Invention Grant
- Patent Title: Self-supervised system generating embeddings representing sequenced activity
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Application No.: US16930279Application Date: 2020-07-15
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Publication No.: US12062059B2Publication Date: 2024-08-13
- Inventor: Mayank Shrivastava , Sagar Goyal , Sahil Bhatnagar , Pin-Jung Chen , Pushpraj Shukla , Arko P. Mukherjee
- Applicant: Microsoft Technology Licensing, LLC
- Applicant Address: US WA Redmond
- Assignee: Microsoft Technology Licensing, LLC.
- Current Assignee: Microsoft Technology Licensing, LLC.
- Current Assignee Address: US WA Redmond
- Agency: Foley IP Law, PLLC
- Priority: IN 2041021829 2020.05.25
- Main IPC: G06Q30/0202
- IPC: G06Q30/0202 ; G06N3/04 ; G06N3/084 ; G06N20/00

Abstract:
The disclosure herein describes a system for generating embeddings representing sequential human activity by self-supervised, deep learning models capable of being utilized by a variety of machine learning prediction models to create predictions and recommendations. An encoder-decoder is provided to create user-specific journeys, including sequenced events, based on human activity data from a plurality of tables, a customer data platform, or other sources. Events are represented by sequential feature vectors. A user-specific embedding representing user activities in relationship to activities of one or more other users is created for each user in a plurality of users. The embeddings are updated in real-time as new activity data is received. The embeddings can be fine-tuned using labeled data to customize the embeddings for a specific predictive model. The embeddings are utilized by predictive models to create product recommendations and predictions, such as customer churn, next steps in a customer journey, etc.
Public/Granted literature
- US20210365965A1 SELF-SUPERVISED SYSTEM GENERATING EMBEDDINGS REPRESENTING SEQUENCED ACTIVITY Public/Granted day:2021-11-25
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