Personalized User Model LLP v. Google Inc.

Filing 119

CLAIM CONSTRUCTION OPENING BRIEF filed by Personalized User Model LLP. (Attachments: # 1 Exhibit A)(Tigan, Jeremy)

Personalized User Model LLP v. Google Inc.

Doc. 119 Att. 1

EXHIBIT A

Dockets.Justia.com

Claim 1 of '040 Patent 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; 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.

Claim 1 of '276 Patent 1. A computer-implemented method for providing personalized information services to a user, the method comprising: [a] transparently monitoring user interactions with data while the user is engaged in normal use of a browser program running on the computer; [b] analyzing the monitored data to determine documents of interest to the user;

[c] estimating parameters of a user-specific learning machine based at least in part on the documents of interest to the user; [d] receiving a search query from the user; [e] retrieving a plurality of documents based on the search query;

[f] for each retrieved document of said plurality of retrieved document, and applying the identified properties of the retrieved document to the user-specific learning machine to estimate a probability that the retrieved document is of interest to the user; and [g] using the estimated probabilities for the respective plurality of retrieved documents to present at least a portion of the retrieved documents to the user.