DETAILED ACTION
Applicant's response, filed 10/24/2025, has been fully considered. The following rejections and/or objections are either reiterated or newly applied. Herein, "the previous Office action" refers to the Final Rejection of 04/24/2025.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 10/24/2025 has been entered.
Status of the Claims
Claims 1-10 and 13-14 are examined; claims 11-12 and 15-26 are canceled.
Priority
This application is a 371 of PCT/ KR2020/003464 (03/12/2020), which claims priority from Foreign Application No. KR2019/0028278 (03/12/2019), Foreign Application No. KR2020/0030597 (03/12/2020), and Foreign application No. KR2019/0040367 (04/05/2019) as reflected in the filing receipt mailed on Jan. 14, 2022. The claims to the benefit of priority are acknowledged and the effective filing date of claims 1-10, and 13-14 is 03/12/2019.
Nucleotide and/or Amino Acid Sequence Disclosures
REQUIREMENTS FOR PATENT APPLICATIONS CONTAINING NUCLEOTIDE AND/OR AMINO ACID SEQUENCE DISCLOSURES
Items 1) and 2) provide general guidance related to requirements for sequence disclosures.
37 CFR 1.821(c) requires that patent applications which contain disclosures of nucleotide and/or amino acid sequences that fall within the definitions of 37 CFR 1.821(a) must contain a "Sequence Listing," as a separate part of the disclosure, which presents the nucleotide and/or amino acid sequences and associated information using the symbols and format in accordance with the requirements of 37 CFR 1.821 - 1.825.In this instant application, Figs. 15-17 include amino acid sequence disclosures and there is no sequence listing or associated SEQ ID Nos with each of these sequences. This "Sequence Listing" part of the disclosure may be submitted:
In accordance with 37 CFR 1.821(c)(1) via the USPTO patent electronic filing system (see Section I.1 of the Legal Framework for Patent Electronic System (https://www.uspto.gov/PatentLegalFramework), hereinafter "Legal Framework") as an ASCII text file, together with an incorporation-by-reference of the material in the ASCII text file in a separate paragraph of the specification as required by 37 CFR 1.823(b)(1) identifying:
the name of the ASCII text file;
ii) the date of creation; and
iii) the size of the ASCII text file in bytes;
In accordance with 37 CFR 1.821(c)(1) on read-only optical disc(s) as permitted by 37 CFR 1.52(e)(1)(ii), labeled according to 37 CFR 1.52(e)(5), with an incorporation-by-reference of the material in the ASCII text file according to 37 CFR 1.52(e)(8) and 37 CFR 1.823(b)(1) in a separate paragraph of the specification identifying:
the name of the ASCII text file;
the date of creation; and
the size of the ASCII text file in bytes;
In accordance with 37 CFR 1.821(c)(2) via the USPTO patent electronic filing system as a PDF file (not recommended); or
In accordance with 37 CFR 1.821(c)(3) on physical sheets of paper (not recommended).
When a “Sequence Listing” has been submitted as a PDF file as in 1(c) above (37 CFR 1.821(c)(2)) or on physical sheets of paper as in 1(d) above (37 CFR 1.821(c)(3)), 37 CFR 1.821(e)(1) requires a computer readable form (CRF) of the “Sequence Listing” in accordance with the requirements of 37 CFR 1.824.
If the "Sequence Listing" required by 37 CFR 1.821(c) is filed via the USPTO patent electronic filing system as a PDF, then 37 CFR 1.821(e)(1)(ii) or 1.821(e)(2)(ii) requires submission of a statement that the "Sequence Listing" content of the PDF copy and the CRF copy (the ASCII text file copy) are identical.
If the "Sequence Listing" required by 37 CFR 1.821(c) is filed on paper or read-only optical disc, then 37 CFR 1.821(e)(1)(ii) or 1.821(e)(2)(ii) requires submission of a statement that the "Sequence Listing" content of the paper or read-only optical disc copy and the CRF are identical.
Specific deficiencies and the required response to this Office Action are as follows:
Specific deficiency - This application fails to comply with the requirements of 37 CFR 1.821 - 1.825 because it does not contain a "Sequence Listing" as a separate part of the disclosure or a CRF of the “Sequence Listing.”.
Required response - Applicant must provide:
A "Sequence Listing" part of the disclosure; together with
An amendment specifically directing its entry into the application in accordance with 37 CFR 1.825(a)(2);
A statement that the "Sequence Listing" includes no new matter as required by 37 CFR 1.821(a)(4); and
A statement that indicates support for the amendment in the application, as filed, as required by 37 CFR 1.825(a)(3).
If the "Sequence Listing" part of the disclosure is submitted according to item 1) a) or b) above, Applicant must also provide:
A substitute specification in compliance with 37 CFR 1.52, 1.121(b)(3) and 1.125 inserting the required incorporation-by-reference paragraph, consisting of:
A copy of the previously-submitted specification, with deletions shown with strikethrough or brackets and insertions shown with underlining (marked-up version);
A copy of the amended specification without markings (clean version); and
A statement that the substitute specification contains no new matter.
If the "Sequence Listing" part of the disclosure is submitted according to item 1) c) or d) above, applicant must also provide:
A CRF in accordance with 37 CFR 1.821(e)(1) or 1.821(e)(2) as required by 1.825(a)(5); and
A statement according to item 2) a) or b) above.
Specific deficiency – Nucleotide and/or amino acid sequences appearing in the drawings are identified by sequence identifiers in accordance with 37 CFR 1.821(d); however the upload of the sequence listing files have not been done).
Withdrawal / Revision of Objections and/or Rejections
In view of the amendment and remarks from 10/24/2025, the rejection of claims 12 and 15 under 35 U.S.C. § 101 is hereby withdrawn in view of Applicant's amendments, rendering both of these grounds of rejection moot. In view of the amendment and remarks from 10/24/2025, the rejection of claim 12 under 35 U.S.C. § 103 over Hacohen, Brusic, Aggarwal, Han and Gundampati is hereby withdrawn in view of Applicant's amendments, rendering both of these grounds of rejection moot. In view of the amendment and remarks from 10/24/2025, the rejection of claim 15 under 35 U.S.C. § 103 over Hacohen, Brusic, Aggarwal and Han is hereby withdrawn in view of Applicant's amendments, rendering both of these grounds of rejection moot. The following rejections and/or objections are either maintained or newly applied for claims 1-10 and 13-14. They constitute the complete set presently being applied to the instant application.
Claim Objections
Claim 13 is objected to because of the following informality: the recited “obtaining a prediction value of the in silico binding in step (C) is performed by generating binding models for a number of types of antigens comparing and determining the energy difference and RMSD difference therebetween” should read “obtaining the prediction value of the in silico binding in step (C) is performed by generating binding models for a number of types of antigens, and comparing and determining the energy difference and RMSD difference therebetween”.
Claim 14 is objected to because of the following informality: the recited “obtaining a prediction value of the in silico binding in step (C)” should read “obtaining the prediction value of the in silico binding in step (C)”.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-10 and 13-14 are rejected under 35 USC § 101 because the claimed inventions are directed to an abstract idea without significantly more. "Claims directed to nothing more than abstract ideas (such as a mathematical formula or equation), natural phenomena, and laws of nature are not eligible for patent protection" (MPEP 2106.04 § I). Abstract ideas include mathematical concepts, and procedures for evaluating, analyzing or organizing information, which are a type of mental process (MPEP 2106.04(a)(2)).
MPEP 2106 organizes JE analysis into Steps 1, 2A (Prong One & Prong Two), and 2B as analyzed below.
Step 1: Are the claims directed to a process, machine, manufacture, or composition of matter (MPEP 2106.03)?
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e., a law of nature, a natural phenomenon, or an abstract idea (MPEP 2106.04(a-c))?
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application by an additional element (MPEP 2106.04(d))?
Step 2B: Do the claims recite a non-conventional arrangement of elements in addition to any identified judicial exception(s) (MPEP 2106.05)?
Step 1: Are the claims directed to a 101 process, machine, manufacture, or composition of matter (MPEP 2106.03)?
The instant claims are directed to a method (claims 1-10, and 13-14), which falls within one of the categories of statutory subject matter. [Step 1: Yes]
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e., a law of nature, a natural phenomenon, or an abstract idea (MPEP 2106.04(a-c))?
With respect to Step 2A, Prong One, the claims recite judicial exceptions in the form of abstract ideas. MPEP § 2106.04(a)(2) further explains that abstract ideas are defined as:
• mathematical concepts (mathematical formulas or equations, mathematical relationships
and mathematical calculations) (MPEP 2106.04(a)(2)(I));
• certain methods of organizing human activity (fundamental economic principles or practices, managing personal behavior or relationships or interactions between people) (MPEP 2106.04(a)(2)(II)); and/or
• mental processes (concepts practically performed in the human mind, including observations, evaluations, judgments, and opinions) (MPEP 2106.04(a)(2)(III)).
Mathematical concepts recited in instant claims 1, 13-14 include the terms “ratio of a predicted drug response”, “obtaining a prediction value”, “determining in silico binding affinities”, “determining the energy difference”, and “determining a correlation”; which are mathematical concepts. The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one having ordinary skill in the art. Thus, the recited corresponds to verbal equivalents of mathematical concepts (MPEP 2106.04(a)(2)). 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." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). The recited “obtaining a prediction value”, “determining in silico binding affinities”, “determining the energy difference”, and “determining a correlation”; constitutes actions executed by a group of mathematical steps in a form of a mathematical algorithm; thus mathematical concepts.
Mental processes, defined as concepts or steps practically performed in the human mind such as steps of observations, evaluations, judgments, and opinions, include “comparing in silico binding affinities” (claim 1); “identifying neoantigen candidates through a genomic mutation” (independent claim 1), “filtering the specificities of the neoantigen candidates for tissue and disease” (independent claim 1), “ranking TCR activity” (independent claim 1), “selecting the major clone genes from cancer cells” (claim 6), “collecting read sequences” (claim 9), “aligning the HLA gene read sequences” (claim 9), “determining the types of the HLA genes according to the aligned ranks of the HLA genes” (claim 9), “comparing the energy difference” (claim 13), “comparing the correlation” (claim 14), and “generating a phi-psi angle Ramachandran plot based on MHC-peptide docking data” (claim 14). Under the BRI, the recited limitations are mental processes because a human mind is sufficiently capable of comparing the data obtained, identifying, collecting and evaluating information to rank values and generate a plot giving a set of values.
Dependent claims 2-3 recite further details about the “genomic mutation” used to identify neoantigen candidates to be used by the prediction algorithm; dependent claims 4 and 6-7 recite further details about the “neoantigen candidates” used by the prediction algorithm; dependent claims 5 and 8-9 recite further details about the MHC molecules used by the prediction algorithm; and dependent claim 10 recites further details about the filtering step; not reciting any additional non-abstract elements; all reciting further aspects of the information being analyzed, the manner in which that analysis is performed. Hence, the claims explicitly recite numerous elements that, individually and in combination, constitute abstract ideas. The instant claims must therefore be examined further to determine whether they integrate that abstract idea into a practical application (MPEP 2106.04(d)). [Step 2A Prong One: Yes]
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application by an additional element (MPEP 2106.04(d))?
Instant claims 1, and 13-14 recite additional elements that are not abstract ideas: performing the predictions in-silico with a deep learning AI model with at least 10 hidden layers and 128 neurons (independent claims 1 and 13-14); and “performing dynamics simulation for MHC-peptide docking complexes” (claim 14).
The recited “in-silico” and deep learning AI model limitations in these claims are interpreted to require the use of a computer. The use of a computer is broadly interpreted and not actually described in the claims or specification. Under BRI, in-silico and the computer nature of the drawings generated in the instant application amounts to applying computer methods. Hence, the claims explicitly recite steps executed by computers and therefore can be described as computer functions. Therefore, claims 1 and 13-14 relate to computers and do not provide any details of how specific structures of the computer are used to implement these functions. As such, these limitations equate to mere instructions to implement the abstract idea on a generic computer, and therefore the claim does not integrate that abstract idea into a practical application (see MPEP 2106.04(d) § I; and MPEP 2106.05(f)).
The limitation of claim 14 for performing dynamics simulations for MHC-peptide docking complexes equates to mere data gathering activity because the limitation represents steps to gather information that is used as input for the subsequent mathematical calculations, which is an insignificant extra-solution activity (MPEP 2106.05(g)).
Claims directed to carrying out the abstract idea with a computer system are not sufficient to integrate an abstract idea into a practical application (see MPEP 2106.05(f)); since steps that can be performed mentally and merely performing the mental process in a computer environment do not negate the fact that something that can be carried out in the human mind. See MPEP 2106.04(a)(2).III.C.
Hence, these are mere instructions to apply the abstract idea using a computer and insignificant extra-solution activity and therefore the claims do not integrate that abstract idea into a practical application (see MPEP 2106.04(d) § I; 2106.05(f); and 2106.05(g)). None of the dependent claims recite any additional non-abstract elements; they are all directed to further aspects of the information being analyzed, the manner in which that analysis is performed, or the mathematical operations performed on the information. [Step 2A Prong Two: No]
Step 2B: Do the claims recite a non-conventional arrangement of elements in addition to any identified judicial exception(s) (MPEP 2106.05)?
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself. Step 2B of the 35 USC § 101 analysis determines whether the claims contain additional elements that amount to an inventive concept, and an inventive concept cannot be furnished by an abstract idea itself (MPEP 2106.05).
Molecular dynamics simulations use for docking is well-understood, routine and conventional (Pagadala N. et. al. “Software for molecular docking: a review” Biophys. Rev. (2017) 9:91–102). Furthermore, these simulations are conventionally carried out in a computer so the combination of a computer with molecular dynamics simulations is conventional. The claims recited are directed to computer functions, interpreted as instructions to apply the abstract idea using a computer, where the computer does not impose meaningful limitations on the judicial exceptions; which can be performed without the use of a computer (MPEP 2106.04(d) § I; and MPEP 2106.05(f)), such as storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; and performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199.
When the claims are considered as a whole, they do not integrate the abstract idea into a practical application; they do not confine the use of the abstract idea to a particular technology; they do not solve a problem rooted in or arising from the use of a particular technology; they do not improve a technology by allowing the technology to perform a function that it previously was not capable of performing; and they do not provide any limitations beyond generally linking the use of the abstract idea to a broad technological environment. See MPEP 2106.05(a) and 2106.05(h). [Step 2B: No]
Conclusion: Instant claims are directed to non-statutory subject matter
For the reasons described, the claims in this instant application, when the limitations are considered individually and as a whole, are directed to an abstract idea and lack an inventive concept. Hence, the claimed invention does not constitute significantly more than the abstract idea, so instant claims 1-10, and 13-14 are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
Response to applicant's remarks in regards of Claim Rejection 35 U.S.C. ~ 101
The Remarks of 10/24/2025 have been fully considered but are not persuasive for the reasons below.
Applicant asserts “In addition, claim 1 is also amended to clarify the activities of the steps as nonmathematical calculation or a simple mental step, wherein the steps requires a processing of instruction from human and complex data mining and processing which can benefit from the computer, and impossible to perform using the human mental process only. As explained in MPEP § 2106.04(a)(2), "[c]laims do not recite a mental process when they do not contain limitations that can practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitations. Id. (citing SRI Int'l, Inc. v. Cisco Systems, Inc., 930 F.3d 1295, 1304 (Fed. Cir. 2019)). Various examples of analyses, determinations, and calculations that cannot be practically performed in the human mind are provided in this section of the MPEP. See id. These examples include, GPS-related calculations, data encryption-methods, digital image manipulation, and the analysis of network traffic at the packet level. Indeed, each of these operations involves mathematical calculations or the manipulation of computer data structures for which the human mind is ill-equipped to handle in any practical sense” – pg. 8 para. 3-4. The examiner recognizes the applicant’s amendments aiming to clarify the activities of the steps as nonmathematical calculation or a simple mental step. However, the recited steps directed to “in-silico binding affinities” are indeed identified as a mathematical concept and whether or not the limitation can be performed in the human mind is not a factor for mathematical concept. Additionally, the claims indeed recite mental steps - “comparing in silico binding affinities” (claim 1); “identifying neoantigen candidates through a genomic mutation” (independent claim 1), “filtering the specificities of the neoantigen candidates for tissue and disease” (independent claim 1), “ranking TCR activity” (independent claim 1), “selecting the major clone genes from cancer cells” (claim 6), “collecting read sequences” (claim 9), “aligning the HLA gene read sequences” (claim 9), “determining the types of the HLA genes according to the aligned ranks of the HLA genes” (claim 9), “comparing the energy difference” (claim 13), “comparing the correlation” (claim 14), and “generating a phi-psi angle Ramachandran plot based on MHC-peptide docking data” (claim 14) – because, under the BRI, a human mind is sufficiently capable of comparing the data obtained, identifying, collecting and evaluating information to rank values and generate a plot giving a set of values; which reads on concepts practically performed in the human mind, including observations, evaluations, judgments, and opinions (MPEP 2106.04(a)(2)(III)).
Applicant asserts “Turning to the present application, claim 1 recites determining in silico binding affinities "using a deep learning AI model comprising at least 10 hidden layers and 128 neurons." There is no reasonable basis to conclude that this operation can be practically performed in the human mind or with pen and paper. The examples in MPEP § 2106.04(a)(2)111.A noted above are directly relevant to this analysis. Similar to the case of GPS calculations, digital image manipulation, or encryption analysis - while a human could theoretically perform such calculations given pen and paper and an enormous amount of time - they cannot be practically performed, and this qualifier is a key aspect of the analysis. The human mind is simply not equipped to perform the vector calculations needed to utilize a deep learning model of this complexity. Indeed, the PTAB has recognized in multiple instances that claims that require complex. AI/ML models cannot be practically performed as a mental process. See, e.g., Ex Parte Baker, Appeal No. 2025-000081, 2024 WL 4899906 (PTAB Nov. 21, 2024) (reversing a§ 101 rejection because the human mind cannot perform the vector calculations needed to generate or adjust a trained AI/ML classifier)” – pg. 9 para. 2-3. This argument is unpersuasive because the deep learning AI model was not identified as an abstract idea, but rather it was treated as an additional element. Claims directed to “determining the in silico binding affinities through an AI model " reads on "applying a mathematical calculation on a computer" but doesn't really change the fact that the function that is being performed is a mathematical calculation; and whether or not the limitation can be performed in the human mind is not a factor for mathematical concept. The example cited - Ex Parte Baker, Appeal No. 2025-000081, 2024 WL 4899906 (PTAB Nov. 21, 2024) – differs from this instant application because here there is no indication that the claimed calculations are improving the AI model itself; therefore the claims considered as a whole, do not integrate the abstract idea into a practical application.
Applicant asserts “Other PTAB panels have reached this same conclusion in view of the guidance in the MPEP noted above and relevant case law. The Examiner is invited to contact the undersigned if these cumulative examples would be helpful. However, it is believed that additional examples should be unnecessary, as there is no plausible basis to conclude that a human mind can practically perform the method of claim 1. Accordingly, this limitation is not a "mental process" and should be found to support a finding of eligibility at Step 2A Prong Two, or Step 2B. Furthermore, the complexity of the AI model recited by claim 1 must also be taken into account when the dependent claims are analyzed - i.e., the additional limitations of these claims relate to processing operations that implicate this same AI model and would thus also be impractical to perform in the mind or with pen and paper, given the scale of the model. As such, all of the dependent claims based on claim 1 should be found to be eligible, by extension” – pg. 9 para. 4-5. Carrying out the abstract idea with a computer system are not sufficient to integrate an abstract idea into a practical application (see MPEP 2106.05(f)); since steps that can be performed mentally and merely performing the mental process in a computer environment do not negate the fact that something that can be carried out in the human mind. See MPEP 2106.04(a)(2).III.C. Additionally, as explained above, there is no indication the AI model is being improved by the judicial exception . If the improvement is not realized in the additional elements then the improvement is merely in the judicial exception itself, which is not considered an improvement to technology. The use of mathematical calculations in any capacity is not sufficient to integrate a judicial exception into a practical application because they improvement cannot be in the judicial exception itself.
Finally, the amendments does not render the rejection under 35 U.S.C. § 101 moot as it has been explained in detail in this action. Furthermore, in this instant application, the amendments support existing claim rejections for claims 1-10, and 13-14, in which the recited limitations are all addressed, see Claim Rejections above.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter 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 pre-AIA 35 U.S.C. 103(a) are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims under pre-AIA 35 U.S.C. 103(a), the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the examiner to consider the applicability of pre-AIA 35 U.S.C. 103(c) and potential pre-AIA 35 U.S.C. 102(e), (f) or (g) prior art under pre-AIA 35 U.S.C. 103(a).
Claims 1-5 and 8-10 are rejected under 35 U.S.C. 103(a) as being unpatentable over Hacohen et. al. (KR-20130119845A – provided in IDS dated 09/13/2021 and referred to in the action as Hacohen – as evidenced by Nielsen M. et. al. (Nielsen M. et. al. “NetMHCpan, a Method for Quantitative Predictions of Peptide Binding to Any HLA A and -B Locus Protein of Known Sequence” PLoS One 2:8: e796 (2007) – referred to in the action as Nielsen – in view of Brusic V. et. al. “Prediction of MHC class II-binding peptides using an evolutionary algorithm and artificial neural network” Bioinformatics: 14:2 121-130 (1998) – referred to in the action as Brusic – in view of Aggarwal C. C. “Neural networks and deep learning” Vol. 10. No. 978. Cham: Springer, 2018 – referred to in the action as Aggarwal – in view of Han Y. et. al. “Deep convolutional neural networks for pan-specific peptide-MHC class I binding prediction” BMC Bioinformatics 18:585 (2017) – referred to in the action as Han.
Determination of the Scope and Content of the Prior Art
(MPEP §2141.01)
Independent claim 1 recites “method of providing neoantigen immunotherapy information for identifying a neoantigen using artificial intelligence ("AI")-based molecular dynamics, the method comprising steps of: (A) identifying neoantigen candidates through a genomic mutation; (B) filtering the specificities of the neoantigen candidates for tissue and disease; (C) obtaining a prediction value of the in silico binding of the neoantigens to one or more MHC proteins”. Hacohen teaches a method for generating tumor specific neoantigens via tumor genome sequencing to identify mutated genes (pg. 9 col. 2 para. 4) which involves: (1) Identifying DNA mutations using whole exome sequencing of tumor samples from each patient (i.e. identifying neoantigen candidates through a genomic mutation) (pg. 43 col. 2 para. 3); (2) applying a highly validated peptide-MHC binding prediction algorithm to generate a set of candidate T cell epitopes based on mutations present in the (i.e., reading on obtaining a prediction value of the in silico binding of the neoantigens to one or more MHC proteins); and (3) generating antigen specific T cells for the mutated peptide (pg. 45 col. 2 para. 3); wherein the described algorithm namely NetMHC algorithm is a computer algorithm (i.e., a system) that predicts binding of peptides to any MHC molecule of known sequence using artificial neural networks – as evidenced by Nielsen – pg. 5 Table 1 and Title) wherein a pharmaceutical composition of the neoantigen may be configured such that the selection, number and/or amount of peptides present in the composition is a selection, number and / or amount specific for tissues, cancers and/or patients (i.e. filtering the specificities of the neoantigen candidates for tissue and disease) (pg. 33 col. 2 para. 2); reading on the recited limitations in claim 1.
Independent claim 1 recites “wherein the IBAs are produced … by the ratio of a predicted drug response (IC50) of a mutant gene to a predicted drug response (IC50) of a wildtype gene”. Hacohen teaches a series of standard dilutions for each produced peptides to determine the concentration of peptide required for 50% killing (IC50), concluding a differential recognition of these peptides by T cells (i.e., binding affinity) when the ratio of wild-type peptide to mutant peptide required for 50% kill exceeds 10-fold (pg. 46 col. 2 para. 3 and Fig. 8); wherein the ratio described was used in the method for confirming the peptide-HLA binding prediction (i.e. reading on “ratio used to determine the binding affinity”) (pg. 42 col. 2 para. 5); which reads on claim 1.
Dependent claim 2 recites “wherein the genomic mutation is a mutation present in tumor exomes or tumor transcriptomes”. Hacohen teaches tumor specific immunotherapy by identifying DNA mutations using whole genomes or whole exome (i.e., captured exons only) (pg. 9 col. 2 para. 4), which reads on claim 2.
Dependent claim 3 recites “wherein the genomic mutation is any one of neo-mutations, exposed features or mal-functions, and verification of exome and transcriptome expression is performed by determining over-expression or differential expression in the transcriptome”. Hacohen teaches that identified mutations provide potential neoepitopes for immunization, where frameshift, readthrough and splice site (e.g., using retained introns) mutations result in longer novel peptide stretches (pg. 43 col. 2 para. 4), showing differential expression of the mutations between different patients (Fig. 5), which reads on claim 3.
Dependent claim 4 recites “wherein the neoantigen candidates in step (A) comprise any one or more of major clone genes selected from cancer cells, mesenchymal stroma cell (MSC) genes selected from cancer cells, or six HLA types of cancer cells”. Dependent claim 5 recites “wherein the six HLA types are HLA-A, HLA-B, HLA-C, HLA-DR, HLA-DP and HLA-DQ”. Hacohen teaches an automated prediction of mutated MHC binding peptides that binds to each of the six HLA alleles (i.e., HLA-A, HLA-B, HLA-C, HLA-DR, HLA-DP and HLA-DQ) of a patient (pg. 14 col. 2 para. 2-3 and Fig. 6), which reads on claims 4-5.
Dependent claim 8 recites “wherein the HLA types of the cancer cells are selected through genomic HLA typing”. Hacohen teaches that genotyping was used to detect the presence of specific mutations or alleles in an individual's DNA (pgs. 16 col. 2 para. 1), which reads on claim 8. Dependent claim 9 recites, “wherein determination of the HLA types of the cancer cells is performed by a method comprising steps of: (a1) collecting read sequences of HLA genes; (a2) aligning the HLA gene read sequences to a human reference genome sequence according to allele types; and (a3) determining the types of the HLA genes according to the aligned ranks of the HLA genes”. Hacohen teaches identifying tumor specific mutations in the expressed genes of the subject with cancer by nucleic acid sequencing, selecting one or more mutant peptides or polypeptides that bind to HLA proteins (pg. 2 col. 2 claim 1), wherein mutations are aligned (left to right) according to the decreasing frequency (pg. 51 col. 2 para. 1) and matched to germline samples (i.e., wild type reference genome) (pg. 9 col. 2 para. 3), which reads on claim 9.
Dependent claim 10 recites wherein step (B) is performed by determining a tissue in which the neoantigen candidates are expressed. Hacohen teaches that a pharmaceutical composition of the neoantigen may be configured such that the selection, number and/or amount of peptides present in the composition is a selection, number and / or amount specific for tissues, cancers and/or patients (pg. 33 col. 2 para. 2), which reads on claim 10.
Ascertainment of the Difference Between Scope the Prior Art and the Claims
(MPEP §2141.02)
Regarding independent claim 1, Hacohen does not explicitly teach “(D) calculating and ranking TCR activity”. However, Brusic teaches a method to predict MHC class II binding peptides using artificial neural network to calculate and classify the binding affinity of the predicted peptide for MHC, wherein the affinity is ranked as: high/ moderate/ low or none (Table 2 Brusic); wherein MHC molecules play a critical role in initiating and regulating immune responses, binding to short peptides and display them on the cell surface for recognition by the T-cell receptor (i.e. “TCR activity”) (pg. 121 col. 1 para. 2); reading on the recited limitation in claim 1.
Regarding independent claim 1, Hacohen does not explicitly teach “wherein the IBAs are produced using a deep learning AI model comprising at least 10 hidden layers and 128 neurons”. Aggarwal teaches a training process that can be performed in layer wise fashion for a deep neural network containing any number of hidden layers (pg. 191 para. 1) and teaches an example of neural network - AlexNet -with the fully connected layers having 4096 neurons (pg. 328 para. 2); reading on the recited limitation in claim 1.
Regarding independent claim 1, Hacohen does not explicitly teach “wherein the obtaining the prediction value of the in silico binding in step (C) is performed by comparing and determining in silico binding affinities ("IBAs") based on the three-dimensional structures of peptides based on somatic mutation of tissue-specific genes, produced through steps (A) and (B), and a selected MHC protein”. However, Han teaches encoding a peptide binding structure into an image like array. The left panel shows the nonapeptide (green)-HLA-A*02:01 (magenta) complex (PDB entry 1qsf) (i.e. reading on 3D-structure data used as input) (pg. 3 Fig. 2); reading on the recited limitation in claim 1.
Finding of Prima Facie Obviousness Rationale and Motivation
(MPEP §2142-2143)
Regarding claims 1-5 and 8-10; it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings by Brusic, Aggarwal and Han to the method for generating tumor specific neoantigens via tumor genome sequencing to identify mutated genes in tumors of patients using a peptide-MHC binding prediction computer algorithm by Hacohen to calculate and rank TCR activity based on MCH binding affinity, applying a deep learning AI model of at least 10 hidden layers and 128 neurons. One of ordinary skill in the art would be motivated to apply the teachings by Brusic, Aggarwal and Han to the method by Hacohen to: 1. improve the fundamental to understanding the basis of immunity, and for the development of immunotherapeutics for autoimmune disease and cancer (pg. 121 col. 2 para. 1 Brusic); 2 to provide a very low training error (pg. 192 para. 1 Aggarwal); and 3. to generate more reliable prediction models (pg. 8 col. 1 para. 2 Han). One of ordinary skill in the art would be able to combine the teachings in these references with a reasonable expectation of success due to the same nature of the problem to be solved and methods involved (i.e., prediction of MHC binding peptides using artificial intelligence) (Id. at 1276, 69 USPQ2d at 1690 - MPEP 2143.01).
Claim 6 is rejected under 35 U.S.C. 103(a) as being unpatentable over Hacohen, Brusic, Aggarwal and Han as applied to claim 1 above further in view of Ware J.H. et. al. “Establishment of human cancer cell clones with different characteristics: a model for screening chemopreventive agents” Anticancer Research 27(1A):1-16 (2007) – referred to in the action as Ware.
Determination of the Scope and Content of the Prior Art
(MPEP §2141.01)
Dependent claim 6 recites “wherein the neoantigen candidates in step (A) comprise major clone genes selected from cancer cells, and wherein, for selecting the major clone genes from cancer cells, a clone having the largest number of cancer cells is selected as a major clone from cancer cells composed of the major clone and subclones”.
Ascertainment of the Difference Between Scope the Prior Art and the Claims
(MPEP §2141.02)
Regarding claim 6, neither Hacohen, Brusic, Aggarwal, nor Han teach “wherein the neoantigen candidates in step (A) comprise major clone genes selected from cancer cells, and wherein, for selecting the major clone genes from cancer cells, a clone having the largest number of cancer cells is selected as a major clone from cancer cells composed of the major clone and subclones”. Ware teaches that newly established clones of human cancer cells were characterized in terms of plating efficiency, population doubling time (i.e. metric that defines the clone with the largest number of cells), saturation density, hormone sensitivity and anchorage-independent growth; wherein these clones are useful for studying cancer progression and determining the efficacy of cancer preventive and therapeutic agents (i.e. neoantigens) (pg. 1 col. 2 para. 1); reading on the recited limitation in claim 6.
Finding of Prima Facie Obviousness Rationale and Motivation
(MPEP §2142-2143)
Regarding claim 6, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings by Ware to the method for generating tumor specific neoantigens via tumor genome sequencing to identify mutated genes in tumors of patients using a peptide-MHC binding prediction computer algorithm by Hacohen, Brusic, Aggarwal and Han. One of ordinary skill in the art would be motivated to apply the teachings by Ware to the method by Hacohen, Brusic, Aggarwal and Han to screen for the chemopreventive efficacy of potential agents during cancer progression (pg. 1 col. 2 para. 1 Ware). One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for screening molecules.
Claim 7 is rejected under 35 U.S.C. 103(a) as being unpatentable over Hacohen, Brusic, Aggarwal and Han as applied to claim 1 above further in view of Wu W. K. K. et. al. “Emerging roles of the host defense peptide LL-37 in human cancer and its potential therapeutic applications” Int. J. Cancer, 127:1741-1747 (2010) – referred to in the action as Wu.
Determination of the Scope and Content of the Prior Art
(MPEP §2141.01)
Dependent claim 7 recites “wherein the neoantigen candidates in step (A) comprise mesenchymal stroma cell (MSC) genes selected from cancer cells, and MSC genes selected from cancer cells are collected based on somatic mutation of genes expressed in the stroma cells”.
Ascertainment of the Difference Between Scope the Prior Art and the Claims
(MPEP §2141.02)
Regarding claim 7, neither Hacohen, Brusic, Aggarwal, nor Han teach “wherein the neoantigen candidates in step (A) comprise mesenchymal stroma cell (MSC) genes selected from cancer cells, and MSC genes selected from cancer cells are collected based on somatic mutation of genes expressed in the stroma cells”. Wu teaches a peptide that has been shown to promote tumor progression (i.e. a neoantigen candidate) through its influence on a particular progenitor cell population known as mesenchymal stromal/stem cells (pg. 1743 col. 1 para. 3); reading on the recited limitation in claim 7.
Finding of Prima Facie Obviousness Rationale and Motivation
(MPEP §2142-2143)
Regarding claim 7, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings by Wu to the method for generating tumor specific neoantigens via tumor genome sequencing to identify mutated genes based on somatic mutations of genes expressed in tumors of patients using a peptide-MHC binding prediction computer algorithm by Hacohen, Brusic, Aggarwal and Han to select MSC genes from cancer cells are collected based on somatic mutation of genes expressed in the stroma cells. One of ordinary skill in the art would be motivated to apply the teachings by Wu to the method by Hacohen, Brusic, Aggarwal and Han develop novel modulators of tumor growth and metastasis in carcinogenesis of various types of cancers cells (pg. 1745 col. 1 para. 2 Wu). One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for potential therapeutic application in cancer treatments.
Claims 13-14 are rejected under 35 U.S.C. 103(a) as being unpatentable over Hacohen, Brusic, Aggarwal and Han as applied to claim 1 above further in view of Gundampati et. al. “Protein-protein docking on molecular models of Aspergillus niger RNase and human actin: novel target for anticancer therapeutics” J. Mol. Model 18:653–662 (2012) – referred to in the action as Gundampati.
Determination of the Scope and Content of the Prior Art
(MPEP §2141.01)
Dependent claim 13 recites “wherein the obtaining a prediction value of the in silico binding in step (C) is performed by generating binding models for a number of types of antigens comparing and determining the energy difference and RMSD difference therebetween”. Dependent claim 14 recites “wherein the obtaining a prediction value of the in silico binding in step (C) is performed by a method comprising steps of: (C1) performing dynamics simulation for MHC-peptide docking complexes; (C2) generating a phi-psi angle Ramachandran plot based on MHC-peptide docking data; (C3) comparing and determining the correlation between rmsds through the phi-psi angles and structures; (C4) comparing and determining the correlation between selected features and each structure rmsd; and (C5) determining the in silico binding affinities through an AI model based on features generated from MHC-peptide complexes”.
Ascertainment of the Difference Between Scope the Prior Art and the Claims
(MPEP §2141.02)
Regarding claims 13-14, neither Hacohen, Brusic, Aggarwal, nor Han teach the recited limitations. However, Gundampati teaches a molecular docking method performed for the molecular models of a ligand (i.e., A. niger RNase) and a receptor (i.e., human actin) to analyze the energy of binding affinity, where stereochemical quality of the models was judged by Ramachandran plot (pg. 653 col. 1 para. 1) and the interaction energy was calculated via molecular docking algorithms (pg. 653 col. 2 para. 1), where the backbone rmsd (i.e. reading on structure rmsd and feature of the complex) of each complex was calculated (pg. 659 Table 3); which reads on claims 13-14.
Finding of Prima Facie Obviousness Rationale and Motivation
(MPEP §2142-2143)
Regarding claims 13-14, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings by Gundampati to the method for generating tumor specific neoantigens via tumor genome sequencing to identify mutated genes in tumors of patients using predicting binding affinities through an AI model by Hacohen, Brusic, Aggarwal and Han. One of ordinary skill in the art would be motivated to apply a molecular docking method for obtaining the energy of binding affinity, RMSD measurements and Ramachandran plots as taught by Gundampati to the MHC-peptide complex to predict binding affinity as taught by Hacohen, Brusic, Aggarwal and Han to develop accurate three-dimensional models of the ligand-receptor complex and analyze all features of the bound complex in detail (pg. 661 col. 1 para. 2 Gundampati). One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for potential therapeutic application in cancer treatments.
Response to applicant's remarks in regards of Claim Rejection 35 U.S.C. ~ 103
The Remarks of 10/24/2025 have been fully considered but are not persuasive for the reasons below.
Applicant asserts “Claim 1 recites "the IBAs are produced using a deep learning AI model comprising at least 10 hidden layers and 128 neurons and by the ratio of a predicted drug response (IC50) of a mutant gene to a predicted drug response (IC50) of a wildtype gene" which are not taught or suggested by the cited references. The additional subject matters was previously presented in claim 15 and none of the references teaches this limitation. Thus, it is respectfully urged that, even for at least this reason, claim 1 and each of its dependent claims are nonobvious over the cited prior art.” – pg. 11 para. 2. This argument is unpersuasive because the described limitation previously recited in claim 15 was indeed already taught in the previous office action by Hacohen (which is cited in detail in the rejection above). Applicant offers no counter-evidence or explanation against the examiner's interpretation of the art. Instead, the arguments merely contradict the examiner's findings, disregard the described teachings in the previous office action, and insist that limitations have not been addressed when, in fact, the examiner's rebuttal does address them. While Applicant is free to dispute the examiner's interpretation of prior art teaching, contradicting the examiner and predicating arguments on an unreasonable standard for obviousness does not show that the examiner's rationale is in error.
Applicant asserts “Applicant also maintains position that Nielsen does not provide additional base to render the claims to be obvious as it teaches away from the inventions in the instant claims: Nielsen describes NetMHC having the ability to predict binding affinities using a three-dimensional approach exploiting both peptide and primary HLA sequence as input information for artificial neural network driven predictions pooling all available data and at the same time incorporate all HLA specificities (Introduction). Applicant has reviewed the cited portion of Nielsen (i.e., the Introduction), and the remainder of this reference, and finds no support for this assertion. Based on the reference to the "Introduction" section, left col., reproduced below:… To be clear, this passage does not state that NetMHCpan uses three-dimensional structural data as an input. In this passage, Nielsen states that prior methods utilized structural modeling, but indicates that such methods were limited by the lack of available structures. In view of tis shortcoming, Nielsen proposes an alternative solution. "Search for alternative solutions, we here propose a novel method, NetMHCpan, exploiting both peptide and primary HLA sequence as input information for ANN-driven predictions .... " Id. Thus, Nielsen (1) teaches that structure-based approaches were disfavored due to the lack of suitable structures, and (2) teaches that a sequence based method is a solution to this problem, not the use of both sequence and 3D-structural data, as recited by former claim 11 - and now amended claim 1. In view of the above, Nielsen does not in fact teach the limitation to render the instant claims obvious and, moreover, actually teaches away from the combination of sequence and structural data as an input for the type of analysis recited by claim 1” – pg. 11 para. 3 to pg. 12 para. 4. Nielsen teachings read on the recited “obtaining a prediction value of the in silico binding of the neoantigens to one or more MHC proteins” and not on the recited “wherein the obtaining the prediction value of the in silico binding in step (C) is performed by comparing and determining in silico binding affinities ("IBAs") based on the three-dimensional structures of peptides based on somatic mutation of tissue-specific genes, produced through steps (A) and (B), and a selected MHC protein”; which is taught by Han. "One cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references" (MPEP 2145 § IV). This argument is unpersuasive, because it analyzes the teachings of the references separately and independently, whereas the rejection is based on the combined teachings of the references. While none of the references teach all claim limitations, and the examiner does not dispute Applicant's identification of material missing from each one, all the claim limitations are taught by the combination of references, as explained previously. Additionally, "the prior art’s mere disclosure of more than one alternative does not constitute a teaching away from any of these alternatives because such disclosure does not criticize, discredit, or otherwise discourage the solution claimed…." In re Fulton, 391 F.3d 1195, 1201, 73 USPQ2d 1141, 1146 (Fed. Cir. 2004) MPEP 2141.02 VI.
Due to all the described reasons above, it is interpreted that the claims do not patentably distinguish the claimed invention to the teachings by the prior art; therefore this action aimed to articulate the reasons for obviousness as described in the claim rejections above.
Conclusion
No claims are allowed.
All claims are identical to or patentably indistinct from, or have unity of invention with claims in the application prior to the entry of the submission under 37 CFR 1.114 (that is, restriction (including a lack of unity of invention) would not be proper) and all claims could have been finally rejected on the grounds and art of record in the next Office action if they had been entered in the application prior to entry under 37 CFR 1.114. Accordingly, THIS ACTION IS MADE FINAL even though it is a first action after the filing of a request for continued examination and the submission under 37 CFR 1.114. See MPEP § 706.07(b). 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FRANCINI A FONSECA LOPEZ whose telephone number is (571)270-0899. The examiner can normally be reached Monday - Friday 8AM - 5PM ET.
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/F.F.L./Examiner, Art Unit 1685
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685
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