A two-model ML platform for NFT lifecycle management. The auto-segmentation model scores each NFT using owner profile, account context, network conditions, and ownership history — routing it to distributed ledger storage or local storage based on a tier threshold. The validation model gates event processing requests by scoring their validity. Both models self-refine in iterative feedback loops. Social media signals update scores in real time.
Distributed ledger storage for NFTs — blockchain or similar infrastructure — is computationally and economically expensive. Storing every NFT on-ledger regardless of its value, liquidity, or usage frequency wastes resources on low-activity assets while potentially under-provisioning high-value ones. There's no automated mechanism to dynamically route NFTs to the appropriate storage tier based on real-time signals about their value and activity.
The platform runs two distinct machine learning models. The auto-segmentation model takes NFT information — owner profile, account data, network conditions, geo signals, ownership history — and produces a tier score. If the tier score meets or exceeds the threshold, the NFT is stored on the distributed ledger (cloud/high-value tier). If below, it's stored locally (low-activity tier).
The validation model operates on event processing requests: when a transfer, listing, or other event is requested for an NFT, the model scores the validity of that request and generates a validation rating. Requests below the validity threshold are gated — the user receives a fallback UI notification (Claim 4). Both models iterate in feedback loops: each outcome refines the next scoring cycle, improving segmentation and validation accuracy over time.
The two models solve different problems in the NFT lifecycle. Auto-segmentation is a storage optimization problem: given an NFT's current signals, where should it live? Validation is a security/quality problem: given an event request, should it be processed?
Claim 7 gives the broadest definition of what an NFT can be in this context: digital signatures, digital assets, domain names, digital artwork, account information, transaction information, or digital currency. The platform is not limited to image-based NFTs — it encompasses any digital token with ownership semantics.
The system is designed for dynamic storage migration. Claim 10 specifies the triggers: when an NFT's tier score changes due to network conditions, NFT value change, ownership transfer, or market condition changes, the system migrates the NFT between storage tiers accordingly.
Claim 8 specifies that training data for the auto-segmentation model includes multiple historical tier scores over time for the same NFT — not just a snapshot. This enables the model to learn from NFT lifecycle trajectories: tokens that start low-tier and move up, high-value tokens that become dormant and move down, and the signal patterns that precede each transition. The historical series is the feature, not just the current state.
Ledger congestion, gas costs, or network degradation can make local storage temporarily preferable even for high-value NFTs. Score adjusts, storage migrates.
Market appraisal of the NFT increases or decreases. High-value NFTs move to distributed ledger for security and liquidity. Low-value tokens drop to local storage.
NFT transfers to a new owner whose profile scores differently. New owner context (account signals, geographic data, transaction history) triggers re-evaluation.
Broader market signals — collection floor prices, trading velocity, social media activity — affect the tier score calculation. Score updates propagate to storage decisions.
Select a scenario to trace the auto-segmentation or validation pipeline from input signals through storage routing decision.
Claim 3 extends the auto-segmentation model with social media signal integration. The platform connects to social media APIs to pull real-time signals: mention velocity, sentiment, trending status, and influencer engagement for NFTs or NFT collections. These signals feed directly into the tier score calculation.
This creates a live feedback loop between social activity and storage routing. An NFT collection that goes viral — rapid mention increase, positive sentiment spike, sudden trading volume — will see tier scores rise in real time, with high-demand tokens automatically migrating from local to distributed ledger storage before the demand peak hits. The system anticipates storage needs from social signals rather than reacting after value has already shifted.
Rate of new social mentions for the NFT or its collection. Sudden acceleration is a leading indicator of demand increase — tier score responds before market price catches up.
Positive/negative/neutral classification of social mentions. Sustained positive sentiment signals long-term value appreciation; negative signals potential devaluation.
Whether the NFT or collection is trending on connected platforms. Trending status can spike tier scores dramatically — ensuring high-availability storage for surging demand.
Social signals update tier scores continuously via API polling. No batch reprocessing required — the auto-segmentation model receives a live social signal feed as an input feature.
The two-model architecture — segmentation routing + validation gating — applies to any digital asset system where cost optimization, fraud prevention, and dynamic re-tiering are simultaneous requirements.
No forward citations found as of this check. US12153794B2 was granted November 2024 and has a continuation pending (US20250028443A1, filed Oct 9, 2024). Citation data is still accumulating for this recently-issued patent.
Claim 7's definition of NFT includes: digital signatures, digital assets, domain names, digital artwork, account information, transaction information, and digital currency. This is deliberate claim drafting — the patent's scope is not limited to the image-collection NFTs that dominated popular discourse in 2022. It covers any digitally unique, ownership-tracked token that the ML platform can score and route.
A continuation application (US20250028443A1, filed Oct 2024) extends the platform further — building on the foundation of this patent with additional claims coverage. The combination of parent grant and pending continuation gives the platform architecture broad and growing protection.
The most commonly recognized NFT type. Images, video, music, generative art. Tier score driven by social signals, trading volume, collection floor price.
Web3 domain names (ENS, Handshake, etc.) with unique ownership. Tier score driven by length, keyword value, secondary market demand, trending topics.
Financial account ownership tokens and transaction records represented as NFTs. Enables the two-model platform to operate in financial infrastructure contexts.
Unique digital attestations and tokenized currency instruments. Claim 7's breadth ensures the platform architecture applies to future digital asset types not yet standardized.