Patent application number | Description | Published |
20080208839 | Method and system for providing information using a supplementary device - 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. | 08-28-2008 |
20090025054 | Method and system for access to content in a content space - A method and system for access to content is provided. Providing access to content involves constructing a smart channel that facilitates adaptive content selection, identifying known content matching the smart channel content selection, performing a smart channel query to discover new content that is related to the known content, and prefetching newly discovered relevant content from a content space. The content includes video content for display on a display such as a TV. | 01-22-2009 |
20090077195 | Method and system for accessing audio/visual content - A method and system for accessing audio/visual content is provided. Such access involves initiating a download of selected content over a communication link, for display on a display device, and monitoring the download status to detect download conditions that may lead to a delay in the display of the selected content. Then, upon detecting download conditions that may lead to delay in display of the selected content, displaying alternate available content on the display device. | 03-19-2009 |
20090132519 | CLIPRANK: RANKING MEDIA CONTENT USING THEIR RELATIONSHIPS WITH END USERS - A method of ranking a plurality pieces of media content is provided. Each of the plurality pieces of media content has at least one relationship with at least one of a plurality of users. Each of the plurality of users has at least one relationship with at least one of the plurality pieces of media content. Each of the plurality pieces of media content is associated with a weight, each of the plurality of users is associated with a weight, and each relationship is associated with a weight. For each of the plurality pieces of media content and each of the plurality of users, recursively calculating and updating the weight associated with the piece of media content or the user until a difference between the weights associated with the plurality pieces of media content and the plurality of users calculated during a current iteration and the weights associated with the plurality pieces of media content and the plurality of users calculated during a previous iteration is less than a predefined threshold. 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. Ranking the plurality pieces of media content according to their respectively associated weights. | 05-21-2009 |
20090132520 | COMBINATION OF COLLABORATIVE FILTERING AND CLIPRANK FOR PERSONALIZED MEDIA CONTENT RECOMMENDATION - Various methods for combining ClipRank and Collaborative Filtering are provided. According to one embodiment, the ClipRank weights associated with a plurality of pieces of media content are calculated based on the relationships among the plurality of pieces of media content and a plurality of users. Those pieces having ClipRank weights greater than or equal to a predefined weight threshold are selected from the plurality of pieces of media content to obtain a plurality of selected pieces of media content. Collaborative Filtering is then performed on the plurality of selected pieces of media content and the plurality of users. According to another embodiment, Collaborative Filtering on a plurality of pieces of media content and a plurality of users is performed for one of the plurality of users. Personalized ClipRank weights associated with the plurality of pieces of media content is calculated for the user based on Collaborative Filtering ratings obtained for the plurality of pieces of media content for the user. | 05-21-2009 |
20090132527 | PERSONALIZED VIDEO CHANNELS ON SOCIAL NETWORKS - In a first embodiment of the present invention, a method for automatically creating a list of media items for a user is provided. Information relating to the user is obtained from a social networking site. One or more keywords are then extracted from the information. The one or more keywords are then sent to a media item search engine. A list of media items relating to the keywords are received from the media item search engine. | 05-21-2009 |
20090133048 | SYSTEM AND METHOD FOR AUTOMATICALLY RATING VIDEO CONTENT - 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. | 05-21-2009 |
20090133059 | PERSONALIZED VIDEO SYSTEM - 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. | 05-21-2009 |
20090158161 | COLLABORATIVE SEARCH IN VIRTUAL WORLDS - In a first embodiment, first information regarding an object in a virtual world is received from a virtual world client. The first information is stored in a database. Second information regarding the object is received from a second virtual world client. The second information is then also stored in the database. In a second embodiment, a request is received from a virtual world client. Then a database is searched based on the request and based on tags corresponding to virtual world objects, wherein the tags are stored in the database, wherein the searching returns one or more tagged virtual world objects. Then the one or more tagged virtual world objects are sent to the virtual world client. | 06-18-2009 |
20090292672 | SYSTEM AND METHOD FOR FACILITATING ACCESS TO AUDO/VISUAL CONTENT ON AN ELECTRONIC DEVICE - 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. | 11-26-2009 |
20090307296 | METHOD FOR ANONYMOUS COLLABORATIVE FILTERING USING MATRIX FACTORIZATION - System and method for performing Collaborative Filtering while preserving complete user anonymity are provided. Each of a group of client devices sends a rating vector anonymously to a server. The cells in each rating vector correspond to a set of items, and selected cells have ratings provided by the user associated with the corresponding client device for the corresponding items. The server aggregates all the rating vectors into a rating matrix, and factorizes the rating matrix into a user feature matrix and an item feature matrix through approximation, such that the rating matrix equals the product of the user feature matrix and the item feature matrix. The item feature matrix is sent to the client devices. Each of the client devices calculates its own user feature vector based on its rating vector and the item feature matrix, and provides personalized recommendations on selected items based on the client's user feature vector and the item feature matrix. | 12-10-2009 |
20100005084 | METHOD AND SYSTEM FOR PREFETCHING INTERNET CONTENT FOR VIDEO RECORDERS - 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, pre-fetching 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. | 01-07-2010 |
20100057694 | SEMANTIC METADATA CREATION FOR VIDEOS - 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. | 03-04-2010 |
20100131590 | EXTENDING THE CAPABILITY OF COMPUTING DEVICES BY USING DYNAMICALLY SCALABLE EXTERNAL RESOURCES - 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. | 05-27-2010 |
20100131592 | EXECUTION ALLOCATION COST ASSESSMENT FOR COMPUTING SYSTEMS AND ENVIRONMENTS INCLUDING ELASTIC COMPUTING SYSTEMS AND ENVIRONMENTS - 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., weblets) between a mobile phone and a Cloud. The invention is especially suited for Elastic computing environment and systems. 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. | 05-27-2010 |
20100161149 | ADAPTIVE AND USER LOCATION-BASED POWER SAVING SYSTEM - 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. | 06-24-2010 |
20100205041 | DETERMINING THE INTEREST OF INDIVIDUAL ENTITIES BASED ON A GENERAL INTEREST - 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. The interest of a particular entity can be determined, for example, by considering the difference between a predicted CF interest value (determined based on CF data) and a group interest value and/or by considering the difference between a predicted individual interest value (determined based on non-collaborative data) and the group interest value. | 08-12-2010 |
20110004574 | EXECUTION ALLOCATION COST ASSESSMENT FOR COMPUTING SYSTEMS AND ENVIRONMENTS INCLUDING ELASTIC COMPUTING SYSTEMS AND ENVIRONMENTS - 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. | 01-06-2011 |
20110004916 | SECURELY USING SERVICE PROVIDERS IN ELASTIC COMPUTING SYSTEMS AND ENVIRONMENTS - 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 cancellation. | 01-06-2011 |
20110282940 | CLOUD-BASED WEB WORKERS AND STORAGES - In accordance with one aspect of the invention, web workers and local storages can be extended to a cloud-based environment. This allows web workers to be executed on any of a number of different cloud platforms located in a cloud, leveraging available resources to provide a quicker and more efficient processing environment for the various web workers. The present invention also provides these functionalities in a way that is transparent to not just the user, but also to the web page developer as well, eliminating the need for the web page developer to be aware of the cloud-based environment and design the web page for use therewith. | 11-17-2011 |
20120222059 | METHOD AND SYSTEM FOR PROVIDING INFORMATION USING A SUPPLEMENTARY DEVICE - 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. | 08-30-2012 |
20120246172 | METHOD AND SYSTEM FOR FACILITATING INFORMATION SEARCHING ON ELECTRONIC DEVICES - 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. | 09-27-2012 |
20120265884 | EXECUTION ALLOCATION COST ASSESSMENT FOR COMPUTING SYSTEMS AND ENVIRONMENTS INCLUDING ELASTIC COMPUTING SYSTEMS AND ENVIRONMENTS - 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. | 10-18-2012 |
20130191722 | HARDWARE ACCELERATION OF WEB APPLICATIONS - In a first embodiment of the present invention, a method for enabling hardware acceleration of web applications is provided, comprising: parsing a web page using a scripting engine, wherein the web page necessitates running a web application; accessing one or more Application Program Interfaces (APIs) that provide parallelization, and distribute tasks of the web application among multiple cores of a multi-core central processing unit (CPU) or graphical processing unit (GPU), wherein the accessing uses a compute context class that, when instantiated, creates a compute context object that acts as a bridge between the scripting engine and the one or more APIs; and creating one or more kernels to operate on the multiple cores. | 07-25-2013 |
20130304791 | BROWSER ENGINE INTERFACING FOR ACCELERATED PHYSICS ENGINE - 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. | 11-14-2013 |
20140074763 | EXECUTION ALLOCATION COST ASSESSMENT FOR COMPUTING SYSTEMS AND ENVIRONMENTS INCLUDING ELASTIC COMPUTING SYSTEMS AND ENVIRONMENTS - 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. | 03-13-2014 |