Microsoft Technology Licensing, LLC

Christian Rudnick

Ravi Kiran Reddy Poluri

Sharada Shirish Acharya

Mainak Sen

Yi Luo

Weigsheng Li

Mohit Sewak

Weisheng Li

Milenko Drinic

William Blum

Michael Abraham Betser

Pak On Chan

Sihong Liu

Emails or other communications are labeled with a category label such as 'spam' or “good” without using confidential or Personally Identifiable Information (PII). The category label is based on features of the emails such as metadata that do not contain PII. Graphs of inferred relationships between email features and category labels are used to assign labels to emails and to features of the emails. The labeled emails are used as a training dataset for training a machine learning model (“MLM”). The MLM identifies unwanted emails such as spam, bulk email, phishing email, and emails that contain malware.

Privacy-preserving labeling and classification of email

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.

Automatic labeling of text data

Emails or other communications are labeled with a category label such as 'spam' or 'good' without using confidential or Personally Identifiable Information (PII). The category label is based on features of the emails such as metadata that do not contain PII. Graphs of inferred relationships between email features and category labels are used to assign labels to emails and to features of the emails. The labeled emails are used as a training dataset for training a machine learning model (&#x201c;MLM&#x201d;). The MLM model identifies unwanted emails such as spam, bulk email, phishing email, and emails that contain malware.

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Seattle, WA, United States of America

Christian Rudnick