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 .
DETAILED ACTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office Action has been withdrawn pursuant to 37 CFR 1.114.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 September 1, 2026 has been entered.
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 and 4-15 are currently pending in the instant application. Claims 1, 4, 5 and 7-13 have been amended by Applicants’ amendment filed 07-28-2026-2026. No claims have been canceled by Applicants’ amendment filed 07-28-2026. Claim 2 has been canceled by Applicants’ amendment filed 07-28-2026.
Therefore, claims 1 and 4-15 are under consideration to which the following grounds of rejection are applicable.
Priority
The present application filed February 24, 2023 is a CON of PCT/KR2021/011388, filed August 25, 2021, which claims the benefit of Republic of Korea Patent KR10-2020-0107932, filed August 26, 2020.
Acknowledgment is made of Applicant's claim for foreign priority based on an application filed in the Republic of Korea on August 26, 2020.
Acknowledgment is made of Applicant's filing of a certified English translation of KR10-2020-0107932, in the reply filed 09-01-2026
Withdrawn Objections/Rejections
Applicants’ amendment and arguments filed July 28, 2026 are acknowledged and have been fully considered. The Examiner has re-weighed all the evidence of record. Any rejection and/or objection not specifically addressed below are herein withdrawn.
Claim Rejections - 35 USC § 102
The rejection of claims 1 and 4-15 is withdrawn under 35 U.S.C. 102(a1)/102(a2) as being anticipated by Xuelian Bai (hereinafter “Bai”) (Dissertation, EWHA Womans University, Department of Life Sciences, July 2020, 1-102) as evidenced by Villa et al. (hereinafter “Villa”) (US Patent No. 8735331, issued May 27, 2014).
Due to filing a certified English translation of the foreign priority document, Bai is not prior art to the instant claims.
In view of the withdrawn rejection, Applicant’s arguments are rendered moot.
Maintained Objections/Rejections
Claim Rejections - 35 USC § 112(b)
The rejection of claims 1 and 4-15 are maintained under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which applicant regards as the invention.
Claim 1 is indefinite as being incomplete for omitting essential steps, such omission amounting to a gap between the steps (See MPEP § 2172.01). The method recited in claim 1 including designing, selecting, synthesizing, etc. require steps that are not recited in the instant claim. The omitted steps can be, for example: “next generation sequencing”, “panning an antibody library against an antigen”, “obtaining a number of reads before panning the antibody library against an antigen”, “obtaining a number of reads after panning the antibody library against an antigen”, “calculating predicted scores”, “calculating enrichment scores”, “comparing predicted ES with calculated ES”, “selecting a candidate sequence”, etc. The equation recited in claim 1 requires panning an antibody library against an antigen, and acquiring a number of NGS reads before and after the step of panning. However, claim 1 does not recite the steps required to calculate and/or to predict an enrichment score upon which the design and/or selection of a candidate CDR-H3 sequence is based (See, as-filed Specification, Example 1, paragraph [0090] and, thus, the metes and bounds of the claim cannot be determined.
Claim 5-9 are indefinite for the recitation of the terms “CDR-H2”, “CDR-L1”, “CDR-L2”, “CDR-L3”, and “light chain” such as recited in claim 5, line 3 because it is unclear how these CDRs are designed and/or synthesized given that the machine learning model recited in instant claim 1 is only trained on CDR-H3 data, such that no information is provided to the model regarding any other CDRs and, thus, the metes and bounds of the claim cannot be determined.
Claims 5 is indefinite for the recitation of the term “excluding VH1, VH4 or VH5 family” such as recited in claim 5, line 4 because claim 5 depends from instant claim 1, wherein claim 1 does not recite any particular gene family from which the CDR sequences are derived from, such that VH1, VH4 or VH5 can be excluded and, thus, the metes and bounds of the claim cannot be determined.
Claims 7 and 8 are indefinite for the recitation of the terms “germline immunoglobulin gene”, “utilization frequencies”, “frequency of mutation”, and “natural human antibodies” such as recited in claim 7, lines 6 and 10 because claim 7 depends from instant claim 1, wherein claim 1 does not recite the presence of a plurality of germline immunoglobulin genes, utilization frequencies, frequency of mutation and/or natural human antibodies and, thus, the metes and bounds of the claim cannot be determined.
Claims 7, 8, 10 and 13-15 are indefinite for the recitation of the terms “natural human antibodies”, “natural human mature antibodies”, “human antibody”, and “the antibody” such as recited in claims 7, line 10. There is insufficient antecedent basis for the terms “natural human antibodies”, “natural human mature antibodies”, “human antibody”, and “the antibody” in the claims because claim 1, lines 1 and 4 recite the term “an antibody library” and “antibodies”.
Claims 8 and 10 are indefinite for the recitation of the terms “designed” and/or “designing” such as recited in claim 8, lines 5, 11, 16 and 19. There is insufficient antecedent basis for the terms “designed” and/or “designing” in the claim because claim 1, lines 2 and 6 recite the term “individually designing”.
Claim 9 is indefinite for the recitation of the terms “kappa light chain or lambda light chain” such as recited in claim 9, lines 4-5 because claim 5 depends from instant claim 1, wherein claim 1 does not recite the presence of only human antibodies, which comprise the kappa light chain and lambda light chain and, thus, the metes and bounds of the claim cannot be determined.
Claim 10 is indefinite for the recitation of the terms “designing the candidate CDR-H3 sequence comprises…and calculated frequencies”, “natural human mature antibodies” and “natural human antibodies” such as recited in claim 10, lines 2-11 and 15-16 because claim 10 depends from instant claim 1, wherein claim 1, lines 6-10 already recites what “designing” comprises; and instant claim 1 does not recite the presence of “natural human antibodies and/or “natural human mature antibodies” and, thus, the metes and bounds of the claim cannot be determined.
Claim 10 is indefinite for the recitation of the terms “designing the candidate CDR-H3 sequence”, and “simulating” such as recited in claim 10, lines 2, 10 and 15-16. There is insufficient antecedent basis for the terms “designing”, “the candidate” and “natural human antibodies” because claim 1, line 2 recites the terms “individually designing” and “CDR-H3”, while claim 10, line 10 recites the term “natural human mature antibodies”. Moreover, instant claim 1 does not recite a step of “designing a candidate CDR-H3 sequence”, the presence of natural human antibodies, and/or simulating and, thus, the metes and bounds of the claim cannot be determined.
Claim 10 is indefinite for the recitation of the terms “designing the 3 amino acids” such as recited in claim 10, line 7 because it is unclear whether the “3 amino acids” referred to in line 7 are the “3 amino acids” excluded from the C-terminus in line 3, or whether the term refers to some other 3 amino acids. Moreover, claim 10 depends from claim 1, wherein claim 1 does not recite a step of designing 3 amino acids and, thus, the metes and bounds of the claim cannot be determined.
Claim 12 is indefinite for the recitation of the terms “potential” and “strongly” in claim 12, lines 3 and 4 because the terms “potential” and “strongly” are relative terms that render the claim indefinite. The terms “potential” and “strongly” are not defined by the claim, and the Specification does not provide a standard for ascertaining the requisite amount of “potential” and/or binding strength as compared to some other values that qualifies as a “potential” predicted immunogenic sequence and/or what qualifies as binding “strongly” to a MHC class II allele, such that one of ordinary skill in the art would not be reasonably appraised of the scope of the invention and, thus, the metes and bounds of the claim cannot be determined.
Claim 12 is indefinite for the recitation of the terms “potential immunogenic sequences predicted”, “in silico” and MHC class II allele” such as recited in claim 12, lines 3-4 because claim 12 depends from instant claim 1, wherein claim 1 does not recite the presence of potential immunogenic sequences predicted, in silico methods, and/or MHC class II alleles. Moreover, it is unclear what the term “in silico” refers to including whether the term refers to in silico excluding, in silico prediction, in silico binding, etc. and, thus, the metes and bounds of the claim cannot be determined.
Claims 4 and 11 are indefinite insofar as they ultimately depend from instant claim 1.
Claim Rejections - 35 USC § 112(d)
The rejection of claims 5-10 and 12 is maintained, and claim 9 is newly rejected, under 35 U.S.C. 112(d) as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends.
Claim 5 recites (in part): “wherein the individually designing further comprises…excluding sequences derived from a VH1, VH4 or VH5 family” in lines 1-3 because claim 5 depends from instant claim 1, wherein claim 1 does not recite any particular gene family from which the CDR sequences are derived from, such that VH1, VH4 or VH5 can be excluded. Moreover, the machine learning model recited in instant claim 1 is only trained on CDR-H3 data, such that the machine learning model is not capable of designing CDR-H2 sequences. Thus, claim 5 is an improper dependent claim for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends.
Claim 6 recites (in part): “wherein the antibody library comprises a polymorphism in a heavy chain complementarity determining region 1 (CDR-H1)…in the antibody library” in lines 2-7 because claim 6 depends from instant claim 1, wherein claim 1 recites a machine learning model recited in instant claim 1 is only trained on CDR-H3 data, such that the machine learning model is not capable of designing CDR-H1, CDR-H2, CDR-L1, CDR-L2 and CDR- L3 sequences. Thus, claim 6 is an improper dependent claim for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends.
Claim 7 recites (in part): “wherein the individually designing further comprises…to the CDR-L3 being designed” in lines 2-10 because claim 7 depends from instant claim 1, wherein claim 1 does not recite the presence of a device to simulate utilization frequency and/or natural human antibodies. Additionally, not all cells (including cells from all species) comprise immunoglobulin genes and/or immunoglobulin genes in a germline configuration. Moreover, the machine learning model recited in instant claim 1 is only trained on CDR-H3 data, such that the machine learning model is not capable of designing CDR-L1, CDR- L2 and/or CDR-L3 sequences as recited in claim 7. Thus, claim 7 is an improper dependent claims for failing to further limit the subject matter of the claim upon which they depend, or for failing to include all the limitations of the claim upon which it depends.
Claim 8 recites (in part): “wherein the individually designing further comprises…to the CDR-L3 being designed” in lines 2-19 because claim 8 depends from instant claim 1, wherein claim 1 does not recite the presence of a device to simulate utilization frequency and/or natural human antibodies. Additionally, not all cells (including cells from all species) comprise immunoglobulin genes and/or immunoglobulin genes in a germline configuration. Moreover, the machine learning model recited in instant claim 1 is only trained on CDR-H3 data, such that the machine learning model cannot design CDR-L3 sequences recited in claim 8. Thus, claim 8 is an improper dependent claims for failing to further limit the subject matter of the claim upon which they depend, or for failing to include all the limitations of the claim upon which it depends.
Claim 9 recites (in part): “wherein the individually designing further comprises…a lambda light chain” in lines 1-4 because claim 9 depends from instant claim 1, wherein claim 9 does not recite the presence of only human antibodies, which comprise the kappa light chain and lambda light chain. Moreover, the machine learning model recited in instant claim 1 is only trained on CDR-H3 data, such that the machine learning model is not capable of designing CDR-L1, CDR- L2 and/or CDR-L3 sequences. Thus, claim 9 is an improper dependent claim for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends.
Claim 10 recites (in part): “wherein the designing of the CDR-H3 sequences comprises designing each sequence…CDR-H3 sequence being designed” in lines 2-17 because claim 1 already recites what “designing the CDR-H3 comprises” and instant claim 1 does not recite designing a candidate CDR-H3 sequence, the presence of natural human mature antibodies, humans, human samples, natural human antibodies, designing a candidate CDR-H3 sequence, and/or simulating. Thus, claim 10 is an improper dependent claim for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends.
Claim 12 recites (in part): “further comprising: after the individually designing, deimmunizing…at least one MHC class II allele” in lines 2-4 because claim 12 depends from instant claim 1, wherein claim 1 does not recite the presence of potential immunogenic sequences, predicting such sequences, in silico methods, and/or any binding including to MHC class II alleles. Thus, claim 12 is an improper dependent claim for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends.
Applicant may cancel the claim, amend the claim to place the claim in proper dependent form, rewrite the claim in independent form, or present a sufficient showing that the dependent claim complies with the statutory requirements.
Response to Arguments
Applicant’s arguments filed July 28, 2026 have been fully considered but they are not persuasive. Applicants essentially assert that: (a) regarding claims 5, 7 and 8 each further recite that designing step by specifying additional design constraints or methodologies (Applicant Remarks, pg. 11, fourth full paragraph); and (b) claim 12 recites an additional processing step that is proper feature under 35 USC 112(d) (Applicant Remarks, pg. 11, last partial paragraph through pg. 12, first partial paragraph).
Regarding (a), with respect to claims 5, 7 and 8, it is noted that the machine learning model recited in instant claim 1 is only trained on CDR-H3 data, such that the machine learning model can only be used to design CDR-H3 sequences. Moreover, the antibodies recited in claim 1 are not identified, wherein not all antibodies comprise VH1, VH4 or VH5 gene families (claims 5, 7 and 8); instant claim 1 does not recite any device that can simulate utilization frequency, and claim 1 does not recite the presence of natural human antibodies (claims 7 and 8). Additionally, not all cells (including cells from all species) comprise immunoglobulin genes and/or immunoglobulin genes in a germline configuration (claims 7 and 8). Thus, the claims remain rejected.
Regarding (b), claim 12 depends from instant claim 1, wherein claim 1 does not recite the presence of potential immunogenic sequences, predicting such sequences, in silico methods, and/or any binding including to MHC class II alleles. Thus, the claim remains rejected.
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.
The rejection of claims 1 and 4-15 is maintained under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. An analysis with respect to the claims as a whole reveals that they do not include additional elements that are sufficient to amount to significantly more than the judicial exception. See; Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 134 S. Ct. 2347, 110 U.S.P.Q.2d 1976 (2014); Ass’n for Molecular Pathology v. Myriad Genetics, Inc., 133 S. Ct. 2107, 2116, 106 U.S.P.Q.2d 1972 (2013); Mayo Collaborative Svcs. v. Prometheus Laboratories, Inc., 132 S. Ct. 1289, 101 U.S.P.Q.2d 1961 (2012). See also 2014 Interim Guidance on Patent Subject Matter Eligibility, available at http://www.gpo.gov/fdsys-/pkg/FR-2014-12-16/pdf/2014-29414.pdf (“2014 Interim Guidance”), and the Office’s examples to be considered in conjunction with the 2014 Interim Guidance in examination of nature-based products, available online at: http://www.uspto.gov/patents/law/exam/mdc_examples_ nature-based_products.pdf (“Nature-Based Products Examples”). This rejection is proper.
Analysis of subject-matter eligibility under 35 U.S.C. § 101 requires consideration of three issues: (1) whether the claim is directed to one of the four categories recited in §101; (2) whether the claim recites or involves a judicial exception (i.e., a law of nature, natural phenomenon, or natural product); and (3) whether the claim as a whole recites something that amounts to significantly more than the judicial exception. The claims as a whole are directed to a method of preparing an antibody library, comprising a method of preparing an antibody library, comprising; individually designing complementarity determining region (CDR) sequences of antibodies; and synthesizing antibodies including the individually designed CDR sequences to prepare the antibody library, wherein the individually designing comprises designing a heavy chain complementarity determining region 3 (CDR-H3) by predicting enrichment scores of candidate CDR-H3 sequences using a machine learning model and selecting a candidate CDR-H3 sequence based on the predicted enrichment scores, wherein the machine learning model is trained by setting, as input values, sequence information of at least one CDR-H3 sequence and setting, as target output values, enrichment scores calculated according to following Equation 1 based on Next Generation Sequencing (NGS) reads obtained from the antibody library before panning against an antigen and NGS reads obtained from the antibody library after panning against the antigen:
Equation 1:
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100
900
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and wherein in the Equation 1, Npre is a total number of NGS reads of the antibody library including at least one of the candidate CDR-H3 sequences before panning, Npost is a total number of NGS reads of the antibody library after panning, ni-pre is a number of reads of a specific CDR-H3 sequence i in the antibody library before panning, ni-post is a number of reads of the specific CDR-H3 sequence i in the antibody library after panning, npre is a set of read numbers of the individually designed CDR sequences in the antibody library before panning, npost is a set of read numbers of the individually designed CDR sequences in the antibody library after panning, median(npre) is a median of the npre, and median(npost) is a median of the npost. In the instant case, the claims are directed to an abstract idea. Therefore, they must each be considered to determine whether, given their broadest reasonable interpretation, they amount to significantly more than the judicial exception.
The claimed invention is not directed to patent eligible subject matter. Based upon an analysis with respect to the claims as a whole, claims 1 and 4-15 do not recite something significantly different than a judicial exception. The rationale for this determination is explained below:
In the instant case, claim 1 is broadly directed to a method of preparing an antibody library, comprising individually designing CDR sequences of antibodies; and synthesizing antibodies including the designed CDR sequences, wherein designing comprises designing a CDR-H3, predicting enrichment scores using a machine learning model; and selecting a candidate CDR-H3 sequence based on the predicted enrichment score using equation 1
Beginning with Step I of the analysis, which asks whether the claimed invention falls within a statutory category, such that the instant claims are directed to a process, thus, the instant claims are directed to a statutory category. Step I [YES].
Proceeding to Step IIA – Prong One of the analysis, which asks if the claimed invention is directed to a judicial exception. Instant claims 1 and 4-15 are directed to an abstract idea in the form of mathematical concepts such as mathematical relationships, formulas or equations, and/or calculations; and/or mental process such as observation, judgement and opinion (e.g., individually designing CDR sequences including designing CDR-H3; encompassing in silico design; machine learning models; predicting and calculating enrichment scores using Equation 1, etc.); where the instant claims are directed to a generic machine learning model, a generic in silico system and/or a generic simulation program, such that the invention merely applies generic machine learning and generic computer systems/programs to a new data environment. The claims broadly recite a method of preparing an antibody library. Step IIA – Prong One [YES].
Step IIA - Prong Two asks whether the claim recites additional elements that integrate the exception into a practical application of the exception. In the instant case, the claims are directed to a judicial exception in the form of a natural product and an abstract idea. Claim 1 recites: a method of preparing an antibody library, comprising: “individually designing CDR sequences of antibodies” in lines 2-3; “synthesizing antibodies including the individually designed CDR sequences to prepare the antibody library” in lines 4-5; “wherein individually designing comprises designing CDR-H3 by predicting enrichment scores of candidate CDR-H3 sequences using a machine learning model and selecting a candidate CDR-H3 sequence based on the predicted enrichment scores” in lines 6-10; “wherein the machine learning model is trained by setting, as input values, sequence information of at least one CDR-H3 sequence and setting, as target output values, enrichment scores calculated according to following Equation 1 based on Next Generation Sequencing (NGS) reads obtained from the antibody library before panning against an antigen and NGS reads obtained from the antibody library after panning against the antigen: Equation 1
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”
in lines 11-18; “and, wherein in the Equation 1, Npre is a total number of NGS reads of the antibody library
including at least one of the candidate CDR-H3 sequences before panning Npost is a total
number of NGS reads of the antibody library after panning ni-pre is a number of reads of a
specific CDR-H3 sequence i in the antibody library before panning ni-post is a number of reads of
the specific CDR-H3 sequence i in the antibody library after panning npre is a set of read
numbers of the individually designed CDR sequences in the antibody library before panning.
npost is a set of read numbers of the individually designed CDR sequences in the antibody library
after panning, median(npre) is a median of the npre, and median(npost} is a median of the npost” in lines 19-27. These limitations simply describe a process of collecting and analyzing information, which is analogous to “obtaining and comparing intangible data” (i.e. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 99 U.S.P.Q.2d 1690 (Fed. Cir. 2011)); as well as, “collecting information, analyzing it, and displaying certain results of the collection analysis” (i.e. Electric Power Group, LLC, v. Alstom, 830 F.3d 1350, 119 U.S.P.Q.2d 1739 (Fed. Cir. 2016)). Moreover, many of Applicant’s process steps can be practiced as a mental process performed in the human mind, by pen and paper, or through the use of a generic computer, for example, “comparing information regarding a sample or test subject to a control or target data” (i.e. Univ. of Utah Research Found. v. Ambry Genetics Corp. (Also known as In re BRCA1– and BRCA2–Based Hereditary Cancer Test Patent Litigation), 774 F.3d 755, 113 U.S.P.Q.2d 1241 (Fed. Cir. 2014) or Association for Molecular Pathology v. USPTO (Also known as Myriad CAFC), 689 F.3d 1303, 103 U.S.P.Q.2d 1681 (Fed. Cir. 2012)). Hence, these limitations are akin to an “abstract idea itself” which was at issue in Alice Corp. and has been identified among non-limiting examples to be an abstract idea. Additionally, the dependent limitations of claims 4-15 also suffer from the same issue. In other words, the dependent limitations do not rectify the rejection of the independent claim. By way of example, the limitations of claim 4 recites “wherein the selecting the candidate CDR-H3 sequence based on the predicted enrichment scores comprises selecting the candidate CDR-H3 sequence with the enrichment scores calculated to be above 0”; which clearly resembles “organizing information through mathematical correlations” (i.e. Digitech Image Techs., LLC v Electronics for Imaging, Inc., 758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)); and is analogous to “obtaining and comparing intangible data” (i.e. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 99 U.S.P.Q.2d 1690 (Fed. Cir. 2011)); “collecting information, analyzing it, and displaying certain results of the collection analysis” (i.e. Electric Power Group, LLC, v. Alstom, 830 F.3d 1350, 119 U.S.P.Q.2d 1739 (Fed. Cir. 2016)); and “comparing information regarding a sample or test subject to a control or target data” (i.e. Univ. of Utah Research Found. v. Ambry Genetics Corp. (Also known as In re BRCA1– and BRCA2–Based Hereditary Cancer Test Patent Litigation), 774 F.3d 755, 113 U.S.P.Q.2d 1241 (Fed. Cir. 2014) or Association for Molecular Pathology v. USPTO (Also known as Myriad CAFC), 689 F.3d 1303, 103 U.S.P.Q.2d 1681 (Fed. Cir. 2012)). The claims do not provide other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment, e.g., an immunization step that integrates an abstract idea of data comparison into a specific process of immunizing that lowers the risk that immunized patients will later develop chronic immune-mediated diseases, as discussed in Classen Immunotherapies Inc. v. Biogen IDEC, 659 F.3d 1057, 1066-68, 100 USPQ2d 1492, 1499-1502 (Fed. Cir. 2011) (see MPEP § 2106.05(e)). Thus, the claims do not integrate the judicial exceptions into a practical application of the exceptions. Step IIA – Prong Two [NO].
Proceeding to Step IIB of the analysis: the question then becomes what element or what combination of elements is sufficient to amount to significantly more than the abstract idea?
The instant independent claim is recited at a high level of generality, such that substantially all practical applications of the judicial exception are covered. For instance, claim 1 is recited without any specificity as to identity of the antibodies in the library; the structure of the antibodies; the number of antibodies in the library; the method of individually designing CDR sequences (e.g., algorithms, limitations, assumptions, specifications, etc.); the identity of the machine learning model; the method of synthesizing; the method of predicting; the method of selecting; the method of setting training values; the training values, input values, and sequence information of the at least one CDR-H3 sequence; the enrichment scores obtained; the cut-off values for selection; how ES are calculated and/or predicted without the steps of NGS, panning, obtaining NGS reads, an antigen, etc.; the number of reads obtained before and after panning; the candidate sequences selected; the pre-panning and post-panning read numbers, the pre-panning and post-panning median read numbers, etc., wherein the steps of the method are well-known in the art.
For example, the use of high-capacity machine learning for antibody complementarity determining region design including CDR-H3; as well as, wherein the machine learning model is trained, such that it predicts panning enrichment, and provides antibody sequence enrichment data is known in the art as evidenced by Zeng (Dissertation, 2019, 133-137; and Figure 7-1); and wherein determining enrichment scores was known in the art as evidenced by Jang (Molecular Cells, 2019; pg. 240, col 2). Moreover, it was known that many large synthetic antibody libraries have been designed, constructed, and successfully generated high-quality antibodies suitable for various demanding applications, such that while synthetic antibody libraries have many advantages such as optimized framework sequences and a broader sequence landscape than natural antibodies, their sequence diversities typically are generated by random combinatorial synthetic processes which cause the incorporation of many undesired CDR sequences, wherein the construction of a synthetic scFv library is described using oligonucleotide mixtures that contain predefined, non-combinatorially synthesized CDR sequences, such that each CDR is first inserted to a master scFv framework sequence and the resulting single-CDR libraries are subjected to a round of proofread panning, where the proofread CDR sequences are assembled to produce the final scFv library with six diversified CDRs as evidenced by Bai and Shim (Methods in Molecular Biology, 2017, Chapter 2, Abstract). It is also known that the ability of monoclonal antibodies to specifically bind a target antigen and neutralize or stimulate its activity is the basis for the rapid growth and development of the therapeutic antibody field, such that in recent years, traditional immunoglobulin antibodies have been further engineered for better efficacy and safety, and technological developments in the field enabled the design and production of engineered antibodies capable of mediating therapeutic functions hitherto unattainable by conventional antibody formats; and representative of this newer generation of therapeutic antibody formats are bispecific antibodies and antibody–drug conjugates, each with several approved drugs and dozens more in the clinical development phase as evidenced by Shim (Biomolecules, 2020, Abstract); and a method for constructing an antibody library, wherein in the individual designing of the complementarity determining region sequences, for CDR-H1, CDR-H2, CDR-L1, or CDR-L2, the sequences therefor are designed by simulating i) an utilization frequency of each germline immunoglobulin gene, ii) a frequency of mutation into any amino acid types by somatic hypermutations at each amino acid position, iii) a length distribution frequency of sequences comprising each complementarity determining region, or iv) a frequency of each amino acid at each position calculated by analyzing a combination thereof, of the complementarity determining regions of actual human-derived mature antibodies was known in the art as evidenced by Shim (US2017362306, paragraph [0029]). Thus, the claims as a whole simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality to the judicial exception, wherein the steps are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 134 S. Ct. at 2359-60, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)). Step IIA [YES].
In sum, when the relevant factors are analyzed, the claims as a whole do NOT recite additional elements that amount to significantly more than the judicial exception itself. Accordingly, claim 1 DOES NOT qualify as eligible subject matter.
Dependent claims 4-15 when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because they do not add anything that makes the abstract idea recited in claim 1, significantly different. For example, claim 5 encompasses the method of claim 1, wherein the individually designing further comprises designing a heavy CDR-H2 by excluding sequences derived from a VH1, VH4 or VH5 family, but it does not add anything that makes the abstract idea in claim 1 significantly different.
In light of the above consideration and the new guidance, claims 1 and 4-15 are non-statutory. This rejection is newly recited as necessitated by the new Guidance set forth in the Memorandum of July 30, 2015 updating the June 25, 2014 guidance (see June 25, 2014 memorandum from Deputy Commissioner for Patent Examination Policy Andrew Hirshfeld titled Preliminary Examination Instructions in view of the Supreme Court Decision in Alice Corporation Pty. Ltd. v. CLS Bank International, et al. (Alice Corp. Preliminary Examination Instructions) and the Revised Patent Subject Matter Eligibility Guidance (See, Federal Register, vol. 84, No. 4, January 7, 2019).
Response to Arguments
Applicant’s arguments filed July 28, 2026 have been fully considered but they are not persuasive. Applicants essentially assert that: (a) the claims are not directed to a judicial exception because claim 1 does not recite a mere mathematical concept or mental process because the claim, under its broadest reasonable interpretation, does not cover performance in the mind. For example, claim 1 recites synthesizing antibodies (Applicant Remarks, pg. 14, last partial paragraph; and pg. 15, first partial paragraph); (b) even if the patent application claims were directed to a judicial exception such as an abstract idea, then the judicial exception is not "directed to" the judicial exception because the judicial exception is integrated into a practical application of the judicial exception; and because the additional elements amount to significantly more than the exception itself because the claimed combination as a whole improves existing biotechnology process for constructing antibody libraries rather than merely performing data analysis. Thus, the claim recites "significantly more" than the alleged abstract idea and the claim is eligible (Applicant Remarks, pg. 15, first partial paragraph; pg. 16, last partial paragraph; and pg. 17); and (c) the claims are not well understood, routine or conventional in the art (Applicant Remarks, pg. 18 through pg. 19, first partial paragraph).
Regarding (a), the Examiner contends that the claims are directed to an abstract idea in the form of mathematical concepts such as mathematical relationships, formulas or equations, and/or calculations; and/or mental process such as observation, judgement and opinion including, for example, designing CDR-H3 by predicting enrichment scores of candidate CDR-H3 sequences using a machine learning model and selecting sequences based on the predicted enrichment score; the machine learning model is trained by setting values including input values, sequence information, target values, etc.; calculating enrichment scores using equation 1, wherein equation 1 requires NGS, obtaining numbers of reads pre-panning, obtaining numbers of reads post-panning against an antigen, and median read numbers without reciting the steps of NGS, panning against an antigen, obtaining read numbers, etc. Although Applicant states that the claims recite operations that require high-throughput sequencing instruments and bioinformatic processing of large sequencing datasets (pg. 16), the instant claims do not recite NGS sequencing, sequencing instruments, and/or bioinformatic processing of large sequencing datasets. Additionally, it is noted that the instant claims, the as-filed Specification and/or the claims as originally filed do not recite, teach and/or disclose any specific machine learning model, in silico system and/or simulation program, such that the invention merely applies generic machine learning and generic computer systems/programs to a new data environment. The claims are clearly directed to a judicial exception without significantly more. Thus, the claims remain rejected for the reasons already of record.
Regarding (b), it is unclear to the Examiner how any library of antibodies is prepared when the claims do not recite critical steps that are necessary for the design and selection of CDR sequences for the preparation of an antibody library. For example, Equation 1 requires obtaining the number of NGS reads from pre-panning and post-panning with an antigen, as well as, median numbers of reads. However, claim 1 does not recite the steps of: next generation sequencing, panning an antibody library against an antigen, obtaining a number of reads before panning the antibody library against an antigen, obtaining a number of reads after panning the antibody library against an antigen, calculating predicted scores, calculating enrichment scores, etc. Moreover, the claims are very broadly recited such that the machine learning model, the settings, input values, sequence information, targets output values, antibody structures, reference values for selecting a CDR-H3 sequence, the cut-off values with enrichment scores for selecting a candidate CDR-H3 sequence for all machine learning models, etc. are not identified. Clearly, one of ordinary skill in the art could not use the method as recited in the instant claims to individually design and prepare an antibody library having the properties as recited in the as-filed Specification. Thus, the claims do not integrate the judicial exception into a practical application of the exception, and they do not recite significantly more than the abstract idea. Thus, the claims remain rejected.
Regarding (c), as previously noted, the steps of designing and synthesizing antibody libraries are well-understood, routine, conventional activities as evidenced by the cited references. For example:
The design and synthesis of large libraries of quality antibodies including scFv libraries with six diversified CDRs is known in the art as evidenced by Bai and Shim.
The individual designing of the complementarity determining region sequences, for CDR-H3, CDR-H1, CDR-H2, CDR-L1, or CDR-L2, synthetic antibody library construction, wherein the sequences therefor are designed by simulating (i) a utilization frequency of each germline immunoglobulin gene, (ii) a frequency of mutation into any amino acid types by somatic hypermutations at each amino acid position, (iii) a length distribution frequency of sequences comprising each complementarity determining region, or (iv) a frequency of each amino acid at each position calculated by analyzing a combination thereof, of the complementarity determining regions of actual human-derived mature antibodies was known in the art as evidenced by Shim ‘306.
The calculation of an enrichment score is known in the art as evidenced by Jang.
The use of high-capacity machine learning for antibody complementarity determining region design including CDR-H3; as well as, wherein the machine learning model is trained, such that it predicts panning enrichment, predicts which region of the CDR will be in contact with an antigen, and provides antibody sequence enrichment data is known in the art as evidenced by Zeng.
Thus, the claims remain rejected.
New Objections/Rejections
Claim Rejections - 35 USC § 112(a) – Scope of Enablement
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.
Claims 1 and 4-15 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the enablement requirement. The claims contain subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention. The instant as-filed Specification does not teach or disclose: (1) a machine learning model to individually design CDR-H3 by predicting enrichment scores of candidate CDR-H3 sequences, and selecting a candidate CDR-H3 based on the enrichment score; (2) “in silico” systems and/or programs for the exclusion of potential immunogenic sequences predicted, in silico, to bind strongly to at least one MHC class II allele; and/or (3) methods, systems and/or programs to design CDRs by simulating utilization frequency of each germline immunoglobulin gene, a frequency of mutation into each of 20 amino acids by somatic hypermutations at each amino acid position, a frequency of each CDR sequence length, a frequency of each amino acid at each position calculated by analyzing and/or simulating sequences that reflect analyzed and/or calculated frequencies of each amino acid at each position in the CDRs of natural human antibodies. Thus, the as-filed Specification fails to identify a Machine Learning Model for the individual design and selection of CDR-H3 sequences.
The test of enablement is whether one skilled in the art could make and use the claimed invention from the disclosures in the patent coupled with information known in the art without undue experimentation (United States v. Telectronics, Inc., 8 USPQ2d 1217 (Fed. Cir. 1988)). Whether undue experimentation is required is not based on a single factor but is rather a conclusion reached by weighing many factors (See Ex parte Forman, 230 USPQ 546 (Bd. Pat. App. & Inter, 1986) and In re Wands, 8USPQ2d 1400 (Fed. Cir. 1988); these factors include the following:
Nature of invention. The invention recites a method of preparing an antibody library, comprising: individually designing complementarity determining region (CDR) sequences of antibodies synthesizing antibodies including the individually designed CDR sequences to prepare the antibody library, wherein the individually designing comprises designing a heavy chain complementarity determining region 3 (CDR-H3) by predicting enrichment scores of candidate CDR-H3 sequences using a machine learning model and selecting a candidate CDR-H3 sequence based on the predicted enrichment scores, wherein the machine learning model is trained; and enrichment scores calculated according to Equation 1 based on Next Generation Sequencing (NGS) reads obtained from the antibody library before panning against an antigen and NGS reads obtained from the antibody library after panning against the antigen. This invention utilizes disciplines of antibody engineering, cloning, and in vitro antibody phage display technology; organic synthesis; immunogenicity; artificial intelligence; molecular modeling; computer design, simulation and imaging; biochemical assay techniques, and spectrometric detection techniques.
Scope of the invention. The method encompasses a genus of preparing libraries of antibodies by individually designing CDR sequences of antibodies by designing CDR-H3 by predicting enrichment scores (ES) of candidate CDR-H3 using a machine learning model and selecting candidate CDR-H3 sequences based on the predicted ES, wherein the machine learning model has been training by setting values; and ES are calculated using Equation 1 based on the number of NGS reads obtained from the antibody library before and after panning with an antigen; and synthesizing antibodies to prepare an antibody library having excellent physical properties, artificial diversity that efficiently simulates natural diversity, improved amplification efficiency after panning, and/or for the identification and development of therapeutic agents.
Number of working examples and guidance. In the instant case, the instant as-filed Specification predicting enrichment scores by a machine learning model that has been trained on at least one CDR-H3 sequence, wherein the instant Application discloses a commercial website (Amazon.com) as providing a machine learning model that was used to construct and evaluate a machine learning model for the prediction of enriched sequences (paragraph [0097]), wherein the model can calculate and output predicted enrichment scores for a specific CDR-H3 using Equation 1, which requires the total number of NGS read of a library including a candidate CDR-H3 sequence before panning with an antigen, after panning with an antigen, such that optimized sequences can be designed using the enrichment scores can be calculating or predicting the enrichment scores of candidate CDR-H3 sequences to select candidate CDR-H3 sequences with the enrichment scores calculated or predicted to be above 0 (paragraphs [0059]-[0065]). Example 1 describes NGS after three rounds of panning, wherein the CDR sequences were extracted from FASTA files using a self-developed Python script in .csv(comma-separated values) format (paragraph [0090]). Example 2 describes designing CDR sequences, which were evaluated through potential bonding to human MHC class 2 molecules using netMHCII pan-3.1 software; and relative frequencies for each individual CDR sequence were compared before and after panning against protein A to produce enrichment scores, wherein the CDR-H3 sequences and their ESs were used to train a machine learning model for efficient prediction of enriched sequences (paragraphs [0090]; [0093]; and [0097]). Example 7 discloses the construction of a final scFv library (paragraph [0114]); such that for CDR-L3, 2 or 3 amino acids at the terminus of CDR-L3 were designed by simulating the sequence frequency at the corresponding positions in CDR-L3 of the mature human antibodies (paragraph [0132]); and sequence analysis of a synthetic human antibody library known as OPLAS (paragraph [0135]); and the construction of an improved novel antibody library known as OPALT (paragraph [0146]). Although the commercially available Amazon Machine Learning Model was used to construct and evaluate a machine learning model for use in the instant invention, no specific machine learning model is identified in the examples. No specific machine learning model, which includes Equation 1, is used to individually design CDR sequences by predicting enrichment scores for all antibody types (e.g., synthetic antibodies, reference antibodies, natural human antibodies, natural human mature antibodies, etc.). The machine learning model is not trained for any other CDR type (e.g., CDR-L3, L2, L1, H1 and H2), wherein CDR-H3 enrichment scores were calculated by measuring relative frequencies of the sequences before and after panning. No specific antibody library and/or antigen is used for panning and/or for designing the CDR sequences; and no specific ESs were predicted, compared with calculated values and/or were selected for any of CDR-H3, CDR-H2, CDR-H1, CDR-L3, CDR-L2 and CDR-L1; and no specific simulation method and/or system was employed to design other CDRs such as CDR-L3. A synthetic antibody library OPALT was synthesized from OPALS using the unidentified machine learning model to produce antibody libraries having excellent physical properties.
State of the art. Although the fields of organic synthesis, biochemical assay techniques, antibody library synthesis and phage display are highly developed, the process of designing and synthesizing antibody libraries using artificial design, artificial intelligence, and/or generic Machine Learning Models, generic simulations and/or in silico techniques to produce antibody libraries, wherein the antibodies have excellent physical properties and/or artificial diversity that efficiently simulates natural diversity, is not highly developed. The art must therefore be considered to be poorly developed.
Unpredictability of the art. At the time the invention was made, it was known that the quality of the library is important for obtaining good antibodies with high binding affinity, and particularly, the size and functional diversity of the library and the quality of clones are important; and when artificial diversity is designed, efficient simulation of natural diversity is very important in the design and construction of a synthetic antibody library as evidenced by Shim (US20230332141, paragraphs [0005]; [0008], lines 13-15); and it was known that the precise targeting of antibodies and other protein therapeutics is required for their proper function and the elimination of deleterious off-target effects, such that often the molecular structure of a therapeutic target is unknown and randomized methods are used to design antibodies without a model that relates antibody sequence to desired properties. Most computational methods for antibody design optimization assume that the structure of a target is known; however, target structure is unknown in many cases, such that other approaches seek to optimize antibody properties primarily focused on predicting the structural conformation of the CDR-H3 loop, which to date remains a difficult unsolved challenge; and many such approaches are based on calculations of binding free energies, where the multitude of possible expressions have been found to be of highly variable quality for predicting actual affinities in experiments. More recently, neural network methods have been successfully applied to predict which region of the CDR will be in contact with an antigen without reliance on structure prediction, but this work does not provide insights on how to obtain improved affinity, wherein existing models of antibody binding do not produce gradients for sequence optimization that directly relate antibody sequence to a desired objective; and to produce design improved antibody sequences with a two-stage approach, named Ens-Grad, by modeling antibody affinity with an ensemble of neural networks and efficiently optimizing it with gradient-based optimization, such that Ens-Grad produced accurate predictive estimates of affinity enrichment for previously unseen sequences; and in addition to our neural network ensemble (Ens-Grad), three other machine learning frameworks were also applied to generate novel CDR-H3 sequences (described in Supplementary Data) as evidenced by Liu (Bioinformatics, 2019; Abstract, Motivation, pg. 2126, col 2; and pg. 2127, col 1, first partial paragraph; and third full paragraph). Moreover, it is known that three machine learning models were designed that predict transcription factor binding and DNA methylation, two fundamental epigenetic phenotypes closely tied to gene regulation, from DNA sequence alone, wherein the predicted change in phenotype between the reference and alternate allele of a genetic variant accurately reflect its functional impact and improves the identification of regulatory variants causal for complex diseases. Additionally, two machine learning models were devised that improve the prediction of peptides displayed by the major histocompatibility complex (MHC) on the cell surface, wherein computational modeling of peptide-display by MHC is central in the design of peptide-based therapeutics, such that the first machine learning model introduces the capacity to quantify uncertainty in the computational prediction and proposes a new metric for peptide prioritization that reduces false positives in high-affinity peptide design, while the second model improves the state-of-the-art performance in MHC-ligand prediction by employing a deep language model to learn the sequence determinants for auxiliary processes in MHC-ligand selection, such as proteasome cleavage, that are omitted by existing methods due to the lack of labeled data. Finally, a machine learning frameworks were developed to model the enrichment of an antibody sequence in phage-panning experiments against a target antigen, such that antibodies with low specificity can be reduced by a computational procedure using machine learning models trained for multiple targets as evidenced by Zeng (Thesis, 2019, pg.3, second full paragraph through pg. 4). Furthermore, new workflows are known in the art including AutomaTedTool For Immunoglobulin Analysis (ATTILA)—which makes it possible to identify variable domains enriched after selection, wherein the methodology combines the generation of VH and VL amplicons from antibody-displaying phages, before and after selection, and the use of NGS to acquire their coding sequences, such that after a filtering process, ATTILA can establish the frequency of each variable domain sequence in a given selection round. It generates a report with the fold change of each enriched sequence, giving the amino acid sequence, complementarity determining region (CDR) identification, and germline classification of the variable domains VH-VL combinatorial library displayed on phage,11 which was examined to validate the ATTILA workflow, wherein ATTILA analyses based on heavy variable domain (VH) sequences, submitted to NGS sequencing in either Illumina or 454 sequencers; and assesses the variability of the library and the error rate of the whole process, as well as, changes in selected VHs throughout the selection cycles from 5 different biopanning experiments as evidenced by Maranhao (Bioinformatics and Biology Insights, 2020, pg. 1, col 2, last partial paragraph; and pg. 2 col 1, first partial paragraph). The references indicate that a myriad of machine learning models are known in the art and/or are commercially available for the preparation of antibody libraries, such that without identifying the machine learning model, in silico systems, the NGS methods, the simulation programs, and/or cut-off values used in the instant method, different CDR sequences will be individually designed and selected, such that the different machine learning models directly dictate the diversity, specificity, and developability of synthetic or focused antibody libraries and, thus, the particular machine learning model will affect the artificial diversity including whether it simulates natural diversity, the amplification efficiency after panning, and can adversely affect the physical properties and/or development of the antibodies produced.
Amount of Experimentation Required. Given the unpredictability of the art, the poorly developed state of the art with regard to individually designing, selecting, predicting, calculating, simulating, and/or synthesizing antibody libraries using all Machine Learning Models, simulations, NGS methods, and/or in silico techniques, in all combinations, such that all of the antibody libraries individually designed and produced will have excellent physical properties including high binding affinity, and will demonstrate artificial diversity that efficiently simulates natural diversity; the lack of working examples; and the lack of guidance provided by applicants, the skilled artisan would have to have conducted undue, unpredictable experimentation to practice the claimed invention.
The Examiner suggests that Applicant amend claim 1 to recite the steps necessary to prepare an antibody library such as, for example, next generation sequencing, panning against an antigen, obtaining reads, predicting enrichment scores, calculating enrichment scores, comparing predicted ES with calculated ES, selecting a candidate sequence, synthesizing antibodies with individually designed CDR-H3 sequences, etc.; as well as, resolving 35 USC 112(b) issues.
Conclusion
Claims 1 and 4-15 are rejected.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AMY M BUNKER whose telephone number is (313) 446-4833. The examiner can normally
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/AMY M BUNKER/Primary Examiner, Art Unit 1684
