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 .
Status of Claims
In response to communications filed on 10 March 2026, claims 1-20 are presently pending in the application, of which, claims 1, 8, and 14 are presented in independent form. The Examiner acknowledges amended claims 1, 2, 8, 9, and 14-15. No claims were cancelled or newly added.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 14 May 2026 has been entered. In addition, the ‘After-Final’ amendment, filed 30 April 2026, has been entered with this RCE.
Response to Remarks/Arguments
All objections and/or rejections issued in the previous Office Action, mailed 25 March 2026, have been withdrawn, unless otherwise noted in this Office Action.
Applicant’s arguments, see pages 8-11, filed 30 April 2026, with respect to the rejections of claims 1-20 under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new grounds of rejection is made in view of in further view of Li, Junnan, et al (U.S. 2021/0374553 and known hereinafter as Li).
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 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 1-2, 4-9, 11-15, and 17-23 are rejected under 35 U.S.C. 103 as being anticipated by Yuan, Luo, et al (U.S. 2021/0103838, and known hereinafter as Yuan) in view of Rawal, Kalvayla, et al (U.S. 2024/0311685, filed 16 March 2023, and known hereinafter as Rawal)(previously presented) in further view of Li, Junnan, et al (U.S. 2021/0374553 and known hereinafter as Li)(newly presented).
As per claim 1, Yuan teaches a method, comprising:
generating, by a multi-label joint autoencoder, latent embeddings of a plurality of predictions of a machine learning model by positioning datapoints representing the plurality of predictions within an embedding space, wherein the datapoints are positioned within the embedding space based on a semantic labeling of each datapoint (e.g. Yuan, see paragraphs [0041-0045], which discloses a decision-making system that uses machine learning for decision making based on one or more sets of data, where the machine is trained to learn how to perform different tasks that also includes an interactive computing environment that includes an explainability system that provides labeling for each of the data provided.);
generating, based on the latent embeddings, a plurality of computer-searchable data structures (e.g. Yuan, see paragraphs [0053-0056], which discloses a decision-making system that applies feature engineering on decision data, where the feature engineering generates features using domain knowledge to transform raw data in order to facilitate the working of one or more machine learning algorithm, which then allows the user to search through the explainability system.).
Yuan does not explicitly disclose configuring a nearest flipped neighbor determiner based on the plurality of computer-searchable data structures for identifying a nearest flipped neighbor of datapoints within the embedding space; determining a contrastive explanation of a prediction generated by the machine learning model based on the nearest flipped neighbor of datapoints; and outputting the contrastive explanation of the prediction.
Rawal teaches configuring a nearest flipped neighbor determiner based on the plurality of computer-searchable data structures for identifying a nearest flipped neighbor of datapoints within the embedding space (e.g. Rawal, see paragraphs [0019-0021], which discloses local interpretable model-agnostic (LIME) is a known technique that can sample ‘n’ points in the neighborhood of an explicand and fetch model predictions using the generated samples using model artifacts. The model predictions are then used to train a model to generate explanations, where model predictions are fetched for the nearest neighbor points from the prediction log.);
determining a contrastive explanation of a prediction generated by the machine learning model based (e.g. Rawal, see paragraphs [0012-0015], which discloses accessing only the set of inputs issued to a machine learning model and corresponding prediction outputs, which can be readily obtained from the prediction logs associated with the deployed machine language model by querying the model directly, where kernel shapley additive explanations (SHARP) attributes based on post-hoc explanation used multiple ‘predict()’ function calls. See further paragraphs [0035-0036], which discloses explanation includes indication of a contrastive explanation.) on the nearest flipped neighbor of datapoints (e.g. Rawal, see paragraph [0015], which discloses k-nearest neighbors regressors can be used to predict feature attributions directly instead of computing feature contributions from example predictions stored in the database.); and
outputting the contrastive explanation of the prediction (e.g. Rawal, see paragraphs [0036-0037], which discloses subset of inputs and subset of outputs can be generated, where the output from the set of outputs was produced by the machine language model in response to the input received.)
Yuan is directed to explainability framework of machine learning based decision making. Rawal is directed to providing information security for machine learning model by generating an explanation for the machine learning model. Both are analogous art because they are directed to enhancing machine learning efficiencies and therefore it would have been obvious to one of ordinary skilled in the art at the time the invention was filed to modify the teachings of Yuan with the teachings of Rawal to include the claimed features with the motivation to improve machine learning model predictions.
The modified teachings of Yuan and Rawal do not explicitly teach wherein the determining the contrastive explanation includes interpolating, utilizing an interpolation parameter and a linear interpolation, between a datapoint within the embedding space representing the prediction and a datapoint within the embedding space representing the nearest flipped neighbor.
Li teaches determining the contrastive explanation includes interpolating (e.g. Li, see paragraphs [0052-0054], which discloses a prototypical contrastive learning module that may be configured to compute prototypical contrastive loss based on a class prototype and a normalized embedding of the input, where the learning module includes input interpolation, etc.), utilizing an interpolation parameter and a linear interpolation (e.g. Li, see paragraphs [0037-0040], which discloses using normalized embeddings of an augmented sample for prototypical contrastive learning, the interpolated input may be used by creating virtual training samples by linearly interpolating a sample with another sample.), between a datapoint within the embedding space representing the prediction and a datapoint within the embedding space representing the nearest flipped neighbor (e.g. Li, see paragraphs [0072-0075], which discloses the normalized embeddings is acquired by aggregating the soft labels from its k nearest-neighbors in the low-dimensional subspace.).
Yuan is directed to explainability framework of machine learning based decision making. Rawal is directed to providing information security for machine learning model by generating an explanation for the machine learning model. Li is directed to noise-robust contrastive learning. All are analogous art because they are directed to enhancing machine learning efficiencies and therefore it would have been obvious to one of ordinary skilled in the art at the time the invention was filed to modify the teachings of Yuan with the teachings of Rawal and with the further teachings of Li to include the claimed features with the motivation to improve machine learning model predictions.
As per claim 8, Yuan teaches a system, comprising:
one or more processors configured to initiate operations including:
generating, by a multi-label joint autoencoder, latent embeddings of a plurality of predictions of a machine learning model by positioning datapoints representing the plurality of predictions within an embedding space, wherein the datapoints are positioned within the embedding space based on a semantic labeling of each datapoint (e.g. Yuan, see paragraphs [0041-0045], which discloses a decision-making system that uses machine learning for decision making based on one or more sets of data, where the machine is trained to learn how to perform different tasks that also includes an interactive computing environment that includes an explainability system that provides labeling for each of the data provided.);
generating, based on the latent embeddings, a plurality of computer-searchable data structures (e.g. Yuan, see paragraphs [0053-0056], which discloses a decision-making system that applies feature engineering on decision data, where the feature engineering generates features using domain knowledge to transform raw data in order to facilitate the working of one or more machine learning algorithm, which then allows the user to search through the explainability system.).
Yuan does not explicitly disclose configuring a nearest flipped neighbor determiner based on the plurality of computer-searchable data structures for identifying a nearest flipped neighbor of datapoints within the embedding space; determining a contrastive explanation of a prediction generated by the machine learning model based on the nearest flipped neighbor of datapoints; and outputting the contrastive explanation of the prediction.
Rawal teaches configuring a nearest flipped neighbor determiner based on the plurality of computer-searchable data structures for identifying a nearest flipped neighbor of datapoints within the embedding space (e.g. Rawal, see paragraphs [0019-0021], which discloses local interpretable model-agnostic (LIME) is a known technique that can sample ‘n’ points in the neighborhood of an explicand and fetch model predictions using the generated samples using model artifacts. The model predictions are then used to train a model to generate explanations, where model predictions are fetched for the nearest neighbor points from the prediction log.);
determining a contrastive explanation of a prediction generated by the machine learning model based (e.g. Rawal, see paragraphs [0012-0015], which discloses accessing only the set of inputs issued to a machine learning model and corresponding prediction outputs, which can be readily obtained from the prediction logs associated with the deployed machine language model by querying the model directly, where kernel shapley additive explanations (SHARP) attributes based on post-hoc explanation used multiple ‘predict()’ function calls. See further paragraphs [0035-0036], which discloses explanation includes indication of a contrastive explanation.) on the nearest flipped neighbor of datapoints (e.g. Rawal, see paragraph [0015], which discloses k-nearest neighbors regressors can be used to predict feature attributions directly instead of computing feature contributions from example predictions stored in the database.); and
outputting the contrastive explanation of the prediction (e.g. Rawal, see paragraphs [0036-0037], which discloses subset of inputs and subset of outputs can be generated, where the output from the set of outputs was produced by the machine language model in response to the input received.)
Yuan is directed to explainability framework of machine learning based decision making. Rawal is directed to providing information security for machine learning model by generating an explanation for the machine learning model. Both are analogous art because they are directed to enhancing machine learning efficiencies and therefore it would have been obvious to one of ordinary skilled in the art at the time the invention was filed to modify the teachings of Yuan with the teachings of Rawal to include the claimed features with the motivation to improve machine learning model predictions.
The modified teachings of Yuan and Rawal do not explicitly teach wherein the determining the contrastive explanation includes interpolating, utilizing an interpolation parameter and a linear interpolation, between a datapoint within the embedding space representing the prediction and a datapoint within the embedding space representing the nearest flipped neighbor.
Li teaches determining the contrastive explanation includes interpolating (e.g. Li, see paragraphs [0052-0054], which discloses a prototypical contrastive learning module that may be configured to compute prototypical contrastive loss based on a class prototype and a normalized embedding of the input, where the learning module includes input interpolation, etc.), utilizing an interpolation parameter and a linear interpolation (e.g. Li, see paragraphs [0037-0040], which discloses using normalized embeddings of an augmented sample for prototypical contrastive learning, the interpolated input may be used by creating virtual training samples by linearly interpolating a sample with another sample.), between a datapoint within the embedding space representing the prediction and a datapoint within the embedding space representing the nearest flipped neighbor (e.g. Li, see paragraphs [0072-0075], which discloses the normalized embeddings is acquired by aggregating the soft labels from its k nearest-neighbors in the low-dimensional subspace.).
Yuan is directed to explainability framework of machine learning based decision making. Rawal is directed to providing information security for machine learning model by generating an explanation for the machine learning model. Li is directed to noise-robust contrastive learning. All are analogous art because they are directed to enhancing machine learning efficiencies and therefore it would have been obvious to one of ordinary skilled in the art at the time the invention was filed to modify the teachings of Yuan with the teachings of Rawal and with the further teachings of Li to include the claimed features with the motivation to improve machine learning model predictions.
As per claim 14, Yuan teaches a computer program product, the computer program product comprising:
one or more computer-readable storage media and program instructions collectively stored on the one or more computer-readable storage media, the program instructions executable by a processor to cause the processor to initiate operations including:
generating, by a multi-label joint autoencoder, latent embeddings of a plurality of predictions of a machine learning model by positioning datapoints representing the plurality of predictions within an embedding space, wherein the datapoints are positioned within the embedding space based on a semantic labeling of each datapoint (e.g. Yuan, see paragraphs [0041-0045], which discloses a decision-making system that uses machine learning for decision making based on one or more sets of data, where the machine is trained to learn how to perform different tasks that also includes an interactive computing environment that includes an explainability system that provides labeling for each of the data provided.);
generating, based on the latent embeddings, a plurality of computer-searchable data structures (e.g. Yuan, see paragraphs [0053-0056], which discloses a decision-making system that applies feature engineering on decision data, where the feature engineering generates features using domain knowledge to transform raw data in order to facilitate the working of one or more machine learning algorithm, which then allows the user to search through the explainability system.).
Yuan does not explicitly disclose configuring a nearest flipped neighbor determiner based on the plurality of computer-searchable data structures for identifying a nearest flipped neighbor of datapoints within the embedding space; determining a contrastive explanation of a prediction generated by the machine learning model based on the nearest flipped neighbor of datapoints; and outputting the contrastive explanation of the prediction.
Rawal teaches configuring a nearest flipped neighbor determiner based on the plurality of computer-searchable data structures for identifying a nearest flipped neighbor of datapoints within the embedding space (e.g. Rawal, see paragraphs [0019-0021], which discloses local interpretable model-agnostic (LIME) is a known technique that can sample ‘n’ points in the neighborhood of an explicand and fetch model predictions using the generated samples using model artifacts. The model predictions are then used to train a model to generate explanations, where model predictions are fetched for the nearest neighbor points from the prediction log.);
determining a contrastive explanation of a prediction generated by the machine learning model based (e.g. Rawal, see paragraphs [0012-0015], which discloses accessing only the set of inputs issued to a machine learning model and corresponding prediction outputs, which can be readily obtained from the prediction logs associated with the deployed machine language model by querying the model directly, where kernel shapley additive explanations (SHARP) attributes based on post-hoc explanation used multiple ‘predict()’ function calls. See further paragraphs [0035-0036], which discloses explanation includes indication of a contrastive explanation.) on the nearest flipped neighbor of datapoints (e.g. Rawal, see paragraph [0015], which discloses k-nearest neighbors regressors can be used to predict feature attributions directly instead of computing feature contributions from example predictions stored in the database.); and
outputting the contrastive explanation of the prediction (e.g. Rawal, see paragraphs [0036-0037], which discloses subset of inputs and subset of outputs can be generated, where the output from the set of outputs was produced by the machine language model in response to the input received.)
Yuan is directed to explainability framework of machine learning based decision making. Rawal is directed to providing information security for machine learning model by generating an explanation for the machine learning model. Both are analogous art because they are directed to enhancing machine learning efficiencies and therefore it would have been obvious to one of ordinary skilled in the art at the time the invention was filed to modify the teachings of Yuan with the teachings of Rawal to include the claimed features with the motivation to improve machine learning model predictions.
The modified teachings of Yuan and Rawal do not explicitly teach wherein the determining the contrastive explanation includes interpolating, utilizing an interpolation parameter and a linear interpolation, between a datapoint within the embedding space representing the prediction and a datapoint within the embedding space representing the nearest flipped neighbor.
Li teaches determining the contrastive explanation includes interpolating (e.g. Li, see paragraphs [0052-0054], which discloses a prototypical contrastive learning module that may be configured to compute prototypical contrastive loss based on a class prototype and a normalized embedding of the input, where the learning module includes input interpolation, etc.), utilizing an interpolation parameter and a linear interpolation (e.g. Li, see paragraphs [0037-0040], which discloses using normalized embeddings of an augmented sample for prototypical contrastive learning, the interpolated input may be used by creating virtual training samples by linearly interpolating a sample with another sample.), between a datapoint within the embedding space representing the prediction and a datapoint within the embedding space representing the nearest flipped neighbor (e.g. Li, see paragraphs [0072-0075], which discloses the normalized embeddings is acquired by aggregating the soft labels from its k nearest-neighbors in the low-dimensional subspace.).
Yuan is directed to explainability framework of machine learning based decision making. Rawal is directed to providing information security for machine learning model by generating an explanation for the machine learning model. Li is directed to noise-robust contrastive learning. All are analogous art because they are directed to enhancing machine learning efficiencies and therefore it would have been obvious to one of ordinary skilled in the art at the time the invention was filed to modify the teachings of Yuan with the teachings of Rawal and with the further teachings of Li to include the claimed features with the motivation to improve machine learning model predictions.
As per claims 2, 9, and 15, the modified teachings of Yuan with Rawal and Li teaches the method of claim 1, the system of claim 8, and the computer program product of claim 14, respectively, wherein
the contrastive explanation corresponds to a nearest flipped neighbor determined by the nearest flipped neighbor determiner (e.g. Rawal, see paragraphs [0012-0015], which discloses accessing only the set of inputs issued to a machine learning model and corresponding prediction outputs, which can be readily obtained from the prediction logs associated with the deployed machine language model by querying the model directly, where kernel shapley additive explanations (SHARP) attributes based on post-hoc explanation used multiple ‘predict()’ function calls. See further paragraphs [0035-0036], which discloses explanation includes indication of a contrastive explanation.).
As per claims 4, 11, and 17, the modified teachings of Yuan with Rawal and Li teaches the method of claim 3, the system of claim 10, and the computer program product of claim 16, respectively, wherein the interpolating further includes generating an optimal interpolation parameter using a greedy heuristic (e.g. Yuan, see paragraphs [0055-0059], which discloses features that include historic alerts, the type of account, and aggregated transaction amount as illustrations of parameters for decision making system.).
As per claims 5, 12, and 18, the modified teachings of Yuan with Rawal and Li teaches the method of claim 1, the system of claim 8, and the computer program product of claim 14, respectively, wherein nearest flipped neighbor determiner is configured as k-d tree that can be searched to identify a nearest neighbor (e.g. Yuan, see paragraphs [0054-0055], which discloses the machine learning algorithm includes tree-based models, feed-forward neural network, clustering methods, and linear model that are used for performing explainability of the decision-making system.).
As per claims 6, 13, and 19, the modified teachings of Yuan with Rawal and Li teaches the method of claim 1, the system of claim 8, and the computer program product of claim 14, respectively, wherein the machine learning model is a machine learning classifier trained to generate predictions by assigning an input to one of multiple classes (e.g. Yuan, see paragraphs [0056-0057], which discloses the algorithm traverses the tree-based model and generates the explanation for each prediction given by the decision-making system.).
As per claims 7, 13, and 20, the modified teachings of Yuan with Rawal and Li teaches the method of claim 6, the system of claim 8, and the computer program product of claim 19, respectively, wherein the nearest flipped neighbor determiner comprises multiple k-d trees, each of the k-d trees uniquely corresponding to one of the multiple classes (e.g. Yuan, see paragraphs [0054-0055], which discloses the machine learning algorithm includes tree-based models, feed-forward neural network, clustering methods, and linear model that are used for performing explainability of the decision-making system.).
As per claims 21-23, the modified teachings of Yuan with Rawal and Li teaches the method of claim 1, the system of claim 8, and the computer program product of claim 14, respectively, wherein the interpolation parameter is an optimal interpolation parameter corresponding to a vector (e.g. Li, see paragraphs [0037-0040], which discloses using normalized embeddings of an augmented sample for prototypical contrastive learning, the interpolated input may be used by creating virtual training samples by linearly interpolating a sample with another sample.).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. See attached PTO-892 that includes additional prior art of record describing the general state of the art in which the invention is directed to.
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Any inquiry concerning this communication or earlier communications from the examiner should be directed to FARHAN M SYED whose telephone number is (571)272-7191. The examiner can normally be reached M-F 8:30AM-5:30PM.
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/FARHAN M SYED/Primary Examiner, Art Unit 2161 June 12, 2026
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