Abstract: Techniques for extending the capabilities of computing environments and/or systems are disclosed. A scalable and dynamic external computing resource can be used in order to effectively extend the internal computing capabilities of a computing environment or system. The scalable and dynamic external computing resource can provide computing resources that far exceed the internal computing resources, and provide the services as needed, and in a dynamic manner at execution time. As a result, a computing device may function with relatively limited and/or reduced computing resources (e.g., processing power, memory) but have the ability to effectively provide as much computing services as may be needed, and provide the services when needed, on demand, and dynamically during the execution time.

Abstract: A method and system for providing access to information via a supplementary device is provided. User access to primary information via a primary device is monitored. Key information related to the primary content is obtained by extracting and analyzing metadata sources for the primary information. Then, supplementary information related to the primary information is obtained based on the key information. The supplementary information is provided for user access via the supplementary device.

Abstract: A media device suitable for playing video content including television programming is provided. The media device comprises a device usage monitor configured to substantially automatically monitor selected usage information related to video content that is played on the media device; a rating engine configured to substantially automatically generate content ratings for specific video content that has been played by the media device, wherein the content ratings are based at least in part of the usage information; and a user interface suitable for presenting a plurality of content channels to the user, wherein at least some of the presented channels are personalized channels that include video content that is selected based at least in part on the content ratings generated by the rating engine.

Abstract: A method and system for facilitating information searching for a user of an electronic device is provided. Facilitating searches involves obtaining information about the user interests, identifying potential data of interest to the user, extracting data related to the data of interest to the user, and collecting the extracted related data for presentation to the user on the device.

Abstract: Techniques for assessing the cost of allocation of execution and affecting the allocation of execution are disclosed. The cost of allocation of execution between a first computing device (e.g., mobile device) and one or more computing resource providers (e.g., Clouds) can be determined during runtime of the code. A computing system can operate independently of the first computing device and a computing resource provider and provide execution allocation cost assessment. Execution allocation cost can be assessed based on execution allocation data pertaining to the first computing device and computing resource providers. Power consumption of a mobile device can be used as a factor in determining how to allocate individual components of an application program between a mobile phone and a Cloud. In an Elastic computing environment, external computing resources can be used to extend the computing capabilities beyond that which can be provided by internal computing resources.

Abstract: Techniques for allocating individually executable portions of executable code for execution in an Elastic computing environment are disclosed. In an Elastic computing environment, scalable and dynamic external computing resources can be used in order to effectively extend the computing capabilities beyond that which can be provided by internal computing resources of a computing system or environment. Machine learning can be used to automatically determine whether to allocate each individual portion of executable code (e.g., a Weblet) for execution to either internal computing resources of a computing system (e.g., a computing device) or external resources of an dynamically scalable computing resource (e.g., a Cloud). By way of example, status and preference data can be used to train a supervised learning mechanism to allow a computing device to automatically allocate executable code to internal and external computing resources of an Elastic computing environment.

Abstract: Access permission can be assigned to a particular individually executable portion of computer executable code (“component-specific access permission”) and enforced in connection with accessing the services of a service provider by the individually executable portion (or component). It should be noted that least one of the individually executable portions can request the services when executed by a dynamically scalable computing resource provider. In addition, general and component-specific access permissions respectively associated with executable computer code as a whole or one of it specific portions (or components) can be cancelled or rendered inoperable in response to an explicit request for cancelation.

Abstract: A method for application interfacing a native physics engine includes embedding access to a native physics engine within a browser engine. Bindings are provided for supporting multiple application classes from the browser engine to the native physics engine and a JavaScript engine.

Abstract: Techniques for allocating individually executable portions of executable code for execution in an Elastic computing environment are disclosed. In an Elastic computing environment, scalable and dynamic external computing resources can be used in order to effectively extend the computing capabilities beyond that which can be provided by internal computing resources of a computing system or environment. Machine learning can be used to automatically determine whether to allocate each individual portion of executable code (e.g., a Weblet) for execution to either internal computing resources of a computing system (e.g., a computing device) or external resources of an dynamically scalable computing resource (e.g., a Cloud). By way of example, status and preference data can be used to train a supervised learning mechanism to allow a computing device to automatically allocate executable code to internal and external computing resources of an Elastic computing environment.

Abstract: A method and system for providing information related to content accessed by a user of an electronic device is provided. An implementation involves determining content of interest to the user for access via an electronic device; obtaining metadata for said content; prefetching information related to said metadata; upon detecting availability of further metadata for said content, prefetching additional information related to said further metadata; and upon access to the content by the user via the electronic device, selectively providing the prefetched information to the user.

Abstract: An adaptive, user-centric system and network for controlling power consumption by an appliance is described. The appliance may be any type of powered apparatus, such as A/C units, heaters, computers, lights, kitchen appliances, home media centers, and so on. The power to these appliances is based on an estimated arrival time of the user to the destination where the appliance is located. It may also be based on previous performance data for the particular appliance, that is, given the current conditions (e.g., various environment temperature readings), how long has it taken in the past for the appliance to reach a certain level of operation. The location of the user is determined by a device that has some location-based services and is able to transmit this location/position data in a message to a power-control server. The server applies rules contained in the message to derive an estimated arrival time for the user which is used to power appliances at the user's destination.

Abstract: Techniques for assessing the cost of allocation of execution and affecting the allocation of execution are disclosed. The cost of allocation of execution to or between a first computing device (e.g., a mobile device) and one or more computing resource providers (e.g., one or more Clouds) can be determined during runtime of the executable code. It will be appreciated that a computing system can operate independently of the first computing device and one or more computing resource providers and provide execution allocation cost assessment as a service to the first computing device and/or one or more computing resource providers. Execution allocation cost can be assessed (or determined) based on execution allocation data pertaining to the first computing device and/or one or more computing resource providers. By way of example, power consumption of a mobile device can be used as a factor in determining how to allocate individual components of an application program (e.g.

Abstract: A method and system for facilitating information searching for a user of an electronic device is provided. Facilitating searches involves obtaining information about the user interests, identifying potential data of interest to the user, extracting data related to said data of interest to the user, and collecting the extracted related data for presentation to the user on the device.

Abstract: A method and system for providing access to information via a supplementary device is provided. User access to primary information via a primary device is monitored. Key information related to the primary content is obtained by extracting and analyzing metadata sources for the primary information. Then, supplementary information related to the primary information is obtained based on the key information. The supplementary information is provided for user access via the supplementary device.

Abstract: A computing system creates and stores semantic metadata on content, such as videos, that enables efficient searching of the content. The existing metadata of a video file, for example, is examined and a keyword list is created. The processes used to derive the keyword list may depend on the type and format of the existing metadata. The keywords from the list are compared against external structured knowledge data sources that are topic oriented. Based on these comparisons and the matches found, semantic data, including topic, topic type, and attribute data are inserted into a topic table. This uniform and structured table is stored on the computing system and is efficiently searchable for finding relevant videos and for finding relationships between videos.

Abstract: A method of ranking pieces of media content is provided. Each of the plurality pieces of media content has at least one relationship with at least one user. Each of the users has at least one relationship with at least one of the pieces of media content. Weights are associated with each piece of media content, user and relationship. The weight of a particular piece of media content is determined by recursively calculating and updating the weights of the pieces of media content and the users. The weight associated with a piece of media content or a user is calculated based on the weights of the at least one relationship and the weights of the at least one piece of media content or the at least one user with which the piece of media content or the user has the at least one relationship.

Abstract: Various methods for combining ClipRank and Collaborative Filtering are provided. According to one embodiment, the ClipRank weights associated with pieces of media content are calculated based on the relationships among the pieces of media content and various users. Those pieces having ClipRank weights greater than or equal to a predefined weight threshold are selected from the pieces of media content to obtain selected pieces of media content. Collaborative Filtering is then performed on the selected pieces of media content and the users.

Abstract: System and method for automatically rating the content of video media based on video operations performed on a media device and in reference to a plurality of rating rules are provided. Usage of the media device is continuously monitored and user actions with respect to operating the video media on the media device are automatically logged. Each rating rule includes a device usage pattern with respect to operating videos on the media device and a rating action indicating adjustments to content ratings of the videos based upon characteristics described by the device usage pattern that are inferred from the recorded user inputted video control operations. When the device usage pattern of a rating rule is inferred from one or more user actions operating a piece of video media directly on the media device, the content rating of the piece of video media is adjusted based on the rating rule.

Abstract: A method and system for facilitating access to content on an electronic device is provided. Facilitating access involves maintaining a temporal log of metadata for content accessed by one or more users, segregated based on time slots; searching the log to detect a pattern related to the metadata for one or more times slots; and constructing a temporal usage profile based on the pattern. The temporal usage profile may be used for recommending appropriate content to a user at an appropriate time.

Abstract: An interest value indicative of the interest of a particular entity in one or more items can be determined based on a general interest value (e.g., a group interest/preference value) associated with a plurality of entities (e.g., persons, members of a group) that include that particular entity. The interest value can be determined based on Collaborative Filtering (CF) data and/or individual (or non-collaborative) data. In contrast to the Collaborative Filtering (CF) data which may include data associated with various entities, the individual (or non-collaborative) data typically pertains to one entity, namely, the entity whose interest is to be determined. It will be appreciated that both collaborative and non-collaborative data pertaining to individuals can be considered, thereby allowing for a better estimation of individual interests.