Patent 8271315

Derivative works

Defensive disclosure: derivative variations of each claim designed to render future incremental improvements obvious or non-novel.

Active provider: Google · gemini-2.5-flash

Derivative works

Defensive disclosure: derivative variations of each claim designed to render future incremental improvements obvious or non-novel.

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Defensive Disclosure: Derivative Variations of US Patent 8271315

This document outlines various derivative concepts and implementations building upon the core principles of US Patent 8271315, specifically targeting Independent Claims 1 (Personal Information Utilization System) and 2 (Non-Transitory Computer-Readable Medium with Program). The objective is to proactively disclose variations that render future incremental improvements or alternative embodiments in this technological space obvious or non-novel, thereby strengthening the public domain as prior art.

Derivative Variations

1. Material & Component Substitution: Distributed Ledger Technology for Storage and Communication

Enabling Description:
This derivative implements the personal information storage means (12) and communication means (8) of US8271315 using a distributed ledger technology (DLT), such as a permissioned blockchain network. Each "personal information disclosing person" maintains a self-sovereign identity (SSI) on the DLT, where their personal information (ID number, name, contact, etc.) is stored as verifiable credentials (VCs) issued by trusted authorities. The "commodity provision information, etc." (CPI) is recorded as immutable transactions on the ledger, linking an anonymized or pseudonymized identifier of the disclosing person to the attribute information of the commodity/service and its provider. Smart contracts govern the receipt, checking, identification, and transmission steps. The "information search side terminal" (second commodity provider) initiates a query via a smart contract, presenting "second commodity provision information" as a parameter. The smart contract, upon execution and verification of appropriate consent tokens (managed by the disclosing person's SSI wallet), checks this against the on-chain "first commodity provision information." Identification occurs by resolving on-chain identifiers linked to matching CPI, and the transmission of (optionally revealed) personal information is facilitated by encrypted data exchange or selective disclosure proofs validated by the smart contract. Data is stored across a network of decentralized nodes, replacing a single hard disk (12) and router (8) with a distributed, redundant, and auditable infrastructure.

flowchart TD
    subgraph Information Disclosing Person (IDP) Network
        IDP1(IDP Wallet/SSI) -- Grants Consent --> SC(Smart Contract)
        IDP2(IDP Wallet/SSI) -- Grants Consent --> SC
    end

    subgraph DLT Network
        SC -- Query First CPI --> DLT_Storage(Distributed Ledger Storage: Personal Info VCs + CPI Transactions)
        DLT_Storage -- Immutable Record --> NodeA
        DLT_Storage -- Immutable Record --> NodeB
        DLT_Storage -- Immutable Record --> NodeC
    end

    subgraph Information Search Side Terminal (ISST) Network
        ISST(Second Commodity Provider) -- Sends Second CPI --> SC
    end

    SC -- Checks Second CPI vs First CPI --> DLT_Storage
    SC -- Identifies Matching IDPs --> DLT_Storage
    SC -- Requests Personal Info (VCs) --> DLT_Storage
    SC -- Transmits Personal Info (Encrypted/SD) --> ISST

    style IDP1 fill:#f9f,stroke:#333,stroke-width:2px
    style IDP2 fill:#f9f,stroke:#333,stroke-width:2px
    style SC fill:#ccf,stroke:#333,stroke-width:2px
    style DLT_Storage fill:#cfc,stroke:#333,stroke-width:2px
    style NodeA fill:#eee,stroke:#333,stroke-width:1px
    style NodeB fill:#eee,stroke:#333,stroke-width:1px
    style NodeC fill:#eee,stroke:#333,stroke-width:1px
    style ISST fill:#f9f,stroke:#333,stroke-width:2px

2. Material & Component Substitution: Quantum-Resistant Cryptography and Homomorphic Encryption for Privacy

Enabling Description:
This derivative enhances the privacy aspects of US8271315 by employing post-quantum cryptographic algorithms and homomorphic encryption (HE). The personal information storage means (12) encrypts all "personal information" and "commodity provision information, etc." using a hybrid encryption scheme where the symmetric keys are themselves encrypted with quantum-resistant public-key cryptography (e.g., Lattice-based cryptography such as CRYSTALS-Kyber for key encapsulation and CRYSTALS-Dilithium for digital signatures). Furthermore, critical attributes of "personal information" (e.g., age ranges, location hashes) and "commodity provision information" (e.g., commodity types, provider categories) are homomorphically encrypted. The processing means (14) is augmented with a specialized homomorphic computation unit, allowing it to perform the "checking" and "identifying" steps directly on the encrypted data without decryption. This means the server never sees the plaintext personal information or search queries. The "read out personal information" transmitted to the "information search side terminal" is still in an encrypted state, with decryption keys only released upon verified consent from the "information disclosing person" via an out-of-band secure channel (e.g., multi-party computation protocol or secure enclave interaction).

sequenceDiagram
    participant IDP as Information Disclosing Person
    participant ISST as Information Search Side Terminal
    participant PP as Processing Means (Server)
    participant PS as Personal Information Storage Means

    IDP->>PS: Stores HE-encrypted Personal Info (PI_HE) + CPI_HE
    ISST->>PP: Sends HE-encrypted Search Query (SQ_HE)
    PP->>PS: Requests CPI_HE relevant to SQ_HE
    PS-->>PP: Returns CPI_HE
    PP->>PP: Performs Homomorphic Checking (SQ_HE vs CPI_HE)
    PP->>PP: Identifies IDP_HE
    PP->>PS: Requests PI_HE for IDP_HE
    PS-->>PP: Returns PI_HE
    PP->>ISST: Transmits PI_HE (Encrypted, awaiting IDP consent)
    IDP->>ISST: Grants Decryption Consent (Out-of-band, secure channel)
    ISST->>ISST: Decrypts PI_HE to PI (plaintext)

3. Operational Parameter Expansion: Hyper-Scale Industrial Data Lake for B2B Information Exchange

Enabling Description:
This derivative scales the system of US8271315 to operate on a hyper-scale industrial data lake infrastructure, specifically designed for business-to-business (B2B) information exchange across a global consortium of enterprises. The personal information storage means (12) is realized as a distributed object storage layer (e.g., Apache Hadoop HDFS or AWS S3), capable of storing petabytes of structured and unstructured "personal information" and "commodity provision information, etc." from millions of "information disclosing persons" and thousands of "first commodity providers." The processing means (14) leverages a big data processing framework (e.g., Apache Spark on Kubernetes) for highly parallelized ingestion, indexing, and querying. The "checking" and "identifying" operations are performed using advanced data warehousing techniques and federated query engines, optimized for high-throughput batch analysis (e.g., daily customer segment identification) and low-latency ad-hoc searches (e.g., real-time lead generation). Communication means (8) are industrial-grade secure APIs and data pipelines, ensuring reliable and high-volume data transfer between the central system and geographically dispersed "information search side terminals." Data governance and anonymization pipelines (e.g., differential privacy techniques) are integrated to manage privacy at scale.

graph TD
    subgraph Data Ingestion Layer
        FP1(First Provider 1) --> DLI
        FP2(First Provider 2) --> DLI
        FPn(First Provider n) --> DLI
        DLI[Data Lake Ingest Service] --> DataLake(Hyper-Scale Data Lake: Raw PI + CPI)
    end

    subgraph Data Processing & Analytics Layer
        DataLake -- ETL/Processing --> DWS(Data Warehouse & Indexing Service)
        DWS -- Batch Processing --> BPE(Big Data Processing Engine - Spark/Kubernetes)
        DWS -- Real-time Query --> FQE(Federated Query Engine)
    end

    subgraph API & Communication Layer
        ISST(Information Search Side Terminal) -- API Request (Second CPI) --> API(Secure API Gateway)
        API --> FQE
        FQE --> BPE -- Results --> API
        API -- Transmits PI --> ISST
    end

    style FP1 fill:#f9f,stroke:#333,stroke-width:2px
    style FP2 fill:#f9f,stroke:#333,stroke-width:2px
    style FPn fill:#f9f,stroke:#333,stroke-width:2px
    style ISST fill:#f9f,stroke:#333,stroke-width:2px
    style DataLake fill:#cfc,stroke:#333,stroke-width:2px
    style BPE fill:#ccf,stroke:#333,stroke-width:2px
    style FQE fill:#ccf,stroke:#333,stroke-width:2px

4. Operational Parameter Expansion: Edge Computing for Localized Micro-Commodity Matching

Enabling Description:
This derivative deploys the system of US8271315 onto localized edge computing devices for highly granular, real-time matching within a constrained geographical or personal environment. Each "information disclosing person" possesses a personal edge device (e.g., a smart home hub, a local server, or a powerful wearable). This edge device acts as the personal information storage means (12) for that individual, holding their "personal information" and "first commodity provision information" locally. The processing means (14) is implemented as a lightweight, optimized microservice running on the edge device itself. "Second commodity provision information" (e.g., availability of a local delivery service, a nearby event, or a personalized offer from a specific local vendor) is received from "information search side terminals" (local businesses, service providers) via short-range wireless communication means (e.g., Bluetooth Low Energy, local Wi-Fi, NFC, or secure mesh networks). The checking, identification, and reading out of (optionally anonymized) personal information occur directly on the edge device, minimizing latency and enhancing privacy by reducing cloud data transfers. Only highly aggregated or strictly consented-to information, or a direct notification to the user, is transmitted back to the external "information search side terminal."

graph TD
    subgraph Edge Device (Information Disclosing Person)
        Local_Storage(Local PI & CPI Storage)
        Local_Processor(Local Matching Engine)
        Local_Comm(Local Wireless Comm. Module)
        Local_Storage -- Stores/Retrieves --> Local_Processor
        Local_Comm -- Receives Second CPI --> Local_Processor
        Local_Processor -- Identifies/Reads --> Local_Comm
    end

    subgraph Local Environment (Information Search Side Terminal)
        LBP(Local Business Provider) -- Sends Second CPI --> Local_Comm
        LSS(Local Service Supplier) -- Sends Second CPI --> Local_Comm
        Local_Comm -- Transmits Matched Info/Notification --> LBP
        Local_Comm -- Transmits Matched Info/Notification --> LSS
    end

    style Local_Storage fill:#cfc,stroke:#333,stroke-width:2px
    style Local_Processor fill:#ccf,stroke:#333,stroke-width:2px
    style Local_Comm fill:#f9f,stroke:#333,stroke-width:2px
    style LBP fill:#f9f,stroke:#333,stroke-width:2px
    style LSS fill:#f9f,stroke:#333,stroke-width:2px

5. Cross-Domain Application: Precision Agriculture (AgTech)

Enabling Description:
In a precision agriculture context, the system is used to optimize resource allocation and procurement for farms. The "personal information storage means" (12) stores farm profiles ("personal information" of the "information disclosing person" - the farm owner/manager), including attributes like soil composition, crop types, historical yields, and environmental conditions. The "first commodity provision information" stored pertains to past purchases of seeds, fertilizers, pesticides, machinery, or received agronomical services from "first commodity providers" (e.g., seed distributors, chemical companies, equipment dealers). An "information search side terminal" (e.g., a new fertilizer supplier, a farm machinery leasing company - the "second commodity provider") submits "second commodity provision information" (e.g., a query for farms that grow corn and used a specific type of nitrogen fertilizer last season). The processing means (14) checks this query against the stored CPI and identifies farms (disclosing persons) that match these criteria. The system then reads out and transmits relevant farm profile information (e.g., contact details, farm size, location, current crop cycle) to the new supplier, enabling targeted offers for complementary products or services.

graph LR
    subgraph Farm Data Hub (Server Device 1)
        FarmDB(Farm Profiles & Historical CPI Storage)
        AgronomicProcessor(Matching & Retrieval Processor)
    end

    subgraph Farm Owner / Manager Network (Information Disclosing Persons)
        FO1(Farm Owner 1) -- Registers Farm Profile & CPI --> FarmDB
        FO2(Farm Owner 2) -- Registers Farm Profile & CPI --> FarmDB
    end

    subgraph AgTech Vendor Network (Information Search Side Terminals)
        FSup(Fertilizer Supplier - 2nd Provider) -- Queries (2nd CPI) --> AgronomicProcessor
        MfrLease(Machinery Lessor - 2nd Provider) -- Queries (2nd CPI) --> AgronomicProcessor
    end

    AgronomicProcessor -- Checks CPI --> FarmDB
    AgronomicProcessor -- Identifies Matching Farms --> FarmDB
    AgronomicProcessor -- Transmits Farm Profiles (PI) --> FSup
    AgronomicProcessor -- Transmits Farm Profiles (PI) --> MfrLease

    style FarmDB fill:#cfc,stroke:#333,stroke-width:2px
    style AgronomicProcessor fill:#ccf,stroke:#333,stroke-width:2px
    style FSup fill:#f9f,stroke:#333,stroke-width:2px
    style MfrLease fill:#f9f,stroke:#333,stroke-width:2px

6. Cross-Domain Application: Disaster Response & Humanitarian Aid

Enabling Description:
In the context of disaster relief, this system facilitates efficient allocation of aid. The "personal information storage means" (12) stores profiles of affected individuals and families ("information disclosing persons"), including essential personal details, medical needs, family composition, and shelter/resource preferences. "First commodity provision information" records aid received from "first commodity providers" (e.g., food rations from NGO A, medical supplies from Agency B, temporary shelter from Government C). An "information search side terminal" (e.g., a new medical team arriving in the region, a water purification unit, a clothing distributor - the "second commodity provider") transmits "second commodity provision information" (e.g., "available medical capacity for trauma patients," "purified water for 500 people," "winter clothing for children"). The processing means (14) matches this against the stored CPI to identify individuals (disclosing persons) who have unmet needs corresponding to the available aid. The system then reads out and transmits relevant anonymized or aggregated personal information (or direct encrypted contact details with explicit prior consent) to the "second commodity provider," ensuring aid reaches those most in need without duplication of efforts.

flowchart TD
    subgraph Humanitarian Data Exchange (Server Device 1)
        AIDB(Aid Recipient Profiles & Aid Provision History Storage)
        ReliefCoordinator(Matching & Prioritization Processor)
    end

    subgraph Affected Persons Network (Information Disclosing Persons)
        AP1(Affected Person 1) -- Registers Needs & Received Aid --> AIDB
        AP2(Affected Person 2) -- Registers Needs & Received Aid --> AIDB
    end

    subgraph Aid Provider Network (Information Search Side Terminals)
        MedTeam(Medical Team - 2nd Provider) -- Queries (2nd CPI - e.g., Trauma Capacity) --> ReliefCoordinator
        WaterPurif(Water Purification - 2nd Provider) -- Queries (2nd CPI - e.g., Water for X ppl) --> ReliefCoordinator
    end

    ReliefCoordinator -- Checks CPI --> AIDB
    ReliefCoordinator -- Identifies Unmet Needs --> AIDB
    ReliefCoordinator -- Transmits Anonymized PI/Direct Contact --> MedTeam
    ReliefCoordinator -- Transmits Anonymized PI/Direct Contact --> WaterPurif

    style AIDB fill:#cfc,stroke:#333,stroke-width:2px
    style ReliefCoordinator fill:#ccf,stroke:#333,stroke-width:2px
    style MedTeam fill:#f9f,stroke:#333,stroke-width:2px
    style WaterPurif fill:#f9f,stroke:#333,stroke-width:2px

7. Cross-Domain Application: Scientific Research Collaboration

Enabling Description:
This derivative supports inter-institutional scientific research by facilitating the identification of collaborators. The "personal information storage means" (12) stores researcher profiles ("personal information" of "information disclosing persons"), including their areas of expertise, publications, current projects, and institutional affiliations. "First commodity provision information" records usage of specialized equipment, access to unique datasets, or participation in specific research grants ("commodities") provided by "first commodity providers" (e.g., core lab facilities, data repositories, funding agencies). An "information search side terminal" (e.g., a principal investigator seeking collaborators for a new grant, a research team with a novel assay technique - the "second commodity provider") transmits "second commodity provision information" (e.g., "seeking researchers who used Cryo-EM within the last year," "looking for labs with access to genomic data from specific patient cohorts"). The processing means (14) checks this query against stored CPI and identifies researchers (disclosing persons) with the relevant equipment usage or data access history. The system then reads out and transmits pertinent anonymized researcher profile information (e.g., institutional contact, high-level expertise) to the "second commodity provider," fostering new collaborative opportunities.

graph TD
    subgraph Research Data Exchange (Server Device 1)
        ResearcherDB(Researcher Profiles & Resource Usage History Storage)
        CollaborationMatcher(Matching & Discovery Processor)
    end

    subgraph Research Community (Information Disclosing Persons)
        R1(Researcher A) -- Registers Profile & Resource Usage --> ResearcherDB
        R2(Researcher B) -- Registers Profile & Resource Usage --> ResearcherDB
    end

    subgraph Collaborative Seeker Network (Information Search Side Terminals)
        PI_Seeking(PI seeking collaborators - 2nd Provider) -- Queries (2nd CPI - e.g., Cryo-EM users) --> CollaborationMatcher
        Team_Offering(Team with new assay - 2nd Provider) -- Queries (2nd CPI - e.g., Genomic data access) --> CollaborationMatcher
    end

    CollaborationMatcher -- Checks CPI --> ResearcherDB
    CollaborationMatcher -- Identifies Relevant Researchers --> ResearcherDB
    CollaborationMatcher -- Transmits Anonymized PI --> PI_Seeking
    CollaborationMatcher -- Transmits Anonymized PI --> Team_Offering

    style ResearcherDB fill:#cfc,stroke:#333,stroke-width:2px
    style CollaborationMatcher fill:#ccf,stroke:#333,stroke-width:2px
    style PI_Seeking fill:#f9f,stroke:#333,stroke-width:2px
    style Team_Offering fill:#f9f,stroke:#333,stroke-width:2px

8. Integration with Emerging Tech: AI-Driven Predictive Recommendation Engine

Enabling Description:
This derivative augments the processing means (14) of US8271315 with an advanced AI-driven predictive recommendation engine. Beyond simple matching, the engine analyzes "personal information" and historical "first commodity provision information" using machine learning models (e.g., collaborative filtering, deep learning-based recommender systems) to predict future needs or preferences of "information disclosing persons." When "second commodity provision information" is received, the AI engine not only performs the direct matching but also identifies information disclosing persons who are likely to be interested in the "second commodity" even if they haven't directly received a perfectly matching "first commodity." This involves:

  1. Feature Engineering: Extracting latent features from personal and commodity data.
  2. Model Training: Training a predictive model on historical data of successful cross-selling and service adoption.
  3. Scoring & Ranking: The AI scores potential matches and ranks them by predicted relevance.
    The "read out personal information" transmitted to the "information search side terminal" includes not just the identified personal details but also a "propensity score" or predicted interest level generated by the AI, enabling the second commodity provider to prioritize their outreach.
graph TD
    subgraph Server Device 1 (Processing Means 14)
        PI_Storage(Personal Info & CPI Storage)
        DataIngest(Data Ingestion & Feature Eng.)
        AI_Model_Train(AI Model Training - Offline)
        AI_Rec_Engine(AI Recommendation Engine - Online)
    end

    subgraph External Inputs
        IDP_Input(IDP Personal Info & First CPI) --> DataIngest
        ISST_Query(Second CPI from Search Terminal) --> AI_Rec_Engine
    end

    DataIngest -- Feeds --> PI_Storage
    PI_Storage -- Training Data --> AI_Model_Train
    AI_Model_Train -- Deploys --> AI_Rec_Engine
    AI_Rec_Engine -- Matches & Predicts --> PI_Storage
    AI_Rec_Engine -- Transmits PI + Propensity Score --> ISST_Output(Information Search Side Terminal)

    style PI_Storage fill:#cfc,stroke:#333,stroke-width:2px
    style DataIngest fill:#eee,stroke:#333,stroke-width:1px
    style AI_Model_Train fill:#add8e6,stroke:#333,stroke-width:2px
    style AI_Rec_Engine fill:#add8e6,stroke:#333,stroke-width:2px
    style IDP_Input fill:#f9f,stroke:#333,stroke-width:2px
    style ISST_Query fill:#f9f,stroke:#333,stroke-width:2px
    style ISST_Output fill:#f9f,stroke:#333,stroke-width:2px

9. Integration with Emerging Tech: IoT-Enabled Real-time Contextual Matching

Enabling Description:
This derivative integrates real-time data streams from Internet of Things (IoT) sensors as a dynamic source of "commodity provision information, etc." The personal information storage means (12) stores static personal profiles and historical CPI. However, the processing means (14) continuously ingests and processes live sensor data (e.g., smart home appliance usage patterns, environmental conditions, vehicle diagnostics, biometric data from wearables – representing very granular "first commodity provision information" or implicit needs derived therefrom) via the communication means (8). An "information search side terminal" (e.g., a utility company, an appliance repair service, a health & wellness provider - "second commodity providers") submits "second commodity provision information" (e.g., "identified abnormal energy consumption," "predictive maintenance alert for specific appliance model," "wearable detected elevated stress levels"). The processing means (14) then performs real-time checking against the current and historical IoT-derived CPI. It identifies "information disclosing persons" whose real-time context matches the "second commodity provision information" and transmits a portion of their (consented) personal information or a direct contextual alert to the "information search side terminal." This enables highly timely and context-aware service provision.

graph TD
    subgraph Server Device 1 (Processing Means 14)
        PI_Storage(Personal Info & Historical CPI)
        IoT_DataStream(IoT Data Stream Ingest)
        RealtimeProcessor(Real-time Matching & Alerting Engine)
    end

    subgraph IoT Devices & Networks
        IoT_Sensor1(Smart Appliance 1) --> IoT_DataStream
        IoT_Sensor2(Wearable Device) --> IoT_DataStream
        IoT_Sensorn(Vehicle Diagnostic) --> IoT_DataStream
    end

    subgraph Information Search Side Terminal Network
        UtilityCo(Utility Company - 2nd Provider) -- Queries (2nd CPI - e.g., High Energy Usage) --> RealtimeProcessor
        RepairSvc(Repair Service - 2nd Provider) -- Queries (2nd CPI - e.g., Appliance Fault) --> RealtimeProcessor
    end

    IoT_DataStream -- Feeds Real-time CPI --> RealtimeProcessor
    RealtimeProcessor -- Checks Real-time + Historical CPI --> PI_Storage
    RealtimeProcessor -- Identifies IDP in Context --> PI_Storage
    RealtimeProcessor -- Transmits PI/Contextual Alert --> UtilityCo
    RealtimeProcessor -- Transmits PI/Contextual Alert --> RepairSvc

    style PI_Storage fill:#cfc,stroke:#333,stroke-width:2px
    style IoT_DataStream fill:#eee,stroke:#333,stroke-width:1px
    style RealtimeProcessor fill:#ccf,stroke:#333,stroke-width:2px
    style UtilityCo fill:#f9f,stroke:#333,stroke-width:2px
    style RepairSvc fill:#f9f,stroke:#333,stroke-width:2px

10. The "Inverse" or Failure Mode: Privacy-Preserving Anonymized Aggregation Mode

Enabling Description:
This derivative describes an operational mode where the system prioritizes privacy by default, functioning primarily as an anonymized aggregation and trend analysis platform, and only escalating to individual personal information disclosure under stringent, explicit, and audited consent. In this "Privacy-Preserving Anonymized Aggregation Mode," the personal information storage means (12) and processing means (14) are configured to:

  1. Anonymize & Aggregate: When "first commodity provision information" is stored, it is immediately anonymized and aggregated with other similar data points. "Personal information" is stored in a segregated, highly secured, and encrypted vault, accessed only under exceptional circumstances.
  2. Trend Analysis: The "information search side terminal" receives "second commodity provision information" as a query, but the processing means (14) defaults to returning only anonymized, aggregated demographic trends or commodity uptake statistics that coincide with the query (e.g., "50% of households in zip code XYZ that bought a baby blanket also bought furniture within 6 months"). No individual "personal information" is read out or transmitted.
  3. Granular Consent for Individual Disclosure: If an "information search side terminal" explicitly requests individual "personal information," a multi-stage, auditable consent process is initiated directly with the "information disclosing person" via their "information disclosure side terminal." Only upon receiving explicit, time-limited, and purpose-bound consent for specific data fields, is that portion of the encrypted personal information read out from the vault and transmitted. This mode also acts as a safe-failure mechanism, preventing accidental or unauthorized individual data disclosure by default.
graph TD
    subgraph Server Device 1 (Privacy-Preserving Mode)
        Vault_PI(Encrypted Personal Info Vault)
        CPI_Aggregator(Anonymized CPI Aggregation & Trend Storage)
        PrivacyProcessor(Processing Means - Default Aggregation)
    end

    subgraph User Interaction
        IDP(Information Disclosing Person) -- Granular Consent Request --> PrivacyProcessor
        IDP -- Stores PI (Encrypted) & CPI --> Vault_PI
        IDP -- Consent Confirmation --> ISST_Receiver(Information Search Side Terminal)
    end

    subgraph Information Search Side Terminal
        ISST_Sender(Search Terminal - Sends Query for 2nd CPI) --> PrivacyProcessor
    end

    PrivacyProcessor -- Default: Returns Aggregated Trends --> ISST_Receiver
    PrivacyProcessor -- Explicit Request for PI --> Vault_PI
    Vault_PI -- Triggers Consent Request --> IDP
    PrivacyProcessor -- IF Consent GRANTED: Reads Specific PI --> Vault_PI
    PrivacyProcessor -- Transmits Specific PI (Encrypted) --> ISST_Receiver

    style Vault_PI fill:#cfc,stroke:#333,stroke-width:2px
    style CPI_Aggregator fill:#cfc,stroke:#333,stroke-width:2px
    style PrivacyProcessor fill:#ccf,stroke:#333,stroke-width:2px
    style IDP fill:#f9f,stroke:#333,stroke-width:2px
    style ISST_Sender fill:#f9f,stroke:#333,stroke-width:2px
    style ISST_Receiver fill:#f9f,stroke:#333,stroke-width:2px

Combination Prior Art Scenarios with Open-Source Standards

These scenarios illustrate how the system and method of US8271315, and its derivatives, can be combined with existing open-source standards, thereby contributing to the obviousness of further developments.

  1. US8271315 System + OAuth 2.0 / OpenID Connect for Consent Management:

    • Description: The "information on disclosure limits" functionality described in dependent claims 5 and 8 of US8271315, which allows "information disclosing persons" to manage their privacy, can be readily implemented using the OAuth 2.0 authorization framework and OpenID Connect (OIDC) authentication layer. An "information disclosing person" would grant specific scopes (e.g., "read personal contact," "read commodity type") to a "second commodity provider" (acting as a client application) through the system (acting as an authorization server). OIDC would handle user authentication and identity verification. This standard integration makes the user consent mechanism robust, interoperable, and well-understood by a person having ordinary skill in the art.
    • Open-Source Standard: OAuth 2.0 (RFC 6749) and OpenID Connect. Many open-source implementations exist (e.g., Keycloak, Auth0, Dex).

  2. US8271315 System + Apache Kafka for Real-time CPI Ingestion:

    • Description: For large-scale and real-time environments, the process of receiving "commodity provision information, etc." from "information register side terminals" (as per dependent claims 3 and 6) and from "first commodity providers" (implicitly in claims 1 and 2) can be implemented using Apache Kafka. Kafka, a distributed streaming platform, would act as the communication means (8) for high-throughput, fault-tolerant ingestion of CPI into the "personal information storage means" (12). "First commodity providers" would publish CPI events to Kafka topics, which the processing means (14) would then consume for real-time updating of personal information records and immediate availability for "information search side terminal" queries. This addresses the need for timely data updates mentioned in the patent's objectives (e.g., "in good timing").
    • Open-Source Standard: Apache Kafka.

  3. US8271315 DLT Derivative (as described in Derivative 1) + Hyperledger Fabric for Permissioned Blockchain Implementation:

    • Description: The derivative concept of using distributed ledger technology for storage and communication (Derivative 1) can be concretely realized using Hyperledger Fabric. Fabric provides a permissioned blockchain framework where "first commodity providers" and the central system operator (Keysoft Inc. in the original patent) can operate as authenticated peers. Smart contracts (chaincode in Fabric) would implement the logic for receiving "second commodity provision information," checking it against on-chain "first commodity provision information," identifying "information disclosing persons," and conditionally releasing encrypted or selectively disclosed personal information. User identities and consent tokens could be managed via Fabric's identity services and private data collections, providing a robust, auditable, and secure environment for cross-industry data sharing.
    • Open-Source Standard: Hyperledger Fabric.

Generated 5/19/2026, 12:04:09 PM