DETAILED ACTION
Comments
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 (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.
Claims 1-3, 11-20, and 27-33 are withdrawn from further consideration pursuant to 37 CFR 1.142(b), as being drawn to a nonelected Inventions, there being no allowable generic or linking claim. Applicant timely traversed the restriction (election) requirement in the reply filed on 1 September 2025.
Claim 1-4 and 6-38 are pending in the application.
Claims 4, 6-10, 21-26, and 34-38 are examined in the instant Office action.
Information Disclosure Statements
The IDSs filed on 1/5/2022 and 13/2023 have been considered.
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.
The following rejection is reiterated:
Claim(s) 4, 6-8, 21-24, and 34-36 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea/law of nature/natural phenomenon without significantly more. Claims 4 and 6-8 are drawn to devices, claims 21-24 are drawn to methods, and claims 34-36 are drawn to non-transitory computer-readable media.
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:
The claims recite the mental step of extracting predictive feature vectors expressing a feature from a cyclic peptide that is a target for biostability prediction for instances in which each of a plurality of residues contained in the cyclic peptide is at a start point of a cyclic sequence.
The claims recite the mental step of generating a predicted value for biostability of the prediction target cyclic peptide by inputting a plurality of predictive feature vectors into a trained model pre-trained to output a predicted value of peptide biostability from a feature vector expressing a feature of a cyclic peptide.
The claims recite the mental step of generating a trained model for outputting a predicted value of biostability of a cyclic peptide from a feature vector expressing a feature of a cyclic peptide by executing a machine learning algorithm based on training data.
The claims recite the mental step of inputting each or the plurality of predictive feature vectors into a machine learning trained model pre-trained to output a predicted value of peptide biostability from a feature vector expressing a feature of a cyclic peptide.
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 mathematical relationships. Therefore, these limitations fall under the “Mental process” and “Mathematical concepts” groupings of abstract ideas. 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, claim(s) 4, 6-8, 21-24, and 34-36 recite(s) an abstract idea/law of nature/natural phenomenon (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 affect 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.
There are no limitations that indicate that the claimed analysis engine 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. As such, claims 4, 6-8, 21-24, and 34-36 is/are directed to an abstract idea/law of nature/natural phenomenon (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.
As discussed above, there are no additional limitations to indicate that the claimed analysis engine 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 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 4, 6-8, 21-24, and 34-36 is/are not patent eligible.
Claim 9-10, 25-26, and 37-38 are not rejected under this statute because the core of each of the claims involves using convolutional neural networks, which are too complex to be conducted using the human mind.
Response to arguments:
Applicant's arguments filed 23 December 2025 have been fully considered but they are not persuasive.
Applicant argues that the amendments to the claims overcome the rejection. The major amendments to the claims involve the addition of the use of a trained machine learning model. This argument is not persuasive because, absent a limiting description from the specification, the machine learning model is broadly construed to comprise simpler machine learning models, such as linear regression, which can be carried using pen and paper.
Applicant argues that, in view of recent memoranda and court decision, paragraphs 24 and 25 of the specification recite the practical application of improvements to technology. This argument is not persuasive paragraphs 24 and 25 of the specification are general assertions that attempt connect machine learning to improved prediction of biostability, but are deficient because paragraphs 24 and 25 of the specification lack substance in describing the manner in which this improvement is accomplished (i.e. and a nexus between this manner of improvement and claim limitations).
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
The following rejection is reiterated:
Claim(s) 4-10, 21-26, and 34-38 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pirrotte et al. [US PGPUB 2019/0034586 A1] in view of Wei et al. [WO 2019/191777 A1].
The claims are drawn to the device, method, and non-transitory computer readable medium for extracting each predictive feature vector expressing a feature from a cyclic peptide that is a target for biostability prediction for instances in which each of a plurality of residues contained in the cyclic peptide is at a start point of a cyclic sequence. The claims recite inputting each of the plurality of predictive feature vectors into a machine learning trained model pre-trained to output a predicted value of peptide biostability from a feature vector expressing a feature of a cyclic peptide. The claims recite obtaining a predicted value for biostability of the prediction target cyclic peptide generated by the machine learning trained model.
The document of Pirrotte et al. studies methods of profiling mass spectral data using neural networks [title]. Figures 1A and 1B and the abstract of Pirrotte et al. teach using a trained CNN as a predictive model to classify and identify features of mass spectral data by inputting predictive feature vectors corresponding to the twenty naturally occurring amino acids and the mass spectral data for the peptide. Paragraph 17 of Pirrotte et al. teaches that the aforementioned analysis is applicable to cyclic peptides.
While Pirrotte et al. is applicable to structure of the cyclic peptide, Pirrotte et al. does not apply the machine learning to analyze the function/biostability of the cyclic peptide.
The document of Wei et al. studies systems and methods for drug design and discovery comprising applications of machine learning with differential geometric modeling [title]. The abstract of Wei et al. teaches applying machine learning to the structure of drugs or proteins to assess functions of the drugs related to biostability. Paragraph 14 of Wei et al. teaches use of feature vector to assess properties of the drugs or proteins.
It would have been obvious to someone of ordinary skill in the art at the time of the effective filing date of the instant application to modify the use of machine learning to analyze structure of cyclic peptides as in Pirrotte et al. by use of machine learning to analyzing biostability of peptides as in Wei et al. wherein the motivation would have been that that the biostability analysis of Wei et al. gives a biological functional application to the machine learning of Pirrotte et al. [abstract of Wei et al.]. There would have been a reasonable expectation of success in combining Pirrotte et al. and Wei et al. because both studies are analogously applicable to applying machine learning to peptides.
It would have been further obvious to modify the linear vectors of Pirrotte et al. to be both-end-adjacent (i.e. cyclic) because it is obvious to try the cyclic vector variant because cyclic vectors correspond to the cyclic peptides studies in paragraph 17 of Pirrotte et al.
Response to arguments:
Applicant's arguments filed 23 December 2025 have been fully considered but they are not persuasive.
Applicant addresses the prior art rejection in the Remarks as Wei et al. in view of Pirrotte et al. and not as in the rejection statement above as Pirrotte et al. in view of Wei et al.
Applicant argues that the prior art does not teach machine learning or feature vectors. This argument is not persuasive because Figures 1A and 1B of Pirrotte et al. and the abstract of Wei et al. teach machine learning and feature vectors.
Applicant argues that the prior art does not teach cyclic peptides. This argument is not persuasive because paragraph 17 of Pirrotte et al. teaches cyclic peptides.
Applicant argues that the prior art does not teach cyclic sequences. This argument is not persuasive because it would have been further obvious to modify the linear vectors of Pirrotte et al. to be both-end-adjacent (i.e. cyclic) because it is obvious to try the cyclic vector variant because cyclic vectors correspond to the cyclic peptides studies in paragraph 17 of Pirrotte et al.
In response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, it would have been obvious to someone of ordinary skill in the art at the time of the effective filing date of the instant application to modify the use of machine learning to analyze structure of cyclic peptides as in Pirrotte et al. by use of machine learning to analyzing biostability of peptides as in Wei et al. wherein the motivation would have been that that the biostability analysis of Wei et al. gives a biological functional application to the machine learning of Pirrotte et al. [abstract of Wei et al.].
Related Prior Art
The document of Ito et al. [Journal of Medicinal Chemistry, volume 63, 13 November 2020, pages 14045-14053; on IDS] studies structural basis for the binding mechanism of human serum albumin complexes with cyclic peptide dalbavancin [title]. The Methods section on page 14051 of Ito et al. teaches performing molecular dynamics simulations using the cyclic peptide.
E-mail Communications Authorization
Per updated USPTO Internet usage policies, Applicant and/or applicant’s representative is encouraged to authorize the USPTO examiner to discuss any subject matter concerning the above application via Internet e-mail communications. See MPEP 502.03. To approve such communications, Applicant must provide written authorization for e-mail communication by submitting the following statement via EFS-Web (using PTO/SB/439) or Central Fax (571-273-8300):
Recognizing that Internet communications are not secure, I hereby authorize the USPTO to communicate with the undersigned and practitioners in accordance with 37 CFR 1.33 and 37 CFR 1.34 concerning any subject matter of this application by video conferencing, instant messaging, or electronic mail. I understand that a copy of these communications will be made of record in the application file.
Written authorizations submitted to the Examiner via e-mail are NOT proper. Written authorizations must be submitted via EFS-Web (using PTO/SB/439) or Central Fax (571-273-8300). A paper copy of e-mail correspondence will be placed in the patent application when appropriate. E-mails from the USPTO are for the sole use of the intended recipient, and may contain information subject to the confidentiality requirement set forth in 35 USC § 122. See also MPEP 502.03.
Conclusion
No claim is 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.
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to Russell Negin, whose telephone number is (571) 272-1083. This Examiner can normally be reached from Monday through Thursday from 8 am to 3 pm and variable hours on Fridays.
If attempts to reach the Examiner by telephone are unsuccessful, the Examiner’s Supervisor, Larry Riggs, Supervisory Patent Examiner, can be reached at (571) 270-3062.
/RUSSELL S NEGIN/ Primary Examiner, Art Unit 1686 5 March 2026