Voice Request Routing via Machine Learning Feedback
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Solution Overview
Problem
Existing systems for information retrieval within internal networks are inefficient, as external users often need to wait on hold or experience difficulties in accessing information, consuming significant networking and bandwidth resources.
Innovation Solution
An apparatus with machine learning algorithms that convert voice requests into text, determine if automatic responses can be generated, and either provide the response or forward the request to an agent, optimizing resource usage by distinguishing between suitable automatic and agent-driven responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If external individuals connect to an agent over an external network to access information stored within an internal network, then the information can be retrieved, but significant networking and bandwidth resources are consumed
Solution Approach 1:
The patent introduces an automated response system that acts as an intermediary between external individuals and the internal network. This system includes a voice-to-text conversion module, a machine learning model for intent classification, and an automated response generation module. The intermediary processes requests locally without requiring direct connection to the internal network, thereby enabling information retrieval while minimizing networking and bandwidth resource consumption.
Solution Approach 2:
The patent implements a self-service mechanism where the automated response system independently handles information retrieval requests from external individuals. The system converts voice requests to text, classifies intent using machine learning, queries the internal network autonomously, and generates responses without human intervention. This self-service approach eliminates the need for external individuals to connect to agents, significantly reducing networking and bandwidth resource usage while maintaining reliable information access.
2Reliability
If external individuals are placed on hold while waiting to speak to an agent, then the request can be processed, but time is lost and networking resources are consumed
Solution Approach 1:
The patent implements preliminary action by having the automated response system immediately process voice requests as they are received, converting them to text and classifying intent before the individual would otherwise be placed on hold. The system proactively queries the internal network and generates responses in advance, eliminating the need for waiting periods while ensuring reliable request processing.
Solution Approach 2:
The automated response system provides self-service by independently handling the entire request processing workflow without requiring human agent intervention. The system converts voice to text, classifies intent, retrieves information from the internal network, and generates responses automatically. This eliminates hold times completely while maintaining reliable request processing, as the system operates autonomously without human involvement.
3Measurement precision
If agents manually locate requested information within the internal network, then accurate information can be provided, but time is lost and agent resources are consumed
Solution Approach 1:
The patent replaces the mechanical process of manual information location with an automated computational system. The machine learning model classifies intent from voice requests, automatically queries the internal network using structured protocols, and retrieves precise information without human intervention. This substitution maintains information accuracy through systematic data retrieval while dramatically improving agent productivity by eliminating manual search processes.
Solution Approach 2:
The automated response system performs self-service by independently executing the entire information retrieval process. It converts voice requests to text, classifies intent using machine learning, autonomously queries the internal network, and generates accurate responses without requiring agent involvement. This self-service mechanism ensures measurement precision through systematic data retrieval while maximizing productivity by completely eliminating manual agent work for routine information requests.
4Loss of energy
If the system automatically generates responses to voice requests, then resource consumption is reduced, but the accuracy of information retrieval may be compromised
Solution Approach 1:
The patent implements feedback mechanisms where the automated response system continuously learns from user interactions and response outcomes. The machine learning model is trained on historical voice requests and their corresponding accurate responses, enabling it to improve its intent classification and information retrieval accuracy over time. This feedback loop allows the system to maintain high measurement precision while operating autonomously with minimal resource consumption.
Solution Approach 2:
The patent replaces manual agent information location with an automated machine learning-based system that uses structured protocols to query the internal network. The system converts voice requests to text, classifies intent with high precision using trained models, and retrieves accurate information through systematic automated processes. This mechanical substitution maintains measurement precision through algorithmic accuracy while dramatically reducing computational and networking resource consumption by eliminating redundant human involvement.
Data Source
AI summary
An apparatus includes a memory and a processor. The memory stores a machine learning algorithm configured to select between forwarding a request to an agent device and transmitting an automatically generated reply to the request. The processor receives feedback for a decision made by the algorithm, indicating whether the automatically generated reply includes the information sought by the request. If the algorithm decided to forward the request to the agent device, a reward is assigned to feedback that indicates that the reply does not include the information, while a punishment is assigned to feedback that indicates that the reply includes the information. If the algorithm decided to transmit the reply, a reward is assigned to feedback that indicates that the reply includes the information, and a punishment is assigned to feedback that indicates that the reply does not include the information. The processor updates the algorithm using the reward/punishment.