DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 2/10/2026 has been entered and considered. Rejections and/or objections not reiterated from the previous office action mailed 12/16/2025 are hereby withdrawn. The following rejections and/or objections are either newly applied or are reiterated and are the only rejections and/or objections presently applied to the instant application.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Status of Claims
Claims 1-5, 7-8, 10, and 12-21 pending and examined on the merits.
Claims 6, 9, and 11 canceled.
Response to Amendments
Applicant claims there is support for the amendment of claim 1 filed on 2/10/2026 at least in paragraphs 0623-0660 in the US publication of the instant application, however the instant specification ends at para.0404 (filed 12/3/2021). Examiner notes support is found in the instant specification at para.0244 and 0261.
Priority
The instant application filed on 12/3/2021 is a 371 national stage entry of PCT/JP2020/022576 having an international filing date of 6/8/2020, and claims the benefit of priority to Japanese Patent Application No. 2019-106814 filed on 6/7/2019. Thus, the effective filing date of the claims is 6/7/2019.
The applicant is reminded that amendments to the claims and specification must comply with 35 U.S.C. § 120 and 37 C.F.R. § 1.121 to maintain priority to an earlier-filed application. Claim amendments may impact the effective filing date if new subject matter is introduced that lacks support in the originally filed disclosure. If an amendment adds limitations that were not adequately described in the parent application, the claim may no longer be entitled to the priority date of the earlier filing.
Specification
The disclosure is objected to because of the following informalities:
URL present on page 111 para.0309 of the specification
Appropriate correction is required.
Withdrawn Rejections
35 USC § 112(d)
The rejection of claim 7 under 35 U.S.C. 112(d) withdrawn in view of Applicant's claim amendments filed on 2/10/2026.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
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 of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
Claims 1-2 and 13-15 recites "An information processing system comprising at least one processor configured to: perform machine learning on the basis of first sequence information [to] generate a first trained model [and] generate a second trained model" and "actually measure characteristics of an actual antigen-binding molecule having the predicted values estimated". Here the generic placeholders which is used instead of means is “on the basis of" and "actually measure characteristics of".
It is noted that in the specification of the instant application there are descriptions of how the machine learning is performed, and on what data the training is performed on, in order to achieve a trained model: Para.0029 "a trained model is generated by performing machine learning on the basis of sequence information on antigen-binding molecules and evaluation result information on characterization of the antigen-binding molecules. Examples of a non-limiting mode of characterization of antigen-binding molecules include evaluation of affinity, evaluation of pharmacological activity, evaluation of physical properties, evaluation of kinetics, and evaluation of safety of antigen-binding molecules, but are not limited to these evaluations", para.0084-0094 gives an example of the data and database used for training the models, and para.0039 lists possible machine learning technique to employ.
It is noted that in the specification of the instant application there are descriptions of how the actual measuring of the characteristics is achieved, however, as noted below (section "Claim Rejections - 35 USC 112"), the written description of the instant specification does not explicitly disclose a means for "actually measuring" said characteristics: Para.0036-0037 "Evaluation of Physical Properties. The technique of evaluation of physical properties for antigen-binding molecules is not limited in any way, and examples of physical properties include thermal stability, chemical stability, solubility, viscosity, photostability, long-term storage stability, and non-specific adsorptivity. In evaluation of the various physical properties exemplified, they can be measured with methods known to those skilled in the art. The evaluation methods are not limited in any way, and, in evaluation of stability such as thermal stability, chemical stability, photostability, stability to mechanical stimulation, and long-term storage stability, for example, evaluation can be made by measuring the decomposition, chemical modification, and association of an antigen-binding molecule of interest before and after treatment intended the evaluation of stability such as heat treatment, exposure to a low-pH environment, exposure to light, stirring with a machine, and long-term storage. Examples of one non-limiting mode of the measurement method involving such evaluation of stability include techniques using chromatography such as ion-exchange chromatography chromatography and size exclusion chromatography, mass spectrometry, and electrophoresis, but are not limited thereto, and measurement can be performed with various techniques known to those skilled in the art. Examples of evaluation of physical properties other than the above-described evaluations include evaluation of solubility of protein by a polyethylene glycol precipitation method, evaluation of viscosity by a small-angle X-ray scattering method, and evaluation of non-specific binding based on evaluation of binding to the Extracellular Matrix (ECM), but are not limited thereto. Even for evaluation of protein expression levels, evaluation of binding to a resin for purification or ligand for purification, and evaluation of surface electric charge, evaluation can be made as evaluation of physical properties as long as measurement can be performed with a technique known to those skilled in the art. Evaluation of Kinetics. The technique of evaluation of kinetics for antigen-binding molecules is not limited in any way, and evaluation can be made by administering an antigen-binding molecule to an animal such as a mouse, a rat, a monkey, and a dog and measuring the amount of the antigen-binding molecule in the blood after administration over time, and evaluation can be made with a technique widely known to those skilled in the art as pharmacokinetics (PK) evaluation. In addition to the technique to directly evaluate PK, the behavior of an antigen-binding molecule in kinetics can be predicted from the amino acid sequence of an antigen-binding molecule by calculating the surface electric charge, isoelectric point, and so on of the antigen-binding molecule with software"
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-5, 7-8, 10, and 12-21 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claims 1-2 and 13-15, the metes and bounds are unclear. The claims fail to particularly point out and distinctly claim the steps for training the model, beyond "perform machine learning on the basis of first sequence information representing first sequences [] and thereby generate a first trained model that has learned characteristics of the sequences". Likewise, the approach used for model training of the second model, and their retraining is not clear. The claims fail to set forth the type of model which would appropriately process any potentially received data (the obtaining of which is also omitted) or the “generat[ing] virtual sequence information indicating a sequence obtained by mutating a first sequence represented by the first sequence information”. Furthermore, the type of training performed on the unspecified model fails to set forth how any data was received or obtained for the training, how that data is used for the training, or how it can be used to generate a virtual sequence. While claims are read in light of the specification, limitations from the specification cannot be read into the claims. To further prosecution, the limitation is interpreted as using at least one of the possible machine learning techniques indicated in para.0039 of the instant specification.
Additionally, claims 1-2 and 13-15 recite "actually measure characteristics of an actual antigen-binding molecule having the predicted values estimated" on lines 17-18. It is not clear what steps are being performed to "actually measure" characteristics (instrumentation, etc.), or what characteristics are being measured (binding affinity, pharmacological activity, etc.). Furthermore, it is not clear by what means "an actual antigen-binding molecule having the predicted values estimated" is being physically produced in order to be measured. While the instant specification discloses the synthesis and/or isolation of specific antibodies in para.0020, it remains silent regarding a means for "actually measuring" (see subsection 112(a) below).
Claim 15 recites "A method for producing an antigen-binding molecule with use of the information processing system according to claim 1". It is not clear how the "system according to claim 1" is a process for physically producing an antigen-binding molecule via some unspecified natural or technological process. Claim 1 under a broadest reasonable interpretation is a process for identifying a specific sequence of an antigen-binding molecule. Additionally, claim 15 attempts to claim a process without setting forth any steps involved in the process, because claim 1, to which claim 15 references, contains no steps for how the antigen-binding molecule is physically produced from the system of claim 1. Therefore, the claim is indefinite because it merely recites a use without any active, positive steps delimiting how this use is actually practiced similar to the findings in Ex parte Erlich, 3 USPQ2d 1011 (Bd. Pat. App. & Inter. 1986). While claims are read in light of the specification, limitations from the specification cannot be read into the claims. To further prosecution, the limitation is interpreted as "A method for identifying an antigen-binding molecule sequence with use of the information processing system according to claim 1".
All other claims depend from claims 1-2 and 13-15, therefore are also rejected as being indefinite under 35 USC 112(b).
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Regarding claims 1-2 and 13-15, the written description of the instant specification does not explicitly disclose a means to "actually measure characteristics of an actual antigen-binding molecule having the predicted values estimated". The computer system has no disclosed parts or features that are described for physical sample manipulation. To further prosecution, the limitation is interpreted as "actually measure characteristics of an actual antigen-binding molecule having the predicted values estimated by any testing method known in the art".
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 1-5, 7-8, 10, and 12-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of a mental process, a mathematical concept, organizing human activity, or a law of nature or natural phenomenon without significantly more. In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A, Prong 1). In the instant application, the claims recite the following limitations that equate to an abstract idea:
Claims 1-2 and 13-15: “perform machine learning on the basis of first sequence information representing first sequences including some or all of sequences of a plurality of antigen-binding molecules and thereby generate a first trained model that has learned characteristics of the sequences”, “perform machine learning on the basis of second sequence information representing second sequences including some or all of the sequences of a plurality of antigen-binding molecules and results of characterization of the antigen-binding molecules represented by the second sequences and thereby generate a second trained model that has learned predicted values for the characterization of the antigen-binding molecules”, and “perform further machine learning for the first trained model and the second trained model based on virtual sequence information of the actual antigen-binding molecule having the actually measured characteristics and characteristic information indicating the actually measured characteristics to newly generate the first trained model and the second trained model” provides mathematical calculations (performing machine learning to generate a trained model using sequence information [both simulated or actual] and executing arithmetic processing involves mathematical calculations) that are considered a mathematical concept, which is an abstract idea.
“generate virtual sequence information indicating a sequence obtained by mutating a first sequence represented by the first sequence information on the basis of the first trained model” and “newly generate the virtual sequence information based on the newly generated first trained model” provides an evaluation (generating information about a mutated sequence) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
Claim 12: “the at least one processor is configured to perform the machine learning on the basis of the sequence information represented by character strings, numeric vectors, or physical property values of constituent elements constituting sequences” provides mathematical calculations (performing machine learning to generate a trained model using sequence information) that are considered a mathematical concept, which is an abstract idea.
These recitations are similar to the concepts of collecting information, analyzing it, and displaying certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), organizing and manipulating information through mathematical correlations in Digitech Image Techs., LLC v Electronics for Imaging, Inc. (758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)) and comparing information regarding a sample or test to a control or target data in Univ. of Utah Research Found. v. Ambry Genetics Corp. (774 F.3d 755, 113 U.S.P.Q.2d 1241 (Fed. Cir. 2014)) and Association for Molecular Pathology v. USPTO (689 F.3d 1303, 103 U.S.P.Q.2d 1681 (Fed. Cir. 2012)) that the courts have identified as concepts that can be practically performed in the human mind or are mathematical relationships. Therefore, these limitations fall under the “Mental process” and “Mathematical concepts” groupings of abstract ideas. Additionally, while claims 1-2 and 13-15 recite performing some aspects of the analysis on “An information processing system” and “A non-transitory computer readable recording medium storing a program configured to allow a computer in an information processing system to execute”, there are no additional limitations that indicate that this requires anything other than carrying out the recited mental processes or mathematical concepts in a generic computer environment. Merely reciting that a mental process is being performed in a generic computer environment does not preclude the steps from being performed practically in the human mind or with pen and paper as claimed. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental processes” grouping of abstract ideas. As such, claims 1-5, 7-8, 10, and 12-21 recite an abstract idea (Step 2A, Prong 1: YES).
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). The judicial exceptions listed above are not integrated into a practical application because the claims do not recite an additional element or elements that reflects an improvement to technology. Specifically, the claims recite the following additional elements:
Claims 1-2 and 13: “An information processing system comprising at least one processor configured to” provides insignificant extra-solution activities (running a program on generic computer components) that do not serve to integrate the judicial exceptions into a practical application.
Claim 1: “input the virtual sequence information into the second trained model, execute arithmetic processing of the second trained model, and thereby estimate the predicted values for the characterization of the antigen-binding molecules with sequences represented by the inputted virtual sequence information” and “input the newly generated virtual sequence information into the newly generated second trained model to estimate the predicted values for the characterization of antigen-binding molecules with sequences represented by the newly generated virtual sequence information” provides insignificant extra-solution activities (inputting data and executing a model are pre-solution activities involving data manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
“actually measure characteristics of an actual antigen-binding molecule having the predicted values estimated” provides insignificant extra-solution activities (measuring protein characteristics is a post-solution activity involving data gathering steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 4: “changing at least one of constituent elements in preset sites including one or more of the constituent elements in a sequence” provides insignificant extra-solution activities (changing information is a post-solution activity involving data manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 10: “the at least one processor is configured to, according to the predicted values estimated by the estimator, output on the basis of virtual sequence information and the predicted values” provides insignificant extra-solution activities (outputting data from a model is a post-solution activity involving data manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 14: “A non-transitory computer readable recording medium storing a program configured to allow a computer in an information processing system to execute []” provides insignificant extra-solution activities (running a program on generic computer components) that do not serve to integrate the judicial exceptions into a practical application.
Claims 16-21: “the antigen-binding molecules are acquired from a sequence of panning in which a subsequent round of panning is carried out with a target antigen for the antigen-binding molecules that have appeared in an operation of panning in the previous round” and “the sequence of panning includes a plurality of rounds of panning, in the first round of panning, a collection of a plurality of molecules is subjected to panning, and in the second or later round of panning, a collection of antigen-non-binding molecules in the previous round is not subjected to panning” provides insignificant extra-solution activities (acquiring molecule samples by "panning" is a pre-solution activity involving sample and/or data gathering steps) that do not serve to integrate the judicial exceptions into a practical application.
The steps for changing, inputting, and outputting data, panning, and measuring protein characteristics are insignificant extra-solution activities that do not serve to integrate the recited judicial exceptions into a practical application because they are pre- and post-solution activities involving data gathering and manipulation, and sample gathering and manipulation steps (see MPEP 2106.04(d)(2)). Furthermore, the limitations regarding implementing program instructions do not indicate that they require anything other than mere instructions to implement the abstract idea in a generic way or in a generic computing environment. As such, this limitation equates to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. Therefore, claims 1-5, 7-8, 10, and 12-21 are directed to an abstract idea (Step 2A, Prong 2: NO).
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). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that are insignificant extra-solution activities that do not serve to integrate the recited judicial exceptions into a practical application, or equate to mere instructions to apply the recited exception in a generic way or in a generic computing environment.
As discussed above, there are no additional elements to indicate that the claimed “information processing system” nor the “non-transitory computer readable recording medium storing a program configured to allow a computer in an information processing system to execute” requires anything other than generic computer components in order to carry out the recited abstract idea in the claims. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. MPEP 2106.05(f) discloses that mere instructions to apply the judicial exception cannot provide an inventive concept to the claims. Additionally, the limitations for: changing, inputting, and outputting data; and measuring protein characteristics are insignificant extra-solution activities that do not serve to integrate the recited judicial exceptions into a practical application. Furthermore, no inventive concept is claimed by the limitations as they are demonstrated to be well-understood, routine, and conventional. Specifically for claims 16-21 as demonstrated by Abbott et al.: Under "Approaches to fine epitope mapping and their limitations", page 3 Peptide-based approaches: "In these methods, overlapping peptides are synthesized that cover the whole sequence of the antigen and are then immobilized onto a solid surface as an array and the binding to the antibody of interest is determined in an ELISA format.42-45 It is ideally suited to situations where the epitope is a linear peptide sequence, although it is also possible to constrain peptides via one or more disulphide bonds to mimic discontinuous and conformation-dependent epitopes.46 It is simple and quick to perform. A variation on the use of synthetic peptide libraries is the use of random phage libraries that can be linear or constrained by disulphide bonds.47-51 In this approach, very large phage libraries can be ‘panned’ with the antibodies and the recombinant peptide genes from binding phage particles can be sequenced to determine the sequences and therefore putative epitopes or ‘mimotopes’ as they are sometimes called" (Abbott et al. "Current approaches to fine mapping of antigen–antibody interactions." Immunology 142.4 (2014): 526-535).
The additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: No). As such, claims 1-5, 7-8, 10, and 12-21 are not patent eligible.
Response to Arguments under 35 USC § 101
Applicant’s arguments filed 2/10/2026 are fully considered but they are not persuasive.
Applicant asserts that claim 1 as amended "is not directed to an Abstract idea because [it] integrates the so-called abstract idea into a practical application" because it "defines an integral link or feedback loop between the actual measurement of the characteristics and the re-execution of the training and prediction process" (Remarks 2/10/2026 Page 3). Applicant further asserts that "since the actual measurement is utilized for the re-running the learning and prediction, it is not an insignificant extra-solution activity, and rather integrates the so-called abstract idea into a practical application" (Remarks 2/10/2026 Page 3). Again, Applicant cites para.0660 of the instant specification, which is not present, however they assert that amended claim 1 provides a significant technical advantage of converging sequence information into virtual sequences having "higher characteristics" (Remarks 2/10/2026 Page 3). Applicant further asserts that the additional steps of actually measuring characteristics of a physical antigen-binding molecule for generating training data for the first model amounts to significantly more than the so-called abstract idea (Remarks 2/10/2026 Pages 3-4). The Examiner notes that the feedback loop, while linking training and measuring, is also a routine optimization of the machine learning models being utilized. Furthermore, Examiner notes that the additional elements cited by Applicant of inputting and measuring data to and from the models are insignificant extra-solution activities that do not serve to integrate the recited judicial exceptions into a practical application, and no inventive concept is claimed by this amendment as inputting and measuring data are well-understood, routine, and conventional.
The Examiner also notes that MPEP 2106(I) states that if the claims are directed to a judicial exception, the second part of the Mayo test is to determine whether the claim recites additional elements that amount to significantly more than the judicial exception. Id. citing Mayo, 566 U.S. at 72-73, 101 USPQ2d at 1966). In the “search for an ‘inventive concept’” (the second part of the Alice/Mayo test), the additional elements identified do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception because inputting and measuring data (data gathering and manipulation steps) are all well-understood, routine, and conventional techniques that are insignificant extra-solution activities that do not serve to integrate the recited judicial exceptions into a practical application. Therefore, combining insignificant extra-solution activities with any of the identified judicial exceptions would not result in patent eligible subject matter because integrating well-understood, routine, and conventional techniques does not yield “significantly more” to a mental process, a mathematical concept, organizing human activity, or a law of nature or natural phenomenon.
Therefore, the rejection of claim 1 is maintained. Likewise, these same amendments also appear in claims 2 and 13-14, and their rejection is likewise maintained. All other claims depend from these claims and therefore are also maintained.
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 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-5, 7-8, 10, and 12-21 rejected under 35 U.S.C. 103 as being unpatentable over Bremel et al. (US-20170039314) in view of Shim et al. (US-20170362306) and Karimi et al. (Karimi et al., DeepAffinity: interpretable deep learning of compound–protein affinity through unified recurrent and convolutional neural networks, Bioinformatics, Volume 35, Issue 18, September 2019, Pages 3329–3338, https://doi.org/10.1093/bioinformatics/btz111).
Regarding claims 1-2 and 13-15, Bremel teaches performing machine learning on the basis of first sequence information representing first sequences including some or all of sequences of a plurality of antigen-binding molecules and thereby generate a first trained model that has learned characteristics (instant application para.0047 "The affinity information is information indicating whether each antibody binds to the target antigen (binding antibody) or not (non- binding antibody)") of the sequences (Para.0016 "In some embodiments, the processes further comprise constructing a neural network via the computer, wherein the neural network is used to predict the binding affinity to one or more MHC [major histocompatibility complex] binding region. In some embodiments, the neural network provides a quantitative structure activity relationship. In some embodiments, the first three principal components represent more than 80% of physical properties of an amino acid." and Para.0018 " the neural network is trained to predict binding to more than one MHC binding region").
Bremel also teaches inputting the virtual sequence information into the second trained model, execute arithmetic processing of the second trained model, and thereby estimate the predicted values for the characterization of the antigen-binding molecules with sequences represented by the inputted virtual sequence information (Para.0012 "The present invention is directed to a method for identification in silico of peptides and sets of peptides internal to or on the surface of microorganisms and cells which have a high probability of being effective in stimulating humoral and cell mediated immune responses. The method combines multiple predictive tools to provide a composite of both topology and multiple sets of binding or affinity characteristics of specific peptides within an entire proteome. This allows us to predict and characterize specific peptides which are B-cell epitope sequences and MHC binding regions in their topological distribution and spatial relationship to each other").
Bremel also teaches measuring characteristics of an actual antigen-binding molecule having the predicted values estimated (Testing protein properties (including pharmacological activity) identified by various methods is common practice as evidenced by Bremel (para.0006 "The field of reverse vaccinology adopts the approach of starting with the genome and identifying open reading frames and proteins which are suitable vaccine components and then testing their B-cell immunogenicity (Musser, J. M. 2006. Nat. Biotechnol. 24:157-158; Serruto, D., L. et al. 2009. Vaccine 27:3245-3250)").
Bremel also teaches: performing further machine learning for the first trained model and the second trained model based on virtual sequence information of the actual antigen-binding molecule having the actually measured characteristics and characteristic information indicating the actually measured characteristics to newly generate the first trained model and the second trained model; newly generating the virtual sequence information based on the newly generated first trained model; and inputting the newly generated virtual sequence information into the newly generated second trained model to estimate the predicted values for the characterization of antigen-binding molecules with sequences represented by the newly generated virtual sequence information. These limitations are akin to iterative training of models from measured data, which is similar in nature to a cross validation, where training data from measurements is used for training then testing the models. An iteration of producing predicted molecules, gathering data to validate those predictions, then using this data to further improve the models is conventional scientific experimentation and therefore would be obvious to one of ordinary skill in the art (para.0225 "In developing NN predictive tools, a common feature is a process of cross validation of the results by use of “training sets” in the “learning” process. In practice, the prediction equations are computed using a subset of the training set and then tested against the remainder of the set to assess the reliability of the method. Binding affinities of peptides of known amino acid sequence have been determined experimentally and are publicly available at http://mhcbindingpredictions.immuneepitope.org/dataset.html. During training, the experimentally determined natural logarithm of the affinity of the particular peptide was used as the output layer. Most of the available training sets consist of about 450 peptides, whose binding affinity to various MHC molecules have been determined in the laboratory").
Bremel does not explicitly teach generating virtual sequence information indicating a sequence obtained by mutating a first sequence represented by the first sequence information on the basis of the first trained model; performing machine learning on the basis of second sequence information representing second sequences including some or all of the sequences of a plurality of antigen-binding molecules and results of characterization of the antigen-binding molecules represented by the second sequences and thereby generate a second trained model that has learned predicted values for the characterization of the antigen-binding molecules; nor acquiring a selection condition for each of one or more characteristics selected, in a user terminal, from the at least one evaluation of the antigen-binding molecules; select the first sequence information that satisfies the selection condition; and performing the machine learning for learning the characteristics of the sequences, based on the selected first sequence information, and thereby generate the first trained model.
However, Shim teaches generating virtual sequence information indicating a sequence obtained by mutating a first sequence represented by the first sequence information on the basis of the first trained model (Para.0117 "First, for heavy chain and light chain CDR1s and CDR2s with only somatic hypermutation without recombination, CDR sequences were designed to have similar sequences and germline CDR sequences of the human-derived mature antibodies by introducing virtual mutations into the human germline CDR sequence, through simulation using a computer. 1,500 simulated sequences for each CDR were designed").
Shim also teaches acquiring a selection condition for each of one or more characteristics selected, in a user terminal, from the at least one evaluation of the antigen-binding molecules; select the first sequence information that satisfies the selection condition; and performing the machine learning for learning the characteristics of the sequences, based on the selected first sequence information, and thereby generate the first trained model (Para.0007 "The quality of individual clones constituting a library, that is, the expression property, stability, immunogenicity, and the like are factors that determine the performance of the antibody library. In antibody engineering perspective, these factors need to be considered during the design phase of the synthetic antibody library construction in order to select high-quality clones from the library", and the described process simply mimics the natural process of antibody selection of an immune response as evidenced by para.0008 "When antibodies are produced in an animal body, antibody sequences having very high diversity are generated through the recombination of tens to hundreds of germline immunoglobulin genes present in the genome, and, among those antibody sequences, antibodies responding to particular antigens are selected. The binding strength of the selected antibodies to antigens is improved through a hypermutation process, finally resulting in mature antibodies", and performing training using the select sequences is a kind of optimization of the model, which is generally considered routine optimization).
However, Karimi teaches performing machine learning on the basis of second sequence information representing second sequences including some or all of the sequences of a plurality of antigen-binding molecules and results of characterization of the antigen-binding molecules represented by the second sequences and thereby generate a second trained model that has learned predicted values for the characterization of the antigen-binding molecules (Page 3 col 2 section 2.3 paragraph 1 "We used a recurrent neural network (RNN) model, seq2seq (Sutskever et al., 2014), that has seen much success in natural language processing and was recently applied to embedding compound SMILES strings into fingerprints (Xu et al., 2017). A Seq2seq model is an auto-encoder that consists of two recurrent units known as the encoder and the decoder, respectively (see the corresponding box in Fig. 1). The encoder maps an input sequence (SMILES/SPS in our case) to a fixed-dimension vector known as the thought vector. Then the decoder maps the thought vector to the target sequence (again, SMILES/SPS here)").
Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the methods of Bremel as taught by Shim in order to design antibody libraries with excellent physical properties against many antigens (Abstract "The antibody library prepared according to the present invention contains antibodies having excellent physical properties against a plurality of antigens, thereby having functional diversity and containing a plurality of unique sequences, and thus can be favorably used as an antibody library"). One skilled in the art would have a reasonable expectation of success because both methods use binding affinity data and essentially are focused on designing antibody libraries.
Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the methods of Bremel and Shim as taught by Karimi because it is the most appropriate model for learning compound/protein representations (page 4 col 2 paragraphs 1-2 "Third, whereas bi-directional GRUs lowered perplexity by about 2 ~3.5 folds and the default attention mechanism did much more for compounds or proteins, they together achieved the best performances (perplexity being 1.0002 for compound SMILES and 1.001 for protein SPS). Therefore, the last seq2seq variant, bidirectional GRUs with attention mechanism, is regarded the most appropriate one for learning compound/protein representations and adopted thereinafter."). One skilled in the art would have a reasonable expectation of success because both approaches have the goal of modeling protein binding affinity.
Regarding claim 3, Bremel in view of Shim and Karimi teach the methods of Claim 1 on which this claim depends. Shim also teaches the character of the sequences is a character including positions of the constituent elements in the sequences and anteroposterior relationship of the constituent element (Para.0117 "First, for heavy chain and light chain CDR1s and CDR2s with only somatic hypermutation without recombination, CDR [complementarity-determining region] sequences were designed to have similar sequences and germline CDR sequences of the human-derived mature antibodies by introducing virtual mutations into the human germline CDR sequence, through simulation using a computer. 1,500 simulated sequences for each CDR were designed").
Regarding claim 4, Bremel in view of Shim and Karimi teach the methods of Claim 1 on which this claim depends. Shim also teaches the virtual sequence information is generated by changing at least one of the constituent elements in preset sites including one or more of the constituent elements in a sequence (same excerpt as claim 3, para.0117, mutations within the CDR sequence constitutes preset sites).
Regarding claim 5, Bremel in view of Shim and Karimi teach the methods of Claim 4 on which this claim depends. Shim also teaches the at least one preset sites is included in a sequence of a heavy chain variable region, a light chain variable region, or a constant region of an antibody (same excerpt as claim 3, para.0117, CDRs are within both the variable heavy- and light- chain domains).
Regarding claims 7 and 8, Bremel in view of Shim and Karimi teach the method of Claim 1 on which these claims depend. Karimi also teaches a sequence model that performs the machine learning with use of a deep learning model in the form of a gate recurrent unit (Page 3 col 2 section 2.3 paragraph 1 "We choose gated recurrent unit (GRU) (Cho et al., 2014) as our default seq2seq model and treat the thought vectors as the representations learned from the SMILES/SPS inputs.").
Regarding claim 10, Bremel in view of Shim and Karimi teach the methods of Claim 1 on which this claim depends. Shim also teaches an output configured to, according to the predicted values estimated by the estimator, output on the basis of virtual sequence information and the predicted values (Para.0117 "First, for heavy chain and light chain CDR1s and CDR2s with only somatic hypermutation without recombination, CDR sequences were designed to have similar sequences and germline CDR sequences of the human-derived mature antibodies by introducing virtual mutations into the human germline CDR sequence, through simulation using a computer. 1,500 simulated sequences for each CDR were designed").
Regarding claim 12, Bremel in view of Shim and Karimi teach the methods of Claim 1 on which this claim depends. Bremel also teaches the sequence learner performs the machine learning on the basis of the sequence information represented by character strings, numeric vectors, or physical property values of constituent elements constituting sequences (Para.0203 provides Table 1 displaying the training resources utilized for NN training: "General immunology resources", "Amino acid physical properties", "Web NN & Training sets", etc.).
Regarding claim 15, Bremel in view of Shim and Karimi teach the methods of Claim 1 on which this claim depends. Bremel also teaches the antigen-binding molecule or protein is represented by a virtual sequence, and a predicted value for characterization has been estimated for the virtual sequence (Para.0012 "The present invention is directed to a method for identification in silico of peptides and sets of peptides internal to or on the surface of microorganisms and cells which have a high probability of being effective in stimulating humoral and cell mediated immune responses. The method combines multiple predictive tools to provide a composite of both topology and multiple sets of binding or affinity characteristics of specific peptides within an entire proteome. This allows us to predict and characterize specific peptides which are B-cell epitope sequences and MHC binding regions in their topological distribution and spatial relationship to each other.").
Regarding claims 16-21, Bremel in view of Shim and Karimi teach the methods of Claims 1, 13, and 14 on which these claims depend. Shim also teaches the antigen-binding molecules are acquired from a sequence of panning in which a subsequent round of panning is carried out with a target antigen for the antigen-binding molecules that have appeared in an operation of panning in the previous round, and the sequence of panning includes a plurality of rounds of panning, in the first round of panning, a collection of a plurality of molecules is subjected to panning, and in the second or later round of panning, a collection of antigen-non-binding molecules in the previous round is not subjected to panning (Para.0082 "The term “panning” refers to a process of selectively amplifying only those clones that bind to a specific molecule from a library of proteins, such as antibodies, displayed on a phage surface. The procedure is that a phage library is added to a target molecule immobilized on the surface to induce binding, unbound phage clones are removed by washing, only bound phage clones are eluted and again infect the E. Coli host, and target-bound phage clones are amplified using helper phages. In most cases, this process is repeated three to four times or more to maximize the percentage of bound clones" and Figure 2).
Response to Arguments under 35 USC § 103
Applicant’s arguments filed 2/10/2026 are fully considered but they are not persuasive.
Applicant asserts that Karimi fails to teach or suggest training a first machine learning model on measured antigen-binding molecule characteristics, generating new sequence information, and using that as input into a second machine learning model to estimate predicted values of the newly generated molecule (Remarks 2/10/2026 Page 4). Applicant also asserts that Bremel and Shim do not cure these deficiencies and the cited art would not have rendered obvious amended claim 1, and therefore the independent claims 1-2 and 13-14 are allowable (Remarks 2/10/2026 Page 5). The Examiner notes that the amendments to the independent claims are similar in nature to a cross validation, where training data from measurements is used for training then testing the models which is taught by Bremel. An iteration of producing predicted molecules, gathering data to validate those predictions, then using this data to further improve the models is conventional scientific experimentation and therefore would be obvious to one of ordinary skill in the art (see details above).
Therefore, the rejection of claim 1 is maintained. Likewise, these same amendments also appear in claims 2 and 13-14, and their rejection is likewise maintained. All other claims depend from these claims and therefore are also maintained.
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Mason et al. "Deep learning enables therapeutic antibody optimization in mammalian cells by deciphering high-dimensional protein sequence space." BioRxiv (2019): 617860, utilizes deep learning of protein sequence space for antibody optimization
Conclusion
No claims are allowed.
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/R.A.P./Examiner, Art Unit 1686
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686