Record-to-Event Conversion with Event Scoring for Real-Time Parsing
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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
1Measurement precision
If traditional record management systems are used to convert unstructured digital communications into structured event data, then the system structure remains simple, but the conversion accuracy is low and processing speed is slow
Solution Approach 1:
The patent segments the conversion process into multiple specialized modules: unstructured data parser, entity recognition module, event detection module, temporal relationship analyzer, and structured data generator. Each module handles a specific aspect of the conversion process, improving accuracy through specialized processing while managing complexity through modular architecture.
Solution Approach 2:
The patent introduces an intermediary processing layer that transforms unstructured communication data into a standardized intermediate representation before final structured output. This intermediary layer includes normalization rules, entity linking mechanisms, and event schema validation that bridge the gap between raw data and structured event data, enhancing conversion accuracy.
2Productivity
If traditional record management approaches are used, then the system complexity remains low, but the processing speed is slow and decision-making is delayed
Solution Approach 1:
The patent implements preliminary action by pre-compiling event schemas, pre-loading entity dictionaries, and pre-establishing conversion rules before actual data processing. The system performs offline training and validation to prepare processing templates that accelerate real-time conversion operations, reducing processing delays without adding operational complexity.
Solution Approach 2:
The patent dynamically adjusts processing parameters such as parsing depth, entity recognition sensitivity, and event detection thresholds based on input data characteristics. This adaptive parameter adjustment optimizes processing speed for different types of communications while maintaining extraction accuracy, managing system complexity through intelligent parameter control rather than structural complexity.
3Reliability
If advanced NLP and generative AI models are used for real-time conversion, then data extraction accuracy and processing speed improve, but the system complexity increases
Solution Approach 1:
The patent implements a universal event schema framework that can handle multiple types of digital communications (emails, messages, documents) through a single standardized processing architecture. The multi-functional system uses the same core NLP and AI models across different communication types, improving reliability through consistent processing while managing complexity through universal design rather than multiple specialized systems.
Solution Approach 2:
The patent incorporates feedback mechanisms where conversion results are validated against expected schemas, and errors are fed back to adjust processing parameters and improve future conversions. The system includes validation loops that check extracted data against domain knowledge and event templates, enhancing reliability while using automated feedback control rather than complex manual intervention systems.
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.