CTNF 18/327,619 CTNF 82697 DETAILED ACTION 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 communications filed on 06/01/2023. Claims 1-20 are pending and have been examined. Priority Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. Information Disclosure Statement 06-52 The information disclosure statement (IDS) submitted was filed on 06/01/2023. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. Specification 07-29 AIA The disclosure is objected to because of the following informalities: The use of a trade name or a mark used in commerce (e.g. MICROSOFT, CISCO, APPLE, etc.) has been noted in this application. It should be capitalized (each letter) wherever it appears and be accompanied by the generic terminology or, where appropriate, include a proper symbol indicating use in commerce, such as ™, SM, or ® following the word. Although the use of trade names and marks used in commerce (i.e., trademarks, service marks, certification marks, and collective marks) are permissible in patent applications, the proprietary nature of the marks should be respected and every effort made to prevent their use in any manner which might adversely affect their validity as commercial marks . Appropriate correction is required. Claim Objections 07-29-01 AIA Claim s 1-2 and 17 are objected to because of the following informalities: As per claim 1, it appears that “the steps of” in line 3 should be removed (note: this lacks antecedent basis). This similarly applies to claims 2 and 17 . Appropriate correction is required. Claim Rejections - 35 USC § 112 07-30-02 AIA The following is a quotation of 35 U.S.C. 112(b): (b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. 07-34-01 Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Independent claims 1 and 17 appear to recite contingent limitations, e.g. based on a state of convergence not occurring. As the Examiner construes the claim, the contingency conditions themselves are not actively recited. For example, it not explicitly clear what is the state of convergence in the claim. Accordingly, it is not clear which, if any, of the contingencies are satisfied, and therefore which, if any, of the claim's consequential actions to a satisfied contingency are required. See, e.g., MPEP 2111.04(II). The effect is that the claim is rendered vague and indefinite under 35 U.S.C. 112(b) and therefore rejected accordingly. The dependent claims 2-16 and 18-20 include the same or similar limitations as claims 1 and 17 discussed here, without curing its deficiencies, and are therefore rejected under the same rationale. Applicants can overcome the rejection by making affirmative the condition, thereby necessitating the performance of a consequential action. Note further that claims 5-8 and 19 are also additionally rejected under similar rationale because they contain further contingent limitations with respect to the loss function. As per claim 9, there is lack of antecedent basis for “the algorithm” in line 3. This similarly applies to claim 19. As per claim 14, there is lack of antecedent basis for “the end” in line 1. 07-34-03 AIA The term “ significantly”/“significantly larger ” in claim 13 is a relative term which renders the claim indefinite. The term “ significantly ” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. What is considered “significantly”/“significantly larger” varies depending on person, context, etc. As such, this renders the claim indefinite . 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 abstract idea without significantly more. The claims recite a system and method associated with storing, selecting, applying, executing, comparing, determining, and updating. The limitations “select… select… apply… execute… compare… determine... update” as recited in claim 1 are each a process, under the broadest reasonable interpretation, covering performance of the limitations in the mind or by pen and paper (See Berkheimer v. HP, Inc., 881 F.3d 1360, 1366, 125 USPQ2d 1649 (Fed. Cir. 2018) ) but for the recitation of generic computer components. That is, other than reciting “ a computing device including at least one processor in communication with at least one memory device, wherein the at least one processor is programmed to perform ”, “ randomly select a sequence including a subset of continuous data points in the plurality of historical time series data ” in the context of the claim encompasses the user making choices. “ randomly select a mask length for a mask for the selected sequence ” in the context of the claim encompasses the user making choices. The limitation “ apply the mask to the selected sequence, wherein the mask is applied to the plurality of forecast variables in the selected sequence ” in the context of the claim encompasses the user making evaluations. The limitation “ execute a model with the masked selected sequence to generate predictions for the masked forecast variables ” in the context of the claim encompasses the user making calculations (note: “ model ” broadly includes an equation or formula). The limitations “ compare the predictions for the masked forecast variables to the actual forecast variables in the selected sequence… determine if convergence occurs based upon the comparison ” in the context of the claim encompasses the user making evaluations. The limitation “ if convergence has not occurred, update one or more parameters of the model and return to step b ” in the context of the claim encompasses the user making calculations. If a claimed limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “mental processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Accordingly, the claim recites an abstract idea. This judicial exception is not integrated into a practical application. In particular, the claim recites additional elements. The claim recites “ computing device including at least one processor in communication with at least one memory device, wherein the at least one processor is programmed to perform ”. The elements are recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using a generic computer component (e.g. See MPEP 2106.05(f)). Moreover, the limitation “ store a plurality of historical time series data including a plurality of predictor variables and a plurality of forecast variables ” is considered as insignificant extra-solution activity (see MPEP 2106.05(g)). Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are no more than a generic computer component and/or field of use. With respect to “ store a plurality of historical time series data including a plurality of predictor variables and a plurality of forecast variables ” considered as insignificant extra-solution activity, MPEP 2106.05(d)(II) indicates that mere storing of information in memory is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here; note e.g. Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93 ). Therefore, the claims are not patent eligible. Claim 17 recites similar claim language as claim 1, and thus have the same issues. Similarly, with respect to claim 8, that the claim recites “ implemented by a computing device including at least one processor in communication with at least one memory device ”. The elements are recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using a generic computer component (e.g. See MPEP 2106.05(f)). Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea and are not sufficient to amount to significantly more than the judicial exception. Regarding claim 2 , the claim does not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception. For example, the claim further describes the comparing comprising determining a difference, which is a mental step (amounting to a user making calculations) and does not include any additional elements. Regarding claim 3 , the claim does not include any additional elements that are sufficient to amount to significantly more than the judicial exception. For example, the claim further describes calculating a loss function, which is a mental step (amounting to a user making calculations) and does not include any additional elements. This similarly applies to claim 18 . Regarding claim 4 , the claim does not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception. For example, the claim further describes the loss function, which is part of the mental steps (amounting to a user making calculations) and does not include any additional elements. This similarly applies to claim 19 . Regarding claim 5 , the claim does not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception. For example, the claim merely further describes determining, which is a mental step (amounting to a user making evaluations and calculations) and does not include any additional elements. This similarly applies to claims 6-9 . Regarding claim 10 , the claim does not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception. For example, the claim further describes determining, selecting, determining, and executing, which are mental steps (amounting to a user making evaluations and calculations) and does not include any additional elements. This similarly applies to claim 20. Regarding claim 11 , the claim does not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception. For example, the claim further includes masking, which is a mental step (amounting to a user making evaluations) and does not include any additional elements. Regarding claim 12 , the claim does not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception. For example, the claim further includes selecting, which is a mental step (amounting to a user making choices) and does not include any additional elements. Regarding claim 13 , the claim does not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception. For example, the claim further describes the historical data compared to the selected sequence, which is part of the mental steps (amounting to a user making choices) and does not include any additional elements. Regarding claim 14 , the claim does not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception. For example, the claim further describes the masked data, which is part of the mental steps (amounting to a user making evaluations) and does not include any additional elements. Regarding claim 15 , the claim does not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception. For example, the claim further describes the predictor variables, which is part of the insignificant extra-solution activity (as noted, MPEP 2106.05(d)(II) indicates that mere storing of information in memory is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here; note e.g. Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93 )). Regarding claim 16 , the claim does not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception. For example, the claim further describes the forecast variables, which is part of the insignificant extra-solution activity (as noted, MPEP 2106.05(d)(II) indicates that mere storing of information in memory is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here; note e.g. Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93 )). 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. 07-20-02-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 Claims 1-4, 6, 8-15, and 17-20 are rejected under 35 U.S.C. 10 3 as being unpatentable over Litt et al (US 6658287 B1) in view of Basil ico et al. (US 20220180186 A1). As per independent claim 1, Litt teaches a system comprising a computing device including at least one processor in communication with at least one memory device, wherein the at least one processor is programmed to perform (e.g. in column 28 lines 17-24, “ implemented entirely through software programs(s) executed by a processor… embodied as a processor readable memory medium storing instructions, which when executed by a processor, perform ”) the steps of: (a) store a plurality of historical time series data including a plurality of predictor variables and a plurality of forecast variables (e.g. in column 5 lines 14-48, column 7 lines 51-56, column 11 lines 45-48, column 25 lines 44-64, and column 28 lines 15-23, “ observation window during which time processing of the brain activity signal is continuous [i.e. time series data] … pre-ictal time frame for seizure prediction… large set of independent, instantaneous and historical features are extracted from the intracranial EEG, real-time brain activity data and/or other physiologic data…for each individual patient… memory… features are extracted… "feature library" is a collection of features which are extracted by algorithms from raw brain activity data… feature behavior… a processor readable memory medium ”) ; (b) randomly select a sequence including a subset of continuous data points in the plurality of historical time series data (e.g. in column 12 lines 40-44, column 21 lines 15-31 and column 24 lines 9-14, “ a window length… non-overlapping 10-minute baselines (>8 hrs. away from onsets) with random starting times… 24 randomly chosen baseline (.gtoreq.8 hrs from seizure) 50-60 minute IEEG segments ”) ; (c) randomly select a mask length for a mask for the selected sequence (e.g. in column 12 lines 40-44 and column 24 lines 9-14, “ a window [i.e. mask] length… 10-minute… randomly chosen…50-60 minute IEEG segments [i.e. mask]”) ; (d) apply the mask to the selected sequence, wherein the mask is applied to the plurality of forecast variables in the selected sequence (e.g. in column 12 lines 40-44, column 21 lines 15-31 and column 24 lines 9-14, and column 25 lines 44-59, “ a window length… non-overlapping 10-minute baselines (>8 hrs. away from onsets) with random starting times… 24 randomly chosen baseline (.gtoreq.8 hrs from seizure) 50-60 minute IEEG segments… feature vector for a particular patient is generated that contains windowed (i.e. calculated over a particular time window, such as 1.25 seconds) features ”) ; (e) execute a model with the masked selected sequence to generate predictions for the masked forecast variables (e.g. in column 18 lines 25-35, column 25 lines 44-59, and column 26 lines 39-48, “ WNN module… number of prediction horizons and their corresponding time interval… probabilistic forecasting ”) ; (f) compare the predictions for the masked forecast variables to the actual forecast variables in the selected sequence (e.g. in column 16 lines 44-63 and column 18 lines 55 – column 19 line 1, “ derived from actual brain activity for an individual, are first individually scored based on validation error… minimization problem with respect to the empirical average squared error function… minimizes the expected value of this measure…over all future data ”), but does not specifically teach (g) determine if convergence occurs based upon the comparison; and (h) if convergence has not occurred, update one or more parameters of the model and return to step b . However, Basilico teaches determine if convergence occurs based upon the comparison and if convergence has not occurred, update one or more parameters of the model and return to a previous step (e.g. in paragraphs 63-63, 67, and 69-70, “ training engine 122 updates the parameters of bias-reduction pre-processing module 210 based on a loss function. In some embodiments, training engine 122 updates the model parameters of bias-reduction pre-processing module 210 at each training iteration to reduce the value of mean square error, mean absolute error, smooth mean absolute error, log-cosh loss, quantile loss, or the like for the loss function… repeats the training process for multiple iterations until a threshold condition is achieved… threshold condition is achieved when the training process reaches convergence… When the threshold condition has not been achieved, the training engine 122 repeats a portion of the personalized prediction training procedure beginning with step ”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Litt to include the teachings of Basilico because one of ordinary skill in the art would have recognized the benefit of facilitating model training. As per claim 2, the rejection of claim 1 is incorporated and the combination further teaches wherein to compare the predictions for the masked forecast variables to the actual forecast variables in the selected sequence the at least one processor is further programmed to perform the steps of: for each masked forecast variable, determine a difference between the masked forecast variable and the forecast variable prior to masking (e.g. Litt, in column 12 lines 40-44, column 18 lines 55 – column 19 line 1, column 21 lines 15-31 and column 24 lines 9-14, and column 25 lines 44-59, “ a window length of 30 seconds… minimizes the expected value of this measure with respect to the empirical average squared error function [showing differences] …over all future data… non-overlapping 10-minute baselines (>8 hrs. away from onsets) with random starting times… 24 randomly chosen baseline (.gtoreq.8 hrs from seizure) 50-60 minute IEEG segments… feature vector for a particular patient is generated that contains windowed (i.e. calculated over a particular time window, such as 1.25 seconds) features ”). As per claim 3, the rejection of claim 2 is incorporated and the combination further teaches wherein the at least one processor is further programmed to calculate a loss function based on the plurality of differences (e.g. Litt, in column 18 lines 55 – column 19 line 1, “ minimizes the expected value of this measure with respect to the empirical average squared error [i.e. loss] function [showing differences] …over all future data ”). As per claim 4, the rejection of claim 3 is incorporated and the combination further teaches wherein the loss function includes at least one of means square error (MSE) and means absolute percentage error (MAPE) (e.g. Litt, in column 18 lines 55 – column 19 line 1, “ minimizes the expected value of this measure with respect to the empirical average [i.e. means] squared error [i.e. loss] function ”). As per claim 6, the rejection of claim 3 is incorporated and the combination further teaches wherein the at least one processor is further programmed to determine that convergence has occurred if a value of the loss function has not changed in a predetermined number of passes (e.g. Basilico, in paragraph 69, “ convergence is reached when the mean square error, mean absolute error, smooth mean absolute error, log-cosh loss, quantile loss, or the like associated with for the loss function stays constant after a certain number of iterations ”). As per claim 8, the rejection of claim 3 is incorporated and the combination further teaches wherein the at least one processor is further programmed to determine that convergence has occurred if an amount of change of the loss function has not exceeded a threshold for a predetermined number of passes (e.g. Basilico, in paragraph 69, “ convergence is reached when the mean square error, mean absolute error, smooth mean absolute error, log-cosh loss, quantile loss, or the like associated with for the loss function stays constant after a certain number of iterations ”). As per claim 9, the rejection of claim 1 is incorporated and the combination further teaches wherein the at least one processor is further programmed to determine that convergence has occurred after a predetermined plurality of passes through the algorithm (e.g. Basilico, in paragraph 69, “ convergence is reached… the threshold condition is a certain number of iterations of the training process (e.g., 50 epochs, 800 epochs) ”). As per claim 10, the rejection of claim 1 is incorporated and the combination further teaches determine a future period of time to predict (e.g. Litt, in column 7 lines 1-10, “ estimates the probability… will occur within one or more fixed or adjustable time periods. Examples of time periods are the next 1 minute, 10 minutes, 1 hour, and 1 day ”) ; select a plurality of historical data points that precede the future period of time to predict, wherein the plurality of historical data points includes predictor variables and forecast variables (e.g. Litt, in column 5 lines 14-48, column 11 lines 45-48, column 25 lines 44-64, and column 28 lines 15-23, “ observation window… large set of independent, instantaneous and historical features are extracted from the intracranial EEG, real-time brain activity data and/or other physiologic data… features are extracted… "feature library" is a collection of features which are extracted by algorithms from raw brain activity data… feature behavior ”) ; determine predictor variables for the future period of time to predict (e.g. Litt, in column 5 lines 14-48, column 16 lines 44-63 and column 18 lines 55 – column 19 line 1, “ large set of independent, instantaneous and historical features are extracted from the intracranial EEG, real-time brain activity data and/or other physiologic data… derived from actual brain activity for an individual, are first individually scored based on validation error… minimization problem with respect to the empirical average squared error function… minimizes the expected value of this measure…over all future data ”) ; and execute the model with the plurality of historical data points and the predictor variables for the future period of time to generate forecast variables for the future period of time (e.g. in column 16 lines 44-63 and column 18 lines 55 – column 19 line 1, “ derived from actual brain activity for an individual, are first individually scored based on validation error… minimization problem with respect to the empirical average squared error function… minimizes the expected value of this measure…over all future data ”). As per claim 11, the rejection of claim 10 is incorporated and the combination further teaches wherein the at least one processor is further programmed to mask the forecast variables for the future period of time (e.g. in column 12 lines 40-44, column 21 lines 15-31 and column 24 lines 9-14, and column 25 lines 44-59, “ a window length… non-overlapping 10-minute baselines (>8 hrs. away from onsets) with random starting times… 24 randomly chosen baseline (.gtoreq.8 hrs from seizure) 50-60 minute IEEG segments… feature vector for a particular patient is generated that contains windowed (i.e. calculated over a particular time window, such as 1.25 seconds) features ”). As per claim 12, the rejection of claim 1 is incorporated and the combination further teaches wherein the at least one processor is further programmed to randomly select the sequence including a subset of continuous data points in the plurality of historical time series data, wherein a first selected sequence in a first pass is different than a second selected sequence in a second pass (e.g. Litt, in column 12 lines 40-44, column 21 lines 15-31 and column 24 lines 9-14, and column 25 lines 44-59, “ a window length… non-overlapping 10-minute baselines (>8 hrs. away from onsets) with random starting times… 24 randomly chosen baseline (.gtoreq.8 hrs from seizure) 50-60 minute IEEG segments… feature vector for a particular patient is generated that contains windowed (i.e. calculated over a particular time window, such as 1.25 seconds) features ”) ; As per claim 13, the rejection of claim 1 is incorporated and the combination further teaches wherein the plurality of historical time series data is significantly larger than the selected sequence (e.g. Litt, in column 12 lines 40-44, column 21 lines 15-31 and column 24 lines 9-14, “ a window length… non-overlapping 10-minute baselines (>8 hrs. away from onsets) with random starting times… 24 randomly chosen baseline (.gtoreq.8 hrs from seizure) 50-60 minute IEEG segments ”). As per claim 14, the rejection of claim 1 is incorporated and the combination further teaches wherein the mask is applied to the end of the selected sequence, wherein the masked selected sequence includes unmasked forecast variables followed by masked forecast variables (e.g. Litt, in column 12 lines 40-44, column 21 lines 15-31 and column 24 lines 9-14, “ a window length… non-overlapping 10-minute baselines (>8 hrs. away from onsets) with random starting times… 24 randomly chosen baseline (.gtoreq.8 hrs from seizure) 50-60 minute IEEG segments ”). As per claim 15, the rejection of claim 1 is incorporated and the combination further teaches wherein the predictor variables include at least one of date, time, weather conditions (e.g. Litt, in column 12 lines 40-44, column 21 lines 15-31 and column 24 lines 9-14, “ a window length of 30 seconds… non-overlapping 10-minute baselines (>8 hrs. away from onsets) with random starting times… 24 randomly chosen baseline (.gtoreq.8 hrs from seizure) 50-60 minute IEEG segments ”). Claims 17-18 and 20 are the method claims corresponding to system claims 1-3 and 10, and are rejected under the same reasons set forth. As per claim 19, the rejection of claim 18 is incorporated and the combination further teaches determining that convergence has occurred if the loss function is below a threshold, if a value of the loss function has not changed in a predetermined number of passes, if an amount of change of the loss function has not exceeded a threshold, if an amount of change of the loss function has not exceeded a threshold for a predetermined number of passes, or after a predetermined plurality of passes through the algorithm (e.g. Basilico, in paragraph 69, “ convergence is reached when the mean square error, mean absolute error, smooth mean absolute error, log-cosh loss, quantile loss, or the like associated with for the loss function stays constant after a certain number of iterations ”) . 07-21-aia AIA Claim s 5 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Litt et al (US 6658287 B1) in view of Basilico et al. (US 20220180186 A1) and further in view of Hajimirsadeghi et al. (US 20200076841 A1) . As per claim 5, the rejection of claim 3 is incorporated, but the combination does not specifically teach determine that convergence has occurred if the loss function is below a threshold . However, Hajimirsadeghi teaches determine that convergence has occurred if a loss function is below a threshold (e.g. in paragraph 229, “ convergence is achieved and training ceases when training loss 1480 fall beneath a threshold or fails to improve (i.e. decline) by at least a threshold amount ”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of the combination to include the teachings of Hajimirsadeghi because one of ordinary skill in the art would have recognized the benefit of facilitating assessment of whether training should cease. As per claim 7, the rejection of claim 3 is incorporated, but the combination does not specifically teach determine that convergence has occurred if an amount of change of the loss function has not exceeded a threshold . However, Hajimirsadeghi teaches determine that convergence has occurred if an amount of change of a loss function has not exceeded a threshold (e.g. in paragraph 229, “ convergence is achieved and training ceases when training loss 1480 fall beneath a threshold or fails to improve (i.e. decline) by at least a threshold amount ”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of the combination to include the teachings of Hajimirsadeghi because one of ordinary skill in the art would have recognized the benefit of facilitating assessment of whether training should cease . 07-21-aia AIA Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Litt et al (US 6658287 B1) in view of Basilico et al. (US 20220180186 A1) and further in view of Kato (US 20150254554 A1) . As per claim 16, the rejection of claim 1 is incorporated, but the combination does not specifically teach wherein the forecast variables include electricity demand . However, Kato teaches forecast variables including electricity demand (e.g. in paragraphs 3 and 36, “ predictions are made with time-series data (time-series prediction) in a wide range of applications, such as…electricity demand forecasting… prediction target type is power consumption ”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of the combination to include the teachings of Kato because one of ordinary skill in the art would have recognized the benefit of allowing predictions to be made on well-known applications . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. For example, Jaganathan et al. (US 20200302224 A1) “ upon error convergence after a final iteration, storing the updated parameters of the neural network in memory to be applied to further neural network-based template generation and base calling… wherein the loss function is mean squared error and the error is minimized ” (e.g. in paragraph 1399 and claim 14) Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM WONG whose telephone number is (571)270-1399. The examiner can normally be reached Monday-Friday 9am-5pm. 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, TAMARA KYLE can be reached at (571)272-4241. 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 applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. 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. /W.W/Examiner, Art Unit 2144 05/16/2026 /T.T.K/Supervisory Patent Examiner, Art Unit 2144 Application/Control Number: 18/327,619 Page 2 Art Unit: 2144 Application/Control Number: 18/327,619 Page 3 Art Unit: 2144 Application/Control Number: 18/327,619 Page 4 Art Unit: 2144 Application/Control Number: 18/327,619 Page 5 Art Unit: 2144 Application/Control Number: 18/327,619 Page 6 Art Unit: 2144 Application/Control Number: 18/327,619 Page 7 Art Unit: 2144 Application/Control Number: 18/327,619 Page 8 Art Unit: 2144 Application/Control Number: 18/327,619 Page 9 Art Unit: 2144 Application/Control Number: 18/327,619 Page 10 Art Unit: 2144 Application/Control Number: 18/327,619 Page 11 Art Unit: 2144 Application/Control Number: 18/327,619 Page 12 Art Unit: 2144 Application/Control Number: 18/327,619 Page 13 Art Unit: 2144 Application/Control Number: 18/327,619 Page 14 Art Unit: 2144 Application/Control Number: 18/327,619 Page 15 Art Unit: 2144 Application/Control Number: 18/327,619 Page 16 Art Unit: 2144 Application/Control Number: 18/327,619 Page 17 Art Unit: 2144 Application/Control Number: 18/327,619 Page 18 Art Unit: 2144 Application/Control Number: 18/327,619 Page 19 Art Unit: 2144 Application/Control Number: 18/327,619 Page 20 Art Unit: 2144 Application/Control Number: 18/327,619 Page 21 Art Unit: 2144 Application/Control Number: 18/327,619 Page 22 Art Unit: 2144 Application/Control Number: 18/327,619 Page 23 Art Unit: 2144 Application/Control Number: 18/327,619 Page 24 Art Unit: 2144
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