Cloud-Based Call Filtering with Dynamic Query Verification
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Solution Overview
Problem
Current call filtering systems are ineffective in blocking unwanted calls, particularly robocalls, due to limitations in differentiating between human and machine calls, and in filtering based on geographic location, with previous methods being unreliable and prone to spoofing, which makes it difficult for users to manage incoming calls effectively.
Innovation Solution
A user-customized call filtering system that utilizes a cloud-based platform with a dynamic query engine to generate queries for incoming calls, allowing users to create blacklists and whitelists, schedule filters based on geographic location and time, and includes a verification filter to determine human responses, thereby selectively blocking unwanted calls.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional call blocking devices compare incoming numbers with pre-stored lists, then calls from known numbers can be blocked, but robocallers can easily circumvent this by using different numbers or spoofing legitimate numbers
Solution Approach 1:
The system dynamically adjusts filtering criteria based on call patterns, geographic location, and time schedules rather than relying on static pre-stored number lists. This allows the filter to adapt to new robocalling techniques in real-time, preventing evasion through number switching or spoofing.
Solution Approach 2:
The system introduces an intermediary verification layer that analyzes call characteristics, geographic origin, and temporal patterns before allowing calls through. This intermediary assessment mechanism detects robocalls based on behavioral patterns rather than just number matching, making evasion significantly more difficult.
2Reliability
If users manually manage call blocking lists, then some unwanted calls can be blocked, but users spend excessive time managing lists and cannot keep up with new robocaller numbers
Solution Approach 1:
The system performs automatic call filtering based on geographic location, time schedules, and call pattern analysis without requiring users to manually maintain blocking lists. The system serves itself by autonomously identifying and blocking robocalls, freeing users from time-consuming list management while maintaining high blocking effectiveness.
Solution Approach 2:
The system pre-configures geographic location filters and time schedules before robocalls occur, establishing filtering criteria in advance. This preliminary setup allows automatic rejection of calls from high-risk areas or inappropriate times without requiring real-time user intervention.
3Reliability
If call filters block all unknown numbers, then robocalls are reduced, but important calls from unrecognized numbers may be missed
Solution Approach 1:
The system applies different filtering strictness to different geographic locations and time periods rather than uniformly blocking all unknown numbers. Calls from trusted geographic areas or during appropriate time schedules are allowed through with lower filtering intensity, reducing false positives while maintaining robust robocall blocking in high-risk scenarios.
Solution Approach 2:
The filtering mechanism dynamically adjusts its strictness based on real-time analysis of call characteristics, geographic origin, and temporal context. This dynamic adjustment allows the system to be more permissive with potentially important calls while maintaining high blocking rates for confirmed robocalls, balancing security with accessibility.
4Measurement precision
If sophisticated verification methods are used to detect robocalls, then filtering accuracy improves, but system complexity and processing time increase
Solution Approach 1:
The verification system is segmented into multiple independent analysis modules: geographic location verification, time schedule checking, call pattern analysis, and behavioral assessment. Each module operates independently with relatively simple logic, achieving high overall accuracy through modular decomposition rather than a single complex verification algorithm.
Solution Approach 2:
The system applies verification checks selectively based on risk assessment rather than uniformly to all calls. Low-risk calls receive minimal verification, while high-risk calls undergo more thorough analysis. This partial application of sophisticated verification maintains accuracy for critical cases while reducing overall system complexity and processing overhead.
Data Source
AI summary
A user-customized call filtering system and method for vetting and filtering incoming calls from unwanted callers including robocallers. The system and method includes a user-customized call filter software application hosted on a cloud-based system that communicates with a native call filter application stored on end-user devices including smart phones, over a wired or wireless communication network. The native call filter application includes a host of pages that are selected by and presented to, end-users to customize a blacklist and whitelist with telephone numbers associated with incoming calls that are unwanted and terminated, granted immediate access to end-users, or granted access to end-users based on predefined schedule filters that are defined by geographic location of originating callers, geographic location of end-users, and/or on a particular date, day, and/or time. The system and method includes a dynamic query engine that generates and presents queries to callers associated with unknown and unverified incoming calls to solicit human responses for determining whether such callers are human or likely robocallers.