AI Workspace Content Generation in Multi-User Collaboration
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Solution Overview
Problem
Existing collaboration systems lack the ability to efficiently generate and manage digital assets in a multi-user search and collaboration environment, particularly in time-sensitive situations like emergencies, where manual data gathering and arrangement are inefficient.
Innovation Solution
A system and method for artificial intelligence-based workspace content generation using sources of digital assets, where a server node sends a spatial event map to client nodes, allowing users to input prompts for a trained machine learning model to generate AI-based digital assets or layouts, which are then displayed across all client nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual data gathering and arrangement is used to generate digital assets in a collaboration workspace, then users have full control over the content creation process, but the time and effort required increases significantly, especially in time-sensitive situations
Solution Approach 1:
The system enables self-service by allowing the workspace to automatically generate digital assets and dashboards using AI/ML models. Users simply provide prompts or select from templates, and the system autonomously performs data gathering, processing, and arrangement, eliminating the need for manual content creation while maintaining full control over the output.
Solution Approach 2:
The system performs preliminary actions by pre-configuring templates and data sources that can be quickly activated. When a user needs a digital asset, the system has already prepared the underlying frameworks, data connection templates, and layout structures, allowing rapid generation without starting from scratch each time.
2Productivity
If AI-based automated generation is implemented, then productivity and speed of digital asset creation improve, but the complexity of the system increases due to integration of machine learning models and automated processes
Solution Approach 1:
The system introduces an intermediary layer between users and the complex AI/ML infrastructure. This intermediary provides simplified interfaces where users can interact with high-level concepts like prompts and templates, while the complex model training, data processing, and asset generation happen automatically in the background, shielding users from technical complexity.
Solution Approach 2:
The system achieves universality by creating a multi-functional platform that handles multiple tasks through a single integrated interface: users can generate digital assets, create dashboards, process various data types, and customize outputs all through the same AI-driven workspace. This consolidates what would otherwise require multiple separate tools and processes into one unified system.
3Quantity of substance
If comprehensive digital assets are generated automatically, then the quantity and quality of workspace content improves, but the loss of manual control and customization options may increase
Solution Approach 1:
The system implements dynamics by making the generation process adaptive and flexible. Users can dynamically adjust parameters, select from multiple AI model options, modify prompts in real-time, and iterate on generated assets. The system responds to user inputs by dynamically regenerating content with different parameters, maintaining full user control throughout the automated process.
Solution Approach 2:
The system incorporates feedback loops where users can review generated digital assets and provide corrections or adjustments. The AI/ML models learn from user feedback and iteratively improve the generated content. Users can request regeneration with modified parameters, and the system adjusts based on their preferences, ensuring the final output matches their vision while maintaining automated efficiency.
Data Source
AI summary
The technology disclosed relates to a system and methods for artificial intelligence-based workspace content generation using sources of digital assets in a multi-user search and collaboration environment. The disclosed methods can include sending a portion of a spatial event map that locates events in a virtual workspace; sending data to allow the client node to display a digital asset identified by events in the spatial event map; receiving an input for a trained machine learning model wherein the input comprises the identification of a digital asset selected by a user or desired features in an artificial intelligence (AI)-based digital asset; sending the input received to the trained machine learning model; receiving the AI-based digital asset as output by the trained machine learning model; and sending the AI-based digital asset to a plurality of client nodes, allowing the client nodes to display the AI-based digital asset in respective digital displays.