Derivative works — US Patent 8862508

Defensive Disclosure: Derivative Works of US Patent 8862508

This Defensive Disclosure aims to broaden the scope of existing prior art related to US Patent 8862508, "System and method for unifying e-banking touch points and providing personalized financial services." By describing derivative works and integrations, this document intends to render future incremental improvements in the field obvious or non-novel, thereby limiting the patentability of such advancements. The descriptions are provided with sufficient technical detail to enable a person having ordinary skill in the art to reproduce each variation.

Derivative Variations for Method Claims (e.g., Claim 1, Claim 7)

1. Material & Component Substitution: Cloud-Native Serverless Architecture with Biometric Thin Clients

Enabling Description:
A method for constructing a unified electronic banking environment utilizing a cloud-native, serverless computing architecture for the multi-channel server and thin-client biometric terminals for e-banking touch points. The multi-channel server functionality is implemented as a collection of stateless microservices orchestrated by a cloud provider's serverless platform (e.g., AWS Lambda, Azure Functions, Google Cloud Functions). User authentication at touch points is performed via embedded multi-modal biometric scanners (e.g., vein pattern, facial recognition, voice recognition) integrated into ultra-low-power ARM-based thin clients. These thin clients communicate encrypted actionable inputs (e.g., transaction requests, biometric data hash) to API Gateway endpoints that trigger the appropriate serverless functions. Previously stored data, including user preferences and financial institution data, is retrieved from a globally distributed, eventually consistent NoSQL database (e.g., Amazon DynamoDB, Google Cloud Datastore) and delivered to the thin client. Real-time monitoring and selection of targeted marketing content involve event-driven serverless functions that process streams of interaction data from touch points via a managed message queue (e.g., Amazon SQS, Azure Service Bus) and trigger content delivery services. User responses are captured as events and processed by further serverless functions for immediate action (e.g., initiating another serverless workflow for additional information delivery).

flowchart TD
    User -- Actionable Input --> BiometricThinClient
    BiometricThinClient -- Encrypted Input --> API_Gateway
    API_Gateway -- Triggers --> ServerlessAuthFunction
    ServerlessAuthFunction -- Authenticates --> BiometricDB(Biometric Database)
    ServerlessAuthFunction -- Queries User Profile/Preferences --> NoSQLDB(NoSQL User Data Store)
    NoSQLDB --> ServerlessPersonalizationFunction
    ServerlessPersonalizationFunction -- Delivers Content --> BiometricThinClient
    BiometricThinClient -- Sends Txn Data --> ServerlessTxnProcessing
    ServerlessTxnProcessing -- Stores Txn Usage Data --> NoSQLDB
    ServerlessTxnProcessing -- Emits Event --> MessageQueue(Message Queue)
    MessageQueue -- Triggers --> ServerlessMarketingMonitor
    ServerlessMarketingMonitor -- Selects Targeted Content --> MarketingContentService
    MarketingContentService --> ServerlessMarketingDelivery
    ServerlessMarketingDelivery -- Transmits Content --> BiometricThinClient
    BiometricThinClient -- User Response --> API_Gateway
    API_Gateway -- Triggers --> ServerlessResponseHandler
    ServerlessResponseHandler -- Updates Profile/Delivers More Info --> NoSQLDB

2. Operational Parameter Expansion: Ultra-High-Frequency, Global, Micro-Transaction Network

Enabling Description:
A method for constructing a unified electronic banking environment designed to process ultra-high-frequency, geographically dispersed micro-transactions on a global scale. The system is engineered for transactional throughput exceeding 1 million transactions per second (TPS) with an end-to-end latency of less than 50 milliseconds, accommodating millions of e-banking touch points across diverse regulatory jurisdictions and time zones. The multi-channel server comprises a distributed network of edge-computing nodes, each with local caching and decision-making capabilities, synchronized via a global consensus mechanism (e.g., Paxos, Raft variants) for transactional integrity. Touch points are miniaturized, low-power IoT devices (e.g., smart NFC payment rings, public transport fare gates, smart vending machines) embedded with cryptographic modules for secure, rapid authentication. Actionable inputs are batched into cryptographic proofs (e.g., zero-knowledge proofs) before transmission to the edge nodes. Previously stored data, including fractional user preferences and micro-marketing profiles, is distributed across content delivery networks (CDNs) and localized edge caches. Real-time monitoring for targeted marketing content selection operates on stream processing frameworks (e.g., Apache Flink, Kafka Streams) analyzing aggregated, anonymized transaction streams in near real-time. Marketing content, often consisting of sub-second video snippets or haptic feedback patterns, is pre-rendered and served from local edge caches for instantaneous delivery. User responses, even passive (e.g., dwell time, gaze tracking via embedded cameras at public touch points), are continuously fed back into the stream processing engine to dynamically adjust subsequent marketing content delivery.

graph TD
    A[Global User Base] --> B(Millions of IoT Touch Points)
    B --> C(Edge Computing Nodes - Geo-distributed)
    C -- Data Aggregation --> D(Global Data Consensus Network)
    D -- Real-time Streams --> E(Stream Processing Engine)
    E -- AI/ML Models --> F(Micro-Marketing Decision Engine)
    F -- Pre-rendered Content --> G(Global CDN/Edge Caches)
    G --> B
    C -- Transactional Data --> H(Distributed Ledger / Global Financial Records)
    B -- User Response/Telemetry --> E

3. Cross-Domain Application: Personalized Public Safety & Emergency Response Coordination System

Enabling Description:
A method for unifying public safety and emergency response touch points to provide personalized information and coordinated services to citizens, and a common point of control for emergency management agencies. The multi-channel server, termed a "Unified Crisis Response Platform (UCRP)," is coupled to diverse touch points including smart city kiosks, wearable emergency beacons, autonomous drone communication nodes, and citizen mobile applications. When an actionable input is received (e.g., distress signal from a wearable, citizen reporting an incident via kiosk), the UCRP retrieves previously stored data associated with the input, which includes citizen profiles (e.g., medical history, known locations, emergency contacts, language preferences) and historical incident data. This data is delivered to the originating touch point and other relevant personnel (e.g., first responders). Transactional usage data (e.g., incident report details, response times, resource deployment) is stored. The UCRP server monitors active sessions in real-time for selection of targeted public safety content (e.g., evacuation routes, shelter information, first aid instructions) correlated to user-defined preferences (e.g., accessibility needs) and real-time situational data (e.g., fire spread models). This content is transmitted in real-time to relevant touch points for acceptance (e.g., confirming receipt of evacuation order), rejection (e.g., reporting a route impassable), or no response by a user, wherein the response is used to determine whether transmission of additional related information occurs during the active session (e.g., alternative routes, direct medical assistance).

graph LR
    A[Citizen Wearable/Mobile App] -- Distress Signal --> B(Smart City Kiosk)
    B -- Actionable Input --> C(Unified Crisis Response Platform - UCRP)
    C -- Retrieve Citizen Profile/Sit. Data --> D[Citizen Database/GIS System]
    D --> C
    C -- Deliver Personalized Info --> B
    B --> A
    C -- Store Incident Data --> E[Incident Log/Resource DB]
    E --> C
    C -- Monitor Active Session --> F(Real-time Situational Awareness Module)
    F -- Select Targeted Safety Content --> G(Dynamic Content Engine)
    G --> C
    C -- Transmit Safety Info --> H[First Responder Devices]
    H --> B
    B --> A
    A -- Response --> C
    C -- Adjust Info/Coord. --> H

4. Integration with Emerging Tech: AI-Driven Predictive Personalization with Decentralized Data Governance

Enabling Description:
A method for constructing a unified electronic banking environment integrating AI-driven predictive personalization with a blockchain-based decentralized data governance model. The multi-channel server leverages a distributed AI inference engine that continuously analyzes transactional usage data and user-defined preferences, augmented by external data feeds (e.g., market trends, social sentiment). This AI engine predicts future user needs and optimal marketing content with high confidence scores, prior to or concurrently with actionable input. All customer data and preferences are stored on a permissioned blockchain (e.g., Hyperledger Fabric, Corda) as encrypted tokens, where each customer owns their data and grants granular access permissions via smart contracts. When an actionable input is received, the system retrieves previously stored data by querying the blockchain through an authorized oracle, decrypting only the necessary data segments as permitted by the user's smart contract. The AI engine then performs real-time selection of targeted marketing content based on its predictive model and the blockchain-verified user preferences. Content delivery to e-banking touch points (which are equipped with local AI accelerators for rendering and basic interaction processing) is optimized by the AI for modality and timing. User responses, including implicit feedback (e.g., interaction patterns, gaze duration), are recorded as immutable transactions on the blockchain, serving as auditable consent or rejection, triggering smart contracts for further information transmission or profile updates.

sequenceDiagram
    participant U as User
    participant ET as E-banking Touch Point
    participant AI as AI Inference Engine
    participant BC as Blockchain (Data Governance)
    participant MCS as Multi-Channel Server
    participant MCDS as Marketing Content Delivery System

    U->>ET: Actionable Input (e.g., Login)
    ET->>MCS: Transmit Actionable Input
    MCS->>BC: Request Encrypted User Data (via Smart Contract)
    BC-->>MCS: Authenticate & Provide Encrypted Data Token
    MCS->>AI: Decrypt & Feed User Data, Input to AI
    AI->>AI: Predict Needs & Optimal Content
    AI-->>MCS: Predicted Marketing Content + Confidence
    MCS->>ET: Deliver Retrieved Personalized Data
    ET->>U: Display Personalized Interface
    MCS->>AI: Stream Transactional Usage Data
    AI->>AI: Continuously Learn & Update Models
    loop Real-time Monitoring & Selection
        AI->>MCS: Real-time Targeted Marketing Content Selection
        MCS->>MCDS: Request Content Delivery
        MCDS->>ET: Transmit Targeted Marketing Content
        ET->>U: Display Marketing Content
        U->>ET: Accept/Reject/No Response
        ET->>MCS: Report User Response (Immutable TX)
        MCS->>BC: Record User Response (via Smart Contract)
        BC-->>MCS: Acknowledge & Update Permissions
        MCS->>AI: Feed Response to AI (Refine Model)
        alt If User Accepted & More Info Needed
            MCS->>ET: Transmit Additional Info (AI-optimized)
        end
    end

5. The "Inverse" or Failure Mode: Resilient "Safe-Mode" Operation

Enabling Description:
A method for constructing a unified electronic banking environment with a resilient "Safe-Mode" operation for e-banking touch points during network degradation, server overload, or security incidents. Upon detection of critical system anomalies (e.g., latency exceeding threshold, excessive error rates, denial-of-service attack indicators), the multi-channel server (or a designated fault-tolerant controller) initiates a system-wide "Safe-Mode" transition. In Safe-Mode, the system reverts to a limited-functionality state. Actionable inputs are restricted to essential, pre-approved transactions (e.g., cash withdrawal up to a pre-defined low limit, balance inquiry from cached data, emergency fund transfers). Personalized marketing content selection and transmission are entirely suspended to conserve computational resources and prevent potential exploitation of personalization engines during compromised states. Instead, generic, pre-loaded public service announcements or system status messages are delivered to touch points. Previously stored data retrieval is limited to locally cached, cryptographically signed account balances and basic user identity information, ensuring data integrity without real-time complex queries. Transactional usage data in Safe-Mode is captured via local resilient logging mechanisms at the touch points and batched for secure, deferred upload once normal operations resume. The system is designed to autonomously detect resolution of the anomaly and progressively restore full functionality, starting with re-enabling personalized content delivery and then full transactional capabilities.

stateDiagram
    [*] --> Normal_Operation
    Normal_Operation --> Anomaly_Detected : System Anomaly
    Anomaly_Detected --> Safe_Mode : Initiate Safe-Mode
    Safe_Mode --> Limited_Functionality : Restrict Services
    Limited_Functionality --> Generic_Content : Suppress Personalization
    Generic_Content --> Local_Data_Access : Cached Data Only
    Local_Data_Access --> Deferred_Logging : Batched Data Capture
    Deferred_Logging --> Anomaly_Resolved : Anomaly Resolved
    Anomaly_Resolved --> Normal_Operation : Restore Full Functionality
    Safe_Mode --> Normal_Operation : Manual Override (High Authority)

Derivative Variations for System Claim (e.g., Claim 13)

1. Material & Component Substitution: Quantum-Resistant Cryptosystem with Optical Fiber Touch Point Network

Enabling Description:
A unified electronic banking system employing a common multi-channel server that is implemented with quantum-resistant cryptographic modules (e.g., lattice-based cryptography, hash-based signatures) for all data at rest and in transit. The server communicates with e-banking touch points exclusively via a dedicated, high-speed optical fiber network, utilizing wavelength-division multiplexing (WDM) for concurrent data streams and physical layer quantum key distribution (QKD) protocols for channel encryption, making it resilient against quantum computing attacks. Each e-banking touch point (e.g., ATM, SSCC, kiosk) integrates a tamper-resistant hardware security module (HSM) equipped with quantum-resistant algorithms for local key management and transaction signing. User interfaces at the touch points are based on holographic projection displays, reducing physical contact points and increasing hygiene. The data storage device is a distributed, fault-tolerant object storage system with homomorphic encryption applied to sensitive customer profiles and transactional usage data, allowing computations on encrypted data without decryption, thus maintaining privacy even if the data store is compromised.

graph TD
    User --> HolographicDisplay(Holographic Display)
    HolographicDisplay --> HSMSensor(HSM & Sensor Array)
    HSMSensor -- Encrypted Actionable Input (QR) --> OpticalFiberNetwork(Optical Fiber Network with QKD)
    OpticalFiberNetwork -- WDM Streams --> MultiChannelServer(Multi-Channel Server)
    MultiChannelServer -- QR Cryptographic Modules --> QRMCS(Quantum-Resistant MCS)
    QRMCS -- AI Marketing Engine --> AIengine(AI Marketing Engine)
    QRMCS -- Homomorphic Encrypted Data --> ObjectStorage(Distributed Object Storage)
    ObjectStorage -- Homomorphic Query --> QRMCS
    QRMCS -- Encrypted Targeted Content (QR) --> OpticalFiberNetwork
    OpticalFiberNetwork --> HolographicDisplay

2. Operational Parameter Expansion: Hyper-Localized Bio-Feedback Loop for Ambient Financial Services

Enabling Description:
A unified electronic banking system operating within a hyper-localized ambient financial services environment, where e-banking touch points are spatially distributed micro-sensors (e.g., embedded in retail shelves, public benches, personal smart garments) that detect bio-feedback and environmental cues from users. The common multi-channel server consists of a federated network of ultra-low-power, event-driven micro-servers deployed at the edge of physical locations (e.g., within a retail store, a park district). These micro-servers receive actionable inputs from micro-RFID tags, smart fabrics, and proximity sensors in real-time, such as a user's physiological state (e.g., stress levels via galvanic skin response, heart rate variability) or interaction with specific products. The data storage device is a distributed time-series database optimized for high-volume, ephemeral sensor data, where data is tokenized and stored with very short retention periods in compliance with privacy regulations. The system monitors ambient bio-feedback and contextual data to select targeted marketing content that adapts not only to user preferences but also to their instantaneous emotional state or immediate physical environment (e.g., offering a calming financial product if stress is detected). Content delivery is via haptic feedback (e.g., a vibrating wristband), subtle audio cues from nearby speakers, or personalized, ephemeral projections on nearby surfaces.

classDiagram
    class User {
        +Bio-feedback
        +Contextual Interaction
    }
    class MicroSensor[IoT Touch Point] {
        +Detects Bio-feedback
        +Reads Contextual Cues
        +Transmits Event Data
    }
    class EdgeMicroServer {
        +Receives Sensor Data
        +Local Processing/Caching
        +Federated Network
    }
    class FederatedMultiChannelServer {
        +Aggregates Edge Data
        +Distributed Time-Series DB
        +Real-time Analytics
    }
    class AI_EmotionalMarketingEngine {
        +Processes Bio-feedback
        +Generates Adaptive Content
        +Predictive Personalization
    }
    class AmbientContentDelivery {
        +Haptic Feedback
        +Subtle Audio Cues
        +Ephemeral Projections
    }
    User --|> MicroSensor : Interacts with
    MicroSensor --> EdgeMicroServer : Transmits Events
    EdgeMicroServer --> FederatedMultiChannelServer : Syncs Data
    FederatedMultiChannelServer --> AI_EmotionalMarketingEngine : Feeds Data
    AI_EmotionalMarketingEngine --> AmbientContentDelivery : Directs Content
    AmbientContentDelivery --> User : Delivers Experience

3. Cross-Domain Application: Personalized Agricultural Resource Optimization System

Enabling Description:
A unified electronic banking system adapted for agricultural resource optimization and personalized farm management. The "multi-channel server" is a cloud-based Farm Operations Management Platform (FOMP) connected to diverse "e-banking touch points" which include autonomous farming robots (e.g., planters, harvesters), soil sensor arrays, drone surveillance units, and farmer-operated mobile terminals. The FOMP receives actionable input from these touch points, such as real-time crop health data, soil nutrient levels, weather forecasts, or farmer-entered planting schedules. It retrieves previously stored data, including specific crop profiles, historical yield data, personalized nutrient application strategies, and market pricing for various produce, and delivers tailored recommendations back to the touch points (e.g., adjusting irrigation for a specific plot, optimal harvest timing). The data storage device accumulates transactional usage data, such as resource consumption per acre, machinery operational metrics, and market transactions for harvested goods. The FOMP monitors active sessions to select targeted "marketing content," which in this context refers to personalized agricultural advice, supply chain optimization offers, or financial hedging strategies correlated to user-defined farm preferences (e.g., organic farming methods, specific crop types) and real-time agricultural conditions. This content is transmitted to the farmer's mobile terminal or the autonomous robots' control interfaces for acceptance (e.g., confirming a new fertilizer schedule), rejection, or no response, with the response determining if additional detailed information (e.g., chemical composition of recommended fertilizer, alternative market buyers) is transmitted during the session.

flowchart LR
    FarmRobots(Autonomous Farming Robots) -- Crop Data --> FOMP(Farm Operations Management Platform)
    SoilSensors(Soil Sensor Arrays) -- Nutrient Data --> FOMP
    DroneUnits(Drone Surveillance Units) -- Imagery Data --> FOMP
    FarmerTerminals(Farmer Mobile Terminals) -- Manual Input --> FOMP

    FOMP -- Retrieve Profiles/Strategies --> FarmDB(Farm Profile & Historical Data)
    FarmDB --> FOMP

    FOMP -- Deliver Recommendations --> FarmRobots
    FOMP -- Deliver Recommendations --> FarmerTerminals

    FOMP -- Store Usage Data --> FarmDB
    FOMP -- Monitor Active Session --> AI_AgriAdvisor(AI-driven Agricultural Advisor)
    AI_AgriAdvisor -- Targeted Advice/Offers --> FOMP

    FOMP -- Transmit Content --> FarmerTerminals
    FarmerTerminals -- Response --> FOMP

4. Integration with Emerging Tech: Decentralized Autonomous Organization (DAO)-Managed Financial Services with Digital Twin Integration

Enabling Description:
A unified electronic banking system where the common multi-channel server functions as a Decentralized Autonomous Organization (DAO) governed by a community of financial institutions and customers, operating on a public blockchain (e.g., Ethereum, Solana) using smart contracts for governance and data access. Each financial institution maintains a "digital twin" of its operations within this DAO ecosystem, allowing for aggregated and anonymized data sharing while preserving proprietary information. E-banking touch points (e.g., smart contract-enabled DeFi apps, biometric payment terminals, virtual reality banking environments) communicate with the DAO through blockchain nodes. An actionable input (e.g., a cryptocurrency transaction, a request for a loan defined by a smart contract) is received. Previously stored data, represented as self-sovereign identity credentials and verifiable claims (e.g., credit history, investment preferences) issued on the blockchain, is retrieved based on the user's explicit consent via cryptographic signatures. This data is delivered to the touch point. Transactional usage data, including successful smart contract executions and user interactions with DAO proposals, is recorded immutably on the public ledger. The system monitors active sessions in real-time for selection of targeted marketing content (e.g., new DeFi investment opportunities, personalized liquidity pool suggestions, DAO governance proposals) correlated to user-defined preferences and their on-chain financial behavior. This content is transmitted as an encrypted message through the blockchain to the user's digital wallet or VR banking avatar for acceptance (e.g., voting on a proposal, initiating a new smart contract), rejection, or no response. The user's on-chain response determines whether further smart contract interactions or information about DAO protocols are initiated.

flowchart TD
    User --> DEFI_App(DeFi App/VR Banking)
    DEFI_App -- Actionable Input (Blockchain TX) --> BlockchainNetwork(Public Blockchain Network)
    BlockchainNetwork -- Smart Contract Execution --> DAO_MCS(DAO-Managed Multi-Channel Server)
    DAO_MCS -- Queries Verifiable Credentials --> UserWallet(User Digital Wallet/SSI)
    UserWallet -- Verifiable Claims/Consent --> BlockchainNetwork
    BlockchainNetwork --> DAO_MCS
    DAO_MCS -- Deliver Personalized Data --> DEFI_App
    DAO_MCS -- Stores Immutable TX Data --> BlockchainNetwork
    BlockchainNetwork -- On-Chain Activity --> AI_DAO_Advisor(AI-driven DAO Advisor)
    AI_DAO_Advisor -- Personalized DeFi Content --> DAO_MCS
    DAO_MCS -- Encrypted Content (Blockchain TX) --> DEFI_App
    DEFI_App -- User Response (On-Chain) --> BlockchainNetwork
    BlockchainNetwork --> DAO_MCS
    DAO_MCS -- Update Smart Contracts/Deliver More Info --> BlockchainNetwork

5. The "Inverse" or Failure Mode: Forensic Read-Only Audit System

Enabling Description:
A unified electronic banking system designed to transition into a "Forensic Read-Only Audit System" (FROAS) mode upon detection of a severe data integrity compromise (e.g., large-scale unauthorized data modification, ransomware attack) or critical system component failure. In FROAS mode, all write operations to the data storage device and all transactional capabilities at e-banking touch points are immediately halted. The common multi-channel server, which includes a dedicated, isolated "Forensic Core" module, switches to only serving immutable, cryptographically verifiable historical data. E-banking touch points (e.g., ATMs, online banking interfaces) enter a display-only state, presenting users with a clearly marked "Audit Mode" interface. Actionable inputs from users are explicitly rejected or converted into support requests, with no financial transactions allowed. Previously stored data retrieval is limited to digitally signed, historical snapshots from an immutable ledger (e.g., a write-once, read-many archive or a private blockchain of historical records) accessible only through the Forensic Core. Personalized marketing content is disabled, and instead, all touch points display standardized, tamper-proof messages regarding the system status and estimated recovery time. No new transactional usage data is stored, and any attempts to write data are logged as security incidents. This mode prioritizes forensic analysis and data integrity verification over service availability, ensuring that no further compromise occurs and providing a clean, uncorrupted dataset for post-incident investigation.

stateDiagram
    [*] --> Normal_Operation_System
    Normal_Operation_System --> Compromise_Detected : Data Integrity Compromise / Critical Failure
    Compromise_Detected --> Initiate_FROAS : Activate Forensic Read-Only Audit System
    Initiate_FROAS --> Halt_Write_Operations : Block all write access
    Halt_Write_Operations --> Display_Audit_Mode : Touch Points show "Audit Mode"
    Display_Audit_Mode --> Reject_Transactions : No Financial Transactions
    Reject_Transactions --> Disable_Marketing : Suppress Marketing Content
    Disable_Marketing --> Serve_Immutable_Data : Access Historical Snapshots via Forensic Core
    Serve_Immutable_Data --> Log_Write_Attempts : Record Security Incidents
    Log_Write_Attempts --> Investigate_Recover : Post-Incident Investigation & Recovery
    Investigate_Recover --> Normal_Operation_System : System Restored / Compromise Resolved

Combination Prior Art Scenarios with Open-Source Standards

These scenarios combine the concepts of US Patent 8862508 with existing open-source standards to demonstrate further obviousness.

Open Banking APIs (e.g., FDX API) + US8862508 for Cross-Institution Personalized Marketing:
- Description: A financial institution implements the principles of US8862508 to unify its e-banking touch points for personalized services and targeted marketing. This system is then integrated with the open-source Financial Data Exchange (FDX) API standard. By leveraging FDX, the multi-channel server can securely access (with explicit customer consent) aggregated financial data from multiple other FDX-compliant financial institutions. This allows the system to offer hyper-personalized financial advice and marketing content (e.g., debt consolidation offers, investment opportunities) based on a holistic view of the customer's finances across all their banking relationships, not just within a single institution. For example, if FDX APIs reveal a customer has high-interest debt at another institution, the originating institution's ATM could, in real-time, offer a personalized low-interest loan consolidation product. The "user-defined preferences" (Claim 1/7) would include explicit consent flags managed through FDX's authentication and authorization flows.
- Prior Art Value: This combination renders obvious the extension of personalized multi-channel banking across multiple financial institutions through standardized, open-source APIs, which was a natural progression given the industry's move towards open banking.
Apache Kafka + US8862508 for Real-Time Event-Driven Personalization:
- Description: The multi-channel server described in US8862508 collects actionable inputs and transactional usage data from diverse e-banking touch points. This data is streamed in real-time into an Apache Kafka distributed streaming platform. Kafka topics are configured for various event types (e.g., ATM withdrawals, online login, SSCC deposits). A stream processing application (e.g., using Kafka Streams or ksqlDB, both open-source components of Kafka) continuously analyzes these event streams to detect patterns, update customer profiles, and trigger real-time personalization decisions. This allows the "monitoring via said server an active session in real-time for selection of targeted marketing content" (Claim 1/7) to be performed with extremely low latency and high scalability across thousands of touch points. For instance, a customer's recent large deposit event from an SSCC, streamed via Kafka, could immediately trigger a personalized investment product offer at a nearby plasma signage display (PSD) touch point before they even leave the branch.
- Prior Art Value: This combination makes obvious the use of mature, high-performance, open-source streaming technologies for the real-time data processing and monitoring components central to the personalization and marketing aspects of US8862508, thereby enhancing its real-time capabilities.
OpenID Connect (OIDC) / OAuth 2.0 + US8862508 for Unified Single Sign-On and Consent Management:
- Description: In a system implementing US8862508, all e-banking touch points (e.g., ATM, online banking website, mobile app) are configured as clients to an identity provider utilizing the open-source OpenID Connect (OIDC) protocol built on OAuth 2.0. When a user attempts to log in to any touch point, OIDC is used to authenticate the user against a central identity store, issuing secure tokens. This provides a unified single sign-on experience across all channels. Furthermore, "user-defined preferences" (Claim 1/7) regarding data sharing for personalization and marketing content are managed via granular consent mechanisms inherent in OAuth 2.0 scopes, allowing users to explicitly grant or revoke permissions for different types of data use or marketing outreach. This ensures that the retrieval and delivery of previously stored data and targeted marketing content strictly adhere to user-controlled consent through an auditable, open-standard framework.
- Prior Art Value: This combination renders obvious the integration of robust, open-source identity and access management standards to provide secure, unified authentication and user preference/consent management across a multi-channel banking environment, which is fundamental to the personalized services claimed in US8862508.