Patent Document ID: 8825853
Application ID: 13907115

Base Claim:
1. A computer-implemented method for providing automatic, personalized information services to a user u, the method comprising: a) transparently monitoring user interactions with data while the user is engaged in normal use of a computer; b) updating user-specific data files, wherein the user-specific data files comprise the monitored user interactions with the data and a set of documents associated with the user; c) estimating parameters of a learning machine, wherein the parameters define a User Model specific to the user and wherein the parameters are estimated in part from the user-specific data files; d) analyzing a document d to identify properties of the document, the properties including (a) words of the document and (b) at least one additional property, the words of the document having (i) a first word frequency within the document and (ii) a second word frequency within other documents; e) estimating a probability P(u|d) that an unseen document d is of interest to the user u, wherein the probability P(u|d) is estimated by applying the identified properties of the document to the learning machine having the parameters defined by the User Model; and f) using the estimated probability to provide automatic, personalized information services to the user.

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Claim 7:
7. The method of claim 1 wherein the parameters of the learning machine define a user u-dependent function selected from the group consisting of: a) a user topic probability distribution P(t|u) representing interests of the user u in various topics t; b) a user product probability distribution P(p|u) representing interests of the user u in various products p; c) a user product feature probability distribution P(f|u,p) representing interests of the user u in various features f of each of the various products p; d) a web site probability distribution P(s|u) representing interests of the user u in various web sites s; e) a cluster probability distribution P(c(u)|u) representing similarity of the user u to users in various clusters c(u); f) a phrase model probability distribution P(w|u) representing interests of the user u in various phrases w; g) an information theory based measure I(I w ; I u ) representing mutual information between various phrases w and the user u; h) an information theory based measure I(I t ; I u ) representing mutual information between various topics t and the user u; i) an information theory based measure I(I s ; I u ) representing mutual information between various web sites s and the user u; j) an information theory based measure I(I p ; I u ) representing mutual information between various products p and the user u; and k) an information theory based measure I(I f ; I u ) representing mutual information between various features f of each of the various products p and the user u.