Lifecycle Inference Models for Predictive Event Mapping
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Solution Overview
Problem
Existing predictive data analysis systems struggle to efficiently map indirectly related data records, leading to underutilization of secondary data records and requiring significant computational resources.
Innovation Solution
The use of machine learning frameworks, specifically lifecycle inference and code co-occurrence models, to detect inferred lifecycles and co-occurring event codes, allowing for the direct mapping of related secondary events to primary events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional predictive data analysis systems are used to map indirectly related data records, then comprehensive data analysis can be performed, but computational resources are significantly consumed and processing efficiency is low
Solution Approach 1:
The system performs preliminary actions by pre-processing event data to extract lifecycle attributes and co-occurrence patterns before the main mapping task. The lifecycle inference model and code co-occurrence model are trained in advance on historical data, so that when mapping is needed, the models are already prepared and can quickly infer relationships without performing full computational analysis from scratch.
Solution Approach 2:
The patent introduces intermediary models (lifecycle inference model and code co-occurrence model) that act as mediators between raw event data and the final mapping results. These intermediary models process and transform the data into structured representations (lifecycle attributes, co-occurrence scores) that make the subsequent mapping task more efficient and less computationally intensive.
2Measurement precision
If traditional mapping approaches are used for indirectly related data records, then all candidate secondary events can be considered, but the mapping accuracy is reduced and relevant events are missed
Solution Approach 1:
The system changes parameters by transforming event data into different feature spaces - extracting lifecycle attributes (start time, end time, duration) and co-occurrence features from raw event sequences. These transformed parameters enable more accurate mapping by capturing the essential characteristics of event relationships without losing information about secondary events that occur outside traditional time windows.
3Reliability
If comprehensive processing of all candidate secondary events is performed, then no data is lost, but processing time increases significantly
Solution Approach 1:
The patent applies partial action by using the lifecycle inference model to identify a focused subset of relevant secondary events within inferred lifecycle time windows, rather than processing all candidate events. The code co-occurrence model further refines this subset by selecting events with high co-occurrence scores. This partial processing approach maintains reliability for the most relevant events while significantly reducing processing time.
Solution Approach 2:
The system performs preliminary filtering using lifecycle time windows and co-occurrence thresholds before detailed mapping analysis. This preliminary action identifies and prioritizes the most relevant secondary events, so that subsequent processing focuses only on high-probability candidates rather than exhaustively analyzing all possible events.
Data Source
AI summary
There is a need for more accurate and more efficient predictive data analysis steps/operations. This need can be addressed by, for example, techniques for efficient predictive data analysis steps/operations. In one example, a method includes mapping a primary event having a primary event code to a related subset of a plurality of candidate secondary events by at least processing one or more lifecycle-related attributes for the primary event code using a lifecycle inference machine learning model to detect an inferred lifecycle for the primary event.