AI Engine Training Through Transaction-Data Commonality Clustering
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Solution Overview
Problem
Existing CRM systems fail to efficiently develop client relationships by manually combing through transaction data to identify new clients and enhance existing client relationships, as this data is not organized for relationship development opportunities.
Innovation Solution
An AI system analyzes transaction data using unsupervised learning to identify commonalities and correlations, which are reviewed by human experts to pursue relationship development opportunities, with ongoing supervised or semi-supervised learning for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sales people manually comb through transaction data to identify client prospects, then they can identify relationship development opportunities, but the process is inefficient and time-consuming
Solution Approach 1:
The patent replaces the mechanical manual analysis process with an automated AI system that uses machine learning algorithms to analyze transaction data. The system automatically identifies patterns, commonalities, and potential client prospects without human intervention, thereby eliminating time loss while maintaining or improving identification accuracy.
Solution Approach 2:
The AI system performs self-learning through unsupervised and semi-supervised learning methods, automatically improving its ability to identify client prospects without requiring continuous manual programming or intervention. The system serves itself by learning from data patterns and refining its identification capabilities autonomously.
2Reliability
If transaction data is structured to facilitate accurate recording of transactions, then data integrity is maintained, but the data is not organized for identifying relationship development opportunities
Solution Approach 1:
The patent makes the transaction data system multi-functional by enabling it to serve both its original purpose of accurate transaction recording and a new purpose of identifying relationship development opportunities. The AI system analyzes the existing structured data to extract additional insights without requiring complete restructuring, thereby maintaining data integrity while adding versatility.
Solution Approach 2:
The patent adds a new analytical dimension to the existing transaction data by applying machine learning algorithms that identify patterns and relationships across multiple data points. This creates a new layer of insight from the same data structure, enabling relationship development identification without altering the original data's integrity or structure.
3Ease of operation
If businesses use traditional CRM systems to manage client information, then client data is organized and accessible, but the systems do nothing to develop client relationships
Solution Approach 1:
The patent implements preliminary action by having the AI system proactively identify potential relationship development opportunities and client prospects before sales people need to manually search for them. The system continuously analyzes transaction data in the background, preparing and presenting actionable insights that enable immediate relationship development activities.
Solution Approach 2:
The patent introduces an AI system as an intermediary between the existing CRM data and the sales people. This intermediary automatically processes the client information and transaction data to generate relationship development opportunities, bridging the gap between data management and relationship development without requiring sales people to manually analyze the data.
Data Source
AI summary
A system for identifying connections between individuals based on relationships found in data. The system includes a database containing data records and fields and identifying individuals involved in each record. The database is provided to a computer which executes a machine learning algorithm configured to identify connections between the individuals based on clusters in the data contained in the database, where the machine learning algorithm provides output data identifying clusters of activity relationships, a group label for each cluster when known, and scores for each of the individuals for each of the clusters in which they appear. A communication system algorithm sends actionable communications to particular ones of the individuals based on the output data. Unsupervised learning may be used for initial system training, and supervised learning for ongoing training.