Contact List Prioritization via Predicted Reachability
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Solution Overview
Problem
Current contact list management methods in mobile devices are inefficient when trying to reach contacts within a specific role, as users often have to manually attempt calls in a brute force manner, especially when the most reachable contact is deep within the list, leading to wasteful and time-consuming efforts.
Innovation Solution
A method and system for contact list prioritization based on predicted reachability, using usage data such as accelerometer data, recency, and frequency of use to compute a reachability value, which sorts contacts to display the most reachable ones first, allowing for efficient dialing attempts within a specific role.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If contacts are listed in traditional order (alphabetical or manual), then the contact list structure is simple and easy to maintain, but finding and reaching the most reachable contact becomes time-consuming and inefficient
Solution Approach 1:
The system pre-computes reachability values for contacts based on usage data before a call is needed. This preliminary action ranks contacts by their likelihood of being reached, so when a user needs to contact someone, the most reachable contact is already positioned at the top of the list, eliminating the need for manual searching through multiple entries.
Solution Approach 2:
The contact list dynamically reorders contacts based on computed reachability values rather than maintaining a static alphabetical or manual order. The list adapts its structure based on usage patterns and reachability predictions, allowing the most accessible contacts to automatically rise to the top while maintaining the underlying data structure for efficient retrieval.
2Loss of time
If users manually attempt calls in brute force manner, then no additional computing resources are needed, but significant time and effort are wasted when the reachable contact is deep in the list
Solution Approach 1:
The system uses usage data as feedback to continuously improve contact reachability predictions. By analyzing past calling patterns, message exchanges, and contact interaction frequency, the system refines its reachability values to better predict which contacts are most likely to be reached, thereby reducing the time needed to successfully contact someone.
Solution Approach 2:
The contact list automatically selects and prioritizes the most reachable contact based on computed reachability values without requiring user intervention. The system serves itself by autonomously ordering contacts based on predictive algorithms, eliminating the need for users to manually search through the list or remember which contacts are most accessible.
3Measurement precision
If contacts are sorted by frequency or recency only, then the sorting criterion is simple to implement, but it does not accurately reflect actual reachability which may involve multiple factors
Solution Approach 1:
The system segments the analysis of contact reachability into multiple independent factors: usage frequency, recency of interaction, and other usage data metrics. Each factor is computed separately based on specific usage data, then combined to form an overall reachability value. This segmentation allows for precise measurement of each dimension while maintaining manageable computational complexity.
Solution Approach 2:
The reachability computation system serves multiple functions simultaneously: it analyzes usage frequency, measures recency of interaction, processes accelerometer data, and generates a comprehensive reachability ranking. This multi-functional approach allows a single system to handle diverse data types and sorting criteria, improving measurement precision without proportionally increasing complexity.
Data Source
AI summary
A method for contact list prioritization according to predicted reachability includes loading a contact list of contacts and retrieving a reachability value for each corresponding contact in the contact list that has been computed for each corresponding contact according to usage data of each corresponding contact for an associated mobile telephone. Thereafter, contacts in the contact list that are determined to be most reachable according to a corresponding reachability value are displayed first. In one aspect, the usage data for the associated mobile telephone is based upon accelerometer data of the associated mobile telephone. In another aspect, the usage data is based upon recency or frequency of use of the mobile telephone to place telephone calls. In yet another aspect, the usage data is based upon recency or frequency of use of a messaging application executing in the associated mobile telephone. Finally, in even yet another aspect, the usage data is based upon recency or frequency of posting content to a social network through the associated mobile telephone.