Automated Research System for Biomedical Experimentation
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Solution Overview
Problem
There is a need to improve access to automated experimentation and data processing in complex systems, particularly in biomedical research, to accelerate the pace of research and provide a standardized, computerized method for conducting and managing medical or biomedical services.
Innovation Solution
An automated research system (ARS) is developed, comprising multiple hardware and software components that automate research steps, including a modularized code framework, a rules engine, and a data acquisition component, to facilitate iterative experimental cycles and goal-directed research processes. This system includes a Library of Possible Experiments (LOPE), an Experiment Director module, and a data processing engine, enabling automated experimentation and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual experimental techniques are used, then research processes are flexible and adaptable, but research pace is slow and productivity is low
Solution Approach 1:
The patent replaces manual mechanical experimental techniques with automated robotic systems. Robots perform experimental operations such as liquid handling, sample preparation, and data collection, eliminating the need for manual manipulation while maintaining experimental flexibility through programmable control.
Solution Approach 2:
The automated research system performs self-service by automatically executing experimental protocols, processing data, and generating results without continuous human intervention. The system autonomously manages experimental workflows, reducing dependency on manual operations while accelerating research throughput.
2Productivity
If automated experimentation is implemented, then research productivity increases and data processing improves, but system complexity increases
Solution Approach 1:
The patent employs universal robotic platforms that can perform multiple experimental functions through reconfigurable end-effectors and programmable control. A single robotic system can execute diverse experimental protocols across different assays and sample types, reducing the need for multiple specialized devices and simplifying system architecture.
Solution Approach 2:
The automated research system is divided into modular functional components including sample preparation modules, experimentation modules, data processing modules, and control modules. Each module operates independently but integrates through standardized interfaces, allowing complex functionality to be achieved through composition of simpler, manageable units.
3Productivity
If high throughput screening is used, then data collection speed increases, but measurement precision and data quality may deteriorate
Solution Approach 1:
The patent incorporates feedback mechanisms where experimental data is continuously monitored and validated during high-throughput screening. Automated quality control algorithms analyze data in real-time, comparing results against expected ranges and experimental parameters, and trigger corrective actions or repeat measurements when anomalies are detected, thereby maintaining precision despite high speeds.
Solution Approach 2:
Manual measurement and data verification processes are replaced with automated optical detection systems and computational analysis algorithms. These systems use consistent, programmable measurement protocols and automated image analysis to maintain uniform precision across thousands of measurements, eliminating human variability while preserving high throughput.
Data Source
AI summary
Systems and methods that provide for automation-assisted research into the workings of one or more studied systems include software modules that communicate with domain knowledge bases, research professionals, automated laboratories, research service objects, and data analysis processes. When implemented in conjunction with online business methods and systems, ordering processes can interface directly with research services, such as medical research services and biomedical research services. In some implementations, automatically selected research service objects can correspond to 3rd-party research services that can produce research results, and subsequent data-processing can update the knowledge bases and provide guidance to a next phase of research.