Invention Grant
- Patent Title: Determining feature contributions to data metrics utilizing a causal dependency model
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Application No.: US16813424Application Date: 2020-03-09
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Publication No.: US11797515B2Publication Date: 2023-10-24
- Inventor: Pulkit Goel , Naman Poddar , Gaurav Sinha , Ayush Chauhan , Aurghya Maiti
- Applicant: Adobe Inc.
- Applicant Address: US CA San Jose
- Assignee: Adobe Inc.
- Current Assignee: Adobe Inc.
- Current Assignee Address: US CA San Jose
- Agency: Keller Preece PLLC
- Main IPC: G06F16/23
- IPC: G06F16/23

Abstract:
The present disclosure relates to methods, systems, and non-transitory computer-readable media for determining causal contributions of dimension values to anomalous data based on causal effects of such dimension values on the occurrence of other dimension values from interventions performed in a causal graph. For example, the disclosed systems can identify an anomalous dimension value that reflects a threshold change in value between an anomalous time period and a reference time period. The disclosed systems can determine causal effects by traversing a causal network representing dependencies between different dimensions associated with the dimension values. Based on the causal effects, the disclosed systems can determine causal contributions of particular dimension values on the anomalous dimension value. Further, the disclosed systems can generate a causal-contribution ranking of the particular dimension values based on the determined causal contributions.
Public/Granted literature
- US20210279230A1 DETERMINING FEATURE CONTRIBUTIONS TO DATA METRICS UTILIZING A CAUSAL DEPENDENCY MODEL Public/Granted day:2021-09-09
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