Virtual Environment Object Emphasis Through NLP Matching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing virtual environments struggle to effectively draw attention to specific objects or information based on natural language conversations, leading to potential miscommunication and ambiguity among users.
Innovation Solution
A computer-implemented method using natural language processing and machine learning to analyze conversational inputs, identify elements within virtual environments, and apply emphasis effects such as highlighting or audio cues to draw attention to relevant objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If natural language processing and machine learning are applied to analyze conversational inputs and identify elements in virtual environments, then clarity and user understanding are improved, but system complexity and computational resources increase
Solution Approach 1:
The patent introduces an intermediary system comprising natural language processing modules and machine learning models that mediate between user conversations and virtual environment elements. This intermediary layer analyzes conversational inputs, identifies referenced elements, and applies emphasis effects, thereby resolving the contradiction by providing a structured approach to enhance communication clarity while managing system complexity through modular architecture
Solution Approach 2:
The patent replaces traditional manual or rule-based methods for identifying and emphasizing elements with automated natural language processing and machine learning systems. This substitution enables the system to automatically analyze conversations, identify elements, and apply emphasis effects without manual intervention, improving communication clarity while the automation helps manage the complexity through intelligent algorithms
2Measurement precision
If emphasis effects are applied to draw attention to specific objects based on conversation, then user attention and understanding are improved, but processing time and computational load increase
Solution Approach 1:
The patent implements preliminary action by pre-processing and indexing virtual environment elements before runtime conversations occur. Elements are tagged with metadata and organized in searchable structures in advance, allowing the natural language processing system to quickly match conversational references to elements during runtime, thereby improving identification accuracy while reducing real-time processing time
Solution Approach 2:
The patent applies partial action by selectively analyzing only relevant portions of conversational inputs that contain element references rather than processing entire conversations. The system identifies and focuses computational resources on specific phrases or sentences that mention virtual environment elements, improving element identification accuracy while minimizing overall processing time by avoiding unnecessary analysis of irrelevant conversation portions
Data Source
AI summary
A computer implemented method may include: analyzing, via natural language processing, a conversational input between a plurality of users to identify a description of an element; applying, via a machine learning model, a matching procedure between the conversational input and a virtual environment to identify the element matching the description of the element; generating an emphasis representation in a programmatic model of the virtual environment based on the description of the element; and applying an emphasis effect to the element within the virtual environment corresponding to the emphasis representation.