# US 2020/0401120 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 2020/0401120 A1
- Document ID: US20200401120A1
- Status: Published application
- Kind: A1
- Application number: 16/663,245
- Filed: 2019-10-24
- 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/US20200401120A1

## 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 control values associated with a process station in a manufacturing process, predicting an expected value for an article of manufacture output from the process station, and determining if the deep-learning controller can control the manufacturing process based on the expected value. Systems and computer-readable media are also provided.

## Claims

### Claim 1

A computer-implemented method for controlling a manufacturing process, comprising:
receiving, by a deep-learning controller, one or more control values associated with a process station in the manufacturing process, wherein the one or more control values are attributes of the process station;
predicting, by the deep-learning controller, an expected value for an article of manufacture output from the process station, based on the one or more control values;
determining if the deep-learning controller can control the manufacturing process based on the expected value; and
assuming control of the manufacturing process by the deep-learning controller based on the expected value.

### Claim 2 (depends on 1)

The computer-implemented method of claim 1, wherein determining if the deep-learning controller can control the manufacturing process further comprises:
receiving, by the deep-learning controller, an actual value associated with the article of manufacture;
comparing the expected value with an actual value; and
if the expected value is within a threshold confidence level of the actual value, determining that the deep-learning controller can control the manufacturing process.

### Claim 3 (depends on 1)

The computer-implemented method of claim 1, further comprising:
indicating that the deep-learning controller is ready to take control of the manufacturing process.

### Claim 4 (depends on 1)

The computer-implemented method of claim 1, wherein determining if the deep-learning controller can control the manufacturing process further comprises:
receiving, by the deep-learning controller, an actual value associated with the article of manufacture;
comparing the expected value with an actual value; and
if the expected value is not within a threshold confidence level of the actual value, determining that the deep-learning controller cannot control the manufacturing process.

### Claim 5 (depends on 1)

The computer-implemented method of claim 1, wherein the one or more control values are received from one or more process controllers associated with the process station.

### Claim 6

(canceled)

### Claim 7 (depends on 1)

The computer-implemented method of claim 1, wherein the expected value is based on two or more physical attributes of the article of manufacture.

### 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, by a deep-learning controller, one or more control values associated with a process station in a manufacturing process, wherein the one or more control values are attributes of the process station;
predicting, by the deep-learning controller, an expected value for an article of manufacture output from the process station, based on the one or more control values;
determining if the deep-learning controller can control the manufacturing process based on the expected value; and
assuming control of the manufacturing process by the deep-learning controller based on the expected value.

### Claim 9 (depends on 8)

The system of claim 8, wherein determining if the deep-learning controller can control the manufacturing process further comprises:
receiving, by the deep-learning controller, an actual value associated with the article of manufacture;
comparing the expected value with an actual value; and
if the expected value is within a threshold confidence level of the actual value, determining that the deep-learning controller can control the manufacturing process.

### Claim 10 (depends on 8)

The system of claim 8, wherein the one or more processors are further configured to perform operations comprising:
indicating that the deep-learning controller is ready to take control of the manufacturing process.

### Claim 11 (depends on 8)

The system of claim 8, wherein determining if the deep-learning controller can control the manufacturing process further comprises:
receiving, by the deep-learning controller, an actual value associated with the article of manufacture;
comparing the expected value with an actual value; and
if the expected value is not within a threshold confidence level of the actual value, determining that the deep-learning controller cannot control the manufacturing process.

### Claim 12 (depends on 8)

The system of claim 8, wherein the one or more control values are received from one or more process controllers associated with the process station.

### Claim 13

(canceled)

### Claim 14 (depends on 8)

The system of claim 8, wherein the expected value is based on two or more physical attributes of the article of manufacture.

### 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, by a deep-learning controller, one or more control values associated with a process station in a manufacturing process, wherein the one or more control values are attributes of the process station;
predicting, by the deep-learning controller, an expected value for an article of manufacture output from the process station, based on the one or more control values;
determining if the deep-learning controller can control the manufacturing process based on the expected value; and
assuming control of the manufacturing process by the deep-learning controller based on the expected value.

### Claim 16 (depends on 15)

The non-transitory computer-readable storage medium of claim 15, wherein determining if the deep-learning controller can control the manufacturing process further comprises:
receiving, by the deep-learning controller, an actual value associated with the article of manufacture;
comparing the expected value with an actual value; and
if the expected value is within a threshold confidence level of the actual value, determining that the deep-learning controller can control the manufacturing process.

### Claim 17 (depends on 15)

The non-transitory computer-readable storage medium of claim 15, wherein the one or more processors are further configured to perform operations comprising:
indicating that the deep-learning controller is ready to take control of the manufacturing process.

### Claim 18 (depends on 15)

The non-transitory computer-readable storage medium of claim 15, wherein determining if the deep-learning controller can control the manufacturing process further comprises:
receiving, by the deep-learning controller, an actual value associated with the article of manufacture;
comparing the expected value with an actual value; and
if the expected value is not within a threshold confidence level of the actual value, determining that the deep-learning controller cannot control the manufacturing process.

### Claim 19 (depends on 15)

The non-transitory computer-readable storage medium of claim 15, wherein the one or more control values are received from one or more process controllers associated with the process station.

### Claim 20

(canceled)