CTNF 18/238,531 CTNF 89242 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 1-7 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Step 1 : All claims are directed towards either a method, a device or a non-transitory computer-readable recording medium and thus satisfies Step 1 as falling into one of the statutory categories. Step 2A, Prong One : Independent Claim 1 recites (the same analysis applies to similar independent Claims 6 and 7): identifying frequency components stronger than a predetermined reference among frequency components of time-series data ; this limitation, under its broadest reasonable interpretation, covers concepts that can be performed in the human mind and therefore would fall under the “Mental Processes” groupings of abstract ideas. That is the human mind is capable of identifying frequency components stronger than a predetermined reference among frequency components using observation and evaluation. calculating values that indicate a relationship between one or more parameters used when generating a plurality of time-series features of the time- series data and periods having the identified frequency components, as features for the parameters ; this limitation, under its broadest reasonable interpretation, also covers concepts that can be performed in the human mind and therefore would fall under the “Mental Processes” groupings of abstract ideas. That is the human mind is capable of calculating values using evaluation and pen and paper. and predicting importance of time-series features for new time-series data this limitation, under its broadest reasonable interpretation, also covers concepts that can be performed in the human mind and therefore would fall under the “Mental Processes” groupings of abstract ideas. That is the human mind is capable of making predictions on feature importance for data using observation and evaluation. Step 2A, Prong Two : Claim 1 recites the additional elements of (the same analysis applies to similar independent Claims 6 and 7): executing training of a first machine learning model by using importance of each of the time-series features on prediction that uses the time-series features and the features for each of the parameters to predict the importance of the time-series features from the features for each of the parameters, the time- series features being generated based on the parameters ; this limitation is considered as using a machine learning model as a tool to perform the abstract idea, which also includes training the machine learning model - see MPEP 2106.05(f). by using the trained first machine learning model . this limitation is also considered as using a machine learning model as a tool to perform the abstract idea, which also includes training the machine learning model - see MPEP 2106.05(f). The further additional elements of a “computer” and/or “processor” as recited in these independent claims are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are therefore directed to an abstract idea. Step 2B : The claims 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 considered as using a machine learning model as a tool to perform the abstract idea, which also includes training the machine learning model - see MPEP 2106.05(f); and the further additional elements of a “computer” and/or “processor” as recited in these independent claims amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are therefore not patent eligible. Dependent Claim 2 is also considered as falling under the “Mental Processes” groupings of abstract ideas. That is the human mind is capable of calculating values using evaluation and pen and paper. The first limitation of Claim 3 is also considered as falling under the “Mental Processes” groupings of abstract ideas. That is the human mind is capable of calculating values using evaluation and pen and paper. The second limitation being considered as using a machine learning model as a tool to perform the abstract idea - see MPEP 2106.05(f). Dependent Claim 4 last limitation is also considered as using a machine learning model as a tool to perform the abstract idea - see MPEP 2106.05(f). The first limitation being considered as falling under the “Mental Processes” groupings of abstract ideas. The first two limitations of Claim 5 are also considered as falling under the “Mental Processes” groupings of abstract ideas. That is the human mind is capable of calculating values (features and importance) using evaluation and pen and paper. The last limitation being considered as using a machine learning model as a tool to perform the abstract idea, which also includes training the machine learning model - see MPEP 2106.05(f). 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. 07-21-aia AIA Claim s 1-7 are rejected under 35 U.S.C. 103 as being unpatentable over Yoshida , US 2022/0036237 A1, in view of Shibahara , US 2020/0082286 A1 . Regarding Claim 1 , Yoshida teaches: A non-transitory computer-readable recording medium storing a program for causing a computer to execute a process, the process comprising: identifying frequency components stronger than a predetermined reference among frequency components of time-series data (paragraph 65: “the comparing unit is configured to calculate a frequency difference between the frequency domain data at frequency peak locations”; And, paragraph 93: “the frequency difference between the frequency domain data at locations where frequency peaks of the frequency domain data are determined to be high according to a preset standard”); With Yoshida teaching the identified frequency components as pointed out above, Yoshida may not have taught all of the following, however, Shibahara shows: calculating values that indicate a relationship between one or more parameters used when generating a plurality of time-series features of the time-series data and periods having the identified frequency components, as features for the parameters (paragraph 63: “the decision unit 305 gives the time series feature vectors…to Equation (9), thereby calculating the unknown predicted value…for the time series feature vectors; And, paragraph 64: “In Equation (9), an importance vector…corresponds to a parameter of the local plane 103 for identifying the time series feature vector”. The importance vector representative of the relationship. And paragraph 57: “At the time of prediction by the prediction section 262, the importance unit 306 calculates importance vectors. To describe an operation by the importance unit 306, a calculation method of an Hadamard product between the vector w and the time series vector u (t−1, . . . , T) is defined”. The time series vector having those identified frequency components as shown); (Emphasis added) executing training of a first machine learning model by using importance of each of the time-series features on prediction that uses the time-series features and the features for each of the parameters to predict the importance of the time-series features from the features for each of the parameters, the time-series features being generated based on the parameters (paragraph 64: “In Equation (9), an importance vector…corresponds to a parameter of the local plane 103 for identifying the time series feature vector”); and predicting importance of time-series features for new time-series data by using the trained first machine learning model (paragraph 69: “the prediction section 262 reads the time series feature vector… that is the test data set 252 from the client DB… The prediction section 262 then calculates the importance of each feature”. The test data being the new data). It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to use the teachings of Shibahara with that of Yoshida for calculating values that indicate a relationship between one or more parameters used when generating a plurality of time-series features and their importance and predicting the importance of the time-series features. The ordinary artisan would have been motivated to modify Yoshida in the manner set forth above for the purposes of calculating an importance of each feature [Shibahara: paragraph 81]. Regarding Claim 2 , Yoshida further teaches: The non-transitory computer-readable recording medium according to claim 1, the process further comprising: calculating the values based on results of dividing constant multiples of the periods by time widths among the parameters (paragraph 31: “the frequency characteristic comparing unit 12 calculates a required window width T=1/Δf for each explanatory variable”. The window width being the time width. Examiner’s note: see also Natsumeda, US 20250044785 A1, for example paragraph 100: “dividing the multivariate time-series data 152 into m pieces along the time axis”). (Emphasis added). Regarding Claim 3 , Shibahara further teaches: The non-transitory computer-readable recording medium according to claim 1, the process further comprising: calculating features for the parameters for the new time-series data; and inputting the calculated features for the parameters to the trained first machine learning model to predict importance of the time-series features for the new time-series data (paragraph 81: “in the case of the patient's time series data, the importance of each feature at every acquisition time can be calculated for an individual patient”; And, paragraph 69: “the prediction section 262 reads the time series feature vector… that is the test data set 252 from the client DB… The prediction section 262 then calculates the importance of each feature”. The test data being the new data). Regarding Claim 4 , Shibahara further teaches: The non-transitory computer-readable recording medium according to claim 1, the process further comprising: determining specific time-series features to be used for training of a second machine learning model that performs prediction with time-series features as input data, based on the predicted importance of the time-series features for the new time-series data (paragraph 10: “generating second internal data based on time of one piece of second feature data among plural pieces of the second feature data each containing a plurality of features, the second internal data being generated for each piece of the second feature data on a basis of second feature data groups in each of which the plural pieces of the second feature data each containing the plurality of features are present in time series, a second internal parameter that is at least part of other piece of the second feature data at time before the time of the one piece of the second feature data, and a first learning parameter optimized by the optimization process; a second transform process transforming a position of the one piece of the second feature data in the feature space on a basis of a plurality of second internal data generated by the second generation process for each piece of the second feature data and a second learning parameter optimized by the optimization process; and an importance calculation process calculating importance data indicating an importance of each piece of the second feature data”. The second feature data being the specific time-series features); and executing the training of the second machine learning model by using the specific time-series features for the time-series data (paragraph 66: “In executing the learning parameter generation process (Step S402), the learning section 261 gives the time series feature vector… that is part of the training data set 264 to the neural network”). Regarding Claim 5 , Yoshida further teaches: The non-transitory computer-readable recording medium according to claim 1, the process further comprising: calculating features for each of time widths included in the parameters as the features for the parameters; calculating importance of each of the time widths as the importance of each of the time-series features; and executing the training of the first machine learning model by using the importance of each of the time widths and the features for each of the time widths (paragraph 47: “the learning unit 7 selects an appropriate window width from among window width candidates found for the respective explanatory variables, and learns a learned model by using the window width as an initial window width. Then, after learning is performed k times, the variable importance degree reflecting unit 14 (an importance degree calculating unit) calculates an importance degree of each explanatory variable based on a ratio at which the explanatory variable contributes to the output of the learned model”. The window width being the time width and the explanatory variables representative of the features of that time width). Claims 6 and 7 are similar to Claim 1 and are rejected under the same rationale as stated above for that claim. Examiner’s Note : The Examiner cites particular pages, sections, columns, line numbers, and/or paragraphs in the references as applied to the claims above for the convenience of the applicant. Although the specified citations representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in its entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner and the additional related prior arts made of record that are considered pertinent to applicant's disclosure to further show the general state of the art. The Examiner's interpretations in parenthesis are provided with the cited references to assist the applicants to better understand how the examiner interprets the prior art to read on the claims. Such comments are entirely consistent with the intent and spirit of compact prosecution . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO-892 for the relevant prior art where for example Achin, US 20180046926 A1, teaches determining a forecast range and a skip range associated with a prediction problem represented by time series data . Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVE MISIR whose telephone number is (571)272-5243. The examiner can normally be reached M-R 8-5 pm, F some hours. 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 Al Kawsar can be reached at 5712703169. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /DAVE MISIR/Primary Examiner, Art Unit 2127 Application/Control Number: 18/238,531 Page 2 Art Unit: 2127 Application/Control Number: 18/238,531 Page 3 Art Unit: 2127 Application/Control Number: 18/238,531 Page 4 Art Unit: 2127 Application/Control Number: 18/238,531 Page 5 Art Unit: 2127 Application/Control Number: 18/238,531 Page 6 Art Unit: 2127 Application/Control Number: 18/238,531 Page 7 Art Unit: 2127 Application/Control Number: 18/238,531 Page 8 Art Unit: 2127 Application/Control Number: 18/238,531 Page 9 Art Unit: 2127 Application/Control Number: 18/238,531 Page 10 Art Unit: 2127 Application/Control Number: 18/238,531 Page 11 Art Unit: 2127 Application/Control Number: 18/238,531 Page 12 Art Unit: 2127
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