Automatic labeling of text data

US12197486B2 · kind B2 · utility

Assignee

• MICROSOFT TECHNOLOGY LICENSING, LLC · US

Inventors

• Mohit Sewak · Lucknow, IN
• Ravi Kiran Reddy Poluri · Redmond, US
• William R. Blum · Bellevue, US
• Pak On Chan · Redmond, US
• Weisheng Li · Chongqing, CN
• Sharada Shirish Acharya · Seattle, US
• Christian Rudnick · Seattle, US
• Michael Abraham Betser · Kirkland, US
• Milenko Drinic · Los Angeles, US
• Sihong Liu · Redmond, US

Key dates

• Filing date: Apr 1, 2022
• Grant date: Jan 14, 2025
• Expiry date: Jul 14, 2042

Classification

• Technology area (CPC G): Physics
• CPC primary: G06N3/096
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

Abstract

The technology described herein determines whether a candidate text is in a requested class by using a generative model that may not be trained on the requested class. The present technology may use of a model trained primarily in an unsupervised mode, without requiring a large number of manual user-input examples of a label class. The may produce a semantically rich positive example of label text from a candidate text and label. Likewise, the technology may produce from the candidate text and the label a semantically rich negative example of label text. The labeling service makes use of a generative model to produce a generative result, which estimates the likelihood that the label properly applies to the candidate text. In another aspect, the technology is directed toward a method for obtaining a semantically rich example that is similar to a candidate text.