Multi-Mode Bot Interaction Switching for Seamless Omni-Channel Support
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Solution Overview
Problem
Existing customer service bots lack the ability to seamlessly switch between audio, video, and textual interactions based on user preference or network connection, requiring users to disconnect and reconnect for mode changes, and do not support personalized interaction or network strength-based adaptation.
Innovation Solution
A system and method enabling a multi-bot interface that integrates voice, video, and text capabilities, allowing users to toggle between modes using machine learning to provide a unified, omni-channel experience with automatic mode switching and human agent handover.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing bots support only text or voice interaction modes, then the bot implementation is simple, but users cannot seamlessly switch between modes and must disconnect and reconnect
Solution Approach 1:
The patent implements a unified bot architecture that supports multiple interaction modes (text, voice, video) within a single system. The bot engine can dynamically switch between these modes based on user preference or network conditions, eliminating the need for separate bot implementations for each mode and resolving the contradiction between versatility and complexity.
Solution Approach 2:
The bot system dynamically adapts its interaction mode based on real-time conditions such as user preference settings and network strength. This dynamic switching capability allows the bot to seamlessly transition between text, voice, and video modes without requiring user disconnection, thereby improving interaction flexibility while maintaining manageable system complexity through centralized control.
2Ease of operation
If users must disconnect and reconnect to switch interaction modes, then the bot system is simple to implement, but user experience deteriorates due to interruption
Solution Approach 1:
The patent enables continuous interaction by maintaining the bot connection while allowing users to switch between interaction modes. The system preserves the active session and seamlessly transitions between text, voice, and video modes without requiring disconnection and reconnection, thereby eliminating interruption and saving user time while enhancing operational ease.
3Reliability
If existing bots do not support network strength-based adaptation, then the bot implementation is straightforward, but video streaming fails under poor network conditions
Solution Approach 1:
The bot system continuously monitors network strength and automatically adjusts the interaction mode based on detected network conditions. When poor network quality is detected, the system switches from video mode to voice or text mode to maintain reliable communication. This feedback-driven adaptation ensures interaction reliability while managing system complexity through intelligent, condition-based decision-making.
4Adaptability or versatility
If existing bots lack personalized preference support, then the bot setup is simple, but users cannot choose their preferred interaction mode
Solution Approach 1:
The patent implements a user preference management system that allows users to define their preferred interaction modes and conditions. The bot respects these user-defined preferences and automatically adjusts its behavior accordingly, enabling personalized interaction experiences. This self-service approach to preference management enhances adaptability while keeping the system relatively simple by leveraging user input rather than complex automated decision-making.
Data Source
AI summary
Embodiments relate to a system for adapting a bot interaction mode, comprising a processor configured to execute instructions stored in a memory. Upon execution, the processor operates a plurality of bots, each in a distinct interaction mode selected from text, voice, or video, for respective interactions with corresponding users. The system detects and analyzes user queries, retrieves responses from a knowledgebase, and identifies corresponding intents forming part of ongoing bot interactions. It evaluates the ongoing interactions based on suitability of the current mode and auto-detection of user equipment. Based on this evaluation and the combined analysis of user queries, responses, and intents, the system dynamically suggests, in real time, a change in interaction mode for the ongoing bot interaction. This adaptive adjustment across the plurality of bots enhances user engagement, improves communication efficiency, and reduces cognitive load by selecting the most appropriate mode for each user context.