Smart Dialing Recommendation Using Time-Stamped Machine Learning
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Solution Overview
Problem
The increasing amount of call records and contacts on mobile devices makes it less convenient for users to find the correct number to dial, as they need to search through extensive lists.
Innovation Solution
A smart dialing recommendation method using a machine learning algorithm to establish a database correlating time intervals with communication numbers, allowing for real-time recommendations based on user dialing habits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users search through extensive call records and contacts to find the correct number to dial, then they can locate the desired contact, but the dialing process becomes less convenient and more time-consuming
Solution Approach 1:
The system performs preliminary analysis of user dialing habits and establishes a database correlating time intervals with communication numbers in advance. When the user needs to dial, the system has already prepared personalized recommendations based on historical data, eliminating the need for users to search through extensive records at the moment of dialing.
Solution Approach 2:
The patent replaces the manual mechanical searching process with an automated machine learning system. The machine learning algorithm automatically analyzes dialing patterns and generates recommendations, substituting the user's manual search action with an intelligent automated system that provides relevant contacts based on time and historical behavior.
2Reliability
If the system continuously updates the database with machine learning to improve recommendation accuracy, then the relevance of recommendations improves, but the computational complexity and processing time increase
Solution Approach 1:
The system performs machine learning updates periodically rather than continuously. The database is established and updated at specific intervals, allowing the system to maintain accurate recommendations while avoiding the excessive computational burden of continuous real-time updates. This periodic approach balances accuracy with system complexity.
Solution Approach 2:
The system applies machine learning selectively to analyze and update only the necessary portions of the database based on changing user patterns, rather than processing the entire database continuously. This partial update approach maintains recommendation accuracy while reducing overall system complexity and processing requirements.
Data Source
AI summary
A smart dialing recommendation method, a non-transitory computer-readable medium, and a mobile device are disclosed. The smart dialing recommendation method includes: establishing a database according to a correspondence between a plurality of different time intervals in a past time range and a plurality of communication numbers by using a machine learning algorithm; obtaining a time stamp; and determining whether the time stamp has expired. When it is determined that the time stamp has not expired, according to the correspondence in the database and a current time point, a recommended number corresponding to the current time point is retrieved from the plurality of communication numbers. When it is determined that the time stamp has expired, the machine learning algorithm is performed again to update the plurality of communication numbers.