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 .
Detailed Action
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 6/10/26 has been entered.
In amendments dated 6/10/26, Applicant amended claims 1-4, 8-9, 11-14, and 18-20, canceled no claims, and added no new claims. Claims 1-20 are presented for examination.
Rejections under 35 U.S.C. 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to mental processes without significantly more. Independent claims 1, 11, and 20 each recites constructing a plurality of simulated family trees, wherein each simulated family tree corresponds to a population having a mixed origin and is built using the reference-panel individuals chosen based on an origin composition corresponding to the population, wherein constructing each simulated family tree comprises generating simulated descendants of the reference-panel individuals by simulating inheritance events comprising meiosis and recombination based on the genetic markers of the genomic datasets of the plurality of reference-panel individuals so as to generate simulated genomic datasets for the simulated descendants; tracking, for genomic segments in the simulated genomic datasets for the simulated descendants, inheritance of the genomic segments from ancestral sources in the simulated family trees; assigning actual inheritance labels to genomic windows of the simulated genomic datasets for the simulated descendants based on the tracked inheritance of the genomic segments from the ancestral sources; generating, from the plurality of simulated family trees, which correspond to different populations, a plurality of simulated genomic datasets representing a plurality of the simulated descendants; training a machine learning model that is configured to determine inheritance labels, wherein training the machine learning model comprises: applying the plurality of simulated genomic datasets as training samples; applying the machine learning model to predict inheritance labels for the training samples; comparing the predicted inheritance labels with the actual inheritance labels assigned based on the plurality of simulated family trees; and
adjusting the origin-specific weight parameters based on the comparing. Constructing a plurality of simulated family trees, generating simulated descendants of the reference-panel individuals, tracking inheritance of genomic segments, assigning actual inheritance labels, and generating simulated genomic datasets are each recited broadly and are mental processes accomplishable in the human mind or on paper. Training a machine learning model and applying the machine learning model to predict inheritance labels are each conventional activities of a machine learning model and are not significantly more than the recited mental processes per Recentive Analytics v. Fox Broadcasting Corp. (134 F.4th 1205, 2025 U.S.P.Q.2d 628), and applying simulated genomic datasets as training samples and adjusting origin-specific weight parameters are each recited broadly and are mental processes accomplishable in the human mind or on paper. Each claim recites additional elements of receiving a plurality of genomic datasets of a plurality of reference-panel individuals, each genomic dataset corresponding to an origin and comprising genetic markers representative of the origin, a data gathering step and insignificant extra-solution activity; and initiating origin-specific weight parameters in the machine learning model, which is an input step and also insignificant extra-solution activity. Claim 11 recites one or more processors and memory configured to store instructions and claim 20 recites a non-transitory computer readable medium, which are generic components of a computer system. Examiner notes Applicant’s discussion of the improvement of the invention on pages 14-15 of his Remarks as “improving machine-learning training through biologically constrained, ground-truth dataset creation,” and further discusses claim 1 as “also related to a technical problem arising in computerized genomic ancestry modeling: conventional training of inheritance-label models lacks reliable ground-truth data for admixed populations, because actual ancestry labels at genomic windows are difficult or impossible to obtain directly at the scale needed for model training.“ Examiner found support in the specification for these in paragraphs 0098-0099 and found support for some specific techniques for the generating, tracking, and assigning steps in paragraphs 0097, 0116, 0199, and 0206 but none of the alleged specific techniques are recited in the claims. The claim steps do not recite a particular improvement in said technology or function of a computer per MPEP 2106.04(d) and do not recite any unconventional steps in the invention per MPEP 2106.05(a). Therefore, the recited mental processes are not integrated into a practical application. Taking the claim as a whole, receiving a plurality of genomic datasets is recited broadly and amounts to receiving data across a network per specification paragraphs 0249 and 0256 and figure 8 network 820, which is routine and conventional activity per the list of such activities in MPEP 2106.05(d) part II. Initiating parameters in a machine learning model is routine and conventional per Sprenkle (US 20250112126 paragraph 0060, initiating parameters in machine learning model and later adjusting said parameters based on the difference (comparison) between predicted outputs and ground-truth labels) and Guar et al (US 20240281887 paragraph 0080, adjusting weights in a machine learning based on comparison of labels associated with input data). The one or more processors, memory configured to store instructions, and non-transitory computer readable medium, are still generic components of a computer system. Therefore, the claims do not include additional elements that are sufficient to amount to significantly more than the recited mental processes.
Claims 2 and 12 each recites filtering a plurality of candidates based on inheritance labels (mental process accomplishable in the human mind or on paper); identifying, for each candidate, a number of the individuals in a particular data-inheritance origin whose genomic datasets match the inheritance dataset of the candidate (identifying is evaluating and a mental process); and selecting a candidate to be added to the genomic datasets based on the number of matched individuals that correspond to the candidate compared to numbers of matched individuals of other candidates (selecting is evaluating and a mental process). Claims 3 and 13 each recites generating a candidate pool that include a plurality of candidates based on inheritance labels related to a particular origin (recited broadly and a mental process accomplishable in the human mind or on paper); determining that one of the candidates has a first number of matches associated with a particular origin and a second number of matches associated with a second origin, the second number of matches exceeding a threshold (determining is evaluating and a mental process); and removing said one of the candidates from the candidate pool (mental process accomplishable in the human mind or on paper).
Claims 4 and 14 each recites accessing a population composition of the geographical location, the population composition comprising information related to percentage of individuals with a plurality of origins (accessing a population composition is retrieving data and routine and conventional activity per the list of such activities in MPEP 2016.05(d) part II); sampling, based on the population composition of the geographical location, reference- panel datasets from the plurality of genomic datasets for the plurality of origins (sampling datasets is retrieving data and routine and conventional activity per the list of such activities in MPEP 2016.05(d) part II); and representing sampled reference-panel datasets in nodes of the particular simulated family tree (representing datasets in nodes is storing the datasets which is routine and conventional activity per the list of such activities in MPEP 2016.05(d) part II). Claims 5 and 15 each recites selecting placements of the sampled reference-panel datasets based on the generation- specific composition (selecting placements is evaluating and a mental process). Claims 6 and 16 each recites treating the particular simulated individual as a descendant individual of the genomic datasets that are placed in the particular simulated family tree (treating an entity as data is a mental process accomplishable in the human mind or on paper); simulating a plurality of inheritance events (simulating an event is a mental process accomplishable in the human mind or on paper); and generating the particular simulated inheritance dataset of the particular simulated individual based on the plurality of inheritance events (generating a dataset is recited broadly and is a mental process accomplishable in the human mind or on paper).
Claims 7 and 17 each recites wherein the machine learning model is a hidden Markov model with windows that represent segments of inheritance data, each window comprising a plurality of nodes and each node representing an origin, wherein the origin-specific weight parameters are associated with weights of the plurality of nodes (training a machine learning model is merely applying it and is not significantly more than the recited mental processes). Claims 8 and 18 each recites comparing the predicted inheritance labels with the actual inheritance labels to identifying under- represented origins and over-represented origins (comparing labels is evaluating and a mental process); for an under-represented origin, increasing a value of the origin-specific weight parameter corresponding to the under-represented origin (increasing a value is a mental process accomplishable in the human mind or on paper); and for an over-represented origin, decreasing a value of the origin-specific weight parameter corresponding to the over-represented origin (decreasing a value is a mental process accomplishable in the human mind or on paper).
Claims 9 and 19 each recites dividing a particular simulated inheritance dataset in a particular training sample into a plurality of windows (dividing data is a mental process accomplishable in the human mind or on paper); examining how a segment of the particular simulated inheritance dataset in a particular window is inherited in a particular simulated family tree (examining is recited broadly and is a mental process accomplishable in the human mind or on paper); identifying a reference-panel individual in the particular family tree who passes down the segment to the simulated inheritance dataset (identifying is evaluating and a mental process); determining an origin label of said reference-panel individual (determining is a mental process accomplishable in the human mind or on paper); and using the origin label as the actual inheritance label (using data is a mental process accomplishable in the human mind or on paper). Claim 10 recites wherein the training samples comprises admixed individuals that are simulated from plurality of simulated family trees and non-admixed individuals that are sampled from actual user datasets (data is a mental process accomplishable in the human mind or on paper).
Relevant Prior Art
During his search for prior art, Examiner found the following references to be relevant to Applicant's claimed invention. Each reference is listed on the Notice of References form included in this office action:
Zhang et al (US 20220382770) teaches embodiments for updating family tree nodes for individuals to complete genomic datasets, does not teach simulating descendants of said individuals, tracking inheritance of genomic segments for said family trees, and assigning actual labels for genomic windows of simulated descendants (paragraphs 0004, 0033, 0039, 0065, 0071, 0112-0120 figure 6); and
Curtis et al (20220076789) teaches generating a graph of genomic datasets for a plurality of individuals and filtering the graph based on features common to the individuals, does not teach simulating descendants of said individuals, tracking inheritance of genomic segments for said family trees, and assigning actual labels for genomic windows of simulated descendants (paragraphs 0004, 0006, 0056, 0068-0074 figure 4).
Responses to Applicant’s Remarks
Regarding objections to claims 1, 11, and 20 for antecedent basis of “the individuals selected from the reference panel,” in view of amendments reciting “reference panel individuals” this objection is withdrawn. Regarding objections to claims 1, 11, and 20 for antecedent basis of “the plurality of simulated family trees that correspond to different populations,” in view of amendments reciting “generating, from the plurality of simulated family trees, which correspond to different populations,” these objections are withdrawn. Regarding objections to claims 1, 11, and 20 for unclear language “a plurality of simulated genomic of simulated descendants,” in view of amendments reciting “so as to generate simulated genomic datasets for the simulated descendants,” these objections are withdrawn. Regarding objections to claims 1, 11, and 20 for unclear language “applying the plurality of simulated genomic as training samples,” in view of amendments reciting “applying the plurality of simulated genomic datasets as training samples,” these objections are withdrawn. Regarding objections to claims 2 and 12 for unclear language “whose genomic match the inheritance dataset of the candidate,” in view of amendments reciting “whose genomic datasets match the inheritance dataset of the candidate,” these objections are withdrawn. Regarding rejections of claims 1-20 under 35 U.S.C. 103 by Montserrat and Curtis in further view of McMaster-Schraiber, Applicant’s amendments overcome Montserrat’s, Curtis’ and McMaster-Schraiber’s teachings and these rejections are withdrawn.
Regarding rejections of claims 1-20 under 35 U.S.C. 101 for reciting mental processes without significantly more, Applicant’s arguments have been considered but are not persuasive. On pages 14-15 Applicant asserts "Claim 1 recites a specific technical process for generating training data used to improve operation of a machine learning model that determines inheritance labels from genomic data." and "Claim 1 is also related to a technical problem arising in computerized genomic ancestry modeling: conventional training of inheritance-label models lacks reliable ground-truth data for admixed populations, because actual ancestry labels at genomic windows are difficult or impossible to obtain directly at the scale needed for model training." Examiner noted this in the rejections above and found support in specification paragraphs 0097, 0116, 0199, and 0206. The amended limitations (“generating simulated descendants of the reference-panel individuals by simulating inheritance events comprising meiosis and recombination based on the genetic markers of the genomic datasets of the plurality of reference-panel individuals so as to generate simulated genomic datasets for the simulated descendants; tracking, for genomic segments in the simulated genomic datasets for the simulated descendants, inheritance of the genomic segments from ancestral sources in the simulated family trees; and assigning actual inheritance labels to genomic windows of the simulated genomic datasets for the simulated descendants based on the tracked inheritance of the genomic segments from the ancestral sources;”) are each recited broadly and lack specific details about how the invention accomplishes each step. For example, these limitations do not recite details on how the simulated descendants are generated, how the invention simulates inheritance events, how the invention tracks inheritance of genomic segments in the simulated family trees, how actual inheritance labels are assigned. Each of generating simulated descendants, tracking inheritance of genomic segments in the simulated family trees, and assigning actual inheritance labels uses a computer as a tool to perform the action and a BRI of each includes use of a physical aid and is thus a mental process accomplishable in the human mind or on paper per MPEP 2106.04(a)(2)(III). MPEP 21006.04(d)(1) states “if the specification sets forth an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement. That is, the claim includes the components or steps of the invention that provide the improvement described in the specification.”
Inquiry
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRUCE M MOSER whose telephone number is (571)270-1718. The examiner can normally be reached M-F 9a-5p.
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/BRUCE M MOSER/Primary Examiner, Art Unit 2154 9/16/26
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