Telephony Risk Assessment via ML Call Log Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current technologies face challenges in effectively enforcing regulations against nuisance and fraudulent calls due to the inability to accurately determine the legitimacy of incoming calls, as existing methods rely on unreliable caller ID information and lack the necessary insights to differentiate between legitimate and illegitimate communications.
Innovation Solution
A distributed system for conducting risk assessments in telephony communications, utilizing a machine learning engine to generate models based on call log records, which processes incoming call information to assign a category and likelihood value, enabling endpoint communication devices to present this information to users for decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If carrier collects personal information to enforce regulations, then enforcement capability is improved, but consumer privacy is compromised and information collection cost increases
Solution Approach 1:
The patent extracts only the essential information needed for enforcement (caller ID, call timing, call frequency) from the broader set of personal information that could be collected. This selective extraction enables regulation enforcement while preserving consumer privacy by not collecting unnecessary personal data.
Solution Approach 2:
The patent introduces an intermediary analysis system that processes call data and generates risk scores without requiring carriers to directly access or store sensitive personal information. This intermediary layer enables enforcement capability while acting as a privacy shield between raw data and enforcement decisions.
2Reliability
If carrier monitors all incoming calls to detect fraudulent activity, then security is improved, but system complexity and processing overhead increase
Solution Approach 1:
The patent applies preliminary risk assessment by analyzing call patterns and generating risk scores before calls are connected. This preliminary action identifies potentially fraudulent calls in advance, enabling security measures to be applied only to high-risk calls rather than all calls, thereby reducing system complexity.
Solution Approach 2:
The patent applies different levels of monitoring and analysis to different calls based on their risk profiles. High-risk calls receive intensive scrutiny while low-risk calls receive minimal processing, creating a localized quality approach that improves security where needed without unnecessarily complicating the entire system.
3Ease of operation
If traditional caller ID verification is used, then implementation simplicity is maintained, but accuracy in identifying legitimate calls deteriorates
Solution Approach 1:
The patent transforms the single parameter of caller ID into a multi-parameter risk assessment model that includes call timing, frequency, duration, and pattern analysis. This parameter expansion maintains relative implementation simplicity while dramatically improving the accuracy of call legitimacy identification through composite scoring.
Data Source
AI summary
Systems and methods for using machine-learning techniques for labeling incoming calls with categories relating to a risk level. A model is generated using call log data. The call log data is augmented using information from additional data sources to generate features for the model. The model may then be used to categorize additional incoming calls. The model may be used in real-time to categorize incoming calls, or categorization results may be stored for a plurality of calling numbers. Various embodiments provide various technical advantages by virtue of how the components of the system are deployed between an endpoint communication device, a telephony provider system, and possibly other systems.