The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Aug. 15, 2023

Filed:

Mar. 13, 2019
Applicant:

Tibco Software Inc., Palo Alto, CA (US);

Inventors:

Thomas Hill, Tulsa, OK (US);

David Katz, Ashland, OR (US);

Piotr Smolinski, Munich, DE;

Siva Ramalingam, Emeryville, CA (US);

Steven Hillion, San Francisco, CA (US);

Assignee:

Other;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 16/00 (2019.01); G06F 16/2458 (2019.01); G06F 16/22 (2019.01); G06F 16/28 (2019.01); G05B 17/02 (2006.01); G06F 17/15 (2006.01);
U.S. Cl.
CPC ...
G06F 16/2465 (2019.01); G05B 17/02 (2013.01); G06F 16/2272 (2019.01); G06F 16/2477 (2019.01); G06F 16/287 (2019.01); G06F 17/15 (2013.01);
Abstract

A process control tool for processing wide data from automated manufacturing operations. The tool including a feature selector, an analysis server, and a visualization engine. The feature selector receives process input data from at least one manufacturing process application, wherein the process input data includes a plurality of observations and associated variables, converts the received process input data to a stacked format having one row for each variable in each observation, converts identified categorical variables into numerical variables and identified time-series data into fixed numbers of intervals, computes statistics that measure the strengths of relationships between predictor values and an outcome variable, orders, filters, and pivots the predictor values. The analysis server performs at least one operation to identify interactions between predictor values, e.g. using maximum Likelihood computations or predefined searches, in the filtered predictor values. The visualization engine displays the interactions for use in managing the manufacturing operations.


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