# US 2016/0155315 A1 · Adaptive Handling of Operating Data > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2016/0155315 A1 - Document ID: US20160155315A1 - Status: Published application - Kind: A1 - Application number: 14/732,320 - Filed: 2015-06-05 - Published: 2016-06-02 - Earliest claimed: 2014-12-01 - Assignees: Uptake, LLC - Applicants: Uptake, LLC - Inventors: Adam McElhinney; John Ciasulli; Michael Horrell - Web page: https://patentgrep.com/patent/US20160155315A1 ## CPC - G06F 11/079: {Root cause analysis, i.e. error or fault diagnosis (in a hardware test environment G06F11/22; in a software test environment G06F11/36)} - G01D 3/08: with provision for safeguarding the apparatus, e.g. against abnormal operation, against breakdown - G01M 99/005: {Testing of complete machines, e.g. washing-machines or mobile phones (testing of machine parts G01M13/00; testing of electric apparatus or components G01R31/50)} - G01M 99/008: {by doing functionality tests} - G05B 19/18: Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of program data in numerical form (G05B19/418 takes precedence) - G05B 23/024: {Quantitative history assessment, e.g. mathematical relationships between available data; Functions therefor; Principal component analysis [PCA]; Partial least square [PLS]; Statistical classifiers, e.g. Bayesian networks, linear regression or correlation analysis; Neural networks} - G05B 23/0254: {based on a quantitative model, e.g. mathematical relationships between inputs and outputs; functions: observer, Kalman filter, residual calculation, Neural Networks} - G05B 23/0275: {Fault isolation and identification, e.g. classify fault; estimate cause or root of failure} - G06F 11/008: {Reliability or availability analysis} - G06F 11/0709: {in a distributed system consisting of a plurality of standalone computer nodes, e.g. clusters, client-server systems} - G06F 11/0721: {within a central processing unit [CPU]} - G06F 11/0751: {Error or fault detection not based on redundancy (power supply failures G06F1/30; network fault management H04L41/06)} - G06F 11/0754: {by exceeding limits} - G06F 11/0772: {Means for error signaling, e.g. using interrupts, exception flags, dedicated error registers} - G06F 11/0787: {Storage of error reports, e.g. persistent data storage, storage using memory protection} - G06F 11/0793: {Remedial or corrective actions (recovery from an exception in an instruction pipeline G06F9/3861; by retry G06F11/1402; for recovering from a failure of a protocol instance or entity H04L69/40)} - G06F 11/2007: {using redundant communication media} - G06F 11/26: Functional testing - G06F 11/263: Generation of test inputs, e.g. test vectors, patterns or sequences {; with adaptation of the tested hardware for testability with external testers} - G06N 5/02: Knowledge representation; Symbolic representation - G06N 5/04: Inference or reasoning models - G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks - G06Q 10/04: Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" (market predictions or forecasting for commercial activities G06Q30/0202) - G06Q 10/06312: {Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling} - G06Q 10/0633: Workflow analysis - G06Q 10/06334: {using forecasting or predicting} - G06Q 10/067: Enterprise or organisation modelling - G06Q 10/20: Administration of product repair or maintenance - G06Q 50/04: Manufacturing - G06Q 50/08: Construction - G07C 5/008: {communicating information to a remotely located station (transmission systems for measured values G08C)} - G07C 5/0808: {Diagnosing performance data (testing of vehicles G01M17/00; testing of electrical installation on vehicles G01R31/005)} - G07C 5/0825: {using optical means} - G08B 21/18: Status alarms (G08B21/02 takes precedence) - G06F 2201/85: Active fault masking without idle spares - G16H 40/40: for the management of medical equipment or devices, e.g. scheduling maintenance or upgrades - Y02P 90/30: Computing systems specially adapted for manufacturing - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G01D: MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR - G01M: TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR - 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 - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR - G07C: TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE - G08B: SIGNALLING SYSTEMS, e.g. PERSONAL CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA - Y02P: CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS ## Abstract Disclosed herein are systems, devices, and methods related to assets and asset operating conditions. In particular, examples involve defining and executing predictive models for outputting health metrics that estimate the operating health of an asset or a part thereof, analyzing health metrics to determine variables that are associated with high health metrics, and modifying the handling of abnormal-condition indicators in accordance with a prediction of a likely response to such abnormal-condition indicators, among other examples. ## Claims ### Claim at position 1 1-20. (canceled) ### Claim 21 A computing system comprising: at least one processor; a non-transitory computer-readable medium; and program instructions stored on the non-transitory computer-readable medium that are executable by the at least one processor to cause the computing system to: receive historical response data indicating a plurality of past responses to a given abnormal-condition indicator, wherein the given abnormal-condition indicator indicates an occurrence of a given abnormal condition at an asset; based on the historical response data, define a response pattern for the given abnormal-condition indicator; receive, for a given asset, operating data that indicates a given occurrence of the given abnormal condition at the given asset and is associated with a given instance of the given abnormal-condition indicator; make a prediction, based at least on the response pattern, of a likely response to the given instance of the given abnormal-condition indicator; and handle the given instance of the given abnormal-condition indicator in accordance with the prediction. ### Claim 22 (depends on 21) The computing system of claim 21, wherein handling the given instance of the given abnormal-condition indicator in accordance with the prediction comprises: causing a computing device to output the given instance of the given abnormal-condition indicator with a recommendation of the likely response. ### Claim 23 (depends on 21) The computing system of claim 21, wherein making the prediction, based at least on the response pattern, of the likely response to the given instance of the given abnormal-condition indicator comprises: predicting that the given abnormal-condition indicator is likely to be disregarded or ignored. ### Claim 24 (depends on 23) The computing system of claim 23, wherein handling the given instance of the given abnormal-condition indicator in accordance with the prediction comprises: causing the given instance of the given abnormal-condition indicator to be suppressed. ### Claim 25 (depends on 23) The computing system of claim 23, wherein the response pattern comprises a percentage of the plurality of past responses that is attributed to decisions to disregard or ignore the given abnormal-condition indicator, and wherein handling the given instance of the given abnormal-condition indicator in accordance with the prediction comprises: if the percentage is below a given threshold, causing a computing device to output the given instance of the given abnormal-condition indicator with a recommendation to disregard or ignore the given abnormal-condition indicator; and if the percentage is at or above the given threshold, causing the given instance of the given abnormal-condition indicator to be suppressed. ### Claim 26 (depends on 21) The computing system of claim 21, further comprising program instructions stored on the non-transitory computer-readable medium that are executable by the at least one processor to cause the computing system to: based at least on the response pattern, modify criteria for identifying an occurrence of the given abnormal condition. ### Claim 27 (depends on 21) The computing system of claim 21, wherein making a prediction, based at least on the response pattern, of the likely response to the given instance of the given abnormal-condition indicator comprises: making a prediction, based on the response pattern and external data associated with a location of the given asset, of the likely response to the given instance of the given abnormal-condition indicator. ### Claim 28 (depends on 27) The computing system of claim 27, wherein the external data associated with the location of the given asset comprises weather data associated with the location of the given asset. ### Claim 29 (depends on 21) The computing system of claim 21, wherein the operating data that indicates the given occurrence of the given abnormal condition comprises the given instance of the abnormal-condition indicator. ### Claim 30 (depends on 21) The computing system of claim 21, wherein the operating data that indicates the given occurrence of the given abnormal condition comprises sensor data that causes the given instance of the abnormal-condition indicator to be invoked. ### Claim 31 A non-transitory computer-readable medium having instructions stored thereon that are executable to cause a computing system to: receive historical response data indicating a plurality of past responses to a given abnormal-condition indicator, wherein the given abnormal-condition indicator indicates an occurrence of a given abnormal condition at an asset; based on the historical response data, define a response pattern for the given abnormal-condition indicator; receive, for a given asset, operating data that indicates a given occurrence of the given abnormal condition at the given asset and is associated with a given instance of the given abnormal-condition indicator; make a prediction, based at least on the response pattern, of a likely response to the instance of the given abnormal-condition indicator; and handle the instance of the given abnormal-condition indicator in accordance with the prediction. ### Claim 32 (depends on 31) The non-transitory computer-readable medium of claim 31, wherein handling the instance of the given abnormal-condition indicator in accordance with the prediction comprises: causing a computing device to output the instance of the given abnormal-condition indicator with a recommendation of the likely response. ### Claim 33 (depends on 31) The non-transitory computer-readable medium of claim 31, wherein making the prediction, based at least on the response pattern, of the likely response to the instance of the given abnormal-condition indicator comprises: predicting that the given abnormal-condition indicator is likely to be disregarded or ignored. ### Claim 34 (depends on 33) The non-transitory computer-readable medium of claim 33, wherein handling the instance of the given abnormal-condition indicator in accordance with the prediction comprises: causing the instance of the given abnormal-condition indicator to be suppressed. ### Claim 35 (depends on 33) The non-transitory computer-readable medium of claim 33, wherein the response pattern comprises a percentage of the plurality of past responses that is attributed to decisions to disregard the given abnormal-condition indicator, and wherein handling the instance of the given abnormal-condition indicator in accordance with the prediction comprises: if the percentage is below a given threshold, causing a computing device to output the instance of the given abnormal-condition indicator with a recommendation to disregard the given abnormal-condition indicator; and if the percentage is at or above the given threshold, causing the instance of the given abnormal-condition indicator to be suppressed. ### Claim 36 A computer-implemented method, the method comprising: receiving historical response data indicating a plurality of past responses to a given abnormal-condition indicator, wherein the given abnormal-condition indicator indicates an occurrence of a given abnormal condition at an asset; based on the historical response data, defining a response pattern for the given abnormal-condition indicator; receive, for a given asset, operating data that indicates a given occurrence of the given abnormal condition at the given asset and is associated with a given instance of the given abnormal-condition indicator; making a prediction, based at least on the response pattern, of a likely response to the given instance of the given abnormal-condition indicator; and handling the given instance of the given abnormal-condition indicator in accordance with the prediction. ### Claim 37 (depends on 36) The computer-implemented method of claim 36, wherein handling the given instance of the given abnormal-condition indicator in accordance with the prediction comprises: causing a computing device to output the given instance of the given abnormal-condition indicator with a recommendation of the likely response. ### Claim 38 (depends on 36) The computer-implemented method of claim 36, wherein making the prediction, based at least on the response pattern, of the likely response to the given instance of the given abnormal-condition indicator comprises: predicting that the given abnormal-condition indicator is likely to be disregarded or ignored. ### Claim 39 (depends on 38) The computer-implemented method of claim 38, wherein handling the given instance of the given abnormal-condition indicator in accordance with the prediction comprises: causing the given instance of the given abnormal-condition indicator to be suppressed. ### Claim 40 (depends on 38) The computer-implemented method of claim 38, wherein the response pattern comprises a percentage of the plurality of past responses that is attributed to decisions to disregard the given abnormal-condition indicator, and wherein handling the given instance of the given abnormal-condition indicator in accordance with the prediction comprises: if the percentage is below a given threshold, causing a computing device to output the given instance of the given abnormal-condition indicator with a recommendation to disregard the given abnormal-condition indicator; and if the percentage is at or above the given threshold, causing the given instance of the given abnormal-condition indicator to be suppressed.