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
This action is in response to the amendments filed 08 October 2025. Claims 1, 7, 11, and 16 are amended. Claims 1-20 are pending and have been examined.
Response to Arguments
Applicant's arguments, see page 6, filed 08 October 2025, with respect to the rejections of Claims 7 and 16 under 35 U.S.C. 112(b) have been fully considered and are persuasive. The rejections of Claims 7 and 16 under 35 U.S.C. 112(b) have been withdrawn.
APPLICANT'S ARGUMENT: Applicant argues (page 6, paragraph 2) that "the claims have been amended so as to more particularly point out and distinctly claim the subject matter of the invention."
EXAMINER'S RESPONSE: Examiner agrees. The rejection has been withdrawn in light of arguments and/or amendments.
Applicant's arguments, see pages 6-8, filed 08 October 2025, with respect to the rejections of Claims 1-20 under 35 U.S.C. 101 have been fully considered but they are not persuasive.
APPLICANT'S ARGUMENT: Applicant argues (page 7, paragraph 1) that "the independent claims now recite specific technical steps that extend far beyond anything that could reasonably be considered a mental process. ... These operations involve probabilistic modeling, large-scale structured data manipulation, and vector computations that cannot practically be carried out by the human mind."
Applicant argues (page 7, paragraph 2) that "Even if certain aspects of the claims could be characterized as involving mathematical relationships, the claims do not merely recite formulas or generic computer implementation."
EXAMINER'S RESPONSE: Examiner respectfully disagrees. Amended Claim 1 recites mental process steps involving, in part, determining a score for decision trees, generating an collection of trees according to the scores, determining a distribution of trees according to the scores, and assigning a weight to the trees according to the distribution statistics. These steps appear to recite mental concepts -- such as observations, evaluations, and judgments – that may be performed in the human mind at the claimed level of generality, or with the aid of pen and paper.
Amended Claim 1 recites an additional element step of processing training observations with decision trees. This step appears to invoke the use of a generic computer or computing machinery merely as a tool to perform an abstract idea, rather than integrating the invention into a practical application or providing significantly more according to Step 2A Prong Two and Step Two of the Alice/Mayo test. See MPEP 2106.05(f). Since amended Claim 1 does not recite an additional element doing so, it recites ineligible subject matter.
APPLICANT'S ARGUMENT: Applicant argues (page 7, paragraph 2) that "The diversity array is a new data structure that captures per-observation and per-tree behavior in a way that conventional random forest training does not. ... Taken together, these features provide a structured, machine-implemented process that improves both the reliability and the interpretability of random forest predictions."
EXAMINER'S RESPONSE: Examiner respectfully disagrees. Amended Claim 1 appears to recite a mental process step of collecting decision trees according to a previously determined score. Applicant's argued improvement appears to represent an improvement in an abstract idea, which would result in an improved abstract idea but not necessarily an improvement in computers or technology.
Examiner further notes that, while amended Claim 1 recites an explainability measure, it does not currently appear to recite or reflect an improvement in reliability of random forests.
APPLICANT'S ARGUMENT: Applicant argues (page 8, paragraph 1) that "the claims recite significantly more than the mere application of a mathematical formula on a generic computer. They require a particular combination of technical features that is neither routine nor conventional.... These elements work together to provide a solution to the specific technical problem of opacity in ensemble machine learning models. They deliver a transparent and explainable prediction process that conventional systems do not provide."
EXAMINER'S RESPONSE: Examiner respectfully disagrees. Examiner notes that, in the rejection of amended Claim 1 its dependent claims under 35 U.S.C. 101 below, the claimed invention was not found to recite limitations that are well-understood, routine, or conventional. However, the claims were found not to positively recite an additional element or combination of elements providing either integration into a practical application or significantly more.
Applicant's arguments, see pages 8-10, filed 08 October 2025, with respect to the rejections of Claims 1-20 under 35 U.S.C. 103 have been fully considered and are persuasive. The rejections of Claims 1-20 under 35 U.S.C. 103 have been withdrawn.
APPLICANT'S ARGUMENT: Applicant argues (page 9, paragraph 1) that "Tsou describes weighting decision trees in a random forest using entropy-based measures. Although Tsou refers to generalized entropy as a basis for weighting trees, it does not disclose or suggest constructing a diversity array that tracks, on a per-observation basis, the tree with the lowest score. Tsou treats entropy as a global measure of uncertainty, not as a per-sample data structure."
Applicant argues (page 9, paragraph 2) that "Quadrianto applies the Dirichlet distribution to model posterior probabilities of class labels at the node level. It does not describe generating a diversity array that links training observations to the decision trees producing the lowest scores. ... Quadrianto's approach is focused on sampling trees from prior distributions, not on building a model of tree tendencies from diversity data."
Applicant argues (page 9, paragraph 3) that "Tsou provides no suggestion to generate a diversity array or to track tree-observation relationships in the manner claimed. Quadrianto, on the other hand, does not use a diversity array as input to a categorical model, but instead applies Bayesian updates at the node level. The claimed sequence ... is absent from both references and is not suggested by their combination."
Applicant argues (page 10, paragraph 1) that "the present claims are directed to enhancing the transparency and explainability of predictions from ensemble models. Neither reference recognizes this problem."
EXAMINER'S RESPONSE: Examiner notes that Applicant's arguments are now moot. The rejections of Claims 1-20 under 35 U.S.C. 103 have been withdrawn in view of arguments and/or amendments.
Claim Rejections - 35 USC § 112
The rejections of Claims 7 and 16 under 35 U.S.C. 112(b) is withdrawn in light of arguments and/or amendments.
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.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1
Claim 1 is ineligible.
Step 1
Claim 1 recites a method, and thus the claimed process falls within a statutory category of invention.
Step 2A Prong 1
The claim recites determining a diversity score from each of the decision trees for each of the training observations, which is a mental process. The claim recites generating a diversity array, wherein each entry in the diversity array includes an index of a decision tree that generated a lowest diversity score for a corresponding training observation, which is a mental process. The claim recites determining a tendency of each decision tree in the random forest based on the diversity array by building a Bayesian Dirichlet categorical model using the diversity array as input to compute a mode-based bias vector for each decision tree, which is a mental process. The claim recites weighting each of the decision trees based on the tendencies of the decision trees based on a complement of the bias vector for the decision tree, wherein decision trees associated with training observations that have higher diversity scores than training observations of other decision trees in the random forest are weighted more heavily, which is a mental process. The claim recites using the weighted decision trees to generate prediction outputs accompanied by an explainability measure that quantifies how representative each decision tree is of the training observations, which is a mental process.
Thus, the claim recites an abstract idea.
Step 2A Prong 2, Step 2B
The additional element running training observations through decision trees of a random forest invokes a computer or other machinery merely as a tool to perform an existing process (see MPEP 2106.05(f), "apply it").
The claim lacks additional elements that integrate it into a practical application or provide significantly more, so it is directed to an abstract idea and is ineligible.
Regarding Claim 2
Step 1
Regarding Claim 2, the rejection of Claim 1 is incorporated.
Step 2A Prong 1
Claim 2 recites the abstract ideas recited by parent Claim 1.
Step 2A Prong 2, Step 2B
The additional element training the decision trees of the random forest with the training observations invokes a computer or other machinery merely as a tool to perform an existing process (see MPEP 2106.05(f), "apply it").
The claim lacks additional elements that integrate it into a practical application or provide significantly more, so it is directed to an abstract idea and is ineligible.
Regarding Claim 3
Step 1
Regarding Claim 3, the rejection of Claim 1 is incorporated.
Step 2A Prong 1
The claim recites enriching each of the decision trees such that each node in each of the decision trees is associated with a set of ... observations that traversed the corresponding node, which is a mental process.
Thus, the claim recites an abstract idea.
Step 2A Prong 2, Step 2B
The additional element a set of training observations does not amount to more than generally linking the use of a judicial exception to a particular field of use (see MPEP 2106.05(h), "limit the use of the abstract idea to a particular technological environment").
The claim lacks additional elements that integrate it into a practical application or provide significantly more, so it is directed to an abstract idea and is ineligible.
Regarding Claim 4
Step 1
Regarding Claim 4, the rejection of Claim 1 is incorporated.
Step 2A Prong 1
The claim recites determining the tendency of each decision tree by building a categorical model configured to identify a bias of each of the decision trees, which is a mental process.
Thus, the claim recites an abstract idea.
Step 2A Prong 2, Step 2B
The claim lacks additional elements that integrate it into a practical application or provide significantly more, so it is directed to an abstract idea and is ineligible.
Regarding Claim 5
Step 1
Regarding Claim 5, the rejection of Claim 4 is incorporated.
Step 2A Prong 1
The claim recites determining the tendency of each decision tree by building a categorical model configured to identify a bias of each of the decision trees (as recited by Claim 4), wherein the categorical model is a Bayesian Dirichlet categorical model, the method comprising updating the Bayesian Dirichlet categorical model which is a mental process.
Thus, the claim recites an abstract idea.
Step 2A Prong 2, Step 2B
The claim lacks additional elements that integrate it into a practical application or provide significantly more, so it is directed to an abstract idea and is ineligible.
Regarding Claim 6
Step 1
Regarding Claim 6, the rejection of Claim 4 is incorporated.
Step 2A Prong 1
The claim recites constructing a weight vector, which is a mental process.
Thus, the claim recites an abstract idea.
Step 2A Prong 2, Step 2B
The additional element applying the weight vector to the decision trees invokes a computer or other machinery merely as a tool to perform an existing process (see MPEP 2106.05(f), "apply it").
The claim lacks additional elements that integrate it into a practical application or provide significantly more, so it is directed to an abstract idea and is ineligible.
Regarding Claim 7
Step 1
Regarding Claim 7, the rejection of Claim 6 is incorporated.
Step 2A Prong 1
The claim recites constructing a weight vector (as recited by Claim 6), wherein the weight vector is configured to give a higher weight to decision trees associated with ... observations that assigns, for each decision tree, a weight inversely proportional to a count of entries in the diversity array corresponding to that decision tree, which is a mental process.
Thus, the claim recites an abstract idea.
Step 2A Prong 2, Step 2B
The additional element training observations does not amount to more than generally linking the use of a judicial exception to a particular field of use (see MPEP 2106.05(h), "limit the use of the abstract idea to a particular technological environment").
The claim lacks additional elements that integrate it into a practical application or provide significantly more, so it is directed to an abstract idea and is ineligible.
Regarding Claim 8
Step 1
Regarding Claim 8, the rejection of Claim 1 is incorporated.
Step 2A Prong 1
The claim recites performing outlier detection on new observations that are not included in the ... observations, which is a mental process.
Thus, the claim recites an abstract idea.
Step 2A Prong 2, Step 2B
The additional element training observations does not amount to more than generally linking the use of a judicial exception to a particular field of use (see MPEP 2106.05(h), "limit the use of the abstract idea to a particular technological environment").
The claim lacks additional elements that integrate it into a practical application or provide significantly more, so it is directed to an abstract idea and is ineligible.
Regarding Claim 9
Step 1
Regarding Claim 9, the rejection of Claim 8 is incorporated.
Step 2A Prong 1
The claim recites detecting outliers at a time of prediction, which is a mental process.
Thus, the claim recites an abstract idea.
Step 2A Prong 2, Step 2B
The claim lacks additional elements that integrate it into a practical application or provide significantly more, so it is directed to an abstract idea and is ineligible.
Regarding Claim 10
Step 1
Regarding Claim 10, the rejection of Claim 8 is incorporated.
Step 2A Prong 1
The claim recites generating a final diversity score for each of the new observations by aggregating weighted diversity scores associated with the decision trees, which is a mental process.
Thus, the claim recites an abstract idea.
Step 2A Prong 2, Step 2B
The claim lacks additional elements that integrate it into a practical application or provide significantly more, so it is directed to an abstract idea and is ineligible.
Regarding Claim 11
Claim 11 is ineligible.
Step 1
Claim 11 recites a non-transitory storage medium having stored therein instructions that are executable by one or more hardware processors to perform operations, and thus the claimed manufacture falls within a statutory category of invention.
Step 2A Prong 1
The claim recites determining a diversity score from each of the decision trees for each of the training observations, which is a mental process. The claim recites generating a diversity array, wherein each entry in the diversity array includes an index of a decision tree that generated a lowest diversity score for a corresponding training observation, which is a mental process. The claim recites determining a tendency of each decision tree in the random forest based on the diversity array by building a Bayesian Dirichlet categorical model using the diversity array as input to compute a mode-based bias vector for each decision tree, which is a mental process. The claim recites weighting each of the decision trees based on the tendencies of the decision trees based on a complement of the bias vector for the decision tree, wherein decision trees associated with training observations that have higher diversity scores than training observations of other decision trees in the random forest are weighted more heavily, which is a mental process. The claim recites the explainability indicator identifying whether the prediction is supported by decision trees representative of the training observations, which is a mental process.
Thus, the claim recites an abstract idea.
Step 2A Prong 2
The additional element running training observations through decision trees of a random forest invokes a computer or other machinery merely as a tool to perform an existing process (see MPEP 2106.05(f), "apply it"). The additional element outputting, for each prediction generated by the random forest, an explainability indicator derived from the diversity array and the bias vector amounts to insignificant extra-solution activity (see MPEP 2106.05(g), "mere data gathering").
Step 2B
The additional element running training observations through decision trees of a random forest invokes a computer or other machinery merely as a tool to perform an existing process (see MPEP 2106.05(f), "apply it"). The additional element outputting, for each prediction generated by the random forest, an explainability indicator derived from the diversity array and the bias vector is well-understood, routine, conventional activity (see MPEP 2106.05(d), "receiving or transmitting data over a network").
The claim lacks additional elements that integrate it into a practical application or provide significantly more, so it is directed to an abstract idea and is ineligible.
Claims 12-19, dependent on Claim 11, incorporate the rejection of Claim 11. Claims 12-19 incorporate substantively all the limitations of Claims 2-4 and 6-10, respectively and are rejected under the same rationale.
Regarding Claim 20
Step 1
Regarding Claim 20, the rejection of Claim 14 is incorporated.
Step 2A Prong 1
The claim recites determining the tendency of each decision tree by building a categorical model configured to identify a bias of each of the decision trees (as recited by Claim 14), wherein the categorical model is a Bayesian Dirichlet categorical model, which is a mental process. The claim recites performing continuous learning using new observations, which is a mental process.
Thus, the claim recites an abstract idea.
Step 2A Prong 2, Step 2B
The claim lacks additional elements that integrate it into a practical application or provide significantly more, so it is directed to an abstract idea and is ineligible.
Claim Rejections - 35 USC § 103
The rejections of Claims 1-20 under 35 U.S.C. 103 have been withdrawn in view of arguments and/or amendments.
Conclusion
Claims 1-20 are rejected only under 35 U.S.C. 101 and are not rejected under 35 U.S.C. 102. A complete search of Claims 1-20 did not uncover any prior art that teaches or fairly suggests:
1. A method, comprising:
running training observations through decision trees of a random forest;
determining a diversity score from each of the decision trees for each of the training observations;
generating a diversity array, wherein each entry in the diversity array includes an index of a decision tree that generated a lowest diversity score for a corresponding training observation;
determining a tendency of each decision tree in the random forest based on the diversity array by building a Bayesian Dirichlet categorical model using the diversity array as input to compute a mode-based bias vector for each decision tree;
weighting each of the decision trees based on the tendencies of the decision trees based on a complement of the bias vector for the decision tree, wherein decision trees associated with training observations that have higher diversity scores than training observations of other decision trees in the random forest are weighted more heavily; and
using the weighted decision trees to generate prediction outputs accompanied by an explainability measure that quantifies how representative each decision tree is of the training observations.
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 ROBERT N DAY whose telephone number is (703)756-1519. The examiner can normally be reached M-F 9-5.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kakali Chaki can be reached at (571) 272-3719. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/R.N.D./Examiner, Art Unit 2122
/KAKALI CHAKI/Supervisory Patent Examiner, Art Unit 2122
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