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: 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: 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: Implementations described herein disclose a method, an article of manufacture, and a system for displaying user profile information using extendible user profiles for user groups. A user profile with an application user profile is maintained. A value provided by a user joining a group for a group specific field is used to override a user profile field maintained in the application user profile.

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: 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: 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: Technology is disclosed for selecting at least one icebreaker people highlight, with respect to a person of interest in a target group, to be shown on one or more user devices. The target group is determined, along with a set of feature values in common among the target group. Similarly, a reference group related to the target group is determined, along with a set of feature values for the reference group. For each feature value in the set of common target group feature values, a frequency of the feature value with respect to the determined reference group is determined. Based on the determined frequency, at least one icebreaker people highlight from the determined set of feature values in common among the target group is caused to be presented on the user device.

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: 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 provide improved user experiences, such as personalization, modifying functionality of user devices, or providing more efficient consumption of bandwidth or power.

Abstract: Implementations described herein disclose an informed join recommendation system that allows users to make informed decision about joining various information sources such as a feed, an application, a group, a blog, a news source, etc., based on an activity grade of the information sources. The informed join recommendation system analyzes information dissemination activity of information sources over a predetermined time period to determine an activity grade of the information source, and in response to the request from a user to join the information source, provides the user the activity grade of the information source.

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 provide improved user experiences, such as personalization, modifying functionality of user devices, or providing more efficient consumption of bandwidth or power.

Abstract: One or more techniques and/or systems are provided for surfacing scheduling conflicts and/or for facilitating appointment scheduling based upon conflict detection. A user may maintain user appointment data comprising user appointments (e.g., calendar entries within a work calendar). Because user activity may not be fully reflected in the appointment data (e.g., no calendar entries for routine grocery shopping, soccer practice, etc.), potential conflicts may exist between user appointments and the true availability of the user. Accordingly, user signals (e.g., email(s), location check-ins, etc. associated with soccer practice) may be evaluated to generate a user inferred behavior pattern that may be used to identify a potential conflict with a user appointment (e.g., where the user consents to the gathering and/or use of such user signals). A warning of the potential conflict may be surfaced to the user (e.g., a Tuesday dinner calendar meeting conflicts with routine soccer practice on Tuesday evenings).

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 provide improved user experiences, such as personalization, modifying functionality of user devices, or providing more efficient consumption of bandwidth or power.

Abstract: Implementations described herein disclose a method, an article of manufacture, and a system for displaying user profile information using extendible user profiles for user groups. A user profile with an application user profile is maintained. A value provided by a user joining a group for a group specific field is used to override a user profile field maintained in the application user profile.

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: 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: Aspects of the technology described herein provide a personalized computing experience for a user based on a predicted future semantic location of the user. In particular, a likely future location (or sequences of future locations) for a user may be determined, including contextual information about the future location. Using information from the current context of the user's current location with historical observations about the user and expected user events, out-of-routine events, or other lasting or ephemeral information, a prediction of one or more future semantic locations and corresponding confidences may be determined and used for providing personalized computing services to the user. The prediction may be provided to an application or service such as a personal assistant service associated with the user, or may be provided as an API to facilitate consumption of the prediction information by an application or service.

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.