Abstract: A method for automatic recognition of human activity is provided and includes the steps of decomposing human activity into a plurality of fundamental component attributes needed to perform an activity and defining ontologies of fundamental component attributes from the plurality of the fundamental component attributes identified during the decomposing step for each of a plurality of different targeted activities. The method also includes the steps of converting a data stream captured during a performance of an activity performed by a human into a sequence of fundamental component attributes and classifying the performed activity as one of the plurality of different targeted activities based on a closest match of the sequence of fundamental component attributes obtained during the converting step to at least a part of one of the ontologies of fundamental component attributes defined during the defining step. A system for performing the method is also disclosed.

Abstract: While a user views a primary content item (for example, a movie on a television screen), secondary content items are selected and presented to the user, either on the same screen or on a screen of the user's companion device. To choose selections that are relevant to the user's current interests, the selection process considers information beyond the realm of primary and secondary content. Over time, the selection process learns to make more relevant selections by monitoring selection choices made by other systems.

Abstract: Disclosed are methods in which ontologies are automatically (i.e., with little or no human interaction) constructed from data. The constructed ontology may be provided for use by an application or device. An ontology may be constructed as follows. Firstly, a lexical graph is constructed. Secondly, a concept-detection process is performed on the lexical graph to detect concepts (prominent terms) in the lexical graph. Thirdly, a clustering method is performed on the lexical graph to form groups of concepts. Fourthly, relationships between the concepts are determined based on the interconnections of the concepts in the groups. Lastly, the concepts and detected relationships therebetween are structured using a formal ontology specification.

Abstract: One disclosed method includes receiving correlation instructions related to a plurality of meta-content elements that are associated with a primary content. The primary content may be multimedia content such as, but not limited to, an audiovisual content. The method includes performing a correlation in response to receiving the instructions. The correlation is between the meta-content elements, where the meta-content elements each have an arbitrary granularity defining meta-content segments. The method returns a result based on the correlation. Another disclosed method include receiving a request having correlation instructions related to a plurality of meta-content elements, where the meta-content elements are associated with a primary content. Again, each meta-content element has an arbitrary granularity defining meta-content segments.

Abstract: A disclosed content rights management system defines a content usage policy via a conditional rule set contained in metadata. The conditional rule set is correlated to at least one second content. An access control manager determines, dynamically, access rights conferrable to a user device or a server, based on the content usage policy and user history parameters. The embodiments may confer limited access rights for a first activity by a user device, or by a server, with respect to the protected content and the second content, and block a second activity with respect to the protected content and the second content, in response to determining that the request for the second content, in conjunction with the user history parameters, does not comply with the conditional rule set for the second activity.

Abstract: A method intercepts correlation instructions related to a plurality of meta-content elements associated with a primary content. The primary content or the meta-content elements may have associated privacy rules. At least one meta-content element of the group is selected as having privacy protected information specified in the privacy rules. A set of meta-content items, of meta-content element, are determined that are subject to a correlation restriction based on evaluation of the privacy rules with respect to each meta-content item contained in the meta-content element, and the privacy rules for the set of meta-content items are enforced.

Abstract: Disclosed are a variety of methods and systems for processing access-only user-behavior data and developing and using user-preference models. In one example embodiment, a method for ascribing a score to a first portion of preference data includes establishing a model of user-preference data and receiving the first portion of preference data at a first computerized device and storing that data. The method further includes calculating at least one statistic in relation to the first portion of the preference data by way of a processing device of either the first computerized device or a second computerized device and performing at least one additional operation, by way of either the processing device or another processing device, by which the at least one statistic is evaluated in relation to the model, whereby as a result of being evaluated, the at least one statistic is converted into the score.

Abstract: One automatically accesses (101) a hierarchical task model as corresponds to a particular task to be executed by a given end user to thereby identify one or more presently un-executed agenda elements. One also automatically accesses (102) characterizing information for that given end user. (By one approach, for example, this characterizing information can comprise information regarding the given end user's knowledge.) This information regarding the un-executed agenda elements and the characterizing information are then used (103) to identify a highest hierarchical level within the hierarchical task model as corresponds to the given end user's wherewithal to successfully execute without also needing lower hierarchical level content within the hierarchical task model. The identified highest hierarchical level within the hierarchical task model is then used (104) to generate a presentation to present to the given end user to thereby facilitate execution of the particular task by the given end user.

Abstract: In a hierarchical profile, each node represents at least one feature of behavioral data collected about an entity profiled, with the topmost node selected as the “statistically most informative” feature of the data. A profile can cover numerous domains and be predictively very powerful in each domain. A number of observations can be “aggregated” together into a single datapoint. In use, the structure of the profile is compared against current information associated with the entity to produce a recommendation or prediction. If the profile represents at least some data aggregation, then new observations are folded into the profile based on statistical weights of the aggregations. Because of the way the profile is created and updated, its hierarchical structure maps the collected observations. Therefore, as new observations are incorporated, if the new observations change the profile's structure significantly, then it can be hypothesized that something “interesting” has happened to the entity.

Abstract: In a hierarchical profile, each node represents at least one feature of behavioral data collected about an entity profiled, with the topmost node selected as the “statistically most informative” feature of the data. A profile can cover numerous domains and be predictively very powerful in each domain. A number of observations can be “aggregated” together into a single datapoint. In use, the structure of the profile is compared against current information associated with the entity to produce a recommendation or prediction. If the profile represents at least some data aggregation, then new observations are folded into the profile based on statistical weights of the aggregations. Because of the way the profile is created and updated, its hierarchical structure maps the collected observations. Therefore, as new observations are incorporated, if the new observations change the profile's structure significantly, then it can be hypothesized that something “interesting” has happened to the entity.

Abstract: In a hierarchical profile, each node represents at least one feature of behavioral data collected about an entity profiled, with the topmost node selected as the “statistically most informative” feature of the data. A profile can cover numerous domains and be predictively very powerful in each domain. A number of observations can be “aggregated” together into a single datapoint. In use, the structure of the profile is compared against current information associated with the entity to produce a recommendation or prediction. If the profile represents at least some data aggregation, then new observations are folded into the profile based on statistical weights of the aggregations. Because of the way the profile is created and updated, its hierarchical structure maps the collected observations. Therefore, as new observations are incorporated, if the new observations change the profile's structure significantly, then it can be hypothesized that something “interesting” has happened to the entity.

Abstract: Disclosed are methods in which ontologies are automatically (i.e., with little or no human interaction) constructed from data. The constructed ontology may be provided for use by an application or device. An ontology may be constructed as follows. Firstly, a lexical graph is constructed. Secondly, a concept-detection process is performed on the lexical graph to detect concepts (prominent terms) in the lexical graph. Thirdly, a clustering method is performed on the lexical graph to form groups of concepts. Fourthly, relationships between the concepts are determined based on the interconnections of the concepts in the groups. Lastly, the concepts and detected relationships therebetween are structured using a formal ontology specification.

Abstract: Natural-language words are associated with content. The natural-language words are identified from, for example, metadata and/or the actual content itself. Each word identified for the content is associated with the identified genre of the content (from, for example, its tagged metadata). A database is then maintained having a number of occurrences of each word from the multiple content items for each genre. Once the word/genre database is created, subgenres for a particular program/content can be created by once again using statistics from the words identified for that program to rank the most appropriate genres for the words and produce sets of the highest ranked genres.

Abstract: A method of generating recommendations for content items comprises providing a domain ontology where concepts are characterized by a term vector with terms and associated weights. Associated term sets, each of which comprises a set of terms that characterize a content item, are further provided. A concept set is generated for each associated term set by determining the concepts of the domain ontology that match the terms of the associated term set. In addition, a user profile for a user is provided where the user profile comprises at least some of the concepts of the ontology coupled with preference weights. Recommendations for content items are generated based on the plurality of associated concept sets and the user profile. The invention may allow improved and/or facilitated generation of recommendations from text based characterizing data.

Abstract: A Statechart for management of an interaction is generated automatically from a frame that describes the interaction by extracting a data model and a set of goals from frame and then generating a Statechart from the data model and the set of goals. The data model includes a set of data fields to be completed during the interaction Procedural logic is extracted from the frame and used to annotate the Statechart with, for example, initial and final rules, prompts and grammar. The Statechart may be stored for future use or editing, or used to manage a current interaction.

Abstract: One receives input (101) from a given end user as pertains to a particular task to be executed and automatically accesses (102) a hierarchical task model as corresponds to a particular task to be executed by a given end user to thereby identify one or more presently un-executed agenda elements. One also automatically accesses (103) characterizing information for that given end user. (By one approach, for example, this characterizing information can comprise information regarding the given end user's knowledge.) This information regarding the un-executed agenda elements and the characterizing information are then used (104) to identify a highest hierarchical level within the hierarchical task model as corresponds to the given end user's wherewithal to successfully execute without also needing lower hierarchical level content within the hierarchical task model. This hierarchical level is then used (105) to facilitate correlating the input with a corresponding meaning.

Abstract: One automatically accesses (101) a hierarchical task model as corresponds to a particular task to be executed by a given end user to thereby identify one or more presently un-executed agenda elements. One also automatically accesses (102) characterizing information for that given end user. (By one approach, for example, this characterizing information can comprise information regarding the given end user's knowledge.) This information regarding the un-executed agenda elements and the characterizing information are then used (103) to identify a highest hierarchical level within the hierarchical task model as corresponds to the given end user's wherewithal to successfully execute without also needing lower hierarchical level content within the hierarchical task model. The identified highest hierarchical level within the hierarchical task model is then used (104) to generate a presentation to present to the given end user to thereby facilitate execution of the particular task by the given end user.

Abstract: A method for reducing an effective lateral resistance of a buried layer in an IC includes forming first and second circuit sections in a common substrate, the second circuit section being spaced laterally from the first circuit section. The method further includes forming an isolation buried layer in the substrate under at least a portion of the first circuit section and forming a conductive layer on a surface of the substrate, the conductive layer overlaying at least a portion of the first circuit section. A plurality of conductive plugs are formed in the substrate for operatively connecting the isolation buried layer to the conductive layer, whereby an effective lateral resistance of the isolation buried layer is reduced.

Abstract: A Statechart is generated automatically from a set of goals for completion of a data model. A set of states is generated corresponding to completion states of the data model, an initial pseudo-state and a final state. Transitions are generated from the initial pseudo-state to an empty state of the set of states and between each state and its subset states. Outgoing transitions are generated from any state that satisfies at least one goal of the set of goals and a transition is generated from that state to the final state. The resulting Statechart may be used to manage an interactive dialog to complete a data model having a set of data fields.

Abstract: A method and apparatus for performing a voice search in a mobile communication device is disclosed. The method may include receiving a search query from a user of the mobile communication device, converting speech parts in the search query into linguistic representations, comparing the query linguistic representations to the linguistic representations of all items in the voice search database to find matches, wherein the voice search database has indexed all items that are associated with the device, displaying the matches to the user, receiving the user's selection from the displayed matches, and retrieving and executing the user's selection.