Context-Aware Caller Identification for Delivery Call Verification
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Solution Overview
Problem
Existing caller ID systems lack contextual information about callers, leading to potential telecommunication security breaches and inefficiencies.
Innovation Solution
Implementing a context-aware caller identification system using machine learning models to determine when a phone number is associated with a service provider for a user's activity within a specific time window, and displaying enhanced caller ID information on user devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional caller ID systems are used, then the system is simple and easy to operate, but the caller ID information lacks contextual details leading to security breaches
Solution Approach 1:
The system performs preliminary actions by pre-establishing a machine learning model that predicts service provider associations before calls occur. The model is trained on historical data about service provider call patterns, allowing the system to proactively identify and display contextual caller information without requiring complex real-time analysis during the call itself.
Solution Approach 2:
The machine learning model acts as an intermediary component between the simple caller ID display and the complex contextual analysis. It processes historical data and call patterns to generate predictions about service provider associations, thereby bridging the gap between basic caller identification and enhanced contextual information without requiring the entire system to be complex.
2Measurement precision
If machine learning models are used to determine service provider associations, then caller identification accuracy improves, but processing time increases
Solution Approach 1:
The machine learning model is pre-trained on historical data about service provider call patterns, delivery times, and user interactions. This preliminary training allows the model to make rapid predictions during actual calls without requiring complex real-time computation, as the computational heavy lifting is done beforehand during model training.
Solution Approach 2:
The system changes the parameters of the machine learning model based on time windows and service provider characteristics. By adjusting model parameters such as time-based weights and service provider-specific factors, the system optimizes prediction accuracy while maintaining efficient processing speeds, balancing precision and time requirements.
3Reliability
If contextual information is displayed to users, then communication security improves, but user interface complexity increases
Solution Approach 1:
The system merges the enhanced contextual caller information with the existing caller ID display interface. The machine learning predictions about service provider associations are integrated into the standard caller ID screen, allowing users to see both the basic caller identification and the contextual security information in a single unified interface, thereby maintaining ease of use while improving security.
Solution Approach 2:
The system applies local quality by providing contextual information selectively based on the specific call characteristics and service provider associations. Rather than overwhelming users with all possible contextual data, the interface displays only the relevant security information locally at the point of call identification, such as service provider name and associated delivery details, when they are most pertinent to the current situation.
Data Source
AI summary
Systems and methods of context-aware caller identification via machine learning techniques are disclosed. In one embodiment, an exemplary computer-implemented method may comprise: obtaining a trained call verification machine learning model that determines when a phone number is associated with a service provider that is associated with a delivery portion of a user activity with an entity within a time window; receiving, from a computing device of a first user, an indication of a communication being received from a second user; receiving activity information of a particular activity associated with the first user and a particular entity; utilizing the trained call verification machine learning model to determine, based at least in part on the activity information and the communication, that the second user is the particular service provider that is associated with a delivery portion of the particular activity associated with the particular entity.