US 11435994B1

US Patent 11435994B1, titled "Multi-platform application integration and data synchronization," was issued to People Center Inc.

Here is a summary of the patent details:

• Title: Multi-platform application integration and data synchronization
• Assignee: People Center Inc
• Inventors: Siddhartha Gunda, Kyle Michael Boston, Daniel Robert Buscaglia, Dilanka Theshan Dharmasena, Ruhitaj Reddypalli, Nilay Pochhi
• Filing Date: 2021-07-01
• Issue Date: 2022-09-06
• Abstract: The patent describes a computer-implemented method, computer-readable media, and a computing system for integrating and synchronizing distributed applications and computer systems over networks. This involves obtaining third-party application data separate from a computing system holding organizational data, analyzing this third-party data based on rules and integration information, processing the data according to the integration information, and performing operations on the organizational data based on this processing. The system may also generate and transmit information to other integrated applications. The goal is to improve efficiency, consistency, accuracy, and availability of organizational data across various disparate applications and systems, reducing the need for custom or manual processes.

Independent Claims Overview:

US11435994B1 has three independent claims: Claim 1 (method), Claim 16 (non-transitory computer-readable media), and Claim 17 (computing system).

1. Claim 1 (Computer-implemented method): This claim describes a method executed by one or more processors. It involves:

   • Obtaining data from a third-party application, where this application is distinct from a computing system that stores an organization's data.
   • Analyzing this third-party data using predefined rules associated with the computing system and specific integration information that details how the third-party application integrates with the organization's data.
   • Processing the third-party data based on this integration information.
   • Performing one or more operations on the organizational data within the computing system, triggered by the processed third-party data.

2. Claim 16 (Tangible non-transitory computer-readable media): This claim covers computer storage media containing instructions. When these instructions are executed by one or more processors, they cause the processors to perform the same operations as described in Claim 1. This includes obtaining, analyzing, processing, and performing operations on organizational data based on third-party application data and integration rules.

3. Claim 17 (Computing system): This claim describes a computing system equipped with one or more processors coupled to non-transitory computer-readable media. When the instructions on the media are executed by the processors, they cause the system to carry out the same steps outlined in Claim 1. This encompasses obtaining third-party application data, analyzing it with rules and integration information, processing it, and subsequently performing operations on the organizational data.

CAFC 2026 Dockets:
A search of the CAFC 2026 dockets for "11435994B1" did not return any specific cases or dockets directly related to this patent number as of April 26, 2026.

As of April 26, 2026, a search for litigation involving US Patent 11435994B1 did not return any specific cases or dockets on Unified Patents, PACER, or general legal search engines. Therefore, no known litigation involving US patent 11435994B1 can be reported at this time.

Proceedings overview

There are no AIA trial proceedings on file for US Patent 11435994B1 as of August 12, 2026. This indicates that the patent has not yet faced challenges before the Patent Trial and Appeal Board (PTAB), leaving its claims untested by this forum.

Strategic summary

Currently, all claims of US11435994B1 are UNTESTED by PTAB proceedings. No claims have been canceled or sustained through an AIA trial. This means that if a defendant is facing assertion of this patent, all claims (1-17) remain presumptively valid from a PTAB perspective, and there is no estoppel landscape established against potential petitioners for any prior-art grounds. The absence of PTAB activity suggests that the patent has not yet been targeted for invalidation in this forum, which can be common for newer patents or those not yet widely asserted.

Recommended next steps

Given that no PTAB activity exists for US Patent 11435994B1, a defendant considering challenging the patent should evaluate the prior art landscape independently to determine if strong invalidity grounds exist under 35 U.S.C. §§ 102 or 103. If such grounds are identified, filing a petition for Inter Partes Review (IPR) or Post-Grant Review (PGR) (depending on the patent's effective filing date relative to the AIA) would be a viable defensive strategy, as no estoppel would apply from previous PTAB proceedings. The absence of prior PTAB challenges means there's no "hardened" aspect to the patent from this venue, and a first mover could set the precedent.

Inventors

• Siddhartha Gunda (People Center Inc)
• Kyle Michael Boston (People Center Inc)
• Daniel Robert Buscaglia (People Center Inc)
• Dilanka Theshan Dharmasena (People Center Inc)
• Ruhitaj Reddypalli (People Center Inc)
• Nilay Pochhi (People Center Inc)

All named inventors were employed by the original assignee, People Center Inc., at the time of filing, as indicated by the "Current Assignee" and "Original Assignee" information on the Google Patents page.

Original assignee

The original assignee is People Center Inc. Their primary line of business appears to be providing an organizational management platform that controls and leverages organizational data to manage applications such as payroll, HR, benefits, and IT device management, as described in the patent's detailed description. They aim to provide automated integration and synchronization of organizational data across various disparate applications and systems. As of the current date, People Center Inc. appears to be an active operating company, as the patent's legal status is "Active" and their website is operational, offering "People Operations Platform" services.

Assignment timeline

• 2021-07-02 (executed) / recorded 2021-07-02 — Reel 056742/0168
  • Conveyance: Assignment
  • Assignor: BOSTON, KYLE MICHAEL; BUSCAGLIA, DANIEL ROBERT; REDDYPALLI, RUHITAJ; DHARMASENA, DILANKA THESHAN; GUNDA, SIDDHARTHA; POCHHI, NILAY
  • Assignee: PEOPLE CENTER, INC.
  • Correspondent: GREGORY D. ALLEN, VAN PELT, YI & JAMES LLP, 10050 N FOOTHILL BLVD STE 200, CUPERTINO, CA 95014-4116
  • Context: Transfer of inventor's interest to the original assignee.
• 2021-08-30 (executed) / recorded 2021-08-30 — Reel 057049/0517
  • Conveyance: Corrective Assignment
  • Assignor: BOSTON, KYLE MICHAEL; BUSCAGLIA, DANIEL ROBERT; REDDYPALLI, RUHITAJ; DHARMASENA, DILANKA THESHAN; GUNDA, SIDDHARTHA; POCHHI, NILAY
  • Assignee: PEOPLE CENTER, INC.
  • Correspondent: GREGORY D. ALLEN, VAN PELT, YI & JAMES LLP, 10050 N FOOTHILL BLVD STE 200, CUPERTINO, CA 95014-4116. This correspondent recurred on the prior assignment in this chain.
  • Context: Corrective assignment to update address lines for the original assignment.

Timeline diagram

timeline
    title Ownership of US 11435994B1
    2021 : Inventors assigned to People Center Inc
         : Corrective assignment filed
    2022 : Issued to People Center Inc

NPE / troll-pattern signals

1. Shell-entity transfer — not present. The assignments are from the individual inventors to People Center Inc., the original operating company assignee.
2. Known asserter in the chain — not present. People Center Inc. is not identified on public NPE lists.
3. Repeat correspondent across the chain — not present. While the same correspondent (Gregory D. Allen, Van Pelt, Yi & James LLP) appears on both recorded assignments, these are both within the initial assignment from inventors to the operating company, and a corrective assignment, not across a chain of different assignees.
4. Cascading transfers — not present. There are only two assignments, both to the initial assignee (one being a corrective filing), occurring within a short period, which is typical for initial patent ownership establishment, not for rapid transfers between shell entities.
5. Pre-litigation transfer — not present. No litigation has been identified for this patent.
6. Bankruptcy fire-sale — not present. There is no indication of People Center Inc. undergoing bankruptcy proceedings.
7. Privateering — not present. No evidence of privateering has been found.
8. Defensive aggregator (anti-NPE) — not present. The patent is currently assigned to People Center Inc., not a defensive aggregator.

Verdict

Insufficient data. The available assignment records only show transfers from the individual inventors to the original operating company assignee, People Center Inc. There are no subsequent assignments that would indicate a change in ownership to an NPE, a defensive aggregator, or any other pattern suggesting assertion or neutralization.

Link to USPTO Assignment Center search: https://assignmentcenter.uspto.gov/patent/index.html?qs=11435994

Unfortunately, the provided patent text for US11435994B1 does not include a "References Cited" section or a list of prior art documents that were considered during its examination. While it lists "Prior art keywords" such as "data," "organizational," "party application," and "applications," these are general terms and do not refer to specific patent or non-patent literature citations.

To accurately identify the most relevant prior art, it would be necessary to consult the full patent document on an official database like Google Patents or the USPTO, which would contain the "References Cited" section. As I am constrained to the provided text and cannot browse external websites in real-time to extract this specific list, I cannot fulfill the request to list the prior art, their details, and potential anticipation claims.

Unfortunately, I cannot perform an obviousness analysis for US patent 11435994B1 under 35 U.S.C. § 103. The "Prior Art" section of this analysis explicitly states: "the provided patent text for US11435994B1 does not include a 'References Cited' section or a list of prior art documents that were considered during its examination."

Without a list of specific prior art references, their details, and their disclosures, it is not possible to identify combinations of references or explain the motivation for a person having ordinary skill in the art (POSA) to combine them to render the claims obvious.

To provide a comprehensive analysis of US Patent 11435994B1 regarding patent term adjustments (PTA), patent term extensions (PTE), continuation applications, divisional applications, related family members, and the projected expiration date, direct access to the official USPTO Patent Center or Patent Public Search is typically required. While general information on these topics is available, the specific details for this patent are not provided directly in the search snippets.

Based on the available information and general patent principles:

Patent Term Adjustment (PTA)

Patent Term Adjustment (PTA) is granted to compensate for certain delays caused by the U.S. Patent and Trademark Office (USPTO) during the prosecution of a utility or plant patent application. This can add additional days to the 20-year lifespan of the patent. Delays are categorized as "A," "B," and "C" delays, relating to specific USPTO processing timeframes, such as issuing a first office action within 14 months, responding to applicant replies within four months, and issuing the patent within three years of the filing date. The USPTO automatically determines the PTA and notifies the applicant in the Issue Notification Letter.

The specific PTA for US11435994B1 is not available from the provided search results. To determine the exact PTA, the patent's file history on the USPTO Patent Center would need to be consulted.

Patent Term Extension (PTE)

Patent Term Extension (PTE) is available for patents on certain products, such as human drugs, food or color additives, medical devices, animal drugs, and veterinary biological products, to restore patent term lost during regulatory review periods prior to commercial marketing or use. This extension aims to compensate for the time spent awaiting premarket government approval from a regulatory agency like the FDA. PTE is distinct from PTA and is governed by 35 U.S.C. § 156.

Given the title "Multi-platform application integration and data synchronization" and the nature of the invention, it is highly unlikely that US11435994B1 would be eligible for a Patent Term Extension under 35 U.S.C. § 156, as it does not appear to relate to products requiring regulatory approval from agencies such as the FDA.

Continuation Applications

A continuation application is a new, complete patent application derived from an earlier-filed "parent" application, allowing an applicant to pursue additional claims to an invention already disclosed in the parent application. It benefits from the earlier priority date of the parent application and cannot introduce new matter.

The provided information does not explicitly state whether US11435994B1 is a continuation application or if it has any continuation applications filed from it. This information would typically be found in the application data sheet or the patent's front page data on the USPTO website.

Divisional Applications

A divisional patent application is a separate application claiming a distinct invention disclosed but not claimed in a parent application, filed in response to a USPTO restriction requirement. It is entitled to the parent's filing date as its priority date.

The provided information does not explicitly state whether US11435994B1 is a divisional application or if it has any divisional applications filed from it. This information would also be found in the application data sheet or the patent's front page data on the USPTO website.

Related Family Members

Patent family members can include continuation, divisional, and continuation-in-part applications, as well as foreign counterparts. The Google Patents page for US11435994B1 lists the following priority claims, which indicate related applications:

• US17/365,666 (priority date: 2021-07-01)
• AU2022301193A (priority date: 2022-06-29)
• PCT/US2022/035507 (priority date: 2022-06-29)
• CA3224565A (priority date: 2022-06-29)
• US17/902,382 (priority date: 2022-09-02)
• US18/672,761 (priority date: 2024-05-23)

These entries indicate that US11435994B1 is related to these applications through priority claims. Specifically, US17/365,666 is the application number for US11435994B1 itself, indicating its own filing date. The other US application numbers (US17/902,382 and US18/672,761) suggest potential continuation, divisional, or continuation-in-part applications or related filings. The AU, PCT, and CA numbers indicate international patent family members.

Projected Expiration Date

Generally, for utility patents filed on or after June 8, 1995, the patent term is 20 years from the earliest effective filing date of the application, subject to any Patent Term Adjustments (PTA) or extensions (PTE) and reductions due to terminal disclaimers.

• Filing Date: The filing date for US11435994B1 is 2021-07-01.
• Base Expiration Date (20 years from filing): 2041-07-01.

The previous "Patent summary" section already stated an "Anticipated expiration" of "2041-07-01". This aligns with the base 20-year term from the filing date. Without specific PTA information from the USPTO file history, this remains the projected expiration date, assuming no terminal disclaimers or PTE apply.

Defensive Disclosure: US Patent 11435994B1 Derivative Works

This document details derivative variations of US Patent 11435994B1, "Multi-platform application integration and data synchronization," to serve as defensive prior art. These disclosures aim to render future incremental improvements or alternative implementations of the patent's core claims obvious or non-novel to a person having ordinary skill in the art, as of April 26, 2026. The derivations focus on the core method steps of obtaining, analyzing, processing, and performing operations on organizational data based on third-party application data and integration information.

---

1. Material & Component Substitution

Variation 1.1: Distributed Ledger Technology for Data Storage

Enabling Description:
Instead of relying on a centralized relational or object database, the organizational data, acting as the system of record, is implemented on a permissioned distributed ledger technology (DLT) framework, such as Hyperledger Fabric or Corda. The "obtaining" step involves third-party application data being ingested via a dedicated gateway service that translates proprietary application data formats into a standardized schema compatible with ledger transactions. After initial analysis and processing, which may occur off-chain to preserve transactional efficiency, relevant state changes or new data entries are recorded as transactions on the DLT. These transactions are validated by a set of trusted peer nodes through a consensus mechanism (e.g., Practical Byzantine Fault Tolerance). The integration information, including data mapping rules and validation criteria, is encoded within smart contracts deployed on the ledger. "Performing operations" associated with the organizational data directly translates to invoking smart contract functions that update the ledger state, ensuring immutability and verifiable audit trails for all data modifications. Data retrieval for analytical "obtaining" or internal querying utilizes read-only access to the ledger state via specific client SDKs, with data integrity implicitly guaranteed by the blockchain's cryptographic properties.

Combination Prior Art Scenarios:

1. US11435994B1 combined with Hyperledger Fabric (an open-source DLT framework) for decentralized organizational data management.
2. US11435994B1 combined with JSON-LD (JSON for Linking Data – an open standard for linked data on the web) for semantic interoperability of third-party data within a blockchain context.
3. US11435994B1 combined with OAuth 2.0 (an open standard for access delegation) to securely authorize third-party applications to submit or query data on the DLT.

flowchart TD
    TPA[Third-Party Application] --> A{Obtain Data (DLT Gateway Service)};
    A --> B{Analyze Data (Off-Chain Processor)};
    B --> C{Process Data (Transaction Payload Prep)};
    C --> D[Record on Distributed Ledger (Org Data Smart Contract)];
    D -- Trigger --> E[Perform Operations (Smart Contract Execution)];
    E -- Update/Sync --> TPA;

Variation 1.2: Serverless Function Architecture for Integration Processing

Enabling Description:
The modular architecture for multi-platform application integration and data synchronization is implemented entirely using a serverless computing paradigm. The "obtaining" step is realized through event-driven triggers; for instance, a third-party application pushing data to an API Gateway endpoint or a message queue (e.g., Apache Kafka), which then automatically invokes a dedicated serverless function (e.g., AWS Lambda, Azure Functions, Google Cloud Functions). This function is responsible for initial data ingestion and validation. Subsequent "analyzing" and "processing" steps are orchestrated as a chain of independent serverless functions, each responsible for a specific micro-task: schema validation, rule evaluation, data transformation (e.g., using custom Python scripts or Node.js functions), and conflict resolution. The "integration information" is stored as configuration parameters or environment variables accessible by these functions. "Performing one or more operations" on the organizational data involves another serverless function that interacts with a backend data store (e.g., DynamoDB, Cosmos DB, Cloud Spanner) via its respective SDK, committing changes or triggering subsequent workflows. This approach inherently offers elastic scalability and a pay-per-execution cost model, minimizing idle resource allocation.

Combination Prior Art Scenarios:

1. US11435994B1 combined with Apache Kafka (an open-source distributed streaming platform) for high-throughput, real-time data ingestion from third-party applications into serverless functions.
2. US11435994B1 combined with OpenFaaS (an open-source serverless framework) for deploying and managing portable serverless functions on diverse infrastructure.
3. US11435994B1 combined with OpenAPI Specification (an open standard for describing RESTful APIs) for defining the interfaces through which third-party applications interact with the serverless integration layer.

sequenceDiagram
    participant TPA as Third-Party App
    participant GW as API Gateway/Message Queue
    participant SF1 as Obtain Data Function
    participant SF2 as Analyze Data Function
    participant SF3 as Process Data Function
    participant SF4 as Perform Ops Function
    participant ODS as Organizational Data Store

    TPA->>GW: Push Data (Event)
    GW->>SF1: Trigger (New Data Event)
    SF1->>SF2: Data acquired/validated
    SF2->>SF3: Analysis result/rules applied
    SF3->>ODS: Processed data commit
    ODS->>SF4: Data change notification
    SF4->>TPA: Operation confirmation/feedback

Variation 1.3: Quantum-Resistant Cryptography for Secure Data Exchange

Enabling Description:
To future-proof data security against advancements in quantum computing, all data communications involved in the "obtaining" and "performing operations" steps are secured using quantum-resistant (post-quantum) cryptographic algorithms. This includes key exchange, digital signatures, and bulk data encryption. For example, key establishment utilizes a lattice-based algorithm like CRYSTALS-Kyber, while digital signatures for authentication and data integrity employ hash-based signatures like CRYSTALS-Dilithium or Falcon. During the "obtaining" phase, third-party application data is encrypted at the source using these algorithms before transmission over the network. The "analyzing" step includes rigorous verification of the post-quantum digital signatures to ensure data authenticity and integrity. Similarly, when "performing operations" that transmit data back to third-party applications or update shared distributed systems, the organizational computing system signs and encrypts the information with quantum-resistant methods. The "integration information" is expanded to include configuration parameters for selecting specific post-quantum cipher suites and managing quantum-resistant certificate authorities.

Combination Prior Art Scenarios:

1. US11435994B1 combined with Open Quantum Safe (an open-source project integrating quantum-resistant cryptography into existing protocols) for securing data channels.
2. US11435994B1 combined with Transport Layer Security (TLS) 1.3 (an open standard for secure communication, conceptually modified to incorporate quantum-resistant algorithms) for end-to-end encryption.
3. US11435994B1 combined with JSON Web Tokens (JWT) (an open standard for securely transmitting information between parties) where the JWTs are signed using quantum-resistant digital signature algorithms.

flowchart TD
    TPA(Third-Party App) -- QRC Encrypt/Sign --> Net(Secure Network Channel)
    Net -- QRC Decrypt/Verify --> CS(Organizational Computing System)
    CS -- Obtain TPA Data --> A[Analyze Data (QRC Signature Verify)]
    A -- Process Data --> B[Perform Operations (Org Data)]
    B -- QRC Encrypt/Sign --> Net
    Net -- QRC Decrypt/Verify --> TPA

---

2. Operational Parameter Expansion

Variation 2.1: Hyper-Scale, Real-time Integration for Global Enterprises

Enabling Description:
This variation extends the multi-platform integration to handle petabytes of third-party application data daily from thousands of globally distributed enterprise applications, maintaining sub-second end-to-end latency. The "obtaining" mechanism relies on high-throughput, fault-tolerant stream processing frameworks (e.g., Apache Flink, Apache Storm) deployed across a massively parallel processing (MPP) architecture. Data is ingested from global endpoints via content delivery networks (CDNs) and routed to regional processing clusters. "Analyzing" and "processing" rules leverage distributed in-memory computing grids (e.g., Apache Ignite, Redis Cluster) augmented with GPU-accelerated analytics engines (e.g., NVIDIA RAPIDS) for real-time rule evaluation, complex event processing, and data transformation at extreme velocity. Organizational data is stored in a globally distributed, eventually consistent NoSQL database (e.g., Apache Cassandra, Amazon DynamoDB Global Tables) optimized for high write and read throughput. "Performing operations" involves triggering distributed transactions or eventual consistency updates across replicated organizational data stores, ensuring that changes propagate and become available to all relevant third-party applications globally within milliseconds, often using idempotent messaging patterns to handle potential network partitions.

Combination Prior Art Scenarios:

1. US11435994B1 combined with Apache Flink (an open-source stream processing framework) for real-time, high-volume data ingestion and processing.
2. US11435994B1 combined with Apache Cassandra (an open-source distributed NoSQL database) for storing organizational data with extreme scalability and availability across global deployments.
3. US11435994B1 combined with Kubernetes (an open-source container orchestration system) for managing and scaling the distributed microservices underpinning the integration platform.

graph TD
    subgraph Global Data Ingestion
        TPA1[TPA 1 (Region A)] --> CDN[Global CDN]
        TPA2[TPA 2 (Region B)] --> CDN
        ...
        TPAN[TPA N (Region Z)] --> CDN
    end

    subgraph Real-time Processing Clusters
        CDN --> Kafka[Distributed Message Bus (e.g., Kafka)]
        Kafka --> Flink[Stream Processors (Flink/Storm)]
        Flink -- Analyze/Process (GPU-Accel.) --> MemGrid[In-Memory Compute Grid]
    end

    subgraph Distributed Org Data Store
        MemGrid -- Update Millisecond Latency --> GlobalNoSQL[Global NoSQL DB (e.g., Cassandra)]
    end

    GlobalNoSQL -- Sync/Trigger Ops --> TPA1
    GlobalNoSQL -- Sync/Trigger Ops --> TPA2
    GlobalNoSQL -- Sync/Trigger Ops --> TPAN

Variation 2.2: Ultra-Low Power, Edge-Based Integration for IoT Devices

Enabling Description:
This derivative implements the integration and synchronization logic on highly resource-constrained edge devices within an Internet of Things (IoT) ecosystem. The "computing system" itself is a low-power microcontroller (e.g., ARM Cortex-M based) with limited flash memory and RAM, optimized for minimal energy consumption (e.g., operating in the microwatt to milliwatt range). "Third-party applications" are co-located sensors or actuators. "Obtaining" third-party application data is performed using lightweight messaging protocols such as MQTT-SN (MQTT for Sensor Networks) or CoAP (Constrained Application Protocol) over low-power wide-area networks (LPWANs) like LoRaWAN or NB-IoT, or even short-range protocols like Zigbee. "Analyzing" and "processing" involve highly optimized, compiled rule engines (e.g., using eBPF or custom bytecode interpreters) with a minimal memory footprint, executing pre-configured rules to filter, aggregate, and compress data locally. Only critical, highly processed organizational data updates are transmitted to a central cloud system, typically in batches. "Performing operations" often involves direct local command execution on attached actuators or display devices, with remote synchronization serving as a backup or for reporting. The "integration information" is pre-loaded onto the device during provisioning and optimized for static analysis to reduce runtime overhead.

Combination Prior Art Scenarios:

1. US11435994B1 combined with MQTT (an open-source messaging protocol for IoT) for efficient data exchange with third-party applications.
2. US11435994B1 combined with FreeRTOS (an open-source real-time operating system for embedded systems) to manage tasks and resources on the low-power edge device.
3. US11435994B1 combined with EdgeX Foundry (an open-source IoT edge platform) for standardizing device connectivity and data services at the edge.

graph TD
    IoTSensor[IoT Sensor (Third-Party App)] -- Raw Data --> EdgeDev[Edge Device (Computing System)]
    EdgeDev -- (1) Obtain Data (MQTT/CoAP) --> Microcontroller[Microcontroller Unit]
    Microcontroller -- (2) Analyze/Process (Optimized Rule Engine) --> LocalData[Local Organizational Data]
    LocalData -- (3) Perform Local Ops --> Actuator[Actuator (Third-Party App)]
    LocalData -- (Batch Sync, LPWAN) --> CloudODS[Cloud Organizational Data Store]

Variation 2.3: High-Frequency, Algorithmic Trading Platform Integration

Enabling Description:
This variant applies the integration principles to the extreme latency and throughput demands of algorithmic trading. The "computing system" is a specialized low-latency trading engine, often implemented with Field-Programmable Gate Arrays (FPGAs) or highly optimized C++ code running on bare-metal servers. "Third-party applications" include direct market data feeds from exchanges, order execution management systems (OEMS), and various risk management systems. "Obtaining" third-party application data (e.g., Level 2 order book data, trade confirmations) occurs via Direct Market Access (DMA) protocols or proprietary exchange APIs, operating at nanosecond to microsecond latencies. "Analyzing" involves Complex Event Processing (CEP) engines and custom hardware logic to identify predefined trading signals, arbitrage opportunities, or regulatory compliance breaches based on organizational portfolio rules and real-time market conditions. "Processing" translates these signals into validated trade orders. "Performing operations" involves rapidly dispatching trade orders to an OEMS or directly to exchange gateways, with strict latency guarantees, and instantaneously updating the organizational portfolio (risk, position) data. The "integration information" includes detailed low-level protocol specifications, data serialization formats, and pre-compiled trading strategies.

Combination Prior Art Scenarios:

1. US11435994B1 combined with FIX Protocol (an open standard for electronic communication of financial transactions) for standardized communication with trading venues.
2. US11435994B1 combined with Aeron (an open-source high-performance messaging system) for ultra-low latency inter-process communication within the trading engine.
3. US11435994B1 combined with Google gRPC (an open-source high-performance RPC framework) for efficient service-to-service communication with internal risk or data systems.

graph TD
    TPA_MarketData[Third-Party Market Data Feed] -- DMA/Proprietary API --> FPGA_Engine[FPGA-Accelerated Trading Engine]
    FPGA_Engine -- (1) Obtain Data --> CEP_Engine[Complex Event Processing Engine]
    CEP_Engine -- (2) Analyze Rules --> AlgorithmicStrategizer[Algorithmic Strategizer]
    AlgorithmicStrategizer -- (3) Process Orders --> RiskManagement[Real-time Risk Management (Org Data)]
    RiskManagement -- (4) Perform Operations (Send Orders) --> TPA_OEMS[Third-Party OEMS/Exchange Gateway]

---

3. Cross-Domain Application

Variation 3.1: Precision Agriculture Data Synchronization

Enabling Description:
The multi-platform integration system is repurposed for precision agriculture. The "computing system" functions as a central farm management platform. The "organizational data" comprises detailed farm-specific information, including soil nutrient maps, crop health indices, historical yield data, equipment maintenance logs, and aggregated sensor readings. "Third-party applications" include diverse data sources such as drone-based multispectral imaging services (for crop health, pest detection), local weather station APIs, soil moisture and nutrient sensor networks, and telemetry systems from autonomous agricultural machinery (tractors, harvesters). The "obtaining" step involves automated data acquisition from these varied sources (e.g., image files, JSON payloads from APIs, CSV logs). "Analyzing" and "processing" utilize rules based on agronomic models, historical data trends, and real-time inputs to determine optimal irrigation schedules, precise fertilizer application rates, pest control interventions, or predictive maintenance needs for machinery. "Performing operations" translates these insights into actionable commands, such as sending control signals to smart irrigation systems, programming robotic sprayers with variable rate application maps, generating work orders for equipment, or updating planting schedules in farm planning software.

Combination Prior Art Scenarios:

1. US11435994B1 combined with OGC SensorThings API (an open standard for integrating IoT devices and spatial data in real-time) for sensor network integration.
2. US11435994B1 combined with OpenAgri (an open-source platform for agricultural data management) to handle the diverse data types and models.
3. US11435994B1 combined with GeoJSON (an open standard for encoding geographical data structures) for representing spatial agricultural data within the organizational data store and for third-party map-based applications.

graph TD
    DroneImagery(Drone Imaging Service) --> FarmMgmt[Farm Management Platform (Org Data)]
    WeatherAPI(Weather Forecast API) --> FarmMgmt
    SoilSensors(Soil Sensor Network) --> FarmMgmt
    MachineryTelemetry(Agri-Machinery Telemetry) --> FarmMgmt

    FarmMgmt -- (1) Obtain Data --> DataIngestion[Data Ingestion Module]
    DataIngestion -- (2) Analyze Rules (Agronomic Models) --> DecisionEngine[Decision Engine]
    DecisionEngine -- (3) Process Actions --> ActionPlanner[Action Planner]
    ActionPlanner -- (4) Perform Ops --> IrrigationControl(Automated Irrigation System)
    ActionPlanner --> RobotSprayer(Robotic Sprayer)
    ActionPlanner --> EquipmentMaint(Equipment Maintenance System)

Variation 3.2: Smart City Infrastructure Management

Enabling Description:
The multi-platform integration system is applied to manage urban infrastructure within a smart city context. The "computing system" serves as the city's central operations platform. "Organizational data" includes real-time traffic flow models, public transportation schedules, utility grid load data, waste collection routes, and environmental sensor readings (air quality, noise levels). "Third-party applications" encompass traffic camera feeds, public transit GPS tracking systems, smart street lighting controllers, smart waste bin sensors, and public-facing alert systems. The "obtaining" step involves continuous streaming data intake from these diverse urban sensors and systems. "Analyzing" and "processing" utilize dynamic rules and predictive algorithms to identify congestion hotspots, optimize traffic signal timings, reroute public transport in real-time, predict utility overloads, optimize waste collection logistics, or trigger emergency alerts for citizens. "Performing operations" involves issuing commands to traffic light controllers, updating public transport digital signage, adjusting energy distribution to specific grid segments, dispatching waste collection vehicles, or broadcasting public safety announcements through integrated alert systems.

Combination Prior Art Scenarios:

1. US11435994B1 combined with FIWARE (an open-source platform for developing smart solutions) as the underlying framework for context data management.
2. US11435994B1 combined with CityGML (an open standard for 3D city models) for semantic interoperability of urban infrastructure data.
3. US11435994B1 combined with GTFS (General Transit Feed Specification) (an open standard for public transportation data) for integrating public transit schedules and real-time updates.

graph LR
    TrafficCams(Traffic Camera Feeds) --> SmartCityOps[Smart City Operations Platform (Org Data)]
    TransitGPS(Public Transit GPS) --> SmartCityOps
    GridSensors(Smart Grid Sensors) --> SmartCityOps
    WasteBins(Smart Waste Bin Sensors) --> SmartCityOps

    SmartCityOps -- (1) Obtain Data --> DataAggregator[Data Aggregation Service]
    DataAggregator -- (2) Analyze Rules (Predictive Models) --> UrbanDecisionEngine[Urban Decision Engine]
    UrbanDecisionEngine -- (3) Process Actions --> CommandOrchestrator[Command Orchestrator]
    CommandOrchestrator -- (4) Perform Ops --> TrafficSignals(Traffic Light Controllers)
    CommandOrchestrator --> TransitRouting(Public Transit System)
    CommandOrchestrator --> UtilityControl(Utility Grid Control)
    CommandOrchestrator --> WasteDispatch(Waste Collection Dispatch)

Variation 3.3: Digital Twin Integration for Manufacturing

Enabling Description:
This implementation focuses on creating and maintaining a comprehensive digital twin of a manufacturing facility. The "computing system" acts as the Digital Twin Platform. The "organizational data" is the dynamic representation of the physical plant, including real-time machine performance parameters, product inventory levels, work-in-progress status, production schedules, energy consumption, and quality control metrics. "Third-party applications" include various operational technology (OT) and information technology (IT) systems: IoT sensors embedded in machinery (for vibration, temperature, throughput), manufacturing execution systems (MES), enterprise resource planning (ERP) systems (for raw material inventory, order fulfillment), supply chain management (SCM) platforms, and computer vision systems for quality inspection. "Obtaining" involves ingesting data streams from these disparate sources, often via industrial communication protocols (e.g., OPC UA, Modbus TCP). "Analyzing" and "processing" apply rules derived from engineering specifications, operational tolerances, and predictive maintenance algorithms to identify anomalies, predict equipment failures, optimize production flow, or detect quality deviations. "Performing operations" translates these analyses into actions such as triggering maintenance work orders, adjusting machine parameters, re-sequencing production batches, reordering materials via ERP/SCM, or alerting human operators.

Combination Prior Art Scenarios:

1. US11435994B1 combined with OPC UA (Open Platform Communications Unified Architecture) (an open standard for industrial interoperability) for secure data exchange with OT systems.
2. US11435994B1 combined with Industry 4.0 Asset Administration Shell (an open standard for digital twins in manufacturing) for structuring and managing the digital twin data.
3. US11435994B1 combined with Eclipse Ditto (an open-source framework for building and managing digital twins) for maintaining the state of connected devices.

graph TD
    MachineIoTSensors(Machine IoT Sensors) --> DigitalTwin[Digital Twin Platform (Org Data)]
    MES(Manufacturing Execution System) --> DigitalTwin
    ERP(Enterprise Resource Planning) --> DigitalTwin
    SCM(Supply Chain Management) --> DigitalTwin
    QCVision(Quality Control Vision) --> DigitalTwin

    DigitalTwin -- (1) Obtain Data (OPC UA/MQTT) --> DataIngest[Industrial Data Ingestion]
    DataIngest -- (2) Analyze Rules (Predictive Algorithms) --> AnomalyDetector[Anomaly Detector/Optimizer]
    AnomalyDetector -- (3) Process Actions --> ActionGenerator[Action Generator]
    ActionGenerator -- (4) Perform Ops --> PredictiveMaint(Maintenance System)
    ActionGenerator --> ProductionControl(Machine Control System)
    ActionGenerator --> MaterialMgmt(ERP/SCM Updates)
    ActionGenerator --> OperatorAlerts(Operator HMI)

---

4. Integration with Emerging Tech

Variation 4.1: AI-Driven Optimization of Integration Rules

Enabling Description:
The organizational computing system incorporates an Artificial Intelligence (AI) module designed to dynamically optimize the integration rules and parameters. This AI module, specifically a reinforcement learning (RL) agent, continuously monitors key performance indicators (KPIs) of the integration process: data freshness, accuracy of organizational data synchronization, latency of "obtaining" and "performing operations," resource utilization (CPU, memory, network bandwidth), and the frequency of data reconciliation failures. The RL agent receives feedback based on these KPIs and iteratively adjusts the "one or more rules associated with the computing system" and "integration information for integrating the third-party application." This includes modifying data mapping heuristics, adjusting data pull/push frequencies, re-prioritizing data streams, and refining conflict resolution strategies. For example, if a particular third-party API becomes intermittently slow, the AI might automatically reduce its polling frequency and increase batch sizes, or shift to a push-based webhook model if available, to maintain overall system performance and data consistency, reducing manual configuration effort.

Combination Prior Art Scenarios:

1. US11435994B1 combined with TensorFlow (an open-source machine learning framework) for implementing and training the AI optimization engine.
2. US11435994B1 combined with Apache Airflow (an open-source platform for programmatically authoring, scheduling, and monitoring workflows) to manage the MLOps pipeline for AI model training and deployment.
3. US11435994B1 combined with PostgreSQL (an open-source relational database) for storing historical integration performance data and AI model parameters.

graph TD
    TPA --> Obtain[Obtain Data]
    Obtain --> Analyze[Analyze Data]
    Analyze --> Process[Process Data]
    Process --> Perform[Perform Operations (Org Data)]
    Perform --> ODS(Organizational Data Store)

    subgraph AI Optimization Loop
        Monitor[Monitor Integration Performance (KPIs)] --> AI_RL[AI Reinforcement Learning Agent]
        AI_RL -- Adjust Rules/Integration Info --> Adjuster[Rule/Config Adjuster]
        Adjuster --> Analyze
        Adjuster --> Process
        Adjuster --> Perform
    end

Variation 4.2: IoT Sensor-Triggered Proactive Data Synchronization

Enabling Description:
The integration system leverages IoT sensors to proactively trigger data synchronization based on real-world events. The "computing system" is interconnected with a network of physical IoT sensors (e.g., proximity sensors, environmental monitors, asset trackers) that act as implicit "third-party applications." These sensors continuously monitor conditions relevant to the organizational data. Instead of scheduled polling, the "obtaining" step is dynamically initiated by specific events detected by these IoT sensors. For instance, a smart inventory shelf sensor detecting low stock levels (a third-party data point) could immediately trigger the system to "obtain" corresponding product information from an inventory management application. "Analyzing" and "processing" then occur in real-time to update the organizational data (e.g., warehouse inventory records). "Performing operations" could involve generating automated re-order requests to a procurement system or dispatching a physical asset tracking signal to a logistics application. This proactive, event-driven approach ensures that organizational data reflects physical reality with minimal latency.

Combination Prior Art Scenarios:

1. US11435994B1 combined with Zigbee (an open standard for wireless personal area networks) for connecting and obtaining data from local IoT sensors.
2. US11435994B1 combined with Node-RED (an open-source flow-based programming tool) for visually wiring together IoT device events, APIs, and the integration logic.
3. US11435994B1 combined with InfluxDB (an open-source time-series database) for efficiently storing and querying high-volume, event-driven IoT sensor data that feeds into the integration process.

graph TD
    IoTSensor1[IoT Sensor 1 (e.g., Temp)] -- Threshold Exceeded Event --> EventBus[IoT Event Bus (e.g., MQTT)]
    IoTSensor2[IoT Sensor 2 (e.g., Proximity)] -- State Change Event --> EventBus
    EventBus -- Proactive Trigger --> Obtain[Obtain Related Third-Party Data]
    Obtain --> Analyze[Analyze Event Data & Rules]
    Analyze --> Process[Process Data for Org Records]
    Process --> ODS[Organizational Data Store]
    ODS -- Data Change --> Perform[Perform Operations (e.g., Alert, Trigger Action)]
    Perform --> TPA_Action[Third-Party Action System (e.g., HVAC Control)]

Variation 4.3: Blockchain for Supply Chain Data Verification and Integration

Enabling Description:
This variation integrates blockchain technology to enhance the trust, transparency, and verifiability of supply chain data within the organizational system. The "organizational data" specifically includes immutable records of product provenance, chain of custody, certifications, and compliance status for goods moving through a supply chain. "Third-party applications" are the various participants in the supply chain (e.g., manufacturers, logistics providers, distributors), each operating their own applications and contributing data to a shared, permissioned blockchain network (e.g., Ethereum, Hyperledger Fabric). The "obtaining" step involves the computing system querying specific smart contracts or ledger states on the blockchain to retrieve verified supply chain events (e.g., shipment acceptance, quality inspection completion, ownership transfer). "Analyzing" includes validating the integrity and authenticity of this on-chain data against predefined organizational rules and smart contract logic, ensuring that all provenance information is accurate and untampered. "Processing" updates the internal organizational supply chain records with this cryptographically secured information. "Performing operations" could include triggering automated payments upon proof of delivery, releasing goods from customs based on verified certifications, or initiating audits based on discrepancies detected in the immutable ledger.

Combination Prior Art Scenarios:

1. US11435994B1 combined with Ethereum (an open-source blockchain platform) or Hyperledger Fabric for establishing a shared, verifiable supply chain ledger.
2. US11435994B1 combined with ERC-721/ERC-1155 (open standards for non-fungible and semi-fungible tokens on Ethereum) for representing and tracking physical goods as unique tokens on the blockchain.
3. US11435994B1 combined with OpenTelemetry (an open-source observability framework for distributed systems) for monitoring the performance and tracing of interactions with the blockchain network.

graph TD
    TPA_Mfg(Manufacturer App) --> Blockchain[Blockchain Network]
    TPA_Log(Logistics App) --> Blockchain
    TPA_Dist(Distributor App) --> Blockchain
    Blockchain -- Query/Event --> Obtain[Obtain Blockchain Data (Org Data Smart Contract)]
    Obtain --> Analyze[Analyze Data (On-Chain Verification)]
    Analyze --> Process[Process Data (Update Internal Org Records)]
    Process --> ODS[Organizational Data Store (Supply Chain)]
    ODS -- Trigger --> Perform[Perform Operations (e.g., Payment, Release Goods)]
    Perform --> TPA_Payment(Payment System)
    Perform --> TPA_Logistics(Logistics System)

---

5. The "Inverse" or Failure Mode

Variation 5.1: Safe-Fail Mode for Data Corruption Detection

Enabling Description:
The organizational computing system incorporates an advanced "safe-fail" mode specifically designed to mitigate the impact of data corruption or inconsistency originating from third-party applications. This mode is activated when the "analyzing" step detects significant deviations from expected data schemas, checksum mismatches, logical inconsistencies (e.g., negative inventory, impossible dates), or violations of data integrity rules during the ingestion of third-party application data. Upon activation, the system immediately ceases all "performing operations" that involve writing or modifying core organizational data. The problematic third-party data stream is isolated and quarantined, preventing further corrupted data from entering the system. The system then enters a "limited-functionality" state, allowing only read-only access to organizational data, while "obtaining" attempts for the affected third-party application are reduced in frequency (e.g., switching from real-time to hourly batch polls). Comprehensive audit logs and high-priority alerts are generated, prompting manual intervention from data administrators. Any partially processed transactions are automatically rolled back to their last known consistent state to prevent data propagation.

Combination Prior Art Scenarios:

1. US11435994B1 combined with Apache Avro (an open-source data serialization system) to enforce robust data schema evolution and integrity checks, aiding in corruption detection.
2. US11435994B1 combined with Prometheus (an open-source monitoring system and time series database) for real-time error detection and alerting when data inconsistencies are identified.
3. US11435994B1 combined with Open Policy Agent (OPA) (an open-source policy engine) to define and enforce runtime policies for activating and governing the safe-fail mode.

stateDiagram
    [*] --> OPERATIONAL: System Fully Active
    OPERATIONAL --> DETECT_CORRUPTION: Data Analysis Detects Corruption
    DETECT_CORRUPTION --> SAFE_FAIL_MODE: Activate Safe-Fail Mode
    SAFE_FAIL_MODE --> ISOLATE_STREAM: Pause Writes, Isolate TPA Stream
    SAFE_FAIL_MODE --> READ_ONLY_ACCESS: Limit Org Data to Read-Only
    SAFE_FAIL_MODE --> REDUCED_PULL_FREQ: Reduce TPA Data Intake Frequency
    SAFE_FAIL_MODE --> GENERATE_ALERTS: Notify Administrators
    READ_ONLY_ACCESS --> OPERATIONAL: Manual Resolution Complete
    REDUCED_PULL_FREQ --> OPERATIONAL: Manual Resolution Complete
    ISOLATE_STREAM --> OPERATIONAL: Manual Resolution Complete

Variation 5.2: Low-Power, Asynchronous Integration for Resource-Constrained Periods

Enabling Description:
This derivative introduces a "low-power" operational mode for the integration system, intended for scenarios with anticipated resource constraints (e.g., off-peak hours, periods of high energy costs, deployment on mobile/battery-powered infrastructure, or during network instability). The system dynamically detects or is manually switched into this mode. In low-power mode, the "obtaining" of third-party application data shifts from continuous polling or real-time streaming to infrequent, scheduled batch processing, significantly reducing network activity and computational load. The "analyzing" and "processing" steps employ simplified rule sets and cached transformation logic, prioritizing essential data fields and deferring non-critical data enrichment. "Performing operations" on the organizational data are not executed immediately but are instead queued asynchronously (e.g., in a persistent message queue) for later execution during periods of higher resource availability or upon exiting low-power mode. This minimizes instantaneous CPU and memory usage, enabling the system to operate effectively with fewer resources while maintaining eventual consistency.

Combination Prior Art Scenarios:

1. US11435994B1 combined with Apache ActiveMQ (an open-source message broker) for asynchronous queuing of operations and data during low-power periods.
2. US11435994B1 combined with SQLite (an open-source, in-process, embedded relational database) for local, low-power storage of queued integration tasks on edge devices or during temporary disconnections.
3. US11435994B1 combined with GNU/Linux (an open-source operating system) configured with advanced power management features (e.g., CPU frequency scaling, intelligent sleep states) to support the low-power operational mode.

graph TD
    SystemStart(System Initialized) --> NormalMode(Normal Operation)
    NormalMode -- Resource Constraint Detected --> LowPowerMode(Low-Power Mode)
    LowPowerMode -- Resources Recovered --> NormalMode

    LowPowerMode -- (1) Batch Obtain Data --> DataCollectorLP[Low-Power Data Collector]
    DataCollectorLP -- (2) Simplified Analyze/Process --> ProcessingUnitLP[Simplified Processing Unit]
    ProcessingUnitLP -- (3) Queue Operations --> AsyncQueue[Asynchronous Operations Queue]
    AsyncQueue --> ODS(Organizational Data Store)

    NormalMode -- (Executes Queued Ops) --> AsyncQueue

Variation 5.3: Limited-Functionality Mode for Graceful Degradation

Enabling Description:
This variation implements a "limited-functionality" mode to ensure graceful degradation when connectivity to one or more third-party applications or their APIs experiences significant degradation (e.g., high latency, frequent errors, partial outages). The system's "analyzing" step continuously monitors the health and responsiveness of integrated third-party applications. When thresholds for degradation are met, the system automatically enters limited-functionality mode for the affected applications. In this mode, "obtaining" is restricted to a pre-defined subset of critical data (e.g., essential personnel updates, emergency notifications) from the problematic third-party application, while non-essential data streams are temporarily paused or downgraded to less frequent updates. "Processing" utilizes cached data or default rules to synthesize information for organizational data, prioritizing data integrity over completeness. "Performing operations" are strictly limited to core functionalities (e.g., maintaining basic employee records, activating emergency alerts) and non-critical updates are deferred or logged for later reconciliation. This ensures that the core organizational data management system remains operational and stable, even when external dependencies are compromised.

Combination Prior Art Scenarios:

1. US11435994B1 combined with the Circuit Breaker pattern (an open-source software design pattern) to gracefully handle failures in calls to third-party services by preventing repeated attempts to a failing service.
2. US11435994B1 combined with Istio (an open-source service mesh) for implementing sophisticated traffic management, fault injection, and resilience patterns for microservices interacting with third-party applications.
3. US11435994B1 combined with Grafana (an open-source analytics and monitoring solution) for visualizing the health of third-party integrations and triggering the limited-functionality mode based on dashboard alerts.

stateDiagram
    [*] --> FULL_FUNCTIONALITY: All Integrations Active
    FULL_FUNCTIONALITY --> TPA_DEGRADED: Third-Party App Health Degrades
    TPA_DEGRADED --> LIMITED_FUNCTIONALITY: Activate Limited Mode
    LIMITED_FUNCTIONALITY --> CRITICAL_DATA_ONLY: Obtain Critical Data Subset
    LIMITED_FUNCTIONALITY --> FALLBACK_PROCESSING: Use Default/Cached Rules
    LIMITED_FUNCTIONALITY --> CORE_OPS_ONLY: Perform Essential Org Data Ops
    LIMITED_FUNCTIONALITY --> FULL_FUNCTIONALITY: Third-Party App Health Restored