Communications Recording Controls for Real-Time Compliance Analytics
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Solution Overview
Problem
Existing systems struggle to automatically establish recording parameters according to specified recording restrictions and generate analytics for communications sessions while ensuring compliance with legal and user consent requirements.
Innovation Solution
A computer-implemented method and system that dynamically establishes recording parameters based on real-time identification of recording restrictions, processes communications in real-time to generate transcriptions and agent analytics, and uses machine learning to infer user sentiment and generate agent training needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the system processes communications in real-time to generate transcriptions and analytics, then productivity is improved, but device complexity increases due to the need for dynamic recording parameter establishment and compliance monitoring
Solution Approach 1:
The system establishes recording parameters and identifies recording restrictions before processing communications. By determining compliance requirements and configuring recording settings in advance, the system enables real-time processing without complex dynamic decision-making during communication flow.
Solution Approach 2:
A compliance determination module acts as an intermediary between the communication processing system and recording restrictions. This module interprets recording restrictions, determines compliance status, and configures recording parameters automatically, reducing the complexity burden on the main processing system.
2Measurement precision
If the system dynamically trains machine learning algorithms using historic recordings and feedback, then measurement precision of user sentiment analysis is improved, but loss of time increases due to continuous training requirements
Solution Approach 1:
The system uses feedback from historic communications and recordings to dynamically train machine learning algorithms. By continuously learning from past data and adjusting models accordingly, the system improves sentiment analysis precision while managing training time through efficient learning techniques.
Solution Approach 2:
The system pre-trains machine learning models using historic recordings before they are needed for real-time sentiment analysis. This preliminary training prepares the models to perform accurate sentiment analysis without requiring extensive computation time during actual communication processing.
3Reliability
If the system automatically identifies and processes recording restrictions in real-time, then reliability of compliance is improved, but device complexity increases due to the need for automated restriction identification and parameter adjustment
Solution Approach 1:
The system automatically identifies recording restrictions and configures recording parameters without manual intervention. By implementing self-service functionality where the system monitors compliance requirements and adjusts settings autonomously, the patent improves compliance reliability while managing complexity through automation.
Solution Approach 2:
The system determines recording restrictions and configures recording parameters before processing communications. By establishing compliance settings in advance based on identified restrictions, the system ensures reliable compliance while reducing the complexity of real-time adjustments.
Data Source
AI summary
Disclosed embodiments provide a framework for automatically establishing recording parameters according to specified recording restrictions and generating analytics corresponding to communications recorded subject to the recording restrictions. During a communications session between a user and an agent, a system can identify any recording restrictions corresponding to user communications exchanged during the communications session. The system automatically processes, in real-time, communications exchanged during the communications session as these communications are exchanged to identify the user communications and agent communications. The system generates a transcript that includes the agent communications but selectively records and transcribes the user communications according to the recording restrictions. A machine learning algorithm is trained to generate a set of inferences corresponding to a user sentiment based on historic recordings and transcripts of historic communications sessions between users and agents, as well as corresponding feedback. From the set of inferences, the system generates agent analytics.