Caller ID Verification Platform for Wireless Networks
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Solution Overview
Problem
Mobile network operators (MNOs) often receive inaccurate caller ID information from third-party vendors, leading to incorrect caller ID data being provided to subscribers, which can result in users answering unwanted or scam calls and affecting their ability to reach intended recipients.
Innovation Solution
A caller ID verification platform that collects contact lists from user devices, collates contact name variations for each unique telephone number, and designates the most frequently occurring name as the accurate caller ID, while also allowing subscribers to request trusted number status and using machine-learning algorithms to classify call patterns for more accurate caller ID information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If MNOs obtain caller ID information from third-party vendors, then the process is simple and fast, but the accuracy of caller ID information deteriorates
Solution Approach 1:
The patent introduces a mediator system that collects contact list information from multiple user devices and uses it to verify caller ID data. This intermediary layer between the third-party vendor and the final caller ID display allows for accuracy improvement without requiring direct changes to the vendor's system, thus managing complexity through a dedicated verification component.
Solution Approach 2:
The system implements feedback by collecting actual contact list data from user devices, comparing it with vendor-provided caller ID information, and using the discrepancy analysis to improve future caller ID accuracy. This feedback loop enables continuous improvement of information accuracy while maintaining a manageable verification process.
2Object-affected harmful factors
If MNOs use third-party vendor data, then implementation is easy, but harmful factors increase due to scam calls
Solution Approach 1:
The system performs preliminary verification by collecting and analyzing contact list information from multiple user devices before displaying caller ID data. This preliminary action allows the system to identify potential scam calls in advance by checking against aggregated contact data, thereby reducing harmful effects while maintaining verification efficiency through proactive rather than reactive measures.
3Measurement precision
If contact lists from multiple user devices are collected, then caller ID accuracy improves, but information processing complexity increases
Solution Approach 1:
The patent merges contact list information from multiple user devices into a unified dataset for verification purposes. By combining data sources and processing them through a centralized system, the patent achieves improved caller ID accuracy while managing complexity through consolidation rather than distributed processing across multiple independent systems.
Data Source
AI summary
A call pattern associated with a telephone number of a subscriber of a wireless carrier network is monitored. The call pattern is then analyzed via a machine-learning algorithm to classify the subscriber into a subscriber classification category of multiple subscriber classification categories. A determination is made as to whether the subscriber classification category of the subscriber corresponds to a service plan type of a specific wireless service plan subscribed to by the subscriber for the telephone number. When the subscriber classification category fails to correspond to the plan type, an offer of an additional wireless service plan that corresponds to the subscriber classification category of the subscriber is sent to a user device of the subscriber. When the subscriber classification category corresponds to the service plan type, a caller category label is assigned to the subscriber that indicates the subscriber classification category of the subscriber.