Microsoft Technology Licensing, LLC

Zicheng Huang

Guihong Cao

Jiayin Ge

Jianshu Ji

Jun Liu

Ling Li

Xiaodong Fan

Mridu Baldevraj Narang

Eugene Joonsup So

Julian Manuel Varanda

Alyssa Nicole Ricken

Astha Suneja

Hao Peng

Sharath Rao

Innocent Mutabazi Ndimubanzi

Chao Gao

Yu Jia

Yufei Zhong

The present disclosure relates to systems and methods for automatically identifying additional content for a webpage. The systems and methods may create a side pane placed next to a webpage to supplement the webpage in a browser. The side pane may provide additional information or additional content to help users consume the webpage. The additional content may provide webpage insights and help the users further explore the webpage.

Automatic identification of additional content for webpages

The technology described herein makes improved use of limited screen space on a search results page by determining whether to present a question-and-answer experience and/or an entity details experience. This determination effects the amount of information presented and the format in which it is presented. In general, the question-and-answer experience provides less information and is more targeted to a question and query terms other than the entity. In contrast, the entity details experience provides more information about the entity that is not tailored to the query beyond the entity being included in the query. In one aspect, the determination of whether to show a question-and-answer experience and/or an entity details experience is based, at least in part, on an entity-details intent classification score ('intent classification score') generated by a machine classification system. The classification score may be processed in combination with other criteria to make a final determination.

Query entity-experience classification

A method of disambiguating user queries in a multi-turn dialogue include a set of user utterances includes using a predefined language model to recognize an ambiguous entity in an unresolved user utterance from the multi-turn dialogue. The method further includes outputting a clarifying question about the ambiguous entity, and receiving a clarifying user utterance. The method further includes identifying a disambiguating entity in the clarifying user utterance. The method further includes rewriting the unresolved user utterance as a rewritten utterance that replaces the ambiguous entity with the disambiguating entity, and outputting the rewritten utterance to one or more query answering machines.

Clarifying questions for rewriting ambiguous user utterance

A method of disambiguating user queries in a multi-turn dialogue including a set of user utterances. The method includes using a predefined language model to recognize an ambiguous entity in an unresolved user utterance from the multi-turn dialogue, and using the predefined language model to recognize entity constraints of the ambiguous entity. The method further includes, in a computer-accessible conversation history of the multi-turn dialogue, searching a set of previously-resolved entities for a candidate entity having entity properties with a highest confidence correspondence to the entity constraints of the ambiguous entity. The unresolved user utterance is rewritten as a rewritten utterance that replaces the ambiguous entity with the candidate entity. The rewritten utterance is output to one or more query answering machines.

Architecture for resolving ambiguous user utterance

A computer processing pipeline is automatically computer monitored. The computer processing pipeline includes a plurality of ordered computer stages. At least one computer stage is configured to receive an input data set and perform one or more computer processing operations on the input data set to produce an output data set. The output data set is provided as input to another computer stage of the computer processing pipeline. A historical expected schedule is automatically computer generated for compliant execution of the at least one computer stage. The output data set is automatically computer sampled at a designated time dictated by the historical expected schedule. The sampled output data set is automatically computer tested for compliance with one or more detection rules. An anomaly alert that identifies one or more anomalies is automatically computer issued based on non-compliance of the output data set with the one or more detection rules.

Automatic anomaly detection in computer processing pipelines

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Bothell, WA, United States of America

Zicheng Huang