公开(公告)号：US20140258304A1

公开(公告)日：2014-09-11

申请号：US13792913

申请日：2013-03-11

Applicant: GM GLOBAL TECHNOLOGY OPERATIONS LLC

Inventor： Gregory D. Sabanski ,  Martin Case ,  Soumen De ,  Dnyanesh Rajpathak

IPC: G06F17/30

CPC classification number: G06F16/904

Abstract: A warranty database stores service repair verbatims. An ontology database that specifies relationships between service terms includes linking relationships between vehicle terminology and cluster categories. The ontology database is reconfigurable for allowing a user to add, delete, and modify contents within the ontology database. A verbatim extraction tool extracts service repair verbatims from the warranty database as function of user selected parameters and a user selected ontology. The user selected ontology is a subset of the ontology database. The service verbatims are segregated into a plurality of cluster categories as a function of the selected parameters and the user selected ontology. A report generating device selectively generated reports based on segregating service verbatims into a plurality of cluster categories. Each respective cluster category includes associated service repair verbatims that are selected as a function of the linking relationship of terms within the service verbatim and the user selected ontology.

Abstract translation: 保修数据库将服务维修逐字记录。 指定服务术语之间的关系的本体数据库包括链接车辆术语和集群类别之间的关系。 本体数据库是可重构的，用于允许用户添加，删除和修改本体数据库内的内容。 逐字提取工具根据用户选择的参数和用户选择的本体的功能，从保修数据库中逐字提取服务修复。 用户选择的本体是本体数据库的一个子集。 根据所选择的参数和用户选择的本体，将服务逐字节分隔为多个集群类别。 报告生成装置基于将服务逐字分类为多个集群类别来选择性地生成报告。 每个相应的集群类别包括根据服务逐字逐句和用户选择的本体中的术语的链接关系来选择的相关联的服务修复逐字节。

公开(公告)号：US20190130028A1

公开(公告)日：2019-05-02

申请号：US15794670

申请日：2017-10-26

Applicant: GM Global Technology Operations LLC

Inventor： Dnyanesh G. Rajpathak ,  Susan H. Owen ,  Joseph A. Donndelinger ,  John A. Cafeo ,  Martin Case ,  Carolyn Nguyen ,  Charles M. Chandler

IPC: G06F17/30 ,  G06Q10/00 ,  G06Q30/00 ,  G06Q30/02

Abstract: A system having an annotation module that annotates, using a master ontology, unstructured verbatim regarding a product and related issue, and a customer-observable (CO) construction module determining associations amongst terminology in the annotated output, yielding a group of CO pairs. A CO merging module merges at least one first CO pair into a second CO pair based on similarities. A pointwise mutual-information module determines which CO pairs of the group of merged CO pairs are relatively more-severe or more-relevant, yielding a group of critical CO pairs. An output module initiates activity to implement the results, such as by automated repair of the product or change to product design or manufacturing process. The system in some embodiments identifies, using a subject-matter-expert (SME) database, features of false-positive associations, and in machine-learning implements the features to improve CO formation going forward.