Sender-Based Contact Profile Ranking for Address Input
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Solution Overview
Problem
Existing communication systems and applications lack efficient methods for assisting users in completing address inputs during message composition, particularly in identifying relevant contacts based on user messaging habits and profiles.
Innovation Solution
A computer-implemented method that scans messages to generate profiles for individuals, computes relevancy scores based on message data, and presents ranked suggestions for address completion in real-time as the user types, utilizing both message history and external data sources for comprehensive profile information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system presents all contacts to the user for selection, then the user can find any contact, but the time and effort required to locate the desired contact increases significantly
Solution Approach 1:
The system pre-processes message history data to generate ranked contact profiles before the user needs to compose a message. By scanning past messages and computing relevancy scores in advance, the system prepares contact suggestions that are immediately available when the user starts typing, eliminating the need to search through all contacts during the composition process.
Solution Approach 2:
The system changes the parameter of contact presentation from an unranked complete list to a ranked subset based on relevancy scores. By transforming the contact data structure to include priority rankings derived from message frequency and recency, the system enables rapid contact identification while filtering out less relevant contacts.
2Measurement precision
If the system scans all message history to generate comprehensive profiles, then the accuracy of contact suggestions improves, but the processing time and computational resources increase
Solution Approach 1:
The system performs partial scanning of message history by focusing on the most recent and frequent communications rather than analyzing every single message. By computing relevancy scores based on a representative subset of message data (prioritizing recent and frequent interactions), the system achieves sufficiently accurate contact suggestions without the excessive processing time required for complete message history analysis.
3Loss of information
If the system integrates multiple data sources for profile information, then the comprehensiveness of contact profiles increases, but the system complexity and data integration difficulty increase
Solution Approach 1:
The system creates a universal profile data structure that can accommodate information from multiple data sources (message history, contact lists, external sources). By designing a flexible profile format with standardized fields for contact information, message statistics, and relevancy scores, the system integrates diverse data sources without requiring separate processing pipelines for each source.
Data Source
AI summary
Systems and methods for searching of information regarding persons or to provide assistance for completion during address input. In one embodiment, a method includes automatically generating profiles for persons identified in messages, such as outgoing emails. Based on how the addresses of the persons are used in outgoing messages, the relevancy scores of the addresses are determined. When a user is searching for information or typing an address, the profiles and the relevancy scores are used to identify and sort search results or suggestions for the completion of the address input.