Voice-Controlled Information Retrieval via Machine Learning Intermediary
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems for information access and retrieval within internal networks often require external individuals to wait on hold or consume significant networking resources, as they rely solely on agents to respond to user requests, leading to inefficient use of computational and bandwidth resources.
Innovation Solution
Implementing machine learning algorithms to automatically determine whether a user request is suitable for automatic response or requires agent intervention, thereby reducing wait times and resource consumption by converting voice signals to text, generating responses, and providing real-time suggestions to agents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If external individuals connect to an agent over an external network to obtain information from the internal network, then the information access is achieved, but significant networking and bandwidth resources are consumed
Solution Approach 1:
The patent introduces an automated response system as an intermediary between external users and the internal network. This system receives requests from external users, converts voice signals to text, processes requests through machine learning algorithms, and generates responses without requiring direct connection to human agents. The intermediary handles routine information retrieval automatically, reducing the need for external network connections and bandwidth consumption while maintaining reliable information access.
Solution Approach 2:
The system enables self-service by allowing external users to obtain information automatically without human agent intervention. The machine learning algorithms process requests independently, retrieve information from the internal network autonomously, and generate responses directly to users. This self-service capability eliminates the need for continuous external network connections to agents, significantly reducing bandwidth resource consumption while maintaining information access reliability.
2Reliability
If external individuals are placed on hold while waiting to speak to an agent, then the request can be processed by an agent, but significant time is consumed
Solution Approach 1:
The system performs preliminary actions by automatically processing requests before human agents are needed. When external users submit requests, the machine learning algorithms immediately begin processing them, converting voice to text, searching the internal network for information, and preparing responses. This preliminary automated processing eliminates the need for users to wait on hold while agents become available, as the system handles routine requests independently and simultaneously.
Solution Approach 2:
The automated response system provides self-service processing for routine information requests, eliminating the need for users to wait for human agents. The machine learning algorithms independently handle request processing, information retrieval, and response generation without requiring user waiting time. This self-service capability maintains reliable request processing while eliminating hold times completely for automated requests.
3Measurement precision
If agents manually locate requested information, then accurate information can be provided, but significant time and computational resources are consumed
Solution Approach 1:
The patent replaces the manual mechanical process of agents locating information with an automated electronic system. Machine learning algorithms automatically process requests, convert voice signals to text, search the internal network database, and retrieve information without human intervention. This substitution maintains information accuracy through systematic processing while dramatically improving resource efficiency by eliminating the time and computational resources consumed by manual agent operations.
Solution Approach 2:
The system performs self-service information retrieval by autonomously processing requests and locating information without human agents. The machine learning algorithms independently execute the complete workflow from request reception to information retrieval and response generation. This self-service capability maintains measurement precision through consistent automated processing while significantly improving productivity by eliminating the resource consumption associated with manual agent operations.
Data Source
AI summary
An apparatus includes a memory and a processor. The memory stores first and second machine learning algorithms. The processor receives, from a user, voice signals associated with an information request and converts them into text. The processor uses the first machine learning algorithm to determine, based on the text, to automatically generate a reply to the request, rather than transmitting the request to an agent. The processor uses the second machine learning algorithm to generate, based on the set of text, the reply, which it transmits to the user. The processor receives feedback associated with the reply, indicating that the reply does or does not include the requested information. The processor uses the feedback to update either or both machine learning algorithms.