Abstract: Embodiments create and label contextual slices from observation data and aggregate slices into a hierarchical storyline for a user. A context is a (possibly partial) specification of what a user was doing in the dimensions of time, place, and activity. A storyline is composed of a time-ordered sequence of contexts that partition a given span of time that are arranged in groups at one or more hierarchical levels. A storyline is created through a process of data collection, slicing, labeling, and aggregating. Raw context data can be collected from a variety of observation sources with various error characteristics. Slicing refines the raw context data into a consistent storyline composed of a sequence of contexts representing homogeneous time intervals. Labeling adds more specific and semantically meaningful data (e.g., geography, venue, activity) to the slices. Aggregation identifies groups of slices that correspond to a single semantic concept.

Abstract: A user interface for an electronic calendar represents different locations or different users or different user calendars in different portions of the display. Calendar entries can be associated with one or more locations, one or more users, and with one or more user calendars. The different locations may reside in different time zones and a timeline for each time zone is displayed. The position of the calendar entry provides a visual identifier of the timeline with which the event is associated. Travel time to and from events in the calendar are calculated for calendared events and shown adjacent to the beginning and end of the event. A user's future location at a point in time is inferred from patterns in the user's locations and by analyzing the user's calendared events and correspondence in order to calculate travel time to calendared events.

Abstract: A user interface for an electronic calendar represents different locations or different users or different user calendars in different portions of the display. Calendar entries can be associated with one or more locations, one or more users, and with one or more user calendars. The different locations may reside in different time zones and a timeline for each time zone is displayed. The position of the calendar entry provides a visual identifier of the timeline with which the event is associated. Travel time to and from events in the calendar are calculated for calendared events and shown adjacent to the beginning and end of the event. A user's future location at a point in time is inferred from patterns in the user's locations and by analyzing the user's calendared events and correspondence in order to calculate travel time to calendared events.

Abstract: When a mobile device is known to be at a venue, readings from one or more sensors of the device are used to generate a sensor fingerprint of the venue and/or a venue category corresponding to the venue. The sensor fingerprint indicates typical sensor readings for the one or more physical sensors for devices that are located at the venue and/or venues of the same category. Sensor fingerprints can also be generated for other circumstances associated with the mobile device, such as the activity being undertaken by the corresponding user and a type of event currently occurring at the venue. Fingerprints can also be generated for combinations of circumstances, such as a particular activity at a certain category of venue. When location data cannot distinguish at which venue a mobile device is located, sensor fingerprints are compared to sensor readings received from the device to resolve this ambiguity.

Abstract: A mobile device includes a plurality of sensors, each with one or more costs associated with it. In order to reduce the cost associated with using the sensors to infer information about the device's current context, a sensor manager first collects readings from relatively low-cost sensors, and attempts to infer the device's context based on these readings. If the context cannot be unambiguously determined (within an acceptable degree of tolerance) using the low-cost sensor readings, the sensor manager activates one or more higher-cost sensors to identify the current context. In some instances, if the higher-cost sensor is still not adequate to determine the context, one or more even higher-cost sensors are activated. The weighting of the various costs associated with the sensors can be adjusted based on previous and/or predicted contexts.

Abstract: A social network model, based on data relevant to a user, is used for semantic processing to enable improved entity recognition among text accessed by the user. An entity extraction module of the server, with reference to a general training corpus, general gazetteers, user-specific gazetteers, and entity models, parses text to identify entities. The entities may be, for example, people, organizations, or locations. A social network module of the server builds the social network model implicit in the data accessed by the user. The social network model includes the relationships between entities and an indication of the strength of each relationship. The social network module is also used to disambiguate names and unify entities based on the social network model.

Abstract: A user's context history is analyzed to identify transitions between contexts therein. The identified transitions are used to build a routine model for the user. The routine model includes transition rules indicating a source context, a destination context, and, optionally, a probability that the user will transition from the source context to the destination context, based on the user's historical behavior. A customized recommendation agent for the user is built using the routine model. The customized recommendation agent selects recommendations from a corpus to present to the user, based on the routine model and the user's current or predicted future context.

Abstract: A user query including one or more references to objects of various types and/or text keywords is received from a user and processed into sub-queries. Information sources execute the sub-queries and returns search results matching text keywords and/or relating to objects referenced in the user queries. The search results are organized based on their object types and relationships, and displayed in a manner exposing their relationships to the user.

Abstract: A user's context history is analyzed to build a personality model describing the user's personality and interests. The personality model includes a plurality of metrics indicating the user's position on a plurality of personality dimensions, such as desire for novelty, tendency for extravagance, willingness to travel, love of the outdoors, preference for physical activity, and desire for solitude. A customized recommendation agent is then built based on the personality model, which selects a recommendation from a corpus to present to the user based on an affinity between the user's personality and the selected recommendation.

Abstract: A mobile device includes a plurality of sensors, each with one or more costs associated with it. In order to reduce the cost associated with using the sensors to infer information about the device's current context, a sensor manager first collects readings from relatively low-cost sensors, and attempts to infer the device's context based on these readings. If the context cannot be unambiguously determined (within an acceptable degree of tolerance) using the low-cost sensor readings, the sensor manager activates one or more higher-cost sensors to identify the current context. In some instances, if the higher-cost sensor is still not adequate to determine the context, one or more even higher-cost sensors are activated. The weighting of the various costs associated with the sensors can be adjusted based on previous and/or predicted contexts.

Abstract: When a mobile device is known to be at a venue, readings from one or more sensors of the device are used to generate a sensor fingerprint of the venue and/or a venue category corresponding to the venue. The sensor fingerprint indicates typical sensor readings for the one or more physical sensors for devices that are located at the venue and/or venues of the same category. Sensor fingerprints can also be generated for other circumstances associated with the mobile device, such as the activity being undertaken by the corresponding user and a type of event currently occurring at the venue. Fingerprints can also be generated for combinations of circumstances, such as a particular activity at a certain category of venue. When location data cannot distinguish at which venue a mobile device is located, sensor fingerprints are compared to sensor readings received from the device to resolve this ambiguity.

Abstract: A user's context history is analyzed to build a personality model describing the user's personality and interests. The personality model includes a plurality of metrics indicating the user's position on a plurality of personality dimensions, such as desire for novelty, tendency for extravagance, willingness to travel, love of the outdoors, preference for physical activity, and desire for solitude. A customized recommendation agent is then built based on the personality model, which selects a recommendation from a corpus to present to the user based on an affinity between the user's personality and the selected recommendation.

Abstract: Embodiments create and label contextual slices from observation data and aggregate slices into a hierarchical storyline for a user. A context is a (possibly partial) specification of what a user was doing in the dimensions of time, place, and activity. A storyline is composed of a time-ordered sequence of contexts that partition a given span of time that are arranged in groups at one or more hierarchical levels. A storyline is created through a process of data collection, slicing, labeling, and aggregating. Raw context data can be collected from a variety of observation sources with various error characteristics. Slicing refines the raw context data into a consistent storyline composed of a sequence of contexts representing homogeneous time intervals. Labeling adds more specific and semantically meaningful data (e.g., geography, venue, activity) to the slices. Aggregation identifies groups of slices that correspond to a single semantic concept.

Abstract: A user's context history is used to help select contextual information to provide to the user. Context data describing the user's current context is received and a plurality of information items corresponding to the user's current context are identified from a contextual information corpus. A personalized user behavior model for the user is applied to determine the likelihood that each of the identified information items will be of value to the user. One or more of the information items are selected based on the corresponding likelihoods and the selected information items are provided for presentation to the user.

Abstract: Embodiments create and label context slices from observation data that together define a storyline of a user's movements. A context is a (possibly partial) specification of what a user was doing in the dimensions of time, place, and activity. Contexts can vary in their specificity, their semantic content, and their likelihood. A storyline is composed of a time-ordered sequence of contexts that partition a given span of time. A storyline is created through a process of data collection, slicing and labeling. Raw context data can be collected from a variety of observation sources with various error characteristics. Slicing refines the chaotic collection of contexts produced by data collection into a single consistent storyline composed of a sequence of contexts representing homogeneous time intervals. Labeling adds more specific and semantically meaningful data (e.g., geography, venue, activity) to the storyline produced by slicing.

Abstract: A customized recommendation agent for the user is built using a behavioral model. The customized recommendation agent selects recommendations from a corpus to present to the user, based on the behavioral model and the user's current or predicted future context. The customized recommendation agent can be shared by the user with others, thus allowing others to access recommendations that may appeal to the user, for example, for use in planning joint activities. Because the user's recommendation agent is independent from the user's actual history, preferences can be shared without revealing a user's specific behavior.

Abstract: Embodiments create and label context slices from observation data that together define a storyline of a user's movements. A context is a (possibly partial) specification of what a user was doing in the dimensions of time, place, and activity. Contexts can vary in their specificity, their semantic content, and their likelihood. A storyline is composed of a time-ordered sequence of contexts that partition a given span of time. A storyline is created through a process of data collection, slicing and labeling. Raw context data can be collected from a variety of observation sources with various error characteristics. Slicing refines the chaotic collection of contexts produced by data collection into a single consistent storyline composed of a sequence of contexts representing homogeneous time intervals. Labeling adds more specific and semantically meaningful data (e.g., geography, venue, activity) to the storyline produced by slicing.

Abstract: A user's context history is used to help select contextual information to provide to the user. Context data describing the user's current context is received and a plurality of information items corresponding to the user's current context are identified from a contextual information corpus. A personalized user behavior model for the user is applied to determine the likelihood that each of the identified information items will be of value to the user. One or more of the information items are selected based on the corresponding likelihoods and the selected information items are provided for presentation to the user.

Abstract: Embodiments create and label context slices from observation data that together define a storyline of a user's movements. A context is a (possibly partial) specification of what a user was doing in the dimensions of time, place, and activity. Contexts can vary in their specificity, their semantic content, and their likelihood. A storyline is composed of a time-ordered sequence of contexts that partition a given span of time. A storyline is created through a process of data collection, slicing and labeling. Raw context data can be collected from a variety of observation sources with various error characteristics. Slicing refines the chaotic collection of contexts produced by data collection into a single consistent storyline composed of a sequence of contexts representing homogeneous time intervals. Labeling adds more specific and semantically meaningful data (e.g., geography, venue, activity) to the storyline produced by slicing.

Abstract: A user's context history is analyzed to build a personality model describing the user's personality and interests. The personality model includes a plurality of metrics indicating the user's position on a plurality of personality dimensions, such as desire for novelty, tendency for extravagance, willingness to travel, love of the outdoors, preference for physical activity, and desire for solitude. A customized recommendation agent is then built based on the personality model, which selects a recommendation from a corpus to present to the user based on an affinity between the user's personality and the selected recommendation.