DETAILED ACTION
Election/Restrictions
Applicant’s election without traverse of Group I and Species III in the reply filed on 7/22/2026 is acknowledged.
Claims 4-6 and 8-20 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention or a nonelected species, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 7/22/2026.
Claim Status
Claims 4-6 and 8-20 are withdrawn, see above.
Claims 1-3 and 7 are rejected.
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 .
Priority
This application makes no claim of foreign priority or domestic benefit. The application was filed on 05/31/2023. Therefore, the effective filing date of claims 1-3 and 7 is 05/31/2023.
Information Disclosure Statement
The Information Disclosure Statement filed on 09/04/2025 is in compliance with the provisions of 37 CFR 1.97 and has been considered in full. A signed copy of list of references cited from each IDS is included with this Office Action.
Drawings
Color photographs and color drawings are not accepted in utility applications unless a petition filed under 37 CFR 1.84(a)(2) is granted. Any such petition must be accompanied by the appropriate fee set forth in 37 CFR 1.17(h), one set of color drawings or color photographs, as appropriate, if submitted via the USPTO patent electronic filing system or three sets of color drawings or color photographs, as appropriate, if not submitted via the via USPTO patent electronic filing system, and, unless already present, an amendment to include the following language as the first paragraph of the brief description of the drawings section of the specification:
The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee.
Color photographs will be accepted if the conditions for accepting color drawings and black and white photographs have been satisfied. See 37 CFR 1.84(b)(2).
Claim Objections
Claim 7 objected to because of the following informalities: the abbreviation “ESM” should be spelled out before being used in a sentence. Appropriate correction is required.
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 and 7 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea 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:
1. A machine-learning based method for protein sequence design
1. determining if any residue of the protein sequence is known;
1. performing entire sequence design if it is determined that no residue of the protein sequence is known;
1. performing partial sequence design if it is determined that at least one residue of the protein sequence is known; and
1. generating an entire sequence non-iteratively.
2. The machine-learning based method of claim 1, wherein the performing entire sequence design comprises performing an entropy-based prediction-selection method in combination with a base model to remove noise in input residue context.
3. The machine-learning based method of claim 2, wherein the performing an entropy-based prediction-selection method comprises computing an entropy of predicted distributions at each position, retaining residues having entropies lower than or equal to a threshold value, and masking other residues having entropies greater than the threshold value.
The limitations for “determining,” “performing,” and “generating” are all commands to take mental or algorithmic actions over a dataset and perform the act of designing a sequence based on those mental actions, which could be performed by a human being using a pen and paper. Therefore, these limitations fall under the “Mental process” grouping of abstract ideas. While claims 1-3 and 7 recite performing some aspects of the analysis with a “machine-learning” or “ESM model,” there are no additional limitations that indicate that this “machine-learning” or “ESM model” requires anything other than carrying out the recited mental process or mathematical concept 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 if falls within the “Mental processes” grouping of abstract ideas. As such, claims 1-3 and 7 recites 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). This judicial exception is not integrated into a practical application because the claims do not recite an additional element that reflects an improvement to technology or applies or uses the recited judicial exception to effect a particular treatment for a condition. Rather, the instant claims recite additional elements that amount to mere instructions to implement the abstract idea in a generic computing environment or mere instructions to apply the recited judicial exception via a generic treatment. Specifically, the claims recite the following additional elements:
1. receiving information of residues of a protein sequence;
7. The machine-learning based method of claim 2, wherein the base model is an ESM model.
There are no limitations that indicate that the claimed “machine-learning”, “ESM model,” or the formats of the provided data require anything other than generic computing systems. As such, these limitations equate 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. The limitation for receiving information is a form of mere data gathering, similar to selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016). As such, claims 1-3 and 7 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 equate to mere instructions to apply the recited exception in a generic way or in a generic computing environment. The instant claims recite additional elements enumerated above, in the section on step 2A.
As discussed above, there are no additional limitations to indicate that the claimed “machine-learning” or “ESM model” 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. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. The limitation for receiving information is a form of storing and retrieving information in memory, which the courts have found to be well-understood, routine and conventional, similar to Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93. Evidentiary reference Csicsery-Ronay et al. (bioRxiv, 2022) is a review article that compared protein prediction models, including ESM (table 1), providing evidence that ESM is a well-understood, routine and conventional machine learning strategy. 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 and 7 are not patent eligible.
Claim Rejections - 35 USC § 103
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.
Claim 1 is rejected under 35 U.S.C. 103 as being unpatentable over Jeliazkov et al (bioRxiv, 2023) in view of Huang et al (ICLR, 2021, IDS Reference).
Regarding claim 1, Jeliazkov teaches a machine-learning based method for protein sequence design (abstract). Jeliazkov’s method masks stretches of protein sequences for training (pg 2 ¶ 1).
Jeliazkov’s method generates proteins in a “zero-shot” manner, which is non-iterative (abstract).
Regarding claim 1, Jeliazkov is silent as to a differential design based on known and unknown sequences.
Regarding claim 1, Huang teaches a sequence-to-sequence machine learning model architecture for human dance movement prediction based on music types, that works based on masking stretches of the sequence for training (Huang abstract). Huang’s method determines if a vector (equivalent to a residue in Jeliazkov) is one of its ground-truth movements, and performs partial design in that case, but uses generated vectors in cases where the vectors are not known (pg 5 ¶ 2-3).
Regarding claim 1, An invention would have been prima facie obvious to one of ordinary
skill in the art at the time of the effective filing date of the invention if some teaching, suggestion, or motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. There is a teaching to use the teacher-suggesting partial design learning scheme in the text of Huang, because it helps to guide sequence design without error accumulation found in teacher-forcing schemes (pg 5 ¶ 2-3). There would be a reasonable expectation of success in making this combination to a person of ordinary skill in the art, as they are both sequence design machine learning models. There is nothing about the movement data that makes it different from protein sequence data computationally, other than each unit of the sequence being a vector of numbers denoting a pose (pg 5 ¶ 2-3), which is simple to reduce to a single character. Therefore, it would have been prima facie obvious to one of ordinary skill in the art at the time to modify the method of Jeliazkov by implementing the teacher-suggesting partial design learning scheme of Huang, in order to reduce error accumulation (pg 5 ¶ 2-3).
Claims 2, 3 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Jeliazkov and Huang as applied to claim 1 above, and further in view of Szymborski (https://jszym.com/blog/dna_protein_complexity/, 2017).
Szymborski explains that “People who poke at DNA or protein sequences as part of their job occaisionally need to find regions that are repetitive or made up of a few nucleic/amino acids. That’s because those regions (1) can have important biological roles such as secondary structure, function, and in the unique case of DNA, expression, and (2) can confuse algorithms such as the BLAST alignment algorithm.” (¶ 1).
Regarding claims 2 and 3, Szymborski describes an entropy-based method of prediction-selection (§ Computing Compositional Complexity from a RepVec, § Establishing an Entropy Threshold). The method computes entropy at each given position (§ Computing Compositional Complexity from a RepVec). In Szymborski’s example, the entropy threshold retains high-entropy sequences and masks low-entropy sequences, because the author is interested in the second use case for entropy masking about BLAST confusion, but makes clear from the background (¶ 1) that the opposite would be helpful for the first use case.
Regarding claim 7, Jeliazkov uses an ESM model (abstract).
Regarding claims 2, 3 and 7, an invention would have been prima facie obvious to one of ordinary skill in the art at the time of the effective filing date of the invention if some teaching, suggestion, or motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. There is a suggestion to use entropic prediction-selection in the text of Szymborski, because it can help find regions important to protein structure and function (¶ 1). There would be a reasonable expectation of success in making this combination to a person of ordinary skill in the art, as all the methods are related to sequence analysis and design. Therefore, it would have been prima facie obvious to one of ordinary skill in the art at the time to modify the method of Jeliazkov and Huang by implementing the entropic prediction-selection of Szymborski, in order to find regions important to protein structure and function (¶ 1).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GRACELYN M HILL whose telephone number is (571)272-9871. The examiner can normally be reached Monday-Friday 8:30-5pm.
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/G.M.H./Examiner, Art Unit 1685
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685
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