Derivative works

Defensive Disclosure: Derivative Works and Obvious Implementations of Distributed Content Identification Systems

Publication Date: April 26, 2026
Subject: This document discloses foreseeable and obvious implementations, extensions, and applications of the core methods described in U.S. Patent 6460050, "Distributed content identification system." The purpose of this disclosure is to place these derivative concepts into the public domain, thereby establishing prior art against future patent applications claiming these incremental improvements.

1. Algorithmic & Component Substitution

1.1 Perceptual Hashing for Near-Duplicate Image and Video Detection

The core patent describes using cryptographic hashes (e.g., MD5) which are sensitive to single-bit changes. This disclosure extends the concept to perceptual hashing for identifying visually similar, but not identical, multimedia content.

Enabling Description: A client agent, upon encountering an image or video file, generates a perceptual hash (pHash). For an image, this involves resizing the image to a standard small size (e.g., 32x32 pixels), converting it to grayscale, applying a Discrete Cosine Transform (DCT) to the pixel matrix, retaining only the low-frequency components (e.g., the top-left 8x8 block), calculating the median DCT value, and generating a 64-bit hash where each bit represents whether the corresponding DCT value is above or below the median. This pHash is sent to the central server. The server, instead of checking for exact ID matches, calculates the Hamming distance between the submitted pHash and the pHashes in its database. A Hamming distance below a predetermined threshold (e.g., <= 5) indicates a visually similar image, which is then flagged. The same principle applies to video by sampling keyframes.

flowchart TD
    A[Client Agent receives Image/Video] --> B{Generate Perceptual Hash};
    B --> B1[Resize to 32x32];
    B1 --> B2[Convert to Grayscale];
    B2 --> B3[Apply DCT];
    B3 --> B4[Truncate to 8x8 Low-Frequency Components];
    B4 --> B5[Compute Median DCT Value];
    B5 --> B6[Generate 64-bit pHash based on Median];
    B6 --> C[Transmit pHash to Server];
    C --> D{Server receives pHash};
    D --> E[Query Database for pHashes with Hamming Distance <= 5];
    E --> F{Match Found?};
    F -- Yes --> G[Transmit 'Near-Duplicate' Characteristic to Client];
    F -- No --> H[Store pHash and Increment Appearance Count];
    G --> I[Client processes file based on reply];

1.2 Locality-Sensitive Hashing (LSH) for Document Similarity

This variation replaces the file-level cryptographic hash with Locality-Sensitive Hashing (LSH) to identify documents with substantial content overlap, even with rephrasing or minor edits.

Enabling Description: The client agent processes a text document by first converting it into a set of shingles (overlapping k-grams, e.g., 9-grams of words). From this set, it computes a MinHash signature, which is a compact representation of the document's content. This signature, which serves as the "digital ID," is sent to the server. The server utilizes a banding technique, where the MinHash signature is partitioned into several bands. If a new signature matches an existing signature in the database on at least one full band, the two documents are considered candidate pairs for a more detailed similarity check. This allows for sub-linear time similarity searches, making it highly scalable for large text corpora.

sequenceDiagram
    participant Client
    participant Server
    Client->>Client: Process Document into k-gram Shingles
    Client->>Client: Compute MinHash Signature from Shingles
    Client->>Server: Transmit MinHash Signature (ID)
    Server->>Server: Partition Signature into Bands
    Server->>Server: Query Database for Matching Bands
    alt Candidate Match Found
        Server-->>Client: Reply: 'High-Similarity Document'
    else No Match
        Server->>Server: Index Signature Bands in Database
        Server-->>Client: Reply: 'Unique Document'
    end

2. Operational Parameter Expansion

2.1 Edge-Native Implementation for IoT Anomaly Detection

The system is adapted to operate on resource-constrained IoT devices at the network edge to identify anomalous behavior across a fleet of sensors.

Enabling Description: An IoT device (e.g., a vibration sensor on an industrial motor) locally runs a lightweight agent. The agent continuously captures time-series sensor data. It uses a dimensionality reduction algorithm, such as Symbolic Aggregate Approximation (SAX), to convert a window of sensor readings (e.g., 1024 samples) into a short alphanumeric string (e.g., abacbabc). This string is the "digital ID" representing the motor's operational state. Under normal conditions, the agent does not transmit. If the generated ID deviates from a locally stored set of "normal" IDs, it is transmitted to a gateway. The gateway aggregates these anomaly IDs from thousands of devices and forwards them to a central server, which uses the frequency of specific anomalous IDs to identify systemic fleet-wide issues (e.g., a bad batch of bearings).

graph TD
    subgraph IoT Device
        A[Sensor Capture] --> B[SAX Transformation];
        B --> C{ID matches local normal profile?};
    end
    subgraph Edge Gateway
        D[Aggregate IDs] --> E[Forward to Cloud]
    end
    subgraph Cloud Server
        F[Correlate Fleet-wide IDs] --> G[Identify Systemic Anomaly]
    end
    C -- No --> D;
    E --> F;
    G --> H[Alert Operators];

2.2 Real-Time Stream Processing for Live Media Content Identification

The system is implemented for extreme low-latency, high-throughput environments such as identifying copyrighted content within user-generated live video streams.

Enabling Description: The client agent, integrated into a streaming server or client, samples video frames at a rate of 1 frame per second. For each frame, it uses a pre-trained convolutional neural network (CNN) to generate a feature vector (embedding) of a fixed size (e.g., 512 floating-point numbers). This vector is the "digital ID." These vectors are streamed via a high-throughput message queue (e.g., Apache Kafka) to a processing cluster. The server-side system uses a specialized vector database (e.g., Milvus or FAISS) that performs Approximate Nearest Neighbor (ANN) searches. It compares the incoming stream of vectors against a database of vectors from known copyrighted works in real-time. A match is flagged when the cosine similarity between an incoming vector and a database vector exceeds a threshold (e.g., 0.95).

sequenceDiagram
    participant StreamingClient
    participant ProcessingServer
    participant VectorDB
    loop Real-time
        StreamingClient->>StreamingClient: Sample video frame
        StreamingClient->>StreamingClient: Generate CNN feature vector (ID)
        StreamingClient->>ProcessingServer: Stream vector
        ProcessingServer->>VectorDB: Perform ANN search for vector
        VectorDB-->>ProcessingServer: Return nearest neighbors and similarity scores
        alt Similarity > 0.95
            ProcessingServer-->>StreamingClient: Send 'Content Match' notification
        end
    end

3. Cross-Domain Application

3.1 Genomic Sequence Variant Tracking

The system is applied to bioinformatics to enable a global, distributed network of labs to track the emergence and spread of specific genetic variants (e.g., viral mutations or antibiotic resistance genes).

Enabling Description: A sequencing lab's client agent takes a new genomic sequence (e.g., from a SARS-CoV-2 sample). It normalizes the sequence and applies a canonical hashing algorithm, such as ntHash, to a specific gene of interest (e.g., the Spike protein gene). The resulting hash is the "digital ID." This ID, along with anonymized metadata (timestamp, geographical region), is submitted to a central epidemiological server. The server aggregates these submissions globally. A sudden increase in the frequency of a new, previously unseen hash from multiple regions indicates the rapid spread of a novel mutation, allowing for real-time public health monitoring without sharing the full, sensitive sequence data.

flowchart TD
    A[Multiple Sequencing Labs] --> B{Process Sample & Extract Gene Sequence};
    B --> C[Generate ntHash of Sequence];
    C --> D[Submit Hash + Anonymized Geo/Time Metadata];
    D --> E[Central Epidemiology Server];
    E --> F{Aggregate and Analyze Hash Frequencies};
    F --> G[Detect Emergence/Spread of New Variant Hash];
    G --> H[Publish Real-time Public Health Alerts];

3.2 Supply Chain Counterfeit Detection

The system is used in logistics to identify counterfeit products by analyzing unique physical characteristics.

Enabling Description: At a distribution center, a product is scanned using a high-resolution optical scanner that captures its unique, unclonable surface texture (e.g., the grain pattern of a paper label). A feature extraction algorithm generates a compact digital signature from this texture, creating a "digital ID" based on this physical unclonable function (PUF). This ID is sent from the client agent (at the scanner) to a central server. The first time an authentic product is scanned, its ID is registered. If the same ID is later seen at a different location or time than is logically possible according to the supply chain records, it is flagged as a potential clone or counterfeit.

stateDiagram-v2
    [*] --> Unseen
    Unseen --> Registered: First scan (product induction)
    Registered --> In_Transit: Scanned at logistics hub
    In_Transit --> Delivered: Scanned at retail
    Delivered --> Flagged_Counterfeit: Second scan of same ID at another location
    In_Transit --> Flagged_Counterfeit: Second scan of same ID at another location
    state Fork <<fork>>
    Registered --> Fork
    Fork --> Flagged_Theft: Product never arrives at next hub
    Fork --> In_Transit

4. Integration with Emerging Technologies

4.1 AI-Driven Polymorphic Threat Detection

The system is integrated with machine learning to identify not just identical content, but entire "campaigns" of similar-but-not-identical malicious content (e.g., polymorphic malware or spam).

Enabling Description: Client agents generate and submit digital IDs (hashes) as in the base patent. The server, however, does not merely count frequencies. It uses a technique like SimHash, which produces hashes where the Hamming distance is proportional to the edit distance of the source files. The server constructs a massive graph where each hash is a node. An edge is created between two nodes if their Hamming distance is small. A graph neural network (GNN) is trained on this data to identify dense clusters of nodes, which represent a polymorphic campaign. When a new hash is submitted, the system checks if it connects to a known malicious cluster, allowing it to proactively block new variants of an attack.

classDiagram
    class Server {
        +receiveHash(hash)
        +findSimilarHashes(hash)
        +updateGraph(hash, similarHashes)
        +classifyCluster(cluster)
    }
    class GraphModel {
        -GNN_Classifier
        +isMalicious(cluster)
    }
    class HashNode {
        <<Node>>
        string hashValue
        int frequency
    }
    class SimilarityEdge {
        <<Edge>>
        int hammingDistance
    }
    Server --> GraphModel : Uses
    Server "1" -- "many" HashNode : Manages
    HashNode "1" -- "0..*" SimilarityEdge : has
    HashNode "1" -- "0..*" SimilarityEdge : has

4.2 Blockchain-based Decentralized Reputation System

The central server and database are replaced with a public blockchain and a smart contract, creating a trustless and censorship-resistant content identification system.

Enabling Description: Client agents are configured as blockchain clients (e.g., Ethereum nodes). When a client wants to check or report a file, it interacts with a smart contract. To report a file, the agent computes its hash and calls a reportHash(bytes32 fileHash) function in the contract. This function logs the hash and the reporter's address. To check a file, the agent calls a getReputation(bytes32 fileHash) function, which returns the number of unique addresses that have reported that hash. A file is considered malicious if its report count exceeds a threshold. Users can stake cryptocurrency to increase the weight of their reports, creating a decentralized web-of-trust.

sequenceDiagram
    participant UserAgent
    participant SmartContract
    participant Blockchain
    UserAgent->>UserAgent: Compute Hash of File
    UserAgent->>SmartContract: call reportHash(fileHash)
    SmartContract->>Blockchain: Record Hash and UserAgent's Address
    Blockchain-->>SmartContract: Transaction Confirmed
    SmartContract-->>UserAgent: Report Successful

    UserAgent->>SmartContract: call getReputation(fileHash)
    SmartContract->>Blockchain: Read Report Count for Hash
    Blockchain-->>SmartContract: Return Count
    SmartContract-->>UserAgent: Reputation Score (Count)

5. The "Inverse" or Failure Mode

5.1 Distributed Fallback via Gossip Protocol

The system is designed to fail gracefully. If the central characteristic server becomes unreachable, the client agents dynamically switch from a client-server model to a peer-to-peer (P2P) network.

Enabling Description: A client agent periodically sends a heartbeat to the central server. If the heartbeat fails for a specified duration (e.g., 60 seconds), the agent enters "decentralized mode." In this mode, it connects to a set of pre-configured or discovered peers. It uses a gossip protocol (e.g., SWIM) to exchange information about recently seen hashes and their local frequency counts. Each agent maintains a small, local bloom filter of high-frequency hashes reported by its peers. While less accurate than the central server, this allows the system to continue providing a baseline level of protection against widespread threats during a central outage. When the server becomes available again, the agents switch back to centralized mode.

stateDiagram-v2
    [*] --> Centralized_Mode
    Centralized_Mode --> Centralized_Mode: Heartbeat OK
    Centralized_Mode --> Decentralized_Mode: Heartbeat Fail
    Decentralized_Mode --> Centralized_Mode: Heartbeat Recovered
    Decentralized_Mode --> Decentralized_Mode: Gossip with Peers

6. Combination Prior Art Scenarios

6.1 Integration with SMTP via the Milter Protocol

The distributed content identification system is implemented as a mail filter (milter) that hooks into the open SMTP protocol standard used by mail servers like Sendmail and Postfix.

Enabling Description: A milter process is written in C and linked against the libmilter library. It registers callbacks for SMTP stages, specifically xxfi_eom (end of message). When the mail server receives a full email, the xxfi_eom callback is triggered. Inside this function, the milter computes the hash(es) of the message body and subject as described in the patent. It then makes a synchronous network call to the central ID server. If the server replies with a "spam" characteristic, the milter returns SMFIS_REJECT to the mail server, causing the SMTP transaction to be rejected with a 554 5.7.1 Message content rejected error before it is ever queued for local delivery.

6.2 Integration with the ClamAV Open Source Antivirus Engine

The system's logic is integrated as a custom signature type within the open-source ClamAV antivirus engine, allowing it to leverage ClamAV's widespread deployment for data collection.

Enabling Description: A new signature type is defined in the ClamAV source code, e.g., ReputationCheck:Host:Port:Options. A custom database file (.crb - Clam Reputation Database) is created containing these directives. When the clamd scan daemon encounters a file, it checks for standard byte-based signatures. If none match, it checks for reputation directives. If one is found, a new function within the engine computes the file's MD5 hash and sends it in a UDP packet to the specified Host and Port. The function waits a short time for a reply. A reply indicating a malicious characteristic causes ClamAV to flag the file as if it had matched a traditional virus signature (e.g., Win.Trojan.Reputation-1).

6.3 Integration with Apache Kafka and KSQL for Stream Processing

The entire backend system is built on open-source distributed streaming standards, specifically using Apache Kafka as the data bus for high-volume ID ingestion and KSQL for real-time characteristic analysis.

Enabling Description: Client agents are configured as Kafka producers. They serialize the digital ID and client metadata into a JSON or Avro object and publish it to a Kafka topic named content-ids. The server-side logic is a persistent KSQL query running on a Kafka cluster. The query is defined as: CREATE TABLE spam_counts AS SELECT id, COUNT(*) as appearance_count FROM content_ids WINDOW TUMBLING (SIZE 30 MINUTE) GROUP BY id HAVING COUNT(*) > 100;. This continuously analyzes a 30-minute rolling window of IDs. Any ID seen more than 100 times is automatically published to a new spam_alerts topic. A separate microservice consumes from this topic to update the master characteristic database that client agents query.