Asset class-backed tokenization platform

Abstract

This document describes a system, method, and apparatus relating to asset class-backed tokens. The system, method, and apparatus includes an interconnected set of tools, services, and applications that enable users to design, develop, simulate, deploy, monitor, and analyze cryptographically secure and tradable tokens, such as those linked to or backed by one or more other asset classes.

Description

Technical Field

[0001] (Cross - References to Related Applications) This application claims priority to the following U.S. provisional applications: U.S. Provisional Patent Application No. 63 / 381547, filed on October 28, 2022, entitled 'ASSET CLASS BACKED TOKEN DESIGN'; and U.S. Provisional Patent Application No. 63 / 472223, filed on June 9, 2023, entitled 'ASSET - CLASS BACKED TOKENIZATION PLATFORM'.

[0002] Each of the foregoing applications is hereby incorporated by reference in its entirety into this specification.

[0003] U.S. Patent Application No. 17 / 554663, filed on December 17, 2021, entitled 'MARKET ORCHESTRATION SYSTEM FOR FACILITATING ELECTRONIC MARKETPLACE TRANSACTIONS' is also hereby incorporated by reference in its entirety.

Background Art

[0004] (Background) It can be said that cryptocurrencies are becoming established. From major players such as Bitcoin and Ethereum to emerging players like CryptoKitties, the vast wealth and largely private valuation they generate are astonishing. However, many challenges still remain, such as high volatility, lack of 'true utility' to enable fundamental value analysis, and expected future regulatory intensification. For this reason, most trading platforms for institutional investors either do not support cryptocurrency trading by customers or offer it in a very restricted form. Nevertheless, despite these limitations, investors' interest continues to grow.

[0005] While custom tokens linked to other asset classes such as patents and works of art already exist, it is almost certain that even more diverse types of tokens will emerge in the future, which will create new challenges for token designers, buyers, sellers, lenders, and stakeholders using trading platforms such as marketplaces and exchanges. [Overview of the Initiative]

[0006] (overview) This invention provides a set of tokens that solve or mitigate the problems of existing cryptocurrencies, and a platform that enables the convenient design, development, and deployment of these tokens. The particularly important issue of "utility" can be solved by backing cryptocurrencies with other assets or asset classes. In particular, backing them with asset classes that facilitate fundamental valuation analysis is effective. This invention provides a robust platform that facilitates the design, development, simulation, deployment, monitoring, and analysis of new token sets linked to a wide range of alternative asset classes, combinations of multiple asset classes, specific aspects of asset classes (risk profiles, volatility profiles, value profiles, etc.), and even value responses to specific event types.

[0007] This invention proposes a tokenization platform backed by an asset class consisting of a series of interconnected tools, services, and applications. This platform enables users to design, develop, simulate, deploy, monitor, and analyze cryptographically secure, tradable tokens. The tokens are linked to or backed by one or more other asset classes, including alternative asset classes such as revenue streams derived from intellectual property rights, farms, manufacturing capacity, energy production facilities, and real estate. On the platform, users can discover the characteristics of various asset classes and associated tokens, and configure token sets to achieve specific goals or adhere to strategies (e.g., by designing token sets or inclusive tokens that provide risk / return characteristics tailored to the designer's objectives). These tokens can increase the transparency of volatility factors by linking them to other asset classes, and mitigate and manage volatility by blending or combining the economic characteristics of multiple asset classes (e.g., to provide a desirable position in the risk / return space). Furthermore, these tokens can be configured to achieve diversification and asset allocation goals, and are applicable to a variety of other uses.

[0008] Tokens linked to alternative asset classes may be referred to as "asset-backed tokens" or "asset-linked tokens" in this specification. These terms are interchangeable unless otherwise specified in the context. There are many ways to link tokens to alternative asset classes or their components, including, but not limited to, the following: indexing the value of a token against a set of economic indicators relating to an asset class or a specific asset, or the value stream within it (e.g., the return the asset generates, the token's valuation in the secondary market, lending market, or insurance market, the value of an index relating to the underlying asset class or component, and many others); making tokens exchangeable for a set of units and / or specific parts of a value stream (e.g., a percentage share, a period of time, or a combination thereof); using tokens as an access control mechanism for holders to access the value stream (e.g., the right to control the production of units by a 3D printer); establishing rules, algorithms, or similar mechanisms for determining the value of a token, where these rules are based on underlying economic data related to the asset class or its components (e.g., determining the value of a real estate token based on the average selling price of real estate in a specific area); comparing tokens to other tokens with similar characteristics (e.g., comparison with other tokens exchangeable for similar asset classes or their components); and others. Where the terms “linking” or “supporting” are used throughout this disclosure, they are intended to encompass either one or a combination thereof, unless specifically indicated in the context.

[0009] According to several embodiments of this disclosure, a method for generating asset-class-backed token portfolios using a tokenization platform is disclosed. In this method, the tokenization platform receives multiple asset performance attributes from a user device. Furthermore, the tokenization platform prioritizes the multiple asset performance attributes by inputting them into a first artificial intelligence (AI) model, which is trained to detect inconsistencies between these attributes. This method also includes the process by which the tokenization platform maps the prioritized multiple asset performance attributes to multiple real-world assets. This mapping process identifies the type and quantity of assets corresponding to the prioritized token performance attributes. Furthermore, this method simulates the performance of the multiple real-world assets using a token simulator consisting of a second AI model. This AI model is configured to simulate value fluctuations of real-world assets based on different input scenarios. It also includes the process of iteratively adjusting the type and quantity of assets to improve the mapping between real-world assets and the prioritized token performance attributes based on the simulation results. Finally, non-fungible tokens backed by the asset classes are generated on a distributed ledger. Non-fungible tokens backed by this asset class are backed by a first quantity of a first asset and a second quantity of a second asset, based on iteratively adjusted asset types and quantities, where the first asset is an asset of a first type and the second asset is an asset of a second type. Non-fungible tokens backed by this asset class have multiple token attributes, including a first asset identification attribute that identifies the first asset, a first asset quantity attribute that identifies a portion of the first asset, a second asset identification attribute that identifies the second asset, and a second asset quantity attribute that identifies a portion of the second asset. In some embodiments, this method further includes the process of generating non-fungible tokens backed by a second asset class on a distributed ledger.The nonfungible tokens backed by this second asset class are backed by a third and a fourth asset selected from a set of assets, where the third asset is of the first type and the fourth asset is of the second type. Furthermore, the nonfungible tokens backed by this asset class are exchangeable for either the first or the second asset. In some embodiments, the nonfungible tokens backed by this asset class are also exchangeable for either the first or the second asset. Furthermore, the prioritization of multiple performance attributes is performed by ranking these attributes using a trained model. In some of these embodiments, this method also includes the process of training the model using a training dataset consisting of user-provided attribute sets and ranking labels for each attribute set. In some embodiments, the first asset includes ownership of at least a portion of land, and the second asset includes the right to use said land. In some embodiments, the first asset includes ownership of at least a portion of production assets, and the second asset includes at least a portion of the revenue stream generated from said production assets. Furthermore, in some embodiments, the first asset is a derivative asset corresponding to a first asset group, and the second asset is a derivative asset corresponding to a second asset group. In some of these embodiments, each of the first asset groups is associated with a first infrastructure project, and each of the second asset groups is associated with a second infrastructure project. In some embodiments, at least one of the first or second assets is an intellectual property asset. Furthermore, the process of optimizing multiple assets involves iteratively adjusting the number of assets within an asset group and resimulating the performance of the adjusted asset group until the simulation results are optimized. Furthermore, the asset group may include oil and gas production facilities. The asset group may also include digital twins of revenue-generating facilities. Furthermore, the method includes the following processes: generating multiple governance rules based on one or more governance requirements associated with multiple assets; and setting up a token management smart contract stored in a distributed ledger based on the generated multiple governance rules.In a specific embodiment, the process of generating asset-backed non-fungible tokens on a distributed ledger consists of the following steps: generating a token description for the asset-backed non-fungible tokens; setting up a smart contract on the distributed ledger based on the token description; and initiating a distributed ledger transaction based on the token description, configured so that the set up smart contract issues asset-backed non-fungible tokens. Furthermore, the attribute set includes one or more of the following: an availability trigger specifying the timing of the first asset becoming redeemable, or an expiration trigger specifying the deadline by which the first asset becomes non-redeemable. Furthermore, the attribute set includes one or more of the following: token redemption rules or token governance rules. Furthermore, the method includes the following processes: generating an issuance schedule based on a portfolio strategy; and, after the period specified in the issuance schedule has elapsed, generating non-fungible tokens backed by additional asset classes backed by multiple assets. In a specific embodiment, the smart contract further has the following function: collecting revenue related to at least the first and second assets, and distributing the collected revenue among token holders.

[0010] According to several embodiments of this disclosure, a method is disclosed for issuing (minting) tokens backed by multiple asset classes on a distributed ledger. This method further includes the steps of: a tokenization platform receiving token configuration information indicating multiple revenue-generating assets and issuers, where the multiple revenue-generating assets include at least two different assets. Furthermore, this method includes the steps of: a tokenization platform deploying at least one smart contract on a distributed ledger, where the deployed smart contract is configured to receive revenue related to the multiple revenue-generating assets, and the deployed smart contract includes the following functions: a first function configured to issue tokens backed by multiple asset classes backed by multiple assets; and a second function configured to distribute the received revenue among the blockchain addresses of the current owners of the tokens backed by multiple asset classes. This method further includes the steps of: in response to the tokenization platform receiving one or more purchase requests, the tokenization platform invoking the first function of the smart contract, triggering the issuance of tokens backed by multiple asset classes and the allocation of each issued token backed by asset class to the blockchain addresses of the purchasing users. Furthermore, periodically, the tokenization platform invokes a second function of the smart contract to distribute the received revenue among the blockchain addresses of the current owners of the tokens backed by multiple asset classes. In some embodiments, this method further includes the following steps: the tokenization platform verifies that the issuer owns multiple revenue-generating assets before deploying at least one smart contract. Furthermore, the issuer may be a crowdfunding pool. In this case, the blockchain addresses of the current owners of the tokens backed by multiple asset classes are associated with users of the crowdfunding pool, and the distribution of received revenue is proportional to the amount contributed by the corresponding user to the crowdfunding pool. Furthermore, the issuer may be a portfolio distributor.In this case, the blockchain address corresponds to the portfolio purchaser. Furthermore, tokens backed by each asset class may be exchangeable for a portion of multiple assets. Furthermore, token composition information is received from a token strategy engine configured to generate token composition information based on a plurality of token performance attributes provided by the issuer. Furthermore, or alternatively, the plural of asset includes ownership of land and the right to use said land. Furthermore, or alternatively, the plural of asset includes ownership of production assets and the revenue streams generated from said production assets. Furthermore, or alternatively, the plural of asset includes derivative assets corresponding to a first set of assets and derivative assets corresponding to a second set of assets. In some of these embodiments, the first set of assets is each associated with a first oil and gas production facility, and the second set of assets is each associated with a second oil and gas production facility. Furthermore, or alternatively, at least one of the plural of asset is an intellectual property asset. In some of these embodiments, the owner of an asset class backed token has the right to sublicense at least a portion of the intellectual property rights corresponding to said intellectual property asset. Furthermore, or alternatively, the method further includes the steps of: the tokenization platform receiving multiple governance rules based on one or more governance requirements relating to the plural of the asset; and the tokenization platform setting up one or more smart contracts based on the multiple governance rules. Furthermore, or alternatively, each token of an asset class backed by a first asset and a second asset from the plural of the asset, where the first asset is the first type of asset and the second asset is the second type of asset. Each asset class backed by a token has multiple attributes, including a first asset identification attribute (identifying the first asset), a first asset amount attribute (identifying a portion of the first asset), a second asset identification attribute (identifying the second asset), and a second asset amount attribute (identifying a portion of the second asset). In some of these embodiments, the set of attributes further includes at least one of the following: an availability trigger specifying when the first asset becomes redeemable, or an expiration trigger specifying when the first asset is no longer redeemable.Furthermore, or alternatively, the set of attributes further includes one or more of the following: token redemption rules or token governance rules. Furthermore, or alternatively, the method further includes the steps of: generating an issuance schedule based on the token configuration; and generating additional asset class backed by the plural of assets after the period specified in the issuance schedule has elapsed. Furthermore, or alternatively, the method further includes the steps of: generating an estimated value of the plural of assets using a token simulator system; determining an estimated value of each asset class backed token based on the estimated value of the plural of assets; and transmitting information indicating the estimated value of each asset class backed token to a marketplace system. In some of these embodiments, the marketplace system sets a minimum price for each token of the asset class backed tokens based on the estimated value of each token of the asset class backed tokens. Furthermore, or alternatively, the estimated value of the plural of assets includes the estimated revenue generated by the revenue stream assets.

[0011] One embodiment of this disclosure discloses a method for setting up at least one smart contract for managing tokens backed by multiple asset classes on a distributed ledger. This method further includes the steps of: a tokenization platform receiving a token configuration representing multiple assets, including at least two different assets, of which at least one is a revenue-generating asset. This method also includes the steps of: a tokenization platform setting up at least one smart contract on a distributed ledger to issue and manage tokens backed by multiple asset classes backed by the aforementioned multiple assets. This smart contract consists of: approving token transfers from a first distributed ledger account to a second distributed ledger account in accordance with one or more governance rules, except that transfers to the second distributed ledger account are prohibited unless the account meets the conditions set forth in the aforementioned one or more governance rules; monitoring a distributed ledger oracle and detecting one or more events related to the assets; receiving revenue related to the multiple assets; and distributing the received revenue to the current owners of the asset-class backed tokens. In response to the tokenization platform receiving one or more purchase requests: the tokenization platform initiates the issuance of tokens backed by multiple asset classes and allocates each issued token to the purchaser's blockchain address. Furthermore: the tokenization platform activates a smart contract to distribute the received revenue to the blockchain addresses of the current owners of the tokens backed by the multiple asset classes. This distribution is performed in response to the aforementioned asset-related events. In some embodiments, asset-related events include one or more of the following: the new purchase or license acquisition of at least one of the multiple assets; fluctuations in periodic revenue in a revenue-generating asset; commencement of operations for a revenue-generating asset; decommissioning of operations for a revenue-generating asset; or receipt of revenue related to an asset.Furthermore, or alternatively, the multiple assets include intellectual property rights, in which case the distributed ledger oracle is configured to maintain a list of licensees for the intellectual property rights, and the tokenization platform sets up at least one smart contract to enforce revenue collection from licensees of the intellectual property rights. In some of these embodiments, at least one smart contract is configured to execute a breach workflow against licensees that do not transfer revenue to at least one smart contract. Furthermore, or alternatively, owners of tokens backed by multiple asset classes have the right to sublicense at least a portion of the intellectual property rights. The estimated value of the multiple assets includes the estimated revenue generated by revenue stream assets. Furthermore, or alternatively, the asset collection includes property rights, in which case the distributed ledger oracle is configured to maintain a list of tenants for such property, and the tokenization platform sets up at least one smart contract to enforce revenue collection from tenants of such property. Furthermore, or alternatively, tokens corresponding to each asset class are interchangeable with portions of the asset collection. Furthermore, or alternatively, the token composition is received from a token strategy engine configured to generate the token composition based on multiple portfolio objectives provided by the issuer. Furthermore, or alternatively, the asset set includes ownership of land and the right to use such land. Furthermore, or alternatively, the asset set includes ownership of production assets and the revenue streams generated from such production assets. Furthermore, or alternatively, the asset set includes derivative assets corresponding to a first asset set and derivative assets corresponding to a second asset set. In some of these embodiments, the first asset set is associated with a first oil and gas production facility, and the second asset set is associated with a second oil and gas production facility. Furthermore, or alternatively, the asset set includes a digital twin of a revenue-generating facility.Furthermore, or alternatively, each asset class-based token is backed by a first asset and a second asset of the asset set, where the first asset is an asset of a first type and the second asset is an asset of a second type, and each asset class-based token has multiple attributes, including a first asset identification attribute (identifying the first asset), a first asset value attribute (identifying a portion of the first asset), a second asset identification attribute (identifying the second asset), and a second asset value attribute (identifying a portion of the second asset). In some of these embodiments, the attribute set further includes at least one of the following: an availability trigger specifying when the first asset becomes redeemable, or an expiration trigger specifying when the first asset is no longer redeemable. Furthermore, or alternatively, the attribute set further includes one or more of the following: a token redemption rule or a token governance rule. Furthermore, the method also includes the steps of: generating an issuance schedule based on the token configuration; and generating additional non-fungible tokens backed by the asset set after the period specified in the issuance schedule has elapsed. Furthermore, or alternatively, the method may also include the following steps: calculating an estimated value of an asset portfolio using a token simulator system; determining an estimated value for each asset class-specific token based on the estimated value of the asset portfolio; and transmitting information indicating the estimated value of each asset class-specific token to a marketplace system. In some of these embodiments, the marketplace system sets a minimum price for each asset class-specific token based on its estimated value. Additionally, the estimated value of the asset portfolio includes the estimated revenue generated by revenue stream assets.

[0012] One embodiment of this disclosure discloses a method for simulating the value of tokens backed by asset classes associated with a product under development. This method includes the following steps: receiving asset component specifications for the product under development in a tokenization platform. These asset component specifications identify several components necessary for the manufacture of the product. Furthermore, the method also includes the following steps: receiving material input specifications for at least one component, defining the materials used to manufacture that component. Furthermore, using the tokenization platform, simulating the development process of the several components using an artificial intelligence (AI) model. This simulation is based on the material input specifications, and the AI ​​model is trained to simulate the development process of the several components under different market conditions. Furthermore, using the tokenization platform, performing a performance simulation of the product using a second AI model. This AI model is trained to predict market sales volume under different market conditions. Furthermore, in the tokenization platform, integrating the development simulation results of the several components under different market conditions with the performance simulation results of the planned product, generates a simulation result. Furthermore, based on this simulation result, estimating the value of tokens backed by several asset classes associated with the product. Furthermore, creating a sales list of tokens backed by asset classes. The value of this sales list is set based on the estimated value. Furthermore, a smart contract is deployed on the blockchain. This smart contract consists of the following functions: the ability to issue tokens backed by multiple asset classes; the ability to distribute the initial revenue from the sale of said tokens to the product producers; the ability to receive a second revenue related to the sale of the asset class-backed tokens; and the ability to distribute this second revenue to the current asset class-backed token holders. In some embodiments, the planned product is an electronic device, and the multiple components consist of a processor, display, memory, and software components. In another embodiment, a machine learning model is used for development simulation of the multiple components.The input data to this machine learning model includes the estimated manufacturing cost of a first component of a group of components and the estimated cost of the materials used to manufacture a second component. In some of these embodiments, the machine learning model is trained to output the total manufacturing cost of the group of components under first market environmental conditions, and another machine learning model is trained to output the total manufacturing cost of the group of components under second market environmental conditions. Furthermore, in yet another embodiment of this method, a machine learning model configured to estimate one or more economic, geopolitical, or meteorological factors is used for performance simulation of the planned product. In yet another embodiment, this method includes the steps of generating tokens backed by a group of asset classes corresponding to the planned product and selling these asset-backed tokens to raise funds for product manufacturing. In some of these embodiments, the asset-backed tokens are backed by a percentage of the planned product's sales revenue. In yet another embodiment, these asset-backed tokens are backed by fees paid by software developers who develop applications for the planned product. In yet another embodiment, these asset-backed tokens are backed by a percentage of the planned product's customer subscription revenue. In yet another embodiment, the most important market conditions for achieving the best results are identified from the simulation output.

[0013] One embodiment of this disclosure discloses a method for deploying an automated market maker (AMM) smart contract on a blockchain. This method includes the step of a tokenization platform receiving a token configuration representing multiple assets, where the multiple assets include at least two different types of assets, of which at least one type of asset is a revenue-generating asset. Furthermore, the tokenization platform includes the step of setting up at least one smart contract on the blockchain to generate and manage asset class-based tokens associated with the multiple assets. The tokenization platform also includes the step of generating an AMM configuration using a trained machine learning model. This AMM configuration includes at least one trading pair and a trading function, where one of the tokens in the trading pair is an asset class-based token, and the trading function defines a formula for exchanging a first type of token for an asset class-based token, and a formula for exchanging an asset class-based token for a first type of token. Furthermore, the tokenization platform includes the step of deploying the AMM smart contract on the blockchain. This deployed AMM smart contract is configured to receive revenue into a liquidity pool managed by the AMM smart contract and to execute a trading function for the trading pair. Furthermore, the tokenization platform includes the step of generating a graphical user interface that allows users who own at least a certain amount of a first type of token to exchange it for at least a second amount of a second type of token in a trading pair, based on the trading functionality. In addition, the tokenization platform includes the step of receiving AMM trading requests associated with the user's user blockchain account. The tokenization platform also performs the step of generating an AMM trading transaction for the user to sign using the private key associated with the blockchain account. Finally, the tokenization platform sends the signed AMM trading transaction to the blockchain. This signed AMM transaction triggers the exchange of tokens via an AMM smart contract based on the trading functionality.In some embodiments, the AMM is one or more of the following: constant product market makers, constant sum market makers, or constant mean market makers. Furthermore, it may be one or more of the following: hybrid automated market makers, dynamic automated market makers, proactive market makers, or virtual automated market makers. The process of generating the AMM configuration includes using a trained machine learning model, which generates additional AMM configuration parameters based on user input. This user input may include AMM configuration parameters (such as trading pairs, trading functions, liquidity pool rules, risk mitigation rules, and distribution rules). In some embodiments, the AMM configuration parameters include at least one of the following: trading pairs, trading functions, liquidity pool rules, risk mitigation rules, distribution rules, etc. Furthermore, the tokenization platform is configured to receive transactions related to the AMM smart contract and generate one or more analyses of these transactions. This one or more analyses may include at least one of the following: trading volume analysis, liquidity pool analysis, price fluctuation analysis, etc. In some embodiments, a graphical user interface is further configured to display one or more of these analyses. Furthermore, the graphical user interface may be configured to generate liquidity pool funding transactions, which can be signed by liquidity pool contributors. Multiple assets may include partial ownership of land and the right to use said land. Multiple assets may also include partial ownership of production assets and at least a portion of the revenue stream generated from said production assets. Furthermore, multiple assets may include derivative assets corresponding to a first set of assets and derivative assets corresponding to a second set of assets. Alternatively, multiple assets may include a first set of assets related to a first infrastructure project and a second set of assets related to a second infrastructure project. Furthermore, multiple assets may include intellectual property assets. Alternatively, multiple assets may include oil and gas production facilities. Furthermore, multiple assets may include digital twins of revenue-generating facilities.

Brief Description of the Drawings

[0014] [Figure 1] Figure 1 schematically shows the components and interactions of an asset-backed tokenization platform, applications, and support systems according to various embodiments.

[0015] [Figure 2] Figure 2 schematically shows an asset-based tokenization platform according to various embodiments.

[0016] [Figure 3] Figure 3 schematically shows a token design system according to various embodiments.

[0017] [Figure 4] Figure 4 shows token attributes according to various embodiments.

[0018] [Figure 5] Figure 5 shows asset class attributes according to various embodiments.

[0019] [Figure 6] Figure 6 shows asset token framework parameters according to various embodiments.

[0020] [Figure 7] Figure 7 shows the parameters of a token simulator according to various embodiments.

[0021] [Figure 8] Figure 8 shows an exemplary system for creating asset-linked tokens according to various embodiments.

[0022] [Figure 9] Figure 9 shows an exemplary tokenization controller according to various embodiments.

[0023] [Figure 10] Figure 10 shows an exemplary token link description according to various embodiments.

[0024] [Figure 11] Figure 11 shows an exemplary token expansion controller according to various embodiments.

[0025] [Figure 12] Figure 12 shows an exemplary token marketplace controller according to various embodiments.

[0026] [Figure 13] Figure 13 shows an exemplary asset class description according to various embodiments.

[0027] [Figure 14] Figure 14 shows exemplary beneficial characteristics according to various embodiments.

[0028] [Figure 15] Figure 15 shows exemplary performance representations according to various embodiments.

[0029] [Figure 16] Figure 16 shows an exemplary procedure for generating asset class-linked tokens according to various embodiments.

[0030] [Figure 17] Figure 17 shows an exemplary procedure for generating asset-linked tokens according to various embodiments.

[0031] [Figure 18] Figure 18 shows an exemplary procedure for adjusting asset-linked tokens according to various embodiments.

[0032] [Figure 19]Figure 19 shows exemplary strategy engines in various embodiments.

[0033] [Figure 20] Figure 20 illustrates exemplary methods for designing portfolio strategies according to various embodiments.

[0034] [Figure 21] Figure 21 shows examples of smart contracts for generating asset class-backed tokens on a distributed ledger according to various embodiments.

[0035] [Figure 22] Figure 22 illustrates exemplary methods for issuing asset class-backed tokens on a distributed ledger according to various embodiments.

[0036] [Figure 23] Figure 23 shows an example environment that includes a smart contract for managing tokens stored on the blockchain, according to various embodiments.

[0037] [Figure 24] Figure 24 illustrates exemplary methods for authorizing the transfer of asset class-backed tokens using smart contracts, according to various embodiments.

[0038] [Figure 25] Figure 25 illustrates exemplary methods for updating a token management smart contract on a blockchain based on off-chain events, according to various embodiments.

[0039] [Figure 26] Figure 26 illustrates exemplary methods for receiving and distributing revenue related to asset class back tokens, according to various embodiments.

[0040] [Figure 27] Figure 27 shows an exemplary token simulator for simulating token performance according to various embodiments.

[0041] [Figure 28] Figure 28 illustrates exemplary methods for simulating the performance of planned commodities and tokens backed by corresponding asset classes, according to various embodiments.

[0042] [Figure 29] Figure 29 illustrates exemplary methods for generating token purchase recommendations backed by asset classes, according to various embodiments. [Modes for carrying out the invention]

[0043] The systems and methods described herein significantly advance the state of distributed ledger and blockchain technology by providing a variety of hybrid tokens and other advanced use cases. For example, in some embodiments of this disclosure, an entire portfolio can be represented using a single digital token backed by multiple assets, such digital tokens configured for specific investment profiles (e.g., age, volatility sensitivity, sophistication, etc.) and can be offered to any user to satisfy a full set of their investment or business objectives. The systems described herein can take the goals of a user or group of users and design a token strategy for generating and / or acquiring the appropriate token or token. Designing a token strategy may further involve predicting the future value of the token so that the strategy, embodied by linking the token to one or more asset classes or value streams, is likely to satisfy the user's needs both now and in the future. In embodiments, the value stream of an asset class-backed token may refer to the return granted to the token holder (e.g., cash, cryptocurrency, or other forms of value transfer). In such a scenario, the asset class-backed token may provide a value stream to its holder while simultaneously offering potential for long-term growth. For example, tokens backed by an asset class can be linked to a pension to meet ongoing spending needs, and by partially redeeming the tokens, income can be received at regular intervals while simultaneously being linked to another asset class for long-term wealth creation.

[0044] Advanced use cases described herein may further include “tokens on tokens,” which may allow users to hold tokens representing groups of tokens or fractions of tokens. Similarly, tokens described herein may enable fractional token ownership, allowing users to have cross-ownership (for example, in the case of works of art or other non-corrosive physical assets, having rules for sharing the asset and / or an interest only in the value of the asset). Additionally or alternatively, tokens described herein may include bundles of rights relating to objects / machines / products / entities, and different types of ownership may be tokenized together or individually. Such multiple types of ownership may include, for example, ownership of the digital rights to a film and ownership of the rights to exhibit it in an art gallery. Furthermore, rights may also be divided by geographical region, and rights to asset classes of value streams in specific regions may be tokenized.

[0045] In embodiments, advanced token configurations such as those described herein may include derivative tokens that may be used to hold market option positions that may be traded independently of market transactions. Additionally or alternatively, tokens may be configured to offer futures positions on the value of the token in the market.

[0046] To enhance and enable these and other advanced use cases, the asset class-backed tokenization platform may be configured with multiple systems and functions. These systems and functions may include a strategy engine for designing and issuing (minting) sets of asset class-backed tokens according to user-defined priorities and goals, while taking into account legal and other governance standards; a token simulator configured to simulate the current and future value of tokens under various conditions; one or more smart contracts configured to automatically issue and / or manage tokens; and / or various other systems, components, and methods as described further herein.

[0047] Referring to Figure 1, additional details, components, subsystems, and other elements of an optional embodiment of the asset-class-backed tokenization platform layer 3302 and associated application 3312 are illustrated. The data processing layer automatically collects relevant information through edge, IoT, and other networking systems, stores the relevant data for use by relevant services and applications (including those involved in token design, configuration, simulation, deployment, monitoring, and analysis), and can apply various algorithms and intelligences to the data (including generating relevant analyses, simulating token behavior, similarity and clustering, and other uses). All of these capabilities enable the various core functions of the asset-class-backed tokenization platform described throughout this disclosure. The asset class-backed tokenization platform 3302 may, in various arbitrary embodiments, include a set of applications, systems, solutions, interfaces, etc., collectively referred to for convenience as applications 3312, thereby enabling operators or owners of assets, tokens, transactions, or financial entities, or other users, to manage, monitor, control, analyze, or otherwise interact with one or more elements of entities 3330. In embodiments, the asset class-backed tokenization platform layer 3302 may include a set of data processing layers 3308, each data processing layer 3308 configured to provide a set of functions to facilitate the development and deployment of intelligence for a wide variety of financial and trading applications and end uses, including automation, machine learning, artificial intelligence applications, intelligent transactions, state management, event management, process management, and many others.In embodiments, the data processing layer 3308 includes a financial and transaction monitoring system layer 3306, a financial and transaction entity-oriented data storage system layer 3310 (which may, in some cases, be simply referred to herein as the data storage layer 3310 for convenience), an adaptive intelligence system layer 3304, and an asset class-backed tokenization platform layer 3302. Each of the data processing layers 3308 may include various services, programs, applications, workflows, systems, components, and modules, as further described herein and in documents incorporated herein by reference. In embodiments, each of the data processing layers 3308 (and optionally the asset class-backed tokenization platform layer 3302 as a whole) is configured such that one or more of its elements can be accessed as a service by other data processing layers 3308 or other systems (for example, configured as a platform-as-a-service deployed on a set of cloud infrastructure components in a microservices architecture). For example, the data processing layer 3308 may have a set of interfaces 3316, including application programming interfaces (APIs), brokers, services, connectors, wired or wireless links, ports, human-accessible interfaces, and software interfaces, through which the data processing layer 3308 interacts with other layers, systems, or subsystems of the asset class-backed tokenization platform layer 3302, as well as with other systems such as financial entities 3330, or cloud-based or on-premises enterprise systems (e.g., accounting systems, resource management systems, CRM systems, supply chain management systems, etc.).Each of the data handling layers—3308—may include a set of data handling services (e.g., microservices), including facilities for data extraction, transformation, and loading; facilities for data cleansing and deduplication; facilities for data normalization; facilities for data synchronization; facilities for data security; facilities for computation (e.g., facilities for performing predefined computational operations on a data stream and providing an output stream); facilities for compression and decompression; and facilities for analysis (e.g., providing automated generation of data visualizations).

[0048] In an embodiment, each data processing layer 3308 has a set of application programming interfaces 3316 for automating data exchange with each of the other data processing layers 3308. These may include data integration functions such as extracting, transforming, loading, normalizing, compressing, decompressing, encoding, decoding, and otherwise processing data packets, signals, and other information exchanged between layers and / or applications 3312. In an embodiment, the data processing layer 3308 is configured in a topology that facilitates shared data collection and distribution across multiple applications and uses within the asset class-backed tokenization platform layer 3302, with the financial and transaction monitoring system layer 3306. The financial and trading monitoring system layer 3306 includes, and may integrate with and / or cooperate with, various data collection and management systems 3318, sometimes conveniently referred to as data collection systems 3318, for collecting and organizing data collected from or about financial and trading entities 3330, as well as data collected from or about various data handling layers 3308 or their services or components.

[0049] The set of applications 3312 may include, but are not limited to, one or more of the following broad types of applications: investment applications 3402 (for example, but not limited to, those for investment in stocks, equity, currencies, commodities, options, futures, derivatives, real estate, trusts, cryptocurrencies, tokens, and other asset classes); asset management applications 3404 (investment assets, real estate, fixtures, movable property, real estate, equipment, intellectual property, vehicles, human resources, software, information technology resources, data processing resources, data storage resources, power generation and / or (e.g., for managing energy storage resources, computing resources, and other assets); Lending applications 3410 (personal loans, commercial loans, secured loans, microloans, peer-to-peer loans, insurance-related loans, asset-class secured loans, secured debt loans, corporate debt loans, student loans, mortgage loans, car loans, etc.); Risk management applications 3408 (e.g., for managing risks or liabilities relating to products, assets, people, homes, vehicles, equipment, parts, information technology systems, security systems, security events, cybersecurity systems, etc.); Property, health condition, death, fire, flood, weather, malfunction, error, business interruption, infringement, advertising damage, defamation, libel, infringement of privacy rights or publicity rights, injury, property damage, business damage, breach of contract, and others); payment applications 3433 (including credit cards, debit cards, wire transfers, ACH, current accounts, currency and other payments, etc., to enable various payments within and between marketplaces); marketing applications 3412 (applications for marketing financial or trading products or services, advertising applications, marketplace platforms or systems for goods, services or other items, marketing analytics applications, customer relationship management applications, search engine optimization applications, sales management applications, advertising network applications, behavior tracking applications, marketing analytics applications, location-based product or service targeting applications, collaborative filtering applications, recommendation engines for products or services, and others);Trading applications 3428 (buy applications, sell applications, bidding applications, auction applications, reverse auction applications, bid / ask matching applications, securities trading applications, commodity trading applications, options trading applications, futures trading applications, derivatives trading applications, cryptocurrency trading applications, token trading applications, analytical applications for analyzing financial or trading performance, yields, investment returns, or other indicators, bookbuilding applications, etc.); Tax applications 3414 (for example, but not limited to, sales tax, income tax, property tax, municipal fees, pollution tax, renewable energy credits, pollution mitigation credits, value-added tax, import duties, export duties, etc., for managing, calculating, reporting, optimizing, or otherwise processing data, events, workflows, or other elements relating to taxes, levies, duties, credits, fees, or other government charges);Fraud prevention applications 3416 (e.g., identity verification applications, biometric identity verification applications, transaction pattern-based fraud detection applications, location-based fraud detection applications, user behavior-based fraud detection applications, network address-based fraud detection applications, blacklist applications, whitelist applications, content inspection-based fraud detection applications, or other fraud detection applications), financial services, applications or solutions 3409 (collectively, but not limited to, "Financial Services": financial planning services, tax planning services, portfolio management services, trading services, lending services, banking services, currency conversion services, currency exchange services, remittance services, asset management services, estate planning services, investment banking services, commercial banking services, foreign exchange services, insurance services, investment services, investment management services, hedge funds) Investment management services, hedge fund services, custody services, credit card services, custody services, current account services, debit card services, lending services, ATM services, ETF services, wire transfer services, overdraft services, reporting services, certified check services, notarization services, capital markets services, brokerage services, broker-dealer services, private banking services, insurance services, insurance brokerage services, underwriting services, annuity services, life insurance services, health insurance services, retirement insurance services, property and casualty insurance services, financial insurance services, reinsurance services, brokerage services, trade clearing services, private equity services, venture capital services, angel investment services, family office investment services, exchange services, settlement services, interbank network services, debt settlement services, or other financial services;Security applications, solutions, or services 3418 (hereinafter referred to as security applications, for example, but not limited to any of the fraud prevention applications 3416 described above, as well as physical security systems (e.g., access control systems (using biometric access control, fingerprint authentication, retinal scanning, passwords, and other access controls), safes, vaults, cages, safe rooms, etc.), surveillance systems (using cameras, etc., surveillance systems (using cameras, motion sensors, infrared sensors, and other sensors, etc.), cybersecurity systems (VIMS detection and remediation, intrusion detection and remediation, spam detection and remediation, phishing detection and remediation, social engineering detection and remediation, cyberattack detection and remediation, packet inspection, traffic inspection, DNS attack remediation and detection, etc.)), or other security applications); underwriting applications 3420 (e.g., for underwriting any insurance solicitation, any loan, or any other transaction, and the entirety of this disclosure) 3422 (including any application for detecting, characterizing, or predicting the potential and / or scope of risk, including underwriting based on any data source, event, or entity pointed out through documents incorporated herein by reference); redemption applications 3422 (including, but not limited to, distribution systems for providing investors or owners with the output of an underlying asset class or its members linked to or backing a token, whether the output is financial dividends, profit distributions, production equipment, etc., and including any of the linking or backing frameworks pointed out herein (e.g., using the token as evidence of redemption, using the token as an access control mechanism for factors of production, linking the value of the token to an index, etc.); real estate applications 3424 (including, but not limited to, real estate brokerage applications, real estate valuation applications, real estate investment trust applications, real estate mortgage or lending applications, real estate valuation applications, real estate marketing applications, or others);Regulatory applications 3426 (such as applications for regulating applications, services, transactions, activities, workflows, events, entities, or other items described herein and in documents incorporated herein for reference, such as regulations on pricing, marketing, offering of securities, offering of insurance, underwriting of brokerage or dealer activities, use of data (including data privacy regulations, data retention regulations, etc.), banking, marketing, sales, financial planning, etc.); Marketplace applications, solutions, or services operated by the platform 3327 (sometimes simply referred to as marketplace applications (this term may also include various types of external marketplaces 3390, to the extent the context allows), e.g., e-commerce marketplaces, auction marketplaces, physical goods marketplaces, virtual goods marketplaces, advertising marketplaces, reverse auction marketplaces) Marketplaces, advertising networks, resource marketplaces, energy trading marketplaces, computing resource marketplaces, network resource marketplaces, frequency allocation marketplaces, internet advertising marketplaces, television advertising marketplaces, print advertising marketplaces, radio advertising marketplaces, in-game advertising marketplaces, virtual reality advertising marketplaces, augmented reality marketplaces, real estate marketplaces, hospitality marketplaces, travel services marketplaces, financial services marketplaces, blockchain-based marketplaces, cryptocurrency marketplaces, token-based marketplaces, loyalty program marketplaces, timeshare marketplaces, rideshare marketplaces, mobility marketplaces, transportation marketplaces, space-sharing marketplaces, or other marketplaces);Warranty applications 3417 (such as warranties or guarantee applications relating to products, services, offerings, solutions, physical products, software, service levels, service quality, financial instruments, liabilities, collateral items, service performance, or other items); analytical solutions 3419 (such as analytical applications relating to any of the data types, applications, events, workflows, or entities referred to throughout this disclosure or the documents incorporated herein by reference, including but not limited to big data applications, user behavior applications, predictive applications, classification applications, dashboards, pattern recognition applications, econometric applications, financial yield applications, return on investment applications, scenario planning applications, decision support applications); pricing applications 3421 (for example, but not limited to goods, services (throughout this disclosure and the documents incorporated herein by reference) This includes, but is not limited to, smart contract applications, solutions, or services (collectively referred to herein as smart contract applications, but not limited to, any of the smart contract types referred to in this disclosure or in the documents incorporated herein by reference, such as smart contracts that use tokens or cryptocurrency as consideration, smart contracts that grant rights, options, or interests, smart contracts that grant rights, options, future or future conditions, smart contracts such as securities, commodities, future, options, derivatives, and present or current; (This includes smart contracts configured to consider or respond to future resource smart contracts, tax, regulatory, or compliance parameters, smart contracts configured to execute arbitrage trades, or many others.) Thus, the asset class-backed tokenization platform layer 3302 can host interactions between a wide range of heterogeneous applications 3312 (such terms include the above and other financial or trading applications, services, solutions, etc.) through shared microservices, shared data infrastructure, and shared intelligence, so that any set or larger combination or permutation of such services may be improved compared to isolated applications of the same type.

[0050] In embodiments, the adaptive intelligence system layer 3304 may collectively include a set of systems, components, services, and other capabilities that facilitate the coordinated development and deployment of intelligent systems, such as those capable of enhancing one or more applications 3312 in the asset-class-backed tokenization platform layer 3302. These adaptive intelligence system layers 3304 may include an adaptive edge computing management solution 3430, a robotic process automation system 3442, a set of protocol adapters 3491, a packet acceleration system 3434, an edge intelligence system 3438, an adaptive networking system 3440, a set of state and event managers 3444, a set of opportunity mining units 3446, an artificial intelligence system 3448, and other systems. The robotic process automation system 3442 may be configured to take outputs and results 3328 from various applications.

[0051] In embodiments, the financial and transaction monitoring system layer 3306 and its data acquisition system 3318 may include a wide range of systems for data acquisition. This layer may include, but is not limited to,: real-time monitoring systems 3468 (such as in-vehicle monitoring systems like event and status reporting systems for ATMs, POS systems, kiosks, vending machines, etc., in-vehicle diagnostic and telematics systems for vehicles and equipment, systems that provide diagnostic codes and events via event buses, communication ports, or other communication systems); monitoring infrastructure (such as cameras, motion sensors, beacons, RFID systems, smart lighting systems, asset tracking systems, person tracking systems, and ambient sensing systems installed in various environments where transactions and other events take place); and removable and replaceable monitoring systems such as portable and mobile data collectors, RFID and other tag readers, smartphones, tablets, and other mobile devices capable of data acquisition); and software interaction monitoring systems. 3450 (for recording and tracking events related to software user interfaces and user interactions, such as mouse movements, touchpad interactions, mouse clicks, cursor movements, keyboard interactions, navigation actions, eye movements, finger movements, gestures, menu selections, and many others, and software interactions that occur as a result of other programs, such as via APIs); mobile data collectors 3452 (as extensively described herein and in documents incorporated by reference); visual monitoring systems 3454 (such as video and still image systems, LiDAR, IR, and other systems capable of visualizing items, people, materials, components, machines, equipment, personnel, gestures, facial expressions, location, configuration, and other factors or parameters of entities 3330, as well as inspection systems for monitoring processes, worker activities, etc.);Interaction point systems 3470 (such as POS systems, kiosks, ATMs, vending machines, touchpads, camera-based interaction tracking systems, smart shopping carts, user interfaces for online and in-store automated vending and commerce systems, tablets, and other systems at points of sale or other interaction by customers or workers involved in shopping and / or transactions); physical process interaction observation systems 3458 (for tracking the physical activities of customers, the physical activities of trading parties (traders, vendors, merchants, customers, negotiators, brokers, etc.), physical interactions between workers and other workers, interactions between workers and physical entities such as machines and equipment, and interactions of physical entities with other physical entities, by, but not limited to, the use of video and still image cameras, motion detection systems (including optical sensors, LIDAR, IR and other sensor sets, etc.), robot motion tracking systems (such as tracking the motion of a human or a system attached to a physical entity), and many others); No; Machine condition monitoring systems 3460 (including onboard and external monitors of the condition, status, operating parameters, or other measures of condition of machines such as clients, servers, cloud resources, ATMs, kiosks, vending machines, POS systems, sensors, cameras, smart shopping carts, smart shelves, vehicles, robots, or other machines); sensors and cameras 3462 and other IoT data acquisition systems 3464 (including onboard sensors, sensors or other data collectors (including click tracking sensors), cameras for monitoring the entire environment, dedicated cameras for specific machines, processes, workers, etc., wearable cameras, portable cameras, cameras placed on mobile robots, cameras on portable devices such as smartphones and tablets, and many others (including any of the many sensor types disclosed throughout this disclosure or in documents incorporated herein by reference));Indoor location monitoring systems 3472 (including cameras, IR systems, motion detection systems, beacons, RFID readers, smart lighting systems, triangulation systems, RF and other spectral detection systems, time-of-flight systems, chemical noses and other chemical sensor sets, and other sensors); user feedback systems 3474 (including survey systems, touchpads, voice-based feedback systems, evaluation systems, facial expression monitoring systems, impact monitoring systems, gesture monitoring systems, etc.); behavioral monitoring systems 3478 (for monitoring actions, shopping behavior, purchasing behavior, clicking behavior, fraudulent or deceptive behavior, user interface interactions, product return behavior, behaviors indicating interest, attention, boredom, etc., mood-indicating behavior (such as fidgeting, staying still, approaching, changing posture, etc.), and many other things); and any of the various Internet of Things (IoT) data collectors 3464, including those described in this disclosure as a whole and in documents incorporated herein by reference.

[0052] Financial and Transaction Entities 3330 may include any of the many different assets, systems, devices, machines, facilities, individuals or other entities referred to in this disclosure or in documents incorporated herein by reference, for example, but not limited to, financial machines 3352 and their components (e.g., teller machines, point-of-sale machines, vending machines, kiosk terminals, smart card-enabled machines, and many others), financial and transaction processes 3350 (lending processes, software processes (including applications, programs, services, and others), production processes, banking processes (including lending processes, underwriting processes, investment processes, and many others), financial service processes, diagnostic processes, security processes, safety processes, and many others), wearables and portable devices 3348 (mobile phones, tablets, portable devices for financial applications, data collectors (including mobile data collectors), sensor-based devices, watches, glasses, hearables, head-mounted devices). Devices, garment-integrated devices, bracelets, neck-worn devices, AR / VR devices, headphones, and many others); workers 3344 (bankers, financial service personnel, managers, engineers, floor managers, vaulters, inspectors, delivery personnel, currency processing personnel, process supervisors, security personnel, safety personnel, and many others); interaction points 3342 (e.g., kiosks, ATMs, POS systems, cash registers, checkout systems, points of use, etc.); and asset management facilities 3340 (currency production facilities, storage facilities, vaults). These include, but are not limited to, bank branches, office buildings, banking facilities, financial service facilities, cryptocurrency mining facilities, data centers, trading floors, high-frequency trading operations, and many others. In particular, they include custody and financial service facilities 3338 (for inventory, components, packaging materials, goods, products, machinery, equipment and other items for financial services); insurance facilities 3334 (for branches, offices, custody facilities, data centers, underwriting operations, etc.); and banking facilities 3332 (for commercial banking operations, investment operations, consumer banking operations, lending operations and many other banking operations, etc.).

[0053] In embodiments, the data storage layer 3310 may include various systems for storing data such as accounting data 3358, access data 3362, pricing data 3364, asset data 3320, worker data 3322, event data 3324, underwriting data 3360, and claims data 3354. Asset data 3320 may include a wide range of historical and / or forecast economic data and other data and attributes related to an asset class or its members, including price data, revenue data, profit data, yield data, rental income data, production volume data, timing data, operational data (e.g., historical and forecast production output), status data (e.g., health status, maintenance status, or operational status), workflow data (e.g., historical and forecast activity of the asset), aggregated data or index data, valuation data, rating data (including credit ratings, quality ratings, etc.), and many others. The storage system layer 3310 may include, but is not limited to, physical storage systems, virtual storage systems, local storage systems, distributed storage systems, databases, memory, network-based storage, network-attached storage systems (such as using NVMe, storage-attached networks, and other network storage systems), and many others. In embodiments, the data storage layer 3310 may store data in one or more knowledge graphs (such as directed, uncycled graphs, data maps, data hierarchies, data clusters including links and nodes, self-organizing maps, etc.). In embodiments, the data storage layer 3310 may store data in digital threads, ledgers, etc., for example, to maintain a chronological record of entity 3330, which includes any of the entities described herein. In embodiments, the data storage layer 3310 may use virtual asset tags 3488 to enable this. The virtual asset tag 3488 may include a data structure that is associated with an asset and can be accessed and managed as if the tag were physically located on the asset, using access control, etc., and data storage and retrieval may be optionally linked to a local process, but may also be optionally open to remote retrieval and storage options.In an embodiment, the data storage layer 3310 may include one or more blockchains 3490 that store ID data, transaction data, entity data of entities 3330, pricing data, ownership transfer data, data for operations by smart contracts 3431, historical interaction data, etc., and may have access control that is role-based or based on credentials associated with entities 3330, services, or one or more applications 3312.

[0054] Referring to Figure 2, the asset-based tokenization platform 200 is shown. The asset-based tokenization platform 200 may include a set of interconnected tools, services, and applications that enable users to design, develop, simulate, deploy, monitor, and analyze cryptographically secure and tradable tokens, such as those linked to or backed by one or more other asset classes, including alternative asset classes such as revenue streams from intellectual property, farms, manufacturing capacity, energy production facilities, and real estate. Any asset class that generates a stream of revenue or value, whether denominated in currency or other units (e.g., energy, attention (page views, clicks, etc.)), can be linked to or associated with a token or set of tokens and provide the backing, utility, or link that supports the token. This may include links or backing to revenue streams (rentals, royalties, revenue-sharing agreements, sales commissions, services, and many others), valuations (securities, bonds, currencies, points, tokens, and other indices), production units (commodities, energy, materials, and other goods), services (data services, physical services, microservices, network services, and others), behaviors (attention, etc.), and others.

[0055] On platform 200, users can discover not only the characteristics of tokens (and other similar things), but also the characteristics of asset classes that can be linked to or back up tokens. This can include data collection from public data sources, proprietary data sources, historical data, etc., using various mechanisms pointed out in relation to the embodiment of the asset class-backed tokenization platform 3302 shown in Figure 1 and related thereto.

[0056] Using the Platform 200 interface, token designers can configure sets of tokens to meet a set of objectives and / or follow a strategy (for example, by designing sets of tokens and / or umbrella tokens that provide a risk / return profile tailored to the designer's objectives). The designed tokens can make volatility drivers more transparent, such as by pegging them to other asset classes; they can mitigate or manage volatility, such as by blending or combining the economic characteristics of multiple asset classes; and they can be configured to meet diversification and / or asset allocation objectives, such as by pegging the set of tokens to multiple asset classes so that they behave economically like a portfolio or fund.

[0057] The asset class collateralized tokenization platform 200 may include a token design system 204, a token deployment system 208, and a token marketplace system 210. The token design system may include a token design interface 302 that allows users to design tokens having a specified token attribute profile 322, a specified asset class attribute profile 324, and an asset-token linking framework 328. Configuring a token in the token design system 204 interface may include selecting and / or configuring the nature of the backing or link between the token and an alternative asset class or its members. Such backing or linking can be implemented in a variety of ways, but is not limited to: indexing the value of a token to a set of economic parameters of an asset class or a specific asset or value flow within it (e.g., revenue generated by the asset, valuation assigned to the token in the secondary market, lending market, insurance market, index value of the underlying asset class or component, and many others); making the token exchangeable for a unit set and / or a specific part of a value flow (e.g., percentage share, period, period, and many others); or making the token exchangeable for a unit set and / or a specific part of a value flow (e.g., percentage share Making tokens exchangeable for stage shares, terms, periods, and many others; making tokens exchangeable for unit sets and / or specific parts of the value flow (e.g., percentage shares, terms, and many others); using tokens as an access control mechanism for holders to access the value flow (e.g., the right to control the production of units by a 3D printer), setting rules or algorithms that determine the value of tokens, basing the value of real estate tokens on the average selling price of real estate in the relevant area, comparing tokens to other tokens with similar characteristics (e.g., other tokens exchangeable for similar asset classes or members thereof). As stated above, references to links or backings throughout this disclosure are intended to encompass one or more of these unless the context specifically indicates otherwise.Selection and configuration may include selecting an asset class (from a dropdown menu, for example), selecting a linking framework from various available options, determining the number of tokens, setting the release timing, and configuring other attributes (such as redemption rules, release timing, and trading rules). Thus, the token design system can interpret a token link description that includes a token performance expression corresponding to at least one asset and / or its asset class, and an asset class description corresponding to at least one asset and / or its asset class. The token design system can also generate at least one asset class link token in response to the token link description and the asset class description. The token deployment system 208 may include a token instance 242, an asset instance 252, an asset-token link 248, a host interface 244, a smart contract 250, and a blockchain 254.

[0058] Once a token is designed, it may be simulated using a token simulator before deployment. A token simulator simulates the behavior of the token based on historical and forecast data related to the underlying or linked asset class. Simulations may include algorithmic simulations, such as aggregating other simulations that cover the underlying asset class. Simulations allow designers to predict and adjust for likely events and to set rules to deal with unlikely events (e.g., "black swan" events). Simulations may use data collection from real markets as well as simulated markets and may utilize various artificial intelligence and machine learning capabilities pointed out throughout this disclosure and the documents incorporated herein by reference.

[0059] Once ready, the tokens may be deployed to the token market, for example, by using the token marketplace system 210, which may include a system 262 for market launch, a market tracking and analysis system 274, and an asset tracking and analysis system 272. The token marketplace system 210 may further include a trading system 264, a governance system 270, and a verification system 268, or further examples of such systems related to one or more asset classes.

[0060] Token designers can interact with the token design interface 302 of the asset class-backed tokenization platform 200 and design the attributes of a proposed asset class-backed token 318 based on the asset classes they represent. Token designers can select blockchains and consensus algorithms for verification, set tokenonomics parameters where applicable, determine the quantity and timing for launching a token tranche, set a starting price, and create trading governance rules. In token design, token attributes can be adjusted to match the attributes of the underlying asset class, such as aligned timing and risk and return curves.

[0061] Referring to Figure 3, the token design system 204 may include a token design interface 302 (also referred to as a user interface) for defining a proposed asset class back-based token 318. The token design interface 302 may include a token attribute profile interface 332, an asset class attribute profile interface 334, an asset-token link framework profile interface 338, and a token simulator interface 340. The token attribute profile interface 332 allows a user or designer to select various token attributes 304 to define the token attribute profile 322 of the proposed asset class back-based token 318. The asset class attribute profile interface 334 allows a user or designer to select various asset class attributes 308 to define the asset class attribute profile 324 of the proposed asset class back-based token 318. The asset-token link framework profile interface 338 allows a user or designer to select asset-token link framework attributes 310 and define the asset-token link framework 328. The token simulator interface 340 allows the user or designer to run the token simulator 312, view the token simulator output 320, and predict the performance of the proposed asset class back-based token 318.

[0062] The token design interface 302 allows users to design the characteristics of a proposed asset class-backed token 318, including reviewing the range of token attributes and asset class attributes, analyzing attributes based on historical analysis datasets, and selecting from a set of asset-token linking framework attributes 310, as well as simulating the market behavior of the proposed asset class-backed token 318. This can be an iterative process, as the token attribute profile 322, asset class attribute profile 324, and asset-token linking framework 328 are modified based on one or more simulation results.

[0063] Referring to Figure 19, another aspect of the present invention relates to a strategy engine 1902 that can be engaged via a token design interface 302. In this embodiment, the strategy engine 1902 includes a machine learning system 1904 that trains a set of machine learning models 1908 to output decisions 1918 relating to a set of token design strategies to be adopted by a token designer, which is optionally trained using a training dataset 1910 consisting of at least one of asset class and / or token features, attributes, parameters, and results. In embodiments, the strategy engine receives a request 1914 for determination 1918 of a set of asset class attributes that may be configured to support a strategy selected and / or configured by the user, and includes an artificial intelligence system 1912 that generates a set of asset class recommendations (associating, for example, the type of asset class, a specific bundle of assets, relative amounts, recommended timing for acquisition or disposal, etc.), optionally generating a set of asset class recommendations for a set of asset classes (optionally including a set of recommended tokens that represent a set of asset classes, are interchangeable with a set of asset classes, are indexed with a set of asset classes, are linked to a set of asset classes, or are otherwise associated) so that the recommended actions for the asset classes (and tokens, if applicable) execute a strategy selected and / or configured by the user. Strategies can be specified in the token design interface, simulated in the simulation interface, and their relationship to each asset class / asset class token can be displayed. The parameters of a strategy may include specific objectives sought by the strategy (e.g., capital preservation, desired risk / return profile, accumulation of a specific amount of value (e.g., to achieve a defined objective)). The behavior of asset classes related to the strategy may be simulated, and the association of asset class recommendations and simulations with token attributes may include a visual representation of past performance, and optionally, the aggregate impact of the token set may be presented in layers, including past and / or predicted volatility / fluctuations.In embodiments, the Strategy Engine 1902 may use a search function that allows a token designer to search for asset classes (including other tokens) that have a set of desired characteristics such as value retention, volatility, liquidity, trading speed, trading volume, legal authorization for trading (e.g., authorization for trading in a given jurisdiction), infrastructure compatibility (e.g., availability of APIs for algorithmic trading, and / or presence of blockchain, smart contracts, wallets, or other infrastructure), and other. This may be achieved through an interface that allows a user to set filters and discover asset classes that satisfy those filters (which may include collaborative filters that leverage similar user selections), or through algorithmic search that is based on the similarity of asset classes to desired asset classes that a user can characterize in the user interface.

[0064] In embodiments, the AI ​​system 1912 can leverage various types of generative AI models, including generative adversarial networks (GANs), variational autoencoders (VAEs), long short-term memory networks (LSTMs), transformer language models, and / or conditional generative models, as well as other types of generative AI models for various use cases described elsewhere in this specification. In these embodiments, the AI ​​system 1912 may include different types of neural network layers, various operations used by different architectures (e.g., multi-head attention layers for transformer models), and / or specialized hardware for efficiently implementing such. In some embodiments, the AI ​​system 1912 can use a transformer-based embedding model (or another type of embedding model) to generate embeddings for some use cases. For example, in a use case where platform 200 provides contextual information to a language model, the language model may use embeddings to access the contextual information, and the embeddings may be generated by the embedding model and made available to the language model for discovery via vector search. The transformer-based embedding model may be a deep learning model that uses a self-attention mechanism to generate vector embeddings. Transmitter-based embedding models can be effective in capturing contextual nuances of language based on word position and other factors through the self-attention mechanism of the transmitter model. Furthermore, other embedding techniques such as Word2Vec FastText can also be employed to generate embeddings. For example, Word2Vec is a shallow neural network model that can generate word embeddings by learning from the co-occurrence of words within a specific window of text. FastText extends the Word2Vec technique by considering subword information, which can increase its effectiveness in handling out-of-vocabulary words and misspellings.

[0065] In embodiments, the strategy engine 1902 may include a set of predetermined strategies and associated asset classes, such as: (i) asset classes with low historical volatility, guaranteed returns, backed by or tethered to the underlying asset value, etc., for capital preservation strategies; (ii) tokens representing returns from speculative asset classes such as emerging market bonds, for more aggressive strategies seeking high returns and accepting higher risk; and (iii) various permutations associated with other strategies, including those with predetermined pivot points (e.g., shifting from aggressive to conservative as a value threshold level accumulates, as the user reaches a certain age, etc.). Pre-configured and / or custom strategies can be selected or designed from among many available strategies, such as buy-and-hold, equity long / short, credit default swap strategies, asset allocation, time-based portfolio selection, pair trading, swing trading, scalping, day trading, news-based, market timing, social trading, front-running, chart-based, computer science-based, automation / algorithmic, and the aforementioned hybrids and combinations. Custom, hybrid, and combined strategies may be supported by sets of tokens representing different aspects necessary to support the underlying strategy. For example, security tokens for growth equity allocation in a portfolio, intellectual property royalty stream tokens or production yield tokens for current yield targets in a portfolio, and emerging market real estate tokens for the token's speculative value accumulation targets.Each set of tokens can be represented by the analytical interfaces described herein, such as the historical behavior of the underlying asset class, predicted or simulated behavior (optionally using machine learning or AI operating on historical datasets to predict behavior), and correlations between asset classes (to indicate where the selected tokens may have underlying economic relationships, e.g., a tendency to increase volatility and / or expected returns (e.g., due to having reinforcing aspects or underlying relationships between two asset classes), or a tendency to decrease volatility and / or returns (e.g., where asset classes / tokens tend to correlate in a way that hedges risk)).

[0066] In embodiments, the strategy engine 1902 may be trained over time, for example on a training dataset 1910 of token designers and results on the platform, to take an input strategy, discover a set of asset classes that are candidates satisfying the strategy, select a set of tokens linked to the asset classes, generate a plan for acquiring and / or disposing of tokens (and possibly other assets) to execute the strategy, and issue a set of instructions (executable over time) to execute the plan. In embodiments, the strategy engine may be trained to monitor and rebalance the set of tokens, for example, based on market changes (e.g., changes in the value of tokens or other assets), changes in the strategy (including pre-planned and spur-of-the-moment changes), and / or other factors, using feedback on similar inputs and results.

[0067] Referring to Figure 4, token attributes 304, which may be part of the token attribute profile 322, may include media type 402, market rules 410 (e.g., how the token can be redeemed, whether the token is launched single or in tranches), proof 408, token design 412 (e.g., static or live), chain 404 (e.g., Bitcoin, Ethereum), etc., and may differ between asset classes. Token attributes 304 may include media and display properties, trading rules, blockchain attributes, consensus algorithm details, tokenonomics details, and smart contract options for token processing (including token launch (e.g., in tranches), burning, redemption, exchange, etc.). The reference design library can be accessed via dropdown menus or drag-and-drop, etc., facilitating the rapid generation of new tokens from precedents and the selection of different token attributes 304.

[0068] A hybrid asset token can have attributes of multiple asset classes, such as commodities, securities, real estate, and an intellectual property basket. Asset class attributes 308 may be part of the token's asset class attribute profile 324. Asset class attributes 308 may include identification data (such as what traditional legal and market frameworks govern the ownership and transfer of the asset), historical data on the asset class (including links to relevant analytical datasets, such as price and volume patterns, volatility, valuation estimates, expert comments, ratings, and many other indicators), and / or predictions or simulations of the asset class's behavior. Referring to Figure 5, asset class attributes 308 may include attribute class 502, asset rating 504, information on the asset's historical performance 508, any governing bodies associated with the asset 510, and information on the asset's current trading mechanism 512.

[0069] Referring to Figure 6, the asset-token linking framework profile interface 338 may allow the user to select different asset-token linking frameworks 328, such as where the asset or token is traded on an index 602 (e.g., DOW, S&P), whether there is physical linkage 604 such as an asset tag, and whether there is digital linkage 608 between the asset and the token, such as a user ID, digital wallet, cryptographic linkage, and link. There may also be data and rules regarding the relevant blockchain 610 and how the asset-to-token link is verified 612 (e.g., IoT sensors, biometrics, signatures, expert authentication).

[0070] The asset token linking framework profile interface 338 allows a user (e.g., a token designer) to determine how a token is linked to an asset class, such as between the token and another digital item, an orchestration system (e.g., when purchase, sale, and other actions on the asset class are orchestrated to correspond to actions on the token), a physical link 604 (e.g., an asset tag), a verification or authentication process (e.g., when a sensor or camera recording actions on the asset class is involved), or a combination of these or other linking frameworks. In embodiments, a user can generate at least one asset class linked token in response to a token link description and an asset class description. For example, a user can interface with an asset class-backed tokenization platform 200 and instruct the asset class-backed tokenization platform 200 to issue (mint) one or more value streams or asset class-backed tokens on a distributed ledger (e.g., a blockchain 254).

[0071] The token simulator 312 allows token designers to simulate the market behavior of a proposed asset class-backed token 318 based on historical asset class behavior, historical token trading data, and a set of economic analysis models and / or tokenomics analysis models. The token simulator interface 340 allows designers to select token simulator parameters that the token simulator 312 will use when simulating the market behavior of the proposed asset class-backed token 318. In embodiments, the token simulator 312 can simulate the contribution of the underlying asset class to which the token is linked, as well as the contributions of tokenization, such as additional liquidity, reduced counterparty discovery costs (such as those provided by automated market making), fractionation (such as a reduction in the trading side that allows more entities to participate), reduced holding costs, improved security, rewards (such as yield farming and mining), or the impact of other factors. This can be achieved, for example, by analytically comparing other similar tokens (based on various similarity parameters, including tokenomics parameters, underlying asset class parameters, and others) with the underlying asset class to which the token is linked or backed.

[0072] Referring to Figure 7, the token simulator output 320 may include predictions such as token volatility 702, token trading volume 704, token price 708, total value 710, and information regarding redemption requests 712 as described elsewhere in this specification. Redemption requests 712 may be indirect, such as when the token is linked by an index, which may optionally include embodiments where the token is simply an underlying asset class or its members, or where the token is a composite representation of a set of rights. In such cases, a smart contract can be defined to retrieve the index value of the asset class or its members and calculate the current redemption value of the token (which may be expressed as currency, cryptocurrency, other tokens, points, or other face value). When a redemption signal is received (such as in a redemption application 712 or its interface), the smart contract may send instructions to the relevant entity (e.g., an account and / or settlement system) to perform its calculation, determine the value, and deliver that value to the redemption user. The smart contract may also instruct, once the redemption is complete, to "burn" the token or otherwise render it unusable. Redemption can be partial, where a portion of the funds is transferred and a smart contract associated with the token sets a new value for future redemptions. Alternatively, there can be direct redemption, where the token represents or embodies access control. For example, a token could be used to unlock access to production equipment such as a 3D printer, with printer production allocated to token holders for a specified period, a specified number of production units, etc. This can be applied to various production types, including energy production, materials, agricultural products, and manufacturing lines. As with other redemptions, a smart contract can embody and manage redemption rules, such as tracking incremental or partial redemptions and burning or invalidating tokens upon full redemption. Redemption can also trigger account changes, such as the transfer of tokens in a distributed ledger or wallet.

[0073] The term "circuit" as used herein should be understood broadly. In certain embodiments, components having terms such as controller, component, engine, and processor may be used instead of and / or in combination with a circuit, and therefore these terms should be understood broadly with similar considerations as those presented with respect to a circuit. The term "circuit" as used herein includes correlated aspects of a system configured to perform its selected operation and may include logic gates, programmable logic circuits, interface devices (e.g., network communication devices and / or connection hardware), I / O devices (e.g., user input devices such as mice, computers, mice, keyboards, touchscreens, voice inputs, and displays), any sensors utilized by the system, any actuators utilized by the system, processors or processing resources, computer-readable memory, data stored in computer-readable memory, data accessed by the system, API interfaces (e.g., to marketplaces, third-party services, etc.), etc. In certain embodiments, an aspect of a circuit may be embodied as instructions stored in computer-readable memory, through which a processor executing instructions is configured to perform one or more operations of the circuit. In certain embodiments, as understood in the context of the specific descriptions herein, a circuit includes and / or communicates with any one or more of these. For the sake of clarity in this disclosure, each controller, circuit, component, engine, memory element, data structure, etc. is depicted as a single device. A given controller, circuit, component, engine, memory element, data structure, etc. may be distributed, all or partially, additionally or alternatively.

[0074] Referring to Figure 8, an exemplary system 800 for creating asset-linked tokens is schematically depicted. The exemplary system 800 may be used with, included with, and / or included in whole or in part with, any systems, assemblies, controllers, components, circuits, etc., as defined throughout this disclosure, including at least the platforms and / or systems defined in relation to Figures 1-3. In addition, or alternatively, the system 800 and / or embodiments thereof may be used to perform any methods, operations, and / or procedures defined throughout this disclosure. The exemplary system 800 includes a tokenization controller 802 that implements a tokenization user interface 808, a token deployment controller 804 that implements asset class-backed token trading, and a token marketplace controller 806 that implements an asset class-backed token trading system.

[0075] Referring to Figure 9, an exemplary tokenization controller 802 is schematically depicted. The exemplary tokenization controller 802 includes a token definition circuit 902 that interprets a token description 910 in response to user operations by the tokenization user interface 808, an asset definition circuit 904 that interprets an asset class description 912 in response to user operations by the tokenization user interface 808, and an asset backing circuit 906 that interprets a token link description 914 which includes a token performance description corresponding to the beneficial properties of at least one asset corresponding to the asset class description 912. The exemplary asset backing circuit 906 generates at least one asset class link token 916 in response to the token link description 914 and the asset class description 912. In an embodiment, the asset backing circuit 906 may be configured to invoke the issuance function of a smart contract 250A stored on a distributed ledger, as will be described in more detail below, in order to generate the asset class link token 916.

[0076] In certain embodiments, the token description 910 includes aspects of the token set by a user creating a token using the tokenization user interface 808, such as the token's name or identifier, a description used with the token (e.g., on the marketplace), and the token's purpose. In certain embodiments, the user's choices for the token description 910 may be limited by the platform and / or the tokenization user interface 808, for example, to limit the maximum or minimum initial value of the token, to make certain information about the token available to potential buyers (e.g., set by regulations, policies, and administrators, e.g., set by the platform administrator), and / or may be limited to choices made available by the platform administrator (e.g., limited to certain types of assets, certain beneficial properties of assets, a limited total number of assets or asset types that can be linked to a token).

[0077] In certain embodiments, the asset class description 912 includes a description of the underlying or linked asset and / or the available beneficial properties of the token-linkable asset. In certain embodiments, the beneficial properties of the token-linkable asset may depend on the asset itself (for example, a real estate-based asset may enable linking to rental income, property taxes, and / or insurance premium collections of the real estate, and a precious metal-based asset may enable linking to specific properties such as value, value volatility, production volume such as country, specific mine, or globally, ore cuts of a mine or region).

[0078] In certain embodiments, the token link description 914 includes the underlying (or linked) asset or asset class, the amount of the asset, and which beneficial properties of the asset are linked to the token. In certain embodiments, the token link description 914 further includes rules governing the token, such as thresholds related to the beneficial properties utilized (e.g., if the token's performance is adjusted based on external factors such as inflation, production capacity, news events, crop yield, time elapsed since the token was created, time it was first acquired, time it was last traded), and / or changes to the asset-token link in response to the selection of any of these factors. In certain embodiments, the underlying (or link) between the asset (and / or asset class) and the token may be a hard link. For example, the token's performance may follow beneficial properties of the asset or asset class due to hard constraints (e.g., ownership of the token is linked to a specific amount of the underlying asset) and / or certain rules (e.g., the token's performance is guaranteed—e.g., another linked token whose value changes in response to the original token's performance not strictly following beneficial properties). In certain embodiments, the backing (or link) between an asset or asset class and a token may be a soft link. For example, the token link and / or rules may be configured such that the token's performance is expected to follow its beneficiary characteristics, but the token's performance in actual operation may not necessarily match that performance. For example, a token linked to the value of gold may instead be linked to a basket of other precious metals or other assets that tend to track the price of gold, but do not actually track the price of gold precisely. The nature of the token link description 914 may or may not be indicated with the asset class linked token 916, depending on, for example, applicable regulations, policy decisions from the platform administrator, or choices made by the user creating the token.For example, a publicly offered asset-linked token 1108 presented for sale on a marketplace may include details such as which asset or asset class is linked to the token, whether the link is a hard link, a constrained link, or a soft link, and what rules apply to the token to enforce the link to the asset or asset class (what thresholds, data, or events apply to the token, and / or how the link is expected or planned to change over time (for example, in the case of a token with characteristics such as value tracking, volatility tracking, or risk tracking, how it changes over time in a known or planned way with respect to certain events or metrics)). In a particular embodiment, the token link description 914 associates a specific asset and / or asset class with the token, such as a revenue stream from a specific farm or hotel. Such assets and / or asset classes may also change over time. It is possible that (for example, switching linked farms based on the season, etc.) In certain embodiments, the token link description 914 does not associate a specific asset with a token. For example, a token backed by a token linked to a commoditable asset, an asset class, and / or a derived asset (e.g., a basket of farms, intellectual property, etc.). In certain embodiments, the token link description 914 is consumable in whole or in part, for example, using actually consumable assets (e.g., consumable assets such as a specific store crop, oil, etc.) and / or consumable assets in a logical sense (e.g., intellectual property assets that enable a specific number of uses, number of users, number of installations, etc.). In certain embodiments, the token link description 914 is associated with a number of assets or asset classes that may change over time, for example, tokens representing a group of subscriptions (and / or revenue streams of subscriptions), a number of license seats, etc.

[0079] Referring to Figure 10, an exemplary token link description 914 is schematically depicted. The exemplary token link description 914 includes a token performance expression 1002 corresponding to a beneficial characteristic 1004 of the linked asset 1006. The linked asset 1006 may be any type of asset, including, but not limited to, any type of asset as defined throughout this disclosure. Examples of linked assets include: precious metal assets (e.g., gold, silver, platinum, precious metal indices, and / or combinations of metals); asset precursors (e.g., planted crops, revenue streams from assets under construction, ore produced from mines before processing); fiat money (e.g., sovereign currency-based assets); real estate assets (e.g., real estate, revenue related to real estate, residual value from the sale or disposal of real estate, profits from real estate investment trusts, etc.); financial assets (e.g., financial instruments, contracts, and / or revenue related to contracts, and / or dividends related to securities or other financial instruments, etc.); securities assets (e.g., stocks, bonds, or secured instruments such as related profits or income therefrom); intellectual property assets (e.g., revenue related to elements of intellectual property such as copyrighted works, group subscriptions, etc.) Linked assets (such as the right to use intellectual property elements such as revenue from and / or copyrighted works and sets of manufacturing instructions), production assets (e.g., crops from a farm, products from a manufacturing facility, and / or revenue from thereon), one or more of the aforementioned beneficiary assets (e.g., incidental or residual rights to any of these based on, for example, the occurrence of a particular event, the elapsed time, etc.), and / or one or more of the aforementioned derivative assets (e.g., a packaged and / or securitized version of any of the aforementioned, such as a share of revenue from a group of manufacturing facilities, or an asset manufactured under a particular license). In certain embodiments, linked asset 1006 may be a combination of these, for example, a share of license revenue from an IP license for manufacturing a particular object and a license for manufacturing a certain number of units of a particular object.In certain embodiments, the combined assets may be of similar nature—for example, income shares related to licensed seeds and harvested crop income from a particular farm—or of dissimilar nature—for example, a combination of an asset (or asset class) related to precious metal assets and an asset related to dividends from a particular stock. By utilizing similar and / or dissimilar assets linked to and / or backed by the token, it becomes possible to adjust the performance expression of the token 1002 independently of beneficial properties 1004. For example, a token 916 linked to an asset class may track the value of a low-volatility asset and leverage (or deleverage) value movements. In certain embodiments, the use of multiple linked assets 1006 makes it possible to generate tokens with specific selected responses to events that may be relevant to a particular user or class of users and that are not linked in the general economic system, such as the occurrence of weather events that may affect crops, housing prices, etc.

[0080] Referring to Figure 11, an exemplary token deployment controller 804 is schematically depicted. The exemplary token deployment controller 804 includes a token providing component 1102 that publishes a published asset class link token (e.g., a token issued on a distributed ledger) to a token marketplace system (e.g., operated by a token marketplace controller 806), which is provided as a published asset class link token 1108 (e.g., a token issued on a distributed ledger) based on an asset class link token 916 published by a token deployment circuit 908 (e.g., issued on a distributed ledger). In a particular embodiment, the asset class link token 916 is published at creation, when the operation of the token deployment controller 804 is performed by the token deployment circuit 908. In a particular embodiment, for example, if the administrator of the token marketplace chooses to verify and / or otherwise approve the token before it is published, and / or if publication controls are provided that allow the asset class link token 916 to only some users and / or selected groups of users, the token providing component 1102 performs the publication 1108 of the asset class link token(s). The publicly released asset class-linked token(s) 1108 may be listed on an interface such as a market operated on a website, web portal, or mobile application, and / or the publicly released asset class-linked token(s) 1108 may be recorded on blockchain 1112 or other ownership and / or transfer record systems, thereby making them available to users who have access to the market and / or blockchain 1112.In an embodiment, the exemplary token deployment controller 804 includes a token contract component 1104 that executes a smart contract 1110 to acquire a publicly traded asset class linked token(s) 1108, for example, in response to a user selection to acquire a publicly traded asset class linked token(s) 1108, and / or in response to a user rule configured to automatically acquire tokens having certain characteristics (e.g., price, expected value, volatility description(s), liquidity description(s), etc.). In a particular embodiment, the smart contract 1110 is executed in any manner as provided throughout this disclosure, including binding the parties (e.g., the token's creator and / or previous owner, as well as the token's acquirer) to terms and conditions related to the token. The terms and conditions related to the token may be created as part of the token description 910 and / or in accordance with terms and conditions applied by the token marketplace administrator. The exemplary token deployment controller 804 includes a token recording component 1106 that records the transaction of the publicly traded token(s) 1108 in response to the executed smart contract 1110. In certain embodiments, for example, if a transaction is not executed using the smart contract 1110, the operation of the token recording component 1106 is performed in response to a received indication that a transaction has occurred, for example, based on a notification or transaction value received from the token market controller 806 in response to user operations of the token creator, buyer, and / or seller.

[0081] In embodiments, the token deployment controller 804 may deploy tokens (or sets of tokens) using a set of robotic deployment agents, such as using automated market maker (AMM) functions, including pairing tokens with other forms of tokens (e.g., cryptocurrency or digital currency) using a fixed-volume trading function. As stated elsewhere in this disclosure, finding counterparties in a transaction can be a challenge, and imposing transaction costs can hinder the development of a robust market that would allow parties to profit through mutually beneficial exchanges. This may be particularly true in the case of a market for a new or unfamiliar item, such as a new token, as described in the various embodiments described herein. To enable such a thin or novel market, or for other reasons, a set of algorithmic automated market-making services or capabilities (such as those provided by the AMM 2350 and / or RDA system 2352, as described in more detail below, for example) may be used in the various embodiments described herein, thereby allowing a set of automated robotic agents to facilitate trading of assets (in embodiments, such as a set of asset stream collateral tokens that may be designed by a token designer). Accordingly, in embodiments, as described above, a trading platform such as those described herein may include a set of automated market maker (AMM) services or capabilities 2350 in which a set of automated robotic agents provides liquidity or otherwise facilitates trading of a set of assets. A set of automated market maker functions 2350 / 2352 may, in embodiments, be partially or fully implemented in an asset stream-backed tokenization platform (e.g., as an RDA system 2352) so that a token designer can design, configure, and deploy automated market making for a set of tokens.The RDA service 2352 may be driven by a set of AI services, artificial intelligence, used to discover, design, and / or configure an appropriate set of automated market maker capabilities 2350 for a given situation (such as providing a desired amount of liquidity, a desired risk profile, a liquidity utilization profile, a desired capital efficiency of deposited liquidity or committed collateral, etc.). Such AI services can be trained on training datasets such as human design behavior or training datasets resulting from the deployment of automated market makers. Like other capabilities, the automated market maker service 2350 can be simulated with a simulation capability of a tokenized platform backed by asset streams.

[0082] In an embodiment, a set of automated market-making services 2350 may have access to a set of liquidity pools, which may include a set of assets (such as cryptocurrencies, digital currencies, or other asset flows indicated throughout this disclosure) that can be used for trading. Assets or sets of assets may be discovered by other mechanisms, including web searches, crowdsourcing, or providing targeted offers to asset holders based on inspections such as public blockchain information or public wallet information. Targeted offers may include smart contract mechanisms that allow a set of liquidity providers to deposit assets into a set of liquidity pools in exchange for a predetermined set of incentives or considerations, or yields, such as a share of trading fees generated by automated market makers.

[0083] In embodiments, the automated market maker service 2350 can be configured so that the price automatically adjusts based on demand for the token, such as in embodiments of a token set designed by a token designer in the case of tokens backed by an asset class. The token designer can choose one of several automated market maker models, including those based on simulation or modeling of the operation of each model, which includes inputs about the underlying asset class or stream to which the token is linked or backed. Such embodiments may include the use of a constant function market maker (CFMM) configured based on a constant function such that the combined asset reserve of trading pairs on the exchange must be immutable (such as trading pairs of tokens designed by the designer with another token, such as a cryptocurrency or digital currency). In embodiments, user deposits may be held by a custodian, or user deposits may be held in a smart contract that traders can use for liquidity, such as trading tokens. In non-custodian embodiments, users can trade via a smart contract with a liquidity pool without having to find a specific counterparty.

[0084] CFMMs include one or more types, such as Constant Product Market Makers (CPMMs), Constant Sum Market Makers (CSMMs), and Constant Mean Market Makers (CMMMs) (where the weighted geometric mean of each liquidity pool is kept constant). While each of these known approaches to automated market making enables market making, each of these models has its own challenges. For example, constant sum market makers can lead to arbitrage and outflows of liquidity pools if there are differences in the market prices of the traded assets. These automated market-making models have also been criticized for issues such as perpetual losses (more profits could have been made by simply holding the assets rather than betting on liquidity pools) and low capital efficiency (due to a lack of control over the price points offered to traders, in contrast to traditional exchanges where order book operators can precisely set prices to efficiently utilize available liquidity).

[0085] In embodiments, more advanced forms of automated market makers can be used, such as in connection with the design and deployment of sets of tokens backed by asset streams using the token design platform described herein. This may include one or more sets of hybrid automated market makers, dynamic automated market makers, proactive market makers, and / or virtual automated market makers. Such embodiments may include the use of advanced hybrid constant-function market makers that combine multiple functions and configuration parameters to enable the degree of risk exposure, the degree of adjustment to price impact, etc. One known example, the stable swap in variant, deploys a constant product market maker and a constant sum market maker, allowing for greater liquidity to be utilized at intermediate prices (whereas more conventional market makers tend to utilize liquidity most effectively only at extreme prices). Another known variant is the dynamic automated market maker (DAMM), which adapts to changing market conditions by dynamically allocating liquidity to various points on the price curve using feeds of price information and volatility data from public sources (such as price feeds and oracles). For example, to improve the capital efficiency of a liquidity pool, liquidity can be concentrated during periods of low volatility and expanded during periods of high volatility. In embodiments, a proactive market maker (PMM) may be used, which attempts to mimic human market-making behavior based on price feed input data, such as seeking increased liquidity close to the current market price, thereby improving capital efficiency. In embodiments, the proactive market maker may be trained to effectively replicate human market-making activity for a given asset class based on a training dataset of human market-making activity and the results from that activity.In other embodiments, a set of virtual automated market makers (VAMMs), such as existing perpetual protocols, may be used. These may use a certain product market maker formula, but instead of using a liquidity pool, collateral is deposited in a smart contract (exposing the collateral to the risk of liquidation in the event of large price fluctuations), and synthetic assets rather than underlying tokens are traded. These and other types of advanced automated market makers should be understood throughout this disclosure as being encompassed within a set of automated market-making services and functions 2350 that may be included in, integrated with, or linked to the trading platform and system. In embodiments, the selection of a particular market-making model 2350 may be based on the characteristics of the underlying asset class in the case of asset class-linked tokens, and / or on the trading characteristics of a pair (or larger set) of tokens, which may include one or more asset class-linked tokens and one or more other tokens linked by an algorithmic market-making function. The selection may also be based on an analysis of trading volume, trading speed, yield (including different points on the exchange's price curve), capital efficiency, and other metrics. In embodiments, an intelligent agent (such as one provided by the RDA system 2352) may be trained, for example, on a human-selected training dataset and / or on a set of market-making results, by supervised, semi-supervised, or deep learning, to select appropriate token pairs, appropriate market-making functions, and / or appropriate configuration parameters for the market-making functions.

[0086] Referring to Figure 12, an exemplary token marketplace controller 806 is schematically depicted. The exemplary token marketplace controller 806 includes a trading system component 1202 that makes the published asset class linked token 1108 available to at least some of the users of the asset class backed token trading system 800, e.g., all users, selected users (e.g., actively selected users, users associated with a particular entity, users associated with a particular submarket of the market, etc.), and / or rule-based user selection (e.g., users who meet a particular asset threshold, subscribed users, users who perform a particular search such as using a particular keyword in a search, and / or users who have performed a trade related to a particular type of token and / or a particular type of asset or asset class, etc.). The exemplary trading system component 1202 further accepts user input for trading the published asset class linked token 1108 (e.g., as user interaction 1208), including at least user interaction 1208 for searching for the token, accessing the token, discovering the token and / or making the token available for trading, providing the token for trading, and / or purchasing the token.

[0087] An exemplary token marketplace controller 806 includes a governance system component 1204 that performs operations supporting a governance scheme(s) 1206 for an asset class-linked token 1108, including operations that may regulate aspects of token creation, token publication, token offering, token purchase, and / or token maintenance. The governance scheme(s) 1206 may include regulatory components that are enforced and / or maintained with respect to the token (e.g., with respect to laws applicable to the token and / or entities that offer and / or purchase the token), and policy components (e.g., to ensure that policies by the token-offering party, trading party, token marketplace administrator, and / or administrator of the token-linked asset or asset class (e.g., a listed company, an exchange) are enforced and / or maintained with respect to the token). In certain embodiments, the applicable governance scheme(s) 1206 may depend, in whole or in part, on the combination of assets or asset classes linked to a particular token, the nature of entities offering and / or purchasing tokens (e.g., size, investor class, indicated preferences, etc.), and the jurisdiction of one aspect of the marketplace or transaction (e.g., the location of the marketplace administrator, the location of the marketplace servers, the location of the offering entity, the location of the purchasing entity, and / or the location of the linked assets and / or asset classes, etc.). Since the linkage of a particular token may change over time, and the related parties to the token may also change over time (e.g., due to changes in related parties away from the marketplace, such as token trading and / or the sale or merger of entities), the governance scheme(s) 1206 associated with the token may also change over time.In certain embodiments, the governance system component 1204 performs one or more operations, such as adjusting the token link description 914 in accordance with the governance scheme 1206 associated with the asset class description 912, and / or verifying the token link description 914 (e.g., verifying that the token's attributes and description remain compliant, and / or verifying that appropriate notices, confirmations, etc., have been completed). In certain embodiments, the governance system component 1204 may provide notice to entities, remove tokens from listing or public disclosure, and / or provide governance information to users and / or operate the user interface to obtain confirmation from users, in response to changes in the applicable governance scheme 1206, and / or tokens, associated entities, transactions with tokens, and / or the governance scheme 1206 itself (e.g., changes in regulations, laws, policies, etc.). In certain embodiments, changes to tokens, such as which assets or asset classes are linked, and their mixtures, may also involve operations of the governance system component 1204 to ensure that tokens, marketplaces, and users of the marketplace remain compliant with the applicable governance scheme 1206.

[0088] The terms asset-backed tokens, asset-backed tokens, and / or asset-linked tokens as used herein should be understood broadly. The tokens of this disclosure are cryptographically secure elements that can be held, traded, or otherwise exercised as digital property. Tokens can be managed, held, and / or traded using any secure digital backbone for managing ownership and access, for example, using digital certificates or blockchains for recording transactions. In certain embodiments, tokens can represent ownership of a single unit, ownership of multiple units, and / or ownership of a shared unit. Without limiting other aspects of this disclosure, tokens used herein may operate in a similar manner to cryptocurrency assets and may be cryptocurrency assets, and / or may include one or more cryptocurrency assets as part or all of the underlying assets or asset classes of the tokens, and / or as asset classes linked to the tokens. Asset-linked tokens may be referred to herein as asset-backed tokens, asset-backed tokens, asset-linked tokens, and / or other similar terms. Asset class-linked tokens represent and / or are linked to a beneficial characteristic of an asset or asset class distinct from the token itself. For example, asset class-linked tokens can be generated to track the risk profile, volatility, liquidity, value trajectory, and value offset profile (e.g., relative to inflation or another dynamic indicator) of a specific asset (e.g., an identifiable asset such as a specific farm or production facility) or asset class (e.g., gold, soybeans, oil, California real estate, downtown Manhattan real estate, or the net profit of a basket of companies in a specific industry). In certain embodiments, even if an identifiable asset is linked to an asset class-linked token, the identifiable asset may change over time.

[0089] The term "beneficial property" as used herein should be understood in a broad sense. A beneficial property is a property that depends on the underlying asset. Therefore, a token that has performance corresponding to a beneficial property of an asset (and / or asset class) (for example, as a token performance representation) is, in a sense, a token that is designed to have performance that follows the performance of that property of the asset and / or asset class. For example, if the beneficial property of an asset is asset value, then a token that is designed to have performance linked to that beneficial property will have value that follows the asset value. In certain embodiments, a token may have performance corresponding to multiple beneficial properties of an asset or asset class (e.g., asset value, asset volatility, asset liquidity, etc.), and / or may have performance corresponding to one or more beneficial properties of multiple assets or asset classes (e.g., a token that has performance corresponding to the value of a quantity of gold assets, and further has performance corresponding to the revenue streams of a production asset). Without limiting other aspects of this disclosure, the performance corresponding to a beneficial characteristic may be performance that matches the beneficial characteristic (e.g., if the value of the linked asset doubles, the value of the token also doubles), and / or performance that has a function selected relative to the beneficial characteristic (e.g., one that scales relative to the beneficial characteristic, such as 1.5x, 2x, 0.5x; one that is offset relative to the beneficial characteristic, such as x+0.5, x-1.0; and / or one that has a more complex relationship with the beneficial characteristic, such as matching, scaling, and / or offsetting, depending on the value or performance of the beneficial characteristic itself—for example, utilizing different functions such as matching returns up to 5% and doubling or halving returns of 5% or more, which can be done for any reason, such as adjusting the leverage of the token based on value returns to limit losses, maximize profits, or adjust volatility).In certain embodiments, a token can have performance corresponding to several beneficial characteristics in any way, such as fractional performance (e.g., 25% for beneficial characteristic A, 75% for beneficial characteristic B), priority-based performance (e.g., following beneficial characteristic A under certain conditions and beneficial characteristic B under other conditions), and / or weighted or cost-based performance (e.g., using a scoring function for beneficial characteristic A and another scoring function for beneficial characteristic B, where the token's performance is adjusted to maximize, progressively improve, and / or enforce a threshold of the overall score value from the scoring function).

[0090] This means that embodiments of this specification can be used to tailor the performance of a token based on an underlying asset class having independent value, create a token with selected performance characteristics based on selected characteristics of the underlying asset, base the performance on token ownership and trading, and give users leverage to exercise the benefits of the token for a selected purpose. Embodiments of this specification enable token creators, holders, and traders to perform a number of real-world operations that cannot be performed using conventionally known means. For example, a token can be configured to track the value of a first asset and emulate the volatility of a second asset. In another example, a token may be configured to trade value related to an asset that is not tradable in conventionally known systems, enabling more complex operations such as trading a portion of the revenue stream from an asset or trading a selectable portion of the revenue stream (e.g., amounts exceeding static (e.g., amounts exceeding $1 million per year) or dynamic (e.g., amounts exceeding 8% of net profit) thresholds of the asset's revenue stream). Embodiments of this specification also provide a convenient interface for creating and executing tokens where similar options for attaching to and / or following aspects of an asset previously required complex and highly specialized agreements between multiple parties to perform similar functions. Embodiments of this specification also provide enhanced liquidity, enabling parties to engage in and trade otherwise complex arrangements without negotiating agreement terms, and enabling a number of parties, including any party with access to a trading market for tokens linked to an asset class, to trade the asset with less burden to enter, reduced costs of creating and maintaining arrangements, and / or improved token liquidity by facilitating token creation, publication, and trading.Embodiments of this specification provide technical tools that enable configured arrangements that can facilitate the creation and execution of projects, investments, and / or trading arrangements that would otherwise be unavailable due to a lack of previously available tools, in order to manage risk, distribute investments across a sufficiently large pool of investors, and / or provide sufficient protection and transparency to investment returns on those projects, investments, and / or trading arrangements.

[0091] Referring to Figure 13, an exemplary asset class description 912 is schematically depicted. The asset class description 912 is determined in response to user entries on the tokenization user interface 808 and / or may be further determined in response to default values, values ​​determined according to governance scheme 1206, preferences generally indicated by the user, and / or values ​​determined according to rules set by the administrator of the tokenization controller 802. In certain embodiments, the type of asset or asset class in the asset class description 912 may be used to adjust or restrict any of these (for example, listing a particular asset or asset class may invoke a set of default values, rules, etc.). The asset class description 912 may be stored in a data structure that potentially includes a blockchain (e.g., blockchain 1112, the same or a separate blockchain used to record token transactions). In certain embodiments, the asset class description 912 may be stored locally (for example, on the user device performing the operation to generate the token), and / or may not be stored, for example, if the asset class description 912 is created and used for a published asset class linked token 1108 in a single workflow, or if the asset class description 912 is used to generate the token and then discarded. In certain embodiments, the asset class description 912 or a portion thereof may be stored, for example, to support the generation of tokens across multiple sessions, and / or to reuse all or part of the asset class description 912 for later use when generating another token using the same asset or asset class.Exemplary asset class descriptions 912 include fungible asset descriptions (e.g., for assets or asset classes based on fungible assets), non-fungible asset descriptions (e.g., for assets or asset classes based on non-fungible assets and / or identifiable assets), value stream descriptions (e.g., assets related to an asset (which may change over time) that enable the linking of tokens to revenue streams), value change descriptions (e.g., enabling the linking of tokens to changes in asset value relative to a baseline, whether asset value is gross value or value streams), and / or value class characterizations (e.g., compliance requirements may relate to storage, maintenance, inspection, location, security, etc., and failure to meet compliance and / or the results of compliance performance, such as inspection results, may affect the value of an asset or asset class).

[0092] Referring to Figure 14, an exemplary beneficial characteristic 1004 is schematically depicted. The exemplary beneficial characteristic 1004 allows all or part of the token to depend on selected characteristics of the linked asset or asset class. Exemplary and non-exclusive beneficial characteristics 1004 include one or more of the following: asset value, asset risk profile, asset volatility (e.g., trends in value changes and / or value change fluctuations within a selected period, and / or statistical descriptions thereof, which may be determined for any value dimension of the asset or asset class, such as sale value, collateral value, or earnings stream value), asset liquidity (e.g., a description of the likelihood that the underlying asset or asset class is likely to be sold, bought, etc., including temporal factors (expected time required for a transaction), pricing factors (how much price fluctuation is expected relative to the market price), and market size factors (e.g., a token likely to involve 10,000 units of an asset may have different liquidity characteristics than a token likely to involve 1,000 units of an asset). In a particular embodiment, the beneficial characteristic 1004 is the entity generating the token. This may depend on the nature of the asset class. For example, if an entity that is a major producer of a particular asset class (e.g., a precious metals mining company) generates tokens linked to a large amount of precious metal assets, it may be expected that the impact on the market will differ from that of another entity that is a major consumer of that asset class (e.g., a high-grade electrical connector company). This difference may be reflected in adjustments to asset value, volatility, liquidity, etc. In certain embodiments, the token performance expression 1002 associated with the token (see, for example, Figure 15) may reflect the target providers and / or acquirers of the token, for example, the token performance adjusted for a particular type of holder of the token. In certain embodiments, the beneficial properties 1004 may additionally or alternatively include the value of the asset class, the risk profile of the asset class, the volatility of the asset class, and / or the liquidity of the asset class.

[0093] Referring to Figure 15, an exemplary token performance representation 1002 is schematically depicted. In a particular embodiment, the token performance representation 1002 reflects the intended performance of the token (e.g., an 8% return, a volatility index of XX, a liquidity index of YY, etc.). In a particular embodiment, the token performance representation 1002 reflects the expected performance of the token. In a particular embodiment, a user interacting with the tokenization user interface 808 utilizes both of these. For example, they set an intended performance value for the token and adjust the asset class description 912 and / or beneficial characteristics 1004 of the linked assets until the expected performance of the token matches or comes sufficiently close to the intended performance value. In a particular embodiment, the tokenization controller 802 inputs recommended assets and / or asset classes based on the intended performance of the token, making it convenient for the user to adjust the link mix of assets and / or asset classes to achieve the intended performance. In certain embodiments, the token performance representation 1002 is used (e.g., by the asset-backed circuit 906) after the token has been generated, published, and / or traded, to adjust the link mix of assets / asset classes in accordance with rules such as those described throughout this disclosure.Exemplary and non-limiting aspects of the token performance expression 1002 include token value (e.g., a value description such as the token's value, the token's value progression, and / or rate of return, net present value), the token's estimated future value (e.g., any of the value parameters such as a selected future time, a range of selected future time, or a perpetual value progression target), the token's risk profile (e.g., relative risk to selected risks such as volatility risk, liquidity risk, total value loss risk, risk to thresholds such as minimum return or minimum value), and / or the token's index description (e.g., current or future asset combination, investment return, liquidity, etc.). Tokens that track current or future scoring parameters such as latitude, and / or tokens that change their performance targets in a selected manner over time and / or in response to events, for example: having a first return / risk profile at an initial point in time and a second return / risk profile at a later point in time; having a first return / risk profile before a first event such as a weather accident or the start of production at a facility and a second return / risk profile after the event; tokens that are linked to a first asset class mix at an initial point in time and to a second asset class mix at a second point in time; and / or trajectories thereof).

[0094] Referring to Figure 16, an exemplary procedure 1600 for generating an asset-class linked token is schematically depicted. The operation of procedure 1600 may be performed by any components, controllers, circuits, etc. as specified herein, including at least the platform components defined in relation to Figures 1-3 and the controllers defined in relation to Figures 8-9 and 11-12. Exemplary procedure 1600 includes operation 1602 for implementing a tokenization user interface, operation 1604 for interpreting a token description, operation 1606 for interpreting an asset-class description, and operation 1608 for interpreting a token-link description. Exemplary procedure 1600 further includes operation 1610 for generating an asset-linked token in response to a token description, an asset-class description, and / or a token-link description. Exemplary procedure 1600 further includes operation 1612 for publishing the asset-linked token to a token marketplace system, which may make it public (e.g., offer) for trading on the token marketplace system.

[0095] Referring to Figure 17, an exemplary procedure 1700 for generating an asset-linked token is schematically depicted. Exemplary procedure 1700 is similar to procedure 1600 but further includes operations for implementing the governance scheme of the asset-linked token. Exemplary procedure 1700 includes operation 1702 for interpreting the governance scheme, and operation 1610 for generating the asset-linked token is further performed in response to the governance scheme—for example, including link rules to ensure that the generated token conforms to the governance scheme and / or enforce the governance scheme throughout the token's lifecycle.

[0096] Referring to Figure 18, an example of procedure 1800 for adjusting asset-linked tokens is schematically depicted. The operations of procedure 1800 can be performed in relation to tokens already held by the acquiring entity, such as through operations with generated tokens, published tokens, and / or tokens with a token marketplace, for example. An exemplary procedure 1800 includes operation 1802 for interpreting token performance characteristics and operation 1804 for adjusting asset-linked tokens in accordance with token performance characteristics (for example, adjusting the mix of linked assets or asset classes). Strategy engine process

[0097] Figure 20 illustrates how to design and / or test a token strategy executable by the Strategy Engine 1902. In 2002, the Strategy Engine 1902 receives a strategy for a token or a collection of tokens from a device associated with the user. The user may be an individual, a company, a group of companies, a group of individuals, or any other entity that wishes to design a strategy for one or more asset class-linked tokens. In some embodiments, the user may wish to generate asset class-linked tokens so that they purchase asset class-linked tokens and subsequently own or have rights to a portfolio of assets. In these embodiments, the user may want to design a portfolio of asset class-linked tokens that aligns with the user's objectives (e.g., investment objectives, business objectives). Furthermore, or alternatively, the user may wish to generate asset class-linked tokens backed by assets (at least some) that the user owns or controls and sell these tokens to other users. For example, the owner may own a first group of assets and wish to design a strategy to generate and sell asset class-linked tokens backed by the first asset class and / or other assets to raise funds.

[0098] In embodiments, the strategy engine 1902 may use AI components such as a language model (e.g., a large-scale language model) to assist the user in designing one or more goals or strategy inputs. For example, an AI chatbot may ask the user a variety of questions about the goals (e.g., a pre-configured list of questions and / or questions generated in real time in response to the user's answers) and use the user's answers to generate a strategy input (e.g., a list of asset performance attributes) aligned with the user's answers. Furthermore, or alternatively, the AI ​​chatbot may summarize the strategy input for the user's review and / or editing, make changes based on user feedback, and / or do so. In addition, or alternatively, the AI ​​component may assist the user in understanding the various mechanisms of asset class collateral tokens by answering questions the user has about, for example, the operation of asset class collateral tokens, various assets, various simulations performed by the strategy engine, and / or such. In embodiments, the machine learning system 1904 and / or the AI ​​system 1912 may implement a language model, which may be a commercial language model, an open-source language model, a fine-tuned language model, and / or such. Additionally or alternatively, the language model may be provided by a third-party system (e.g., a language model provided by a commercial language model provider such as OPENAI). In these embodiments, platform 200 can provide contextual information to the language model so that it can answer various user questions. Contextual information may include, for example, a pre-configured list of questions that may be asked to determine the user's strategy, asset descriptions of available assets and / or asset classes, and / or embeddings for asset descriptions, strategy inputs and formats for data fields, and / or values ​​for strategy inputs. Platform 200 then sends the user query to the language model along with the relevant contextual information, enabling the language model to respond appropriately to the user query.In addition, or alternatively, contextual information is stored as an embedding accessible to the language model. In an embodiment, platform 200 can parse the response provided by the language model, extract information, and use the information in the process (for example, if the language model generates one or more strategic attributes, platform 200 can recognize and extract the attributes).

[0099] Strategies entered by the user (and / or generated by the AI ​​component with the user's assistance) may contain multiple components. A strategy may include one or more asset performance attributes that specify or influence combinations of assets that may be associated with asset class collateral tokens. In embodiments, asset performance attributes include one or more indicators of portfolio composition (e.g., whether the portfolio should aim for growth versus stability, prioritizing one type of asset over others), other asset allocation factors (e.g., desired diversification amount, target sectors, desired risk tolerance, desired dividend or other income amount, income or earnings predictability, desired wealth accumulation amount, desired spending target, desired margin of safety, desired volatility elimination or addition), wealth design objectives (e.g., desired tax shelter), desired non-financial effects (e.g., carbon neutrality through the inclusion of renewable energy credits, pollution reduction), supply chain objectives (e.g., insurance for the reliability / availability / price of specific materials, goods, manufacturing, etc.), desired intellectual property rights (e.g., the right to manufacture and / or sell a specific product in a specific region), risk management objectives (e.g., hedging against specific outcomes or events), access to illiquid or hard-to-tradable assets (e.g., a license to use non-corruptible goods or services), and / or similar. A strategy entered by the user can define any number of attributes. For example, a user can use a device to interface with the token design interface 302. This interface can provide menus, lists, forms, or other user interfaces for specifying various attributes, including predetermined or customizable attributes and / or goals.

[0100] User input to a strategy may further include instructions for desired token values ​​and / or values ​​for a set of tokens. For example, a user may specify that each token be close to or match a target value, fall within a range defined by a minimum or maximum value, and / or so. Additionally or alternatively, a user may specify that a set of tokens constituting a portfolio be close to or match a target value, fall within a range defined by a minimum or maximum value, and / or so. In some of these embodiments, a user may specify an automated market maker (AMM) configuration that may be deployed on the blockchain with tokens backed by an asset class. For example, a user may specify one or more trading pairs to be provided by the AMM, the type of AMM and / or pricing capabilities, the price or price range that the AMM should target, and / or so. The AMM configuration may be manually provided by the user and / or automatically determined by the strategy engine 1902 with AI assistance to meet the user's strategic objectives, as will be described in more detail below. Additionally or alternatively, the strategy engine may follow a hybrid approach in which the user specifies several aspects of one or more AMMs (e.g., trading pairs), and the strategy engine 1902 determines other AMM configuration information algorithmically and / or using AI-assisted techniques.

[0101] The strategy entered by the user may further include token attributes that specify how the token or token collection should be structured. For example, the user in the first example may wish to generate a single non-fungible token backed by a collection of assets. In the second example, a collection of 100 non-fungible tokens is generated, each token backed by different periods of returns from the assets backing that collection, allowing the user to bid on 100 non-fungible tokens in the secondary market. The user in the third example may wish to generate 1,000 mycogenic tokens equivalent to 0.1% of the value of the assets backing the tokens. In the fourth example, the user may wish to generate tokens of tokens, each token containing a number of assets that can be combined in various ways. The fifth example is generating fractional ownership tokens, each token having fractional ownership of a physical object. The user can specify any of the various tokens described herein. In embodiments, the token attributes may be any of the token attributes 304 described herein (e.g., type 402, chain 404, proof 408, design 412, rule 410, etc.). To reiterate, some or all of the user strategy attributes (e.g., token type and / or number, chain, etc.) may be generated or modified by an AI model (e.g., a language model) based on user interaction and / or feedback, as described above. For example, contextual information provided to the language model by platform 200 may include formatting information indicating that attributes may include type 402 attributes, chain 404 attributes, etc., and the language model may then generate specific values ​​for attributes that platform 200 can parse and extract based on user responses and feedback.

[0102] User strategy inputs may further include governance components such as the need to comply with or circumvent specific regulations, restrictions on the trading or ownership of tokens, and / or other regulatory components, policy components, and / or other governance components, as will be described in more detail below. In some embodiments, the user may specify governance components as part of the input to the token design interface 302. In addition, or alternatively, the strategy engine 1902 may automatically select specific governance components based on other user inputs and / or based on various simulations, as will be described in more detail below. For example, if a user strategy indicates that the tokens generated according to the user strategy may include financial instruments subject to specific regulations, the strategy engine 1902 may automatically select governance components corresponding to those regulations. Thus, for example, governance components may include both governance components provided by the user and governance components automatically selected by the strategy engine 1902. As described above, some or all aspects of a user strategy (e.g., one or more governance components) may be generated by an AI model (e.g., a language model) based on user interaction and / or feedback as described above.

[0103] The token design interface 302 can collect specified asset performance attributes, token attributes, and / or governance components and provide them to the strategy engine 1902 as part of a first request 1914. In 2004, the strategy engine 1902 may prioritize the strategic components such that the most important, desirable, or required asset performance attributes, token attributes, and / or governance components are ranked higher than others. In embodiments, the strategy engine 1902 may, in response to the first request 1914, send a first decision 1918 to the token design interface 302 and solicit user input to assist in prioritizing the strategic components. In embodiments, the first decision 1918 may include a default ranking of attributes and / or governance components based on one or more rules and / or models 1908 that are utilized by a machine learning system 1904 and / or an AI system 1912. For example, Model 1908 may be trained to show that certain attributes and / or governance components (e.g., regulatory components, intellectual property rights, supply chain objectives) tend to rank higher than others (e.g., volatility performance attributes) based on a training dataset consisting of previous user prioritization inputs, etc.

[0104] In some embodiments, the machine learning system 1904 and / or the AI ​​system 1912 can leverage a generative AI model (e.g., a large-scale language model) to assist in prioritizing strategic components. For example, the strategy engine 1902 can generate one or more queries specifying attributes and / or governance components, using queries that request the generative AI model to rank strategic components, and send them to the machine learning system 1904 and / or the AI ​​system 1912. The strategy engine 1902 can then receive a ranked list of attributes and / or governance components from the generative AI model implemented by the machine learning system 1904 and / or the AI ​​system 1912. Furthermore, in some embodiments, at least one of the queries generated by the strategy engine 1902 and sent to the generative AI model can request the generative AI model to identify potential conflicts between various attributes and / or governance components. Thus, the strategy engine 1902 can receive a response from the generative AI model indicating potential conflicts.

[0105] The token design interface 302 may display a user interface that includes some or all of the ranked attributes and / or governance components that the user can modify (e.g., changing rankings or resolving conflicts). For example, if the first token performance attribute indicates high growth and the second token performance attribute indicates low risk, the user can consider the potential conflict between these performance attributes and prioritize whether the strategy focuses on higher growth or lower risk. Furthermore, or alternatively, the user can specify that certain attributes or components are mandatory (e.g., certain governance components are mandatory, specified intellectual property rights are mandatory, etc.) and / or that certain attributes or components are optional. In some cases (e.g., when attributes conflict with each other), the user can remove attributes via the prioritization user interface.

[0106] In embodiments, the strategy engine 1902 can leverage one or more trained models implemented by the machine learning system 1904 and / or the AI ​​system 1912 to detect potential conflicts among various user-supplied attributes and / or governance components. For example, a stored training set 1910 may include various user-supplied attributes and / or governance components and indicators of potential conflicts, which can be leveraged by the machine learning system 1904 and / or the AI ​​system 1912 to train a model for detecting conflicts or potential conflicts. In these embodiments, automatically detected conflicts may be flagged for the user during a prioritization step so that the user can indicate their relative importance, and attributes that reduce the likelihood of achieving the conflicting attributes may be removed, and / or so may be done. Thus, the first decision 1918 may indicate potential conflicts detected by the strategy engine 1902.

[0107] In 2006, the Strategy Engine 1902 may map attributes to one or more assets in a pool of asset data maintained by the Strategy Engine 1902 and / or the Tokenization Platform 200. Each asset may be mapped to one or more attributes associated with the asset, such as risk attributes, growth attributes, carbon neutral attributes, minority ownership attributes, IP rights attributes, and / or any of the various asset attributes described herein. Additionally or alternatively (for example, where an attribute indicates that the user wishes to own a particular asset), selected or otherwise indicated assets may be selected from the pool of asset class data. Asset class data and / or attributes associated with asset class data may be obtained from various sources and compiled into a comprehensive dataset (for example, by the Strategy Engine 1902 and / or the Tokenization Platform 200). Sources may include, for example, one or more asset exchanges (e.g., stock or commodity exchanges), one or more IP exchanges, real estate or other asset markets, and / or other data creators that may provide information about assets and attributes associated with assets. Other potential data sources are described in relation to Figure 1 above.

[0108] In an embodiment, the asset data may be part of a training set 1910 that can be used to train a machine learning model 1908 to perform mapping in conjunction with a machine learning system 1904 and / or an AI system 1912. In addition, or alternatively, the strategy engine 1902 may evaluate asset classes using a variety of logical and / or mathematical techniques to achieve specific performance attributes (e.g., balancing carbon emissions through carbon offsets).

[0109] For example, if a user specifies a strategy that includes carbon neutrality as the first priority, a target growth rate of 7% as the second priority, low volatility as the third priority, and a high level of diversification as the fourth priority, the strategy engine 1902 may perform operations to generate an asset mix that is carbon neutral, has a high probability of achieving the target growth rate, has a high or moderate probability of low volatility (e.g., growth rate may be prioritized in ranges where volatility conflicts with growth rate), has a high level of diversification (e.g., growth may be prioritized in ranges where volatility conflicts with growth). In an exemplary embodiment, the strategy engine 1902 may use one or more machine learning models 1908 to generate an asset mix that can output a specific asset mix (e.g., a mix of securities or other types of assets) based on the prioritized attributes. In an embodiment, the asset mix may specify the number of each asset based on the attributes and any target token value supplied as part of the user strategy. In addition, or alternatively, the asset mix can represent the percentage of each asset (for example, token value has not yet been considered).

[0110] In addition to, or as an alternative to, the use of machine learning models, Strategy Engine 1902 can use matching strategies to select potential assets for an asset mix. For example, if a user strategy includes carbon neutrality, Strategy Engine 1902 can automatically select renewable energy credits and / or pollution reduction credits to be part of the asset mix. Then, if the asset mix includes other attributes (e.g., energy stocks, mining stocks) associated with negative carbon reduction attributes (e.g., increases in CO2), renewable energy credits may be added to "balance" the carbon attributes to achieve carbon neutrality. Thus, for example, the total amount of negative carbon reductions (e.g., representing a net increase in CO2) associated with the asset mix can be used to select additional renewable energy credits until the total value of carbon reductions is at least zero.

[0111] Another example of an asset might be an intellectual property asset that can specify one or more patents or patent portfolios, one or more works, rights to perform works, and / or other intellectual property rights corresponding to a specific product, standard (e.g., communication standard), drug, method, etc. If a performance attribute provided by the user specifies a particular intellectual property right and the asset pool contains a corresponding asset that matches the specified intellectual property right, that asset can be added to the asset mix.

[0112] In 2008, the strategy engine 1902 can simulate the performance of a selected asset mix. In embodiments, the strategy engine 1902 may use a token simulator 312, which is described in more detail elsewhere in this specification, to simulate the future performance of a selected asset mix. The token simulator 312 can provide a token simulator output 320 that shows various performance metrics. The token simulator 312 may show, for example, the probability of achieving a particular growth rate over the next 1, 3, 5 years, or other period, the probability of achieving other financial performance indicators, and / or such. In addition, or alternatively, the token simulator 312 may show, as described elsewhere in this specification, the probability that a specified asset will remain available (for example, due to supply chain constraints), that unfavorable economic and / or geopolitical events will not affect the ability to manufacture components for the asset, and / or such probability.

[0113] In some embodiments, the strategy engine 1902 can simulate the pricing of a selected asset mix using an RDA system 2352 (described in more detail below), which may be configured to generate price estimates for various trading pairs using machine learning models or other AI-assisted techniques. For example, if a particular asset mix includes one or more assets corresponding to an existing AI-assisted model that predicts the performance of a subset of assets based on historical trading pair data, the strategy engine 1902 can invoke the RDA system 2352 to predict the performance data for the corresponding one or more assets. In some embodiments, a trained AI model may exist for some (but not all) of the asset mix. In these embodiments, the trained AI model may be used to partially simulate the performance of the asset mix. Furthermore, the strategy engine 1902 may use other techniques to simulate the performance of the remaining assets (for example, as described later for the token simulator 312) and generate a combined simulated performance of the entire asset mix.

[0114] In step 2010, the strategy engine 1902 can adjust the asset mix based on the simulation results and / or user input. The strategy engine 2010 provides the token design interface 302 with the simulation performance of the asset mix generated in step 2006 and the asset mix generated in step 2008, which then presents the asset mix and / or simulation performance to the user or a device associated with the user. The user can then manually adjust the asset mix, and the token design interface 302 can rerun the simulation with the adjusted asset mix (for example, by repeating step 2008). The strategy engine 1902 can iterate repeatedly by adjusting the asset mix and the re-simulated asset mix as many times as the user desires to optimize the portfolio until the user is satisfied with the asset mix and / or the simulated performance of the asset mix.

[0115] In some embodiments (not shown in Figure 20), after simulating the asset mix, the user can prioritize performance attributes or other aspects of the strategy provided by the user, and in response, the token design interface can re-execute step 2006 based on the adjusted strategy. For example, based on the simulation results, the user can recognize that a particular attribute is detrimental to one desired performance aspect and decide to eliminate that attribute accordingly. As another example, if the simulation results show that the simulated performance will not be sufficiently effective unless one attribute is ranked higher than another, the user can change the ranking of the attributes to achieve the desired performance in the updated simulation results.

[0116] In this embodiment, the strategy engine 1902 can use an automated method to optimize the portfolio strategy based on the simulation results. For example, the strategy engine 1902 can adjust the asset mix by randomly swapping the initially selected asset with a second asset of the same type, and then rerun the simulation to see whether the user attributes are likely or unlikely to be achieved based on the adjusted asset mix. If the attributes are likely to be achieved, the adjustment can be maintained; otherwise, the adjustment can be reversed before attempting another iteration. In this way, by running numerous simulations based on randomized or semi-randomized variations, the strategy engine 1902 can iterate toward an optimal asset mix.

[0117] In an embodiment, the strategy engine 1902 may use a generative AI model (such as one implemented by the machine learning system 1904 and / or the AI ​​system 1912) to assist in optimizing the portfolio strategy based on the simulation results. For example, the strategy engine 1902 may submit a query to the generative AI model containing a summary of the simulation results, requesting the generative AI model to provide a set of proposed optimizations based on the summary of the simulation results. The strategy engine 1902 receives the generative AI response, adjusts the portfolio strategy (e.g., asset allocation) using the proposed optimizations, and can then resimulate the performance of the asset allocation (e.g., by looping back to step 2008).

[0118] In 2012, Strategy Engine 1902 can generate one or more governance rules and / or AMM configurations based on the asset mix, the desired performance of the asset mix, and / or any governance components specified by the user strategy. For example, governance rules may be required by law for a particular class of securities, as described elsewhere in this specification, or may be used for other purposes. In embodiments, the user can provide user-selected governance rules, and / or Strategy Engine 1902 can select essential governance rules based on the selected asset mix.

[0119] Additionally or alternatively, Strategy Engine 1902 can generate AMM configurations that specify the design of one or more AMMs to be deployed with tokens backed by asset classes (if any). For example, an AMM configuration might specify, for each AMM, trading pairs (e.g., another token that could be automatically traded with the tokens backed by the asset class using the AMM), pricing features, pool weighting (e.g., the relative weight of each asset in a multi-asset mix in the liquidity pool), fee structure (e.g., the percentage of trades as trading fees, the distribution of trading fees to liquidity providers, etc.), one or more rules for managing the liquidity pool, risk mitigation rules (e.g., trade size limits, use of stop-loss orders, etc.), distribution rules to incentivize liquidity providers to contribute to the liquidity pool, etc.

[0120] In embodiments, the strategy engine 1902 may leverage the RDA system 2352 (described in further detail below) to use one or more AI-assisted techniques to train a model that optimizes the AMM configuration. These techniques may include supervised learning techniques that train a model (such as a neural network, regression model, or decision tree) based on historical training data. Alternatively, the AI ​​techniques may include reinforcement learning techniques for iteratively training the model based on market interactions. Other relevant techniques may include evolutionary algorithms and / or deep learning techniques. In these embodiments, the training dataset may include trading data, market data, asset volatility data, liquidity demand data, and / or these. Furthermore (for example, in the case of supervised learning techniques), the training dataset may also include target data containing AMM parameter data that specifies one of the above AMM configuration data (e.g., trading pair parameters, pricing function, pool weighting parameters, fee structure parameters, liquidity pool management parameters, risk mitigation parameters, distribution parameters, etc.).

[0121] In 2014, the strategy engine 1902 can output a portfolio specification that includes one or more token descriptions 910, asset class descriptions 912, token link descriptions 914, AMM configurations, and / or other configuration data corresponding to an optimized asset mix. Additionally or alternatively, the strategy engine 1902 can output a governance scheme that incorporates one or more token governance rules. The one or more token descriptions 910, asset class descriptions 912, token link descriptions 914, AMM configurations, and / or governance rules may be used to generate tokens backed by one or more asset classes and / or to deploy associated smart contracts (e.g., AMMs), as described elsewhere in this specification. In embodiments, the portfolio specification may specify several different tokens, each token backed by a different asset in the portfolio. Further or alternatively, the portfolio specification may specify one or more hybrid tokens, each hybrid token backed by multiple (or all) assets in the portfolio. Issuance of asset-class collateralized tokens using smart contracts

[0122] Figure 21 shows an exemplary smart contract 250A that may be used by the Asset Class Backed Tokenization Platform 200 to generate one or more asset class backed tokens on a distributed ledger (e.g., a blockchain). In embodiments, the exemplary smart contract 250A may be configured to generate a number of similar asset class backed tokens (e.g., a set of soluble tokens or a set of non-fungible tokens that share at least some common attributes), which may be referred herein to as a collection of asset class backed tokens. The exemplary smart contract 250A can generate tokens backed by one or more asset classes as described above for step 1610. The token design system 204 and / or token deployment system 208 of the Asset Class Backed Tokenization Platform 200 may be configured to interface with any of the other smart contracts 250 described herein to invoke various functions of the smart contract.

[0123] In embodiments of this disclosure, multiple smart contracts 250 configured to issue asset class-backed tokens may be deployed on various blockchains or other distributed ledgers, each such smart contract configured to issue at least one token or class of tokens. For example, different smart contracts 250 may be provided on the Ethereum blockchain, the Solana blockchain, and the like. In these embodiments, the asset class-backed tokenization platform 200 can deploy exemplary smart contracts 250A to trigger the issuance of one or more collections of asset class-backed tokens on a particular distributed ledger (e.g., chain 404 specified by token attributes 304 supplied by the user during the token design phase). In embodiments, multiple collections of tokens may be issued using a single smart contract 250. Furthermore, or alternatively, each collection of tokens may be issued by a different smart contract 250.

[0124] An exemplary smart contract 250A may have one or more functions that can store and / or access configuration data that may be used during the minting process. In embodiments, at least one of the functions may be an issuance function, and at least one of the functions may be a configuration function that can be used to supply configuration data to smart contract 250A. To issue tokens backed by multiple asset classes using smart contract 250A, an asset class-backed tokenization platform 200 may first configure smart contract 250A by supplying configuration data to smart contract 250A using the configuration function of smart contract 250A, and smart contract 250A may store the configuration data. The asset class-backed tokenization platform 200 may then invoke the issuance function of smart contract 250A to issue (e.g., once or repeatedly) tokens backed by one or more asset classes based on the configuration data. Additionally or alternatively, smart contract 250A may be configured to issue tokens backed by an asset class without pre-storing configuration data (for example, if the configuration data is provided directly to the issuing function, or if the configuration data is stored off-chain).

[0125] Smart contract 250A may store configuration data 2102 which may include any data used to issue tokens backed by asset classes (e.g., metadata for asset class-backed tokens). For example, the configuration data may include one or more token attributes (e.g., token attribute 304), one or more asset class attributes (e.g., asset class attribute 308), data from token description 910, data from asset class description 912, data from token link description 914, and / or these. In the illustrated example, the configuration data 2102 for a token backed by a particular asset class or a collection of tokens backed by asset classes may include asset attributes 2104 for multiple assets (e.g., in the case of a hybrid token backed by multiple assets), one or more token redemption rules 2120, one or more token governance rules 2122, and / or token issuance schedule 2124.

[0126] In the illustrated example, configuration data 2102 includes several first asset attributes 2104A, including asset identifier 2106A, asset value 2108A, available trigger 2110A, expiration trigger 2112A, validator link 2114A, and redemption link 2116A. However, these attributes are merely illustrative, and additional or alternative asset attributes may be provided for tokens backed by other assets and / or other asset classes. In the illustrated example, configuration data specifies multiple assets by specifying different sets of asset attributes (e.g., asset attributes 2104A-2104N). Multiple sets of asset attributes may be specified when a single smart contract 250A is used to issue multiple types of tokens (e.g., one type of token is issued for each set of asset attributes), when a single smart contract 250A is used to issue asset class-backed tokens backed by multiple assets (e.g., one token is backed by each type of attribute), when a token is issued, and / or in such cases.

[0127] In exemplary embodiments, the asset identifier 2106A may identify a specific asset (e.g., gold, a type of stock, a type of intellectual property, a revenue stream, a crop harvest, etc.) used to back an asset class backed token. The asset identifier may include a text description of the asset, a unique identifier corresponding to the asset (e.g., a ticker symbol, a registration number, and / or any other unique identifier), and / or any other information that may be used to identify a specific asset (in the case of a non-fungible asset) or a class of assets (in the case of a soluble asset).

[0128] In exemplary embodiments, asset amount 2108A may identify the quantity of an asset identified by asset identifier 2106A. For example, asset amount may represent a specific number of assets, the weight of an asset, a percentage of an asset, fractional ownership of an asset, a specific right or bundle of rights associated with an asset, a period of an asset (e.g., a temporal division of a revenue stream, a temporal division of a usage right or access control), or any other means of dividing ownership or other rights to a specific asset(s) or quantity(s) of a convertible asset(s).

[0129] In an exemplary embodiment, the availability trigger 2110A may specify a trigger for when an asset becomes available, for example, the earliest time when the asset becomes available (for example, for redemption). For example, if an asset class is a right to a particular future period of a revenue stream, the revenue from the revenue stream may not be available for redemption until the future period. Similarly, if an asset corresponds to a percentage of a crop harvest, the asset may not be available for redemption until the actual harvest or a time related to the actual harvest. Therefore, for example, the availability trigger 2110A may store a timestamp after the asset has become available.

[0130] In addition or alternatively, the availability trigger 2110A may specify any other type of trigger that may make the asset available. For example, a particular asset (e.g., insurance payout) may become available in the event of events such as a weather event, property damage, or the implementation of a health procedure. In these embodiments, the availability trigger 2110A may indicate a data source, such as a recognized public data source such as an oracle, that can indicate whether a particular event has occurred. In addition or alternatively, the availability trigger 2110A may indicate that an asset may be available if a particular event or other trigger has a probability of occurring above a threshold, a probability of occurring below a threshold, a probability within a specific range of probabilities, and / or such. In these embodiments, the availability trigger 2110A may indicate a corresponding oracle that can store predictions generated using artificial intelligence techniques (e.g., machine learning models) of the probability of future events occurring.

[0131] In exemplary embodiments, the expiration trigger 2112A may specify an expiration condition, such as the most recent time an asset may be available (for example, for redemption). For example, an expiration timestamp may be used for certain expiring assets, such as futures contracts, options, and / or other assets that may expire after a specific date. In addition, or alternatively, the expiration trigger 2112a may refer to an oracle indicating whether a particular event has occurred, is likely to occur, or will not occur, as described above for the availability trigger 2110a.

[0132] In exemplary embodiments, verifier link 2114A may link to a system, oracle, and / or smart contract for verifying that an asset exists, is owned by a specific party, is stored in a specific location, and / or such. For example, verifier link 2114A may point to an oracle associated with a third party undertaking the asset (for example, by ensuring that the asset's miner owns the asset and / or is likely to own the asset in the future).

[0133] In exemplary embodiments, the redemption link 2116A may include a link to a system and / or smart contract for redeeming asset class-backed tokens to receive assets. For example, the redemption link may include a link to a website where a holder of asset class-backed tokens can verify ownership of their asset class-backed tokens, enter address or bank account information to receive assets, and / or do so. Furthermore, or alternatively, the link may be a link to a smart contract configured to assist in the redemption of asset class-backed tokens.

[0134] In an embodiment, the configuration data 2102 may further include one or more asset attributes 2104B~N for a second, third, and / or any number of up to N assets associated with a token backed by an asset class. In an embodiment, various assets 2~N may be configured using the same and / or different attributes (e.g., depending on the type of asset).

[0135] In embodiments, configuration data 2102 may further include one or more token redemption rules 2120, which may indicate how tokens are redeemed against the asset class backing them. For example, an asset class backing rule may indicate that an asset class backed token corresponding to configuration data 2102 grants its holder either a first asset (e.g., the asset indicated by asset class attribute 2104A) or a second asset (e.g., the asset indicated by asset attribute 2104B), but not both. In this example, the value of an asset class backed token may correspond to the larger of the first and second asset values. In another rule example, an asset class backed token may indicate that its holder receives the first asset, but if the value of the first asset falls below a threshold, the token holder receives the second asset. In this example, the second asset is used as a hedge against a decline in the value of the first asset. Other rules may include, for example, the right of a token holder to receive a particular asset and / or the conditions under which a particular asset can be redeemed (for example, based on the holder's discretion, such as whether the value of a particular asset is less or greater than the values ​​of other assets, based on the corresponding value of each asset, the sum of all assets, etc.), whether a particular asset can be redeemed separately or must be redeemed simultaneously, and / or so. In embodiments, Token Redemption Rule 2120 may be used to constitute an valuation system and / or redemption system, as will be described in more detail elsewhere in this Spec.

[0136] In embodiments, configuration data 2102 may further include one or more token governance rules 2122 that can be used to control one or more aspects of an asset class-backed token. For example, the token governance rules 2122 may include buyer eligibility rules that indicate to whom an asset class-backed token may be sold. Buyer eligibility rules may stipulate, for example, that a buyer of an asset class-backed token must meet accredited investor requirements (e.g., own other assets above a certain value), that a buyer of an asset-backed token must be a holder of an entitlement token (e.g., an NFT that verifies the buyer's right to purchase an asset or a class of assets), that a buyer of an asset class-backed token must be approved by a third party, and / or so. In addition, or alternatively, the token governance rules may indicate which party is responsible for valuing the asset class-backed token, whether or not some or all of the assets backed by the token must be insured before sale or transfer, and / or such.

[0137] In the embodiment, the configuration data 2102 may further include one or more medal issuance schedules 2124. The token issuance schedule 2124 may specify the number of tokens to be issued for the initial issuance and / or instructions for additional token issuance after the initial issuance. For example, the token issuance schedule may constitute one or more future issuances if the asset corresponds to a periodic revenue stream or other periodic asset. The token issuance schedule may define the period (e.g., yearly, monthly, quarterly, etc.) and the number to be issued per period for issuing tokens backed by a new asset class. For example, the token issuance schedule may indicate issuing 100 tokens backed by asset classes each year (e.g., each token corresponds to 1% of the allocated revenue stream for the next year, as defined by the asset attribute). The token issuance schedule may further define which portion of the revenue stream each token backed by an asset class corresponds to. For example, a token issuance schedule could specify that 1200 tokens backed by different asset classes will be issued annually, with the first 100 tokens each corresponding to 1% of the revenue in January of the following year, and the second 100 tokens each corresponding to 1% of the revenue in February of the following year. In this way, a token issuance schedule can define instructions for the periodic issuance of tokens backed by asset classes (for example, in conjunction with asset attributes).

[0138] Additionally or alternatively, the token issuance schedule 2124 can define other conditions (e.g., non-periodic conditions) for issuing tokens. For example, the token issuance schedule can specify the minimum and / or maximum number of tokens that should remain in circulation. For example, if a certain number of tokens backed by an asset class are lost (e.g., because the tokens are exchanged for the corresponding asset), new tokens backed by the new asset class can be automatically issued to replace them until the minimum number of tokens in circulation is reached. Additionally or alternatively, the token issuance schedule can define trigger conditions for issuing a certain number of new tokens (e.g., a batch of new tokens is issued each time a trigger occurs).

[0139] In embodiments, configuration data 2102 may specify one or more token management smart contracts via attribute 2126 that may be managed after the issuance of asset class backed tokens. For example, the management smart contract indicated by attribute 2126 may manage the transfer of asset class backed tokens from one user to another (e.g., ensuring that any eligibility requirements are met), manage the modification of asset class backed tokens if permitted, and manage the revenue stream associated with the tokens (e.g., the management smart contract may receive payments, distribute payments to token holders, and / or so). Additionally or alternatively, in embodiments, smart contract 250A may manage the tokens after they have been issued (e.g., the issuance smart contract 250A and the management smart contract 250B may be the same smart contract).

[0140] In an embodiment, the exemplary smart contract 250A may include one or more functions that can be invoked by the tokenization platform 200 and / or other smart contracts 250. For example, the exemplary smart contract 250A may include a configuration function 2130 that accepts configuration data 2102 and / or stores the configuration data 2102 in the exemplary smart contract 250A. For example, before issuing a set of tokens backed by a configured asset class, the tokenization platform 200 may generate configuration data 2102 for tokens backed by the asset class and then initiate a distributed ledger transaction that invokes the configuration function 2130 of the smart contract 250A. The configuration function 2130 may accept configuration data 2102 and / or equivalent data values ​​as one or more arguments. The configuration function 2130 can then store the arguments as configuration data 2102 and / or perform other actions to configure the exemplary smart contract 250A and / or other smart contracts to issue tokens backed by the corresponding asset class.

[0141] In an embodiment, the exemplary smart contract 250A may be configured without using the configuration function 2130. For example, the tokenization platform 200 can pre-configure the exemplary smart contract 250A using configuration data 2102 and then initiate one or more distributed ledger transactions to store the pre-configured exemplary smart contract 250A on the distributed ledger.

[0142] In an embodiment, exemplary smart contract 250A may include an issuance function 2140 that can trigger or initiate one or more distributed ledger transactions to issue tokens backed by one or more asset classes, based on configuration data 2102. The issuance function 2140 may perform some or all of the steps described in the process shown in Figure 22. In an embodiment, token issuance may include the generation of a new unique token and associated attributes. The new token may be generated and stored within smart contract 250A and / or another smart contract 250B (as further described below). The new token may be stored with a unique identifier, a record indicating the current owner (which may initially be set to a default value such as the issuing party and later changed to indicate the blockchain address of the current owner after purchase or other token transfer), and several attributes. In an embodiment, some of the attributes may include a link to off-chain data, which may be stored in centralized storage (e.g., a file server) or distributed storage (e.g., IPFS).

[0143] Figure 22 illustrates an exemplary process for issuing tokens using the tokenization platform 200 and / or exemplary smart contract 250A. The process in Figure 22 can be performed in whole or in part by exemplary smart contract 250A and / or by the tokenization platform 200, which is backed by an asset class that can invoke various functions of smart contract 250A. For example, as will be described in more detail below, smart contract 250A may be configured to periodically issue tokens according to a token issuance schedule (if provided as part of configuration data 2102), and / or the platform may periodically invoke the smart contract issuance function as indicated by the token issuance schedule.

[0144] In step 2202, the tokenization platform 200 backed by the smart contract 250A and / or the asset class can receive configuration data 2102. In embodiments, the configuration data 2102 may be generated by the asset class-backed tokenization platform 200 based on one or more of the token attributes 304, asset class attributes 308, and / or asset-token linking framework attributes 310. For example, as described above with respect to Figure 3, the token design system 204 may generate a proposed asset class-backed token 318 including a specific token attribute profile 322, an asset class attribute profile 324, and / or an asset-token linking framework 328. Additionally or alternatively, the strategy engine 1902 may generate a portfolio specification, as described elsewhere herein, which may include the token attributes 304, asset class attributes 308, asset-token linking framework attributes 310, and / or configuration data 2102. In some embodiments, data corresponding to tokens 318 backed by the proposed asset class can be converted into configuration data 2102 by the platform 200 (for example, by a token deployment system 208 capable of handling communication with one or more smart contracts 250, including smart contract 250A).

[0145] In these embodiments, platform 200 may leverage a language model provided by AI system 1912 to allow the user to review configuration data 2102, describe the type of tokens (e.g., number, performance, etc.) backed by asset classes issued using the configuration data 2102, and make changes to the configuration data 2102 (e.g., issue a different number of tokens), swap one asset or asset class for another, etc., before proceeding with the process. In other words, the user can interactively modify the configuration data 2102 as desired before proceeding with the configuration / deployment of the smart contract and / or minting. In these embodiments, platform 200 may send prompts to the language model containing various configuration data 2102 and / or other relevant data (e.g., formatting rules for the configuration data 2102, general information about tokens backed by assets or asset classes, etc.) to include contextual information that enables the language model to respond to the user's inquiry, and then display a user interface that enables the user to ask various follow-up questions based on the provided context. After receiving a user query via the user interface, platform 200 can include the user query in its language model along with previous conversation history (which may include, for example, previously provided context information), receive and display the response, and interpret it in various ways (for example, if the language model responds with a modified set of configuration data 2102, platform 200 can detect and use the configuration data 2102 to modify it (for example, using a parser). Similarly, the platform may leverage the language model to describe how a smart contract 250A will function after being deployed / configured with the configuration data 2102 (for example, a user asks about various smart contract functions and receives an answer based on the context provided to the language model by platform 200, such as smart contract code and / or documentation).

[0146] In one embodiment, the asset class-backed tokenization platform 200 may provide configuration data 2102 to a smart contract 250A (for example, by calling the configuration function 2130 of smart contract 250A and providing the configuration data 2102 as an argument to the configuration function). In another embodiment, the asset class-backed tokenization platform 200 may select which of a plurality of smart contracts 250 to configure based on the configuration data 2102 or other data (for example, a token attribute profile 322). For example, if the configuration data 2102, the token attribute profile 322, or some other data indicates that the asset class-backed token is an Ethereum token, the asset class-backed tokenization platform 200 can select and configure a smart contract already deployed on the Ethereum blockchain. The asset class-backed tokenization platform 200 can then call the configuration function of the selected smart contract. Additionally or alternatively, the asset class-supported tokenization platform 200 may deploy a new smart contract on a selected blockchain instead of configuring a smart contract that has already been deployed.

[0147] In step 2204, the asset class-backed tokenization platform 200 and / or smart contract 250A may be configured to validate the assets indicated by the configuration data 2102. For example, the asset class-backed tokenization platform 200 and / or smart contract 250A may use a validator link 2114 for each asset specified in the configuration data to verify that the asset exists, is available (e.g., that the asset is not already backed by another token), is stored in a specified location (e.g., a storage facility), is not unsecured, and / or is in a state to be used to back one or more tokens to be issued. For example, the validator link 2114 may point to an off-chain system related to asset validation and / or storage providers. In embodiments, different validator systems may be used for different assets indicated in the configuration data. Additionally or alternatively, each validator link 2114 may link to an API of a system (e.g., the asset class-backed tokenization platform 200 and / or other system) that interfaces with multiple validators to validate assets. Additionally or alternatively, the tokenization platform 200 and / or smart contract 250A may use one or more oracles 2308 to retrieve off-chain data from validators located at the validator link 2114. In these embodiments, the oracles 2308 may retrieve the validation data periodically and / or on demand (for example, in response to query records stored by the smart contract 250A) and store the validation data on-chain for access by the smart contract 250A.

[0148] In 2206, the asset class-backed tokenization platform 200 and / or smart contract 250A may be configured to verify that the user requesting the issuance has the right to issue tokens. For example, the user may already own the assets indicated by configuration data 2102 (for example, as indicated by verifier 2114 which may indicate the ownership status of each asset). In addition, or alternatively, the user may provide sufficient convertible tokens to enable the user to acquire the assets as part of the issuance process (for example, by transferring the tokens to a wallet maintained by the asset class-backed tokenization platform 200 and / or to the address of smart contract 250A). For example, if the first asset is available for purchase at a fixed unit price (as indicated by, for example, an asset verifier, a marketplace system, a token simulator 312, etc.), and the configuration data indicates that an asset class backed token should be backed by a fixed number of units of the first asset, then in step 2206, the asset class backed tokenization platform 200 and / or smart contract 250A may verify that the user already owns the specified asset or has provided sufficient funds (for example, sufficient funds (e.g., convertible tokens) to purchase a specified number of the first asset at the current unit price).

[0149] In the embodiments, the asset class-backed tokenization platform 200 and / or smart contract 250A may wait until the token pool has sufficient funds (e.g., a sufficient number of Kazible tokens associated with asset class-backed tokens having a defined price and / or a sufficiently high estimated value) before proceeding with the issuance of tokens. In these embodiments, multiple users may contribute funds to the pool, and the asset class-backed tokenization platform 200 and / or smart contract 250A may track the value of the funds (e.g., tokens) contributed by each user so that the issued asset class-backed tokens are distributed to different users according to their contribution value. Thus, for example, the asset class-backed tokenization platform 200 and / or smart contract 250A can provide a crowdfunding model in which users pool funds for the issuing party until a target amount is reached, and then method 2200 proceeds with the issuance of asset class-backed tokens.

[0150] In step 2208, the asset class-backed tokenization platform 200 and / or smart contract 250A can use configuration 2102 to configure one or more smart contracts 250A. In embodiments, the configuration function 2130 of the issuing smart contract 250 may execute one or more instructions to configure the smart contract to issue asset class-backed tokens using configuration data 2102. For example, the configuration function 2130 can cause the smart contract to store some or all of the configuration data 2102 in the smart contract's memory. In addition, or alternatively, the asset class-backed tokenization platform 200 and / or smart contract 250 can use configuration data 2102 to configure other smart contracts and / or systems, such as a token management smart contract, one or more AMMs 2350 and / or RDA systems 2352, and a governance system. For example, the token redemption rules 2120 of the configuration data 2102 may be used (e.g., by the asset class-backed tokenization platform 200 and / or smart contract 250A) to configure the token management smart contract 250B, as described elsewhere in this specification. In embodiments, smart contract 250A may configure the token management smart contract 250B by storing the configuration data on a distributed ledger (e.g., within the storage of smart contract 250A) and enable the token management smart contract 250B to retrieve the configuration data from the distributed ledger. Additionally or alternatively, the asset class-backed tokenization platform 200 may configure a token valuation system and / or a token redemption system by transmitting at least a portion of the configuration data 2102 (e.g., the token redemption rules 2120) to the token management smart contract 250B.

[0151] In embodiments including the deployment of one or more AMM2350s for asset class-backed tokens, the tokenization platform 200 can deploy the AMM2350s based on AMM configuration data generated by the strategy engine, as described above. Additionally or alternatively, the tokenization platform 200 can configure the RDA system 2352 to train, deploy, and / or run one or more AI models to generate pricing data used by the on-chain AMM2350s in order to provide pricing functionality for automated trading of asset class-backed tokens. In embodiments, the tokenization platform 200 can obtain AI models from third-party resources and / or from users who set up the issuance of asset class-backed tokens. In addition or alternatively, the tokenization platform 200 can train custom AI models based on historical data of one or more assets and / or asset classes corresponding to asset class-backed tokens and / or historical data of a second token in a trading pair provided by the AMM2350.

[0152] In step 2210, the configured smart contract 250A may issue one or more asset class backed tokens as indicated by the initial issuance instructions in the token issuance schedule 2124. For example, if the token issuance schedule 2124 indicates that 100 asset class backed tokens should be issued, and each asset class backed token is backed by a specified amount of each of assets A through N (as indicated by, for example, the asset identifiers 2106 and asset amounts 2108 for each of assets A through N), then the smart contract 250A may issue the indicated 100 asset class backed tokens. In addition, or alternatively, tokens may be issued on demand (for example, in response to a user who requests and pays for the issuance of tokens using the functionality of the smart contract 250A). Each issued asset class backed token may be assigned one or more corresponding assets as indicated by the configuration data 2102. Generally, one or all of the data fields stored as configuration data 2102 may be written as attributes of the issued asset class backed tokens. For example, an issued asset class-backed token may include all of the asset attributes 2104A of the first asset backing the token (such as the asset identifier 2106A, asset value 2108A, available trigger 2110A, expiration trigger 2112A, validator link 2114A, and / or redemption link 2116). Similarly, an issued asset class-backed token may include the asset attributes 2014B-N of other assets backing the asset class-backed token. Additionally or alternatively, instead of storing some or all of the attributes in the asset class-backed token, the asset class-backed token may store a link to data stored elsewhere (e.g., in another smart contract or off-chain such as decentralized storage). In some embodiments, the corresponding attributes may be retrieved from configuration data 2102 stored in the smart contract 250A.

[0153] Once issued, tokens backed by an asset class may be stored in a distributed ledger within smart contract 250A and / or smart contract 250B for managing the tokens, as will be described in more detail elsewhere in this specification. In some embodiments, smart contract 250A that issued the tokens and smart contract 250B that manages the tokens may be the same smart contract 250 or different smart contracts.

[0154] In 2212, the asset class-backed tokenization platform 200 and / or smart contract 250A may initiate an evaluation of the issued asset class-backed tokens. For example, the asset class-backed tokenization platform 200 and / or smart contract 250A may invoke a token simulator 312 configured to generate an evaluation of the issued asset class-backed tokens. As will be described in more detail elsewhere in this specification, the token simulator 312 may evaluate each asset class-backed token based on the underlying assets of the tokens (e.g., their present value, projected value, etc.), whether ownership of the assets is verifiable, any rules for redeeming multiple assets, predictions of the likelihood of a particular event occurring, and / or based on these.

[0155] In 2214, the asset class-backed tokenization platform 200 and / or smart contract 250A may facilitate the transfer of tokens. In some embodiments (for example, when a user requesting issuance wishes to hold asset class-backed tokens), the asset class-backed tokenization platform 200 and / or smart contract 250A may trigger the transfer of tokens to the user who initiated the issuance. In embodiments where multiple users contribute funds to acquire the assets backing the tokens (for example, a crowdfunding model), the asset class-backed tokenization platform 200 and / or smart contract 250A may distribute tokens to users in proportion to the amount of funds contributed. In some embodiments (for example, when a user requesting issuance wishes to sell asset class-backed tokens for fundraising), the asset class-backed tokenization platform 200 may automatically generate a marketplace listing of the issued asset class-backed tokens. In some embodiments, the listing may represent the valuation generated in step 2212 and / or be available for purchase for an amount matching or exceeding the valuation generated in step 2212.

[0156] In 2216, the asset class-backed tokenization platform 200 and / or smart contract 250A may issue additional asset class-backed tokens when a trigger specified in the asset issuance schedule is detected. For example, if the issuance schedule specifies the periodic issuance of additional tokens, the asset class-backed tokenization platform 200 and / or smart contract 250A may wait until the corresponding time is reached and then issue the additional assets. In addition, or alternatively, if the issuance schedule specifies the issuance of additional tokens based on an event, the asset class-backed tokenization platform 200 and / or smart contract 250A may periodically poll other systems (e.g., oracles) and / or smart contracts to determine if an even number has occurred and, after detecting that a trigger has occurred, issue the additional assets. Token management of asset classes using smart contracts

[0157] In embodiments, one or more smart contracts 250 may be configured to manage asset class-backed tokens after issuance. In some embodiments, a token management smart contract attribute 2126 may be included in the configuration data 2102 before issuance so that asset class-backed tokens are issued and linked to the corresponding smart contract 250 indicated by the attribute 2126 after issuance. Additionally or alternatively, issued asset class-backed tokens may be stored in the memory of the smart contract 250 configured for token management (for example, wrapped by the smart contract). In embodiments, one or more smart contracts 250 may be configured to have a wide variety of different management functions depending on the corresponding asset class-backed token(s) and associated attributes. Exemplary management functions are described in detail below, but it should be understood that any type of management function can be used to manage asset class-backed tokens as desired.

[0158] According to the first embodiment, a collection of tokens backed by an asset class could be backed by a set of different assets related to a complex project and managed by a smart contract 250. For example, assets could include land, existing or planned infrastructure on the land, a supplier ecosystem, inputs for factories or other productive assets, the productive asset itself under construction or planned for construction (e.g., a factory or power plant), shares in a company managing the power plant, and / or such. Such a collection of tokens backed by an asset class could enable productive investments in long-term revenue sources such as future power plants. In this example, acquiring a token backed by a single asset class in the collection could provide partial ownership of the land used to hold the productive asset, the inputs for the productive asset, the company managing the productive asset, and / or the productive asset itself.

[0159] In this example, one or more smart contracts 250 may be configured to manage the associated mechanisms of tokens backed by an asset class, such as verifying the receipt of rental funds, splitting revenue among token holders, raising new capital, and / or so on. In embodiments, the smart contract(s) may include functionality to implement each or all of these mechanisms. For example, if the tokens are backed by land associated with a project, smart contract 250 may be configured to have the functionality to verify that lease payments for the use of the land are being paid by the land user (e.g., the smart contract may have the functionality to allow the receipt of cryptocurrency or other token-based payments to occur directly to the smart contract, and / or the smart contract may monitor the receipt of payments to a designated payment blockchain address), and may be configured to distribute payments to all token holders (e.g., so that 100k rental income is distributed equally among holders of 1,000 tokens backed by land assets and / or other associated assets). If, additionally or alternatively, some or all of the payment is made off-chain, the smart contract may be configured to communicate with Oracle 2308, which has access to the payment data, and Oracle 2308 is configured to verify receipt of the payment. In these embodiments, the smart contract may be configured to instruct Oracle 2308 to distribute the received payment to the token holders.

[0160] Additionally or alternatively, if a token backed by an asset class is backed by ownership of a productive asset (e.g., a factory, power plant, energy production facility), when the productive asset becomes operational, a smart contract 250 (e.g., the same smart contract that manages rental fees, or another smart contract) may have a function configured to receive (or verify receipt via oracle 2308 to another on-chain address or off-chain settlement system) a portion of the revenue generated from the operation of the productive asset (e.g., 2% of top-line revenue) and distribute this revenue to the blockchain addresses of the token holders. In these embodiments, an availability trigger 2110A specified in configuration data 2102 may indicate that revenue verification and distribution should begin when a specified oracle system indicates that the productive asset is operational. In addition or alternatively, the tokenization platform 200 may periodically (e.g., at regular intervals) invoke a configured function of a smart contract that verifies the receipt of revenue and / or distributes the revenue to the blockchain addresses of the token holders. In these embodiments, the oracle system (e.g., oracle system 2302) may provide information regarding the revenue stream, indicating the amount to be transferred to and distributed to the smart contract 250, and / or so it may do.

[0161] Additionally or alternatively, one or more smart contracts 250 associated with the asset class backed tokens of the first example may be configured to raise new capital for the relevant project (e.g., increasing the production capacity of a production asset) and to manage the distribution of profits from the increase in production (e.g., between contributors of new capital and existing token holders). In these embodiments, the smart contracts 250 may be configured to receive the new capital and distribute it appropriately (e.g., to distribute it to blockchain addresses managed by the party responsible for allocating the raised capital). Furthermore, the smart contracts 250 may be further configured to receive and / or verify profits from the production asset and distribute the profits accordingly (e.g., partially to the blockchain addresses of token holders).

[0162] In some embodiments, a collection of tokens backed by an asset class may support multiple complex projects (e.g., multiple projects to build new power plants or other energy production or power generation facilities, where the tokens backed by the asset class may support multiple plots of land, multiple productive assets, multiple input sets, multiple corporate shares, etc.) in order to diversify risk, reduce potential fluctuations in returns, provide more predictable valuation growth, provide increased upside by focusing on earlier-stage projects, and / or for such purposes. In these embodiments, a smart contract may receive and / or verify rental income, profits produced by productive assets, equity distributions, proceeds from the sale of shares, and / or such, and distribute the returns accordingly (e.g., to the blockchain addresses of token holders).

[0163] In embodiments, asset class-backed tokens may be backed by multiple energy production facilities (e.g., oil and gas facilities such as oil platforms, oil wells, refineries, associated land, associated equipment, and / or such), and energy storage facilities (e.g., oil and / or gas storage, including strategic oil reserves and / or fractional ownership thereof, rights to revenue from the sale of stored oil and / or gas, etc.). In these embodiments, tokens may grant each token holder a shared right to the revenue generated by the energy production and / or storage facilities, and a managing smart contract may receive and / or verify the revenue stream or other profits generated by the production facilities, equity distributions, revenue from the sale or rental of equipment, and / or such, and distribute the revenue accordingly (e.g., to the token holder's blockchain address) as described above.

[0164] In another example, a collection of tokens backed by an asset class may each be associated with a non-exclusive intellectual property right, along with a portion of the revenue or profits derived from the commercialization of the intellectual property. For example, each token may be backed by a license to manufacture, sell, or otherwise use one or more goods, services, performances, and / or other items protected by the intellectual property. In this example, smart contract 250 may be configured to collect revenue from the intellectual property licensee and distribute a portion of the profits to the token holder's blockchain account. In some embodiments, for the collection of tokens according to the second example, some or all of the tokens may be backed by a license to an intellectual property right having a degree of exclusivity (e.g., in terms of geographical, field of use, or other restrictions). In these embodiments, one or more smart contracts 250 may be configured to manage the collection of revenue from various segments (e.g., revenue from a specific location or field of use) and distribute (at least a portion of) the revenue to the corresponding token holder's blockchain account. As described above, smart contract 250 can directly receive revenue and / or verify the receipt of revenue to other blockchain addresses and / or off-chain settlement systems via oracle 2308, and can be triggered for periodic verification and / or distribution by other smart contracts and / or tokenization platforms 200.

[0165] In some embodiments, the smart contract 250 may be configured to allow a token holder to sublicense the IP rights corresponding to that token by, for example, creating a new set of tokens that divide a portion of the IP rights associated with the token. Thus, for example, a token holder can trigger the issuance of new tokens for the IP rights assets backing the token holder's token. In these embodiments, the smart contract 250 can track and process the division of revenue corresponding to the IP rights between the token holder of the original token and the holder of the token backed by the sublicensed IP rights. For example, if a single entity creates tokens that divide revenue from IP rights, the smart contract 250 may be configured to monitor royalty payments (e.g., via an oracle), divide the revenue into equal or otherwise predefined portions, and transfer each portion to the accounts (e.g., digital wallets) of those token holders. Generally, smart wrappers that wrap tokens backed by an asset class may be configured to automatically allocate a specified amount of funds (e.g., cryptocurrency, fiat currency, etc.) to token account holders when the corresponding smart contract (or other programmatic logic) detects a distribution condition (e.g., when it receives notification from an oracle that a certain amount of funds has been received).

[0166] In these embodiments, a collection of asset class-backed tokens may be backed by digital land existing in a virtual reality / metaverse application. The asset class-backed tokens may convey ownership of the digital land, and smart contract 250 may be configured to collect rent from users of the digital land (e.g., digital operators who may lease the digital land) and / or verify that rent has been paid by users of the digital land. In these embodiments, smart contract 250 may be configured to divide the revenue received from users into portions corresponding to the issued asset class-backed tokens (e.g., equal portions) and transfer each respective portion to the respective accounts (e.g., digital wallets) holding the corresponding asset class-backed tokens.

[0167] In this embodiment, a collection of tokens backed by an asset class may be backed not only by one or more real-world assets, but also by one or more digital twins of real-world assets. For example, real-world assets could be specialized machinery, factories, energy production facilities, rental vehicles, robots, etc., and the digital twins could enable simulations of the real-world assets (e.g., for prediction, training, modeling, etc.). In this example, tokens backed by an asset class could provide token holders with revenue from the use of real-world assets and from digital twins of real-world assets. For example, digital twins could generate revenue through their use by simulation systems, data analysis systems, AI systems, etc., which may use the digital twins for analysis, data generation purposes (e.g., generating synthetic AI training data), etc. This revenue is collected by a smart contract (and / or detected via an oracle system) and distributed to token holders as discussed elsewhere in this specification.

[0168] In some embodiments, a smart contract configured to manage tokens backed by an asset class may be configured to include a voting function that allows token holders to vote on one or more governance aspects or other decisions related to one or more assets. For example, the configuration function of a smart contract may allow a facility owner or manager to configure a new voting topic (e.g., by calling the configuration function and signing a transaction that provides arguments to configure the vote, which may include specifying the issue for voting, rules for voting, and various voting options). In these embodiments, the configuration function may be restricted to access only by specific account holders (e.g., a call to the function is only valid if signed with the private key of a specified blockchain account, and the smart contract may verify it using the public key of a specified blockchain account). Each token holder can then call a voting function that includes arguments specifying the issue to be voted on and the user's vote. Again, the smart contract may use public-key / private-key cryptography to gate access to the voting function to token holders. In an example implementation, a collection of tokens backed by an asset class could be associated with a land development project, allowing token holders to collectively manage the land and / or extract value from it. For example, a token could be backed by a first asset corresponding to the energy availability of the land, a second asset corresponding to power generation systems on the land, such as solar or wind power systems, a third asset corresponding to permitted transmission lines, a fourth asset corresponding to an energy rating (e.g., a green rating), a fifth asset corresponding to accessibility and transportation on the land, a sixth asset corresponding to water rights, and / or similar assets.In this example, smart contract 250 can manage the decision-making process for developing land (e.g., allowing token holders to vote on land use), generate capital for each of the various project elements (e.g., by selling tokens), collect and distribute profits from land development (including allocating profits according to token ownership), and / or do so. Various parameters of such tokens can be set by the token designer. For example, some align voting rights with capital investment, project timing, and other factors, while others provide various economic benefits (e.g., rewarding certain token holders who assume different risks with preferential returns (optionally including interest payments), or if profits or gains are paid in tranches). In this example, a set of tokens can be constructed to give token buyers different risk / reward profiles.

[0169] As can be seen from the provided examples, one or more smart contracts 250 can be configured to have a wide variety of management functions. With respect to Figure 23, an example environment is illustrated that includes smart contract 250B configured to have several functions that may be useful in many cases. Smart contract 250B may be the same as smart contract 250A described with respect to Figure 21 (for example, smart contract 250B may contain the functions and data of smart contract 250A, or vice versa), or it may be a different smart contract.

[0170] As shown in Figure 23, smart contract 250B may include (e.g., wrap) tokens 2312 backed by multiple asset classes, which may be fungible or nonfungible tokens issued as described elsewhere in this specification. Each of the wrapped asset class backed tokens 2312 may be associated with an owner attribute indicating the wallet address of the user who currently owns the asset class backed token (e.g., a blockchain address, which may be the public key of a public / private key pair managed by the owner of the address, or may be associated with the public key). Additionally or alternatively, the asset class backed tokens 2312 may be stored elsewhere on the blockchain (e.g., the tokens may not be wrapped by smart contract 250B). Smart contract 250B may include several management functions, among other management functions, such as a transfer function 2314, a modification function 2316, an audit function 2318, a revenue collection function 2320, and / or a revenue distribution function 2322. Various functions may be configured to operate based on data associated with one or more oracles 2308, which may be managed by the oracle system 2302 and / or governance system 1204 of the asset class-backed tokenization platform 200, and / or by other systems such as one or more auditor systems 2304.

[0171] As an example of the use of the oracle system 2302, if the asset class-backed tokens 2312 correspond to a complex project such as that described according to the first example above, one or more audit functions 2318 and / or revenue collection functions 2320 may be configured to verify the receipt of rental income. For example, if the rental income is paid off-chain, the auditor system 2304 and / or oracle system 2302 may detect the off-chain rental income, convert any off-chain funds into blockchain funds (e.g., by purchasing Kanzible tokens through an exchange), and / or update one or more oracles 2308 to indicate the off-chain payment, which may be detected by an audit function 2318 configured to monitor the oracle 2308. Additionally or alternatively, the revenue collection function 2320 may be configured to receive funds via one or more blockchain transactions. The revenue distribution function 2322 may be configured to distribute the received revenue proportionally among token holders (or otherwise, for example, if users hold different classes of tokens, some of which may be associated with different ownership shares, rights to different revenue streams, etc.) as indicated by the address data associated with the tokens 2312 backed by each asset class.

[0172] In embodiments, the revenue collection function 2320 may receive (or monitor off-chain revenue receipts via an oracle) from any of the production assets linked to tokens backed by the asset classes described herein. This includes real estate, facilities thereon (e.g., power plants, factories, transportation, hotels, toll collectors or other municipal toll collectors), intellectual property rights (e.g., patents, trademarks, copyrighted material, publicity rights), other rights (e.g., rental or usage rights of equipment, exhibition rights of works of art or other material, voting rights, other ownership rights), debt flows (e.g., revenue from loans such as microfinance loans), sales revenue (e.g., sales of consumer products, goods, energy, crops, seeds, etc.), and various other revenue sources (e.g., insurance revenue, crowdfunding revenue, revenue from products under development, stocks, bonds, dividends, futures, or other assets, and / or any of the other assets described herein).

[0173] In the embodiments, any of the functions may modify its behavior based on data from Oracle 2308. For example, the revenue distribution function 2322 may not distribute revenue until a trigger event occurs (e.g., until a productive asset such as a power plant, or any other productive or revenue-generating asset disclosed herein, becomes operational), and Oracle 2308 may store data indicating whether or not a trigger event has occurred, based on instructions received from an off-chain system such as Oracle System 2302. Similarly, the revenue collection function 2320 may not request periodic receipt of revenue until a trigger event occurs. As another example, if a first trigger event occurs (e.g., a productive asset becomes operational) and / or a second trigger event does not occur (e.g., revenue collection does not occur after the due date), the revenue collection function 2320 may perform one or more default actions corresponding to the non-occurrence of the second event (e.g., perform a breach workflow by transferring escrowed funds to token holders, burn tokens backed by asset classes held by the breaching party, etc.).

[0174] In this embodiment, the oracle system 2302 may be configured to monitor multiple information sources and generate and / or configure one or more oracles 2308 based on the information sources. For example, if tokens backed by asset classes are backed by intellectual property assets, the oracle system 2302 may be configured to monitor one or more transfer and / or license information sources and provide data to the oracle 2308 indicating ownership and / or licensing information of intellectual property rights. For example, when a new licensee is added to the licensing data source, corresponding data indicating the new licensee may be added to the oracle 2308, which may be detected by the audit function 2318. In this example, the revenue collection function 2320 may then begin to expect payments from the new licensee, and the remediation function 2316 may, and / or may, remediate the tokens 2312 backed by one or more asset classes to indicate the new licensee. Similarly, Oracle System 2302 may monitor sources that provide sales data relating to sales corresponding to licensed intellectual property rights and provide Oracle 2308 with such information and / or information indicating required intellectual property royalties. The audit function 2318 can then configure the revenue collection function 2320 to detect the information and expect one or more payments corresponding to royalties by a certain date (for example, by configuring a breach workflow that may be performed if a payment is not received in full). It is understood that Oracle System 2302 may be configured to collect additional or alternative data sources relating to additional or alternative assets. Oracle 2302 may be configured to monitor banking systems, weather feeds, news feeds, social media feeds, government databases, IoT systems, third-party data feeds, and / or other appropriate data sources. Similarly, the audit function 2318 may be configured to monitor data received from any type of Oracle 2302 for any appropriate conditions associated with tokens backed by an asset class.

[0175] In embodiments, the oracle system 2302 can configure or implement a digital twin of a real-world item, which is at least partially input by sensor data that maintains current information about the state of the real-world item, such as operating status, maintenance status, location, environmental exposure, production capacity, or any of other broad range of relevant parameters, so that a smart contract 250B can consume and operate on such information. In some of these embodiments, the digital twin may receive data relating to the state of the real-world item and update the state of the digital twin after processing the received data. For example, the digital twin may verify the authenticity of the data and preprocess the data (e.g., filter or aggregate the data) before updating the state of the real-world item. In embodiments, the digital twin may report some or all of the determined state of the real-world item to a component of the oracle system 2302 that interfaces with a distributed ledger network of nodes. In some embodiments, the digital twin may be configured to interface directly with a distributed ledger network so that the digital twin provides state information directly to the distributed ledger network.

[0176] In embodiments, the Oracle System 2302 may be embedded in or otherwise integrated into a physical item, such as being installed in the information processing infrastructure of an item having access to onboard sensors and diagnostic systems, communication functions (such as consuming information from other items or infrastructure), processing information, and publishing a stream of information about the physical item, its environment, etc. The information stream from the embedded Oracle 2302 may include various status, operation, capability, environment, or other information as described throughout. In embodiments, the Oracle 2302 may be embedded in any of the following: manufacturing equipment (optionally, additive manufacturing equipment), robots, vehicles, mobile devices, personal computers, wearable devices, augmented reality systems, smart home systems, smart city systems, or a wide range of products and systems. Thus, in these embodiments, the Oracle System 2302 embedded in an item can receive information from one or more sensors mounted on the item. Oracle system 2302 can then process information received from one or more sensors in various ways for any of the use cases described herein (e.g., updating a digital twin, generating price estimates, or otherwise performing any function of Oracle system 2302), and upload the resulting processed information to the blockchain (e.g., to Oracle 2308).

[0177] In embodiments, the oracle system 2302 may be configured to monitor a digital environment (e.g., digital land, digital twin, metaverse entity, etc.) and update one or more oracles 2308 based on the digital environment. Thus, for example, the assets backing the tokens may include digital land or its use, digital twin or its use, and / or other assets that may exist in the virtual and / or metaverse environments. In embodiments, any of these digital entities may include information about revenue streams (e.g., payments) or other valuation data that can be used by various functions of the smart contract 250B, as discussed elsewhere in this specification. In addition, or alternatively, the oracle system 2302 may be configured to monitor sources that provide information about any tangible or intangible real-world assets (e.g., any of the assets discussed elsewhere in this specification, or any other assets).

[0178] In this embodiment, a user system 2306 associated with a holder of an asset class-backed token may have the right to perform various functions of the smart contract 250B. For example, a first token holder (e.g., by owning at least one asset class-backed token 2312) may invoke a modification function 2316 that may allow the first token holder to sublicense the token (which may be backed by licensable IP rights) or a subset of the token-backed assets to another user. In this example, when the first token holder invokes the modification function 2316, the smart contract 250B determines that one or more asset class-backed tokens 2312 are allocated in the first token holder's wallet and may allow the first token holder to sublicense the asset class-backed tokens to another wallet (e.g., by transferring the asset class-backed tokens and / or issuing new tokens for the sublicensee).

[0179] In an embodiment, the governance system 1204 may configure one or more oracles 2308 to control the operation of one or more functions. For example, the governance system 1204 may configure an oracle 2308 having eligibility information to control a transfer function 2314. The transfer function 2314 may refer to the eligibility information before allowing a token backed by an asset class to be transferred from a first blockchain address to a second blockchain address. In an embodiment, the governance system 1204 may operate in accordance with a governance rule 2122 specified in the configuration data 2102 (for example, the governance system 1204 may receive the governance rule 2122 in step 2208 of method 2200).

[0180] As described above, blockchain 3490 may include one or more automated market makers (AMMs) 2350 that can be embodied (at least partially) as smart contracts. An AMM 2350 can function on blockchain 3490 as a decentralized exchange (DEX) for any of the tokens backed by the asset classes described herein. Each AMM 2350 may use a smart contract function consisting of a formula that can determine (or estimate) the price of one or more assets and / or asset classes corresponding to tokens backed by an asset class, thereby improving token trading and increasing liquidity. In some cases, an AMM 2350 can reduce or eliminate the need for intermediaries (e.g., counterparties for price discovery), thereby streamlining the trading process.

[0181] In embodiments, each AMM2350 may be a smart contract that maintains a liquidity pool. Depending on the type of AMM and trading pair, the liquidity pool may include any type of tokenized asset (e.g., Kandible tokens, asset class-backed tokens, etc.). In embodiments, these tokens may be provided by liquidity providers (LPs), which may provide liquidity to the liquidity pool in exchange for trading fees, token rewards, etc. In other words, the AMM2350 may include the ability to accept contributions from LPs and record the contribution amount and the blockchain address associated with each LP. The AMM2350 may further include the ability to pay fees and / or rewards when certain events occur (e.g., paying trading fees to the LP's blockchain address when a transaction occurs through the AMM).

[0182] In embodiments, as described above, the pricing function of AMM2350 may be based on the demand for tokens backed by a particular type of asset class, and / or other analytical metrics related to the use of tokens backed by an asset class. In these embodiments, AMM2350 can obtain dynamic pricing information (e.g., metrics of demand for a type of token) from an oracle 2308 that can periodically store updated metrics or other dynamic pricing information on-chain for access by smart contracts such as AMM2350. Furthermore, in embodiments, the pricing function of AMM2350 may be based on the output of an AI model or other machine learning model that may be run off-chain (e.g., on an RDA system 2352). In these embodiments as well, one or more oracles 2308 can periodically store updated information (e.g., updated output of an AI model running on an RDA system 2352) for use by the pricing function of AMM2350.

[0183] In addition, or alternatively, as mentioned above, different types of AMMs can use different types of pricing functions. For example, a Constant Function Market Maker (CFMM) may use a function that keeps the product of the quantities of the two assets in a trading pair constant, which is known as constant market depth. A variety of other functions may also be used depending on the type of AMM, some of which are described in detail above.

[0184] In some embodiments, the off-chain RDA system 2352 may operate outside the blockchain network (e.g., as part of a tokenization platform 200). For example, the RDA system 2352 can implement various AI techniques (e.g., machine learning models) that can be leveraged by the pricing function of the on-chain AMM2350, and / or otherwise provide off-chain computation for the AMM2350. This hybrid on / off-chain approach may enable more sophisticated pricing capabilities, with improved transaction speed and / or reduced fees.

[0185] In an embodiment, Oracle 2308 can link AMM 2350 with off-chain RDA system 2352, enabling information flow between relevant on-chain and off-chain components. Thus, for example, AMM 2350 can use price data (e.g., price estimates of real-word assets) computed off-chain by RDA system 2352 and uploaded to the chain using Oracle 2308. In addition, or alternatively, RDA system 2352 can access on-chain data of various tokens (e.g., trading volume, price, etc.) and use the on-chain data for off-chain price estimation, model training, deployment, and / or refinement, or for other purposes. In an embodiment, tokenization platform 200 can provide various AMM-related services that can help users (e.g., blockchain account holders) more easily interact with AMM 2350 on the chain. For example, the tokenization platform 200 can generate a graphical user interface that enables users to generate AMM transactions (which can exchange one or more first non-fungible tokens or a certain amount of first fungible tokens for one or more second non-fungible tokens or a certain amount of second fungible tokens) that invoke AMM smart contracts, transfer the first tokens to the AMM, and receive the second tokens from the AMM. For example, the tokenization platform 200 can receive an AMM transaction request from a user associated with a user blockchain account and generate an AMM transaction transaction for the user to sign using the private key associated with the blockchain account.In addition, or alternatively, the tokenization platform 200 can send the generated AMM transaction to the user for signing (e.g., using the user's private blockchain key) and / or send the signed AMM transaction to the blockchain (e.g., if the user's wallet does not have the capability to send the signed AMM transaction to the blockchain, or if the user does not know how to send the signed AMM transaction to the blockchain). Thus, by sending the signed AMM transaction to the blockchain, the tokenization platform may trigger a token exchange using an AMM smart contract based on the transaction functionality.

[0186] The tokenization platform 200 may further provide a variety of analytical tools that can analyze blockchain transactions using the AMM, such as for measuring and / or analyzing transaction volume, liquidity pool size and / or changes in the liquidity pool, price changes (e.g., based on the supply of each token in a trading pair), and / or such. In these embodiments, the tokenization platform 200 can take all transactions or a subset of transactions that invoke the AMM trading function of an AMM smart contract and analyze the transaction data accordingly. In some embodiments, the tokenization platform 200 may take a subset of transactions that occur within a specified period (or another subset such as transactions that invoke a particular trading pair, transactions associated with a particular party, a particular transaction size, etc.) and perform one or more analyses thereof.

[0187] The tokenization platform 200 can further provide tools and / or user interfaces to assist users in contributing to liquidity pools and / or providing other liquidity pool functions such as automatic rebalancing, yield optimization, and loss protection. For example, the graphical user interface generated by the tokenization platform 200 can help blockchain users generate and sign transactions to modify or configure liquidity pool funding transactions and / or AMM smart contracts, broadcast signed transactions to the blockchain, and / or assist such actions.

[0188] Figure 24 shows an exemplary method 2400 for transferring tokens from a first distributed ledger account to a second distributed ledger account. Method 2400 may be performed by an asset class-backed tokenization platform 200 and / or a token management smart contract 250B. Method 2400 may be used to transfer tokens backed by one or more issued asset classes to the first purchaser of the tokens (e.g., primary sale), and / or to transfer tokens backed by asset classes from one owner to another owner (e.g., secondary sale).

[0189] In step 2402, the asset class-backed tokenization platform 200 may first deploy a configured management smart contract (e.g., smart contract 250B and / or any of the smart contracts described above) (for example, for step 2208). For example, the token management smart contract 250B may be deployed on a distributed ledger (e.g., a blockchain) so as to be linked to tokens backed by one or more asset classes that it manages. Thus, for example, if the asset class-backed tokens are securities subject to accredited investor requirements or other governance standards, the token management smart contract may be configured to verify that the requested transfer is authorized under the corresponding governance standards (for example, the transfer function 2314 may be configured to transfer the token to the new owner only if the governance system 1204 authorizes it).

[0190] In 2404, the asset class-backed tokenization platform 200 and / or token management smart contract 250B may receive a token transfer request. For example, a purchasing user and / or a marketplace system may send a transfer request using a transfer function (e.g., using a transfer function built into the smart contract and / or blockchain protocol), where the request contains or indicates sufficient Kandible tokens (e.g., an amount equal to the purchase price of the token(s)) to complete the transfer. The request may further specify the distributed ledger address of the purchasing user's wallet. Then, in 2406, the asset class-backed tokenization platform 200 and / or token management smart contract 250B may request the governance system 1204 to transfer the tokens to the designated wallet (e.g., via Oracle 2308) and receive approval. For example, when smart contract 250B requests approval from governance system 1204, it can do so by storing a query record in blockchain 3490 that can be discovered by oracle system 2302 (for example, because oracle system 2302 monitors blockchain 3490). Oracle system 2302 may then communicate with governance system 1204 to receive authorization. Upon receiving authorization, oracle system 2302 (or another off-chain component of platform 200) can invoke a function of smart contract 250B that proceeds with the authorized transaction.

[0191] The governance system 1204 may use various rules (e.g., governance rule 2122) and data sources to determine whether to approve a token transfer request. For example, the governance system 1204 may determine that a wallet is associated with an authorized investor if the sum of other tokens allocated to the wallet on the distributed ledger is greater than a minimum value, or if the minimum income requirement is verified. In this regard, the governance system 1204 may, for example, refer to a marketplace system and / or a valuation system to determine the value of the tokens associated with a given wallet. In addition or alternatively, the governance system 1204 may verify that the wallet possesses authorization tokens that grant the wallet eligibility to receive the tokens associated with the token transfer request. Other governance rules may require other forms of verification, whether manual, automatic, or semi-automatic, before approving a token transfer request. In embodiments, the governance system 1204 may use an oracle to store an indication on the distributed ledger that the transfer has been approved. For example, the governance system 1204 may interface with the oracle system 2302 to deploy such authorizations, which may be detected by the asset class-backed tokenization platform 200 and / or the token management smart contract 250B.

[0192] In 2408, after receiving authorization from the governance system, the asset class-backed tokenization platform 200 and / or token management smart contract 250B may generate a distributed ledger transaction to transfer the tokens to the wallet indicated in the transfer token request (e.g., using the transfer function 2314), broadcast the distributed ledger transaction to distributed ledger nodes (e.g., blockchain nodes), thereby adding the transaction to the distributed ledger / blockchain.

[0193] Figure 25 shows an exemplary method 2500 for monitoring one or more oracles for token-related events and updating a token management smart contract accordingly. Method 2500 may be implemented by an asset class-backed tokenization platform 200 and / or a token management smart contract 250B. Method 2500 may also be used by tokens backed by a dynamic asset class to enable changes based on real-world events. As a non-limiting example, Method 2500 may be used to update a token management smart contract for a collection of tokens backed by an intellectual property-backed asset class when a new licensee obtains a license for intellectual property rights. For example, the token management smart contract may be reconfigured to monitor sales data of the new licensee, receive revenue from the new licensee, and / or distribute the received revenue to token holders.

[0194] In step 2502, the asset class-backed tokenization platform 200 can initially deploy the configured management smart contract 250B as described above (for example, for step 2208). In an exemplary example where the asset class-backed tokens are backed by licensable IP rights, the token management smart contract may be configured to receive revenue from an initial set of licensees (if any) and distribute the revenue to token holders. Furthermore, the token management smart contract may be configured to update in response to specific events (for example, adding additional licensees when new licensees obtain licenses). For example, the token management smart contract may initially be configured to monitor an oracle (for example, provided by oracle system 2302) that provides an up-to-date list of licensees and update the revenue distribution schedule based on the new licensees in response to the addition of new licensees.

[0195] In 2504, the asset class-backed tokenization platform 200 and / or the token management smart contract 250B may monitor a designated oracle that provides data related to the management of the asset class-backed tokens (e.g., an updated list of licensees). In 2506, when the asset class-backed tokenization platform 200 and / or the token management smart contract 250B detect a token-related event (e.g., a new licensee or a previous licensee being removed from the licensee list), the asset class-backed tokenization platform 200 and / or the token management smart contract 250B may generate an updated token management configuration based on that detection. For example, the updated token management configuration may include an updated list of licensees. Next, in 2508, the asset class-backed tokenization platform 200 and / or the token management smart contract 250B may generate a distributed ledger transaction that updates the token management smart contract with the latest token management configuration (e.g., as follows), and / or the token management smart contract takes action (e.g., set a deadline for receiving revenue from each licensee, distribute revenue from each licensee, etc.) based on the updated configuration data.

[0196] Figure 26 illustrates an exemplary method 2600 for collecting revenue related to the assets backing asset class-backed tokens and distributing the revenue accordingly. Method 2600 may be implemented by an asset class-backed tokenization platform 200 and / or a token management smart contract 250B. For example, Method 2600 may be used to receive revenue from each of the IP rights licensees for a collection of IP rights-backed asset class-backed tokens and distribute the revenue accordingly to token holders.

[0197] In step 2602, the asset class-backed tokenization platform 200 can first deploy the configured management smart contract 250B as described above (for example, for step 2208). In an exemplary example where the asset class-backed tokens are backed by licenseable IP rights, the token management smart contract may be configured to receive revenue from licensees and distribute the revenue to token holders. Additionally or alternatively, method 2600 may be used to manage any tokens backed by revenue streams in order to collect and distribute revenue streams in accordance with a token design strategy. For example, revenue streams may include revenue from licensees, revenue from tenants of a property, revenue from a group of tenants (for example, each token may correspond to a portion of the revenue over a set of time and / or locations), and / or other divisions of any revenue streams.

[0198] In 2604, the token management smart contract 250B can receive revenue from revenue sources (e.g., licensees, tenants, tenant groups). For example, the token management smart contract 250B can receive a number of exchangeable tokens corresponding to the amount a licensee is owed to pay over a period of time as part of one or more distributed ledger transactions. In an embodiment, the smart contract 250B can receive revenue over a payment period (e.g., one month) and can only proceed after the period has ended (e.g., at the end of the month, it can verify and / or distribute the revenue received over the past month).

[0199] In 2606, the token management smart contract 250B may verify compliance with the token management rules associated with the received revenue. For example, the token management smart contract 250B may verify that the requested amount has been received. In an exemplary embodiment, each licensee of an intellectual property right (or other source of revenue) may be required to submit a certain amount (or a percentage of an amount) within a specified period. For example, a licensee may be required to submit a percentage of sales revenue, and a tenant may be required to submit an agreed-upon amount of rent. The token management smart contract 250B may determine the amount of debt based on data provided via an oracle (for example, generated by an oracle system and / or a third party). For example, an oracle 2308 may provide verified figures for sales revenue, lease agreements, and / or any other indicator of how much each licensee / tenant / etc. has a payment obligation. In embodiments, this data may be obfuscated (e.g., encrypted) to prevent access by third parties. For example, the data may be encrypted using the public key associated with the token management smart contract 250B, and only the token management smart contract 250B may be able to decrypt the data using its corresponding private key.

[0200] In some embodiments, the token management smart contract 250B may trigger one or more default / breach provisions for a corresponding revenue source if the revenue source (e.g., a licensee, tenant, etc.) fails to receive the amount due by a certain date (e.g., a certain number of days after the expiration of the revenue collection period). For example, it may generate a distributed ledger transaction indicating that the licensee or tenant has defaulted, which can be detected by the asset class-backed tokenization platform 200. In addition, or alternatively, the default / breach workflow may include automatically transferring escrowed funds to token holders, burning tokens held by the breaching party (e.g., licensee tokens that allow the licensee to use intellectual property rights), and / or similar actions. In some embodiments, method 2500 in Figure 25 may be used to return the debtor to normal control if the debtor subsequently fulfills their obligations (e.g., by posting instructions for the corresponding event to an oracle, thereby updating the token management smart contract and returning the licensee / tenant to normal control).

[0201] In 2608, the token management smart contract 250B may determine a revenue distribution to distribute the received revenue to holders of tokens backed by asset classes. In embodiments, the revenue may be distributed in proportion to the number of tokens held by each wallet. Additionally or alternatively, tokens of different classes may receive revenue in different proportions (for example, first-class tokens may receive a larger proportion of the revenue than second-class tokens). Furthermore, or alternatively, other distribution rules may be used to prioritize or otherwise determine the distribution of revenue. Then, in 2610, the token management smart contract 250B may generate one or more distributed ledger transactions to send the collected revenue to various token holders of tokens backed by asset classes, in accordance with the determined revenue distribution.

[0202] In this embodiment, the token management smart contract 250 can distribute revenue periodically (for example, at regular intervals such as monthly or quarterly) in response to a call to the distribution function by the tokenization platform 200 or an oracle system or other system. In other words, the tokenization platform 200 (or other system) can have the revenue collected by the smart contract distributed periodically to token holders. In addition, or alternatively, the tokenization platform 200 (or other system) can trigger distributions when asset-related events occur, such as when a particular asset starts production (or some other revenue-generating activity), stops production, changes production, and / or so. Example of a token simulator embodiment

[0203] An exemplary embodiment of the token simulator 312 is shown in Figure 27. The token simulator 312 may include a plurality of systems configured to simulate one or more factors that may affect a token backed by an asset class at present and / or in the future, using various models and / or other data. In embodiments, the token simulator 312 may include a token performance simulation system 2702 configured to predict one or more aspects of the token's current and / or future performance, such as current or future value, future price, future volatility, and future profit. Additionally or alternatively, the token simulator 312 may include an event prediction system configured to predict the probability of events that may affect the token's value, such as redemptibility (e.g., whether the asset backing the token is redeemable on a future date) and / or the likelihood of a black swan event occurring. In some embodiments, the event prediction system 2726 may interface with one or more prediction markets to receive prediction market data (e.g., crowdsourced predictions) about future events. In one embodiment, the token performance simulation system 2702 can use the event prediction system 2720 to generate token performance predictions based on various scenarios predicted by the event prediction system 2720 and their estimated probabilities. The token performance simulation system 2702 may include various models, such as an evaluation model 2704, a pricing model 2706, a volatility model 2708, and a profit model 2710.

[0204] Additionally or alternatively, the token simulator 312 may include a market factor simulation system 2740 configured to predict various other market factors, such as economic (e.g., micro and / or macro) factors, geopolitical factors, weather factors, various market scenarios, and supply and demand factors. In addition or alternatively, the token simulator 312 may include a production simulation system 2760 configured to simulate a supply chain or other production system based on data regarding inputs, production system capacity, price, market forecasts, etc.

[0205] In embodiments, the production simulation system 2760 can implement one or more supply chain digital twins 2762 of supply chain components or facilities, which can be used to generate data for simulations by other components and / or to run simulations using variously configured digital twins. For example, digital twins include distribution twins (representing distribution facilities, assets, objects, workers, etc.), warehouse twins (representing warehouse facilities, assets, objects, workers, etc.), port infrastructure twins (representing seaports, airports, and other facilities, assets, objects, workers, etc.), shipping facility twins, operation facility twins, etc.; customer twins (representing groups of customers or individual customers' physical, behavioral, demographic, psychometric, financial, historical, affinity, interests, and other characteristics); worker twins (representing workers' physical attributes, physiological data, state data, psychometric information, emotional state, fatigue / energy state, attention state, skills, training, abilities, roles, authority, responsibilities, work status, activities, and other attributes); wearable / portable Device twins, process twins, machine twins (such as various machines used to support value chain networks), product twins, origin twins, supplier twins, supply factor twins, offshore facility twins, floating asset twins, shipyard twins, destination twins, fulfillment twins, delivery system twins, demand factor twins, retailer twins, e-commerce and online sites, operator twins; waterway twins, road twins, rail twins, aviation facility twins (such as twins of aircraft, runways, airports, hangars, warehouses, air routes, refueling facilities, and other assets, goods, and workers used in connection with the air transport of products), autonomous vehicle twins, robotics twins, drone twins, logistics factor twins, etc.Each of these may have properties including mirroring or reflecting changes in the state of related physical objects or other entities, providing the ability to model the behavior or interactions of related physical objects or other entities, enabling simulation, providing a state display, and many other things.

[0206] In the embodiment, the token simulator 312 acquires data from various sources for use in the simulation. For example, the production simulation system 2760 includes a supply chain data monitor 2764 configured to monitor / receive simulation data from supply chain systems (e.g., real-time monitoring systems such as event and status reporting systems for ships and other floating assets, delivery vehicles, trucks and other transport assets; shipyards, ports, warehouses, distribution centers and other locations; onboard diagnostic (OBD) systems and telematics systems mounted on floating assets, vehicles and equipment; systems that provide diagnostic codes and events via event buses, communication ports or other communication systems; monitoring infrastructure (cameras, motion sensors, beacons, RFID systems, smart lighting systems, asset tracking systems, personnel tracking systems and environmental sensing systems installed in diverse environments where value chain activities and other events occur), as well as removable and replaceable monitoring systems including portable data collectors, RFID and other tag readers, smartphones, tablets and other mobile devices with data collection capabilities. The production simulation system receives simulation data from software interaction observation systems (which log and track events related to user-to-software user interface interactions, such as mouse operations, touchpad operations, mouse clicks, cursor movements, keyboard operations, navigation operations, eye movements, finger movements, gestures, menu selections, and software interactions resulting from other programs, via APIs, etc.), mobile data acquisition systems, and visual monitoring systems (such as video and still image systems, LIDAR, IR, etc., which can visualize objects, people, materials, parts, machinery, equipment, personnel, gestures, facial expressions, position, location, configuration, and other factors and parameters, as well as inspection systems that monitor processes and worker activities).The production simulation system 2760 may receive simulation data from interaction point systems (such as dashboards, user interfaces, and control systems for value chain entities). It may also receive simulation data from physical process observation systems (systems that track the physical activities of operators, workers, customers, etc.; the physical activities of individuals (e.g., shippers, delivery personnel, packers, pickers, assembly workers, customers, vendors, etc.); physical interactions between workers; interactions between workers and physical entities (such as machines and equipment); and interactions between physical entities (including, but not limited to, video cameras and still cameras; motion sensing systems (such as optical sensors, LiDAR, and IR sensor sets); and robot motion tracking systems (such as systems that track the movement of systems attached to humans or physical entities)).Furthermore, monitoring systems for machine condition (including monitoring of conditions, status, and operating parameters via onboard or external monitors) or other indicators that measure the status of entities in the value chain (including machines or their parts), such as machines, clients, servers, cloud resources, control systems, display screens, sensors, cameras, vehicles, robots, or other machines; sensors, cameras, and other IoT data collection systems (including onboard sensors, sensors, or other data collectors (including click-tracking sensors) installed in or around the value chain environment); but not limited to, starting points, loading or unloading docks, vehicles or floating assets for cargo transport, containers, ports, distribution centers, storage facilities, warehouses, delivery vehicles, and destinations); cameras that monitor the entire environment; cameras dedicated to specific machines, processes, workers, or similar subjects; wearable cameras; portable cameras; cameras mounted on mobile robots; cameras mounted on portable devices such as smartphones and tablets; and Many other things (including many types of sensors disclosed in this Specified or documents incorporated herein by reference); indoor location monitoring systems (such as cameras, infrared systems, motion detection systems, beacons, RFID readers, smart lighting systems, triangulation systems, RF and other spectral detection systems, time-of-flight systems, chemical sensor sets (including chemical sensors), etc.); user feedback systems (survey systems, touchpads, voice-based feedback systems, evaluation systems, facial expression monitoring systems, emotion monitoring systems, gesture monitoring systems, etc.); behavioral monitoring systems (e.g., motion monitoring, shopping behavior, purchase behavior, click behavior, fraudulent or deceptive behavior, interaction with user interfaces, product return behavior, behavior indicating interest, attention, boredom, etc., behavior indicating mood (e.g., fidgeting, motionless, approaching, changing posture, etc.), and many others); and a variety of Internet of Things (IoT) data collection devices, including those described herein and in reference documents incorporated herein.

[0207] In embodiments, the models and systems of the token simulator 312 can use data collected from a wide variety of sources, including those described above. For example, various models may be trained on and / or utilized on data including accounting data 3358, access data 3362, asset data 3320, virtual asset tags 3488, worker data 3322, event data 3324, underwriting data 3360, pricing data 3364, claims data 3354, or any other types of data described herein. Data may be collected from various entities 3330 using various monitoring and / or data collection systems 3306, 3318. In embodiments, the token simulator 312 can leverage any of the adaptive intelligence systems 3304 described herein to train, develop, deploy, and run models.

[0208] In this embodiment, the token simulator 312 can leverage a language model provided by the AI ​​system 1912 to facilitate user interaction with the token simulator 312 and / or understanding of the use of information as input to or generated by the token simulator 312. Thus, various inputs and outputs of the token simulator 312 can be provided to the language model as contextual information that enables the user to question various inputs, simulations, assumptions, strategies, and / or such at any stage of the token simulator process and modify various inputs or outputs. Thus, the user can interactively adapt the input and / or output information to better align with the user's beliefs or predictions, confidential information known to the user, and / or such. Furthermore, the language model can provide explanations of various predictions, simulations, token data, etc.

[0209] Several examples are useful in illustrating the functionality of the token simulator 312. In exemplary embodiments, tokens or parts thereof may be exchangeable for future shares (e.g., perpetual or discrete periods) of profits arising from the production output of discrete manufacturing units, such as machinery on a factory floor, additive manufacturing units, closed-environment agricultural systems, or many others. In these embodiments, the token simulator 312 can simulate the future profit distribution of the tokens by determining a suitable initial price for the tokens (e.g., using pricing model 2706 and / or valuation model 2704), the likelihood of profit generation (e.g., using profit model 2710), and other factors, and thus can enable an understanding of the value of the production output and / or any other value (e.g., for tokens representing a hybrid of asset streams as described in this disclosure). To simulate the value of the production output tokens, a token performance simulation system 2702, a market factor simulation system 2740, and / or a production simulation system 2760 can be used. These systems may operate separately or together to simulate the market prices of the relevant items produced by the systems. For commodity items, the simulation may be based on historical commodity market trends, taking into account relevant market inputs such as weather (e.g., as shown by weather model 2746), macroeconomic and / or microeconomic factors (e.g., as shown by economic model 2742), geopolitical factors (e.g., as shown by geopolitical model 2744), and other relevant market inputs. For non-commodity items, the simulation may also take into account other factors such as pricing of complements and substitutes (which may be determined in embodiments using similar clustering models, etc.), which may be based on demand models that model the price elasticity of demand, supply models that model the price elasticity of supply, etc. In embodiments, the production simulation system 2760 may be configured to model such production-related factors for various non-commodity items.In embodiments, the token simulator 312 may leverage a market scenario model 2748, which in particular includes models that simulate scenarios involving market entry by additional competitors, scenarios involving the consolidation of competitors, scenarios involving market exits, and / or scenarios involving government intervention (e.g., price regulation, funding, etc.). In embodiments, the token simulator 312 (e.g., a production simulation system 2760) may use a cost model that models component costs, production input costs (e.g., raw materials), operating costs, maintenance costs, etc. In embodiments, cost information may be aggregated from various third-party data sources (e.g., market reports or pricing streams from proprietary providers or public or government sources), input by one or more experts, crowdsourced, and / or acquired and / or supplemented by sensor data from edge devices or IoT devices, etc. In these embodiments, the token simulator 312 may periodically collect such data from third-party data sources, sensors, and / or such, and use that data to periodically retrain and / or update one or more models.

[0210] The token simulator 312 can also provide production simulations using utilization models (which can include various scenarios, such as maximum utilization (e.g., when the machine is consistently profitable) and partial utilization (e.g., when utilization changes based on whether the output price exceeds input and operating costs, or whether it changes based on other factors such as operator availability, input availability, or whether the system is operational). Utilization may take into account predicted uptime and capacity, such as being determined by an AI system that predicts the need for maintenance and repairs or the possibility of operational interruptions. Production simulations may include simulations based on similar manufacturing systems, such as being determined by industry data, specification information, and / or the operational history of operators of the same machine or a similar group of machines.

[0211] In exemplary embodiments, the prediction market is utilized by the token simulator 312 to predict the likelihood of various events. In embodiments, the prediction market itself may operate using asset class-backed tokens. For example, an asset class-backed token may be backed by one or more predictions, such as a prediction that a particular event will occur or not occur by a particular date, a prediction of the number of items to be produced by a given manufacturer during a certain period (e.g., the number of electric vehicles to be produced in the next quarter), and / or so on. In these embodiments, the asset may be a synthetic asset that pays token holders based on the predictions made by each token holder and how close the token holders' predictions are to the actual numbers. In addition, or alternatively, the market price of the prediction token may reflect the likelihood of a crowdsourced event occurring. In these embodiments, the prediction token may be managed so that token holders receive compensation for insurance data if insurance claims are not paid. Thus, for example, a user may be able to crowdsource and fund insurance contracts (which may otherwise be difficult, for example, an emerging asset class or farmland in a developing country).

[0212] In exemplary embodiments, tokens backed by asset classes may be backed by first-user rights to special future events (e.g., the right to watch a television program, movie or other production and / or write a review before its release, the right to travel, the right to attend a physical event, the right to try various services, the right to make predictions about a lottery or other competition, the right to participate in some venture, etc.). In these embodiments, the asset may exist for the entire duration of the event (e.g., filming of a movie), and the asset owner may have the right to observe and provide commentary (e.g., spoiler-free pre-release reviews). In these embodiments, the token simulator 312 can simulate the value of the token based on economic data such as viewership levels (e.g., for identical or similar shows), social media trend data and / or response data, and social media impact scores. In these embodiments, the token simulator 312 can use one or more AI models trained to predict likely changes in social media followers based on the purchase of tokens backed by asset classes and associated rights. For example, various market scenario models 2748 and / or other models can be used for social media impact prediction and / or other predictions. Token Simulator 312 can use multiple predictive models in various ways. For example, the first model can be used to predict the impact of future events on viewership ratings and / or the financial impact of changes in viewership ratings. Then, the second model can be used to predict the impact of social media influencers purchasing tokens / rights on their followers and / or the financial impact of changes in influencer followers and / or similar factors. The model outputs can be used for different purposes by various users of Token Simulator 312.For example, an event organizer or rights holder could use the first model to set a floor price for tokens backed by a corresponding asset class, and a potential buyer could use the second model to decide whether to bid on and / or purchase tokens backed by an asset class.

[0213] In exemplary embodiments, the token simulator 312 can simulate a distributed ledger using a distributed ledger simulation system 2770. For example, the distributed ledger simulation system 2770 may be configured to use the structure and rules of the corresponding distributed ledger (e.g., proof of work, proof of stake, delegate proof of stake). Furthermore, the distributed ledger simulation system 2770 can operate with variability in functionality based on an actual existing distributed ledger (e.g., a distributed ledger(s) in which the token resides or is planned to reside). The simulation may further consider other factors such as block creation time, on-chain transaction limits, and available transaction throughput. In embodiments, the distributed ledger simulation system 2770 may provide a parallel-operating distributed ledger with tunable variables. Such a simulation system 2770 may be used in particular to simulate the performance of tokens underpinned by correlated items (e.g., gasoline / diesel / electricity / transportation costs / travel costs) and their various complementary / correlated elasticities. For example, such a simulation system 2770 can simulate whether, for instance, rising gasoline prices will generally and / or fall hotel prices in specific regions (e.g., outside metropolitan areas), or how gasoline prices will affect product sales demand (e.g., sales of electric vehicles versus internal combustion engine vehicles). In these embodiments, the distributed ledger simulation system 2770 may, depending on the simulation, use data that reveals various dependent and independent variables (e.g., gasoline cost / barrel, diesel, electricity market, vehicle sales data, hotel costs and hotel classifications, vacation rental data, shipping costs from USPS, UPS, FedEx, DHL, Amazon, Walmart, etc., sentiment data from logistics company executives, weather data, geopolitical impact forecasts, and / or such).In this regard, the distributed ledger simulation system 2770 can leverage other systems and / or models of the token simulator 312, such as various economic models 2742, geopolitical models 2744, weather models 2746, market scenario models 2748, and / or similar. Furthermore, the distributed ledger simulation system 2770 can use various AI and / or ML techniques to detect and analyze correlations between specific pairs / triplets and identify new correlations. For example, a model may be seeded with known impacts and then trained to find additional patterns. In these embodiments, the tokens to be simulated may include any tokens backed by the various assets described herein, including commodity futures, ownership shares of hotels / hotel chains / fractional ownership of accommodations across regions, ownership / profit-sharing tokens of automobile dealers (e.g., tied to regions, brands, etc.), or other such assets.

[0214] In exemplary embodiments, the token simulator 312 can be used to simulate the market value and demand for an upcoming technology consumer product prior to its launch. Using such forecasts, the token simulator 312 can evaluate the value of tokens backed by one or more assets corresponding to the upcoming technology consumer product and / or simulate other impacts. For example, the token simulator 312 can simulate various aspects using one or more supply and demand models 2750 and / or other models. The simulator 312 may use the production simulation system 2760 to simulate, for example, the supply chain and manufacturing process using models based on available raw materials, extraction, manufacturing (e.g., semiconductor manufacturing), assembly, and / or shipment. In embodiments, the simulator 312 may further simulate the cost, complexity, timeliness, and predictability of product development (e.g., using the market factor simulation system 2740). In embodiments, the simulator 312 may further use the demand model 2750 to model demand in the consumer market (e.g., scale, elasticity, seasonality, dependence on add-on products or consumables, dependence on substitutes in adjacent markets). In embodiments, the simulator 312 may simulate the rate of technological innovation in the consumer market (e.g., whether the relevant field is related to slow and / or predictable technological innovations such as storage, or a technological frontier with rapid and potentially disruptive technological innovations such as augmented reality environments and hardware). In embodiments, the simulator 312 can simulate the magnitude of competition in the consumer market (e.g., whether the market is already dominated by large companies or against startups, whether the market is a highly visible market with broad appeal such as game consoles, or a niche market with narrow appeal such as 3D printing, etc.).

[0215] In exemplary embodiments, simulator 312 can further simulate different tokens backed by different assets related to a technology consumer product. For example, a token backed by a first potential asset class might be backed by revenue from consumer sales of the technology product. A token backed by a second potential asset class might be backed by revenue from consumer subscriptions, a token backed by a third potential asset class by revenue from add-on products and services, a token backed by a fourth potential asset class by revenue from third parties, and a token backed by a fifth potential asset class by licensing revenue from IP related to the product, etc. In addition, or alternatively, a token might be backed by a mixture of these various assets or other assets related to the technology consumer product. Simulator 312 can simulate various beneficial characteristics of each type of asset performance, such as one-time revenue, recurring revenue, volatility, sensitivity to popularity or network effects, sensitivity to the accumulation of product momentum, sensitivity to competing products, and dependence on the ongoing development of the product. Simulator 312 can further simulate how different types of beneficial properties respond to market conditions such as hot and cool economies, fluctuating inflation rates, supply chain disruptions, disputes over IP ownership and dominance, geopolitical events such as tariffs, and climate change affecting resource availability. Simulator 312 can use models developed based on vast amounts of historical data from comparable markets, as well as various econometric models.

[0216] In exemplary embodiments, the token simulator 312 can provide a microfinance simulation for tokens representing partial ownership and / or financing of student classes, developer groups, innovator groups, etc. (for example, revenue collected by selling tokens could be used to finance student aid / tuition and could represent partial ownership of some future outputs of students in that class, such as a portion of future salaries and ownership of IP developed by students, developers, innovators, etc.). In these embodiments, the simulator 312 can use data showing class composition to generate economic forecasts, model the probability of future employment by students, predict the likelihood of future IP licensing, model the cost and / or acquisition probability of IP portfolio development, and / or similar.

[0217] In exemplary embodiments, the token simulator 312 can provide a simulation of renewable energy tokens backed by assets representing the electrical output from renewable energy facilities. In these embodiments, the simulator 312 can simulate aspects such as future climate, consumer demand, and output from different types of renewable energy, and can model the performance of various bundles or mixes of renewable energy corresponding to different types of facilities (e.g., solar, wind, hydro, etc.) to simulate the performance of different types of tokens backed by different types of renewable energy assets.

[0218] In exemplary embodiments, the token simulator 312 can provide a simulation of logistics tokens representing the right to move a certain weight and / or volume of goods from one location to another within a given time window. In these embodiments, the tokens may be backed by door-to-door transportation services, which may include packaging, transport, insurance, and bills of lading. In addition, or alternatively, the tokens may be associated with futures that can be used to hedge the transportation costs of the required route / weight / volume. In these embodiments, the simulator 312 can incorporate historical transportation market data for various shipping mechanisms (e.g., air, land, and sea routes) to simulate the pricing and / or performance of the tokens.

[0219] In an exemplary embodiment, the token simulator 312 can provide a simulation of healthcare tokens backed by care packages representing medical services that can be used to care for a particular condition or group of conditions, so that the tokens function as health insurance. The token simulator 312 can simulate current and future market conditions, drug costs, medical service costs, etc., to evaluate the tokens, and can simulate the predicted cost to a patient based on the age, health status, and likelihood of needing the services of the potential token purchaser.

[0220] Therefore, as can be understood from the examples above, the token simulator 312 can simulate the performance of a wide variety of applications by using different models and configurations that may differ depending on the assets backing any given token. In embodiments, a general model may be reusable across a wide variety of tokens (e.g., economic models, geopolitical models, generative AI models, etc.), while a particular model may need to be tailored to a particular type of asset. On the other hand, a particular model may need to be tailored to a particular type of asset (e.g., a valuation model for one type of asset may differ significantly from a valuation model for another type of asset, reflecting different use cases, different dependencies, and / or such). Different tailored models may be developed and provided by stakeholders (e.g., token holders who wish to simulate the performance of a particular type of asset) and / or developed by the token simulator 312 using different tailored datasets. Thus, by configuring the token simulator 312 to operate with both reusable and tailored models, the token simulator 312 can simulate performance across a wide variety of asset classes while maximizing data and model reusability as much as possible.

[0221] In embodiments, the token simulator 312 may be configured to use different workflows for various applications. Two exemplary workflows are described with reference to Figures 28 and 29. Figure 28 shows an exemplary method 2800 of using the token simulator 312 to predict the value of a set of tokens backed by asset classes corresponding to a planned technology product. In 2802, the token simulator 312 can receive specifications for components of the planned technology product (e.g., via the token simulator interface 340) such as a display, speaker, processor, memory, storage, network adapter, enclosure, software, and / or similar. For example, the product designer may provide various specifications for each component, whether hardware (e.g., the type of processor to be developed, the planned manufacturer of the processor, etc.) or software (e.g., the operating system and / or the application to be developed).

[0222] In 2804, the token simulator 312 may receive one or more material inputs and / or development models for each component (e.g., via the token simulator interface 340). For example, raw materials and / or upstream components of a planned processor may be identified, and a model may be provided to simulate the development of the processor (e.g., based on various inputs). In this example, the model inputs may be raw materials, labor, other components (e.g., including price and supply factors), and / or so. In embodiments, the specifications of the raw materials or components may be generated by a generative AI model (e.g., based on a user description of the product provided via the token simulator interface 340). The model may output predicted development time and / or cost, or it may be configured to simulate the development process of various required components of the product and / or the product as a whole.

[0223] In 2806, the token simulator 312 can use the provided model and / or other models (e.g., various market scenario models 2748) to simulate the development of various components and entire products under various market conditions. For example, the token simulator 312 can use various economic models 2742 to estimate the costs of labor, raw materials, or other inputs. In embodiments, the range of economic estimates output by the economic model 2742 may vary based on the outputs of geopolitical models 2744, weather models 2746, black swan models 2724, and / or similar models that can predict supply chains, labor costs, and / or events and / or trends that may affect them. In other words, various outputs of the token simulator model can be used as inputs to other models to generate detailed predictions of possible scenarios for various market conditions and to simulate development costs and / or time for a representative range of various market conditions. Next, in 2808, the token simulator 312 may simulate the performance (e.g., sales volume) of a planned technical product under various market conditions based on various product-specific factors such as the type of product, the predictive power of the product compared to other products in the same field, the software available through the product, and / or such. In embodiments, the token simulator 312 may perform the simulation using a digital twin, thereby comprising a library and behavior appropriate for performing the financial simulation. In some of these embodiments, the token simulator 312 can perform such simulation by leveraging a digital twin system, as described elsewhere in this disclosure.

[0224] In 2810, the token simulator 312 can aggregate various scenarios to generate various simulation outputs and / or probabilities for each simulation output. In embodiments, the simulation outputs can include best-case results for various market conditions, revealing which market conditions are most important for achieving the best-case results. Similarly, the outputs can include worst-case results for various market conditions, revealing which market conditions are most unfavorable to the results. In addition, or alternatively, the outputs of the token simulator 312 can reveal the components (e.g., processor, software) that have the strongest impact on performance based on various simulations and market results.

[0225] In 2812, the token simulator 312 can simulate the value of a set of tokens backed by an asset class, which can be linked to a simulated product in various ways. For example, the token simulator 312 can simulate the value of tokens backed by a percentage of revenue from the sale of a technology product, tokens backed by a percentage of fees paid by software developers to make the software work on the product, tokens backed by a percentage of subscription revenue from consumers paid in connection with the product, and / or tokens backed by various combinations of these and / or other asset-token partnerships. Thus, by comparing various ways of constructing asset-class backed tokens, product developers may be able to determine the most profitable way to raise capital for product development.

[0226] Figure 29 illustrates an exemplary method 2900 that uses a token simulator to predict the value of a set of tokens backed by an asset class for token miners and predicts the value of tokens backed by an asset class for token buyers, thereby generating purchase recommendations. Method 2900 is particularly applicable when the value of an asset may vary significantly among buyers, such as IP rights (which may be of high value to customers who use IP rights efficiently or effectively) or health insurance (which may be of high value to buyers who are likely to have medical needs), to give two specific examples. However, as will be described in detail herein, it will be understood that Method 2900 can be used in a wide variety of use cases in a variety of ways for different tokens backed by different assets.

[0227] In 2902, the token simulator 312 may receive simulation parameters for a set of asset-class backed tokens backed by one or more types of assets. In embodiments, the simulation parameters may specify the assets and their associations with the asset-class backed tokens (e.g., rights obtained by owning the tokens, the required purchase price of the asset-class backed tokens, ongoing royalties arising from the use of the assets, payments to be paid to token holders, etc.). Additionally or alternatively, the simulation parameters may include one or more models that the token simulator 312 can use to simulate the value of the asset-class backed tokens. According to an exemplary embodiment, the simulation parameters may specify data for a set of intellectual property rights, including the current number of licensees, established loyalty rates, the total addressable market, and / or such. Additionally or alternatively, the simulation parameters may include models trained to calculate the value of intellectual property rights based on a variety of input parameters, which may include the specified parameters, their derivatives, and / or other inputs that may be generated by the token simulator 312 (e.g., figures for projected market growth or contraction, the impact of supply chain issues, etc.).

[0228] In 2904, the token simulator 312 can simulate the value of a token backed by an asset class based on simulation parameters. For example, the token simulator 312 may use an evaluation model that can be trained to predict revenue associated with an asset (e.g., intellectual property rights). In embodiments, multiple simulations may be generated for different market scenarios (e.g., average market growth, contraction, supply chain constraints, etc.) based on predictions generated using other models of the token simulator 312, and the results may be aggregated based on the likelihood of each scenario.

[0229] In some embodiments, the token simulator 312 can generate market scenarios or estimates, which include leveraging a generative AI model to perform automatic summarization of market forecast reports generated by third parties, assigning numerical estimates to various text-based forecasts, and / or otherwise processing market forecast data or other simulation data into a format usable by the token simulator 312. In embodiments, the evaluation model may consider various forecast applications of the asset by potential buyers of the asset, some of which may be generated by a generative AI model.

[0230] In some embodiments, simulated values ​​of asset class-backed tokens may be provided to token holders or other rights holders for the purpose of pricing the corresponding asset class-backed tokens. Additionally or alternatively, simulated values ​​may be used automatically (e.g., by an automated token marketplace) to set the sales price and / or floor price of the asset class-backed tokens.

[0231] In 2906, the token simulator 312 can receive simulation parameters from potential buyers of tokens backed by an asset and / or asset class. The simulation parameters can specify parameters relating to the buyer and / or the intended use of the asset by the buyer. For example, if a token backed by an asset class grants an IP license, the simulation parameters could indicate how the buyer plans to use the license (e.g., the number of products the buyer wishes to manufacture using the license, the planned selling price of the products, the planned marketing budget for the products, the buyer's current social media followers, etc.).

[0232] In 2908, the token simulator 312 can simulate changes in the value of a potential buyer based on the intended use of the asset. For example, the token simulator 312 may predict revenue from the intended use of the license, the projected market share of the product, changes in the buyer's social media followers, and / or such. In some of these embodiments, the token simulator can leverage a generative AI model to generate estimates of these changes.

[0233] In 2910, the token simulator 312 can generate a buy recommendation if the token buyer's predicted value exceeds the current and / or simulated value of the asset class-backed token (e.g., exceeding a threshold amount or percentage). In addition, or alternatively, the token simulator 312 can output a display of the amount by which the simulated value change exceeds the token's purchase price, representing the buyer's predicted increase in value by purchasing the asset class-backed token.

[0234] Various embodiments envision a microservices architecture having many platform components corresponding to many of the components disclosed herein for many different lending use cases.

[0235] In embodiments, various use cases include the platform being a general-purpose provider across asset classes for backing tokens, including precious metal-backed tokens, fiat currency-backed tokens, real estate-backed tokens, and IP-backed tokens (for large IP holders - patent rights, royalties, brands, and publicity rights). In these embodiments, the platform may further leverage blockchain for knowledge functions as described in this disclosure and elsewhere in the documents incorporated herein by reference.

[0236] In embodiments, asset-backed tokens include tokens representing a specific production capacity of a machine or system, such as energy generated by a generator or turbine, and token holders can buy, sell, redeem, lend, borrow (such as using tokens as collateral), or otherwise trade tokens that include tokens as a representation of the machine's nominal and / or actual physical capacity. Tokens, like other tokens referred to herein, may be for fractional shares of production, which may be funnelable or non-fungible fractional shares (e.g., representing a specific production period and / or a specific quantity of capacity, such as the production of a given unit amount of energy). Production capacity may include all types of production, including energy production, heat production, material production, commodity production, service production (where the machine or system produces services, such as in an "as-a-service" business model), and many others.

[0237] In various embodiments described herein, artificial intelligence (including various types of artificial intelligence disclosed herein, such as machine learning, natural language processing, and robotic process automation) can be used to improve the performance or extend the functionality of a tokenization platform backed by an asset class. For example, generative AI systems (such as those using large language model sets, transformer model sets, or similar) can be used to generate content related to tokens and / or their underlying asset classes, thereby improving the understanding of token designers and / or end users and saving them time. Robotic process automation can also be trained on training datasets of token discovery tasks, token design tasks, token configuration tasks, token deployment tasks, or other tasks performed by token designers within the platform, and can perform or recommend supervised, semi-supervised, or autonomous actions, and may be further optimized by deep learning based on the results from the platform. Furthermore, the AI ​​may learn from the results and be used to adjust the smart contract configuration, token economic parameters, and other configurable elements of the platform.

[0238] In embodiments, the methods and systems described herein include a platform for enabling transactions such that a set of tokens is generated, linked to an alternative asset class, and the value of the tokens in the set represents a defined unit of the alternative asset class.

[0239] In embodiments, the methods and systems described herein include a platform for enabling transactions such that a set of tokens is generated, linked to an alternative asset class, and the value of the tokens in the set represents a defined unit of the alternative asset class, where the tokens are non-fungible tokens representing a specific unit of the alternative asset class.

[0240] In embodiments, the methods and systems described herein include a platform for enabling transactions such that a set of tokens is generated, linked to an alternative asset class, the value of the tokens in the set represents a defined unit of the alternative asset class, and the tokens are fungible tokens representing a quantity of the alternative asset class.

[0241] In embodiments, the methods and systems described herein include a platform that enables trading such that a set of tokens is generated, linked to an alternative asset class, the value of the tokens in the set represents a defined unit of the alternative asset class, and the tokens represent the indexed value of the alternative asset class.

[0242] In embodiments, the methods and systems described herein include a platform for generating and enabling transactions linked to an alternative asset class such that the value of the tokens in the set of tokens represents a defined unit of an alternative asset class, and the set of tokens represents fractional ownership of a unit of the alternative asset class.

[0243] In embodiments, the methods and systems described herein include a platform for enabling transactions such that a set of tokens is generated, linked to an alternate asset class, and the value of the tokens in the set represents a defined unit of the alternate asset class, the alternate asset class being one or more of the following: data streams, revenue streams, energy streams, future revenue streams, future production capacity, future intellectual property capacity, and any of the aforementioned future or derivatives.

[0244] In embodiments, the methods and systems described herein include a platform for enabling transactions such that a set of tokens is generated, linked to an alternate asset class, and the value of the tokens in the set represents a defined unit of the alternate asset class, the alternate asset class being a hybrid asset class of at least two of the following assets: data streams, revenue streams, energy streams, future revenue streams, future production capacity, future intellectual property capacity, and any of the aforementioned future or derivative assets.

[0245] In embodiments, the methods and systems described herein include a platform for enabling transactions in which a set of tokens is generated and linked to an alternative asset class such that the value of the tokens in the set represents a defined unit of the alternative asset class, and a set of interfaces for designing tokens that represent a set of alternative assets blended by the interface user to provide a target risk / return profile.

[0246] In embodiments, the methods and systems described herein include a platform for enabling transactions in which a set of tokens is generated and linked to an alternative asset class such that the value of the tokens in the set represents a defined unit of the alternative asset class, and has a digital twin system for representing a set of parameters for the tokens and the alternative asset backing the tokens.

[0247] In embodiments, the methods and systems described herein include a platform for enabling transactions in which a set of tokens is generated and a set of tokens is linked to an alternative asset class such that the value of the tokens in the set represents a defined unit of the alternative asset class, and a data collection system for automatically collecting information about the alternative assets backing the tokens.

[0248] In embodiments, the methods and systems described herein are platforms for enabling transactions in which a set of tokens is linked to an alternative asset class such that a set of tokens is generated and the value of the tokens in the set represents a defined unit of the alternative asset class, the platforms comprising an analytical system that calculates and outputs a set of measures representing parameters of the alternative asset backing the tokens, the measures representing one or more of price, value, volatility, risk measure, trading measure, future price, option price, and derivative price.

[0249] In embodiments, the methods and systems described herein include a platform for generating a set of tokens, linking them to an alternative asset class, and enabling trading such that the value of the tokens in the set represents a defined unit of the alternative asset class; and an analysis system for calculating and outputting a set of measurements representing the parameters of the tokens, the set of measurements including one or more of the following: token price, token value, token volatility, token trading velocity, token future price, token risk metric, token derivative price, and token option price.

[0250] In embodiments, the methods and systems described herein include a platform for deploying a smart contract for managing tokens linked to an alternative asset class, the smart contract for managing access to the alternative assets.

[0251] In embodiments, the methods and systems described herein include a platform for deploying a smart contract for managing tokens linked to an alternative asset class, the smart contract for managing token transactions.

[0252] In embodiments, the methods and systems described herein include a platform for deploying a smart contract for managing tokens linked to a revenue stream, the smart contract for collecting and distributing revenue to token holders.

[0253] In embodiments, the methods and systems described herein include a platform that automatically identifies a set of assets corresponding to a strategy and generates tokens linked to the set of assets.

[0254] In embodiments, the methods and systems described herein include a platform that automatically identifies a set of assets corresponding to a strategy and generates tokens linked to the set of assets, the platform further simulates the future performance of the tokens and optimizes the set of assets according to the simulated performance.

[0255] In embodiments, the methods and systems described herein include a platform that automatically identifies assets based on a set of user priorities, identifies and resolves conflicts between user-defined priorities, and generates tokens linked to the assets according to the priorities.

[0256] In embodiments, the methods and systems described herein include a platform for deploying a smart contract for automatically issuing tokens linked to iterative assets according to an issuance schedule.

[0257] In embodiments, the methods and systems described herein include a platform for crowdfunding asset purchases and automatically issuing tokens linked to asset purchases.

[0258] In embodiments, the methods and systems described herein include a platform that provides a distributed ledger oracle with information to correct the behavior of a smart contract used to manage tokens backed by an asset class.

[0259] In embodiments, the methods and systems described herein include a platform for simulating the future value of tokens linked to a set of related assets corresponding to productive assets. ...

Claims

1. A method for simulating the value of tokens backed by asset classes linked to a product under development, In a tokenization platform, the step of receiving asset component specifications for a product under development, wherein the asset component specifications specify multiple components for manufacturing the product, The steps include receiving a material input specification that defines the material used to manufacture the component for at least one of several components, The tokenization platform provides a step of simulating the development of multiple components using an artificial intelligence (AL) model, wherein the simulation is performed based on material input specifications, and the AL model is trained to simulate the development of multiple components under different market conditions. The tokenization platform allows for the simulation of product performance using a second AI model trained to predict market sales under different market conditions, and The tokenization platform aggregates simulations of the development of multiple components under different market conditions and simulations of the product's performance under different market conditions to generate simulation output; and based on the simulation output, estimates the value of tokens backed by multiple asset classes linked to the product. A step of generating a sales list of tokens backed by an asset class, the value of the sales list being based on an estimated value, and a step of deploying a smart contract on the blockchain, the smart contract being Issuing tokens backed by multiple asset classes, The first revenue from the sale of tokens backed by multiple asset classes is distributed to the producers of the product. Receiving a second revenue related to the sale of tokens backed by an asset class, and A method that includes the steps of distributing a second return to the current owners of the asset class-backed tokens.

2. The method according to claim 1, wherein the product is an electronic device, and the plurality of components include a processor, a display, memory, and software components.

3. The method according to claim 1, wherein the simulation of the development of the plurality of components uses a machine learning model, and the input to the machine learning model includes the estimated cost of a first component of the plurality of components and the estimated cost of the materials used to manufacture a second component of the plurality of components.

4. The method according to claim 3, wherein the machine learning model is trained to output the costs of the plurality of components in a first set of market conditions, and the second machine learning model is trained to output the costs of the plurality of components in a second set of market conditions.

5. The method according to claim 1, wherein simulating the performance of the said product includes using a machine learning model configured to estimate one or more economic, geopolitical, or weather factors.

6. The method according to claim 1, further comprising generating tokens backed by a plurality of asset classes corresponding to the product, and selling the plurality of asset class-backed tokens to raise funds for manufacturing the product.

7. The method according to claim 6, wherein the tokens backed by the aforementioned asset classes are backed by a percentage of the sales revenue of the product.

8. The method according to claim 6, wherein the tokens backed by the aforementioned asset classes are backed by fees paid by software developers of applications for the product.

9. The method according to claim 6, wherein the tokens backed by the aforementioned asset classes are backed by a percentage of the subscription revenue from customers of the product.

10. The method according to claim 1, wherein the output of the simulation indicates which market conditions are most important for achieving the best results.

11. A method for generating tokens backed by an asset class using a tokenization platform, The tokenization platform receives multiple asset performance attributes from the user's device, The tokenization platform enables the prioritization of multiple asset performance attributes by detecting competition between them and providing those asset performance attributes to a first artificial intelligence (AI) model trained to rank them. The tokenization platform maps prioritized asset performance attributes to multiple real-world assets, the mapping comprising the steps of determining the type and number of assets corresponding to the prioritized token performance attributes, The steps involve simulating the performance of multiple real-world assets using a token simulator that includes a second AI model configured to simulate changes in the value of real-world assets based on different input scenarios, and The steps include iteratively adjusting the types and number of assets based on simulated performance to improve the mapping between multiple real-world assets and multiple prioritized token performance attributes, A method comprising the steps of generating an asset class-backed nonfungible token on a distributed ledger, wherein the asset class-backed nonfungible token is backed by a first number of a first asset and a second number of a second asset selected from an iteratively adjusted set of asset types and numbers, wherein the first asset is an asset of a first type and the second asset is an asset of a second type, and the asset class-backed nonfungible token includes a plurality of token attributes, including a first asset identifier attribute that identifies the first asset, a first asset value attribute that identifies a portion of the first asset, a second asset identifier attribute that identifies the second asset, and a second asset value attribute that identifies a portion of the second asset.

12. The method according to claim 11, further comprising generating a second asset class-backed nonfungible token on a distributed ledger, wherein the second asset class-backed nonfungible token is backed by a third asset and a fourth asset among the plurality of assets, the third asset being the first type of asset and the fourth asset being the second type of asset.

13. The method according to claim 11, wherein the non-fungible tokens backed by the asset class are exchangeable for the first asset and the second asset.

14. The method according to claim 11, wherein the non-fungible token backed by the asset class is exchangeable for either the first asset or the second asset.

15. The method according to claim 11, wherein prioritizing the multiple performance attributes comprises using a model trained to rank the multiple performance attributes.

16. The method according to claim 15, further comprising training a model using a training dataset which includes a set of user-provided attributes and ranking labels for each set of user-provided attributes.

17. The method according to claim 11, wherein the first asset includes ownership of at least a portion of the land, and the second asset includes the right to use the land.

18. The method according to claim 11, wherein the first asset includes ownership of at least a portion of a productive asset, and the second asset includes at least a portion of the revenue from the productive asset.

19. The method according to claim 11, wherein the first asset is a derivative asset corresponding to a first set of assets, and the second asset is a derivative asset corresponding to a second set of assets.

20. The method according to claim 19, wherein each of the first asset sets relates to a first infrastructure project, and each of the second asset sets relates to a second infrastructure project.

21. The method according to claim 11, wherein at least one of the first asset and the second asset is an intellectual property asset.

22. The method according to claim 11, wherein optimizing the plurality of assets includes iteratively adjusting the number of assets in the plurality of assets and resimulating the performance of the adjusted plurality of assets until the simulated performance is optimized.

23. The method according to claim 11, wherein the plurality of assets include oil and / or gas production facilities.

24. The method according to claim 11, wherein the plurality of assets include a digital twin of a revenue-generating facility.

25. A step of generating multiple governance rules based on one or more governance requirements related to multiple assets, The method according to claim 11, further comprising the step of setting up a token management smart contract stored in a distributed ledger based on a set of governance rules.

26. The step of generating asset-class-backed, non-fungible tokens on a distributed ledger is, The steps include generating a token description for an asset class-backed, non-fungible token, The steps include setting up a smart contract on a distributed ledger based on the token description, The method according to claim 11, comprising the step of initiating a distributed ledger transaction configured to cause a configured smart contract to issue non-fungible tokens backed by an asset class based on a token description.

27. The method according to claim 11, wherein the plurality of attributes further include at least one of an availability trigger specifying when the first asset becomes redeemable, or an expiration trigger specifying when the first asset becomes non-redeemable.

28. The method according to claim 11, wherein the aforementioned multiple attributes further include one or more of the token redemption rules or token governance rules.

29. Steps include generating an issuance schedule based on the performance attributes of multiple assets, The method according to claim 11, further comprising the step of generating non-fungible tokens backed by an additional asset class backed by multiple assets after a period specified by the issuance schedule.

30. The method according to claim 26, wherein the smart contract is further configured to collect revenue related to at least one of the first asset and the second asset, and to distribute the collected revenue to token holders.

31. A method for issuing tokens backed by multiple asset classes on a distributed ledger, Steps include: receiving a token configuration representing multiple revenue-generating assets and issuers through a tokenization platform, wherein the multiple revenue-generating assets include at least two different types of assets; A tokenization platform deploys at least one smart contract on a decentralized ledger, wherein the deployed at least one smart contract is configured to receive revenue related to multiple revenue-generating assets, and the deployed at least one smart contract is A first function configured to issue multiple asset-class backed tokens backed by multiple assets, The step includes a second function configured to distribute the received revenue among the blockchain addresses of the current owners of multiple asset class back tokens, A step of responding to one or more purchase requests received by a tokenization platform, the step of causing the tokenization platform to initiate a first function of a smart contract, which involves issuing multiple asset class back tokens and allocating each issued asset class back token to the blockchain address of the purchasing user, A method that includes the steps of periodically activating a second function of a smart contract via a tokenization platform, and distributing the received revenue to the blockchain addresses of the current owners of multiple asset class back tokens.

32. The method according to claim 31, further comprising verifying by the tokenization platform that the issuing party owns the plurality of revenue-generating assets before deploying the at least one smart contract.

33. The method according to claim 31, wherein the issuing party is a crowdfunding pool, the blockchain addresses of the current owners of the tokens backed by the multiple asset classes are associated with users of the crowdfunding pool, and the distribution of the received revenue is proportional to the amount contributed by the corresponding user to the crowdfunding pool.

34. The method according to claim 31, wherein the issuing party is the portfolio seller and the blockchain address corresponds to the portfolio buyer.

35. The method according to claim 31, wherein tokens backed by each asset class are exchangeable for a portion of a plurality of assets.

36. The method according to claim 31, wherein the token configuration is received from a token strategy engine configured to generate a token configuration based on the performance attributes of a plurality of tokens provided by the issuing party.

37. The method according to claim 31, wherein the aforementioned multiple assets include ownership of land and the right to use land.

38. The method according to claim 31, wherein the plurality of assets include ownership of productive assets and sources of income from said productive assets.

39. The method according to claim 31, wherein the plurality of assets include derivative assets corresponding to a first set of assets and derivative assets corresponding to a second set of assets.

40. The method according to claim 39, wherein each first set of assets relates to a first oil and / or gas production facility, and each second set of assets relates to a second oil and / or gas production facility.

41. The method according to claim 31, wherein at least one of the multiple assets is an intellectual property asset.

42. The method according to claim 41, wherein the owner of the token backed by the aforementioned asset classes has the right to sublicense at least a portion of the intellectual property rights corresponding to the aforementioned intellectual property assets.

43. The tokenization platform receives multiple governance rules based on one or more governance requirements related to multiple assets, The method according to claim 31, further comprising the step of setting up one or more smart contracts based on a set of governance rules using a tokenization platform.

44. The method according to claim 31, wherein each of the plurality of asset class backed tokens is backed by a first asset and a second asset from the plurality of assets, wherein the first asset is a first type of asset and the second asset is a second type of asset, and each asset class backed token includes a plurality of attributes, including a first asset identifier attribute that identifies the first asset, a first asset value attribute that identifies a portion of the first asset, a second asset identifier attribute that identifies the second asset, and a second asset value attribute that identifies a portion of the second asset.

45. A method according to claim 44, wherein the plurality of attributes further include at least one of an availability trigger specifying when the first asset becomes redeemable, or an expiration trigger specifying when the first asset becomes non-redeemable.

46. The method according to claim 44, wherein the aforementioned multiple attributes further include one or more token redemption rules or token governance rules.

47. Steps include generating an issuance schedule based on the token configuration, The method according to claim 31, further comprising the step of generating additional asset class backed by multiple assets after a period specified by the issuance schedule.

48. The steps involve using a token simulator system to generate estimated values ​​for multiple assets, The steps include determining the estimated value of each token of the plurality of asset class back tokens based on the estimated values ​​of the plurality of assets, The method according to claim 31, further comprising the step of sending a display of estimated values ​​for each token of a multi-asset-backed token to a marketplace system.

49. The method according to claim 48, wherein the marketplace system sets a minimum price for each of the multiple asset class back tokens based on an estimated value for each of the multiple asset class back tokens.

50. The method according to claim 48, wherein the estimated value of the plurality of assets includes the estimated income generated by the income stream assets.

51. A method for managing multiple asset class back tokens by configuring at least one smart contract on a distributed ledger, Steps include receiving a token configuration representing multiple assets through a tokenization platform, wherein the multiple assets include at least two different types of assets, and at least one of the asset types includes a revenue-generating asset; The tokenization platform sets up at least one smart contract on a decentralized ledger to issue and manage multiple asset-backed tokens of multiple asset classes, wherein at least one smart contract is: In accordance with one or more governance rules, token transfers from the first distributed ledger account to the second distributed ledger account are permitted, and the second distributed ledger account, In a distributed ledger oracle, you can monitor one or more asset-related events. Receiving income related to multiple assets, and The profits received will be distributed to the current owners of the tokens backed by the asset class. If the eligibility specified by one or more governance rules is not met, the governance rules are configured to prohibit the transfer to the second distributed ledger account, step, The tokenization platform, in response to one or more purchase requests it has received, initiates the issuance of multiple asset class-backed tokens and the allocation of each issued asset class-backed token to the blockchain address of the purchasing user. A method comprising the steps of: causing a tokenization platform to cause a smart contract to distribute received revenue to the blockchain addresses of the current owners of tokens backed by multiple asset classes, wherein the distribution is in response to one or more asset-related events.

52. The aforementioned one or more asset-related events are: New purchase or license of at least one of multiple assets, Changes in the operating income of revenue-generating assets, To begin managing assets that generate income, To cease managing income-generating assets, or Receipt of income related to assets, The method according to claim 51, comprising at least one of the following.

53. The method according to claim 51, wherein the plurality of assets include intellectual property rights, the distributed ledger oracle is configured to hold a list of licensees of the intellectual property rights, and the tokenization platform configures the at least one smart contract to enforce the collection of revenue from the licensees of the intellectual property rights.

54. The method according to claim 53, wherein at least one smart contract is configured to execute a breaching workflow against a licensee that does not transfer revenue to at least one smart contract.

55. The method according to claim 53, wherein the owners of multiple asset class back tokens have the right to sublicense at least a portion of the intellectual property rights.

56. The method according to claim 51, wherein the plurality of assets include property rights, the distributed ledger oracle is configured to hold a list of tenants of the real estate, and the tokenization platform constitutes the at least one smart contract to enforce the collection of revenue from the tenants of the real estate.

57. The method according to claim 51, wherein each asset class back token is exchangeable for a portion of a plurality of assets.

58. The method according to claim 51, wherein the token configuration is received from a token strategy engine configured to generate a token configuration based on a plurality of asset performance attributes provided by the issuing party.

59. The method according to claim 51, wherein the aforementioned multiple assets include ownership of land and the right to use land.

60. The method according to claim 51, wherein the plurality of assets include ownership of productive assets and sources of income from said productive assets.

61. The method according to claim 51, wherein the plurality of assets include derivative assets corresponding to a first set of assets and derivative assets corresponding to a second set of assets.

62. The method according to claim 61, wherein each of the first asset sets relates to a first oil and / or gas production facility, and each of the second asset sets relates to a second oil and / or gas production facility.

63. The method according to claim 51, wherein the plurality of assets include a digital twin of the revenue-generating facility.

64. The method according to claim 51, wherein each of the plurality of asset class backed tokens is backed by a first asset and a second asset from the plurality of assets, wherein the first asset is a first type of asset and the second asset is a second type of asset, and each asset class backed token includes a plurality of attributes, including a first asset identifier attribute that identifies the first asset, a first asset value attribute that identifies a portion of the first asset, a second asset identifier attribute that identifies the second asset, and a second asset value attribute that identifies a portion of the second asset.

65. The method according to claim 64, wherein the plurality of attributes further include at least one of an availability trigger specifying when the first asset becomes redeemable, or an expiration trigger specifying when the first asset becomes non-redeemable.

66. The method according to claim 64, wherein the plurality of attributes further include one or more of the token redemption rules or token governance rules.

67. Steps include generating an issuance schedule based on the token configuration, The method according to claim 51, further comprising the step of generating non-fungible tokens backed by an additional asset class backed by multiple assets after a period specified by the issuance schedule.

68. The steps involve using a token simulator system to generate estimated values ​​for multiple assets, The steps include determining the estimated value of each token of the plurality of asset class back tokens based on the estimated values ​​of the plurality of assets, The method according to claim 51, further comprising the step of sending a display of estimated values ​​for each token of a multi-asset-backed token to a marketplace system.

69. The method according to claim 68, wherein the marketplace system sets a minimum price for each of the multiple asset class back tokens based on an estimated value for each of the multiple asset class back tokens.

70. The method according to claim 68, wherein the estimated value of the plurality of assets includes the estimated income generated by the income stream assets.

71. A method for configuring the deployment of an automated market maker (AMM) smart contract on a blockchain, Steps include receiving a token configuration representing multiple assets through a tokenization platform, wherein the multiple assets include at least two different types of assets, and at least one of the asset types includes a revenue-generating asset; The tokenization platform involves the steps of setting up at least one smart contract on the blockchain and issuing and managing multiple asset-class back tokens linked to multiple assets, A tokenization platform provides a step of generating an AMM configuration using a trained machine learning model, wherein the AMM configuration comprises at least one trading pair and a trading function, where one of the tokens in the trading pair is an asset class-backed token, and the trading function defines an expression for trading an asset class-backed token with a first token type, and an expression for trading an asset class-backed token with a first token type. The steps include: deploying an AMM smart contract on the blockchain using a tokenization platform, where the deployed AMM smart contract is configured to receive revenue into a liquidity pool managed by the AMM smart contract and to implement trading functionality for trading pairs; The tokenization platform generates a graphical user interface configured to allow users who own at least a first type of token in a trading pair to exchange a first amount of the first type of token for a second amount of at least a second type of token in the trading pair, based on the trading function. The tokenization platform receives AMM transaction requests from users associated with user blockchain accounts, The tokenization platform uses the private key associated with the blockchain account to generate an AMM transaction for user signature, and A method comprising the steps of: sending a signed AMM transaction to the blockchain via a tokenization platform, wherein the signed AMM transaction triggers a token exchange using an AMM smart contract based on the transaction function.

72. The method according to claim 71, wherein the AMM is one or more of a constant volume market maker, a constant sum market maker, and a constant average market maker.

73. The method according to claim 71, wherein the AMM is one or more of a hybrid automated market maker, a dynamic automated market maker, a proactive market maker, or a virtual automated market maker.

74. Generating an AMM configuration involves using a trained machine learning model, and using such a trained machine learning model is The system receives one or more user inputs specifying AMM configuration parameters, The method according to claim 71, comprising using a trained machine learning model to generate additional AMM configuration parameters based on one or more user inputs.

75. The AMM configuration parameters are: Trading pairs, Trading function, One or more liquidity pool rules, One or more risk mitigation rules, or One or more distribution rules, The method according to claim 74, comprising one or more of the above.

76. The method according to claim 71, wherein the tokenization platform is further configured to receive a transaction including the AMM smart contract and generate one or more analyses of the transaction, the one or more analyses including one or more of a volume analysis, a liquidity pool analysis, or a price change analysis.

77. The method according to claim 76, wherein a graphical user interface is further configured to display one or more analyses.

78. The method according to claim 71, wherein the graphical user interface is further configured to generate liquidity pool funding transactions for signing by liquidity pool contributors.

79. The method according to claim 71, wherein the plurality of assets include ownership of at least a portion of the land and the right to use the land.

80. The method according to claim 71, wherein the plurality of assets include ownership of at least a portion of the productive assets and at least a portion of the revenue sources from the productive assets.

81. The method according to claim 71, wherein the plurality of assets include derivative assets corresponding to a first set of assets and derivative assets corresponding to a second set of assets.

82. The method according to claim 71, wherein the plurality of assets include a first set of assets related to a first infrastructure project and a second set of assets related to a second infrastructure project.

83. The method according to claim 71, wherein the plurality of assets include intellectual property assets.

84. The method according to claim 71, wherein the plurality of assets include oil and / or gas production facilities.

85. The method according to claim 71, wherein the plurality of assets include a digital twin of a revenue-generating facility.

86. A system configured to perform the method described in any one of claims 11 to 85.

87. A non-transient computer-readable medium, when executed by one or more processors, containing instructions that cause one or more processors to perform the method described in any one of claims 11 to 85.

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