Spatial Graph Object Tracking for Accurate Indoor Positioning

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Solution Overview

Problem

Indoor positioning systems (IPS) face challenges when GPS is ineffective indoors, and existing technologies lack efficient methods for accurate object tracking and location determination in indoor environments.

Innovation Solution

A spatial graph-based system using wearable devices, edge compute units, and remote servers that collect and synthesize data from various sensors to create nodes and edges, forming a spatial graph that tracks objects and determines their locations within indoor environments.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If GPS is used for positioning, then outdoor location accuracy is improved, but indoor positioning effectiveness deteriorates

Engineering Contradiction:
Improvelocation accuracyVSAvoidindoor positioning effectiveness
Core Design Contradiction:
Measurement precisionVSReliability

Solution Approach 1:

The patent introduces Wi-Fi access points and Bluetooth beacons as intermediary devices to mediate positioning in indoor environments where GPS fails. These intermediaries create artificial reference points that enable triangulation and signal strength-based location determination, effectively bridging the gap between outdoor GPS capability and indoor positioning needs.

Inventive Principle:
Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent replaces the GPS satellite-based electromagnetic positioning system with alternative indoor positioning mechanisms including Wi-Fi signal triangulation, Bluetooth beacon proximity detection, and RFID tag recognition. These substitutions maintain the core function of location determination while adapting to indoor environmental constraints that block satellite signals.

Inventive Principle:
Principle #28Mechanics substitution (Replace mechanical system)

2Adaptability or versatility

If multiple sensor technologies are integrated for comprehensive tracking, then object tracking capability is improved, but system complexity increases

Engineering Contradiction:
Improveobject tracking capabilityVSAvoidsystem complexity
Core Design Contradiction:
Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent implements a multi-functional tracking system where a single integrated platform supports multiple sensor types (GPS, Wi-Fi, Bluetooth, RFID, cameras, accelerometers) that can operate independently or in combination. This universal system adapts its sensor suite based on environmental context, using only the necessary sensors for each scenario, thereby managing complexity while maintaining versatility.

Inventive Principle:
Principle #6Universality (Multi-functionality)

Solution Approach 2:

The patent segments the tracking system into modular functional components: location determination modules (GPS, Wi-Fi triangulation, Bluetooth proximity), object recognition modules (cameras, RFID), motion sensing modules (accelerometers, gyroscopes), and data fusion modules. Each segment operates semi-independently and processes data through standardized interfaces, reducing overall system complexity while enabling comprehensive tracking capability.

Inventive Principle:
Principle #1Segmentation

Data Source

PatentEP3746743B1Object and location tracking with a graph-of-graphs
Publication Date: 2025.12.31 MICROSOFT TECHNOLOGY LICENSING LLC
  • EP3746743B1 patent drawingFigure 1
  • EP3746743B1 patent drawingFigure 2
  • EP3746743B1 patent drawingFigure 3

AI summary

A wearable device is configured with various sensory devices that recurrently monitor and gather data for a physical environment surrounding a user to help locate and track real-world objects. The various heterogeneous sensory devices digitize objects and the physical world. Each sensory device is configured with a threshold data change, in which, when the data picked up by one or more sensory devices surpasses the threshold, a query is performed on each sensor graph or sensory device. The queried sensor graph data is stored within a node in a spatial graph, in which nodes are connected to each other using edges to create spatial relationships between objects and spaces. Objects can be uploaded into an object graph associated with the spatial graph, in which the objects are digitized with each of the available sensors. This digital information can be subsequently used to, for example, locate the object.