DETAILED ACTION This is a non-final, first office action on the merits. Claims 1-21 are pending. 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 1-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Specifically, claims 1-21 are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea. With respect to Step 2A Prong One of the framework, claims 1, 5, 9, and 15 recite an abstract idea. Claims 1, 5, 9, and 15 include “ inputting a data set comprising a single-layer predictor; inputting the data set comprising a multi-layer predictor; processing the data set into a feature set; processing the data set into raw data; creating from the raw data, a prediction set with multiple layers; mapping the prediction set against the feature set to identify to what extent each feature contributed to a final prediction from the prediction set; based on the mapping, generating an explanation of the predictive behavior ”. The limitations above recite an abstract idea under Step 2A Prong One. More particularly, the elements above recite mental processes-concepts performed in the human mind (including an observation, evaluation, judgment, opinion) because the elements describe a process for generating an explanation of the predictive behavior. As a result, claims 1, 5, 9, and 15 recite an abstract idea under Step 2A Prong One. Claims 2-4, 6-8, 10-14, and 16-21 further describe the process for generating an explanation of the predictive behavior. As a result, claims 2-4, 6-8, 10-14, and 16-21 recite an abstract idea under Step 2A Prong One for the same reasons as stated above with respect to claims 1, 5, 9, and 15. With respect to Step 2A Prong Two of the framework, claims 1, 5, 9, and 15 do not include additional elements that integrate the abstract idea into a practical application. Claims 1, 5, 9, and 15 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements of claims 1, 5, 9, and 15 include a processor, artificial-intelligence, a shallow learning system, and a deep learning system. When considered in view of the claim as a whole, the additional elements do not integrate the abstract idea into a practical application because the additional computing elements are generic computing elements that are merely used as a tool to perform the recited abstract idea. As a result, claims 1, 5, 9, and 15 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two. Claims 2, 6, 10, and 16 do not include any additional elements beyond those recited with respect to claims 1, 5, 9, and 15. As a result, claims 2, 6, 10, and 16 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two for the same reasons as stated above with respect to claims 1, 5, 9, and 15. Claims 3-4, 7-8, 11-14, and 17-21 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements of claims 3-4, 7-8, 11-14, and 17-21 include a machine learning model, a neural network, a shallow learning system, and a natural language . When considered in view of the claims as a whole, the additional elements do not integrate the abstract idea into a practical application because the additional computing elements do no more than generally link the use of the recited abstract idea to a particular technological environment. As a result, claims 3-4, 7-8, 11-14, and 17-21 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two. With respect to Step 2B of the framework, claims 1, 5, 9, and 15 do not include additional elements amounting to significantly more than the abstract idea. As noted above, claims 1, 5, 9, and 15 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements of claims 1, 5, 9, and 15 include a processor, artificial-intelligence, a shallow learning system, and a deep learning system. The additional elements do not amount to significantly more than the abstract idea because the additional computing elements are generic computing elements that are merely used as a tool to perform the recited abstract idea. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, independent claims 1, 5, 9, and 15 do not include additional elements that amount to significantly more than the abstract idea under Step 2B. Claims 2, 6, 10, and 16 do not include any additional elements beyond those recited with respect to claims 1, 5, 9, and 15. As a result, claims 2, 6, 10, and 16 do not include additional elements that amount to significantly more than the abstract idea under Step 2B for the same reasons as stated above with respect to claims 1, 5, 9, and 15. Claims 3-4, 7-8, 11-14, and 17-21 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements of claims 3-4, 7-8, 11-14, and 17-21 include a machine learning model, a neural network, a shallow learning system, and a natural language . The additional elements do not amount to significantly more than the abstract idea because the additional computing elements do no more than generally link the use of the recited abstract idea to a particular technological environment. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, claims 3-4, 7-8, 11-14, and 17-21 do not include additional elements that amount to significantly more than the abstract idea under Step 2B. Therefore, the claims are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea. Accordingly, claims 1-21 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. 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 of this title, 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 set forth in Graham v. John Deere Co. , 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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. Claims 1, 3-5, 7-9, 11-15, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Wei et al. (US Pat No. 12,079,719) (hereinafter Wei et al . ) in view of De et al. (US Pub No. 2021/0142169) (hereinafter De et al. ). Regarding claims 1 and 5, Wei discloses a method for generating explainable artificial-intelligence, the method comprising: inputting a data set into a shallow learning system, the shallow learning system comprising a single-layer predictor (see Wei, column 7, lines 26-35, wherein the first and second sigmoid outputs are combined in a wide and shallow neural network that provide a third sigmoid output as a final output of the machine learning model….obtained from one or more data sources for each of a plurality of patients as a dataset; column 4, lines 36-39, wherein each layer contains one or more nodes (alternatively referred to as "vertices") as representatively indicated by reference numeral 230. The vertices are connected by edges that represent weighting in the neural network); inputting the data set into a deep learning system, the deep learning system comprising a multi-layer predictor (see Wei, column 6, lines 20-24, wherein input features are concatenated in an embedding layer 420 which is utilized as an input to one or more multi-layer neural networks 425 that each utilize activation functions provided by a plurality of rectified linear units; and column 3, lines 53-56, wherein the model implements lifelong learning by integrating wide and deep learning components with traditional tree models to leverage results from prior model learnings into future predictions); processing, at the shallow learning system, the data set into a feature set (see Wei, column 7, lines 26-35, wherein the first and second sigmoid outputs are combined in a wide and shallow neural network that provide a third sigmoid output as a final output of the machine learning model….obtained from one or more data sources for each of a plurality of patients as a dataset; and column 7, lines 60-64, wherein utilize current non-sequential data from the healthcare dataset as non-sequential input features and provide predictions of identified patient profiles using a first multi-level neural network); processing, at the deep learning system, the data set into raw data (see Wei, column 3, line 65 & column 4, line 1, wherein receive real-world healthcare data 110 for patients from one or more data sources 120; and column 3, lines 53-56, wherein the model implements lifelong learning by integrating wide and deep learning components with traditional tree models to leverage results from prior model learnings into future predictions); creating, at the deep learning system, from the raw data, a prediction set with multiple layers (see Wei, column 3, line 65 & column 4, line 1, wherein receive real-world healthcare data 110 for patients from one or more data sources 120; column 3, lines 53-56, wherein the model implements lifelong learning by integrating wide and deep learning components with traditional tree models to leverage results from prior model learnings into future predictions; column 8, lines 1-2, wherein provide predictions of identified patient profiles using a second multi-level neural network); generating an explanation of the predictive behavior of the deep learning system (see Wei, column 4, lines 30-32, wherein establish terminology that is used in the detailed explanation of the inventive Bayesian deep neural network-based LML model 105; and column 1, lines 39-42, wherein Lifelong learning is implemented by dynamically integrating present learning from the wide and deep learning components with past learning from traditional tree models in the prior component into future predictions). Wei et al. fails to explicitly disclose mapping the prediction set against the feature set to identify to what extent each feature contributed to a final prediction from the prediction set; based on the mapping, generating an explanation of the predictive behavior of the deep learning system Analogous art De discloses mapping the prediction set against the feature set to identify to what extent each feature contributed to a final prediction from the prediction set (see De, para [0046], wherein the data cluster assembler 130 may implement the artificial intelligence component 212 to map the actual outcome to each of the case instances 206 to generate predictions; para [0029], wherein each of the plurality of instance clusters may be comprising the case instances that may be grouped together based on similarity features, which are represented by the hidden neuron contribution scores; para [0025], wherein the case instances may refer to various variable features associated with the case result prediction requirement); Analogous art De discloses based on the mapping, generating an explanation of the predictive behavior of the deep learning system (see De, para [0015], wherein explanation considers the structure of the model to derive an explanation for the prediction. The interpretability based approaches may aim to find trends and patterns to map inputs to the network predicted output; para [0081], wherein defaulting behavior and the behavior may have been deteriorating overtime; and para [0068], wherein decision rules from the decision tree 222 and may provide human interpretable explanation for deep learning prediction). Wei directed to a system for identifying patient profiles by continuously learning, as new data become available. De directed to improving prediction interpretation. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Wei, regarding the System for Lifelong Machine Learning (LML) Model for Patient, to have included mapping the prediction set against the feature set to identify to what extent each feature contributed to a final prediction from the prediction set; based on the mapping, generating an explanation of the predictive behavior of the deep learning system because both inventions teach improving explanations for the model outcome. Further, the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Regarding claims 3, 7, 12, and 18, Wei discloses the method of claim 1 wherein the single-layer predictor is a machine learning model (see Wei, column 3, lines 50-53, wherein machine learning (LML) model is based on a Bayesian influencing concept that provides for updating model parameters dynamically through a sequence of model application and learning). Regarding claims 4, 8, 14, and 20, Wei discloses the method of claim 1 wherein the multi-layer predictor is a neural network (see Wei, column 2, lines 6-8, wherein a shallow and wide neural network which provides a final sigmoid output from the Bayesian deep neural network based LML model). Regarding claims 9 and 15, Wei discloses a system for generating explainable artificial-intelligence, the system comprising: a shallow learning system operable to: receive a data set (see Wei, column 7, lines 26-35, wherein the first and second sigmoid outputs are combined in a wide and shallow neural network that provide a third sigmoid output as a final output of the machine learning model….obtained from one or more data sources for each of a plurality of patients as a dataset; and column 4, lines 36-39, wherein each layer contains one or more nodes (alternatively referred to as "vertices") as representatively indicated by reference numeral 230. The vertices are connected by edges that represent weighting in the neural network); and process the data set into a feature set (see Wei, column 7, lines 26-35, wherein the first and second sigmoid outputs are combined in a wide and shallow neural network that provide a third sigmoid output as a final output of the machine learning model….obtained from one or more data sources for each of a plurality of patients as a dataset; and column 7, lines 60-64, wherein utilize current non-sequential data from the healthcare dataset as non-sequential input features and provide predictions of identified patient profiles using a first multi-level neural network); a deep learning system operable to: receive the data set (see Wei, column 6, lines 20-24, wherein input features are concatenated in an embedding layer 420 which is utilized as an input to one or more multi-layer neural networks 425 that each utilize activation functions provided by a plurality of rectified linear units; and column 3, lines 53-56, wherein the model implements lifelong learning by integrating wide and deep learning components with traditional tree models to leverage results from prior model learnings into future predictions); process the data set into raw data (see Wei, column 3, line 65 & column 4, line 1, wherein receive real-world healthcare data 110 for patients from one or more data sources 120; and column 3, lines 53-56, wherein the model implements lifelong learning by integrating wide and deep learning components with traditional tree models to leverage results from prior model learnings into future predictions); and create, from the raw data, a prediction set with multiple layers (see Wei, column 3, line 65 & column 4, line 1, wherein receive real-world healthcare data 110 for patients from one or more data sources 120; column 3, lines 53-56, wherein the model implements lifelong learning by integrating wide and deep learning components with traditional tree models to leverage results from prior model learnings into future predictions; column 8, lines 1-2, wherein provide predictions of identified patient profiles using a second multi-level neural network); a processor (see Wei, column 8, line 18, wherein a processor 805, a system memory 811) operable to: map the prediction set against the feature set; and based on the map: identify to what extent each feature, included in the feature set, contributed to a final prediction from the prediction set, as set forth above with claim 1; and generate an explanation of the predictive behavior of the deep learning system, as set forth above with claim 1. Regarding claims 11 and 17, Wei discloses the system of claim 9 wherein the shallow learning system comprises a single-layer predictor (see Wei, column 7, lines 26-35, wherein the first and second sigmoid outputs are combined in a wide and shallow neural network that provide a third sigmoid output as a final output of the machine learning model….obtained from one or more data sources for each of a plurality of patients as a dataset; and column 4, lines 36-39, wherein each layer contains one or more nodes (alternatively referred to as "vertices") as representatively indicated by reference numeral 230. The vertices are connected by edges that represent weighting in the neural network). Regarding claims 13 and 19, Wei discloses the system of claim 9 wherein the deep learning system comprises a multi-layer predictor (see Wei, column 6, lines 20-24, wherein input features are concatenated in an embedding layer 420 which is utilized as an input to one or more multi-layer neural networks 425 that each utilize activation functions provided by a plurality of rectified linear units). Claims 2, 6, 10, 16, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Wei et al. (US Pat No. 12,079,719) (hereinafter Wei et al . ), in view of De et al. (US Pub No. 2021/0142169) (hereinafter De et al. ), and further in view of Garvey et al. (US Pub No. 2024/0354789) (hereinafter Garvey et al. ). Regarding claims 2 and 6, Wei discloses the method of claim 1 wherein the mapping, as set forth above with claim 1. Wei et al. fails to explicitly disclose utilizes a heatmap. Analogous art Garvey discloses a heatmap (see Garvey, para [0064], wherein generating heatmap). Wei directed to a system for identifying patient profiles by continuously learning, as new data become available. Garvey directed to quantitative split driven quote. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Wei, regarding the System for Lifelong Machine Learning (LML) Model for Patient, to have included a heatmap because both inventions teach improving the overall experience. Further, the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Regarding claims 10 and 16, Wei discloses the system of claim 9 wherein the processor, as set forth with claim 9. Wei et al. fails to explicitly disclose a heatmap of the feature set and a heatmap of the prediction set to map the prediction set against the feature set. Analogous art Garvey discloses a heatmap of the feature set and a heatmap of the prediction set to map the prediction set against the feature set (see Garvey, para [0064], wherein generating heatmap; para [0146], wherein Machine learning: identify patterns and features that are indicative of sentiment. The features may include the tokens (words/phrases) in the text, the sequence/order of the words, adjacency of one word to another, the part of speech of a token, and/or other semantic/linguistic attributes of the text; and para [0042], wherein UX tests may capture heatmap elements, which may include qualitative and/or quantitative test result data that is tied to a particular location within a webpage or application page…..Heatmap data may be captured by the user selecting a particular area within a page and inputting qualitative and/or quantitative data that is associated with the selected page region. The user may input one or more quotations that describes a positive attribute and/or a negative attribute of a particular user interface element on the page). One of ordinary skill in the art would have recognized that applying the known technique of Garvey would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 2. Regarding claim 21, Wei discloses the system of claim 15 wherein the explanation, as set forth above with claim 15. Wei et al. fails to explicitly disclose formatted in natural language. Analogous art Garvey discloses formatted in natural language (see Garvey, para [0068], wherein quotations in an unstructured natural language format). One of ordinary skill in the art would have recognized that applying the known technique of Garvey would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 2. Conclusion The prior arts made of record and not relied upon is considered pertinent to applicant's disclosure. (US Pub No. 2008/0046562; US Pub No. 2019/0379589; US Pub No. 2023/0130567; US Pub No. 2017/0323239; US Pub No. 2021/0406693; US Pub No. 2020/0104704; S De Cnudde, Y Ramon, D Martens, F Provost (Deep learning on big, sparse, behavioral data) - Big data, 2019 - journals.sagepub.com; and V Swamy, B Radmehr, N Krco, M Marras (Evaluating the explainers: black-box explainable machine learning for student success prediction in MOOCs)… - arXiv preprint arXiv …, 2022 - arxiv.org. Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT HAFIZ A KASSIM whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)272-8534 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT 9:00 - 5:00 PM . Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. 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For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /HAFIZ A KASSIM/ Primary Examiner, Art Unit 3623 03/26/202 6