Contact Relationship Scoring for Message Disambiguation
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Solution Overview
Problem
Existing communication systems lack the ability to effectively determine and utilize the strength of association between user contacts based on message interactions, leading to difficulties in identifying related contacts and disambiguating references in messages.
Innovation Solution
The method involves analyzing messages sent and received by a user to calculate a strength of relationship score between contacts, forming groups based on co-occurrence and message properties, and providing suggestions for additional contacts within identified groups when composing new messages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system analyzes message interactions to determine strength of association between contacts, then the accuracy of identifying related contacts is improved, but the computational complexity and processing time increase
Solution Approach 1:
The patent segments the contact analysis process into distinct components: message interaction analysis, strength of association calculation, and contact grouping. This modular approach allows the system to process message data in manageable segments rather than analyzing all contacts simultaneously, reducing computational complexity while maintaining accuracy in identifying related contacts.
Solution Approach 2:
The system performs preliminary analysis of message interactions to pre-calculate strength of association scores between contacts before actual contact identification is needed. By pre-processing message data and establishing baseline association strengths, the system reduces the computational burden during actual contact relationship determination, resolving the contradiction between accuracy and complexity.
2Reliability
If the system calculates strength of relationship scores based on message co-occurrence, then the ability to disambiguate contact references is improved, but the data processing requirements increase
Solution Approach 1:
The patent extracts only the essential features from message data needed for disambiguation - specifically the co-occurrence patterns of contacts within messages. Rather than processing entire message contents, the system extracts and analyzes only the relevant contact relationship indicators, reducing data processing requirements while maintaining the reliability needed for accurate contact reference disambiguation.
Solution Approach 2:
The system applies different analysis depths to different contact pairs based on their local context in messages. For contacts that frequently co-occur together, the system performs more detailed analysis, while for contacts with minimal interaction, simpler analysis suffices. This localized quality approach optimizes data processing requirements by focusing computational resources where they are most needed for reliable disambiguation.
3Ease of operation
If the system provides suggested additional contacts based on contacts groups, then the ease of message composition is improved, but the risk of suggesting incorrect contacts increases
Solution Approach 1:
The patent implements feedback mechanisms where the system monitors user responses to suggested contacts - whether users accept, reject, or modify suggestions. This feedback is used to refine the strength of association calculations and adjust future contact suggestions. By continuously learning from user interactions, the system improves the reliability of suggestions over time while maintaining ease of operation.
Solution Approach 2:
The contact suggestion system is dynamic rather than static - it adapts suggestions based on changing message contexts, evolving contact relationships, and user preferences. The strength of association scores are dynamically updated as new message interactions occur, allowing the system to provide accurate suggestions that reflect current relationship states, thereby maintaining both ease of operation and reliability.
Data Source
AI summary
Methods and apparatus related to identifying one or more messages sent by a user-identifying two or more contacts that are associated with one or more of the messages, determining a strength of relationship score between identified contacts, and utilizing the strength of relationship scores to provide additional information related to the contacts. A strength of relationship score between a contact and one or more other contacts may be determined based on one or more properties of one or more of the messages. In some implementations, contacts groups maybe determined based on the strength of relationship scores. In some implementations, contacts groups may be utilized to disambiguate references to contacts in messages. In some implementations, contacts group may be utilized to provide suggestions to the user of additional contacts of a contacts group that includes the indicated recipient contact of a message.