# US 2021/0279518 A1 · Learning method, learning system, and learning program > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2021/0279518 A1 - Document ID: US20210279518A1 - Status: Published application - Kind: A1 - Application number: 17/185,018 - Filed: 2021-02-25 - Published: 2021-09-09 - Earliest claimed: 2020-03-03 - Assignees: Toyota Jidosha Kabushiki Kaisha - Applicants: Toyota Jidosha Kabushiki Kaisha - Inventors: Taro Takahashi - Web page: https://patentgrep.com/patent/US20210279518A1 ## CPC - G06N 3/08: Learning methods - G01P 3/44: for measuring angular speed (G01P3/56 takes precedence) - G06F 18/2148: {characterised by the process organisation or structure, e.g. boosting cascade} - G06F 18/25: Fusion techniques - G06N 3/044: Recurrent networks, e.g. Hopfield networks - G06N 3/0442: characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU] - G06N 3/045: Combinations of networks - G06N 3/0464: Convolutional networks [CNN, ConvNet] - G06N 3/09: Supervised learning - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G01P: MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION, OR SHOCK; INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT (measuring or recording blood flow A61B5/02, A61B8/06; monitoring speed or deceleration of electrically-propelled vehicles B60L3/00; vehicle lighting systems adapted to indicate speed B60Q1/54; determining position or course in navigation, measuring ground distance in geodesy or surveying G01C; combined measuring devices for measuring two or more variables of movement G01C23/00; measuring velocity of sound G01H; measuring velocity of light G01J7/00; measuring direction or velocity of solid objects by reception or emission of radiowaves or other waves and based on propagation effects, e.g. Doppler effect, propagation time, direction of propagation, G01S; measuring speed of nuclear radiation G01T; measuring acceleration of gravity G01V; {measuring or recording the speed of trains B61L23/00; speed indicators incorporated in motor vehicles B60K35/00; measuring frequency or phase G01R; traffic control G08G}) - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract To reduce a learning time while improving learning accuracy. A learning method includes generating first time-series data by performing sampling from a predetermined sensor value at a first sampling cycle, generating second time-series data by performing sampling from the predetermined sensor value at a second sampling cycle different from the first sampling cycle, and generating learning data by combining the generated first and second time-series data with each other, and performing learning by using the generated learning data. ## Claims ### Claim 1 A learning method comprising: generating first time-series data by performing sampling from a predetermined sensor value at a first sampling cycle, generating second time-series data by performing sampling from the predetermined sensor value at a second sampling cycle different from the first sampling cycle, and generating learning data by combining the generated first and second time-series data with each other; and performing learning by using the generated learning data. ### Claim 2 (depends on 1) The learning method according to claim 1, wherein at least one time series data is generated by performing sampling at a sampling cycle different from the first and second sampling cycles, and the learning data is generated by combining the generated at least one time series data with the first and second time-series data. ### Claim 3 (depends on 1) The learning method according to claim 1, wherein the first time-series data is generated by performing sampling in a retrospective manner starting from a present time from the predetermined sensor value at the first sampling cycle, and the second time-series data is generated by performing sampling in a retrospective manner starting from the present time from the predetermined sensor value at the second sampling cycle different from the first sampling cycle. ### Claim 4 (depends on 1) The learning method according to claim 1, wherein the same number of data may be included in each of the time-series data. ### Claim 5 (depends on 1) The learning method according to claim 1, wherein when the first sampling cycle is shorter than the second sampling cycle, a multiplication value obtained by multiplying the first sampling cycle by the number of data included in the first time-series data is smaller than a multiplication value obtained by multiplying the second sampling cycle by N (N=1 to 5), and when the first sampling cycle is longer than the second sampling cycle, a multiplication value obtained by multiplying the first sampling cycle by N (N=1 to 5) is larger than a multiplication value obtained by multiplying the second sampling cycle by the number of data included in the second time-series data. ### Claim 6 (depends on 1) The learning method according to claim 1, wherein the learning data is generated by combining the first and second time-series data with a sensor value or an estimated value different from the predetermined sensor value. ### Claim 7 (depends on 1) The learning method according to claim 1, wherein the predetermined sensor value is an angular velocity of a rotation mechanism, and a frictional torque of the rotation mechanism is estimated by performing learning using the learning data. ### Claim 8 A learning system comprising: a data generation unit that generates first time-series data by performing sampling from a predetermined sensor value at a first sampling cycle, generates second time-series data by performing sampling from the predetermined sensor value at a second sampling cycle different from the first sampling cycle, and generates learning data by combining the generated first and second time-series data with each other; and a learning unit that performs learning by using the learning data generated by the data generation unit. ### Claim 9 A non-transitory computer readable medium storing a learning program for causing a computer to execute: a process for generating first time-series data by performing sampling from a predetermined sensor value at a first sampling cycle, generating second time-series data by performing sampling from the predetermined sensor value at a second sampling cycle different from the first sampling cycle, and generating learning data by combining the generated first and second time-series data with each other; and a process for performing learning by using the generated learning data.