DETAILED ACTION Notice of Pre-AIA or AIA Status The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claims 1-20 are presented for examination. This Office action is Non-Final . Information Disclosure Statement The information disclosure statement (IDS) filed on 08/28/2023 has been considered by the Examiner and made of record in the application file. Allowable Subject Matter Claims 4, 5, 14, 15 , 19 and 20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. The following is a statement of reasons for the indication of allowable subject matter: The prior art of record, including US 2021/0081492 A1 and US 10,635,563 B2 (noted below) , teaches forecasting time-series data detecting potential change points in time-series signals, including analyzing forecasted data to identify possible structural changes and up dating or adapting models in response to detected changes. However, the applied references do not teach or suggest the limitations recited in dependent claims 4, 14 and 19 , which require predicting one or more change point attributes of a forecast value based upon determining that the forecast value is a change point and predicting forecast attribute values based upon those change point attributes . Claim Rejections - 35 USC § 102 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale , or otherwise available to the public before the effective filing date of the claimed invention. Claim s 1-3, 6- 9, 11- 13 and 16-18 are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Higginson et al. (US 2021/0081492 A1) hereinafter “Higginson” . With respect to claims 1, 11 and 16, the Higginson reference discloses a method, system and non-transitory computer-readable media [ see Abstract, disclosing t ime-series analysis for forecasting computational workloads ], comprising: one or more processors [ see ¶0114, disclosing one or more devices that include a hardware processor and that are configured to perform any of the operations ] ; and a computer-readable medium including instructions that, when executed by the one or more processors [ see ¶0119, disclosing m ain memory 806 also may be used for storing temporary variables or other intermediate information during execution of instructions to be executed by processor 804 ] , cause the computing system to: receive a request to predict a value for a variable at a future time point based upon a time series, the time series comprising a sequence of data points, each data point in the sequence of data points identifying a time point and at least one value associated with the time point [ see Abstract, disclosing t he resource management system tests a first time-series model that incorporates a first exogenous variable corresponding to a first exogeneous factor to determine that the first time-series model fits both the first portion of the set of historical data points and the second portion of the set of historical data points within an error threshold ; Then, the resource management system selects the first time-series model to predict future data points of the data set ] ; and predict, using a first trained machine learning model and based upon the times series, a plurality of forecast values for the future time point, the plurality of forecast values [ see ¶0036, disclosing time-series models analyze time-series data that includes metrics collected from monitored systems to predict future values in the time-series data based on previously observed values in the time-series data ] including: a first forecast value predicted for the variable at the future time point [ see ¶0036, disclosing time-series models analyze time-series data that includes metrics collected from monitored systems to predict future values in the time-series data based on previously observed values in the time-series data ] ; and a set of one or more forecast attribute values for one or more attributes of the time series, each of the set of one or more forecast attribute values predicted for the future time point [ see ¶0056, disclosing t he time-series model outputs predictions 135 of future values in the time series as a predicted workload, resource utilization, and/or performance associated with the entity ] . With respect to claim s 2 , 12 and 17 , Higginson discloses the method , system and non-transitory computer-readable media of claim s 1 , 11 and 16 , as referenced above. Higginson further discloses it comprises: providing the plurality of forecast values as input to a second trained machine learning model [ see ¶0038, disclosing t hese components of the time-series models 221 improve the accuracy of the models and allow the models 220 to be adapted to various types of time-series data collected from the monitored systems ; In one embodiment, the time-series models 220 include an exogenous variable 221d that accounts for outliers 214 in the historical time-series data 210, so that the outliers 214 in the model generated with the historical time-series data 210 do not affect values of the metrics in the forecasts of the forecast module 134 ] ; and predicting, using the second trained machine learning model and based upon the plurality of forecast values provided as input to the second trained machine learning model, a second forecast value for the variable at the future time point [ see ¶0093, disclosing a resource management system selects a version of a time-series model with a best performance in predicting metrics from among multiple versions of the time-series model fitted to historical time-series data containing the metrics collected from a monitored system (Operation 601) ; For example, the version may be selected from multiple versions with different combinations of parameters used to create the time-series model ] . With respect to claim s 3 , 13 and 18 , Higginson discloses the method, system and non-transitory computer-readable media of claims 2 , 1 2 and 1 7 , as referenced above. Higginson further discloses it comprises: comparing a difference between the first forecast value and the second forecast value to a threshold difference [ see ¶0030, disclosing t he system selects a time-series model, incorporating one or more exogenous factors, that represents the data set including the outliers within an error threshold ] ; determining whether to output the first forecast value or the second forecast value based upon comparing the difference between the first forecast value and the second forecast value to the threshold difference [ see ¶0057, disclosing a plot of metrics as a function of time. The plot additionally includes representations of one or more thresholds for metrics and/or forecasted values of metrics from a time-series model for the corresponding entity ] ; and outputting the determined first forecast value or second forecast value [ see ¶0057, disclosing w hen the forecasted values violate a given threshold, the user interface displays highlighting, coloring, shading, and/or another indication of the violation as a prediction of a future anomaly or issue in the entity's use of the monitored systems ] . With respect to claim 6 , Higginson discloses the method of claim 1 , as referenced above. Higginson further discloses wherein the first trained machine learning model is a deep learning model that is trained to receive the time series and output the plurality of forecast values based upon the time series [ see ¶0045, disclosing the training module 133 applies Fourier terms 221e to the time-series models 220 ; For example, when multiple seasons are detected in the time series, seasonal patterns may be represented using Fourier terms 221e, which are added as external regressors in the ARIMA mode l ] . With respect to claim 7, Higginson discloses the method of claim 6 , as referenced above. Higginson further discloses wherein the deep learning model is trained by adjusting a plurality of weights of the deep learning model to predict a change point at the future time point, predict a change point type at the future time point, and predict a change point shift direction at the future time point [ see ¶0076, disclosing the time-series model incorporates an influence of the exogenous variable on future data points predicted by the first time-series model by reducing a weight given to the exogenous variable relative to other variables in the first time-series model representing a seasonality pattern in the historical data ] . With respect to claim 8 , Higginson discloses the method of claim 2 , as referenced above. Higginson further discloses wherein the second trained machine learning model is a regressor model that is trained to receive the plurality of forecast values and output the second forecast value based upon the plurality of forecast values [ see ¶0045, disclosing the training module 133 applies Fourier terms 221e to the time-series models 220 ; For example, when multiple seasons are detected in the time series, seasonal patterns may be represented using Fourier terms 221e, which are added as external regressors in the ARIMA mode l ] . With respect to claim 9 , Higginson discloses the method of claim 8 , as referenced above. Higginson further discloses wherein the regressor model is trained by adjusting a plurality of weights of the regressor model to predict the second forecast value based upon the first forecast value, predict a change point type at the future time point, and predict a change point shift direction at the future time point [ see ¶0076, disclosing the time-series model incorporates an influence of the exogenous variable on future data points predicted by the first time-series model by reducing a weight given to the exogenous variable relative to other variables in the first time-series model representing a seasonality pattern in the historical data ] . Claim Rejections - 35 USC § 103 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. Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Higginson, further view of O’Shea et al. (US 11,259,260 B2) hereinafter “Chu” . With respect to claim 10 , Higginson discloses the method of claim 1 , as referenced above. Higginson does not teach wherein the first trained machine learning model is trained to receive the time series as an analog signal and to output the plurality of forecast values as respective binary values in parallel. However, O’Shea discloses an analog signal and to output the plurality of forecast values as respective binary values in parallel [ see cols. 3-4, lines 55-67 and 1-2, respectively, discloses the one or more processing devices to perform operations includes: transmitting input information through a first communication channel; obtaining first information as an output of the first communication channel; transmitting the input information through a second communication channel implementing a channel machine- learning network, the second communication channel representing a model of the first communication channel; obtaining second information as an output of the second communication channel; providing the first information or the second information to a discriminator machine-learning network as an input; obtaining an output of the discriminator machine-learning network; and updating the channel machine-learning network using the output of the discriminator machine-learning network ; also, see col. 4, lines 9-10, disclosing the output of the discriminator machine-learning network is a binary output ] . It would have been obvious before the effective filing date of the invention to a person having ordinary skill in the art to which said subject matter pertains to modify the machine-learning forecaster as taught by Higginson with the signal-processing architectures that receive analog signal representations of input data and produce output values as taught b y O’Shea. Doing so would have improved computational efficiency of the predictive model and implemented hardware that represented predictions using binary outputs that can be processed in parallel. Prior Art Made of Record The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Fauelhammer et al. discloses body-mounted sensor for improved pose forecasting. Curtis et al. discloses hyperparameter tuning for anomaly detection machine learning forecasting. Adar et al. discloses time series forecasting using univariate ensemble model. Pat et al. discloses predictive analytics using multidimensional event representation in customer journeys. Chakraborty et al. discloses a neural network for model-blended time series forecast. Saleh et al. disclos es an attention mechanism and dataset bagging for time series forecasting using deep neural network models. Higginson et al. (‘664) discloses anomaly detection using forecasting computational workloads. Higginson et al. (‘591) discloses time series analysis for forecasting computational workloads. Xu et al. discloses an artificial intelligence system incorporating automatic model updates based on change point detection using likelihood ratios. Li et al. discloses metric forecasting employing a similarity determination in a digital medium environment. Dong et al. discloses processing irradiation forecast, method for training stacked generalization model. Vasseur et al. discloses forecasting network KPIs. Chu et al. discloses combining what-if and goal seeking analyses for prescriptive time series forecasting. Saluke et al. discloses an unsupervised method for baselining and anomaly detection in time-series data for enterprise systems. Grichnik et al. discloses forecasting using attenuated forecast function. Nishiuma et al. discloses a forecasting apparatus. Conclusions/Points of Contacts Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT JORGE A CASANOVA whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)270-3563 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT M-F: 9 a.m. to 6 p.m. (EST) . Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. 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For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /JORGE A CASANOVA/ Primary Examiner, Art Unit 2165