Record-to-Event Conversion for Real-Time Communication Extraction
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Solution Overview
Problem
Traditional record management systems face challenges in efficiently converting unstructured digital communications into structured, actionable event data, particularly in time-sensitive environments, due to the complexity and variability of human communication patterns, leading to inaccurate data extraction and delayed decision-making.
Innovation Solution
A system leveraging advanced natural language processing and generative AI models to detect, classify, and convert communication records into standardized event entries within time-enumerated data structures, utilizing a multi-stage processing pipeline and real-time processing capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional record management approaches are used to handle diverse record types, then the system can maintain simplicity in architecture, but the efficiency and accuracy of information extraction deteriorates
Solution Approach 1:
The system segments the record-to-event conversion process into distinct modular components: record ingestion module, classification module, extraction module, and validation module. Each module handles specific aspects of the conversion process, allowing the system to process diverse record types efficiently while maintaining clear architectural boundaries that prevent complexity from escalating.
Solution Approach 2:
The patent introduces intermediary components including a standardized event schema that acts as a mediator between diverse record formats and the target event structure. This schema serves as an intermediate representation layer that simplifies the conversion process by providing a common framework for different record types, thereby improving extraction efficiency without requiring complex custom processing for each record type.
2Loss of time
If manual record processing methods are used, then the system maintains high accuracy in data extraction, but the processing time and decision-making delay increase
Solution Approach 1:
The system implements feedback mechanisms through validation modules that automatically check extracted data against predefined criteria and confidence thresholds. When extraction confidence is below the threshold, the system triggers re-processing or flags for manual review, creating a feedback loop that maintains high accuracy while enabling rapid processing of high-confidence records through automated pipelines.
Solution Approach 2:
The patent applies preliminary action by pre-configuring classification rules, extraction templates, and validation criteria before processing begins. The system pre-processes record metadata to predict processing requirements and pre-allocates resources accordingly, enabling rapid processing while maintaining accuracy through pre-established extraction patterns that have been validated against known data formats.
3Productivity
If automated conversion systems are implemented, then processing speed improves, but the accuracy of data extraction deteriorates due to complexity in handling diverse communication patterns
Solution Approach 1:
The system employs dynamic processing that adapts to the characteristics of each record being processed. The classification module dynamically selects appropriate extraction strategies based on record type, and the extraction module adjusts its processing depth and methods according to the complexity and confidence indicators of the input record, maintaining high accuracy across diverse formats while preserving automated processing speed.
Data Source
AI summary
Systems and methods are disclosed comprising techniques for record-to-event conversion, such as retrieving at least one alphanumeric record associated with a monitored digital communication transmitted among two or more users, generating a time-enumerated data structure that stores an event entry set for the monitored digital communication, selectively identifying at least one discrete event for the monitored digital communication, generating one or more relevance scores for the at least one discrete event, identifying at least one valid discrete event from the at least one discrete event, generating an event attribute set for the at least one valid discrete event, updating the normalized event attribute set for a new event entry within the event entry set of the time-enumerated data structure, and transmitting the updated time-enumerated data structure within an elapsed duration after retrieving the at least one alphanumeric record.