Visual Graph Platform for Multi-Domain Decision Support
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Solution Overview
Problem
Current decision-making processes in industries such as healthcare and pharmaceuticals are hindered by the inability to integrate and interpret complex, domain-specific data from multiple sources in real-time, leading to suboptimal treatment decisions and outcomes for patients, particularly in cases involving cancer and rare diseases.
Innovation Solution
A platform utilizing machine vision and data ingestion technologies to transform diverse data sources into standardized graphic representations, enabling real-time, comprehensive decision support by prioritizing and analyzing domain-specific information across multiple domains, including scientific, historical, and socioeconomic data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional algorithm-based decision support systems are used, then they can provide structured clinical guidance, but they are clunky and slow when handling complex, data-dense scientific diagnostics
Solution Approach 1:
The patent replaces traditional mechanical algorithm-based decision support systems with a visual machine vision assessment system. Instead of using complex if/then algorithms to process clinical data, the system transforms diagnostic data into visual representations that can be rapidly interpreted, thereby increasing productivity while managing complexity through visual intuition rather than computational complexity
Solution Approach 2:
The patent creates visual copies or representations of complex diagnostic data in the form of standardized graphic formats. These visual copies allow clinicians to rapidly assess diagnostic information without processing the underlying complex data structures, enabling faster decision-making while the system handles the complexity of data integration in the background
2Reliability
If comprehensive multi-domain data is integrated for decision-making, then decision quality improves, but the complexity of data processing and interpretation increases
Solution Approach 1:
The patent segments comprehensive multi-domain data into distinct visual representations for different domains (scientific, historical, socioeconomic). Each domain's data is transformed into its own standardized graphic format, allowing the system to integrate comprehensive data for reliable decision-making while managing complexity by dividing the processing into separate, standardized visual modules
Solution Approach 2:
The patent implements a universal visual representation framework that can handle multiple types of domain-specific data through standardized graphic formats. This multi-functional approach allows the same visual assessment system to process diverse data types (scientific publications, historical data, socioeconomic factors) uniformly, improving decision quality through comprehensive data integration while simplifying the processing complexity through standardization
3Speed
If domain-specific data is transformed into standardized graphic representations, then machine vision can rapidly assess information, but the transformation process adds computational overhead
Solution Approach 1:
The patent performs preliminary transformation of domain-specific data into standardized graphic representations during data ingestion and storage phases. By pre-processing and standardizing data before it needs to be assessed, the system enables rapid machine vision assessment during clinical decision-making while the transformation overhead occurs in advance during batch processing or data entry operations
Solution Approach 2:
The patent changes the parameter representation of data from raw domain-specific formats to standardized visual parameters. By transforming data into standardized graphic representations with consistent visual parameters (shapes, colors, sizes representing different data attributes), the system enables rapid machine vision assessment while the transformation is optimized through automated processes that minimize the time loss
Data Source
AI summary
The described platform's infrastructure transforms domain-specific data into unique graphic component parts (a shape), providing a visual graph representation of the current status of the domain data. The software on the platform provides instructions to allow users to manipulate the domain-specific data and shapes. The domain-specific information may be used in hypothesis generation, prioritization among domains, decision support, and domain modeling over a time period. The platform enables industry contributors and consumers to collaborate using all the domains. The domain-specific shape is created then used by Platform Machine Vision, enabling time-saving decision support across all domains and industries. Additional benefit of creating each domain shape is the shapes may be combined for an Overall Graphic, which is a new kind of visual representation across domains. The Overall Graphic is a portable industry status.