DETAILED ACTION
Notice of 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 .
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 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.
Withdrawal of Objections and Rejections
Applicant's response, filed 05/25/2026, has been fully considered.
In view of the amendment and remarks from 05/25/2026, the objection to the specification, the objection to the claims are withdrawn.
The following rejections and/or objections are either maintained or newly applied for claims 1-10 and 13. They constitute the complete set applied to the instant application. Herein, "the previous Office action" refers to the Final Rejection of 11/28/2025.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 05/25/2026 has been entered.
Status of the Claims
Claims 11-12 and 14-26 are canceled.
Claims 1 and 28-29 are objected to.
Claims 1-10, 13 and 27-29 are rejected.
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 is 03/12/2019.
Specification
The amendments to the abstract and the specification submitted 05/25/2026 are accepted.
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 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 - The size of the Sequence Listing file is presented in the specification (pg. 1, lines 9-11) in kilobytes. 37 CFR 1.823(b)(1) requires the size of the ASCII text file in bytes.
Required response – Applicant must provide:
Replacement and annotated drawings in accordance with 37 CFR 1.121(d) inserting the required sequence identifiers;
AND/OR
A substitute specification in compliance with 37 CFR 1.52, 1.121(b)(3) and 1.125 inserting the required sequence identifiers into the Brief Description of the Drawings, 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.
Claim objections
Claim 1 is objected to because of the following informality: the recited "and" at the end of step (D) should be removed because the following step (A2) is not the last step in the list of main steps (A1) to (A3).
Claim 28 is objected to because of the following informality: the recited "cancel-specific neoantigens" should read "cancer-specific neoantigens" for proper agreement between the claims.
Claim 29 is objected to because of the following informality: the recited "wherein the genomic mutation presents in tumor exomes and tumor transcriptomes in a cancer patient biopsies" should read "wherein the genomic mutation presented in tumor exomes and tumor transcriptomes is identified via cancer patient biopsies." If such interpretation is not intended by the applicant, the claim may be amended to reflect the correct interpretation and proper grammar.
Appropriate correction is required.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION —The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claim 1-10, 13 and 27-29 are rejected under 35 U.S.C. 112(b)as being indefinite for failing to particularly point out and distinctly claim the subject matter the invention. Dependent claims are rejected similarly, unless otherwise noted below. The following issues cause the respective claims to be rejected under 112(b) as indefinite:
The following recitations require but lack antecedent basis, rendering their claims indefinite because there is no previous recitations of the followings terms as written:
Claim 1, "the in silico binding" (step (C))
Claim 1, "the three-dimensional structures of peptides" (second line of first wherein clause)
Claim 1, "the ratio of a predicted drug response" (second line of second wherein clause)
Claim 1, "the correlation between RMSD" (step (C3))
Claim 1, "the correlation between selected features" (step (C4))
Claim 1, "the phi-psi angles" (step (C3))
Claim 6, "the largest number of cancer cells"
Claim 9, "the aligned ranks of the HLA genes"
Claim 13, " the energy difference"
In claim 13 the recited “obtaining a prediction value of the in silico binding in step (C)” is unclear. It is unclear if the recited prediction value refers to the same prediction value recited in claim 1 or not. To overcome this rejection the claim may be amended to “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, 13 and 27-29 are rejected under 35 USC § 101 because the claimed inventions are directed to one or more Judicial Exceptions (JEs) without significantly more. Regarding JEs, "Claims directed to nothing more than abstract ideas..., 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)). Any newly recited portions are necessitated by claim amendment.
101 background
MPEP 2106 organizes JE analysis into Steps 1, 2A (Prong One & Prong Two), and 2B as analyzed below. MPEP 2106 and the following USPTO website provide further explanation and case law citations: uspto.gov/patent/laws-and-regulations/examination-policy/examination-guidance-and-training-materials.
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)?
Analysis of instant claims
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, 13 and 27-29) which falls within one of the categories of statutory subject matter. [Step 1: claims 1-10, 13 and 27-29: 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))?
Background
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)).
Analysis of instant claims
With respect to the instant claims, under the Step 2A, Prong One evaluation, the claims are found to recite abstract ideas that fall into the grouping of mathematical concepts (in particular mathematical relationships and formulas) and mental processes (in particular procedures for observing, analyzing and organizing information) are as follows.
Mathematical concepts (in particular mathematical relationships and formulas) include:
• "(A1) obtaining neoantigen immunotherapy information using artificial intelligence ("AI)-based molecular dynamics" (independent claim 1);
• "C) obtaining a prediction value of the in silico binding of the neoantigens to one or more MHC proteins" (independent claim 1);
• "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" (independent claim 1); and
• "(C5) determining the in silico binding affinities through an AI model based on features generated from MHC-peptide complexes" (independent claim 1).
The claims identified above read on math. The abstract ideas recited in the claims are evaluated under the Broadest Reasonable Interpretation and determined each element performed by mathematical operation. The step directed to “executing algorithms for the prediction and identification of neoantigens” requires mathematical techniques as the only supported embodiments because it describes the mathematical technique of adding numbers together in words (MPEP 2106.04(a)(2) pertains). Further support for the mathematical techniques used in the claims is provided in the specification at pg. 3 para. 1, which discloses algorithms implemented for the prediction and identification of neoantigens. Thus, the recited terms correspond to verbal equivalents of mathematical concepts because they constitute actions executed by a group of mathematical steps in a form of a mathematical algorithm; thus 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). MPEP 2106.04(a)(2) pertains.
Mental processes, defined as concepts or steps practically performed in the human mind such as steps of observations, evaluations, judgments, analysis, opinions or organizing information include:
• "(A) identifying neoantigen candidates through a genomic mutation" (independent claim 1);
• "(B) filtering the specificities of the neoantigen candidates for tissue and disease" (independent claim 1);
• "(D) obtaining and ranking TCR activity" (independent claim 1);
• "comparing …in silico binding affinities" (independent claim 1);
• "(A2) identifying immunogenic neoantigens for the disease" (independent claim 1);
• "(C2) generating a phi-psi angle Ramachandran plot based on MHC-peptide docking data" (independent claim 1);
• "(C3) comparing and determining the correlation between RMSD (Root Means Square Deviation) through the phi-psi angles and structures" (independent claim 1);
• "(C4) comparing and determining the correlation between selected features and each structure RMSD" (independent claim 1); and
• "generating binding models for a number of types of antigens, and comparing and determining the energy difference and RMSD difference there between" (claim 13).
The abstract ideas recited in the claims are evaluated under the Broadest Reasonable Interpretation (BRI) and determined to each cover performance either in the mind (i.e. concepts practically performed in the human mind, including observations, evaluations, judgments, and opinions) or because the method only requires a user to manually determine action based on an added number. Under the BRI, the recited limitations are mental processes because a human mind is also sufficiently capable of identifying candidate neoantigens based on genomic data; filtering/ranking/comparing data, and generating a model using pen and paper along with comparing and determining an energy difference based on such comparison.
Dependent claims 2-10 and 27-29 recite further steps that limit the judicial exceptions in independent claim 1 and, as such, also are directed to those abstract ideas. For example, claims 2-3 and 29 recite further details about the “genomic mutation” used to identify neoantigen candidates to be used by the prediction algorithm; claims 4 and 6-7 and 28 recite further details about the “neoantigen candidates” used by the prediction algorithm; claims 5 and 8-9 recite further details about the MHC molecules used by the prediction algorithm; claim 10 recites further details about the filtering step; and claim 27 recites further details about the disease.
[Step 2A Prong One: claims 1-10, 13 and 27-29: 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))?
Background
MPEP 2106.04(d).I lists the following example considerations for evaluating whether a judicial exception is integrated into a practical application:
An improvement in the functioning of a computer or an improvement to other technology or another technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a);
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, as discussed in MPEP § 2106.04(d)(2);
Implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, as discussed in MPEP § 2106.05(b);
Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP § 2106.05(c); and
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP § 2106.05(e).
Analysis of instant claims
Instant claim 1 recites additional elements that are not abstract ideas:
• "(A3) administering an immunotherapy using the identified neoantigens to a subject in need thereof" (independent claim 1);
• "deep learning AI model" (independent claim 1);
• "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; and wherein the IBAs are produced using a deep learning AI model comprising at least 10 hidden layers and 128 neurons" (independent claim 1) and
• "(C1) performing dynamics simulation for MHC-peptide docking complexes" (independent claim 1).
Considerations under Step 2A, Prong Two
The recited limitations in claims 1-10, 13 and 27-29 are interpreted as requiring the use of a computer. Hence, the claims explicitly recite steps executed by computers and therefore can be described as computer functions or instructions to implement on a generic computer.
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, the claims relate to computers and do not provide any details of how specific structures of the computer are used to implement these functions. The claims state nothing more than a generic computer which performs the functions that constitute the judicial exceptions.
The judicial exceptions in the claims are considered to perform the claimed abstract idea with a computer, which is 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.
The recited 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)).
With respect to claim 1, the computer-related elements or the general purpose computer and the recited deep learning AI model comprising at least 10 hidden layers and 128 neurons does not rise to the level of significantly more than the judicial exception. The claims state nothing more than a generic computer which performs the functions that constitute the judicial exceptions. Hence, these are mere instructions to apply the judicial exceptions using a computer, which the courts have found to not provide significantly more when recited in a claim with a judicial exception (Alice Corp., 573 U.S. at225-26, 110 USPQ2d at 1984; see MPEP 2106.05(A)). The additional elements are set forth at such a high level of generality that they can be met by a general purpose computer. Therefore, the computer components constitute no more than a general link to a technological environment, which is insufficient to constitute an inventive concept that would render the claims
Claims directed to "administering an immunotherapy using the identified neoantigens to a subject in need thereof" read on a generic "apply it" step because the claim recites an idea of a solution or outcome without any indication of how the judicial exception impacts or influences this step. There is no clear link between how the steps and data in step A1 to the step related to identifying the neoantigens in A2, therefore, using the information from step A2 in step A3 is not connected to A1.
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)).
In Step 2A, Prong One above, claim steps and/or elements were identified as part of one or more judicial exceptions (JEs).
In this Step 2A, Prong Two immediately above claim steps and/or elements were identified as part of one or more additional elements. Additional elements are further discussed in Step 2B below.
Here in Step 2A, Prong Two, no additional step or element clearly demonstrates integration of the JE(s) into a practical application.
[Step 2A Prong Two: claims 1-10, 13 and 27-29: No]
Step 2B: Do the claims recite a non-conventional arrangement of elements in addition to any identified judicial exception(s) (MPEP 2106.05)?
According to analysis so far, the additional elements described above do not provide significantly more than the judicial exception. A determination of whether additional elements provide significantly more also rests on whether the additional elements or a combination of elements represents other than what is well-understood, routine, and conventional. Conventionality is a question of fact and may be evidenced as: a citation to an express statement in the specification or to a statement made by an applicant during examination that demonstrates a well-understood, routine or conventional nature of the additional element(s); a citation to one or more of the court decisions as discussed in MPEP 2106(d)(II) as noting the well-understood, routine, conventional nature of the additional element(s); a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s); and/or a statement that the examiner is taking official notice with respect to the well-understood, routine, conventional nature of the additional element(s).
Claims 1-10, 13 and 27-29 recite a computer or 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)).
The computer-related elements or the general purpose computer and the recited deep learning AI model comprising at least 10 hidden layers and 128 neurons do not rise to the level of significantly more than the judicial exception. The claims state a generic computer which performs the functions that constitute the judicial exceptions. Hence, these are mere instructions to apply the judicial exceptions using a computer, which the courts have found to not provide significantly more when recited in a claim with a judicial exception (Alice Corp., 573 U.S. at225-26, 110 USPQ2d at 1984; see MPEP 2106.05(A)).
With respect to the instant claims, the prior art review Patronov (“T-cell epitope vaccine design by immunoinformatics” Open Biol. 3(1):120139 (2013)); cited on the attached PTO-892 Form) discloses that performing molecular dynamics simulations for MHC-peptide docking complexes and "administering an immunotherapy using the identified neoantigens to a subject in need thereof" is routine, well-understood and conventional in the art. Said portions of the prior art are, for example, pg. 91 Abstract.
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).
The instant claims constitute insignificant extra solution activity, and when considered individually, are insufficient to constitute inventive concepts that would render the claims significantly more than an abstract idea (see MPEP 2106.05(g)). Hence, these elements, when considered individually, are insufficient to constitute inventive concepts that would render the claims significantly more than an abstract idea (see MPEP 2106.05(d)).
[Step 2B: claims 1-10, 13 and 27-29: No]
Conclusion: Instant claims are directed to non-statutory subject matter
For the reasons above, 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 not clearly anything significantly more.
Response to applicant's remarks in regard to Claim Rejection 35 U.S.C. ~ 101
The Remarks of 05/25/2026 have been fully considered but are not persuasive for the reasons below:
Applicant asserts starting in pg. 8 para. 2:
Claim 1 is now directed to a practical and applicable method of treating a disease. Claim 1 already recited a deep learning AI model with at least 10 hidden layers and 128 neurons. This is a meaningful structural limitation that goes beyond simply invoking "AI" or "machine learning" generically. These architectural parameters are not merely an abstract mathematical concept but define a specific technical implementation that was selected to achieve a particular technical result such as accurate prediction of in silico binding affinities for neoantigen-MHC protein complexes. The claims are directed to neoantigen immunotherapy, a concrete medical application. The claimed method integrates the alleged abstract idea into a practical application because the output (ranked neoantigens with predicted binding affinities) is used to guide immunotherapy treatment decisions based on the result obtained from the claimed method. This ties the claims to the Vanda Pharmaceuticals line of reasoning, where claims applying information to a specific treatment context were found patent-eligible. The instant claims are directed to "a method of treating a disease using a neoantigen identified using the AI based molecular dynamics" obtained as described in claim 1. Based on the MEMORANDUM from the USPTO dated June 7, 2018 under the Subject of Recent Subject Matter Eligibility Decision: Vanda Pharmaceuticals Inc. v. West-Ward Pharmaceuticals, the subject matter of the instant claims are to be evaluated as a whole when determining the claim for patent eligibility
It is respectfully submitted that this is not persuasive because the fact that prediction of in silico binding affinities provide an "accurate prediction of in silico binding affinities for neoantigen-MHC protein complexes" does not negate the fact that mathematical operations are being applied to arrive at such prediction, thus the claims recite mathematical steps (i.e. judicial exceptions). The analysis at Step 2A, Prong 2, considers the claims as a whole, i.e., the additional elements in combination with the judicial exceptions (see MPEP 2106.05(a)), although the integration or improvement provided in the claim must flow from the additional elements and not the judicial exceptions to be considered persuasive.
Regarding the analogy to Vanda case law, the amended claims are not analogous because Vanda's case also involved the administration of a "particular treatment" when considered in context of the claim as a whole; which is not the case in the instant application. The claims in Vanda recited a method of treating a patient having schizophrenia with iloperidone, a drug known to cause QTc prolongation (a disruption of the heart’s normal rhythm that can lead to serious health problems) in patients having a particular genotype associated with poor drug metabolism. Whereas the instant claims do not recite a particular treatment and the recited "administering" step does not indicate how the judicial exception output impacts or influences this step. The claims recite an idea of a solution or outcome without any indication of how the judicial exception impacts or influences this step because there is no clear link between how the steps and data in step A1 to the step related to identifying the neoantigens in A2, therefore, using the information from step A2 in step A3 is not connected to A1.
Applicant asserts starting in pg. 8 para. 5:
The claims are not merely generic data processing. They operate on specific biological inputs: tumor exomes/transcriptomes, major clone genes and MSC genes selected from cancer cells, and, for example, six specific HLA types (HLA-A, HLA-B, HLA-C, HLA-DR, HLA-DP, and HLA-DQ). The method also includes genomic HLA typing with concrete steps such as collecting read sequences, aligning to a human reference genome, and determining types according to aligned ranks. These biologically specific operations are not the type of abstract concepts the 101 exception was designed to exclude. The claims also contain specific three-dimensional structural modeling including energy difference and RMSD difference, etc. This feature makes the claims more technically grounded and not a routine data analysis. For example, the IBAs are generated from MHC-peptide docking complexes and compared against specific structural features, which ties the AI model to a concrete physical/biological substrate rather than generic data. The specific method of treating a disease using a neoantigens identified by combination of a deep learning model with defined architecture applied to MHC-peptide docking simulations using Ramachandran plots and RMSD analysis is urged not to be routine and conventional and be patentably eligible. … Claim 1 now is directed to a method of treating a disease using neoantigens identified by AI-based method which in tum is incorporated into an immunotherapy. The method is not an operation which can be achieved in human mind. In addition, the AI-based method steps for identifying the neoantigens are also too complex to be performed in human mind.
It is respectfully submitted that this is not persuasive because the claimed method recites the argued " in steps "(C2) generating a phi-psi angle Ramachandran plot based on MHC-peptide docking data" (independent claim 1) and "(C3) comparing and determining the correlation between RMSD (Root Means Square Deviation) through the phi-psi angles and structures" (independent claim 1); which have been identified as mental processes under step 2A Prong 1. A human mind is sufficiently capable of identifying candidate neoantigens based on genomic data; filtering/ranking/comparing data, and generating a model/plot of angles and RMSD data using pen and paper along with comparing and determining an energy difference based on such comparison. The use of a 3D model to display the output of mathematical calculations reads on gathering and insignificant extra-solution activity related to instructions to apply the abstract idea using a computer under step 2A Prong 2. There are no additional limitations to indicate details of exactly how the judicial exception is being integrated by the additional elements. Claim 1 is not directed to a particular treatment because the claims recite an idea of a solution or outcome without any indication of how the judicial exception impacts or influences this step.
The judicial exceptions in the claims are considered to perform the claimed abstract idea with a computer, which is 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.
Claim Rejections - 35 USC § 103
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.
A. Claims 1-5, 8-10, 13 and 27-29 are rejected under 35 U.S.C. 103(a) as being unpatentable over Hacohen (KR-20130119845A – provided in IDS dated 09/13/2021 as cited on the 11/28/25 Form PTO-892 – in view of Patronov (“T-cell epitope vaccine design by immunoinformatics” Open Biol. 3(1):120139 (2013)- newly cited) in view of Brusic (“Prediction of MHC class II-binding peptides using an evolutionary algorithm and artificial neural network” Bioinformatics: 14:2 121-130 (1998) as cited on the 11/28/25 Form PTO-892) in view of Han (“Deep convolutional neural networks for pan-specific peptide-MHC class I binding prediction” BMC Bioinformatics 18:585 (2017) as cited on the 11/28/25 Form PTO-892) in view of Aggarwal (“Neural networks and deep learning” Vol. 10. No. 978. Cham: Springer, 2018) in view of Gundampati (“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) as cited on the 11/28/25 Form PTO-892). The instant rejection is newly stated and is necessitated by further consideration of the claims.
Claim 1 recites:
(A1) obtaining neoantigen immunotherapy information 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; …
(A2) identifying immunogenic neoantigens for the disease; and
(A3) administering an immunotherapy using the identified neoantigens to a subject in need thereof;
• 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 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) wherein the present invention provides a method of inducing a tumor specific immune response in a subject with said identified neoantigen by vaccinating a tumor, or treating and / or alleviating symptoms of cancer in a subject by administering a neoantigenic peptide or vaccine composition of the invention to a subject (i.e. reading on steps (A2) and (A3)) (pg. 31 col. 2 para. 4).
• Hacohen does not teach "(A1) obtaining neoantigen immunotherapy information using artificial intelligence ("AI)-based molecular dynamics." However Patronov teaches the study of MHC peptide–protein complexes via molecular dynamics simulations using energy scoring functions (i.e. reading on step (A1)) (pg. 8 col. 1 para. 3).
(D) obtaining and ranking TCR activity;
• Hacohen does not teach the recitation above. 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).
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
• Hacohen does not teach the recitation above. However, Han teaches encoding a peptide binding structure into an image like array wherein 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).
wherein the IBAs are produced using a deep learning AI model comprising at least 10 hidden layers and 128 neurons
• Hacohen does not teach the recitation above. However, 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).
wherein the IBAs are produced … and 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).
wherein 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 RMSD (Root Means Square Deviation) 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
• Hacohen does not teach the recitation above. However, Patronov teaches "(C1) performing dynamics simulation for MHC-peptide docking complexes" as the study of MHC peptide–protein complexes via molecular dynamics simulations using energy scoring functions (pg. 8 col. 1 para. 3). Furthermore, Gundampati teaches "performing dynamics docking simulation for protein-peptide complexes … determining the in silico binding affinities through an AI model based on features generated from protein-peptide complexes … generating a phi-psi angle Ramachandran plot based on docking complexes" as a molecular docking method performed for the molecular models of a ligand and a receptor to analyze the energy of binding affinity where stereochemical quality of the models was judged (i.e. reading on steps C3-C4) 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).
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).
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).
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
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 as in claims 4-5) of a patient (pg. 14 col. 2 para. 2-3 and Fig. 6).
Claim 8 recites:
wherein the HLA types of the cancer cells are selected through genomic HLA typing
Claim 9 recites:
wherein determination of the HLA types of the cancer cells is performed by a method comprising steps of:(al) 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), 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).
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).
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 and comparing and determining the energy difference and RMSD difference therebetween
• Hacohen does not teach the recitation above. 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 (i.e. obtaining a prediction value of the in silico binding), 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 (i.e. comparing and determining the energy difference and RMSD difference therebetween) (pg. 653 col. 2 para. 1), where the backbone rmsd of each complex was calculated (pg. 659 Table 3).
Claim 27 recites:
wherein the disease is a cancer
Claim 28 recites:
wherein the neoantigen is a cancel-specific neoantigens
Claim 29 recites:
wherein the genomic mutation presents in tumor exomes and tumor transcriptomes in a cancer patient biopsies.
• Hacohen teaches a method for generating tumor specific neoantigens via tumor genome sequencing to identify mutated genes (i.e. reading on cancer-specific neoantigens as in claims 27-28) (pg. 9 col. 2 para. 4) by identifying DNA mutations using whole genomes or whole exome (i.e. captured exons only) sequencing of tumor samples (i.e. reading genomic mutation presents in tumor exomes and tumor transcriptomes in a cancer patient biopsies as in claim 29) versus matched germline samples from each patient (pg. 9 col. 2 para. 4).
Rationale for combining (MPEP §2142-2143)
Regarding claims 1-5, 8-10, 13 and 27-29, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the method of Hacohen in view of Patronov, Brusic, Han, Aggarwal and Gundampati because all references disclose 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).
The motivation would have been to:
• effectively leverage computational techniques to deliver effective and utilitarian advantage in the search of new vaccines (pg. 8 col. 2 para. 3 Patronov);
• 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);
• generate more reliable prediction models (pg. 8 col. 1 para. 2 Han);
• provide a very low training error for the proposed algorithm (pg. 192 para. 1 Aggarwal); and
• 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).
Therefore it would have been obvious to one of ordinary skill in the art to substitute the prediction of MHC binding peptides using artificial intelligence of Hacohen to the methods by Patronov, Brusic, Han, Aggarwal and Gundampati because such a substitution is no more than the simple substitution of one known element for another. 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 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).
B. Claim 6 is rejected under 35 U.S.C. 103(a) as being unpatentable over Hacohen, Patronov, Brusic, Han, Aggarwal and Gundampati as applied to claim 1 above further in view of Ware (“Establishment of human cancer cell clones with different characteristics: a model for screening chemopreventive agents” Anticancer Research 27(1A):1-16 (2007) as cited on the 11/28/25 Form PTO-892).
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
• Neither Hacohen or Patronov or Brusic or Han or Aggarwal or Gundampati teach the recitation above. However, 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).
Rationale for combining (MPEP §2142-2143)
Regarding claim 6, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the method of Hacohen, Patronov, Brusic, Han, Aggarwal and Gundampati in view of Ware because all references disclose methods for screening molecules. The motivation would have been to screen for the chemopreventive efficacy of potential agents during cancer progression (pg. 1 col. 2 para. 1 Ware).
Therefore it would have been obvious to one of ordinary skill in the art to substitute the method for screening molecules of Hacohen, Patronov, Brusic, Han, Aggarwal and Gundampati to the methods by Ware because such a substitution is no more than the simple substitution of one known element for another. 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.
C. Claim 7 is rejected under 35 U.S.C. 103(a) as being unpatentable over Hacohen, Patronov, Brusic, Han, Aggarwal and Gundampati as applied to claim 1 above further in view of Wu (“Emerging roles of the host defense peptide LL-37 in human cancer and its potential therapeutic applications” Int. J. Cancer, 127:1741-1747 (2010) as cited on the 11/28/25 Form PTO-892).
Claim 7 recites:
wherein the neoantigen candidates in step (A) comprise mesenchymal stroma cell (MSC) genes selected from cancer cells, and wherein the mesenchymal stroma cell (MSC) genes selected from cancer cells are collected based on somatic mutation of genes expressed in the stroma cells
• Neither Hacohen or Patronov or Brusic or Han or Aggarwal or Gundampati teach the recitation above. However, 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).
Rationale for combining (MPEP §2142-2143)
Regarding claim 7, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the method of Hacohen, Patronov, Brusic, Han, Aggarwal and Gundampati in view of Wu because all references disclose methods for potential therapeutic application in cancer treatments. The motivation would have been to develop novel modulators of tumor growth and metastasis in carcinogenesis of various types of cancers cells (pg. 1745 col. 1 para. 2 Wu).
Therefore it would have been obvious to one of ordinary skill in the art to substitute the methods for potential therapeutic application in cancer treatments of Hacohen, Patronov, Brusic, Han, Aggarwal and Gundampati to the methods by Wu because such a substitution is no more than the simple substitution of one known element for another. 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 regard to Claim Rejection 35 U.S.C. ~ 103
The Remarks of 05/25/2026 have been fully considered but are not persuasive for the reasons below:
Applicant asserts starting in pg. 10 para. 3:
While the Examiner acknowledges that Nielsen does not teach a use of both sequence and 3D-structural data as in the present claim 1, the Examiner insisted that Nielsen can be an evidentiary art for the "suggestion to search for an alternative solution like a sequence based method."…Applicant respectfully note that the Examiner's argument is overbroad to assume that a simple suggestion for search of an alternative solution to be directed to the present invention. On the contrary, an ordinary skill in the art would not naturally pinpointedly go the method as presented in the instant claims. It would be like a searching a needle in a hay … The Examiner assumption is again overbroad that any suggestion can lead to the specifics of the present invention. It is like searching for a needle in a hay to search for the solution such as the present invention … The detailed method for step (C) are not taught or suggested by the cited references. The additional subject matter was previously presented in claim 14 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. Applicant still urges 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 …
It is respectfully submitted that the arguments are not persuasive because the Examiner is not assuming a simple suggestion for search of an alternative solution neither stating that one would naturally pinpointedly go to the method as presented in the instant claims. Each claim recited has been properly mapped and the teachings from each reference of the combination of references have been explained in detail, therefore one could easily arrive at the claimed invention; contrary to the analogy of searching a needle in a hay.
The Examiner agrees with the one argument regarding "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" and also agrees that Nielsen does not tech the use of molecular dynamics simulations for MHC complexes in steps (A2) and (C). Patronov – newly cited art – now addresses such limitation in step (A2) and – along with teachings by Gundampati – also addresses all sub steps in step (C).
Here, the combination of teachings by Hacohen, Patronov, Brusic, Han, Aggarwal and Gundampati allows one of ordinary skill to arrive at the claimed solution as described in detail in the rejections above. Obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007).
Applicant asserts starting in pg. 13 para. 2:
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... In addition, step (B) of claim 1 recite "filtering the specificities of the neoantigen candidates for tissue and disease" which is not taught by any of the cited references or in combination. As the Examiner indicated, Hacohen's method contains a step of" Identifying DNA mutations using whole exome sequencing of tumor samples from each patient" and 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 I or amount specific for tissues, cancers and/or patients in par. [O 138]. Hacohen teaches a method wherein neoantigen is configured to create a pharmaceutical composition, while the instant claim is directed to a method where the neoantigens are filtered by their specificities for tissue and disease. Thus, it is hardly that Hacohen teaches the subject matter which was presented in claims 1 and previously claim 14. The function of neoantigens are different in these two disclosures. The cited references teach separate techniques used for various reason in the molecular science or molecular modeling, etc. However, there is no proper reason for a person of ordinary skill in the art would have been motivated to combine limited to the teachings of the cited references. In other words, mere suggestion for searching an alternative solution, for example, would not be a sufficient motivation for an ordinary skill in the art to make the specifics of the present invention and thus, not an enough base for this rejection
It is respectfully submitted that this is not persuasive because the combination of teachings by Hacohen, Patronov, Brusic, Han, Aggarwal and Gundampati allows one of ordinary skill to arrive at the claimed solution as described in detail in the rejections above. The Examiner agrees with the one argument regarding "Nielsen does not in fact teach the limitation to render the instant claims obvious" and to address the limitations involving the use of molecular dynamics simulations for MHC complexes in steps (A2) and (C), the teachings by Patronov and Gundampati have been described.
The argument about the art to Nielsen referring to "this passage does not state that NetMHCpan uses three-dimensional structural data as an input" is not persuasive because Nielsen was not applied to the limitation related to 3D structural data, Han was. Han teaches encoding a peptide binding structure into an image like array wherein 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).
Regarding the argued "filtering" step, claim 1 recites "filtering the specificities of the neoantigen candidates for tissue and disease" and not "the neoantigens are filtered by their specificities for tissue and disease" as argued. The actual recited limitation is taught by the primary art to Hacohen in pg. 33 col. 2 para. 2 which recites 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; which reads on the filtering step.
The detailed explanation of subject matter suggested by a prior art references is entirely acceptable when establishing a rationale for obviousness: "to support the conclusion that the claimed invention is directed to obvious subject matter, either the references must expressly or impliedly suggest the claimed invention or the examiner must present a convincing line of reasoning as to why the artisan would have found the claimed invention to have been obvious in light of the teachings of the references" (Ex parte Clapp, 227 USPQ 972 at 973 (BPAI 1985)); "Office personnel may also take into account 'the inferences and creative steps that a person of ordinary skill in the art would employ' (KSR v. Teleflex Inc., 82 USPQ2d 1385 at 1396 (SC 2007))" (MPEP 2141 § 11.C); "the rationale to support a rejection under 35 U.S.C. 103 may rely on logic and sound scientific principle" (MPEP 2144.02). The examiner's explanation of how the combination of teachings read over the claimed invention presents a proper and convincing line of reasoning for a reasonable expectation of success, and takes into account and explains the motivation behind why one of ordinary skill would arrive at the claimed solution are described in detail in this office action.
Conclusion
No claims are allowed.
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
/JANNA NICOLE SCHULTZHAUS/Examiner, Art Unit 1685
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