Elance, Inc.

Daniel-Augustin Grad

Kwan-Min Chiu

Nidhi Gupta

Embodiments of the present invention are directed to a matching method that implements a machine learning algorithm to serve one or more matches to an individual and to ensure that an outcome of each match is more likely than not to be successful. A recommendation engine uses the machine learning algorithm to perform predictive analysis to thereby select one or more members of an online community to match or pair with the individual, based on at least structured data, unstructured data or both of the individual and/or each selected member. The recommendation engine is configured to continuously learn from past user behavior, including the individual's, to further improve future matches provided to the individual.

Matching method based on a machine learning algorithm and a system thereof

Embodiments of the present invention are directed to a system for assisting creation of messages, by automatically recommending data to be included therein. The system includes an application configured to accept a set of user inputs of a first classification to determine and provide a group of suggested data relevant to the set of user inputs of the first classification. The group of data is chosen from an evolving collection of data of a second classification. The user is able to select any or all from the group of suggested data to adopt as the user's own and/or to provide additional or different data, which are of the second classification. The user inputs and the user's selection(s) are values collected for use in subsequent determinations.

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Sunnyvale, CA, United States of America

Daniel-Augustin Grad