Server-Based Contact Information Pattern Extraction
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Solution Overview
Problem
Existing electronic messaging systems face challenges in efficiently updating user contact information, such as email addresses, phone numbers, and physical addresses, as this information often changes over time, leading to manual, time-consuming, and error-prone updates.
Innovation Solution
A computer-implemented technique that uses a server to identify patterns in training electronic messages to extract and suggest alternate contact information, such as virtual addresses, physical addresses, and telephone numbers, by analyzing context and usage rates, thereby automatically updating user profiles and providing intelligent suggestions to users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual updating of contact information is used, then users can update their profiles, but the process is time-consuming and error-prone
Solution Approach 1:
The system automatically extracts contact information from electronic messages and updates user profiles without requiring manual user intervention. The server identifies patterns in message contexts, extracts contact details, and autonomously updates the database, allowing the system to serve itself rather than requiring users to manually update their contact information.
Solution Approach 2:
The system performs preliminary extraction and validation of contact information from electronic messages before the user needs to update their profile. By continuously monitoring and pre-processing message data, the system prepares updated contact information in advance, so when updates are needed, they are already ready and verified, eliminating the need for time-consuming manual entry.
2Reliability
If manual updating of contact information is used, then users can maintain their profiles, but errors are frequent
Solution Approach 1:
The system uses feedback loops to continuously verify extracted contact information against multiple criteria including pattern matching, context validation, and usage frequency analysis. The server monitors the reliability of extracted information and adjusts extraction patterns based on feedback from successful and unsuccessful extractions, progressively improving accuracy while maintaining automated operation.
Solution Approach 2:
The patent replaces the mechanical manual process of copying and pasting contact information with an automated information extraction system that uses pattern recognition and contextual analysis. This substitution eliminates human errors associated with manual transcription while maintaining the simplicity of the update process through automated server-side operations.
3Productivity
If automated pattern recognition is implemented, then contact information extraction is faster, but system complexity increases
Solution Approach 1:
The pattern recognition system is segmented into distinct modular components: pattern definition modules, context analysis modules, extraction modules, and validation modules. Each module handles a specific aspect of the extraction process, making the overall complex system manageable through clear separation of concerns. The server executes these segmented functions in sequence, achieving high extraction speed while maintaining system organization.
Solution Approach 2:
The system introduces intermediary pattern templates that serve as mediators between raw electronic messages and extracted contact information. These predefined patterns act as intermediate representations that simplify the extraction process by providing structured frameworks for identifying contact details, reducing the computational complexity while maintaining extraction speed through template-based matching.
4Measurement precision
If comprehensive pattern analysis is performed, then extraction accuracy is improved, but processing time increases
Solution Approach 1:
The system performs partial pattern analysis by focusing on the most relevant contextual patterns and contact information types based on message classification. Rather than analyzing every possible pattern in every message, the server applies selective pattern matching that concentrates computational effort on high-probability extraction targets, achieving sufficient accuracy without the full processing time of comprehensive analysis.
Data Source
AI summary
Computer-implemented techniques for automatic identification and use of alternate user contact information can include identifying, at a server having one or more processors, a set of patterns from training electronic messages, each pattern indicating a pattern of contact information context. The techniques can include storing and utilizing, at the server, the set of patterns to obtain a set of alternate contact information for a target user. In response to a use of a specific alternate contact information for the target user by a source user at a computing device, the techniques can include providing, from the server to the computing device, a suggestion for the source user. Examples of the suggestion may include a virtual address for an electronic message or at a social network, a physical address for navigation, and a telephone number for calling or incoming caller identification.