Transaction Affinity Recommendation System Using Segmented Processing

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Solution Overview

Problem

Existing systems lack the capability to dynamically analyze transaction data to provide personalized transaction affinity recommendation data, which can be used to optimize subsequent transactions and user interactions.

Innovation Solution

The implementation of a system that performs transaction affinity processes to collect and analyze transaction data, determining transaction affinity relationships between transactions and merchants, and generating dynamic recommendation data based on these relationships.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If transaction data is collected and analyzed to generate personalized recommendation data, then the quality and personalization of recommendations improve, but the complexity of the system increases

Engineering Contradiction:

Improvepersonalization of recommendationsVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system segments the recommendation generation process into distinct modules: transaction data collection, affinity relationship determination, and recommendation generation. This modular segmentation allows each component to be independently developed and maintained, reducing overall system complexity while enabling personalized recommendations through coordinated operation of specialized subsystems

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system introduces an intermediary affinity determination module that acts as a mediator between raw transaction data and final recommendations. This intermediary layer processes transaction data to extract affinity relationships, transforming complex unstructured data into structured relationship information that can be efficiently used for personalized recommendations without requiring the entire system to handle full complexity

Inventive Principle:

Principle #24Intermediary (Mediator)

2Speed

If real-time transaction data analysis is performed to provide dynamic recommendations, then the responsiveness and relevance of recommendations improve, but the processing time and computational resources increase

Engineering Contradiction:

Improveresponsiveness of recommendationsVSAvoidcomputational resources

Core Design Contradiction:

SpeedVSUse of energy by moving object

Solution Approach 1:

The system performs preliminary actions by continuously collecting and pre-processing transaction data in the background, maintaining an updated affinity relationship database before recommendations are needed. This preliminary data preparation allows the recommendation engine to generate personalized suggestions rapidly when requested, as the computationally intensive affinity analysis has already been performed on accumulated transaction data

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements periodic action by analyzing transaction data at regular intervals and updating affinity relationships periodically rather than processing every transaction in real-time. This periodic batch processing reduces instantaneous computational load and energy consumption while still providing timely updated recommendations, balancing responsiveness with resource efficiency

Inventive Principle:

Principle #19Periodic action

Data Source

PatentUS12307484B2Systems and methods for providing transaction affinity information

Publication Date: 2025.05.20 CAPITAL ONE SERVICES LLC

AI summary

In certain aspects, the disclosed implementations include methods and systems for dynamically generating and providing transaction affinity recommendation data. In certain implementations, the transaction affinity recommendation data may include information that identifies a target merchant and associated merchant promotion data that may be generated based on a dynamic analysis of transaction data corresponding to an account record. The disclosed implementations may determine temporal relationships between pairs of sequential transactions corresponding to the account record, and based on such relationships, may identify pairs of transactions involving separate merchants. The disclosed implementations may determine affinity relationships associated with one or more pairs of the temporally related transactions and a target merchant, and based on such relationships, generate and provide the transaction affinity recommendation data.