PATENT CLAIM ANALYSIS

Application Number: 15953844
Application Type: Utility
Filing Date: 2018-04
Publication Date: 2019-08
Patent Classification: ["705", "044000"]

Abstract:
Systems and methods for aggregating commercial transaction information from a plurality of transaction systems of merchants, and evaluating the aggregated information utilizing machine learning and artificial intelligence algorithms are disclosed. The commercial transaction information is parsed, aggregated, and evaluated based on patterns recognized by the artificially intelligent system. One or more fraud clusters are generated based on the recognized patterns. The fraud clusters are utilized to generate a predictive fraud score for a transaction initiated by a customer. Interpretation of the predictive fraud score by a transaction system of a merchant allows for a determination as to whether the transaction initiated by the customer is likely a legitimate transaction or a fraudulent transaction.

Claim (Index 1):
A method of detecting fraudulent transactions by a first transaction system of a plurality of related transaction systems, the method comprising:\n creating a fraud cluster, by a cluster generation module, from aggregated transaction attributes for use in detecting fraudulent transactions, the fraud cluster created based at least in part on historical transaction data from a plurality of transactions conducted by the plurality of related transaction systems, over a period of time, the fraud cluster including at least one link between a first transaction attribute and a second transaction attribute present together in multiple transactions of the plurality of transactions, the first transaction attribute associated with known fraudulent transactions; receiving, by an input source interface module, a request from a second transaction system to evaluate a likelihood of an initiated transaction being a fraudulent transaction; parsing, by an input source parser module, the initiated transaction into a plurality of discrete data attributes of the initiated transaction; determining, by a vertical check module, utilizing the fraud cluster, whether any of the parsed plurality of discrete data attributes of the initiated transaction is similar to the second transaction attribute of the aggregated transaction attributes; generating, by the vertical check module, a ratio of legitimate to fraudulent transactions based on at least one data attribute; generating, by a score generation module, a predictive fraud score for the initiated transaction based on the created fraud cluster, the determination of whether any of the parsed plurality of discrete data attributes of the initiated transaction match the second transaction attribute of the aggregated transaction attributes, and the ratio of legitimate to fraudulent transactions, the predictive fraud score generated using artificial intelligence gained from a plurality of machine learning algorithms; transmitting the generated predictive fraud score to the requesting second transaction system; receiving confirmation information from the requesting second transaction system regarding whether the initiated transaction was completed as legitimate or fraudulent, the confirmation information comprising a plurality of discrete data attributes for the completed transaction; automatically recalculating the fraud cluster based on the confirmation information feedback from the second transaction system; and monitoring changes of the fraud cluster over time to determine how a criminal organization's tactics evolve.

Metadata:
- Claim Count in Document: 1.0
- Percentile: 91.0
- Lexical Diversity: 1.98361
- Patent Class: 705.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['09782681', '14991099', '14525273', '09675412', '14922643']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.1202690608494083
- 35 USC 102 Novelty (BERT): 0.4993561771087036
- Combined Prediction Score: 0.1581777724753378
- Mean Citation Score: 248.072402
- Max Citation Score: 254.3634
- Similarity Product: 176.8107004422784

Labels:
- Claim Label 101: 0
- Claim Label 102: 1
- Claim Label 103: 1
- Claim Label 112: 1
- Combined Label: 0
- Label 101 Adjusted: 0

Dataset: test