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 15):
A system for detecting fraudulent transactions among a plurality of related transaction systems via at least one fraud cluster, the system comprising:\n a processor; a memory for storing executable instructions; an input source interface module to receive transaction data regarding a plurality of completed transactions from a plurality of related transaction systems, the received transaction data comprising a multi-attribute data set for each completed transaction; a vertical check module to generate a ratio of legitimate to fraudulent transactions based on at least one data attribute of the multi-attribute data set; and a cluster generation module to:\n identify any of outliers and singularities in the received transaction data via at least one pattern recognition artificially intelligent algorithm; \n group two or more of the completed transactions into one or more groups based on correspondence between the multi-attribute data sets; \n automatically calculate and generate one or more fraud clusters for the one or more groups with correspondence between the multi-attribute data sets using the ratio of legitimate to fraudulent transactions, the one or more fraud clusters including at least one correspondence between a first transaction attribute and a second transaction attribute of at least one of the multi-attribute data sets; \n generate an interactive graphical user interface that displays the one or more fraud clusters to a user, wherein the user can select any portion of each of the one or more fraud clusters to receive additional information regarding underlying transaction data for each point on the one or more fraud clusters; and \n monitor changes of the one or more fraud clusters 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): 0.1208208709479797
- 35 USC 102 Novelty (BERT): 0.4933590598000154
- Combined Prediction Score: 0.1580746898331833
- Mean Citation Score: 248.072402
- Max Citation Score: 254.3634
- Similarity Product: 193.51573381640912

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

Dataset: test