CTNF 18/368,341 CTNF 99377 DETAILED ACTION This action is responsive to the Application/amendment filed on 09/14/2023 . Claims 1-20 are pending in the case. Claims 1, 10, and 19 are independent claims. 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. 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. 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 claims are directed towards abstract ideas/mental processes without significantly more. Regarding Claim 1: Subject Matter Eligibility Analysis Step 2A Prong 1: The claim recites generating…based on the first information…a set of network weights which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass choosing values based on data. See 2106.04.(a)(2).III.C. The claim recites modulating…based on the first information…a set of sine activation amplitudes of the functional representation of the first time series which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass choosing different values for a variable. See 2106.04.(a)(2).III.C The claim recites imputing…based on the set of network weights and a result of the modulating…the at least one missing value of the first time series which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass evaluating a pattern and using judgement to determine a value. See 2106.04.(a)(2).III.C. The claim recites that is usable by a sinusoidal representation network model for obtaining a functional representation of the first time series which is an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))). Subject Matter Eligibility Analysis Step 2A Prong 2: at least one processor which merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)) receiving…first information that relates to a latent vector representation of a first time series for which at least one value is missing which amount to mere extra solution activity of obtaining and/or gathering data over a network, see MPEP §2106.05(g) Subject Matter Eligibility Analysis Step 2B: Additional elements (a) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f). Additional element (b) of obtaining a network input is well understood, routine, and conventional activity of “transmitting or receiving data over a network" (see MPEP 2106.05(d)(II)(i) using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362). The additional element(s) (a) and (b) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible. Regarding Claim 2: The rejection of claim 1 is incorporated and further claim recites further additional elements/limitations: Subject Matter Eligibility Analysis Step 2A Prong 1: The claim does not contain elements that would warrant a Step 2A Prong 1 analysis. Subject Matter Eligibility Analysis Step 2A Prong 2: wherein the sinusoidal representation network model comprises a first neural network model which merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)) generating of the set of network weights is performed by using a second neural network model which merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)) the modulating of the set of sine activation amplitudes is performed by using a third neural network model which merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)) Subject Matter Eligibility Analysis Step 2B: Additional elements (a) (b) and (c) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f). The additional element(s) (a) (b) and (c) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible. Regarding Claim 3: The rejection of claim 1 is incorporated and further claim recites further additional elements/limitations: Subject Matter Eligibility Analysis Step 2A Prong 1: The claim recites assessing an accuracy of the imputing which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement to determine an opinion on the similarity of two values/sets of values. See 2106.04.(a)(2).III.C. The claim recites obtaining…a first metric that relates to a mean- squared error between the imputed at least one missing value of the first time series and at least one ground truth value that corresponds to the at least one missing value of the first time series which are abstract ideas(Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))). The claim recites obtaining…a second metric that relates to a maximum error between the imputed atleast one missing value of the first time series and an evaluation of a model output that corresponds to the at least one missing value of the first time series which are abstract ideas(Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))). The claim recites obtaining…a third metric that relates to a Euclidean distance in a feature space between the imputed at least one missing value of the first time series and the at least one ground truth value that corresponds to the at least one missing value of the first time series which are abstract ideas(Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))). Subject Matter Eligibility Analysis Step 2A Prong 2: The claim does not contain elements that would warrant a Step 2A Prong 2 analysis. Subject Matter Eligibility Analysis Step 2B: The claim does not include any additional element, when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible. Regarding Claim 4: The rejection of claim 1 is incorporated and further claim recites further additional elements/limitations: Subject Matter Eligibility Analysis Step 2A Prong 1: The claim recites wherein the modulating is further based on the second information which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations appears to clarify using information for the mental process of modulating a variable and encompass choosing different values for a variable based on a data. See 2106.04.(a)(2).III.C Subject Matter Eligibility Analysis Step 2A Prong 2: receiving second information that relates to frequency modes of a superset of data that includes the first time series and at least a second time series which merely recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)). Subject Matter Eligibility Analysis Step 2B: Additional element (a) obtaining a network input is well understood, routine, and conventional activity of “transmitting or receiving data over a network" (see MPEP 2106.05(d)(II)(i) using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 ). The additional element(s) (a) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible. Regarding Claim 5: The rejection of claim 1 is incorporated and further claim recites further additional elements/limitations: Subject Matter Eligibility Analysis Step 2A Prong 1: The claim recites with respect to a predetermined loss function which is an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))). Subject Matter Eligibility Analysis Step 2A Prong 2: training the sinusoidal representation network model which merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)) using historical data and optimizing the sinusoidal representation network model which merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)) Subject Matter Eligibility Analysis Step 2B: Additional element (a) and (b) is merely clarifying the type of information that is obtained as a network input. Obtaining a network input is well understood, routine, and conventional activity of “transmitting or receiving data over a network" (see MPEP 2106.05(d)(II)(i) using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362) The additional element(s) (a) and (b) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible. Regarding Claim 6: The rejection of claim 1 is incorporated and further claim recites further additional elements/limitations: Subject Matter Eligibility Analysis Step 2A Prong 1: The claim does not contain elements that would warrant a Step 2A Prong 1 analysis. Subject Matter Eligibility Analysis Step 2A Prong 2: wherein the first time series comprises a univariate time series which amount to mere extra solution activity of obtaining and/or gathering data over a network, see MPEP §2106.05(g) Subject Matter Eligibility Analysis Step 2B: Additional element (a) is merely clarifying the type of information that is obtained as a network input. Obtaining a network input is well understood, routine, and conventional activity of “transmitting or receiving data over a network" (see MPEP 2106.05(d)(II)(i) using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362). The additional element(s) (a) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible. Regarding Claim 7: The rejection of claim 6 is incorporated and further claim recites further additional elements/limitations: Subject Matter Eligibility Analysis Step 2A Prong 1: The claim does not contain elements that would warrant a Step 2A Prong 1 analysis. Subject Matter Eligibility Analysis Step 2A Prong 2: wherein the univariate time series comprises a time series that relates to stock market data which amount to mere extra solution activity of obtaining and/or gathering data over a network, see MPEP §2106.05(g) Subject Matter Eligibility Analysis Step 2B: Additional element (a) is merely clarifying the type of information that is obtained as a network input. Obtaining a network input is well understood, routine, and conventional activity of “transmitting or receiving data over a network" (see MPEP 2106.05(d)(II)(i) using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362) The additional element(s) (a) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible. Regarding Claim 8: The rejection of claim 1 is incorporated and further claim recites further additional elements/limitations: Subject Matter Eligibility Analysis Step 2A Prong 1: The claim does not contain elements that would warrant a Step 2A Prong 1 analysis. Subject Matter Eligibility Analysis Step 2A Prong 2: wherein the first time series comprises a multivariate time series which amount to mere extra solution activity of obtaining and/or gathering data over a network, see MPEP §2106.05(g) Subject Matter Eligibility Analysis Step 2B: Additional element (a) is merely clarifying the type of information that is obtained as a network input. Obtaining a network input is well understood, routine, and conventional activity of “transmitting or receiving data over a network" (see MPEP 2106.05(d)(II)(i) using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362) The additional element(s) (a) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible. Regarding Claim 9: The rejection of claim 8 is incorporated and further claim recites further additional elements/limitations: Subject Matter Eligibility Analysis Step 2A Prong 1: The claim does not contain elements that would warrant a Step 2A Prong 1 analysis. Subject Matter Eligibility Analysis Step 2A Prong 2: wherein the multivariate time series comprises one from among a time series that relates to yield rate curve data, a time series that relates to weather forecasting data, and a time series that relates to medical diagnosis data which amount to mere extra solution activity of obtaining and/or gathering data over a network, see MPEP §2106.05(g) Subject Matter Eligibility Analysis Step 2B: Additional element (a) is merely clarifying the type of information that is obtained as a network input. Obtaining a network input is well understood, routine, and conventional activity of “transmitting or receiving data over a network" (see MPEP 2106.05(d)(II)(i) using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362) The additional element(s) (a) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible. Regarding Claim 10: Subject Matter Eligibility Analysis Step 2A Prong 1: The claim recites generating…based on the first information…a set of network weights which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass choosing values based on data. See 2106.04.(a)(2).III.C. The claim recites modulating…based on the first information…a set of sine activation amplitudes of the functional representation of the first time series which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass choosing different values for a variable. See 2106.04.(a)(2).III.C The claim recites imputing…based on the set of network weights and a result of the modulating…the at least one missing value of the first time series which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass evaluating a pattern and using judgement to determine a value. See 2106.04.(a)(2).III.C. The claim recites that is usable by a sinusoidal representation network model for obtaining a functional representation of the first time series which is an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))). Subject Matter Eligibility Analysis Step 2A Prong 2: a processor; a memory; and a communication interface coupled to each of the processor and the memory, wherein the processor which merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)) at least one processor which merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)) receiving…first information that relates to a latent vector representation of a first time series for which at least one value is missing which amount to mere extra solution activity of obtaining and/or gathering data over a network, see MPEP §2106.05(g) Subject Matter Eligibility Analysis Step 2B: Additional elements (a) and (b) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f). Additional element (c) of obtaining a network input is well understood, routine, and conventional activity of “transmitting or receiving data over a network" (see MPEP 2106.05(d)(II)(i) using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362) The additional element(s) (a) (b) and (c) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible. Regarding Claim 11: The rejection of claim 10 is incorporated and further claim recites further additional elements/limitations: Claim 11 is rejected under that same 101 claim analysis due to the substantially similarity of the limitations and additional elements of claim 2 found in claim 11 . Regarding Claim 12: The rejection of claim 10 is incorporated and further claim recites further additional elements/limitations: Claim 12 is rejected under that same 101 claim analysis due to the substantially similarity of the limitations and additional elements of claim 3 found in claim 12 . Regarding Claim 13: The rejection of claim 10 is incorporated and further claim recites further additional elements/limitations: Claim 13 is rejected under that same 101 claim analysis due to the substantially similarity of the limitations and additional elements of claim 4 found in claim 13 . Regarding Claim 14: The rejection of claim 10 is incorporated and further claim recites further additional elements/limitations: Claim 14 is rejected under that same 101 claim analysis due to the substantially similarity of the limitations and additional elements of claim 5 found in claim 14 . Regarding Claim 15: The rejection of claim 10 is incorporated and further claim recites further additional elements/limitations: Claim 15 is rejected under that same 101 claim analysis due to the substantially similarity of the limitations and additional elements of claim 6 found in claim 15 . Regarding Claim 16: The rejection of claim 15 is incorporated and further claim recites further additional elements/limitations: Claim 16 is rejected under that same 101 claim analysis due to the substantially similarity of the limitations and additional elements of claim 7 found in claim 16 . Regarding Claim 17: The rejection of claim 10 is incorporated and further claim recites further additional elements/limitations: Claim 17 is rejected under that same 101 claim analysis due to the substantially similarity of the limitations and additional elements of claim 8 found in claim 17 . Regarding Claim 18: The rejection of claim 10 is incorporated and further claim recites further additional elements/limitations: Claim 18 is rejected under that same 101 claim analysis due to the substantially similarity of the limitations and additional elements of claim 9 found in claim 18 . Regarding Claim 19: Subject Matter Eligibility Analysis Step 2A Prong 1: The claim recites generating…based on the first information…a set of network weights which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass choosing values based on data. See 2106.04.(a)(2).III.C. The claim recites modulating…based on the first information…a set of sine activation amplitudes of the functional representation of the first time series which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass choosing different values for a variable. See 2106.04.(a)(2).III.C The claim recites imputing…based on the set of network weights and a result of the modulating…the at least one missing value of the first time series which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass evaluating a pattern and using judgement to determine a value. See 2106.04.(a)(2).III.C. The claim recites that is usable by a sinusoidal representation network model for obtaining a functional representation of the first time series which is an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))). Subject Matter Eligibility Analysis Step 2A Prong 2: a non-transitory computer readable storage medium storing instructions for performing time series imputation, the storage medium comprising executable code which, when executed by a processor, causes the processor which merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)) at least one processor which merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)) receiving…first information that relates to a latent vector representation of a first time series for which at least one value is missing which amount to mere extra solution activity of obtaining and/or gathering data over a network, see MPEP §2106.05(g) Subject Matter Eligibility Analysis Step 2B: Additional elements (a) and (b) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f). Additional element (c) of obtaining a network input is well understood, routine, and conventional activity of “transmitting or receiving data over a network" (see MPEP 2106.05(d)(II)(i) using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362) The additional element(s) (a) (b) and (c) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible. Regarding Claim 20: The rejection of claim 19 is incorporated and further claim recites further additional elements/limitations: Claim 20 is rejected under that same 101 claim analysis due to the substantially similarity of the limitations and additional elements of claim 2 found in claim 20 . Claim Rejections - 35 USC § 103 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. 07-103 AIA The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action. 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) 1-6 , 8 , 10-15 , 17 and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sitzmann et al(“Implicit Neural Representations with Periodic Activation Functions” henceforth known as Sitzmann) in view of Mehta et al(“Modulated Periodic Activations for Generalizable Local Functional Representations” henceforth known as Mehta) and further in view of Naouret al(“Time Series Continuous Modeling for Imputation and Forecasting with Implicit Neural Representations” henceforth known as Naour) . Regarding claim 1: Sitzmann discloses receiving, by the at least one processor, first information that relates to a latent vector representation (Sitzmann, Page 8, Paragraph 2, “we condition these latent code vectors on partial observations of the signal O ϵ R m through an encoder”) of a first time series (Sitzmann, Supplementary Material, Page 16, Figure 8, “Example frames from fitting a video with SIREN and ReLU MLPs” where a video corresponds to a time series as a video is pixels over time (See also Sitzmann, Supplementary Material, Page 16, Paragraph 5, “Due to the highly periodic nature of audio signals with structure at various time scales, we expect that SIRENs could accurately represent such signals efficiently and provide an alternative representation for audio signals” where the model handling audio at time scales corresponds to handling a time series )) for which at least one value is missing( Sitzmann, Supplementary Material, Page 17, Figure 7, “ We sample 10% of pixels from the ground truth image for training, learning a representation which can inpaint the missing values” where the inpainting of missing pixels corresponds to receiving data with values missing ) Sitzmann discloses generating, by the at least one processor based on the first information, a set of network weights that is usable by a sinusoidal representation network model for obtaining a functional representation of the first time series (Sitzmann, Supplementary Material, Page 19, Paragraph 4, “We use a hypernetwork…which maps the latent code to the weights of a 5-layer SIREN”) Sitzmann does not explicitly disclose modulating, by the at least one processor based on the first information, a set of sine activation amplitudes of the functional representation of the first time series. Mehta discloses modulating, by the at least one processor based on the first information, a set of sine activation amplitudes of the functional representation of the first time series( Mehta, Page 4, Col. 1, Paragraph 2, “the latent codes z can modulate the amplitude of the sine activations of each hidden layer”) References Sitzmann and Mehta are analogous art because they are from the same field of endeavor if using machine learning with implicit neural representations using SIREN’s. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Sitzmann and Mehta before him or her, to modify the SIREN of Sitzmann to include the modulation of Mehta to adapt to different signals latent code. The suggestion/motivation for doing so would have been Mehta , Page 2, Col. 1, Paragraph 1, “functional mapping that uses two MLPs: a modulator and a synthesis network…The modulator is the key to generalization.” The Sitzmann-Mehta does not explicitly disclose imputing, by the at least one processor based on the set of network weights and a result of the modulating, the at least one missing value of the first time series. Naour discloses imputing, by the at least one processor based on the set of network weights and a result of the modulating, the at least one missing value of the first time series (Naour , Page 8, Paragraph 5, “We compared TimeFlow with the following two-step processing baseline: linear interpolation handling the missing values within the partially observed look-back window,” where imputing the missing values of the time series correspond imputing…the at least one missing value of the first time (See Also Naour , Page 20, Table 13)) References Sitzmann-Mehta and Naour are analogous art because they are from the same field of endeavor if using machine learning with implicit neural representations. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Sitzmann-Mehta and Naour before him or her, to modify the inference of Sitzmann-Mehta to include the imputation of previous missing data of Naour to better handle real-world data as real-world data has missing values. The suggestion/motivation for doing so would have been Naour , Page 2, Paragraph 1, “Practical considerations, such as the installation of new sensors, or legal restrictions, such as the EU GDPR, may prevent the simultaneous availability of all samples over time.” Regarding claim 3: The rejection of claim 1 with prior art Sitzmann-Naour-Mehta is incorporated and further: Sitzmann discloses further comprising assessing an accuracy of the imputing by obtaining at least one from among a first metric that relates to a mean- squared error between the imputed at least one missing value of the first time series and at least one ground truth value that corresponds to the at least one missing value of the first time series (Sitzmann, Supplementary Material, Page 16, Figure 8, “Tab. 3 shows the converged SIREN mean-squared error on the original audio signal and statistics on these metrics” where the use of mean square error to show how close the reconstructed audio matches the original ground-truth audio corresponds to a first metric that relates to a mean- squared error between the imputed at least one missing value of the first time series and at least one ground truth value that corresponds to the at least one missing value of the first time series (See also, Sitzmann, Page 4, Paragraph 4, “which can be translated into the loss PNG media_image1.png 29 280 media_image1.png Greyscale ” Due to the claim language stating at least one from among the following limitations are not quired as the first metric is found in prior art: a second metric that relates to a maximum error between the imputed atleast one missing value of the first time series and an evaluation of a model output that corresponds to the at least one missing value of the first time series, and a third metric that relates to a Euclidean distance in a feature space between the imputed at least one missing value of the first time series and the at least one ground truth value that corresponds to the at least one missing value of the first time series. Regarding claim 5: The rejection of claim 1 with prior art Sitzmann-Naour-Mehta is incorporated and further: Sitzmann further discloses further comprising training the sinusoidal representation network model (Sitzmann, Page 4, Paragraph 6“We present a principled initialization scheme necessary for the effective training of SIRENs.”) using historical data (Sitzmann, Page 7, Paragraph 2, “Training is performed on randomly sampled points x within the domain” where the samples of x are considered historical data as it is data that exists ) and optimizing the sinusoidal representation network model with respect to a predetermined loss function (Sitzmann, Page 4, Paragraph 4, “which can be translated into the loss PNG media_image1.png 29 280 media_image1.png Greyscale ” Regarding claim 6: The rejection of claim 1 with prior art Sitzmann-Naour-Mehta is incorporated and further: Naour, further discloses wherein the first time series comprises a univariate time series (Naour, Page 1, Paragraph 2, “In this paper, we focus on these univariate time series with multiple instances/samples and address two critical tasks: imputation and long-term time series forecasting (LTSF)”) Regarding claim 8: The rejection of claim 1 with prior art Sitzmann-Naour-Mehta is incorporated and further: Naour, further discloses wherein the first time series comprises a multivariate time series (Naour, Page 10, Paragraph 1, “To extend our findings to the multi-sample multivariate case, a simple approach is to modify the output dimension of the INR network”) Regarding claim 4: The rejection of claim 1 with prior art Sitzmann-Naour-Mehta is incorporated and further: Mehta further discloses receiving second information that relates to frequency modes of a superset of data that includes the first time series and at least a second time series, wherein the modulating is further based on the second information (Mehta, Page 4, Col. 1, Equation 4 and Paragraph 3, “As can be seen from Equations 4 and 2, the latent codes z can modulate the amplitude of the sine activations of each hidden layer in the synthesis network, through the modulation parameters α i ”) Regarding claim 2: The rejection of claim 1 with prior art Sitzmann-Naour-Mehta is incorporated and further: Sitzmann further discloses wherein the sinusoidal representation network model comprises a first neural network model (Sitzmann, Page 4, Paragraph 1, “We propose SIREN, a simple neural network architecture for implicit neural representations that uses the sine as a periodic activation function”) and the generating of the set of network weights is performed by using a second neural network model,( Sitzmann, Page 8, Paragraph 3, “and use a ReLU hypernetwork [53], to map the latent code to the weights of a SIREN, as in” where the hypernetwork outputting the weights θ of the SIREN network corresponds to generating a set of network weights being performed by using a neural network model ) Mehta further discloses the modulating of the set of sine activation amplitudes is performed by using a third neural network model( Mehta, Page 3, Col. 1, Paragraph 4, “The latent codes are processed by a modulation network, which conditionally modulates the activations of a synthesis network, that acts as a template for the functional mapping”) Regarding claim 10: Sitzmann discloses a processor; a memory; and a communication interface coupled to each of the processor and the memory, wherein the processor is configured to (Sitzmann, Page 8, Paragraph 6, “The networks are trained using NVIDIA GTX Titan X GPUs with 12 GB of memory”) Sitzmann discloses receive, via the communication interface, first information that relates to a latent vector representation (Sitzmann, Page 8, Paragraph 2, “we condition these latent code vectors on partial observations of the signal O ϵ R m through an encoder”) of a first time series (Sitzmann, Supplementary Material, Page 16, Figure 8, “Example frames from fitting a video with SIREN and ReLU MLPs” where a video corresponds to a time series as a video is pixels over time (See also Sitzmann, Supplementary Material, Page 16, Paragraph 5, “Due to the highly periodic nature of audio signals with structure at various time scales, we expect that SIRENs could accurately represent such signals efficiently and provide an alternative representation for audio signals” where the model handling audio at time scales corresponds to handling a time series )) for which at least one value is missing( Sitzmann, Supplementary Material, Page 17, Figure 7, “ We sample 10% of pixels from the ground truth image for training, learning a representation which can inpaint the missing values” where the inpainting of missing pixels corresponds to receiving data with values missing ) Sitzmann discloses generating, based on the first information, a set of network weights that is usable by a sinusoidal representation network model for obtaining a functional representation of the first time series (Sitzmann, Supplementary Material, Page 19, Paragraph 4, “We use a hypernetwork…which maps the latent code to the weights of a 5-layer SIREN”) Sitzmann does not explicitly disclose modulate, based on the first information, a set of sine activation amplitudes of the functional representation of the first time series. Mehta discloses modulate, based on the first information, a set of sine activation amplitudes of the functional representation of the first time series( Mehta, Page 4, Col. 1, Paragraph 2, “the latent codes z can modulate the amplitude of the sine activations of each hidden layer”) References Sitzmann and Mehta are analogous art because they are from the same field of endeavor if using machine learning with implicit neural representations using SIREN’s. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Sitzmann and Mehta before him or her, to modify the SIREN of Sitzmann to include the modulation of Mehta to adapt to different signals latent code. The suggestion/motivation for doing so would have been Mehta , Page 2, Col. 1, Paragraph 1, “functional mapping that uses two MLPs: a modulator and a synthesis network…The modulator is the key to generalization.” The Sitzmann-Mehta does not explicitly disclose impute, based on the set of network weights and a result of the modulating, the at least one missing value of the first time series. Naour discloses impute, based on the set of network weights and a result of the modulating, the at least one missing value of the first time series (Naour , Page 8, Paragraph 5, “We compared TimeFlow with the following two-step processing baseline: linear interpolation handling the missing values within the partially observed look-back window,” where imputing the missing values of the time series correspond imputing…the at least one missing value of the first time (See Also Naour , Page 20, Table 13)) References Sitzmann-Mehta and Naour are analogous art because they are from the same field of endeavor if using machine learning with implicit neural representations. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Sitzmann-Mehta and Naour before him or her, to modify the inference of Sitzmann-Mehta to include the imputation of previous missing data of Naour to better handle real-world data as real-world data has missing values. The suggestion/motivation for doing so would have been Naour , Page 2, Paragraph 1, “Practical considerations, such as the installation of new sensors, or legal restrictions, such as the EU GDPR, may prevent the simultaneous availability of all samples over time.” Regarding claim 11: The rejection of claim 10 incorporated in claim 11 . Claim 11 is rejected under the same rationale as set forth in the rejection of claim 2 . Regarding claim 12: The rejection of claim 10 incorporated in claim 12 . Claim 12 is rejected under the same rationale as set forth in the rejection of claim 3 . Regarding claim 13: The rejection of claim 10 incorporated in claim 13 . Claim 13 is rejected under the same rationale as set forth in the rejection of claim 4 . Regarding claim 14: The rejection of claim 10 incorporated in claim 14 . Claim 14 is rejected under the same rationale as set forth in the rejection of claim 5 . Regarding claim 15: The rejection of claim 10 incorporated in claim 15 . Claim 15 is rejected under the same rationale as set forth in the rejection of claim 6 . Regarding claim 17: The rejection of claim 10 is incorporated in claim 17 . Claim 17 is rejected under the same rationale as set forth in the rejection of claim 8 . Regarding claim 19: Sitzmann discloses a non-transitory computer readable storage medium storing instructions for performing time series imputation, the storage medium comprising executable code which, when executed by a processor, causes the process (Sitzmann, Page 8, Paragraph 6, “The networks are trained using NVIDIA GTX Titan X GPUs with 12 GB of memory”) Sitzmann discloses receive, via the communication interface, first information that relates to a latent vector representation (Sitzmann, Page 8, Paragraph 2, “we condition these latent code vectors on partial observations of the signal O ϵ R m through an encoder”) of a first time series (Sitzmann, Supplementary Material, Page 16, Figure 8, “Example frames from fitting a video with SIREN and ReLU MLPs” where a video corresponds to a time series as a video is pixels over time (See also Sitzmann, Supplementary Material, Page 16, Paragraph 5, “Due to the highly periodic nature of audio signals with structure at various time scales, we expect that SIRENs could accurately represent such signals efficiently and provide an alternative representation for audio signals” where the model handling audio at time scales corresponds to handling a time series )) for which at least one value is missing( Sitzmann, Supplementary Material, Page 17, Figure 7, “ We sample 10% of pixels from the ground truth image for training, learning a representation which can inpaint the missing values” where the inpainting of missing pixels corresponds to receiving data with values missing ) Sitzmann discloses generating, based on the first information, a set of network weights that is usable by a sinusoidal representation network model for obtaining a functional representation of the first time series (Sitzmann, Supplementary Material, Page 19, Paragraph 4, “We use a hypernetwork…which maps the latent code to the weights of a 5-layer SIREN”) Sitzmann does not explicitly disclose modulate, based on the first information, a set of sine activation amplitudes of the functional representation of the first time series. Mehta discloses modulate, based on the first information, a set of sine activation amplitudes of the functional representation of the first time series( Mehta, Page 4, Col. 1, Paragraph 2, “the latent codes z can modulate the amplitude of the sine activations of each hidden layer”) References Sitzmann and Mehta are analogous art because they are from the same field of endeavor if using machine learning with implicit neural representations using SIREN’s. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Sitzmann and Mehta before him or her, to modify the SIREN of Sitzmann to include the modulation of Mehta to adapt to different signals latent code. The suggestion/motivation for doing so would have been Mehta , Page 2, Col. 1, Paragraph 1, “functional mapping that uses two MLPs: a modulator and a synthesis network…The modulator is the key to generalization.” The Sitzmann-Mehta does not explicitly disclose impute, based on the set of network weights and a result of the modulating, the at least one missing value of the first time series. Naour discloses impute, based on the set of network weights and a result of the modulating, the at least one missing value of the first time series (Naour , Page 8, Paragraph 5, “We compared TimeFlow with the following two-step processing baseline: linear interpolation handling the missing values within the partially observed look-back window,” where imputing the missing values of the time series correspond imputing…the at least one missing value of the first time (See Also Naour , Page 20, Table 13)) References Sitzmann-Mehta and Naour are analogous art because they are from the same field of endeavor if using machine learning with implicit neural representations. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Sitzmann-Mehta and Naour before him or her, to modify the inference of Sitzmann-Mehta to include the imputation of previous missing data of Naour to better handle real-world data as real-world data has missing values. The suggestion/motivation for doing so would have been Naour , Page 2, Paragraph 1, “Practical considerations, such as the installation of new sensors, or legal restrictions, such as the EU GDPR, may prevent the simultaneous availability of all samples over time.” Regarding claim 20: The rejection of claim 19 incorporated in claim 20 . Claim 20 is rejected under the same rationale as set forth in the rejection of claim 2 . 07-21-aia AIA Claim (s) 7 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sitzmann et al(“Implicit Neural Representations with Periodic Activation Functions” henceforth known as Sitzmann) in view of Mehta et al(“Modulated Periodic Activations for Generalizable Local Functional Representations” henceforth known as Mehta) and further in view of Naouret al(“Time Series Continuous Modeling for Imputation and Forecasting with Implicit Neural Representations” henceforth known as Naour) and Mehtab et al(“Stock Price Prediction Using Convolutional Neural Networks on a Multivariate Timeseries” henceforth known Mehtab) . Regarding claim 7: The rejection of claim 6 with prior art Sitzmann-Naour-Mehta is incorporated and further: The Sitzmann-Naour-Mehta combination does not disclose wherein the univariate time series comprises a time series that relates to stock market data , however Mehtab discloses representing stock market data as a time series(Mehtab Page 4, Col. 1, Paragraph 2, “In the first case, we design a CNN for multi-step time series forecasting using only the univariate sequence of the close_perc values” and Mehtab Page 4, Col. 1, Paragraph 3, “The multi-step time series forecasting approach is essentially an autoregression process. Whether univariate or multivariate, the prior time series data is used for forecasting the values for the next week.”) References Sitzmann-Naour-Mehta and Mehtab are analogous art because they are from the field of endeavor as using time series with machine learning models to forecast data. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Sitzmann-Naour-Mehta and Mehtab before him or her, to modify the time series of Sitzmann-Naour-Mehta with the stock information in a time series of Mehtab as stocks naturally form a time series as they track movements over time. The suggestion/motivation for doing so would have been Mehtab Page 2, Col. 1, Paragraph 2, “We believe that past movement patterns of daily NIFTY index values can be learned by powerful machine learning and deep learning-based approaches, and that knowledge can be gainfully applied for predicting future movement NIFTY index values.” Regarding claim 16: The rejection of claim 15 incorporated in claim 16 . Claim 16 is rejected under the same rationale as set forth in the rejection of claim 7 . 07-21-aia AIA Claim (s) 9 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sitzmann et al(“Implicit Neural Representations with Periodic Activation Functions” henceforth known as Sitzmann) in view of Mehta et al(“Modulated Periodic Activations for Generalizable Local Functional Representations” henceforth known as Mehta) and further in view of Naouret al(“Time Series Continuous Modeling for Imputation and Forecasting with Implicit Neural Representations” henceforth known as Naour) and Festag et al(“Medical multivariate time series imputation and forecasting based on a recurrent conditional Wasserstein GAN and attention” henceforth known Festag) . Regarding claim 9: The rejection of claim 8 with prior art Sitzmann-Naour-Mehta is incorporated and further: The Sitzmann-Naour-Mehta combination does not disclose wherein the multivariate time series comprises one from among a time series that relates to yield rate curve data, a time series that relates to weather forecasting data, and a time series that relates to medical diagnosis data however Festag discloses representing medical data as a time series(Festag Page 1, Abstract, “…the focus of our work lay on a…multivariate generative approach that samples infillings or forecasts…for this task, we developed…networks that consist of…encoders and decoders…and can learn the distribution of intervals from multivariate time… The presented generative probabilistic system for the imputation and forecasting of (medical) time series ” and Festag Page 2, Col. 2, Paragraph 5, “The data basis for training and testing consists of multivariate time series 𝐒 ∈ R 𝑛 × 𝑑 with 𝑑 time steps and 𝑛 channels”) References Sitzmann-Naour-Mehta and Festag are analogous art because they are from the field of endeavor as using time series with machine learning models to forecast and impute data. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Sitzmann-Naour-Mehta and Festag before him or her, to modify the time series of Sitzmann-Naour-Mehta with the medical information in a time series of Festag as biomedical measurements naturally form a time series as they track physiological variables over time. The suggestion/motivation for doing so would have been Festag Page 1, Col. 1, Paragraph 1, “In the biomedical domain, many measured values are part of time series representing the development of the measurand over time. Prominent examples of the temporal documentation of repeated readings in the clinical context are bedside monitors that continuously output health-related data of patients.” Regarding claim 18: The rejection of claim 17 incorporated in claim 18 . Claim 18 is rejected under the same rationale as set forth in the rejection of claim 9 . Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHARLES JEFFREY JONES JR whose telephone number is (703)756-1414. The examiner can normally be reached Monday - Friday 8:00 - 5:00 EST. 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, Kakali Chaki can be reached at 571-272-3719. 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. /C.J.J./Examiner, Art Unit 2122 /KAKALI CHAKI/Supervisory Patent Examiner, Art Unit 2122 Application/Control Number: 18/368,341 Page 2 Art Unit: 2122 Application/Control Number: 18/368,341 Page 3 Art Unit: 2122 Application/Control Number: 18/368,341 Page 4 Art Unit: 2122 Application/Control Number: 18/368,341 Page 5 Art Unit: 2122 Application/Control Number: 18/368,341 Page 6 Art Unit: 2122 Application/Control Number: 18/368,341 Page 7 Art Unit: 2122 Application/Control Number: 18/368,341 Page 8 Art Unit: 2122 Application/Control Number: 18/368,341 Page 9 Art Unit: 2122 Application/Control Number: 18/368,341 Page 10 Art Unit: 2122 Application/Control Number: 18/368,341 Page 11 Art Unit: 2122 Application/Control Number: 18/368,341 Page 12 Art Unit: 2122 Application/Control Number: 18/368,341 Page 13 Art Unit: 2122 Application/Control Number: 18/368,341 Page 14 Art Unit: 2122 Application/Control Number: 18/368,341 Page 15 Art Unit: 2122 Application/Control Number: 18/368,341 Page 16 Art Unit: 2122 Application/Control Number: 18/368,341 Page 17 Art Unit: 2122 Application/Control Number: 18/368,341 Page 18 Art Unit: 2122 Application/Control Number: 18/368,341 Page 19 Art Unit: 2122 Application/Control Number: 18/368,341 Page 20 Art Unit: 2122 Application/Control Number: 18/368,341 Page 21 Art Unit: 2122 Application/Control Number: 18/368,341 Page 22 Art Unit: 2122 Application/Control Number: 18/368,341 Page 23 Art Unit: 2122 Application/Control Number: 18/368,341 Page 24 Art Unit: 2122 Application/Control Number: 18/368,341 Page 25 Art Unit: 2122 Application/Control Number: 18/368,341 Page 26 Art Unit: 2122 Application/Control Number: 18/368,341 Page 27 Art Unit: 2122 Application/Control Number: 18/368,341 Page 28 Art Unit: 2122 Application/Control Number: 18/368,341 Page 29 Art Unit: 2122 Application/Control Number: 18/368,341 Page 30 Art Unit: 2122 Application/Control Number: 18/368,341 Page 31 Art Unit: 2122 Application/Control Number: 18/368,341 Page 32 Art Unit: 2122 Application/Control Number: 18/368,341 Page 33 Art Unit: 2122 Application/Control Number: 18/368,341 Page 34 Art Unit: 2122
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