DETAILED ACTION 1. This office action is in response to application 18/280,727 filed on 9/7/2023. Claims 1-20 are pending in this office action. Notice of Pre-AIA or AIA Status 2. 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 § 102 3. 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. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. Claim(s) 1-4, 6, 7, 9-13, 16, 17 and 19 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by US 2020/0387797 (hereinafter Ryan) . As for claim 1 Ryan discloses: 1. A system for detecting an anomaly in a multivariate time series, the system comprising: at least one processor programmed or configured to: receive a dataset of a plurality of data instances (See paragraphs 0176 and 0179 note processors and conductors are configured to receive the data) , wherein each data instance comprises a time series of data points ( See paragraph 0075 ) ; determine a set of target data instances based on the dataset, wherein each target data instance of the set of target data instances is associated with a first time period; determine a set of historical data instances based on the dataset (See paragraphs 0075, 0101 and 0160) , wherein each historical data instance of the set of historical data instances is associated with a second time period, wherein the second time period is prior to the first time period (See paragraphs 0075, 0101 and 0203 ; generate, based on the set of target data instances, a true value matrix, a true frequency matrix, and a true correlation matrix; generate a forecast value matrix based on the set of target data instances and the set of historical data instances (See paragraphs 0066, 0101, 0165, 0203 and 0207-0208) ; determine an amount of forecasting error, wherein when determining the amount of forecasting error, the at least one processor is programmed or configured to determine the amount of forecasting error between (See paragraph 0068) : the forecast value matrix, the forecast frequency matrix, and the forecast correlation matrix, and the true value matrix, the true frequency matrix, and the true correlation matrix (See paragraphs 0066, 0097, 0101 and 0165) ; and determine whether the amount of forecasting error corresponds to an anomalous event associated with the dataset of the plurality of data instances (See paragraphs 0066 and 0068) . As for claim 2 the rejection of claim 1 is incorporated and further Ryan discloses: wherein, when determining whether the amount of forecasting error corresponds to an anomalous event associated with the dataset of the plurality of data instances (See paragraphs 0066 and 0068) , the at least one processor is programmed or configured to: determine whether the amount of forecasting error satisfies a threshold value of forecasting error (See paragraph 0176) ; and determine whether the dataset of the plurality of data instances includes an anomalous event based on determining whether the amount of forecasting error satisfies the threshold value of forecasting error (See paragraphs 0066 and 0068) . As for claim 3 the rejection of claim 1 is incorporated and further Ryan discloses: wherein when determining the amount of forecasting error, the at least one processor is programmed or configured to: concatenate the true value matrix, the true frequency matrix, and the true correlation matrix to generate a forecasting input matrix (See paragraphs 0066, 0068, 0176 and 0097 note the matrix with the highest value is determined) ; concatenate the forecast value matrix, the forecast frequency matrix, and the forecast correlation matrix to generate a forecasting output matrix (See paragraph 0172 note Output from the special CNNs 418 and fully connected block 414 are provided to an anomaly detection block 420, which may be configured to adjustably define the normal/anomaly threshold of classification."; see also paragraph 0066, 0097, 0101, 0165, 0203 and 0207-0208); ; and determine the amount of forecasting error based on the forecasting input matrix and the forecasting output matrix (See paragraphs 0066, 0068 and 0097). As for claim 4 the rejection of claim 1 is incorporated and further Ryan discloses: wherein when determining the amount of forecasting error (See paragraph 0066 and 0068) , the at least one processor is programmed or configured to: determine a measure of loss associated with a forecasting error mean for batches of input data instances of the dataset of the plurality of data instances based on the forecasting input matrix and the forecasting output matrix; determine a measure of loss associated with a variance of forecasting error for each batch of input data instances of the dataset of the plurality of data instances based on the forecasting input matrix and the forecasting output matrix (See paragraphs 0066, 0097, 0101, 0165, 0172, 0176, 0203 and 0207-0208) ; and determine the amount of forecasting error based on the measure of loss associated with a forecasting error mean for batches of data instances and the measure of loss associated with the variance of forecasting error for each batch of data instances (See paragraphs 0097, and 0143) . As for claim 6 the rejection of claim 1 is incorporated and further Ryan discloses: wherein the at least one processor is further programmed or configured to: generate a historical true value matrix based on a number of time series of data points in the set of target data instances and a number of time steps in a target window segment of data points (See paragraphs 0097, 0101, 0203 and 0208). As for claim 7 the rejection of claim 1 is incorporated and further Ryan discloses: wherein, when generating the forecast value matrix, the at least one processor is programmed or configured to: provide the historical true value matrix as an input to a dilated convolutional neural network (CNN) to generate an output of the dilated CNN; and generate the forecast value matrix based on the output of the dilated CNN (See paragraphs 0065, 0097, 0101, 0203 and 0208 note the windowing technique) . As for claim 9 the rejection of claim 1 is incorporated and further Ryan discloses: wherein, when generating the forecast correlation matrix, the at least one processor is programmed or configured to: generate a sequence of window segments based on the historical true value matrix (See paragraphs 0097, 0101 0203 and 00208) ; generate a plurality of frequency matrices based on a discrete Fourier transform of the sequence of window segments 097, 0101 0203 and 0165) ; provide the plurality of frequency matrices as an input to a convolutional long short-term memory (ConvLSTM) neural network to generate an output of the ConvLSTM neural network; provide the output of the ConvLSTM neural network as an input to an attention mechanism to generate an output of the attention mechanism; and generate the forecast correlation matrix based on the output of the attention mechanism (See paragraphs 0097, 0101, 0165 and 0174). Claims 10, 12 and 13 are method claims substantially corresponding to the system of claims 1, 3 and 4 and are thus rejected for the same reasons as set forth in the rejection of claims 1, 3 and 4. As for claim 11 Ryan discloses: wherein determining whether the amount of forecasting error corresponds to an anomalous event associated with the dataset of the plurality of data instances comprises: determining whether the amount of forecasting error satisfies a threshold value of forecasting error; and determining whether the dataset of the plurality of data instances includes an anomalous event based on determining whether the amount of forecasting error satisfies the threshold value of forecasting error (See paragraphs 0066 and 0068). Claims 15-17 and 19 are program product claims substantially corresponding to the method of claims 1-3 and 7 and are thus rejected for the same reasons as set forth in the rejection of claims 1-3 and 7. Claim Rejections - 35 USC § 103 4. 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. The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. Claim(s) 5, 8, 14, 18 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ryan as applied to claim s 1, 10 and 15 above, and further in view of US 2021/0004682 (hereinafter Gong) . As for claim 5 Ryan discloses: 5 wherein, when generating the true value matrix, the true frequency matrix, and the true correlation matrix, the at least one processor is programmed or configured to: generate the true value matrix based on a number of time series of data points in the set of target data instances and a number of time steps in a target window segment of data points (See paragraphs 0066, 0101, 0165, 0172, 0203 and 0207-208) ; generate the true frequency matrix based on a discrete Fourier transform of the true value matrix; and generate the true correlation matrix based on scores between a plurality of time series of data points of the plurality of data instances (See paragraphs 0066, 0101, 0165, 0172, 0203 and 0207-208) . Ryan does not disclose a cosine similarity score . Gong however discloses: a cosine similarity score (See paragraphs 0075 and 0098 note the system determine the similarity based on the cosine). It would have been obvious to an artisan of ordinary skill in the pertinent at the time the instantly claimed invention was filed to have incorporated the teaching of Gong into the system of Ryan. The modification would have been obvious because the two references are concerned with the solution to problem of data processing, therefore there is an implicit motivation to combine these references (i.e. motivation from the references themselves). In other words, the ordinary skilled artisan, during his/her quest for a solution to the cited problem, would look to the cited references at the time the invention was made. Consequently, the ordinary skilled artisan would have been motivated to combine the cited references since Gong’s teaching would enable users of the Ryan system to have more efficient processing. As for claim 8 the rejection of claim 6 is incorporated and further Ryan discloses: wherein, when generating the forecast frequency matrix, the at least one processor is programmed or configured to: generate a sequence of window segments based on the historical true value matrix (See paragraphs 0097, 0101, 0203 and 0208) ; generate a plurality of correlation matrices based on scores of the sequence of window segments; provide the plurality of correlation matrices as an input to a convolutional long short-term memory (ConvLSTM) neural network to generate an output of the ConvLSTM neural network; provide the output of the ConvLSTM neural network as an input to an attention mechanism to generate an output of the attention mechanism; and generate the forecast frequency matrix based on the output of the attention mechanism (See paragraphs 0066, 0068, 0097 and 0165). It would have been obvious to an artisan of ordinary skill in the pertinent at the time the instantly claimed invention was filed to have incorporated the teaching of Gong into the system of Ryan. The modification would have been obvious because the two references are concerned with the solution to problem of data processing, therefore there is an implicit motivation to combine these references (i.e. motivation from the references themselves). In other words, the ordinary skilled artisan, during his/her quest for a solution to the cited problem, would look to the cited references at the time the invention was made. Consequently, the ordinary skilled artisan would have been motivated to combine the cited references since Gong’s teaching would enable users of the Ryan system to have more efficient processing. Claim 14 is a method claim substantially corresponding to the system of claim 5 and is thus rejected for the same reasons as set forth in the rejection of claim 5. Claim s 18 and 20 are program product claim s substantially corresponding to the system of claim 5 and 8 and are thus rejected for the same reasons as set forth in the rejection of claim s 5 and 8 . Information Any inquiry concerning this communication or earlier communications from the examiner should be directed to ELIYAH STONE HARPER whose telephone number is (571)272-0759 . The examiner can normally be reached on Monday-Friday 10:00 am - 6:00 pm . Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration t ool. To schedule an interview, a pplicant 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, Sanjiv Shah can be reached on (571) 272-40 98. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /Eliyah S. Harper/ Primary Examiner, Art Unit 2166 March 24, 2026