Natural Language Intent Recognition for VR Object Rendering
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Solution Overview
Problem
Current virtual reality (VR) and augmented reality (AR) systems rely on unnatural input methods, such as physical controllers, which limit user interaction and can be cumbersome, whereas natural language processing is not effectively integrated for intuitive interaction within virtual environments.
Innovation Solution
A computer-implemented method using machine learning models to identify user intents from natural language inputs, allowing for the generation and rendering of virtual objects in VR or AR environments, enabling users to interact naturally without specific activation phrases, and improving the accuracy of intent identification and resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional input devices (keyboards, mice, controllers) are used in VR/AR systems, then device complexity and control precision are maintained, but ease of operation and naturalness of interaction deteriorate
Solution Approach 1:
The patent replaces mechanical input devices (controllers, keyboards, mice) with acoustic field-based natural language processing. Users speak commands naturally without physical interaction devices, transforming mechanical interaction into acoustic signal processing. The system captures spoken commands via microphones, processes them through NLP pipelines, and executes corresponding actions, eliminating the need for specialized VR/AR controllers.
Solution Approach 2:
The patent introduces natural language processing and machine learning models as intermediaries between the user and the VR/AR system. Instead of direct mechanical control, user intent is conveyed through spoken language, which is interpreted by NLP components that translate speech into system commands. This intermediary layer enables natural interaction while maintaining system control.
2Ease of operation
If natural language processing is integrated into VR/AR systems, then ease of operation improves, but measurement precision and reliability of intent recognition deteriorate
Solution Approach 1:
The patent segments the natural language processing pipeline into multiple specialized components: speech-to-text conversion, intent classification, entity recognition, and command execution. Each component handles a specific aspect of processing, allowing for targeted optimization and error correction at each stage. This modular approach improves overall accuracy by addressing specific processing challenges independently.
Solution Approach 2:
The patent implements feedback mechanisms where the system confirms interpreted intents to users before execution, and learns from correction patterns. Machine learning models are continuously trained on user interactions and corrections, improving intent recognition accuracy over time. The system adapts to individual user speech patterns and preferences, enhancing precision through experience.
3Productivity
If machine learning models process streaming natural language inputs in real-time, then productivity and responsiveness improve, but use of energy and computational resources increases
Solution Approach 1:
The patent applies partial processing by analyzing only the most salient features of incoming speech streams rather than processing every detail. The system uses incremental processing where previously analyzed context is retained, and only new information requires full processing. This reduces computational overhead while maintaining real-time responsiveness to user commands.
Solution Approach 2:
The patent implements periodic processing intervals where full machine learning model inference occurs at optimized frequencies, with lighter-weight processing between intervals. The system batches similar processing tasks and uses caching mechanisms to reduce redundant computations, maintaining responsiveness while managing energy consumption through rhythmic processing cycles.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques for rendering visual artifacts in virtual worlds using machine learning models. An example method generally includes identifying, based on a machine learning model and a streaming natural language input, an intent associated with the streaming natural language input; generating, based on the identified intent associated with the streaming natural language input, one or more virtual objects for rendering in a virtual environment displayed on one or more displays of an electronic device; and rendering the generated one or more virtual objects in the virtual environment.