Invention Grant
- Patent Title: Deep learning using activity graph to detect abusive user activity in online networks
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Application No.: US17705146Application Date: 2022-03-25
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Publication No.: US11991197B2Publication Date: 2024-05-21
- Inventor: Yi Wu , Mariem Boujelbene , James R. Verbus , Jason Paul Chang , Beibei Wang , Ting Chen
- 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: Schwegman, Lundberg & Woessner, P.A.
- Main IPC: H04L9/40
- IPC: H04L9/40 ; G06F18/2431 ; G06N3/04

Abstract:
In an example embodiment, a deep learning algorithm is introduced that operates on a transition matrix formed from user activities in an online network. The transition matrix records the frequencies that particular transitions between paths of user activity have occurred (e.g., the user performed a login activity, which has one path in the network, and then performed a profile view action, which has another path in the network). Each transition matrix corresponds to a different user's activities.
Public/Granted literature
- US20230164157A1 DEEP LEARNING USING ACTIVITY GRAPH TO DETECT ABUSIVE USER ACTIVITY IN ONLINE NETWORKS Public/Granted day:2023-05-25
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