PATENT CLAIM ANALYSIS

Application Number: 16366794
Application Type: Utility
Filing Date: 2019-03
Publication Date: 2019-10
Patent Classification: ["706", "012000"]

Abstract:
A feature extraction is performed on transaction data to obtain a user classification feature and a transaction classification feature. A first dimension feature is constructed based on the user classification feature and the transaction classification feature. A dimension reduction processing is performed on the first dimension feature to obtain a second dimension feature. A probability that the transaction data relates to a risky transaction is determined based on a decision classification of the second dimension feature, where the decision classification is based on a pre-trained deep forest network including a plurality of levels of decision tree forest sets.

Claim (Index 7):
The computer-implemented method of  claim 6 , wherein a number of the black samples is not equal to a number of the white samples, and the method further comprises:\n prior to training each base classifier:\n dividing data associated with the black samples and data with the white samples through a k-fold cross validation to obtain a train set and a validation set; \n training a base classifier using the train set to obtain a model; and \n testing the model using the validation set to obtain a performance indicator that evaluates the base classifier.

Metadata:
- Claim Count in Document: 38.0
- Percentile: 99.0
- Lexical Diversity: 2.5122
- Patent Class: 706.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: True
- Related Applications: ['16366841', '12436667', '16185860', '15756263', '10993651']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.4288543345362448
- 35 USC 102 Novelty (BERT): 0.5173675914951004
- Combined Prediction Score: 0.4377056602321303
- Mean Citation Score: 258.753976
- Max Citation Score: 312.76273
- Similarity Product: 219.11602205804584

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