Entity Association Confidence Score Maintenance
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Solution Overview
Problem
Manual input of electronic communication data into systems of record is challenging due to high volume and heterogeneity, leading to errors and inefficiencies, and existing methods fail to accurately and efficiently populate node profiles in member node networks.
Innovation Solution
A system that generates node profiles for member nodes using electronic activity data, employing statistical analysis and data from multiple sources to verify and update field values, thereby ensuring accurate and efficient data population.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual input methods are used to enter electronic communication data into systems of record, then data can be entered into the system, but the process becomes time-consuming and error-prone due to high volume and heterogeneity of data
Solution Approach 1:
The system automatically extracts entity information from electronic communications and populates node profiles without human intervention. The automated entity recognition and profile population mechanisms enable the system to serve itself, eliminating manual data entry while maintaining high accuracy through confidence score validation.
Solution Approach 2:
The patent replaces manual mechanical data entry processes with automated computational systems. Machine learning models extract entities from communications, and algorithms automatically populate and validate node profiles, substituting human operators with intelligent software systems that process data faster and more accurately.
2Measurement precision
If existing methods are used to populate node profiles, then some data can be captured, but the profiles are not accurate or up-to-date due to failure to verify against multiple sources
Solution Approach 1:
The system merges data from multiple sources including electronic communications, systems of record, and external data sources into unified node profiles. By combining these diverse data sources and cross-referencing them, the system achieves high profile accuracy while the integration layer manages the complexity of coordinating multiple sources.
Solution Approach 2:
The system implements feedback loops where node profiles are continuously verified against multiple data sources, and confidence scores are calculated based on the consistency of information across sources. This feedback mechanism ensures profiles remain accurate and up-to-date by automatically detecting and correcting discrepancies.
3Productivity
If automated entity recognition is implemented to extract data from electronic communications, then data entry efficiency improves, but challenges arise in accurately identifying and verifying entity associations
Solution Approach 1:
The system introduces confidence scores as an intermediary metric to evaluate the reliability of automatically extracted entity associations. These confidence scores act as a mediator between automated extraction and final data acceptance, allowing the system to process data rapidly while maintaining quality control through threshold-based validation.
Solution Approach 2:
The system dynamically adjusts confidence score thresholds and verification parameters based on the specific context and data source reliability. By changing these parameters adaptively, the system optimizes the balance between processing speed and association accuracy for different types of electronic communications and data sources.
Data Source
AI summary
The present disclosure is directed to systems and methods of maintaining confidence scores of entity associations derived from systems of record. The system can access a record objects of systems of record. The system can identify, from a record object corresponding to a first group entity, an account relationship data structure specifying a relationship. The system can identify a first group node profile corresponding to the first group entity. The system can identify, for each second group entity, a second group node profile. The system can detect a change in a relationship of the group entities in the account relationship data structure or from electronic activities. The system can determine, between the first and a second group node profile, a relationship type in the change. The system can update, in a node graph, an edge between a first and a second group node profile to indicate the relationship type.