Abstract: In one embodiment, a current context of a mobile device may be ascertained, where the current context includes an indication of a last application opened via the mobile device, wherein the last application opened is one of a plurality of applications installed on the mobile device. A probability, for each of the plurality of applications, that a user of the mobile device will use the corresponding application under the current context may be determined, where the probability for at least a portion of the plurality of applications is determined by applying a computer-generated model to the current context. One or more of the plurality of the applications may be identified based, at least in part, upon the probability, for each one of the plurality of applications, that the user of the mobile device will use the corresponding application.

Abstract: In one embodiment, a current context of a mobile device may be ascertained, where the current context includes an indication of a last application opened via the mobile device, wherein the last application opened is one of a plurality of applications installed on the mobile device. A probability, for each of the plurality of applications, that a user of the mobile device will use the corresponding application under the current context may be determined, where the probability for at least a portion of the plurality of applications is determined by applying a computer-generated model to the current context. One or more of the plurality of the applications may be identified based, at least in part, upon the probability, for each one of the plurality of applications, that the user of the mobile device will use the corresponding application.

Abstract: In one embodiment, a current context of a mobile device may be ascertained, where the current context includes an indication of a last application opened via the mobile device, wherein the last application opened is one of a plurality of applications installed on the mobile device. A probability, for each of the plurality of applications, that a user of the mobile device will use the corresponding application under the current context may be determined, where the probability for at least a portion of the plurality of applications is determined by applying a computer-generated model to the current context, wherein the computer-generated model is associated with the mobile device. One or more of the plurality of the applications may be identified based, at least in part, upon the probability, for each one of the plurality of applications, that the user of the mobile device will use the corresponding application.

Abstract: Disclosed is a system and method for displaying search results upon the occurrence or detection of an event and/or time-delay. The present provides a user with the ability to view search results upon satisfaction of an event trigger or specified time delay (e.g., a rule). The disclosed systems and methods enable features that allow a user to link a search query and/or a set or sets of results for the search query to a trigger, whereby upon detection and satisfaction of such trigger, the search (and/or related results) are displayed. Such trigger can be associated with, but not limited to, a calendar event, to-do list item, location, a product or contact person, among other types of event based information events.

Abstract: Disclosed is a system and method for displaying search results upon the occurrence or detection of an event and/or time-delay. The present provides a user with the ability to view search results upon satisfaction of an event trigger or specified time delay (e.g., a rule). The disclosed systems and methods enable features that allow a user to link a search query and/or a set or sets of results for the search query to a trigger, whereby upon detection and satisfaction of such trigger, the search (and/or related results) are displayed. Such trigger can be associated with, but not limited to, a calendar event, to-do list item, location, a product or contact person, among other types of event based information events.

Abstract: In one embodiment, a current context of a mobile device may be ascertained, where the current context includes an indication of a last application opened via the mobile device, wherein the last application opened is one of a plurality of applications installed on the mobile device. A probability, for each of the plurality of applications, that a user of the mobile device will use the corresponding application under the current context may be determined, where the probability for at least a portion of the plurality of applications is determined by applying a computer-generated model to the current context, wherein the computer-generated model is associated with the mobile device. One or more of the plurality of the applications may be identified based, at least in part, upon the probability, for each one of the plurality of applications, that the user of the mobile device will use the corresponding application.

Abstract: Various embodiments for automatically distinguishing between users of a handheld device are described. An embodiment includes collecting sensor data from a user interacting with a handheld device, where the sensor data is collected via embedded sensors in the handheld device. The embodiment further includes distinguishing the user from other users of the handheld device via the collected sensor data, at least one embedded machine learning algorithm and a profile for the user. Other embodiments are described and claimed.

Abstract: Disclosed is a system and method for displaying search results upon the occurrence or detection of an event and/or time-delay. The present provides a user with the ability to view search results upon satisfaction of an event trigger or specified time delay (e.g., a rule). The disclosed systems and methods enable features that allow a user to link a search query and/or a set or sets of results for the search query to a trigger, whereby upon detection and satisfaction of such trigger, the search (and/or related results) are displayed. Such trigger can be associated with, but not limited to, a calendar event, to-do list item, location, a product or contact person, among other types of event based information events.

Abstract: Various embodiments for automatically distinguishing between users of a handheld device are described. An embodiment includes collecting sensor data from a user interacting with a handheld device, where the sensor data is collected via embedded sensors in the handheld device. The embodiment further includes distinguishing the user from other users of the handheld device via the collected sensor data, at least one embedded machine learning algorithm and a profile for the user. Other embodiments are described and claimed.

Abstract: An electronic computer appliance, method, and computer program product are provided for providing a user of a mobile electronic device with a predetermined message during a communication network page loading latency time. The method comprises pre-caching the predetermined message; detecting the beginning of a page loading latency time; displaying the predetermined message on the mobile electronic device during the page loading latency time; and supplanting the predetermined message with a page that had been loaded during the page loading latency time.

Abstract: Techniques are disclosed that involve the detection of persons. For instance, embodiments may receive, from an image sensor, one or more images (e.g., thermal images, infrared images, visible light images, three dimensional images, etc.) of a detection space. Based at least on the one or more images, embodiments may detect the presence of person(s) in the detection space. Also, embodiments may determine one or more characteristics of such detected person(s). Exemplary characteristics include (but are not limited to) membership in one or more demographic categories and/or activities of such persons. Further, based at least on such person detection and characteristics determining, embodiments may control delivery of content to an output device.

Abstract: Various embodiments for automatically distinguishing between users of a handheld device are described. An embodiment includes collecting sensor data from a user interacting with a handheld device, where the sensor data is collected via embedded sensors in the handheld device. The embodiment further includes distinguishing the user from other users of the handheld device via the collected sensor data, at least one embedded machine learning algorithm and a profile for the user. Other embodiments are described and claimed.

Abstract: A method, system and apparatus for wrapping objects for liveness in a collaborative environment. In a preferred aspect of the present invention, a method for enabling liveness for a non-live object can include the step of wrapping the non-live object to emulate liveness in a collaborative environment. Preferably, the wrapping step can include selecting at least one attribute of the non-live object, repeatedly updating a status of the at least one attribute for the non-live object, and, providing the repeatedly updated status to the collaborative environment in order to emulate liveness for the non-live object.

Abstract: A method, system and apparatus for creating and managing an activity in a collaborative environment. In a preferred aspect of the present invention, a unified activity manager for use in a collaborative environment can include an activity list including a hierarchical listing of activities. Each of the activities can include at least one task. The unified activity manager further can include an activity view including a rendering of properties associated with a selected activity in the activity view. Finally, the unified activity manager yet further can include a persons and roles view including at least a listing of collaborators available for association with the selected activity in the activity view.

Abstract: A system for identification and tracking of a tag distributed in a room is presented. A laser base station is configured to scan laser beams through a portion of a room. A tag is configured to be reactive to incident laser beams scanned from the laser base station to provide a data signal back to the laser base station. A tag tracking system is configured to receive input derived from the data signal from the laser base station. The tag tracking system stores state records of position and informational content of the tag generated from the input. The tag tracking system determines angular position of the tag with respect to the laser base station based on the position and information content of the tag stored in the state records.

Abstract: A partially collapsing hierarchical display system aids information navigation by providing a dynamic mechanism for labeling items with one or more states. In one embodiment, states comprise “hidden”, “visible” and “unmarked”. An expanded list of items displays all items. A partially expanded list of items displays all visible and unmarked items, removing hidden items from the display. A further collapsed list of items displays all visible items. A completely collapsed list of items hides all items. Users customize the amount of detail displayed in a sub-tree, reducing the display size of the sub-tree. Expansion of a hierarchical structure is combined with filtering, allowing users to partially collapse or partially expand a sub-tree structure. Partially collapsed lists comprise one or more focus points, allowing users to view several relevant sections of a hierarchical structure concurrently.

Abstract: A system for N-space navigation of digital data sets. The system includes an electronic tag having a digitally readable identifier, an electronic tag reader configured to read the identifier of the electronic tag, and a computing system connected to the electronic tag reader to provide digital navigation services of N-space data sets in response to reading the identifier of the electronic tag. The computing system generates at least one transitional data point in N-space for output between a currently displayed start point and a target point referenced by the identifier.