Personalized User Model LLP v. Google Inc.

Filing 116

CLAIM CONSTRUCTION OPENING BRIEF filed by Google Inc.. (Attachments: # 1 Appendix A-D)(Moore, David) (Additional attachment(s) added on 11/22/2010: # 1 Appendix A-D) (ntl).

Personalized User Model LLP v. Google Inc.

Doc. 116 Att. 1

Appendix A For the Court's convenience, the asserted claims and claim 32 of the '040 patent are listed below, with the disputed claim language emphasized. U.S. Patent No. 6,981,040 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(uld) that an unseen document d is of interest to the user u, wherein the probability P(uld) 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. 11. The method of claim 1 further comprising estimating a posterior probability P(uld,q) that the document d is of interest to the user u, given a query q submitted by the user. 21. The method of claim 1 further comprising sending to a third party web server user interest information derived from the User Model, whereby the third party web server may customize its interaction with the user. 22. The method of claim 1 wherein the monitored user interactions include a sequence of interaction times. 32. A program storage device accessible by a central computer, tangibly embodying a program of instructions executable by the central computer to perform method steps for providing automatic, personalized information services to a user u, the method steps comprising: a) transparently monitoring user interactions with data while the user is engaged in normal use of a client computer in communication with the central computer;

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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(uld) that an unseen document d is of interest to the user u, wherein the probability P(uld) 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. 34. The program storage devise of claim 32 wherein analyzing the document d provides for the analysis of documents having multiple distinct media types.

U.S. Patent No. 7,685,276 1. A computer-implemented method for providing personalized information services to a user, the method comprising: transparently monitoring user interactions with data while the user is engaged in normal use of a browser program running on the computer; analyzing the monitored data to determine documents of interest to the user; estimating parameters of a user-specific learning machine based at least in part on the documents of interest to the user; receiving a search query from the user; retrieving a plurality of documents based on the search query; for each retrieved document of said plurality of retrieved documents: identifying properties of the 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 using the estimated probabilities for the respective plurality of retrieved documents to present at least a portion of the retrieved documents to the user.

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3. The method of claim 1, wherein transparently monitoring user interactions with data comprises monitoring user interactions with data during multiple different modes of user interaction with network data. 5. The method of claim 1, further comprising analyzing the monitored data to determine documents not of interest to the user, and wherein estimating parameters of a user-specific learning machine further comprises estimating parameters of a user-specific learning machine based at least in part on the documents not of interest to the user. 6. The method of claim 1, wherein monitoring user interactions with data for a document comprises monitoring at least one type of data selected from the group consisting of information about the document, whether the user viewed the document, information about the user's interaction with the document, context information, the user's degree of interest in the document, time spent by the user viewing the document, whether the user followed at least one link contained in the document, and a number of links in the document followed by the user. 7. The method of claim 1, wherein said plurality of retrieved documents correspond to a respective plurality of products. 14. The method of claim 1, wherein identifying properties of the retrieved document comprises determining whether at least one of said documents of interest contains a link to said retrieved document. 21. The method of claim 1, wherein using the estimated probabilities for the respective plurality of retrieved documents to present at least a portion of the retrieved documents to the user comprises presenting to the user at least said portion of the retrieved documents based on the estimated probability that the retrieved document is of interest to the user and the relevance of the retrieved document to the search query. 22. The method of claim 1, wherein identifying properties of the retrieved document comprises identifying properties selected from the properties consisting of a topic associated with the retrieved document, at least one product feature extracted from the retrieved document, an author of the retrieved document, an age of the retrieved document, a list of documents linked to the retrieved document, a number of users who have accessed the retrieved document, and a number of users who have saved the retrieved document in a favorite document list. 23. A computer-implemented method for providing personalized information services to a user, the method comprising transparently monitoring user interactions with data while the user is engaged in normal use of a browser program running on the computer; transparently monitoring user interactions with data while the user is engaged in normal use of a browser program running on the computer; analyzing the monitored data to determine documents of interest to the user; estimating parameters of a user-specific learning machine based at least in part on the documents of interest to the user; 3

collecting a plurality of documents of interest to a user; for each of said plurality of collected documents: identifying properties of the collected document, and applying the identified properties of the collected document to the userspecific learning machine to estimate a probability that the collected document is of interest to the user; using the estimated probabilities for the respective plurality of collected documents to select at least a portion of the collected documents; presenting said selected collected documents to said user. 24. The method of claim 23, wherein presenting said selected collected documents to said user comprises displaying said selected collected documents to said user on a personal web page associated with the user.

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Appendix B ­ Antecedent Basis Terms

Term/Phrase "user u"/"the user" and "the user u" ('040 patent, claims 1, 11, 21, 32) "user"/"the user" ('276 patent, claims 1, 6, 21, 23) "user-specific data files"/"the user-specific data files" ('040 patent, claims 1 and 32) "a document d"/"the document" ('040 patent, claims 1, 11, 32) "a document"/"the document" ('276 patent, claim 6) "a learning machine"/"the learning machine" ('040 patent, claims 1 and 32) "a user-specific learning machine"/"the user-specific learning machine" ('276 patent, claims 1, 5, 23) "a probability P(u|d) that an unseen document d is of interest to the user u"/"the probability P(u|d)"/"the estimated probability" ('040 patent, claims 1 and 32) "parameters of a learning machine"/"the parameters" ('040 patent, claims 1 and 32) "a user model"/"the user

Google's Construction "a user u" and "the user"/"the user u refer to the same user "a user" and "the user" refer to the same user "user-specific data files" and "the user-specific data files" refer to the same files "a document d" and "the document" refer to the same document "a document" and "the document" refer to the same document "a learning machine" and "the learning machine" refer to the same learning machine "a user-specific learning machine" and "the user-specific learning machine" refer to the same user-specific learning machine "a probability P(u|d) that an unseen document d is of interest to the user u," "the probability P(u|d)," and "the estimated probability" refer to the same probability. "parameters of a learning machine" and "the parameters" refer to the same parameters "a user model" and "the user

Plaintiff's Construction no construction necessary

no construction necessary no construction necessary

no construction necessary

no construction necessary

no construction necessary

no construction necessary

no construction necessary

no construction necessary

no construction necessary

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Term/Phrase model" ('040 patent, claims 1, 21, 32) "a search query"/"the search query" ('276 patent, claim 1, 21)

Google's Construction model" refer to the same user model "a search query" and "the search query" refer to the same search query

Plaintiff's Construction

no construction necessary

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Appendix C ­ Antecedent Basis Terms

'040 Patent, claim 1: "User u"/"the user" and "the user u";

"user-specific data files" / "the user-specific data files"

"a document d" / "the document";

"a learning machine" / "the learning machine";

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"a probability P(u|d) that an unseen document d is of interest to the user u"/ "the probability P(u|d)" / "the estimated probability";

"parameters of a learning machine" / "the parameters";

"a user model" / "the user model"

'040 Patent, claim 32: "User u"/"the user" and "the user u";

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"user-specific data files" / "the user-specific data files"

"a document d" / "the document";

"a learning machine" / "the learning machine";

"a probability P(u|d) that an unseen document d is of interest to the user u"/ "the probability P(u|d)" / "the estimated probability";

"parameters of a learning machine" / "the parameters";

"a user model" / "the user model" 9

'276 Patent, claim 1: "user" / "the user"

"a user-specific learning machine" / "the user-specific learning machine"

"a search query" / "the search query"

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'276 Patent, claim 6: "a document" / "the document"

'276 Patent, claim 23: "user" / "the user"

"a user-specific learning machine" / "the user-specific learning machine"

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Appendix D ­ Order of Steps in '276 Patent '276 patent, claim 23:

(annotations added, including lettered paragraphs)

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