Head Mounted Display Shared Information Fields
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Solution Overview
Problem
In collaborative augmented reality environments, participants wearing head-mounted displays face challenges in effectively sharing and distinguishing between new and previously known information during collaboration events, leading to difficulties in synchronizing and visualizing relevant data among participants.
Innovation Solution
A method that classifies and displays electronic data sets relevant to a collaboration context using head-mounted displays, applying natural language processing to identify and categorize data into classes of new and known information, allowing participants to visualize and interact with a shared visual information field that highlights new and known data sets differently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If all collaboration data sets are displayed to each participant, then complete information sharing is achieved, but information overload and difficulty in distinguishing new from known information occurs
Solution Approach 1:
The patent segments the collaboration data sets into two distinct categories: new information and known information. This segmentation is achieved through classification algorithms that analyze each data set and assign it to the appropriate category based on whether the participant has previously accessed it. The visual representation then displays these segmented categories differently, allowing participants to quickly distinguish between new and known information without being overwhelmed by the complete data set.
Solution Approach 2:
The patent applies local quality by providing different visual representations for different types of information. New information is highlighted with distinct visual characteristics (such as different colors, icons, or positioning) compared to known information. This allows participants to quickly identify and focus on new information while still having access to the complete data set, thereby maintaining information completeness while improving distinguishability.
2Productivity
If classification processing is applied to data sets, then information organization and retrieval efficiency is improved, but processing time and system complexity increases
Solution Approach 1:
The patent implements preliminary action by pre-processing and classifying data sets before they are displayed to participants. The system proactively analyzes each data set, determines whether it is new or known information, and organizes it accordingly before presentation. This preliminary classification reduces the processing time during actual collaboration events, as the heavy lifting of categorization has already been completed in advance.
Solution Approach 2:
The system employs self-service mechanisms where data sets automatically undergo classification and organization without requiring manual intervention from participants. The classification algorithms autonomously process incoming data, assign appropriate categories, and update the visual representation in real-time, thereby improving retrieval efficiency while minimizing the time burden on users.
3Loss of information
If visual representations of all data sets are displayed, then complete information availability is achieved, but visual clutter and difficulty in focusing on relevant content occurs
Solution Approach 1:
The visual representation segment s the display into distinct regions or zones for new information and known information. This spatial segmentation allows participants to quickly locate and focus on new information without being overwhelmed by the complete data set. The segmentation maintains information availability while improving the ease of detecting relevant content through clear visual organization.
Solution Approach 2:
The patent applies local quality by赋予different visual characteristics to different types of information in the visual representation. New information is highlighted with distinctive visual properties (such as brighter colors, larger icons, or prominent positioning) while known information is displayed with different characteristics. This differential visual treatment maintains complete information availability while significantly improving the ability to detect and focus on relevant new content.
Data Source
AI summary
Disclosed are techniques for classifying and displaying electronic data sets relevant to a collaboration context using head mounted displays. Context information corresponding to a collaboration event is leveraged to identify participants for the collaboration event, where natural language processing is applied across the contents of electronic data sets presently available to the participants across their computer devices and network-based storage environments to collect electronic data sets relevant to the collaboration event. The collected data sets are then classified, from the perspective of individual participants, into classes including previously known and previously unknown data sets. The collected data sets are then displayed based on their classes to the participants wearing head mounted displays such that each participant views the same overall set of data sets through their head mounted display, but the data sets displayed within each class may differ between them.