Machine learning techniques to predict geographic talent flow

US11238352B2 · kind B2 · utility

Assignee

• MICROSOFT TECHNOLOGY LICENSING, LLC · US

Inventors

• Chi-Yi Kuan · Fremont, US
• Shen Huang · San Jose, US
• Yu Wang · Redmond, US
• Yongzheng Zhang · San Jose, US
• Paul Hyunwoo Ko · San Francisco, US
• Shady Elasra · San Francisco, US
• Fanbin Bu · Fremont, US

Key dates

• Filing date: Mar 30, 2018
• Grant date: Feb 1, 2022
• Expiry date: Dec 3, 2040

Classification

• Technology area (CPC H): Electricity
• CPC primary: H04L67/306
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

Abstract

Techniques are provided for predicting talent flow to and/or from a geographical region. In one technique, multiple entity profiles are stored and analyzed to generate training data that is labeled indicating whether a corresponding entity has moved to or moved from a region. A machine-learned prediction model is generated or trained based on the training data. Using the machine-learned prediction model, a prediction is made whether, for each entity corresponding to another entity profile, that entity will move to or move from a particular geographic region. Based on multiple predictions, a number of entities that are predicted to move to or move from the particular geographic region is determined. Talent flow data that is based on the number of entities is presented on a computer display.