Invention Grant
- Patent Title: Learning personalized actionable domain models
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Application No.: US15475551Application Date: 2017-03-31
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Publication No.: US11295230B2Publication Date: 2022-04-05
- Inventor: Lydia Manikonda , Shirin Sohrabi Araghi , Biplav Srivastava , Kartik Talamadupula
- Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION
- Applicant Address: US NY Armonk
- Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
- Current Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
- Current Assignee Address: US NY Armonk
- Agency: Griffiths & Seaton PLLC
- Main IPC: G06N20/00
- IPC: G06N20/00 ; G06N7/00 ; G06N5/04 ; G06F40/35 ; G06Q50/00 ; G06Q30/04

Abstract:
Embodiments for learning personalized actionable domain models by a processor. A domain model may be generated according to a plurality of actions, extracted from one or more online data sources, of a plurality of cluster representatives. The plurality of actions achieve a goal. A hierarchical action model may be generated based on probabilities of the domain model and the plurality of actions. The hierarchical action model comprises a sequence of actions of the plurality of actions for achieving the goal. The hierarchical action model may be personalized by filtering to a selected set of actions according to weighted actions of the plurality of actions.
Public/Granted literature
- US20180285770A1 LEARNING PERSONALIZED ACTIONABLE DOMAIN MODELS Public/Granted day:2018-10-04
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