Virtual World Data Mapping for Enterprise Decision Comprehension
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Enterprise systems face challenges in presenting vast amounts of data in a comprehensible manner due to their complexity and heterogeneity, making it difficult for users to understand and utilize effectively.
Innovation Solution
A virtual world environment, termed 'Storyverse', is created using machine learning to represent and interact with Enterprise system data as real-world objects, allowing users to navigate and grasp data insights through immersive experiences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If data is presented in traditional textual or tabular formats, then data completeness is maintained, but user comprehension and ease of understanding deteriorate due to the overwhelming volume and complexity of enterprise data
Solution Approach 1:
The patent transforms flat, two-dimensional data tables into three-dimensional virtual world environments where data points become spatially distributed objects. This dimensional transformation allows users to navigate and comprehend enterprise data through spatial relationships and immersive exploration, dramatically improving understanding while preserving complete data sets through the virtual environment.
Solution Approach 2:
The patent introduces a virtual world environment as an intermediary layer between raw enterprise data and user comprehension. This virtual environment acts as a mediator that translates complex data structures into intuitive spatial representations, allowing users to interact with and understand data without being overwhelmed by its raw complexity.
2Measurement precision
If machine learning models are trained with more diverse and complex enterprise data, then model accuracy improves, but training time and computational resources increase
Solution Approach 1:
The patent performs preliminary data transformation by converting enterprise data into virtual world representations before training the machine learning model. This pre-processing step organizes data into a structured spatial format that facilitates more efficient model training, allowing the system to leverage diverse data sources without proportionally increasing training time.
Solution Approach 2:
The patent replaces traditional mechanical data processing approaches with immersive virtual world interactions. Users can explore and validate data insights through spatial navigation and interactive manipulation in the virtual environment, which accelerates the feedback loop during model development and reduces iterative training cycles.
Data Source
AI summary
In an example embodiment, machine learning is utilized to create a virtual world where a user can view and interact with data in a graphical environment. This virtual world may be termed a “Story Verse” environment, which can create multiple different virtual world universes capable of segmenting the traditional complexities of Enterprise system data into an easily usable and holistic set. In a further example embodiment, the virtual world is presented in a way that data is represented as real world objects, such as amusement park rides, clouds, etc.