Abstract: A content creation application can include a feature that receives an inline note within a document and communicates the content of the inline note and a user identifier associated with an author of the inline note to an intelligence service. The intelligence service can identify, from the content of the inline note, one or more agents and a request, the identified one or more agents being the author, one or more person agents, one or more bot agents, or a combination thereof. Based on the identified agent (or lack thereof), the intelligence service can generate a message to each of the one or more agents and communicate the message to the each of the one or more agents over a communication channel. A person agent or the author can receive the message and view the message using the appropriate communication application without accessing the original document.

Abstract: Techniques provide time-aware ranking, such as ranking of information, files or URL (uniform resource locator) links. For example, time-aware modeling assists in determining user intent of a query to a search engine. In response to the query, results are ranked in a time-aware manner to better match the user intent. The ranking may model query, URL and query-URL pair behavior over time to create time-aware query, URL and query-URL pair models, respectively. Such models may predict behavior of a query-URL pair, such as frequency and timing of clicks to the URL of the pair when the query of the pair is posed to the search engine. Results of a query may be ranked by predicted query-URL behavior. Once ranked, the results may be sent to the user in response to the query.

Abstract: A content creation application can include a feature that receives an inline note within a document and communicates the content of the inline note and a user identifier associated with an author of the inline note to an intelligence service. The intelligence service can identify, from the content of the inline note, one or more agents and a request, the identified one or more agents being the author, one or more person agents, one or more bot agents, or a combination thereof. Based on the identified agent (or lack thereof), the intelligence service can generate a message to each of the one or more agents and communicate the message to the each of the one or more agents over a communication channel. A person agent or the author can receive the message and view the message using the appropriate communication application without accessing the original document.

Abstract: Systems, methods, and computer storage media are provided for analyzing a large amount of social media data from a large population of social media users and constructing correlational data models between one or more events that occur within each user's timeline. Social media posts directed to personal experiences of a large number of social media users are extracted. Event timelines are generated for each of the social media users, based on their personal experiences. The event timelines are analyzed with a particular event of interest to measure correlations between events occurring within the timelines and the particular event of interest. Using the measured correlations, a correlational data model is thereby constructed. The correlational data model may be used for application to decision-making calculations by one or more systems in an active or passive manner.

Abstract: Some examples include transitioning between an individual mode and a collaborative mode in response to an orientation change of a device. Further, some implementations include identifying data to be shared with one or more other devices (e.g., co-located devices) in the collaborative mode. In some examples, the individual mode may be associated with an individual search, the collaborative mode may be associated with a collaborative search, and the devices may transition between the individual mode and the collaborative mode in response to orientation changes.

Abstract: Some examples include transitioning between an individual mode and a collaborative mode in response to an orientation change of a device. Further, some implementations include identifying data to be shared with one or more other devices (e.g., co-located devices) in the collaborative mode. In some examples, the individual mode may be associated with an individual search, the collaborative mode may be associated with a collaborative search, and the devices may transition between the individual mode and the collaborative mode in response to orientation changes.

Abstract: Systems, methods, and computer storage media are provided for analyzing a large amount of social media data from a large population of social media users and constructing correlational data models between one or more events that occur within each user's timeline. Social media posts directed to personal experiences of a large number of social media users are extracted. Event timelines are generated for each of the social media users, based on their personal experiences. The event timelines are analyzed with a particular event of interest to measure correlations between events occurring within the timelines and the particular event of interest. Using the measured correlations, a correlational data model is thereby constructed. The correlational data model may be used for application to decision-making calculations by one or more systems in an active or passive manner.

Abstract: Techniques provide time-aware ranking, such as ranking of information, files or URL (uniform resource locator) links. For example, time-aware modeling assists in determining user intent of a query to a search engine. In response to the query, results are ranked in a time-aware manner to better match the user intent. The ranking may model query, URL and query-URL pair behavior over time to create time-aware query, URL and query-URL pair models, respectively. Such models may predict behavior of a query-URL pair, such as frequency and timing of clicks to the URL of the pair when the query of the pair is posed to the search engine. Results of a query may be ranked by predicted query-URL behavior. Once ranked, the results may be sent to the user in response to the query.

Abstract: Techniques provide time-aware ranking, such as ranking of information, files or URL (uniform resource locator) links. For example, time-aware modeling assists in determining user intent of a query to a search engine. In response to the query, results are ranked in a time-aware manner to better match the user intent. The ranking may model query, URL and query-URL pair behavior over time to create time-aware query, URL and query-URL pair models, respectively. Such models may predict behavior of a query-URL pair, such as frequency and timing of clicks to the URL of the pair when the query of the pair is posed to the search engine. Results of a query may be ranked by predicted query-URL behavior. Once ranked, the results may be sent to the user in response to the query.

Abstract: Some examples include transitioning between an individual mode and a collaborative mode in response to an orientation change of a device. Further, some implementations include identifying data to be shared with one or more other devices (e.g., co-located devices) in the collaborative mode. In some examples, the individual mode may be associated with an individual search, the collaborative mode may be associated with a collaborative search, and the devices may transition between the individual mode and the collaborative mode in response to orientation changes.

Abstract: Techniques provide time-aware ranking, such as ranking of information, files or URL (uniform resource locator) links. For example, time-aware modeling assists in determining user intent of a query to a search engine. In response to the query, results are ranked in a time-aware manner to better match the user intent. The ranking may model query, URL and query-URL pair behavior over time to create time-aware query, URL and query-URL pair models, respectively. Such models may predict behavior of a query-URL pair, such as frequency and timing of clicks to the URL of the pair when the query of the pair is posed to the search engine. Results of a query may be ranked by predicted query-URL behavior. Once ranked, the results may be sent to the user in response to the query.

Abstract: The claimed subject matter provides a system and/or a method that facilitates generating a personalized query result for a specific user. An interface can receive at least one of a portion of a text query to be searched or a portion of personalized content related to a user that submits the portion of the text query. A personalization component can combine the portion of personalized content related to the user with a portion of personalized content related to one or more disparate users to create group personalized content, wherein the group personalized content is compared with the portion of the text query to identify a relationship there between to generate a personalized query result in accordance with the relationship.

Abstract: The claimed subject matter provides a system and/or a method that facilitates generating a personalized query result for a specific user. An interface can receive at least one of a portion of a text query to be searched or a portion of personalized content related to a user that submits the portion of the text query. A personalization component can combine the portion of personalized content related to the user with a portion of personalized content related to one or more disparate users to create group personalized content, wherein the group personalized content is compared with the portion of the text query to identify a relationship there between to generate a personalized query result in accordance with the relationship.