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 .
Claim Status
Claims 1-10, 16, 18-19, 21-24, and 32-33 are pending and are examined on the merits.
Claims 12-15, 17, 20, and 25-3 are canceled.
Claims 1 and 32-33 are independent.
Priority
Applicant's claim for the benefit of a prior-filed application, U.S. Provisional App. No. 63/340,332 filed 05/10/2022 is acknowledged. As detailed on the Filing Receipt, this application claims priority to as early as 05/10/2022. At this point in examination, all claims have been interpreted as being accorded this priority date. In future actions, the effective filing date of one or more claims may change, due to amendments to the claims, or further analysis of the disclosure(s) of the priority application(s).
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 08/08/2023 and 03/30/2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the list of cited references was considered in full by the examiner. A signed copy of the corresponding 1449 form has been included with this Office action.
Drawings
The drawings filed 05/09/2023 and 07/25/2023 are accepted.
Specification
The amendments to the specification filed 07/25/2023 are accepted.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-10, 16, 18-19, 21-24, and 32-33 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The Supreme Court has established a two-step framework for this analysis, wherein a claim does not satisfy § 101 if (1) it is “directed to” a patent-ineligible concept, i.e., a law of nature, natural phenomenon, or abstract idea, and (2), if so, the particular elements of the claim, considered “both individually and as an ordered combination,” do not add enough to “transform the nature of the claim into a patent-eligible application.” Elec. Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1353 (Fed. Cir. 2016) (quoting Alice, 134 S. Ct. at 2355). Applicant is also directed to MPEP 2106.
Step 1: The instantly claimed invention (claim(s) 32-33 being representative) is directed to a system and (claim(s) 1-10, 16, 18-19, 21-24 being representative) is directed to method. Therefore, the instantly claimed invention falls into one of the four statutory categories. [Step 1: YES]
Step 2A: First it is determined in Prong One whether a claim recites a judicial exception, and if so, then it is determined in in Prong Two if the recited judicial exception is integrated into a practical application of that exception.
Step 2A, Prong 1: Under the MPEP § 2106.04, the Step 2A (Prong 1) analysis requires determining whether a claim recites an abstract idea, law of nature, or natural phenomenon.
Claim(s) 1-10, 16, 18-19, 21-24, and 32-33 recite the following steps which fall under the mathematical concepts, mental processes, and/or certain methods of organizing human activity groupings of abstract ideas:
Claims 1, 32, and 33 recite screening based on machine learning model predicted thermostability; the limitation screening, given the plain meaning of screening, encompasses observation, evaluation, judgment, and opinion (See MPEP 2106.04(a)(2), subsection III.) performable by human mind (mental process), since human mind is capable of screening based on the result of an analysis.
Claims 1, 32, and 33 further recite determining a thermostability indication for each scFv in the set of scFvs to obtain a plurality of thermostability indications; the limitation determining falls within the mental process groupings of abstract ideas because it covers concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III.
Claims 1, 32, and 33 further recite generating a first set of features to provide as input to the trained machine learning model, the generating comprising including the interaction energy metrics in the first set of features; the limitation generating metrics is considered a mental process, since human mind is capable of generating metrics, as disclosed in specification [0013], “generating the first set of features comprises… generating a respective two- dimensional (2D) matrix of values of the particular energy metric”.
Claims 1, 32, and 33 further recite identifying a subset of the set of scFvs for subsequent production based on the plurality of thermostability indications; the limitation “identifying” given the plain meaning of identifying encompasses observation, evaluation, judgment, and opinion (See MPEP 2106.04(a)(2), subsection III.) performable by human mind (mental process), since human mind is capable of identifying based on known information/thermostability indications.
Claim 2 recites generating a second set of features to provide as input to the trained machine learning model, the generating comprising including the second interaction energy metrics in the second set of features (mental process of generating metrics).
Claim 7 recites classifying the first scFv into one of a plurality of classes using the first set of features (mental process of classifying data).
Claim 8 recites determining the information indicative of the 3D structure of the first scFv to generate the information indicative of the 3D structure from the first residue sequence (mental process of determining structure by using pen and paper or computer).
Claim 9 recites determining the interaction energy metrics to generate the interaction energy metrics using the information indicative of the 3D structure of the first scFv (mental process of determining metrics by using pen and paper or computer).
Claim 10 recites generating a respective two-dimensional (2D) matrix of values of the particular energy metric, and including the generated 2D matrix in the first set of features (mental processes of generating a matrix and including the matrix in a set).
Claim 16 recites encoding the first residue sequence to obtain an encoded sequence; and including the encoded sequence in the first set of features (mathematical process of using a mathematical algorithm to change data forms (specification [0017]: “encoding the first residue sequence comprises: one-hot- encoding …”); also, mental process of changing data from one form to another).
Claim 22 recites determining the first thermostability as either: (i) a temperature range in the plurality of temperature ranges associated with [[the]] a highest probability in the first plurality of probabilities; or (ii) a temperature determined as a weighted linear combination of mean values of the plurality of temperature ranges weighted by the probabilities in the first [[set]] plurality of probabilities (determining a range: mathematical relationship/ determining a mean: mathematical calculations/ mathematical processes).
Claim 23 recites determining whether the first thermostability for the first scFv satisfies at least one criterion; and after determining that the first thermostability satisfies the at least one criterion, identifying the first scFv for subsequent production (mental process of determining based on criterion).
Claims 3-6 and 11 provide additional information.
The identified claims recite a law of nature, a natural phenomenon (product of nature) or fall into one of the groups of abstract ideas of mathematical concepts, mental processes, and/or certain methods of organizing human activity for the reasons set forth above. See MPEP 2106.04 (a)(2) III and MPEP 2106.04 (b) I. Therefore, claims are directed to one or more judicial exception(s) and require further analysis in Prong Two. [Step 2A, Prong 1: YES]
Step 2A: Prong 2: Under the MPEP § 2106.04, the Step 2A, Prong 2 analysis requires identifying whether there are any additional elements recited in the claim beyond the judicial exception(s), and evaluating those additional elements to determine whether they integrate the exception into a practical application of the exception. This judicial exception is not integrated into a practical application for the following reasons.
The additional elements of claims include the following.
Claims 1, 32-33 recite at least one computer hardware processor, using a trained machine learning model, obtaining interaction energy metrics for each of a plurality of pairs of residues, providing the first set of features as input to the trained machine learning model to obtain a corresponding output indicative of a first thermostability for the first scFv, and producing at least one of the scFvs in the identified subset.
Claim 2 recites obtaining second interaction energy metrics for each of a second plurality of pairs of second residues, and providing the second set of features as input to the trained machine learning model to obtain a corresponding output.
Claim 7 recites using the trained machine learning model.
Claim 8 recites using protein structure prediction software.
Claim 9 recites using molecular modeling software.
Claim 18 a trained neural network model.
Claim 19 recites a trained convolutional neural network (CNN) model.
Claim 21 recites output a plurality of probabilities that an scFv is thermostable in each of a plurality of temperature ranges.
Claim 22 recites providing the first set of features to the trained CNN model to obtain a first plurality of probabilities.
Claim 24 recites testing the thermostability of the at least one of the scFvs in an in vitro assay.
Claim 32 recites a system, comprising: at least one computer hardware processor; and at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one computer hardware processor.
Claim 33 recites at least one non-transitory computer-readable storage medium storing processor- executable instructions that, when executed by at least one computer hardware processor, cause the at least one computer hardware processor to perform a method.
The additional elements of a system comprising a hardware processor, at least one non-transitory computer-readable storage medium, and executable instructions are generic computer components and/or processes. There are no limitations that indicate that these component and processes require anything other than generic computing systems. The courts have found the use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Furthermore, the additional elements of obtaining metrics, providing features to a model/inputting, and outputting amount to necessary data gathering and outputting. The courts have found the limitations that amount to necessary data gathering and outputting are insignificant extra-solution activity that do not integrate a recited judicial exception into a practical application in Mayo, 566 U.S. at 79, 101 USPQ2d at 1968 and O/P Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (see MPEP 2106.05(g)).
Furthermore, the additional element of using a trained machine learning model, using protein structure prediction software, using molecular modeling software, a trained neural network model, and a trained convolutional neural network (CNN) model provide nothing more than mere instructions to implement an abstract idea on a generic computer or a computer environment. See MPEP 2106.05(f). additionally, said limitations amount to generally linking the use of a judicial exception to a particular technological environment or field of use See MPEP 2106.05(h).
Furthermore, the additional element of testing the thermostability of the at least one of the scFvs in an in vitro assay amount to nothing more than gathering the data necessary to perform the abstract idea. The testing step does not impose meaningful limitations on the scope of the claims: they would be performed in exactly the same manner if the samples were analyzed using a different abstract idea, or none at all. As such, said limitations are considered insignificant extra-solution activity. The courts have found that adding insignificant extra-solution activity does not integrate the judicial exception into a practical application. See MPEP 2106.05(g).
Furthermore, the limitation producing at least one of the scFvs in the identified subset amounts to generally linking the use of a judicial exception to a particular technological environment or field of use. The recited judicial exceptions only result in the step of producing and does not provide details as to how and/or what type of scFvs is produced. Applying or using the judicial exception in some meaningful way beyond generally linking the use of the judicial exception to a particular technological environment may help to overcome a 101 rejection, as discussed at Step 2A/2nd Prong, fifth consideration in MPEP 2106.04(d) and (d)(1). Such application requires providing details as to how and/or what type of scFvs is produced. As appropriate, the independent claims may include limitations such as limitations recited in claim 23, “determining whether the first thermostability for the first scFv satisfies at least one criterion; and after determining that the first thermostability satisfies the at least one criterion, identifying the first scFv for subsequent production.”
Therefore, the additionally recited elements amount to generic computer and insignificant extra-solution activity and, as such, the claims as a whole do no integrate the abstract idea into practical application.
MPEP 2106.04(d).I lists the following example considerations for evaluating whether a judicial exception is integrated into a practical application:
An improvement in the functioning of a computer or an improvement to other technology or another technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a);
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, as discussed in MPEP § 2106.04(d)(2);
Implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, as discussed in MPEP § 2106.05(b);
Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP § 2106.05(c); and
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP § 2106.05(e).
In Step 2A, Prong 1 above, claim steps and/or elements were identified as part of one or more judicial exceptions (JEs).
In Step 2B below, any remaining steps and/or elements are therefore in addition to the identified JE(s). Any such additional steps and additional elements are further discussed in Step 2B.
Here in Step 2A, Prong 2, no additional step or element clearly demonstrates integration of the JE(s) into a practical application.
At this point in examination, it is not yet the case that any of the Step 2A, Prong 2 considerations enumerated above clearly demonstrates integration of the identified JE(s) into a practical application. Referring to the considerations above, none of 1. an improvement, 2. treatment, 3. a particular machine or 4. a transformation is clear in the record.
In conclusion regarding Prong 2, claims 1-10, 16, 18-19, 21-24, and 32-33 are directed to an abstract idea. [Step 2A, Prong 2: NO]
Step 2B: In the second step it is determined whether the claimed subject matter includes additional elements that amount to significantly more than the judicial exception. An inventive concept cannot be furnished by an abstract idea itself. See MPEP § 2106.05.
The additional elements of claims include the following.
Claims 1, 32-33 recite at least one computer hardware processor, using a trained machine learning model, obtaining interaction energy metrics for each of a plurality of pairs of residues, providing the first set of features as input to the trained machine learning model to obtain a corresponding output indicative of a first thermostability for the first scFv, and producing at least one of the scFvs in the identified subset.
Claim 2 recites obtaining second interaction energy metrics for each of a second plurality of pairs of second residues, and providing the second set of features as input to the trained machine learning model to obtain a corresponding output.
Claim 7 recites using the trained machine learning model.
Claim 8 recites using protein structure prediction software.
Claim 9 recites using molecular modeling software.
Claim 18 a trained neural network model.
Claim 19 recites a trained convolutional neural network (CNN) model.
Claim 21 recites output a plurality of probabilities that an scFv is thermostable in each of a plurality of temperature ranges.
Claim 22 recites providing the first set of features to the trained CNN model to obtain a first plurality of probabilities.
Claim 24 recites testing the thermostability of the at least one of the scFvs in an in vitro assay.
Claim 32 recites a system, comprising: at least one computer hardware processor; and at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one computer hardware processor.
Claim 33 recites at least one non-transitory computer-readable storage medium storing processor- executable instructions that, when executed by at least one computer hardware processor, cause the at least one computer hardware processor to perform a method.
The additional elements of a system comprising a hardware processor, at least one non-transitory computer-readable storage medium, and executable instructions are conventional computer components and/or processes. The courts have found the use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TU Communications LLC v. AV Auto, LLC, 823 F.3d 607,613,118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit).
Furthermore, the additional elements of obtaining metrics, providing features to a model/inputting, and outputting amount to necessary data gathering and outputting. The courts have found the limitations that amount to necessary data gathering and outputting are insignificant extra-solution activity that do amount to significantly more (see MPEP 2106.05(g)).
Furthermore, the additional element of using a trained machine learning model, using protein structure prediction software, using molecular modeling software, a trained neural network model, and a trained convolutional neural network (CNN) model provide nothing more than mere instructions to implement an abstract idea on a generic computer or a computer environment. See MPEP 2106.05(f). additionally, said limitations amount to generally linking the use of a judicial exception to a particular technological environment or field of use See MPEP 2106.05(h).
Furthermore, the additional element of testing the thermostability of the at least one of the scFvs in an in vitro assay amount well-known, routine, and conventional thermostability testing of scFvs. This position is supported by Kang (Solubility, Stability, and Avidity of Recombinant Antibody Fragments Expressed in Microorganisms, Frontiers in Microbiology, 25 September 2020, pages: 1-10). Kang reviews production of antibodies or antibody fragments in microorganisms but also scFv stabilization via (i) directed evolution of variants with increased stability using display systems, (ii) stabilization of the interface between variable regions of heavy (VH) and light (VL) chains through the introduction of a non-native covalent bond between the two chains, (iii) rational engineering of VH-VL pair, based on the structure, and (iv) computational approaches (abstract).
Therefore, these additional elements are not sufficient to amount to significantly more than the judicial exception. See MPEP 2106.05(g).
Taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception(s). Even when viewed as a combination, the additional elements fail to transform the exception into a patent-eligible application of that exception. Thus, the claims as a whole do not amount to significantly more than the exception itself. [Step 2B: NO]
Therefore, the instantly rejected claims are not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-3, 7-11, 16, 18, 23-24, and 32-33 are rejected under 35 U.S.C. 103 as being unpatentable over Gibson (US20230122168A1; as newly cited in the attached 892 form) in view of Mason (US20220157403A1; as newly cited in the attached 892 form).
Regarding claims 1, 32, and 33, the recited method for computationally screening a set of single-chain variable fragments (scFvs) based on thermostability of the scFvs predicted by a trained machine learning model comprising scFvs having different residue sequences, is taught as, devices, software, systems, and methods for optimizing design of biopolymer sequences, trained plurality of observed biopolymer sequences, where the biopolymer include newly designed sequences and dataset consists of binding affinities for scFv antibody (Gibson: claim 1, [0089], [0106], and [0115]).
The recited determining, using the trained machine learning model, a thermostability indication for each scFv, the set of scFvs having a first residue sequence, is taught as, predicting protein stability according to thermostability based on a trained machine learning model, where the biopolymer sequence can include at least one of an amino acid sequence of all proteins including antibodies (Gibson: [0010], [0015] and [0092]).
The recited obtaining, using information indicative of a three-dimensional (3D) structure of the first scFv, interaction energy metrics for each of a plurality of pairs of residues, is taught as, predicting stability according to tertiary structure of the protein and capturing non-linear interactions between positions and amino acids (Gibson: [0091-0092] and [0105]).
The recited generating a first set of features to provide as input to the trained machine learning model, the generating comprising including the interaction energy metrics in the first set of features, is taught as, machine learning input data comprises amino acid sequences and information in addition to the primary amino acid sequence such as, for example, surface charge, hydrophobic surface area, measured or predicted solubility, or other relevant information; the data inputs (e.g., primary amino acid sequence) are augmented by random mutation and/or biologically informed mutation to the primary amino acid sequence, multiple sequence alignments, contact maps of amino acid interactions, and/or tertiary protein structure. (Gibson; [0090-0091]).
The recited providing the first set of features as input to the trained machine learning model to obtain a corresponding output indicative of a first thermostability for the first scFv, is taught as, devices, software, systems, and methods for evaluating input data comprising protein or polypeptide information such as amino acid sequences (or nucleic acid sequences that code for the amino acid sequences) to predict one or more specific functions or properties based on the input data leveraging the capabilities of artificial intelligence or machine learning techniques for polypeptide or protein analysis to make predictions about structure and/or function, where the properties include thermostability [0089] and [0092].
The recited identifying a subset of the set of scFvs for subsequent production based on the plurality of thermostability indications, is taught as selecting at least one candidate biopolymer sequence having an optimized linear combination of the conformal inference interval and the predicted value of the labeled biopolymer sequences (claim 1).
The recited producing at least one of the scFvs in the identified subset, is taught as, the one or more selected biopolymer sequences are manufactured by an in vitro method of chemical synthesis [0022].
Further regarding claims 32 and 33, the recited system, comprising at least one computer hardware processor; and at least one non-transitory computer-readable storage medium storing processor-executable instructions, is taught as, a computer system (e.g., processor, disk storage, memory, input/output ports, network ports, ... non-volatile storage for computer software instructions and data used to implement an embodiment of the present invention (Gibson: [0119], FIG. 7).
Further regarding claims 1, 32, and 33, Gibson teaches that biopolymer can include at least one of an amino acid sequence, a nucleic acid sequence, all proteins such as, for example, enzymes, growth factors, cytokines, hormones, signaling proteins, structural proteins, kinetic proteins, antibodies (including both immunoglobulin-based molecules and alternative molecular scaffolds), and combinations of the foregoing, including fusion proteins and conjugates; biopolymer sequences can include either known sequences (e.g., previously encountered, previously observed, or natural sequences) or newly designed sequences (Gibson: [0010], and [0115]).
Gibson does not teach that the biopolymer is single-chain variable fragments (scFvs). Mason teaches this limitation.
Mason systems and methods to make predictions classifying one or more properties of a binding protein such as an antibody, for example, antibody affinity, thermal stability, or specificity for an antigen using machine learning model, where the generated sequences are used to produce proteins (Mason: abstract, [0036]). Mason further teaches that the protein or peptide comprises an amino acid sequence generated as an scFv [0011].
Regarding claim 2, the recited second scFv different from the first scFv, the second scFv having a second residue sequence, is taught as, determining the conformal inference interval based on a second set of observed biopolymer sequences (Gibson: claim 5, [0007]).
The recited obtaining, using information indicative of a three-dimensional (3D) structure of the first scFv, interaction energy metrics for each of a plurality of pairs of residues, is taught as, predicting stability according to tertiary structure of the protein and capturing non-linear interactions between positions and amino acids (Gibson: [0091-0092] and [0105]).
The recited generating a first set of features to provide as input to the trained machine learning model, the generating comprising including the interaction energy metrics in the first set of features, is taught as, machine learning input data comprises amino acid sequences and information in addition to the primary amino acid sequence such as, for example, surface charge, hydrophobic surface area, measured or predicted solubility, or other relevant information; the data inputs (e.g., primary amino acid sequence) are augmented by random mutation and/or biologically informed mutation to the primary amino acid sequence, multiple sequence alignments, contact maps of amino acid interactions, and/or tertiary protein structure. (Gibson; [0090-0091]).
Regarding claim 3, the recited output indicative of the first thermostability indicates a first temperature at which the first scFv is thermostable, is taught as, determining thermostability above a certain melting temperature (Gibson: [0096]).
Regarding claim 7, the recited classifying the first scFv into one of a plurality of classes using the first set of features, wherein each of the plurality of classes corresponds to a respective temperature range, is taught as, a prediction comprises a classification such as a binary, multi-label, or multi-class classification to predict a discrete class or label based on input parameters; a binary classification includes a positive or negative prediction for a property or function for a protein or polypeptide sequence that includes any quantitative readout subject to a threshold such as, for example, binding to a DNA sequence above some level of affinity, catalyzing a reaction above some threshold of kinetic parameter, or exhibiting thermostability above a certain melting temperature. (Gibson: [0096]).
Regarding claim 8, the recited determining the information indicative of the 3D structure of the first scFv by using protein structure prediction software, is taught as, methods described herein can be used to generate a variety of predictions; The predictions can involve protein functions and/or properties… prediction comprises one or more structural features such as, for example, secondary structure, tertiary protein structure, quaternary structure, or any combination thereof [0092].
Regarding claim 9, the recited using molecular modeling software to generate the interaction energy metrics, is taught as, using Rosetta to compute energy functions (Mason: [0135).
Regarding claim 10, the recited generating a respective two-dimensional (2D) matrix of values of the particular energy metric and including the generated 2D matrix in the first set of features, is taught as, converting the sequence into an input vector, generate a 2D matrix, where each row of the matrix corresponds to a respective value (e.g., position) of the input vector. Each column of the matrix corresponds to a different possible amino acid that can fill each respective value of the input vector (Mason: [0071], [0078], and [0109]).
Regarding claim 11, the recited generated 2D matrix including a row for at least 75% of the residues in the first residue sequence, is taught as, each variant of the training data can be stored separately (e.g., as a single string or vector) or collectively (e.g., as a matrix where each column or row corresponds to a different variant) (Mason: [0071]).
Regarding claim 16, the recited encoding the first residue sequence to obtain an encoded sequence; and including the encoded sequence in the first set of features, is taught as, The training data can be labeled and passed to the neural networks as a one-hot encoded matrix (Mason: [0073], [0098], and [0109]).
Regarding claim 18, the recited trained machine learning model comprises a trained neural network model, is taught as, the machine learning model is a neural network fine-tuned using the observed biopolymer sequences and their labels (Gibson: claim 4).
Regarding claim 23, the recited determining whether the first thermostability for the first scFv satisfies at least one criterion and identifying the first scFv for subsequent production, is taught as, calculating conformal intervals for predictions from a fine-tuned neural network using nearest-neighbors in sequence space and calculating a cutoff score for selected biopolymers; manufacturing the selected biopolymer (Gibson: [0068-0077], [0022], [0096], claim 25, FIG. 2).
Regarding claim 24, the recited testing the thermostability of the at least one of the scFvs in an in vitro assay, is taught as, the one or more selected biopolymer sequences are manufactured by an in vitro method of chemical synthesis (Gibson: [0022]).
Rationale for combining Gibson and Mason:
It would have been prima facie obvious to one ordinary skilled in the art before the effective filling date of the invention to use the known single-chain variable fragments (scFvs) of Mason in the biopolymer function prediction method of Gibson based on a finding that Mason contained a known technique that is applicable to the base method of Gibson. One ordinary skilled in the art would have recognized that the applying the known technique of Mason would have yielded predicted result and result in an improved method of thermostability prediction for biopolymers such as single-chain variable fragments (scFvs).
Claims 4-6 are rejected under 35 U.S.C. 103 as being unpatentable over Gibson in view of Mason, as applied to claims 1-3, 7-11, 16, 18, 23-24, and 32-33 above, and further in view of Lee (Computer-based Engineering of Thermostabilized Antibody Fragments, AIChE J. 2020 March 01; 66(3): page: 1-15; as newly cited in the attached 892 form).
Claims 4-6 depend on claims 3 and 1. Limitations of claims 1 and 3 are taught in the above rejections.
Regarding claims 4-6, Gibson discloses quantifying binding affinity [0097]. Gibson does not teach quantifying half maximal binding of the first scFv. Lee teaches a computer-based engineering of thermostabilized antibody fragments where they generated scFvs, calculated percent binding affinity by setting half maximal effective concentration (EC50) for comparisons between different variants. Lee further teaches calculating percent binding affinity retained for each variant by setting the half maximal effective concentration (EC50) of each variant without the heat treatment as 100% and the EC50 of the wildtype after the thermal challenge as 0% (the C7, C8, and C10 variants were worse than the wildtype and set to 0) (Lee: abstract, Fig. 2).
Rationale for combining Gibson, Mason, and Lee:
It would have been prima facie obvious to one ordinary skilled in the art before the effective filling date of the invention to use the known half-maximal binding quantification of Lee in the binding affinity quantification of Gibson and Mason based on a finding that Lee contained a known technique that is applicable to the base method of Gibson and Mason. One ordinary skilled in the art would have recognized that the applying the known technique of Lee would have yielded predicted result and result in an improved method of thermostability prediction.
Claims 16, 19, and 21-22 are rejected under 35 U.S.C. 103 as being unpatentable over Gibson in view of Mason, as applied to claims 1-3, 7-11, 16, 18, 23-24, and 32-33 above, and further in view of Feala (US20220270711A1; as newly cited in the attached 892 form).
Claim 16 depend on claim 1. Limitations of claims 1 are taught in the above rejections. Claim 19 depends on claim 18 and claim 1. Limitations of claims 1 and 18 are taught in the above rejections.
Regarding claim 16, Gibson does not teach the recited encoding the first residue sequence to obtain an encoded sequence; and including the encoded sequence in the first set of features. This limitation is taught by Feala.
Feala teaches a method of engineering an improved biopolymer sequence as assessed by a function, where the model network comprises an encoder network providing the embedding of biopolymer sequences in a functional space representing the function, and the decoder network trained to provide a probabilistic biopolymer sequence, given an embedding of a biopolymer sequence. Feala further teaches obtaining a probabilistic improved biopolymer sequence from the decoder.
Further regarding limitations of claim 16, Feala further teaches that the encoder network can receive a sequence of amino acids, which may be represented as a sequence of one-hot vectors, and generate the embedding for that protein (Feala: [0058]).
Regarding claim 19, the recited trained neural network model comprises a trained convolutional neural network (CNN) model having a plurality of 2D convolutional layers, is taght as, first model and the second model can be a supervised model using a convolutional architecture in the form of a 1D convolution (e.g., primary amino acid sequence), a 2D convolution (e.g., contact maps of amino acid interactions), or a 3D convolution (e.g., tertiary protein structures) (Feala: [0146]).
Regarding claim 21, the recited trained CNN model configured to output a plurality of probabilities that an scFv is thermostable in each of a plurality of temperature ranges, is taught as, calculating the probability for the conformational score (Gibson: [0055-0057]). Additionally, said limitation, is taught as, providing a predicted probabilistic biopolymer sequence, given an embedding of the predicted biopolymer sequence in the functional space (Feala: claim 1, and claim 88).
Regarding claim 22, the recited providing the first set of features to the trained CNN model to obtain a first plurality of probabilities that the first scFv is thermostable in each of the plurality of temperature ranges; and determining the first thermostability as either: (i) a temperature range in the plurality of temperature ranges associated with [[the]] a highest probability in the first plurality of probabilities; or (ii) a temperature determined as a weighted linear combination of mean values of the plurality of temperature ranges weighted by the probabilities in the first [[set]] plurality of probabilities, is taught as, determining a candidate biopolymer sequences to observe having a highest predicted value of the labeled biopolymer sequences based on the machine learning model, where the machine learning model is a neural network (Gibson: [0115]). Additionally, said limitation is taught as, selection of initial source sequences can be based on rational means (e.g., the protein(s) with the highest level of function) or by some other means, (e.g., random selection) (Feala: [0070] and [0101]).
Rationale for combining Gibson, Mason, and Feala:
It would have been prima facie obvious to one ordinary skilled in the art before the effective filling date of the invention to use the known technique of encoding sequence into a machine learning model, as taught by Feala, in the neural network of Gibson and Mason based on a finding that Feala contained a known technique that is applicable to the base method of Gibson and Mason. One ordinary skilled in the art would have recognized that the applying the known technique of Feala would have yielded predicted result and result in an improved method of thermostability prediction.
Conclusion
No claims are allowed.
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/G.S./ Examiner, Art Unit 1686
/G. STEVEN VANNI/ Primary patents examiner, Art Unit 1686
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