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 .
Applicant's response filed 5/7/2026 has been fully considered. The following rejections
and/or objections are either reiterated or newly applied.
Status of Claims
Claims 1, 3-4, 7-10, 13-17, 19-23, and 26-29 pending and examined on the merits.
Claims 2, 5-6, 11-12, 18, and 24-25 canceled.
Priority
The instant application filed on 11/9/2021 is a 371 national stage entry of PCT/GB2020/051143 having an international filing date of 5/11/2020, and claims the benefit of foreign priority to Application No. GB1906566.3 filed on 5/9/2019. Thus, the effective filing date of the claims is 5/9/2019.
The applicant is reminded that amendments to the claims and specification must comply with 35 U.S.C. § 120 and 37 C.F.R. § 1.121 to maintain priority to an earlier-filed application. Claim amendments may impact the effective filing date if new subject matter is introduced that lacks support in the originally filed disclosure. If an amendment adds limitations that were not adequately described in the parent application, the claim may no longer be entitled to the priority date of the earlier filing.
Specification
The objection to the specification withdrawn in view of Applicant's claim amendments filed on 5/7/2026.
Claim Objections
The objection to claims 1-2 and 20 withdrawn in view of Applicant's claim amendments filed on 5/7/2026.
Withdrawn Rejections
35 USC § 112(b)
The rejection of claims 1-4, 7-10, 12-17, 19-23, and 26-29 under 35 USC 112(b) withdrawn in view of Applicant's claim amendments filed on 5/7/2026.
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, 3-4, 7-10, 13-17, 19-23, and 26-29 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of a mental process, a mathematical concept, organizing human activity, or a law of nature or natural phenomenon without significantly more. In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A, Prong 1). In the instant application, the claims recite the following limitations that equate to an abstract idea:
Claim 1: “a library design step, in which a nucleic acid library comprising at least 10⁴ sequence variants is designed, wherein each sequence variant comprises a coding sequence for a protein and each sequence variant comprises at least one constant region and at least one variable region, wherein one or more constant regions are common to all sequence variants within the library, and the one or more variable regions are not common to all sequence variants within the library” provides an evaluation (designing a nucleic acid library to specific parameters) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
“the sequence variants are each assigned a fitness score based at least in part on the result of the library testing step” provides an evaluation (assigning a score based on a result involves evaluating results) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
“a machine learning algorithm uses the fitness score of each of the sequence variants to train a model to predict the fitness score for new sequence variants” provides a mathematical calculation (using the fitness scores with label data [continuous variables between 0 and 1 according to specification page 5] to train "black box" neural network classifiers or "white box" machine learning algorithms [page 32] involves mathematical calculations) that is considered a mathematical concept, which is an abstract idea.
Claim 7: “designing at least one of the one or more variable regions to include random variability in at least one position, optionally wherein the library design step (a) comprises designing at least one of the one or more variable regions to include random variability in one or more specific positions of the at least one variable region” provides an evaluation (designing a nucleic acid library to specific parameters) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
Claim 9: “selecting a nucleic acid sequence encoding for a protein that has at least one of the one or more desired properties” provides an evaluation (selecting a sequence involves evaluating desired protein properties) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
“identifying one or more regions of the sequence where variability is expected to result in an improvement” provides an evaluation (identifying regions of the sequence where variability might result in an improvement in desired properties) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
“defining the one or more variable parts to include the one or more regions of the sequence where variability is expected to result in an improvement” provides an evaluation (defining regions involves making selections based on evaluations) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
Claim 10: “identifying one or more regions of the sequence where variability is expected to be detrimental to the integrity of the protein and /or to at least one of the one or more desired properties” provides an evaluation (identifying regions of the sequence where variability might result in a degradation of desired properties) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
“defining one or more of the one or more constant regions to include the one or more regions of the sequence where variability is expected to be detrimental” provides an evaluation (defining regions involves making selections based on evaluations) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
Claim 15: “separating the protein library into at least 2 samples depending on the results of the one or more assays, wherein the results of the one or more assays on which the separation depends are the desired properties as identified by the one or more assays being run on the sample” provides an evaluation (performing an action based on a result involves evaluation of results) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
Claim 16: “aligning the sequences obtained by sequencing with the sequences designed in step (a)” provides a comparison (aligning sequences involves comparison of strings) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
“quantifying the number of times that each sequence appears in each sample” provides a mathematical calculation (quantifying the number of times a sequence appears in a sample involves arithmetic) that is considered a mathematical concept, which is an abstract idea.
Claim 20: “training a plurality of machine learning algorithms, wherein each machine learning algorithm is trained to predict at least one of the plurality of fitness scores for new sequence variants” provides a mathematical calculation (training machine learning algorithms involves mathematical calculations) that is considered a mathematical concept, which is an abstract idea.
Claim 21: “the one or more fitness scores associated with each sequence variant depends on the number of times that each sequence appears in a first sample and the number of times that each sequence appears in a second sample” provides an evaluation (assessing sequence alignments and counts before calculating a fitness score) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
These recitations are similar to the concepts of collecting information, analyzing it, and displaying certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), organizing and manipulating information through mathematical correlations in Digitech Image Techs., LLC v Electronics for Imaging, Inc. (758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)) and comparing information regarding a sample or test to a control or target data in Univ. of Utah Research Found. v. Ambry Genetics Corp. (774 F.3d 755, 113 U.S.P.Q.2d 1241 (Fed. Cir. 2014)) and Association for Molecular Pathology v. USPTO (689 F.3d 1303, 103 U.S.P.Q.2d 1681 (Fed. Cir. 2012)) that the courts have identified as concepts that can be practically performed in the human mind or are mathematical relationships. Therefore, these limitations fall under the “Mental process” and “Mathematical concepts” groupings of abstract ideas. Additionally, while claims 28-29 recite performing some aspects of the analysis on “a processor adapted to implement the method of claim 1”, there are no additional limitations that indicate that this requires anything other than carrying out the recited mental processes or mathematical concepts in a generic computer environment. Merely reciting that a mental process is being performed in a generic computer environment does not preclude the steps from being performed practically in the human mind or with pen and paper as claimed. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental processes” grouping of abstract ideas. As such, claims 1, 3-4, 7-10, 13-17, 19-23, and 26-29 recite an abstract idea (Step 2A, Prong 1: YES).
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). The judicial exceptions listed above are not integrated into a practical application because the claims do not recite an additional element or elements that reflects an improvement to technology. Specifically, the claims recite the following additional elements:
Claim 1: “a step of producing the proteins encoded by each sequence variant of the nucleic acid library to obtain a protein library, wherein the nucleic acid library is translated in such a way as to maintain a relationship between each coding sequence and its encoded protein by using a display technology” provides insignificant extra-solution activities (producing proteins of a sequence library and assaying them are a pre-solution activities involving sample manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
“a library testing step, implemented at least in part via a computer system configured to interact with and control automated laboratory equipment, in which at least a portion of the sequence variants of the protein library are tested in parallel in one or more assays, for the one or more desired properties” provides insignificant extra-solution activities (testing nucleic acid libraries in parallel using known methods is a pre-solution activity involving sample manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
“the machine learning model trained in step (c) is used to design a new library of sequence variants with an improved distribution of fitness scores” provides insignificant extra-solution activities (using a model for library design is a pre- and post-solution activity involving data gathering and manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 4: “synthesizing a second DNA strand by single primer extension to form double stranded DNA” provides insignificant extra-solution activities (synthesizing nucleic acids is a pre-solution activity involving sample manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 13: “transcribing and translating the DNA library, wherein translating the library comprises synthesising RNA-polypeptide fusion molecules each comprising an RNA sequence variant bound to the protein that it encodes” provides insignificant extra-solution activities (producing proteins of a sequence library using RNA-polypeptide fusion molecules is a pre-solution activity involving sample manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 14: “transcribing and translating the DNA library, wherein translating the library comprises propagating phage that display a coat protein-polypeptide fusion, wherein the polypeptide fused to the coat protein corresponds to a sequence variant of the DNA library” provides insignificant extra-solution activities (producing proteins of a sequence library using RNA-polypeptide fusion molecules is a pre-solution activity involving sample manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 15: “separating the protein library into at least 2 samples” and “sequencing the nucleic acids present in at least one of the at least 2 samples” provides insignificant extra-solution activities (separating a protein library into multiple samples and sequencing them is a pre-solution activity involving sample manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 19: “incubating the protein library with the specific target immobilised on a surface and separating the protein library into a sample that is bound to the surface and a sample that is not bound to the surface” provides insignificant extra-solution activities (running an immobilization assay is a pre-solution activity involving sample manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 23: “the machine learning model trained in step (c) is used to design a new library of sequence variants by iteratively optimising a library of sequence variants in silico, optionally wherein the library of sequence variants is iteratively optimised using a genetic algorithm” provides insignificant extra-solution activities (using a model for library design and optimization is a pre- and post-solution activity involving data gathering and manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 28: “a processor adapted to implement the method of claim 1” provides insignificant extra-solution activities (running instructions on generic computer components) that do not serve to integrate the judicial exceptions into a practical application.
The steps for using models and providing sequence data; and synthesizing, testing/assaying, separating, and sequencing libraries are insignificant extra-solution activities that do not serve to integrate the recited judicial exceptions into a practical application because they are pre- and post-solution activities involving data gathering, data manipulation, and sample manipulation steps (see MPEP 2106.04(d)(2)). Furthermore, the limitations regarding implementing program instructions do not indicate that they require anything other than mere instructions to implement the abstract idea in a generic way or in a generic computing environment. As such, this limitation equates to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. Therefore, claims 1, 3-4, 7-10, 13-17, 19-23, and 26-29 are directed to an abstract idea (Step 2A, Prong 2: NO).
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that are insignificant extra-solution activities that do not serve to integrate the recited judicial exceptions into a practical application, or equate to mere instructions to apply the recited exception in a generic way or in a generic computing environment.
As discussed above, there are no additional elements to indicate that the claimed “processor adapted to implement the method of claim 1” requires anything other than generic computer components in order to carry out the recited abstract idea in the claims. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. MPEP 2106.05(f) discloses that mere instructions to apply the judicial exception cannot provide an inventive concept to the claims. Additionally, the limitations for using models and providing sequence data; and synthesizing, testing/assaying, separating, and sequencing libraries are insignificant extra-solution activities that do not serve to integrate the recited judicial exceptions into a practical application. Furthermore, no inventive concept is claimed by these limitations as they are well-understood, routine, and conventional as evidenced by: Kimmerlin et al. ("‘100 years of peptide synthesis’: ligation methods for peptide and protein synthesis with applications to β‐peptide assemblies." The Journal of peptide research 65.2 (2005): 229-260) page 2 col 1 first paragraph "Since the first synthesis of a dipeptide by Emil Fischer in 1901, peptide science has made tremendous progress and with recent innovations it is currently possible to ‘routinely’ synthesize proteins, well over 200 amino acids in length"; and Armbruster et al. ("Clinical chemistry laboratory automation in the 21st century-Amat Victoria curam (Victory loves careful preparation)." The Clinical Biochemist Reviews 35.3 (2014): 143) page 10 col 2 paragraph 3 "The history of automation in the clinical laboratory is long and varied. The Latin aphorism at the beginning of this review, loosely translated as “Victory loves careful preparation,” is apt for laboratories picking and choosing among the various automation options available in the 21st century. Manual testing is clearly of the past century for a modern laboratory except for a few very specialised tests. Even if a laboratory’s workload is of such a low volume and TAT is not a concern, the inherent variability of manual procedures makes them nonviable in comparison to modern automated methods and it is a given that laboratories will inevitably adopt automation"
The additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: No). As such, claims 1, 3-4, 7-10, 13-17, 19-23, and 26-29 are not patent eligible.
Response to Arguments under 35 USC § 101
Applicant’s arguments filed 5/7/2026 are fully considered but they are not persuasive.
Applicant asserts that the claims "recite a concrete technological workflow for improving protein engineering and exploration of protein sequences" because they are an "integrated laboratory and computational platform for generating, testing, evaluating, and redesigning protein libraries" (Remarks 5/7/2026 pages 5-7). Specifically, Applicant asserts that "assigning a fitness score" is similar to "MPEP 2106.04(a)(1), example vi" where "determining the amount of use" of an icon does not recite an abstract idea, and that evaluating the claims "as a whole" would not result in "characterizing those limitations at a high level of abstraction" (Remarks 5/7/2026 pages 7-8). Examiner notes that the limitation of assigning sequence variants "a fitness score based at least in part on the result of the library testing step" is not similar to the cited example vi because the instant limitation (as indicated above) provides a mathematical calculation (using the fitness scores with label data [continuous variables between 0 and 1 according to specification page 5] to train "black box" neural network classifiers or "white box" machine learning algorithms [page 32] involves mathematical calculations) that is considered a mathematical concept, which is an abstract idea. Example vi simply contains a limitation that is gathering data (memory use over time), which is not considered an abstract idea.
Applicant also asserts that the characterization of "all limitations apart from the purported abstract ideas" are not insignificant extra-solution activity because they "are not ancillary data-gathering steps appended to an otherwise abstract process" and "[r]ather, they are central aspects of the claimed technological workflow and are integral to the claimed improvement in protein engineering" and "are the mechanisms through which the claimed technological improvement is achieved" (Remarks 5/7/2026 pages 9-10). Additionally, Applicant asserts that the wet-lab operations "are fundamental to the claimed iterative redesign process" in order "to produce improved subsequent libraries" and therefore "are not 'insignificant extra-solution activity'", and that the claims integrate computational techniques into an improvement of another technology or technical field" which is patent eligible (Remarks 5/7/2026 pages 11-12). And Applicant argues that the automated laboratory equipment recited in the claims are not merely generic computer components and "therefore recite a specific technological environment involving coordinated computational and laboratory operations rather than merely instructing a generic computer to perform abstract calculations (Remarks 5/7/2026 page 12). Finally, Applicant asserts that the Office Action's "over-abstraction is inconsistent with the requirement to evaluate the claims as a whole" and that there is no evidentiary support for the laboratory operations being well-understood, routine, and conventional. Furthermore, Applicant asserts that taken as a whole, the ordered combination of claim elements renders them patent-eligible (Remarks 5/7/2026 page 13). Examiner has indicated above evidence of the well-understood, routine, and conventional nature of the additional elements as evidenced by Kimmerlin et al. and Armbruster et al. The Examiner also notes that MPEP 2106(I) states that if the claims are directed to a judicial exception, the second part of the Mayo test is to determine whether the claim recites additional elements that amount to significantly more than the judicial exception. Id. citing Mayo, 566 U.S. at 72-73, 101 USPQ2d at 1966). In the “search for an ‘inventive concept’” (the second part of the Alice/Mayo test), the additional elements identified do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception because using models and providing sequence data; and synthesizing, testing/assaying, separating, and sequencing libraries (data and sample gathering and manipulation steps) are all well-understood, routine, and conventional techniques that are insignificant extra-solution activities that do not serve to integrate the recited judicial exceptions into a practical application. Therefore, combining insignificant extra-solution activities with any of the identified judicial exceptions would not result in patent eligible subject matter because integrating well-understood, routine, and conventional techniques does not yield “significantly more” to a mental process, a mathematical concept, organizing human activity, or a law of nature or natural phenomenon.
Therefore, the rejection of claims 1, 3-4, 7-10, 13-17, 19-23, and 26-29 under 35 USC 101 is maintained.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3-4, 7-10, 13-17, 19-20, 22-23, and 26-29 rejected under 35 U.S.C. 103 as being unpatentable over Baynes et al. (US-20110160071) in view of Gustafsson et al. (US-20040161796).
Regarding claim 1, Baynes teaches a method for producing a protein having one or more desired properties (Para.0002 "Methods and compositions of the invention relate to novel proteins, protein variant libraries and methods of designing and using the same. More particularly, methods and compositions of the invention relate to novel protein variants that exhibit a desired characteristic").
Baynes also teaches step (a) a library design step, in which a nucleic acid library comprising at least 10^4 sequence variants is designed, wherein each sequence variant comprises a coding sequence for a protein and each sequence variant comprises at least one constant region and at least one variable region, wherein one or more constant regions are common to all sequence variants within the library, and the one or more variable regions are not common to all sequence variants within the library (Para.0132 "Practitioners have a desire to synthesize and test more than about 10 of their in silico designs, perhaps 100 to 1000 or even 10000 proteins instead, to avoid missing possible solutions to the design problem due to only a slight error in the model", para.0253 "Each variable region may include between about 5 and about 10,000 different variants (e.g., about 10, about 50, about 100, about 1,000 or more). However, fewer or more variants may be included in a variable region. According to the invention, the theoretical final number of variants will be the product of the number of variants in each variable region that are combined together to form the final library. By assembling a plurality of relatively short variable regions each with relatively few variants, a relatively large number of final variants may be generated. []. one or more constant regions may be identified or selected (e.g., between variable regions) [] each variable region is separated by a constant region", and para.0259 "Each fragment represent a pool of variants containing one or more varied bases within the variable region and sequences that are common (identical) among the variants within the pool of fragments. For example, a variable region (e.g., VI) may encode a peptide that corresponds to a defined motif of a protein, where a set of residues are selected to be varied for altered function, stability and/or structure, etc. The adjacent constant regions represent sequences that are identical among the variants of the particular pool of oligonucleotides. Therefore, a constant region is at least one base, but preferably more (e.g., about 2, 3, 4, 5, 6, 7, 8, 9, 10, 10-100, 100-1,000, or more than 1,000)").
Baynes also teaches step (a’’) a step of producing the proteins encoded by each sequence variant of the nucleic acid library to obtain a protein library, wherein the nucleic acid library is translated in such a way as to maintain a relationship between each coding sequence and its encoded protein by using a display technology (para.0099 "To validate the improvement of properties due to a pre-filtering strategy, parallel DNA libraries may be generated initially with and without the theoretical pre-filtering step. Randomly selected members of pre-filtered and unfiltered libraries may then be translated into protein and tested for the property under investigation. In addition, in-vitro selections may be performed under identical conditions for pre-filtered and unfiltered libraries, and the properties of the selected proteins from each may be compared").
Baynes also teaches step (b) a library testing step, implemented at least in part via a computer system configured to interact with and control automated laboratory equipment, in which at least a portion of the sequence variants of the protein library are tested in parallel in one or more assays, for the one or more desired properties (Para.0030 "the system further includes a testing module for testing the fabricated specific construct against a predetermined criterion", para.0099 "To validate the improvement of properties due to a pre-filtering strategy, parallel DNA libraries may be generated initially with and without the theoretical pre-filtering step. Randomly selected members of pre-filtered and unfiltered libraries may then be translated into protein and tested for the property under investigation. In addition, in-vitro selections may be performed under identical conditions for pre-filtered and unfiltered libraries, and the properties of the selected proteins from each may be compared", para.0024 "Further, aspects of the invention provide methods and systems for evaluating, designing, assembling, testing, and/or licensing constructs that may be used for biological applications. In some embodiments, constructs may be polynucleotide polymers. In certain embodiments, constructs may be polypeptide polymers", and para.0128 "Aspects of the invention may include automating one or more acts described herein. For example, an analysis may be automated in order to generate an output automatically. Acts of the invention may be automated using, for example, a computer system").
Baynes also teaches an iterative in silico design process (Para.0133 "In silico designs can be made to produce a library of constructs that can serve as a pool or plural separate species that can be tested or selected for a good candidate, or can serve as a starting places for other purposeful design iterations or for evolutionary techniques utilizing random mutagenesis. A screen or selection can be applied to the pool, and if necessary, the process (starting from design or another library expansion) can be iterated").
Baynes does not explicitly teach: step (c) a learning step, in which the sequence variants are each assigned a fitness score based at least in part on the result of the library testing step, and a machine learning algorithm uses the fitness score of each of the sequence variants to train a model to predict the fitness score for new sequence variants; or the machine learning model trained in step (c) is used to design a new library of sequence variants with an improved distribution of fitness scores.
However, Gustafsson teaches step (c) a learning step, in which the sequence variants are each assigned a fitness score based at least in part on the result of the library testing step, and a machine learning algorithm uses the fitness score of each of the sequence variants to train a model to predict the fitness score for new sequence variants (Para.0102 "Examples of the mathematical/logical form of models include linear and non-linear mathematical expressions of various orders, neural networks, classification and regression trees/graphs, clustering approaches, recursive partitioning, support vector machines, and the like" and para.0154 "The phrase "most suitable for artificial evolution" refers to those members of the variant population that lie at least proximal to a Pareto front, e.g., when the variants are scored (e.g., screened or selected) and plotted for desired objectives. These variants are generally the most suitable for artificial evolution, because they are not dominated by other variants (or at least most other variants) in at least one of the desired objectives").
Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the methods of Baynes as taught by Gustafsson in order to develop a model for protein activity versus sequence information (para.0103 "Models are developed from a training set of activity versus sequence information to provide the mathematical/logical relationship between activity and sequence. This relationship is typically validated prior to use for predicting activity of new sequences or residue importance"). One skilled in the art would have a reasonable expectation of success because both methods are concerned with identifying functional biomolecules using an iterative design/test/model/update approach.
Gustafsson also teaches the machine learning model trained in step (c) is used to design a new library of sequence variants with an improved distribution of fitness scores (Para.0121 "The rank ordered list of regression coefficients can be used to construct a new library of protein variants that is optimized with respect to a desired activity (i.e., improved fitness)").
Regarding claims 3 and 4, Baynes in view of Gustafsson teach the methods of Claims 1 and 2 on which this claim depends/these claims depend, respectively. Baynes also teaches: the library design step (a) utilizes USER assembly, Darwin assembly and/or inverse PCR; and the nucleic acid molecules corresponding to each of the one or more variable parts are provided as single stranded DNA, optionally wherein providing a plurality of nucleic acid molecules corresponding to the variants of one or more variable parts comprises synthesizing a second DNA strand by single primer extension to form double stranded DNA (Para.0124 "assembly reactions may be performed using assembly nucleic acids that have not been amplified (e.g., assembly oligonucleotides that were synthesized and released from an array without an amplification step). In some embodiments, a plurality of non-amplified overlapping nucleic acids may be assembled to generate one variant sequence for a library. This variant fragment may be amplified. In some embodiments, this variant fragment may be amplified using one or more universal primers if the flanking assembly nucleic acids have sequences (e.g., sequences that may need to be removed) that are complementary to the universal primers").
Regarding claim 7, Baynes in view of Gustafsson teach the methods of Claim 1 on which this claim depends/these claims depend. Baynes also teaches the library design step (a) comprises designing at least one of the one or more variable regions to include random variability in at least one position (Para.0133 "In silico designs can be made to produce a library of constructs that can serve as a pool or plural separate species that can be tested or selected for a good candidate, or can serve as a starting places for other purposeful design iterations or for evolutionary techniques utilizing random mutagenesis").
Regarding claim 8, Baynes in view of Gustafsson teach the methods of Claim 7 on which this claim depends/these claims depend. Baynes also teaches including random variability comprises constraining the variability to sequences that correspond to a DNA codon (Para.0197 "a target nucleic acid may include a functional sequence (e.g., a protein binding sequence, a regulatory sequence, a sequence encoding a functional protein, etc., or any combination thereof)", a functional protein being one assembled from a mature RNA (i.e. comprised of coding portions of spliced exons where codons are found).
Regarding claim 9, Baynes in view of Gustafsson teach the methods of Claim 1 on which this claim depends/these claims depend. Baynes also teaches the library design step (a) comprises: selecting a nucleic acid sequence encoding for a protein that has at least one of the one or more desired properties, and automatically identifying one or more regions of the sequence, and defining the one or more variable parts where variability is expected to result in an improvement of the at least one of the one or more desired properties and/or acquisition of at least one of the one or more desired properties (Para.0324 "It should be appreciated that the building blocks may be selected in any suitable manner, e.g. specified by a designer (or any other user), selected automatically from a data repository or otherwise. It should also be appreciated that the desired construct and/or construct building blocks may be divided into any suitable (smaller) building blocks (e.g., molecular segments), depending on the specification and properties, structure and other features relating to the construct and/or construct building blocks").
Regarding claim 10, Baynes in view of Gustafsson teach the methods of Claim 1 on which this claim depends/these claims depend. Baynes also teaches the library design step (a) further comprises: identifying one or more regions of the sequence where variability is expected to be detrimental to the integrity of the protein and /or to at least one of the one or more desired properties; and defining one or more of the one or more constant regions to include the one or more regions of the sequence where variability is expected to be detrimental to the integrity of the protein and /or to at least one of the one or more desired properties (Para.0017 "methods of the invention are useful for screening nucleic acid sequences that are candidates for inclusion in an expression library and identifying those sequences that encode polypeptides with one or more undesirable properties (e.g., poor solubility, high immunogenicity, low stability, etc.)").
Regarding claims 13 and 14, Baynes in view of Gustafsson teach the methods of Claim 12 on which this claim depends/these claims depend. Baynes also teaches: the nucleic acid library is a DNA library and producing the protein library comprises transcribing and translating the DNA library, wherein translating the library comprises synthesising RNA-polypeptide fusion molecules each comprising an RNA sequence variant bound to the protein that it encodes; and the nucleic acid library is a DNA library and producing the protein library comprises transcribing and translating the DNA library, wherein translating the library comprises propagating phage that display a coat protein-polypeptide fusion, wherein the polypeptide fused to the coat protein corresponds to a sequence variant of the DNA library (Para.0111 "Examples of display libraries include those generated by phage, bacterial, yeast, mRNA, or ribosome display, where each nucleic acid and corresponding polypeptide are part of the same physical particle (e.g., a bacteriophage, a bacterium, a yeast cell, covalent mRNA-polypeptide fusion, or non-covalent mRNA/ribosome/polypeptide complex)").
Regarding claim 15, Baynes in view of Gustafsson teach the methods of Claim 12 on which this claim depends/these claims depend, respectively. Baynes also teaches the library testing step (b) of separating the protein library into at least 2 samples depending on the results of the one or more assays, wherein the results of the one or more assays on which the separation depends are the desired properties as identified by the one or more assays being run on the sample, and sequencing the nucleic acids present in at least one of the at least 2 samples (Para.0021 "the library nucleic acid may be amplified, sequenced or cloned after it is made" and para.0409 "Up to 100 variant clones with the highest level of the desired characteristic (such as polymerase activity or processivity) are sequenced").
Regarding claim 16, Baynes in view of Gustafsson teach the methods of Claim 15 on which this claim depends/these claims depend, respectively. Baynes also teaches the learning step (c) comprises aligning the sequences obtained by sequencing with the sequences designed in step (a), and quantifying the number of times that each sequence appears in each sample (Para.0157 "As is known in the art, there are a number of sequence alignment methodologies that can be used. For example, sequence homology based alignment methods can be used to create sequence alignments of proteins related to the target structure (Altschul et al., J. Mol. Biol. 215(3):403 (1990), incorporated by reference). These sequence alignments are then examined to determine the observed sequence variations. These sequence variations are tabulated to define a primary library").
Regarding claim 17, Baynes in view of Gustafsson teach the methods of Claim 1 on which this claim depends/these claims depend, respectively. Baynes also teaches the one or more desired properties is selected from the group consisting of physico-chemical properties of the proteins, activity-related properties, physiologically-relevant properties, and pharmacokinetic properties (Para.0015 "the invention relates to expression libraries that can be used to screen or select for polypeptides having one or more functional and/or structural properties (e.g., one or more predetermined catalytic, enzymatic, receptor-binding, therapeutic, or other properties)").
Regarding claim 19, Baynes in view of Gustafsson teach the methods of Claim 15 on which this claim depends/these claims depend, respectively. Gustafsson also teaches one of the one or more desired properties is binding to a specific target, and the library testing step (b) comprises incubating the protein library with the specific target immobilised on a surface and separating the protein library into a sample that is bound to the surface and a sample that is not bound to the surface (Para.0267-268 "Other methods of physical assays, suitable for use in the methods herein, can be based on the use of biosensors specific for reaction product(s), including those comprising antibodies with reporter properties, or those based on in vivo affinity recognition coupled with expression and activity of a reporter gene. Enzyme-coupled assays for reaction product detection and cell life-death-growth selections in vivo can also be used where appropriate. Regardless of the specific nature of the physical assays, they all are used to select a desired activity, or combination of desired activities, provided or encoded by a biomolecule of interest. The specific assay used for the selection will depend on the application. Many assays for proteins, receptors, ligands and the like are known. Formats include binding to immobilized components, cell or organismal viability, production of reporter compositions, and the like").
Regarding claim 20, Baynes in view of Gustafsson teach the methods of Claim 1 on which this claim depends/these claims depend, respectively. Gustafsson also teaches the library testing step comprises testing the variants for a plurality of properties, and the learning step comprises assigning a plurality of fitness scores to each variant tested, wherein each fitness score corresponds to one of the plurality of properties, wherein the learning step comprises training a plurality of machine learning algorithms, wherein each machine learning algorithm is trained to predict at least one of the plurality of fitness scores for new sequence variants (Figure 15 box J3 "score at least one target polypeptide character string using the motif scoring function to predict the at least one property of the at least one target polypeptide character string").
Regarding claim 22, Baynes in view of Gustafsson teach the methods of Claim 1 on which this claim depends/these claims depend, respectively. Gustafsson also teaches the machine learning algorithm is a classifier, wherein the machine learning algorithm is a neural network (Para.0102 "Examples of the mathematical/logical form of models include linear and non-linear mathematical expressions of various orders, neural networks, classification and regression trees/graphs, clustering approaches, recursive partitioning, support vector machines, and the like").
Regarding claim 23, Baynes in view of Gustafsson teach the methods of Claim 1 on which this claim depends/these claims depend, respectively. Baynes also teaches the machine learning model trained in step (c) is used to design a new library of sequence variants by iteratively optimising a library of sequence variants in silico, optionally wherein the library of sequence variants is iteratively optimised using a genetic algorithm (Para.0133 "In silico designs can be made to produce a library of constructs that can serve as a pool or plural separate species that can be tested or selected for a good candidate, or can serve as a starting places for other purposeful design iterations or for evolutionary techniques utilizing random mutagenesis. A screen or selection can be applied to the pool, and if necessary, the process (starting from design or another library expansion) can be iterated").
Regarding claim 26, Baynes in view of Gustafsson teach the methods of Claim 1 on which this claim depends/these claims depend, respectively. Gustafsson also teaches the new library of sequence variants with an improved distribution of fitness scores is one wherein at least 30% of the sequence variants have one or more variable regions having a DNA sequence similarity of less than 95% with respect to the corresponding one or more variable regions of all, or a proportion of, the sequence variants within the library prepared in step (a) (Para.0052 "It is generally assumed that two nucleic acids have common ancestry when they demonstrate sequence similarity. However, the exact level of sequence similarity necessary to establish homology varies in the art. In general, for purposes of this disclosure, two nucleic acid sequences are deemed to be homologous when they share enough sequence identity to permit direct recombination to occur between the two sequences").
Regarding claim 27, Baynes in view of Gustafsson teach the methods of Claim 1 on which this claim depends/these claims depend, respectively. Gustafsson also teaches a higher proportion of sequence variants of the new library display one or more improved desirable properties compared to the sequence variants within the library prepared in step (a) (Para.0121 "The rank ordered list of regression coefficients can be used to construct a new library of protein variants that is optimized with respect to a desired activity (i.e., improved fitness)").
Regarding claims 28-29, Baynes in view of Gustafsson teach the methods of Claim 1 which are implemented via processor in claim 28 (claim 29 depending from claim 28). Baynes also teaches a laboratory automation apparatus consisting of well-understood, routine, and conventional instrumentation (Para.0128-129 "Aspects of the invention may include automating one or more acts described herein. For example, an analysis may be automated in order to generate an output automatically. Acts of the invention may be automated using, for example, a computer system. Aspects of the invention may be used in conjunction with any suitable multiplex nucleic acid assembly procedure involving at least two nucleic acids with complementary regions (e.g., at least one pair of nucleic acids that have complementary 3' regions). For example, library assembly may involve one or more of the multiplex nucleic acid assembly procedures described below"; and entire section on "Automated Applications" para.0279-285).
Claim 21 rejected under 35 U.S.C. 103 as being unpatentable over Baynes et al. (US-20110160071) in view of Gustafsson et al. (US-20040161796) as applied to claims 1, 3-4, 7-10, 13-17, 19-20, 22-23, and 26-29 above, and further in view of Rau et al. (Rau et al. "Data-based filtering for replicated high-throughput transcriptome sequencing experiments." Bioinformatics 29.17 (2013): 2146-2152).
Baynes et al. in view of Gustafsson et al. are applied to claims 1, 3-4, 7-10, 13-17, 19-20, 22-23, and 26-29.
Regarding claim 21, Baynes in view of Gustafsson teach the method of Claim 16 on which this claim depends/these claims depend.
Baynes nor Gustafsson explicitly teach a step for filtering true reads or variants by requiring that the variant will not be assigned a fitness score unless the associated reads for the variant are present in both samples at least one time (as interpreted above).
However, Rau teaches a filtering method depending on a threshold of sequence read counts being greater than some cutoff value in multiple samples (Page 2 col 2 section 2.2 "For each of the filter types previously defined, a biologically pertinent cutoff (or alternatively, number of genes to be filtered) must be chosen; in practice, arbitrary thresholds are routinely used with little or no discussion of their impact on the downstream analysis. To address this issue, we propose a data-based choice for the threshold to be used in maximum-based filters. The main idea underlying this choice is to identify the threshold that maximizes the filtering similarity among replicates, i.e. one where most genes tend to either have normalized counts less than or equal to the cutoff in all samples (i.e. filtered genes) or greater than the cutoff in all samples (i.e. non-filtered genes)").
While this technique is applied to normalized RNA-seq data, this is only because RNA-seq data has systematic inter-sample biases (Page 2 col 2 section 2.1), but could easily be applied to WGS data where counting sequences is involved. Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the methods of Baynes and Gustafsson as taught by Rau in order to reduce false-discovery rates (page 1 Abstract "As tests are performed on a large number of genes, stringent false-discovery rate control is required at the expense of detection power. Ad hoc filtering techniques are regularly used to moderate this correction by removing genes with low signal"). One skilled in the art would have a reasonable expectation of success because both approaches are filtering sequence read count data with the goal of reducing false positives.
Response to Arguments under 35 USC § 103
Applicant’s arguments filed 5/7/2026 are fully considered but they are not persuasive.
Applicant asserts that "each claim includes limitations that no reference teaches or suggests" and because the references "taken separately or in combination, would not motivate a person skilled in the art to create the claimed invention" (Remarks 5/7/2026 page 14). Specifically, Applicant argues that Baynes does not teach or suggest "the testing step is implemented at least in part by a computer, which is adapted to interact with and control automated laboratory equipment" (Remarks 5/7/2026 page 15). Examiner notes above that Baynes does in fact teach or suggest integrating testing with automated laboratory equipment (para.0024 "Further, aspects of the invention provide methods and systems for evaluating, designing, assembling, testing, and/or licensing constructs that may be used for biological applications. In some embodiments, constructs may be polynucleotide polymers. In certain embodiments, constructs may be polypeptide polymers" and para.0128 "Aspects of the invention may include automating one or more acts described herein. For example, an analysis may be automated in order to generate an output automatically. Acts of the invention may be automated using, for example, a computer system").
Applicant also asserts that "Baynes [nor Gustafsson] teach that such an integrated 'closed-loop' system is possible, nor how to construct it, nor what advantages it would provide" (Remarks 5/7/2026 pages 15-16). Examiner notes that only the testing step of the method is claimed as being automated (claim 28), and it has already been noted above that Baynes does in fact teach or suggest automating a patentably indistinct testing step. Finally, Applicant asserts that Rau (included for the rejection of claim 21) does not cure the deficiencies of Bayes or Gustafsson. Examiner again notes above that the cited art does teach or suggest the independent claim, and that Rau is not required for this.
Therefore, the rejection of claims 1, 3-4, 7-10, 13-17, 19-23, and 26-29 under 35 USC 103 is maintained.
Conclusion
No claims are allowed.
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 TH REE-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 finaI action.
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/R.A.P./Examiner, Art Unit 1686
/Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687
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