CTNF 18/337,099 CTNF 90182 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 § 102 07-07-aia AIA 07-07 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 – 07-08-aia AIA (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention. 07-12-aia AIA (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. 07-15 AIA Claim 1 is rejected under 35 U.S.C. 102( a)(1) and 102(a)(2 ) as being anticipated by Sun et al. U.S. PGPub 2018/0260704 A1 (hereinafter Sun) . Regarding Claim 1, Sun teaches a method for generating matrix data for a convolutional neural network that performs a convolution operation based on the matrix data in which predetermined information is arranged as a matrix element (Sun, Figs. 3-4; Para. [0027]), wherein: the matrix data is composed of predetermined time-series data in which each row of the matrix data changes continuously in terms of time in an arrangement direction of each column of the matrix data (Sun, Fig. 12; Paras. [0004] – [0007] and [0073]); the time-series data is composed of first data of which a degree of influence on the convolution operation is high (Sun, Figs. 10-11; Para. [0026], “high-dimensional time series data”, and Paras. [0068] – [0071], “first sequence of multi-dimensional time series data”) and second data of which the degree of influence is lower than the first data (Sun, Figs. 10-11; Paras. [0068] – [0071], “second {and subsequent} sequence of multi-dimensional time series data”), the convolution operation is performed using a kernel that partitions the matrix data into the rows and the columns corresponding to a predetermined coefficient (Sun, Fig. 12, Element 1202; Paras. [0072] – [0073], “data matrix”); at least one row of the first data is arranged for each set of the rows corresponding to the coefficient; and the second data is arranged in the remaining rows except for the row in which the first data is arranged (Sun, Figs. 9-11; Paras. [0068] – [0071], “first sequence of multi-dimensional time series data”) . Allowable Subject Matter 12-151-07 AIA 07-97 12-51-07 Claim s 2-5 are allowed. Reasons for Allowance The following is an examiner’s statement of reasons for allowance: Regarding Claim 2: Though the prior art discloses a method for generating matrix data for a convolutional neural network that performs a convolution operation based on the matrix data, wherein: the matrix data is composed of time-series data in which each row of the matrix data changes continuously in terms of time in an arrangement direction of each column of the matrix data; the time-series data is composed of first data of which a degree of influence on the convolution operation is high and second data of which the degree of influence is lower than the first data; the convolution operation is performed using a kernel that partitions the matrix data into the rows and the columns corresponding a predetermined coefficient; at least one row of the first data is arranged for each set of the rows corresponding to the coefficient; and the second data is arranged in the remaining rows except for the row in which the first data is arranged, it fails to teach or suggest the aforementioned limitations of claim 2, and further including the combination of: a method for generating matrix data for a convolutional neural network that performs a convolution operation based on the matrix data in which vehicle information with a behavior and a state of each component of a vehicle detected is arranged as a component of a matrix and estimates a state of a temporal change of a predetermined element of the vehicle, wherein: the matrix data is composed of time-series data of the vehicle information in which each row of the matrix data changes continuously in terms of time in an arrangement direction of each column of the matrix data; the time-series data is composed of first data of which a degree of influence on the convolution operation is high and that includes at least data related to a primary factor of the temporal change … Regarding Claim 4: Though the prior art discloses a learning system using a convolutional neural network performing a convolution operation based on matrix data in which predetermined information is arranged as a component of a matrix, wherein: the time-series data using first data of which a degree of influence on the convolution operation is high and second data of which the degree of influence is lower than the first data, performs the convolution operation using a kernel that partitions the matrix data into the rows and the columns corresponding to a predetermined coefficient, arranges at least one row of the first data for each set of the rows corresponding to the coefficient, arranges the second data in the remaining rows except for the row in which the first data is arranged, and estimates the state of the temporal change by performing the convolution operation, it fails to teach or suggest the aforementioned limitations of claim 4, and further including the combination of: a learning system provided with a control unit mounted on a vehicle and a server installed outside the vehicle, the learning system using a convolutional neural network that estimates a state of a temporal change of a predetermined element of the vehicle by performing a convolution operation based on matrix data in which predetermined information is arranged as a component of a matrix, wherein: the control unit acquires vehicle information with a behavior and a state of each component of the vehicle detected, and transmits the vehicle information to the server; and the server configures the matrix data using time-series data of the vehicle information in which each row of the matrix data changes continuously in terms of time in an arrangement direction of each column of the matrix data, configures the time-series data using first data of which a degree of influence on the convolution operation is high and that includes at least data related to a primary factor of the temporal change … Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Ryan et al. U.S. Patent 12,468,951 teaches a time-series event prediction using convolution neural network system. Kim KR-20200119383 teaches a time-series data being battery deterioration. Takahashi et al. U.S. PGPub 2020/0292620 teaches a battery life learning device. Any inquiry concerning this communication or earlier communications from the examiner should be directed to JERRY D ROBBINS whose telephone number is (571)272-7585. The examiner can normally be reached 9:00AM - 6:00PM Tuesday-Saturday. 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, Julian Huffman can be reached at 571-272-2147. 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. /JERRY D ROBBINS/ Examiner, Art Unit 2859 Application/Control Number: 18/337,099 Page 2 Art Unit: 2859
Prosecution Insights