Multi-faceted Bot System with Adaptive Interaction Mode Switching
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Solution Overview
Problem
Existing bots lack the ability to provide seamless customer interactions across multiple modes such as chat, audio, and video, and do not adapt to user preferences or network conditions, leading to a suboptimal user experience.
Innovation Solution
A system and method utilizing a machine learning-based architecture to generate and switch between textual, audio, and visual responses based on user preferences and network conditions, enabling a 3-in-one Chat, Audio, and Video service integration with bot capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing bots provide only text-based interaction, then implementation is simple, but user experience is suboptimal and lacks personalization
Solution Approach 1:
The bot system is designed to perform multiple interaction functions (text, audio, video) through a single unified platform. The bot maker engine can generate bots that automatically switch between different interaction modes based on user preferences and network conditions, eliminating the need for separate text-only, audio-only, or video-only bot systems.
Solution Approach 2:
The bot system dynamically adapts its interaction mode based on real-time conditions. The ML engine continuously monitors user preferences, network strength, and device capabilities to dynamically switch between text, audio, and video modes, making the bot flexible and adaptive rather than static.
2Ease of operation
If bots use video streaming for visual responses, then user experience is enhanced, but network performance degrades under poor network conditions
Solution Approach 1:
The system changes the parameter of interaction mode based on network conditions. When network strength is high, the bot provides video responses for enhanced user experience. When network strength deteriorates, the bot automatically switches to audio or text modes, maintaining reliability and usability under varying network parameters.
Solution Approach 2:
The bot dynamically adjusts its response format based on real-time network monitoring. The ML engine detects network degradation and automatically transitions from resource-intensive video streaming to lighter audio or text interactions, ensuring continuous reliable operation across different network conditions.
3Adaptability or versatility
If bots provide personalized interaction based on user preferences, then user experience is improved, but system complexity increases
Solution Approach 1:
The bot maker engine automatically generates personalized bot configurations without requiring manual intervention. The ML engine autonomously analyzes user preferences from conversation history and device information, then automatically configures the appropriate interaction modes, eliminating the need for complex manual personalization setups.
Solution Approach 2:
The system implements continuous feedback loops where the ML engine monitors user interactions, preferences, and device information to automatically adjust personalization parameters. This feedback mechanism enables the bot to learn and adapt to user preferences over time, improving personalization while using automated processes rather than manual configuration.
4Adaptability or versatility
If bots support multiple interaction modes (text, audio, video), then user experience is enhanced, but device complexity increases
Solution Approach 1:
The patent merges text, audio, and video interaction capabilities into a single unified bot system. The bot maker engine creates one bot that can handle all three interaction modes, consolidating what would traditionally require separate bot implementations into a single integrated solution.
Solution Approach 2:
The bot system is designed as a universal platform capable of performing multiple interaction functions through a single architecture. The ML engine and bot maker engine work together to provide text, audio, and video responses through one unified system, reducing the need for multiple specialized bot systems.
Data Source
AI summary
The present disclosure relates to a system and method for generating an executable multi-faceted specific to an entity. In an exemplary implementation, the proposed system receives a knowledgebase comprising a set of potential queries associated with the entity, and receives responses that can be switched to a video form, an audio form or a textual form corresponding to the potential queries based on any or a combination of user preference, network conditions and user device features. The system processes, through a machine learning model, training data comprising the set of potential queries, the video frame responses, and the intent mapped to each potential query to generate a trained model, based on which a prediction engine is configured to process an end-user query and predict an intent associated with the end-user query, and facilitate response to the end-user query based on video frame response that is mapped with the predicted intent.