Abstract: A system and a method for an automated intelligent information mining includes receiving product-related queries and respective product-related information from various text sources; extracting multiple key-phrases from the product-related information and received queries; generating two or more layers of contextual relation maps by mapping the extracted key-phrases to two-dimensional maps using a self organizing map, and a technique including a combination of Hessian matrix and Perturbation technique to enhance the learning process and to categorize the extracted key-phrases based on a contextual meaning. Further, the technique includes forming word clusters and constructing corresponding key phrase frequency histograms for each of the generated contextual relation maps.

Abstract: An automated customer support tool. A web server receives one or more queries regarding the product from a customer. A categorizer coupled to the web server categorizes the received queries based on a type of query, to determine whether the answers to the queries can be automatically communicated to the customer. An FAQ extractor extracts one or more corresponding product FAQs from respective product FAQ data bases. A key-word extractor extracts one or more key-words from the received queries and the extracted product FAQs. The key-word extractor further transforms the extracted key words to unique numerical representations. An analyzer coupled to the key-word extractor represents the transformed key-words into respective query and product FAQ vector forms. The analyzer further applies a convolution algorithm to each of the query vector forms, with each of the product FAQ vector forms separately and obtains one or more appropriate answers to the queries.