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Solution Overview
Problem
Existing CRM systems fail to efficiently develop client relationships by manually combing through transactional data to identify new clients and enhance existing relationships, as this data is not organized for relationship development opportunities.
Innovation Solution
An AI system analyzes transaction data using machine learning algorithms, particularly neural networks, to identify commonalities and correlations that suggest new clients and enhance existing relationships, 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 transactional 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 machine learning system. The ML model processes transactional data to identify client relationship opportunities, substituting human manual effort with automated computational analysis. This resolves the contradiction by maintaining identification accuracy while dramatically reducing time consumption through automation.
Solution Approach 2:
The system enables self-service by allowing the transactional data to speak for itself through automated pattern recognition. The ML model automatically identifies relationships and opportunities without requiring human intervention to comb through data, making the system self-sufficient in extracting insights from raw transactional data.
2Reliability
If transactional 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 transactional data multi-functional by using the same data structure for both accurate transaction recording and relationship development identification. The ML model extracts multiple types of insights (client prospects, relationship opportunities, life events) from the same transactional data, making it versatile without compromising its primary function of accurate recording.
Solution Approach 2:
The system adds another dimension of analysis to the existing transactional data structure. Instead of reorganizing the data, it applies ML algorithms that analyze the data from a new perspective (pattern recognition, clustering, association rules), enabling relationship identification while preserving the original data integrity and structure.
3Ease of operation
If businesses use traditional CRM systems to manage client information, then client data is organized, but the systems do nothing to develop client relationships
Solution Approach 1:
The patent implements preliminary action by proactively identifying client relationship opportunities before human intervention is needed. The ML system continuously analyzes transactional data to predict future needs, identify prospects, and flag relationship development opportunities in advance, enabling businesses to act ahead rather than reactively.
Solution Approach 2:
The system incorporates feedback loops where the ML model continuously learns from identified opportunities and their outcomes. The system refines its predictions and identifications based on results, creating a self-improving cycle that enhances relationship development productivity over time while maintaining ease of data management.
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.