# US 2020/0401119 A1 · Predictive process control for a manufacturing process > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2020/0401119 A1 - Document ID: US20200401119A1 - Status: Published application - Kind: A1 - Application number: 16/519,102 - Filed: 2019-07-23 - Published: 2020-12-24 - Earliest claimed: 2019-06-24 - Assignees: Nanotronics Imaging, Inc. - Applicants: Nanotronics Imaging, Inc. - Inventors: Matthew C. Putman; John B. Putman; Vadim Pinskiy; Damas Limoge - Web page: https://patentgrep.com/patent/US20200401119A1 ## CPC - G05B 19/41875: {characterised by quality surveillance of production} - G05B 13/027: {using neural networks only} - G05B 2219/32193: Ann, neural base quality management - G05B 2219/32194: Quality prediction - G05B 2219/32195: Feedforward quality control - 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) ## Abstract Aspects of the disclosed technology encompass the use of a deep learning controller for monitoring and improving a manufacturing process. In some aspects, a method of the disclosed technology includes steps for: receiving a plurality of control values from two or more stations, at a deep learning controller, wherein the control values are generated at the two or more stations deployed in a manufacturing process, predicting an expected value for an intermediate or final output of an article of manufacture, based on the control values, and determining if the predicted expected value for the article of manufacture is in-specification. In some aspects, the process can further include steps for generating control inputs if the predicted expected value for the article of manufacture is not in-specification. Systems and computer-readable media are also provided. ## Claims ### Claim 1 A computer-implemented method comprising: receiving a plurality of control values from two or more stations, at a deep learning controller, wherein the control values are generated at the two or more stations deployed in a manufacturing process; predicting, by the deep learning controller, an expected value for an intermediate value at a downstream station or final output of an article of manufacture, based on the control values; determining, by the deep learning controller, that the predicted expected value for the article of manufacture is not in-specification; and based on the determining, generating, by the deep learning controller, one or more control inputs, wherein the one or more control inputs are configured to cause the intermediate or final output for the article of manufacture to be in-specification. ### Claim 2 (depends on 1) The computer-implemented method of claim 1, further comprising: outputting the one or more control inputs to one or more station controllers, if a confidence level associated with the one or more control inputs exceeds a predetermined threshold. ### Claim 3 (depends on 2) The computer-implemented method of claim 2, wherein outputting the one or more control inputs to the one or more station controllers, further comprises: providing a first control input to a first station controller associated with a first station; and providing a second control input to a second station controller associated with a second station, and wherein the second station is downstream from the first station in the manufacturing process. ### Claim 4 (depends on 1) The computer-implemented method of claim 1, wherein predicting the intermediate or final output of the article of manufacture, further comprises: identifying one or more key influencers associated with the manufacturing process; and adjusting one or more control inputs associated with at least one of the one or more key influencers. ### Claim 5 (depends on 1) The computer-implemented method of claim 1, further comprising: permitting control of the manufacturing process by the one or more station controllers, if the predicted expected value for the article of manufacture is in-specification. ### Claim 6 (depends on 1) The computer-implemented method of claim 1, further comprising: receiving a plurality of station values at the deep learning controller, wherein the station values are associated with the one or more stations, and wherein predicting the intermediate or final output of the article of manufacture is further based on the station values. ### Claim 7 (depends on 1) The computer-implemented method of claim 1, wherein the plurality of control values comprises one or more of: speed, temperature, pressure, vacuum, rotation, current, voltage, resistance, or power. ### Claim 8 A system comprising: one or more processors; and a non-transitory memory storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising: receiving a plurality of control values from two or more stations, at a deep learning controller, wherein the control values are generated at the two or more stations deployed in a manufacturing process; predicting, by the deep learning controller, an intermediate value at a downstream station or a final output of an article of manufacture, based on the control values; determining, by the deep learning controller, that the predicted expected value for an intermediate or final output for the article of manufacture is not in-specification; and based on the determining, generating, by the deep learning controller, one or more control inputs, wherein the one or more control inputs are configured to cause an intermediate or final output for the article of manufacture to be in-specification. ### Claim 9 (depends on 8) The system of claim 8, wherein the processors are further configured to perform operations comprising: outputting the one or more control inputs to one or more station controllers, if a confidence level associated with the one or more control inputs exceeds a predetermined threshold. ### Claim 10 (depends on 9) The system of claim 9, wherein outputting the one or more control inputs to the one or more station controllers, further comprises: providing a first control input to a first station controller associated with a first station; and providing a second control input to a second station controller associated with a second station, and wherein the second station is downstream from the first station in the manufacturing process. ### Claim 11 (depends on 8) The system of claim 8, wherein predicting the intermediate or final output of the article of manufacture, further comprises: identifying one or more key influencers associated with the manufacturing process; and adjusting one or more control inputs associated with at least one of the one or more key influencers. ### Claim 12 (depends on 8) The system of claim 8, further comprising: permitting control of the manufacturing process by the one or more station controllers, if the predicted expected value for the article of manufacture is in-specification. ### Claim 13 (depends on 8) The system of claim 8, wherein the processors are further configured to perform operations comprising: receiving a plurality of station values at the deep learning controller, wherein the station values are associated with the one or more stations, and wherein predicting the expected value of the article of manufacture is further based on the station values. ### Claim 14 (depends on 8) The system of claim 8, wherein the plurality of control values comprises one or more of: speed, temperature, pressure, vacuum, rotation, current, voltage, resistance, or power. ### Claim 15 A non-transitory computer-readable storage medium comprising instructions stored therein, which when executed by one or more processors, cause the one or more processors to perform operations comprising: receiving a plurality of control values from two or more stations, at a deep learning controller, wherein the control values are generated at the two or more stations deployed in a manufacturing process; predicting, by the deep learning controller, an expected value for an intermediate value at a downstream station or a final output of an article of manufacture, based on the control values; determining, by the deep learning controller, that the predicted expected value for the article of manufacture is not in-specification; and based on the determining, generating, by the deep learning controller, one or more control inputs, wherein the one or more control inputs are configured to cause an intermediate or final output for the article of manufacture to be in-specification. ### Claim 16 (depends on 15) The non-transitory computer-readable storage medium of claim 15, further comprising instructions configured to cause the one or more processors to perform operations comprising: outputting the one or more control inputs to one or more station controllers, if a confidence level associated with the one or more control inputs exceeds a predetermined threshold. ### Claim 17 (depends on 16) The non-transitory computer-readable storage medium of claim 16, wherein outputting the one or more control inputs to the one or more station controllers, further comprises: providing a first control input to a first station controller associated with a first station; and providing a second control input to a second station controller associated with a second station, and wherein the second station is downstream from the first station in the manufacturing process. ### Claim 18 (depends on 15) The non-transitory computer-readable storage medium of claim 15, wherein predicting the intermediate or final output of the article of manufacture, further comprises: identifying one or more key influencers associated with the manufacturing process; and adjusting one or more control inputs associated with at least one of the one or more key influencers. ### Claim 19 (depends on 15) The non-transitory computer-readable storage medium of claim 15, further comprising instructions configured to cause the one or more processors to perform operations comprising: permitting control of the manufacturing process by the one or more station controllers, if the predicted expected value for the article of manufacture is in-specification. ### Claim 20 (depends on 15) The non-transitory computer-readable storage medium of claim 15, further comprising instructions configured to cause the one or more processors to perform operations comprising: receiving a plurality of station values at the deep learning controller, wherein the station values are associated with the one or more stations, and wherein predicting the expected value of the article of manufacture is further based on the station values.