Extended Reality Context Analysis for Accurate Machine Learning Answers
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Solution Overview
Problem
Conventional systems fail to provide proper context and background information from extended reality environments for machine learning models to generate relevant and accurate answers to user queries, often resulting in irrelevant, too simple, or too complex responses, and may generate false information.
Innovation Solution
A method is implemented to identify extended reality components relevant to user queries, generate prompts based on these components, and transmit them to a machine learning model to improve answer data generation, incorporating context and background information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional systems use machine learning models to answer user questions without providing context from extended reality environment, then the system operation is simple, but the answer data becomes irrelevant or inaccurate
Solution Approach 1:
The system performs preliminary actions by capturing and storing context information (user location, viewed components, interactions) from the extended reality environment before the user asks a question. This pre-captured context is then readily available when generating the prompt, eliminating the need for complex real-time analysis and improving answer accuracy without proportionally increasing system complexity.
Solution Approach 2:
The system introduces an intermediary prompt generation component that translates raw context information from the extended reality environment into a structured prompt format suitable for the machine learning model. This intermediary layer simplifies the integration between context capture and answer generation, managing complexity while improving answer relevance.
2Loss of information
If the extended reality environment contains infinitely many components, then the environment provides comprehensive information, but identifying relevant components becomes difficult
Solution Approach 1:
The system applies local quality by focusing context capture on the user's immediate vicinity and recent interactions rather than attempting to process all components in the infinite extended reality environment. By tracking user location, viewed components, and recent interactions, the system identifies relevant context locally, maintaining information completeness while reducing detection difficulty.
Solution Approach 2:
The system segments the infinite extended reality environment into manageable context categories (user location, viewed components, recent interactions, current task). This segmentation allows the system to process and identify relevant components systematically without being overwhelmed by the environment's infinite complexity.
3Reliability
If machine learning models generate answers without contextual prompts, then the model operation is simple, but the responses may include hallucinations or irrelevant information
Solution Approach 1:
The system performs preliminary action by constructing context-rich prompts before submitting queries to the machine learning model. By pre-assembling relevant context information from the extended reality environment into structured prompts, the system improves answer reliability without requiring complex modifications to the model's core operation.
Solution Approach 2:
The prompt generation component serves as an intermediary that bridges the extended reality context and the machine learning model. This intermediary translates environmental context into model-appropriate input formats, improving reliability while keeping the model itself relatively simple.
Data Source
AI summary
Disclosed herein are methods, systems, and computer-readable media for causing a machine learning model to generate improved answer data based on an extended reality environment. In an embodiment, a method may include receiving the query, identifying at least one extended reality component associated with the extended reality environment as relating to the query, the at least one extended reality component comprising at least one of an object, a recording, or transcript information, and generating a prompt based on the query and the at least one extended reality component. The method may further include transmitting the prompt to a machine learning model, in response to the transmitted prompt, receiving answer data from the machine learning model, and based on the received answer data, generating content in the extended reality environment.