AI Voice Agent and AR/VR Interaction With Multimodal Personalization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing communication systems lack the ability to provide natural, context-aware, and personalized interactions across diverse user environments, particularly in augmented and virtual reality, while ensuring secure and efficient integration with backend systems and adaptive response generation.
Innovation Solution
A computer-implemented system utilizing advanced voice recognition, natural language processing, and reinforcement learning to generate contextually relevant responses, integrate with CRM and ERP systems, and apply biometric authentication, while dynamically adjusting tactile feedback based on user biometrics and preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If advanced voice recognition and natural language processing are implemented to provide context-aware interactions, then the quality of user interaction is improved, but the device complexity increases
Solution Approach 1:
The system divides complex voice processing into separate modules: automatic speech recognition converts voice to text, natural language processing analyzes intent and sentiment, and machine learning models generate responses. Each module handles a specific aspect of the interaction, making the overall complex system manageable and maintainable while providing high-quality context-aware interactions.
Solution Approach 2:
The patent introduces intermediary components such as conversation history management and sentiment analysis layers that mediate between raw voice input and system responses. These intermediaries process and structure information before passing it to response generation systems, enabling context-aware interactions without requiring complete reprocessing of all inputs at each level.
2Adaptability or versatility
If multiple modalities and sensors are integrated for personalized interactions, then the adaptability is improved, but the device complexity increases
Solution Approach 1:
The system merges multiple data sources including voice inputs, biometric sensors, and behavioral data into a unified user profile. This consolidation allows the system to access comprehensive user information through a single integrated structure, enabling personalized interactions across different contexts without managing separate complex systems for each data type.
Solution Approach 2:
The patent creates a universal user profile structure that can accommodate various types of data (voice patterns, biometric readings, behavioral metrics) and apply them across multiple interaction scenarios. This multi-functional profile system serves as a common foundation for personalization whether the interaction is voice-based, sensor-driven, or context-dependent, reducing the need for separate specialized systems.
3Measurement precision
If continuous learning and model updates are implemented using conversation logs, then the system accuracy is improved, but the loss of time for processing and updating increases
Solution Approach 1:
The system performs preliminary actions by continuously collecting and storing conversation logs and feedback data in structured formats during normal operation. This pre-processing and organization of learning data occurs in the background without interrupting service, so when model updates are needed, the prepared data can be quickly processed, reducing the actual update time while maintaining high accuracy through continuous learning.
4Reliability
If biometric authentication and encryption are implemented for security, then the reliability is improved, but the ease of operation decreases
Solution Approach 1:
The system implements self-service authentication by using biometric sensors to automatically capture and verify user identity without requiring manual intervention. The biometric data is collected and processed by the system itself, eliminating the need for users to manually enter credentials or follow complex authentication procedures, thereby maintaining high security while preserving ease of operation.
Data Source
AI summary
A computer-implemented method for operating an artificial intelligence voice agent system includes receiving voice input through communication channels; analyzing converted text through natural language processing (NLP) pipelines implementing intent recognition and sentiment analysis detecting emotional cues using a multimodal large language model (LLM); generating response content using machine learning models trained on domain-specific corpora; converting generated responses to synthetic speech through text-to-speech (TTS) engines; integrating with a customer relationship management (CRM) platforms or an enterprise resource planning (ERP) database; and implementing continuous learning by updating language understanding models using conversation logs, voice recognition parameters based on user feedback, and response generation patterns. One implementation is a computer-implemented system and method that operates a suite of intelligent interactive devices and platforms including an artificial intelligence voice agent, enhanced communication platforms, an intimacy companion system, and augmented/virtual reality eyeglasses. Further, one implementation includes AR/VR eyeglasses that project visual content onto interchangeable lenses or directly onto the user's retina via laser-based retinal projection, provide prescription adjustments, incorporate ear-mounted sensors for monitoring physiological parameters like heart rate, oxygen saturation, and blood pressure, and utilize wireless data transmission, onboard environmental sensing, and remote calibration, all designed to offer dynamically adaptive, secure, and context-aware interactions across communication, personal assistance, health monitoring, and immersive augmented or virtual reality environments.