Derivative works — US Patent 10444943

Defensive Disclosure Document for US Patent 10444943

Date: April 26, 2026

Patent: US10444943B2 - Interactive electronically presented map

Inventors: Michael Abramson, Erika Jakubassa, Michael Grisham, Geoff Atkin

The following derivatives of the core claims of US10444943 are disclosed as prior art to prevent future claims of novelty or non-obviousness for incremental improvements in interactive electronic map systems. The core elements considered for these derivations include:

Displaying an unmagnified electronic map with associated item information.
Simultaneous display of a highlighted portion of the unmagnified map and a magnified view thereof.
Smooth, user-controlled movement of the highlighted portion and corresponding smooth update of the magnified view.
Bidirectional interaction: selecting information from the magnified view to display additional details, and selecting item information to reposition the highlighted portion/magnified view on the unmagnified map.

1. Material & Component Substitution

This section details variations involving alternative materials and mechanical/electronic components for implementing the interactive map system.

Derivative 1.1: Flexible Organic Light-Emitting Diode (OLED) Display with Haptic Feedback and Electroluminescent Highlight

Enabling Description:
An interactive electronic map system utilizes a flexible OLED display panel (e.g., using polyimide substrates and solution-processed emissive layers) as the unmagnified map display. The highlighted portion is rendered via an embedded, dynamically addressable electroluminescent polymer layer that changes luminescence intensity and color (e.g., yellow phosphorescent dopants) when electrically excited, creating a visually distinct boundary or fill. User interaction for smooth movement is achieved via an integrated haptic feedback array (e.g., piezoelectric actuators or vibrotactile motors) beneath the flexible display surface, providing tactile cues as the user's finger (serving as the input device) traverses map features. The magnified view is displayed on a separate, rigid micro-LED display with ultra-high pixel density (>1000 PPI) and a transparent capacitive touch overlay for precise information selection. Data transmission employs a low-latency, high-bandwidth optical fiber network using 100 Gigabit Ethernet (100GbE) for real-time updates.

graph TD
    A[User Finger Input] -- Haptic Feedback Array --> B(Flexible OLED Display)
    B -- Electroluminescent Polymer Layer --> C{Highlighted Portion}
    C --> D(Magnified View Micro-LED Display)
    D -- Transparent Capacitive Touch --> E[Information Selection]
    E -- 100GbE Optical Network --> F(Processing Unit)
    F -- Database Query/Update --> G(Map Data Backend)
    G -- Item Information Retrieval --> E

Derivative 1.2: E-Ink Display Array with Electro-Fluidic Magnifier and Ultrasonic Gesture Control

Enabling Description:
The unmagnified electronic map is presented on a large-format tiled array of monochrome or multi-color E-Ink display modules (e.g., electrophoretic microcapsule technology), offering persistent display with minimal power consumption. The "magnifier" is implemented as a mobile, transparent electro-fluidic lens overlay, where localized electric fields manipulate a refractive liquid (e.g., silicon oil with embedded nanoparticles) to create a variable focal length optical magnification directly above the E-Ink surface. The highlighted portion is defined by the physical boundary of this fluidic lens. User input for smooth movement of the lens is achieved through an array of ultrasonic transceivers (e.g., time-of-flight sensors operating at 40 kHz) that detect precise 3D hand gestures above the E-Ink array, allowing non-contact manipulation. Information associated with the magnified view is rendered on a secondary, high-refresh-rate LCD panel, triggered by a specific "pinch" gesture detected by the ultrasonic array when hovering over a point of interest.

graph TD
    A[Ultrasonic Gesture Control] --> B(Electro-Fluidic Lens Overlay)
    B -- Physical Movement --> C{Highlighted Portion}
    C -- Optical Magnification --> D(E-Ink Display Array)
    D -- Information Trigger (Gesture) --> E(Secondary LCD Panel)
    E -- Display Information --> F(Processing Unit)
    F -- Database Query --> G(Map Data Backend)

Derivative 1.3: Ferrofluidic Topography Map with Magnetic Stylus and Photonic Crystal Magnifier

Enabling Description:
A physically reconfigurable map surface is created using a ferrofluid contained within a matrix of addressable electromagnets. The unmagnified map is displayed by varying the magnetic field strength to create topographical relief patterns corresponding to geographical features (e.g., mountains, rivers). Item information locations are represented by embedded, individually addressable micro-LEDs within the ferrofluidic matrix. User interaction for defining and moving the highlighted portion is performed with a magnetic stylus that senses local magnetic field gradients and provides haptic feedback. The "magnified view" is generated by a portable photonic crystal lens, which when placed above the ferrofluid, optically magnifies the underlying relief and activates a corresponding subset of micro-LEDs to display granular item information. The magnified information is captured by a miniature camera integrated into the photonic crystal lens, processed, and displayed on a wearable micro-display (e.g., waveguide display glasses).

graph TD
    A[Magnetic Stylus Input] -- Magnetic Field Manipulation --> B(Ferrofluidic Topography Map)
    B -- Embedded Micro-LEDs --> C{Item Information}
    C -- Photonic Crystal Lens --> D(Magnified View)
    D -- Integrated Camera --> E(Wearable Micro-Display)
    E -- Data Processing --> F(Processing Unit)
    F -- Control Signals --> B

Derivative 1.4: Multi-Layer Transparent LCD with Infrared Touch and Dynamic Lightfield Magnification

Enabling Description:
The unmagnified map is presented across a stack of transparent liquid crystal displays (LCDs), allowing for depth perception and overlaying different map data layers (e.g., terrain, utilities, property lines). User interaction for defining the highlighted portion occurs via an integrated infrared (IR) multi-touch sensor grid within the top LCD layer. The "magnified view" is achieved through dynamic lightfield display technology, where a region of the transparent LCD stack selectively renders a magnified, parallax-aware view by controlling individual light rays, giving the impression of a holographic magnifier floating above the surface. Movement of the highlighted area by user touch smoothly updates this lightfield magnification. Item information (e.g., 3D models of buildings, utility schematics) is streamed in real-time from a cloud-based geospatial database (e.g., using OGC CDB standard) and rendered within the lightfield, allowing for interactive manipulation through IR touch gestures.

graph TD
    A[Multi-Layer Transparent LCD] --> B{IR Multi-Touch Sensor}
    B -- User Input (Touch/Gesture) --> C{Highlighted Portion}
    C -- Dynamic Lightfield Display --> D(Magnified View)
    D -- 3D Object Rendering --> E(Cloud Geospatial Database)
    E -- Data Stream (OGC CDB) --> F(Processing Unit)
    F -- Control Signals --> A

2. Operational Parameter Expansion

This section describes variations where the interactive map technology operates at extreme scales or conditions.

Derivative 2.1: Nanoscale Molecular Map with Electron Beam Magnifier for Chemical Synthesis Guidance

Enabling Description:
An unmagnified map represents a 2D projection of molecular structures or a reaction pathway on a substrate at the nanoscale (e.g., 10 nm scale). Item information includes atomic species, bond types, or active sites, visualized as color-coded regions. A focused electron beam (e.g., from a scanning electron microscope modified for interaction) acts as the position indicator, raster-scanning a highlighted portion. The "magnified view" is generated by increasing the electron beam dwell time and resolution over the highlighted area, achieving up to 1,000,000x effective magnification, revealing individual atoms and their orbitals. User input for moving the electron beam is achieved through a haptic joystick interface connected to piezoelectric stage actuators with sub-nanometer precision. Selection of molecular features in the magnified view triggers display of quantum chemical properties (e.g., HOMO/LUMO orbitals, bond energies) via a computational chemistry backend, which can then guide automated atomic force microscopy (AFM) manipulation or electron-beam lithography for direct chemical synthesis.

stateDiagram-v2
    [*] --> Unmagnified_Molecular_Map
    Unmagnified_Molecular_Map --> Electron_Beam_Magnifier: Raster Scan
    Electron_Beam_Magnifier --> Highlighted_Area
    Highlighted_Area --> Magnified_Atomic_View: Increased Resolution/Dwell
    Magnified_Atomic_View --> Quantum_Chemistry_Backend: Feature Selection (e.g., Atom, Bond)
    Quantum_Chemistry_Backend --> AFM_Manipulation: Synthesis Guidance
    AFM_Manipulation --> Unmagnified_Molecular_Map: Updated Substrate

Derivative 2.2: Planetary-Scale Dynamic Weather Map with Global Telemetry and Adaptive Resolution Magnifier

Enabling Description:
The unmagnified electronic map displays real-time weather patterns across an entire planetary body (e.g., Earth, Mars) at a global scale (e.g., 1:100,000,000 resolution), with item information representing atmospheric pressure, temperature, wind vectors, and precipitation derived from a network of orbital satellites and ground-based IoT telemetry stations. The system handles petabytes of data updated every minute. The "highlighted portion" is a user-defined region (e.g., a hurricane path) that dynamically adapts its resolution based on meteorological significance. The "magnified view" provides localized weather forecasts down to a 1:1,000 resolution, leveraging an adaptive mesh refinement (AMR) algorithm to increase computational and display resolution only within the highlighted area, processing data from high-resolution radar and ground sensors. User input for defining and moving the highlighted region is managed by a geographically distributed network of meteorologists using collaborative virtual reality (VR) interfaces, where haptic gloves simulate atmospheric conditions.

flowchart TD
    A[Orbital Satellites & IoT Telemetry] --> B(Global Weather Data Stream - Petabytes/min)
    B --> C{Processing Unit - Distributed Computing}
    C --> D[Unmagnified Planetary Map - 1:100M]
    D -- User Defines Region --> E{Adaptive Resolution Magnifier}
    E -- AMR Algorithm --> F[Magnified Local Forecast - 1:1K]
    F -- Collaborative VR Interface --> G[Meteorologist Input (Haptic Gloves)]
    G --> E
    F --> H(Real-time Weather Prediction Models)

Derivative 2.3: Ultra-High Frequency Spectral Map with Hyperspectral Magnifier for RF Interference Localization

Enabling Description:
An unmagnified electronic map visualizes the radio frequency (RF) spectrum (e.g., 0 Hz to 3 THz) across multiple dimensions (frequency, power, time, spatial origin) as a dynamic heatmap. Item information denotes active transmitters, interference sources, or unoccupied bandwidth, represented by color and intensity. The "highlighted portion" is a user-selected frequency band or geographic area of interest within the spectrum, defined by a directional antenna array and a high-speed field-programmable gate array (FPGA) for real-time spectral analysis. The "magnified view" provides a hyperspectral analysis of the highlighted RF signal, breaking it down into minute frequency sub-bands (e.g., 1 Hz resolution) and identifying modulation schemes, signal characteristics, and precise geolocation of interference sources using advanced triangulation algorithms. User input is provided via a gestural control system interpreted by a quantum processor for rapid pattern recognition in high-dimensional spectral data, allowing fluid navigation through complex RF environments.

sequenceDiagram
    participant U as User (Gestural Input)
    participant QP as Quantum Processor
    participant DA as Directional Antenna Array
    participant FPGA as High-Speed FPGA
    participant RF as RF Spectrum Map (Unmagnified)
    participant HS as Hyperspectral Magnifier (Magnified)
    participant SA as Spectral Analysis Backend
    participant GL as Geolocation & Modulation ID

    U->>QP: Gesture (Select Frequency/Area)
    QP->>DA: Direct Antenna Scan
    DA->>FPGA: Raw RF Data
    FPGA->>RF: Update Unmagnified Map
    FPGA->>HS: Generate Highlighted Portion & Magnified View
    HS->>SA: Hyperspectral Analysis Request
    SA->>GL: Triangulate/Identify Source
    GL->>HS: Return Detailed Info
    HS->>U: Display Magnified View & Item Info

3. Cross-Domain Application

This section illustrates the application of the core interactive map mechanism in three unrelated industries.

Derivative 3.1: Aerospace - Dynamic Airspace Management and Drone Fleet Command

Enabling Description:
An unmagnified electronic map displays a three-dimensional representation of controlled airspace, with item information representing aircraft, flight paths, weather systems, and restricted zones. For drone fleet management, it tracks individual drones, their mission parameters, battery status, and sensor feeds. A highlighted portion is dynamically drawn around a specific region of airspace (e.g., airport approach, drone delivery corridor) or a cluster of aircraft. The magnified view provides granular detail for that region, showing individual aircraft identification, altitude, speed, predicted trajectory, and real-time telemetry from transponders (e.g., ADS-B data for manned aircraft, custom drone telemetry protocols). User input via a multi-modal interface (voice commands for high-level tasks, haptic joystick for precise control) allows air traffic controllers or drone operators to smoothly navigate the airspace, identify potential conflicts, and initiate automated re-routing procedures. Selecting an aircraft in the magnified view displays its flight plan, origin/destination, and estimated time of arrival.

classDiagram
    class AirspaceMap {
        +displayUnmagnified(3D_Airspace_Data)
        +updateHighlightedRegion(Region_ID)
        +updateMagnifiedView(Region_ID)
    }
    class Aircraft {
        +ID: String
        +Position: 3D_Coordinates
        +Velocity: Vector
        +Altitude: Float
        +FlightPath: List<3D_Coordinates>
        +Telemetry: RealtimeData
    }
    class Drone {
        +ID: String
        +MissionPlan: Object
        +BatteryStatus: Float
        +SensorFeed: Video/Data
    }
    class ControllerInput {
        +voiceCommand(Command_String)
        +joystickControl(Vector)
    }
    AirspaceMap "1" -- "*" Aircraft : contains
    AirspaceMap "1" -- "*" Drone : contains
    ControllerInput --> AirspaceMap : controls
    AirspaceMap <-- Aircraft : transmits telemetry
    AirspaceMap <-- Drone : transmits telemetry

Derivative 3.2: Biotechnology - High-Throughput Microscopy for Tissue Analysis

Enabling Description:
An unmagnified electronic map displays a macroscopic view of a tissue sample (e.g., a stained histology slide) at low optical magnification (e.g., 2x objective), with item information representing different cell types, structural anomalies, or labeled biomarkers identified by image processing algorithms. The highlighted portion, controlled by a digital microscope stage, defines a region of interest. The magnified view automatically switches to a high optical magnification (e.g., 40x or 100x objective) for the highlighted area, rendering individual cells, subcellular structures, and quantifying biomarker expression (e.g., immunohistochemistry scoring). Smooth movement of the highlighted region corresponds to precise stepper motor control of the microscope stage, continuously updating the high-resolution image stream. User selection of a cell or anomaly in the magnified view triggers display of its classification (e.g., cancerous, necrotic), morphological parameters (e.g., nuclear-to-cytoplasmic ratio), and links to genomic or proteomic databases for that specific cell type.

stateDiagram-v2
    [*] --> Macroscopic_Tissue_View: Low Magnification (2x)
    Macroscopic_Tissue_View --> Highlighted_ROI: User Defines Area
    Highlighted_ROI --> High_Resolution_View: High Magnification (40x/100x)
    High_Resolution_View --> Cell_Analysis_Module: Cell/Structure Selection
    Cell_Analysis_Module --> Genomic_Proteomic_DB: Data Retrieval
    Genomic_Proteomic_DB --> User_Display: Display Cell Info
    High_Resolution_View --> Macroscopic_Tissue_View: Move ROI/Return to Overview

Derivative 3.3: Industrial Automation - Smart Factory Floor Layout and Robotic Path Planning

Enabling Description:
An unmagnified electronic map presents a real-time, overhead 2D/3D layout of a factory floor, showing static machinery, dynamic automated guided vehicles (AGVs), robotic arms, and raw material/finished goods inventory locations. Item information includes machine status (e.g., operational, maintenance, error), AGV routes, and material stock levels. A highlighted portion indicates a specific work cell, an AGV's current operating area, or a bottleneck region. The magnified view provides a detailed, often 3D, schematic of the highlighted work cell, displaying individual sensor readings, robot joint positions, tool status, and micro-movements of components. User input via a specialized industrial touchscreen or holographic projection interface enables fluid navigation and immediate visualization of operational data. Selecting a robotic arm in the magnified view brings up its operational logs, predictive maintenance alerts, and allows for real-time adjustment of its programmed path or task sequence (with appropriate safety interlocks).

graph TD
    A[Factory Floor Map (2D/3D)] --> B{Item Info (Machine Status, AGV Routes)}
    B -- User Selects/Moves --> C{Highlighted Work Cell/Area}
    C -- Real-time Data Stream --> D(Magnified Work Cell View)
    D -- Sensor Readings, Robot Poses --> E[Detailed Operational Data]
    E -- User Interaction (Industrial Touch/Holographic) --> F(Robotic Control System)
    F -- Path Planning, Task Adjustment --> A

4. Integration with Emerging Tech

This section details integration of the interactive map with AI, IoT, and Blockchain technologies.

Derivative 4.1: AI-Driven Predictive Maintenance Map for Urban Infrastructure

Enabling Description:
An unmagnified electronic map displays an urban area with overlaid infrastructure networks (e.g., water pipes, electrical grids, communication lines). Item information includes current status, age, material type, and predictive failure scores for each segment, generated by an AI model trained on historical maintenance records, sensor data (IoT), and environmental factors. The highlighted portion automatically focuses on areas with high predicted failure probability or critical events detected by IoT sensors (e.g., sudden pressure drops in water mains, power outages). The magnified view provides a detailed engineering schematic of the affected infrastructure, displaying real-time IoT sensor data (e.g., flow rates, voltage, fiber optic integrity), projected repair timelines from AI algorithms, and resource allocation recommendations. User input (e.g., natural language queries processed by an AI assistant) enables smooth navigation and filtering of predictions. Selecting a pipe segment in the magnified view prompts the AI to simulate the impact of its failure and suggest optimal preventative actions.

flowchart TD
    A[IoT Sensors (Pressure, Voltage, etc.)] --> B(Real-time Infrastructure Data)
    C[Historical Maintenance Data] --> D(AI Predictive Failure Model)
    B --> D
    D --> E[Unmagnified Urban Map (Failure Scores Overlay)]
    E -- AI-Driven Highlight/User Input --> F{Highlighted Critical Area}
    F --> G(Magnified Engineering Schematic)
    G -- AI Recommendations, IoT Data --> H[Detailed Incident Information]
    I[Natural Language Query] --> J(AI Assistant)
    J --> F

Derivative 4.2: IoT-Enhanced Environmental Monitoring Map with Real-time Anomaly Detection

Enabling Description:
An unmagnified electronic map represents a natural reserve or agricultural zone, overlaid with real-time environmental data streams from a dense network of IoT sensors (e.g., soil moisture, air quality, wildlife trackers, temperature, pollution levels). Item information is dynamically generated by fusing sensor data and displayed as heatmaps or animated indicators. Anomaly detection AI continuously monitors these streams. The highlighted portion automatically follows detected environmental anomalies (e.g., wildfire hotspots, abnormal chemical spills, unauthorized human presence) or user-selected areas for detailed inspection. The magnified view provides granular sensor readings, time-series graphs, and specific alarm triggers for the highlighted anomaly, enabling rapid response. User input via a ruggedized tablet with touch and voice control allows smooth movement and deep dives into environmental conditions. Selecting a sensor node in the magnified view displays its operational status, battery life, and communication logs, with data visualized in a GIS-compatible format.

graph TD
    A[IoT Sensor Network (Environmental)] --> B(Real-time Data Stream)
    C[Anomaly Detection AI] --> B
    B -- Data Fusion --> D[Unmagnified Environmental Map (Dynamic Overlays)]
    D -- AI-Detected Anomaly/User Input --> E{Highlighted Anomaly/Area}
    E --> F(Magnified Sensor Readings/Graphs)
    F -- Alarm Triggers, GIS Data --> G[Detailed Environmental Information]
    H[Ruggedized Tablet (Touch/Voice)] --> E

Derivative 4.3: Blockchain-Secured Supply Chain Logistics Map with Immutable Asset Tracking

Enabling Description:
An unmagnified electronic map visualizes a global supply chain network, showing manufacturing facilities, distribution centers, shipping routes, and current locations of various assets (e.g., containers, pallets, individual high-value goods). Item information, sourced from a distributed ledger (blockchain), includes asset ID, ownership history, origin, destination, customs clearance status, and environmental conditions during transit (e.g., temperature for pharmaceuticals). Each asset's journey and status updates are cryptographically secured on the blockchain, ensuring immutability and auditability. The highlighted portion is a user-selected shipment, a specific transportation corridor, or a high-risk customs zone. The magnified view provides a transparent and verifiable record of the highlighted asset's journey, displaying cryptographic proofs of each waypoint, smart contract execution logs, and an auditable trail of custody from the blockchain. User input via secure biometric authentication allows authorized parties (e.g., customs officials, logistics managers) to smoothly trace assets, verify compliance, and drill down into the immutable transaction history.

sequenceDiagram
    participant AU as Authorized User (Biometric Input)
    participant LM as Logistics Map (Unmagnified)
    participant HCA as Highlighted Critical Asset/Area
    participant MV as Magnified Verified Asset Trail
    participant SC as Smart Contracts
    participant DLT as Distributed Ledger Technology (Blockchain)
    participant IOT as IoT Tracking Devices

    IOT->>DLT: Asset Location & Sensor Data (Immutable Transaction)
    DLT->>LM: Update Asset Positions & Status
    AU->>LM: Select Shipment/Area (Authenticated)
    LM->>HCA: Highlighted Item/Area
    HCA->>DLT: Query Immutable Record
    DLT->>SC: Verify Smart Contract State
    DLT->>MV: Return Verifiable Trail & Info
    MV->>AU: Display Magnified View

5. The "Inverse" or Failure Mode

This section explores variations involving inverse functionality, safe failure modes, or limited-functionality operation.

Derivative 5.1: "Privacy-Preserving" Map with Dynamic Anonymization and Role-Based Information Filtering

Enabling Description:
An interactive electronic map operates in a "privacy-preserving" mode for public displays or sensitive applications. The unmagnified map dynamically anonymizes item information based on configurable privacy policies and user roles. For instance, residential addresses are blurred or replaced with generic markers, while real-time individual movement data is aggregated and displayed as heatmaps rather than specific tracks. The highlighted portion, when defined by an authorized user, triggers a gradual de-anonymization process within its boundary, controlled by a secure multi-party computation (MPC) framework to protect underlying sensitive data. The magnified view only reveals specific, granular item information (e.g., individual names, precise GPS coordinates) if the accessing user's credentials (verified via a Zero-Knowledge Proof (ZKP) system) meet the strict privacy policy requirements for that specific data point. In "low-power" mode, the map reverts to a static, low-resolution grayscale display with only essential public service information.

graph TD
    A[Unmagnified Map (Aggregated/Anonymized Data)] --> B{Configurable Privacy Policies}
    C[User Role/Credentials (ZKP Verified)] --> B
    B -- Dynamic Anonymization --> A
    A -- User Defines/Authorized Highlight --> D{Highlighted De-anonymization Zone}
    D -- MPC Framework --> E(Magnified View - Granular/Sensitive Data)
    E -- Role-Based Filtering --> F[Displayed Information (Permitted)]
    G[Low-Power Mode] --> A: Grayscale/Static

Derivative 5.2: "Debug & Diagnostic" Map for Complex Software Systems with Runtime Trace Visualization

Enabling Description:
An unmagnified electronic map visually represents the architecture of a complex distributed software system (e.g., microservices, cloud functions), where item information denotes individual services, data queues, or network endpoints. Color coding indicates service health (e.g., green for operational, red for error), and animated flow lines represent real-time message traffic. The highlighted portion, user-defined or triggered by a runtime error, focuses on a specific microservice or data pipeline. The magnified view provides a "deep dive" into that component, showing stack traces, log entries, CPU/memory utilization, and variable states in real-time. This magnified view integrates a sophisticated trace visualization tool (e.g., OpenTracing, Jaeger) that visually represents the call graph and latency of individual requests. User input via a specialized debugging console or command-line interface allows developers to smoothly traverse the system's execution flow, pinpoint performance bottlenecks, and inject test data into the magnified service for live debugging, all without affecting production traffic in "safe failure" debug mode.

classDiagram
    class SystemMap {
        +displayUnmagnified(System_Architecture)
        +updateHighlightedService(Service_ID)
        +updateMagnifiedDebugView(Service_ID)
    }
    class Microservice {
        +ID: String
        +HealthStatus: Enum
        +CPU_Usage: Float
        +Memory_Usage: Float
        +LogEntries: List<String>
        +StackTraces: List<String>
    }
    class DataQueue {
        +ID: String
        +Throughput: Float
        +Latency: Float
    }
    class DebugConsole {
        +commandInput(Command_String)
        +visualizeTrace(Trace_ID)
    }
    SystemMap "1" -- "*" Microservice : contains
    SystemMap "1" -- "*" DataQueue : contains
    DebugConsole --> SystemMap : controls/queries
    SystemMap <-- Microservice : transmits telemetry

Derivative 5.3: Disaster Response Map with Limited Bandwidth "Emergency Mode" and Prioritized Information Display

Enabling Description:
An interactive electronic map is deployed for disaster response scenarios (e.g., earthquake, hurricane). In normal operation, it displays full geographical data, infrastructure status, and real-time incident reports. However, in "emergency mode," triggered by network degradation or power loss, the system automatically shifts to a limited-bandwidth, low-power profile. The unmagnified map displays only critical information (e.g., emergency shelters, safe evacuation routes, known hazard zones) using a highly compressed vector graphics format (e.g., GeoJSON optimized for size) and monochromatic color palette. Item information is strictly prioritized: human casualties, critical infrastructure failures, and emergency resource locations are displayed, while non-essential data is suppressed. The highlighted portion, manually or autonomously positioned over an active incident area, fetches and displays a magnified view with only the most recent (e.g., within 5 minutes) and crucial updates, such as victim count, specific resource needs, or structural integrity reports from first responders. Communication uses a resilient mesh network (e.g., LoRaWAN) to ensure smooth, albeit low-throughput, updates for the magnifier and magnified view, ensuring critical information reaches responders despite degraded conditions.

stateDiagram-v2
    [*] --> Normal_Operation: Full Data, High Bandwidth
    Normal_Operation --> Emergency_Mode: Network Degradation/Power Loss
    Emergency_Mode --> Unmagnified_Emergency_Map: Compressed Vector, Critical Info Only
    Unmagnified_Emergency_Map --> Highlighted_Incident_Area: Manual/Autonomous Positioning
    Highlighted_Incident_Area --> Magnified_Critical_Updates: Resilient Mesh Network (LoRaWAN)
    Magnified_Critical_Updates --> Prioritized_Information_Display: First Responder Data
    Prioritized_Information_Display --> Emergency_Mode: Continuous Monitoring
    Emergency_Mode --> Normal_Operation: Network Restored

Combination Prior Art Scenarios

Here are three scenarios where US10444943 could be combined with existing open-source standards to demonstrate obviousness.

Combination with OpenStreetMap (OSM) and Leaflet.js:
The core method of displaying an unmagnified map, a highlighted portion, and a magnified view (Claim 1) is rendered obvious when combined with the capabilities of OpenStreetMap (an open-source collaborative mapping project providing free geographic data) and Leaflet.js (an open-source JavaScript library for mobile-friendly interactive maps). A developer could readily implement a system using Leaflet to render an OSM base map as the "unmagnified electronic map." Custom Leaflet layers or plugins could serve as the "highlighted portion" (e.g., a circular SVG overlay) and an additional map instance or a dynamically resized viewport could provide the "magnified view." Smooth movement and updates would be achieved using Leaflet's pan and zoom controls, which respond to touch or mouse input. Item information (e.g., Points of Interest from OSM data) could be pulled from the OSM API and associated with map coordinates. The bidirectional interactivity is a standard feature of many interactive map libraries where clicking a marker brings up information, and searching for an item focuses the map on it. This combination clearly anticipates the functional aspects of US10444943 using widely available open-source tools.
Combination with QGIS and OGC Web Map Service (WMS):
The system described in Claim 7, comprising a display, input, and processing unit configured to provide the interactive map functionality, becomes obvious when considering Geographic Information System (GIS) software like QGIS (an open-source desktop GIS application) integrated with Open Geospatial Consortium (OGC) Web Map Service (WMS) standards. QGIS can display various geospatial data layers as an "unmagnified electronic map." A WMS server can serve different resolution layers of map data. A user, through QGIS's interface (display and input device), can define a "highlighted portion" (e.g., using a selection tool) and request a higher-resolution WMS layer for that specific area, effectively creating a "magnified view" that updates as the selection moves. The processing unit (computer running QGIS) handles the rendering and WMS requests. Item information, such as attributes associated with GIS features, can be selected from the magnified view, and searching for specific attributes can automatically pan/zoom the QGIS display to the relevant feature, demonstrating the two-way interactivity.
Combination with HTML5 Canvas API and WebSockets for Real-time Updates:
Independent Claim 14, describing a computer program product for the interactive map, is anticipated by combining standard web technologies like the HTML5 Canvas API and WebSockets. A web application could use the Canvas API to draw a vector-based "unmagnified electronic map" and its associated item information (e.g., rendered text labels, SVG icons). A second Canvas element or a manipulated viewport could act as the "magnified view." User input (mouse, touch) could directly manipulate coordinates on the primary Canvas to define and smoothly move a "highlighted portion" (e.g., a dynamically rendered circle or rectangle). WebSockets could be employed to push real-time updates of map data or item information from a backend server, ensuring the smooth updates of both views. JavaScript event listeners handle user selection of items for displaying additional details, and client-side JavaScript functions would handle repositioning the map based on search queries for item information. This setup inherently provides the core functionality of US10444943 using widely understood and implemented open web standards.