Protocol Definition Autocomplete Using Weighted Master Trees
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Solution Overview
Problem
Current tools for designing protocol definitions do not leverage information from stored protocol definitions, preventing the incorporation of lessons learned from previous designs into new definitions.
Innovation Solution
A computer-implemented method that generates a master tree from a plurality of protocol definitions, using dynamic and static nodes with associated weights, to auto-complete partial protocol definitions by identifying common nodes and assessing similarity scores through machine-learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual protocol definition design is used, then flexibility and adaptability are maintained, but time consumption and efficiency are reduced
Solution Approach 1:
The system enables self-service by automatically generating protocol definitions through machine learning models that analyze historical data and patterns, allowing the system to complete protocol definitions autonomously without requiring extensive manual input, thus improving efficiency while maintaining flexibility
Solution Approach 2:
The system incorporates feedback mechanisms by continuously learning from historical protocol definitions and performance data to refine and improve automated generation accuracy over time, enabling the system to adapt to varying requirements while reducing manual intervention needs
2Manufacturing precision
If historical protocol definitions are not leveraged, then system simplicity is maintained, but knowledge reuse and accuracy are reduced
Solution Approach 1:
The system performs preliminary action by pre-processing and storing historical protocol definitions in a structured format that can be quickly retrieved and analyzed by machine learning models, enabling accurate automated generation without requiring complex real-time analysis of historical data
Solution Approach 2:
The system uses copying by replicating successful patterns from historical protocol definitions through machine learning models, allowing new protocol definitions to be generated by copying proven effective structures and avoiding known pitfalls from historical data
3Productivity
If automated protocol generation is implemented, then productivity is improved, but reliability and adaptability to edge cases may be reduced
Solution Approach 1:
The system applies dynamics by making the automated generation process adaptable through machine learning models that can adjust their output based on contextual requirements and historical performance data, enabling the system to maintain high reliability across different scenarios while preserving speed benefits
Solution Approach 2:
The system uses parameter changes by adjusting generation parameters such as complexity level, detail depth, and structural patterns based on historical protocol characteristics and performance metrics, allowing the automated system to adapt to different reliability requirements without sacrificing productivity
Data Source
AI summary
Various techniques can include accessing a master tree that was generated using a plurality of protocol definitions. The plurality of protocol definitions can identifies an ordered set of actions and specifies, for each sequential pair of actions in the ordered set of actions, an action-advancement condition that identifies a criterion for advancing across the sequential pair of actions in the ordered set of actions so as to trigger a later of the sequential pair of actions. A master tree includes a set of dynamic nodes and a set of static nodes. The technique can include accessing a partial protocol definition that includes at least one action. The technique can include generating an auto-completion of the partial protocol definition using the master tree, at least some of the dynamic-node weights, and at least some of the static-node weights. The technique can output a representation of an auto-completed protocol definition.