Metabyte, Inc.

Saiba Singh

Manu Mehta

Lynn Randolph Slater, Jr

Nitin Mehta

Anjali Dayal

Yiu Wan Lau

Aradhana Mehta

An experience rating and skill discovery system (ERSDS) and a method for determining credibility of experience ratings provided by one or more reviewers and discovering skills of opportunity seekers based on a relationship between the reviewers and the opportunity seekers are provided. A skill profile module of the ERSDS reads profile data from a user profile list and generates a skills profile list. An invitation module transmits invitations to reviewer devices for providing the experience ratings. An aggregation module aggregates the experience ratings and generates an aggregated experience credibility measure and an aggregated experience plausibility measure, and further an aggregated skill amount measure and an aggregated skill credibility measure corresponding to the experience summaries in an experience summary list and the skills of the opportunity seeker respectively, using a relationship depth computed from the relationship data and the experience ratings received by a user association module and a rating module respectively.

Relationship and credibility based experience rating and skill discovery system

A method and a multi-directional attribute matching system (MAMS) determining a degree of match between item profiles with respective attributes possessed (AP) and respective attributes needed (AN) of varying credibility and varying importance are provided. The MAMS receives an attributes possessed list including the AP and an attributes needed list including the AN, from a predefined attribute list database. The AP and the AN have parent attributes or hierarchical sub-attributes. The MAMS merges multiple occurrences of AP and inputs to the attributes possessed list. The MAMS generates a matched attribute list for a side by matching the AP in an item profile with the AN in an item profile of another item by matching the AP with the AN. The MAMS computes a raw score and an attribute match score for each side using match formulae and computes a final score of the multi-directional match between the item profiles.

Matching of multiple sides with multiple levels of mixed credibility attributes

A method of determining a Trac score representing an individual's future employment behavior using past and present employment behavior that are unrelated to skills and job match within a job and across multiple jobs of an individual when combined with public and available external records. A method employing a Trac score calculation system comprises at least one processor to execute computer program instructions for obtaining one or more ratings from one or more users through one or more user devices connected to the Trac score calculation system that assigns the credibility to the ratings. A combination of the Trac sub-score obtained by accumulating the ratings weighted by estimated credibility of each rating, and Adjustment sub-score obtained by processing available external records in public and private forms, creates a Trac score which is a numerical and reflects an aggregation of available data weighted by what is believed to affect the employment behavior.

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Pleasanton, CA, United States of America

Saiba Singh