Derivative works

Defensive Disclosure and Prior Art Derivations for U.S. Patent 11,301,898

Document ID: DP-20260429-898
Publication Date: April 29, 2026
Art Domain: Computer Science, Network Advertising, Distributed Systems, Information Security

This document discloses novel variations, extensions, and applications of the methods and systems described in U.S. Patent 11,301,898 (hereinafter "the '898 patent"). The purpose of this disclosure is to place these concepts into the public domain, thereby establishing them as prior art for any future patent applications in this domain.

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Axis 1: Component & Protocol Substitution

The core method of the '898 patent relies on specific components like "tags" and a "redirection" mechanism. The following disclosures substitute these with alternative but functionally equivalent technologies.

Derivative 1.1: Cryptographic Token-Based Authentication Instead of Tags

• Enabling Description: The "tag" sent from the first computer system (Targeting System) to the second computer system (Ad Network) is replaced with a signed JSON Web Token (JWT). When the user visits the first website, the Targeting System generates a JWT containing a unique user identifier (sub), an expiration time (exp), and the intended audience (aud, the domain of the Ad Network). This JWT is cryptographically signed using the Targeting System's private key. The Ad Network, possessing the Targeting System's public key, can verify the token's authenticity without needing a shared secret or a database lookup. The "condition" is then passed in a subsequent, secure server-to-server call authenticated by this token, preventing client-side manipulation.

• Mermaid Diagram:

  sequenceDiagram
      participant UserDevice
      participant WebsiteA
      participant TargetingSystem
      participant AdNetwork
      UserDevice->>WebsiteA: Visits
      WebsiteA->>TargetingSystem: Request to profile user
      TargetingSystem-->>TargetingSystem: Generate signed JWT (sub, exp, aud: AdNetwork)
      TargetingSystem->>UserDevice: Set JWT in secure, HttpOnly cookie
      UserDevice->>AdNetwork: Subsequent request with JWT
      AdNetwork-->>AdNetwork: Verify JWT signature with public key
      AdNetwork->>TargetingSystem: Server-to-server call to get condition
      TargetingSystem-->>AdNetwork: Return condition (e.g., max_bid_price)
  

Derivative 1.2: WebSockets for Real-Time Condition Updates

• Enabling Description: Instead of a one-time transfer of a static condition, the Ad Network and Targeting System maintain a persistent WebSocket connection for a given active user session. After the initial tagging, the Ad Network's server establishes this connection. The Targeting System can then push real-time updates to the condition based on the user's ongoing behavior (e.g., mouse movements, time on page) or changing market dynamics. When the user visits a second website, the Ad Network checks the most recently received condition from the WebSocket stream before deciding to serve the ad, enabling sub-second adjustments to targeting logic.

• Mermaid Diagram:

  sequenceDiagram
      participant UserDevice
      participant AdNetwork
      participant TargetingSystem
      UserDevice->>AdNetwork: Initial visit, tagged
      AdNetwork->>TargetingSystem: Establish WebSocket connection for user session
      TargetingSystem-->>AdNetwork: Push Condition v1 (bid=$0.50)
      loop Real-time Updates
          TargetingSystem-->>AdNetwork: Push Condition v2 (bid=$0.75)
          TargetingSystem-->>AdNetwork: Push Condition v3 (bid=$0.60)
      end
      UserDevice->>AdNetwork: Visits new page (ad opportunity)
      AdNetwork-->>AdNetwork: Check latest condition (v3)
      alt Condition Met
          AdNetwork->>TargetingSystem: Request Ad
      else Condition Not Met
          AdNetwork->>AdNetwork: Serve fallback ad
      end
  

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Axis 2: Operational Parameter Expansion

This section describes the application of the '898 patent's method under extreme or specialized operational conditions.

Derivative 2.1: High-Frequency Trading (HFT) Ad Bidding

• Enabling Description: The disclosed method is applied to a real-time bidding (RTB) environment operating at microsecond latencies. The "first computer system" is an algorithmic trading platform, and the "second computer system" is an ad exchange. The "condition" is a complex algorithmic rule set, not just a price, that includes parameters like market volatility, latency to user, and competitor bid density. This condition is pre-cached at edge data centers of the ad exchange. The check-and-redirect process must complete within a 10-millisecond budget, requiring the condition check to be performed in-memory on specialized hardware (FPGAs) to avoid disk or network I/O latency.

• Mermaid Diagram:

  graph TD
      subgraph Ad Exchange Edge DC
          A[User Ad Request] --> B{FPGA: In-Memory Condition Check};
          C[HFT System] -- Pre-cache --> D[(Condition Store)];
          D --> B;
          B -- Condition Met --> E[Redirect to HFT System];
          B -- Condition Not Met --> F[Run Standard Auction];
      end
      E --> G[HFT System Bids];
  

Derivative 2.2: Large-Scale IoT Device Command and Control

• Enabling Description: The method is scaled to manage millions of IoT devices. The "Targeting System" is a central IoT device management platform. The "Ad Network" is a regional edge computing provider. A device (e.g., a smart thermostat) periodically connects to the edge provider (first website visit). The management platform tags the device and provides a "condition" to the edge provider, such as "only redirect for a firmware update if device battery > 80% AND local network traffic < 1 Mbps." When the device next connects, the edge provider checks these locally-sensed conditions before redirecting the device to the central platform for a potentially data-intensive update, thus distributing the decision-making load and conserving network resources.

• Mermaid Diagram:

  flowchart LR
      subgraph Central IoT Platform
          A(Device Management)
      end
      subgraph Regional Edge Provider
          B(Device Connection Point)
          C{Condition Check}
          D[(Local Condition Store)]
      end
      E(IoT Device)
  
      E -- 1. Connects & gets tagged --> B
      A -- 2. Sends condition --> D
      D -- 3. Feeds rule --> C
      E -- 4. Later connection --> B -- Triggers --> C
      C -- Battery > 80%? --> E_OK
      subgraph E_OK["Redirect"]
          direction LR
          C -- Yes --> F(Redirect to Central Platform) --> A
      end
      C -- No --> G(Wait for next connection)
  

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Axis 3: Cross-Domain Application

The core mechanism is applied to industries unrelated to online advertising.

Derivative 3.1: Aerospace - Conditional Satellite Tasking

• Enabling Description: A satellite operator (System 1) manages a constellation. A third-party data consumer, like a weather agency (System 2), can request satellite imagery. The operator provides the agency with a tag for a specific region of interest and a "condition" for tasking, e.g., "Only redirect a tasking request to the satellite command bus if cloud cover over the target coordinates is < 15% based on your real-time meteorological model." The agency's system continuously checks its own weather model. Only when the condition is met does it redirect the authenticated request to the satellite operator, preventing wasted satellite time and bandwidth on unusable, cloudy images.

• Mermaid Diagram:

  sequenceDiagram
      participant WeatherAgency as System 2
      participant SatelliteOperator as System 1
      participant Satellite
      SatelliteOperator->>WeatherAgency: Provides tag for region + condition (cloud < 15%)
      loop Continuous Monitoring
          WeatherAgency-->>WeatherAgency: Check own weather model for region
      end
      alt Cloud Cover < 15%
          WeatherAgency->>SatelliteOperator: Redirect authenticated tasking request
          SatelliteOperator->>Satellite: Task to capture image
      end
  

Derivative 3.2: AgTech - Precision Irrigation Control

• Enabling Description: A central farm analytics platform (System 1) models crop water needs. An on-field irrigation controller (System 2) manages water valves. The platform provides the controller with a tag for a specific field zone and a "condition," such as "Permit irrigation only if the local soil moisture sensor reading is below 25% AND the 6-hour weather forecast shows no rain." The irrigation controller, which is connected to local sensors and has access to a weather API, checks these conditions locally. It only redirects a request to the central platform for the master irrigation schedule if the conditions are met, ensuring autonomous, fault-tolerant operation even if connectivity to the central platform is intermittent.

• Mermaid Diagram:

  stateDiagram-v2
      [*] --> Idle
      Idle --> Checking: On schedule
      Checking: Check local sensors & weather API
      Checking --> Idle: Conditions not met
      Checking --> Requesting: Conditions met
      Requesting: Redirect to Central Platform for schedule
      Requesting --> Irrigating: Schedule received
      Irrigating --> Idle: Cycle complete
  

Derivative 3.3: Consumer Electronics - Smart Home Voice Assistant Skill Invocation

• Enabling Description: A third-party service provider (e.g., a pizza delivery company, System 1) registers with a smart home voice assistant platform (e.g., Amazon Alexa, System 2). A user links their pizza account. The service provider sends a tag and a "condition" to the assistant platform, such as "Only invoke the 'reorder my usual' skill if the user's GPS location is within their 'Home' geofence AND the time is between 5 PM and 9 PM." When the user says "Alexa, I'm hungry," the voice platform checks these user-specific, pre-supplied conditions before deciding whether to offer the pizza reorder skill as a suggestion, making the interaction more contextually relevant.

• Mermaid Diagram:

  graph TD
      A[User: "Alexa, I'm hungry"] --> B{Voice Assistant Platform (System 2)};
      B -- 1. Parse intent --> C{Check Conditions};
      D(Pizza Service [System 1]) -- Pre-loads --> E[(User-specific Condition: at_home AND 5-9pm)];
      E --> C;
      C -- Conditions Met? --> F[Suggest: "Want to reorder from Domino's?"];
      C -- No --> G[Generic response: "What would you like to eat?"];
  

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Axis 4: Integration with Emerging Tech

Derivative 4.1: AI-Driven Predictive Condition Generation

• Enabling Description: The Targeting System (System 1) incorporates a machine learning model that predicts a user's likelihood to convert. Instead of a static condition (e.g., price), the condition sent to the Ad Network (System 2) is a "minimum required conversion probability score," e.g., p(convert) > 0.85. The Ad Network, in turn, may have its own local model that estimates the current context's influence on conversion. It redirects to the Targeting System only if its local context score, combined with the required probability score, exceeds a threshold. This creates a two-party AI validation gate.

• Mermaid Diagram:

  sequenceDiagram
      participant TargetingSystem as System1
      participant AdNetwork as System2
      TargetingSystem->>TargetingSystem: ML Model predicts P(Convert) for user
      TargetingSystem->>AdNetwork: Send condition: min_p_convert = 0.85
      AdNetwork->>AdNetwork: User visits, local ML model scores context (e.g., 0.9)
      AdNetwork->>AdNetwork: Check if (local_score * min_p_convert) > threshold
      alt Check Passes
          AdNetwork->>TargetingSystem: Redirect for Ad
      end
  

Derivative 4.2: Blockchain for Verifiable Consent and Ad Delivery

• Enabling Description: The entire process is mediated by a smart contract on a public blockchain. The "tag" is a transaction that writes a user's anonymized ID to the smart contract, and the "condition" is encoded as a function requirement within that contract. The Ad Network (System 2) is an oracle that reports page-visit events to the smart contract. The contract itself checks the condition. If met, it emits an event that the Targeting System (System 1) listens for, which triggers the ad serving. The redirection is thus a cryptographically verifiable state change on the blockchain, providing an immutable audit trail of user consent and ad delivery events for all parties.

• Mermaid Diagram:

  flowchart TB
      U[User on Website A] --> T{Targeting System};
      T -- 1. Writes Tag+Condition --> SC(Smart Contract on Blockchain);
      V[User on Website B] --> AN{Ad Network (Oracle)};
      AN -- 2. Reports visit to --> SC;
      SC -- 3. Executes & checks condition --> R{Result};
      R -- Condition Met --> EV(Emit "ServeAd" Event);
      EV -- 4. Listened for by --> T;
      T -- 5. Serves Ad --> V;
  

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Axis 5: The "Inverse" or Failure Mode

Derivative 5.1: Graceful Degradation to Privacy-Preserving Mode

• Enabling Description: The system is designed to fail-safe into a non-targeted mode. The "condition" sent from the Targeting System to the Ad Network includes a Time-To-Live (TTL) parameter. If the Ad Network cannot refresh the condition from the Targeting System before the TTL expires (due to network failure, high load, etc.), the condition is automatically nullified. Upon the next user visit, the Ad Network sees the null condition and is programmed to not redirect the user. Instead, it serves a generic, non-profiled, context-based ad from its own inventory. This ensures user privacy and system functionality during partial outages.

• Mermaid Diagram:

  stateDiagram-v2
      state Active {
          [*] --> Condition_Fresh
          Condition_Fresh --> Condition_Stale: TTL expires
          Condition_Stale --> Condition_Fresh: Refresh success
          Condition_Stale --> Inactive: Refresh fails
      }
      [*] --> Active: System Normal
      Active --> Inactive: Network Failure
      Inactive --> [*]: Serve non-targeted ad
  

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Combination Prior Art with Open-Source Standards

1. Combination with Prebid.js: The method of the '898 patent is implemented as a Prebid.js adapter. The Targeting System (System 1) is a "bidder." When a publisher website (controlled by System 2) initiates a Prebid auction, the Targeting System's adapter is called. Instead of returning a bid, it returns a signal (the "tag") and simultaneously makes a server-to-server call to the publisher's ad server to lodge the "condition." The publisher's ad server, using its own logic, checks this condition against other bids received. If the condition is met (e.g., "accept a redirect if no other bidder offers > $2.00 CPM"), it forgoes the standard auction winner and instead returns a creative that performs the claimed redirection to the Targeting System.

2. Combination with OpenRTB Protocol: The "condition" is transmitted as a new, non-standard object within the ext (extension) field of an OpenRTB Bid Request. The Ad Exchange (System 2) is modified to parse this custom object. If the object is present, the exchange's internal logic is short-circuited. Instead of running a full auction, it evaluates the rule described in the object. If the rule evaluates to true, it responds to the initial request with an HTTP 302 redirect to the endpoint specified by the Bidder (System 1), effectively implementing the patent's logic within the framework of the existing OpenRTB standard.

3. Combination with IAB Tech Lab's Seller-Defined Audiences (SDA): The "tag" sent from System 1 to System 2 is an encrypted identifier that corresponds to a segment in the IAB's SDA taxonomy. System 1 (the Targeting System) is a data provider that has profiled the user into a specific segment. System 2 (the publisher) receives this segment ID. The "condition" is a rule that System 1 provides to System 2 regarding that segment, such as "Only serve my ads to users in segment [seg_id] on your 'Sports' section pages." The publisher's ad server checks this condition (user is on a sports page AND has the specified segment ID from System 1) before redirecting the ad call to System 1. This uses the industry-standard SDA framework for audience naming but applies the patent's specific conditional logic for ad call routing.