ML Digital Assistant for Real-Time Service Transcript Analysis
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Solution Overview
Problem
Existing technologies face challenges in reliably and efficiently identifying and addressing processing events on client devices without expending additional computing resources, due to variations in device configurations and application data processing types.
Innovation Solution
A data processing system utilizing large language models hosted on scalable cloud-based infrastructure for real-time natural language processing, generating queries to provide content recommendations, agent routing suggestions, and targeted automation solutions by transcribing ongoing user-service provider communications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time transcription and analysis of communication sessions is implemented, then client issue identification accuracy is improved, but computing resource consumption increases
Solution Approach 1:
The system performs preliminary actions by pre-processing audio samples into transcripts and pre-training machine learning models on historical performance event data before actual client interactions. This allows the system to have ready-to-use analysis capabilities without performing heavy computation in real-time during client communications, thus improving identification accuracy while controlling resource consumption.
Solution Approach 2:
The patent introduces an intermediary machine learning model that acts as a mediator between the raw communication data and the final issue identification. The model translates audio transcripts into structured performance event detections, enabling accurate client issue identification without requiring complex real-time processing of raw audio data, thereby reducing computing resource requirements.
2Reliability
If comprehensive analysis of communication transcripts is performed, then troubleshooting service reliability is improved, but processing time increases
Solution Approach 1:
The system extracts only the essential and relevant portions of communication transcripts that contain performance event indicators, rather than analyzing the entire transcript comprehensively. By focusing on key trigger phrases and performance-related segments, the system maintains high troubleshooting reliability while significantly reducing processing time.
Solution Approach 2:
The patent applies partial action by performing analysis on selected portions of transcripts that are most likely to contain performance issues, rather than analyzing every word. The machine learning model identifies and focuses on critical segments containing performance event keywords and patterns, achieving reliable detection with reduced processing overhead.
3Measurement precision
If machine learning models are trained on historical data, then performance event detection accuracy is improved, but model training complexity increases
Solution Approach 1:
The patent develops a universal machine learning model that can detect multiple types of performance events across different applications and devices using a single trained model. This multi-functional approach improves detection accuracy for various performance issues while reducing the complexity that would arise from training separate specialized models for each type of performance event.
4Productivity
If real-time detection prior to session termination is implemented, then service response efficiency is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary detection of performance events during the communication session before it terminates. By continuously monitoring transcripts and detecting performance events in advance, the system can provide real-time alerts and recommendations while the session is still active, improving service response efficiency without requiring complex post-session analysis systems.
Data Source
AI summary
A machine learning (ML)-based service performance digital assistant is provided. The system can include a computing system with a processor and memory that can analyze an electronic transcript from audio samples of a client-service communication and detect a trigger phrase related to the performance event of an application using a first ML model. The system can generate, based on the trigger phrase, a search query using a second ML model trained on data corresponding to performance events. The system can select, based on the search query, an electronic resource via a search engine and send it to the provider device during the ongoing communication session between the client and the service device.