Adaptive Electronic Data Platform for Lab Testing
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Solution Overview
Problem
Conventional electronic data platforms in laboratory settings lack personalization and efficiency in capturing observations, requiring significant time and not fully utilizing the potential of electronic lab notebooks (ELNs) for data capture and analysis.
Innovation Solution
The method involves generating templates based on test types, parsing data for sample-based and time-based provenance, and using machine learning models to infer updates for improving data capture and analysis efficiency, with AI modules assisting in data processing and template updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional electronic data platforms use predefined forms for capturing observations, then the structure of data capture is standardized, but the personalization and efficiency of data capture are reduced
Solution Approach 1:
The patent applies dynamics by making the data capture forms adaptive and dynamic rather than static. The system automatically generates forms based on test types, sample information, and provenance data, allowing the form structure to change according to the specific experimental context. This enables personalization for different users and test scenarios while maintaining standardization through automated generation.
Solution Approach 2:
The system changes parameters of the data capture process by automatically adjusting form fields, required information, and capture methods based on test type, sample characteristics, and provenance requirements. This allows the same platform to adapt to different experimental needs without manual configuration, resolving the contradiction between personalization and complexity.
2Productivity
If manual data capture methods are used in laboratory settings, then flexibility in capturing observations is maintained, but the time required for data capture increases significantly
Solution Approach 1:
The system performs preliminary actions by pre-configuring data capture forms based on test types and sample information before the actual data collection begins. The forms are generated in advance with appropriate fields and structures, eliminating the need for manual form creation during the experiment and significantly reducing data capture time.
Solution Approach 2:
The data capture system serves itself by automatically generating forms, validating data, and managing the capture process without requiring manual intervention. The system self-adjusts based on test types and sample characteristics, enabling rapid data capture while maintaining flexibility and accuracy.
3Adaptability or versatility
If electronic data platforms use fixed templates for all test types, then the system complexity is reduced, but the adaptability to different test scenarios is limited
Solution Approach 1:
The system achieves universality by creating a single platform that can generate appropriate data capture forms for multiple test scenarios automatically. Rather than maintaining separate fixed templates for each test type, the system uses a universal form generation mechanism that adapts to different test types, sample types, and provenance requirements, thereby achieving multi-functionality without proportionally increasing complexity.
Solution Approach 2:
The patent applies segmentation by dividing the data capture system into modular components: test type identification, sample information processing, provenance data handling, and form generation. This segmentation allows each component to be simple and focused, while their combination produces highly adaptable forms for different scenarios without overall system complexity.
4Measurement precision
If data capture forms are highly customized for each test type, then the relevance and accuracy of data collection is improved, but the time to prepare and configure forms increases
Solution Approach 1:
The system performs preliminary form generation based on test type and sample information before data collection begins. By pre-configuring the forms automatically, the system eliminates manual preparation time while maintaining high accuracy and relevance through intelligent form generation based on the specific test scenario requirements.
Solution Approach 2:
The system uses feedback from test type identification and sample information to automatically adjust form configuration. This feedback mechanism ensures that forms are both accurate and relevant to the specific test scenario while eliminating manual configuration time, as the system self-adjusts based on input data characteristics.
Data Source
AI summary
A method performed by a computing device includes generating a template for receiving data based on a type of a test conducted in a testing environment. The method also includes receiving data input to the computing device based on the template. The method further includes parsing the received data to identify data corresponding to a sample-based provenance and a time-based provenance. The method still further includes updating at least one of the time-based provenance and the sample-based provenance based on the identified data. The method also includes generating an inference at a machine learning model based on at least one of the time-based provenance and the sample-based provenance, and updating the template based on the inference.