# US 2021/0276191 A1 · Method and Safety Oriented Control Device for Determining and/or Selecting a Safe Condition > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2021/0276191 A1 - Document ID: US20210276191A1 - Status: Published application - Kind: A1 - Application number: 17/190,639 - Filed: 2021-03-03 - Published: 2021-09-09 - Earliest claimed: 2020-03-04 - Assignees: Siemens Aktiengesellschaft - Applicants: Siemens Aktiengesellschaft - Inventors: Frank Dittrich SCHILLER - Web page: https://patentgrep.com/patent/US20210276191A1 ## CPC - B25J 9/1674: {characterised by safety, monitoring, diagnostic} - G05B 19/406: characterised by monitoring or safety (G05B19/19 takes precedence) - B25J 9/161: {Hardware, e.g. neural networks, fuzzy logic, interfaces, processor} - B25J 9/163: {learning, adaptive, model based, rule based expert control} - G05B 19/058: {Safety, monitoring} - G06F 13/409: {Mechanical coupling (back panels H05K7/1438)} - G06N 3/0455: Auto-encoder networks; Encoder-decoder networks - G06N 3/0499: Feedforward networks - G06N 3/09: Supervised learning - G06N 3/092: Reinforcement learning - G05B 19/0428: {Safety, monitoring (G05B19/0423 takes precedence)} - G05B 2219/34465: Safety, control of correct operation, abnormal states - 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}) - 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) - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract A method and safety-oriented control device for determining and/or selecting a safe condition using a safety-oriented control device configured for safety-oriented control of an apparatus or installation via execution of a safety-oriented control program which, when executed, results in a safe reaction being triggered in the safety-oriented controller, wherein the method is implemented such that an ML model is configured and formed as a result, which is stored in a memory device, of the application of a machine learning method, such that data relevant to the determination of a safe condition are stored in connection with the triggering of the safe reaction, and such that a first safe condition is determined via the data relevant to the determination of the safe condition being applied to the ML model. ## Claims ### Claim 1 A method for determining a safe condition by utilizing a safety-oriented control device which is configured for safety-oriented control of an apparatus or installation via execution of a safety-oriented control program which, when executed, results in a safe reaction being triggered in the safety-oriented controller, the method comprising: storing an ML model in a memory device of an application of a machine learning method, the ML model being configured as and forming a result; storing data relevant to the determining the safe condition in connection with the triggering of the safe reaction; and determining a first safe condition via the data relevant to the determining of the safe condition being applied to the ML model. ### Claim 2 (depends on 1) The method as claimed in claim 1, wherein a plurality of safe conditions are stored in reference to the safety-oriented control of the apparatus or installation; and wherein the first safe condition is selected from the plurality of safe conditions via the data relevant to the determining of the safe condition being applied to the ML model. ### Claim 3 (depends on 2) The method as claimed in claim 2, wherein a succession of safe conditions is selected from the plurality of safe conditions via the data relevant to the determining of the safe condition being applied to the ML model; wherein the succession of safe conditions comprises the first safe condition and at least one further safe condition. ### Claim 4 (depends on 1) The method as claimed in claim 1, wherein the first safe condition is stipulated by at least one apparatus and/or installation parameter, and said at least one apparatus and/or installation parameter comprises at least one parameter value range; and wherein the application of the data relevant to the determining of the safe condition to the ML model further results in determination of a parameter value or a succession of parameter values from the parameter value range. ### Claim 5 (depends on 1) The method as claimed in claim 1, wherein the ML model is formed and configured as a result, which is stored in the memory device, of the application of the machine learning method to ML training data. ### Claim 6 A safety-oriented control device for safety-oriented control of an apparatus or installation via the execution of a safety-oriented control program, comprising: a processor; and wherein the processor is configured to: store an ML model in a memory device of an application of a machine learning method, the ML model being configured as and forming a result; store data relevant to the determining the safe condition in connection with the triggering of the safe reaction; and determine a first safe condition via the data relevant to the determining of the safe condition being applied to the ML model. ### Claim 7 (depends on 6) The safety-oriented control device as claimed in claim 6, wherein one of: (i) the safety-oriented control device further comprises the memory device having the ML model and (ii) the safety-oriented control device is communicatively coupled to the memory device having the ML model. ### Claim 8 (depends on 6) The safety-oriented control device as claimed in claim 6, wherein the safety-oriented control device is formed and configured as a modular safety-oriented control device having a safety-oriented central module; and wherein the safety-oriented central module comprises the memory device having the ML model. ### Claim 9 (depends on 7) The safety-oriented control device as claimed in claim 7, wherein the safety-oriented control device is formed and configured as a modular safety-oriented control device having a safety-oriented central module; and wherein the safety-oriented central module comprises the memory device having the ML model. ### Claim 10 (depends on 6) The safety-oriented control device as claimed in claim 6, wherein the safety-oriented control device is formed and configured as a modular safety-oriented control device having a safety-oriented central module and a KI module; wherein the safety-oriented central module and the KI module are communicatively coupled via a backplane bus of the safety-oriented control device; and wherein the KI module comprises the memory device having the ML model. ### Claim 11 (depends on 7) The safety-oriented control device as claimed in claim 7, wherein the safety-oriented control device is formed and configured as a modular safety-oriented control device having a safety-oriented central module and a KI module; wherein the safety-oriented central module and the KI module are communicatively coupled via a backplane bus of the safety-oriented control device; and wherein the KI module comprises the memory device having the ML model. ### Claim 12 (depends on 10) The safety-oriented control device as claimed in claim 10, wherein the KI module is formed and configured as a safety-oriented KI module.