Derivative works — US Patent 12337716

Defensive Disclosure: Portable User Profiles for Controlled-Environment Systems

Publication Date: April 26, 2026
Assignee: Cerulean Dynamics Corporation
Reference: US Patent 12337716

Abstract: This publication discloses a series of derivative implementations and enhancements to the core concepts of cloud-based, transferable user profiles for preference and settings management in vehicles, as detailed in US Patent 12337716. The described embodiments are intended to enter the public domain to act as prior art against future patent applications in this and analogous fields. The disclosures below extend the core concepts into new domains, integrate them with emergent technologies, and explore alternative materials, operational parameters, and failure modes.

Core Technology Basis (Hypothesized from US Patent 12337716)

A cloud-based system that allows a user's profile, containing personalized settings and preferences, to be transferred between different vehicles. The process involves:

A vehicle's onboard system sending a request to a central cloud server to access a user profile.
The server verifying the user's identity, potentially through credentials or biometrics.
The server transmitting the user's profile data to the vehicle.
The vehicle's systems adjusting to the settings specified in the profile (e.g., seat position, climate control, infotainment preferences).
The system learning from user adjustments and updating the cloud-based profile.

Derivative Variation Set 1: Material and Component Substitution

1.1. Profile Transfer via Ferroelectric Polymer Memory

Enabling Description: This variation replaces the standard CMOS-based flash memory in the in-vehicle receiver with a non-volatile ferroelectric polymer memory substrate. The user's profile is encoded as a series of polarization domains on a thin film of a copolymer like Poly(vinylidene fluoride-trifluoroethylene) (P(VDF-TrFE)). The cloud server transmits the profile data as a modulated microwave signal. The vehicle's antenna, coupled with a specialized transducer, directly "prints" the polarization domains onto the P(VDF-TrFE) film. This method offers extreme durability against thermal cycling and electromagnetic interference common in automotive environments. The in-vehicle computer reads the profile by measuring the piezoelectric response of the film.

Mermaid Diagram:

sequenceDiagram
    participant CloudServer as Cloud Server
    participant VehicleAntenna as Vehicle Antenna/Transducer
    participant FerroelectricMemory as P(VDF-TrFE) Memory
    participant VehicleECU as Vehicle ECU

    CloudServer->>VehicleAntenna: Transmit Profile (Modulated Microwave Signal)
    VehicleAntenna->>FerroelectricMemory: "Prints" Polarization Domains
    VehicleECU->>FerroelectricMemory: Apply Read Voltage
    FerroelectricMemory-->>VehicleECU: Piezoelectric Response (Profile Data)
    VehicleECU->>VehicleSystems: Apply User Settings

1.2. Biometric Authentication via Graphene-Based Biosensors

Enabling Description: Instead of traditional cameras or fingerprint scanners, user verification is achieved using a multi-modal graphene-based biosensor integrated into the steering wheel or gear shift. This sensor array is functionalized with specific aptamers to detect unique biomarkers in the user's sweat, such as cortisol levels, glucose variations, and specific proteins, creating a unique "chemical fingerprint." The raw sensor data is transmitted to the cloud server, where a machine learning model (trained on the user's baseline biomarker profile) performs the authentication. This provides a continuous and passive authentication method that is difficult to spoof.

Mermaid Diagram:

graph TD
    A[User Touches Steering Wheel] --> B{Graphene Biosensor Array};
    B --> C[Measures Sweat Biomarkers];
    C --> D[Transmit Raw Sensor Data];
    D --> E[Cloud Authentication Server];
    E --> F{ML Model Analysis};
    F -- Authenticated --> G[Download User Profile];
    F -- Denied --> H[Lock Vehicle Systems];
    G --> I[Apply Vehicle Settings];

1.3. Quantum Dot-Based Display for Profile Visualization

Enabling Description: The in-vehicle display that shows the profile being loaded and its settings is a Quantum Dot (QD) display on a flexible, transparent substrate. This allows the display to be integrated into the windshield as a Heads-Up Display (HUD) or on curved dashboard surfaces. When a profile is transferred, different sets of quantum dots are excited based on the profile's color-scheme settings, providing a vibrant, high-contrast, and power-efficient visual confirmation. The specific emission spectra can also serve as a secondary, optical authentication layer, where a small in-cabin sensor verifies the displayed light signature against a stored cryptographic hash.

Mermaid Diagram:

classDiagram
class UserProfile {
    +string userID
    +ColorThemeSetting colorTheme
    +OpticalHash opticalSignature
}
class QuantumDotDisplay {
    -substrateType: "Flexible Polymer"
    +displayProfile(UserProfile)
    +emitLightSignature(OpticalHash)
}
class OpticalSensor {
    +verifySignature(OpticalHash) bool
}
class VehicleECU {
    +loadProfile(UserProfile)
}
VehicleECU --> UserProfile
VehicleECU -- controls --> QuantumDotDisplay
VehicleECU -- receives from --> OpticalSensor

Derivative Variation Set 2: Operational Parameter Expansion

2.1. Nanoscale Profile Transfer for Smart Dust and Medical Nanobots

Enabling Description: The concept of profile transfer is scaled down to networks of nanomachines or "smart dust." A central server broadcasts an acoustic or optical signal containing operational profiles for a swarm of nanobots within a patient's bloodstream. Each nanobot, equipped with a nanoscale receiver, adopts a specific behavior profile (e.g., "seek and destroy cancer cells," "deliver targeted drug payload," "report on blood glucose levels"). The profile dictates their movement, payload release schedule, and sensor readings. The system operates at frequencies in the high MHz to GHz range for acoustic communication or THz for optical, within a highly lossy medium (human tissue).

Mermaid Diagram:

flowchart LR
    subgraph Cloud Control
        A[Master Profile Database]
        B[Acoustic/Optical Transmitter]
    end
    subgraph In-Vivo Environment
        C(Nanobot Swarm)
        D{Target: Cancer Cells}
        E{Action: Drug Delivery}
    end
    A -- Profile Selection --> B
    B -- Modulated Signal (GHz) --> C
    C -- Adopts 'Seek' Profile --> D
    C -- Adopts 'Act' Profile --> E

2.2. Industrial-Scale Profile Transfer for Modular Factories

Enabling Description: An entire manufacturing floor is treated as the "vehicle." The "user profile" is a "Production Profile" that dictates the configuration of hundreds of modular robotic arms, CNC machines, and autonomous guided vehicles (AGVs). When a new product run is initiated, the central factory server downloads the corresponding Production Profile from a cloud repository. This profile reconfigures the entire factory floor in minutes: robotic arms switch end-effectors and motion paths, CNC machines load new toolsets and G-code, and AGVs adjust their routes and schedules. The system is designed to handle massive data payloads (gigabytes per profile) and requires robust, low-latency industrial ethernet or 5G connectivity.

Mermaid Diagram:

stateDiagram-v2
    [*] --> Idle
    Idle --> Configuring: New Production Order
    Configuring --> Production: Profile Download Complete
    Production --> Idle: Order Complete
    Production --> Maintenance: Fault Detected

    state Configuring {
        direction LR
        RoboticArms: Loading Programs
        CNCMachines: Swapping Tools
        AGVs: Recalculating Paths
    }
    state Production {
        direction LR
        RoboticArms: Assembling
        CNCMachines: Milling
        AGVs: Transporting
    }

2.3. High-Frequency Profile Synchronization for Drone Swarms

Enabling Description: A swarm of autonomous drones ("vehicle") shares a collective "swarm profile" that is continuously updated from a ground control server at high frequency (e.g., >100 Hz). The profile dictates the swarm's formation, collective behavior (e.g., "search pattern," "surveillance," "follow target"), and sensor data fusion parameters. Each drone receives a micro-update to its portion of the profile, ensuring real-time, coordinated maneuvers. The communication protocol is a time-sensitive networking (TSN) variant over a mesh radio network, operating at microwave frequencies (e.g., 5.8 GHz or higher) to ensure minimal latency and jitter, which is critical for maintaining stable formation flight.

Mermaid Diagram:

sequenceDiagram
    participant GroundControl as Ground Control Server
    participant MasterDrone as Master Drone
    participant Drone_N as Drone (N)

    loop High-Frequency Update Loop (100Hz)
        GroundControl->>MasterDrone: Swarm Profile Delta-Update
        MasterDrone->>Drone_N: Propagate Micro-Update
        Drone_N-->>MasterDrone: Acknowledge & Telemetry
        MasterDrone-->>GroundControl: Aggregated Telemetry
    end

Derivative Variation Set 3: Cross-Domain Application

3.1. Aerospace: Pilot Profile Transfer for Commercial Airliners

Enabling Description: A pilot's certified profile, containing their type ratings, flight hour logs, and personal instrument layout preferences (for "glass cockpit" displays), is stored in a secure cloud managed by an aviation authority (e.g., FAA). When a pilot logs into a new aircraft, their profile is downloaded. The system automatically verifies the pilot is rated for that specific aircraft model against the airframe's digital identity. It then reconfigures the Primary Flight Display (PFD) and Multi-Function Display (MFD) to the pilot's preferred layout, sets communication frequency presets, and loads their preferred checklists. This reduces setup time and potential for human error during pre-flight.

Mermaid Diagram:

graph TD
    A[Pilot Logs into Flight Computer] --> B{Request Profile};
    B --> C[Aviation Authority Cloud];
    C -- Verifies Pilot & Airframe --> D{Profile Validated};
    C -- Not Certified --> E[Access Denied];
    D --> F[Download Profile to Aircraft];
    F --> G[Configure PFD/MFD Layout];
    F --> H[Load Comms Presets];
    F --> I[Set Flight Management System Defaults];

3.2. AgTech: Farmer Profile Transfer for Autonomous Tractors

Enabling Description: In a large-scale agricultural operation with a fleet of autonomous tractors and combines, each "Farmer Profile" corresponds to a specific crop type and field. The profile contains parameters for seeding depth, fertilizer and pesticide application rates (based on soil sensor data), harvesting speed, and tilling patterns. A farm manager can remotely assign a profile to a tractor. The tractor downloads the profile and autonomously executes the task for that specific field. As the tractor works, it collects data (yield, soil moisture, etc.), which is sent back to the cloud to refine and optimize the profile for the next planting season.

Mermaid Diagram:

erDiagram
    FARMER_PROFILE {
        string profileID PK
        string cropType
        float seedingDepth
        json applicationRates
    }
    TRACTOR {
        string tractorID PK
        string currentProfileID FK
    }
    FIELD {
        string fieldID PK
        string currentProfileID FK
    }
    FARMER_PROFILE ||--|{ TRACTOR : "is applied to"
    FARMER_PROFILE ||--|{ FIELD : "is optimized for"

3.3. Consumer Electronics: User Profile Portability for Smart Homes

Enabling Description: A user's "Home Profile" is stored in a cloud service. When the user visits a friend's smart home or stays in a smart hotel room, they can temporarily log in. The host system downloads their profile, and for the duration of their stay, the environment adjusts: the lighting changes to their preferred color temperature and brightness, smart speakers log into their music streaming service, the thermostat adjusts to their comfort zone, and digital photo frames display their personal photos. Access is time-limited and sandboxed, preventing the guest's profile from permanently altering the host's settings. Upon logout, the system reverts to its original state.

Mermaid Diagram:

sequenceDiagram
    actor User
    participant GuestPhone as User's Phone
    participant HostHome as Host Smart Home Hub
    participant CloudService as Profile Cloud Service

    User->>GuestPhone: Scan QR Code in Host Home
    GuestPhone->>HostHome: Initiate Guest Session Request
    HostHome->>CloudService: Request Temporary Profile for User
    CloudService->>HostHome: Transmit Sandboxed User Profile
    activate HostHome
    HostHome->>HostHome: Adjust Lights, HVAC, Music
    deactivate HostHome
    User->>GuestPhone: Select 'End Stay'
    GuestPhone->>HostHome: End Guest Session
    HostHome->>HostHome: Revert to Owner's Settings

Derivative Variation Set 4: Integration with Emerging Tech

4.1. AI-Driven Predictive Profile Pre-loading

Enabling Description: An AI/ML model on the cloud server analyzes a user's calendar, GPS location, historical travel patterns, and real-time traffic data. Based on this, it predicts which vehicle the user is likely to use next and at what time. For instance, if the user has a 9 AM meeting across town, the AI predicts they will use their personal car around 8:30 AM. It then pre-emptively "pushes" the user's profile to that specific car's memory before the user even approaches it. When the user enters, the car is already configured, resulting in a zero-latency experience. The AI can also create a "situational profile," automatically adjusting radio preferences to a news station if traffic is heavy or pre-conditioning the cabin to be cooler if the user is coming from the gym.

Mermaid Diagram:

flowchart TD
    subgraph Cloud AI Engine
        A[User Calendar Data]
        B[GPS & Location History]
        C[Real-time Traffic]
        D[ML Predictive Model]
    end
    subgraph Vehicle Fleet
        V1[Car A]
        V2[Car B]
    end
    A & B & C --> D
    D -- Prediction: User will use Car A at 8:30 --> E{Push Profile to Car A};
    E --> V1;
    F[User Enters Car A at 8:32] --> G{Instant Profile Activation};

4.2. IoT Sensor Fusion for Dynamic Profile Adaptation

Enabling Description: The vehicle is equipped with a suite of IoT sensors: biometric sensors in the seat (heart rate, respiration), an internal cabin air quality sensor (CO2, VOCs), and an external weather sensor (temperature, humidity, UV index). These sensors provide a real-time data stream to the vehicle's ECU. The downloaded user profile now contains not just static preferences, but "preference curves." For example, instead of a fixed temperature, it specifies a function of heart rate and external temperature. If the driver's heart rate increases (indicating stress), the system might automatically activate the seat massager and switch the audio to a calming playlist, dynamically adapting the in-car environment based on real-time physiological and environmental data.

Mermaid Diagram:

graph TD
    subgraph IoT_Sensors
        A[Heart Rate Sensor]
        B[Cabin CO2 Sensor]
        C[External UV Sensor]
    end

    subgraph Cloud_Profile
        P[User Profile w/ Preference Curves]
    end

    subgraph Vehicle_ECU
        D[Real-time Adaptation Engine]
    end

    A & B & C -- Data Stream --> D
    P -- Preference Functions --> D
    D --> E[Adjust Climate Control]
    D --> F[Activate Seat Massager]
    D --> G[Change Ambient Lighting]

4.3. Blockchain for Profile Security and Inter-Operator Roaming

Enabling Description: The user's profile is not stored in a centralized database but as a non-fungible token (NFT) or a secure record on a private, permissioned blockchain run by a consortium of automotive manufacturers and car-sharing services. When a user approaches a vehicle, they sign a transaction with their private key (stored in a secure element on their phone) to grant the vehicle temporary, read-only access to their profile on the blockchain. This provides a decentralized, auditable, and highly secure record of every profile access. It also enables seamless "roaming," where a user's profile from Manufacturer A can be securely accessed and trusted by a rental car from Manufacturer B without pre-existing partnership agreements, as both are members of the same blockchain consortium.

Mermaid Diagram:

sequenceDiagram
    participant UserDevice as User's Mobile Device
    participant Vehicle as Vehicle (from Manuf. B)
    participant Blockchain as Automotive Profile Blockchain
    participant CloudServiceA as Cloud Service (from Manuf. A)

    UserDevice->>Vehicle: Initiate Access (Signs w/ Private Key)
    Vehicle->>Blockchain: Request Profile Access Tx
    Blockchain->>Blockchain: Validate Signature & Grant Temp Access
    Blockchain-->>Vehicle: Return Decrypted Profile Data
    Vehicle->>Vehicle: Apply Settings
    Note right of Blockchain: All transactions are immutable and auditable.

Derivative Variation Set 5: The "Inverse" or Failure Mode

5.1. Graceful Degradation / Safe Mode Profile

Enabling Description: In the event of a lost connection to the cloud server, the vehicle's ECU activates a "Graceful Degradation Profile." This is a minimal, locally-stored profile that prioritizes safety and essential functions. It disables all non-essential features like infotainment, complex climate controls, and personalized lighting. It locks the seat and mirrors into a neutral, safe position (e.g., based on 50th percentile human ergonomics), sets the throttle response to a low-power "eco" mode, and displays a prominent message on the dashboard indicating that it is operating in a limited capacity. This ensures the vehicle remains safely operable until a connection can be re-established. The profile is stored in a write-protected section of the onboard memory to prevent corruption.

Mermaid Diagram:

stateDiagram-v2
    state Connected {
        direction LR
        [*] --> Full_Profile
        Full_Profile: All settings active
    }
    state Disconnected {
        direction LR
        [*] --> Safe_Mode_Profile
        Safe_Mode_Profile: Infotainment disabled
        Safe_Mode_Profile: Seat/Mirrors in neutral
        Safe_Mode_Profile: Low-power engine map
    }

    Connected --> Disconnected: Connection Lost
    Disconnected --> Connected: Connection Restored

5.2. Valet Profile with Geofenced Restrictions

Enabling Description: This is a specific, limited-functionality user profile designed for valet or service scenarios. The owner activates "Valet Mode" from their smartphone app. The cloud server pushes a special "Valet Profile" to the car. This profile severely limits functionality: top speed is capped at 25 mph, engine RPM is limited, the glove box and trunk are electronically locked, and access to navigation history and contacts is disabled. Crucially, the profile includes a geofence with a small radius (e.g., 500 meters) around the drop-off point. If the vehicle crosses this boundary, the owner receives an instant alert on their phone with the vehicle's location, and the vehicle may begin to subtly pulse its hazard lights and horn.

Mermaid Diagram:

flowchart TD
    A[Owner Activates Valet Mode via App] --> B[Cloud Server];
    B --> C{Push Valet Profile to Vehicle};
    C --> D[Vehicle enters Valet Mode];
    subgraph Valet Mode Restrictions
        D1[Speed limited to 25 mph]
        D2[Trunk & Glove Box Locked]
        D3[Geofence Active (500m)]
    end
    D---D1 & D2 & D3
    E{Vehicle Crosses Geofence?} -- Yes --> F[Send Alert to Owner's Phone];
    F --> G[Pulse Hazard Lights];
    E -- No --> H[Normal Valet Operation];

Combination Prior Art Scenarios

Combination with W3C Verifiable Credentials: The user's vehicle profile is structured as a W3C Verifiable Credential (VC). The cloud server acts as the "Issuer." The user's mobile device is the "Holder," storing the VC in a digital wallet. The vehicle is the "Verifier." When the user enters the car, their phone presents the VC. The vehicle verifies the issuer's digital signature and the credential's validity without needing to send all the user's data back to the cloud, enhancing privacy and enabling offline authentication using a cached public key of the issuer. This combines the profile transfer concept with a standardized, open-source framework for digital identity.
Combination with MQTT Protocol: The communication between the vehicle and the cloud server is implemented using the ISO standard MQTT (Message Queuing Telemetry Transport) protocol. The vehicle "subscribes" to a specific MQTT topic unique to its VIN (e.g., vehicle/VIN12345/profile/update). The cloud server "publishes" the user profile as a JSON payload to that topic. This leverages a lightweight, open-source, and widely adopted IoT messaging protocol, making the system more efficient in low-bandwidth scenarios and easily interoperable with standard IoT platforms and brokers like Mosquitto or HiveMQ.
Combination with Android Automotive OS: The entire profile management system is implemented as an application layer on top of the open-source Android Automotive OS. The "user profile" is a data object within the Android user profile framework. Transferring the profile simply involves logging the user into their Google Account on the vehicle's head unit. The system then leverages the existing Android framework to manage user-specific settings for installed apps (e.g., Spotify, Google Maps), as well as vehicle-specific settings via the Android Automotive vehicle HAL (Hardware Abstraction Layer). This grounds the patented concept in a widely available, open-source automotive operating system.