PATENT CLAIM ANALYSIS

Application Number: 16355439
Application Type: Utility
Filing Date: 2019-03
Publication Date: 2019-09
Patent Classification: ["706", "020000"]

Abstract:
Techniques for identifying fraudulent transactions are described. In one example method, an operation sequence and time difference information associated with a transaction are identified by a server. A probability that the transaction is a fraudulent transaction is predicted based on a result provided by a deep learning network, where the deep learning network is trained to predict fraudulent transactions based on operation sequences and time differences associated with a plurality of transaction samples, and where the deep learning network provides the result in response to input including the operation sequence and the time difference information associated with the transaction.

Claim (Index 4):
The computer-implemented method of  claim 3 , wherein performing a feature conversion and a selection on each of the operation sequences and each of the time difference information comprises:\n performing a feature conversion on each of the operation sequences and each of the time difference information to obtain an initial operation feature and an initial time feature; and separately performing a dimension reduction and an irrelevant feature removal on the initial operation feature and the initial time feature to select a plurality of operation features and time difference features.

Metadata:
- Claim Count in Document: 13.0
- Percentile: 99.0
- Lexical Diversity: 1.94444
- Patent Class: 706.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: True
- Related Applications: ['15980208', '15572082', '15921386', '15697375', '15521751']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.383362657499241
- 35 USC 102 Novelty (BERT): 0.49028002659345
- Combined Prediction Score: 0.3940543944086619
- Mean Citation Score: 158.20166799999996
- Max Citation Score: 176.34726
- Similarity Product: 112.00352776180029

Labels:
- Claim Label 101: 1
- Claim Label 102: 1
- Claim Label 103: 1
- Claim Label 112: 1
- Combined Label: 1
- Label 101 Adjusted: 1

Dataset: test