Offline Map Data Updates Using Quadtree Diff Compression

Solution Overview

Problem

Updating offline map data on mobile devices is resource-intensive in terms of storage, bandwidth, and processing, as existing methods do not efficiently manage data compression and updates.

Innovation Solution

A system that uses a server device to organize map data into a quadtree structure, generating update data using binary and semantic difference algorithms to determine the smallest compressed size, and sends only necessary updates to client devices, optimizing data compression and update efficiency.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If offline map data is stored and updated on mobile devices, then map services can be used offline, but data storage space, network bandwidth, processing cycles, and update time are consumed

Engineering Contradiction:

Improveoffline map service availabilityVSAvoiddata storage space

Core Design Contradiction:

ReliabilityVSQuantity of substance

Solution Approach 1:

The patent segments map data into a quadtree structure, dividing the map data into hierarchical nodes (root node, intermediate nodes, leaf nodes). Each node contains only the map data objects relevant to its spatial region, allowing selective storage and update of only the necessary portions of map data on mobile devices, thereby reducing overall storage requirements while maintaining offline accessibility.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent extracts and stores only the essential map data objects needed for offline functionality, separating them from the complete map dataset. By using the quadtree structure, the system extracts only the relevant spatial data objects for each region and stores them on mobile devices, rather than storing entire map datasets, thus reducing storage space while preserving offline service capability.

Inventive Principle:

Principle #2Taking out (Extraction)

2Reliability

If complete map data is downloaded and stored for offline use, then offline map services are enabled, but network bandwidth and data transfer are increased

Engineering Contradiction:

Improveoffline map service availabilityVSAvoidnetwork bandwidth

Core Design Contradiction:

ReliabilityVSLoss of substance

Solution Approach 1:

The patent segments map data into a quadtree structure, dividing the map data into hierarchical nodes (root node, intermediate nodes, leaf nodes). Each node contains only the map data objects relevant to its spatial region, allowing selective storage and update of only the necessary portions of map data on mobile devices, thereby reducing overall storage requirements while maintaining offline accessibility.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent implements partial action by downloading and storing only the necessary portions of map data (specific quadtree nodes and their contained objects) rather than complete map datasets. The system performs exactly the amount of data transfer needed for offline functionality, avoiding excessive data download, thus conserving network bandwidth while enabling offline services.

Inventive Principle:

Principle #16Partial or excessive action

3Loss of substance

If map data is organized for efficient compression, then update data size is reduced, but data organization complexity increases

Engineering Contradiction:

Improveupdate data sizeVSAvoiddata organization structure

Core Design Contradiction:

Loss of substanceVSDevice complexity

Solution Approach 1:

The patent segments map data into a quadtree structure, dividing the map data into hierarchical nodes (root node, intermediate nodes, leaf nodes). Each node contains only the map data objects relevant to its spatial region, allowing selective storage and update of only the necessary portions of map data on mobile devices, thereby reducing overall storage requirements while maintaining offline accessibility.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent changes the organizational parameter of map data from traditional formats to a quadtree hierarchical structure defined by spatial coordinates and depth levels. This parameter change enables more efficient compression by grouping related objects together in the tree structure, reducing update data size despite the increased organizational complexity.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS11487752B2Optimizing offline map data updates

Publication Date: 2022.11.01 APPLE INC

AI summary

In some implementations, a system can optimize offline map data updates. For example, a server device in the system can determine a metric for identifying map data objects based on attributes of the map data objects. The server device can then generate a quadtree that stores the map data objects in nodes of the quadtree based on the metric. When processing an update to the map data stored at the server device, the server device can generate update data describing the updates for each node in the quadtree based on a binary difference algorithm and/or a semantic difference algorithm. The server device can select the algorithm based on which algorithm results in the smallest compressed size of the update data.