Caller Reputation Analysis for Robocall Detection
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Solution Overview
Problem
Robocalls and unwanted communications continue to be a significant issue despite regulatory efforts, with nefarious callers using spoofing technology to evade detection, making it difficult for consumers to identify and prevent such calls.
Innovation Solution
A communications device system that generates a reputation value for a called party based on analysis from communications service providers, caller-name lookup services, and identity information providers, which is then used to display a visual indicator or alert the user before initiating a call, potentially preventing unwanted communications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If spoofing technology is used to mask caller identity, then caller anonymity is improved, but detection accuracy deteriorates
Solution Approach 1:
The patent introduces a called-party analyzer as an intermediary component that sits between the communication network and the calling device. This analyzer receives caller identifiers, performs reputation analysis using multiple data sources (call detail records, crowd-sourced data, machine learning models), and returns reputation values to the calling device. This intermediary approach enables sophisticated detection without requiring complex changes to the core telephony infrastructure, thereby improving caller identity verification while managing system complexity.
2Measurement precision
If real-time reputation analysis is performed for every call, then call filtering accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary reputation analysis by pre-processing and storing reputation data in databases before actual calls occur. Call detail records, crowd-sourced reputation data, and machine learning models are prepared in advance. During the call setup phase, the system queries pre-computed reputation values rather than performing full analysis in real-time, significantly reducing call setup time while maintaining assessment accuracy.
Solution Approach 2:
The system implements a tiered reputation analysis approach where not all callers receive the same level of analysis. High-risk callers (based on initial filters) receive comprehensive multi-source reputation analysis, while low-risk callers receive simplified verification. This partial action approach maintains high accuracy for problematic calls while reducing processing overhead for normal calls, balancing precision with time efficiency.
Data Source
AI summary
In one implementation, a communications device operable by a calling party may include one or more processors configured to receive an identifier associated with a called device from the calling party, and receive an input command to establish a communications session with the called device from the calling party. After the receiving of the input command, the one or more processors may attempt to establish the communications session with the called device. Further, the one or more processors may transmit the identifier to a called-party analyzer. The called-party analyzer, in response to receiving the identifier, may generate a reputation value based on analysis of data originating from at least one of: (i) a communications service provider, (ii) a caller-name lookup service provider, and (iii) an identity information provider. The reputation value may be indicative of a likelihood that at least one of the called device and a called party associated with the called device is involved in attempting to establish unwanted communications sessions. Further, the called-party analyzer may transmit the reputation value destined for the communications device. The communications device may receive the reputation value originating from the called-party analyzer and cause an output generated based on the reputation value on an output the communications device.