Derivative works — US Patent 9532164

Defensive Disclosure Document for US Patent 9532164

This document outlines various derivative works and technical disclosures intended to serve as prior art, thereby rendering future incremental improvements in the domain of "Mashing mapping content displayed on mobile devices" obvious or non-novel. The derivations are based on the independent claims of US patent 9532164, utilizing specific axes for expansion.

Independent Claim 1: Method for displaying mapping content

Core Claim Summary: A method performed by an electronic device, involving displaying an initial map with existing mapping content. When a separate, non-mapping application is active and displaying mappable information, and a user selects this information, the map is then automatically presented again. The previously selected mappable information is displayed on this same map, alongside the original mapping content, potentially adjusting the map's zoom level to ensure everything is visible.

Derivative 1.1: Multi-Modal Haptic-Visual Mapping System for Extreme Environments

Enabling Description: This derivative employs a ruggedized mobile electronic device featuring a flexible e-ink display for low-power visual output and an integrated haptic feedback array. The initial map, derived from a cached, pre-processed geospatial dataset (e.g., military grid reference system or subterranean geological survey), is presented visually on the e-ink screen. A non-mapping application, such as a specialized sensor telemetry reader or a geological stratum analyzer, displays mappable information (e.g., "anomaly at N40.7128 W74.0060, depth 15m," or "seismic event at [lat, long]"). Upon user selection via gesture control (e.g., swipe on a capacitive sensor embedded in the device casing), the device re-presents the original map. The anomaly location is visually overlaid as a distinct, flashing icon on the e-ink display, and concurrently, a spatially correlated haptic pattern (e.g., localized vibration or pressure pulse) is generated on the haptic array, guiding the user's finger to the mapped anomaly. The system dynamically adjusts the map's scale and haptic intensity to ensure both the original map context and the new anomaly are discernible. Power consumption is minimized by selective refresh rates of the e-ink display and intelligent activation of haptic feedback only when new data is integrated or user focus shifts.
Combination Prior Art:
1. This derivative combined with the OpenStreetMap (OSM) data model for base map data representation and the OSMAnd (OSM for Android) mobile mapping application for offline rendering capabilities.
2. This derivative combined with GeoJSON for standardized exchange of mappable anomaly information and the Bluetooth Low Energy (BLE) Mesh Profile for proximity-based haptic cue synchronization among multiple field units.
3. This derivative combined with the Haptic Feedback API (e.g., Android HapticManager or iOS Core Haptics) for programmatic control of haptic patterns and a Portable Network Graphics (PNG) format for efficient, low-bandwidth transmission of overlay icons.

graph TD
    A[Rugged Mobile Device] --> B(E-Ink Display)
    A --> C(Haptic Feedback Array)
    A --> D(Gesture Control Interface)
    E[Non-Mapping App: Sensor Telemetry] --> F{Mappable Info Selected}
    F --> G[Map Processor]
    G --> H[Geospatial Dataset Cache]
    H --> I(Initial Map Display)
    F -- User Selects Mappable Info --> J[Relay Selected Info]
    J --> G
    G --> K(Update Map with New POI)
    K --> B
    K --> C
    K --> L{Adjust Map Scale/Haptic Intensity}
    L --> B
    L --> C

Derivative 1.2: Real-time, Ultra-Low Latency Drone Swarm Mapping for Dynamic Environments

Enabling Description: This derivative describes a system where a swarm of autonomous drones, each equipped with LiDAR, high-resolution cameras, and embedded edge computing units (e.g., NVIDIA Jetson Nano), performs rapid-response mapping of a dynamic environment (e.g., wildfire, active disaster zone). The mobile device is a ground control station tablet with a specialized mapping application. The initial map is a rapidly generated topographical or environmental hazard map. A non-mapping application, running on the same tablet, streams real-time environmental data (e.g., thermal hotspots from IR sensors, gas concentrations, structural integrity data from drone-mounted sonar). When a user on the ground control tablet selects a critical data point (e.g., a rapidly expanding thermal hotspot), the mapping application instantly overlays this information on the existing topographical map. The system employs predictive pathfinding algorithms to determine the optimal drone for visual confirmation and updates the map with a dynamic vector field representing expected hazard spread. The map display automatically re-centers and zooms to ensure the new critical data, the affected area, and relevant drone positions are clearly visible within a sub-50ms latency.
Combination Prior Art:
1. This derivative combined with MAVLink (Micro Air Vehicle Link) protocol for drone-to-ground communication and CesiumJS for 3D globe visualization of dynamic geospatial data.
2. This derivative combined with GDAL/OGR libraries for geospatial data processing and the ROS (Robot Operating System) framework for inter-drone communication and task orchestration.
3. This derivative combined with WebRTC (Web Real-Time Communication) for direct, low-latency video and sensor data streaming from drones to the ground control tablet and the PostGIS extension for PostgreSQL for advanced spatial querying of dynamically updated map features.

graph LR
    A[Drone Swarm] -- Real-time Sensor Data (LiDAR, IR, Gas) --> B{Edge Compute Units on Drones}
    B -- MAVLink --> C[Ground Control Tablet]
    C --> D(Mapping Application)
    C --> E(Non-Mapping App: Environmental Data Streamer)
    E -- User Selects Critical Data --> F[Map Integration Module]
    F --> D
    D -- Overlays Data + Predictive Paths --> G(Dynamic Map Display)
    G -- Auto Re-center/Zoom --> G

Derivative 1.3: Agricultural Field Health Mapping with Sensor Integration and Predictive Overlays

Enabling Description: A mobile device, such as a ruggedized agricultural tablet, displays an initial map of a farm field, showing soil composition and historical yield data. A non-mapping application running simultaneously pulls real-time data from a network of IoT soil sensors, displaying metrics like moisture levels, nutrient deficiencies, or pest infestations. When a farmer selects a specific sensor reading (e.g., "low nitrogen at [lat, long]") within the non-mapping application, the field map automatically updates. A point-of-interest indicator representing the selected sensor location is overlaid, alongside a dynamically generated polygon highlighting the predicted affected area based on soil type and water flow models. The map view adjusts to encompass both the original field context and the newly identified problem area, with color-coding to indicate severity. This system enables targeted intervention.
Combination Prior Art:
1. This derivative combined with OGC SensorThings API for standardized access to IoT sensor data and QGIS for open-source geospatial data management and visualization on the tablet.
2. This derivative combined with LoRaWAN for long-range, low-power sensor communication and GeoTIFF for raster image data storage of historical field health maps.
3. This derivative combined with MQTT for lightweight messaging from field sensors and OpenLayers for rendering interactive maps in the browser-based mapping application on the tablet.

graph TD
    A[IoT Soil Sensors] -- LoRaWAN --> B(Sensor Gateway)
    B -- MQTT --> C[Agricultural Tablet]
    C --> D(Non-Mapping App: Sensor Dashboard)
    C --> E(Mapping App: Field Map)
    D -- User Selects Sensor Data --> F[Location Data Processor]
    F --> E
    E -- Overlays Sensor POI + Predictive Polygon --> G(Updated Field Map)
    G -- Auto Zoom/Pan --> G

Derivative 1.4: AI-Optimized Real-time Contextual Mapping

Enabling Description: A mobile device displays a base map. An AI-driven background process, acting as a non-mapping application, continuously monitors user activity across various foreground applications (e.g., email, calendar, messaging, web browsing) and passively extracts potential mappable information. This AI leverages Natural Language Processing (NLP) models (e.g., BERT-based entity recognition) and context-aware algorithms (e.g., temporal relevance, user interest profiles). When the AI identifies high-confidence mappable information that is highly relevant to the user's current context (e.g., a meeting address mentioned in an email coinciding with an upcoming calendar event), it automatically "selects" this information. The mapping application is then brought to the foreground, and the extracted mappable information is displayed on the existing map, alongside any previously displayed relevant POIs (e.g., user's current location, other meeting points). The AI dynamically adjusts the map's zoom and orientation, prioritizing a view that clearly shows the newly identified location relative to existing points of interest and the user's anticipated route. This obviates explicit user selection.
Combination Prior Art:
1. This derivative combined with TensorFlow Lite for on-device NLP processing of text content and GeoPackage (GPKG) for storing and managing local, contextual geospatial data.
2. This derivative combined with OpenNLP for open-source natural language processing components and Protobuf for efficient serialization of location data between applications.
3. This derivative combined with the Android/iOS Accessibility Services API for monitoring foreground application content (with user permission) and Mapbox GL JS for highly customizable client-side map rendering.

graph TD
    A[User's Mobile Device] --> B(Foreground Apps: Email, Calendar, Messaging)
    B -- User Activity & Content --> C(AI Context Engine - Non-Mapping App)
    C -- NLP/Context Analysis --> D{High-Confidence Mappable Info Identified}
    D -- Auto "Select" --> E[Mapping Application]
    E -- Existing Map Content --> E
    E -- Overlay New POI + AI-Optimized View --> F(Updated Contextual Map)
    E -- Auto Zoom/Orientation --> F

Derivative 1.5: Limited-Functionality "Privacy Mode" Mapping

Enabling Description: This derivative features a mobile electronic device offering a "Privacy Mode" for its mapping capabilities. When activated, the initial map is displayed with existing content, but all location data, including the user's current position, is intentionally obfuscated or quantized to a predefined grid cell (e.g., 1 km square). A non-mapping application (e.g., a local event finder) is active, displaying mappable information (e.g., "Concert Hall, city center"). When a user selects this information, the map is automatically presented again. The selected mappable information is displayed on the same map, alongside the obfuscated original mapping content. The system, in this mode, prevents precise coordinate display or storage. Instead, it displays the general vicinity of the new point of interest within a larger, non-specific area (e.g., "within 2km of city center"). Zoom functionality is limited to prevent granular detail, and route guidance is provided as cardinal directions and estimated time/distance to the general area, rather than turn-by-turn. This ensures basic utility while rigorously protecting precise location privacy.
Combination Prior Art:
1. This derivative combined with Open Location Code (Plus Codes) for generating short, approximate location codes instead of precise coordinates.
2. This derivative combined with DP-Means algorithm for differential privacy in clustering user locations before display and the SQLite database for local, anonymized map tile caching.
3. This derivative combined with Homomorphic Encryption libraries (e.g., SEAL) for processing location queries on encrypted, generalized location data without revealing exact positions and GeoHashing for location data aggregation into grid cells.

stateDiagram-v2
    state "Normal Mode" as Normal
    state "Privacy Mode" as Privacy
    Normal --> Privacy : Activate Privacy Mode
    Privacy --> Normal : Deactivate Privacy Mode

    state "Privacy" {
        [*] --> Map_Displayed_Obfuscated : Initial Map
        Map_Displayed_Obfuscated --> Non_Mapping_App_Active : Displaying Mappable Info
        Non_Mapping_App_Active --> User_Selects_Info : User Interaction
        User_Selects_Info --> Map_RePresented_Obfuscated : Display New Info on Same Map
        Map_RePresented_Obfuscated --> Limited_Zoom : Zoom Constraints Applied
        Limited_Zoom --> Map_Displayed_Obfuscated
    }

Independent Claim 10: Method for processing and displaying location information

Core Claim Summary: A method where a mobile device with a mapping application receives location information from an external source (e.g., dragged and dropped, copied and pasted). This received information is then processed to extract valid location identifiers (like street addresses or landmark names). These identifiers are then passed to the mapping application, which determines their geographical coordinates and displays them as points of interest on a map.

Derivative 10.1: Quantum-Resistant Geocoding with Decentralized Storage

Enabling Description: This derivative outlines a method where a mobile device receives location information (e.g., "Quantum Computing Research Institute, Menlo Park, CA") via secure input (e.g., encrypted clipboard paste). This information is transmitted to an on-device processing unit that leverages a specialized geospatial co-processor designed for quantum-resistant cryptographic operations. The processing unit uses a homomorphic encryption scheme (e.g., based on lattice-based cryptography) to tokenize and extract location identifiers without revealing plaintext. These encrypted identifiers are then sent to a decentralized, blockchain-based geocoding service. This service, composed of a network of trusted nodes, performs coordinate lookup against a globally distributed, immutable ledger of geographical coordinates. The results, also homomorphically encrypted, are returned to the mobile device. The mapping application on the device then decrypts the coordinates using its quantum-safe keys and displays them as points of interest. This ensures the integrity and confidentiality of location data throughout the entire geocoding process, resilient to future quantum computing threats.
Combination Prior Art:
1. This derivative combined with IPFS (InterPlanetary File System) for decentralized storage of encrypted map tiles and location data.
2. This derivative combined with Ethereum Smart Contracts for managing the trusted nodes and incentive mechanisms of the decentralized geocoding service.
3. This derivative combined with Open Geocoding Initiative (OGI) standards for location data formats and liboqs (Open Quantum Safe) for implementing quantum-resistant cryptographic algorithms.

sequenceDiagram
    participant U as User (Mobile Device)
    participant OP as On-Device Processor (Quantum-Resistant)
    participant B as Blockchain Geocoding Service (Decentralized Nodes)
    participant M as Mapping Application

    U->>OP: Receive Encrypted Location Info (e.g., "Quantum Computing Research Institute")
    OP->>OP: Homomorphic Encryption & Tokenization
    OP->>B: Transmit Encrypted Location Identifiers
    B->>B: Query Immutable Ledger of Geo-Coordinates (Encrypted)
    B-->>OP: Return Encrypted Geo-Coordinates
    OP->>OP: Decrypt Geo-Coordinates (Quantum-Safe)
    OP->>M: Pass Decrypted Geo-Coordinates
    M->>U: Display POI on Map

Derivative 10.2: Real-time, Massively Scalable Stream Processing of Ambiguous Location Data

Enabling Description: This derivative focuses on processing high-volume, real-time streams of highly ambiguous location information (e.g., Twitter feeds, emergency dispatch transcripts containing phrases like "incident near old factory," or "suspect heading towards the river") on a mobile device or a connected edge server. The device receives this raw, unstructured data. A machine learning pipeline, utilizing recurrent neural networks (RNNs) or transformer models trained for geospatial entity recognition and disambiguation, processes the stream. This pipeline dynamically extracts potential location identifiers and assigns confidence scores. For ambiguous terms, it employs contextual inference based on historical data, user profiles, and real-time environmental factors (e.g., weather, traffic). The extracted identifiers, even if partially confident, are passed to the mapping application. The mapping application then displays these as "fuzzy" points of interest (e.g., a shaded area or multiple low-transparency markers), with the transparency or size of the marker correlating to the confidence score. The system continuously refines these POIs as more data streams in, demonstrating dynamic resolution of ambiguity.
Combination Prior Art:
1. This derivative combined with Apache Kafka for streaming data ingestion and Apache Flink for real-time stream processing of location text on a backend server, with results pushed to the mobile device.
2. This derivative combined with SpaCy for open-source NLP entity recognition and Elasticsearch for fast, full-text search and spatial queries of historical location references.
3. This derivative combined with Protobuf for efficient data serialization of ambiguous location predictions and Leaflet.js for rendering dynamic, confidence-weighted POIs on a web-based mapping interface accessible via the mobile device.

graph TD
    A[Raw Unstructured Data Stream (e.g., Twitter)] --> B{Mobile Device / Edge Server}
    B --> C(ML Pipeline: Geospatial NER & Disambiguation)
    C -- Extracted (Ambiguous) Identifiers + Confidence --> D[Mapping Application]
    D --> E(Display Fuzzy POIs on Map)
    E -- Continuous Refinement --> C

Derivative 10.3: Forensic Location Data Mapping for Digital Investigations

Enabling Description: This derivative describes a specialized mobile forensic device designed for digital investigations. It receives location information from extracted digital artifacts (e.g., EXIF data from images, geo-tags from social media posts, GPS logs from vehicle telematics, Wi-Fi hotspot logs) obtained from a target device. The forensic application, acting as the external source, provides a structured export of this diverse data. The mobile forensic device's processing module then parses and normalizes this rich dataset, correlating timestamps and locations to reconstruct movement patterns. It filters out irrelevant data and resolves inconsistencies, generating a consolidated list of distinct location identifiers and their temporal context. These identifiers are then passed to a specialized mapping application on the forensic device. This mapping application not only plots the individual points of interest but also generates animated heatmaps showing "time spent" in an area, sequential paths, and spatio-temporal clusters, enabling investigators to visualize movements and significant locations over a specified period.
Combination Prior Art:
1. This derivative combined with GRASS GIS for robust geospatial data processing and analysis within the forensic application.
2. This derivative combined with KML (Keyhole Markup Language) for standardized export and import of forensic location paths and points between different analysis tools.
3. This derivative combined with SQLite for efficient on-device storage of parsed location artifacts and D3.js (or similar JavaScript library) for advanced, interactive spatio-temporal visualizations within the mapping application.

graph TD
    A[Extracted Digital Artifacts (EXIF, GPS Logs, Wi-Fi)] --> B(Forensic Application)
    B -- Structured Export --> C[Mobile Forensic Device]
    C --> D(Location Data Parser & Normalizer)
    D -- Validated Location Identifiers + Time --> E[Specialized Mapping Application]
    E --> F(Display Animated Heatmaps, Paths, Clusters)

Derivative 10.4: Federated Learning for Crowd-Sourced POI Validation and Coordinate Refinement

Enabling Description: This derivative details a system where a mobile device receives location information from an external source (e.g., a user submitting a new business address). This initial submission is considered unverified. The device processes this to extract the identifier. Before passing to the local mapping application, this identifier, along with locally derived contextual information (e.g., cell tower triangulation data, Wi-Fi SSIDs), is fed into a federated learning client. This client contributes to a global, decentralized model for POI validation and coordinate refinement. When other mobile devices, operating under the same federated learning scheme, encounter this same unverified POI or gather corroborating location data, their local models update and contribute to the global consensus. Once the federated model reaches a high confidence threshold for the POI's coordinates, the refined, validated coordinates are pushed back to the originating mobile device. The mapping application then displays this now-verified POI with higher confidence, potentially using a distinct visual indicator. This method continually improves mapping accuracy through collective intelligence without centralizing raw user location data.
Combination Prior Art:
1. This derivative combined with OpenStreetMap's crowd-sourcing methodology for geographic data collection and TensorFlow Federated for distributed machine learning model training.
2. This derivative combined with GeoJSON for standardized representation of POI data and associated confidence scores during federated updates.
3. This derivative combined with MQTT for lightweight communication between federated learning clients and a central orchestrator, and PostGIS for managing the validated geographic database on a federated server.

graph TD
    A[Mobile Device 1] --> B(External Source: New POI Submission)
    B --> C(Location Identifier Extraction)
    C --> D(Federated Learning Client)
    D -- Local Updates --> E[Global Federated Learning Model]
    F[Mobile Device N] -- Local Updates --> E
    E -- Refined Coordinates (High Confidence) --> G(Federated Learning Server)
    G -- Push Refined Coordinates --> D
    D --> H[Mapping Application]
    H --> I(Display Validated POI)

Derivative 10.5: Decentralized, User-Controlled Location Data Processing with Privacy-by-Design

Enabling Description: This derivative focuses on a mobile device where all location information processing is executed locally and transparently, adhering to privacy-by-design principles. When the device receives location information from an external application, instead of immediately passing it to a centralized geocoding service, it first presents the raw input to the user for explicit permission to process. A user interface module, part of the mapping application, then allows the user to manually select privacy filters (e.g., "blur exact coordinates," "only show region," "anonymize source"). The device then performs local geocoding using an offline, open-source dataset (e.g., OpenStreetMap Nominatim data). If network assistance is required, the request is routed through a mix network (e.g., Tor) and only for privacy-preserving sub-queries. The derived (and potentially privacy-filtered) coordinates are then immediately displayed on the map. All intermediate processing steps are logged locally and are auditable by the user, and no raw location data is ever transmitted or stored off-device without explicit, granular user consent, enforced by cryptographic proofs.
Combination Prior Art:
1. This derivative combined with OpenStreetMap Nominatim for local, offline geocoding using open-source data.
2. This derivative combined with Tor network client integration for anonymized network requests for supplementary geocoding data.
3. This derivative combined with Decentralized Identifiers (DIDs) and Verifiable Credentials for user-controlled consent management and auditable privacy policies for location data sharing.

graph TD
    A[External App] -- Raw Location Info --> B{Mobile Device}
    B --> C(User Consent & Privacy Filter UI)
    C -- User Permissions --> D(Local Geocoding Engine - Offline OSM Data)
    D -- (Optional) Mix Network Request --> E(External Geocoding Service)
    E -- (Partial/Obfuscated Result) --> D
    D --> F[Mapping Application]
    F --> G(Display Privacy-Filtered POI)
    B -- Auditable Logs --> H(Local Audit Log)

Independent Claim 16: Mobile device with mapping functionality

Core Claim Summary: A mobile electronic device equipped with a display, a processor, and memory. The device is configured to display a map with initial content from a mapping application. It also runs a non-mapping application capable of displaying mappable content. When the user selects this mappable content from the non-mapping application and issues a command, the device automatically displays the selected mappable content on the same map from the mapping application, in addition to the original map content.

Derivative 16.1: Ruggedized Modular Device for Expeditions and First Responders

Enabling Description: This mobile electronic device is constructed with an MIL-STD-810G compliant, modular casing (e.g., reinforced carbon fiber composite) and an impact-resistant, transflective LCD display for readability in direct sunlight. Its processor is a low-power, fanless ARM-based SoC (e.g., NXP i.MX series), optimized for energy efficiency in remote operations, and features expanded non-volatile memory for offline map caching. The device includes a mapping application displaying initial content from pre-loaded topographic maps, satellite imagery, and critical infrastructure overlays. The modularity allows attachment of specific non-mapping application sensor modules (e.g., chemical hazard detector, advanced medical vital sign monitor, encrypted radio communication unit). When the user, via a large, gloved-hand friendly button or voice command, selects mappable data (e.g., "high radiation detected at [coordinates], source: Module 3") from the attached non-mapping sensor module, the device automatically presents the existing map. The detected hazard's location is overlaid as a color-coded icon (e.g., red for high radiation), with a dynamically expanding danger zone polygon, in addition to the original map content. The device's processor prioritizes rendering of critical hazard information and automatically adjusts the map extent and zoom to ensure the hazard and relevant escape routes are immediately visible.
Combination Prior Art:
1. This derivative combined with NATO's STANAG 4586 for UAV control protocols for integrating drone-collected hazard data from a modular sensor.
2. This derivative combined with ESRI's ArcGIS Runtime SDK for robust offline map rendering and geospatial analysis capabilities on the mobile device.
3. This derivative combined with MGRS (Military Grid Reference System) for standardized coordinate display and NMEA 0183 for communication with external GPS receivers and other navigation equipment.

classDiagram
    class RuggedizedMobileDevice {
        +Display: Transflective LCD
        +Processor: ARM SoC
        +Memory: Non-Volatile (Offline Maps)
        +Casing: MIL-STD-810G Modular
        +Input: Large Buttons, Voice Cmd
        +MappingApp: Topographic, Satellite, Infra Maps
    }
    class NonMappingSensorModule {
        +SensorType: Chemical, Medical, Radio
        +MappableData: string
        +CommInterface: Proprietary/Standard
    }
    class MappingApplication {
        +displayMap(content, newPOI, zoom)
        +renderHazardOverlay(location, type)
    }
    class UserInputProcessor {
        +selectMappableContent()
        +issueMappingCommand()
    }

    RuggedizedMobileDevice "1" -- "*" NonMappingSensorModule : integrates
    RuggedizedMobileDevice "1" -- "1" MappingApplication : runs
    RuggedizedMobileDevice "1" -- "1" UserInputProcessor : handles
    NonMappingSensorModule --> MappableData : provides
    UserInputProcessor --> MappableData : selects
    UserInputProcessor --> MappingApplication : commands
    MappingApplication --> RuggedizedMobileDevice : displays

Derivative 16.2: Low-Resource Offline Mapping on Legacy Devices

Enabling Description: This derivative features an older generation mobile electronic device (e.g., pre-2010 smartphone) with limited processing power (e.g., single-core 600MHz CPU), small display (e.g., 3.5-inch resistive touchscreen), and restricted memory (e.g., 256MB RAM). The device is configured to display a highly optimized, vector-tile based map (e.g., using Mapbox GL JS with aggressive caching) with initial content from a lightweight mapping application designed for minimal resource consumption. A non-mapping application, such as a basic text editor or an RSS reader, runs in a background process with minimal overhead, displaying mappable content (e.g., "meet at library main entrance"). When the user selects this text (e.g., via a long-press context menu optimized for legacy touchscreens) and issues a mapping command, the device's processor intelligently manages memory and CPU cycles. It pauses non-critical background tasks, re-activates the mapping application, and displays the selected mappable content on the same map, generating a simple, low-polygon marker. Map rendering is simplified (e.g., no 3D elements, reduced detail levels), and zoom adjustments are pre-calculated to avoid real-time complex rendering, ensuring responsiveness despite hardware limitations.
Combination Prior Art:
1. This derivative combined with OpenStreetMap vector tiles for lightweight map data distribution and rendering.
2. This derivative combined with Progressive Web App (PWA) architecture for the mapping application to enable offline functionality and minimal installation footprint.
3. This derivative combined with SQLite for efficient on-device storage of pre-processed POI data and HTML5 Geolocation API for basic location services.

graph TD
    A[Legacy Mobile Device] --> B(Limited Processor & Memory)
    A --> C(Resistive Touchscreen Display)
    D[Lightweight Mapping App (Vector Tiles)] --> C
    E[Non-Mapping App (Text Editor/RSS)] -- Mappable Content --> C
    C -- User Selects Text (Long-Press) --> F[Resource Manager]
    F -- Commands Mapping App --> D
    D -- Displays New POI on Existing Map --> C
    F -- Optimizes CPU/Memory --> B

Derivative 16.3: Specialized Tourism Guide with AR Map Overlay

Enabling Description: This derivative is a mobile electronic device purpose-built as a tourism guide, featuring a high-resolution display with integrated augmented reality (AR) capabilities (e.g., LiDAR scanner, powerful GPU). The device runs a mapping application displaying an initial map of a historical district, highlighting key landmarks and walking paths. A non-mapping application, a "historical trivia" game or a "local recommendations" app, displays mappable content (e.g., "visit this hidden alley at [address] for local street art"). When the user, via a gesture (e.g., "pinch-to-map" on the touchscreen), selects this mappable content, the device automatically activates an AR overlay. The selected street art location is visually integrated as a dynamic waypoint projected onto the real-world view through the device's camera feed, aligning with the existing map content. The map application concurrently displays the location on its 2D map, and adjusts its zoom to show both the user's current position, the new point of interest, and relevant contextual information (e.g., historical markers) from the base map, providing an immersive, guided exploration experience.
Combination Prior Art:
1. This derivative combined with ARToolKit or ARCore/ARKit for augmented reality rendering and tracking.
2. This derivative combined with GeoJSON for structured representation of tourist points of interest and street art locations.
3. This derivative combined with OpenStreetMap data for base map information and OpenLayers for rendering the 2D map interface.

graph TD
    A[Tourism Mobile Device] --> B(High-Res Display + AR Module)
    A --> C(Powerful GPU/LiDAR)
    D[Mapping App: Historical Map] --> B
    E[Non-Mapping App: Trivia/Recommendations] -- Mappable Content --> B
    B -- User Selects (Pinch-to-Map) --> F[AR Integration Engine]
    F --> D
    D -- Overlay POI on 2D Map + AR Waypoint --> G(Interactive AR Map View)
    G -- Auto Zoom/Alignment --> G

Derivative 16.4: AI-Powered Voice-Activated Mapping Device

Enabling Description: A mobile electronic device features an array of high-fidelity microphones and a dedicated neural processing unit (NPU) for on-device AI inference. The device is configured to display an initial map in its mapping application. A non-mapping application, which could be an email client, a calendar, or a web browser, is capable of displaying mappable content. Instead of physical selection, the user issues a voice command, "Map the location mentioned in the last email." The device's NPU processes this command using a pre-trained speech-to-text model and an on-device NLP model (e.g., distilled BERT) to extract the location from the specified application. This extracted mappable content is then automatically fed to the mapping application, which intelligently displays it on the same map with the original content. The AI further optimizes the map presentation by inferring user intent from the voice command (e.g., "show me the fastest route," "display nearby restaurants") and automatically adjusting zoom, filters, and overlays.
Combination Prior Art:
1. This derivative combined with Kaldi or Mozilla DeepSpeech for open-source speech-to-text processing on-device.
2. This derivative combined with Hugging Face Transformers library for implementing distilled NLP models for entity extraction.
3. This derivative combined with Mapbox GL JS for dynamic map styling and integration with voice-driven interaction logic.

graph TD
    A[Mobile Device] --> B(Microphone Array)
    A --> C(Neural Processing Unit (NPU))
    D[Mapping App (Initial Map)] --> E(Display)
    F[Non-Mapping App (Email/Calendar)] --> E
    B -- Voice Command ("Map last email location") --> G(Speech-to-Text)
    G --> H(NLP Entity Extraction on NPU)
    H -- Extracted Location --> D
    D -- Displays New POI + AI-Optimized View --> E

Derivative 16.5: Limited-Scope "Zone Specific" Mapping Device

Enabling Description: This mobile electronic device is configured to operate only within a predefined geographical zone (e.g., a specific industrial park, a campus, or a wildlife reserve). Its internal memory contains only the map data relevant to this zone, enforced by a geofencing module. The mapping application, therefore, always displays an initial map limited to this zone. A non-mapping application (e.g., asset tracking, personnel management within the zone) displays mappable content related to entities within this designated zone. When a user selects specific mappable content (e.g., "maintenance crew at Sector 4, Building B") and issues a command, the device automatically displays the selected mappable content on the same zone-specific map. The device prevents any map rendering or display functionality outside this predefined geofenced area, and any attempt to map an external location is met with a "location out of bounds" error, thereby limiting its functionality by design.
Combination Prior Art:
1. This derivative combined with GeoPackage (GPKG) for storing immutable, zone-specific map data offline.
2. This derivative combined with MQTT for localized real-time updates of asset positions within the geofenced zone.
3. This derivative combined with OSMAnd (OpenStreetMap for Android) for its ability to load and render custom, offline map packages.

stateDiagram-v2
    state "Inactive Outside Zone" as OutOfZone
    state "Active Within Zone" as InZone
    
    [*] --> OutOfZone : Device Startup (Initial Location Check)
    OutOfZone --> InZone : Enter Predefined Zone (Geofence Trigger)
    InZone --> OutOfZone : Exit Predefined Zone (Geofence Trigger)

    state "InZone" {
        [*] --> Initial_Map_Displayed : Mapping App active (Zone-Specific Map)
        Initial_Map_Displayed --> Non_Mapping_App_Active : Non-Mapping App displays zone-specific content
        Non_Mapping_App_Active --> User_Selects_Content : User interaction
        User_Selects_Content --> Validate_Location_In_Zone : Check if selected location is within zone
        Validate_Location_In_Zone --> Display_On_Same_Map : If valid, overlay on existing map
        Validate_Location_In_Zone --> Out_Of_Bounds_Error : If invalid, show error
        Display_On_Same_Map --> Initial_Map_Displayed
        Out_Of_Bounds_Error --> Initial_Map_Displayed
    }

Independent Claim 22: System for mapping and displaying location data

Core Claim Summary: A system that includes a first electronic device and a second electronic device (which may be the same as the first). The first device runs an application capable of displaying location information. When a user selects this location information, a mapping component receives it. This mapping component then transmits the location information to a mapping application on the second electronic device. The mapping application then displays a map showing the location information.

Derivative 22.1: Satellite-Integrated Decentralized Mesh Network for Disaster Response Mapping

Enabling Description: This system comprises a first electronic device (a handheld field terminal for first responders) and a second electronic device (a command center console). The field terminal operates an application displaying real-time incident reports, including location information (e.g., "collapsed building at 34.0522 N, 118.2437 W"). When a first responder selects this location, an integrated mapping component on the terminal receives it. This component transmits the location information over a satellite-backhauled, decentralized mesh network (e.g., using LoRaWAN or Meshtastic protocols over Iridium SBD) to the command center console. The command center console, acting as the second device, receives this data. Its mapping application processes the satellite-derived location, correlates it with local ground truth data received from other mesh nodes, and displays a dynamically updated disaster area map, indicating the collapsed building. The system prioritizes low-bandwidth, robust communication for critical location updates in areas with compromised infrastructure.
Combination Prior Art:
1. This derivative combined with Meshtastic firmware for peer-to-peer mesh networking and OpenStreetMap for base map data.
2. This derivative combined with MQTT-SN (MQTT for Sensor Networks) for constrained device communication over satellite links and GeoJSON for location data interchange.
3. This derivative combined with OGC Web Map Service (WMS) for serving satellite imagery and Leaflet.js for interactive map rendering on the command center console.

graph TD
    A[First Responder Terminal (1st Device)] --> B(Incident Reporting App)
    B -- User Selects Location --> C(Integrated Mapping Component)
    C -- Encrypted LoRaWAN over Iridium SBD --> D(Decentralized Mesh Network + Satellite Backhaul)
    D --> E[Command Center Console (2nd Device)]
    E --> F(Mapping Application)
    F --> G(Display Disaster Map with Incident Location)

Derivative 22.2: Global Real-time Mapping with Sub-Meter Precision via Hyper-Local Sensor Fusion

Enabling Description: This system features a first electronic device (a mobile robot or autonomous vehicle) and a second electronic device (a cloud-based mapping platform accessed via any user device). The mobile robot, equipped with RTK-GPS, LiDAR, and high-resolution optical sensors, runs an application generating highly precise location-tagged data (e.g., "surface anomaly at [RTK-GPS coordinates] with 5cm deviation"). Upon detecting a significant anomaly, the robot's onboard mapping component automatically captures and transmits this sub-meter precise location information. This data is streamed via 5G to the cloud-based mapping platform. The cloud platform (second electronic device) fuses this real-time, precise data with other sensor inputs (e.g., atmospheric conditions from weather stations) to contextually enrich the anomaly. Its mapping application then displays a global map, dynamically zooming into the robot's location, showing the anomaly with its sub-meter precision and any fused environmental data, continuously updated as the robot moves.
Combination Prior Art:
1. This derivative combined with ROS (Robot Operating System) for robot sensor data integration and communication.
2. This derivative combined with PostGIS for managing and querying high-precision geospatial data in the cloud-based mapping platform.
3. This derivative combined with OGC Web Feature Service (WFS) for real-time querying of dynamically updated features and Mapbox GL JS for high-performance 3D map rendering in the web browser.

graph TD
    A[Mobile Robot (1st Device)] --> B(RTK-GPS, LiDAR, Sensors)
    B --> C(Onboard Application: Anomaly Detection)
    C -- Auto Transmit Sub-Meter Location --> D(Mapping Component)
    D -- 5G Stream --> E[Cloud Mapping Platform (2nd Device)]
    E --> F(Sensor Fusion & Data Enrichment)
    F --> G(Mapping Application)
    G --> H(Global Map Display with Sub-Meter Anomaly)

Derivative 22.3: Smart City Infrastructure Management with Digital Twin Integration

Enabling Description: This system uses a first electronic device (a maintenance technician's smart glasses with integrated camera and AR capabilities) and a second electronic device (a central smart city operations dashboard). The smart glasses run an application that provides augmented reality overlays for infrastructure (e.g., highlighting underground pipes, electrical conduits). The technician visually identifies a problem (e.g., "leaking pipe segment ID #456 at intersection of Main St & Elm Ave") and, through gaze tracking or voice command, selects this location information within the smart glasses' AR interface. A mapping component embedded in the smart glasses transmits the precise location and asset ID to the central operations dashboard. The dashboard's mapping application, integrated with a digital twin of the city infrastructure, displays a 3D model of the city. It pinpoints the exact pipe segment, highlights its status (e.g., red for leak), and integrates real-time sensor data from the pipe, providing a comprehensive operational view for dispatching repair crews.
Combination Prior Art:
1. This derivative combined with glTF (GL Transmission Format) for efficient 3D model exchange between the smart glasses and the digital twin platform.
2. This derivative combined with OPC UA (Open Platform Communications Unified Architecture) for standardized communication with industrial IoT sensors monitoring infrastructure.
3. This derivative combined with CityGML for semantic modeling of urban objects and Three.js for rendering the 3D city model and map on the operations dashboard.

graph TD
    A[Maintenance Tech Smart Glasses (1st Device)] --> B(AR Infrastructure View App)
    B -- Gaze/Voice Select Problem Area --> C(Mapping Component)
    C -- Asset ID + Precise Location --> D[Central Operations Dashboard (2nd Device)]
    D --> E(Digital Twin Integration Module)
    E --> F(Mapping Application: 3D City Model)
    F --> G(Display Pinpointed Asset + Sensor Data Overlay)

Derivative 22.4: Blockchain-Verified Supply Chain Tracking System

Enabling Description: This system involves a first electronic device (a smart tag or IoT sensor attached to a shipping container) and a second electronic device (a global supply chain monitoring platform accessible via web browser). The smart tag, running an application, periodically records its precise GPS location and environmental conditions (e.g., temperature, humidity). When a predefined event occurs (e.g., container door opened, temperature deviation), or at regular intervals, the smart tag's embedded mapping component signs the location information with its unique cryptographic key and commits it as an immutable transaction to a permissioned blockchain. The global supply chain monitoring platform (second electronic device) retrieves these verified location transactions from the blockchain. Its mapping application reconstructs the container's journey by displaying a map with a chain of verified, timestamped location points. Each point is associated with its cryptographic proof of authenticity and an audit trail, allowing stakeholders to verify the container's route and conditions transparently.
Combination Prior Art:
1. This derivative combined with Hyperledger Fabric for the permissioned blockchain network for supply chain transactions.
2. This derivative combined with GeoJSON for standardized representation of location data within blockchain transactions.
3. This derivative combined with MQTT for lightweight sensor data transmission from the smart tag to an initial gateway before blockchain commitment, and Leaflet.js for interactive map visualization.

sequenceDiagram
    participant S as Smart Tag (1st Device)
    participant BC as Permissioned Blockchain Network
    participant P as Supply Chain Platform (2nd Device)
    participant M as Mapping Application

    S->>S: Record GPS + Environmental Data
    S->>S: Detect Event / Timer
    S->>S: Sign Location Data (Cryptographic Key)
    S->>BC: Commit Signed Location as Transaction
    BC->>BC: Verify & Add to Ledger
    P->>BC: Retrieve Verified Location Transactions
    BC-->>P: Return Verified Location Data
    P->>M: Pass Location Data + Proofs
    M->>P: Display Map with Verified Journey & Audit Trail

Derivative 22.5: Redundant Backup Mapping System for Critical Infrastructure

Enabling Description: This system consists of a primary electronic device (a critical infrastructure monitoring server) and a secondary, offline electronic device (a hardened, geographically distant backup server). The primary server runs an application that continuously displays the operational status and location of critical infrastructure assets (e.g., power grid nodes, water pipelines) on a high-resolution display. If the primary server detects a fault in an asset and the primary mapping component goes offline or fails, the system automatically triggers a failover. The primary server, before failure, transmits its last known state, including all displayed location information and active map layers, to the backup server via a dedicated, redundant link. The backup server, acting as the second device, initiates its own mapping application using this received data. It immediately displays a map of the critical infrastructure, showing the fault location and the last known operational status. This ensures continuous situational awareness even during a catastrophic primary system failure, operating in a 'recovery' mode that prioritizes data integrity and availability over real-time processing of new, incoming external data.
Combination Prior Art:
1. This derivative combined with MODBUS/TCP for communication with critical infrastructure control systems.
2. This derivative combined with OGC GeoPackage (GPKG) for storing and exchanging the complete state of map data and layers during failover.
3. This derivative combined with Zabbix for infrastructure monitoring and alerting the system to primary server failure.

stateDiagram-v2
    state "Primary System Active" as PrimaryActive
    state "Backup System Standby" as BackupStandby
    state "Primary System Failed" as PrimaryFailed
    state "Backup System Active (Recovery Mode)" as BackupActive

    [*] --> PrimaryActive : System Start
    [*] --> BackupStandby : System Start

    PrimaryActive --> PrimaryFailed : Primary Server Failure Detected
    PrimaryFailed --> BackupActive : Failover Triggered; Last State Transmitted
    BackupStandby --> BackupActive : Failover Triggered; Last State Received

    state "PrimaryActive" {
        PrimaryApp : Critical Infra Monitoring
        PrimaryMap : Display Operational Map
        PrimaryMap -- Transmit State (Periodic) --> BackupStandby
    }

    state "BackupActive" {
        BackupMapApp : Loads Last Known State
        BackupMapApp : Displays Operational Map (Recovery Mode)
    }

Independent Claim 25: Method for selecting a map

Core Claim Summary: A method for a computing device to select a map for displaying new mapping data. After receiving the new mapping data and a command to map it, the method determines if a default map-display application has been designated. If so, the data is sent to that default application. If not, it checks for open map-display applications and, if found, selects one (e.g., the last used or one geographically closest to the mapping data) to display the new data, optionally setting it as the new default. If no applications are open, a new one is launched.

Derivative 25.1: Brain-Computer Interface (BCI) driven Map Selection

Enabling Description: This derivative describes a computing device integrated with a non-invasive Brain-Computer Interface (BCI) (e.g., EEG headset). The device receives new mapping data (e.g., a meeting address from a calendar application) and a command to map it, which can also be issued via BCI (e.g., a specific thought pattern recognized by the BCI). The system first checks for a default map-display application designated by the user's implicit BCI training data (e.g., an application frequently activated when thinking "map work locations"). If a default is recognized, the data is sent there. If not, the BCI monitors the user's cognitive response (e.g., alpha wave patterns, attention spikes) as a dynamically generated list of open map-display applications (or suggested new ones) is briefly displayed. The application eliciting the strongest "interest" or "recognition" signal from the BCI is automatically selected to display the new mapping data, and optionally set as the new BCI-inferred default. If no BCI-driven preference is detected or no applications are open, a new instance of a system-defined default mapping application is launched.
Combination Prior Art:
1. This derivative combined with OpenBCI GUI and API for real-time EEG data acquisition and processing.
2. This derivative combined with OpenStreetMap Nominatim for reverse geocoding to quickly determine the geographic relevance of map suggestions.
3. This derivative combined with Electron framework for developing cross-platform desktop map-display applications that can interface with BCI data streams.

graph TD
    A[User (EEG Headset)] --> B(BCI Signal Acquisition)
    C[Computing Device] -- New Mapping Data + BCI Command --> D(Map Selector Module)
    D -- Check BCI-Inferred Default --> E{Default Map Designated?}
    E -- Yes --> F[Transmit to Default App]
    E -- No --> G{Open Map-Display Apps?}
    G -- Yes --> H(Dynamically Display App List)
    H -- BCI Monitor Cognitive Response --> I{BCI Selects App}
    I -- Selected App --> J[Transmit to Selected App]
    J -- (Optional) Set as BCI Default --> E
    G -- No --> K[Launch New Default App]
    K --> L[Transmit to New App]

Derivative 25.2: Multi-Dimensional Data Map Selection for Geospatial Simulation

Enabling Description: This derivative describes a computing device used for advanced geospatial simulations (e.g., climate modeling, urban growth projections), which receives complex new mapping data, including not only geographic coordinates but also time-series data (e.g., temperature over decades) and volumetric data (e.g., atmospheric pollutant concentrations at different altitudes). Upon a command to map this data, the system evaluates available map-display applications. Instead of simple geographic proximity, it employs a multi-criteria optimization algorithm. This algorithm considers the map's native dimensionality (2D, 3D), its ability to render time-series animations, its support for volumetric data visualization, and its thematic relevance to the new data (e.g., a climate modeling application for temperature data). The map-display application that best matches the multi-dimensional attributes of the new data, minimizing data transformation requirements and maximizing visualization fidelity, is automatically selected. This selection might prioritize an application that can display a 4D (3D + time) map over a simpler 2D map, even if the 2D map is geographically closer.
Combination Prior Art:
1. This derivative combined with Open Geospatial Consortium (OGC) standards (e.g., WCS for Coverage data, WKT for geometry) for handling complex geospatial data types.
2. This derivative combined with NASA WorldWind or CesiumJS for powerful 3D globe visualization and time-series animation capabilities.
3. This derivative combined with NetCDF (Network Common Data Form) for storing multi-dimensional scientific data (e.g., climate model outputs).

flowchart TD
    A[Computing Device] --> B(Receive Multi-Dimensional Mapping Data)
    B --> C(Command to Map Data)
    C --> D(Multi-Criteria Optimization Algorithm)
    D -- Evaluate Open Map-Display Apps --> E{App 1 (2D, time-series, Volumetric support)}
    D -- Evaluate Open Map-Display Apps --> F{App 2 (3D, time-series, Volumetric support)}
    D -- Evaluate Open Map-Display Apps --> G{App N (...)}
    D -- Best Match Score --> H(Select Optimal Map-Display App)
    H --> I[Transmit Data to Selected App]
    I --> J(Display Multi-Dimensional Map)

Derivative 25.3: Medical Imaging Analysis with Anatomical Map Selection

Enabling Description: This derivative is for a computing device in a medical context, receiving new imaging data (e.g., a detected lesion at specific anatomical coordinates within the brain, derived from an MRI scan). The command to map this data is issued by a clinician. The method determines if a default anatomical map-display application (e.g., a specialized neuro-imaging viewer) has been designated. If so, the lesion data is sent to that application. If not, it checks for open anatomical map-display applications (e.g., different brain atlas viewers, 3D anatomical models). The selection algorithm prioritizes an application that already displays a relevant anatomical region (e.g., the specific lobe of the brain) and can effectively render the lesion with appropriate medical overlays. It would favor a map-display application that matches the modality and resolution of the incoming data, and can display the new data in a 3D context with relevant anatomical landmarks, rather than a generic 2D map.
Combination Prior Art:
1. This derivative combined with DICOM (Digital Imaging and Communications in Medicine) standard for medical image data.
2. This derivative combined with 3D Slicer or ITK-SNAP for open-source medical image visualization and analysis.
3. This derivative combined with NIfTI (Neuroimaging Informatics Technology Initiative) format for brain imaging data.

graph TD
    A[Computing Device] --> B(Receive New Medical Imaging Data - Lesion Coordinates)
    B --> C(Clinician Command to Map)
    C --> D(Anatomical Map Selector Module)
    D -- Check Default Medical Viewer --> E{Default Viewer Designated?}
    E -- Yes --> F[Transmit to Default Viewer]
    E -- No --> G{Open Anatomical Viewers?}
    G -- Yes --> H(Select Viewer: Matches Region/Modality/3D)
    H --> I[Transmit to Selected Viewer]
    G -- No --> J[Launch New Medical Viewer]
    J --> K[Transmit to New Viewer]

Derivative 25.4: Reinforcement Learning (RL) for Personalized Map Recommendations

Enabling Description: This derivative describes a computing device that, after receiving new mapping data and a command, uses a Reinforcement Learning (RL) agent to select the optimal map. Initially, the system might follow predefined rules (default, last used, closest geography). However, the RL agent (e.g., using Q-learning or Deep Q-Networks) continuously observes user interactions with selected maps (e.g., how long they spend on a map, if they zoom/pan, if they immediately switch apps after map display). Based on these observations, the RL agent learns a personalized policy. When new mapping data arrives, the RL agent, acting as the map selection logic, predicts which available map-display application (or new instance thereof) is most likely to satisfy the user's implicit needs and preferences for that specific type of mapping data. Over time, the system learns to select the "best" map, dynamically adapting to evolving user behavior without explicit configuration. The "reward" signal for the RL agent is derived from positive user engagement metrics with the displayed map.
Combination Prior Art:
1. This derivative combined with OpenAI Gym for designing the reinforcement learning environment for map selection.
2. This derivative combined with Google's Protocol Buffers (Protobuf) for efficient data exchange of user interaction logs for RL training.
3. This derivative combined with Leaflet.js for building customizable and extensible web-based map applications whose interaction data can be easily captured for RL feedback.

sequenceDiagram
    participant U as User
    participant C as Computing Device
    participant S as Map Selector (RL Agent)
    participant M as Map-Display Applications

    U->>C: Input Mapping Data + Command
    C->>S: Request Map Selection
    S->>S: Observe User Interaction History
    S->>S: Apply Learned Policy (Predict Best Map)
    S-->>C: Recommend/Select Map App (e.g., M_App_X)
    C->>M: Launch/Activate M_App_X
    M->>U: Display Map with New Data
    U->>M: Interact with Map (Zoom, Pan, Switch)
    M->>S: Send Engagement Metrics (Reward Signal)
    S->>S: Update Learned Policy

Derivative 25.5: "Least Data Exposure" Map Selection for Sensitive Information

Enabling Description: This derivative describes a computing device designed for handling sensitive mapping data, where the primary objective is to minimize exposure of that data. When the device receives new mapping data (e.g., classified facility location) and a command to map it, the system first determines if a default map-display application has been designated that adheres to stringent data handling protocols (e.g., isolated network segment, air-gapped display). If so, the data is transmitted there. If not, it checks for open map-display applications and evaluates them against predefined security and privacy criteria, such as network connectivity status (offline preferred), data logging policies, and access controls. The system selects the map-display application that offers the "least data exposure" profile (e.g., an offline, sandboxed mapping application that does not log coordinates, even if it requires more panning than a geographically closer, but internet-connected, option). If no applications meet the minimum security threshold or none are open, a specialized, disposable, temporary "secure viewer" is launched, which automatically purges all map data upon closing.
Combination Prior Art:
1. This derivative combined with SELinux/AppArmor for mandatory access control policies to sandbox map-display applications.
2. This derivative combined with Zero-knowledge Proof (ZKP) protocols to verify map data origins and integrity without revealing the underlying sensitive information.
3. This derivative combined with OpenStreetMap vector tiles for local, offline rendering in a disconnected environment.

graph TD
    A[Computing Device] --> B(Receive Sensitive Mapping Data + Command)
    B --> C(Secure Map Selector Module)
    C -- Check Secure Default App --> D{Default App (Secure) Designated?}
    D -- Yes --> E[Transmit to Secure Default App]
    D -- No --> F{Open Apps Meet Security Threshold?}
    F -- Yes --> G(Select App with Least Data Exposure)
    G --> H[Transmit to Selected App]
    F -- No --> I[Launch Temporary Secure Viewer]
    I --> J[Transmit to Temporary Viewer]
    I -- Auto-Purge on Close --> B