Spatial-Temporal Goal-Seeking With Hierarchical Cluster Prediction

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Solution Overview

Problem

Large systems with thousands or millions of data sources and outcome predictors make accurate goal-seek analysis difficult due to the complexity of determining location-based activities associated with desired goals.

Innovation Solution

A method that generates a spatial-temporal hierarchical cluster model using spatial-temporal data, optimizing locations and predictors associated with the goal, and distributing optimal values throughout the model to predict future system states.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If comprehensive spatial-temporal data from thousands or millions of data sources is analyzed, then prediction accuracy improves, but system complexity increases

Engineering Contradiction:

Improveprediction accuracyVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent segments the large-scale spatial-temporal data system into multiple hierarchical levels: individual data sources, local clusters of related data sources, regional aggregations, and global system level. This hierarchical segmentation allows accurate local predictions while managing complexity through modular organization, where each level processes data independently before aggregation to higher levels.

Inventive Principle:

Principle #1Segmentation

2Manufacturing precision

If location-based activities are precisely determined for goal achievement, then goal attainment improves, but computational difficulty increases

Engineering Contradiction:

Improvegoal attainment precisionVSAvoidcomputational difficulty

Core Design Contradiction:

Manufacturing precisionVSDifficulty of detecting and measuring

Solution Approach 1:

The patent introduces temporal dimension to the spatial analysis by analyzing data across multiple time points. This transforms the problem from static spatial analysis to dynamic spatio-temporal analysis, enabling precise determination of location-based activities by observing changes over time. The temporal dimension provides additional context that simplifies identifying causal relationships between activities and goal outcomes.

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

3Adaptability or versatility

If spatial-temporal hierarchical cluster model is generated with multiple layers, then analysis comprehensiveness improves, but processing time increases

Engineering Contradiction:

Improveanalysis comprehensivenessVSAvoidprocessing time

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The patent performs preliminary actions by pre-processing and organizing spatial-temporal data into hierarchical clusters before actual goal-seek analysis. Data is pre-aggregated at multiple hierarchical levels with spatial and temporal relationships established in advance. This preliminary organization significantly reduces processing time during actual analysis while maintaining comprehensive multi-layered analysis capabilities.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS12572792B2Goal-seek analysis with spatial-temporal data

Publication Date: 2026.03.10 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

Performing a goal-seek analysis of spatial-temporal data by generating a hierarchical cluster according to spatial temporal data, determining a spatial-temporal location input for a target, determining spatial-temporal predictor values for the spatial-temporal location, and adjusting the hierarchical cluster according to and the spatial-temporal predictors.