Address Quality Engine for Messaging Recipient Verification
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Solution Overview
Problem
Existing messaging systems fail to accurately distinguish between individuals with the same name, leading to potential misdirection of communications, as they rely on token association without considering communication patterns, times, and channels.
Innovation Solution
An Address Quality Engine (AQE) utilizes a personalized communication model seeded with social, spatial, temporal, and logical data from a W4 database to validate and verify the accuracy of communications by matching recipient names with tokens based on communication channels, times, and frequencies, ensuring correct recipient selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If string analysis is used to predict intended recipient, then recipient selection becomes automated, but accuracy decreases when multiple individuals share the same name
Solution Approach 1:
The system performs preliminary actions by analyzing communication patterns, time, and channel data before final recipient selection. The AQE pre-processes communication data to build a personalized model that predicts the most likely intended recipient, resolving ambiguity before the user completes the addressing process.
Solution Approach 2:
The system adds new dimensions to recipient identification beyond just name matching. By incorporating communication channel, time of day, and frequency dimensions into the verification process, the system creates a multi-dimensional identification space that distinguishes between individuals with the same name.
2Productivity
If token association is used for recipient identification, then messaging speed is maintained, but reliability decreases due to inability to distinguish namesakes
Solution Approach 1:
The system implements feedback by using historical communication data to verify and refine recipient identification. The AQE continuously learns from past communication patterns, channel preferences, and timing data to improve the accuracy of token association, creating a self-correcting system that maintains speed while improving reliability.
Solution Approach 2:
The system changes the parameters used for recipient identification from static token association alone to dynamic parameters including communication channel, time of day, and frequency. This transforms the identification process from a single-parameter system to a multi-parameter system that adapts to contextual variations.
3Measurement precision
If manual verification of recipient identity is implemented, then accuracy improves, but time consumption increases
Solution Approach 1:
The system applies partial verification by performing automated analysis on key distinguishing factors (channel, time, frequency) without requiring complete manual review of all communication history. This partial automation provides sufficient verification accuracy while avoiding the time cost of exhaustive manual checking.
Solution Approach 2:
The system performs self-service by automatically analyzing communication patterns and verifying recipient identity without user intervention. The AQE independently processes communication data, applies verification logic, and confirms recipient selection, eliminating the need for time-consuming manual verification while maintaining high accuracy.
Data Source
AI summary
A method and system for communicating a message in an electronic messaging environment is provided. A method employed by the system may include generating a personalized communication model related to a user, determining the validity of a token associated with an intended recipient of the message based on information in the personalized communication model, extracting entities from the message, determining whether the entities extracted match the intended recipient, and indicating to the user whether the token is valid and whether the entities match the intended recipient. The tokens correspond to email addresses, phone numbers, and addresses associated with intended recipients. The personalized communication model includes the names and tokens associated with those individuals with whom the user communicates. The personalized communication model is seeded with information including social, spatial, temporal and logical information related to the user. The personalized communication model is generated by a network processor.