Derivative works — US Patent 11949962

Defensive Disclosure and Prior Art Generation for Cross-Device Association and Measurement

Publication Date: May 1, 2026
Subject: Methods and systems for multi-device interaction analysis, attribution, and targeting based on proxy location and indirect association. This document is intended to enter the public domain to be used as prior art.

Introduction

This defensive publication describes a series of methods, systems, and architectural variations derived from the core concept of using a primary device (OD1) to establish a proxy location for a central content-delivery apparatus (e.g., a Set-Top Box or STB), and subsequently associating secondary devices (OD2s) with that apparatus based on their presence within the established proxy location. These derivative inventions are intended to make obvious any future incremental improvements in the fields of cross-device advertising measurement, smart-home automation, industrial IoT, and secure credentialing.

Derivative 1: Component Substitution using Ultrasonic Beacons and Hardware-Based Identifiers

Enabling Description

This variation replaces IP-based geolocation and cookie-based device identification with a more precise and physically-bound method. The STB is equipped with an ultrasonic audio transducer that continuously emits an inaudible, time-varying, and location-specific audio signal (an "audio beacon"). The primary online device (OD1), such as a smart speaker, is pre-registered with the STB. OD1 listens for this ultrasonic beacon and, upon successful validation, reports its own hardware-based identifier (e.g., a TPM-generated key or a secure enclave ID) to a central server, confirming its physical proximity to the STB. This establishes the STB Proxy Location with room-level accuracy.

A secondary device (OD2), such as a smartphone, with its microphone enabled by a user-authorized application, can then also detect this same ultrasonic beacon. OD2 independently reports the decoded beacon signature and its own hardware ID to the server. The server associates OD2 with the STB only if the reported beacon signature matches the one currently being emitted and recorded by OD1. This method ensures that OD2 is not just on the same network, but physically in the same room, while using un-spoofable hardware IDs for device recognition.

sequenceDiagram
    participant STB
    participant OD1_SmartSpeaker as OD1 (Smart Speaker)
    participant OD2_Phone as OD2 (Phone)
    participant Server

    STB->>OD1_SmartSpeaker: Emits Ultrasonic Beacon (sig-A, time-T1)
    OD1_SmartSpeaker->>Server: Beacon(sig-A) detected. My ID is HWID-1. Establishing Proxy Location.
    Server-->>OD1_SmartSpeaker: ACK. Proxy Location established for HWID-1.

    loop Ad Display & Measurement
        Server->>STB: Deliver Ad (ad_id_123)
        STB->>OD1_SmartSpeaker: Emits Ultrasonic Beacon (sig-B, time-T2, ad_id_123)
        OD2_Phone->>OD2_Phone: User enters room, App detects Beacon(sig-B)
        OD2_Phone->>Server: Beacon(sig-B, ad_id_123) detected. My ID is HWID-2.
        Server->>Server: Verify Beacon signature and time. Associate HWID-2 with STB via Proxy Location.

        Note over OD2_Phone: User performs online action (e.g., website visit).
        OD2_Phone->>Server: Reporting conversion event for HWID-2.
        Server->>Server: Correlate conversion with ad_id_123 via HWID-2 association.
    end

Derivative 2: Operational Parameter Expansion for Micro-Location Retail Analytics

Enabling Description

This disclosure expands the core patent's logic to a high-density, micro-location environment such as a supermarket aisle. The "STB" is a digital price display on a shelf for a specific product. The "ad" is a video promotion playing on that display. The primary device (OD1) is a fixed BLE (Bluetooth Low Energy) beacon mounted on the same shelf, which defines a hyper-local "Proxy Location" with a radius of approximately 1-2 meters.

As a shopper's smartphone (OD2) moves down the aisle, an installed retailer application scans for BLE signals in the background. When the phone comes within range of OD1, the app records this "nearness" event. If the user then uses the retailer app on their phone (OD2) to scan the product's barcode or search for reviews within a predefined time window (e.g., 2 minutes) after entering the proxy location, this action is recorded as a conversion. The system measures the effectiveness of the on-shelf video promotion by calculating the ratio of conversions to the total number of unique devices detected within the proxy location. This scales the concept down from a household to a shelf, measuring near-immediate consumer response.

flowchart TD
    subgraph "Supermarket Aisle"
        A[Digital Shelf Display <br> "STB"] -- Plays Ad --> B((Shelf BLE Beacon <br> "OD1"));
        B -- Defines -->> C{Proxy Location <br> (1-2m Radius)};
        D[Shopper's Phone <br> "OD2"] -- Enters Proximity --> C;
    end

    subgraph "Measurement System"
        C -- Notifies Event --> E[Analytics Server];
        D -- User Action --> F[Scans Barcode / Searches Product];
        F -- Sends Action Data --> E;
        E -- Correlates --> G[Ad Effectiveness Report <br> (Conversion %)];
    end

Derivative 3: Cross-Domain Application in In-Flight Entertainment (IFE) Systems

Enabling Description

This variation applies the method to the aerospace domain to measure passenger engagement with in-flight advertisements and services. The "STB" is the aircraft's seat-back IFE screen. The primary device (OD1) is the seat's Wi-Fi Access Point (WAP) or a logical network port assigned to that specific seat, which is directly associated with the IFE screen via the aircraft's cabin management system. The "Proxy Location" is the micro-network segment serving that individual seat.

When a passenger connects their laptop or tablet (OD2) to the in-flight Wi-Fi, they are authenticated through a captive portal which associates their device (OD2) with the seat's network port (OD1). An advertisement for a travel visa service is displayed on the IFE screen (STB). The system then monitors traffic from the associated OD2 device. If the passenger navigates to the visa service website on their laptop, the system logs this as a conversion event. The system provides airlines with analytics on which on-screen promotions effectively drive passengers to use their personal devices for purchases or service sign-ups during the flight.

graph LR
    subgraph "Aircraft Seat 14A"
        IFE[IFE Screen <br> "STB"] --- SeatWAP[Seat Wi-Fi Port <br> "OD1"];
        SeatWAP --- Laptop[Passenger Laptop <br> "OD2"];
    end

    subgraph "Data Flow"
        Server -- Delivers Ad --> IFE;
        Laptop -- Connects & Authenticates --> SeatWAP;
        SeatWAP -- Association Info --> Server;
        Server -- Establishes Link --> Laptop;
        Laptop -- Visits Advertiser Site --> Internet;
        Server -- Monitors Traffic & Records Conversion --> Analytics;
    end

    style IFE fill:#f9f,stroke:#333,stroke-width:2px
    style Laptop fill:#ccf,stroke:#333,stroke-width:2px

Derivative 4: Integration with AI for Predictive Association and Dynamic Targeting

Enabling Description

This method integrates a machine learning model to enhance the accuracy and timeliness of the device association process. The system collects metadata from all devices detected on a home network (the proxy location), including device type, operating system, connection times, and data consumption patterns. A recurrent neural network (RNN) is trained on this data to establish a "household rhythm."

The AI model distinguishes between permanent resident devices (OD2s) and transient guest devices. It achieves this by recognizing patterns; for example, a device that connects every weekday evening is likely a resident, while a device that connects once on a Saturday for three hours is likely a guest. The system only associates predicted resident devices with the STB for ad measurement, significantly reducing attribution errors. Furthermore, the AI uses the combined (anonymized) profile data of all currently present resident devices to select the next TV ad in real-time. For instance, if two residents are interested in cars and one is interested in travel, it might select an ad for an SUV road trip, dynamically targeting the household's aggregate interest profile.

stateDiagram-v2
    [*] --> Detecting_Devices
    state Detecting_Devices {
        [*] --> Analyzing_Metadata
        Analyzing_Metadata --> Prediction: RNN processes connection times, OS, etc.
        Prediction --> Resident: High confidence score from model
        Prediction --> Guest: Low confidence score from model
        Resident --> Associated_with_STB
        Guest --> Ignored
    }
    Associated_with_STB --> Monitoring_Activity: Ad measurement is active
    Monitoring_Activity --> Associated_with_STB: User remains in household
    Associated_with_STB --> Disassociated: Model detects pattern break (e.g., device absent for 1 week)
    Disassociated --> [*]
    Ignored --> [*]

Derivative 5: The "Inverse" Failure Mode for Privacy-Preserving Disassociation

Enabling Description

This disclosure describes a system designed to operate in a "privacy-first" or "safe-fail" mode. The system actively works to sever or refuse to create associations based on specific triggers. The "Proxy Location" is defined not just by a network ID but by a user-configured geofence and time window (e.g., "Home" from 6 PM to 8 AM). Any device (OD2) detected outside this window is automatically ignored.

Furthermore, the system is designed to detect "visitor patterns." If a new device (OD2) connects to the network and an associated resident device (OD1 or another OD2) sends a calendar event invitation or a messaging app notification containing keywords like "guest," "visitor," or "party" to another device, the system flags the new device as temporary. It is placed in a "privacy sandbox" where its activity is not tracked for ad measurement purposes. This failure mode prioritizes preventing incorrect associations over capturing every possible data point, thereby enhancing user trust and reducing data contamination from non-residents.

erDiagram
    HOUSEHOLD ||--o{ DEVICE : has
    DEVICE {
        string hardware_id PK
        string status
        string type
    }
    HOUSEHOLD {
        int household_id PK
        string geofence_data
        string active_time_window
    }
    DEVICE ||--|{ ASSOCIATION_RULE : applies
    ASSOCIATION_RULE {
        string rule_id PK
        string condition "e.g., 'is_visitor'"
        string action "e.g., 'DISASSOCIATE'"
    }
    DEVICE ||--o{ ACTIVITY_LOG : generates
    ACTIVITY_LOG {
        int log_id PK
        string event_type
        datetime timestamp
        bool is_tracked "Default FALSE unless status is Resident"
    }

Combination Prior Art Scenarios with Open-Source Standards

Combination with the Matter Connected Home Standard: The device association process is fully managed within an open-source Matter fabric. The STB, OD1, and OD2 are all Matter-compliant endpoints. The STB-OD1 association is achieved via the standard Matter commissioning process (e.g., scanning a QR code). The "Proxy Location" becomes the Matter fabric itself. An OD2 is determined to be "near" when it is commissioned into the same fabric. A central server subscribes to Matter events; it receives a notification when an ad is displayed on the STB (a custom Matter event) and another event when a user on OD2 takes an online action (e.g., launching an app with Matter integration), enabling measurement via open, interoperable, and local network protocols.
Combination with IAB Tech Lab's OpenRTB Protocol: To standardize communication, the notification that an ad was shown on the STB is structured as a custom extension (x_stb_imp) within an imp object sent via an HTTPS call from the STB to the measurement server, conforming to OpenRTB principles. The payload includes stb_id, household_ip, and ad_id. When a conversion happens on OD2 from the same household_ip, the tracking pixel firing to the server includes the ad_id in its payload. The server-side logic then matches the ad_id and household_ip from the two separate events (STB impression and OD2 conversion), linking them according to an open data-passing standard used throughout the advertising industry.
Combination with W3C Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs): To enhance privacy and user control, all devices (STB, OD1, OD2) are issued DIDs. The TV Provider issues a Verifiable Credential to the user's DID wallet, cryptographically attesting that DID:STB:123 is associated with DID:OD1:456. When OD2 wants to participate, the user provides consent via their wallet, which generates a temporary, time-bound Verifiable Presentation proving that DID:OD2:789 was present at the same location as DID:OD1:456 (using a location oracle or peer-to-peer attestation). The measurement server receives this VC as proof of association, allowing it to correlate ad views and conversions without ever processing raw PII, cookies, or IP addresses, using a W3C open standard for identity.