Projected Occurrence Detection From Nonadjacent Process Data
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Solution Overview
Problem
Existing resource allocation processes fail to fully utilize available data, particularly human communication data, due to the difficulty in converting it into a usable dataset.
Innovation Solution
An apparatus and method using a processor and memory to identify nonadjacent occurrences in process data, determine characteristic features through machine learning, generate potential projected occurrences, weight them based on optimization constraints, and select the most relevant ones for resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If resource allocation is done upon need arising or decision being made, then resource allocation responsiveness is improved, but data utilization efficiency deteriorates
Solution Approach 1:
The system performs preliminary analysis of process data to identify nonadjacent occurrences and project their future impacts before resource allocation decisions are made. By analyzing historical patterns and projecting forward, the system enables proactive resource allocation that both responds to needs and utilizes available data effectively.
2Quantity of substance
If human communication data is converted into datasets, then data availability for analysis is improved, but conversion complexity increases
Solution Approach 1:
The system uses an intermediary processing layer that automatically converts human communication data into structured process data representations. This intermediary layer handles the complexity of data conversion by transforming unstructured communication patterns into analyzable datasets, making the conversion process transparent and manageable.
3Measurement precision
If machine learning models are trained on historical occurrences, then prediction accuracy is improved, but processing time increases
Solution Approach 1:
The system segments the machine learning process into efficient components that process only relevant features from historical occurrences. By focusing on nonadjacent occurrences and their characteristic features rather than processing all historical data uniformly, the system achieves accurate predictions with reduced processing time.
Data Source
AI summary
Described herein is an apparatus and a method for determining a projected occurrence. An apparatus may include at least a processor; and a memory communicatively connected to the at least processor, the memory containing instructions configuring the at least processor to identify a series of nonadjacent occurrences within process data; determine a plurality of characteristic features corresponding to occurrences in the series of non-adjacent occurrences using a feature learning algorithm; generate a plurality of potential projected occurrences as a function of the plurality of characteristic features; weight the plurality of potential projected occurrences as a function of at least an optimization constraint in the process data; and select a projected occurrence as a function of the weighted plurality of potential projected occurrences.