Virtual Environment Action Suggestion System
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Solution Overview
Problem
Users in immersive virtual environments often face challenges in discovering available actions and interactions within unfamiliar locations, leading to a tedious process of trial and error, and existing solutions require extensive time and effort to provide instructions.
Innovation Solution
A computer-implemented method that monitors user interactions and creates a searchable actions index based on user histories, matching characteristics to suggest relevant actions graphically within the virtual environment, reducing the need for extensive instruction preparation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users explore unfamiliar locations through trial and error, then they can discover available actions, but the process is tedious and time-consuming
Solution Approach 1:
The system performs preliminary action by pre-processing and indexing action data from multiple users before the current user needs to discover actions. User action histories are collected, processed, and stored in an actionable format in advance, so when a user enters a new location, the system can immediately query and present relevant suggested actions without requiring the user to explore through trial and error.
Solution Approach 2:
The system introduces an intermediary mechanism - a suggestion engine that acts as a mediator between the user and the virtual environment's action database. This intermediary processes user queries against indexed action data and returns curated suggestions, eliminating the need for users to directly interact with all possible actions through trial and error.
2Ease of operation
If extensive instructions are provided to guide users, then users can understand available actions, but preparation time and effort increase significantly
Solution Approach 1:
The system implements self-service by enabling users to autonomously discover relevant actions through intuitive queries without requiring pre-prepared comprehensive instructions. Users can naturally ask questions about actions in a location, and the system automatically retrieves and presents relevant suggestions based on aggregated user histories, eliminating the need for extensive manual instruction preparation.
Solution Approach 2:
The system employs feedback mechanisms by continuously collecting action data from multiple users, analyzing patterns, and using this feedback to refine and improve action suggestions. This iterative feedback loop allows the system to learn from actual user behavior and provide increasingly accurate recommendations without requiring manual instruction updates.
3Measurement precision
If the system monitors and analyzes user histories to suggest actions, then action recommendations become more relevant, but system complexity increases
Solution Approach 1:
The system applies segmentation by dividing the complex task of action recommendation into separate modular components: data collection module for gathering user actions, data processing module for indexing and storing actions, query processing module for handling user questions, and suggestion generation module for presenting results. This segmentation reduces overall system complexity by making each component independent and manageable.
Solution Approach 2:
The system uses copying by creating an indexed copy of action data from multiple user histories that can be quickly queried and matched against current user queries. Instead of analyzing complete user histories in real-time, the system maintains a copied and pre-processed version of action patterns that enables fast retrieval and matching, reducing computational complexity while maintaining recommendation precision.
Data Source
AI summary
Embodiments of the invention provide techniques for suggesting actions to users of an immersive virtual environment based on previous user actions within the virtual environment. Generally, characteristics of actions performed by various users of the virtual environment may be stored in a searchable actions index. Subsequently, the actions index may be used to suggest actions based on similarity of the stored characteristics to those of a current user and/or actions. The suggested actions may be presented to the user as graphical indications visible within the user's view of the virtual environment.