Humanoid Customer Support Routing for Surge-Scale Service Quality
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Solution Overview
Problem
Companies face challenges in providing timely and effective customer support due to staffing resource limitations and unpredictable surges in support requests, often resulting in long wait times and ineffective service, especially during large-scale events.
Innovation Solution
A humanoid system utilizing machine learning models and automation capabilities to provide automated customer support, capable of identifying and resolving issues independently or with human oversight, mimicking human dialog to handle various support campaigns, and integrating with external systems for complex tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human operators are used to provide customer support, then service quality and adaptability are improved, but wait times increase and productivity decreases during large-scale events
Solution Approach 1:
The patent segments customer support into two distinct pathways: routine issues handled by automated systems and complex issues escalated to human operators. This segmentation allows the system to maintain high productivity for common queries while preserving service quality for specialized problems, directly resolving the contradiction between automated capacity and human-quality support.
Solution Approach 2:
The patent introduces an automated support system as an intermediary between customers and human operators. This intermediary handles the majority of routine support requests, freeing human operators to focus on complex issues that require human judgment and adaptability, thereby increasing overall productivity without compromising service quality.
2Adaptability or versatility
If more human operators are hired to handle support requests, then service coverage is improved, but operational cost increases
Solution Approach 1:
The patent implements a self-service automated support system that handles routine customer inquiries without requiring additional human operators. This self-service capability expands service coverage to handle more requests simultaneously while avoiding the increased operational costs that would result from hiring additional staff.
Solution Approach 2:
The patent uses machine learning models that are trained on historical support interactions to create virtual agents that replicate human problem-solving capabilities. These copied intelligence models can handle multiple queries simultaneously without the linear cost increase associated with hiring more human operators, thereby expanding service coverage cost-effectively.
3Productivity
If automated systems are used to handle support requests, then productivity is improved, but service quality and adaptability decrease
Solution Approach 1:
The patent implements a dynamic support system that automatically routes queries based on their complexity and the capabilities of available resources. Routine queries are dynamically assigned to automated systems for high-speed handling, while complex queries are dynamically escalated to human operators, ensuring both productivity and service quality are optimized according to the specific needs of each interaction.
Solution Approach 2:
The patent incorporates feedback mechanisms where human operators review and correct automated system responses, and where customer satisfaction data feeds back into the machine learning models. This continuous feedback loop enables the automated system to learn from human expertise and improve service quality over time while maintaining high productivity.
4Adaptability or versatility
If human operators handle all support requests, then adaptability to complex issues is improved, but wait times increase during surges
Solution Approach 1:
The patent implements preliminary action by having the automated system immediately respond to and resolve routine support issues before they can queue up for human operator attention. This preliminary handling of common queries eliminates wait times for those issues while preserving human operator availability for complex problems that genuinely require human adaptability.
Solution Approach 2:
The patent segments support requests by complexity level, automatically identifying and routing routine issues to automated handling while escalating only complex issues requiring human adaptability. This segmentation eliminates unnecessary wait times for routine queries while ensuring complex issues receive appropriate human attention, optimizing both response time and adaptability.
Data Source
AI summary
A computer executed process for mimicking human dialog, referred to herein as a “humanoid” or “humanoid system,” can be configured to provide automated customer support. The humanoid can identify a support issue for a customer, as well as a customer support campaign corresponding to the support issue. The humanoid can identify at least one machine learning model associated with the customer support campaign and can communicate with the customer using the at least one machine learning model. The humanoid can execute a support action to resolve the support issue.