Invention Grant
- Patent Title: Generating and applying a trained structured machine learning model for determining a semantic label for content of a transient segment of a communication
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Application No.: US15139807Application Date: 2016-04-27
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Publication No.: US10540610B1Publication Date: 2020-01-21
- Inventor: Jie Yang , Amr Ahmed , Luis Garcia Pueyo , Mike Bendersky , Amitabh Saikia , Marc-Allen Cartright , Marc Alexander Najork , MyLinh Yang , Hui Tan , Weinan Zhang , Vanja Josifovski , Alexander J. Smola
- Applicant: Google Inc.
- Applicant Address: US CA Mountain View
- Assignee: GOOGLE LLC
- Current Assignee: GOOGLE LLC
- Current Assignee Address: US CA Mountain View
- Agency: Middleton Reutlinger
- Main IPC: G06N7/00
- IPC: G06N7/00 ; G06N20/00 ; H04L12/58

Abstract:
Methods, apparatus, and computer-readable media are provided for analyzing a cluster of communications, such as B2C emails, to generate a template for the cluster that defines transient segments and fixed segments of the cluster of communications. More particularly, methods, apparatus, and computer-readable media are provided for generating and/or applying a trained structured machine learning model for a generated template that can be used to determine, for one or more transient segments of subsequent communications, a corresponding probability that a given semantic label is the correct semantic label for extracted content of the transient segment(s).
Information query
IPC分类:
G | 物理 |
G06 | 计算;推算或计数 |
G06N | 基于特定计算模型的计算机系统 |
G06N7/00 | 基于特定数学模式的计算机系统 |