Microsoft Technology Licensing, LLC

Beijing Medisan Technology Co., Ltd

Corning Optical Communications LLC

Harbin Medisan Pharmaceutical Co., Ltd.

Lei Ji

Jian Wang

Fan Yang

Zheng Chen

Yan Wang

Jun Yan

Ning Liu

Ming Zhou

Dmitriy Meyerzon

Benyu Zhang

Nan Duan

Chenxi Lin

HuaJun Zeng

Zhongyuan Wang

Welly Sugiarto Lee

Qing Xu

Steven J Hanks

Mingxin Wang

Ailing Wang

Jinai Liu

Xiao Bi Liang

Eric Raymond Logan

Jian-Tao Sun

Weizhu Chen

Yan Zhu

Christopher Mark Quinn

Jin Cui

Zhimin Zhang

Nikolay Anatolyevich Panin

Jianwen Zhang

Jiguo Sun

Jiulong Wang

The present disclosure provides a technical solution of multi-modal chatting, which may provide response to user query by using multi-modal response in the interaction between chatbot and human beings, so that the expressing ways and the expressed content by the chatbot could be richer by using such response in a multi-modal way.

Videochat

VideoChat

A computing system comprises, in one example, a display system, a user profile update system configured to update a user profile for a given user, a data extraction system configured to analyze a data source and to extract, from the data source, at least one work attribute associated with the given user, and a surfacing system. The surfacing system comprises a suggestion generator component configured to generate a user profile suggestion based on the at least one work attribute, and a display system controller configured to control the display system to generate a user interface display, with user input mechanisms, that displays the user profile suggestion and to detect a user interaction with the user input mechanisms. The user profile update system updates the user profile based on the detected user interaction.

Extracting and surfacing user work attributes from data sources

Some examples include displaying a user interface that includes attributes and entities that are determined to be related to an input entity identified by a user. Further, some implementations include displaying a structured data table that identifies attribute values associated with the input entity and selected related entities.

Identifying and structuring related data

An optical communication cable and related method is provided. The cable includes a cable body and a plurality of optical transmission elements surrounded by the cable body. The cable includes a reinforcement layer surrounding the plurality of optical transmission elements and located between the cable body and the plurality of optical transmission elements. The reinforcement layer includes an outer surface and a channel defined in the outer surface that extends in the longitudinal direction along at least a portion of the length of the cable. The cable includes an elongate strength element extending in the longitudinal direction within the channel.

Optical fiber cable with elongate strength member recessed in armor layer

Disclosed are a glycine reuptake inhibitor and the use thereof. The glycine reuptake inhibitor is a compound of formula I, or isomers, pharmaceutically acceptable salts or solvates thereof, wherein R1 is one or more groups selected from hydrogen, halogen, C1-6 alkyl, C1-6 alkoxy, nitro, cyano, amino, hydroxyl, C1-6 haloalkyl, and C1-6 haloalkoxy; R2 is selected from hydrogen, C1-6 alkyl, C1-6 alkoxy, C1-6 haloalkyl, and C1-6 haloalkoxy; and each of R3 and R4 is independently selected from hydrogen, halogen, C1-6 alkyl, C1-6 alkoxy, nitro, cyano, amino, hydroxyl, C1-6 haloalkyl, and C1-6 haloalkoxy. 

Glycine reuptake inhibitor and use thereof

Embodiments facilitate greater flexibility in definition of user segments for targeted advertising, by employing indexed semantic user profiles. Semantic user profiles are built through extraction of online user behavior data such as user search queries and page views, and include user interest information that is inferred based on user behavior. Semantic user profiles are then indexed to facilitate search for a set of users that fit specified semantic search terms. Search results for semantic profiles are ranked according to a ranking model developed through machine learning. In some embodiments, building and indexing of semantic profiles and learning of the ranking model is performed offline to facilitate more efficient online processing of queries.

Semantic user profiles for targeting user segments

Techniques are described for generating structured information from semi-structured web pages, and retrieving the structured knowledge in response to a user query that indicates a query intent. The structured information is automatically extracted offline from semi-structured web pages, through the use of an auto wrapper solution that is noise tolerant, scalable, and automatic. The structured information is stored in a knowledge base, and provided in response to a user search query that indicates a query intent. Extraction of structured information may also include clustering of pages based on their measured similarities. The clusters may be determined based on similar elements in the tag path text data of the pages. A minimum size threshold may be applied to the clusters.

Web knowledge extraction for search task simplification

The present invention relates to a norepinephrine and selective serotonin receptor blocker and the use thereof. The norepinephrine and selective serotonin receptor blocker is a compound of formula I, or isomers, pharmaceutically acceptable salts or solvates thereof, wherein R1 is selected from the group consisting of C1-6 alkyl, C1-6 alkoxy, C6-10 aralkyl, C6-10 arylalkoxy, C1-6 haloalkyl and C1-6 haloalkoxy; and R2 is one or more groups each independently selected from the group consisting of hydrogen, halogen, C1-6 alkyl, C1-6 alkoxy, nitro, cyano, amino, hydroxyl, C1-6 haloalkyl, and C1-6 haloalkoxy. R3 is one or more groups each independently selected from the group consisting of hydrogen, halogen, C1-6 alkyl, C1-6 alkoxy, nitro, cyano, amino, hydroxyl, C1-6 haloalkyl, and C1-6 haloalkoxy. The compound of the present invention is effective norepinephrine and selective serotonin receptor blocker. 

Norepinephrine and selective serotonin receptor blocker and use thereof

A smart user-centric information aggregation system allows a user to define a region of content displayed in a display of a device and performs information aggregation on behalf of the user. The smart user-centric information aggregation system searches, aggregates and groups information related to content included in the region of content for the user while the user can continue to perform his/her original course of actions without interruption. After finding information related to the desired content, the smart user-centric information aggregation system may notify the user and present the found information to the user upon receiving confirmation from the user. The smart user-centric information aggregation system may continue to find new related information and update the presentation with the newly found information periodically, in some instances without user intervention or input.

Smart user-centric information aggregation

A classification process may reduce the computational resources and time required to collect and classify training data utilized to enable a user to effectively access online information. According to some implementations, training data is established by defining one or more seed queries and query patterns. A bi-partite graph may be constructed using the seed query and query pattern information. A traversal of the bi-partite graph can be performed to expand the training data to encompass sufficient data to perform classification of the present search task.

Random walk on query pattern graph for query task classification

Transfer of learning trains a new domain for the classification of search queries according to different tasks, as well as the generation of a corresponding domain-specific query classifier that may be used to classify the search queries according to the different tasks in the new domain. The transfer of learning may include preparing a new domain to receive classification knowledge from one or more source domains by populating the new domain with preliminary query patterns extracted for a search engine log. The transfer of learning may further include preparing the classification knowledge in each source domain for transfer to the new domain. The classification knowledge in each source domain may then be transferred to the new domain.

Transfer of learning for query classification

Indexing semantic user profiles for targeted advertising

Growing community of inventors

Beijing, China

Lei Ji