Extended Display Generator for Predictive Content Integration
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Solution Overview
Problem
Current display systems lack the ability to effectively generate and integrate predictive and generative content across multiple user interfaces and environments, limiting user interaction and productivity in multi-tasking and collaborative applications.
Innovation Solution
An extended display generator system that includes an input stream module, function module, and graphical user interface to generate and display content across multiple layers and depths, utilizing AI and machine learning to provide predictive and generative visual information, and allowing user-defined templates and functions for enhanced user interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current display systems are used, then basic display functionality is provided, but the ability to generate and integrate predictive and generative content across multiple user interfaces is limited
Solution Approach 1:
The system divides functionality into separate modules: a display system for rendering content, a computational module for processing input streams and generating predictive/generative content, and communication interfaces for multi-device connectivity. This segmentation allows each component to be optimized independently while working together to provide advanced content generation capabilities.
Solution Approach 2:
The display system is designed to handle multiple types of input streams (video, audio, text, sensor data) and generate various types of content (predictive suggestions, generative visual elements, annotated overlays) across different user interfaces. The system can adapt to different应用场景 whether collaborative work, entertainment, or training, making it universally applicable.
2Productivity
If multiple input streams and processing functions are integrated, then predictive and generative content can be generated, but processing time and computational load increase
Solution Approach 1:
The system performs preliminary processing of input streams by pre-computing predictive models and generative content templates before actual display is needed. Historical data and patterns are analyzed in advance to establish prediction frameworks, reducing real-time computational requirements during actual content generation.
Solution Approach 2:
The computational module dynamically adjusts processing intensity based on the complexity of input streams and the immediacy of content generation needs. The system can operate in different modes: high-speed processing for real-time interactive content, or batch processing for complex predictive analysis, optimizing the balance between processing time and content generation capability.
3Ease of operation
If content is displayed across multiple layers and depths, then immersive visual experiences are achieved, but display system complexity increases
Solution Approach 1:
The display system implements nested content layers where predictive suggestions, generative elements, and annotated information are superimposed at different depth levels over the base video feed. This nesting allows multiple types of information to coexist without interfering with each other, creating an immersive experience while managing complexity through hierarchical organization.
Solution Approach 2:
The computational module acts as an intermediary between raw input streams and the final multi-layered display output. It processes and translates complex data from multiple sources into coordinated visual elements that can be rendered at different depths, simplifying the overall system architecture by centralizing the complexity management function.
4Adaptability or versatility
If AI and machine learning functions are integrated, then predictive and generative content can be created, but device complexity and computational requirements increase
Solution Approach 1:
The AI and machine learning functions are extracted as a separate computational module that can operate independently from the core display system. This modular extraction allows the display system to maintain simplicity while still benefiting from advanced AI capabilities, as the computational module can be upgraded or replaced without affecting the display hardware and software architecture.
Data Source
AI summary
Systems and methods are described for improving the utilization of an extended display system. Some aspects relate to an extended display generator having an input stream module to generate or receive input streams. Input streams may be generated locally (e.g., by a game engine) or remotely (e.g., from the internet). A function module of the generator provides functions that modify or extract information from the input streams. Then extended display generator applies a template to the input streams and function outputs, defining how such display content is presented to a user. A graphical user interface is used to specify which input streams, functions, and visual template should be used. The extended display shows the selected input stream(s) and the functional output(s) in a format defined by the visual template.