Digital Map Zone Ranking for Structured Physical System Analysis

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Solution Overview

Problem

Existing technologies struggle to efficiently process and analyze structured attribute data from digital maps of physical systems, leading to challenges in optimizing resource allocation, traffic flow, and infrastructure deployment in various contexts such as event spaces, buildings, and geographic areas.

Innovation Solution

A method and system for processing digital maps to determine and analyze structured attribute data by performing image processing, point of interest determination, zone definition, and data ranking, using machine learning algorithms to identify and rank zones based on predefined criteria, enabling dynamic updates and efficient decision-making.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If traditional methods are used to process and analyze digital map data, then the processing complexity and time consumption increase, but the analysis accuracy and decision-making efficiency deteriorate

Engineering Contradiction:

Improvedata processing efficiencyVSAvoidtime consumption for analysis

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The patent segments the digital map processing into distinct functional modules: image processing operations (shape detection, text recognition), point of interest determination, zone definition, attribute data extraction, and ranking. This modular segmentation enables parallel processing and reduces overall analysis time while maintaining comprehensive data extraction.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system performs preliminary image processing operations on the digital map before detailed analysis, pre-identifying shapes, text labels, and key features. This preliminary action prepares structured data that accelerates subsequent zone definition and attribute extraction processes, reducing total processing time.

Inventive Principle:

Principle #10Preliminary action

2Loss of information

If comprehensive attribute data is extracted from digital maps, then the data quality and actionable insights improve, but the processing complexity and computational resources increase

Engineering Contradiction:

Improveinformation completenessVSAvoidprocessing system complexity

Core Design Contradiction:

Loss of informationVSDevice complexity

Solution Approach 1:

The patent extracts specific structured attribute data from the digital map, including zone boundaries, point of interest locations, text labels, and spatial relationships. By selectively extracting only the necessary attributes rather than processing all possible data, the system maintains information completeness while reducing processing complexity.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The system transforms unstructured or semi-structured digital map data into structured attribute data with defined parameters and formats. This parameter transformation enables efficient storage, analysis, and decision-making while reducing the complexity of subsequent processing operations.

Inventive Principle:

Principle #35Parameter changes

3Adaptability or versatility

If manual analysis of digital map data is performed, then the flexibility and adaptability improve, but the processing speed and scalability deteriorate

Engineering Contradiction:

Improveanalysis flexibilityVSAvoidprocessing throughput

Core Design Contradiction:

Adaptability or versatilityVSProductivity

Solution Approach 1:

The patent replaces manual mechanical analysis with automated image processing operations including shape detection algorithms, optical character recognition, and computational geometry calculations. This substitution maintains analytical flexibility through programmable parameters while dramatically increasing processing throughput and scalability.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system performs self-service automated analysis of digital maps, independently identifying zones, points of interest, and attributes without requiring manual intervention. This automation maintains adaptability through configurable processing parameters while achieving high processing speeds and scalability.

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS12555403B1Determining and analyzing structured attribute data for physical systems based on a digital map

Publication Date: 2026.02.17 VIGILANTE STRATEGY LLC

AI summary

In a general aspect, structured attribute data for physical systems is generated based on a digital map. In some implementations, a system receives a digital map representing a physical system. The system performs a set of image processing operations using the digital map as input. The system determines a set of points representing points of interest within the physical system. The system determines a set of zones defined with respect to the digital map. The system calculates distances between the set of points and the set of zones. The system creates a structured dataset that includes entries for each zone in the set of zones, wherein each entry includes zone attributes corresponding to a respective zone in the set of zones. The system determines and outputs a ranking of the entries of the structured dataset based on a set of weights corresponding to zone attributes of the structured dataset.