Microsoft Technology Licensing, LLC

Robert Boschgmbh

Linkedin Corporation

Baoshi Yan

Fuliang Weng

Zhe Feng

Anmol Bhasin

Ke Wang

Zhongnan Shen

Songtao Guo

Alex Ching Lai

Mohammad Shafkat Amin

Liu Ren

Daniel Tunkelang

Lincan Zou

Christian Posse

Kui Xu

Craig Martell

Vita G Markman

Alexis Blevins Baird

Yanen Li

Lili Wu

Haishan Liu

You-Chi Cheng

Liang Tang

Katrina Li

Sripad Sriram

Jiaqi Guo

William Chang

Methods, systems and computer program products for identifying a relationship between sub-units of an entity are described. The sub-units are segmented into one or more candidate related groups based on one or more general attributes and a pair of the sub-units of the one or more candidate related sub-units is selected. The pair of sub-units is analyzed to determine a relationship between the sub-units and the relationship between the sub-units is identified based on the determined relationship.

Determining an entity's hierarchical relationship via a social graph

Techniques for leveraging a social graph to facilitate the delivery of relevant recommendations. For example, a recommender is identified based on the recommender being a member of a social networking service who has interacted with an item of web-based content. A recommendee is identified based on the recommendee being another member of the social networking service who is connected to the recommender via a social graph maintained by the social networking service and based on having an affinity score for the item that exceeds a recommendee affinity score threshold and a connection strength to the recommender that exceeds a connection strength threshold. The recommender is sent a communication that invites the recommender to recommend the item to the recommendee. With some example embodiments, the communication is sent to the recommender within a pre-determined time measured from the time the recommender initiated an interaction with the item of web-based content.

Leveraging a social graph to deliver relevant recommendations

Techniques for determining a location of an entity are described. A determination module using member profile data may determine the location of the entity. Member profile data can include information about a member's employer and the employer's location. The determination module accesses member profiles from a social network. Additionally, the determination module may create a subgroup of entity-related member profiles from the accessed member profiles. The entity-related member profiles can be associated with a specific entity. Furthermore, the determination module may determine a suggested location for the specific entity based on location information derived from the related member profiles. Moreover, the determination module may calculate a connection density for the specific entity based on connections associated with the entity-related member profiles. Subsequently, the determination module may determine that the suggested location is a physical location when the calculated connection density is above a pre-determined threshold value.

Surfacing an entity's physical locations via social graph

Techniques for determining a location of an entity are described. A determination module using member profile data may determine the location of the entity. Member profile data can include information about a member's employer and the employer's location. The module accesses member profiles from a social network. Additionally, the module may create a subgroup of entity-related member profiles from the accessed member profiles. The entity-related member profiles can be associated with a specific entity. Furthermore, the module may determine a suggested location for the specific entity based on location information derived from the related member profiles. Moreover, the module may calculate a connection density for the specific entity based on connections associated with the entity-related member profiles. Subsequently, the module may determine that the suggested location is a physical location when the calculated connection density is above a pre-determined threshold value.

Calculating an entity'S location size via social graph

This disclosure provides systems and methods for determining a classification model for a secondary language different from a primary language. A social networking server is configured to obtain primary language content written in a first spoken language and secondary language content written in a second spoken language. The social networking server further obtains a machine translation of the primary language content. The social networking server then determines an initial language model from the machine translation. The social networking further determines a language model perturbation using the initial language model, where the language model perturbation accounts for a difference between the machine translation and the secondary language content. The social networking server also determines a classification model from the initial language model and the language model perturbation, which is then applied to a plurality of comments associated with an item of interest provided by a social networking service.

Transfer learning for bilingual content classification

Systems and methods are disclosed for determining whether a short amount of text is irrelevant. Initially, an article is selected having one or more comments of varying length. Depending on the number of comments available, a native context may be constructed based on a given comment and other neighboring comments. In other embodiments, a transferred context may be constructed from the given comment and topically similar comments extracted from other, topically similar articles. A native context-aware feature may be determined from the constructed native context and a transferred context-aware feature may be determined from the constructed transferred context. These features may be leveraged by a language classifier to determine whether a given comment is irrelevant.

Context-aware approach to detection of short irrelevant texts

Systems and methods are disclosed that recommend one or more electronic presentations to a user based on one or more factors. These factors may include contextual information, behavioral information, profile information, or combinations of the foregoing. Contextual information may include content and/or features extracted from a given electronic presentation. Behavioral information may include user behavioral data, such as the number of times a user has viewed a presentation, the amount of the presentation viewed by the user, presentations previously viewed by the user, and other such behavioral data. Profile information may include user professional profile information, such as skills the user has identified as possessing, employment history information, and other such user professional profile information.

Providing recommendations for electronic presentations based on contextual and behavioral data

A method of receiving input from a user includes providing a surface within reach of a hand of the user. A plurality of locations on the surface that are touched by the user are sensed. An alphanumeric character having a shape most similar to the plurality of touched locations on the surface is determined. The user is audibly or visually informed of the alphanumeric character and/or a word in which the alphanumeric character is included. Feedback is received from the user regarding whether the alphanumeric character and/or word is an alphanumeric character and/or word that the user intended to be determined in the determining step.

Gesture-based information and command entry for motor vehicle

Embodiments of a dialog system that utilizes a multi-modal input interface for recognizing user input in human-machine interaction (HMI) systems are described. Embodiments include a component that receives user input from a plurality of different user input mechanisms (multi-modal input) and performs certain synchronization and disambiguation processes. The multi-modal input components synchronizes and integrates the information obtained from different modalities, disambiguates the input, and recovers from any errors that might be produced with respect to any of the user inputs. Such a system effectively addresses any ambiguity associated with the user input and corrects for errors in the human-machine interaction.

System and method for multi-modal input synchronization and disambiguation

Embodiments of an interface system that enables a call center agent to access and intervene in an interaction between an automated call center system and a caller whenever necessary for complex application tasks is described. The system includes a user interface that presents the agent with one or more categories of information, including the conversation flow, obtained semantic information, the recognized utterances, and access to the utterance waveforms. This information is cross-linked and attached with a confidence level for better access and navigation within the dialog system for the generation of appropriate responses to the caller.

System and method for interacting with live agents in an automated call center

A system, apparatus, computer-program product and method are provided for identifying members of a particular user segment. One particular user segment of interest includes members who work for employers having a number of employees within a predetermined range. Members of an online service provide data purporting to identify their employers and/or other personal or professional attributes. The data entries are normalized by standardizing terms, removing superfluous or unneeded terms, and/or performing other processing. The members are then clustered according to their normalized employer names, and members within clusters that have sizes within the range are added to the user segment. Invalid employer names (e.g., fictitious companies, non-existent entities) may be filtered out. Within a cluster, a professional social networking site (or other site) may be analyzed to determine if the clustered members have developed relationships; if not, the cluster may be cancelled.

System and method for determining users working for the same employers in a social network

Systems and methods are described that automatically control modules of dialog systems. The systems and methods include a dialog module that receives and processes utterances from a speaker and outputs data used to generate synthetic speech outputs as responses to the utterances. A controller is coupled to the dialog module, and the controller detects an abnormal output of the dialog module when the dialog module is processing in an automatic mode. The controller comprises a mode control for an agent to control the dialog module by correcting the abnormal output and transferring a corrected output to a downstream dialog module that follows, in a processing path, the dialog module. The corrected output is used in further processing the utterances.

Dialog system for human agent to correct abnormal output

Systems and methods are described that automatically generate interactive systems configured for collecting dialog data of human-machine interactions in dialog systems. The systems and methods comprise receiving a task flow that describes operations of a dialog system. A formal description of the task flow is generated, and an interactive system comprising a graphical user interface (GUI) is automatically generated from the formal description. The GUI consists of templates for control of the dialog system and real-time collection and annotating of dialog data during a live dialog between only the dialog system and callers to the dialog system. The dialog data consists of data of the live dialog.

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Belmont, CA, United States of America

Baoshi Yan