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 10):
The non-transitory, computer-readable medium of  claim 9 , wherein training the deep learning network comprises:\n obtaining a black sample associated with a fraudulent transaction and a white sample associated with a non-fraudulent transaction; separately extracting, from the black sample and from the white sample, an operation sequence and time difference information; performing a feature conversion and a selection on each of the operation sequences and on each of the time difference information that are extracted from the black sample and the white sample to obtain a plurality of operation features and a plurality of time difference features; calculating a similarity between each pair of the operation feature and the time difference feature that corresponds to a specific time point; combining more than one operation features based on the calculated similarity to obtain a combined operation feature; and training the deep learning network through classification based on the combined operation feature.

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): 0.3859018027268026
- 35 USC 102 Novelty (BERT): 0.4799697424220136
- Combined Prediction Score: 0.3953085966963237
- Mean Citation Score: 158.20166799999996
- Max Citation Score: 176.34726
- Similarity Product: 123.98152231520534

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