DETAILED ACTION
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Han et al. (US 2022/0366439 A1), hereinafter “Han”, and in view of Jordan et al. (US 2021/0182690 A1), hereinafter “Jordan”.
As per claim 1, Han teaches a system comprising: at least on data processor; and at least one memory storing instructions, which when executed by the at least one processor result in operations comprising:
“binning input samples in a first dimension associated with a first predictor of an outcome based at least on a sample minimum; binning in input samples in a second dimension associated with a second predictor of the outcome based at least on binning the input samples in the first dimension” at [0015], [0039]-[0043] and Fig. 3;
(Han teaches segmenting a group of objects based on two sets of metrics. The group of objects is segmented based on the first metric (i.e., “first dimension”). Then the segmented group objects are further segmented based on a second metric (i.e., “second dimension”). The first metric is associated with a first predictor to predict loan default risk for the objects in each segment, and the second metric is associated with a second predictor to predict loan default risk for each segment)
“determining a two-dimensional risk pattern based at least on a first one-dimensional risk pattern associated with the first predictor along the first dimension and a second one dimensional risk pattern associated with the second predictor along the second dimension” at [0016], [0029];
(Han teaches obtaining the first risk metric and a second risk metric, segmenting the entities based on the first risk metric and the second risk metric)
“comparing a first divergence of a first machine learning model to a second divergence of a second machine learning model” at [0048]-[0051];
(Han teaches determining a number of segments of the plurality of segment to maximize a divergence in credit risk by determining divergence metric of reach segment and comparing the divergence between neighboring segments)
“wherein the first machine learning model is trained to generate a first output based at least on the first predictor, the first one-dimensional risk pattern associated with the first predictor, the second predictor, the second one-dimensional risk pattern associated with the second predictor, and a baseline score generated based on the input samples and wherein the second machine learning model is trained to generate a second output based at least one the baseline score and a cross-effect term including the two-dimensional risk pattern” at [0022],[0037], [0051]-[0080];
(Han teaches a first machine learning model is trained to generate a first output/prediction based on first metric associated with first predictor and second matric associated with a second predictor, wherein the first and second metrics represent risk patterns associated with the first and the second predictor. The divergence metric (i.e., “baseline score”) is
Han does not teach “predicting a strength of an interaction effect between the first predictor and the second predictor based on the comparison, wherein the strength of the interaction effect indicates a marginal contribution of the interaction between the first predictor and the second predictor to at least the second output” as claimed. However, Jordan teaches a method for optimizing neural networks for generating predictive output including the steps of “predicting a strength of an interaction effect between the first predictor and the second predictor based on the comparison, wherein the strength of the interaction effect indicates a marginal contribution of the interaction between the first predictor and the second predictor to at least the second output” at [0029]-[0036].
Thus, it would have been obvious to one of ordinary skill in the art to combine Jordan with Han’s teaching in order to provide an “optimized neural network can be used both for accurately determining response variables using predictor variables, which indicates an effect or an amount of impact that a given predictor variable has on the response variable”, as suggested by Han at [0004].
As per claim 2, Han and Jordan teach the system of claim 1 discussed above. Han also teaches: wherein “the operations further comprise: generating a visualization representing the strength of a plurality of interaction effect between a plurality of predictor, wherein the strength of the plurality of interaction effects includes the strength of the interaction effect between the first predictor and the second predictor, and wherein the plurality of predictors includes the first predictor and the second predictor” at [0044]-[0053] and Figs. 3-5.
As per claim 3, Han and Jordan teach the system of claim 2 discussed above. Han also teaches: wherein “the visualization includes at least one of a paragraph, a heat map, a tabulation, and a matrix” at [0044]-[0053] and Figs. 3-5.
As per claim 4, Han and Jordan teach the system of claim 2 discussed above. Han also teaches: wherein “the visualization includes a ranking of strength of the plurality of interaction effects” at [0044]-[0053] and Figs. 3-5.
As per claim 5, Han and Jordan teach the system of claim 1 discussed above. Han also teaches: wherein “the sample minimum indicates a minimum quantity of samples associated with a corresponding label that are included in each bin in the first dimension, and wherein input samples are binned in the first dimension such that each bin in the first dimension includes a quantity of samples associated with the corresponding label that is greater than or equal to the minimum quantity” at [0039]-[0045].
As per claim 6, Han and Jordan teach the system of claim 5 discussed above. Han also teaches: wherein “the input samples are binned in the second dimension according to bin breaks separating each bin determined during binning the input samples in the first dimension” at [0039]-[0049].
As per claim 7, Han and Jordan teach the system of claim 1 discussed above. Han also teaches: wherein “the first one-dimensional risk pattern includes at least one of increasing, decreasing, concave, and convex, and wherein the second one-dimensional risk pattern includes at least one of increasing, decreasing, concave, and convex” at [0039]-[0049].
As per claim 8, Han and Jordan teach the system of claim 1 discussed above. Han also teaches: wherein “the first one-dimensional risk pattern and the second one-dimensional risk pattern are applied as separate constraint on the first machine learning model, wherein the two-dimensional risk pattern is applied as a single constraint on the second machine learning model” at [0016], [0029].
As per claim 9, Han and Jordan teach the system of claim 1 discussed above. Han also teaches: wherein “the two-dimensional risk pattern represents the first one-dimensional risk pattern and the second one-dimensional risk pattern of a bin at an intersection between the first predictor and the second predictor” at [0016], [0029] and Fig. 3.
As per claim 10, Han and Jordan teach the system of claim 2 discussed above. Han also teaches: wherein “the first machine learning model is at least one of a neural network, a generalized additive model, a scorecard model, and wherein the second machine learning model is at least one of a neural network, a generalized additive model, a scorecard model” at [0022], [0037].
Claims 11-20 recite similar limitations as in claims 1-10 and are therefore rejected by the same reasons.
Conclusion
Examiner's Note: Examiner has cited particular columns and line numbers in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
In the case of amending the Claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KHANH B PHAM whose telephone number is (571)272-4116. The examiner can normally be reached Monday - Friday, 8am to 4pm.
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/KHANH B PHAM/Primary Examiner, Art Unit 2166
February 11, 2026