# US 11,815,882 B2 · Industrial bottleneck detection and management method and system > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,815,882 B2 - Document ID: US11815882B2 - Status: Granted - Kind: B2 - Application number: 17/268,255 - Filed: 2019-09-10 - Published: 2021-06-10 - Granted: 2023-11-14 - Earliest claimed: 2018-09-11 - Assignees: Throughput, Inc. - Applicants: Throughput, Inc. - Inventors: Ali Hussan Raza; Bhaskar Subbarao Ballapragada; Seth Rom Page; Anzar Kamdar - Web page: https://patentgrep.com/patent/US11815882B2 ## CPC - G05B 13/0205: {not using a model or a simulator of the controlled system} - G05B 19/41865: {characterised by job scheduling, process planning, material flow} - G05B 23/0221: {Preprocessing measurements, e.g. data collection rate adjustment; Standardization of measurements; Time series or signal analysis, e.g. frequency analysis or wavelets; Trustworthiness of measurements; Indexes therefor; Measurements using easily measured parameters to estimate parameters difficult to measure; Virtual sensor creation; De-noising; Sensor fusion; Unconventional preprocessing inherently present in specific fault detection methods like PCA-based methods} - G06Q 10/0637: Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals - G05B 2219/24065: Real time diagnostics - G05B 2219/32015: Optimize, process management, optimize production line - G05B 2219/32191: Real time statistical process monitoring - Y02P 90/02: Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS] - 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) - 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 - Y02P: CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS ## Abstract The present invention includes: (a) a method for improving data to be processed for bottleneck detection, by cleaning corrupt or outlier data; (b) a method for improved analysis of bottleneck data using a plurality of rules for categorization; and (c) a method for improved display and/or allowing improved user feedback for bottleneck data using multivariate analysis and display. These methods can be used alone, or preferably be combined in whole or in part together to improve performance of an industrial process. A system is also provided. ## Claims ### Claim 1 A method for improving data to be processed by a bottleneck detection system comprising the steps of: receiving in a processor a first set of a plurality of data items related to an industrial process, each data item being time stamped so that each data item includes time stamp and industrial process data regarding an industrial process occurring at the time; identifying in the processor corrupt industrial process data and outlier industrial process data and corrupt time stamp data in the first set of the data items, the outlier process data being determined using a first statistical calculation performed on the first set of the plurality of data items, the first statistical calculation including comparing a cumulative density function to a cutoff to identify the outlier process data; providing a second set of the plurality of data items excluding data items in the first set having the identified corrupt industrial process data and outlier industrial process data and the corrupt time stamp data and performing in the processor a second different statistical calculation using the second set of the plurality of data items excluding the data items in the first set having the identified corrupt industrial process data and outlier industrial process data and the corrupt time stamp data, the second different statistical calculation including a standard deviation calculation on the second set of the plurality of data items excluding the data items having the identified corrupt industrial process data and the outlier industrial process data and corrupt time stamp data; discarding the data items in the first set with corrupt industrial process data and time stamp data; and providing a third set of the plurality of data items by including items with the outlier industrial process data to the second set but without the discarded data items and then using the third set to identify abnormal patterns or behaviors. ### Claim 2 (depends on 1) The method as recited in claim 1 wherein the receiving occurs continuously in real-time during the industrial process. ### Claim 3 (depends on 1) The method as recited in claim 1 wherein the outlier industrial process data is identified when the cumulative density function is beyond a cutoff of 0.997. ### Claim 4 (depends on 1) The method as recited in claim 1 wherein identifying step identifies all null values and negative values for the industrial process data as corrupt industrial process data and all null values and negative values of time stamp data as corrupt time stamp data of the data items. ### Claim 5 (depends on 1) The method as recited in claim 1 wherein the first statistical calculation calculates a standard deviation higher than a second standard deviation calculated by the second different statistical calculation. ### Claim 6 (depends on 1) The method as recited in claim 1 wherein the second different statistical calculation is used to build a control chart with control rules. ### Claim 7 (depends on 6) The method as recited in claim 6 wherein the control rules include a first rule of a defect rate above a mean plus three standard deviations or below a defect rate lower than a mean minus three standard deviations, and a second rule where two of three consecutive values fall above a mean plus two standard deviations or below a mean minus two standard deviations. ### Claim 8 (depends on 7) The method as recited in claim 7 wherein the identification of the abnormal patterns or behaviors occurs by applying the control rules to each of the plurality of data items in the third set.