Patent 10419805

Derivative works

Defensive disclosure: derivative variations of each claim designed to render future incremental improvements obvious or non-novel.

Active provider: Google · gemini-2.5-pro

Derivative works

Defensive disclosure: derivative variations of each claim designed to render future incremental improvements obvious or non-novel.

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Defensive Disclosure and Prior Art Document

Pertaining to U.S. Patent No. 10,419,805: "Data service"

Publication Date: May 10, 2026
Author: Advanced Research Division, Defensive Publishing Initiative
Abstract: This document discloses a series of technical implementations, variations, and applications derived from the architectural principles described in U.S. Patent No. 10,419,805. The purpose of this disclosure is to place these concepts into the public domain, thereby establishing prior art against future patent applications claiming these or obvious variations thereof. The described embodiments expand upon the core concepts of a modular, service-oriented architecture for content aggregation in a client device by exploring alternative components, novel operational domains, integration with emerging technologies, and specialized functional modes.

Axis 1: Material & Component Substitution

1.1. Microservice-Based Content Aggregation Framework

  • Enabling Description: The monolithic "internal content provider module" is decomposed into a set of independent, containerized microservices. A central API Gateway service, acting as the primary entry point for the User Interface Application, routes requests to specific microservices (e.g., a VOD-Service, an EPG-Service, a LiveTV-Service). These microservices are analogous to the original "subservices" but operate as fully independent processes, potentially written in different programming languages (e.g., Go for high-concurrency I/O, Python for data processing). Communication between services is handled via a lightweight messaging bus like RabbitMQ or a direct gRPC protocol. This architecture allows for independent scaling, updating, and fault isolation of each content aggregation function. Each microservice manages its own set of "source plug-ins," which are dynamically loaded libraries or sidecar containers that handle the protocol-specific communication with external content providers.

  • Mermaid Diagram:

    graph TD
    A[User Interface Application] --> B{API Gateway};
    B -- /vod --> C[VOD Microservice];
    B -- /epg --> D[EPG Microservice];
    B -- /live --> E[Live TV Microservice];

    subgraph VOD Microservice
        C --> F1[Netflix Plugin];
        C --> F2[Hulu Plugin];
        C --> F3[Local Media Plugin];
    end

    subgraph EPG Microservice
        D --> G1[Gracenote Plugin];
        D --> G2[OTA ATSC Plugin];
    end

    subgraph Live TV Microservice
        E --> H1[Cable Tuner Plugin];
        E --> H2[IPTV Stream Plugin];
    end

1.2. GraphQL-based Data Federation Layer

  • Enabling Description: The communication protocol between the primary application layer and the various subservices is implemented using a GraphQL Federation Gateway. Instead of each subservice exposing a RESTful API, they each run as a GraphQL service with a defined schema for the data they provide (e.g., EPG data, VOD metadata). The Federation Gateway combines these individual schemas into a single, unified data graph. The client-side "internal content provider module" can then make a single, complex query to the Gateway to fetch all necessary data from multiple subservices simultaneously. This eliminates the "N+1" query problem, reduces network chattiness, and allows the client application to request only the specific data fields it needs, thereby optimizing data transfer for low-bandwidth environments.

  • Mermaid Diagram:

    sequenceDiagram
    participant App as UI Application
    participant Gateway as GraphQL Federation Gateway
    participant VOD as VOD Subservice
    participant EPG as EPG Subservice

    App->>Gateway: Query { nowPlaying { title }, recommendations { title, posterUrl } }
    Gateway->>EPG: Resolve nowPlaying { title }
    EPG-->>Gateway: nowPlaying Data
    Gateway->>VOD: Resolve recommendations { title, posterUrl }
    VOD-->>Gateway: recommendations Data
    Gateway-->>App: Combined JSON Response

1.3. WebAssembly (WASM) Plugin Architecture

  • Enabling Description: The "source plug-ins" are compiled to WebAssembly (WASM) bytecode instead of being native-code libraries. The data service runtime includes a secure, sandboxed WASM execution environment. This approach provides significant advantages:

    1. Portability: A single WASM plugin binary can run on any "Intelligent TV" platform (e.g., ARM-based, x86-based) that includes a WASM runtime, eliminating the need for platform-specific compilation.
    2. Security: The WASM sandbox restricts the plugin's access to the underlying system, preventing a buggy or malicious plugin from compromising the entire device.
    3. Dynamic Loading: New plugins can be securely downloaded and hot-loaded from an application store without requiring a full system firmware update. The subservice communicates with the WASM module through a well-defined memory interface for passing data requests and receiving results.

  • Mermaid Diagram:

    graph TD
    subgraph Data Service
        A[Media Subservice] -- WASI Call --> B{WASM Runtime};
        B -- Memory Buffer I/O --> C[Plugin.wasm];
    end
    C -- HTTP/Socket --> D[External Content API];
    D --> C;
    C --> B;
    B --> A;
    A --> E[UI Application];

Axis 2: Operational Parameter Expansion

2.1. Industrial IoT Data Aggregation at Scale

  • Enabling Description: The architecture is scaled up for an industrial control system dashboard. The "Intelligent TV" is a large display in a factory control room. The "subservices" are specialized for industrial data types: TimeSeries-Subservice for sensor data, Alarm-Subservice for critical alerts, and Video-Subservice for security cameras. "Source plug-ins" are protocols like Modbus, OPC-UA, and RTSP. The system is designed for high-throughput, low-latency ingestion of data from thousands of endpoints. The TimeSeries-Subservice plugin for OPC-UA polls PLCs every 100ms, aggregates the data into 1-second windows, and pushes it to a time-series database (e.g., InfluxDB). The UI module then queries this database to render real-time dashboards and historical trend charts.

  • Mermaid Diagram:

    flowchart LR
    subgraph Factory Floor
        A[PLC 1] -- Modbus --> P1(Modbus Plugin)
        B[Sensor Array] -- OPC-UA --> P2(OPC-UA Plugin)
        C[IP Camera] -- RTSP --> P3(RTSP Plugin)
    end

    subgraph Control Room System
        P1 --> S1[TimeSeries Subservice]
        P2 --> S1
        P3 --> S2[Video Subservice]

        S1 --> DB[(Time-Series DB)]
        S2 --> VMS(Video Management System)

        UI[Control Dashboard] --> S1
        UI --> S2
    end

2.2. Sub-Sea Remote Operated Vehicle (ROV) Control System

  • Enabling Description: The architecture is adapted for an ROV operating in extreme sub-sea environments with high pressure and low-bandwidth, high-latency acoustic communication links. The "Intelligent TV" is the pilot's console on the support vessel. A Sonar-Subservice uses a "CHIRP Sonar Plugin" to process acoustic data. A Telemetry-Subservice uses a custom "Acoustic-Modem Plugin" to receive vehicle health data (pressure, battery, thruster status). The plugins are designed with aggressive data compression (e.g., delta compression for telemetry) and state-synchronization protocols tolerant of packet loss. The Telemetry-Subservice maintains a predictive model of the ROV's state, updating the UI instantly and correcting when a new data packet arrives, mitigating the effects of latency.

  • Mermaid Diagram:

    sequenceDiagram
    participant PilotConsole as Pilot Console (TV)
    participant TelemetrySvc as Telemetry Subservice
    participant AcousticPlugin as Acoustic Modem Plugin
    participant ROV as Remote Operated Vehicle

    loop Data Poll Cycle (High Latency)
        PilotConsole->>TelemetrySvc: Request State Update
        TelemetrySvc->>AcousticPlugin: Send(Poll Command)
        AcousticPlugin-->>ROV: (Acoustic Transmission)
        ROV-->>AcousticPlugin: (Acoustic Response)
        AcousticPlugin->>TelemetrySvc: Receive(Telemetry Packet)
        TelemetrySvc->>PilotConsole: Push Updated State
    end

Axis 3: Cross-Domain Application

3.1. Aerospace: Integrated Vehicle Health Management (IVHM)

  • Enabling Description: In an aircraft cockpit, the '805 architecture is used for an IVHM system. The "Intelligent TV" is a Multi-Function Display (MFD). An Engine-Subservice uses an "ARINC 429 Plugin" to read data from engine FADECs. A Navigation-Subservice uses a "GPS/INS Plugin" for positioning data. A Weather-Subservice uses a "SATCOM Plugin" to download real-time graphical weather data. The "internal content provider module" is a flight management application that synthesizes this data, overlaying engine performance metrics on a moving map with real-time weather, providing pilots with a unified situational awareness display.

  • Mermaid Diagram:

    graph TD
    subgraph Aircraft Systems
        A[Engine 1 FADEC] --> B[ARINC 429 Bus];
        C[Engine 2 FADEC] --> B;
        D[GPS/INS Unit] --> E[Avionics Bus];
        F[SATCOM Antenna] --> G[Datalink Unit];
    end

    subgraph IVHM Computer
        H(ARINC 429 Plugin) --> I[Engine Subservice];
        J(GPS/INS Plugin) --> K[Navigation Subservice];
        L(SATCOM Plugin) --> M[Weather Subservice];
        B --> H;
        E --> J;
        G --> L;
    end

    I --> N[Flight Management App];
    K --> N;
    M --> N;

    N --> O[Cockpit MFD];

3.2. AgTech: Precision Farming Decision Support System

  • Enabling Description: A farm management platform uses this architecture to provide decision support. The "Intelligent TV" is a dashboard on a farmer's tablet. A Soil-Subservice uses a "LoRaWAN Plugin" to collect data from a distributed network of wireless soil moisture and nutrient sensors. A Weather-Subservice uses a "Weather.com API Plugin" to get forecast data. A Imagery-Subservice uses an "FTP Plugin" to retrieve NDVI (Normalized Difference Vegetation Index) maps from a drone imaging provider. The "internal content provider module" is an analytics application that fuses these data streams to generate a real-time field health map, recommending specific irrigation or fertilization actions for different zones in the field.

  • Mermaid Diagram:

    graph TD
    A[Soil Sensors] -- LoRaWAN --> B(LoRaWAN Plugin);
    C[Weather.com] -- REST API --> D(API Plugin);
    E[Drone Data FTP] -- FTP --> F(FTP Plugin);

    B --> G[Soil Subservice];
    D --> H[Weather Subservice];
    F --> I[Imagery Subservice];

    G --> J[Farm Analytics App];
    H --> J;
    I --> J;

    J --> K[Farmer's Tablet UI];

3.3. Automotive: In-Vehicle Infotainment (IVI) & Telematics

  • Enabling Description: A modern vehicle's IVI system implements this architecture. The "Intelligent TV" is the center console touchscreen. A Vehicle-Bus-Subservice uses a "CAN/LIN Plugin" to access vehicle data like speed, fuel level, and tire pressure. A Media-Subservice has "Spotify Plugin" and "FM-Tuner Plugin" to handle audio. A Navigation-Subservice has a "TomTom Traffic Plugin" and a "GPS Plugin." The "internal content provider module" is the main HMI application, which can display navigation directions on the instrument cluster while showing media information on the center screen, and can automatically lower the music volume when the navigation subservice issues a voice prompt.

  • Mermaid Diagram:

    classDiagram
  direction LR
  class IVI_HMI_Application {
    +displayMap()
    +playMedia()
    +showVehicleStatus()
  }
  class VehicleBusSubservice {
    +getSpeed()
    +getFuelLevel()
  }
  class MediaSubservice {
    +getCurrentTrack()
    +setSource(plugin)
  }
  class NavigationSubservice {
    +getCurrentRoute()
    +getETA()
  }
  class CAN_Plugin {
    +read_can_frame(id)
  }
  class Spotify_Plugin {
    +api_call(endpoint)
  }
  class GPS_Plugin {
    +read_nmea_sentence()
  }

  IVI_HMI_Application --|> VehicleBusSubservice
  IVI_HMI_Application --|> MediaSubservice
  IVI_HMI_Application --|> NavigationSubservice
  VehicleBusSubservice "1" *-- "1..*" CAN_Plugin : uses
  MediaSubservice "1" *-- "1..*" Spotify_Plugin : uses
  NavigationSubservice "1" *-- "1..*" GPS_Plugin : uses

Axis 4: Integration with Emerging Tech

4.1. AI-Driven Predictive Content Caching

  • Enabling Description: The system incorporates an AI-Prediction-Subservice which monitors user viewing habits, time of day, EPG data, and even external event calendars (e.g., major sports events). It uses a machine learning model (e.g., a recurrent neural network) to predict what content the user is likely to watch next. This subservice doesn't have its own plugins but instead issues commands to other subservices. For example, it might command the VOD-Subservice to use its "Netflix Plugin" to pre-cache the first 10 minutes of the next episode of a series the user is binge-watching, ensuring an instant start when the user selects it.

  • Mermaid Diagram:

    sequenceDiagram
    participant UI
    participant AI_Service
    participant VOD_Service
    participant EPG_Service

    UI->>VOD_Service: playEpisode(series_X, ep_2)
    VOD_Service->>AI_Service: logEvent(user, series_X, ep_2)
    EPG_Service->>AI_Service: logEvent(user, channel_Y, time)

    loop Predictive Analysis
        AI_Service->>AI_Service: model.predict(user_history)
    end
    Note right of AI_Service: Prediction: User likely to watch series_X, ep_3 next
    AI_Service->>VOD_Service: command: preCache(series_X, ep_3)
    VOD_Service->>VOD_Service: Initiates background download via plugin

    UI->>VOD_Service: playEpisode(series_X, ep_3)
    Note over VOD_Service: Content is already cached.
    VOD_Service-->>UI: Start playback immediately

4.2. IoT-Driven Ambient Experience Adaptation

  • Enabling Description: The Intelligent TV is part of a broader IoT ecosystem. A new Ambient-Subservice is added, which uses a "Smart Home Hub Plugin" (e.g., for Home Assistant or a proprietary hub) to subscribe to state changes from various sensors. When a user starts a movie via the VOD-Subservice, the UI application notifies the Ambient-Subservice. This service then commands the smart hub to dim the lights, close the blinds, and adjust the thermostat to a "movie mode" temperature. The sensor data is treated as just another content stream to be aggregated and acted upon by the system's logic.

  • Mermaid Diagram:

    flowchart TD
    A[User selects "Play Movie" on UI] --> B{UI Application};
    B -- "play_event" --> C[VOD Subservice];
    B -- "context_change: movie" --> D[Ambient Subservice];

    C --> E[Video Player];

    subgraph Smart Home
        D --> F(Smart Hub Plugin);
        F -- "set_light_brightness(10%)" --> G[Smart Lights];
        F -- "set_blinds_position(closed)" --> H[Motorized Blinds];
    end

Axis 5: The "Inverse" or Failure Mode

5.1. Graceful Degradation for Low-Bandwidth Networks

  • Enabling Description: The system includes a Network-Monitor-Subservice that constantly measures available bandwidth and latency. When it detects poor network conditions (e.g., bandwidth drops below 5 Mbps), it broadcasts a "low_bandwidth_mode" system state. The VOD-Subservice, upon receiving this state change, instructs all its streaming plugins to request lower-bitrate streams (e.g., 480p instead of 4K). The EPG-Subservice tells its "Internet-EPG Plugin" to stop fetching rich media (posters, trailers) and only fetch essential text data. The UI module, also subscribed to this state, renders a simplified interface without high-resolution images to conserve bandwidth.

  • Mermaid Diagram:

    stateDiagram-v2
    [*] --> HighBandwidth
    HighBandwidth --> LowBandwidth: Network Degraded
    LowBandwidth --> HighBandwidth: Network Restored

    state HighBandwidth {
        VOD_Service: Requests 4K streams
        EPG_Service: Fetches full metadata
        UI_Module: Renders rich graphics
    }

    state LowBandwidth {
        VOD_Service: Requests 480p streams
        EPG_Service: Fetches text-only data
        UI_Module: Renders simplified UI
    }

5.2. Fail-Safe Redundancy for Critical Data Streams

  • Enabling Description: This variation is designed for a system where data availability is critical, such as a remote security monitoring station. The Video-Subservice is configured with both a primary "Fiber-Optic Plugin" and a secondary "5G-Cellular Plugin" for each camera feed. The subservice continuously health-checks the primary plugin by monitoring for data packets. If a packet is not received within a specified timeout (e.g., 500ms), it immediately activates the secondary plugin to take over the stream, ensuring minimal interruption of the video feed to the operator. It also raises an alert in the UI, indicating that it is now operating on the backup connection.

  • Mermaid Diagram:

    graph TD
    subgraph Data Sources
        A[Camera via Fiber] --> P1(Fiber Optic Plugin);
        B[Camera via 5G] --> P2(5G Cellular Plugin);
    end

    subgraph Redundant Subservice
        P1 -- Primary Stream --> S1{Video Subservice};
        P2 -- Backup Stream --> S1;
        S1 -- Health Check --> P1;
    end

    S1 -- Video Data --> UI[Operator Console];
    S1 -- Alert --> UI;

    style P1 fill:#9f9,stroke:#333,stroke-width:2px
    style P2 fill:#f99,stroke:#333,stroke-width:2px

Combination Prior Art Scenarios

  1. Combination with DLNA/UPnP for Local Media Aggregation: The Media-Subservice is designed to be fully compliant with the Digital Living Network Alliance (DLNA) and Universal Plug and Play (UPnP A/V) open standards. An integrated "UPnP Control Point Plugin" is included, which, upon activation by the subservice, uses the Simple Service Discovery Protocol (SSDP) to discover all DLNA-certified Digital Media Servers (DMS) on the local network (e.g., NAS drives, personal computers running Plex). Once a DMS is discovered, the plugin queries its Content Directory Service (CDS) to retrieve a browseable hierarchy of the available media, which the subservice then formats according to its internal data model and presents to the "internal content provider module" for display in a unified "My Network" section of the UI.

  2. Combination with Matter for Smart Home Control: The system is extended to act as a Smart Home controller by incorporating a Home-Automation-Subservice. This subservice includes a "Matter Controller Plugin" built upon the open-source Matter (formerly Project CHIP) SDK. This plugin manages a Thread or Wi-Fi network to securely commission and control Matter-compliant devices (lights, switches, thermostats) from any manufacturer. The status of these devices (e.g., light on/off, temperature) is treated as a stream of "content." The "internal content provider module" can then render interactive widgets on the TV screen, allowing a user to control their smart home devices using the TV remote, effectively turning the television into a central home hub.

  3. Combination with ActivityPub for Decentralized Social Media Feeds: A Social-Subservice is created to aggregate content from the decentralized "Fediverse." This service utilizes an "ActivityPub Plugin" which acts as a client for the W3C ActivityPub protocol. The user can authenticate with their accounts from different Fediverse platforms (e.g., Mastodon, Pixelfed, PeerTube). The plugin polls the user's home feeds from these services, receiving content (posts, images, videos) formatted as ActivityStreams 2.0 objects. The Social-Subservice normalizes this data into its own data model and passes it to the main UI application, which can then display a unified, chronologically-sorted feed of social updates alongside traditional media content, all without relying on proprietary, centralized social media APIs.

Generated 5/10/2026, 2:45:20 PM