Automatic, personalized online information and product services
DC CAFC
First Claim
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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 include documents of interest to the user u and documents that are not of interest to the user u;
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 distinct treatment of the documents of interest and the documents that are not of interest;
d) analyzing a document d having multiple distinct media types 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.
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Abstract
A method for providing automatic, personalized information services to a computer user includes the following steps: transparently monitoring user interactions with data during normal use of the computer; updating user-specific data files including a set of user-related documents; estimating parameters of a learning machine that define a User Model specific to the user, using the user-specific data files; analyzing a document to identify its properties; estimating the probability that the user is interested in the document by applying the document properties to the parameters of the User Model; and providing personalized services based on the estimated probability. Personalized services include personalized searches that return only documents of interest to the user, personalized crawling for maintaining an index of documents of interest to the user; personalized navigation that recommends interesting documents that are hyperlinked to documents currently being viewed; and personalized news, in which a third party server customized its interaction with the user. The User Model includes continually-updated measures of user interest in words or phrases, web sites, topics, products, and product features. The measures are updated based on both positive examples, such as documents the user bookmarks, and negative examples, such as search results that the user does not follow. Users are clustered into groups of similar users by calculating the distance between User Models.
81 Citations
11 Claims
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1. A computer-implemented method for providing automatic, personalized information services to a user u, the method comprising:
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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 include documents of interest to the user u and documents that are not of interest to the user u; 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 distinct treatment of the documents of interest and the documents that are not of interest; d) analyzing a document d having multiple distinct media types 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. - View Dependent Claims (2, 3)
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4. A computer-implemented method for providing automatic, personalized information services to a user u, the method comprising:
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a) transparently monitoring multiple distinct modes of user interaction with network data while the user is engaged in normal use of a computer, the multiple distinct modes of user interaction selected from the group consisting of a network searching mode, a network navigation mode, a network browsing mode, an email reading mode, an email writing mode, a document writing mode, a viewing “
pushed”
information mode, a finding expert advice mode, and a product purchasing mode;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.
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5. A computer-implemented method for providing automatic, personalized information services to a user u, the method comprising:
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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, and in part from product parameters characterizing a product p; 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 the product parameters including an estimate of a probability P(p|d) that unseen document d refers to product p; and f) using the estimated probability to provide automatic, personalized information services to the user. - View Dependent Claims (6, 7)
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8. A computer-implemented method for providing automatic, personalized information services to a user u, the method comprising:
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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 and the parameters further define a user product probability distribution that P(p|u) representing interests of the user u in various products p; and
a user product feature probability distribution P(f|u,p) representing interests of the user u in various products p;d) analyzing a document d to identify properties of the document; e) estimating a probability P(u|d) that the 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 defined by an estimated probability P(u|d, product described=p) that a document d that describes a product p is of interest to the user u, wherein the probability is estimated in part from the user product proability distribution and the user product feature probability distribution; and f) using the estimated probability to provide automatic, personalized information services to the user. - View Dependent Claims (9)
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10. A computer-implemented method for providing automatic, personalized information services to a user u, the method comprising:
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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) crawling network documents, wherein the crawling comprises parsing crawled documents for links, calculating probable user interest in the parsed links using the learningmachine, and preferentially following links likely to be of interest to the user u; e) analyzing a document d to identify properties of the document; f) estimating a probability P(u|d) that the 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 g) using the estimated probability to provide automatic, personalized information services to the user. - View Dependent Claims (11)
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Specification
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Litigation Data
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Current AssigneePersonalized User Model LLP
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Original AssigneeUTOPY, Inc. (Genesys Cloud Services Incorporated)
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InventorsKonig, Yochai, Twersky, Roy, Berthold, Michael R.
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Primary Examiner(s)Barot; Bharat
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Application NumberUS11/316,785Publication NumberTime in Patent Office754 DaysField of Search709201-203, 709223-228, 707 1- 3, 707 7- 10, 715/500, 715/736, 715513-514US Class Current709/224CPC Class CodesG06F 16/9535 Search customisation based ...G06N 20/00 Machine learningH04L 67/306 User profilesH04L 67/53 using third party service p...H04L 67/55 Push-based network servicesH04L 67/75 Indicating network or usage...H04N 21/44224 Monitoring of user activity...
