Predicting likelihood of request classifications using deep learning
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United States of America
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Jan 7, 2025
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N/A
app pub date -
May 23, 2024
filing date -
May 23, 2024
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Abstract
A system and method include receiving a first set of variables associated with a real-time request, extracting a predetermined subset of the first set of variables for generating a second set of variables, identifying historical request data, computing a set of parameters based on the first set of variables and the historical request data, generating a plurality of numeric sequences and a plurality of string sequences for the real-time request, converting each of the plurality of string sequences into an encoded string sequence to obtain a plurality of encoded string sequences, inputting the plurality of numeric sequences and the plurality of encoded string sequences into a trained deep machine learning model, and computing a score from the trained deep machine learning model, the score indicative of a likelihood that the real-time request belongs to an unauthorized classification.

First Claim
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- 15 United States
- 10 France
- 8 Japan
- 7 China
- 5 Korea
- 2 Other
Patent Owner(s)
Patent Owner | Address | |
---|---|---|
SAS INSTITUTE INC | SAS CAMPUS DRIVE CARY NC 27513 |
International Classification(s)

- [Classification Symbol]
- [Patents Count]
Inventor(s)
Inventor Name | Address | # of filed Patents | Total Citations |
---|---|---|---|
Arangala, Adheesha Sanjaya | Chapel Hill, US | 3 | 41 |
Armagan, Artin | Raleigh, US | 1 | 0 |
Hare, Brian Christopher | Raleigh, US | 1 | 0 |
Jung, Jin-Whan | Raleigh, US | 22 | 109 |
Liao, Yi | Apex, US | 37 | 189 |
Oothongsap, Phoemphun | Raleigh, US | 1 | 0 |
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