Abstract: The technology described herein solicits user feedback in order to improve the processing of contextual signal data to identify automation setting preferences. Users have limited availability or willingness to provide explicit feedback. The technology calculates an impact score that measures a possible improvement to the automation system that could result from receiving feedback. Feedback is solicited when the impact score exceeds a threshold. Other rules can be provided in conjunction with the impact score to determine when feedback is solicited, such as a daily cap on feedback solicitations.

Abstract: Traffic data reconciliation and brokering are provided. A traffic data brokering system ingests traffic-related data provided from a plurality of data sources, analyzes the data, and reconciles the data for identifying accurate, up-to-date, and comprehensive traffic data. The system identifies current traffic conditions based on identified relationships between pieces of received data, calculates confidence scores, and determines which pieces of data are accurate based on the calculated confidence scores. The traffic data brokering system provides the reconciled traffic data to various users of traffic data, such as individuals or third-party services. One aspect includes a route generation engine that determines and provides recommended route(s) to clients. Another aspect includes a forecast engine that predicts traffic conditions based on past traffic data. The forecasted data can be used to determine recommended routes.

Abstract: A user activity pattern may be ascertained using signal data from a set of computing devices. The activity pattern may be used to infer user intent with regards to a user interaction with a computing device or to predict a likely future action by the user. In one implementation, a set of computing devices is monitored to detect user activities using sensors associated with the computing devices. Activity features associated with the detected user activities are determined and used to identify an activity pattern based on a plurality of user activities having similar features. Examples of user activity patterns may include patterns based on time, location, content, or other context. The inferred user intent or predicted future actions may be used to facilitate understanding user speech or determining a semantic understanding of the user.

Abstract: Intelligent agents (IA) for automatically generating responses to content within a communication session (CS) are disclosed. An IA is trained to target the responses to a user and the user's context within the CS. An IA receives CS content that includes natural language expressions encoding users' conversations and determines content features based on natural language models. The content features indicate intended semantics of the expressions. The IA identifies likely-relevant content to the targeted user, to generate a response for. Identifying such content includes determining a relevance of the content based on content features, a context of the CS, a user-interest model, and a content-relevance model. Identifying the likely-relevant content to respond to is based on the determined relevance of the content and relevance thresholds. Various responses to the identified portions of the content are automatically generated and provided based on a natural language response-generation model targeted to the user.

Abstract: Traffic data reconciliation and brokering are provided. A traffic data brokering system ingests traffic-related data provided from a plurality of data sources, analyzes the data, and reconciles the data for identifying accurate, up-to-date, and comprehensive traffic data. The system identifies current traffic conditions based on identified relationships between pieces of received data, calculates confidence scores, and determines which pieces of data are accurate based on the calculated confidence scores. The traffic data brokering system provides the reconciled traffic data to various users of traffic data, such as individuals or third-party services. One aspect includes a route generation engine that determines and provides recommended route(s) to clients. Another aspect includes a forecast engine that predicts traffic conditions based on past traffic data. The forecasted data can be used to determine recommended routes.

Abstract: Technology for interoperability is disclosed by enabling the sharing of user context or preferences for a computing experience across computing devices, operating systems, applications, or locations. A platform and application programming interface (API) are provided for computer applications and services to store and retrieve context data associated with a computing experience. Access to the context data for sharing may be managed by an access controller, which enables a user to manage access permissions for the sharing of the context data. The context data may be defined according to a common schema, which specifies the information for sharing and may be communicated using common communication channels or protocols. Thus context data may be shared across nearly any application or service including those developed in different computer programming languages or operating on different types of computing devices or devices running different operating systems or by different software developers.

Abstract: A user activity pattern may be ascertained using signal data from a set of computing devices. The activity pattern may be used to infer user intent with regards to a user interaction with a computing device or to predict a likely future action by the user. In one implementation, a set of computing devices is monitored to detect user activities using sensors associated with the computing devices. Activity features associated with the detected user activities are determined and used to identify an activity pattern based on a plurality of user activities having similar features. Examples of user activity patterns may include patterns based on time, location, content, or other context. The inferred user intent or predicted future actions may be used to facilitate understanding user speech or determining a semantic understanding of the user.

Abstract: The technology described herein solicits user feedback in order to improve the processing of contextual signal data to identify automation setting preferences. Users have limited availability or willingness to provide explicit feedback. The technology calculates an impact score that measures a possible improvement to the automation system that could result from receiving feedback. Feedback is solicited when the impact score exceeds a threshold. Other rules can be provided in conjunction with the impact score to determine when feedback is solicited, such as a daily cap on feedback solicitations.

Abstract: Pooling computing resources based on inferences about a plurality of hardware devices. The method includes identifying inference information about the plurality of devices. The method further includes based on the inference information optimizing resource usage of the plurality of hardware devices.

Abstract: One or more techniques and/or systems are provided for determining a user work schedule. A user may seldom update actual work hours, such as within a calendar, to reflect an actual schedule of the user, which may result in erroneous information being exposed to services that may notify other users as to when the user is inside or outside work hours. Accordingly, user signals (e.g., a current device location, execution of a work-related app, access to a work VPN, participation in a conference call from home, etc.) may be evaluated to generate a set of user activity patterns that may be indicative of recurring work patterns of the user. A user work schedule for the user may be determined based upon the set of user activity patterns. The user work schedule may be used to modify user interfaces (e.g., a conferencing website, a phone app, etc.) exposed to other users.

Abstract: Intelligent agents (IA) for automatically generating responses to content within a communication session (CS) are disclosed. An IA is trained to target the responses to a user and the user's context within the CS. An IA receives CS content that includes natural language expressions encoding users' conversations and determines content features based on natural language models. The content features indicate intended semantics of the expressions. The IA identifies likely-relevant content to the targeted user, to generate a response for. Identifying such content includes determining a relevance of the content based on content features, a context of the CS, a user-interest model, and a content-relevance model. Identifying the likely-relevant content to respond to is based on the determined relevance of the content and relevance thresholds. Various responses to the identified portions of the content are automatically generated and provided based on a natural language response-generation model targeted to the user.

Abstract: Pooling computing resources based on inferences about a plurality of hardware devices. The method includes identifying inference information about the plurality of devices. The method further includes based on the inference information optimizing resource usage of the plurality of hardware devices.

Abstract: A computer-implemented technique is described herein by which a first user can forward notification information to one or more other users, including a second user. The first user can interact with the notification information via a first computing device, while the second user can interact with the notification information using a second computing device. In some implementations, both the first computing device and the second computing device have access to an application which has generated the notification information or to which the notification information otherwise pertains. In other implementations, the second computing device may lack access to the application. In some implementations, the first user receives the notification information and then explicitly and manually controls the forwarding of the notification information (or a version of the notification information) to the second user.

Abstract: Technology for interoperability is disclosed by enabling the sharing of user context or preferences for a computing experience across computing devices, operating systems, applications, or locations. A platform and application programming interface (API) are provided for computer applications and services to store and retrieve context data associated with a computing experience. Access to the context data for sharing may be managed by an access controller, which enables a user to manage access permissions for the sharing of the context data. The context data may be defined according to a common schema, which specifies the information for sharing and may be communicated using common communication channels or protocols. Thus context data may be shared across nearly any application or service including those developed in different computer programming languages or operating on different types of computing devices or devices running different operating systems or by different software developers.

Abstract: A computer-implemented technique is described herein by which a first user can forward notification information to one or more other users, including a second user. The first user can interact with the notification information via a first computing device, while the second user can interact with the notification information using a second computing device. In some implementations, both the first computing device and the second computing device have access to an application which has generated the notification information or to which the notification information otherwise pertains. In other implementations, the second computing device may lack access to the application. In some implementations, the first user receives the notification information and then explicitly and manually controls the forwarding of the notification information (or a version of the notification information) to the second user.

Abstract: Technology for interoperability is disclosed by enabling the sharing of user context or preferences for a computing experience across computing devices, operating systems, applications, or locations. A platform and application programming interface (API) are provided for computer applications and services to store and retrieve context data associated with a computing experience. Access to the context data for sharing may be managed by an access controller, which enables a user to manage access permissions for the sharing of the context data. The context data may be defined according to a common schema, which specifies the information for sharing and may be communicated using common communication channels or protocols. Thus context data may be shared across nearly any application or service including those developed in different computer programming languages or operating on different types of computing devices or devices running different operating systems or by different software developers.

Abstract: Traffic data reconciliation and brokering are provided. A traffic data brokering system ingests traffic-related data provided from a plurality of data sources, analyzes the data, and reconciles the data for identifying accurate, up-to-date, and comprehensive traffic data. The system identifies current traffic conditions based on identified relationships between pieces of received data, calculates confidence scores, and determines which pieces of data are accurate based on the calculated confidence scores. The traffic data brokering system provides the reconciled traffic data to various users of traffic data, such as individuals or third-party services. One aspect includes a route generation engine that determines and provides recommended route(s) to clients. Another aspect includes a forecast engine that predicts traffic conditions based on past traffic data. The forecasted data can be used to determine recommended routes.

Abstract: One or more techniques and/or systems are provided for constructing a complementary calendar and/or a shadow calendar. A user may maintain a primary calendar within which the user may schedule user specified tasks and/or meetings, but may inadvertently omit other entries. Accordingly, a complementary calendar may be constructed based upon inferences derived from user signal data (e.g., information used to identify/infer activities of the user, such as social network profile information, messages, temporal information, locational information, documents, etc.). The complementary calendar may be automatically populated with entries corresponding to inferences derived from user signals and/or real-time conditions (e.g., weather, store hours, traffic conditions, etc.). The primary calendar and the complementary calendar may be merged to create a shadow calendar.

Abstract: Methods for providing enhanced services to users participating in communication sessions (CS), via a virtual assistant, are disclosed. One method receives content that is exchanged by users participating in the CS. The content includes natural language expressions that encode a conversation carried out by users. The method determines content features based on natural language models. The content features indicate intended semantics of the natural language expressions. The method determines a relevance of the content and identifies portions of the content that are likely relevant to the user. Determining the relevance is based on the content features, a context of the CS, a user-interest model, and a content-relevance model of the natural language models. Identifying the likely relevant content is based on the determined relevance of the content and a relevance threshold. A summary of the CS is automatically generated from summarized versions of the likely relevant portions of the content.

Abstract: Implementations described herein disclose a system for providing extendible user profiles for user groups by providing a group formation user interface to a group administrator, wherein the group formation user interface allows the group administrator to add group specific fields, creating a group table including the group specific fields, and linking the group table to the user profile tables.