CTNF 18/163,711 CTNF 101375 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. This action is in response to the application and claims filed 02/02/2023. Claims 1-20 are pending and have been examined. Claims 1-20 are rejected. Information Disclosure Statement The information disclosure statement (IDS) submitted on 08/28/2024 and 10/03/2025 is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. Claim Objections 07-29-01 AIA Claim 2, 11, 16 and 17 are objected to because of the following informalities: Regarding claim 2, 11, and 16 “includes at a predicted value” is grammatically ambiguous. It should likely read “includes a predicted value”. The current preposition phrase ‘at’ obscures whether the inference is the value or a location defined by the value. Regarding claim 17 “ herein identifying the plurality…” should read “ wherein identifying the plurality” to be grammatically correct . Appropriate correction is required. Specification 07-29 AIA The disclosure is objected to because of the following informalities: There are many conflicts regarding the numbering of the “Token Importance Information” and “Spatial Saliency Information” between Figure 1 and the detailed description. Figure 1 labels “Token Importance Information” as 126(1)-(N) and “Spatial Saliency Information” as 124(1)-(N). However, paragraph [0024] states “…determine token importance information 124(1)-(n)…” This contradicts Figure 1, which assigns 126 to token importance information. Similarly, Paragraph [0026] states “…determine the spatial saliency information 126”. This contradicts Figure 1, which assigns 124 to Spatial Saliency Information. This error is consistently made in many of the subsequent paragraphs. Paragraph [0027] “…determine token importance information 128”. Reference number 128 does not appear in the drawings provided . Appropriate correction is required. Drawings 06-22-02 AIA The drawings are objected to as failing to comply with 37 CFR 1.84(p)(4) because reference characters " 124(1)-(N) " and " 126(1)-(N) " have both been used to designate Spatial Saliency Information in Figure 1 and Figure 5 respectively . Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 an abstract idea without significantly more. The analysis of the claims will follow the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 (“2019 PEG”). Claim 1 Step 1: The claim recites a method; therefore, it is directed to the statutory category of processes. Step 2A prong 1: The claim recites the following abstract ideas: identifying, based on a token-based importance method, a plurality of tokens of a predefined importance to a machine learning (ML) inference; This limitation falls within the metal process grouping because it describes an act of evaluation or judgement. A person can mentally judge and identify items based on importance. generating frequency distribution information based on the plurality of tokens of the predefined importance; (This limitation falls with the mathematical concepts grouping because it involves statistical math operations of organizing data to determine a distribution.) generating, based on the frequency distribution information, quantile information for the plurality of tokens of a predefined importance; (This limitation falls within the mathematical concepts grouping because it involves finding quantile information which involve a mathematical calculation to divide a probability distribution into intervals of equal probability.) calculating spatial saliency information based on the frequency distribution information and quantile information, the spatial saliency information including a spatial saliency value for a quantile of the quantile information; ( This limitation falls within the mathematical concepts grouping because it recites the act of calculating a numerical value using mathematical methods.) Step 2A prong 2: This judicial exception is not integrated into a practical application. The claim further recites: and presenting the spatial saliency information via a graphical user interface. (This limitation is merely a post-solution step of presenting the data that does not meaningfully limit the claim. Presenting is recited at a high level of generality and simply implementing the abstract idea in a generic method is not a practical application of the abstract idea. Therefore, this step is an insignificant extra-solution activity (MPEP 2106.05(g)).) ) Step 2B : and presenting the spatial saliency information via a graphical user interface. (MPEP 2106.05(d)(II) indicates that merely presenting offers and gathering statistics is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer) The additional elements considered individually or in combination do not amount to significantly more than the judicial exception. Therefore, the claim is not patent eligible. Claim 2 Step 1: A process, as above. Step 2A prong 1: See the rejection of Claim 1 above, which claim 2 depends on. Claim 2 further recites: further comprising generating the ML inference based on time series data, wherein the ML inference includes at a predicted value of a time stamp. (This limitation falls within the metal process grouping because it describes an act of evaluation or judgement. A person can mentally make an inference that’s based on time series data at a particular time stamp. Step 2A prong 2: The claim does not recite additional elements therefore the judicial exception is not integrated into a practical application. Step 2B : The claim does not recite additional elements that amount to significantly more than the judicial exception. Therefore, the claim is not patent eligible. Claim 3 Step 1: A process, as above. Step 2A prong 1: See the rejection of Claim 1 above, which claim 3 depends on. Claim 3 further recites: wherein identifying the plurality of tokens of the predefined importance, comprises: identifying the plurality of the tokens as a predefined number of tokens having the highest importance values according to the token-based importance method . (This limitation falls within mental process grouping of abstract ideas. A person can perform this limitation in their mind or using pen and paper. The process amounts to simply observation, evaluation, and sorting, which are concepts performed in the human mind.) Step 2A prong 2: The claim does not recite additional elements therefore the judicial exception is not integrated into a practical application. Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception. Therefore, the claim is not patent eligible. Claim 4 Step 1: A process, as above. Step 2A prong 1: See the rejection of Claim 1 above, which claim 4 depends on. Claim 4 further recites: wherein token-based importance method includes a local interpretable model-agnostic explanations (LIME) method or a Shapley additive explanations (SHAP) method. ( This limitation falls within the mathematical concepts grouping because it recites mathematical algorithms (LIME and SHAP) which are used to calculate feature contribution values and importance scores. Step 2A prong 2: The claim does not recite additional elements therefore the judicial exception is not integrated into a practical application. Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception. Therefore, the claim is not patent eligible. Claim 5 Step 1: A process, as above. Step 2A prong 1: See the rejection of Claim 1 above, which claim 5 depends on. Claim 5 further recites: wherein generating frequency distribution information based on the plurality of tokens of the predefined importance comprises generating a frequency distribution histogram based on the plurality of tokens of the predefined importance. ( This limitation falls within the mathematical concepts grouping because creating a histogram relies on the mathematical operations of defining numerical ranges and calculating how data falls within those ranges. As stated in paragraph [0028] – “the SSE module 116 generates a frequency distribution histogram HistLKT,Nb by binning LKT into a predefined number of bins N B , and determines the quantiles of the HistLKT,Nb where N Q is the number of quantiles.” Step 2A prong 2: The claim does not recite additional elements therefore the judicial exception is not integrated into a practical application. Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception. Therefore, the claim is not patent eligible. Claim 6 Step 1: A process, as above. Step 2A prong 1: See the rejection of Claim 1 above, which claim 6 depends on. Claim 6 further recites: wherein calculating spatial saliency information based on the frequency distribution information and quantile information comprises: determining an aggregated importance of a timestamp range of the quantile of the quantile information; and determine the spatial saliency value based on the aggregated importance and a size of the quantile. ( This limitation falls within the mathematical concepts grouping because it describes a mathematical formula for density. The specification explicitly defines this calculation as a mathematical equation (see paragraph [0028]). Step 2A prong 2: The claim does not recite additional elements therefore the judicial exception is not integrated into a practical application. Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception. Therefore, the claim is not patent eligible. Claim 7 Step 1: A process, as above. Step 2A prong 1: See the rejection of Claim 1 above, which claim 7 depends on. Step 2A prong 2: This judicial exception is not integrated into a practical application. The claim further recites: wherein presenting the spatial saliency information via the graphical user interface comprises generating the graphical user interface to include a table presenting the spatial saliency information; and applying, based on the spatial saliency value, within the graphical user interface, one or more graphical effects to table information associated with the quantile of the quantile information. (The additional elements merely present the result of the abstract idea (the calculated saliency information) in a table format with visual effects. This is a post-solution step of displaying data, which is an insignificant addition to the claim that does not meaningfully limit the claim. Simply implementing the abstract idea and displaying the result in a generic table is not practical application of the abstract idea. Therefore, this is insignificant extra-solution step (MPEP 2106.05(g)).) Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception. Therefore, the claim is not patent eligible. wherein presenting the spatial saliency information via the graphical user interface comprises generating the graphical user interface to include a table presenting the spatial saliency information; and applying, based on the spatial saliency value, within the graphical user interface, one or more graphical effects to table information associated with the quantile of the quantile information. (MPEP 2106.05(d)(II) indicates that merely presenting offers and gathering statistics is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer) The additional elements considered individually or in combination do not amount to significantly more than the judicial exception. Therefore, the claim is not patent eligible. Claim 8 Step 1: A process, as above. Step 2A prong 1: See the rejection of Claim 1 above, which claim 8 depends on. Step 2A prong 2: This judicial exception is not integrated into a practical application. The claim further recites: wherein presenting the spatial saliency information via the graphical user interface comprises: generating the graphical user interface to include a graph representation of time sample information used to generate the ML inference, wherein the graph representation identifies the quantile of the quantile information; and applying, based on the spatial saliency value, within the graphical user interface, one or more graphical effects to graph information associated with the quantile of the quantile information. (The additional elements merely present the result of the abstract idea (the calculated saliency information) in a graph format with visual effects. This is a post-solution step of displaying data, which is an insignificant addition to the claim that does not meaningfully limit the claim. Simply implementing the abstract idea and displaying the result in a generic table is not practical application of the abstract idea. Therefore, this is insignificant extra-solution step (MPEP 2106.05(g)).) Step 2B: wherein presenting the spatial saliency information via the graphical user interface comprises: generating the graphical user interface to include a graph representation of time sample information used to generate the ML inference, wherein the graph representation identifies the quantile of the quantile information; and applying, based on the spatial saliency value, within the graphical user interface, one or more graphical effects to graph information associated with the quantile of the quantile information. (MPEP 2106.05(d)(II) indicates that merely presenting offers and gathering statistics is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer) The additional elements considered individually or in combination do not amount to significantly more than the judicial exception. Therefore, the claim is not patent eligible. Claim 9 Step 1: A process, as above. Step 2A prong 1: See the rejection of Claim 1 above, which claim 9 depends on. Step 2A prong 2: This judicial exception is not integrated into a practical application. The claim further recites: wherein presenting the spatial saliency information via the graphical user interface comprises transmitting, to a client device in response to a client request, the spatial saliency information for display via the graphical user interface. (This additional element recites a mere instruction to apply an exception with a recitation of the words "apply it" (or an equivalent) as identified in MPEP 2106.05(f), and does not provide integration into a practical application. This limitation amounts to transmitting data over a network, which is merely an instruction to apply the abstract idea using a generic computer component. Step 2B: wherein presenting the spatial saliency information via the graphical user interface comprises transmitting, to a client device in response to a client request, the spatial saliency information for display via the graphical user interface. (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer) The additional elements considered individually or in combination do not amount to significantly more than the judicial exception. Therefore, the claim is not patent eligible. Claim 10 Step 1: The claim recites a system; therefore, it is directed to the statutory category of machine. Step 2A prong 1: The claim recites the following abstract ideas: identify, based on a token-based importance method, a plurality of tokens of a predefined importance to a machine learning (ML) inference; This limitation falls within the metal process grouping because it describes an act of evaluation or judgement. A person can mentally judge and identify items based on importance. generate frequency distribution information based on the plurality of tokens of the predefined importance; (This limitation falls with the mathematical concepts grouping because it involves statistical math operations of organizing data to determine a distribution.) generate, based on the frequency distribution information, quantile information for the plurality of tokens of a predefined importance; (This limitation falls within the mathematical concepts grouping because it involves finding quantile information which involve a mathematical calculation to divide a probability distribution into intervals of equal probability.) determine spatial saliency information based on the frequency distribution information and quantile information, the spatial saliency information including a spatial saliency value for a quantile of the quantile information; ( This limitation falls within the mathematical concepts grouping because it recites the act of calculating a numerical value using mathematical methods.) Step 2A prong 2: This judicial exception is not integrated into a practical application. The claim further recites: A system comprising: a memory storing instructions thereon; and at least one processor coupled with the memory and configured by the instructions to: (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).) and present the spatial saliency information via a graphical user interface. (This limitation is merely a post-solution step of presenting the data that does not meaningfully limit the claim. Presenting is recited at a high level of generality and simply implementing the abstract idea in a generic method is not a practical application of the abstract idea. Therefore, this step is an insignificant extra-solution activity (MPEP 2106.05(g)).) ) Step 2B : A system comprising: a memory storing instructions thereon; and at least one processor coupled with the memory and configured by the instructions to: (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).) and present the spatial saliency information via a graphical user interface. (MPEP 2106.05(d)(II) indicates that merely presenting offers and gathering statistics is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer) The additional elements considered individually or in combination do not amount to significantly more than the judicial exception. Therefore, the claim is not patent eligible. Claim 11 Claim 11 is a system (machine) claim that recites identical limitations to method claim 2. Therefore, claim 11 is rejected using the same rationale as claim 2. Claim 12 Claim 12 is a system (machine) claim that recites identical limitations to method claim 3. Therefore, claim 12 is rejected using the same rationale as claim 3. Claim 13 Claim 13 is a system (machine) claim that recites identical limitations to method claim 4. Therefore, claim 13 is rejected using the same rationale as claim 4. Claim 14 Claim 14 is a system (machine) claim that recites identical limitations to method claim 6. Therefore, claim 14 is rejected using the same rationale as claim 6. Claim 15 Step 1: The claim recites a non-transitory computer-readable device; therefore, it is directed to the statutory category of machine. Step 2A prong 1: The claim recites the following abstract ideas: identifying, based on a token-based importance method, a plurality of tokens of a predefined importance to a machine learning (ML) inference; This limitation falls within the metal process grouping because it describes an act of evaluation or judgement. A person can mentally judge and identify items based on importance. generating frequency distribution information based on the plurality of tokens of the predefined importance; (This limitation falls with the mathematical concepts grouping because it involves statistical math operations of organizing data to determine a distribution.) generating, based on the frequency distribution information, quantile information for the plurality of tokens of a predefined importance; (This limitation falls within the mathematical concepts grouping because it involves finding quantile information which involve a mathematical calculation to divide a probability distribution into intervals of equal probability.) calculating spatial saliency information based on the frequency distribution information and quantile information, the spatial saliency information including a spatial saliency value for a quantile of the quantile information; ( This limitation falls within the mathematical concepts grouping because it recites the act of calculating a numerical value using mathematical methods.) Step 2A prong 2: This judicial exception is not integrated into a practical application. The claim further recites: A non-transitory computer-readable device having instructions thereon that, when executed by at least one computing device, cause the at least one computing device to perform operations comprising: (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).) and presenting the spatial saliency information via a graphical user interface. (This limitation is merely a post-solution step of presenting the data that does not meaningfully limit the claim. Presenting is recited at a high level of generality and simply implementing the abstract idea in a generic method is not a practical application of the abstract idea. Therefore, this step is an insignificant extra-solution activity (MPEP 2106.05(g)).) ) Step 2B : A non-transitory computer-readable device having instructions thereon that, when executed by at least one computing device, cause the at least one computing device to perform operations comprising: (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).) and presenting the spatial saliency information via a graphical user interface. (MPEP 2106.05(d)(II) indicates that merely presenting offers and gathering statistics is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer) The additional elements considered individually or in combination do not amount to significantly more than the judicial exception. Therefore, the claim is not patent eligible. Claim 16 Claim 16 is a non-transitory computer-readable device type claim that recites identical limitations to method claim 2. Therefore, claim 16 is rejected using the same rationale as claim 2. Claim 17 Claim 17 is a non-transitory computer-readable device type claim that recites identical limitations to method claim 3. Therefore, claim 17 is rejected using the same rationale as claim 3. Claim 18 Claim 18 is a non-transitory computer-readable device type claim that recites identical limitations to method claim 4. Therefore, claim 18 is rejected using the same rationale as claim 4. Claim 19 Claim 19 is a non-transitory computer-readable device type claim that recites identical limitations to method claim 5. Therefore, claim 19 is rejected using the same rationale as claim 5. Claim 20 Claim 20 is a non-transitory computer-readable device type claim that recites identical limitations to method claim 6. Therefore, claim 20 is rejected using the same rationale as claim 6. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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. 07-20-aia AIA 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. 07-23-aia AIA The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. 07-20-02-aia AIA This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention. 07-21-aia AIA Claim s 1-4, 6-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over non-patent literature Nayebi et al. ("WindowSHAP: An Efficient Framework for Explaining Timeseries Classifiers based on Shapley Values", hereinafter "Nayebi") in view of Takagi et al. (US-20220076049-A1), hereinafter "Takagi" . Regarding claim 1, Nayebi teaches: A method comprising: (Section 3.3 “We introduce our efficient framework called WindowSHAP… In this method, we compute Shapley values for each individual time window…”) identifying, based on a token-based importance method, a plurality of tokens of a predefined importance to a machine learning (ML) inference; (Section 3.1 “Shapley values assign an importance (contribution) score 𝜙 i to the 𝑖 th feature, indicating how much the model output for a single instance is influenced by its 𝑖 th feature.”) generating frequency distribution information based on the plurality of tokens of the predefined importance; (Figure 1 and Figure 8 – Examiner Note (EN): this depicts a visual distribution of importance (bar charts) across the time axis, which is similar to “frequency distribution” of importance. Creating a “frequency distribution” is simply a statistical method of visualizing where the “importance tokens” identified previously are located. Nayebi discloses determining the location and density of importance). generating, based on the frequency distribution information, quantile information for the plurality of tokens of a predefined importance; (Section 3.3.3 Dynamic WindowSHAP – EN: this denotes generating variable-length windows (ranges) based on the importance (density) of the data. It splits regions with high importance into smaller windows.) calculating spatial saliency information based on the frequency distribution information and quantile information, the spatial saliency information including a spatial saliency value for a quantile of the quantile information; (Section 1 “Instead of calculating Shapley values for every possible time step and variable combinations, we simply calculate Shapley values for each time window (see Figure 1 for conceptual demonstration).”) presenting the spatial saliency information (…) (Figure 8 – EN: this shows bar charts of Shapley values for specific windows.) Nayebi does not disclose “via a graphical user interface.” However, Takagi teaches “via a graphical user interface.” . (para 0024, “The display controller 14 causes the display 5 to display various types of information. For example, the display controller 14 visualizes the distribution.”) Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to combine the presentation of spatial saliency information of Nayebi with the display controller of Takagi. This will allow users observe and analyze behaviors of importances corresponding to features. (Takagi Para 0046, “ Since importances are visualized for each of the groups formed according to estimated output values, it is possible to observe and analyze behaviors of importances corresponding to estimated output values. Since importances of each feature amount are visualized for each group, it is possible to observe and analyze behaviors of importances corresponding to feature amounts, differences in behavior corresponding to estimated output values, and the like.”) Nayebi teaches all the core concepts of the claimed invention, including identifying token-based importance for time-series data and aggregating that importance into spatial ranges (windows) to determine saliency. The claimed generation of “frequency distribution information” and “quantile information” merely represents a slight statistical variation of the Nayebi’s density-based windowing. These statistical grouping methods are obvious modifications that are well-known and standard in the art of visualizing machine learning predictions to prevent data loss from simple averaging. For instance, Takagi (US-20220076049-A1) explicitly teaches an importance analysis apparatus. (Para 0017, “The distribution calculator calculates a distribution of the importances of each of the feature amounts across the input data samples. Para 0041, The type of distribution can be discretionarily selected from among… a quantile…) Takagi further discloses generating graphs like violin plots to display these “probability density functions” (frequency distributions), noting that such distribution analysis is necessary because simple averages can overlook multimodal distributions of importances. Therefore, it would have been obvious to a person of ordinary skill in the art to apply the standard statistical distribution and quantile grouping methods of Takagi to the time-series windows of Nayebi to achieve the claimed invention. Regarding claim 2 , as discussed above, Nayebi in view of Takagi teaches all of the limitations of claim 1. Nayebi further teaches: generating the ML inference based on time series data, wherein the ML inference includes at a predicted value of a time stamp. (Section 3.5 “We developed a prediction model to predict the long-term functional outcome of patients”. Section 3.5 "The third clinical prediction model is based on the MIMIC data set and uses the initial 48 hours of clinical data to predict patient mortality in the subsequent 48 hours.” EN: This denotes the paper’s application to time-series predictive models used to forecast patient outcomes at future time points. ) Regarding claim 3, as discussed above, Nayebi in view of Takagi teaches all of the limitations of claim 1. Nayebi further teaches: identifying the plurality of the tokens as a predefined number of tokens having the highest importance values according to the token-based importance method. ( Figure 7 – “The top 15 variables depicted on the y axis are ranked according to their importance.” EN: This depicts a visualization where it identifies and presents exactly the top 15 variables based on their importance ranking. This figure is based on the Dynamic WindowSHAP algorithm which uses Shapley values, therefore it is based on SHAP (a token-based importance method. ) Regarding claim 4, as discussed above, Nayebi in view of Takagi teaches all of the limitations of claim 1. Nayebi further teaches: wherein token-based importance method includes a local interpretable model-agnostic explanations (LIME) method or a Shapley additive explanations (SHAP) method. (Section 1 Introduction - “In summary, the main contributions of this study are as follows: Developing the WindowSHAP framework, a variation of Shapley additive explanations for time-series data…”) Regarding claim 6 , as discussed above, Nayebi in view of Takagi teaches all of the limitations of claim 1. Nayebi further teaches: determining an aggregated importance of a timestamp range of (…) (Section 3.3 “Considering each window for each variable as a feature, the Shapley value for the 𝑘 th time window of variable 𝑖 is calculated as see equation 4” and Section 5 “First, by aggregating nearby time steps as a time window, WindowSHAP lowers the dependence of the elements…” PNG media_image1.png 215 1448 media_image1.png Greyscale EN: this denotes determining the importance (shapley value) of a timestamp range (window). Equation 4 treats each window as a single feature, therefore equation 4 determines the collective (aggregated) importance of all the time steps within that time range (window) ). and determine the spatial saliency value based on the aggregated importance and a size of (…) (Section 3.3 “The Shapley value of any variable-time point combination can be estimated by distributing the importance of a time window equally among its time points, i.e., see equation 5” ) PNG media_image2.png 254 1125 media_image2.png Greyscale Regarding claim 7 , as discussed above, Nayebi in view of Takagi teaches all of the limitations of claim 1. Nayebi further teaches: (…) to include a table presenting the spatial saliency information; and applying, based on the spatial saliency value, within the graphical user interface, one or more graphical effects to table information associated with (…) (Section 4 “Figure 7. Heatmaps depicting the importance of all time steps for the important features for a certain patient record” EN: This denotes a Heatmap which includes a matrix of data organized in rows and columns and the graphical effect is the color intensity of the cell.) Nayebi does not distinctly disclose: “generating the graphical user interface…” and “…the quantile of the quantile information.“ However, Takagi teaches: “generating the graphical user interface…” (Para 0024, “Specifically, the display controller 14 generates a graph showing a distribution for each of a plurality of feature amounts, and causes the display 5 to display the graph.”) “…the quantile of the quantile information.“ ( Para 0041, The type of distribution can be discretionarily selected from among… a quantile… ) Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to combine the heatmap (table information) and graphical effect of Nayebi with the statistical distribution analysis which includes quantiles and the display controller of Takagi. Combining the teaching of Nayebi and Takagi allows the heatmap (table information) to be structured in quantiles and be displayed via a graphical user interface. Thereby, allowing users observe and analyze behaviors of importances corresponding to features. (Takagi Para 0062, “The configuration also reduces the possibility of overlooking the case where there are feature amounts of high importances only in some input data samples out of all input data samples, the case where a distribution of importances is multimodal, or the like.” Takagi Para 0046, “ Since importances are visualized for each of the groups formed according to estimated output values, it is possible to observe and analyze behaviors of importances corresponding to estimated output values. Since importances of each feature amount are visualized for each group, it is possible to observe and analyze behaviors of importances corresponding to feature amounts, differences in behavior corresponding to estimated output values, and the like.”) Regarding claim 8 , as discussed above, Nayebi in view of Takagi teaches all of the limitations of claim 1. Nayebi further teaches: (…) to include a graph representation of time sample information used to generate the ML inference, (Figure 8 depicts a graph of time sample information used to generate the prediction (heart rate variable) wherein the graph representation identifies (…); (Figure 8 EN: this depicts (identifies) the two divided blocks, a blue block and red block) and applying, based on the spatial saliency value, within the graphical user interface, one or more graphical effects to graph information associated with ( Figure 8, EN: this depicts the graphical effect (red and blue) and the height of the bars corresponds to the Shapley values (calculated in equation 5), this corresponds to “spatial saliency value” as stated in claim 6.) Nayebi does not distinctly disclose: “generating the graphical user interface” and “the quantile of the quantile information;” However, Takagi teaches: “generating the graphical user interface…” (Para 0024, “Specifically, the display controller 14 generates a graph showing a distribution for each of a plurality of feature amounts, and causes the display 5 to display the graph.”) “…the quantile of the quantile information.“ ( Para 0041, The type of distribution can be discretionarily selected from among… a quantile… ) Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to combine the graph representation of time samples and the graphical effect of Nayebi with the statistical distribution analysis which includes quantiles and the display controller of Takagi. Combining the teaching of Nayebi and Takagi allows the graph representation of time sample to be structured in quantiles and be displayed via a graphical user interface. Thereby, allowing users observe and analyze behaviors of importances corresponding to features. (Takagi Para 0062, “The configuration also reduces the possibility of overlooking the case where there are feature amounts of high importances only in some input data samples out of all input data samples, the case where a distribution of importances is multimodal, or the like.” Takagi Para 0046, “ Since importances are visualized for each of the groups formed according to estimated output values, it is possible to observe and analyze behaviors of importances corresponding to estimated output values. Since importances of each feature amount are visualized for each group, it is possible to observe and analyze behaviors of importances corresponding to feature amounts, differences in behavior corresponding to estimated output values, and the like.”) Regarding claim 9 , as discussed above, Nayebi in view of Takagi teaches all of the limitations of claim 1. Nayebi further teaches: the spatial saliency information (Section 3.3 “The Shapley value of any variable-time point combination can be estimated by distributing the importance of a time window equally among its time points, i.e., see equation 5” Examiner Note ( EN): the numerator of the equation denotes the aggregated importance and the denominator is the size of the time window. The result 𝜙 (i,t) is the importance density of the size of the window. Similar to Para 0028 of the instant application “determines the spatial saliency information 126 by calculating the density of feature contribution to a model prediction for each quantile” ) Nayebi does not disclose: transmitting, to a client device in response to a client request, (…) for display via the graphical user interface. However, Takagi teaches: transmitting, to a client device in response to a client request, (…) for display via the graphical user interface. (Figure 1 depicts an input device 3, communication device 4, display 5, and display controller 14) Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to take the density information (spatial saliency information) of Nayebi and apply it to the configuration system of Takagi to present the information. Thereby, allowing users to observe and analyze behaviors of importances corresponding to features. (Takagi Para 0046, Since importances are visualized for each of the groups formed according to estimated output values, it is possible to observe and analyze behaviors of importances corresponding to estimated output values. Since importances of each feature amount are visualized for each group, it is possible to observe and analyze behaviors of importances corresponding to feature amounts, differences in behavior corresponding to estimated output values, and the like.) Regarding Claim 10, Takagi teaches: A system comprising: (Para 0019, “FIG. 1 shows a configuration example of an importance analysis apparatus 100 according to the present embodiment. The importance analysis apparatus 100 is a computer for analyzing an importance of a feature amount in machine learning.”) a memory storing instructions thereon; and at least one processor coupled with the memory and configured by the instructions to: (Para 0020, “The processing circuitry 1 includes a processor such as a central processing unit (CPU), and a memory such as a random access memory (RAM). The processing circuitry 1 executes importance visualization processing for calculating and visualizing an importance of a feature amount in machine learning.” Para 0025, “The storage device 2 stores results of various operations by the processing circuitry 1, various programs executed by the processing circuitry 1, and the like.”) The rest of claim 10 recites identical limitations to method claim 1. Therefore, claim 10 is rejected using the same rationale as claim 1. Regarding claim 11, Claim 11 is a system type claim that recite the same limitations as claim 2, Therefore, claim 11 is rejected using the same rationale as claim 2. Regarding claim 12, Claim 12 is a system type claim that recite the same limitations as claim 3, Therefore, claim 12 is rejected using the same rationale as claim 3. Regarding claim 13, Claim 13 is a system type claim that recite the same limitations as claim 4, Therefore, claim 13 is rejected using the same rationale as claim 4. Regarding claim 14, Claim 14 is a system type claim that recite the same limitations as claim 6, Therefore, claim 14 is rejected using the same rationale as claim 6. Regarding claim 15, Takagi teaches: A non-transitory computer-readable device having instructions thereon that , (Para, 0002, “Embodiments described herein relate generally to an importance analysis apparatus, method, and non-transitory computer readable medium.” Para 0063, “The function of each unit according to the present embodiment, and the program for causing a computer to implement the function may be stored in a non-transitory computer readable medium.”) when executed by at least one computing device, (Para 0020, “The processing circuitry 1 executes a program stored in the storage device 2 to implement an estimation unit 11, an importance calculator 12, a distribution calculator 13, and a display controller 14.”) cause the at least one computing device to perform operations comprising: (Para 0020, “The processing circuitry 1 executes importance visualization processing for calculating and visualizing an importance of a feature amount in machine learning.”) The rest of claim 15 recites identical limitations to method claim 1. Therefore, claim 15 is rejected using the same rationale as claim 1. Regarding claim 16, Claim 16 is a non-transitory computer-readable device type claim that recite the same limitation of claim 2. Therefore, claim 16 is rejected using the same rationale as claim 2. Regarding claim 17, Claim 17 is a non-transitory computer-readable device type claim that recite the same limitation of claim 3. Therefore, claim 17 is rejected using the same rationale as claim 3. Regarding claim 18, Claim 18 is a non-transitory computer-readable device type claim that recite the same limitation of claim 4. Therefore, claim 18 is rejected using the same rationale as claim 4. Regarding claim 20, Claim 20 is a non-transitory computer-readable device type claim that recite the same limitation of claim 6. Therefore, claim 20 is rejected using the same rationale as claim 6 . 07-21-aia AIA Claim s 5 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over non-patent literature Nayebi et al. ("WindowSHAP: An Efficient Framework for Explaining Timeseries Classifiers based on Shapley Values", hereinafter "Nayebi") in view of Takagi et al. (US-20220076049-A1), hereinafter "Takagi" further in view of Venkataramani et al. (US-20170154181-A1), hereinafter "Venkataramani”.Regarding claim 5, as discussed above, Nayebi in view of Takagi teaches all of the limitations of claim 1 Nayebi further teaches:wherein generating frequency distribution information based on the plurality of tokens of the predefined importance comprises generating (…) based on the plurality of tokens of the predefined importance. (Figure 1 and Figure 8 – Examiner Note (EN): this depicts a visual distribution of importance (bar charts) across the time axis, which is similar to “frequency distribution” of importance. Creating a “frequency distribution” is simply a statistical method of visualizing where the “importance tokens” identified previously are located. Nayebi discloses determining the location and density of importance). Nayebi does not disclose: “a frequency distribution histogram” However, Venkataramani teaches “a frequency distribution histogram” (figure 7 – EN: this depicts Histogram which includes frequency in the Y axis ) Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to combine the importance density calculation of Nayebi with the statistical distribution analysis (frequency and quantiles) of Takagi to include a histogram as taught by Venkataramani in order to visualize and identify clusters of importance within the time series data while filtering out noise. (Venkataramani, Figure 7 Event Density Histogram) Regarding claim 19, Claim 19 is a non-transitory computer-readable device type claim that recite the same limitation of claim 5. Therefore, claim 19 is rejected using the same rationale as claim 5 . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. For example, non-patent literature Ismail, Aya (“Benchmarking Deep Learning Interpretability in Time Series Predictions”, hereinafter “Ismail”) discloses standard token-based saliency methods (e.g. SNAP, Gradient) in time-series contexts, specifically their failure to distinguish informative features . Any inquiry concerning this communication or earlier communications from the examiner should be directed to NAYMUR RAHMAN ALI whose telephone number is (571)272-0007. The examiner can normally be reached Mon-Fri. 7:30-5 pm. 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, Alexey Shmatov can be reached at (571)270-3428. The fax phone number for the organization where this application or proceeding is assigned is (571)273-8300. Information regarding the status of published or unpublished applicatio