Machine learned models for contextual editing of social networking profiles

US10678997B2 · kind B2 · utility

Assignee

• MICROSOFT TECHNOLOGY LICENSING, LLC · US

Inventors

• Karan Ahuja · New Delhi, IN
• Befekadu Ayenew Ejigou · San Leandro, US
• Ningfeng Liang · Cupertino, US
• Lokesh P. Bajaj · Fremont, US
• Wei Wang · Redmond, US
• Paul Fletcher · Edinburgh, GB
• Wei Lu · San Jose, US
• Shaunak Chatterjee · Sunnyvale, US
• Souvik Ghosh · Santa Clara, US
• Yang Li · Chapel Hill, US
• Wei Deng · Taipei, TW
• Qiang Wu · 流花街道, CN

Key dates

• Filing date: Nov 29, 2017
• Grant date: Jun 9, 2020
• Expiry date: Mar 25, 2038

Classification

• Technology area (CPC H): Electricity
• CPC primary: H04L67/535
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

Abstract

In an example, first and second machine learned models corresponding to a particular context of a social networking service are obtained, the first machine learned model trained via a first machine learning algorithm to output an indication of importance of a social networking profile field to obtaining results in the particular context, and the second machine learned model trained via a second machine learning algorithm to output a propensity of the user to edit a social networking profile field if requested. One or more missing fields in a social networking profile for the user are identified. For each of one or more of the one or more missing fields, the field and an identification of the user are passed through the first and second machine learned models, and outputs of the first and second machine learned models are combined to identify one or more top missing profile fields.