Invention Grant
- Patent Title: Autonomous learning of actionable models from unstrutured data
-
Application No.: US15420433Application Date: 2017-01-31
-
Publication No.: US10699199B2Publication Date: 2020-06-30
- Inventor: Lydia Manikonda , Anton Viktorovich Riabov , Shirin Sohrabi Araghi , Biplav Srivastava , Kartik Talamadupula , Deepak Srinivas Turaga
- Applicant: International Business Machines Corporation
- Applicant Address: US NY Armonk
- Assignee: INTERNATIONAL BUSINESS MACHINES CORPROATION
- Current Assignee: INTERNATIONAL BUSINESS MACHINES CORPROATION
- Current Assignee Address: US NY Armonk
- Agency: Amin, Turocy & Watson, LLP
- Main IPC: G06N5/04
- IPC: G06N5/04 ; G06F30/20 ; G06F40/35 ; G06F40/289

Abstract:
Techniques for autonomously generating a domain model and/or an action model based on unstructured data are provided. In one example, a computer implemented method can comprise extracting, by a system operatively coupled to a processor, a plurality of actions from a non-numerical language. The plurality of actions can achieve a goal. The computer-implemented method can also comprise generating, by the system, a domain model based on the plurality of actions. Further, the computer-implemented method can comprise generating, by the system, an action model based on the domain model. In various embodiments, the action model can comprise an action transition for accomplishing the goal.
Public/Granted literature
- US20180218270A1 AUTONOMOUS LEARNING OF ACTIONABLE MODELS FROM UNSTRUTURED DATA Public/Granted day:2018-08-02
Information query