Cloud Platform for Automated Scientific Method Recommendation
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Solution Overview
Problem
Current scientific laboratory systems face challenges in efficiently managing complex arrangements of movable components, sensors, input and output ports, energy sources, and consumable components, leading to increased time and resources required for configuring projects and performing scientific experiments.
Innovation Solution
A cloud-based platform that connects scientific instruments, manages schedules, tracks projects, and uses machine learning to recommend methods for analyzing samples based on their type and desired analysis, while also optimizing the use of available instruments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If scientific instruments include a complex arrangement of movable components, sensors, input and output ports, energy sources, and consumable components, then the functionality and versatility of the instruments are improved, but the time and resources required for configuring projects and performing experiments increase
Solution Approach 1:
The system enables self-service through automated method development where the computational platform automatically generates experimental methods based on input parameters without requiring manual configuration by users. The system self-configures instrument parameters, selects appropriate methods, and manages experimental workflows autonomously, reducing the time and expertise required for complex instrument setup.
Solution Approach 2:
A computational platform acts as an intermediary between the user and the complex scientific instrument system. This intermediary layer handles the complexity of configuring movable components, sensors, and other instrument elements by automatically translating high-level experimental goals into detailed instrument configurations, thereby shielding users from the underlying complexity while maintaining full instrument functionality.
2Productivity
If many instruments and many individuals interact with different instruments in lab systems, then the productivity and research output are improved, but the complexity of managing and coordinating these interactions increases
Solution Approach 1:
The computational platform provides universal functionality that serves multiple instruments and multiple users through a single unified system. It can manage diverse instrument types (mass spectrometers, chromatographs, etc.) and support various experimental workflows through common interfaces and standardized method templates, reducing the need for instrument-specific configuration expertise while maintaining high productivity across different research teams.
Solution Approach 2:
The system implements feedback mechanisms where experimental results and instrument performance data are automatically captured and used to optimize future experimental configurations. This feedback loop enables continuous improvement of experimental methods and reduces the coordination complexity by learning from past interactions between users and instruments, automatically adjusting parameters based on observed outcomes.
3Adaptability or versatility
If manual configuration and setup of scientific experiments are performed, then flexibility and customization are improved, but the time consumption and resource requirements increase
Solution Approach 1:
The system performs preliminary action by pre-configuring experimental methods and parameters based on historical data and standardized protocols. Common experimental configurations are prepared in advance and stored as reusable templates, allowing users to quickly customize experiments by selecting and modifying pre-prepared methods rather than configuring everything from scratch, thereby maintaining flexibility while dramatically improving setup efficiency.
Solution Approach 2:
The computational platform enables efficient parameter changes by allowing users to modify high-level experimental parameters through intuitive interfaces while automatically handling the cascading changes to underlying instrument settings. Users can adjust experimental conditions (temperature, flow rates, detection parameters) without manually configuring each individual instrument component, maintaining full customization capability while reducing the time and complexity of parameter modification.
Data Source
AI summary
Methods and systems for scientific method creation. One method includes building a data store including a plurality of linked data sets linking a predetermined experiment method defining a plurality of experiment parameters, a predetermined sample type, and a predetermined analysis; training a model with the plurality of linked data set stored in the data store; inputting to the model, as trained, at least one selected from a group consisting of a sample type and a desired analysis; and outputting, from the model, a list of one or more recommended methods based on the at least one of the sample and the desired analysis. The method may also include receiving actual experiment results collected via one or more scientific instructions, the actual experiment results associated with performance of a method from the list; determining expected results for the performed methods; and verifying the actual experiment results based on expected results.