DETAILED ACTION
Applicant’s response, filed 09/22/2026, has been fully considered. Rejections and/or objections not reiterated from previous Office Actions are hereby withdrawn. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
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 .
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 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.
Restriction election
Applicant’s election without traverse species claims 4, 14, 24, 28, and 23-24 in the reply filed on 05/27/2026 is acknowledged.
Claims 5, 15, 25, 30, and 32 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a non-elected invention.
Claim Status
Claims 1-5, 7-11, 14-15, 17, 19, 21-28, 30, 32, 34-36, and 39-40 are pending.
Claims 6, 12-13, 16, 18, 20, 29, 31, 33, and 37-38 are canceled.
Claims 5, 15, 17, 25, 30, 32, and 34 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a non-elected species, as described above.
Claims 1-4, 7-11, 14, 19, 21-24, 26-28, 35-36, and 39-40 are under examination.
Claims 39 and 40 are objected to.
Claims 1-4, 7-11, 14, 19, 21-24, 26-28, 35-36, and 39-40 are rejected.
Priority
This application is a CON of PCT/US2024/013935, filed 02/01/2024, which claims benefit of 63/588,437 10/06/2023, and claims benefit of 63/502,497 05/16/2023, and claims benefit of 63/482,756 02/01/2023.
Accordingly, each of claims 1-4, 6-11, 14, 19, 21-24, 26-28, 35-36, and 39-40 are afforded the effective filing date of 02/01/2023.
Information Disclosure Statement
The information disclosure statement (IDS) filed on 08/01/2025 is in compliance with the provisions of 37 CFR 1.97 and has therefore been considered. A signed copy of the IDS document is included with this Office Action.
Drawings
The Drawings submitted 08/01/2025 are accepted.
Claim Objections
The outstanding objections to the claims are withdrawn in view of the amendments submitted herein.
Claim Rejections- 35 USC § 112
The outstanding rejections to the claims are withdrawn in view of the amendments submitted herein as which energy based model has been clarified.
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.
For the following rejections, underlined text indicates newly recited portions necessitated by claim amendment.
Claims 1-4, 7-11, 14, 19, 21-24, 26-28, 35-36, and 39-40 are rejected under 35 U.S.C. 101 because the claimed invention is directed to one or more judicial exceptions without significantly more. Any newly recited portions are necessitated by claim amendment.
Response to Applicant Arguments
MPEP 2106 organizes judicial exception analysis into Steps 1, 2A (Prongs One and Two) and 2B as follows below. MPEP 2106 and the following USPTO website provide further explanation and case law citations: uspto.gov/patent/laws-and-regulations/examination-policy/examination-guidance-and-training-materials.
Framework with which to Evaluate Subject Matter Eligibility:
Step 1: Are the claims directed to a process, machine, manufacture, or composition of matter;
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e. a law of nature, a natural phenomenon, or an abstract idea;
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application (Prong Two); and
Step 2B: If the claims do not integrate the judicial exception, do the claims provide an inventive concept.
Framework Analysis as Pertains to the Instant Claims:
Step 1
With respect to Step 1: yes, the claims are directed to method and systems, i.e., a process, machine, or manufacture within the above 101 categories [Step 1: YES; See MPEP § 2106.03].
Step 2A, Prong One
With respect to Step 2A, Prong One, the claims recite judicial exceptions in the form of abstract ideas. The MPEP at 2106.04(a)(2) further explains that abstract ideas are defined as:
mathematical concepts (mathematical formulas or equations, mathematical relationships and mathematical calculations);
certain methods of organizing human activity (fundamental economic practices or principles, managing personal behavior or relationships or interactions between people); and/or
mental processes (procedures for observing, evaluating, analyzing/ judging and organizing information).
With respect to the instant claims, under the Step 2A, Prong One evaluation, the claims are found to recite abstract ideas that fall into the grouping of mental processes (in particular procedures for observing, analyzing and organizing information) and mathematical concepts (in particular mathematical relationships and formulas) a are as follows:
Independent claims 1 and 39:
identifying a sample sequence from the data distribution of protein sequences;
generating a noisy sample sequence by at least adding noise to the -sample sequence
generating a training dataset including a plurality of noisy sample sequences, where the plurality of noisy sample sequences form a noisy data distribution, and where the training dataset is generated to include the noisy sample sequence;
training a protein design computation model to approximate a data distribution of protein sequences exhibiting the one or more properties of interest using the plurality of noisy sample sequences from the training dataset the noisy data distribution by at least, wherein the training the protein design computation model includes applying the protein design computation model having a first adjustment to generate a first sample output sequence,
applying the protein design computation model having a second adjustment to generate a second sample output sequence, and upon determining that the first sample output sequence is more similar to the plurality of noisy sample sequences in the training dataset than the second sample output sequence, further training the protein design computation model by at least adjusting the protein design computation model having the first adjustment
applying the protein design computation model to generate a sample output sequence, and
adjusting the protein design computation model to reduce a difference between the sample output sequence generated by the protein design computation model and the plurality of noisy sample sequences in the training dataset;
applying the trained protein design computation model to modify the input sequence to generate an output sequence exhibiting the one or more properties of interest.
Dependent claim 3:
adjusting a plurality of parameters of the energy-based model, and wherein the plurality of parameters parameterize an energy function associated with the energy-based model.
Dependent claim 9:
generating an additional training dataset including a plurality of sample sequences from a different data distribution;
determining a first adjustment to the additional energy-based model that reduces a difference between a first output sample sequence generated by the additional energy-based model and the plurality of noisy sample sequences in the training dataset;
determining a second adjustment to the additional energy-based model that reduces a difference between a second output sample sequence generated by the additional energy based model;
training the additional energy-based model by at least applying, to the additional energy-based model, a third adjustment determined based on the first adjustment and the second adjustment.
Dependent claim 11:
encoding each sample sequence from the data distribution to generate an embedding of each sample sequence, wherein the encoding includes enriching with structural information that identifies, for at least one amino acid residue in each sample sequence, one or more neighboring amino acid residue in three-dimensional space
generating the plurality of noisy sample sequences in the training dataset by at least adding noise to the embedding of each sample sequence.
Dependent claim 14:
generating a noisy input sequence by at least adding noise to the input sequence,
applying an energy-based model to generate a noisy output sequence by at least modifying, based at least on an energy function of the energy-based model, the noisy input sequence, and
generating the output sequence by at least denoising the modified noisy output sequence generated by the energy-based model.
Dependent claim 19:
generating a fixed-length representation of the input sequence by at least
aligning each amino acid residue in the input sequence to a fixed set of structural roles such that each amino acid residue in the input sequence is assigned an integer position corresponding to a structural role of the amino acid residue, and
inserting a gap character at one or more positions where the input sequence fails to include an amino acid residue having a corresponding structural role; and
applying the trained protein design computation model to generate the output sequence by at least modifying the fixed length representation of the input sequence.
Independent claims 22 and 40:
identifying an input sequence having a plurality of amino acid residues;
generating a noisy embedding of the input sequence by at least adding noise to the input sequence;
modifying the noisy embedding of the input sequence by at least applying a protein design computation model trained to approximate a data distribution of protein sequences exhibiting one or more properties of interest, wherein the protein design computation model is trained to approximate the data distribution of protein sequences exhibiting the one or more properties of interest using a plurality of noisy sample sequences, wherein each noisy sample sequence of the plurality of noisy sample sequences are generated by at least adding noise to a known protein sequence exhibiting the one or more properties of interest, and wherein the protein design computation model modifies the noisy embedding of the input sequence to increase a likelihood of a modified noisy embedding resulting from the modifying being in the data distribution, the protein design computation model modifying the noisy embedding of the input sequence to increase a likelihood of a modified noisy embedding resulting from the modifying being in the data distribution; and
generating an output sequence by at least denoising the modified noisy embedding generated by the protein design computation model.
Dependent claim 23:
encoding the input sequence to generate an embedding of the input sequence; generating the noisy embedding of the input sequence by at least adding noise to the embedding of the input sequence; and
generating the output sequence by decoding a denoised embedding generated by the denoising of the modified noisy embedding
Dependent claim 27:
generating a first modified noisy embedding by applying an energy-based model (EBM) trained to approximate the data distribution to modify the noisy embedding of the input sequence,
generating a second modified noisy embedding by applying the energy-based model (EBM) to modify the noisy embedding of the input sequence,
applying an energy function parameterized by a plurality of parameters of the energy- based model (EBM) to determine an energy value of the first modified noisy embedding and an energy value of the second modified noisy embedding, and
applying the energy-based model (EBM) to further modify, based at least on the energy value of the first modified noisy embedding and the energy value of the second modified noisy embedding, at least one of the first modified noisy embedding and the second modified noisy embedding
Dependent claims 2, 4, 7-8, 10, 21, 24, 26, 28, and 35-36 recite further steps that limit the judicial exceptions in independent claims 1-3, 6, 9, 22-23, and 27 and, as such, also are directed to those abstract ideas. For example, claim 2 further limits the model of claim 1, claim 4 further limits the energy value of claim 3, claim 7 further limits the criteria of claim 6, claim 8 further limits the model of claim 2, claim 10 further limits the third adjustment of claim 9, claim 21 further limits the difference of claim 1, claim 24 further limits the encoding of claim 23, claim 26 further limits the input sequence of claim 23, claim 28 further limits the criteria of claim 27, claim 35 further limits the properties of claim 22, and claim 36 further limits the input sequence of claim 22;
The claims recite mathematical concepts of identifying a sample sequence from the data distribution of protein sequences, generating a noisy sample sequence, generating a training dataset, training a protein design computation model, applying the protein design computation model, adjusting the protein design computation model, applying the trained protein design computation model, generating an output sequence exhibiting the one or more properties, adjusting a plurality of parameters, applying the energy-based model, modifying the energy based model, generating an additional training dataset, determining a first adjustment to the energy-based model, determining a second adjustment to the additional energy-based model, training the energy-based model, encoding each sample sequence from the data distribution, generating the plurality of noisy sample sequences, generating a noisy input sequence, applying an energy-based model, generating the output sequence, generating one or more structural tokens, generating a fixed-length representation of the input sequence, aligning each amino acid residue in the input sequence to a fixed set of structural roles, inserting a gap character, identifying an input sequence having a plurality of amino acid residues, generating a noisy embedding of the input sequence by at least adding noise to the input sequence, modifying the noisy embedding of the input sequence by at least applying a protein design computation model, generating an output sequence by at least denoising the modified noisy embedding, encoding the input sequence to generate an embedding, generating the noisy embedding of the input sequence, generating the output sequence by decoding a denoised embedding, generating a first modified noisy embedding by applying an energy-based model (EBM), generating a second modified noisy embedding by applying the energy-based model (EBM), applying an energy function, applying the energy-based model (EBM), changing an identity of one or more amino acid residues in the input sequence, deleting an amino acid residue, and inserting an amino acid residue at a position.
Therefore, claims 1, 22, 39-40 and those claims dependent therefrom recite an abstract idea [Step 2A, Prong 1: YES; See MPEP § 2106.04].
Step 2A, Prong Two
Because the claims do recite judicial exceptions, direction under Step 2A, Prong Two, provides that the claims must be examined further to determine whether they integrate the judicial exceptions into a practical application (MPEP 2106.04(d)). A claim can be said to integrate a judicial exception into a practical application when it applies, relies on, or uses the judicial exception in a manner that imposes a meaningful limit on the judicial exception. This is performed by analyzing the additional elements of the claim to determine if the judicial exceptions are integrated into a practical application (MPEP 2106.04(d).I.; MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the judicial exceptions, the claim is said to fail to integrate the judicial exceptions into a practical application (MPEP 2106.04(d).III).
Additional elements, Step 2A, Prong Two
With respect to the instant recitations, the claims recite the following additional elements:
Independent claims 1 and 39-40:
receiving a data distribution of protein sequences, each of the protein sequences exhibiting one or more desired properties.
receiving an input sequence
The claims also include non-abstract computing elements. For example, independent claims 1 and 22 includes a system, processor, and memory.
Considerations under Step 2A, Prong Two
With respect to Step 2A, Prong Two, the additional elements of the claims do not integrate the judicial exceptions into a practical application for the following reasons. Those steps directed to data gathering, such as “receiving”, perform functions of collecting the data needed to carry out the judicial exceptions. Data gathering and outputting do not impose any meaningful limitation on the judicial exceptions, or on how the judicial exceptions are performed. Data gathering and outputting steps are not sufficient to integrate judicial exceptions into a practical application (MPEP 2106.05(g)).
Further steps directed to additional non-abstract elements of “a system, processor, and memory” do not describe any specific computational steps by which the “computer parts” perform or carry out the judicial exceptions, nor do they provide any details of how specific structures of the computer, such as the computer-readable recording media, are used to implement these functions. The claims state nothing more than a generic computer which performs the functions that constitute the judicial exceptions. Hence, these are mere instructions to apply the judicial exceptions using a computer, and therefore the claim does not integrate that judicial exceptions into a practical application. The courts have weighed in and consistently maintained that when, for example, a memory, display, processor, machine, etc.… are recited so generically (i.e., no details are provided) that they represent no more than mere instructions to apply the judicial exception on a computer, and these limitations may be viewed as nothing more than generally linking the use of the judicial exception to the technological environment of a computer (MPEP 2106.05(f)).
Thus, none of the claims recite additional elements which would integrate a judicial exception into a practical application, and the claims are directed to one or more judicial exceptions [Step 2A, Prong 2: NO; See MPEP § 2106.04(d)].
Step 2B (MPEP 2106.05.A i-vi)
According to analysis so far, the additional elements described above do not provide significantly more than the judicial exception. A determination of whether additional elements provide significantly more also rests on whether the additional elements or a combination of elements represents other than what is well-understood, routine, and conventional. Conventionality is a question of fact and may be evidenced as: a citation to an express statement in the specification or to a statement made by an applicant during prosecution that demonstrates a well-understood, routine or conventional nature of the additional element(s); a citation to one or more of the court decisions as discussed in MPEP 2106(d)(II) as noting the well-understood, routine, conventional nature of the additional element(s); a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s); and/or a statement that the examiner is taking official notice with respect to the well-understood, routine, conventional nature of the additional element(s).
With respect to the instant claims, the courts have found that receiving and outputting data are well-understood, routine, and conventional functions of a computer when claimed in a merely generic manner or as insignificant extra-solution activity (see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network), Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015), and OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93, as discussed in MPEP 2106.05(d)(II)(i)).
As such, the claims simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (MPEP2106.05(d)). The data gathering steps as recited in the instant claims constitute a general link to a technological environment which is insufficient to constitute an inventive concept which would render the claims significantly more than the judicial exception (MPEP2106.05(g)&(h)).
With respect to claims 1, 22 and those claims dependent therefrom, the computer-related elements or the general purpose computer do not rise to the level of significantly more than the judicial exception. The claims state nothing more than a generic computer which performs the functions that constitute the judicial exceptions. Hence, these are mere instructions to apply the judicial exceptions using a computer, which the courts have found to not provide significantly more when recited in a claim with a judicial exception (see MPEP 2106.06(A)). The specification also notes that computer processors and systems, as example, are commercially available or widely used at [0183-191]. The additional elements are set forth at such a high level of generality that they can be met by a general purpose computer. Therefore, the computer components constitute no more than a general link to a technological environment, which is insufficient to constitute an inventive concept that would render the claims significantly more than the judicial exceptions (see MPEP 2106.05(b)I-III).
Taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception(s). Even when viewed as a combination, the additional elements fail to transform the exception into a patent-eligible application of that exception. Thus, the claims as a whole do not amount to significantly more than the exception itself [Step 2B: NO; See MPEP § 2106.05].
Therefore, the instant claims are not drawn to eligible subject matter as they are directed to one or more judicial exceptions without significantly more. For additional guidance, applicant is directed generally to the MPEP § 2106.
Response to Applicant Arguments
1. Applicant submits Akin to McRO, claims 1 and 22 recite specific details for realizing, through a protein design computation model trained "approximate a data distribution of protein sequences exhibiting the one or more properties of interest using the plurality of noisy sample sequences," a technological improvement to conventional approaches to computational protein design [p. 17, par. 2].
It is respectfully found not persuasive. The improvement to approximating a data distribution of protein sequences exhibiting the one or more properties of interest using the plurality of noisy sample sequences does not provide an improvement in computer animation and use rules to automate a subjective task of humans to create a sequence of synchronized, animated characters as in McRo. In McRO, it was not the mere presence of unconventional rules that led to patent eligibility. In McRO, "[t]he claimed improvement was to how the physical display operated (to produce better quality images)." SAP Am. v. InvestPic, LLC, 898 F.3d 1161, 1167 (Fed. Cir. 2018). The claims in McRO recited a step of applying the data sets generated using the specific claimed rules to a sequence of animated characters to produce lip synchronization and facial expression control of those animated characters. McRO, 837 F.3d at 1308. Thus, the claims were directed to an improvement in computer animation and used rules to automate a subjective task of humans to create a sequence of synchronized, animated characters. Id. at 1314--15.
In the claims at issue here, there is no such application of specifically claimed rules to produce an improved technological result. The process of approximate a data distribution of protein sequences exhibiting the one or more properties of interest using the plurality of noisy sample sequences using a protein design computation model is not a technological process; it is information evaluation. It is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. Furthermore, it is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements or by the additional element(s) in combination with the recited judicial exception. See MPEP 2106.05(a).
2. Applicant submits like Desjardin, the subject matter of claims 1 and 22 also addresses the challenges that arise with training a machine learning model. Training the claimed protein design computation model on noisy sample sequences improves the generative performance of protein design computation model by at least preventing the model from being "overfitted" to the sample sequences [p. 18, par. 3].
It is respectfully found not persuasive. Ex parte Desjardins had claims drawn to the use of a machine learning model, trained on one task with a first set of data, and set parameter weights, then trained again using differing data on a different task, adjusting parameters and weights, while protecting performance of the first task. Further, in Desjardins, the retraining of the particular ML changed the structure of that ML in a way that provided "'[a]n improvement in the functioning of a computer, or an improvement to other technology or technical field,' as discussed in MPEP §§ 2106.04(d)(l) and 2106.05(a). In contrast, the claims do not clearly set forth the link between the data gathered, the initial training of the ML, the structure of the ML, and how training or retraining affects the structure to obtain the desired results or asserted improvement.
Claim Rejections - 35 USC § 102
The previous 102 rejection has been withdrawn in view of the amended claims and arguments. The prior art of record does not disclose applying the protein design computation model having a first adjustment to generate a first sample output sequence, applying the protein design computation model having a second adjustment to generate a second sample output sequence, and upon determining that the first sample output sequence is more similar to the plurality of noisy sample sequences in the training dataset than the second sample output sequence, further training the protein design computation model by at least adjusting the protein design computation model having the first adjustment of claims 1, 22, 39, and 40.
Claim Rejections - 35 USC § 103
The previous 103 rejection has been withdrawn in view of the amended claims and arguments. The prior art of record does not disclose applying the protein design computation model having a first adjustment to generate a first sample output sequence, applying the protein design computation model having a second adjustment to generate a second sample output sequence, and upon determining that the first sample output sequence is more similar to the plurality of noisy sample sequences in the training dataset than the second sample output sequence, further training the protein design computation model by at least adjusting the protein design computation model having the first adjustment of claims 1, 22, 39, and 40.
Conclusion
No claims are allowed.
THIS ACTION IS MADE FINAL. 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.
Inquiries
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Dawn Bickham whose telephone number (703)756-1817. The examiner can normally be reached on Monday - Friday 8-4.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Olivia Wise can be reached on (571)272-2249. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/D.M.B./Examiner, Art Unit 1685 /Soren Harward/Primary Examiner, TC 1600
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