Abstract: Methods, systems, and apparatus for receiving data identifying an application and a voice command trigger term, validating the received data, inducting the received data to generate an intent that specifies the application, the voice command trigger term, and one or more other voice command trigger terms that are determined based at least on the voice command trigger term, and storing the intent at a contextual intent database, wherein the contextual intent database comprises one or more other intents.

Abstract: Systems, methods and apparatus for invoking actions at a second user device from a first user device. A method includes determining that a first user device has an associated second user device; accessing specification data that specifies a set of user device actions that the second user device is configured to perform; receiving command inputs for the first user device; for each command input, determining whether the command input resolves to one of the user device actions; for each command input not determined to resolve to any of the user device actions, causing the command input to be processed at the first user device; and for each command input determined to resolve one of the user device actions causing the first user device to display in a user interface a dialog by which a user may either accept or deny invoking the user device action at the second user device.

Abstract: A method and system are disclosed for retrieving information through the use of a multi-stage interaction with a client to identify particular knowledge content associated with a knowledge map. The present invention is an application program running on a server accessed via the world-wide web or other data network using standard Internet protocols, a web browser and web server software. In addition to an automated portion, the present invention allows a human dialog designer to model the way the system elicits information, giving a human feel to the dialog and a better customer experience. In operation, users start a dialog by directing their web browser to a designated web page. This web page asks the user some initial questions that are then passed to a dialog engine. The dialog engine then applies its methods and algorithms to a knowledge map, using dialog control information\ and the user's responses to provide feedback to the user.

Abstract: A method and system organize and retrieve information using taxonomies, a document classifier, and an autocontextualizer. Documents (or other knowledge containers) in an organization and retrieval subsystem may be manually or automatically classified into taxonomies. Documents are transformed from clear text into a structured record. Automatically constructed indexes help identify when the structured record is an appropriate response to a query. An automatic term extractor creates a list of terms indicative of the documents' subject matter. A subject matter expert identifies the terms relevant to the taxonomies. A term analysis system assigns the relevant terms to one or more taxonomies, and a suitable algorithm is then used to determine the relatedness between each list of terms and its associated taxonomy. The system then clusters documents for each taxonomy in accordance with the weights ascribed to the terms in the taxonomy's list and a directed acyclic graph (DAG) structure is created.

Abstract: A method and system are disclosed for retrieving information through the use of a multi-stage interaction with a client to identify particular knowledge content associated with a knowledge map.

Abstract: This document describes, among other things, a visual search experience editor for providing a tailored search experience to one or more end-users. In certain examples, the editor provides a What-You-See-Is-What-You-Get (WYSIWIG)-type interface, so that an administrative user can see what the tailored search experience will look like to the end-user. This may include the ability to review live search results or other specialized transaction interface results responsive to the tailored search. This document also describes various techniques of triggering tailored search experiences, as well as techniques for mapping queries to tailored search experiences, such as to generalize a particular tailored search for a particular query to apply to other similar queries.

Abstract: A method is provided for automatically classifying text into categories. In operation, a plurality of tokens or features are manually or automatically associated with each category. A weight is then coupled to each feature, wherein the weight indicates a degree of association between the feature and the category. Next, a document is parsed into a plurality of unique tokens with associated counts, wherein the counts are indicative of the number of times the feature appears in the document. A category score representative of a sum of products of each feature count in the document times the corresponding feature weight in the category for each document is then computed. Next, the category scores are sorted by perspective, and a document is classified into a particular category, provided the category score exceeds a predetermined threshold.

Abstract: This document discusses, among other things, systems, devices, and methods for implementing a content provider using at least one structured data attribute, with an integer, float, string, or date value or the like. One or more such structured data attributes is obtained from a user query, a user attribute, a user selection, a document or other content resource, or an instance within an interactive user-provider dialog. One or more such structured data attributes is auto-mapped to a set of ordered concepts in an at least partially ordered taxonomy of a knowledge map representing a multidimensional organization of such concepts. A structured data attribute and/or an ordered concept is used to control the dialog, constrain a user's search, or order and present search results, either alone, or in combination with nonstructured (e.g., textual) data and/or one or more concepts that is not ordered using a structured data parameter.

Abstract: A method and system organize and retrieve information using taxonomies, a document classifier, and an autocontextualizer. Documents (or other knowledge containers) in an organization and retrieval subsystem may be manually or automatically classified into taxonomies. Documents are transformed from clear text into a structured record. Automatically constructed indexes help identify when the structured record is an appropriate response to a query. An automatic term extractor creates a list of terms indicative of the documents' subject matter. A subject matter expert identifies the terms relevant to the taxonomies. A term analysis system assigns the relevant terns to one or more taxonomies, and a suitable algorithm is then used to determine the relatedness between each list of terms and its associated taxonomy. The system then clusters documents for each taxonomy in accordance with the weights ascribed to the terms in the taxonomy's list and a directed acyclic graph (DAG) structure is created.

Abstract: A method and system organize and retrieve information using taxonomies, a document classifier, and an autocontextualizer. Documents (or other knowledge containers) in an organization and retrieval subsystem may be manually or automatically classified into taxonomies. Documents are transformed from clear text into a structured record. Automatically constructed indexes help identify when the structured record is an appropriate response to a query. An automatic term extractor creates a list of terms indicative of the documents' subject matter. A subject matter expert identifies the terms relevant to the taxonomies. A term analysis system assigns the relevant terms to one or more taxonomies, and a suitable algorithm is then used to determine the relatedness between each list of terms and its associated taxonomy. The system then clusters documents for each taxonomy in accordance with the weights ascribed to the terms in the taxonomy's list and a directed acyclic graph (DAG) structure is created.

Abstract: A customer relationship management (CRM) or other content provider system and method use a set of predetermined terms organized into predetermined groups. A set of search queries is generated using search templates, ordered generally from approximately more specific to approximately more general, including combinations of such groups. A user-provider session generates session context using, among other things, user query language, user response language, a user selection, or a user attribute. Each search template specifies which ones of the groups require at least one corresponding term in the session context to be present in a document for that document to satisfy a search query generated from that search template. In one example, some groups may include words or terms other than the predetermined terms. Such other words or terms may be defined by other characteristics, such as by their frequency of occurrence in the content.

Abstract: This document discusses, among other things, systems, devices, and methods for implementing an efficient and cost-effective automated content provider that effectively steers a user to relevant stored documents. Word or text features are extracted from user query language, and matched to substantially similar concept features. The concepts are organized in primary groups, such as Activities, Objects, Symptoms, and Products groups, which may be implemented as taxonomies. Documents that include the concept feature are tagged to that concept. A list of links or other document indicators tagged to the matched concepts is displayed for the user. Derived groups map relationships between concepts in the same or different primary groups, so that a particular matched concept results in the display of related concepts for restricting or otherwise changing the documents in play that are displayed for the user. This document also describes techniques for ranking the related concepts for display to the user.

Abstract: This document discusses, among other things, systems and methods for assisting a knowledge engineer in associating intelligence with content. An example system classifies a set of documents to concept nodes in a knowledge map that includes multiple taxonomies. A candidate feature extractor automatically extracts features from the documents. The candidate features are displayed with other information on a user-interface (UI). The other displayed information may include information regarding how relevant terms are to various concept nodes; such information may be obtained from a prior classification iteration. From the candidate features and accompanying information and/or personal knowledge, a knowledge engineer selects features and assigns the selected features to concept nodes. The documents are classified using the user-selected features and corresponding concept node assignments.

Abstract: A method and system are disclosed for retrieving information through the use of a multi-stage interaction with a client to identify particular knowledge content associated with a knowledge map. The present invention is an application program running on a server accessed via the world-wide web or other data network using standard Internet protocols, a web browser and web server software. In addition to an automated portion, the present invention allows a human dialog designer to model the way the system elicits information, giving a human feel to the dialog and a better customer experience. In operation, users start a dialog by directing their web browser to a designated web page. This web page asks the user some initial questions that are then passed to a dialog engine. The dialog engine then applies its methods and algorithms to a knowledge map, using dialog control information\ and the user's responses to provide feedback to the user.

Abstract: A method of reporting suspense transaction detail to acquiring banks includes compiling financial transaction information to a database. The information in the database is then routed to a server operable with the World Wide Web. The financial transaction information from the server is then accessed at a client station operable with the World Wide Web.

Abstract: A method is provided for learning local syntactic relationships for use in an example-based information-extraction-pattern learning element of an automated information extraction system. The example-based learning element learns information extraction patterns from user-provided examples of texts paired with events the texts contain; these patterns can then be used by the information extraction system to recognize similar events in subsequent texts. The learning element learns patterns by analyzing each example text/event pair to determine paths of local syntactic relationships between constituents in the text that indicate the event. The learning element employs an incomplete dictionary of local syntactic relationships for this analysis. The present invention learns new local syntactic relationships for text/event pairs that cannot be analyzed using the learning element's initial, incomplete dictionary of relationships.

Abstract: A system is provided for learning extraction patterns (grammar) for use in connection with an information extraction system. The learning system learns extraction patterns from examples of texts and events. The patterns can then be used to recognize similar events in other input texts. The learning system builds new extraction patterns by recognizing local syntactic relationships between the sets of constituents within individual sentences that participate in events to be extracted. The learning system generalizes extraction patterns it has learned previously through simple inductive learning of sets of words that can be treated synonymously within the patterns. Sets of patterns for a sample extraction task perform nearly at the level of a hand-built dictionary of patterns.