wherein the neural network model includes a plurality of network models for the class II MHC alleles, each network model assigned to a corresponding class II MHC allele of the class II MHC alleles and including a series of nodes arranged in one or more layers which corresponds to claim 26 of US Patent No. 11264117.
Claim 29 recites wherein the neural network model is trained by updating the parameters of the neural network model, and wherein the parameters of at least two network models are jointly updated for at least one training iteration which corresponds to claim 26 of US Patent No. 11264117.
Claim 30 recites wherein the machine-learned presentation model is a deep learning model that includes one or more layers of nodes layers which corresponds to claim 1 of US Patent No. 11264117.
Although claim 1 does not teach the limitations of genetically engineering a plurality of T-cells to express at least one of the one or more identified T-cell receptors, and culturing the plurality of T-cells under conditions that expand the plurality of T-cells, Kato teaches genetically engineering a plurality of T-cells to express at least one of the one or more identified T-cell receptors (see explicit recitation of constructed TCR-engineered t-cells via transfer of cancer-specific T cell receptor genes in T cells that can be isolated; see page 11010) and culturing the plurality of T-cells under conditions that expand the plurality of T-cells (expanding T-cell clones is a known alternative method as recited in “Abstract”; see page 11009). Therefore, it would have been obvious to one of ordinary skill in the art to incorporate Kato’s genetically engineered T-cells into US Patent No. 11264117’s antigen-specific identification pipeline to bypass suboptimal conditions which impede the reactivation of existing T cells or the priming of neoantigen-specific T cells in a patient as evidenced by Kato (“Abstract”; see page 11009). There would have been a reasonable expectation of success as identification of T-cells that are antigen-specific would allow for the generation of TCR-engineered T cells. As such, claims 1-8, 12-22, 27, and 29-30 are rejected on the ground of nonstatutory double patenting in view of Kato et al.
Conclusion
No claims are currently allowed.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Olivia M. Wise can be reached at (571) 272-2249. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/P.N./Examiner, Art Unit 1685
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685
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