Path Prescriber Model Node Hierarchy Simplification

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Solution Overview

Problem

Existing data structures struggle to accurately capture and identify significant causal relationships between nodes in time series data, leading to stale or outdated information due to the complexity and dynamic nature of these relationships, which hinders effective decision-making.

Innovation Solution

A method is introduced that simplifies the model by removing less significant relationships, simulating scenarios to identify nodes at risk of missing target values, and generating action pathways to adjust driver nodes, using a hierarchy of nodes in a data structure to predict future values and maintain actionable insights.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If the model includes all relationships between nodes in the hierarchy, then the accuracy of capturing causal relationships is improved, but the computational complexity increases

Engineering Contradiction:

Improveaccuracy of causal relationshipsVSAvoidcomputational complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent extracts and removes relationships between nodes that have minimal impact on parent nodes. The system calculates impact metrics for each relationship and selectively eliminates those below a threshold, retaining only the most significant causal relationships. This extraction approach maintains measurement precision for important relationships while reducing overall computational complexity.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The patent applies different levels of analysis to different parts of the model. High-impact relationships are analyzed with greater detail and retained in the simplified model, while low-impact relationships are removed. This local quality approach ensures that computational resources are focused on the most critical causal relationships, maintaining accuracy where it matters most.

Inventive Principle:

Principle #3Local quality

2Device complexity

If the model is simplified by removing relationships, then the computational complexity is reduced, but the accuracy of identifying significant causal relationships deteriorates

Engineering Contradiction:

Improvecomputational complexityVSAvoidaccuracy of causal relationships

Core Design Contradiction:

Device complexityVSMeasurement precision

Solution Approach 1:

The patent changes the parameter of relationship significance by calculating impact metrics for each relationship. Relationships are evaluated based on their actual impact on parent nodes, and only those meeting a significance threshold are retained. This parameter-based filtering ensures that the simplified model maintains accuracy for significant causal relationships while reducing complexity.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent applies partial action by retaining only the necessary subset of relationships required to maintain accuracy. Instead of keeping all relationships, the system identifies and preserves only those that significantly contribute to causal understanding, removing redundant relationships that add computational burden without proportional value.

Inventive Principle:

Principle #16Partial or excessive action

3Loss of information

If the system monitors all metrics continuously, then the completeness of information is improved, but the loss of time for processing increases

Engineering Contradiction:

Improvecompleteness of informationVSAvoidprocessing time

Core Design Contradiction:

Loss of informationVSLoss of time

Solution Approach 1:

The patent extracts and focuses monitoring efforts on the most significant relationships and metrics identified through impact analysis. By removing less important metrics from continuous monitoring, the system reduces processing time while maintaining completeness of information for decision-critical parameters.

Inventive Principle:

Principle #2Taking out (Extraction)

4Loss of time

If the system updates metrics frequently, then the timeliness of information is improved, but the loss of energy increases

Engineering Contradiction:

Improvetimeliness of informationVSAvoidenergy consumption

Core Design Contradiction:

Loss of timeVSUse of energy by moving object

Solution Approach 1:

The patent applies partial action by updating only the subset of metrics and relationships that are most significant for decision-making. Instead of frequently updating all metrics, the system focuses computational energy on maintaining timeliness for critical parameters, reducing overall energy consumption while preserving information timeliness where it matters most.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS20210365643A1Natural language outputs for path prescriber model simulation for nodes in a time-series network

Publication Date: 2021.11.25 ORACLE INT CORP

AI summary

A method of generating natural language outputs may include accessing a model of a system, where the system may be represented by a hierarchy of nodes in a data structure, and nodes in the hierarchy of nodes may include time series of data. The method may also include identifying a time series represented by a node in the data structure that will generate a future anomaly; accessing a template corresponding to a type of the time series; populating semantic tags in the template using data from the time series; sending a phrase from the template to a natural language model; receiving a plurality of similar phrases from the natural language model; selecting one of the plurality of similar phrases and replacing the phrase in the template; and causing language from the template to be displayed on a display device.