Contact Configuration With Extracted Context for Contact Differentiation
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Solution Overview
Problem
Users face challenges in managing large contact lists, often forgetting why a contact was added or differentiating between similar contacts, leading to confusion and inefficiency in locating desired contacts.
Innovation Solution
The implementation of contact configuration techniques that include extracted context data, such as relationship, location, and message intent information, to generate or update contact profiles, enhancing their representation and differentiation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If users store a large number of contacts in their contact list, then the contact database becomes comprehensive and useful, but users become unable to locate desired contacts and lose the meaning/context of each contact
Solution Approach 1:
The contact information is segmented into two distinct parts: basic contact details (name, phone number, email) and contextual information (relationship, location, message intent). This segmentation allows the system to manage large numbers of contacts while preserving meaningful context through structured data organization and presentation.
Solution Approach 2:
The patent extracts contextual information from communications and separately stores it as metadata associated with contacts. This extraction process pulls out relationship details, location data, and message intent from raw communication data, enabling the system to maintain contact meaning even as the contact list grows large.
2Reliability
If users manually manage detailed contact information including context, then contact differentiation improves, but the complexity of contact management increases
Solution Approach 1:
The system automatically extracts and stores contextual information from communications without requiring manual user input. The contact management system serves itself by autonomously capturing relationship details, location data, and message intent from incoming communications, thereby improving contact differentiation while avoiding the complexity of manual management.
Solution Approach 2:
The system uses feedback from communication patterns to automatically update and refine contact profiles. By analyzing communication metadata and user interactions, the system continuously improves contact differentiation based on actual usage patterns, reducing the need for complex manual management procedures.
3Loss of information
If users manually track contact context, then memory of contact meaning is improved, but the time and effort required for contact management increases
Solution Approach 1:
The system performs preliminary action by automatically capturing and storing contextual information at the moment communications occur. Relationship details, location data, and message intent are extracted and saved proactively during the communication process itself, eliminating the need for later manual documentation and ensuring contact meaning is preserved without additional time investment.
Solution Approach 2:
The contact management system serves itself by automatically tracking and preserving contact context through metadata extraction from communications. This self-service approach maintains contact meaning retention while requiring zero additional time from users, as the system autonomously performs the information preservation tasks.
Data Source
AI summary
Techniques for contact configuration including extracted context data are described. For instance, the described techniques can be implemented to detect a contact data trigger to perform a contact operation and extract context data pertaining to one or more of the contact data trigger or the contact operation. The context data, for instance, includes an indication of a contextual relationship between a user of the client device and a contact associated with the contact operation. A contact recommendation is generated based at least in part on the context data, and a contact profile is configured based at least in part on the contact recommendation.