Social Awareness Service
OwnerMichael A. Oren, Prasad Siddalingainventors
Life of this patent
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Abstract
Systems and methods are provided for generating recommendations for users of a service. A data processor collects raw user data and provides that data to a learning engine. The learning engine analyzes the data to generate weighted social aspect scores and further to generate a social affinity score using the weighted social aspect scores. The social affinity score is combined with information about people near the user to generate a social context, and the availability of the user is evaluated to determine whether a recommendation should be provided to the user.
Claims20
A system for generating a recommendation for a user, comprising:
- a data processor collecting raw user data about the user;
- and
- a learning engine analyzing the raw user data to generate weighted social aspect scores and further to generate a social affinity score using said weighted social aspect scores, said learning engine combining said social affinity score with information about people near the user to generate a social context, said learning engine further evaluating an availability of the user to determine whether or not to provide said recommendation based on said social context to said user.
A method for generating recommendations for a user comprising:
- gathering raw user data;
- sorting said raw user data into sorted user data in user data categories;
- providing said sorted user data in user data categories to a learning engine;
- using said learning engine to modify said sorted user data using weights for each type of information to create weighted social aspect scores;
- combining said weighted social aspect scores to create a social affinity score;
- combining said social affinity score with an evaluation of people near the user to determine a social context for generating a recommendation;
- determining user availability;
- and
- providing a recommendation to the user if said step of determining user availability shows that the user is available to receive the recommendation.
A method for generating recommendations for a user comprising:
- gathering raw user data;
- sorting said raw user data into sorted user data into user data categories, said user data categories comprising data about proximity, sentiment, action, and awareness;
- providing said sorted user data in said user data categories to a learning engine;
- using said learning engine to modify said sorted user data using weights for each type of information to create weighted social aspect scores comprising a weighted proximity score, a weighted sentiment score, a weighted action score, and a weighted awareness score;
- combining said weighted social aspect scores to create a social affinity score;
- combining said social affinity score with an evaluation of people near the user, along with evaluations of social affinity scores for said people near the user, to determine a social context for generating a recommendation;
- determining user availability;
- and
- providing a recommendation to the user if said step of determining user availability shows that the user is available to receive the recommendation.
Dependencies are inferred from the claim text.