# US 2018/0197112 A1 · Machine learning device that learns shocks to teaching device, shock prevention system of teaching device, and machine learning method > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2018/0197112 A1 - Document ID: US20180197112A1 - Status: Published application - Kind: A1 - Application number: 15/865,920 - Filed: 2018-01-09 - Published: 2018-07-12 - Earliest claimed: 2017-01-10 - Assignees: Fanuc Corporation - Applicants: Fanuc Corporation - Inventors: Yuusuke SUGIYAMA - Web page: https://patentgrep.com/patent/US20180197112A1 ## CPC - G05B 13/0265: {the criterion being a learning criterion} - G06N 3/08: Learning methods - B25J 9/163: {learning, adaptive, model based, rule based expert control} - G06N 3/09: Supervised learning - G06N 3/098: Distributed learning, e.g. federated learning - G06N 20/00: Machine learning - G05B 19/406: characterised by monitoring or safety (G05B19/19 takes precedence) - G05B 19/409: characterised by using manual data input [MDI] or by using control panel, e.g. controlling functions with the panel; characterised by control panel details or by setting parameters (G05B19/408, G05B19/4093 take precedence) - G05B 2219/33098: Several nc machines, dnc, cnc - G05B 2219/33321: Observation learning - G05B 2219/36162: Pendant control box - G05B 2219/39443: Portable, adapted to handpalm, with joystick, function keys, display - G05B: CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS (systems for controlling or regulating non-electric variables G05D; systems for regulating electric or magnetic variables G05F; control devices or systems insofar as characterised by mechanical features only G05G) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - B25J: MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES ({manipulators specially adapted for use in surgery A61B34/70; manipulators used in cleaning hollow articles B08B9/04}; manipulators associated with rolling mills B21B39/20; manipulators associated with forging machines B21J13/10; {manipulators associated with picking-up and placing mechanisms B23P19/007}; means for holding wheels or parts thereof B60B30/00; {vehicles with ground-engaging propulsion means, e.g. walking members B62D57/02, B62D57/032; devices for picking-up and depositing articles or materials between conveyors B65G47/90, B65G47/91; manipulators with gripping or holding means for transferring packages B65H67/065}; cranes B66C; {manipulators used in the protection or supervision of pipe-line installations F17D5/00; walking equipment adapted for nuclear steam-generators F22B37/006}; manipulators specially adapted for, or associated with, nuclear reactors G21C; {apparatus used for handling wafers during manufacture or treatment of semiconductor H10P72/50}) ## Abstract A machine learning device, which learns shocks to a teaching device, includes a state observation unit which observes data based on an inclination of the teaching device or a present position of the teaching device; a label obtaining unit which obtains a label based on a shock received by the teaching device; and a learning unit which generates a learning model based on an output of the state observation unit and an output of the label obtaining unit. ## Claims ### Claim 1 A machine learning device, which learns shocks to a teaching device, comprising: a state observation unit which observes data based on an inclination of the teaching device or a present position of the teaching device; a label obtaining unit which obtains a label based on a shock received by the teaching device; and a learning unit which generates a learning model based on an output of the state observation unit and an output of the label obtaining unit. ### Claim 2 (depends on 1) The machine learning device according to claim 1, wherein the state observation unit further observes data based on at least one of speed at which buttons of the teaching device are pressed, content of an operation involving the teaching device, length of time of the operation, a time zone of the operation, and an identity of an operator of the teaching device. ### Claim 3 (depends on 1) The machine learning device according to claim 1, wherein the label obtaining unit obtains a label based on whether or not the shock received by the teaching device is greater than a threshold value. ### Claim 4 (depends on 1) The machine learning device according to claim 1, wherein the learning unit comprises: an error calculation unit which calculates an error based on outputs of the state observation unit and the label obtaining unit; and a learning model update unit which updates a learning model for determining an error in a shock to the teaching device, based on outputs of the state observation unit and the error calculation unit. ### Claim 5 (depends on 1) The machine learning device according to claim 1, wherein the machine learning device comprises a neural network. ### Claim 6 (depends on 1) The machine learning device according to claim 1, wherein the machine learning device is connectable with at least one other machine learning device and exchanges or shares with the at least one other machine learning device a learning model generated by the learning unit of the machine learning device. ### Claim 7 (depends on 1) A shock prevention system of a teaching device, comprising: the machine learning device according to claim 1; the teaching device, the machine learning device learning shocks to the teaching device; and an output utilization unit which utilizes an output of the machine learning device, wherein the output utilization unit operates in such a manner as to prevent a shock to the teaching device. ### Claim 8 (depends on 7) The shock prevention system of the teaching device according to claim 7, wherein the output utilization unit outputs an alarm or advises caution before the teaching device receives a shock, based on an output of the machine learning device, to prevent the shock to the teaching device. ### Claim 9 (depends on 8) The shock prevention system of the teaching device according to claim 8, wherein the output utilization unit uses at least one of an alarm using light or sound, voice, indication on a display, and vibration before the teaching device receives a strong shock greater than a certain threshold value to prevent the strong shock to the teaching device. ### Claim 10 A machine learning method of learning shocks to a teaching device, comprising: observing state data based on an inclination of the teaching device or a present position of the teaching device; obtaining a label based on a shock received by the teaching device; and generating a learning model based on the observed state data and the label. ### Claim 11 (depends on 10) The machine learning method according to claim 10, wherein the state data further comprises data based on at least one of speed at which buttons of the teaching device are pressed, content of an operation involving the teaching device, length of time of the operation, a time zone of the operation, and an identity of an operator of the teaching device. ### Claim 12 (depends on 10) The machine learning method according to claim 10, wherein the label is based on whether or not the shock received by the teaching device is greater than a threshold value. ### Claim 13 (depends on 10) The machine learning method according to claim 10, wherein the generating the learning model comprises: calculating an error based on the observed state data and the label; and updating a learning model for determining an error in a shock to the teaching device, based on the state data and the calculated error.