Aaron Allen Wilson

US Patent:

6959263, Oct 25, 2005

Filed:

Dec 14, 2002

Appl. No.:

10/318868

Inventors:

Gerald A. Wilson - Cupertino CA, US
Aaron Wilson - Campbell CA, US

Assignee:

Perspicuous Insights LLC - San Marcos CA

International Classification:

G06F015/00

US Classification:

702185, 702183, 702184

Abstract:

The present invention discloses a unique interactive diagnostic system and method driven by expert systems. In particular, a system and method for troubleshooting of networked electronic devices, driven by an inference engine which controls both the user interaction and the operation of and collection of data from diagnostic tools, are disclosed. In an exemplary embodiment, the invention includes a knowledge base , an inference engine , a communications processor for handling requests to and from the inference engine and the other components (except the knowledge base), an interaction management processor for handling interactions with a plurality of end users over a communications network , and a plurality of peripheral control units and their associated peripheral units , which collectively can gather status information and perform diagnostic tests on the said networked electronic devices.

US Patent:

20210302452, Sep 30, 2021

Filed:

Jun 10, 2021

Appl. No.:

17/344183

Inventors:

- Livermore CA, US
Eric B. Duoss - Dublin CA, US
Du Nguyen - Fremont CA, US
William Smith - Oakland CA, US
Sachin Subhash Talathi - Pleasanton CA, US
Aaron Creighton Wilson - Livermore CA, US

International Classification:

G01N 35/00
G05B 13/02
G05D 7/06
B01L 3/00
G01N 15/14
G01N 15/00

Abstract:

A system is provided to automatically monitor and control the operation of a microfluidic device using machine learning technology. The system receives images of a channel of a microfluidic device collected by a camera during operation of the microfluidic device. Upon receiving an image, the system applies a classifier to the image to classify the operation of the microfluidic device as normal, in which no adjustment to the operation is needed, or as abnormal, in which an adjustment to the operation is needed. When an image is classified as normal, the system may make no adjustment to the microfluidic device. If, however, an image is classified as abnormal, the system may output an indication that the operation is abnormal, output an indication of a needed adjustment, or control the microfluidic device to make the needed adjustment.

US Patent:

20190234976, Aug 1, 2019

Filed:

Apr 5, 2019

Appl. No.:

16/376380

Inventors:

- Livermore CA, US
Eric B. Duoss - Dublin CA, US
Du Nguyen - Fremont CA, US
William Smith - Oakland CA, US
Sachin Subhash Talathi - Pleasanton CA, US
Aaron Creighton Wilson - Livermore CA, US

International Classification:

G01N 35/00
G05B 13/02
G05D 7/06
B01L 3/00
G01N 15/00

Abstract:

A system is provided to automatically monitor and control the operation of a microfluidic device using machine learning technology. The system receives images of a channel of a microfluidic device collected by a camera during operation of the microfluidic device. Upon receiving an image, the system applies a classifier to the image to classify the operation of the microfluidic device as normal, in which no adjustment to the operation is needed, or as abnormal, in which an adjustment to the operation is needed. When an image is classified as normal, the system may make no adjustment to the microfluidic device. If, however, an image is classified as abnormal, the system may output an indication that the operation is abnormal, output an indication of a needed adjustment, or control the microfluidic device to make the needed adjustment.

US Patent:

20180311663, Nov 1, 2018

Filed:

Apr 26, 2017

Appl. No.:

15/498282

Inventors:

- Livermore CA, US
Eric Duoss - Dublin CA, US
Du Nguyen - Fremont CA, US
William Smith - Oakland CA, US
Sachin Subhash Talathi - Pleasanton CA, US
Aaron Creighton Wilson - Livermore CA, US

International Classification:

B01L 3/00
G05B 13/02
G05D 7/06
G01N 35/00

Abstract:

A system is provided to automatically monitor and control the operation of a microfluidic device using machine learning technology. The system receives images of a channel of a microfluidic device collected by a camera during operation of the microfluidic device. Upon receiving an image, the system applies a classifier to the image to classify the operation of the microfluidic device as normal, in which no adjustment to the operation is needed, or as abnormal, in which an adjustment to the operation is needed. When an image is classified as normal, the system may make no adjustment to the microfluidic device. If, however, an image is classified as abnormal, the system may output an indication that the operation is abnormal, output an indication of a needed adjustment, or control the microfluidic device to make the needed adjustment.