Google Inc.

Motorola Mobility LLC

Arris Technology, Inc.

Guohua Hao

Paul C Davis

Jianguo Li

Mir Farooq Ali

Dale William Russell

James R Talley

Haifeng Li

Jianguo Liu

User-preference datapoints are collected. At least some of these datapoints are associated with user-preference information about an item (e.g., a movie), and some of these datapoints are associated with user-preference information about an attribute (e.g., a movie genre or an actor). A profile of the user is created based, at least in part, on these datapoints. When a new datapoint is collected, the new datapoint is assigned a user-preference 'score.' If, for example, the new datapoint does not come with an explicit user-preference rating, then the score is based on related item and attribute datapoints already in the profile. Depending upon the relationship of the new datapoint to the already existing datapoints, a confidence value is assigned to the user-preference score. The profile is then updated with the new datapoint along with its assigned score and confidence rating. The user profile can be used in performing any number of actions.

Inferring attribute and item preferences

In a hierarchical profile, each node represents at least one feature of behavioral data collected about an entity profiled, with the topmost node selected as the 'statistically most informative' feature of the data. A profile can cover numerous domains and be predictively very powerful in each domain. A number of observations can be 'aggregated' together into a single datapoint. In use, the structure of the profile is compared against current information associated with the entity to produce a recommendation or prediction. If the profile represents at least some data aggregation, then new observations are folded into the profile based on statistical weights of the aggregations. Because of the way the profile is created and updated, its hierarchical structure maps the collected observations. Therefore, as new observations are incorporated, if the new observations change the profile's structure significantly, then it can be hypothesized that something &#x201c;interesting&#x201d; has happened to the entity.

Hierarchical behavioral profile

Disclosed are methods and apparatus for processing linguistic expressions (e.g., opinionated text documents). The linguistic expressions are processed by, firstly, detecting topics of interest discussed in the linguistic expressions. The sentiment, or sentiments, of an originator with respect to each of the topics detected in the linguistic expressions is then assessed. The originators are then grouped (or clustered) into one or more groups based on the similarities between the originators' respective sets of detected topics and corresponding sentiments. Semantic information is then associated with a given group. Finally, for a given member of a given group, a profile is created or updated. This profile comprises attributes that may be based on a degree of membership of the given member to the given group and the semantic information associated with the given group.

Communication processing

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Vernon Hills, IL, United States of America

Guohua Hao