Abstract: Methods and computer-readable media for associating words or groups of words distilled from content, such as reported speech or an attitude report, of a document to form semantic relationships collectively used to generate a semantic representation of the content are provided. Semantic representations may include elements identified or parsed from a text portion of the content, the elements of which may be associated with other elements that share a semantic relationship, such as an agent, location, or topic relationship. Relationships may also be developed by associating one element that is in relation to, or is about, another element, thereby allowing for rapid and effective comparison of associations found in a semantic representation with associations derived from queries. The semantic relationships may be determined based on semantic information, such as potential meanings and grammatical functions of each element within the text portion of the content.

Abstract: Methods and computer-readable media for associating words or groups of words distilled from content, such as reported speech or an attitude report, of a document to form semantic relationships collectively used to generate a semantic representation of the content are provided. Semantic representations may include elements identified or parsed from a text portion of the content, the elements of which may be associated with other elements that share a semantic relationship, such as an agent, location, or topic relationship. Relationships may also be developed by associating one element that is in relation to, or is about, another element, thereby allowing for rapid and effective comparison of associations found in a semantic representation with associations derived from queries. The semantic relationships may be determined based on semantic information, such as potential meanings and grammatical functions of each element within the text portion of the content.

Abstract: Methods and computer-readable media for associating words or groups of words distilled from content, such as reported speech or an attitude report, of a document to form semantic relationships collectively used to generate a semantic representation of the content are provided. Semantic representations may include elements identified or parsed from a text portion of the content, the elements of which may be associated with other elements that share a semantic relationship, such as an agent, location, or topic relationship. Relationships may also be developed by associating one element that is in relation to, or is about, another element, thereby allowing for rapid and effective comparison of associations found in a semantic representation with associations derived from queries. The semantic relationships may be determined based on semantic information, such as potential meanings and grammatical functions of each element within the text portion of the content.

Abstract: Computer-readable media, computerized methods, and computer systems for conducting semantic processes to present search results that include highlighted regions which are relevant to a conceptual meaning of a query are provided. Initially, content of document(s) is accessed and semantic representations are derived by distilling linguistic representations from the content. These semantic representations may be stored at a semantic index. Also, a proposition is derived from the query by parsing search terms of the query, and distilling the proposition from the search terms. Typically, the proposition is a logical representation of the conceptual meaning of the query. The proposition is compared against the semantic representations at the semantic index to identify a matching set. Regions of the content within the document, from which the matching set of semantic representations are derived, are targeted.

Abstract: Methods and computer-readable media for associating words or groups of words distilled from content, such as reported speech or an attitude report, of a document to form semantic relationships collectively used to generate a semantic representation of the content are provided. Semantic representations may include elements identified or parsed from a text portion of the content, the elements of which may be associated with other elements that share a semantic relationship, such as an agent, location, or topic relationship. Relationships may also be developed by associating one element that is in relation to, or is about, another element, thereby allowing for rapid and effective comparison of associations found in a semantic representation with associations derived from queries. The semantic relationships may be determined based on semantic information, such as potential meanings and grammatical functions of each element within the text portion of the content.

Abstract: Computer-readable media, computerized methods, and computer systems for conducting semantic processes to present search results that include highlighted regions which are relevant to a conceptual meaning of a query are provided. Initially, content of document(s) is accessed and semantic representations are derived by distilling linguistic representations from the content. These semantic representations may be stored at a semantic index. Also, a proposition is derived from the query by parsing search terms of the query, and distilling the proposition from the search terms. Typically, the proposition is a logical representation of the conceptual meaning of the query. The proposition is compared against the semantic representations at the semantic index to identify a matching set. Regions of the content within the document, from which the matching set of semantic representations are derived, are targeted.

Abstract: Personality traits displayed on a workbench are grouped into a character profile that is utilized in determining the personality and/or specific actions of a synthetic character. The personality traits are selectable by an operator from graduated dials. The personality traits may also be displayed in the form of selectable attributes or trait indicative behaviors that, when selected, are mapped into predetermined amounts of each trait in the character profile. The character profile is utilized to compute activation levels for primary behaviors, which are selected to influence or direct behavior of the synthetic character. Secondary behaviors are also selected, but discarded if conflicting with the selected primary behaviors. When behaviors are selected, the corresponding behaviors are translated into classes of ‘personality effectors’ that modulate the synthetic character's behavioral expression to reflect the desired set of traits.

Abstract: Personality traits displayed on a workbench are grouped into a character profile that is utilized in determining the personality and/or specific actions of a synthetic character. The personality traits are selectable by an operator from graduated dials. The personality traits may also be displayed in the form of selectable attributes or trait indicative behaviors that, when selected, are mapped into predetermined amounts of each trait in the character profile. The character profile is utilized to compute activation levels for primary behaviors, which are selected to influence or direct behavior of the synthetic character. Secondary behaviors are also selected, but discarded if conflicting with the selected primary behaviors. When behaviors are selected, the corresponding behaviors are translated into classes of ‘personality effectors’ that modulate the synthetic character's behavioral expression to reflect the desired set of traits.

Abstract: Deliberative and reactive processing are combined to process multi-modal inputs and direct movements and speech of a synthetic character that operates as an interface between a user and a piece of equipment. The synthetic character is constructed as an ally, working with and helping the user learn and operate the equipment. The synthetic character interacts with both a virtual space where the character is displayed, and a physical space (domain) that includes the user. Real-time reactive processing provides lifelike and engaging responses to user queries and conversations. Deliberative processing provides responses to inputs that require more processing time (deep linguistic processing, for example). Knowledge bases are maintained for both dynamic (discourse model, for example) and static (e.g., knowledge about the domain or discourse plans) information types.

Abstract: An architecture including a speech manager that identifies input, input content, and location of the input (speech, for example), a action scheduler, a dialog manager, and an animation system provides reduced message traffic and streamlined processing for support of animated characters (conversational characters, for example). Speech recognition is provided along with location information to the action scheduler for determination of appropriate expressions for interactive behavior (looking, turn taking, etc.), and speech (or input) content is provided to a dialog manager to determine a substantive response (including speech or other content related responses) and any facial expressions or gestures related to content, but not containing content, are identified, and placed in a communication to the animation system.