Predictive Recommendation Model with Real-Time Constraint Indexing
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Solution Overview
Problem
Existing decision-making systems, particularly in engineering and industrial contexts, face challenges in automating recommendations due to the complexity of input variables and the inability to account for new data or situations not considered during model training, leading to suboptimal decisions and resource-intensive updates.
Innovation Solution
A computer-implemented method that selects appropriate decisions by training a model with data representing similar events, generating options, computing target values, indexing these options, and allowing real-time constraints for querying to rank and filter recommendations, enabling decision-makers to influence the process and handle evolving situations with limited resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If a machine learning model is trained with historical data to automate decision-making recommendations, then the automation extent and decision quality are improved, but the model cannot account for new data or situations not considered during training, reducing adaptability
Solution Approach 1:
The system incorporates feedback mechanisms where decision-makers can provide feedback on model recommendations and new situations. This feedback loop allows the model to learn from new data and situations, gradually improving its adaptability while maintaining high automation levels. The feedback is used to refine the model's understanding of complex scenarios.
Solution Approach 2:
The system performs preliminary actions by pre-training the machine learning model on extensive historical data and scenarios before actual decision-making occurs. This preparation enables the model to handle a wide range of situations effectively. The preliminary training includes curating diverse datasets and continuously updating the model with new information to maintain adaptability.
2Manufacturing precision
If complex input variables are processed to provide comprehensive recommendations, then the decision quality is improved, but the device complexity and computational resources required increase significantly
Solution Approach 1:
The system extracts and selects only the most relevant input variables from the complex set of available data. By identifying and focusing on the critical variables that have the most impact on decision outcomes, the system maintains high decision quality while reducing the complexity of processing. This extraction process is performed through feature selection algorithms and expert analysis.
Solution Approach 2:
The system changes parameters by transforming complex input variables into simplified representations or aggregations. This involves summarizing multiple related variables into fewer composite parameters, normalizing data formats, and adjusting the granularity of input data to balance comprehensiveness with processing efficiency.
3Adaptability or versatility
If the number of options and input variables increases beyond human manageable levels, then the comprehensiveness of analysis is improved, but the ease of operation and human understanding deteriorate
Solution Approach 1:
The system segments the complex decision space into manageable categories and levels. It organizes the vast number of options and input variables into hierarchical structures, grouping related items together. This segmentation allows the system to present information in digestible portions, maintaining completeness while improving human understandability and ease of operation.
Solution Approach 2:
The system introduces intermediary layers between the complex data and the decision-maker. These intermediaries include visualizations, summaries, and explanatory interfaces that translate complex input variables and options into intuitive representations. The intermediary layer acts as a bridge, preserving the comprehensiveness of the analysis while making it accessible and easy to operate.
4Manufacturing precision
If empirical models are built to process large numbers of variables, then the decision quality is improved, but the model complexity and computational resources required increase
Solution Approach 1:
The system applies partial action by training empirical models on a carefully selected subset of the most important variables and scenarios rather than all possible variables. This partial approach achieves sufficient decision quality while significantly reducing computational resource requirements. The system identifies the critical variables that provide the most value and focuses computational effort on those.
Data Source
AI summary
The invention notably relates to a computer-implemented method for selecting an appropriate decision by constraining options assessed with a model. The method comprises selecting a model capable of receiving inputs and providing output in response to an input; training a model with a set of data representing similar events; generating options that represent hypothetical events; computing target values by applying the trained model on the generated options; computing index for indexing the generated options and target values associated with the trained model; querying the said index for obtaining a selection of a set of options, the selection being performed according to a specific constraint; returning, as a result of the query, a subset of the set of the generated options, the subset being ranked according to the target values associated with each option.