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, 5-7, 9-15, 18-20, and 26-29 are pending and examined in the instant application.
Information Disclosure Statement
The IDS filed has been considered.
Withdrawn Rejections
The 35 U.S.C. 112, 35 U.S.C. 102, and 35 U.S.C. 103 rejections of the previous Office action are withdrawn in view of amendments filed to the instant set of claims on 27 July 2026.
The first 35 U.S.C. 101 Rejection (i.e. 35 U.S.C. 101 Rejection #1) is withdrawn in view of amendments filed to the instant set of claims on 27 July 2026.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.
The following rejection is reiterated for claims 1-3, 5-7, 9-15, and 18-20, and is necessitated by amendment for claims 26-29:
Claim(s) 1-3, 5-7, 9-15, 18-20, and 26-29 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 1-3, 5-7, 9-15, and 26-29 are drawn to methods, claim 18 is drawn to a non-transitory computer-readable storage medium, and claims 19-20 are drawn to systems comprising processors.
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 18-19 recite the mental step of encoding data related to the patients with latent variables.
Claims 1 and 18-19 recite the mental step of determining one or more importance measures of the latent variables.
Claims 1 and 18-19 recite the mental step of prioritizing the latent variables using importance measures.
Claims 1 and 18-19 recite the mental step of identifying a disease endotype that is represented by one or more of the interpreted latent variables.
Claims 1 and 18-19 recite the mental step of using unsupervised machine learning comprising techniques such as PCA..
Claims 1 and 18-19 recite the mental step of relating data to the patients multiple times and determining the extent of recurrence of each of the latent variables.
Claims 1 and 18-19 recite the mental steps of encoding the data multiple times and determining importance measures of a latent variable comprising determining an extent of recurrence of the latent variable.
Claims 1 and 18-19 recite the mental step of applying gene enrichment analysis to the latent variables.
Claims 1 and 18-19 recite the mental step of identifying a biological process underlying the disease using a gene expression pattern encoded in the latent variables.
Claim 2 recites the mental step of constraining the type of data.
Claims 3 and 20 recite the mental step of performing batch correction on the data.
Claim 5 recites the mental step and mathematical limitation of using linear factor model.
Claim 6 recites the mental step of applying sparsity constraints to the latent variables.
Claim 7 recites the mental steps of extracting data and using latent variables to predict clinical attributes.
Claim 9 recites the mental step of determining the contribution of the latent variable to a proportion of variation.
Claim 10 recites the mental step of determining the ability of the latent variable to separate patients from a control group.
Claim 11 recites the mental step of determining an extent to which the latent variable is predictive of a patient attribute.
Claim 12 recites the mental step of rewarding a latent variable that is predictive of a patient attribute that is relevant to the disease.
Claim 13 recites the mental step of constraining the types of disease attributes.
Claim 14 recites the mental step of penalizing the latent variable that is predictive of a patient attribute that is not relevant to the disease.
Claim 15 recites the mental step of describing attributes not relevant to the disease.
Claim 26 recites the mental step of constraining the dataset to include RNA sequencing data.
Claims 27-28 recite the mental and mathematical steps of constraining the unsupervised machine learning model to comprise a linear factor model comprising a loading matrix.
Claim 29 recites the mental step of ranking the latent variables from most important to least important for the disease.
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) 1-3, 5-7, 9-15, 18-20, and 26-29 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 1-3, 5-7, 9-15, 18-20, and 26-29 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. MPEP 2106.05(f) discloses that mere instructions to apply the judicial exception cannot provide an inventive concept to the claims. 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, 5-7, 9-15, 18-20, and 26-29 is/are not patent eligible.
Response to arguments:
Applicant's arguments filed 27 July 2026 have been fully considered but they are not persuasive.
Applicant argues that encoding using an unsupervised machine learning model is too complex to be conducted in the human mind. This argument is not persuasive because unsupervised machine learning comprises techniques such as principal component analysis, which is simple enough to be conducted with pen and paper.
Applicant argues that the claims result in the practical application of stratifying patients into disease endotypes with more accuracy and efficiency than conventional techniques with analogous objectives. This argument is not persuasive because a judicial exception that is an improvement to a conventional judicial exception remains a judicial exception.
Applicant argues that the claims recite limitations that are not routine and conventional. However, to support this argument, applicant only reiterates limitations that are part of judicial exceptions. The argument regarding limitations that are not routine and convention only applies to limitations outside the judicial exceptions.
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 necessitated by amendment:
Claim(s) 1-3, 5-7, 9-15, 18-20, and 26-29 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wong et al. [US PGPUB 2019/0065666 A1; on IDS] in view of Tsalik et al. [WO 2018/140256 A1; on IDS] in view of Metzger et al. [WO 2020/072977 A1].
Claim 1 is drawn to a method of stratifying a population of patients into disease endotypes. The method comprises encoding data relating to the patients as latent variables using an unsupervised machine learning model. The method comprises determining one or more importance measures of the latent variables, wherein the determining comprises encoding, using the unsupervised machine learning model, the data relating to the patients a plurality of times and determining an extent of recurrence of each of the latent variables. The method comprises prioritizing the latent variables using the importance measures. The method comprises selecting one or more latent variables that satisfy a minimum standard of significance as indicated by the importance measures. The method comprises interpreting the selected one or more latent variables by applying a gene enrichment analysis to the one or more latent variables. The method comprises identifying a disease endotype that is represented by one or more of the interpreted latent variables by identifying a biological process underlying the disease using a gene expression pattern encoded in the one or more latent variables. The method comprises outputting a result comprising a stratification of the patients into disease endotypes.
Claim 5 is further limiting wherein the unsupervised machine learning model comprises an autoencoder.
Claim 18 is drawn to similar subject matter as claim 1, except claim 18 is drawn to a computer readable medium.
Claim 19 is drawn to similar subject matter as claim 1, except claim 19 is drawn to a system.
Paragraphs 7-8, 73, and 76 of Wong et al. teaches stratification into disease endotypes for septic shock. Paragraphs 74 and 77 of Wong et al. teach computer implementation. Paragraphs 175-182 of Wong et al. teach the classification and regression tree methodology involving encoding gene expression data (i.e. data relating to the patients as latent variables) which determine whether the patient data matches the criteria of terminal tree nodes.. Paragraphs 175-182 of Wong et al. teach variable reduction involving determining the important measure of how many subjects in the root node are in each terminal node. Paragraphs 175-182 of Wong et al. teach that the associated latent variables are prioritized using this importance measure since the terminal nodes with <5% of the subjects in the root node are pruned. Paragraphs 175-182 of Wong et al. teach that the only purpose of the classification and regression tree methodology is to apply the decision tree for deriving decisions, which in this case is the identification of the disease (i.e. septic shock) endotype and which necessarily involves interpreting the latent variables associated with the terminal tree nodes. Paragraph 100 of Wong et al. teach identifying a biological process underlying the disease using a gene expression pattern encoded for by latent variables.
Wong et al. does not teach gene enrichment. Wong et al. does not teach all of the machine learning limitations of the claims,
Tsalik et al. studies gene expression signatures useful to predict or diagnose sepsis [title]. Page 26, lines 8-14 of Tsalik et al. teaches gene enrichment.
Wong et al. and Tsalik et al. do not teach all of the machine learning limitations of the claims.
Metzger et al. teaches systems and methods for identifying bioactive agents utilizing unbiased machine learning [title]. Paragraph 82 of Metzger et al. teaches an unsupervised machine learning method comprising a convolutional autoencoder that processes data multiple times to predict toxicity. Paragraph 82 of Metzger et al. teaches a standard deviation which is indicative of proportion of variation and extent of recurrence of the variable. Paragraph 89 of Metzger et al. teaches dimensional reduction into latent space using the unsupervised machine learning technique of PCA. Paragraph 82 of Metzger et al. teaches a disease phenotype from a wild type phenotype. Paragraph 82 of Metzger et al. teaches removing the distance between wild type and disease phenotypes as the distance is not relevant to predicting disease and toxicity.
With regard to claim 2, paragraph 9 of Wong et al. teaches genomic data.
With regard to claims 3 and 20, the last paragraph on page 24 of Tsalik et al. teaches performing batch correction on data.
With regard to claims 6 and 27-28, the first paragraph on page 15 of Tsalik et al. teaches applying sparsity constraints to the latent variables. Paragraph 54 of Metzger et al. teaches loading data of a neural network onto a matrix. Paragraph 89 of Metzger et al. teaches a linear factor model.
With regard to claim 7, the figures of Wong et al. teach extracted and labelled data using latent variables to predict clinical attributes.
With regard to claims 9-10, Paragraph 82 of Metzger et al. teaches a standard deviation which is indicative of proportion of variation and extent of recurrence of the variable, and this variance is used statistically to differentiate between groups.
With regard to claim 11, paragraph 182 and Table 15 of Wong et al. teach the accuracy of prediction of latent variables in patient survival.
With regard to claim 12, the prioritization of latent variables in paragraphs 175-182 of Wong et al. teaches rewarding latent variables (i.e. latent variables prioritized higher) that a predictive of a patient attribute that is relevant for the disease.
With regard to claim 13, paragraph 60 of Wong et al. teaches patient survival time.
With regard to claim 14, paragraph 82 of Metzger et al. teaches removing the distance between wild type and disease phenotypes as the distance is not relevant to predicting disease and toxicity.
With regard to claim 15, Table 1 on pages 23-24 of Tsalik et al. teaches gender as a variable.
With regard to claim 26, page 50, lines 1-3 of Tsalik et al. teach analysis of RNA sequencing data.
With regard to claim 29, page 40, line 26 to page 41, line 7 of Tsalik et al. teaches ranking the latent variables by degree of importance.
It would have been obvious to someone of ordinary skill in the art at the time of the effective filing date of the instant invention to modify the disease stratification algorithm of Wong et al. by use of the gene expression algorithms of Tsalik et al. wherein the motivation would have been that the gene expression algorithms of Tsalik et al. add additional mathematical algorithms to facilitate the disease stratification algorithm [line 1 of page 15 to line 25 of page 16 of Tsalik et al.]. There would have been a reasonable expectation of success in combining Wong et al. and Tsalik et al. because both studies are analogously applicable to analysis of sepsis.
It would have been obvious to someone of ordinary skill in the art at the time of the effective filing date of the instant invention to modify the disease stratification algorithm of Wong et al. and the gene expression algorithms of Tsalik et al. by use of the autoencoder to predict disease and toxicity of Metzger et al. wherein the motivation would have been that the machine learning of Metzger et al. is an additional mathematical technique that facilitates the disease stratification algorithm of Wong et al. and Tsalik et al. [paragraph 82 of Metzger et al.].
There would have been a reasonable expectation of success in combining Wong et al., Tsalik et al., and Metzger et al. because all three studies are analogously applicable to using mathematical and machine learning algorithms to stratify disease statuses in groups of patients.
Response to arguments:
Applicant's arguments filed 27 July 2026 have been fully considered but they are not persuasive.
Applicant argues that the prior art does not teach the prioritizing limitation of the independent claims. This argument is not persuasive because paragraphs 175-182 of Wong et al. teach that the associated latent variables are prioritized using this importance measure since the terminal nodes with <5% of the subjects in the root node are pruned.
Applicant argues that Metzger et al. does not teach using an unsupervised machine learning model a plurality of times to retrieve an extent of recurrence of the variables. This argument is not persuasive because paragraph 82 of Metzger et al. teaches obtaining standard deviations for the variables. A standard deviation is a measure of variance (i.e. extent of recurrence of the variable). A meaningful measure of variance needs a plurality of runs outputting the same variable.
Applicant argues that the prior art does not teach gene enrichment analysis. This argument is not persuasive because page 26, lines 8-14 of Tsalik et al. teaches gene enrichment analysis.
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, the gene expression algorithms of Tsalik et al. add additional mathematical algorithms to facilitate the disease stratification algorithm of Wong et al. [line 1 of page 15 to line 25 of page 16 of Tsalik et al.]. There would have been a reasonable expectation of success in combining Wong et al. and Tsalik et al. because both studies are analogously applicable to analysis of sepsis.
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 11 October 2026
