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 1-12 are pending and examined in the instant Office action.
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 and is necessitated by amendment for claims 3-12:
Claim(s) 1-12 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 1-9 and 12 are directed to methods, and claims 10-11 are drawn to apparatus.
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:
Claims 1 and 10 recite the mental step of receiving a protein mutagenesis dataset containing sequence and phenotype pairs for a set of protein sequence variants.
Claims 1 and 10 recite the mental step of passing the protein sequence variants through a language model to obtain a vector representation of an amino acid at each position of each variant sequence.
Claims 1 and 10 recite the mental step of concatenating or pooling embeddings for each position and applying a dimensional reduction to generate a set of sequence representations.
Claims 1 and 10 recite the mental step of training a statistical model to take the set of sequence representations as input to predict phenotype values.
Claims 1 and 10 recite the mental step of receiving a set of amino acid sequences and applying the language model and the statistical model to predict phenotype values for the set of amino acid sequences.
Claims 2 and 11 recite the mathematical limitation of a Gaussian process regression model.
Claim 3 recites the mental step of performing a protein engineering workflow based on phenotype values.
Claim 4 recites the mental step of the workflow corresponding to design of a protein.
Claim 5 recites the mental step of the workflow corresponding to design of a drug.
Claim 6 recites the mental step of the predicted phenotype values to guide a search or optimization algorithm.
Claim 7 recites the mental step of the phenotype values representing a property of the sequence.
Claim 8 recites the mental step of constraining the Gaussian process regression model to correspond to properties.
Claim 9 recites the mathematical limitation of the dimensional reduction to be PCA.
Claim 12 recites the mathematical limitation of the statistical model providing a transfer learning function.
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. While claims 1-2 involve machine learning, the training can be broadly construed to comprise a look-up table, and in claim 1, the statistical model can be broadly construed to be linear regression. 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) 1-12 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.
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 1-12 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 1-12 is/are not patent eligible.
Response to arguments:
Applicant's arguments filed 13 June 2025 have been fully considered but they are not persuasive.
On page 6 of the Remarks, applicant argues that non-precedential Examples 38 and 39 and non-precedential PTAB decision Ex parte Hannun 2018-003323 provide guidance supporting subject matter eligibility. However, while the claims recite steps regarding training models, the claims do not require the models to comprise a complex model (e.g. neural network) that cannot be performed in the human mind. When broadly construed, each model can be broadly interpreted to comprise linear regression, which can be performed on pen and paper. While the claims do not recite mathematical expressions, the functions recited in the claims are equivalent to mathematical expressions recited using words.
While applicant argues the support sequence-to-phenotype calculations in the specification use large datasets that cannot be performed in the human mind, the claims do not require that these sequence-to-phenotype calculations must use the same large datasets. In the absence of this requirement, the datasets can be broadly construed to only comprise two data points.
On page 8 of the Remarks, applicant argues that the claims result in a practical application of an improves sequence-to-phenotype modeling tools relation to conventional modeling tools. This argument is not persuasive because a judicial exception that is an improvement to a conventional judicial exception remains a judicial exception.
On pages 8-9 of the Remarks, applicant argues that the Examiner has not cited prior art to demonstrate that the limitations outside the judicial exception are well-understood, routine, or conventional. Since there are no limitations of the claims outside the judicial exception beside use of generic computer equipment, the Examiner does not cite prior art in this subject matter eligibility rejection.
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 and is necessitated by amendment for claims 3-4, 6, and 9-10:
35 U.S.C. 103 Rejection #1:
Claim(s) 1, 3-4, 6, and 9-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bepler et al. [arXiv; 16 October 2019; 17 pages] in view of Fox [US PGPUB 2005/0084907 A1].
Claim 1 is drawn to a method comprises receiving a protein mutagenesis dataset containing sequence and phenotype pairs for a set of protein sequence variants. The method comprises training a sequence to phenotype prediction model. The training comprises passing the protein sequence variants through a language model to obtain a vector representation of an amino acid at each position of each variant sequence. The training comprises concatenating or pooling embeddings for each position and applying a dimensional reduction to generate a set of sequence representations. The training comprises training a statistical model to take the set of sequence representations as input to predict one or more phenotype values. The method comprises that following training, receiving a set of amino acid sequences and applying the language model and the statistical model to predict one or more phenotype values for the set of amino acid sequences.
Claim 10 is drawn to similar subject matter as claim 1, except claim 10 is drawn to an apparatus.
Page 28 of the original specification defines the term “phenotype” as being synonymous with “function” or “property.”
The document of Bepler et al. studies learning protein sequence embeddings using information from structure [title]. The abstract of Bepler et al. teaches application of machine learning to vector embedding of protein amino acid sequences. The conclusion on page 10 of Bepler et al. at least suggests that the machine learning can be used to predict functional properties. Section 3.1 on page 4 of Bepler et al. teaches passing the proteins through a language model to obtain a vector representation of an amino acid at each position of the sequence. The paragraph bridging pages 5-6 in Section 3.3 of Bepler et al. concatenation of the embedding vectors. Section 3.4 on page 6 of Bepler et al. teaches training the statistical model and dimensional reduction. Section 4.3 on pages 9-10 of Bepler et al., teaches applying the statistical model and the language model to transmembrane protein prediction.
Bepler et al. does not teach receiving a protein mutagenesis dataset containing sequence and phenotype pairs for a set of protein sequence variants.
The document of Fox studies methods, systems, and software for identifying functional biomolecules [title]. Paragraph 151-159 of Fox teaches training, sequence-activity space, and a library of variant protein sequences. Figure 1 of Fox teaches using a sequence-activity model to predict protein phenotype from sequence data.
With regard to claims 3-4 and 6, Figure 1 of Fox teaches a workflow for searching and screening/”designing” proteins with a modeled phenotype derived from sequence data.
With regard to claim 9, paragraph 151 of Fox teaches PCA.
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 application machine learning to the sequence-phenotype relationship of proteins of Bepler et al. by use of the variant protein sequences of Fox because it is obvious to substitute known elements in the prior art to yield a predictable result. In this instance, the variant protein sequences are an alternative to the wild type protein sequences. There would have been a reasonable expectation of success in combining Bepler et al. and Fox because both studies are analogously applicable to applying machine learning to protein sequence-function relationships.
Response to arguments:
Applicant's arguments filed 13 June 2025 have been fully considered but they are not persuasive.
Applicant argues that the rejection does not teach training a statistical model to take the set of sequence representations as input to predict phenotype values and applying the language model and the statistical model to predict phenotype values for the set of amino acid sequences. This argument is not persuasive because while Bepler et al. teaches the language model, Figure 1 and paragraphs 151-159 of Fox suggest the statistical model.
The following rejection is reiterated and is necessitated by amendment for claims 7-8 and 11:
35 U.S.C. 103 Rejection #2:
Claim(s) 2, 7-8, and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bepler et al. in view of Fox as applied to claims 1, 3-4, 6, and 9-10 above, in further view of Gradinaru et al. [US PGPUB 2020/0087358 A1].
Claims 2 and 11 are further limiting wherein the statistical model is a Gaussian process registration model.
Bepler et al. and Fox make obvious using machine learning to assess sequence-phenotype relationships in variant protein sequences, as discussed above. Figure 1 of Fox teaches predicting the property of a protein sequence.
Bepler et al. and Fox do not teach that the statistical model is the Gaussian process registration model.
The document of Gradinaru et al. studies engineered light-sensitive proteins [title]. Paragraph 121 of Gradinaru et al. teaches use of the Gaussian process registration model to measure similarity between protein sequences.
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 application machine learning to the sequence-phenotype relationship of proteins of Bepler et al. and the variant protein sequences of Fox by use of the Gaussian process registration model of Gradinaru et al. because it is obvious to combine known elements in the prior art to yield a predictable result. In this instance, the Gaussian process registration model is an alternative to probability modeling. There would have been a reasonable expectation of success in combining Bepler et al., Fox, and Gradinaru et al. because all three studies are analogously applicable to analysis of protein sequence data.
Response to arguments:
Applicant has no arguments specific to Gradinaru et al.
The following rejection is necessitated by amendment:
35 U.S.C. 103 Rejection #3:
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bepler et al. in view of Fox as applied to claims 1, 3-4, 6, and 9-10 above, in further view of Leader et al. [Nature Reviews: Drug Discovery, volume 7, 2008, pages 21-39].
Claim 5 is further limiting comprising design of a drug for therapeutic application.
Bepler et al. and Fox make obvious using machine learning to assess sequence-phenotype relationships in variant protein sequences, as discussed above.
Bepler et al. and Fox do not teach that the protein is a drug.
The document of Leader et al. is a review article that assesses the use of proteins as drugs that are therapeutic [title and abstract].
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 application machine learning to the sequence-phenotype relationship of proteins of Bepler et al. and the variant protein sequences of Fox by use of the drugs of Leader et al. wherein the motivation would have been that Leader et al. gives a physiological application for the proteins designed through the modeling.
The following rejection is necessitated by amendment:
35 U.S.C. 103 Rejection #4:
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bepler et al. in view of Fox as applied to claims 1, 3-4, 6, and 9-10 above, in further view of Aburakhia et al. [11th IEEE Annual International Conference and Workshop on Computing and Communication, 2020, 7 pages].
Claim 12 is further limiting requiring that training the statistical model provides a transfer learning function.
Bepler et al. and Fox make obvious using machine learning to assess sequence-phenotype relationships in variant protein sequences, as discussed above.
Bepler et al. and Fox do not teach a transfer learning function.
The document of Aburakhia et al. a transfer learning framework for anomaly detection using a model of normality [title]. The equations of Aburakhia et al. comprise a transfer learning function.
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 application machine learning to the sequence-phenotype relationship of proteins of Bepler et al. and the variant protein sequences of Fox by use of the drugs of Aburakhia et al. wherein the motivation would have been that Aburakhia et al. gives mathematical techniques that facilitate the protein modeling of Bepler et al. and Fox [equations of Aburakhia et al.].
Related Art
The claims of the document of Bepler et al. [US Patent 11,532,378 B2] are distinct from the instantly rejection claims because the claims of ‘378 are drawn to a sequence homology search involving loss functions in sub-linear time that are not in the instantly rejected claims. Likewise, the instantly rejected claims are drawn to machine learning of training, the applying, a sequence-to-phenotype prediction model of protein sequence variants using a language model and a statistical model- limitations which are not in the claims of ‘378.
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.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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 17 August 2025
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