# US 12,449,792 B2 · 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 12,449,792 B2 - Document ID: US12449792B2 - Status: Granted - Kind: B2 - Application number: 18/629,541 - Filed: 2024-04-08 - Published: 2024-10-03 - Granted: 2025-10-21 - 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/US12449792B2 ## 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 first set of in-process inputs from a first processing station, at a deep learning controller, wherein the first set of in-process inputs are generated at the first processing station deployed in a manufacturing process, wherein the first set of in-process inputs are attributes of the first processing station of a plurality of stations in the manufacturing process; identifying, by the deep learning controller, a final quality metric of a plurality of final quality metrics for which to optimize the manufacturing process; generating, by the deep learning controller, an expected value of the final quality metric for an article of manufacture based on the first set of in-process inputs; determining, by the deep learning controller, based on a comparison of the first set of in-process inputs and the expected value that anomalous activity is present; and based on the determining, adjusting, by the deep learning controller, downstream parameters of a downstream processing station to cause the final quality metric to address the anomalous activity. ### Claim 2 (depends on 1) The computer-implemented method of claim 1, wherein the final quality metric is a metric of a completed version of the article of manufacture that cannot be measured until the manufacturing process is complete. ### Claim 3 (depends on 1) The computer-implemented method of claim 1, further comprising: adjusting, by the deep learning controller, further downstream parameters of a further downstream processing station. ### Claim 4 (depends on 1) The computer-implemented method of claim 1, further comprising: generating, by the deep learning controller, one or more control inputs, wherein the deep learning controller adjusts the downstream parameters of the downstream processing station in accordance with the one or more control inputs. ### Claim 5 (depends on 1) The computer-implemented method of claim 1, wherein adjusting, by the deep learning controller, the downstream parameters of the downstream processing station comprises: identifying a key influencer associated with the anomalous activity; identifying a control input that corresponds to the key influencer; and adjusting the downstream parameters based on the control input corresponding to the key influencer. ### Claim 6 (depends on 5) The computer-implemented method of claim 5, wherein the key influencer affects characteristics of outputs from the first processing station. ### Claim 7 (depends on 1) The computer-implemented method of claim 1, further comprising: identifying a plurality of parameters in outputs generated by the first processing station; and determining a subset of the plurality of parameters that affect the outputs generated by the first processing station. ### Claim 8 A non-transitory computer readable medium comprising one or more sequences of instructions, which, when executed by a processor, causes a computing system to perform operations comprising: receiving a first set of in-process inputs from a first processing station, at a deep learning controller, wherein the first set of in-process inputs are generated at the first processing station deployed in a manufacturing process, wherein the first set of in-process inputs are attributes of the first processing station of a plurality of stations in the manufacturing process; identifying, by the deep learning controller, a final quality metric of a plurality of final quality metrics for which to optimize the manufacturing process; generating, by the deep learning controller, an expected value of the final quality metric for an article of manufacture based on the first set of in-process inputs; determining, by the deep learning controller, based on a comparison of the first set of in-process inputs and the expected value that anomalous activity is present; and based on the determining, adjusting, by the deep learning controller, downstream parameters of a downstream processing station to cause the final quality metric to address the anomalous activity. ### Claim 9 (depends on 8) The non-transitory computer readable medium of claim 8, wherein the final quality metric is a metric of a completed version of the article of manufacture that cannot be measured until the manufacturing process is complete. ### Claim 10 (depends on 8) The non-transitory computer readable medium of claim 8, further comprising: adjusting, by the deep learning controller, further downstream parameters of a further downstream processing station. ### Claim 11 (depends on 8) The non-transitory computer readable medium of claim 8, further comprising: generating, by the deep learning controller, one or more control inputs, wherein the deep learning controller adjusts the downstream parameters of the downstream processing station in accordance with the one or more control inputs. ### Claim 12 (depends on 8) The non-transitory computer readable medium of claim 8, wherein adjusting, by the deep learning controller, the downstream parameters of the downstream processing station comprises: identifying a key influencer associated with the anomalous activity; identifying a control input that corresponds to the key influencer; and adjusting the downstream parameters based on the control input corresponding to the key influencer. ### Claim 13 (depends on 12) The non-transitory computer readable medium of claim 12, wherein the key influencer affects characteristics of outputs from the first processing station. ### Claim 14 (depends on 8) The non-transitory computer readable medium of claim 8, further comprising: identifying a plurality of parameters in outputs generated by the first processing station; and determining a subset of the plurality of parameters that affect the outputs generated by the first processing station. ### Claim 15 A system comprising: a processor; a deep learning controller; and a memory having programming instructions stored thereon, which, when executed by the processor, causes the system to perform operations comprising: receiving a first set of in-process inputs from a first processing station, at the deep learning controller, wherein the first set of in-process inputs are generated at the first processing station deployed in a manufacturing process, wherein the first set of in-process inputs are attributes of the first processing station of a plurality of stations in the manufacturing process; identifying, by the deep learning controller, a final quality metric of a plurality of final quality metrics for which to optimize the manufacturing process; generating, by the deep learning controller, an expected value of the final quality metric for an article of manufacture based on the first set of in-process inputs; determining, by the deep learning controller, based on a comparison of the first set of in-process inputs and the expected value that anomalous activity is present; and based on the determining, adjusting, by the deep learning controller, downstream parameters of a downstream processing station to cause the final quality metric to address the anomalous activity. ### Claim 16 (depends on 15) The system of claim 15, wherein the final quality metric is a metric of a completed version of the article of manufacture that cannot be measured until the manufacturing process is complete. ### Claim 17 (depends on 15) The system of claim 15, wherein the operations further comprise: adjusting, by the deep learning controller, further downstream parameters of a further downstream processing station. ### Claim 18 (depends on 15) The system of claim 15, wherein the operations further comprise: generating, by the deep learning controller, one or more control inputs, wherein the deep learning controller adjusts the downstream parameters of the downstream processing station in accordance with the one or more control inputs. ### Claim 19 (depends on 15) The system of claim 15, wherein adjusting, by the deep learning controller, the downstream parameters of the downstream processing station comprises: identifying a key influencer associated with the anomalous activity; identifying a control input that corresponds to the key influencer; and adjusting the downstream parameters based on the control input corresponding to the key influencer. ### Claim 20 (depends on 15) The system of claim 15, wherein the operations further comprise: identifying a plurality of parameters in outputs generated by the first processing station; and determining a subset of the plurality of parameters that affect the outputs generated by the first processing station.