Experiment Design Interface Compatibility Validation

Solution Overview

Problem

Conventional systems for generating and executing complex experiment designs are inflexible, inaccurate, and inefficient, often leading to unpredictable compatibilities and wasteful computational resource usage due to inaccurate mapping of data analysis models with experiment process components.

Innovation Solution

An experiment design interface system that utilizes analysis code validation components to detect and display compatibility metrics between selected data analysis models and experiment designs, enabling flexible and efficient user interfaces for customizable experiment designs by identifying potential errors before execution.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If conventional systems are used to generate and execute complex experiment designs, then basic experiment orchestration is achieved, but operational inflexibility and inaccuracy occur due to inability to detect compatibilities between data analysis models and experiment process components

Engineering Contradiction:

Improveaccuracy of experiment design executionVSAvoidoperational flexibility

Core Design Contradiction:

ReliabilityVSAdaptability or versatility

Solution Approach 1:

The system performs preliminary compatibility validation by extracting validation components from data analysis models and comparing them against experiment process components before execution. This advance checking ensures accurate mapping and detects incompatibilities early, preventing execution errors while maintaining flexible user selections.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements feedback mechanisms by displaying compatibility metrics and validation results to users during experiment design. This feedback enables users to identify and correct incompatibilities between data analysis models and experiment processes, improving both accuracy and operational flexibility through informed adjustments.

Inventive Principle:

Principle #23Feedback

2Productivity

If conventional systems execute complex experiment designs without compatibility validation, then experiment execution is achieved, but computational resource waste occurs due to inaccurate mapping of data analysis models

Engineering Contradiction:

Improvecomputational efficiencyVSAvoidaccuracy of data analysis model mapping

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system performs preliminary compatibility validation by extracting validation components from data analysis models and comparing them against experiment process components before execution. This advance checking ensures accurate mapping and detects incompatibilities early, preventing execution errors while maintaining flexible user selections.

Inventive Principle:

Principle #10Preliminary action

3Reliability

If users manually verify compatibility between data analysis models and experiment processes, then some compatibility detection is achieved, but time consumption and inefficiency increase

Engineering Contradiction:

Improvecompatibility detection accuracyVSAvoidtime for experiment design validation

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system implements self-service validation by automatically extracting validation components from data analysis models and performing compatibility checks against experiment process components. This automated approach eliminates manual verification effort while maintaining high detection accuracy, significantly reducing validation time.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system implements feedback mechanisms by displaying compatibility metrics and validation results to users during experiment design. This feedback enables users to identify and correct incompatibilities between data analysis models and experiment processes, improving both accuracy and operational flexibility through informed adjustments.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20250208980A1Detecting analysis model compatibilities with complex experiment designs to generate experiment design and analysis model compatibility graphical user interfaces

Publication Date: 2025.06.26 RECURSION PHARMACEUTICALS INC

AI summary

The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating and displaying compatibility metrics between experiment designs and data analysis models. For example, the disclosed systems can display an experiment design user interface that enables user selections of a modular experiment design and data analysis models for the experiment design. Furthermore, the disclosed systems can extract (or identify) one or more analysis code validation components from data analysis models that detect design errors (e.g., incompatibilities) between selected data analysis models and the experiment design. Additionally, the disclosed systems can compare the one or more analysis code validation components to the experiment design to detect compatibilities between the selected data analysis models with the experiment design. Moreover, the disclosed systems can also display, within graphical user interfaces, the detected compatibilities between selected data analysis models and the experiment design during scheduling of the modular experiment design.