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 application filed 27 March 2026. Claims 1, 2, 10-12, 17, and 19 are amended. Claims 1-20 are pending and have been examined.
Continued Examination Under 37 CFR 1.114
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 27 March 2026 has been entered.
Response to Arguments
Applicant's arguments, see pages 7-10, filed 27 March 2026, 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 8, paragraph 1) that "The claims recite a specific, computer-implemented sequence of operations that depends on structured data representations and numerical computations that cannot practically be performed in the human mind. ... The claims further require computing local diversity scores at nodes traversed by each observation using those stored probability distribution parameters and aggregating those local scores into a diversity score. ... The evaluation of probability distributions across potentially large numbers of nodes and the aggregation of such values into diversity scores is not a mental process, but rather a concrete computational procedure that requires stored data structures and numerical processing."
EXAMINER'S RESPONSE: Examiner respectfully disagrees. At the claimed levels of generality, the recited mental process steps of amended Claim 1 identified in the 35 U.S.C. 101 rejection below appear to be performable in the human mind or with the aid of pen and paper, similarly to an observation, evaluation, judgment, or opinion. Examiner notes that the Applicant's argued "large numbers of nodes" are not recited in the rejected claim.
Examiner notes that Applicant's argued "computer-implemented sequence of operations" does not appear to be recited by amended Claim 1, and further notes that mere recitation of generic computing equipment or a computing environment does not provide a practical application for mental process. See MPEP 2106.04(a)(2)(III)(c). The additional element steps of amended Claim 1 that pertain to training of the model appear to invoke computing machinery merely to perform a mental process. See MPEP 2106.05(f).
APPLICANT'S ARGUMENT: Applicant argues (page 8, paragraph 2) that "aggregating data across the training set and performing probabilistic modeling using a Dirichlet framework ... cannot practically be performed mentally at the claimed scale."
EXAMINER'S RESPONSE: Examiner respectfully disagrees that, as currently recited, amended Claim 1 recites performance of the claimed steps at a particular scale. Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims.
APPLICANT'S ARGUMENT: Applicant argues (page 9, paragraph 2) that "The claims are directed to a specific improvement in the operation of machine learning models, particularly random forest models, by implementing a closed-loop training and retraining process based on quantified measures of how representative an observation is relative to the training data. The specification explains that conventional random forests are opaque and lack mechanisms for assessing whether predictions are supported by representative training data, which limits trust and reliability. The claimed invention addresses this technical problem."
Applicant argues (page 9, paragraph 3) that "the claims provide a mechanism for identifying unreliable predictions and reducing the influence of decision trees that behave anomalously. The use of the final diversity score to trigger retraining further improves the robustness of the model by incorporating new observations that would otherwise fall outside the distribution of the training data. ... This constitutes a concrete improvement in how the model operates and produces predictions."
EXAMINER'S RESPONSE: Examiner respectfully disagrees. Amended Claim 1 recites additional element steps of training decision trees of a random forest and generating predictions using the random forest. As currently recited, the steps appear merely to recite use of computing machinery to perform a mental process.
Examiner further disagrees that amended Claim 1 recites Applicant's argued "closed-loop training and retraining process." The newly recited limitation of "retraining the random forest ... when the final diversity score indicates" is interpreted as a contingent step per MPEP 2111.04(II):
The broadest reasonable interpretation of a method (or process) claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met.
Therefore, under BRI, Applicant's argued improvements resulting from a training and retraining process are not recited or reflected by amended Claim 1, given that the retraining step is not required to be performed by the claimed method.
Assuming that the newly recited retraining step were not interpreted to be contingent, "retraining the random forest using the new observation" would be considered to amount to an insignificant extra-solution activity step of necessary data gathering and outputting under Step 2A Prong 2 analysis of the Alice/May framework. The retraining step would be considered a well-understood, routine, or conventional activity under Step 2B analysis, as supported by Cheng, et al. (US 2013/0159226 A1).
APPLICANT'S ARGUMENT: Applicant argues (page 10, paragraph 1) that "The additional elements are not generic or routine computer functions, but instead form a specific and unconventional combination of steps.... When considered as an ordered combination, these elements provide a specific technical solution that transforms how random forest models operate and adapt over time, and therefore amount to significantly more than any judicial exception."
EXAMINER'S RESPONSE: Examiner respectfully disagrees. As currently recited, the additional element steps of training and using the random forest model appear to invoke a computer or other machinery merely as a tool to perform a mental process under Step 2B of the Alice/Mayo framework. Thus, the steps do not provide significantly more when taken singly or in combination. As indicated above, the step of retraining the random forest is interpreted under BRI as a contingent limitation, and is thus not a required step of the method.
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 associating nodes of each decision tree with stored probability distribution parameters derived from training observations that traversed the nodes, which is a mental process. The claim recites determining a diversity score from each of the decision trees for each of the training observations by computing, at nodes traversed by each training observation, local diversity scores based on the stored probability distribution parameters and aggregating the local diversity scores into the diversity score, 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 based on counts of occurrences of the indices in the diversity array, 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 to construct a weight vector applied in a weighted voting probability model, 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 computing a final diversity score for a new observation by performing a matrix-vector multiplication between (i) a matrix of diversity scores for the new observation across the decision trees and (ii) the weight vector, which is a mental process. The claim recites determining whether the new observation is an outlier based on the final diversity score, which is a mental process.
Thus, the claim recites an abstract idea.
Step 2A Prong 2
The additional element training decision trees of a random forest using 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 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 running a new observation through the weighted decision trees to generate a prediction 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 retraining the random forest using the new observation when the final diversity score indicates that the new observation is an outlier relative to the training observations is interpreted to be a contingent limitation per MPEP 2111.04, and is thus given no patentable weight. If the additional element step were not interpreted as contingent, retraining the random forest using the new observation would be considered to amount to insignificant extra-solution activity (see MPEP 2106.05(g), "necessary data gathering and outputting").
Step 2B
The additional element training decision trees of a random forest using 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 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 running a new observation through the weighted decision trees to generate a prediction 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 retraining the random forest using the new observation when the final diversity score indicates that the new observation is an outlier relative to the training observations is interpreted to be a contingent limitation per MPEP 2111.04, and is thus given no patentable weight. If the additional element step were not interpreted as contingent, retraining the random forest using the new observation would be considered well-understood, routine, conventional activity (see MPEP 2106.05(d), as supported by Cheng, et al., US 2013/0159226 A1, [0005]: "When a set of sample data is newly collected, a conventional skill uses the newly-collected sample data for model refreshing, so as to refresh or retrain the prediction models. Therefore, the prediction accuracy of the prediction models is closely related to the historical sample data and the new sample data collected during an on-line model-refreshing phase").
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
The claim recites generating probability distribution parameters at nodes based on the training observations, which is a mental process.
Thus, the claim recites an abstract idea.
Step 2A Prong 2, Step 2B
The additional element training the decision trees of the 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 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 computing a final diversity score for a new observation ... (as recited by Claim 1) wherein the final diversity score for each of the new observations is generated 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 associating nodes of each decision tree with stored probability distribution parameters derived from training observations that traversed the nodes, which is a mental process. The claim recites determining a diversity score from each of the decision trees for each of the training observations by computing, at nodes traversed by each training observation, local diversity scores based on the stored probability distribution parameters and aggregating the local diversity scores into the diversity score, 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 based on counts of occurrences of the indices in the diversity array, 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 to construct a weight vector applied in a weighted voting probability model, 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 computing a final diversity score for a new observation by performing a matrix-vector multiplication between (i) a matrix of diversity scores for the new observation across the decision trees and (ii) the weight vector, which is a mental process. The claim recites determining whether the new observation is an outlier based on the final diversity score, which is a mental process.
Thus, the claim recites an abstract idea.
Step 2A Prong 2
The additional element training decision trees of a random forest using 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 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 running a new observation through the weighted decision trees to generate a prediction 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 retraining the random forest using the new observation when the final diversity score indicates that the new observation is an outlier relative to the training observations is interpreted to be a contingent limitation per MPEP 2111.04, and is thus given no patentable weight. If the additional element step were not interpreted as contingent, retraining the random forest using the new observation would be considered to amount to insignificant extra-solution activity (see MPEP 2106.05(g), "necessary data gathering and outputting").
Step 2B
The additional element training decision trees of a random forest using 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 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 running a new observation through the weighted decision trees to generate a prediction 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 retraining the random forest using the new observation when the final diversity score indicates that the new observation is an outlier relative to the training observations is interpreted to be a contingent limitation per MPEP 2111.04, and is thus given no patentable weight. If the additional element step were not interpreted as contingent, retraining the random forest using the new observation would be considered well-understood, routine, conventional activity (see MPEP 2106.05(d), as supported by Cheng, et al., US 2013/0159226 A1, [0005]: "When a set of sample data is newly collected, a conventional skill uses the newly-collected sample data for model refreshing, so as to refresh or retrain the prediction models. Therefore, the prediction accuracy of the prediction models is closely related to the historical sample data and the new sample data collected during an on-line model-refreshing phase").
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 12-16, dependent on Claim 11, incorporate the rejection of Claim 11. Claims 12-16 incorporate substantively all the limitations of Claims 2-4, 6, and 7, respectively and are rejected under the same rationale.
Regarding Claim 17
Step 1
Regarding Claim 17, the rejection of Claim 11 is incorporated.
Step 2A Prong 1
The claim recites determining whether the new observation is an outlier based on the final diversity score (as recited by Claim 11), wherein determining whether the new observation is an outlier comprising performing an 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.
Claims 18 and 19, dependent on Claim 11, incorporate the rejection of Claim 11. Claims 18 and 19 incorporate substantively all the limitations of Claims 9 and 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.
Conclusion
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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/R.N.D./Examiner, Art Unit 2122
/KAKALI CHAKI/Supervisory Patent Examiner, Art Unit 2122
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