US-12619981-B1 - System and method for revenue participation via smart referral and commission tracking

US12619981B1US 12619981 B1US12619981 B1US 12619981B1US-12619981-B1

Abstract

A system and method for token-based revenue participation through smart referral and commission tracking. The system implements cryptographically secure digital certificates representing contributor value in multi-party transactions across various industries. Key components include: a commission allocation engine that programmatically directs transaction revenue to a reward pool; a token acquisition module implementing multi-parametric optimization for cost-efficient token purchases; a digital certificate issuance module generating non-fungible tokens with standardized metadata for contributions; a reward distribution algorithm applying parameterized time-decay functions to certificate values; and a royalty enforcement module that monitors transfers and enforces compliance. The system reduces computational complexity from O(n 2 ) to O(log n) for multi-party attribution, implements programmable time-decay valuation, and creates cross-platform portable contribution records. The protocol-agnostic architecture supports multiple distributed ledger technologies through a unified abstraction layer, enabling consistent business logic across heterogeneous blockchain environments.

Inventors

Ari Kyle Levine
JASON LING
Jamie Tian
Joshua Spooner

Assignees

Ari Kyle Levine
JASON LING
Jamie Tian
Joshua Spooner

Dates

Publication Date: 20260505
Application Date: 20250407

Claims (18)

1 . A computerized system for cryptographically secure management of token-based reward distribution in multi-party transactions, the system comprising: a commission allocation engine implemented on one or more processors with distributed computing architecture and configured to: receive transaction data comprising at least transaction value, commission amount, and contributor identifiers, execute a parameterized allocation algorithm that computes distribution coefficients according to configurable parameters stored in a secure parameter data store, cryptographically sign allocation records using an asymmetric ECDSA key pair with 256-bit security to create tamper-evident distribution instructions, and generate an audit log of all allocation operations with sequential hash linking for tamper detection; a token acquisition module implemented on one or more processors with parallel processing capability and configured to: establish secure API connections to a plurality of digital token exchanges using TLS 1.3 protocol with mutual certificate authentication, execute a multi-parametric optimization algorithm that minimizes acquisition costs by solving the constraint equation min(Σ(P i ·V i )+Σ(G i )+Σ(S i )) where P i represents price, V i represents volume, G i represents gas cost, and S i represents slippage at exchange i, implement temporal distribution of acquisition volume according to a volume-weighted average price (VWAP) formula expressed as VWAP=Σ(P i ·V i )/Σ(V i ) with time distribution function V=(V_total·f(t))/∫f(t)dt, and store acquired digital tokens in hierarchical deterministic wallets implementing BIP-32/BIP-39/BIP-44 standards with m-of-n multi-signature security requiring at least two authorized signatories for withdrawal operations; a digital certificate issuance module implemented on one or more processors with hardware security element and configured to: programmatically generate non-fungible Digital Certificates compatible with multiple distributed ledger protocols including Hedera Token Service (HTS), Ethereum token standards (ERC-721 and ERC-1155), and other blockchain token systems upon cryptographic verification of qualifying transactions, assign generated digital certificates to cryptographic addresses associated with transaction contributors using public-key cryptography compatible with multiple cryptographic curves including secp256k1 and Ed25519, embed standardized JSON-LD metadata within each digital certificate comprising at least hierarchical contribution type classification according to a predefined taxonomy, mathematically weighted point value calculated according to contribution significance, millisecond-precision issuance timestamp encoded in ISO 8601 format, and cryptographic hash of the source transaction for verification purposes, and implement batched certificate issuance operations to optimize gas utilization on gas-metered blockchain networks; a reward distribution module implemented on one or more processors with dedicated memory cache and configured to: apply a parameterized mathematical time-decay function selected from linear decay expressed as D_lin(t)=max(0, 1−(t/T_max)), exponential decay expressed as D_exp(t)=e{circumflex over ( )}(−λt), step-function expressed as D_step(t)={1.0 if t<T 1 , C 1 if T 1 ≤t<T 2 , C 2 if T 2 ≤t<T 3 , . . . , C n if t≥T n }, and parametric decay expressed as D_param(t)=(1+(t/T_ref){circumflex over ( )}β){circumflex over ( )}(−α) to digital certificate point values, cryptographically verify compliance with royalty requirements through transaction history analysis using a binary eligibility function ε(Certificate_id)={1 if compliance_verification(Certificate_id)=TRUE, 0 otherwise}, execute a computationally optimized proportional allocation algorithm with O(n) complexity to calculate token reward distributions using the formula R_i=P·AP_i/(Σ i AP_J) where R_i is the reward amount for Certificate_i, P is the total reward pool size, AP_i is the adjusted points calculated as E(Certificate_i)·OP_i·D(t_i), and implement protocol-optimized batch transfer operations for distributing tokens to eligible digital certificate holders using merkleized proof of allocation for gas efficiency; and a hybrid data management system implemented on one or more processors with encrypted storage and configured to: store sensitive transaction data and personally identifiable information in an encrypted off-chain database with role-based access controls using AES-256 encryption, record cryptographic proofs of reward distributions as Merkle-tree hashes on a distributed ledger using sequential hash linking, and implement a two-level caching system for Digital Certificate state with block-height based invalidation strategy to minimize redundant blockchain queries.
2 . The system of claim 1 , wherein the commission allocation engine is configured to allocate a percentage of transaction revenue from real estate, automotive, insurance, or financial service transactions.
3 . The system of claim 1 , wherein the token acquisition module implements a volume-weighted average price (VWAP) acquisition strategy across multiple exchanges.
4 . The system of claim 1 , wherein the Digital Certificate issuance module is configured to mint distinct Digital Certificate types for agents, consumers, and referrers associated with each transaction.
5 . The system of claim 1 , wherein the time-based decay function comprises at least one of: a linear decay function expressed as max(0, 1−(t/T_max)), where t represents elapsed time and T_max represents maximum time period; an exponential decay function expressed as e{circumflex over ( )}(−λt), where λ represents a decay rate constant; a step function with predefined values at time thresholds; or a parametric curve expressed as (1+(t/T_ref){circumflex over ( )}β){circumflex over ( )}(−α), where T_ref represents a reference time period and α and ρ represent shape parameters.
6 . The system of claim 1 , further comprising a royalty enforcement module configured to monitor Digital Certificate transfers and update compliance status based on adherence to royalty requirements.
7 . The system of claim 6 , wherein the royalty enforcement module is configured to mark Digital Certificates as ineligible for reward distribution when transferred without required royalty payments.
8 . The system of claim 1 , wherein the data management system stores personally identifiable information (PII) and sensitive transaction details off-chain while recording cryptographic proofs of reward distribution on-chain.
9 . The system of claim 1 , further comprising a user dashboard configured to display: Digital Certificate holdings and associated metadata; historical and projected reward distributions based on Digital Certificate holdings; compliance status of owned Digital Certificates; and transfer tools with integrated royalty handling.
10 . A computer-implemented method for cryptographically secure allocation of token-based rewards to contributors in multi-party transactions, the method comprising: receiving, by one or more processors configured with transaction verification hardware, a cryptographically signed transaction record comprising transaction value, commission structure, and contributor identifiers; validating, by the one or more processors, the authenticity of the transaction record by verifying the cryptographic signature using public key cryptography and confirming the record integrity through hash verification; executing, by the one or more processors, a parameterized allocation algorithm that programmatically directs a configurable percentage of transaction revenue to a distributed ledger-controlled reward pool according to the formula A=Σ(C i ·w i )·α, where A is the allocated amount, C i is commission component i, w i is the weight coefficient for component i, and α is the allocation percentage parameter, wherein the algorithm performs the following steps: (a) extracting the individual commission components from the transaction data, (b) applying the corresponding weight coefficients to each component based on transaction type, (c) computing the weighted sum of all components, and (d) multiplying by the allocation percentage parameter to derive the final allocation amount; implementing, by the one or more processors configured with secure API communication hardware, a multi-exchange acquisition protocol that acquires digital tokens using the allocated revenue through the following technical steps: (a) establishing secure connections to multiple digital token exchanges using TLS 1.3 protocol, (b) retrieving real-time pricing and liquidity data from each exchange, (c) computing an optimal acquisition strategy using the constraint optimization algorithm min(Σ(P i ·V i )+Σ(G i )+Σ(S i )), (d) executing token acquisition operations with cryptographically signed API requests, and (e) verifying the completion of acquisition transactions through receipt verification; generating, by the one or more processors configured with specialized cryptographic processing hardware, protocol-compatible non-fungible Digital Certificates for each contributor identified in the transaction record, wherein the Digital Certificates conform to at least one distributed ledger token standard selected from Hedera Token Service (HTS) Digital Certificates, Ethereum token standards (ERC-721 and ERC-1155), Solana Program Library (SPL) tokens, and other distributed ledger token implementations, through the following technical steps: (a) selecting the appropriate token standard based on protocol configuration, (b) constructing the certificate creation transaction according to protocol-specific requirements, (c) signing the transaction using the issuing authority's private key, and (d) submitting the transaction to the corresponding distributed ledger network; embedding, by the one or more processors, standardized JSON-LD metadata within each digital certificate comprising contribution type classification according to a predefined ontology, mathematically computed point value derived from contribution parameters, cryptographically verifiable timestamp encoded as Unix epoch time with millisecond precision, and digital signature of the issuing authority to verify authenticity, wherein the embedding process involves: (a) constructing a structured JSON-LD document according to the standardized schema, (b) computing the point value using the formula P=B·M·F, where B is the base point value, M is the multiplier based on contribution type, and F is the factor based on transaction significance, (c) generating a cryptographic signature over the metadata using the EIP-712 typed data standard, and (d) storing the metadata and signature either on-chain or via content-addressed storage with on-chain reference; applying, by the one or more processors configured with mathematical computation hardware, a parameterized time-decay function to each digital certificate's point value, the function selected from: (a) linear decay expressed as D_lin(t)=max(0, 1−(t/T_max)), where t is the time elapsed since issuance and T_max is the maximum time period, (b) exponential decay expressed as D_exp(t)=e{circumflex over ( )}(−λt), where λ is the decay rate constant defined as In(2)/t_half, (c) step function expressed as D_step(t)={1.0 if t<T 1 , C 1 if T 1 ≤t<T 2 , C 2 if T 2 ≤t<T 3 , . . . , C n if t≥T n }, where T i are threshold timestamps and C i are coefficient values, or (d) parametric decay expressed as D_param(t)=(1+(t/T_ref){circumflex over ( )}β){circumflex over ( )}(−α), where T_ref is a reference time period and α and β are shape parameters; executing, by the one or more processors, a cryptographic verification protocol that confirms the digital certificate's eligibility based on transaction history analysis and royalty compliance verification executing, by the one or more processors, a cryptographic verification protocol that confirms the digital certificate's eligibility based on transaction history analysis and royalty compliance verification through the following technical steps: (a) retrieving the certificate's transfer history from the distributed ledger, (b) analyzing each transfer event for required royalty payments, (c) calculating the compliance status using the binary eligibility function ε(Certificate_id)={1 if compliance_verification(Certificate_id)=TRUE, 0 otherwise}, and (d) recording the verification result in a secure, append-only log; computing, by the one or more processors using parallel processing architecture, each eligible digital certificate's proportional share of the reward pool through the following technical steps: (a) retrieving all eligible certificates using O(log n) complexity sparse Merkle tree indexing, (b) calculating each certificate's adjusted point value by applying the time-decay function and eligibility verification, (c) computing the sum of all adjusted points in parallel using a map-reduce algorithm, (d) calculating each certificate's proportional share using the formula R_i=P·(AP_i/Σ(AP_j)), where R_i is the reward for digital certificate i, P is the total reward pool, AP_i is the adjusted points for digital certificate i, and Σ(AP_j) is the sum of all adjusted points; and implementing, by the one or more processors configured with blockchain interaction hardware, a protocol-optimized batch distribution operation that transfers tokens to cryptographic wallet addresses of eligible contributors through the following technical steps: (a) constructing a merkleized proof of allocation for efficient verification, (b) organizing transfers to minimize gas consumption through optimized transaction ordering, (c) executing batch transfers using protocol-appropriate methods such as ERC-20 transferFrom( ) or EIP-1155 batch transfer, and (d) verifying the completion of all transfers through transaction receipt validation.
11 . The method of claim 10 , wherein the contributor types include at least agent, consumer, and referrer roles with distinct contribution classifications.
12 . The method of claim 10 , wherein the decay function reduces the Digital Certificate's point value according to a mathematical formula based on time elapsed since issuance, with configurable parameters adjustable per vertical implementation.
13 . The method of claim 10 , further comprising recording reward distribution metadata on a distributed ledger with tamper-evident properties.
14 . The method of claim 10 , wherein Digital Certificates transferred without meeting required royalty payments are disqualified from receiving rewards through an automated compliance verification process.
15 . The method of claim 10 , further comprising exposing an application programming interface (API) that enables integration with external transaction management systems through standardized data exchange protocols.
16 . The method of claim 10 , further comprising adapting the metadata structure and point value calculation based on industry-specific transaction characteristics through configurable attribute mappings.
17 . The method of claim 10 , wherein allocating the portion of transaction revenue comprises one of: a fixed percentage allocation, a tiered allocation based on transaction value, or a dynamic allocation based on transaction characteristics.
18 . A non-transitory computer-readable medium storing instructions that, when executed by a processor with specialized cryptographic acceleration hardware, cause the processor to perform operations for managing a protocol-agnostic digital certificate and reward system, the operations comprising: receiving transaction data including transaction value, commission amount, commission structure, and contributor identifiers through a secure API gateway implementing TLS 1.3 encryption; validating transaction authenticity by verifying cryptographic signatures using public key infrastructure (PKI) with support for multiple signature schemes including ECDSA and EdDSA; calculating a reward pool allocation based on predefined allocation rules using the formula A=Σ(C i ·w i )·α, where A is the allocated amount, C i is commission component i, w i is the weight coefficient for component i, and α is the allocation percentage parameter; generating distributed ledger-compatible Digital Certificates conforming to at least one token standard selected from Hedera Token Service (HTS) specifications, Ethereum token standards (ERC-721 and ERC-1155), Solana Program Library (SPL) token specifications, and other distributed ledger non-fungible token implementations, with protocol-specific optimizations for gas efficiency and throughput; embedding standardized metadata within the digital certificates using JSON-LD schema definitions that include: (a) @context field pointing to a standardized schema definition, (b) @type field identifying the certificate type, (c) hierarchical contribution classification according to predefined taxonomy, (d) mathematically computed point value derived from transaction parameters, (e) ISO 8601 formatted timestamp with millisecond precision, and (f) cryptographic signature for data authenticity verification; assigning the digital certificates to cryptographic addresses associated with transaction contributors using public-key based addressing compatible with multiple cryptographic curves including secp256k1 and Ed25519; executing a cryptographically secured token acquisition process across multiple liquidity sources by: (a) establishing secure connections to multiple token exchanges, (b) implementing a volume-weighted average price (VWAP) acquisition strategy, (c) optimizing acquisition cost using the constraint equation min(Σ(P i ·V i )+Σ(G i )+Σ(S i )), (d) fragmenting large orders to minimize market impact, and (e) verifying transaction execution through cryptographic receipt validation; applying a configurable mathematical time-decay function to certificate point values according to elapsed time since issuance, selected from: (a) linear decay expressed as D_lin(t)=max(0, 1−(t/T_max)), (b) exponential decay expressed as D_exp(t)=e{circumflex over ( )}(−λt), (c) step function with predefined thresholds and coefficients, or (d) parametric decay expressed as D_param(t)=(1+(t/T_ref){circumflex over ( )}β){circumflex over ( )}(−α), wherein each function's parameters are configurable per implementation vertical; verifying certificate compliance status through cryptographic proofs by: (a) analyzing transfer history for required royalty payments, (b) validating payment transactions through cryptographic receipt verification, (c) computing compliance status using a binary eligibility function, and (d) storing verification results in a tamper-evident data structure; calculating proportional reward allocations using an optimized distribution algorithm with O(n) complexity implementing the formula R_i=P·(AP_i/Σ(AP_j)), where R_i is the reward for digital certificate i, P is the total reward pool, AP_i is the adjusted points calculated as ε(Certificate_i)·OP_i·D(t_i), and Σ(AP_j) is the sum of all adjusted points; and executing protocol-appropriate token distribution operations to eligible certificate holders through the corresponding distributed ledger networks, with protocol-specific optimizations including: (a) batched transfers for gas efficiency, (b) merkleized proofs for efficient verification, (c) optimal transaction ordering to minimize state changes, and (d) receipt verification for transaction finality confirmation.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of U.S. Provisional Patent Application No. 63/637,059, filed Apr. 22, 2024, entitled “Decentralized Revenue System and Method,” the contents of which are incorporated herein by reference. This non-provisional application does not rely on any DAO governance or decentralized autonomous organization infrastructure. FIELD OF THE INVENTION The present invention relates to systems and methods for enabling token-based revenue participation through cryptographically secure referral attribution, commission allocation, Digital Certificate-linked contribution tracking, and hybrid on-chain/off-chain reward disbursement. PRIOR ART Several existing systems and technologies have attempted to address aspects of referral tracking and commission allocation using both centralized and decentralized approaches: Traditional Centralized Referral Systems: As documented in US Patent Application Publication No. 20190318433 (McGee et al.), these systems utilize centralized relational databases with HTTP cookie tracking and UTM parameters for attribution. While functional, they lack cryptographic verification capabilities, rely on mutable database entries, and cannot provide independent verification through consensus mechanisms or cryptographic proofs. Blockchain-Based Token Rewards: Prior implementations such as US Patent Application Publication No. 20180322597 (Sher) explore using blockchain for real estate transactions but focus primarily on disintermediating agents rather than enhancing attribution capabilities through cryptographically secure commission tracking. These systems implement basic commission distribution on single blockchains and explore using fungible tokens for rewards, but fail to address cross-chain operability. Time-Decay Value Systems: Alternative currency designs and community contribution scoring systems, as seen in US Patent Application Publication No. 20120221390 (Codey), have implemented basic time-based value decay in limited contexts. However, these approaches lack the computational efficiency and temporal value adjustment capabilities necessary for accurate multi-party contribution valuation with mathematical rigor. Certificate-Based Authentication: Non-fungible tokens have been used as certificates of ownership or authentication in various contexts, such as in US Patent Application Publication No. 20200410590 (Regmi et al.), which describes blockchain-based systems for environmental transactions. However, these implementations do not address the specific computational challenges of multi-party commission attribution with temporal value decay. Manual Reconciliation Methods: Prior implementation attempts have included manual reconciliation through spreadsheet calculations, which exhibit O(n2) computational complexity when calculating attributions across complex hierarchies, creating significant performance bottlenecks at scale. Data Structure Incompatibility: Existing solutions maintain contribution data in proprietary, non-interoperable data structures that prevent cross-platform persistence of contribution metrics, lacking standardized metadata schemas necessary for programmatic interpretation across systems. These disparate approaches have failed to address the fundamental technical challenges of cross-chain operability, computational efficiency at scale, secure data partitioning, and programmable time-based value adjustment in a unified system. BACKGROUND OF THE INVENTION Traditional referral programs and commission-based incentive systems suffer from significant technical deficiencies that impair their functionality, accuracy, and interoperability. These systems, typically built on centralized database architectures with limited computational capabilities, exhibit several critical technical limitations: Cryptographic verification deficiency: Existing systems lack immutable, cryptographically secured transaction records, relying instead on mutable database entries that cannot be independently verified through consensus mechanisms or cryptographic proofs, resulting in verification asymmetry between platform operators and participants. Computational inefficiency in multi-party attribution: Current algorithmic approaches to multi-party commission calculations exhibit O(n2) complexity in participant relationships, creating processing bottlenecks when computing weighted distributions across complex transaction hierarchies with interdependent contribution factors. Temporal time-decay computation absence: Legacy systems implement static temporal models that fail to incorporate programmable time-decay functions necessary for mathematically sound representation of diminishing contribution value over time, resulting in computationally inaccurate reward distributions that do not reflect real-world value degradation patterns. Heterogeneous data structure incompatibility: Attribution data remains siloed in proprietary, non-interoperable da