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:
May. 03, 2022

Filed:

Oct. 29, 2020
Applicant:

Adobe Inc., San Jose, CA (US);

Inventors:

Fan Du, Milpitas, CA (US);

Xiao Xie, Hangzhou, CN;

Shiv Kumar Saini, Jhunjhunu, IN;

Gaurav Sinha, Bangalore, IN;

Ayush Chauhan, Bengaluru, IN;

Assignee:

Adobe Inc., San Jose, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 11/20 (2006.01); G06F 16/22 (2019.01); G06F 16/26 (2019.01); G06T 3/40 (2006.01); G06F 16/901 (2019.01);
U.S. Cl.
CPC ...
G06T 11/206 (2013.01); G06F 16/2264 (2019.01); G06F 16/26 (2019.01); G06F 16/9024 (2019.01); G06T 3/40 (2013.01); G06T 2200/24 (2013.01);
Abstract

The present disclosure describes systems, methods, and non-transitory computer readable media for generating and providing a causal-graph interface that visually depicts causal relationships among dimensions and represents uncertainty metrics for such relationships as part of a streamlined visualization of a causal graph. The disclosed systems can determine causality among dimensions of multidimensional data and determine uncertainty metrics associated with individual causal relationships. Additionally, the disclosed system can generate a visual representation of a causal graph with nodes arranged in stratified layers and can connect the layered nodes with uncertainty-aware-causal edges to represent both the causality between the dimensions and the uncertainty metrics. Further, the disclosed systems can provide interactive tools for generating and visualizing predictions or causal relationships in intuitive user interfaces, such as visualizations for dimension-specific (or dimension-value-specific) interventions and/or attribution determinations.


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