Abstract: A system and method for automatic task prioritization comprises one or more task queues, a monitoring module, and a decision engine. The decision engine receives tasks and assigns a priority code to each task. Each task is inserted into one of the task queues according to its priority code and the priority codes of other tasks which may be present in the task queues. Agents select tasks to perform from the task queue according to priority guidelines established by a system user. The monitoring module may monitor the order of the tasks selected by the agents and each task's priority code. The monitoring module feeds this information back to the decision module. In one embodiment, the decision module is a learning system that updates stored data using feedback from the monitoring module to determine priority of each task.

Abstract: A system and method for electronic communication management comprises a universal data model, a modeling engine, and an adaptive knowledge base. The modeling engine includes a natural language processor and a statistical modeler. A communication is translated from its native format into the universal data model. The modeling engine determines the intent of the communication using the natural language processor and the statistical modeler. A response is generated, either automatically or by an agent. An audit module analyzes each response and provides feedback to the modeling engine and the adaptive knowledge base. The modeling engine uses the feedback to update models in the adaptive knowledge base. The modeling engine supports various application specific modules.

Abstract: A system and method for processing a web-based query is provided. The system comprises a web server for transmitting a web form having a text field box for entering a natural language query, and a language analysis server for extracting concepts from the natural language query and classifying the natural language query into predefined categories via computed match scores based upon the extracted concepts and information contained within an adaptable knowledge base. In various embodiments, the web server selectively transmits either a resource page or a confirmation page to the client, based upon the match scores. The resource page may comprise at least one suggested response corresponding to at least one predefined category. The language analysis server may adapt the knowledge base in accordance with a communicative action received from the client after the resource page is transmitted.

Abstract: A system and method for classifying text includes a pre-processor, a knowledge base, and a statistical engine. The pre-processor identifies concepts in the text and creates a structed text object that contains the concepts. structured text object is then passed to a statistical engine, which applies statistical information provided in nodes of a knowledge base to the structured text object in order to calculate a set of match scores, each match score representing the relevance of the text to an associated one of a plurality of predefined categories. The pre-processor may be implemented in the form of an interpreter which selects and executes a script that includes language- and scenario-specific instructions for performing linguistic and semantic analysis of the text.

Abstract: A software tool for creating, training and testing a knowledge base of a computerized customer relationship management system is disclosed. The software tool includes corpus editing processes for displaying and editing text-based corpus items, and assigning selected categories to individual corpus items. Knowledge base construction processes construct a knowledge base by analyzing a first subset of the corpus items, and testing processes test the knowledge base on a second subset of the corpus items. Reporting processes generate reports containing indicia representative of the testing results, which may be utilized to edit the corpus items and retrain the knowledge base so as to improve performance.

Abstract: A system and method for automatic task prioritization comprises one or more task queues, a monitoring module, and a decision engine. The decision engine receives tasks and assigns a priority code to each task. Each task is inserted into one of the task queues according to its priority code and the priority codes of other tasks which may be present in the task queues. Agents select tasks to perform from the task queue according to priority guidelines established by a system user. The monitoring module may monitor the order of the tasks selected by the agents and each task's priority code. The monitoring module feeds this information back to the decision module. In one embodiment, the decision module is a learning system that updates stored data using feedback from the monitoring module to determine priority of each task.