Consona Crm Inc.

Kanisa, Inc.

Mark A Angel

David Kay

Scott B Huffman

Max Copperman

Raya Fratkina

Jeffrey H Rudy

Robert Stern

Monica Anderson

Catherine Wormington

Denis M Lynch

Shafi Mohammed

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. The feedback may include follow-up questions, relevant documents, and instructions to the user (e.g., instructions to contact a human customer service representative). This dialog engine response is rendered as a web page and returned to the user's web browser. The user can then respond further to the follow-up questions he or she is presented, and the cycle repeats. The invention can be implemented so that it can interact with customers through a wide variety of communication channels including the Internet, wireless devices (e.g., telephone, pager, etc.), handheld devices such as a Personal Data Assistant (PDA), email, and via a telephone where the automated system is delivered using an interactive voice response (IVR) and/or speech-recognition system.

System and method for providing an intelligent multi-step dialog with a user

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.

System and method for implementing a knowledge management system

In an information retrieval application, a system and method for detecting content holes. A content body is parsed into a plurality of concepts nodes, including a first concept node. A percentage of successful service interactions is determined as a function of concept node and, if the percentage of successful service interactions at the first concept node is below a predefined threshold, a content hole is flagged.

System and method for measuring the quality of information retrieval

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.

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.

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Cupertino, CA, United States of America

Mark A Angel