DETAILED ACTION
This action is in response to claims filed 13 May 2026 for application 18353572 filed 17 July 2023. Currently claims 1-20 are 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 § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 3-5, 9-11, 13-15, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Huge et al. (Differential Machine Learning) in view of Chen et al. (US 20080228680 A1).
Regarding claims 1, 11 and 20, Huge discloses: A method comprising:
identifying, by a framework and based on a model script, a model that generates an output based on a set of inputs, wherein the inputs include a plurality of parameters (“The evaluation of the twin network returns a predicted value y, and its differentials ¯x wrt the n0 = n inputs x” p6 §1.2 ¶3) p7, Fig 2 input x and output after first feedforward);
selecting, by the framework, a first plurality of parameter values (Fig 2 inputs z, see also p21 §2.1 d learnable parameters);
assembling, by the framework, a set of training samples by observing outputs generated by the model in response to each of the first plurality of parameter values
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(p8 last ¶);
training, by the framework and based on the set of training samples, a surrogate model, wherein the surrogate model is trained to predict outputs of the model (p8 last ¶, Fig 2, the second model of the twin model is interpreted as the surrogate model, it is trained with differentials);
generating, by the framework and using the surrogate model, predicted outputs of the model, wherein each of the predicted outputs of the model is based on a different parameter value in a second plurality of parameter values (Fig 2 multiple outputs of the model);
selecting, by the framework and based on the predicted outputs of the model, a desired parameter value (“The twin network, therefore, predicts prices and risk sensitivities for twice the computation complexity of value prediction alone, irrespective of the number of risks. Hence, a trained twin net approximates prices and risk sensitivities, wrt potentially many states, in a particularly efficient manner.” P7).
Huge does not explicitly disclose, however, Chen teaches: after selecting the desired parameter value, applying the model, using the desired parameter value, to predict a value of interest for an input value (“A method that comprises: determining a system's response to a limited set of input parameter values; deriving a system model that predicts the system's response over a larger set of input parameter values, wherein the system model includes a neural network ensemble comprising multiple local neural network ensembles, each local neural network ensemble selected from a corresponding set of neural network ensembles developed based on a particular weighting for a multi-objective function, wherein said multi-objective function is evaluated based on a first portion of the limited set of input parameter values, and wherein said selection is made based on input parameter values held out from said first portion; and storing or displaying a system response predicted by the system model.” Claim 17).
Huge and Chen are in the same field of endeavor of machine learning models and are analogous. Huge discloses a differential machine learning system with a surrogate model. Chen teaches selecting a desired parameter value and applying a model from a plurality of models to that input. It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the known differential ML system of Huge to apply a model of models to a selected input as taught by Chen to yield predictable results of making better use of sparse data.
Regarding claims 3 and 13, Huge discloses: The method of claim 1, wherein selecting the first plurality of parameter values includes:
performing adaptive sampling to mitigate effects caused by variances across the first plurality of parameter values (“In low dimension, the training states X(i) may be put on a regular grid over a relevant domain. In higher dimension, they may be sampled over a relevant domain with a low discrepancy sequence like Sobol. When the exposure date T1 is today or close, sampling XT1 with Monte-Carlo is nonsensical, an appropriate sampling distribution must be applied depending on context” p19 §Training inputs, note: Sobol sequence is a quasi-MonteCarlo method which is used to achieve variance reduction).
Regarding claims 4 and 14, Huge discloses: The method of claim 1, wherein assembling the set of training samples includes:
assembling training samples that each include a state value, a parameter value of the first plurality of parameter values, the observed output value, and a derivative of the observed output value with respect to the parameter value (p3 last ¶ - p4 ¶2 disclose a an input x having a parameter value and a state value, an observed output y and a derivative dy/dx).
Regarding claims 5 and 15, Huge discloses: The method of claim 1, wherein training the surrogate model includes:
training a deep neural network using least square regression regularized with derivatives of the observed output values with respect to parameter values (p35 the method used is a least square linear regression using differentials).
Regarding claims 9 and 19, Huge discloses: The method of claim 1, wherein selecting the desired parameter value includes:
selecting an optimal parameter value from the second plurality of parameter values, wherein the optimal parameter value tends to maximize the output from the model (§2.1, 2.2 and 2.3 disclose various methods of selecting parameters for training to achieve the desired result, interpreted as maximizing correct output from the model).
Regarding claim 10, Huge discloses: The method of claim 1, wherein predicting the value of interest includes:
predicting a payoff of an interest rate option contract (p2 §online approximation with sampled payoffs).
Claim(s) 2 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Huge in view of Lock et al. (US 7627543).
Regarding claims 2 and 12, Huge does not explicitly disclose, however, Lock teaches: The method of claim 1, further comprising:
receiving, by a control system, the value of interest; interpreting, by the control system, the value of interest to determine an action to take; and outputting, by the control system and over a network to a downstream system, a control signal to control the operation of the downstream system (“, facts of interest and functions for calculating values of interest from items of data, d) evaluating the more specific rule generalisation by applying it to the training data set to identify vulnerabilities, and e) incorporating the more specific rule generalisation in the rule set if it classifies vulnerabilities in the training data set adequately in terms of covering at least some of the positive vulnerability examples, f) applying the rule set to a test program for vulnerability detection therein, and g) providing an alert or a report to a user regarding vulnerability detection in the test program resulting from operation of the method in order to enable corrective action to be taken.” Claim 11).
Huge, Chen and Lock are in the same field of endeavor of machine learning and are analogous. Huge discloses a differential machine learning system with a surrogate model. Chen teaches selecting a desired parameter value and applying a model from a plurality of models to that input. Lock teaches exemplary machine learning which responds to a value of interest with an action. It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the known differential ML system of Huge and Chen to take an action in response to a value as taught by the known system of Lock to yield predictable results of taking automated actions to received data.
Claim(s) 6-8 and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Lock in view of Dushatskiy et al. (A novel surrogate-assisted evolutionary algorithm applied to partition based ensemble learning).
Regarding claims 6 and 16, Huge does not explicitly disclose, however, Dushatskiy teaches: The method of claim 1, wherein training the surrogate model includes:
training a plurality of surrogate models, where each surrogate model is trained starting with a different random seed (Algorithm 2 and §2.4, multiple surrogate models are trained in parallel and evaluated for fitness, a best surrogate model is selected, “when the same dataset partitioning can result in different ensemble accuracy scores because learners in ensembles are initialized with different random seeds.” P589 ¶1).
Huge, Chen and Dushatskiy are in the same field of endeavor of machine learning and are analogous. Huge discloses a differential machine learning system with a surrogate model. Chen teaches selecting a desired parameter value and applying a model from a plurality of models to that input. Dushatskiy teaches a system for generating a plurality of parallel surrogate models from a random seed and selecting the best one. It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the known differential ML system of Huge and Chen to include multiple differential models to select the best one to yield predictable results of optimizing the surrogate model.
Regarding claims 7 and 17, Huge does not explicitly disclose, however, Dushatskiy teaches: The method of claim 6, wherein generating predicted outputs of the model includes:
executing each of the surrogate models in parallel to generate different sets of predicted outputs of the model (Algorithm 2 and §2.4, multiple surrogate models are trained in parallel and evaluated for fitness, a best surrogate model is selected).
Regarding claims 8 and 18, Huge does not explicitly disclose, however, Dushatskiy teaches: The method of claim 7, wherein selecting a desired parameter value includes:
selecting a desired one of the plurality of surrogate models based on an assessment of the robustness of the predicted outputs generated by each of the plurality of surrogate models (Algorithm 2 and §2.4, multiple surrogate models are trained in parallel and evaluated for fitness, a best surrogate model is selected); and
selecting the desired parameter value based on the predicted outputs generated by the desired surrogate model (Algorithm 2 and §2.4, multiple surrogate models are trained in parallel and evaluated for fitness, a best surrogate model is selected).
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ERIC NILSSON whose telephone number is (571)272-5246. The examiner can normally be reached M-F: 7-3.
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/ERIC NILSSON/ Primary Examiner, Art Unit 2151
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