DETAILED ACTION
Applicant’s response filed 04/17/2026 has been fully considered. The following rejections and/or objections are either reiterated or newly applied.
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 4, 6, 12, 15-21, 33, 35 and 40-41 are cancelled by Applicant.
Claims 1-3, 5, 7-11, 13-14, 22-32, 34 and 36-39 are currently pending and are herein under examination.
Claims 1-3, 5, 7-11, 13-14, 22-32, 34 and 36-39 are rejected.
Priority
The instant application claims domestic benefit as a 371 filing of International Application PCT/US2020/061526 filed November 20, 2020, which claims domestic benefit to U.S. Provisional Patent Application No. 62/938,021 filed November 20, 2019. The claims to domestic benefit are acknowledged. As such, the effective filing date for claims 1-3, 5, 7-11, 13-14, 22-32, 34 and 36-39 is November 20, 2019.
Drawings
The objection to the drawings is withdrawn in view of drawing and specification amendments and because Applicant’s argument regarding reference character 204 is persuasive (pg. 9, last para. of remarks filed 4/17/2026).
The drawings filed 05/19/2022 are accepted.
Abstract
The objection to the abstract filed 5/19/2022 is withdrawn in view of the replacement abstract filed 4/17/2026. However, the replacement abstract is objected as discussed below.
The abstract of the disclosure filed 4/17/2026 is objected to because it recites 41 words. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
Applicant is reminded of the proper language and format for an abstract of the disclosure.
The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words in length. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details.
Nucleotide and/or Amino Acid Sequence Disclosures
The deficiency of the nucleotide sequence disclosure is withdrawn in view of amendment to specification para. [3] filed 4/17/2026.
Withdrawn Rejections
35 USC 112(a)
The rejection of claim 14 under 35 USC 112(a) for not providing written description support for “sampling the library exhaustively to identify probably tertiary motifs” is withdrawn in view of claim amendment.
35 USC 112(b)
The rejection of claims 1-3, 5-11, 13-14, 22-32, 34 and 36-39 under 35 USC 112(b), except for the rejection maintained below in section Claim Rejections - 35 USC § 112, is withdrawn in view of claim amendment.
35 USC 101
The rejection of claim 6 under 35 USC 101 is withdrawn because Applicant cancelled the claim.
35 USC 103
The rejection of claims 5-6 under 35 USC 103 as being unpatentable over Mackenzie et al. in view of Kuhlman et al. and PDB (“2KL8”; published 2009) is withdrawn in view of claim amendment.
The rejection of claim 7 under 35 USC 103 Mackenzie et al. in view of Kuhlman et al. and PDB (“2KL8”; published 2009) and in further view of Ramachandran et al. is withdrawn in view of claim amendment.
Claim Objections
The objections to claims 5, 14, 23-24, 26, 28-32, 34 and 36 are withdrawn in view of claim amendment.
Claim Interpretation
35 USC 112(f)
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are described below:
In claims 1 and 37-39 the structural sampler performs the following functions: “sampling at least one configuration of the subject protein according to the topology dataset; calculating a scoring function incorporating a distance between a configuration and the set of self-tertiary motifs or the set of probable pair tertiary motifs; continuously optimizing chain coordinate to minimize the scoring function; and generating a predicted structure representing a local minimum according to the scoring function.”
In claim 3 the structural sampler performs the following function: “sampling … includes sampling the at least one configuration of the subject protein dynamically using at least one of Langevin dynamics and Monte Carlo sampling.”
In claim 14 the structural sampler performs the following function: “sampling the library of tertiary motifs according to their frequency in a reference database to identify probable tertiary motifs; and sampling the library of tertiary motifs to identify probable tertiary motifs.”
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
The “structural sampler” is a computer-implemented means-plus-function limitation, as evidenced by claim 1 limitation “the structural sampler implemented via a processor and a memory.” The structure for computer-implemented means-plus-function limitations is the algorithm that performs the function (MPEP § 2181.II.B). Below is a summary of the algorithms in the specification that perform the claimed functions of the “structural sampler”.
In claims 1 and 37-39: “sampling at least one configuration of the subject protein according to the topology dataset”. Specification para. [36-37] recite that sampling can be performed by Langevin dynamics or Monte Carlo sampling. As such, Langevin dynamics or Monte Carlo sampling, and equivalents thereof, are the structure.
In claims 1 and 37-39: “calculating a scoring function incorporating a distance between a configuration and the set of self-tertiary motifs or the set of probable pair tertiary motifs”. Specification para. [35] and [89] describe scoring functions that take into account distance between tertiary motifs and a subject protein configuration. As such, these scoring functions, and equivalents therefore, are the structure.
In claims 1 and 37-39: “continuously optimizing chain coordinate to minimize the scoring function; and generating a predicted structure representing a local minimum according to the scoring function”. Specification para. [91] recites that steepest descent minimization or conjugate gradients minimization perform this function. As such, these equations and equivalents thereof are the structure.
In claims 1 and 37-39: “generating a predicted structure representing a local minimum according to the scoring function”. Specification para. [73] [91] [104] discuss using the best scoring or lowest score conformation as the final predicted structure. However, there are no algorithms that describe how the structural sampler generates the predicted structure. As such, any algorithm that generates a predicted protein structure based on a local minimum of scoring function will read on this limitation.
In claim 3, the structure for the structural sampler performing “sampling” is the algorithm disclosed in claim 3 which is “sampling at least one configuration of the subject protein dynamically using at least one of Langevin dynamics and Monte Carlo sampling.”
In claim 14: “sampling the library of tertiary motifs according to their frequency in a reference database to identify probable tertiary motifs”. Specification para. [48] recites “the tertiary motifs in the library are sampled according to their frequency in a reference database, such as PDB, that is, sampling first from the most frequently-occurring tertiary motifs to the least frequent tertiary motifs.” This algorithm is the structure.
In claim 14: “sampling the library of tertiary motifs to identify probable tertiary motifs”. Specification para. [48] recites sampling various percentages of the library of tertiary motifs or sampling motifs from the library based on frequency. These algorithms and equivalents thereof are the structure.
If applicant does not intend to have these limitations interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitations to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Response to Arguments under 35 USC 112(f)
Applicant's arguments filed 4/17/2026 have been fully considered but they are not persuasive.
Applicant requests withdrawal of claim interpretation under 35 USC 112(f) (pg. 11, sec 112(f)). Applicant’s argument is not persuasive because the claims still require interpretation under 35 USC 112(f) because they recite computer-implemented means-plus-function limitations. This is because the structural sampler is implemented via a processor rather than being comprised of a processor. Structure for computer-implemented means-plus-function limitations require an algorithm. See MPEP 2181.II.B.
Claim Rejections - 35 USC § 112
35 USC 112(a)
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 1-3, 5, 7-11, 13-14, 22-32, 34 and 36-39 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 written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
This rejection is maintained from the previous Office Action.
Claims dependent from a rejected claim are also rejected, unless otherwise noted.
Claims 1 and 37-39 fail to comply with the written description requirement because they do not adequately link or associate adequately described particular structure, material, or acts to perform the function recited in the claims identified to invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. As discussed in Claim Interpretation, claims 1 and 37-39 recite “generating, by the structural sampler, a predicted structure representing a local minimum according to the scoring function”, which invokes 35 U.S.C. 112(f). Neither the specification nor the drawings disclose an algorithm that performs this recited function of the structural sampler. Therefore, in accordance with MPEP § 2181.IV, the instant specification does not provide written description support for the structural sampler to perform generating a predicted structure because there is no corresponding algorithm.
Response to Arguments under 35 USC 112(a)
Applicant's arguments filed 4/17/2026 have been fully considered but they are not persuasive.
Applicant argues that because the structural sampler is implemented by a processor and memory and/or a server that there is sufficient structure for the computer-implemented means-plus-function limitation of the structural sampler (pg. 11-12, sec. 112(a)). Applicant’s argument is not persuasive because:
Implementation by a processor, memory, and server indicates that the structural sampler is a computer-implemented means-plus-function limitation. MPEP 2181.II.B recites “[f]or a computer-implemented 35 U.S.C. 112(f) claim limitation, the specification must disclose an algorithm for performing the claimed specific computer function”, and “[t]o claim a means for performing a specific computer-implemented function and then to disclose only a general-purpose computer as the structure designed to perform that function amounts to pure functional claiming.” Applicant must recite an algorithm in the claim or point to where in the disclosure an algorithm exists to perform the function of the structural sampler rather just stating that the structural sampler is implemented by a processor and memory (i.e., general-purpose computer).
35 USC 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-3, 5, 7-11, 13-14, 22-32, 34 and 36-39 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
This rejection is either maintained from the previous Office Action or newly recited as necessitated by claim amendment.
Claims dependent from a rejected claim are also rejected, unless otherwise noted.
Claim 32, line 2, recites “the tertiary motifs” which renders the claim indefinite. It is unclear if the recitation refers to tertiary motifs in “a library of tertiary motifs” or “probable pair tertiary motifs” in claim 1. Clarify which tertiary motifs are being referenced.
Claim 38, line 2, recites “the instructions” which renders the claims indefinite. It is unclear which instructions are being referenced because both claim 1, line 7, and claim 37, line 2, recite “instructions”. Clarify which instructions are being referenced.
Claims 1 and 37-39 recite the following limitations that invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph: “generating, by the structural sampler, a predicted structure representing a local minimum according to the scoring function”. As discussed in Claim Interpretation, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and fails to clearly link the structure, material, or acts to the function. Therefore, the claims are indefinite and are rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Response to Arguments under 35 USC 112(b)
Applicant's arguments filed 4/17/2026 have been fully considered but they are not persuasive.
The rejection of claims 1 and 37-39 under 35 USC 112(b) for failing to disclose corresponding structure for the computer-implemented means-plus-function limitation is maintained for the same reasons discussed above under response to argument for 35 USC 112(a). Thus, Applicant’s arguments are not persuasive (pg. 12, sec. 112(b)).
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 1-3, 5, 7-11, 13-14, 22-32, 34 and 36-39 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea and a natural phenomenon without significantly more.
Any newly recited portions herein are necessitated by claim amendment.
Step 1:
Step 1 asks whether the claims recite statutory subject matter. In the instant application, c claims 1-3, 5, 7-11, 13-14, 22-32, 34 and 36 recite a method, claim 37 recites a CRM, claim 38 recites a system, and claim 39 recites a method. As such, these claims recite statutory subject matter (Step 1: YES).
Step 2A, Prong 1:
Claims that recite statutory subject matter are analyzed under Step 2A, Prong 1 to determine if they recite any concepts that equate to an abstract idea, law of nature or natural phenomena. The instant claims recite the following limitations that equate to one or more categories of judicial exception:
Claims 1 and 37-39 recite “A method of predicting the structure of a subject protein comprising: initializing a structural sampler with a topology dataset comprising: a) a set of probable self-tertiary motifs from a library of tertiary motifs for a subject protein and b) a set of probable pair tertiary motifs from the library of tertiary motifs for the subject protein, the structural sampler … ; sampling, by the structural sampler, at least one configuration of the subject protein according to the topology dataset; calculating, by the structural sampler, a scoring function incorporating a distance between a configuration and the set of probable self-tertiary motifs or the set of probable pair tertiary motifs; continuously optimizing chain coordinates, by the structural sampler, to minimize the scoring function; and generating, by the structural sampler, a predicted structure representing a local minimum according to the scoring function.”
Claim 2 recites “calculating the distance as root-mean squared deviation between a tertiary motif from the library and a fragment from the at least one configuration sampled corresponding to a structure of the tertiary motif.”
Claim 3 recites “wherein sampling, by the structural sampler, includes sampling the at least one configuration of the subject protein dynamically using at least one of Langevin dynamics and Monte Carlo sampling.”
Claim 5 recites “wherein the scoring function is:
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, wherein, C denotes a chain configuration, i denotes an i-th topology option, r;(C) is a RMSD value, a weight w; determines a relative importance of the i-th topology option, and parameter B is a pseudo-temperature factor.”
Claim 7 recites “wherein the scoring function is minimized by at least one of steepest descent minimization or conjugate gradients minimization.”
Claim 8 recites “wherein the scoring function, in addition to the topology dataset, utilizes one or more molecular mechanical features.”
Claim 9 recites “wherein the one or more molecular mechanical features include one or more of: bond, angle, and dihedral energies; van der Waals and Coulombic interaction energies; and solvation energies.”
Claim 10 recites “wherein the topology dataset further comprises a set of at least one of triplet tertiary motifs, quadruple tertiary motifs, pentuple tertiary motifs, and probable higher-order tertiary motifs.”
Claim 11 recites “determining the set of probable self-tertiary motifs by evaluating the tertiary motifs in the library by comparing each contiguous segment along a length of the subject protein according to a sequence model of a tertiary motif; calculating a score that indicates a probability of a n-mer conforming to the tertiary motif, or providing a score that indicates a probability of the segments conforming to the tertiary motif, and identifying the set of probable self-tertiary motifs as those for which the score calculated or the score provided meets or exceeds a reference value.”
Claim 13 recites “wherein the reference value is a pre-determined numerical threshold or pre-determined rank-order.”
Claim 14 recites “wherein sampling includes at least one of: sampling the library of tertiary motifs according to their frequency in a reference database to identify probable tertiary motifs; and sampling the library of tertiary motifs to identify probable tertiary motifs.”
Claim 22 recites “wherein the self-tertiary motifs in the library have a length n, and further comprising: generating the self-tertiary motifs by clustering all contiguous n-mers in the library.”
Claim 23 recites “wherein clustering all contiguous n-mers in the library is performed by at least one of best-fit RMSD of backbone atoms or Euclidian distance map norm difference.”
Claim 24 recites “wherein Euclidian distance map norm difference is performed by at least one of greedy clustering, k-means clustering, or hierarchical clustering.”
Claim 25 recites “wherein the pair tertiary motifs in the library have a length n and further comprising: generating the pair tertiary motifs by identifying interacting residue pairs having a distance between alpha carbon atoms and generating a pair of n-mer tertiary motifs having at least one of interacting residue pairs, distance between residue centroids, contact degree-based definition, and other residue orientation-depending geometric descriptors.”
Claim 26 recites “wherein at least one of: interacting residue pairs have a distance between alpha carbon atoms of less than 26 angstroms; distance between residue centroids is less than 25 angstroms; and contract degree-based definition is a contact degree less than 0.8.”
Claim 27 recites “wherein the pair tertiary motifs in the library have a length n, and further comprising: generating the pair tertiary motifs by clustering all pairs of n-mer tertiary motifs in the library.”
Claim 28 recites “wherein clustering all pairs of n-mer tertiary motifs in the library is performed by at least one of best-fit RMSD of backbone atoms and Euclidian distance map norm difference”
Claim 29 recites “wherein the Euclidian distance map norm difference is performed by at least one of greedy clustering, k-means clustering, and hierarchical clustering.”
Claim 30 recites “wherein the component segments of pair tertiary motifs are both the same length.”
Claim 31 recites “wherein the component segments of pair tertiary motifs are different lengths.”
Claim 32 recites “generating the sequence model of the tertiary motifs by employing at least one of a Potts model of tertiary motifs in a cluster and a weak coupling framework of tertiary motifs in a cluster.”
Claim 34 recites “wherein the subject protein is at least one of a de novo protein without a known homologue and less than 3000 amino acids in length.”
Claim 36 recites “wherein the predicted structure exhibits a backbone RMSD less than 3.5 Angstroms, relative to an experimentally-derived structure.”
Claim 39 recites “A method of predicting the structure of a subject protein comprising providing … with a primary amino acid sequence of the subject protein and obtaining the predicted structure.”
Limitations reciting a mental process.
Claims 1, 5, 8-11, 13-14, 22, 25-27, 30-31, 34 and 36-39 contain limitations recited at such a high level of generality that they equate to a mental process because they are similar to the concepts of collecting information, analyzing it, and displaying certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), which the courts have identified as concepts that can be practically performed in the human mind. The paragraphs below discuss the limitations in these claims that recite a mental process under their broadest reasonable interpretation (BRI).
Regarding claims 1 and 37-39, the BRI of initializing a structural sampler includes parameterizing a model, wherein a human can input values into a model. The BRI of sampling includes selecting a configuration at random. The BRI of calculating a scoring function includes performing the calculation of claim 5, which can be done on pen and paper. The BRI of generating a predicted structure representing a local minimum according to the scoring function includes a human evaluating the results of the scoring function to then piece together tertiary fragments that have a local minimum in the subject protein, which can be done using pen and paper.
Regarding claim 5, a human can calculate the scoring function using pen and paper.
Regarding claims 8-10, these limitations are included in the abstract idea in claim 1 because they further limit the data being used to sample the subject protein and calculate the scoring function, but do not change the fact that they are part of the abstract idea.
Regarding claim 11, the BRI of comparing each contiguous segment along a length of the subject protein using a sequence model includes performing a sequence alignment, which a human can do using pen and paper. The BRI of calculating a score that indicates a probability includes a human performing calculations using pen and paper. A human can identify whether a score exceeds a value through analysis and mental determinations.
Regarding claim 13, this limitation is included in the abstract idea in claim 11 of identifying which score meets a reference value because it further limits the reference value but is still an abstract idea.
Regarding claim 14, the BRI of sampling includes a human selecting motifs randomly.
Regarding claims 22 and 27, the motifs having a length n is included in the abstract idea of claim 1 because it further limits the motifs but they are still part of the abstract idea.
Regarding claim 25, the motifs having a length n is included in the abstract idea of claim 1 because it further limits the motifs but they are still part of the abstract idea. The BRI of identifying interacting residue pairs having a distance between alpha carbon atoms includes analyzing data and making a determination. The BRI of generating a pair includes determining that a pair has interacting residues through analysis of data, then piecing them together on pen and paper.
Regarding claim 26, these limitations are included in the abstract idea of claim 25 because they further limit the parameters that a human analyses to generate the motif pairs.
Regarding claims 30-31 and 34, these limitations are included in the abstract idea of claim 1 because they further limit the motifs and subject protein, which are still part of the abstract idea.
Regarding claim 36, this limitation is included in the abstract idea of claim 1 for predicting a structure because it further limits the predicted structure but does not change the fact that it is part of the abstract idea.
Limitations reciting a mathematical concept.
Claims 1, 2-3, 7, 11, 22-24, 27-29, 32 and 37-39 recite limitations that equate to a mathematical concept because they are similar to organizing and manipulating information through mathematical correlations in Digitech Image Techs., LLC v Electronics for Imaging, Inc. (758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)), which the courts have identified as mathematical concepts. The paragraphs below discuss the limitations in these claims that recite a mathematical concept under BRI.
Regarding claims 1 and 37-39, the BRI of a structural sampler includes it being a Metropolis Monte Carlo (MMC), which performs calculations. The BRI of initializing a structural sampler includes parameterizing a MCC, which includes organizing/manipulating numerical variables. The BRI of sampling a configuration includes performing Monte Carlo sampling. The BRI of calculating a scoring function includes using the equation of claim 5. The BRI of optimizing coordinates to minimize the scoring function includes using steepest descent minimization as recited in para. [91].
Regarding claims 2-3 and 5, RMSD, Monte Carlo sampling, and the scoring function are equations.
Regarding claim 7, steepest descent and conjugate gradient minimization are functions.
Regarding claim 11, calculating a score that indicates a probability is a mathematical concept.
Regarding claims 22-24 and 27-29, the BRI of clustering includes using k-means clustering which performs calculations.
Regarding claim 32, the BRI of a Potts model and a weak coupling framework includes use of mathematical equations and calculations.
Limitations reciting a natural phenomenon.
Claim 1 recites limitations that equate to a natural phenomenon because they are similar to the concept of a correlation between variations in non-coding regions of DNA and allele presence in coding regions of DNA, Genetic Techs. Ltd. v. Merial LLC, 818 F.3d 1369, 1375, 118 USPQ2d 1541, 1545 (Fed. Cir. 2016), which the courts have established as a natural phenomenon. Claim 1 predicts the structure of a protein from its primary amino acid sequence by using tertiary structures related to segments of the sequence, under its broadest reasonable interpretation. The relationship between primary amino acid sequence and tertiary structure is a natural phenomenon.
As such, claims 1-3, 5, 7-11, 13-14, 22-32, 34 and 36-39 recite an abstract idea and a natural phenomenon (Step 2A, Prong 1: YES).
Additional Elements:
Once limitations have been identified that recite a judicial exception, the claims are evaluated for additional elements. The additional elements are then analyzed under Step 2A, Prong 2 then Step 2B. The instant claims recite the following additional elements:
Claims 1 and 37-39 recite “implemented via a processor and a memory with instructions stored thereon”
Claim 37 recites “A non-transitory computer-readable medium comprising instructions that, upon execution by a microprocessor, causes the microprocessor to perform the method of claim 1.”
Claim 38 recites “A system comprising the non-transitory computer-readable medium of claim 37 and a processor for executing the instructions, wherein the system comprises a human end-user interface.”
Claim 39 recites “the system of claim 38.”
These above recited additional elements are analyzed below under both Step 2A, Prong 2 and Step 2B:
Step 2A, Prong 2:
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). The judicial exception is not integrated into a practical application because the claims do not recite additional elements that reflect an improvement to a computer, technology, or technical field (MPEP § 2106.04(d)(1) and 2106.5(a)), require a particular treatment or prophylaxis for a disease or medical condition (MPEP § 2106.04(d)(2)), implement the recited judicial exception with a particular machine that is integral to the claim (MPEP § 2106.05(b)), effect a transformation or reduction of a particular article to a different state or thing (MPEP § 2106.05(c)), nor provide some other meaningful limitation (MPEP § 2106.05(e)). Rather, the claims include limitations that equate to an equivalent of the words “apply it” and/or to instructions to implement an abstract idea on a computer (MPEP § 2106.05(f)). The paragraphs below discuss the additional elements recited above in the instant claims.
Regarding the above cited limitation in claims 1 and 37-39 of implemented via a processor and a memory with instructions stored thereon, CRM comprising instructions executed by a microprocessor, and a system comprising a CRM and a processor. There are no limitations requiring anything other than a generic computer and/or generic computing system. Therefore, these limitations equate to mere instructions to implement an abstract idea on a generic computer, which the courts have established does not render an abstract idea eligible in Alice Corp. 573 U.S. at 223, 110 USPQ2d at 1983.
As such, claims 1-3, 5, 7-11, 13-14, 22-32, 34 and 36-39 are directed to an abstract idea and a natural phenomenon (Step 2A, Prong 2: NO).
Step 2B:
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). These claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because these claims recite additional elements that equate to instructions to apply the recited exception in a generic way and/or in a generic computing environment (MPEP § 2106.05(f)) and to well-understood, routine and conventional (WURC) limitations (MPEP § 2106.05(d)). The paragraphs below discuss the additional elements recited above in the instant claims.
Regarding the above cited limitation in claims 37-39 of a computer-readable medium (CRM) comprising instructed executed by a microprocessor and a system comprising a CRM and a processor, there are no limitations requiring anything other than a generic computer and/or generic computing system. Therefore these limitations equate to instructions to implement an abstract idea on a generic computing environment, which the courts have established does not provide an inventive concept in Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015).
Regarding the additional elements in claims of 1 and 37-39 of storing instructions in memory, these limitations equate to storing information in memory, which the courts have established as a WURC function of a generic computer in Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
When these additional elements are considered individually and in combination, they do not provide an inventive concept because they all equate to WURC components/functions of a generic computer and/or generic computing system and to mere instructions to implement an abstract idea on a generic computer. Therefore, these additional elements do not transform the claimed judicial exception into a patent-eligible application of the judicial exception and do not amount to significantly more than the judicial exception itself (Step 2B: No).
As such, claims 1-3, 5, 7-11, 13-14, 22-32, 34 and 36-39 are not patent eligible.
Response to Arguments under 35 USC 101
Applicant's arguments filed 4/17/2026 have been fully considered but they are not persuasive.
Applicant references McRO v. Bandai Namco Games America and argues the claims provide both improved speed and fidelity in structure prediction (pg. 12-13, sec. 101). Applicant’s argument is not persuasive because:
Applicant has not pointed out which additional element(s) alone or in combination that confer the improvement nor explained how the additional element(s) interact with the judicial exception to confer the improvement. Rather, Applicant provides a bare assertion of an improvement, which is not sufficient to show an improvement. See MPEP 2106.04(d)(1) regarding requirements for evaluating improvements to technology or a technical field.
As of record, the only additional elements in the independent claims equate to mere instructions to implement the abstract idea on a generic computer, which does not integrate into a practical application (MPEP 2106.05(f)). If Applicant refers to the limitations that recite a judicial exception, the judicial exception alone cannot provide an improvement (MPEP 2106.05(a)). It is also noted that MPEP 2106.05(a).II recites “an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology.”
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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, 5, 8-11, 13-14, 22-32, 34 and 36-39 are rejected under 35 U.S.C. 103 as being unpatentable over Mackenzie et al. (“Mackenzie”; NPL ref. 2 on IDS filed 05/19/2022; previously cited), as evidenced by Supporting Information of Mackenzie (Proceedings of the National Academy of Sciences, 113(47), E7438-E7447; hereinafter “Mackenzie Supplemental”; previously cited on PTO892 mailed 1/22/2026), in view of Kuhlman et al. (“Kuhlman”; Nature reviews molecular cell biology 20, no. 11 (August, 2019): 681-697; previously cited on PTO892 mailed 1/22/2026), as evidenced by PDB (“2KL8”; published 2009; previously cited on PTO892 mailed 1/22/2026).
The bold and italicized text below are the limitations of the instant claims, and the italicized text serves to map the prior art onto the instant claims.
Any newly recited portions herein are necessitated by claim amendment.
Claims 1 and 37-39:
A method of predicting the structure of a subject protein comprising:
Mackenzie decomposes known protein structures into basic elements called tertiary structural motifs (TERMs), which are used for protein structure design and prediction (abstract).
initializing a structural sampler with a topology dataset comprising: a) a set of probable self-tertiary motifs from a library of tertiary motifs for a subject protein and b) a set of probable pair tertiary motifs from the library of tertiary motifs for the subject protein the structural sampler implemented via processor and a memory with instructions stored thereon;
Mackenzie discloses a dataset DB80 that contains topologies of TERMs (topology dataset) (pg. 7445, col. 2, para. 2) (pg. 7446, col. 2, para. 1). DB80 contains TERMs which can be single-segment motifs (self-tertiary motifs) or multi-segment motifs (pair tertiary motifs) derived from PDB (a library of tertiary motifs) (pg. 7440, col. 1, para. 3) (Figure 6). The method is computer-implemented by use of several software packages such as MASTER (pg. 7440, col. 1, para. 1), wherein computers have memory and processors.
The broadest reasonable interpretation of a structural sampler includes a model. Mackenzie Supplemental teaches using the TERMs in a model to determine sequence structure (pg. 3, col. 1, para. 2).
sampling, by the structural sampler, at least one configuration of the subject protein according to the topology dataset;
Mackenzie recites “the weak coupling framework reported by Weigt and coworkers, we built a two-body statistical sequence model for each TERM from the MSA of its PDB instances (SI Appendix, SI Methods). With this model, we scored all possible alignments for each of the top 4,000 highest-priority universal TERMs (with up to three segments) onto each protein sequence from the above X-ray-1 and NMR-1 sets” (pg. 7443, col. 2, para. 4).
However, Mackenzie does not teach sampling a configuration of a subject protein.
Kuhlman reviews advances in protein structure and prediction and design (title). Monte Carlo (MC) simulations are used for protein structure prediction (Box 1) (Figure 2). The Monte Carlo simulation starts “from a random or fully extended conformation and proceed by repeatedly selecting a random window of the protein (e.g. residues 22–30) and inserting into that window the structure of a randomly selected fragment from the corresponding fragment library” (pg. 684, col. 2, para. 2). Box 1 shows the local and global minima of the MC simulation.
calculating, by the structural sampler, a scoring function incorporating a distance between a configuration and the set of probable self-tertiary motifs or the set of probable pair tertiary motifs;
Mackenzie Supplemental teaches “The top 20 best-scoring alignments were found and recorded for each TERM, with the best alignments from the combined list corresponding to predicted TERM alignments. Alignments were considered structurally correct if the corresponding RMSD was below 1.0 Å for single-segment motifs, below 1.5 Å for two-segment motifs, and below 2.0 Å for three-segment motifs” (pg. 3, col. 1, para. 2).
continuously optimizing chain coordinates, by the structural sampler, to minimize the scoring function; and
Mackenzie discloses backbone coordinates captured by TERMs (pg. 7442, col. 2, para. 2) and scoring functions (pg. 7443, col. 2, para. 4) (pg. 7445, col. 1, para. 1). However, Mackenzie does not teach continuously optimizing chain coordinate to minimize the scoring function.
Kuhlman teaches “In gradient-based optimization approaches, the derivatives of the energy function with respect to the flexible degrees of freedom (e.g. the atomic coordinates or backbone torsion angles) are calculated in order to proceed in the direction in which the energy decreases most rapidly. Gradient-based optimization is effective at finding the nearest local minimum in the energy landscape” (caption of Box 1). Torsion angles (backbone and side chain) are optimized using gradient-based minimization (chain coordinates), indicating that they adjusted to find an energy minimum (pg. 690, col. 2, para. 1) (Box 1). The optimization is continuous because it doesn’t stop until a local or global minima is reached (Box 1).
It would have been prima facie obvious to have modified the method of Mackenzie for predicting protein structure by using a gradient-based optimization approach that optimizes torsion angles (i.e. backbone and side chains) to determine a local minimum as taught by Kuhlman. The motivation for doing so is taught by Kuhlman who states energy functions help guide protein prediction and reaching an energy minimum generates better proteins (caption to the left of Box 1). One of ordinary skill in the art would have had a reasonable expectation of success because Kuhlman states that optimization techniques are commonly used to search for lower-energy sequences and side-chain conformations (pg. 690, col. 1, para. 2).
generating, by the structural sampler, a predicted structure representing a local minimum according to the scoring function.
Mackenzie Supplemental shows in Figure S19 an example of a predicted structure of a protein based on its sequence using TERMs. In Figure S19 single-segment (self-tertiary motifs) and multi-segment (pair tertiary motifs) TERMs are used to construct the structure of the protein from its sequence. Figure 6 shows that the predictions were made for de novo protein sequences as well.
However, Mckenzie does not teach that the predicted structure is based on a local minimum according to the scoring function.
Kuhlman teaches “This hypothesis forms the basis for a general approach to protein structure prediction that combines sampling of alternative conformations with scoring to rank them by energy and identify the lowest energy state” (pg. 682, col. 2). Box 1 also shows the MC simulation evaluating local minima.
It would have been prima facie obvious to have modified the method of Mackenzie for predicting protein structure by using a MC simulation that performs protein configuration sampling to find local energy minima as taught by Kuhlman. The motivation for doing so is taught by Kuhlman who states that MC simulations lead to a lowest-energy model when using fragment assembly (pg. 684, col. 2, para. 2). One of ordinary skill in the art would have had a reasonable expectation of success because Kuhlman teaches that MC simulations are used on fragment assemblies (Box 1) (pg. 684, col. 2, para. 2). Mackenzie teaches using fragments (i.e., TERMs) to predict structure from sequence.
Claim 2:
Mackenzie Supplemental teaches “The top 20 best-scoring alignments were found and recorded for each TERM, with the best alignments from the combined list corresponding to predicted TERM alignments. Alignments were considered structurally correct if the corresponding RMSD was below 1.0 Å for single-segment motifs, below 1.5 Å for two-segment motifs, and below 2.0 Å for three-segment-motifs” (pg. 3, col. 1, para. 2). Each TERM corresponds to a portion of a protein sequence.
Claim 3:
As discussed above regarding claim 1, Mackenzie teaches aligning sequence models of TERMs onto each protein sequence in the X-ray-1 and NMR-1 datasets (pg. 7443, col. 2, para. 4). However, Mackenzie does not teach sampling configurations of a subject protein. Kuhlman teaches using MC simulations to sample configurations based on fragments (Box 1) (Figure 2).
Claim 8:
Mackenzie Supplemental discloses calculating pseudo-energies (molecular mechanical features) for TERMs (topology dataset) used in the structure predictions, wherein the predicted structures are scored in part based on the pseudo-energies (pg. 3, col. 1, para. 2).
Claim 9:
Mackenzie Supplemental teaches scoring the simulated structures (pg. 3, col. 2, para. 2-3). However, Mackenzie does not teach that the scoring function contains van der Waals and Coulombic interaction energies.
Kuhlman teaches “Typical protein energy functions are linear combinations of multiple terms, each term capturing a distinct energetic contribution (van der Waals interactions, electrostatics)” (caption to left of Box 1).
It would have been prima facie obvious to have modified the scoring function of Mackenzie used to predict protein structure based on amino acid sequence by using the scoring function of Kuhlman. The motivation for doing so is taught by Kuhlman who recites that their energy functions navigate protein conformational energy landscapes to allow for accurate protein structure prediction (top caption of Box 1). One of ordinary skill in the art would have had a reasonable expectation of success to use the energy functions of Kuhlman in Mackenzie because they are applicable to fragment assemblies (Box 1). Mackenzie uses a fragment assembly method (i.e., TERMs).
Claim 10:
Mackenzie shows in Figure 6 that 3-segment TERMs were used.
Claim 11:
determining the set of probable self-tertiary motifs by evaluating the tertiary motifs in the library by comparing each contiguous segment along a length of the subject protein according to a sequence model of the tertiary motif of the tertiary motifs in the library,
Mackenzie teaches “Using the weak coupling framework reported by Weigt and coworkers (70), we built a two-body statistical sequence model for each TERM from the MSA of its PDB instances (SI Appendix, SI Methods). With this model, we scored all possible alignments for each of the top 4,000 highest-priority universal TERMs (with up to three segments) onto each protein sequence from the above X-ray-1 and NMR-1 sets” (pg. 7443, col. 2, para. 4).
calculating a score that indicates a probability of a n-mer conforming to the tertiary motif, or providing a score that indicates a probability of the segments conforming to the tertiary motif, and identifying the set of probable self-tertiary motifs as those for which the score calculated or the score provided meets or exceeds a reference value.
Mackenzie Supplemental teaches “The final score for each alignment 𝑘 of TERM 𝑡 was then calculated as:
PNG
media_image2.png
90
656
media_image2.png
Greyscale
where 𝐸 (𝑡) is statistical energy associated with the 𝑘-th alignment of TERM 𝑡, calculated by summing the appropriate self and pair energy components (more positive energies are more favorable by convention in Morcos et al. (6)), the sum in the denominator on the left extends over all possible alignments of 𝑡 in the corresponding benchmark protein, and 𝑝 𝑡 represents the prior probability of observing the TERM and was taken simply as the frequency of 𝑡 in the set-cover database. The top 20 best-scoring alignments were found and recorded for each TERM, with the best alignments from the combined list corresponding to predicted TERM alignments” (pg. 3, col. 2, para. 2).
Claim 13:
Mackenzie Supplemental recites “Alignments were considered structurally correct if the corresponding RMSD was below 1.0 Å for single-segment motifs, below 1.5 Å for two-segment motifs, and below 2.0 Å for three-segment motifs” (pg. 3, col. 2, para. 2).
Claim 14:
Mackenzie teaches using the TERMs to predict the structure of a protein, wherein top-ranking TERMs are selected (pg. 7443, col. 2, para. 4). However, Mackenzie does not teach sampling the TERMS.
Kuhlman teaches “Examples of the move sets used for Monte Carlo simulations include fragment-replacement moves, in which a continuous backbone segment in the current conformation is replaced with an alternative conformation from a fragment library, and side-chain rotamer substitutions”, which is done to achieve an energy minimum (caption of Box 1).
It would have been prima facie obvious to one of ordinary skill in the art to have modified the method of Mackenzie for predicting protein structure by using a MC simulation that performs protein configuration sampling to find local energy minima as taught by Kuhlman. The process would be performed “exhaustively” until a satisfactory energy minimum is found, which Kuhlman teaches is advantageous (pg. 684, col. 2, para. 2). One of ordinary skill in the art would have had a reasonable expectation of success because Kuhlman teaches that MC simulations use fragment assemblies (Box 1) (pg. 684, col. 2, para. 2). Mackenzie uses fragments (i.e., TERMs) to predict structure from sequence (pg. 7443, col. 2, para. 3-4).
Claims 22-24 and 27-29:
Mackenzie teaches single-segment TERMs (self-tertiary motifs) and multi-segment TERMs (pair tertiary motifs) acquired from PDB (the library) (pg. 7440, col. 1, para. 3), which are protein segments with lengths (length of n) (pg. 7440, col. 1, para. 4). These TERMs make up the DB80 dataset (pg. 7445, col. 2, last para.). The TERMs in the DB80 dataset were greedily clustered (clustering all contiguous n-mers in the library) (Euclidean distance map norm difference) (greedy clustering) (pg. 7446, col. 1, para. 1).
Claim 25:
Mackenzie recites “The universal set to cover consisted of all unique residues and PCs in DB80, representing secondary and tertiary/quaternary information, respectively” (the pair tertiary motifs in the library have a length n) (pg. 7445, col. 2, last para.). Mackenzie Supplemental teaches “Residues consecutive in sequence are joined by peptide bonds of relatively fixed geometry, so that the position of each next residue in a chain is highly constrained by the previous residues (e.g., Cα-to-Cα distances between adjacent residues are generally ~3.8 Å) (interacting residue pairs having a distance between alpha carbon atoms) (pg. 1, col. 1, para. 1). Mackenzie teaches how the motifs were created which includes residue pairs capable of making contact (PCs) which is a measure of contact degree (generating a pair of n-mer tertiary motifs having at least one of contact degree-based definition) (pg. 7445, col. 2, para. 3-4).
Claim 26:
Mackenzie teaches how the motifs were created which includes residue pairs capable of making contact (PCs) which is a measure of contact degree (contact degree-based definition) (pg. 7445, col. 2, para. 3-4). The contact degree varies from 0 to 1 (contact degree less than 0.08). MPEP 2144.05.I recites “In the case where the claimed ranges ‘overlap or lie inside ranges disclosed by the prior art’ a prima facie case of obviousness exists.” Therefore, it would have been prima facie obvious to require a contact degree of less than 0.8 in Mackenzie because the claimed range of less than 0.8 overlaps with the ranged disclosed by Mackenzie of 0 to 1.
Claim 30:
Mackenzie Supplemental shows in Figure S2B a motif with two segments of the same length: n = [5,5].
Claim 31:
Mackenzie Supplemental shows in Figure S2 a motif with two segments, wherein one segment is 5 and the other second can be n-residues, which includes the residues being other than 5. See caption of Figure S2.
Claim 32:
Mackenzie teaches “Using the weak coupling framework reported by Weigt and coworkers (70), we built a two-body statistical sequence model for each TERM from the MSA of its PDB instances (SI Appendix, SI Methods)” (pg. 7443, col. 2, para. 4). Mackenzie also teaches that the TERMs are clustered (pg. 7446, col. 1, para. 1).
Claim 34:
Mackenzie teaches referring to predicting protein structure “TERM-based mining appears to be quite applicable to de novo proteins and requires no homology (Fig. 6)” (pg. 7445, col. 1, para. 2). Figure 6 shows that one of the de novo proteins is 2KL8, which was acquired from PDB. As evidenced by PDB, 2KL8 is 85 amino acids in length.
Claim 36:
Mackenzie Supplemental teaches “Alignments were considered structurally correct if the corresponding RMSD was below 1.0 Å for single-segment motifs, below 1.5 Å for two-segment motifs, and below 2.0 Å for three-segment motifs” (pg. 3, col. 1, para. 2). See also Figure S6.
Claims 37-38:
Mackenzie discloses that their method is computer-implemented by use of several software packages such as MASTER (pg. 7440, col. 1, para. 1), wherein computers have memory and processors.
Claim 39:
Mackenzie discloses a computer-implemented method, wherein the structure of a protein is predicted based on its sequence (pg. 7443, col. 2, para. 3-4). See also Figure S6.
Response to Arguments under 35 USC 103
Applicant's arguments filed 4/17/2026 have been fully considered but they are not persuasive.
Applicant argues that the references do not teach continuously optimizing chain coordinates to minimize a scoring function (pg. 13-14, sec. 103). Applicant’s argument is not persuasive because:
Kuhlman teaches that gradient-based optimization techniques find the nearest local minimum in an energy landscape and optimize torsion angles (chain coordinates) (Box 1) (pg. 690, col. 2, para. 1). Gradient-based optimization is continuous because different backbone configurations and side chains are iteratively sampled to find an energy minimum (upper left in Box 1).
Kuhlman also discusses different sequence optimizations that search for lower-energy sequences and side-chain conformations such as dead-end elimination, simulated annealing and genetic algorithms. These methods optimize side-chains by restricting side-chain motion and operate by altering atomic coordinates (pg. 690, col. 2, para. 1) (Box 1).
Alternately, Kuhlman discloses optimization using Rosetta, which uses Monte Carlo sampling with simulated annealing to identify low-energy sequences and rotamers and uses internal coordinates (pg. 690, col. 1, para. 1). This too is a continuous optimization to find a global minimum (Box 1).
Conclusion
No claims are allowed.
Claims 5 and 7 are free from the prior art because the prior art does not teach the scoring function in claim 5, particularly the use of a pseudo-temperature multiplied by an RMSD value.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/N.A.A./Examiner, Art Unit 1687
/KAITLYN L MINCHELLA/Primary Examiner, Art Unit 1685
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