Dynamic Event Data Extraction Templates for Structured Communications
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Solution Overview
Problem
Existing business-to-consumer (B2C) emails and structured communications lack efficient methods to extract event-related data, as current templates do not differentiate between transient and fixed or confidential information, leading to incomplete data extraction.
Innovation Solution
The method involves grouping communications into clusters based on similarities, generating data extraction templates that focus on transient event-related data, and using feedback to refine the extraction process, ensuring non-confidential event data is extracted while ignoring fixed or confidential information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing templates are used to extract data from structured communications, then data extraction can be performed, but the templates cannot differentiate between transient event-related data and fixed or confidential information, leading to incomplete or inaccurate extraction
Solution Approach 1:
The patent implements dynamic template generation by analyzing communication patterns and automatically adapting extraction templates based on observed data characteristics. The system transitions from static pre-defined templates to dynamic templates that evolve based on communication patterns, allowing accurate differentiation between transient event data and fixed confidential information while maintaining adaptability to various communication formats.
Solution Approach 2:
The system changes the parameters of data extraction by introducing confidence scores and probability metrics to distinguish between different types of data. By varying extraction parameters based on pattern recognition results, the system can adjust its behavior to extract only transient event-related data while ignoring fixed confidential information, thereby improving extraction accuracy without sacrificing versatility.
2Quantity of substance
If all data from structured communications is extracted, then comprehensive information is obtained, but confidential and fixed boilerplate information is also captured, reducing data quality and security
Solution Approach 1:
The patent applies the extraction principle by selectively removing unwanted data elements from the extraction process. The system identifies and extracts only transient event-related data while deliberately excluding fixed boilerplate and confidential information through pattern recognition and classification mechanisms, thereby improving data quality by filtering out low-value or sensitive information.
Solution Approach 2:
The system applies different extraction qualities to different parts of the communication data. Rather than uniformly extracting all data, the system applies localized extraction rules that treat transient event data differently from fixed boilerplate sections, assigning higher extraction priority and quality to event-related portions while reducing or eliminating extraction of confidential fixed information.
3Measurement precision
If manual template creation is used to achieve accurate data extraction, then extraction precision can be improved, but the complexity and time required for template development increases significantly
Solution Approach 1:
The patent implements self-service by enabling the system to automatically generate and refine extraction templates without manual intervention. The template generation mechanism analyzes communication patterns autonomously, learns from extracted data, and self-optimizes extraction rules, thereby achieving high extraction precision while eliminating the complexity and time burden of manual template creation and maintenance.
Solution Approach 2:
The system performs preliminary actions by pre-processing communications to identify patterns and characteristics before final extraction. By conducting preliminary analysis and template generation based on observed patterns, the system prepares extraction rules in advance, reducing the complexity of ad-hoc template creation while maintaining high precision through pre-established extraction logic.
4Productivity
If static extraction rules are applied, then processing speed is maintained, but the system cannot adapt to variations in communication formats and patterns
Solution Approach 1:
The patent transforms static extraction rules into dynamic adaptive rules that can adjust to varying communication formats. The system continuously learns from processed communications and modifies extraction patterns in real-time, maintaining processing speed through automated adaptation rather than requiring manual rule updates for each format variation, thereby achieving both speed and adaptability.
Solution Approach 2:
The system creates universal extraction mechanisms that can handle multiple communication formats through a single adaptive framework. Rather than creating separate static rules for each format, the system develops multi-functional extraction logic that automatically adjusts to different patterns, maintaining high processing speed while providing broad format adaptability through unified intelligent processing.
Data Source
AI summary
Techniques are described herein for generating and applying event data extraction templates. In various implementations, a data extraction template may be applied to structured communications to extract, from each structured communication, event data associated with a transient markup language path indicated in the data extraction template. The data extraction template may include an event-related semantic data type assigned to the transient markup language path and a strength of association between the transient structural path and the event-related semantic data type. Feedback may be obtained concerning event data extracted from one or more of the structured communications. Based on the feedback, the strength of association between the transient markup language path and the event-related semantic data type may be altered. The data extraction template may then be applied to a subsequent structured communication to extract new event data from the structured communication based on the altered strength of association.