DETAILED ACTION
Applicant’s response, filed 21 November 2025, has been fully considered. 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 .
Claim Status
Claims 1-5, 7-10, 13, 15-17, 19-22 and 25 are pending and examined herein.
Claims 1-5, 7-10, 13, 15-17, 19-22 and 25 are rejected.
Priority
Claims 1-5, 7-10, 13, 15-17, 19-22 and 25 are granted the claim to the benefit of priority to U.S. Provisional application 62/858007 filed 06 June 2019. Thus, the effective filling date of claims 1-5, 7-10, 13, 15-17, 19-22 and 25 is 06 June 2019.
Information Disclosure Statement
The information disclosure statement (IDS) was received on 10 August 2022. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the examiner.
Drawings
The drawings received 21 November 2025 are accepted.
Claim Objections
The objection of claim 13 in Office action mailed 02 June 2025 is withdrawn in view of the amendment “A non-transitory computer-readable medium” received 21 November 2025.
Claim Interpretation
Claims 1, 13, and 25 recite “training a machine learning model on the first set of features and on the second set of features, wherein training of the machine learning model comprises organizing the first set of features and the second set of features into a partition tree…” and “generating, from the machine learning model, computer-readable instructions in computer memory comprising a classifier”. The instant disclosure provides that training a machine learning model comprising organizing the features in a partition tree is a feature engineering step which provides a list of candidate features (see instant disclosure pages 51-54). The instant disclosure further provides that a Logistic Regression, Linear Discriminatory Analysis, or Random Forest are used for prediction to classify etiological factors based on selected features (see instant disclosure page 55-56). The BRI of training a machine learning model on the first set of features and on the second set of features, wherein training of the machine learning model comprises organizing the first set of features and the second set of features into a partition tree…” and “generating, from the machine learning model, computer-readable instructions in computer memory comprising a classifier” is training the machine learning model to produce a set of candidate features and using candidate features from the list of candidate features to generate the classifier that classifies etiological factors based on candidate features (which represent mutational signatures). Thus, the limitation of “from the machine learning model” is interpreted as features selected by the machine learning model.
Claim Rejections - 35 USC § 112
The rejection on the ground of 112/b claims 1, 13, and 25 for reciting “generating, from the machine learning model, a classifier” in Office action mailed 02 June 2025 is withdrawn in view of the amendment of having claim “wherein training of the machine learning model comprises organizing the first set of features and the second set of features into a partition tree that includes layers of nodes, each node representing a particular type of mutation and each child of the node representing possible mutations that are of the type of mutation in the node” and “generating, from the machine learning model, computer-readable instructions in computer memory comprising a classifier” received 21 November 2025.
The rejection on the ground of 112/b claims 1, 13, and 25 for reciting “training a machine learning model on the first set of features and on the second set of features” in Office action mailed 02 June 2025 is withdrawn in view of the amendment of having claim “wherein training of the machine learning model comprises organizing the first set of features and the second set of features into a partition tree that includes layers of nodes, each node representing a particular type of mutation and each child of the node representing possible mutations that are of the type of mutation in the node” received 21 November 2025.
The rejection on the ground of 112/b claims 15 in Office action mailed 02 June 2025 is withdrawn in view of the amendment of having claim 15 depend from claim 13 received 21 November 2025.
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 rejection has been modified necessitated by amendment.
Claims 1-5, 7-10, 13, 15-17, 19-22 and 25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
(Step 1)
Claims 1-5 and 7-10 fall under the statutory category of a process and claims 13, 15-17, 19-22 and 25 fall under the statutory category of a machine.
(Step 2A Prong 1)
Under the BRI, the instant claims recite judicial exceptions that are an abstract idea of the type that is in the grouping of a “mental process”, such as procedures for evaluating, analyzing or organizing information, and forming judgement or an opinion. The instant claims further recite judicial exceptions that are an abstract idea of the type that is in the grouping of a “mathematical concept”, such as mathematical relationships and mathematical equations.
Independent claims 1, 13, and 25 recite mental processes of “generating a first set of features based on single nucleotide mutations”, “generating a second set of features based on dinucleotide mutations”, “training a machine learning model one the first set of features and on the second set of features, wherein training of the machine learning model comprises organizing the first set of features and the second set of features into a partition tree that includes layers of nodes, each node representing…”, “generating, from the machine learning model, a classifier that is configured to operate by receiving a new-genomic-data object… and generate from the new-genomic-data object, a etiological classification for the new-genomic data object, the etiological-classification indicating a corresponding etiological factor that matches one of the etiological tags”, “generating, from the subject’s genome, a subject-genomic-data-object for the subject”, and “detecting an etiological factor for the subject by providing the subject-genomic-data-object to the classifier”.
Independent claims 1, 13, and 25 recite a mathematical concept of “generating, from the machine learning model, a classifier that is configured to operate by…”.
Dependent claims 4 and 16 recite a mental process of “generating a third set of features based on trinucleotide mutations wherein training the machine learning model further comprises training the machine learning model on the third set of features”. Dependent claims 5 and 17 recite a mental process of “generating a fourth set of features based on all mutations, wherein training the machine learning model further comprises training the machine learning model on the fourth set of features”. Dependent claims 7 and 19 recite a mental process of “wherein the training of the machine learning model further comprises pruning the partition tree by removing a pruned node and all other nodes that are children of the pruned node”. Dependent claims 8 and 20 recite mental processes of “selecting some, but not all, of the nodes as candidate nodes to be used for candidate testing, and testing the candidate nodes to generate first-phase candidate nodes”. Dependent claims 9 and 21 recite mental processes of “generating second-phase candidates by for each particular first-phase candidate node, adjusting a value for each parent node that is also a first-phase candidate node, the adjustment being based on the particular first-phase candidate node, and selecting, as a second-phase candidate, a first-phase candidate with a remaining value above a threshold value”. Dependent claims 10 and 22 recite a mental process of “combining second-phase candidates of training data that did have a particular tag with training data that did not have the particular tag”.
The claims recite steps that fall under evaluating/analyzing data of generating features, training a machine learning model through organizing a partition tree to identifying feature signatures, generating data objects from data, detecting etiological factors, pruning nodes, and combining candidate training data that have a particular tag with training that data that did not have the particular tag, selecting candidate nodes, testing nodes, adjusting values, comparing candidate nodes that are above a threshold value. The human mind is capable of evaluating/analyzing data. The claims recite a step that falls under mathematical calculations of generating a classifier which encompasses building a logistic regression (see instant disclosure page 36) which is a series of calculations for fitting a mathematical function.
Dependent claims 2, 3, and 15 further limit the mental process/mathematical concept recited in the independent claim but do not change their nature as a mental process/mathematical concept. Thus, claims 1-5, 7-10, 13, 15-17, 19-22, and 25 recite abstract ideas.
(Step 2A Prong 2)
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). Integration into a practical application is evaluated by identifying whether there are any additional elements recited in the claim and evaluating those additional elements to determine whether they integrate the exception into a practical application.
The additional element in claims 1 of computer-readable instructions in computer memory which amounts to using a generic computer to perform judicial exceptions, the additional element in claim 13 of a non-transitory computer readable medium containing instruction that when executed cause a generic computer to perform judicial exceptions and the additional element in claim 25 of a generic computer that performs judicial do not integrate the judicial exceptions into a practical application because this is simply applying the judicial exceptions to a generic computer environment without improving computer technology (see MPEP 2106.04(d)(1)).
The additional element in claims 1, 13, and 25 of receiving data does not integrate the judicial exceptions into a practical application because this is extra solution activity of data gathering.
Thus, the additional elements do not integrate the judicial exceptions into a practical application and claims 1-10, 13, 15-22, and 25 are directed to the abstract idea.
(Step 2B)
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 additional element in claims 1 of computer-readable instructions in computer memory which amounts to using a generic computer to perform judicial exceptions, the additional element in claim 13 of a non-transitory computer readable medium containing instruction that when executed cause a generic computer to perform judicial exceptions and the additional element in claim 25 of a generic computer that performs judicial are conventional see MPEP 2106.05(b) and MPEP 2106.05(d)(II).
The additional element in claims 1, 13, and 25 of receiving data is conventional see MPEP 2106.05(b) and MPEP 2106.05(d)(II).
Thus, the additional elements are not sufficient to amount to significantly more than the judicial exception because they are conventional.
Response to Arguments
Applicant's arguments filed 21 November 2025 have been fully considered but they are not persuasive.
Applicant argues that the elements of claim 1 provide a technical solution to various technical problems. Applicant argues one technical problem relates to overcoming obstacles to “accuracy of the supervised approach” to machine learning and applicant argues the structure of the machine learning techniques enable this accuracy (Reply p. 11-13). Applicant argues that the claimed technical solutions of which increase accuracy allows for “detecting an etiological factor of a disease in a subject having the disease” and the training of the machine learning model in the manner of claim 1 supports this solution (Reply p. 13-14). Applicant further argues that the use of the supervised learning in claim 1 is a particular solution to particular problems (Reply p. 14-15).
This argument has been fully considered but found to be not persuasive. The MPEP states at 2106.05(a) “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements… In addition, the improvement can be provided by the additional element(s) in combination with the recited judicial exception”. The determination of an improvement to technology has two steps, the identification of additional elements (which define the technology) and the evaluation of the additional elements to determine if the improvement is provided by or realized in the additional elements either by the additional elements themselves or the additional element in combination with the judicial exception (i.e. the interaction between the judicial exceptions and the additional elements). It is noted that the training of the machine learning model falls under the judicial exception itself because the human mind is capable of building a partition tree with features, pruning nodes of the partition tree, selecting candidate nodes in a partition tree, testing candidate nodes in a partition tree, and combining nodes of a partition tree. Further, the increased accuracy that allows for “detecting an etiological factor for the subject by providing the subject-genomic- data-object to the classifier” provides that the improvement is realized in the judicial exception itself of detecting an etiological factor in a patient and not in the recited additional elements of the claim. The additional elements in the claims are receiving data in a computer environment and using a computer to perform abstract ideas. The step of receiving data is insignificant extra solution activity of data gathering because this additional element only interacts with the judicial exceptions by providing data to the judicial exceptions to process. The generic computer environment only interacts with the judicial exceptions in a manner by being utilized as a tool to perform the judicial exceptions. The judicial exceptions and the computer do not interact in a manner where the computer itself functions in an improved manner. Although, the abstract idea being implemented on the computer may increase the accuracy of detecting etiological factors for a subject (which is an abstract idea of analyzing genetic data) this comes solely from the particular abstract ideas being performed rather than the computer itself functioning in an improved manner. Thus, the argued improvement is not provided by or realized in the additional elements of the claim and thus do not constitute as an improvement to technology.
Claim Rejections - 35 USC § 103
The rejection on the ground of 103 of claims 1-5, 13, 15-17, and 25 in Office action mailed 02 June 2025 is withdrawn in view of the amendment “wherein training of the machine learning model comprises organizing the first set of features and the second set of features into a partition tree that includes layers of nodes, each node representing a particular type of mutation and each child of the node representing possible mutations that are of the type of mutation in the node” and “generating, from the machine learning model, computer-readable instructions in computer memory comprising a classifier” received
.
Conclusion
No claims are 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.
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/J.E.H./Examiner, Art Unit 1685
/KAITLYN L MINCHELLA/Primary Examiner, Art Unit 1685