Microsoft Technology Licensing, LLC

Linkedin Corporation

Workday, Inc.

Matthew Terence Hayes

Peter Nicholas Skomoroch

Vinodh Jayaram

Avery Randolph Moon

Elliot Shmukler

David Michael Breger

Prachi Gupta

Adam Nash

Abhishek Gupta

Eric Lynn Eilebrecht

Dhanurjay A S Patil

Marcus Jon Jager

Steven Robert Sandke

Anastasios Kasiolas

Marc Andrew Power

Samir Mavji Shah

Michael Conover

Scott Mitchell Blackburn

A system for disambiguation of noisy text includes an interface and processor. The interface is configured to receive a set of words. The processor is configured to determine a token from the set of words, wherein the token includes a subset of the set of words; determine candidates for the token; determine a ranking for the candidates based at least in part on a semantic similarity between the token and each of the candidates; determine whether to select a top ranked candidate or to abstain; and in response to a select determination to select the top ranked candidate, indicate the top ranked candidate is selected.

Fast entity linking in noisy text environments

Disclosed in some examples is a method comprising determining a first set of high ranking skills, the first set containing skills possessed by a member of the social networking service based upon the member's user profile; determining a second set of high ranking skills, the second set containing skills for a second member of the social networking service based on the second member's user profile; determining a third set of high ranking skills, the third set being the intersection between the first and second set of high ranking skills; and suggesting one or more of the skills in the third set of high ranking skills to the member for endorsement of the second member with respect to that skill.

Skills endorsements

In an example, disclosed is a machine automated method of identifying a set of skills. In some examples, the method includes extracting a plurality of skill seed phrases from a plurality of member profiles of a social networking site, creating a plurality of disambiguated skill seed phrases by disambiguating the plurality of skill seed phrases using one or more computer processors, and de-duplicating the plurality of disambiguated skill seed phrases to create a plurality of de-duplicated skill seed phrases.

Skill extraction system

Server-side control of client-side quality-of-service tracking is provided. When a service is being provided to a client device, a server provides aggregation rules to a client device as part of the service. The client device executes instructions in the aggregation rules to collect QoS metrics regarding the service on the client device and to manipulate the QoS metrics to generate aggregated QoS data. The client device reports the aggregated QoS data to a server for storage and analysis.

Rule-based system for client-side quality-of-service tracking and reporting

Server-side control of client-side quality-of-service tracking is provided. When an event occurs at a client, a measurement client collects event parameters and generates aggregated measurement data in accordance with aggregation rules that are provided to the measurement client from a server-side measurement service. The measurement client periodically reports the aggregated measurement data to the measurement service for storage and analysis.

Growing community of inventors

Milpitas, CA, United States of America

Matthew Terence Hayes