Notice of Pre-AIA or AIA Status This Non-Final communication is in response to Application No. 18/217,876 filed 07/03/2023. 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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. 101 Subject Matter Eligibility Analysis Step 1: Claims 1-20 are within the four statutory (a process, machine, manufacture or composition of matter.) Claims 1-9 describe a process and 10-20 describes a machine. With respect to claim 1: Step 2A Prong 1: The claim recites an abstract idea enumerated in the 2019 PEG. generating, …, at least one prediction for a target time based on the at least one input; (This is an abstract idea of a "Mental Process." The " generating " step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The generation could be made manually by an individual.) generating, by the at least one processor, a set of common background data for each of the at least one time window based on the at least one input; (This is an abstract idea of a "Mental Process." The " generating " step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The generation could be made manually by an individual.) determining, by the at least one processor, at least one respective mode explanation for each of the at least one time window based on the corresponding set of common background data, the corresponding at least one trained model, and the corresponding at least one prediction; and (This is an abstract idea of a "Mental Process." The " determining " step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The determination could be made manually by an individual.) determining, by the at least one processor, at least one reconciled explanation for a target prediction that corresponds to the target time based on the at least one input and the at least one respective mode explanation. (This is an abstract idea of a "Mental Process." The " determining " step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The determination could be made manually by an individual.) Step 2A Prong 2: The judicial exception is not integrated into a practical application Additional elements: receiving, by the at least one processor via an application programming interface, at least one input; (this limitation amounts to adding insignificant extra-solution activity to the judicial exception). temporally segmenting, by the at least one processor, the at least one input to generate a finite set of at least one time window; (this limitation amounts to adding insignificant extra-solution activity to the judicial exception). training, by the at least one processor, at least one model for each of the at least one time window; (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.) by the at least one processor using each of the at least one trained model, (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.) Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception The additional element s “receiving…”, and “temporally segmenting…” add insignificant extra-solution activity to the judicial exception and cannot provide an inventive concept. Storing and retrieving information in memory is directed to a well understood routine conventional activity of data transmission (MPEP 2106.05(d)(II)(iv)) . The additional element s “training…” and “by the at least one processor…” are recited in a generic level and they represent generic computer components to apply the abstract idea. Mere instructions to apply an exception cannot provide an inventive concept (MPEP 2106.05(f)). When considered in combination, these additional elements represent insignificant extra - solution activity and mere instructions to apply an expectation, which do not provide an inventive concept. Therefore, claim 1 is ineligible With respect to claim 2 : Step 2A Prong 1: claim 2, which incorporates the rejection of claim 1, does not recite an abstract idea. Step 2 A Prong 2: The judicial exception is not integrated into a practical application. the at least one input includes at least one from among raw data, a parameter, a weighting function that prioritizes a plurality of temporal time windows for consistency evaluation, a timestamp for the target time, a data sampling strategy, and a consistency factor. (this limitation amounts to adding insignificant extra-solution activity to the judicial exception). Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception The additional element add s insignificant extra-solution activity to the judicial exception and cannot provide an inventive concept. Storing and retrieving information in memory is directed to a well understood routine conventional activity of data transmission (MPEP 2106.05(d)(II)(iv)) . Therefore, claim 2 is ineligible. With respect to claim 3 : Step 2A Prong 1: claim 3, which incorporates the rejection of claim 2, does not recite an abstract idea. Step 2 A Prong 2: The judicial exception is not integrated into a practical application. the raw data includes a series of data that represents an evolution of information over time, and wherein the parameter includes a required number of the at least one time window. (this limitation amounts to adding insignificant extra-solution activity to the judicial exception). Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception The additional element add s insignificant extra-solution activity to the judicial exception and cannot provide an inventive concept. Storing and retrieving information in memory is directed to a well understood routine conventional activity of data transmission (MPEP 2106.05(d)(II)(iv)) . Therefore, claim 3 is ineligible. With respect to claim 4 : Step 2A Prong 1: claim 4, which incorporates the rejection of claim 1, does not recite an abstract idea. Step 2 A Prong 2: The judicial exception is not integrated into a practical application. each of the at least one respective mode explanation includes at least one respective feature attribution for each of a plurality of segmented time windows with respect to a specific corresponding background data distribution. (this limitation amounts to adding insignificant extra-solution activity to the judicial exception). Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception The additional element add s insignificant extra-solution activity to the judicial exception and cannot provide an inventive concept. Storing and retrieving information in memory is directed to a well understood routine conventional activity of data transmission (MPEP 2106.05(d)(II)(iv)) . Therefore, claim 4 is ineligible. With respect to claim 5 : Step 2A Prong 1: claim 5, which incorporates the rejection of claim 1, does not recite an abstract idea. Step 2 A Prong 2: The judicial exception is not integrated into a practical application. the at least one reconciled explanation corresponds to a consistent explanation for each of the at least one prediction over time. (this limitation amounts to adding insignificant extra-solution activity to the judicial exception). Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception The additional element s add insignificant extra-solution activity to the judicial exception and cannot provide an inventive concept. Storing and retrieving information in memory is directed to a well understood routine conventional activity of data transmission (MPEP 2106.05(d)(II)(iv)) . Therefore, claim 5 is ineligible. With respect to claim 6 : Step 2A Prong 1: claim 6, which incorporates the rejection of claim 1, recites an additional abstract idea: determining, by the at least one processor, a consistency value for a plurality of features in each of the at least one respective mode explanation; and (This is an abstract idea of a "Mental Process." The " determining " step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The determination could be made manually by an individual.) removing, by the at least one processor, at least one feature from the plurality of features based on the consistency value and a consistency factor. (This is an abstract idea of a "Mental Process." The " removing " step under its broadest reasonable interpretation, covers concepts that can be practically performed by a human using a pen and paper.) Step 2 A Prong 2: claim 6 does not recite any additional elements and thus cannot be integrated into a practical application. Step 2B: claim 6 does not recite an additional element. Therefore, claim 6 is ineligible. With respect to claim 7 : Step 2A Prong 1: claim 7, which incorporates the rejection of claim 6, recites an additional abstract idea: weighting, by the at least one processor using a weighting function, at least one remaining feature from the plurality of features based on a result of the removing; and (this is an abstract idea of a “mathematical concept”. The recited “ weighting function ” represents a mathematical function that would fall under the “mathematical concepts” grouping.) determining, by the at least one processor, at least one respective reconciled feature score for each of the at least one remaining feature. (This is an abstract idea of a "Mental Process." The " determining " step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The determination could be made manually by an individual.) Step 2 A Prong 2: claim 7 does not recite any additional elements and thus cannot be integrated into a practical application. Step 2B: claim 7 does not recite an additional element. Therefore, claim 7 is ineligible. With respect to claim 8 : Step 2A Prong 1: claim 8, which incorporates the rejection of claim 6, does not recite an abstract idea. Step 2 A Prong 2: The judicial exception is not integrated into a practical application. the consistency value relates to a feature value distance of a plurality of proximate features that is determined based on a graphical projection of the at least one respective mode explanation. (this limitation amounts to adding insignificant extra-solution activity to the judicial exception). Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception The additional element s add insignificant extra-solution activity to the judicial exception and cannot provide an inventive concept. Storing and retrieving information in memory is directed to a well understood routine conventional activity of data transmission (MPEP 2106.05(d)(II)(iv)) . Therefore, claim 8 is ineligible. With respect to claim 9 : Step 2A Prong 1: claim 9, which incorporates the rejection of claim 1, does not recite an abstract idea. Step 2 A Prong 2: The judicial exception is not integrated into a practical application. the at least one model includes at least one from among a large language model, a deep learning model, a neural network model, a natural language processing model, a machine learning model, a mathematical model, a process model, and a data model. (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.) Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception The additional element is recited in a generic level and they represent generic computer components to apply the abstract idea. Mere instructions to apply an exception cannot provide an inventive concept (MPEP 2106.05(f)). Therefore, claim 9 is ineligible. With respect to claim 10 : The claim recites similar limitations as corresponding to claim 1. Therefore, the same subject matter analysis that was utilized for claim 1, as described above, is equally applicable to claim 10. Therefore, claim 10 is ineligible. With respect to claim 11 : The claim recites similar limitations as corresponding to claim 2. Therefore, the same subject matter analysis that was utilized for claim 2, as described above, is equally applicable to claim 11. Therefore, claim 11 is ineligible. With respect to claim 12 : The claim recites similar limitations as corresponding to claim 3. Therefore, the same subject matter analysis that was utilized for claim 3, as described above, is equally applicable to claim 12. Therefore, claim 12 is ineligible. With respect to claim 13 : The claim recites similar limitations as corresponding to claim 4. Therefore, the same subject matter analysis that was utilized for claim 4, as described above, is equally applicable to claim 13. Therefore, claim 13 is ineligible. With respect to claim 14 : The claim recites similar limitations as corresponding to claim 5. Therefore, the same subject matter analysis that was utilized for claim 5, as described above, is equally applicable to claim 14. Therefore, claim 14 is ineligible. With respect to claim 15 : The claim recites similar limitations as corresponding to claim 6. Therefore, the same subject matter analysis that was utilized for claim 6, as described above, is equally applicable to claim 15. Therefore, claim 15 is ineligible. With respect to claim 16 : The claim recites similar limitations as corresponding to claim 7. Therefore, the same subject matter analysis that was utilized for claim 7, as described above, is equally applicable to claim 16. Therefore, claim 16 is ineligible. With respect to claim 17 : The claim recites similar limitations as corresponding to claim 8. Therefore, the same subject matter analysis that was utilized for claim 8, as described above, is equally applicable to claim 17. Therefore, claim 17 is ineligible. With respect to claim 18 : The claim recites similar limitations as corresponding to claim 9. Therefore, the same subject matter analysis that was utilized for claim 9, as described above, is equally applicable to claim 18. Therefore, claim 18 is ineligible. With respect to claim 19 : The claim recites similar limitations as corresponding to claim 1. Therefore, the same subject matter analysis that was utilized for claim 1, as described above, is equally applicable to claim 19. Therefore, claim 19 is ineligible. With respect to claim 20 : The claim recites similar limitations as corresponding to claim 2. Therefore, the same subject matter analysis that was utilized for claim 2, as described above, is equally applicable to claim 20. Therefore, claim 20 is ineligible. 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 Nayebi (NPL: ‘WindowSHAP: An Efficient Framework for Explaining Time-series Classifiers based on Shapley Values’ ( 2023) ) in view of Raykar ( US 2023/0419136 A1) and Bento (NPL: ‘TimeSHAP: Explaining Recurrent Models through Sequence Perturbations’ (2021)). Regarding claim 1, Nayebi teaches: A method for providing explanations of predictive outputs, the method being implemented by at least one processor, the method comprising: ( Abstract) receiving, by the at least one processor via an application programming interface, at least one input; (Algorithm 1 on pages 8-9 shows input sequence). temporally segmenting, by the at least one processor, the at least one input to generate a finite set of at least one time window; (Page 8, 3.3.1 Stationary WindowSHAP “ In this approach, the time-axis is segmented into fixed-length windows. ”) training, by the at least one processor, at least one model for each of the at least one time window; (Page 13. 3.5 Data sources ” To test the model-agnostic explanation methods (e.g., WindowSHAP), we used three distinct clinical time-series data sets to develop and train three different deep learning prediction models. ”) generating, by the at least one processor using each of the at least one trained model, at least one prediction for a target time based on the at least one input; (Page 13. 3.5 Data sources ” To test the model-agnostic explanation methods (e.g., WindowSHAP), we used three distinct clinical time-series data sets to develop and train three different deep learning prediction models. ” Prediction model is making prediction for the windows) generating, by the at least one processor, a set of common background data for each of the at least one time window based on the at least one input; determining, by the at least one processor, at least one respective mode explanation for each of the at least one time window based on the corresponding set of common background data , the corresponding at least one trained model, and the corresponding at least one prediction; and (Page 6 Section 3 Methods shows how they calculate Shapley values for each window which serve as an explanation which uses the trained prediction model and prediction. ) determining, by the at least one processor, at least one reconciled explanation for a target prediction that corresponds to the target time based on the at least one input and the at least one respective mode explanation. Nayebi does not teach: Prediction model for each time window generating, by the at least one processor, a set of common background data for each of the at least one time window based on the at least one input; determining, by the at least one processor, at least one reconciled explanation for a target prediction that corresponds to the target time based on the at least one input and the at least one respective mode explanation. Raykar teaches: Prediction model for each time window ([0024] “ The input component 110 receives sets of time series forecasting predictions generated from sets of black box models. ”) generating, by the at least one processor, a set of common background data for each of the at least one time window based on the at least one input; ([0040] “ In operation 810 , the model component 130 generates a surrogate data set. The surrogate data set may be generated by backtesting one or more time series forecasting predictions of the set of time series forecasting predictions. ”) Nayebi and Raykar are considered analogous art to the claimed invention because they are in the same field of endeavor being AI Explanation systems . It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the background data of Raykar with the explanation system of Nayebi and include the background data in the explanation determination . One would want to do this to create better explanations (Raykar background). Bento teaches: determining, by the at least one processor, at least one reconciled explanation for a target prediction that corresponds to the target time based on the at least one input and the at least one respective mode explanation. (Page 7 Section 4.2 Global Explanations “ We compute global explanations by applying TimeSHAP to all sequences that contained a positive prediction, and explaining the first positive prediction of each sequence (hence referred as 𝑡 = 0). We use 𝜂 = 0.025 as the tolerance of the temporal coalition pruning algorithm. ”) Nayebi, Raykar, and Bento are considered analogous art to the claimed invention because they are in the same field of endeavor being AI Explanation systems . It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the time window explanation system of Nayebi with the global explanation system of Bento . One would want to understand the model’s decision process as a whole (Bento 4.2 Global Explanations) Regarding claim 2, , Nayebi in view of Raykar and Bento teaches claim 1 as outlined above . Nayebi further teaches: the at least one input includes at least one from among raw data, a parameter, a weighting function that prioritizes a plurality of temporal time windows for consistency evaluation, a timestamp for the target time, a data sampling strategy, and a consistency factor. (Algorithm 1 on pages 8-9 shows input sequence). Regarding claim 3, , Nayebi in view of Raykar and Bento teaches claim 2 as outlined above . Nayebi further teaches: the raw data includes a series of data that represents an evolution of information over time, and wherein the parameter includes a required number of the at least one time window. (Nayebi is working with time-series data. Page 8 Section 3.3.1 “ In this approach, the time-axis is segmented into fixed-length windows. Even though all time windows have the same length, if the length of the sequence is not divisible by the length of the time window, the last time window may be smaller than the others. ”) Regarding claim 4, , Nayebi in view of Raykar and Bento teaches claim 1 as outlined above . Raykar further teaches: each of the at least one respective mode explanation includes at least one respective feature attribution for each of a plurality of segmented time windows with respect to a specific corresponding background data distribution. ([0030] “ The subset of surrogate models may be selected based on features used in the models, feature importance, model simplicity, or any other suitable interpretable aspect. ” And [0031] “ The one or more explanation outputs may be generated based on the subset of surrogate models. ”) Regarding claim 5, , Nayebi in view of Raykar and Bento teaches claim 1 as outlined above . Bento further teaches: the at least one reconciled explanation corresponds to a consistent explanation for each of the at least one prediction over time. (Page 4 Section 3 TimeSHAP “ Additionally, we aim to explain sequential models while preserving the three desirable properties of importance attribution stemming from the Shapley values: local accuracy, missingness ,and consistency ”) Regarding claim 6, , Nayebi in view of Raykar and Bento teaches claim 1 as outlined above . Bento further teaches: determining, by the at least one processor, a consistency value for a plurality of features in each of the at least one respective mode explanation; and (Page 4 Section 3 TimeSHAP “ TimeSHAP attributes an importance value to each feature/event in the input that reflects the degree to which that feature/event affected the fina l prediction. ” removing, by the at least one processor, at least one feature from the plurality of features based on the consistency value and a consistency factor. (Page 5 Section 3.2 Pruning “ One glaring issue with TimeSHAP is that the number of event (temporal) coalitions scales exponentially with the length of the observed sequence, just as in KernelSHAP the number of feature coalitions scales exponentially with the number of input features. Moreover, in a recurrent setting, the input sequence can be arbitrarily long. We address this issue by proposing a temporal coalition pruning algorithm. ”) Regarding claim 7, , Nayebi in view of Raykar and Bento teaches claim 6 as outlined above . Bento further teaches: weighting, by the at least one processor using a weighting function, at least one remaining feature from the plurality of features based on a result of the removing; and (Page 4. Section 3 TimeSHAP “ the bias term 𝑤 0= 𝑓 ( ℎ𝑋 (0)) corresponds to the model’s output with all features toggled off (dubbed base score), the weights 𝑤𝑖 , 𝑖 ∈ {1,..., 𝑚 }, correspond to the importance of each feature ”) determining, by the at least one processor, at least one respective reconciled feature score for each of the at least one remaining feature. (Page 7 Section 4.2 Global Explanations “ We compute global explanations by applying TimeSHAP to all sequences that contained a positive prediction, and explaining the first positive prediction of each sequence (hence referred as 𝑡 = 0). We use 𝜂 = 0.025 as the tolerance of the temporal coalition pruning algorithm. ”) Regarding claim 8, , Nayebi in view of Raykar and Bento teaches claim 6 as outlined above . Bento further teaches: the consistency value relates to a feature value distance of a plurality of proximate features that is determined based on a graphical projection of the at least one respective mode explanation. (Page 8 Section 4.3 Local Explanations “ As expected, the aggregate importance of older events (from the beginning of the sequence up to index 𝑡 ) suffers a steep decrease as its distance to the current event increases ”) Regarding claim 9, Nayebi in view of Raykar and Bento teaches claim 1 as outlined above . Nayebi further teaches: the at least one model includes at least one from among a large language model, a deep learning model, a neural network model, a natural language processing model, a machine learning model, a mathematical model, a process model, and a data model. (Page 3. Introduction: “ For evaluation purposes, we train three deep learning models on time-series data ”) Regarding claim 10, Nayebi teaches: receive, via an application programming interface, at least one input; (Algorithm 1 on pages 8-9 shows input sequence). temporally segment the at least one input to generate a finite set of at least one time window; (Page 8, 3.3.1 Stationary WindowSHAP “ In this approach, the time-axis is segmented into fixed-length windows. ”) train at least one model for each of the at least one time window; (Page 13. 3.5 Data sources ” To test the model-agnostic explanation methods (e.g., WindowSHAP), we used three distinct clinical time-series data sets to develop and train three different deep learning prediction models. ”) generate, by using each of the at least one trained model, at least one prediction for a target time based on the at least one input; (Page 13. 3.5 Data sources ” To test the model-agnostic explanation methods (e.g., WindowSHAP), we used three distinct clinical time-series data sets to develop and train three different deep learning prediction models. ” Prediction model is making prediction for the windows) determine at least one respective mode explanation for each of the at least one time window based on the corresponding set of common background data, the corresponding at least one trained model, and the corresponding at least one prediction; and (Page 6 Section 3 Methods shows how they calculate Shapley values for each window which serve as an explanation which uses the trained prediction model and prediction. ) Nayebi does not explicitly teach: A computing device configured to implement an execution of a method for providing explanations of predictive outputs, the computing device comprising: a processor; a memory; and Prediction model for each time window generate a set of common background data for each of the at least one time window based on the at least one input; determine at least one reconciled explanation for a target prediction that corresponds to the target time based on the at least one input and the at least one respective mode explanation. Raykar teaches: A computing device configured to implement an execution of a method for providing explanations of predictive outputs, the computing device comprising: a processor; a memory; and ([0047] “ As shown in the figure, computer system/server 900 is shown in the form of a general-purpose computing device. The components of computer system/server 900 may include, but are not limited to, one or more processors 902 (e.g., processing units), a system memory 904 (e.g., a computer-readable storage medium coupled to the one or more processors), and a bus 906 that couple various system components including system memory 904 to the processor 902 . ”) Prediction model for each time window ([0024] “ The input component 110 receives sets of time series forecasting predictions generated from sets of black box models. ”) generate a set of common background data for each of the at least one time window based on the at least one input; ([0040] “ In operation 810 , the model component 130 generates a surrogate data set. The surrogate data set may be generated by backtesting one or more time series forecasting predictions of the set of time series forecasting predictions. ”) Nayebi and Raykar are considered analogous art to the claimed invention because they are in the same field of endeavor being AI Explanation systems . It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the background data of Raykar with the explanation system of Nayebi and include the background data in the explanation determination . One would want to do this to create better explanations (Raykar background). Bento teaches: determine at least one reconciled explanation for a target prediction that corresponds to the target time based on the at least one input and the at least one respective mode explanation. (Page 7 Section 4.2 Global Explanations “ We compute global explanations by applying TimeSHAP to all sequences that contained a positive prediction, and explaining the first positive prediction of each sequence (hence referred as 𝑡 = 0). We use 𝜂 = 0.025 as the tolerance of the temporal coalition pruning algorithm. ”) Nayebi, Raykar, and Bento are considered analogous art to the claimed invention because they are in the same field of endeavor being AI Explanation systems . It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the time window explanation system of Nayebi with the global explanation system of Bento . One would want to understand the model’s decision process as a whole (Bento 4.2 Global Explanations) . Regarding claim 1 1 , Nayebi in view of Raykar and Bento teaches claim 10 as outlined above . Claim 1 1 recites similar limitations corresponding to claim 2 and is rejected for similar reasons as claim 2 using similar teachings and rationale. Regarding claim 1 2 , Nayebi in view of Raykar and Bento teaches claim 11 as outlined above . Claim 1 2 recites similar limitations corresponding to claim 3 and is rejected for similar reasons as claim 3 using similar teachings and rationale. Regarding claim 1 3 , Nayebi in view of Raykar and Bento teaches claim 10 as outlined above . Claim 1 3 recites similar limitations corresponding to claim 4 and is rejected for similar reasons as claim 4 using similar teachings and rationale. Regarding claim 1 4 , Nayebi in view of Raykar and Bento teaches claim 10 as outlined above . Claim 1 4 recites similar limitations corresponding to claim 5 and is rejected for similar reasons as claim 5 using similar teachings and rationale. Regarding claim 1 5 , Nayebi in view of Raykar and Bento teaches claim 10 as outlined above . Claim 1 5 recites similar limitations corresponding to claim 6 and is rejected for similar reasons as claim 6 using similar teachings and rationale. Regarding claim 1 6 , Nayebi in view of Raykar and Bento teaches claim 15 as outlined above . Claim 1 6 recites similar limitations corresponding to claim 7 and is rejected for similar reasons as claim 7 using similar teachings and rationale. Regarding claim 1 7 , Nayebi in view of Raykar and Bento teaches claim 15 as outlined above . Claim 1 7 recites similar limitations corresponding to claim 8 and is rejected for similar reasons as claim 8 using similar teachings and rationale. Regarding claim 1 8 , Nayebi in view of Raykar and Bento teaches claim 10 as outlined above . Claim 1 8 recites similar limitations corresponding to claim 9 and is rejected for similar reasons as claim 9 using similar teachings and rationale. Regarding claim 19, Nayebi teaches: receive, via an application programming interface, at least one input; (Algorithm 1 on pages 8-9 shows input sequence). temporally segment the at least one input to generate a finite set of at least one time window; (Page 8, 3.3.1 Stationary WindowSHAP “ In this approach, the time-axis is segmented into fixed-length windows. ”) train at least one model for each of the at least one time window; (Page 13. 3.5 Data sources ” To test the model-agnostic explanation methods (e.g., WindowSHAP), we used three distinct clinical time-series data sets to develop and train three different deep learning prediction models. ”) generate, by using each of the at least one trained model, at least one prediction for a target time based on the at least one input; (Page 13. 3.5 Data sources ” To test the model-agnostic explanation methods (e.g., WindowSHAP), we used three distinct clinical time-series data sets to develop and train three different deep learning prediction models. ” Prediction model is making prediction for the windows) determine at least one respective mode explanation for each of the at least one time window based on the corresponding set of common background data, the corresponding at least one trained model, and the corresponding at least one prediction; and (Page 6 Section 3 Methods shows how they calculate Shapley values for each window which serve as an explanation which uses the trained prediction model and prediction. ) Nayebi does not explicitly teach: A non-transitory computer readable storage medium storing instructions for providing explanations of predictive outputs, the storage medium comprising executable code which, when executed by a processor, causes the processor to: Prediction model for each time window generate a set of common background data for each of the at least one time window based on the at least one input; determine at least one reconciled explanation for a target prediction that corresponds to the target time based on the at least one input and the at least one respective mode explanation. Raykar teaches: A non-transitory computer readable storage medium storing instructions for providing explanations of predictive outputs, the storage medium comprising executable code which, when executed by a processor, causes the processor to: ([0070] “ These computer readable program instructions may be provided to a processor of a general-purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. ”) Prediction model for each time window ([0024] “ The input component 110 receives sets of time series forecasting predictions generated from sets of black box models. ”) generate a set of common background data for each of the at least one time window based on the at least one input; ([0040] “ In operation 810 , the model component 130 generates a surrogate data set. The surrogate data set may be generated by backtesting one or more time series forecasting predictions of the set of time series forecasting predictions. ”) Nayebi and Raykar are considered analogous art to the claimed invention because they are in the same field of endeavor being AI Explanation systems . It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the background data of Raykar with the explanation system of Nayebi and include the background data in the explanation determination . One would want to do this to create better explanations (Raykar background). Bento teaches: determine at least one reconciled explanation for a target prediction that corresponds to the target time based on the at least one input and the at least one respective mode explanation. (Page 7 Section 4.2 Global Explanations “ We compute global explanations by applying TimeSHAP to all sequences that contained a positive prediction, and explaining the first positive prediction of each sequence (hence referred as 𝑡 = 0). We use 𝜂 = 0.025 as the tolerance of the temporal coalition pruning algorithm. ”) Nayebi, Raykar, and Bento are considered analogous art to the claimed invention because they are in the same field of endeavor being AI Explanation systems . It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the time window explanation system of Nayebi with the global explanation system of Bento . One would want to understand the model’s decision process as a whole (Bento 4.2 Global Explanations). Regarding claim 20 , Nayebi in view of Raykar and Bento teaches claim 19 as outlined above . Claim 20 recites similar limitations corresponding to claim 2 and is rejected for similar reasons as claim 2 using similar teachings and rationale. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT DANIEL P GRUSZKA whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)272-5259 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT M-F 9:00 AM - 6:00 PM ET . Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, FILLIN "SPE Name?" \* MERGEFORMAT Li Zhen can be reached at FILLIN "SPE Phone?" \* MERGEFORMAT (571) 272-3768 . 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