Abstract: In accordance with some implementations, a method for requesting user data is disclosed. The method is performed on a server system having one or more processors and memory storing one or more programs for execution by the one or more processors. The server system establishes a user profile for a user. The user profile includes one or more information items associated with the user. The server system receives a request from the user to access additional services. In response to receiving the user request, the server system requests additional information from the user.

Abstract: In accordance with some implementations, a method for responding to requests submitted through a conversational interface is disclosed. The method is performed on a server system having one or more processors and memory storing one or more programs for execution by the one or more processors. The server system receives a request from a user through a conversational interface. The server system determines, based on the request, whether the system is able to fulfill the request automatically. In accordance with a determination that the system is able to fulfill the request automatically, the server system fulfills the user request with no human interaction. In accordance with a determination that the system is not able to fulfill the request without human intervention, the server system sends the request to a human operator for fulfillment.

Abstract: System, methods and apparatus for analyzing electronic messages and grouping them into threads are described. In addition, the present invention may classify threads based on their relationship to other threads.

Abstract: A computer-implemented system and method is disclosed for retrieving documents using context-dependant probabilistic modeling of words and documents. The present invention uses multiple overlapping vectors to represent each document. Each vector is centered on each of the words in the document and includes the local environment. The vectors are used to build probability models that are used for predictions of related documents and related keywords. The results of the statistical analysis are used for retrieving an indexed document, for extracting features from a document, or for finding a word within a document. The statistical evaluation is also used to evaluate the probability of relation between the key words appearing in the document and building a vocabulary of key words that are generally found together. The results of the analysis are stored in a repository. Searches of the data repository produce a list of related documents and a list of related terms.

Abstract: System, methods and apparatus for analyzing electronic messages and grouping them into threads are described. In addition, the present invention may classify threads based on their relationship to other threads.

Abstract: A computer-implemented system and method is disclosed for retrieving documents using context-dependant probabilistic modeling of words and documents. The present invention uses multiple overlapping vectors to represent each document. Each vector is centered on each of the words in the document, and consists of the local environment, i.e., the words that occur close to this word. The vectors are used to build probability models that are used for predictions. In one aspect of the invention a method of context-dependant probabilistic modeling of documents is provided wherein the text of one or more documents are input into the system, each document including human readable words. Context windows are then created around each word in each document. A statistical evaluation of the characteristics of each window is then generated, where the results of the statistical evaluation are not a function of the order of the appearance of words within each window.

Abstract: A computer-implemented system and method is disclosed for retrieving documents using context-dependant probabilistic modeling of words and documents. The present invention uses multiple overlapping vectors to represent each document. Each vector is centered on each of the words in the document and includes the local environment. The vectors are used to build probability models that are used for predictions of related documents and related keywords. The results of the statistical analysis are used for retrieving an indexed document, for extracting features from a document, or for finding a word within a document. The statistical evaluation is also used to evaluate the probability of relation between the key words appearing in the document and building a vocabulary of key words that are generally found together. The results of the analysis are stored in a repository. Searches of the data repository produce a list of related documents and a list of related terms.

Abstract: A computer-implemented system and method is disclosed for retrieving documents using context-dependant probabilistic modeling of words and documents. The present invention uses multiple overlapping vectors to represent each document. Each vector is centered on each of the words in the document, and consists of the local environment, i.e., the words that occur close to this word. The vectors are used to build probability models that are used for predictions. In one aspect of the invention a method of context-dependant probabilistic modeling of documents is provided wherein the text of one or more documents are input into the system, each document including human readable words. Context windows are then created around each word in each document. A statistical evaluation of the characteristics of each window is then generated, where the results of the statistical evaluation are not a function of the order of the appearance of words within each window.