Machine Learning Feedback Management for Dispersed Customer Data
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Solution Overview
Problem
Enterprises face challenges in extracting valuable insights from dispersed customer feedback data across online forums and live interactions due to its time-consuming and error-prone manual processing, limited by customer availability and technical obstacles.
Innovation Solution
Implementing an AI-based feedback data management system that utilizes large language models and retrieval augmented generation techniques to generate virtual feedback profiles by combining and vectorizing data from various sources, including internal and external platforms, to provide context-specific responses to user queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual extraction of insights from dispersed feedback data is performed, then data accuracy can be maintained through human judgment, but the process becomes time-consuming and error-prone
Solution Approach 1:
The patent introduces an AI-based feedback data management system as an intermediary between raw feedback data and insight extraction. This system uses large language models and retrieval augmented generation to automatically process dispersed feedback data from multiple sources, eliminating the need for manual extraction while maintaining accuracy through advanced NLP techniques.
Solution Approach 2:
The patent replaces the manual mechanical process of data extraction with an automated AI-based system. The mechanical system of human analysts reading and extracting insights is substituted with an electronic system using large language models that can process data at scale without time constraints or human error.
2Loss of information
If live customer interactions are used as a source of insights, then valuable real-time feedback can be obtained, but customer availability and technical obstacles limit these opportunities
Solution Approach 1:
The patent creates a universal feedback data management system that can handle multiple types of data sources simultaneously - both structured live customer interactions and unstructured dispersed feedback from online forums. The AI system adapts to process different data formats and sources through its multi-functional architecture, making the system versatile in data collection.
Solution Approach 2:
The patent performs preliminary action by pre-processing and organizing dispersed feedback data from multiple sources before analysis. The system proactively collects, cleans, and structures data from various sources in advance, making it readily available for insight extraction without waiting for specific customer interactions or encountering availability issues.
3Loss of information
If dispersed feedback data from multiple sources is collected, then comprehensive insights can be obtained, but the data becomes difficult to manage and analyze
Solution Approach 1:
The patent merges dispersed feedback data from multiple sources into a unified data structure. The AI-based system combines data from online forums, live interactions, and other sources into a single manageable format, eliminating the complexity of handling separate data streams while maintaining the completeness of feedback from all sources.
Solution Approach 2:
The patent introduces an AI-based feedback data management system as an intermediary between raw feedback data and insight extraction. This system uses large language models and retrieval augmented generation to automatically process dispersed feedback data from multiple sources, eliminating the need for manual extraction while maintaining accuracy through advanced NLP techniques.
Data Source
AI summary
An apparatus includes at least one processing device including a processor coupled to a memory, wherein the at least one processing device is configured to modify first data obtained from one or more sources, wherein the modifying includes adding user context data to the first data to generate second data, the second data representing the first data supplemented with a per-user context and, in response to receipt of a query, generate a response to the query using at least one generative language model supplemented by a retrieval augmented generation process based on at least a portion of the second data.