DETAILED ACTION
This action is in response to the claims filed 07/01/2026 for Application number 18/470,233. Claims 1, 2, 11, and 12 have been amended. Thus, claims 1-20 are currently pending.
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 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,
Step 1 Analysis: Claim 1 is directed to a process, which falls within one of the four statutory categories.
Step 2A Prong 1 Analysis: Claim 1 recites, in part, The limitations of:
determining, [by the arbitrariness prediction computer program], a number of dropout models for the trained machine learning model to generate can be considered to be an evaluation in the human mind
determining, [by the arbitrariness prediction computer program], an arbitrariness for each of the trained machine learning models based on the outputs. can be considered to be an evaluation in the human mind
selecting, [by the arbitrariness prediction computer program], one of the trained machine learning models based on the arbitrariness. can be considered to be an evaluation in the human mind
These limitations as drafted, are processes that, under broadest reasonable interpretation, covers performance of the limitation in the mind or with the aid of pen and paper which falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 Analysis: This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements - “arbitrariness prediction computer program”, “a trained machine learning model”, and “creating, by the arbitrariness prediction computer program and for each of the trained machine learning models, the number of dropout models”. Thus, these elements in the claim are recited at a high level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP 2106.05(f). Additionally, the claim recites the additional element – “wherein the number of dropout models is based on computational resources and a time budget”. This element that is recited is only generally linked to the judicial exception. Please see MPEP 2106.05(h). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
The claim further recites: receiving, by arbitrariness prediction computer program, a trained machine learning model, wherein the trained machine learning model comprises a plurality of nodes, and each node has a weight
providing, by the arbitrariness prediction computer program, sample data to each of the dropout models;
receiving, by the arbitrariness prediction computer program, an output from each of the dropout models;
These limitations amount to mere data gathering and outputting steps and thus are insignificant extra-solution activities. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim as a whole is directed to an abstract idea.
Step 2B Analysis: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of utilizing an arbitrariness prediction computer program and a trained machine learning model to perform the steps of the claimed process amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Additionally, the limitation of wherein the number of dropout models is based on computational resources and a time budget amounts to generally linking the additional element to the judicial exception.
Furthermore, the limitations of:
receiving, by arbitrariness prediction computer program, a trained machine learning model, wherein the trained machine learning model comprises a plurality of nodes, and each node has a weight
providing, by the arbitrariness prediction computer program, sample data to each of the dropout models;
receiving, by the arbitrariness prediction computer program, an output from each of the dropout models;
are well-understood, routine, and conventional, as evidenced by MPEP §2106.05(d)(II)(I), “receiving or transmitting data over a network”. These limitations therefore remain insignificant extra-solution activity even upon reconsideration, and does not amount to significantly more. Even when considered in combination, these additional elements amount to mere instructions to apply the exception using generic computer components, generally linking the additional element to the judicial exception and insignificant extra-solution activity, which cannot provide an inventive concept. The claim is not patent eligible.
Regarding claim 2, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein each of the trained machine learning model comprises a neural network. This limitation amounts to generally linking the judicial exception to a field of use or technological environment. Please see MPEP 2106.05(h).
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 3, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the number of dropout models to generate is received as a parameter. This limitation amounts to generally linking the judicial exception to a field of use or technological environment. Please see MPEP 2106.05(h).
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 4, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the step of creating the number of dropout models comprises: removing, by the arbitrariness prediction computer program, a number or percentage of the plurality of nodes from each of the dropout models. This limitation amounts to mere instructions to apply the judicial exception using a generic computer component. Please see MPEP 2106.05(f).
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 5, the rejection of claim 4 is further incorporated, and further, the claim recites: wherein the number or percentage of the plurality of nodes are removed by setting the weights for the number or percentage of the plurality of nodes to zero. This limitation amounts to mere instructions to apply the judicial exception using a generic computer component. Please see MPEP 2106.05(f).
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 6, the rejection of claim 4 is further incorporated, and further, the claim recites: wherein the plurality of nodes to remove from each of the dropout models are randomly selected. This limitation amounts to more specifics of the judicial exception identified in the rejection of claim 1 above.
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 7, the rejection of claim 4 is further incorporated, and further, the claim recites: wherein the number or the percentage of nodes to remove is received as a parameter. This limitation amounts to mere data gathering thus is an insignificant extra-solution activity.
The claim does not include any additional elements that amount to significantly more than the judicial exception. This limitation is just a nominal or tangential addition to the claim, and is also well-understood, routine and conventional as evidenced by MPEP §2106.05(d)(II)(I), “receiving or transmitting data over a network”. This limitation therefore remains insignificant extra-solution activity even upon reconsideration, and does not amount to significantly more. Even when considered in combination, this additional element represents an insignificant extra-solution activity which cannot provide an inventive concept. The claim is not patent eligible.
Regarding claim 8, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the step of creating the number of dropout models comprises: multiplying, by the arbitrariness prediction computer program, the weights for the plurality of nodes with Gaussian noise having a unit mean and a variance. This claim recites additional mathematical steps in addition to the judicial exception identified in the rejection of claim 1, thus recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 9, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the arbitrariness is a ratio of outputs of the dropout models that are the same over the number of dropout models. This limitation amounts to more specifics of the judicial exception identified in the rejection of claim 1 above.
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 10, the rejection of claim 4 is further incorporated, and further, the claim recites: providing, by the arbitrariness prediction computer program, a second sample to the dropout models; and receiving, by the arbitrariness prediction computer program, second outputs from each of the dropout models for the second sample; wherein the arbitrariness is based on the outputs and the second outputs. These limitations amount to mere data gathering and outputting thus is an insignificant extra-solution activity.
The claim does not include any additional elements that amount to significantly more than the judicial exception. These limitations are just nominal or tangential additions to the claim, and are also well-understood, routine and conventional as evidenced by MPEP §2106.05(d)(II)(I), “receiving or transmitting data over a network”. These limitations therefore remain insignificant extra-solution activity even upon reconsideration, and does not amount to significantly more. Even when considered in combination, these additional elements represent an insignificant extra-solution activity which cannot provide an inventive concept. The claim is not patent eligible.
Claim 11 recites features similar to claim 1 and is rejected for at least the same reasons therein. Claim 11 additionally requires analysis for “A non-transitory computer readable storage medium, including instructions stored thereon, which when read and executed by one or more computer processors, cause the one or more computer processors to perform steps comprising…” however this is an additional element that amounts to mere instructions to apply the judicial exception using a generic computer component. Please see MPEP 2106.05(f).
Regarding Claims 12-20, it recites features similar to claims 2-10 and are rejected for at least the same reasons therein.
Allowable Subject Matter
Claims 1-20 are objected to as being allowable over prior art if all outstanding rejections were withdrawn. None of the prior art, either alone or in combination, fairly discloses limitations of claims 1 and 11 in particular:
determining, by the arbitrariness prediction computer program, a number of dropout models for each of the trained machine learning models to generate, wherein the number of dropout models is based on computational resources and a time budget;
creating, by the arbitrariness prediction computer program and for each of the trained machine learning models, the number of dropout models; providing, by the arbitrariness prediction computer program, sample data to each of the dropout models
determining, by the arbitrariness prediction computer program, an arbitrariness for each of the trained machine learning models based on the outputs; and
selecting, by the arbitrariness prediction computer program, one of the trained machine learning models based on the arbitrariness.
No prior art was uncovered which fairly discloses all of the limitations of claims 1 and 11.
The closest prior art of record is Hsu et al. (“Rashomon Capacity: A Metric for Predictive Multiplicity in Probabilistic Classification”) which discloses Predictive multiplicity which captures potential individual harm introduced by an arbitrary choice of a single model however the reference does not explicitly teach determining, by the arbitrariness prediction computer program, an arbitrariness for each of the trained machine learning models based on the outputs; and
selecting, by the arbitrariness prediction computer program, one of the trained machine learning models based on the arbitrariness.
Lemay et al. (“Improving the repeatability of deep learning models with Monte Carlo dropout”) discloses a dropout method however does not specifically disclose the steps of determining, by the arbitrariness prediction computer program, an arbitrariness for each of the trained machine learning models based on the outputs; and
selecting, by the arbitrariness prediction computer program, one of the trained machine learning models based on the arbitrariness.
Response to Arguments
Applicant’s arguments, see pgs. 10-14, filed 07/01/2026, with respect to claims 1 and 11 have been fully considered and are persuasive. The 35 U.S.C. 103 Rejection of claims 1-20 has been withdrawn in light of applicant’s amendments.
Regarding the 35 U.S.C. §101 Rejection:
Applicant appears to assert that the claims integrate any alleged judicial exception into a practical application by using the output of a plurality of dropout models to determine arbitrariness of a plurality of trained machine learning model and using the arbitrariness to select one of the trained machine learning model. Examiner respectfully disagrees. The claims as currently recited appear to be directed towards an abstract idea. Merely using outputs of the dropout models to make a determination and making a selection of a model based off the arbitrariness can be considered to be evaluations performable in the human mind. There are no details in the claims which reflect any improvement in the actual training process or an existing process in the technical field of ML or operation of a ML model rather the claims merely generically recite the use of ML models as tools to perform the steps of the claimed process. Furthermore, the examiner suggests adding additional details to the claims regarding performing dropout at test time by providing a pre-trained model as noted in ¶0033 would reflect the improvement by reducing the time needed to sample models via re-training.
Conclusion
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 MICHAEL H HOANG whose telephone number is (571)272-8491. The examiner can normally be reached Mon-Fri 8:30AM-4:30PM.
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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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/MICHAEL H HOANG/PRIMARY EXAMINER, Art Unit 2122
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