Dynamic Endpoint Communication Channels for Intent-Based Routing
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Solution Overview
Problem
Existing virtual assistant technologies require users to conform to rigid protocols for communication, limiting their ability to perform tasks beyond those they are trained for and preventing them from learning new tasks organically.
Innovation Solution
A concierge artificial intelligence service that processes natural language communications, determines user intent, and selects appropriate endpoints to assist with tasks, allowing users to interact without adhering to specific protocols.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If virtual assistants use rigid communication protocols, then system structure is maintained, but adaptability to new tasks deteriorates
Solution Approach 1:
The system dynamically selects communication protocols based on the task type and endpoint capabilities. Instead of using a fixed rigid protocol, the virtual assistant adapts the communication format and interaction pattern to match the specific task requirements, enabling both structural stability and task versatility
Solution Approach 2:
The system changes communication parameters such as protocol type, data format, and interaction mode based on the detected task intent. This allows the same virtual assistant infrastructure to handle diverse tasks by adjusting communication parameters rather than requiring separate rigid protocols for each task type
2Reliability
If virtual assistants are trained for specific tasks, then task performance is reliable, but ability to learn new tasks organically deteriorates
Solution Approach 1:
The system introduces an intermediary layer between the trained virtual assistant models and the user requests. This intermediary detects task intent, selects appropriate endpoints with relevant expertise, and facilitates organic learning by routing new task types to endpoints that can handle them, thereby maintaining reliability for trained tasks while enabling learning of new tasks
Solution Approach 2:
The virtual assistant system is designed with multi-functionality to handle both pre-trained tasks and new organic tasks. By incorporating endpoint selection and intent detection, the system becomes universal in its capability to process various task types, combining the reliability of trained models with the flexibility to learn new tasks through endpoint referrals
3Adaptability or versatility
If multiple endpoints are available, then task completion capability improves, but endpoint selection complexity increases
Solution Approach 1:
The system uses feedback from task intent detection and endpoint performance to make intelligent selection decisions. By analyzing the user's request, detecting the task type, and selecting endpoints based on their expertise and past performance, the system reduces selection complexity while maintaining high task completion capability across diverse tasks
Data Source
AI summary
The present disclosure relates generally to systems, methods, and computer-readable storage media for providing a concierge service to handle a wide variety of topics and user intents via a common interface. The concierge service can be part of a connection management system that can dynamically manage and facilitate conversations between a user making a request or providing an instruction and one or more endpoints for the purposes of fulfilling the request or instruction. Such dynamic management may include transferring a communication session to a social network member endpoint based on an intent identified within natural language communications, tracking a dynamic sentiment score, and automatically switching the communication session to another endpoint based on a change in the dynamic sentiment score.