Derivative works — US Patent 12118290

Defensive Disclosure for U.S. Patent 12,118,290

Publication Date: May 13, 2026

Subject: Derivative Works and Improvements for Systems and Methods of Interactive Content Creation

This document discloses novel variations, applications, and integrations related to the core concepts described in U.S. Patent 12,118,290 ("Methods and systems for interactive content creation"). 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 similar incremental innovations. This disclosure is intended for defensive purposes only and does not assert any intellectual property rights.

Derivatives Based on Independent Claim 1: System for Automatic Assembly of an Interactive Document

The core concept involves a server embedding rules into a modular content item, receiving personalization parameters, pulling a second modular item from a different document based on these rules and parameters, and assembling a new derivative document.

Axis 1: Material & Component Substitution

Derivative 1.1: In-Memory Graph Database for Rule Processing.
- Enabling Description: Instead of traditional relational databases or file-based rule storage, the server utilizes an in-memory graph database (e.g., Neo4j, RedisGraph) to store and process the rules and content relationships. Each modular item is a node, and the embedded rules are represented as directed edges with properties defining the logic (e.g., PULL_IF_PARAM_EQUALS). Personalization parameters trigger a graph traversal query (e.g., Cypher query) that identifies the second modular item node with minimal latency. This component substitution drastically reduces I/O bottlenecks and enables real-time assembly for highly complex, multi-level rule dependencies.
- ```
graph TD
    A[User Device] -- Personalization Parameters --> B(Server: API Gateway);
    B -- Cypher Query --> C{In-Memory Graph DB};
    C -- Rule Traversal --> C;
    C -- Node Found --> D(Content Assembler);
    subgraph Content Sources
        E(Document 1: Node M1);
        F(Document 2: Node M2);
    end
    C -- M1 Reference --> D;
    C -- M2 Reference --> D;
    D -- Assembled Document --> A;
```
Derivative 1.2: WebAssembly (WASM) for Rule Execution.
- Enabling Description: The "first set of rules" embedded in the modular item is not stored as simple key-value pairs but as a compiled WebAssembly (WASM) binary. The server, upon receiving personalization parameters, executes this WASM module in a sandboxed runtime. The WASM function takes the parameters as input and returns the identifier for the second modular item. This allows for highly complex, computationally intensive rules (e.g., statistical analysis, probabilistic matching) to be executed securely and efficiently, independent of the server's native programming language.
- ```
sequenceDiagram
    participant User
    participant Server
    participant WASM_Runtime
    participant Doc_Store

    User->>Server: Personalization Request (Params)
    Server->>Doc_Store: Fetch Modular Item 1 (contains WASM binary)
    Doc_Store-->>Server: Item 1 Data
    Server->>WASM_Runtime: Execute WASM(Params)
    WASM_Runtime-->>Server: Return Identifier for Item 2
    Server->>Doc_Store: Fetch Modular Item 2 (using Identifier)
    Doc_Store-->>Server: Item 2 Data
    Server-->>User: Assembled Document
```

Axis 2: Operational Parameter Expansion

Derivative 1.3: Nanoscale Document Assembly for DNA Data Storage.
- Enabling Description: The concept is applied to DNA-based data storage and retrieval. "Documents" are sequences of synthetic DNA, and "modular items" are specific gene-like fragments encoded with information. The "rules" are encoded as primer binding sites. To assemble a derivative "document" (a new DNA sequence), specific chemical primers ("personalization parameters") are introduced. These primers selectively amplify and ligate the target modular fragments from different DNA pools ("documents") via Polymerase Chain Reaction (PCR), creating a new, larger DNA molecule. This enables massively parallel, molecular-scale document assembly.
- ```
flowchart LR
    subgraph "DNA Pool 1 (Document 1)"
        D1_M1("Fragment M1 <br/> [Rule: Primer X]")
    end
    subgraph "DNA Pool 2 (Document 2)"
        D2_M2("Fragment M2")
    end
    subgraph "Reaction Vessel"
        Input(Personalization Parameters <br/> Add Primer X) --> PCR(PCR Amplification & Ligation);
        D1_M1 --> PCR;
        D2_M2 --> PCR;
    end
    PCR --> Output(Derivative DNA Strand <br/> [M1 + M2]);
```

Axis 3: Cross-Domain Application

Derivative 1.4: Aerospace: Dynamic Mission Checklist Generation.
- Enabling Description: In aviation, the system generates dynamic, context-aware pre-flight and emergency checklists. The "first document" is a master checklist template. "Modular items" are individual checklist steps (e.g., 'Check Flaps', 'Verify Fuel Levels'). The "rules" are tied to real-time telemetry data (altitude, engine status, weather) received as "personalization parameters." If the system detects an engine anomaly, it pulls a specific "Emergency Descent Procedure" module from a separate emergency operations manual ("second document") and seamlessly inserts it into the active checklist presented to the pilot on a cockpit multi-function display.
- ```
stateDiagram-v2
    [*] --> Normal_Ops
    Normal_Ops: Standard Checklist
    Normal_Ops --> Emergency_Condition: Engine_Failure_Telemetry
    Emergency_Condition: Assemble Derivative Checklist
    Emergency_Condition --> [*]: Procedure Complete

    state Emergency_Condition {
      direction LR
      [*] --> Pull_Emergency_Module
      Pull_Emergency_Module: Rule: Engine_PSI < Threshold
      Pull_Emergency_Module --> Assemble_New_Checklist: From 'Emergency Manual'
      Assemble_New_Checklist --> Display_To_Pilot
      Display_To_Pilot --> [*]
    }
```
Derivative 1.5: AgTech: Adaptive Crop Treatment Scheduling.
- Enabling Description: The system creates dynamic daily treatment schedules for autonomous farming equipment. The "first document" is a base schedule for a specific crop. "Modular items" are tasks like irrigation, fertilization, or pesticide application. "Rules" are linked to soil moisture, nutrient levels, and pest detection data from IoT sensors, which serve as "personalization parameters." If a sensor detects a specific fungal outbreak in one zone, the system pulls a targeted "fungicide application" module from a central pest management guide ("second document") and assembles a new schedule for the relevant drone or rover.
- ```
erDiagram
    CROP_SCHEDULE {
        string BaseTasks
        string Rules_FK
    }
    SENSOR_DATA {
        string SensorID
        string DataType
        float Value
    }
    TREATMENT_MANUAL {
        string Condition
        string Module
    }
    CROP_SCHEDULE ||--o{ SENSOR_DATA : triggers
    TREATMENT_MANUAL ||--o{ CROP_SCHEDULE : provides_module
```

Axis 4: Integration with Emerging Tech

Derivative 1.6: AI-Driven Predictive Document Assembly.
- Enabling Description: The "rules" are not hard-coded but are managed by a trained machine learning model (e.g., a recommendation engine or a classifier). The model is trained on historical user engagement and document performance data. When a user provides initial "personalization parameters" (e.g., customer industry), the AI model predicts which additional content modules will maximize engagement. It then dynamically generates the rule set to pull the highest-scoring "second modular item" from a content lake ("second document"), effectively personalizing the document based on predicted future success rather than static predefined logic.
- ```
graph TD
    subgraph User Input
        A(Personalization Params);
    end
    subgraph Server
        B(AI/ML Inference Engine);
        C(Rule Generator);
        D(Document Assembler);
    end
    subgraph Data
        E(Content Lake);
        F(Performance Analytics DB);
    end
    A --> B;
    F --> B;
    B -- Predicted best module --> C;
    C -- Dynamic Rule --> D;
    E -- Content pull --> D;
    D -- Predictive Document --> G(End User);
```
Derivative 1.7: Blockchain-Verified Content Provenance.
- Enabling Description: Each "modular item" has its hash and authorship metadata recorded on a distributed ledger (blockchain). The "rules" embedded in the first modular item include a smart contract address. When the server assembles the "derivative document," it executes a transaction on the smart contract. This transaction verifies the provenance of both the first and second modular items by checking their hashes against the blockchain record. The final derivative document is then timestamped and hashed on-chain, creating an immutable audit trail of its composition, which is critical for legal, financial, and regulatory documents.
- ```
sequenceDiagram
    participant Assembler
    participant SmartContract
    participant Blockchain

    Assembler->>Blockchain: Read Hash of Module 1
    Blockchain-->>Assembler: Return Hash1
    Assembler->>Blockchain: Read Hash of Module 2
    Blockchain-->>Assembler: Return Hash2
    Assembler->>SmartContract: AssembleRequest(Hash1, Hash2)
    activate SmartContract
    SmartContract->>Blockchain: Verify(Hash1)
    SmartContract->>Blockchain: Verify(Hash2)
    SmartContract-->>Assembler: Verification Success
    deactivate SmartContract
    Assembler->>Blockchain: Write Hash of Derivative Document
```

Axis 5: The "Inverse" or Failure Mode

Derivative 1.8: Graceful Degradation Assembly.
- Enabling Description: The system is designed for environments with intermittent network connectivity. The "rules" include a primary and a secondary (or cached) reference for the "second modular item." If the server receives a personalization request and cannot reach the "second document" (e.g., a remote microservice is down), the rule engine automatically triggers a fallback. It pulls a locally cached, lower-fidelity, or text-only version of the second modular item. The assembled derivative document is thus generated in a "limited-functionality" mode, ensuring service availability even during partial system failure, and includes a flag indicating that the content is degraded.
- ```
flowchart TD
    A{Receive Request} --> B{Rule: Try to Pull Module from Primary Source};
    B -- Success --> C(Assemble High-Fidelity Document);
    B -- Failure/Timeout --> D{Rule: Pull Module from Local Cache};
    D --> E(Assemble Low-Fidelity Document);
    E --> F(Add 'Degraded Content' Flag);
    C --> G(Deliver Document);
    F --> G;
```

Combination Prior Art Scenarios

Combination 1: Git-Based Content Versioning and Assembly.
- Description: The system described in US 12118290 is combined with the open-source Git version control system. Each "document" is a Git repository. "Modular items" are individual files (e.g., Markdown, JSON). The "rules" embedded in a file within the first repository are scriptable hooks (e.g., a bash script) that, when executed by a CI/CD pipeline (e.g., GitLab CI, GitHub Actions), use the personalization parameters to perform a git clone --depth 1 of a second repository ("second document"). It then uses git filter-branch or git sparse-checkout to extract the specific required file ("second modular item") and assembles it into a new branch, which represents the "derivative document." This creates a fully version-controlled, auditable, and collaborative document assembly process.
Combination 2: OpenAPI (Swagger) for Dynamic API Documentation.
- Description: The system is used to generate personalized API documentation. The "first document" is a static documentation template. The "second document" is an OpenAPI 3.0 specification file (e.g., openapi.json). The "modular items" in the second document are the individual endpoint definitions (/paths/{path}). A user ("personalization parameter") selects their programming language. The rules embedded in the template then trigger a process that uses an open-source code generator like openapi-generator to pull the relevant endpoint definitions and create client-side code snippets ("second modular item") in the selected language. These snippets are then assembled into the final documentation page.
Combination 3: RSS/Atom Feed Aggregation and Personalization.
- Description: The invention is combined with the RSS 2.0 or Atom open standard for web feeds. The "first document" is a user's personalized newsletter template. A "modular item" within this template contains a rule specifying user interests (e.g., 'Artificial Intelligence', 'Cybersecurity') as "personalization parameters." The server continuously polls various RSS feeds ("second documents") from trusted sources. When a new <item> ("second modular item") in a feed matches the user's interest rules, the server pulls that item's content (title, description, link) and assembles it into the user's next scheduled newsletter ("derivative document").