Derivative works — US Patent RE48633

As a Senior Patent Strategist and Research Engineer specializing in Defensive Publishing, I will now generate a comprehensive "Defensive Disclosure" document for US Patent RE48633. The goal is to create "Prior Art" that renders future incremental improvements by competitors "obvious" or "non-novel" based on the patent's core claims.

First, I will search the USPTO database for US patent RE48633 to confirm the current patent claims and any relevant public information.

The patent RE48633 is a reissue patent, and its claims focus on a method, system, and computer-readable medium for creating and managing a virtualized computing system. The core of the claims involves receiving a user specification for a non-yet-instantiated N-tier cloud environment, sending an initializing event to configure that environment, and then sending application data to execute within it.

Defensive Disclosure Document for US Patent RE48633

This Defensive Disclosure aims to broaden the scope of existing prior art related to the automated provisioning and management of virtualized computing systems, particularly in multi-tier cloud environments, thereby reducing the patentability of incremental advancements by competitors. This disclosure is derived from the core concepts of US Patent RE48633 and extends them into various technical axes.

Core Claim 1 (Method for virtualized computing system management)

Claim 1: A method for creating and managing a virtualized computing system, the method comprising:
a) determining, based on an initial user specification, an initial cloud environment that is an N-tier computing environment, wherein the initial cloud environment is not yet instantiated;
b) sending an initializing event based on the requested initial cloud environment, wherein the initialization event is configured to cause an initial cloud environment configuration to be made available to an application; and
c) sending application data that is configured to cause the application to begin execution in the initial cloud environment configuration.

Derivative Variations for Claim 1

1. Material & Component Substitution

Derivative 1.1: Container-Based Orchestration with ARM Processors and NVMe Storage

Enabling Description: Instead of traditional virtual machines and x86 architectures, this derivative utilizes containerization technologies (e.g., Docker, containerd) orchestrated by Kubernetes for defining and managing the N-tier environment. The underlying infrastructure comprises bare-metal servers equipped with ARM-based processors (e.g., Ampere Altra) for increased power efficiency and Non-Volatile Memory Express (NVMe) solid-state drives for high-throughput, low-latency storage. The "initial user specification" (1a) would define a set of container images, resource limits, and Kubernetes deployment descriptors (e.g., YAML files). The "initializing event" (1b) triggers a Kubernetes controller to provision pods, services, and ingresses, deploying the specified container images onto the ARM/NVMe cluster. The "application data" (1c) is distributed as container images or configuration maps, which are pulled and executed by the Kubernetes runtime in the configured N-tier container environment.

graph TD
    A[User Specification (YAML/JSON)] --> B(Kubernetes API Server)
    B --> C{Admission Controller}
    C --> D[Scheduler]
    D --> E(Kubelet on ARM/NVMe Node)
    E --> F[Container Runtime (containerd)]
    F --> G(Pod/Container Instance)
    G --> H{Application Execution}
    subgraph Cluster Provisioning
        E
        F
    end
    subgraph Container Deployment
        G
        H
    end

Derivative 1.2: Serverless Function-as-a-Service (FaaS) with FPGA-Accelerated Inference

Enabling Description: This variation implements the N-tier environment using a serverless Function-as-a-Service (FaaS) model. The "initial user specification" (1a) defines a collection of serverless functions (e.g., AWS Lambda, Google Cloud Functions) and their associated event triggers (e.g., HTTP requests, message queue events), runtime environments (e.g., Python, Node.js), and memory/CPU allocations. The "initializing event" (1b) involves deploying these function definitions to a FaaS platform. For computationally intensive tiers, such as machine learning inference services, the underlying hardware for specific functions can incorporate Field-Programmable Gate Array (FPGA) accelerators. The "application data" (1c) is the function code and its dependencies, uploaded to the FaaS platform, where it is executed on-demand in the configured serverless environment, leveraging FPGA acceleration for critical paths.

graph TD
    A[User Spec (FaaS Func Defs)] --> B(FaaS Deployment Service)
    B --> C{Function Configuration}
    C --> D[Function Orchestrator]
    D -- Deploy --> E(FaaS Runtime Environment)
    E -- Dynamic Scale --> F{FPGA-Accelerated Compute (for ML functions)}
    E --> G{Standard Compute (for other functions)}
    G --> H[Application Code Execution]
    F --> H
    subgraph FaaS Platform
        D
        E
    end

2. Operational Parameter Expansion

Derivative 2.1: Hyperscale Cloudbursting for Exascale Scientific Simulations

Enabling Description: The system is designed to manage an N-tier computing environment that can burst to exascale levels for scientific simulations. The "initial user specification" (1a) defines a high-performance computing (HPC) N-tier environment, potentially comprising a front-end for job submission, compute nodes, and high-throughput storage, with a requirement for dynamic scaling across multiple cloud providers or hybrid cloud setups. The "initializing event" (1b) triggers the provisioning of tens of thousands of compute instances with specialized interconnects (e.g., InfiniBand, high-speed Ethernet) and petabytes of temporary, high-IOPS storage across multiple geographic regions, potentially utilizing spot instances or negotiated reserved capacities. The "application data" (1c) comprises parallelized simulation codes (e.g., MPI applications) and large datasets, distributed across the provisioned compute nodes via high-speed data transfer protocols (e.g., RDMA), with execution initiated by a distributed job scheduler (e.g., Slurm, HTCondor).

flowchart TD
    A[User Spec (HPC N-Tier/Exascale)] --> B(Global Orchestrator)
    B -- Initiate Burst --> C{Cloud Provider A Provisioning}
    B -- Initiate Burst --> D{Cloud Provider B Provisioning}
    C --> E[Compute Nodes (Region 1)]
    D --> F[Compute Nodes (Region 2)]
    E & F -- High-Speed Interconnect --> G[Distributed Storage (PB Scale)]
    G --> H{MPI Application Execution}
    H --> I[Result Aggregation]
    subgraph Hyperscale Cloudburst
        C
        D
        E
        F
        G
    end

Derivative 2.2: Ultra-Low Power Edge Environment for IoT Sensor Networks

Enabling Description: This derivative focuses on deploying N-tier environments at the extreme edge, designed for ultra-low power consumption in geographically dispersed IoT sensor networks. The "initial user specification" (1a) details an N-tier architecture consisting of constrained edge devices (e.g., ARM Cortex-M microcontrollers) for data acquisition, local aggregation nodes (e.g., Raspberry Pi equivalents) for initial processing, and a small cloud gateway. The "initializing event" (1b) involves over-the-air (OTA) provisioning of lightweight operating systems (e.g., FreeRTOS, Zephyr RTOS) and specific firmware configurations to battery-powered edge devices, coupled with configuration of local mesh networks (e.g., LoRaWAN, Zigbee) and secure communication channels to the gateway. The "application data" (1c) consists of highly optimized, event-driven micro-applications or firmware updates, which are pushed to the edge devices and activated based on sensor triggers, operating on minimal power budgets.

sequenceDiagram
    participant U as User
    participant CS as Central Server (Cloud)
    participant GW as Cloud Gateway
    participant AN as Aggregation Node (Edge)
    participant ED as Edge Device (Sensor)

    U->>CS: Initial User Spec (Low-Power N-Tier)
    CS->>GW: Send Initializing Event (OTA Provisioning Cmd)
    GW->>AN: Relay OTA Provisioning (Firmware/OS)
    AN->>ED: Push Firmware/OS Update
    ED-->>AN: Acknowledge Provisioning
    AN-->>GW: Acknowledge Provisioning
    GW-->>CS: Acknowledge Provisioning
    CS->>GW: Send Application Data (Micro-App)
    GW->>AN: Relay Application Data
    AN->>ED: Deploy Micro-App
    ED->>ED: Begin Execution (Low Power Mode)
    ED->>AN: Sensor Data (Event Trigger)
    AN->>GW: Aggregated Data
    GW->>CS: Upload Data

3. Cross-Domain Application

Derivative 3.1: Autonomous Agricultural Robot Fleet Management (AgTech)

Enabling Description: The system is applied to manage an N-tier computing environment for an autonomous agricultural robot fleet. The "initial user specification" (1a) defines an N-tier environment comprising: Tier 1 - Edge processing units on individual robots for real-time sensor data analysis (e.g., crop health, soil conditions); Tier 2 - A local field station server for fleet coordination, path planning optimization, and data aggregation; and Tier 3 - A cloud-based analytics platform for long-term data storage, machine learning model training, and agricultural insights. The "initializing event" (1b) involves deploying software stacks (e.g., ROS 2, custom AI models) to the robot's edge processors, configuring communication protocols (e.g., 5G, LoRa) between robots and the field station, and establishing secure data pipelines to the cloud. The "application data" (1c) includes updated navigation maps, new crop-specific AI models for disease detection, and optimized work schedules, which cause the robots to commence autonomous field operations and data collection.

graph TD
    A[User Spec (Agri-Robot N-Tier)] --> B(Central Control System - Cloud)
    B -- Config/Deploy --> C[Cloud Analytics Platform (Tier 3)]
    B -- Config/Deploy --> D[Field Station Server (Tier 2)]
    D -- Config/Deploy --> E[Autonomous Robots (Tier 1 - Edge)]
    E -- Sensor Data --> D
    D -- Aggregated Data --> C
    C -- ML Models/Maps --> D
    D -- Tasks/Updates --> E
    subgraph Agricultural Domain
        C
        D
        E
    end

Derivative 3.2: Smart City Infrastructure Management for Emergency Response (Public Safety)

Enabling Description: This derivative applies the system to manage N-tier environments for smart city emergency response infrastructure. The "initial user specification" (1a) defines an N-tier environment for a specific urban sector, including: Tier 1 - Distributed IoT sensors (e.g., traffic cameras, air quality, acoustic sensors) and edge gateways for immediate event detection; Tier 2 - A localized emergency operations center (EOC) server for real-time data fusion, incident correlation, and resource allocation; and Tier 3 - A city-wide cloud platform for historical data analysis, predictive modeling of incident spread, and long-term planning. The "initializing event" (1b) triggers the deployment of real-time analytics modules to edge gateways, configuration of secure, low-latency communication networks (e.g., CBRS, dedicated fiber) to the EOC, and instantiation of incident management dashboards in the cloud. The "application data" (1c) consists of updated emergency protocols, AI models for predicting crowd movement, and optimized dispatch algorithms, which cause the integrated system to monitor for incidents and activate automated response sequences.

flowchart LR
    A[User Spec (Emergency N-Tier)] --> B(City Management Platform)
    B -- Deploy/Config --> C[Cloud Platform (Tier 3)]
    B -- Deploy/Config --> D[EOC Server (Tier 2)]
    D -- Config Edge --> E[Edge Gateways (Tier 1)]
    E -- Connect --> F[IoT Sensors (Traffic, Environmental)]
    F --> E
    E -- Real-time Data --> D
    D -- Incident Alerts --> C
    C -- Predictive Models --> D
    D -- Action Directives --> E
    subgraph Smart City Emergency Response
        C
        D
        E
        F
    end

Derivative 3.3: Personalized Adaptive Learning Platform for Educational Technology (EdTech)

Enabling Description: The system is used to create and manage N-tier environments for personalized adaptive learning platforms. The "initial user specification" (1a) outlines an N-tier environment composed of: Tier 1 - Client-side interfaces (web/mobile apps) running on student devices; Tier 2 - A regional learning management system (LMS) server for course content delivery, progress tracking, and student interaction management; and Tier 3 - A central AI-driven personalization engine in the cloud for adaptive content recommendation, performance analytics, and curriculum optimization. The "initializing event" (1b) involves provisioning and configuring the LMS server, deploying client-side application bundles to student devices (via managed device programs or app stores), and setting up secure APIs between tiers. The "application data" (1c) comprises updated course materials, new adaptive learning algorithms, and individualized learning paths, which cause the platform to deliver tailored educational content and dynamically adjust to student performance.

sequenceDiagram
    participant S as Student Device (Tier 1)
    participant LMS as Regional LMS Server (Tier 2)
    participant AI as Cloud AI Engine (Tier 3)
    participant I as Instructor/Admin

    I->>AI: Initial User Spec (Adaptive Learning N-Tier)
    AI->>LMS: Send Initializing Event (LMS Setup)
    LMS->>AI: Acknowledge Configuration
    AI->>S: Push Client App Bundle (via App Store/MDM)
    S->>S: Install Learning App
    AI->>LMS: Send Application Data (Course Content/Adaptive Algos)
    LMS->>S: Deliver Personalized Content
    S->>LMS: Student Interaction/Performance Data
    LMS->>AI: Aggregate Student Data
    AI->>AI: Optimize Learning Paths/Algos
    AI->>LMS: Update Recommendations

4. Integration with Emerging Tech

Derivative 4.1: AI-Driven Optimization with Real-Time IoT Monitoring and Blockchain for Compliance

Enabling Description: This derivative integrates RE48633's core method with advanced technologies. The "initial user specification" (1a) defines an N-tier industrial control environment. The "initializing event" (1b) deploys the infrastructure, including microservices for process control, data ingestion, and predictive maintenance. AI-driven optimization agents are then deployed as "application data" (1c), continuously monitoring system performance, resource utilization, and operational parameters. These agents leverage IoT sensors for real-time data input from physical machinery, feeding metrics into a streaming analytics pipeline. Anomalies detected by the AI trigger automated scaling or reconfiguration of the N-tier environment. Crucially, all deployment events, configuration changes, and AI-driven adjustments are cryptographically signed and recorded on a private blockchain for immutable auditing and supply chain verification of software components and sensor data provenance, ensuring regulatory compliance and trustworthiness in high-stakes environments (e.g., pharmaceutical manufacturing, critical infrastructure).

graph LR
    A[User Spec (N-Tier Industrial Control)] --> B(Cloud Orchestrator)
    B -- Provision Infra --> C[N-Tier Environment (Microservices)]
    C -- Deploy AI Agents --> D[AI Optimization Layer]
    C -- Data Stream --> E[IoT Sensor Network]
    E -- Real-time Data --> D
    D -- Optimized Params/Actions --> C
    D -- Audit Log --> F[Blockchain Network (Immutable Ledger)]
    C -- Event Log --> F
    F --> G[Compliance/Verification Service]
    subgraph Integrated Ecosystem
        D
        E
        F
        G
    end

5. The "Inverse" or Failure Mode

Derivative 5.1: Graceful Degradation and Limited-Functionality Mode for Critical Infrastructure

Enabling Description: This derivative focuses on designing the N-tier system for controlled failure and limited functionality, crucial for critical infrastructure (e.g., energy grids, telecommunications). The "initial user specification" (1a) includes not only the standard N-tier configuration but also a set of predefined "degradation profiles" and "limited-functionality modes" for each tier. The "initializing event" (1b) establishes the robust N-tier environment, but also deploys a dedicated "failure mode controller" and pre-configures fallback mechanisms. When a critical fault or resource exhaustion is detected (e.g., 80% CPU utilization across a tier, network partition), the system automatically triggers a degradation profile. This could involve shedding non-essential services, rerouting traffic to lower-capacity but highly resilient backup nodes, or switching to a read-only or minimum-viable-functionality mode. The "application data" (1c) for these modes might be highly optimized, lightweight versions of critical services, ensuring core functionality (e.g., emergency communication, essential data logging) remains operational even under severe stress, while non-critical operations are paused or terminated safely.

stateDiagram-v2
    [*] --> Healthy_Operational
    Healthy_Operational --> Moderate_Degradation: High Resource Use / Minor Fault
    Moderate_Degradation --> Critical_Degradation: Major Fault / Resource Exhaustion
    Critical_Degradation --> Limited_Functionality: Catastrophic Failure
    Limited_Functionality --> Safe_Shutdown: Sustained Criticality
    Safe_Shutdown --> [*]

    state Healthy_Operational {
        label: Full Service Availability
        Operational_Mode: Standard Application Data
    }
    state Moderate_Degradation {
        label: Non-Essential Services Shed
        Operational_Mode: Reduced Application Data
        Entry: Activate Degradation Profile 1
    }
    state Critical_Degradation {
        label: Core Services Only, Rerouting
        Operational_Mode: Minimal Application Data
        Entry: Activate Degradation Profile 2
    }
    state Limited_Functionality {
        label: Read-Only / Emergency Mode
        Operational_Mode: Essential Application Data Only
        Entry: Activate Limited-Functionality Profile
    }
    state Safe_Shutdown {
        label: Controlled Termination
        Operational_Mode: Data Preservation/Standby
    }

Combination Prior Art Scenarios

Here are three combination prior art scenarios where the core invention of US Patent RE48633 is combined with existing open-source standards to demonstrate obviousness or lack of novelty for future incremental improvements.

1. RE48633 + OpenStack + Puppet/Ansible

Scenario: A system for creating and managing a virtualized computing system in a private cloud environment using OpenStack as the infrastructure-as-a-service (IaaS) platform, combined with configuration management tools like Puppet or Ansible for automated software deployment and configuration.
Combination Logic:
- RE48633 Claim 1(a) (Determining initial cloud environment): The "initial user specification" is analogous to an OpenStack Heat template or a Terraform configuration file that defines an N-tier environment (e.g., compute instances, networks, storage volumes) within an OpenStack private cloud. This environment is not yet instantiated.
- RE48633 Claim 1(b) (Sending initializing event for configuration): The "initializing event" is the deployment action within OpenStack (e.g., heat stack-create or terraform apply), which causes OpenStack services (Nova, Neutron, Cinder) to provision the virtual infrastructure. Once the VMs are available, an event triggers Puppet or Ansible to connect to these VMs and apply system-level and application-level configurations (e.g., installing web servers, database software, setting up firewall rules). This makes the "initial cloud environment configuration" available.
- RE48633 Claim 1(c) (Sending application data for execution): After the configuration management tools have prepared the environment, Puppet or Ansible can be used to deploy the "application data" (e.g., application binaries, web content, database schemas, environment variables) directly to the configured VMs, initiating the application's execution. Alternatively, a CI/CD pipeline integrated with Puppet/Ansible would push the application artifacts.
Obviousness Argument: For a PHOSITA in 2010 (priority date of original patent) or even 2018 (reissue filing date), using an IaaS platform like OpenStack for automated VM provisioning and then using established configuration management tools like Puppet (available since 2005) or Ansible (available since 2012, but concepts were present earlier in tools like Cfengine, Bcfg2) to configure and deploy applications to those VMs would have been a well-known and obvious practice. The combination would yield the predictable result of an automated end-to-end cloud deployment.

2. RE48633 + Apache Mesos + Marathon

Scenario: A system for creating and managing a containerized N-tier computing system using Apache Mesos for resource management and Marathon (a Mesos framework) for orchestrating long-running containerized applications.
Combination Logic:
- RE48633 Claim 1(a) (Determining initial cloud environment): The "initial user specification" defines an N-tier application structure as a Marathon application definition (JSON/YAML), specifying required Docker container images, resource constraints (CPU, RAM), network ports, and scaling rules. This defines an N-tier environment that is not yet instantiated on the Mesos cluster.
- RE48633 Claim 1(b) (Sending initializing event for configuration): The "initializing event" is submitting the Marathon application definition to the Mesos/Marathon cluster. Marathon, acting as a scheduler, requests resources from Mesos. Mesos grants offers, and Marathon then launches the specified Docker containers on available Mesos agents. This process makes the "initial cloud environment configuration" (i.e., the running container instances with their defined network and storage) available.
- RE48633 Claim 1(c) (Sending application data for execution): The "application data" is inherently part of the specified Docker container images. When Marathon launches these containers, the application code within them immediately begins execution in the newly configured, containerized N-tier environment. This represents sending and executing application data.
Obviousness Argument: Apache Mesos (developed at UC Berkeley in 2009, open-sourced in 2010) and Marathon (released 2013) were established open-source projects providing robust container orchestration. For a PHOSITA familiar with cloud orchestration and containerization by the reissue filing date, combining a resource manager (Mesos) with an application orchestrator (Marathon) to define, provision, and deploy containerized N-tier applications would have been a straightforward and obvious extension of existing patterns, leading directly to the claimed method.

3. RE48633 + Cloud-init + Bash Scripting

Scenario: A basic, yet widely used, method for automatically configuring virtual machines and deploying applications using cloud-init for initial instance setup and standard Bash shell scripting for application deployment.
Combination Logic:
- RE48633 Claim 1(a) (Determining initial cloud environment): The "initial user specification" is a cloud provider's API call or a cloud formation template (e.g., AWS EC2, OpenStack Nova) that requests an N-tier set of virtual machines with specific operating system images. The "user data" field within this request contains a cloud-init script. This defines an uninstantiated N-tier environment.
- RE48633 Claim 1(b) (Sending initializing event for configuration): The "initializing event" is the instance launch request. When the VMs boot, cloud-init (an open-source package present in most cloud images since the early 2010s) executes the user-provided script. This script performs the "initial cloud environment configuration" by installing necessary software packages (e.g., web server, database), configuring network settings, creating user accounts, and setting up file systems.
- RE48633 Claim 1(c) (Sending application data for execution): The cloud-init script or a subsequent Bash script (invoked by cloud-init or a separate SSH command) contains commands to download (e.g., wget, curl, git clone) and install/start the "application data" (e.g., pulling code from a repository, compiling, running daemon processes). This causes the application to begin execution in the configured N-tier environment.
Obviousness Argument: Cloud-init was widely adopted by major cloud providers by 2010-2012, and Bash scripting for server automation has been ubiquitous for decades. The practice of spinning up VMs with cloud-init to perform initial setup, followed by scripts to fetch and execute application code, was a fundamental and obvious pattern for automating cloud deployments long before the reissue patent's filing date. This combination clearly anticipates or renders obvious the broad steps of RE48633.