CTNF 18/498,540 CTNF 87824 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. 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 1-5 and 15-18 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite mental processes and mathematical concepts. This judicial exception is not integrated into a practical application and does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements alone or in combination are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above. See the analysis below for further details. Claims 1, 2 and 15 Step 1 : The claim recites a system, method and non-transitory computer-readable medium, therefore, the claims falls into the statutory categories. Step 2A Prong 1 : The claim recites, inter alia: Selecting seasonality as a statistical profile type to identify in the first dataset (claim1); selecting a statistical profile type to identify in the first dataset (claim 2 and 15) (This is a mental process of observation, evaluation and judgement wherein a user determines they type of the profile they want to look for in the data set.) Determining a first statistical profile for the first dataset; (This is mental process wherein a user determines a statistical profile of the data by evaluating the given data, can be done with aid of pen and paper.) Generating a first periodogram for the first dataset using Fourier transform (claim 1); (This a mathematically concept being performed wherein user applies Fourier transform to the time series data to go from time domain to frequency domain. Then creating a periodogram by calculating the squared magnitude of the discrete Fourier transform. See para. [0114] of the instant specification.) Processing the first periodogram using a Fisher G-test; (This is a mathematical concept wherein a Fisher G-test is a statistical test applied to data to detect periodicities by testing for peaks.) Selecting, based on the first statistical profile, a first untrained model from a first plurality of untrained models for training, wherein the first plurality of untrained models comprises respective algorithms for time-series forecasting, and wherein each untrained model comprises default hyperparameter tuning; and (This is mental process of observation, evaluation and judgement wherein a user matches a statistical profile to an untrained model.) Step 2A Prong 2: This judicial exception is no integrated into a practical application. Aside from the limitations above, the claim recites: One or more processors; and one or more non-transitory, computer-readable mediums comprising instructions; (This is using generic computer hardware to implement the abstract idea, see MPEP 2106.05(f).) Receiving a first dataset, wherein the first dataset comprises time-series data having a sequence of datapoints at equally spaced points in time over a dataset time range; Retrieving a statistical model corresponding to the seasonality/statistical profile type; (The above limitations are transmitting data or data collection and is extra-solution activity, see MPEP 2106.05(g).) Based on (using) the statistical model; (This is using the machine learning model (statistical model) to determine statistical properties or values of dataset which is 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 - see MPEP 2106.05(f) – Examiner’s note: high level application of a model to get output). Based on selecting the untrained model, tuning a first hyperparameter of the first untrained model using the first dataset. (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 - see MPEP 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data); The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above. Step 2B : The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Additional elements: One or more processors; and one or more non-transitory, computer-readable mediums comprising instructions; (This is using generic computer hardware to implement the abstract idea, see MPEP 2106.05(f).) Based on (using) the statistical model; (This is using the machine learning model (statistical model) to determine statistical properties or values of dataset which is 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 - see MPEP 2106.05(f) – Examiner’s note: high level application of a model to get output). Based on selecting the untrained model, tuning a first hyperparameter of the first untrained model using the first dataset. (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 - see MPEP 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data); Receiving a first dataset, wherein the first dataset comprises time-series data having a sequence of datapoints at equally spaced points in time over a dataset time range; Retrieving a statistical model corresponding to the seasonality/statistical profile type; (This is well-understood, routine and conventional as it amounts to receiving data and retrieving information (a model) from memory. See MPEP 2106.06(d)(II)(i) and (iv) wherein it cites “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016)”; iv. Storing and retrieving information in memory, 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;”) The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above. Claims 3 and 16 Step 2A Prong 1 : The claim recites, inter alia: determining a threshold percent change for the first dataset; (This is mental process of observation, evaluation and judgment wherein a user chooses a threshold percent for change.) determining a first time range that is less than the dataset time range; (This is mental process of observation, evaluation and judgment wherein a user determines a time range of the data.) determining a first subset of the sequence of datapoints, wherein the first subset begins at a first datapoint in the sequence of datapoints and includes datapoints within the first time range from the first datapoint; (This is mental process of observation, evaluation and judgment wherein a user chooses datapoints in a time range.) determining a first maximum datapoint value of the first subset and a first minimum datapoint value of the first subset; (This is mental process of observation, evaluation and judgment wherein a user determines a max and min value in the dataset.) determining a first difference between the first maximum datapoint value and the first minimum datapoint value; (This is mental process of observation, evaluation and judgment wherein a user determines a difference between two known values.) determining a second time range that is less than the dataset time range; (This is mental process of observation, evaluation and judgment wherein a user determines a time range of the data.) determining a second subset of the sequence of datapoints, wherein the second subset begins at a second datapoint in the sequence of datapoints and includes datapoints within the second time range from the second datapoint, and wherein the second datapoint is immediately after the first datapoint in the sequence of datapoints; (This is mental process of observation, evaluation and judgment wherein a user determines a second time range of the data that needs after the first one.) determining a second maximum datapoint value of the second subset and a second minimum datapoint value of the second subset; (This is mental process of observation, evaluation and judgment wherein a user determines a max and min value in the dataset.) determining a second difference between the second maximum datapoint value and the second minimum datapoint value; (This is mental process of observation, evaluation and judgment wherein a user determines a difference between two known values.) determining a percent change between the first subset and the second subset based on an absolute value of the first difference and the second difference; (This is mental process of observation, evaluation and judgment wherein compares the first difference to the second difference to determine a percent change between them.) comparing the percent change to the threshold percent change; and (This is mental process of observation, evaluation and judgment wherein compares the percent change to a threshold value.) determining the first statistical profile of the first dataset based on comparing the percent change to the threshold percent change. (This is mental process of observation, evaluation and judgment wherein a user picks a statistical profile by based on percent change.) Step 2A Prong 2 : This judicial exception is not integrated into a practical application as there are no additional limitations or elements. Step 2B : The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception, as there are no additional limitations or elements. Claim 4 and 17 Step 2A Prong 1 : The claim recites, inter alia: Claims 4 and 17 inherit the abstract idea of claims 3 and 16. Step 2A Prong 2: This judicial exception is no integrated into a practical application. Aside from the limitations above, the claim recites: Wherein the first statistical profile corresponds to a determination of a spikiness of the first dataset; (This is mere extra-solution activity of what type of data is selected or used, see MPEP 2106.05(g).) The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity in combination being implemented to perform the disclosed abstract idea above. Step 2B : The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Wherein the first statistical profile corresponds to a determination of a spikiness of the first dataset; (This is mere extra-solution activity of what type of data is selected or used, see MPEP 2106.05(g).) The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are mere insignificant extra solution activity in being implemented to perform the disclosed abstract idea above. Claim 5 and 18 Step 2A Prong 1 : The claim recites, inter alia: Claims 5 and 18 inherit the abstract idea of claims 3 and 16. Step 2A Prong 2: This judicial exception is no integrated into a practical application. Aside from the limitations above, the claim recites: Wherein the threshold percent change comprises a number between zero and one ; (This is mere extra-solution activity of what type/value of data is selected or used, see MPEP 2106.05(g).) The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity in combination being implemented to perform the disclosed abstract idea above. Step 2B : The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Wherein the threshold percent change comprises a number between zero and one ; (This is mere extra-solution activity of what type/value of data is selected or used, see MPEP 2106.05(g).) The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are mere insignificant extra solution activity in being implemented to perform the disclosed abstract idea above. 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. 07-21-aia AIA Claim s 2 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Bledsoe et al. (US 2018/0300737 A1 – hereinafter Bledsoe) and further in view of Wang et al. (“Rule Induction For Forecasting Method Selection: Meta-Learning The Characteristics Of Univariate Time Series” – hereinafter Wang) . In regards to claim 2 , Bledsoe discloses a method for automating model selection based on dataset fittings of time-series data that comprises non-standardized variance prior to hyperparameter optimization, the method comprising: receiving a first dataset, wherein the first dataset comprises time-series data having a sequence of datapoints at equally spaced points in time over a dataset time range; (Bledsoe para. [0002] cites “A time series is a sequence of data points representing samples or observations often collected at discrete and equally spaced time intervals.” and para. [0004] cites “The apparatus receives datasets including time series data points that are descriptive of a feature of a given entity.”) selecting a statistical profile type to identify in the first dataset; (Bledsoe para. [0027] and table 1 teaches various types of analysis that are performed on the time-series dataset to identifying information the time-series. It has seasonality analysis, variability analysis, analysis of a number of variables and shape of distribution analysis. Examiners note: para. [0099] of instant specification statistical profile types wherein it cities “For example, the system may determine different statistical profile types (e.g., categories of statistical profiles) such as seasonality, multiple seasonality, nested seasonality, stationary trends, spiky data, smooth data, and/or additional types. The system may use information about whether a dataset corresponds to a category to determine the best model and/or hyperparameter to use.”, thus the various analysis types also do this.) retrieving a statistical model corresponding to the statistical profile type; (Bledsoe para. [0039-0042] teaches determining seasonality (profile type) using autocorrelation function (ACF) and Partial Autocorrelation function (PACF), wherein ACF and PACF are the statistical models used for seasonality. Also see fig. 9 wherein it uses TBATs (which is an automated times series forecasting model for multiple seasonalities, non-integer seasonal lengths and varying trend patterns.), BTST (which is Bayesian Structural Time Series, and statistical model technique used for time series forecasting, causal inference, and feature selection that uses time-series data to determine trends, seasonality (e.g., holiday spikes), and regression effects (e.g., how an ad campaign impacts sales), ARIMA, mean, media, neural networks, standard deviations, and linear regression. All of which are statistical models.) determining a first statistical profile for the first dataset based on the statistical model; (Bledsoe para. [0027] teaches the system analyzes the time series data to determine times series characteristics, which are used to select a forecasting model; and para. [0038] teaches determining time series characteristics wherein it cites “… data analyzer and filtering engine 217 determines time series characteristics such as occurrences of dead data periods, number of sampled time series data points, number of exogenous variables or covariant relevant to the forecast of time series data points, analysis of time intervals values of a time series, constant data analysis, and other suitable time series analysis.” and para. [0039-0042] teaches determining seasonality using ACF and PACF. These characteristics makes up the statical profile of the time-series data is what is used for selecting a forecasting model.) selecting, based on the first statistical profile, a first untrained model from a first plurality of untrained models for training, wherein the first plurality of untrained models comprises respective algorithms for time-series forecasting, and wherein each of the first plurality of untrained models comprises; and (Bledsoe para. [0029] and fig. 5 element 505 teaches selecting an entrant forecasting model from a pool of models based on time-series characteristics. Also fig. 5 element 505 specifically states the models are untrained wherein it cites “Select a set of entrant forecasting models from a pool of untrained and/or untested forecasting models…”. Then para. [0030] teaches training the entrant models with the time-series data, thus it a respective algorithm for time-series forecasting and default parameters.) based on selecting the first untrained model, training the model using the first dataset. (Bledsoe para. [0030] teaches training untrained models with the time-series data (first dataset) wherein it cites “TSF server 101 trains each of the selected entrant forecasting models with received and/or collected time series data points. TSF server 101 executes each of the trained entrant forecasting models to produce a set of forecasted values. The forecasted values indicate forecasted estimations of future data points of the time series. In some implementations, TSF server 101 uses the forecasted values to determine forecast accuracy scores of each of the trained entrant forecasting models.”) However, Bledsoe does not explicitly discloses selecting an untrained model and tuning a first hyperparameter of the first untrained model using the first dataset. Wang discloses based on selecting the first untrained model, tuning a first hyperparameter of the first untrained model using the first dataset. (Wang teaches fitting each method by adjusting parameters (hyperparameters). In section 4.3 it teaches using ARIMA and selecting p, d, and 1 using Akaike’s information criterion (AIC) to penalize likelihood and find best parameters and best ARIMA fitting model. Section 4.2 teaches Exponential Smoothing (ES) using Pegels classification method wherein trend and seasonal components are considered and hyperparameters are automatically determining use state space models. Section 4.4 teaches tuning a neural networks using an equation based on I, j, and d to determine the number of hidden neurons. This is also tuning hyperparameters and it use backpropagation. Additionally, Wang also teaches receiving a time-series having a sequence of datapoints at equally spaced points in time in section 5 wherein it cites “A univariate time series is the simplest form of temporal data and is a sequence of real numbers collected regularly in time, where each number represents a value. We represent a time series as an ordered set of n real-valued variables Y1; . . . ; Yn.”, it teaches statistical profile type in the dataset in abstract wherein it cites “To provide a rich portrait of the global characteristics of univariate time series, we extracted measures from a comprehensive set of features such as trend, seasonality, periodicity, serial correlation, skewness, kurtosis, nonlinearity, self-similarity, and chaos.” Wherein characteristic types (trend, seasonality, nonlinearity, etc.) are statistical profile types; retrieving a statistical model corresponding to a statistical profile type in section 5.1 Trend and Seasonality using Box-Cox transformation and STL decomposition, section 5.2 periodicity using algorithm scanning autocorrelation function (ACF) lags, peaks and trough to detect frequency (see the 4 bullets in section 5.2); in section 5.3 serial correlation uses Box-Pierce statistics; section 5.4 Nonlinear autoregressive structure using Terasvirta’s neural network test for nonlinearity; section 5.5 Skewness and Kurtosis using standard skewness and excess kurtosis formula; section 5.6 Self-similarity using Autoregressive Fractionally integrated moving average (ARFIMA), and section 5.7 Chaos using Hilborns method. Also, Wang teaches a statistical profile of the dataset wherein in section 5 last paragraph it teach “a total of 13 metrics are extracted for nine identified characteristics. A finite set of 13 metrics are used to quantify the global characteristics of univariate time series, regardless of its length and missing values.” Meaning 13 tests are performed on dataset to get characteristics of data series and then section 3 second paragraph says this metrics are combined and section 7.2 teaches 13 characteristics metrics of the data set is combined to a vector representing the dataset, which is the statistical profile of the data set.) It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify the teachings of the Bledsoe with that of Wang in order to allow for tuning hyperparameters of untrained models as both references deal with finding the best model for forecasting using a dataset. The benefit of doing so it creates a more accurate model by tuning the model to the specific dataset and saves time by finding the appropriate or best model to use based on the dataset characteristics . Allowable Subject Matter 12-151-08 AIA 07-43 12-51-08 Claim s 6-14 and 19-20 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. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to PAULINHO E SMITH whose telephone number is (571)270-1358. The examiner can normally be reached Mon-Fri. 10AM-6PM CST. 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, Abdullah Kawsar can be reached at 571-270-3169. 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. /PAULINHO E SMITH/Primary Examiner, Art Unit 2127 Application/Control Number: 18/498,540 Page 2 Art Unit: 2127 Application/Control Number: 18/498,540 Page 3 Art Unit: 2127 Application/Control Number: 18/498,540 Page 4 Art Unit: 2127 Application/Control Number: 18/498,540 Page 5 Art Unit: 2127 Application/Control Number: 18/498,540 Page 6 Art Unit: 2127 Application/Control Number: 18/498,540 Page 7 Art Unit: 2127 Application/Control Number: 18/498,540 Page 8 Art Unit: 2127 Application/Control Number: 18/498,540 Page 9 Art Unit: 2127 Application/Control Number: 18/498,540 Page 10 Art Unit: 2127 Application/Control Number: 18/498,540 Page 11 Art Unit: 2127 Application/Control Number: 18/498,540 Page 12 Art Unit: 2127 Application/Control Number: 18/498,540 Page 13 Art Unit: 2127 Application/Control Number: 18/498,540 Page 14 Art Unit: 2127 Application/Control Number: 18/498,540 Page 15 Art Unit: 2127
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