Tokenomic data structures for enabling smart contract functionality

Abstract

Described is a system for tokenomic data structures that enable smart contract functionality by receiving a first real estate possession record for a first real estate from a first real estate possessor, identifying a value of the first real estate, generating a first digital token by a group of nodes of a blockchain network, transmitting the generated digital token to a digital token storage associated with the first real estate possessor, and recording a lien on the first real estate possession record and a record of the digital token transmitted to the digital token storage associated with the first real estate possessor onto a distributed ledger of the blockchain network. The system then causes execution of a smart contract by broadcasting one or more functions to the blockchain, the execution of the smart contract providing a real estate user with access to the first real estate.

Description

TOKENOMIC DATA STRUCTURES FOR ENABLING SMART CONTRACT FUNCTIONALITYCLAIM OF PRIORITY

[0001] This patent application claims the benefit of U.S. Patent Application No. 18 / 809,231, filed August 19, 2024, entitled “Tokenomic Data Structures for Enabling Smart Contract Functionality”, and U.S. Provisional Patent Application No. 63 / 535,913, filed August 31, 2023, entitled “Tokenomic Data Structures for Enabling Smart Contract Functionality”, which are incorporated by reference herein in their entireties.TECHNICAL FIELD

[0002] The present disclosure relates generally to a tokenomic data structures, and more specifically to tokenomic data structures for enabling smart contract functionality.BACKGROUND

[0003] Traditional home ownership often involves acquiring a mortgage from a financial institution like a bank or a mortgage lender. In this setup, an aspiring homeowner, upon finding a suitable property, applies for a mortgage loan. The lender evaluates the applicant's creditworthiness based on their financial history, current income, and debt levels, among other factors. If the application is approved, the lender provides the funds necessary to purchase the home, and the homebuyer agrees to repay the loan over a predefined period, typically in monthly installments over 15 to 30 years. The property serves as collateral, which means that if the borrower fails to make the required payments, the lender has the right to take possession of the home (foreclosure) and sell it to recover their funds. Over time, as the homebuyer makes their mortgage payments, they gradually build equity in the home, which represents their financial stake or ownership interest in the property. When the loan is fully repaid, the lender releases the lien to the deed, indicating full ownership to the home buyer.BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

[0004] In the drawings, which are not necessarily drawn to scale, like numerals may describe similar components in different views. To identify the discussion of any particular element or act more easily, the most significant digit or digits in a reference number refer tothe figure number in which that element is first introduced. Some non-limiting examples are illustrated in the figures of the accompanying drawings in which:

[0005] FIG. 1 illustrates an architecture for tokenizing of a real estate asset, according to some examples.

[0006] FIG. 2 illustrates an architecture comparing conventional mortgage and rental systems with the tokenization system regarding ownership of the property over time, according to some examples.

[0007] FIG. 3 illustrates an architectural diagram between the tokenization system, asset owners, and individuals, according to some examples.

[0008] FIG. 4 illustrates a method 400 for tokenization of real world assets for smart contract execution, according to some examples.

[0009] FIG. 5 illustrates an architectural diagram of tokenization of various real world assets, according to some examples.

[0010] FIG. 6 illustrates real world asset usage via usage controls and tokenization, according to some examples.

[0011] FIG. 7 illustrates virtual reality changes enabling usage rights to real world assets, according to some examples.

[0012] FIG. 8 is a diagrammatic representation of a machine in the form of a computer system within which a set of instructions may be executed to cause the machine to perform any one or more of the methodologies discussed herein, according to some examples.

[0013] FIG. 9 is a block diagram showing a software architecture within which examples may be implemented.

[0014] FIG. 10 illustrates a machine-learning pipeline, according to some examples.

[0015] FIG. 11 illustrates training and use of a machine-learning program, according to some examples.

[0016] FIG. 12 illustrates tokenization of an asset as a whole, according to some examples.

[0017] FIG. 13 illustrates tokenization of an asset that is divisible into parts, according to some examples.

[0018] FIG. 14 illustrates tokenizing ownership and / or usage across time, according to some examples.

[0019] FIG. 15 illustrate tokenizing ownership and / or usage across time and parts, according to some examples.

[0020] FIG. 16 illustrates tokenization for use allocations, according to some examples.

[0021] FIG. 17 illustrates token generation based on location, according to some examples.

[0022] FIG. 18 illustrates token generation for copies of goods, according to some examples.DETAILED DESCRIPTION

[0023] Traditional systems restrict the ways in which individuals can utilize real-world assets. Ownership often comes with rigid usage rights, and traditional systems do not enable the ability for asset owners to share, rent, or grant temporary or permanent access to others.

[0024] Transferring usage rights of a property in traditional systems involves extensive paperwork, intermediaries, and administrative procedures. This process can be timeconsuming, leading to delays and inefficiencies in granting access to a new user.

[0025] In traditional systems, granting access to a property, such as by providing physical keys, requires manual coordination and supervision. This complexity can result in mismanagement of access rights and potential security vulnerabilities.

[0026] Traditional systems offer limited real-time control over usage rights. Once ownership or access rights are transferred, it's challenging to implement dynamic changes or revoke access promptly in response to changing circumstances.

[0027] Traditional systems lack transparent and tamper-proof methods to verify ownership and access rights. This can lead to disputes, fraud, and uncertainty about the legitimacy of access to a property.

[0028] Traditional systems do not provide straightforward pathways for temporary usage rights. If someone needs to use a property for a short period, they may face difficulties in arranging such arrangements efficiently and securely.

[0029] Traditional real estate systems do not integrate with emerging technologies like the Internet of Things (loT) devices or mixed reality interfaces, which limit their ability to provide innovative and efficient solutions for managing access rights.

[0030] Examples of the example tokenization system as described herein mitigate and / or eliminate the pitfalls of traditional systems as described above. The tokenization system overcomes such pitfalls by enabling fractional ownership and usage rights of real-world assets. Each digital token represents a specific asset, such as a house, car, or apartment unit. This allows asset owners to define and manage different levels of access, from fullownership to temporary usage. Owners can grant usage rights, share assets, and even rent them out, offering unprecedented flexibility in asset utilization.

[0031] With tokenization of real world assets, transferring usage rights is greatly streamlined. Smart contracts executed on a blockchain eliminate the need for extensive paperwork, intermediaries, and administrative procedures. These contracts automatically enforce the terms and conditions of the usage rights transfer, ensuring efficient and secure transfer without the delays associated with traditional systems.

[0032] The asset tokenization addresses the complexity of granting access by utilizing blockchain-based smart contracts. These contracts automatically execute predefined functions, such as enabling access to a property. By integrating with Internet of Things (loT) devices, the smart contract can directly communicate with a device attached to the property's door, automating access control. This reduces manual coordination, minimizes potential for human error, and enhances security.

[0033] Unlike traditional systems, where implementing dynamic changes to usage rights can be challenging, the tokenization system excels in real-time control. Smart contracts can be updated and executed immediately upon authorization. This means that asset owners can promptly adjust access permissions, revoke access, or modify usage rights in response to changing circumstances or user requirements.

[0034] Blockchain technology forms the foundation of the tokenization system, ensuring transparent and tamper-proof verification of ownership and access rights. All transactions related to the asset, including ownership transfers and usage rights changes, are recorded on the blockchain's distributed ledger. This transparency reduces the potential for disputes, fraud, and uncertainty, creating a trustworthy record of access legitimacy.

[0035] The tokenization system inherently supports temporary usage rights. Owners can define and offer short-term access to their assets through smart contracts, allowing individuals to use properties for specific durations without the need for complex agreements. This simplifies the process of arranging temporary usage while maintaining security and transparency.

[0036] The tokenization system leverages emerging technologies like the Internet of Things (loT) devices and mixed reality interfaces to enhance access management. By incorporating loT devices, the system can directly interact with physical properties, automating access control. The integration of these technologies ensures innovative andefficient solutions for managing access rights, overcoming the limitations of traditional systems.

[0037] In summary, the tokenization system revolutionizes the way real-world assets are utilized and accessed. By utilizing blockchain, smart contracts, and cutting-edge technologies, it addresses the shortcomings of traditional systems and offers unprecedented flexibility, efficiency, security, and transparency in managing usage rights of assets.

[0038] When the effects in this disclosure are considered in aggregate, one or more of the methodologies described herein may improve known systems, providing additional functionality (such as, but not limited to, the functionality mentioned above), making such systems easier, faster, or more intuitive to operate, and / or obviating a need for certain efforts or resources that otherwise would be involved in the tokenization process. Computing resources used by one or more machines, databases, or networks may thus be more efficiently utilized or even reduced.

[0039] TOKENIZATION OF ASSET

[0040] FIG. 1 illustrates an architecture 118 for tokenizing of a real estate asset, according to some examples. The process of tokenizing a home or an asset by the tokenization system can be described into several steps.

[0041] One step is for the tokenization system to evaluate the property to determine its current market value. In some cases, the tokenization system employs technological methods for estimating the value of a property. In some cases, the tokenization system compares the property to similar properties in the same area that have been sold recently by retrieving data from third party real estate databases.

[0042] In some cases, the tokenization system applies a regression analysis that can determine how different variables (like location, size, age, number of rooms, and nearby amenities) impact the property's value. The algorithm is trained on a vast dataset of property sales to learn the weight of each variable.

[0043] In some cases, the tokenization system applies Geographic Information System (GIS) data, which includes geographical and topological data about a property and its surroundings. The tokenization system applies this data to assess the value based on physical features like proximity to water bodies, hills, parks, and more.

[0044] In some cases, the tokenization system applies one or more artificial neural networks to predict property values. The neural network is trained on a large dataset and canhandle complex, non-linear relationships between variables (such as data related to the property and other similar assets), making the estimate more accurate.

[0045] Although artificial intelligence, neural networks, and machine learning models are disclosed as performing certain features, it is appreciated that a machine learning model can be trained and applied by the tokenization system to perform any or all of the features of the tokenization system as described herein. For example, a first machine learning model facilitates decisioning by the tokenization system between modules and other machine learning models, whereas a second machine learning model generates a prediction of property values.

[0046] Systems and methods described herein include training a machine learning network, such as training to generate smart contracts, predict property values, mint tokens, facilitate transactions to various individuals and wallets, perform features on deeds and ownership, and / or the like. The machine learning network can be trained to perform one or more of the features for the tokenization system as described herein.

[0047] The machine learning algorithm can be trained using historical information. For example, the machine learning model is trained to generate smart contracts by applying historical real estate transactions for use cases on the tokenization system, resulting in selfexecuting smart contracts which are deployed on the blockchain (e.g., sent to the blockchain network and stored on the distributed ledger).

[0048] Training of models, such as artificial intelligence models, is necessarily rooted in computer technology, and improves modeling technology by using training data to train such models and thereafter applying the models to new inputs to make inferences on the new inputs. Here, the new inputs can be information relating to a new homeowner requesting tokenization of the home to rent and slowly sell the home to a new tenant. The trained machine learning model performs the various features of enabling the homeowner to tokenize the home and enable the new tenant to progressively own the home.

[0049] Such training involves complex processing that typically requires a lot of processor computing and extended periods of time with large training data sets, which are typically performed by massive server systems. Training of models can require logistic regression and / or forward / backward propagating of training data that can include input data and expected output values that are used to adjust parameters of the models.

[0050] Such training is the framework of machine learning algorithms that enable the models to be applied to new and unseen data (such as new tenant or asset owner data) andmake predictions that the model was trained for based on the weights or scores that were adjusted during training. Such training of the machine learning models described herein reduces false positives and increases the performance.

[0051] Once the property value is established, the homeowner submits a digital version of the deed to the tokenization system. This deed serves as a legal proof of ownership and will be held by the tokenization system for the duration of the rental agreement.

[0052] Using the property's evaluated value and a particular value for each token (whether a predefined value or current market value), the tokenization system determines a number of tokens to be minted. For example, if a home 102 of FIG. 1 is valued at $300,000 and each token is worth $100,000, the system mints 3 digital tokens 106a, 106b, 106c (collectively referred to herein as digital tokens 106). These tokens are digital representations of ownership in the property.

[0053] The tokenization system mints new tokens on the blockchain or distributed ledger by creating new digital tokens or coins. First, the tokenization system generates a smart contract and is deployed to the blockchain. This contract serves as the blueprint for the new tokens and contains rules about how the tokens can be transferred, how many will exist, and other necessary specifications.

[0054] Once the smart contract is live, the blockchain invokes the smart contract to mint new tokens. When the mint function is called, a specified number of tokens are created and assigned to the specified owner's address. In this case, an asset owner 104 is assigned as the owner of the digital tokens 106 representing full ownership of the home 102. As the minted tokens are then awarded to the homeowner, the tokenization system effectively converts the real estate into a digital form of ownership that can be divided, sold, or traded.

[0055] Although the examples described herein explain blockchain technology, digital ledger technology, tokens, and / or smart contracts to apply to particular examples, it is appreciated that the features of the tokenization system can be applied to other blockchains, tokens, and / or smart contracts. For example, blockchain technology can be applied to predict property values, and mint tokens, whereas smart contracts can be applied to facilitate a transaction (such as a payment) to various individuals and wallets, perform features on deeds and ownership, and / or the like.

[0056] The advent of blockchain technology, tokenization, and / or smart contracts improve trusts in the tokenization system using various features rooted in technology. Blockchain technology ensures that once a transaction is recorded on the blockchain, it can't bechanged. In the context of the tokenization system, once the owner receives tokens corresponding to their property's value, that transaction is recorded permanently. The same goes for each token that a tenant purchases. This creates a clear, immutable record of who owns the asset, making the system much more trustworthy.

[0057] Moreover, every transaction on the blockchain is visible to all network participants. This means that the process of tokenization, as well as each subsequent token purchase, is completely transparent. No one can secretly change the number of tokens or alter the value of the asset, because such a change would be visible to everyone on the network.

[0058] The decentralized nature of blockchain also contributes to its trustworthiness. Rather than relying on a single entity (like a bank or government) to verify transactions, blockchain uses a network of nodes (computers). Each node has a copy of the blockchain, and transactions are verified through a consensus process. In essence, multiple parties agree on the validity of transactions, making it virtually impossible for fraudulent activity to occur.

[0059] The tokenization system can use smart contracts to facilitate one or more processes of the tokenization system. The tokenization system writes (or a machine learning model automatically generates) smart contracts to automatically perform features of the tokenization system as described herein, such as transferring tokens from the tenant to the owner upon receipt of a transaction (such as a payment), and transferring ownership of the asset once all tokens have been purchased. Smart contracts execute automatically when certain conditions are met, and because they're also stored on the blockchain, they're transparent, immutable, and verifiable.

[0060] Tokenization of asset ownership, such as in the case of real estate, provides enhanced security and privacy in several ways. With the blockchain or similar decentralized technology that underlies tokenization, there's no central authority holding all the data. This makes it harder for cybercriminals to exploit a single point of failure.

[0061] Moreover, once a transaction is recorded and confirmed on the blockchain, it can't be altered or tampered with. This prevents any fraudulent changes to the ownership records. Every token can be tracked from its inception, offering a clear and indisputable lineage of ownership.

[0062] Blockchain uses strong cryptographic algorithms to ensure the data in the blockchain can only be read by those involved in the transaction. This means personal and financial data can be securely stored and transferred. The tokenization system appliescryptography to tokenize real estate or any asset on a blockchain. In some cases, the tokenization system applies a public-key (asymmetric) cryptography using pairs of keys: public keys (which may be known to others), and private keys (which are known only to the owner).

[0063] The generation of such keys depends on cryptographic algorithms based on mathematical problems to produce one-way functions. The owner of the private key can use the key to sign transactions or data, and anyone with the public key can verify the signature. In the context of blockchain tokenization, the ownership of tokens (and therefore the real estate) can be proven by the possession of the private key.

[0064] The tokenization system includes a hash function, which given an input, produces a fixed size string of bytes. Every transaction in a blockchain can be hashed and the hash value is stored in the block. Any change in the transaction data would change the hash, which can easily be checked. These hash functions ensure data integrity.

[0065] When a token owner wants to transfer their tokens (representing ownership or equity in a real estate property), the token owner can create a transaction and sign it with their private key. This digital signature proves that the transaction was created by the actual owner and was not tampered with. Anyone can verify the signature with the corresponding public key, but they cannot forge the signature without the private key.

[0066] In some cases, the tokenization system encrypts sensitive data using the public key which can only be decrypted using the corresponding private key. This means even if someone else gets hold of this encrypted data, they can't read or understand it without the private key.

[0067] These cryptographic features and algorithms of the tokenizing system underpin the security, trust, and immutability aspects of the asset-backed tokens that represent equity in the asset. Such use of keys improves data security by restricting unauthorized use, view, and / or recordation of data onto the tokenization system.

[0068] These keys are used to authenticate users to data (such as ownership) or transactions (such as a request to tokenize a real estate) which increases security, prevents unauthorized access by third parties, and enables users of the tokenization system to apply features in an easily implemented way. Moreover, such encryption features are necessarily rooted in computer technology.

[0069] With tokenization, personal details can be kept private while still proving ownership. Rather than sharing all of your personal information, a token representing ownership can be transferred while your personal data stays secured.

[0070] The tokenization system applies smart contracts which are self-executing contracts embedded with the terms of the agreement directly written into code and / or onto the distributed ledger. The smart contracts permit trusted transactions and agreements to be carried out among disparate parties without the need for a central authority, legal system, or external enforcement mechanism.

[0071] Traditional transactions typically involve intermediaries, such as banks or transaction processors, that have access to all transaction data. With blockchain technology, transactions are made directly between parties, which means that sensitive data, such as transaction information, isn't exposed to third-party companies. This reduces the risk of sensitive data being intercepted or misused.

[0072] In summary, through the use of blockchain and tokenization, you can create a secure, transparent system for real estate ownership and transactions that minimizes the risk of fraud, protects privacy, and enhances the security of sensitive data.

[0073] A tenant 108 rents the property and, in addition to paying rent, begins to purchase tokens from the homeowner. These transactions can be made separately or as part of the rent payment. Over the duration of the lease, the tenant can acquire one or more tokens, such as digital token 106a, thereby gaining a portion of ownership in the property.

[0074] As the tenant begins to acquire tokens, the proceeds or dividends from the property are divided among the token holders based on their percentage of ownership. In the given example, if the tenant 108 has acquired one digital token 106a, the tenant would receive 1 / 3 of the proceeds, while the homeowner would receive 2 / 3. The proceeds to the tenant can be less than 1 / 3 of the rent as the proceeds can be determined by subtracting expenses from the rent (such as insurance, property tax, property management). The homeowner could get more than 2 / 3 if the homeowner is the one performing property management.

[0075] This technical method of tokenization enables gradual transfer of ownership from the homeowner to the tenant and provides both parties with more flexibility and liquidity than traditional systems. It also allows for a more seamless and efficient real estate transaction process, reducing the need for intermediaries and reducing costs.

[0076] The tokenization system uses a combined order of specific procedures that tokenizes real world properties that represent ownership, and these tokens are used in avariety of different and novel ways as described herein. Not only do some examples and features of the tokenization system eliminate the need for intermediaries that are typical in the home purchasing process, the process of the tokenization system is also different than the process for traditional systems.

[0077] The tokenization does not simply automate traditional systems and concepts. By leveraging tokenization technology, the tokenization system enables efficiencies and improvements to the real estate world, such as by leasing of ownership and partial ownership, progressive ownership as a tenant using the property, deed recordation and ownership facilitation, other features of the ownership tokens, and / or the like.

[0078] CONVENTIONAL MORTGAGE AND RENTAL COMPARED WITH TOKENIZATION SYSTEM REGARDING OWNERSHIP OVER TIME

[0079] FIG. 2 illustrates an architecture comparing conventional mortgage and rental systems with the tokenization system regarding ownership of the property over time, according to some examples.

[0080] In a conventional mortgage system, the homebuyer typically pays a large down payment 206, often 20% of the home's value, and borrows the remaining 80% from a bank or other lending institution. The homebuyer gets legal 100% ownership of the property through a deed, but the lender also has a lien on the property (100% ownership with a lien 208), meaning they can foreclose and take possession if the homebuyer fails to make mortgage payments. The deed 202 is provided to the new buyer.

[0081] Integration with land record databases - The system could interface directly with public land records to submit lien documents for recording and retrieve confirmation. In some cases, the tokenization system implements self-executing smart contracts that automatically notifies relevant third party databases that have their own record (such as records of liens and ownerships), transfer lien-related assets, and / or record the lien on the blockchain upon meeting coded conditions.

[0082] In some cases, the tokenization system applies loT sensors, such as sensors on deed documents could track their physical location and confirm when they are processed by the registrar's office. In some cases, the tokenization system applies computer vision algorithms, such as scanning deed documents and verifying registrar stamps and signatures using OCR and image analysis to validate recording (such as lien recording). In some cases, the tokenization system applies web scrapers to scrap public land record sites to check for lien recording and confirm registration details.

[0083] In some cases, the tokenization system applies Application Programming Interfaces (API) that interface with registrar's office databases, and submit via API lien data and retrieve recording confirmation of lien recordation programmatically. In some cases, the tokenization records the lien on a distributed ledger, such as recording the lien cryptographically on a blockchain to decentralized ledger.

[0084] Over the term of the loan, which could be 15 to 30 years, the homebuyer pays off the borrowed amount along with interest. The interest payments can significantly increase the total amount the homebuyer pays for the home. However, throughout this period, the homebuyer gradually gains equity in the home with each mortgage payment the homebuyer makes, and once the mortgage is fully paid, the homebuyer owns the home outright with 100% ownership 210 without a lien.

[0085] When a tenant rents a property, a contract 204 is signed between the tenant and the property owner that includes rental terms. The tenant pays a set amount 212 each month for the use of the property but gains no ownership or equity. Moreover, the rent could increase as time passes. This is typically the least costly option in the short term, as the tenant only pays for the use of the property and don't have to provide a large upfront down payment or pay interest. However, at the end of the lease, the tenant has 0% ownership 214 in the property, and all the money the tenant paid in rent does not contribute to any form of property ownership.

[0086] In contrast, the tokenization system combines elements of both mortgages and rentals while leveraging the advantages of tokenization technology. When the tenant uses this tokenization system, the tenant starts renting the property and also purchases digital tokens 106 over time. Each token represents a fraction of ownership in the property. Each month, the tenant makes payments 216 for the rental of the property and also for the tokens. The tenant gradually builds equity in the property (such as 10% ownership 218 initially) without needing to provide a large down payment upfront or pay high amounts of interest to a lender. Over time, and as leases renew, the tenant accumulates enough tokens to own the property outright at 100% ownership 220 without any liens and risks of default. The tokenization system provides the flexibility to move without the need to sell property, given that the tokens can be sold, transferred, and / or held. The tenant also has the ability to acquire ownership over time, thereby making homeownership more accessible for more people.

[0087] ARCHITECTURAL DIAGRAM BETWEEN THE TOKENIZATION SYSTEM, ASSET OWNERS, AND INDIVIDUALS

[0088] FIG. 3 illustrates an architectural diagram between the tokenization system, asset owners, and individuals, according to some examples. A group of computers 302a, 302b, 302c, 302d, and 302e (collectively referred to as the group of computers 302) connected to the Internet 304 runs the blockchain that forms a decentralized network, also known as nodes in blockchain terminology. These nodes are responsible for maintaining and updating the blockchain ledger, which in this case performs actions that record ownership, transactions, and contractual terms, and execute smart contracts for real estate properties.

[0089] When an asset owner 104 wants to tokenize their home 102, the asset owner submits the necessary documentation (such as a deed) to the tokenization system. The tokenization system receives a digital deed and performs functions using one or more forms of artificial intelligence, data processing, and cryptographic technologies.

[0090] The tokenization system receives a digital copy of the deed from the asset owner. This digital copy could be a scanned document or a photo of the physical deed. The tokenization system performs Optical Character Recognition (OCR), which can be a form of Artificial Intelligence (Al) that identifies text within digital images or scanned documents. The OCR module converts the visual representation of the text in the digital deed into machine-readable text.

[0091] Once the text has been recognized, a Natural Language Processing (NLP) module can be used to identify and extract key pieces of information. NLP, which can be another form of Al, is capable of understanding human language. In this case, the tokenization system identifies information such as the owner's name, the property description, boundaries, and any relevant legal language.

[0092] The extracted information is then standardized and stored in a structured database, enabling easy access and comparison. Standardization may involve transforming the text to conform to set formats, such as converting dates to a YYYY-MM-DD format, or geolocating addresses to standardized coordinates. Information, such as a digital copy of a deed, received from the various data sources can be of a different format.

[0093] In some cases, the machine learning model classifies the property based on the extracted information. The machine learning model identifies certain characteristics of the property that is not explicitly in the extracted information. For example, the machine learning model classifies a unit as a 1 bedroom based on its size and location.

[0094] The tokenization system configures data from multiple different databases that are in their own non-standardized format into a single standardized format. As such, messages can be automatically generated to communicate with individuals such as tenants and asset owners using the standardized format. Moreover, assessments and decisioning made by the tokenization system can be applied back to the asset owner by reapplying nonstandardized formatting of the asset owner.

[0095] In some cases, the tokenization system processes the deed information into a viewable form, such as in a way which mirrors the physical representation of an original paper form of the deed. This reduces the time consuming nature of importing source code into the form. The tokenization system converts a digital copy of the deed into a standardized form which establishes calculations and rule conditions required to fill in the standardized form, import data from the digital copy to populate data fields in the standardized form, and performs calculations on the imported data. This allows the tokenization system to change imported data into a standardized viewable form.

[0096] In some cases, the tokenization system applies such standardization on documents or data received and / or documents generated. The tokenization system generates a standardized form of a deed to enable the tokenization system to generate a viewable deed form. In some cases, the tokenization system generates contracts, such as between the tenant and the asset owner, to rent and purchase tokens. The tokenization system collects data related to the tenant, asset owner, and asset from various different sources and applies standardization to this data to populate fields of the generated documents (e.g., contracts).

[0097] In some cases, the machine learning model performs one or more features of the standardization described herein. In some cases, the machine learning model performs customizations and / or standardizations based on a user’s preferences. For example, the user inputs preferences such as a particular language for translation, customization on classifications and associated parameters, non-linear transformation, and / or the like.

[0098] In some cases, the tokenization system cross-checks information from the deed against a government or public property database. The tokenization system accesses such data via an API (Application Programming Interface) to interface with the relevant public records databases, query the extracted details, and compare the results for verification purposes. This step ensures that the property details match the official records and that the person claiming ownership is indeed the legal owner.

[0099] In some cases, the tokenization system and / or machine learning model cross-checks such information from the deed using other third party database. For example, the tokenization system checks information using global positioning system (GPS) data to verify the location, accesses photographs or data of prior owners such as on social media to verify the interior design of the home, and / or assesses a live camera feed from an augmented reality device. For example, the live camera feed can include a walk through of the property and the machine learning model applies computer vision algorithms to the camera feed to identify characteristics of the home, such as door types, bedroom locations, size, and / or the like.

[0100] Once the ownership is verified, the tokenization system divides the property's value into multiple tokens, as per the value evaluated by the system or provided by the user. The property can be divided based on a ratio of the value of the real estate and the value for each token. These tokens represent fractional ownership in the property. The token ownership records, deed, and other relevant details are encrypted and stored on a blockchain. Each token transfer can be managed via a smart contract, ensuring that all transactions are secure, transparent, and immutable, and the tokens are made available for tenants to purchase.

[0101] In some cases, the tokenization system applies the API to perform a recordation on the property records database, such as a records database of a government entity. In some cases, the tokenization system records a lien on the property based on tokens minted for the property.

[0102] In some cases, the tokenization system creates internal property records. For example, the tokenization system uses these internal property records for a layer of protection (e.g., to prevent multiple entries). In some cases, the tokenization system creates an internal property record to not have to rely on public records and / or to rely on such internal records when public records are unavailable.

[0103] If the tenant becomes the full owner, the tokenization system facilitates the transfer of ownership. The tokenization system initiates a transaction via the API with the property records database, to record the new ownership, such as via a smart contract indicating full ownership. These operations are conducted securely due to the cryptographic principles of the underlying blockchain and / or tokenization technology.

[0104] In some cases, the features related to the deed and / or other features of the tokenization system applies a self-referential table. A self-referential table includes a database table where a foreign key references the primary key of the same table. Thetokenization system, for example, applies such self-referential tables to track the ownership history of the tokens representing asset ownership.

[0105] Each token could be represented as a row in the table, with fields such as token id (the primary key), current owner, previous owner, and originating asset (or depositor). The previous owner field could reference another row in the same table, indicating the previous owner of the token before the current token owner, forming a chain of ownership. Such fields can be recorded onto the digital ledger. The tokenization system uses the originating asset to associate a token with other tokens minted by the same asset owner. Advantageously, this field helps for certain features of the tokenization system, such as exchangeability and fungibility.

[0106] When a token is transferred from one owner to another (e.g., from the asset owner to a tenant), the current owner field of the token's row is updated with the new owner's ID. A new row is also added to the table, representing a new token owner. The previous owner field of this new row points to the row representing the token that was just transferred, creating a link in the chain of ownership.

[0107] Moreover, the tokenization system tracks a history of ownership via the self- referential table through the previous owner field. Starting from a row representing a token's current owner, and previous owner fields that would lead to the previous token owners before the current owner, and so on, until a row is reached where previous owner is null, indicating the original token issued to the asset owner. This traceability adds to the transparency and security of the system, as it provides a tamper-proof log of token ownership changes.

[0108] When tokens are resold back to the asset owner or moved to another property, a similar process to the token transfer can be followed. The current owner of the affected tokens is updated, and new rows are added to represent the new token owners.

[0109] The self-referential tables can include a special row and / or column within the database that stores the pointers to the other portions of the same table or other tables. Instead of having to save the benefit characteristics for each of the transactions or individuals, the tokenization system includes an entry that refers to another portion of the table or other table with the corresponding information. Advantageously, the data stored in each of the databases can be reduced by calling a call function (e.g. a database pointer) when a certain data entry in another table is needed.

[0110] Thus, a tokenization system and / or client devices can perform functions of the tokenization system and have more flexibility in assessing large datasets, which previously required a large network throughput of data and high processing speed. Moreover, a self- referential table can enable more efficient storage and retrieval of larger sized data, faster searching of the asset ownership, token distribution, and / or the like;7and more flexibility in configuring the database.[OHl] In some cases, the tokenization system includes the group of computers 302 and / or facilitates communication among the group of computers 302. The nodes in the network validate information, such as ownership, and if validated, the nodes initiate the token creation process. The value of the property is divided by the chosen token value to determine the number of tokens to be minted.

[0112] These digital tokens 106, representing fractional ownership of the property, are issued to the asset owner (such as a tenant 108), such as to the asset owner's virtual asset storage. The transaction of minting and assigning these tokens is recorded on the ledger.

[0113] The nodes (such as the blockchain nodes) also manage the buying, selling, and leasing of tokens. For instance, when a tenant wants to buy tokens from an owner, the tenant submits a transaction to the network. The nodes verify the transaction, make sure the tenant has sufficient funds, and transfer the tokens from the owner's virtual asset storage to the tenant's. Once the transaction is validated and confirmed by the network (e.g., via the nodes), the transaction is recorded on the blockchain.

[0114] If a tenant accrues enough tokens to fully own the property, the blockchain network facilitates the transfer of ownership. The nodes of the blockchain burn or delete the tokens and update the property's ownership status on the digital ledger. The nodes validate this transaction before recording it on the blockchain. The nodes facilitate transfer over of the deed to the tenant.

[0115] When an asset owner (homeowner) decides to tokenize their property, the tokenization system evaluates the property to determine its current market value. The homeowner then provides the system with the necessary documentation (such as a copy of the deed) to confirm ownership of the property.

[0116] This information is verified by the decentralized network of computers running the blockchain, such as by accessing real estate records of ownership and / or on its own ledger of real estate ownership records. Once the information has been verified and the property's value has been established, the system will proceed with the tokenization process.

[0117] The value of the property is divided by the chosen token value (e.g., if a $300,000 property is divided into tokens each worth $100,000, 3 tokens will be minted as described above). These tokens, representing fractional ownership of the property, are digitally minted on the blockchain and assigned to the homeowner's virtual asset storage.

[0118] If a new tenant moves in or a lease is renewed, the system adjusts the valuation, if the value of the property increases, a certain number of additional tokens are minted and provided to the asset owner and / or the token holders associated with the property.

[0119] The homeowner may request to the tokenization system a re-evaluation of the property's value at any point, such as after significant improvements or renovations (e.g., adding a pool). If the value has changed, the system could initiate a re-tokenization process. For instance, if the property's value has increased from $300,000 to $500,000 and the token value remains at $100,000, two additional tokens would be minted and assigned to the homeowner and / or the token holders. This re-tokenization is recorded on the digital ledger.

[0120] Tokenizing real estate assets allows for flexibility in buying, selling, and transferring the tokenized assets. Individuals can trade tokens on a peer-to-peer basis on the tokenization platform, which is supported by the blockchain network. If a tenant wishes to buy tokens, they can send a transaction request to another individual who owns the tokens. The nodes verify ownership of the token and payment, and facilitate the transfer of ownership for the token.

[0121] The buyer sends the agreed upon amount (often in a form of cryptocurrency or any acceptable payment method on the platform) to the seller. Upon confirmation of payment, a smart contract is executed that transfers the tokens from the seller's virtual asset storage to the buyer's wallet. This transaction is recorded and verified on the blockchain, providing an immutable record and ensuring transparency.

[0122] In some cases, a buyer can buy or sell tokens directly from / to the asset owner. The asset owner lists the tokens for sale on the platform (such as with the specified price). A buyer who wishes to buy these tokens sends a purchase request, pays the specified price, and receives the tokens upon confirmation of payment via a smart contract. The smart contract ensures payment is made and tokens are owned and transferred. The asset owner can also buy back the tokens from the tenant or another token holder using a similar process.

[0123] Blockchain technology's inherent transparency, security, and immutability make it well-suited for this kind of application. Each node in the network independently verifiesevery transaction and maintains a copy of the ledger, making the system highly resilient and reliable. This decentralization also ensures that no single entity has control over the network, increasing trust and participation in the system using technological advances that are not typically used in real estate, let alone real estate ownership scenarios.

[0124] Intermediaries such as property developers or token aggregators could hold a pool of tokens from various properties and offer them for sale to interested buyers. The intermediary can list the tokens for sale on the platform, and buyers can purchase these tokens.

[0125] Intermediaries also can buy tokens. For instance, a token aggregator might be interested in buying tokens from various individual holders to add to their collection. Individual token holders or asset owners could sell their tokens to these intermediaries following a similar transaction process as described herein.

[0126] In these scenarios, the use of smart contracts ensures that transactions are securely executed and recorded. The blockchain's decentralized nature ensures transparency, as all transactions are visible to all participants in the network.

[0127] In some cases, the tokenization system and / or a smart contract can facilitate the use of a property. For example, a tenant can be renting a home while obtaining tokens. The tokenization system can facilitate such use by sending a message to control the property.The tokenization system sends a wireless message to a lockbox on the property enabling the user to access keys to open the home. In some cases, such messages can control the use, type of use, availability of certain operations and features, time period and duration of use, and / or the like using these communications.

[0128] The tokenization system sends such signals to a computing device or server of the asset, such as a vehicle computing device or a server communicating with one or more smart home systems.

[0129] In some cases, depending on the rules set by the platform, tokens are used across properties, meaning a token holder could potentially use their tokens as payment to rent or purchase in another property on the platform. These features make the tokens of the tokenization system truly fungible and provide additional flexibility to the token holders.

[0130] In the context of tokenizing real estate, leasing tokens introduces a level of flexibility and unique opportunities for temporary ownership and use of assets. The tokenization system enables a token holder who owns a certain percentage of an asset to lease tokens to another individual. By doing so, the tokenization system enacts a smartcontract that enables the other individual to gain temporary ownership of the tokens and, by extension, the right to use or benefit from a proportion of the asset represented by these tokens.

[0131] During the lease period, the tokenization system enables the temporary token holder to rent the property to a tenant. The proceeds from the tenant are received by the tokenization system, whereby smart contracts are invoked to provide the proceeds to the token holder and the temporary token owner. At the end of the lease, the tokenization system invokes a smart contract whereby the tokens are automatically returned to the token owner's wallet.

[0132] The distribution of rent proceeds automatically disperse via smart contracts. For instance, if the rent is paid in cryptocurrency, the smart contract automatically distributes the rent to the token owner, the temporary token owner, and property manager based on predefined percentages. For example, the property manager may require a certain amount or percentage of the proceeds.

[0133] Token leasing in this manner not only provides opportunities for passive income for token holders but also increases liquidity of the token in the token market. It further allows those without the capital to purchase tokens outright to benefit from tokenized assets temporarily.

[0134] Token holders in a real estate tokenization system have various investment strategies at their disposal. The token holders can engage in arbitrage, where they buy and sell tokens to take advantage of price discrepancies across different markets or platforms, turning a profit from the difference in token prices. This might occur if tokens representing the same asset are priced differently in distinct markets.

[0135] Token holders can adopt a long-term investment strategy, holding onto tokens to benefit from natural appreciation of the underlying real estate asset. In some cases, over time, as the property value increases, so does the value of each token, providing capital gains to the token holders. In some cases, new tokens are minted and distributed to each owner accordingly, such as if there are multiple owners to a property management company or to multiple properties. Token holders can also deposit or lease their tokens to others, earning a passive income. This approach allows others to use the tokens temporarily, such as for rental income, while the original token holder continues to derive financial benefit.

[0136] In some cases, the asset owner divides the value of a single asset (say, a house) into several tokens. Each token represents a proportional stake in the returns from the asset (likerent). The tokenization system enables transfer of property ownership to a token holder who accumulates tokens equivalent to the asset's total value. In such a case, the tokens corresponding to that asset are removed from circulation or “purged.”

[0137] In some cases, the tokenization system enables an asset owner to have several assets (say, multiple properties). Here, the total value of all assets is divided into tokens, each representing a proportional stake in the returns from all assets. Alternatively, each individual asset can also have its own token representation. The tokenization system enables token holders to acquire ownership of an individual asset or a percentage of a group of assets by accumulating tokens equivalent to the asset's total value. In some cases, different owners of the same or different properties can each tokenize their equity and / or ownership.

[0138] This kind of tokenized asset ownership provides investors with a new way to diversify their portfolios and potentially lower barriers to entry in markets like real estate using the technological advances of tokenization.

[0139] Although examples described herein refer to asset or real estate property, it is appreciated that examples described herein can refer to other types of assets, including both physical and / or intangible assets. For example, assets can refer to vehicles, such as cars, boats, planes, and other vehicles, allowing investors to own a piece of these assets and potentially share in their appreciation over time.

[0140] In some cases, the assets refer to artwork and / or collectibles, such as paintings, sculptures, rare collectibles, and other valuable items that can be tokenized to enable broader ownership. This could lower the barriers to entry in the art investment market, which has traditionally been accessible only to the wealthy.

[0141] In some cases, assets refer to intellectual property, such as copyrights, patents, and other forms of intellectual property. This could enable creators to raise funds while allowing investors to share in the potential profits from these assets.

[0142] In some cases, assets refer to commodities such as gold, oil, or agricultural products, providing another way for investors to gain exposure to these markets. In some cases, assets refer to business equity, allowing investors to buy and sell tokens representing shares in the company. In some cases, assets refer to debt instruments, such as bonds or loans, which could create more flexibility and liquidity in the debt market. In some cases, assets refer to digital assets such as domain names, digital art (such as non-fungible tokens - NFTs), and in-game assets.

[0143] TOKENIZATION OF REAL WORLD ASSETS FOR SMART CONTRACTEXECUTION

[0144] FIG. 4 illustrates a method 400 for tokenization of real world assets for smart contract execution, according to some examples. Although the example method 400 depicts a particular sequence of operations, the sequence may be altered without departing from the scope of the present disclosure. For example, some of the operations depicted may be performed in parallel or in a different sequence that does not materially affect the function of the method 400. In other examples, different components of an example device or system that implements the method 400 may perform functions at substantially the same time or in a specific sequence.

[0145] At block 402, the tokenization system receives a first real estate possession record for a first real estate from a first real estate possessor. The tokenization system acquires a digital representation of the legal rights associated with a real estate, provided by the individual or entity that currently holds those rights. The real estate can be any form of real- world property, such as real estate, vehicles, artwork, or other valuable goods.

[0146] In some cases, the real estate includes a real estate property, such as a home, a building, a car, equipment such as recording equipment, material such as gold or steel, technology such as a server or radio station, factories, production facilities, and / or the like. The real estate can include any real world object that can be divided based on its value, such as into tokens or digital tokens.

[0147] The real estate possession record includes a legal document that establishes the ownership and rights associated with the real estate. This could be a deed for a property, a title for a vehicle, or any other legal document that establishes ownership.

[0148] The process of digitizing this title involves taking a picture or scanning a physical copy of the real estate possession record. The tokenization system converts the information within the real estate possession record into a digital format that can be stored, transmitted, and processed electronically. In some cases, the tokenization system scans the real estate possession record and applies optical character recognition (OCR) to extract text.

[0149] The tokenization system can apply a machine learning model to map data fields in the real estate possession record to relevant data fields in the tokenization system database. In some cases, the tokenization system standardizes data in the real estate possession record. For example, formats and data can be different across different documents, such as abbreviations, acronyms, and / or formats (e.g., zipcodes). The tokenization systemstandardizes such data, such as using a machine learning model, in order to store and process the data.

[0150] With data in standardized format, the tokenization system can compare data to other data in its database. If the tokenization system desires to send data back to the computing system that transmitted the real estate possession record or other third party databases, the tokenization system converts the data into the non-standardized format of the receiving party.

[0151] FIG. 5 illustrates an architectural diagram of tokenization of various real world assets, according to some examples. The real estate possessor 104 provides a real estate possession record (such as a deed 202) to the tokenization system via the Internet 564 for the home 102. The same real estate possessor 104 can provide another real estate possession record (such as a title 540) for a car 552. Another farm land owner 542 can provide the deed 546 for a farmland 550, and another owner 560 of airplane seats can provide an ownership record 558 indicating ownership of one or more seats, such as airplane seat 554 and airplane seat 556.

[0152] At block 404, the tokenization system identifies a value of the first real estate. The tokenization system determines a monetary worth of the real estate. The tokenization system determines the value of the real estate through one or a variety of ways. In some cases, the tokenization system determines the value of the real estate depending on the type of real estate. For instance, in the case of real estate, the value could be determined through a professional appraisal system, comparative market analysis, or valuation models.

[0153] In some cases, the tokenization system applies a valuation model, such as a machine learning model. The tokenization system inputs one or more characteristics of the property. The tokenization system identifies such characteristics based on information from the real estate possession record and / or third party databases. For example, the tokenization system can retrieve an address from the real estate possession record and retrieve characteristics of the property, such as the number of bedrooms, square footage, and / or the like from third party databases.

[0154] In some cases, the tokenization system trains and applies a machine learning model that can generate textual or visual descriptions of assets. In some cases, such a machine learning model is a transformer type machine learning model. The textual or visual descriptions can be searched using non-traditional queries. For example, a user can search “nice place in a gentrified neighborhood” or “apartment looks like the one in friends.” Themachine learning model takes characteristics of the property from multiple different sources, such as google maps or a government database, to generate such textual or visual descriptions. In some cases, a user can draw an asset and the machine learning model, through its generated textual or visual descriptions, identifies the closest matching asset for the user.

[0155] In some cases, the tokenization system inputs characteristics of the real estate into the model, such as the condition, size, location, address, characteristics of the neighborhood, and other factors. The model is trained to identify similar properties (such as properties in a similar neighborhood that share certain characteristics of the real estate) and compares the property to these other similar properties that have recently sold in the same area to determine its estimated market value. In some cases, the machine learning model outputs such characteristics as described herein such as based on other characteristic inputs.

[0156] In some cases, the valuation models use mathematical modeling combined with databases of existing properties and transactions to calculate property values. These models can quickly provide an estimate of a property's value based on available data. This value serves as the basis for generating the digital tokens that represent ownership of the real estate. This value is also used by the tokenization system, machine learning models, and / or smart contracts in accepting transactions if the value is within an acceptable range of values.

[0157] At block 406, the tokenization system generates a first digital token by a group of nodes of a blockchain network, the digital token is generated based on the value of the first real estate, the digital token representing ownership of the first real estate. In some cases, the digital token is generated based on the value of the real estate. For example, the digital token includes a value that corresponds to the value of the real estate property. In some cases, each digital token represents ownership in the real estate. For example, token 504 represents ownership of home 102, token 506 represents ownership of car 552, token 544 represents ownership of farmland 550, and token 548 represents ownership of both airplane seats 554 and 556.

[0158] The tokenization system creates (or mints) digital tokens that represent ownership in the asset. These digital tokens are generated with a value that corresponds to the previously determined value of the real estate. The price of the asset can be set by a user such as the real estate possessor, and / or the price can be set by the market (such as based onbuy and sell orders on an exchange that exchanges digital tokens for other monetary value such as money).

[0159] The tokenization system can initiate a distributed ledger and / or blockchain technology to generate the digital tokens. The tokenization system initiates the blockchain to create unique, non-fungible digital tokens that can be securely tracked and transferred. Each digital token is a digital asset that is stored on the blockchain, providing a transparent and immutable record of ownership.

[0160] Once generated, these digital tokens can be bought, sold, leased, or traded, allowing for ownership in the real estate. In some cases, the asset owner places restrictions on the tenant, and / or vice versa. For example, the asset owner can place restrictions on purchasing, selling or leasing tokens by a tenant. In another example, only an individual under a usage contract can purchase tokens and they cannot sell them while under contract.

[0161] At block 408, the tokenization system transmits the generated digital token to a digital token storage associated with the first real estate possessor. In some cases, the digital token storage includes a software-based system that securely stores users' digital assets, such as cryptocurrencies and / or asset-backed digital tokens.

[0162] The tokenization system initiates the transmission process once the digital tokens have been generated. The tokenization system initiates a transaction on the blockchain network to move the tokens from the system's wallet (or a temporary holding wallet) to the real estate possessor's wallet.

[0163] The tokenization system initiates the creation of a digital signature using its private key, which is then broadcasted to the blockchain network. The network's nodes validate the transaction, ensuring that the tokenization system has the necessary balance of digital tokens to perform the transaction and that the digital signature matches the system's public key.

[0164] Once validated, the transaction is added to a block of transactions, which is then added to the blockchain. This process ensures the immutability and transparency of the transaction, providing a clear record of the transfer of digital tokens from the system to the real estate possessor.

[0165] The real estate possessor's digital token storage will then update to reflect the receipt of the new digital tokens. The real estate possessor then manages these digital tokens within their wallet, including transferring them to other wallets or using them in transactions.

[0166] At block 410, the tokenization system records a lien on the first real estate possession record and a record of the digital token transmitted to the digital token storage associated with the first real estate possessor onto a distributed ledger of the blockchain network.

[0167] The system could interface directly with public land records to submit lien documents for recording and retrieve confirmation. In some cases, the tokenization system implements self-executing smart contracts that automatically notifies relevant third party databases that have their own record (such as records of liens and ownerships), transfer lien- related assets, and / or record the lien on the blockchain upon meeting coded conditions.

[0168] In some cases, the tokenization system applies loT sensors, such as sensors on deed documents could track their physical location and confirm when they are processed by the registrar's office. In some cases, the tokenization system applies computer vision algorithms, such as scanning deed documents and verifying registrar stamps and signatures using OCR and image analysis to validate recording (such as lien recording). In some cases, the tokenization system applies web scrapers to scrap public land record sites to check for lien recording and confirm registration details.

[0169] In some cases, the tokenization system applies Application Programming Interfaces (API) that interface with registrar's office databases, and submit via API lien data and retrieve recording confirmation of lien recordation programmatically. In some cases, the tokenization records the lien on a distributed ledger, such as recording the lien cryptographically on a blockchain to decentralized ledger.

[0170] At block 412, the tokenization system causes execution of a smart contract by broadcasting one or more functions to the blockchain network and receiving validation from the nodes of the blockchain network, the execution of the smart contract providing a real estate user with access to the first real estate.

[0171] In some cases, the tokenization system automatically enables access to the asset. For example, the tokenization system automatically configures digital locks or security systems. In some cases, the tokenization system generates a unique access code for the real estate user upon receipt of the occupy exchange. The tokenization system sends the real estate user this code, allowing them to access the property.

[0172] In some cases, the tokenization system uses smart contracts on the blockchain to automatically grant access rights to the real estate user. The smart contract is programmed to change the status of the asset to 'in use' by the real estate user upon receipt of the occupyexchange. Such a status initiates (and / or the smart contract initiates configuration of) proper technology, as described herein, to enable access to the property.

[0173] In some cases, the tokenization system configures Internet of Things (loT) devices that are connected to the asset. The tokenization system sends commands to these devices to grant access to the real estate user. For example, the tokenization system sends a command to unlock the doors of a rental property or to activate utilities of a car.

[0174] For assets such as rental properties or shared spaces, the tokenization system integrates with existing reservation platforms. Upon receipt of the use occupy exchange, the tokenization system automatically books the property for the real estate user for the agreed- upon period.

[0175] In some cases, the tokenization system generates legal documents, such as lease agreements, that grant the real estate user the right to use the property. In some cases, the tokenization system generates such documents by identifying relevant data fields and populating the fields with information retrieved. The tokenization system applies the standardized data (as described further herein) to the forms to generate legal documents for the parties to sign.

[0176] In some cases, the tokenization system applies a machine learning model to generate such legal documents. The machine learning model is trained to receive information related to the asset, the real estate possessor, and / or the real estate user, and generate legal documents, based on training on historical asset, real estate possessor, and real estate user data.

[0177] In some cases, the tenant 108 acts as an intermediary whereby the tenant 108 gains access to a real world asset, such as the home 102 by purchasing or leasing token 504. The tokenization system generates a legal contract that is stored on the blockchain that records the transfer of permanent and / or temporary ownership. The tenant 108 can rent the home 102 to another tenant 566, receive proceeds from the tenant 566, and have a sublet contract 568 generated by the tokenization system.

[0178] In some cases, the tokenization system generates a single token representative of the ownership of the real world asset. The token can be leased for a temporary period of time, whereby the token is automatically returned to the owner after the lapse of the period of time.

[0179] In some cases, the tokenization system generates at least two tokens for a real world asset, one representing ownership and another representing usage rights. Uponpurchase of the ownership token, the tokenization system updates the deed to reflect new ownership by the new owner via a smart contract on the blockchain. Upon purchase of the usage token, the tokenization system updates the deed to include a lien on the property and provide access to the real world asset via a smart contract on the blockchain.

[0180] REAL WORLD ASSET USAGE VIA USAGE CONTROLS AND TOKENIZATION

[0181] FIG. 6 illustrates real world asset usage via usage controls and tokenization, according to some examples. The real estate possessor 104 submits a real estate possession record 202 to the tokenization system and the tokenization system records the record on the distributed ledger. The tokenization system generates token 504 representing usage rights for the home 102 and transmits the token to the real estate possessor's digital token storage.

[0182] A real estate user 108 desires to gain usage rights for the home 102. The tokenization system generates a legal contract 502 to convey the usage rights for the real estate user 108. In some cases, the real estate possessor is allowed options by the tokenization system the terms of the usage rights for the real estate user. The legal contract 502 is recorded onto the distributed ledger, and the tokenization system records a lien on the real estate possession record 202 onto the distributed ledger.

[0183] The tokenization system initiates a digital token relocation for a real estate user. The first digital token relocation transmit a digital token 504 from the digital token storage associated with the real estate possessor to a digital token storage associated with the real estate user and recording the transmission onto the distributed ledger.

[0184] The tokenization system identifies that a real estate asset transfer or usage rights transfer is taking place from a possessor (current owner) to a recipient (user). The system looks up the digital token storage wallet / account associated with the real estate possessor.

[0185] In some cases, the unique identifier of the token being transferred, the sending and receiving wallets, and the transfer details are immutably recorded on the distributed ledger. The ledger entry initially confirms that the possessor's storage has the digital token, while the recipient's storage after transmission has the corresponding token. The recorded transaction officially verifies the transfer of ownership stake (as represented by the transferred tokens) from the possessor to the recipient or can represent the transfer of usage rights to the user.

[0186] In some cases, the tokenization system receives a restriction on digital token relocations for real estate recipients. In some cases, the restriction comprises a time periodfor submitting digital token relocations by real estate recipients or a required value for the digital token relocations submitted by real estate recipients.

[0187] In some cases, the restrictions include a restriction that real estate token cannot be transferred for the first a number of months after issuance, real estate tokens can only be transferred if the total value of tokens being transferred is at least a certain amount, real estate token transfers are only allowed during a specific time period each term (such as 2 weeks a year), real estate tokens can only be transferred to recipients who have been preapproved by the tokenization system, and / or the like.

[0188] In some examples, the tokenization system applies a machine learning model to receive an expected return for digital token relocations submitted by real estate recipients over a period of time. The tokenization system collects data on factors that impact real estate token values such as market conditions, property attributes, location, demand, etc. This data is inputted into a machine learning algorithm that is trained to analyze the relationships between the variables. The model looks at patterns in the data to make predictions about how real estate token values will change in the future.

[0189] When a real estate token recipient submits a request to transfer tokens, the system can run the request details through the trained ML model. The model will analyze the property details, recipient details, and external factors to generate a customized prediction for the expected return on those tokens over the recipient's defined time period. This provides useful insights to the recipient on the potential appreciation or depreciation of the real estate tokens over time. The ML model can be continuously refined as new data comes in, improving its ability to forecast real estate token returns.

[0190] The tokenization system initiates usage controls 608, such as via smart contracts. The usage controls can communicate with one or more devices of the home 102 to enable the user to access the home. In some cases, the user 108 can provide periodic token relocations for the use of the property and / or enable others to use the property and collect proceeds from the other users.

[0191] The tokenization system periodically receives signals or notifications of digital token relocations (or other payments) from the real estate user during a specified period of real estate use. The real estate user could be a tenant, a renter, any other party who is using the real estate but does not fully own it, or any other party requiring payment from real estate proceeds.

[0192] The tokenization system can receive digital token relocations that can relate to one or more different actions related to the use, partial acquisition of the real estate, or requested or required by the tokenization system for the real estate. For instance, the tokenization system receives an indication of a rent payment, a purchase of additional digital tokens representing ownership in the real estate, or the like. In some cases, the tokenization system determines such payment is made from a third party financial database or server. In other cases, the payment is made directly to the tokenization system. In some cases, the tokenization system determines that a payment request tis made by a third party or a party within the system, such as for the use or fractional ownership of the property.

[0193] In some cases, the tokenization system receives token resource allocations for insurance payments, tax refunds, property taxes, rent for asset usage, payment for supplies such as for property maintenance, and / or other forms of payment. For example, if insurance payments were also made by the tenant, a smart contract initiates payment back to the home owner and tenant according to their proportional ownership.

[0194] The tokenization system receives the indication of the digital token relocation via digital signal or message sent from the real estate user’s digital token storage or account to the system. The indication includes information about the transaction, such as the amount paid or requested, the number of digital tokens purchased, and the time of the transaction.

[0195] The tokenization system receives this indication and processes it to update the records of the real estate and the associated digital tokens. The tokenization system updates the balance of digital tokens in the real estate user's digital token storage, updating the remaining value of the real estate, updating the record of payments made by the real estate user, and / or record payment requests for the real estate asset (as further described herein).

[0196] The system initiates an automated transfer of the calculated first subset of tokens from the possessor's digital token storage to the recipient's digital token storage. The unique identifiers of the tokens being transferred, the sending and receiving wallets, and the transfer details are immutably recorded on the distributed ledger. In some cases, the tokenization system automates a request for transfer of one or more tokens. For example, the tokenization system places a hold on the tokens (such as a lien on each token) until each token owner performs one or more functions, such as making a payment. The tokenization system removes the lien upon payment. In some cases, the tokenization system instead is sent to another entity (such as if the token or asset is sold with the lien and a payment portion is transmitted to the other entity, creditor, debtor, and / or the like. In some cases, thetokenization system automates request for transfers of tokens or payments based on contractual terms (as further described herein).

[0197] The ledger entry confirms that the possessor’s storage now has a reduced number of tokens, while the recipient’s storage has a corresponding increased balance of tokens or payments. The recorded transaction officially verifies the transfer of ownership or usage rights (as represented by the transferred tokens) from the possessor to the recipient. The system cross-checks the token transfer against the token relocation to ensure accuracy.

[0198] The tokenization system transmits a first portion of the second digital token relocation transmitted to the digital token storage associated with the real estate possessor. This first portion can represent a variety of things depending on the specifics of the transaction and the terms of the real estate use. For instance, in a rental scenario, the first portion represents the part of the tenant’s payment that is allocated to rent to the real estate possessor. In some cases, in a supply scenario such as performing landscaping services for a real estate asset, the first portion represents the part of the supplier’s payment request that is allocated to providing the service.

[0199] The tokenization system transmits the first portion of the digital token relocation to the real estate possessor or transmits a request for token relocation on behalf of the real estate possessor. In some cases, tokenization system identifies a payment made through other channels, such as assessing a financial transaction from the real estate user to the real estate possessor.

[0200] The tokenization system identifies this first portion by analyzing the details of the transaction indication received from the real estate user. This could involve parsing the transaction data, applying predefined rules or algorithms, or using machine learning models to classify and quantify the different parts of the transaction.

[0201] Once the first portion is identified, the payment and / or payment request is recorded in the system and used to update the records of the real estate and the associated digital tokens (such as on a distributed ledger). This involves subtracting the value of the first portion from the digital token relocation.

[0202] The tokenization system transmits a second portion of the digital token relocation to the digital token storage associated with the real estate recipient in the case the real estate recipient enables others to use the property and receives proceeds from the other users. As such, the original property possessor and the recipient can collect partial relocations from the proceeds. In some cases for a payment request, the tokenization system does not send aportion of the token relocation to the real estate recipient unless stipulated in the contractual terms with the real estate recipient (as further described herein).

[0203] The tokenization system records a record of the first and second portions being transmitted onto the distributed ledger of the blockchain. The distributed ledger on the blockchain maintains a decentralized, immutable record of all tokenization system transactions.

[0204] When the first subset of tokens is transferred from the possessor to the recipient, the details of this transfer are recorded on the ledger. This includes the sending and receiving wallets, number of tokens, timestamp, etc. This ledger entry documents the change in token balances resulting from the first transfer portion. The possessor's balance is verifiably decreased, and the recipient's balance increased.

[0205] When the second subset of tokens is subsequently transferred, the details of this second transfer are also immutably recorded on the ledger. The ledger now reflects the updated token balances of both parties following the second transfer action. Recording the first and second portions provides a transparent, undisputed record of the continuing asset ownership changes enacted through the token transfers.

[0206] In some cases, another portion is allowed to costs, such as to property management, expenses, utilities, intermediary payment, bill collector (e.g., for payment request), tax authority (e.g., tax payment on stages of the supply chain), and / or the like. In some examples, a portion of the digital token relocation is sent to the property manager. If the real estate possessor is the property manager, the tokenization system sends the portion of the digital token relocation for rent and for property management to the real estate possessor. If the property manager is a third party, the tokenization system sends separate payments to the property manager and to the asset owner.

[0207] In some cases, the transfer process begins with the tokenization system initiating a transaction on the blockchain network. This transaction involves moving the specified number of digital tokens from the real estate possessor's wallet to the real estate user's wallet. The tokenization system creates a digital signature for the transaction using the private key associated with the real estate possessor's wallet. This signature is then broadcasted to the blockchain network, where it is validated by the network's nodes. The nodes check that the real estate possessor's wallet has a sufficient balance of digital tokens and that the digital signature matches the public key associated with the wallet.

[0208] Once validated, the transaction is added to a block of transactions, which is then added to the blockchain. This process ensures the immutability and transparency of the transaction, providing a clear record of the transfer of digital tokens from the real estate possessor to the real estate user.

[0209] In some cases, in response to a lapse of the real estate use period for the real estate user, the tokenization system determines whether the contract 502 enables autorenewal, if the user desires to continue using the home, and / or whether the possessor allows the user to continue to use the home. In some cases, the tokenization system renews the real estate use period.

[0210] In some cases, the tokenization system automatically renews the real estate utilization period. In other cases, the tokenization system generates a new contract to be agreed upon between the asset owner and the tenant for a new real estate utilization period.

[0211] The real estate use period can include a predefined time period, such as a lease term or a use term, during which the real estate user is expected to acquire full or partial usage rights, and full and / or partial ownership of the real estate by purchasing digital tokens.

[0212] The renewal of the real estate use period involves extending the lease term, renewing the loan term of the token, and / or setting a new deadline for the real estate user to acquire full ownership (such as by purchasing the full ownership rights of the home instead of just usage rights). This provides flexibility for the real estate user and allows them to continue using the real estate and providing them time to acquire full ownership.

[0213] In some cases, the tokenization system reassesses the current price of the real estate. For example, the real estate appreciates (or depreciates) naturally over time. In some cases, modifications or damage occurs to the real estate over time, and thus the value appreciates or depreciates. In such cases, the value of the digital token can change automatically.

[0214] Upon completion of the use of the property, the contract 502 is recorded as terminated on the blockchain. The tokenization system releases the lien such that the real estate possessor 104 now owns the real estate free from the recorded lien.

[0215] The real estate possession record includes a digital version of a deed, title, or other legal document that establishes ownership of the real estate. This document is stored in a secure, tamper-proof format of the tokenization system, such as a blockchain or a secure database.

[0216] The tokenization system initiates the transfer of the usage rights back to the possessor by creating a transaction on the blockchain or updating the database to reflect the change in ownership of usage rights. The tokenization system can change the owner field in the real estate possession record to the identifier of the real estate possessor, or creating a new real estate possession record with the real estate possessor with the usage rights and invalidating the previous document.

[0217] The tokenization system initiates broadcasting of the transaction or update to the network or commits the change to the database, where it is validated and recorded. This process ensures the immutability and transparency of the ownership transfer, providing a clear and indisputable record of the real estate user's ownership. The tokenization system also returns the digital token 504 from the wallet of the user to the wallet of the possessor.

[0218] Once the transfer is complete, the real estate possessor has full legal ownership of the real estate, as represented by the real estate possession record. The real estate possessor can exercise all rights and privileges associated with usage again, such as selling the real estate, using it as collateral, making modifications to the real estate, using the real estate, and / or the like.

[0219] In some cases, the tokenization system can provide an option for the real estate possessor to purchase back the usage rights digital token from the real estate user, such as based on a market value. The repurchasing of the digital tokens can be compulsory for the real estate user, for the real estate possessor, and / or both. In some cases, the return of the usage rights is automatic without payment and / or at a certain time period (such as after 1 or 5 years of use by the user).

[0220] Although examples described herein describe a smart contract performing a series of features for the tokenization system, it is appreciated that more than one smart contract can be used to perform such features, and / or the tokenization system can generate and execute smart contracts to perform one or more other features described herein for the tokenization system.

[0221] The blockchain stores a history of the movement of the digital token over time. For example, the digital ledger shows a history of the digital token 504 transmitted from the possessor's wallet to the user's wallet at the start of the usage period, and at a later time upon completion of the usage period, the digital token 504 being transmitted from the user's wallet to the possessor's wallet.

[0222] In some cases, the tokenization system formats transactions to a standardized format to support all parameter combinations, instead of having custom formats for each class of transactions. This will enable easier and more accessible analytics for the digital tokens, assets, individuals, companies, and / or the like along with enhanced transaction transparency and feature expandability.

[0223] In some cases, the tokenization system enables supply chain mapping via digital token records on the distributed ledger. Because digital tokens are unique to specific objects, the transaction history for a particular digital token can be used to identify the location and history of the item. Moreover, such formatting enables seamless application into machine learning models that are trained to determine an expected return of an asset based on the returns of other assets within its supply chain. The tokenization system now has a robust transaction structure to allow for future incorporation of new features and transaction types.

[0224] In some cases, the tokenization system accepts and requests payment in various stages of the supply chain (as further described herein). This allows the tokenization system to enable trade of asset portions (tokens) and / or payments (request and receive). The tokenization system applies machine learning models (as further described herein) to enable transparency of supply, removal of intermediaries thus reducing stages in the supple chain, by recording such payments and payment requests in a distributed ledger within a blockchain, removing the need for centralized authorities. This is especially applicable when parties cannot agree on a centralized authority or when a centralized authority is not needed (e.g. cross border trade). Such features also enable auto payment of various portions (such as to satisfy vat or other customs payments).

[0225] In some cases, upon return of the digital token back to the possessor, the tokenization system purges the token from circulation. If the token is purged, the tokenization system removes the token from the real estate user’s digital token storage and updates the blockchain or database to reflect the reduced supply of tokens. Advantageously, purging of the coins prevents the real estate user from selling the property or usage rights of the property using the tokens and / or selling the property separately using the asset ownership document.

[0226] Examples described herein are described according to one real world property. However, it is appreciated that the examples and features can apply to a collection of assets, such as a portfolio of properties owned by a developer or a real estate company. In this case, the “real estate” referred to herein include multiple individual assets, each of which could bea separate property. For example, the assets can be equivalent to a certain set amount of tokens. In other examples, the assets acquire tokens based on the value of the individual asset (e.g., the tokens are set to a particular price, and the tokens given to an individual asset is based on the number of tokens equivalent to the asset value).

[0227] The real estate possessor provides digitized asset rights documents for each property in the collection. The system identifies the total value of the collection of properties. The system generates digital tokens corresponding to the total value of the collection of assets. Each token represents a fractional ownership interest in the entire collection, not just a single property. Thus, a real estate user who purchases tokens is gaining equity in the entire collection of properties, not just one property.

[0228] The real estate user is able to use one of the tangible properties in the collection, such as by renting a property. The system checks whether the quantity of tokens in the real estate user’s digital token storage equals or exceeds the number of tokens corresponding to the value of the collection of assets. If it does, the tokenization system transfers full ownership to the collection of properties to the real estate user. The system transfers the digitized asset rights documents for the entire collection of assets to the real estate user.

[0229] This approach allows a developer or real estate company to tokenize a portfolio of properties and sell fractional ownership interests to multiple real estate users. It provides a flexible and efficient way for real estate users to gain equity in a collection of properties, and it allows the real estate possessor to raise capital by selling tokens.

[0230] The system ensures proper ownership or usage rights exchange by maintaining a clear and immutable record of all transactions related to the asset, including the initial tokenization of the asset and all subsequent transfers of tokens. This record serves as a digital chain of title, providing a transparent history of the asset’s ownership.

[0231] When the real estate possessor first submits the digitized asset rights document (such as a deed) to the system, the system records this transaction on the blockchain or in a secure database. This initial record includes the real estate possessor’s identity, the value of the asset, and the tokens generated.

[0232] Each time tokens are transferred from one digital token storage to another, the system records the transaction. This includes transfers from the real estate possessor to the real estate user (such as a tenant), as well as any subsequent transfers between different real estate users. Each record includes the identities of the sender and receiver, the number of tokens transferred, and the time of the transfer.

[0233] The system maintains a complete record of all these transactions, creating a digital chain of title for the asset. This chain of title provides a clear and indisputable history of the gradual change in asset’s ownership as well as the final transfer of full ownership.

[0234] By maintaining this digital chain of title, the system ensures that the ownership transfer is transparent, secure, and legally valid. The blockchain technology used in this process provides additional security by making the record immutable, meaning it cannot be altered or deleted once it’s been recorded. This prevents fraud and disputes over ownership, providing peace of mind for all parties involved.

[0235] In some cases, the tokenization system generates legal documents to formalize each transfer of ownership and / or usage rights. For example, when the real estate user acquires enough tokens to become the owner, the system could generate a new deed or title in the real estate user’s name for the real estate user and the real estate possessor to sign. The system can generate a contract for the usage rights of the user. This document would be legally binding and could be recorded with the appropriate government agency.

[0236] In some cases, the tokenization system applies a machine learning model that is trained to generate required documents for a particular property. For example, the machine learning model generates different documents for an apartment complex, a single family home, a commercial property, or for an automobile. In some cases, the machine learning model generates documents required for different jurisdictions, such as based on state law or documents needed for foreign jurisdictions.

[0237] In some cases, upon the real estate user acquiring enough tokens, the system uses a third-party escrow system to hold the digitized asset rights document and oversees the transfer of ownership or usage rights. The escrow system ensures that the real estate user has enough tokens before transferring the document to them.

[0238] In some cases, the tokenization system uses digital signatures to authenticate each transaction. Both the sender and receiver of tokens signs each transaction (such as with their private keys), providing a secure and verifiable record of the transaction.

[0239] In some cases, the system integrates online notary systems to notarize the transfer of ownership. This would provide an additional layer of legal assurance that the transfer is valid.

[0240] In some cases, the system automatically records, such as at a government agency database, a change of ownership. For example, the government agency database can hold a chain of title for a real estate property. The tokenization system initiates transmission of amessage to the government agency database for the recordation of the new ownership to add to the chain of title.

[0241] In some cases, the system creates a new token recordation system that replaces and / or augments a centralized database, such as a government agency database. This can be useful if government agency databases are not complete and / or if no database currently exists.

[0242] In some cases, the tokenization system applies a machine learning model to optimize various aspects of the tokenization system. In some cases, the tokenization system applies historical data to the machine learning model, such as historical real estate possession records, historical contracts between the owner and user of the real estate, and / or the like and trains the model to generate an optimal digital token relocation, such as an amount or value of the asset, and / or usage duration of the asset. The tokenization system uses the amount or value of the asset to verify a transaction as being within an acceptable range of the valuation.

[0243] The tokenization system trains a machine learning model using previous real estate contracts associated with different properties. Based on various factors such as property value, location, market conditions, historical trends, and more, the machine learning model is trained to estimate an optimal transaction amount and / or contract duration for a new property that’s being tokenized.

[0244] For example, if the property is similar to previous assets, the machine learning model is trained from historical data to suggest terms based on an assessment of what happened for the previous assets. The machine learning model is trained to determine how quickly tokens were purchased for past property, any trends in token purchases, and so forth, to suggest a contract duration. Similarly, the machine learning model is trained to look at token prices in relation to the property value to suggest an optimal token price.

[0245] In some cases, the machine learning model is trained to perform one or more features of the tokenization system on assets that are non-similar. The machine learning model receives as input various characteristics of multiple properties, generate hidden latent variables across the different properties that factor into valuation, and applies such latent variables to compare properties that are dissimilar.

[0246] The tokenization system applies such models for the benefit of various entities. The tokenization system can help the real estate possessor in determining the parameters for their token offering. The tokenization system can help the tenant in evaluating differenttokenization offers and finding the one that matches their financial capacity and goals. The tokenization system also applies such models to smart contracts to verify transaction by ensuring the values are within the range of values that the machine learning model estimates or outputs.

[0247] In some cases, the tokenization system trains a machine learning model by applying input lease agreements associated with different assets to determine the forecast expected total cost to a tenant for ownership and provide a comparison for different options.

[0248] The tokenization system trains a machine learning model to analyze past contracts and / or current market conditions to forecast a value a tenant could expect to submit over time to gain ownership of a property using the digital tokens. The machine learning model is also trained to compare this total value against other options, such as compared to traditional home buying or other tokenized processes.

[0249] The tokenization system trains a machine learning model to use various inputs such as the property’s token price, the length of the contract, market trends, and other relevant data. The model can process this information to generate a projection of total value to be submitted and uses this forecast to compare different home ownership options. The tokenization system helps tenants make informed decisions about the most cost-effective way to gain homeownership.

[0250] In some cases, the tokenization system trains a machine learning model to review a smart contract and translate the contract into a form aligned with certain tenant specified criteria. Smart contracts include self-executing contracts with the terms of the agreement directly written into code. The smart contracts detail the terms of the tokenization agreement, including token price, number of tokens, contract duration, and / or the like.

[0251] The machine learning model receives the smart contract code as input and translate the code into a format that aligns with the tenant’s specific criteria or requirements, such as reinterpreting the terms into a more readable format, highlighting key terms and conditions, or mapping terms to specific criteria set by the tenant.

[0252] As such, the tokenization system applies such a machine learning model to help tenants to quickly understand the terms of various tokenization contracts, and how they align with their specific needs and goals. It would make the process of comparing and choosing between different tokenization options more accessible and user-friendly.

[0253] In some cases, the tokenization system applies a machine learning model that receives as input previous asset valuations and a new asset valuation report. The machinelearning model is trained to determine a valid value range for the new asset. The tokenization system applies the value range as a parameter for confirming transaction validity by the distributed network of nodes. In some cases, the tokenization system enables the real estate possessor to apply such data to better calibrate asset tokenization parameters. In some cases, the tokenization system enables a potential token owner to apply such data to evaluate different token purchase options.

[0254] The tokenization system executes smart contracts that apply one or more machine learning models as described herein. In some cases, the machine learning models execute smart contracts to enable certain features of the tokenization system, as described herein.

[0255] VIRTUAL REALITY CHANGES ENABLE USAGE RIGHTS TO REAL WORLD ASSETS

[0256] FIG. 7 illustrates virtual reality changes enabling usage rights to real world assets, according to some examples. In some cases, a real world asset 702 is tokenized in the form of a digital token 504 for an asset possessor 104.

[0257] A user 704 desires usage rights, and the tokenization system enables the digital token 504 to be transmitted to the user. The user can then, in virtual reality, augmented reality, or other form of mixed reality, open a virtual door 706. The tokenization system then transmits a signal, such as via a smart contract, to an loT device on the real world asset 702 that unlocks the front door to the home enabling the user to access the home.

[0258] For example, the user can be in a completely virtual space unlocking the virtual door and / or the user can be standing in front of the real world asset and select a virtual option in augmented reality to unlock the door. The tokenization system can then send a signal to the loT device to unlock the door.

[0259] loT devices are deployed on the real world asset. These devices can include sensors, GPS trackers, cameras, or any other relevant technology that can provide real-time data about the asset’s condition, location, and usage.

[0260] loT devices continuously collect and transmit data related to the asset. For instance, in a vehicle, data might include information about the vehicle’s location, mileage, engine status, fuel levels, and maintenance history.

[0261] The data collected by loT devices is transmitted wirelessly to a centralized data platform. This tokenization platform processes and stores the incoming data for analysis.

[0262] Relevant features are extracted from the incoming data. These features could include factors such as distance traveled, engine health, usage patterns, and location history.

[0263] Historical data from loT devices is used to create a labeled dataset. This dataset is divided into training and validation sets for training and evaluating the model. The machine learning model is trained using the labeled dataset. The model learns to identify patterns and relationships between the loT data and the asset’s condition.

[0264] The trained model and / or other process of the tokenization system can predict the future condition of the asset based on incoming loT data. For example, it can predict when maintenance is required for a vehicle or when an asset’s value might be compromised.

[0265] Machine learning models can identify anomalies in the loT data, flagging instances where the asset’s condition deviates significantly from the expected norm. This helps users or possessors take prompt action in case of potential asset deterioration. If the model detects potential issues with the asset’s condition, it can trigger alerts or notifications to both the user and the possessor, ensuring timely action.

[0266] Real-time data from loT devices enables possessors and / or users to assess the risk associated with the asset more accurately. It allows them to make informed decisions based on the asset’s actual condition rather than relying solely on assumptions. Asset tracking via loT devices enhances transparency between user and possessor, as both parties can access real-time information about the asset’s condition.

[0267] In addition to smart contracts verifying validity of a transaction (such as a payment, token transfer, deed recordation or modification, contract creation, and / or the like) using the blockchain network of nodes, the tokenization system can determine a reasonability metric of a transaction. The reasonability determination can include an objective and / or subjective factor.

[0268] The tokenization system applies an objective reasonability transaction factor by causing the nodes of the blockchain to analyze transactional data and / or other data sources. For example, the nodes can analyze the data through a statistical non-deterministic estimation model (such as Monte-Carlo simulation) to determine the expected transaction value. The tokenization system can enable a voting mechanism that can gather the expected results from the different nodes and determine the final value range upon which the transaction can be validated.

[0269] Smart contracts can have machine learning models, such as random forests or neural networks, directly integrated into their code. For example, an asset valuation model may be embedded in a smart contract governing token issuance. When the smart contract isexecuted by nodes on the network, the embedded machine learning model is initiated to run locally to generate an estimate.

[0270] In some cases, the same smart contract can execute simultaneously on multiple distributed nodes. Each node can run the embedded machine learning model during execution. Since many ML models have stochastic (random) components, each node may get a slightly different result.

[0271] In some cases, the tokenization system collects the results from the nodes executing the machine learning model. The tokenization system can combine the results in various ways like taking the median, mean, or determining an expected range. Parallel decentralized execution allows for independent assessment without a central point of failure. Moreover, combining results helps remove biases and outliers. The network consensus mechanism ensures bad actors cannot manipulate outcomes.

[0272] In some cases, the machine learning models can be used to execute smart contracts. For example, a neural network can be trained to mimic a smart contract's logic and execute its functions. The machine learning model takes contract inputs like asset details, user info, transaction data etc. The machine learning model can predict the corresponding contract outputs like token allocation, access rights, payments etc. based on its learned logic.

[0273] Over time, the machine learning model keeps improving its ability to replicate smart contract execution by learning patterns from more training data. This approach takes the existing rigid logic of smart contracts and makes it more flexible and adaptive using machine learning models.

[0274] In some cases, the machine learning model can optimize and improve on the original smart contract logic by detecting inefficiencies. New contract clauses can be added seamlessly by retraining the model without having to rewrite code. The machine learning model-executed contracts can interface with blockchain infrastructure like wallets, oracles, etc. to implement features of the tokenization system such as transferring relocations of tokens.

[0275] Thus, the tokenization system can not only embed machine learning models in smart contracts, but the tokenization system can also wholly execute smart contracts using machine learning models in an adaptive way using their pattern recognition capabilities, making smart contracts more intelligent and customizable.

[0276] For example, a potential tenant is deciding whether to purchase a token based on its expected value in five years. The network of nodes can simulate token behavior based onrelevant parameters to arrive at a set of expected values at the end of five years. The voting mechanism can create a distribution of values upon which the potential tenant or intermediary application can use this estimate to help make their decision on particular transactions.

[0277] In the case of a subjective reasonability transaction factor, each node evaluates data to arrive at a factor upon which each node casts its vote (such as a valid or invalid response). These votes are gathered and a final decision is made, such as a total percentage of votes meeting a certain threshold. Determining accuracy of such subjective decisions can be done by classifying nodes based on their past performance against actual and training decisions and thus weighing each node’s decision to arrive at a final aggregate decision.

[0278] For example, the asset owner upgraded their asset and would like to issue new tokens based on the upgrade. The network of nodes evaluates the evidence or data (e.g. permits, photos, descriptions of the updates) and arrive at individual conclusions based on computational or comparison verification or any combination thereof to arrive at a local decision upon which a voting mechanism would determine the final overall decision.

[0279] The tokenization system enables the decision making process to be performed within the blockchain network. In some cases, the tokenization system enables the ability to further appeal decisions in multiple stages within the blockchain, such as by enabling users to provide more evidence or data.

[0280] In addition, the nodes responsible for making the decisions can be isolated to the nodes owning tokens that were created by the same asset owner, nodes that own any tokens belonging to our system, any nodes on the block chain network, and / or the like.

[0281] In some cases, a machine learning model can examine previous transactions stored on blockchain by the asset owner to determine their credit worthiness. In some cases, the machine learning model uses this to generate transaction information (such as contract terms).

[0282] A machine learning model can perform creditworthiness checking by analyzing various data points related to an individual's financial history, behavior, and other relevant information to determine the likelihood of them repaying a loan or credit.

[0283] Relevant data is collected from various sources, including credit bureaus, financial institutions, and other data providers. This data includes information such as credit scores, credit reports, income, employment history, payment history, outstanding debts, and more.

[0284] The collected data is analyzed to identify relevant features or variables that can help assess an individual's creditworthiness. These features could include credit utilization ratio, number of open credit accounts, length of credit history, and recent credit inquiries.

[0285] Historical data on credit applicants, including how they've used assets in the past, token and payment history, and / or the like, is used to create a labeled dataset. This dataset is divided into a training set and a validation set for training and evaluating the model. The machine learning model is trained on the labeled dataset to learn patterns and relationships between the input features and the qualifications for usage rights. The model's internal parameters are adjusted during training to minimize prediction errors.

[0286] Relevant data about the borrower is collected, including personal information, credit history, income, employment status, loan amount, and purpose. This data forms the basis for assessing the borrower's creditworthiness. Machine learning models require relevant features or variables to make predictions. These features are extracted from the collected data, and they can include credit score, debt-to-income ratio, employment history, loan term, and more.

[0287] Implementing blockchain technology to create self-executing smart contracts can revolutionize the way usage rights of real world assets are handled. The tokenization system applies a machine learning model to determine contractual terms, and these terms are translated into code and stored on the blockchain as a smart contract. The contract includes details such as different types of usage rights, enabling and disabling usage rights, time periods of allowed and disallowed usage, and / or the like.

[0288] The smart contract can be programmed to automatically execute based on predefined conditions. Once the borrower meets the specified criteria (e.g., creditworthiness assessment), the usage rights can be approved without the need for manual intervention.

[0289] All transactions and changes to the smart contract are recorded on the blockchain, providing an immutable and transparent record of the contractual agreement's execution. The smart contract enforces the terms of the contract automatically, ensuring that both parties adhere to the agreed-upon conditions.

[0290] Smart contracts eliminate the need for intermediaries (e.g., lawyers, banks) and reduce administrative overhead, leading to faster and more cost-effective usage right processes. Moreover, smart contracts are tamper-proof and secure. This reduces the risk of fraud and disputes, as all parties have access to the same, unchangeable record.MACHINE ARCHITECTURE

[0291] FIG. 8 is a diagrammatic representation of the machine 800 within which instructions 802 (e.g., software, a program, an application, an applet, an app, or other executable code) for causing the machine 800 to perform any one or more of the methodologies discussed herein may be executed. For example, the instructions 802 may cause the machine 800 to execute any one or more of the methods described herein. The instructions 802 transform the general, non-programmed machine 800 into a particular machine 800 programmed to carry out the described and illustrated functions in the manner described. The machine 800 may operate as a standalone device or may be coupled (e.g., networked) to other machines. In a networked deployment, the machine 800 may operate in the capacity of a server machine or a client machine in a server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine 800 may comprise, but not be limited to, a server computer, a client computer, a personal computer (PC), a tablet computer, a laptop computer, a netbook, a set-top box (STB), a personal digital assistant (PDA), an entertainment media system, a cellular telephone, a smartphone, a mobile device, a wearable device (e.g., a smartwatch), a smart home device (e.g., a smart appliance), other smart devices, a web appliance, a network router, a network switch, a network bridge, or any machine capable of executing the instructions 802, sequentially or otherwise, that specify actions to be taken by the machine 800. Further, while a single machine 800 is illustrated, the term "machine" shall also be taken to include a collection of machines that individually or jointly execute the instructions 802 to perform any one or more of the methodologies discussed herein. The machine 800, for example, may comprise a user system or any one of multiple server devices forming part of the server system. In some examples, the machine 800 may also comprise both client and server systems, with certain operations of a particular method or algorithm being performed on the server-side and with certain operations of the particular method or algorithm being performed on the client-side.

[0292] The machine 800 may include processors 804, memory 806, and input / output (I / O) components 808, which may be configured to communicate with each other via a bus 810. In an example, the processors 804 (e.g., a Central Processing Unit (CPU), a Reduced Instruction Set Computing (RISC) Processor, a Complex Instruction Set Computing (CISC) Processor, a Graphics Processing Unit (GPU), a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Radio-Frequency Integrated Circuit (RFIC), another processor, or any suitable combination thereof) may include, for example, a processor 812 and a processor 814 that execute the instructions 802. The term "processor"is intended to include multi-core processors that may comprise two or more independent processors (sometimes referred to as "cores") that may execute instructions contemporaneously. Although FIG. 8 shows multiple processors 804, the machine 800 may include a single processor with a single-core, a single processor with multiple cores (e.g., a multi-core processor), multiple processors with a single core, multiple processors with multiples cores, or any combination thereof.

[0293] The memory 806 includes a main memory 816, a static memory 818, and a storage unit 820, both accessible to the processors 804 via the bus 810. The main memory 806, the static memory 818, and storage unit 820 store the instructions 802 embodying any one or more of the methodologies or functions described herein. The instructions 802 may also reside, completely or partially, within the main memory 816, within the static memory 818, within machine-readable medium 822 within the storage unit 820, within at least one of the processors 804 (e.g., within the processor's cache memory), or any suitable combination thereof, during execution thereof by the machine 800.

[0294] The I / O components 808 may include a wide variety of components to receive input, provide output, produce output, transmit information, exchange information, capture measurements, and so on. The specific I / O components 808 that are included in a particular machine will depend on the type of machine. For example, portable machines such as mobile phones may include a touch input device or other such input mechanisms, while a headless server machine will likely not include such a touch input device. It will be appreciated that the I / O components 808 may include many other components that are not shown in FIG. 8. In various examples, the I / O components 808 may include user output components 824 and user input components 826. The user output components 824 may include visual components (e.g., a display such as a plasma display panel (PDP), a lightemitting diode (LED) display, a liquid crystal display (LCD), a projector, or a cathode ray tube (CRT)), acoustic components (e.g., speakers), haptic components (e.g., a vibratory motor, resistance mechanisms), other signal generators, and so forth. The user input components 826 may include alphanumeric input components (e.g., a keyboard, a touch screen configured to receive alphanumeric input, video input (e.g., camera), peer-to- peer input (e.g., chatbot), a photo-optical keyboard, or other alphanumeric input components), point-based input components (e.g., a mouse, a touchpad, a trackball, a joystick, a motion sensor, or another pointing instrument), tactile input components (e.g., a physical button, a touch screen that provides location and force of touches or touch gestures,or other tactile input components), audio input components (e.g., a microphone), and the like.

[0295] In further examples, the I / O components 808 may include biometric components 828, motion components 830, environmental components 832, or position components 834, among a wide array of other components. The motion components 830 include acceleration sensor components (e.g., accelerometer), gravitation sensor components, rotation sensor components (e.g., gyroscope).

[0296] The environmental components 832 include, for example, one or more cameras (with still image / photograph and video capabilities), illumination sensor components (e.g., photometer), temperature sensor components (e.g., one or more thermometers that detect ambient temperature), humidity sensor components, pressure sensor components (e.g., barometer), acoustic sensor components (e.g., one or more microphones that detect background noise), proximity sensor components (e.g., infrared sensors that detect nearby objects), gas sensors (e.g., gas detection sensors to detect concentrations of hazardous gasses for safety or to measure pollutants in the atmosphere), or other components that may provide indications, measurements, or signals corresponding to a surrounding physical environment.

[0297] The position components 834 include location sensor components (e.g., a GPS receiver component), altitude sensor components (e.g., altimeters or barometers that detect air pressure from which altitude may be derived), orientation sensor components (e.g., magnetometers), and the like.

[0298] Communication may be implemented using a wide variety of technologies. The I / O components 808 further include communication components 836 operable to couple the machine 800 to a network 838 or devices 840 via respective coupling or connections. For example, the communication components 836 may include a network interface component or another suitable device to interface with the network 838. In further examples, the communication components 836 may include wired communication components, wireless communication components, cellular communication components, Near Field Communication (NFC) components, Bluetooth® components (e.g., Bluetooth® Low Energy), Wi-Fi® components, and other communication components to provide communication via other modalities. The devices 840 may be another machine or any of a wide variety of peripheral devices (e.g., a peripheral device coupled via a USB).

[0299] Moreover, the communication components 836 may detect identifiers or include components operable to detect identifiers. For example, the communication components 836 may include Radio Frequency Identification (RFID) tag reader components, NFC smart tag detection components, optical reader components (e.g., an optical sensor to detect onedimensional bar codes such as Universal Product Code (UPC) bar code, multi-dimensional bar codes such as Quick Response (QR) code, Aztec code, Data Matrix, Dataglyph™, MaxiCode, PDF417, Ultra Code, UCC RSS-2D bar code, and other optical codes), or acoustic detection components (e.g., microphones to identify tagged audio signals). In addition, a variety of information may be derived via the communication components 836, such as location via Internet Protocol (IP) geolocation, location via Wi-Fi® signal triangulation, location via detecting an NFC beacon signal that may indicate a particular location, and so forth.

[0300] The various memories (e.g., main memory 816, static memory 818, and memory of the processors 804) and storage unit 820 may store one or more sets of instructions and data structures (e.g., software) embodying or used by any one or more of the methodologies or functions described herein. These instructions (e.g., the instructions 802), when executed by processors 804, cause various operations to implement the disclosed examples.

[0301] The instructions 802 may be transmitted or received over the network 838, using a transmission medium, via a network interface device (e.g., a network interface component included in the communication components 836) and using any one of several well-known transfer protocols (e.g., hypertext transfer protocol (HTTP)). Similarly, the instructions 802 may be transmitted or received using a transmission medium via a coupling (e.g., a peer-to- peer coupling) to the devices 840.SOFTWARE ARCHITECTURE

[0302] FIG. 9 is a block diagram 900 illustrating a software architecture 902, which can be installed on any one or more of the devices described herein. The software architecture 902 is supported by hardware such as a machine 904 that includes processors 906, memory 908, and I / O components 910. In this example, the software architecture 902 can be conceptualized as a stack of layers, where each layer provides a particular functionality. The software architecture 902 includes layers such as an operating system 912, libraries 914, frameworks 916, and applications 918. Operationally, the applications 918 invoke API calls 920 through the software stack and receive messages 922 in response to the API calls 920.

[0303] The operating system 912 manages hardware resources and provides common services. The operating system 912 includes, for example, a kernel 924, services 926, and drivers 928. The kernel 924 acts as an abstraction layer between the hardware and the other software layers. For example, the kernel 924 provides memory management, processor management (e.g., scheduling), component management, networking, and security settings, among other functionalities. The services 926 can provide other common services for the other software layers. The drivers 928 are responsible for controlling or interfacing with the underlying hardware. For instance, the drivers 928 can include display drivers, camera drivers, BLUETOOTH® or BLUETOOTH® Low Energy drivers, flash memory drivers, serial communication drivers (e.g., USB drivers), WI-FI® drivers, audio drivers, power management drivers, and so forth.

[0304] The libraries 914 provide a common low-level infrastructure used by the applications 918. The libraries 914 can include system libraries 930 (e.g., C standard library) that provide functions such as memory allocation functions, string manipulation functions, mathematic functions, and the like. In addition, the libraries 914 can include API libraries 932 such as media libraries (e.g., libraries to support presentation and manipulation of various media formats such as Moving Picture Experts Group-4 (MPEG4), Advanced Video Coding (H.264 or AVC), Moving Picture Experts Group Layer-3 (MP3), Advanced Audio Coding (AAC), Adaptive Multi-Rate (AMR) audio codec, Joint Photographic Experts Group (JPEG or JPG), or Portable Network Graphics (PNG)), graphics libraries (e.g., an OpenGL framework used to render in two dimensions (2D) and three dimensions (3D) in a graphic content on a display), database libraries (e.g., SQLite to provide various relational database functions), web libraries (e.g., WebKit to provide web browsing functionality), and the like. The libraries 914 can also include a wide variety of other libraries 934 to provide many other APIs to the applications 918.

[0305] The frameworks 916 provide a common high-level infrastructure that is used by the applications 918. For example, the frameworks 916 provide various graphical user interface (GUI) functions, high-level resource management, and high-level location services. The frameworks 916 can provide a broad spectrum of other APIs that can be used by the applications 918, some of which may be specific to a particular operating system or platform.

[0306] In an example, the applications 918 may include a home application 936, a contacts application 938, a browser application 940, a location application 944, a media application 946, a messaging application 948, and a broad assortment of other applications such as athird-party application 952. The applications 918 are programs that execute functions defined in the programs. Various programming languages can be employed to create one or more of the applications 918, structured in a variety of manners, such as object-oriented programming languages (e.g., Objective-C, Java, or C++) or procedural programming languages (e.g., C or assembly language). In a specific example, the third-party application 952 (e.g., an application developed using the ANDROID™ or IOS™ software development kit (SDK) by an entity other than the vendor of the particular platform) may be mobile software running on a mobile operating system such as IOS™, ANDROID™, WINDOWS® Phone, or another mobile operating system. In this example, the third-party application 952 can invoke the API calls 920 provided by the operating system 912 to facilitate functionalities described herein.MACHINE-LEARNING PIPELINE

[0307] FIG. 11 is a flowchart depicting a machine-learning pipeline 1100, according to some examples. The machine-learning pipelines 1100 may be used to generate a trained model, for example the trained machine-learning program 1102 of FIG. 11, described herein to perform operations associated with searches and query responses.Overview

[0308] Broadly, machine learning may involve using computer algorithms to automatically learn patterns and relationships in data, potentially without the need for explicit programming to do so after the algorithm is trained. Examples of machine learning algorithms can be divided into three main categories: supervised learning, unsupervised learning, and reinforcement learning.• Supervised learning involves training a model using labeled data to predict an output for new, unseen inputs. Examples of supervised learning algorithms include linear regression, decision trees, and neural networks.• Unsupervised learning involves training a model on unlabeled data to find hidden patterns and relationships in the data. Examples of unsupervised learning algorithms include clustering, principal component analysis, and generative models like autoencoders.• Reinforcement learning involves training a model to make decisions in a dynamic environment by receiving feedback in the form of rewards or penalties. Examples of reinforcement learning algorithms include Q-learning and policy gradient methods.

[0309] Examples of specific machine learning algorithms that may be deployed, according to some examples, include logistic regression, which is a type of supervised learning algorithm used for binary classification tasks. Logistic regression models the probability of a binary response variable based on one or more predictor variables. Another example type of machine learning algorithm is Naive Bayes, which is another supervised learning algorithm used for classification tasks. Naive Bayes is based on Bayes' theorem and assumes that the predictor variables are independent of each other. Random Forest is another type of supervised learning algorithm used for classification, regression, and other tasks. Random Forest builds a collection of decision trees and combines their outputs to make predictions. Further examples include neural networks which consist of interconnected layers of nodes (or neurons) that process information and make predictions based on the input data. Matrix factorization is another type of machine learning algorithm used for recommender systems and other tasks. Matrix factorization decomposes a matrix into two or more matrices to uncover hidden patterns or relationships in the data. Support Vector Machines (SVM) are a type of supervised learning algorithm used for classification, regression, and other tasks. SVM finds a hyperplane that separates the different classes in the data. Other types of machine learning algorithms include decision trees, k-nearest neighbors, clustering algorithms, and deep learning algorithms such as convolutional neural networks (CNN), recurrent neural networks (RNN), and transformer models. The choice of algorithm depends on the nature of the data, the complexity of the problem, and the performance requirements of the application.

[0310] The performance of machine learning models is typically evaluated on a separate test set of data that was not used during training to ensure that the model can generalize to new, unseen data. Evaluating the model on a separate test set helps to mitigate the risk of overfitting, a common issue in machine learning where a model learns to perform exceptionally well on the training data but fails to maintain that performance on data it hasn't encountered before. By using a test set, the system obtains a more reliable estimate of the model's real-world performance and its potential effectiveness when deployed in practical applications.

[0311] Although several specific examples of machine learning algorithms are discussed herein, the principles discussed herein can be applied to other machine learning algorithms as well. Deep learning algorithms such as convolutional neural networks, recurrent neural networks, and transformers, as well as more traditional machine learning algorithms likedecision trees, random forests, and gradient boosting may be used in various machine learning applications.

[0312] Two example types of problems in machine learning are classification problems and regression problems. Classification problems, also referred to as categorization problems, aim at classifying items into one of several category values (for example, is this object an apple or an orange?). Regression algorithms aim at quantifying some items (for example, by providing a value that is a real number).PhasesGenerating a trained machine-learning program 1102 may include multiple types of phases that form part of the machine-learning pipeline 1100, including for example the following phases 1000 illustrated in FIG. 10:• Data collection and preprocessing 1002: This may include acquiring and cleaning data to ensure that it is suitable for use in the machine learning model. Data can be gathered from user content creation and labeled using a machine learning algorithm trained to label data. Data can be generated by applying a machine learning algorithm to identify or generate similar data. This may also include removing duplicates, handling missing values, and converting data into a suitable format.• Feature engineering 1004: This may include selecting and transforming the training data 1104 to create features that are useful for predicting the target variable. Feature engineering may include (1) receiving features 1106 (e.g., as structured or labeled data in supervised learning) and / or (2) identifying features 1106 (e.g., unstructured or unlabeled data for unsupervised learning) in training data 1104.• Model selection and training 1006: This may include specifying a particular problem or desired response from input data, selecting an appropriate machine learning algorithm, and training it on the preprocessed data. This may further involve splitting the data into training and testing sets, using cross-validation to evaluate the model, and tuning hyperparameters to improve performance. Model selection can be based on factors such as the type of data, problem complexity, computational resources, or desired performance.• Model evaluation 1008: This may include evaluating the performance of a trained model (e.g., the trained machine-learning program 1102) on a separate testing dataset.This can help determine if the model is overfitting or underfitting and if it is suitable for deployment.• Prediction 1010: This involves using a trained model (e.g., trained machine-learning program 1102) to generate predictions on new, unseen data.• Validation, refinement or retraining 1012: This may include updating a model based on feedback generated from the prediction phase, such as new data or user feedback.• Deployment 1014: This may include integrating the trained model (e.g., the trained machine-learning program 1102) into a larger system or application, such as a web service, mobile app, or loT device. This can involve setting up APIs, building a user interface, and ensuring that the model is scalable and can handle large volumes of data.

[0313] FIG. 11 illustrates two example phases, namely a training phase 1108 (part of the model selection and trainings 1006) and a prediction phase 1110 (part of prediction 1010). Prior to the training phase 1108, feature engineering 1004 is used to identify features 1106. This may include identifying informative, discriminating, and independent features for the effective operation of the trained machine-learning program 1102 in pattern recognition, classification, and regression. In some examples, the training data 1104 includes labeled data, which is known data for pre-identified features 1106 and one or more outcomes.

[0314] Each of the features 1106 may be a variable or attribute, such as individual measurable property of a process, article, system, or phenomenon represented by a data set (e.g., the training data 1104). Features 1106 may also be of different types, such as numeric features, strings, vectors, matrices, encodings, and graphs, and may include one or more of content 1112, concepts 1114, attributes 1116, historical data 1118 and / or user data 1120, merely for example. Concept features can include abstract relationships or patterns in data, such as determining a topic of a document or discussion in a chat window between users. Content features include determining a context based on input information, such as determining a context of a user based on user interactions or surrounding environmental factors. Context features can include text features, such as frequency or preference of words or phrases, image features, such as pixels, textures, or pattern recognition, audio classification, such as spectrograms, and / or the like. Attribute features include intrinsic attributes (directly observable) or extrinsic features (derived), such as identifying square footage, location, or age of a real estate property identified in a camera feed. User data features include data pertaining to a particular individual or to a group of individuals, such as in a geographical location or that share demographic characteristics. User data can include demographic data (such as age, gender, location, or occupation), user behavior (suchas browsing history, purchase history, conversion rates, click-through rates, or engagement metrics), or user preferences (such as preferences to certain video, text, or digital content items). Historical data includes past events or trends that can help identify patterns or relationships over time.

[0315] In training phases 1108, the machine-learning pipeline 1100 uses the training data 1104 to find correlations among the features 1106 that affect a predicted outcome or prediction / inference data 1122.

[0316] With the training data 1104 and the identified features 1106, the trained machinelearning program 1102 is trained during the training phase 1108 during machine-learning program training 1124. The machine-learning program training 1124 appraises values of the features 1106 as they correlate to the training data 1104. The result of the training is the trained machine-learning program 1102 (e.g., a trained or learned model).

[0317] Further, the training phase 1108 may involve machine learning, in which the training data 1104 is structured (e.g., labeled during preprocessing operations), and the trained machine-learning program 1102 implements a relatively simple neural network 1126 capable of performing, for example, classification and clustering operations. In other examples, the training phase 1108 may involve deep learning, in which the training data 1104 is unstructured, and the trained machine-learning program 1102 implements a deep neural network 1126 that is able to perform both feature extraction and classification / clustering operations.

[0318] A neural network 1126 may, in some examples, be generated during the training phase 1108, and implemented within the trained machine-learning program 1102. The neural network 1126 includes a hierarchical (e.g., layered) organization of neurons, with each layer including multiple neurons or nodes. Neurons in the input layer receive the input data, while neurons in the output layer produce the final output of the network. Between the input and output layers, there may be one or more hidden layers, each including multiple neurons.

[0319] Each neuron in the neural network 1126 operationally computes a small function, such as an activation function that takes as input the weighted sum of the outputs of the neurons in the previous layer, as well as a bias term. The output of this function is then passed as input to the neurons in the next layer. If the output of the activation function exceeds a certain threshold, an output is communicated from that neuron (e.g., transmitting neuron) to a connected neuron (e.g., receiving neuron) in successive layers. The connectionsbetween neurons have associated weights, which define the influence of the input from a transmitting neuron to a receiving neuron. During the training phase, these weights are adjusted by the learning algorithm to optimize the performance of the network. Different types of neural networks may use different activation functions and learning algorithms, which can affect their performance on different tasks. Overall, the layered organization of neurons and the use of activation functions and weights enable neural networks to model complex relationships between inputs and outputs, and to generalize to new inputs that were not seen during training.

[0320] In some examples, the neural network 1126 may also be one of a number of different types of neural networks or a combination thereof, such as a single-layer feedforward network, a Multilayer Perceptron (MLP), an Artificial Neural Network (ANN), a Recurrent Neural Network (RNN), a Long Short-Term Memory Network (LSTM), a Bidirectional Neural Network, a symmetrically connected neural network, a Deep Belief Network (DBN), a Convolutional Neural Network (CNN), a Generative Adversarial Network (GAN), an Autoencoder Neural Network (AE), a Restricted Boltzmann Machine (RBM), a Hopfield Network, a Self-Organizing Map (SOM), a Radial Basis Function Network (RBFN), a Spiking Neural Network (SNN), a Liquid State Machine (LSM), an Echo State Network (ESN), a Neural Turing Machine (NTM), or a Transformer Network, merely for example.

[0321] In addition to the training phase 1108, a validation phase may be performed evaluated on a separate dataset known as the validation dataset. The validation dataset is used to tune the hyperparameters of a model, such as the learning rate and the regularization parameter. The hyperparameters are adjusted to improve the performance of the model on the validation dataset.

[0322] The neural network 1126 is iteratively trained by adjusting model parameters to minimize a specific loss function or maximize a certain objective. The system can continue to train the neural network 1126 by adjusting parameters based on the output of the validation, refinement, or retraining block 1012, and rerun the prediction 1010 on new or already run training data. The system can employ optimization techniques for these adjustments such as gradient descent algorithms, momentum algorithms, Nesterov Accelerated Gradient (NAG) algorithm, and / or the like. The system can continue to iteratively train the neural network 1126 even after deployment 1014 of the neural network 1126. The neural network 1126 can be continuously trained as new data emerges, such as based on user creation or system-generated training data.

[0323] Once a model is fully trained and validated, in a testing phase, the model may be tested on a new dataset that the model has not seen before. The testing dataset is used to evaluate the performance of the model and to ensure that the model has not overfit the training data.

[0324] In prediction phase 1110, the trained machine-learning program 1102 uses the features 1106 for analyzing query data 1128 to generate inferences, outcomes, or predictions, as examples of a prediction / inference data 1122. For example, during prediction phase 1110, the trained machine-learning program 1102 is used to generate an output. Query data 1128 is provided as an input to the trained machine-learning program 1102, and the trained machine-learning program 1102 generates the prediction / inference data 1122 as output, responsive to receipt of the query data 1128. Query data can include a prompt, such as a user entering a textual question or speaking a question audibly. In some cases, the system generates the query based on an interaction function occurring in the system, such as a user interacting with a virtual object, a user sending another user a question in a chat window, or an object detected in a camera feed.

[0325] In some examples the trained machine-learning program 1102 may be a generative Al model. Generative Al is a term that may refer to any type of artificial intelligence that can create new content from training data 1104. For example, generative Al can produce text, images, video, audio, code or synthetic data that are similar to the original data but not identical.

[0326] Some of the techniques that may be used in generative Al are:• Convolutional Neural Networks (CNNs): CNNs are commonly used for image recognition and computer vision tasks. They are designed to extract features from images by using filters or kernels that scan the input image and highlight important patterns. CNNs may be used in applications such as object detection, facial recognition, and autonomous driving.• Recurrent Neural Networks (RNNs): RNNs are designed for processing sequential data, such as speech, text, and time series data. They have feedback loops that allow them to capture temporal dependencies and remember past inputs. RNNs may be used in applications such as speech recognition, machine translation, and sentiment analysis• Generative adversarial networks (GANs): These are models that consist of two neural networks: a generator and a discriminator. The generator tries to create realistic content that can fool the discriminator, while the discriminator tries to distinguishbetween real and fake content. The two networks compete with each other and improve over time. GANs may be used in applications such as image synthesis, video prediction, and style transfer.• Variational autoencoders (VAEs): These are models that encode input data into a latent space (a compressed representation) and then decode it back into output data. The latent space can be manipulated to generate new variations of the output data. They may use self- attend on mechanisms to process input data, allowing them to handle long sequences of text and capture complex dependencies.• Transformer models: These are models that use attention mechanisms to learn the relationships between different parts of input data (such as words or pixels) and generate output data based on these relationships. Transformer models can handle sequential data such as text or speech as well as non-sequential data such as images or code.

[0327] In generative Al examples, the prediction / inference data 1122 that is output include trend assessment and predictions, translations, summaries, image or video recognition and categorization, natural language processing, face recognition, user sentiment assessments, advertisement targeting and optimization, voice recognition, or media content generation, recommendation, and personalization.

[0328] USAGE CASES FOR TOKENIZATION, OWNERSHIP, USE, AND / OR THE LIKE

[0329] FIG. 12 illustrates tokenization of an asset as a whole, according to some examples. In some cases, a real world asset is not divided into parts by the tokenization system. For example, a home 102 is tokenized as a whole and not divided into different rooms. In some cases, the tokenization system tokenizes assets that are not easily dividable without changing the asset itself, such as a sculpture 1204, a painting 1202, a stamp in a stamp collection 1208, a coin in a rare coin collection 1206, and / or the like.

[0330] In some cases, the tokenization system generates and / or mints a single token 1210 corresponding to ownership or usage rights for the individual asset. In other cases, the tokenization system generates and / or mints multiple tokens 106a, 106b, 106c representing fractional ownership and / or different usage rights for the individual asset.

[0331] As users gain ownership and / or usage rights for the individual asset, users can start utilizing the asset as per the agreement and / or contractual terms (as further described herein).

[0332] In some cases, asset owners can jointly tokenize assets. For example, the artist and sculptor can jointly tokenize their paintings and sculptures. The joint assets can be tokenized either into a single token or multiple tokens. In some cases, the individual assets are valuated and tokens are assigned respectively. In other cases, a single token provides ownership and / or usage of the joint assets.

[0333] FIG. 13 illustrates tokenization of an asset that is divisible into parts, according to some examples. The tokenization system can divide an asset, such as a building 1310, a mall 1312, a park 1314, farm land 1316, into parts and tokenize individual parts for use and / or ownership. For example, a building can be divided into apartments, a mall can be divided into sections for stores, farm land divided into parcels, and / or the like.

[0334] In some cases, the tokenization system generates an individual usage and / or ownership token for each divisible part, such as token 106a for a first apartment unit, token 106b for a second apartment unit, and 106c for a third apartment unit. In other cases, the tokenization system generates multiple tokens for each part, such as tokens 1302 for a first store front, tokens 1304 for a second store front, tokens 1306 for a third store front, tokens 1308 for a fourth store front and / or the like. In some cases, the amount of tokens for each part is determined using the valuation methods and processes as further described herein.

[0335] In some cases, the tokenization system identifies divisible parts based on third party data, such as data on the number of units in an apartment building retrieved from a real estate or government website. In some cases, the tokenization system applies a machine learning model that is trained to automatically determine divisible portions of a particular asset. For example, the machine learning model can receive as input an address of an asset, a type of asset (such as if the asset type indicates a divisible number of parts such as a duplex), input from the asset owner of characteristics of the asset, and / or the like (other inputs to the machine learning model further described herein).

[0336] FIG. 14 illustrates tokenizing ownership and / or usage across time, according to some examples. In some cases, the tokenization system can tokenize an asset, such as a boat 1402, equipment 508, car 1404, public transportation 1406, and / or the like over time. The tokenization system applies ownership and / or usage rights over time. For example, a boat 1402 can be rented throughout the year for the use in boat tours.

[0337] The tokenization system can generate tokens according to the time and / or time frame desired for ownership and / or usage. For example, the tokenization system determines that boat tours are in demand in certain parts of the year but not in others. The tokenizationsystem can apply the valuation methods and processes as further described herein to value the ownership and / or usage for particular time periods. For example, the time frame for tokens 1410 are in high demand, and thus more tokens are required for ownership and / or usage for these time slots, whereas the time periods for tokens 1408, 1412, and 1414 are in less demand and thus less tokens are minted for these time periods.

[0338] FIG. 15 illustrate tokenizing ownership and / or usage across time and parts, according to some examples. In some cases, the tokenization system tokenizes an asset across time and space. For example, the tokenization system divides an airplane 1528 into multiple seats or a multiple factory production lines in a factory 1526.

[0339] An airplane can have tens or hundreds of seats, each of which could be tokenized. In some cases, a group of seats can be tokenized, such as 4 seats for a family of 4. Such tokens can be tied to a particular airplane or to an airline with a fleet of airplanes.

[0340] In some cases, the tokenization system tokenizes ownership and / or usage across multiple factors, such as time and parts. It is appreciated that the tokenization system can tokenize an asset across one or more other factors, such as time and location, time parts and location, and / or the like.

[0341] As noted herein, the tokenization system can determine a valuation for the token based on these factors. In FIG. 15, the tokenization system determines that the certain seats at a particular time frame corresponding to tokens 1504 and 1520 are the highest in demand or highest in cost. As such, the more tokens are minted for the seats and time corresponding to tokens 1504 and 1520 than that for tokens 1502, 1510, 1518, 1512, 1506, 1514, 1522, 1508, 1516, and 1524.

[0342] In some cases, a factory with multiple production lines can tokenize each production line over different periods of time. Ownership of Tokens allow for the usage of associated production lines and collection of proceeds from the product line output. In some cases, a livestock production facility can tokenize each production line and across multiple cycles within a calendar year and offer those tokens to individual operators. These operators can make use of the facility for their own production and / or further offer the production line to other operators who could make use of the facility.

[0343] FIG. 16 illustrates tokenization for use allocations, according to some examples. The tokenization system can tokenize use allocations for assets. The tokenization system can tokenize cellphone towers 1602 such as based on data bandwidth usage. The tokenization system can tokenize amount of electricity generation by a wind turbine 1608 orsolar farms 1606. The tokenization system can tokenize automobiles 1604 based on mileage. The tokenization system can tokenize use of roads 1610, such as an amount of traffic or length of travel.

[0344] Use allocations can be uniform across use, such as allocating the same amount of tokens for use of an automobile from 0-10 miles, 10-20 miles, 20-30 miles, etc. As shown in FIG. 16, use allocations can be different across use allocations. The automobile can be equivalent to a total token group 1612. However the use of the automobile may be of a higher value when the automobile is new. As such, the first group of miles for the automobile may be worth more tokens, such as tokens 1614, than when the automobile is at the middle of its lifespan, such as tokens 1616, or the end of its lifespan, such as token 1618.

[0345] In some cases, the tokenization system tokenizes cellphone towers (e.g., data use), automobile or farm equipment (e.g., mileage), oil wells, solar farms, wind turbines, other energy sources, mining rights, water rights, fishing quotas, bridges, toll roads, public transport, locations with services (e.g., fitness center, copy center, restaurant), and / or the like.

[0346] The tokenization system can tokenize an internet provider based on a provided bandwidth. The tokenization system can generate tokens representative of portions of bandwidth usage and provide such tokens to a large user base or virtual providers. The owners of these tokens can then use the associated bandwidth or sell the bandwidth to other users. A geographically diverse shared electricity grid can also tokenize its production of electricity and offer tokens to individual electricity producers that best meet the demands of their customers. Both these examples demonstrate the ability of the tokenization system to improve utilization of temporal assets that would be lost if not used immediately.

[0347] FIG. 17 illustrates token generation based on location, according to some examples. In some cases, the tokenization system tokenizes homes 1702, 1706, 1710, and 1714. Depending on the location of the home, the home may be valued differently. The tokenization system takes into consideration location, and / or other characteristics as described further herein in the valuation model, to determine a number of tokens to generate for the home. For example, home 1702 is provided with 3 tokens 1704, home 1706 is provided with 1 token 1708, home 1710 is provided with 2 tokens 1712, and home 1714 is provided with 4 tokens 1716.

[0348] In some cases, the tokenization system tokenizes medical facilities, clinics, wellness centers, companies (legal practices, accounting firms, consulting firms, research centers, etc.), and / or the like based on at least location.

[0349] FIG. 18 illustrates token generation for copies of goods, according to some examples. In some cases, the tokenization system tokenizes copies of goods, such as artwork, creative works, books, movies, designs, architectural plans, educational content, music, software, formulas, recipes, advertisements, intellectual property, machine learning models, virtual objects (objects in virtual reality, augmented reality, mixed reality, etc), ingame items, in-application items, pharmaceuticals, and / or the like.

[0350] In some cases, as more copies are made, the more tokens are generated and / or the reduction of value for each token. For example, a book 1802 without any copies can be equivalent to 8 tokens 1812. The tokenization system can generate a first copy 1804 of the book and with the generated first copy, divide the number of tokens equally (e.g., 1812, 1814) between the original book 1802 and the first copy 1804. As such, the owner can decide how many copies to generate and how granular the owner desires the tokens and asset to be sold. In the next step, second copy 1806 and third copy 1808 are generated, and the tokenization system generates tokens 1818 and 1816 respectively. As shown in FIG. 18, after there are 4 copies in existence, the value for each book is reduced from 8 tokens down to 2 tokens each.

[0351] Although particular examples are described herein, such as a home being non- divisible and tokenized, it is appreciated that the example assets described herein can be applied to other types of tokenization. For example, the home 102 can be tokenized for usage across time, such as a short term rental.

[0352] The previous use cases can be combined whereby different features can be obtained from each case. This allows for a large amount of flexibility according to the underlying assets and intended usage. For example, the tokenization system can use the same tokens for different assets enabling flexibility in exchange of assets. For example, the tokenization system can apply tokens from a token owner issued by the same asset owner for use of different assets even if the assets are different or have different usage models.

[0353] Assets can be owned by a single or multiple asset owners. An asset owner can have a single or multiple assets. Assets can be used by a single or multiple tenants simultaneously. Asset usage can span a single or multiple time periods. A tenant may be allowed to utilize the asset for themselves only or offer it to be utilized by others.Intermediaries can borrow Tokens and acquire some asset ownership rights to offer the assets to other tenants. Asset utilization returns are shared with all token owners and potentially a portion of tenant returns. The asset itself can be made eligible for ownership if enough tokens are owned by a tenant.

[0354] As further described herein, the ownership and / or usage can be on a first come first serve basis, the tokenization system can implement a bidding auction whereby users can bid tokens and / or payments for a certain ownership or usage of an asset, and / or the like.EXAMPLES

[0355] In view of the above-described implementations of subject matter this application discloses the following list of examples, wherein one feature of an example in isolation or more than one feature of an example, taken in combination and, optionally, in combination with one or more features of one or more further examples are further examples also falling within the disclosure of this application.

[0356] Example l is a system comprising: at least one processor; and at least one memory component storing instructions that, when executed by the at least one processor, cause the at least one processor to perform operations comprising: receiving a first real estate possession record for a first real estate from a first real estate possessor; identifying a value of the first real estate; generating a first digital token by a group of nodes of a blockchain network, the first digital token is generated based on the value of the first real estate, the first digital token representing ownership of the first real estate; transmitting the first digital token to a digital token storage associated with the first real estate possessor; recording a lien on the first real estate possession record and a record of the first digital token transmitted to the digital token storage associated with the first real estate possessor onto a distributed ledger of the blockchain network; causing execution of a first smart contract by broadcasting one or more functions to the blockchain network and receiving validation from the nodes of the blockchain network, the execution of the first smart contract providing a real estate user with access to the first real estate.

[0357] In Example 2, the subject matter of Example 1 includes, wherein providing the real estate user with access to the first real estate includes generating a unique access code for a digital lock or security system of the first real estate.

[0358] In Example 3, the subject matter of Examples 1-2 includes, wherein providing the real estate user with access to the first real estate includes transmitting a signal to one ormore Internet of Things (loT) devices associated with the first real estate such that the one or more loT devices grants access to the real estate user.

[0359] In Example 4, the subject matter of Examples 1-3 includes, wherein providing the real estate user with access to the first real estate includes automatically booking the first real estate for the real estate user for a first real estate utilization period.

[0360] In Example 5, the subject matter of Examples 1-4 includes, wherein the operations further comprise displaying a mixed reality user interface to the real estate user and receiving a selection to enable access to the first real estate, wherein causing execution of the first smart contract is in response to receiving the selection to enable access by the real estate user via the mixed reality user interface.

[0361] In Example 6, the subject matter of Examples 1-5 includes, wherein the operations further comprise enabling the real estate user to enable use of the real estate to one or more other users by enabling the real estate user to cause execution of a second smart contract that provides the one or more other users with the access to the first real estate.

[0362] In Example 7, the subject matter of Examples 1-6 includes, wherein the operations further comprise transmitting the first digital token to digital token storage associated with the real estate user for a period of time, and in response to a lapse of the period of time, transmitting the first digital token to the digital token storage of the first real estate possessor and disabling access to the first real estate via execution of a second smart contract.

[0363] In Example 8, the subject matter of Examples 1-7 includes, wherein the operations further comprise generating a second digital token by the group of nodes of the blockchain network, the second digital token one or more usage rights of the first real estate; transmitting the second digital token to digital token storage associated with the real estate user for a period of time, and in response to a lapse of the period of time, transmitting the second digital token to the digital token storage of the first real estate possessor and disabling access to the first real estate via execution of a second smart contract.

[0364] In Example 9, the subject matter of Examples 1-8 includes, wherein the operations further comprise: receiving a second real estate possession record for a second real estate from the first real estate possessor; generating a second digital token by the group of nodes of the blockchain network, the second digital token representing ownership of the second real estate; transmitting the second digital token to a digital token storage associated with the first real estate possessor; recording a lien on the second real estate possession recordand a record of the second digital token transmitted to the digital token storage associated with the first real estate possessor onto a distributed ledger of the blockchain network.

[0365] In Example 10, the subject matter of Examples 1-9 includes, wherein the first real estate includes a real estate property, the first real estate possession record including a digitized deed, and the first real estate possessor including a real estate property owner.

[0366] In Example 11, the subject matter of Examples 1-10 includes, wherein the operations further comprise: performing optical character recognition (OCR) on the first real estate possession record, and converting data identified from performing the OCR into a standardized format, identifying the value of the first real estate being based on the converted data.

[0367] In Example 12, the subject matter of Examples 1-11 includes, wherein the first real estate includes a collection of physical properties, wherein the real estate user is able to use one of the physical properties, wherein the tokens represent fractional ownership for the collection of the physical properties, wherein the value of the tokens required for the transfer of ownership is the value of the collection of the physical properties.

[0368] In Example 13, the subject matter of Examples 1-12 includes, wherein the at least one processor is configured to apply the first real estate possession record to a machine learning model, wherein the machine learning model performs the operations of identifying the value of the first real estate, generating a first digital token by the group of nodes of the blockchain network, and transmitting the first digital token to the digital token storage associated with the first real estate possessor.

[0369] In Example 14, the subject matter of Example 13 includes, wherein the machine learning model further performs the operations of recording the lien on the first real estate possession record and the record of the first digital token transmitted to the digital token storage associated with the first real estate possessor onto the distributed ledger of the blockchain network.

[0370] In Example 15, the subject matter of Example 14 includes, wherein the machine learning model further performs the operations of causing execution of the first smart contract by broadcasting one or more functions to the blockchain network, and receiving validation from the nodes of the blockchain network.

[0371] In Example 16, the subject matter of Examples 14-15 includes, wherein execution of the first smart contract causes the recordation of the lien on the first real estate possession record and the record of the first digital token transmitted to the digital token storageassociated with the first real estate possessor onto the distributed ledger of the blockchain network.

[0372] In Example 17, the subject matter of Examples 1-16 includes, wherein the operations further comprise determining a reasonability factor for the recorded lien on the first real estate possession record.

[0373] In Example 18, the subject matter of Examples 1-17 includes, wherein the distributed ledger includes an association between the digital token and the first real estate, and a history of transfers for the digital token 18 is missing parent: 19. A method comprising: receiving a first real estate possession record for a first real estate from a first real estate possessor; identifying a value of the first real estate; generating a first digital token by a group of nodes of a blockchain network, the digital token is generated based on the value of the first real estate, the digital token representing ownership of the first real estate; transmitting the generated digital token to a digital token storage associated with the first real estate possessor; recording a lien on the first real estate possession record and a record of the digital token transmitted to the digital token storage associated with the first real estate possessor onto a distributed ledger of the blockchain network; causing execution of a smart contract by broadcasting one or more functions to the blockchain network and receiving validation from the nodes of the blockchain network, the execution of the smart contract providing a real estate user with access to the first real estate.

[0374] Example 19 is a non-transitory computer-readable storage medium storing instructions that, when executed by at least one processor, cause the at least one processor to perform operations comprising: receiving a first real estate possession record for a first real estate from a first real estate possessor; identifying a value of the first real estate; generating a first digital token by a group of nodes of a blockchain network, the digital token is generated based on the value of the first real estate, the digital token representing ownership of the first real estate; transmitting the generated digital token to a digital token storage associated with the first real estate possessor; recording a lien on the first real estate possession record and a record of the digital token transmitted to the digital token storage associated with the first real estate possessor onto a distributed ledger of the blockchain network; causing execution of a smart contract by broadcasting one or more functions to the blockchain network and receiving validation from the nodes of the blockchain network, the execution of the smart contract providing a real estate user with access to the first real estate.

[0375] Example 20 is at least one machine-readable medium including instructions that, when executed by processing circuitry, cause the processing circuitry to perform operations to implement any of Examples 1-19.

[0376] Example 21 is an apparatus comprising means to implement any of Examples 1-19.

[0377] Example 22 is a system to implement any of Examples 1-19.

[0378] Example 23 is a method to implement any of Examples 1-19.CONCLUSION

[0379] As used in this disclosure, phrases of the form “at least one of an A, a B, or a C,” “at least one of A, B, or C,” “at least one of A, B, and C,” and the like, should be interpreted to select at least one from the group that comprises “A, B, and C.” Unless explicitly stated otherwise in connection with a particular instance in this disclosure, this manner of phrasing does not mean “at least one of A, at least one of B, and at least one of C.” As used in this disclosure, the example “at least one of an A, a B, or a C,” would cover any of the following selections: {A}, {B}, {C}, {A, B}, {A, C}, {B, C}, and {A, B, C}.

[0380] Unless the context clearly requires otherwise, throughout the description and the claims, the words “comprise,” “comprising,” and the like are to be construed in an inclusive sense, as opposed to an exclusive or exhaustive sense, i.e., in the sense of “including, but not limited to.” As used herein, the terms “connected,” “coupled,” or any variant thereof means any connection or coupling, either direct or indirect, between two or more elements; the coupling or connection between the elements can be physical, logical, or a combination thereof. Additionally, the words “herein,” “above,” “below,” and words of similar import, when used in this application, refer to this application as a whole and not to any particular portions of this application. Where the context permits, words using the singular or plural number may also include the plural or singular number respectively. The word “or” in reference to a list of two or more items, covers all of the following interpretations of the word: any one of the items in the list, all of the items in the list, and any combination of the items in the list. Likewise, the term “and / or” in reference to a list of two or more items, covers all of the following interpretations of the word: any one of the items in the list, all of the items in the list, and any combination of the items in the list.

[0381] Although some examples, e.g., those depicted in the drawings, include a particular sequence of operations, the sequence may be altered without departing from the scope of the present disclosure. For example, some of the operations depicted may be performed in parallel or in a different sequence that does not materially affect the functions as describedin the examples. In other examples, different components of an example device or system that implements an example method may perform functions at substantially the same time or in a specific sequence.

[0382] The various features, steps, and processes described herein may be used independently of one another, or may be combined in various ways. All possible combinations and subcombinations are intended to fall within the scope of this disclosure. In addition, certain method or process blocks may be omitted in some implementations.

Claims

What is claimed is:

1. A system comprising: at least one processor; and at least one memory component storing instructions that, when executed by the at least one processor, cause the at least one processor to perform operations comprising: receiving a first real estate possession record for a first real estate from a first real estate possessor; identifying a value of the first real estate; generating a first digital token by a group of nodes of a blockchain network, the first digital token is generated based on the value of the first real estate, the first digital token representing ownership of the first real estate; transmitting the first digital token to a digital token storage associated with the first real estate possessor; recording a lien on the first real estate possession record and a record of the first digital token transmitted to the digital token storage associated with the first real estate possessor onto a distributed ledger of the blockchain network; and causing execution of a first smart contract by broadcasting one or more functions to the blockchain network and receiving validation from the nodes of the blockchain network, the execution of the first smart contract providing a real estate user with access to the first real estate.

2. The system of claim 1, wherein providing the real estate user with access to the first real estate includes generating a unique access code for a digital lock or security system of the first real estate.

3. The system of claim 1, wherein providing the real estate user with access to the first real estate includes transmitting a signal to one or more Internet of Things (loT) devices associated with the first real estate such that the one or more loT devices grants access to the real estate user.

4. The system of claim 1, wherein providing the real estate user with access to the first real estate includes automatically booking the first real estate for the real estate user for a first real estate utilization period.

5. The system of claim 1, wherein the operations further comprise displaying a mixed reality user interface to the real estate user and receiving a selection to enable access to the first real estate, wherein causing execution of the first smart contract is in response to receiving the selection to enable access by the real estate user via the mixed reality user interface.

6. The system of claim 1, wherein the operations further comprise enabling the real estate user to enable use of the real estate to one or more other users by enabling the real estate user to cause execution of a second smart contract that provides the one or more other users with the access to the first real estate.

7. The system of claim 1, wherein the operations further comprise transmitting the first digital token to digital token storage associated with the real estate user for a period of time, and in response to a lapse of the period of time, transmitting the first digital token to the digital token storage of the first real estate possessor and disabling access to the first real estate via execution of a second smart contract.

8. The system of claim 1, wherein the operations further comprise generating a second digital token by the group of nodes of the blockchain network, the second digital token one or more usage rights of the first real estate; transmitting the second digital token to digital token storage associated with the real estate user for a period of time, and in response to a lapse of the period of time, transmitting the second digital token to the digital token storage of the first real estate possessor and disabling access to the first real estate via execution of a second smart contract.

9. The system of claim 1, wherein the operations further comprise: receiving a second real estate possession record for a second real estate from the first real estate possessor; generating a second digital token by the group of nodes of the blockchain network, the second digital token representing ownership of the second real estate; transmitting the second digital token to a digital token storage associated with the first real estate possessor; and recording a lien on the second real estate possession record and a record of the second digital token transmitted to the digital token storage associated with the first real estate possessor onto a distributed ledger of the blockchain network.

10. The system of claim 1, wherein the first real estate includes a real estate property, the first real estate possession record including a digitized deed, and the first real estate possessor including a real estate property owner.

11. The system of claim 1, wherein the operations further comprise: performing optical character recognition (OCR) on the first real estate possession record, and converting data identified from performing the OCR into a standardized format, identifying the value of the first real estate being based on the converted data.

12. The system of claim 1, wherein the first real estate includes a collection of physical properties, wherein the real estate user is able to use one of the physical properties, wherein the tokens represent fractional ownership for the collection of the physical properties, wherein the value of the tokens required for the transfer of ownership is the value of the collection of the physical properties.

13. The system of claim 1, wherein the at least one processor is configured to apply the first real estate possession record to a machine learning model, wherein the machine learning model performs the operations of identifying the value of the first real estate, generating a first digital token by the group of nodes of the blockchain network, and transmitting the first digital token to the digital token storage associated with the first real estate possessor.

14. The system of claim 13, wherein the machine learning model further performs the operations of recording the lien on the first real estate possession record and the record of the first digital token transmitted to the digital token storage associated with the first real estate possessor onto the distributed ledger of the blockchain network.

15. The system of claim 14, wherein the machine learning model further performs the operations of causing execution of the first smart contract by broadcasting one or more functions to the blockchain network and receiving validation from the nodes of the blockchain network.

16. The system of claim 14, wherein execution of the first smart contract causes the recordation of the lien on the first real estate possession record and the record of the first digital token transmitted to the digital token storage associated with the first real estate possessor onto the distributed ledger of the blockchain network.

17. The system of claim 1, wherein the operations further comprise determining a reasonability factor for the recorded lien on the first real estate possession record.

18. The system of claim 1, wherein the distributed ledger includes an association between the digital token and the first real estate, and a history of transfers for the digital token.

19. A method comprising: receiving a first real estate possession record for a first real estate from a first real estate possessor; identifying a value of the first real estate; generating a first digital token by a group of nodes of a blockchain network, the digital token is generated based on the value of the first real estate, the digital token representing ownership of the first real estate; transmitting the generated digital token to a digital token storage associated with the first real estate possessor; recording a lien on the first real estate possession record and a record of the digital token transmitted to the digital token storage associated with the first real estate possessor onto a distributed ledger of the blockchain network; and causing execution of a smart contract by broadcasting one or more functions to the blockchain network and receiving validation from the nodes of the blockchain network, the execution of the smart contract providing a real estate user with access to the first real estate.

20. A non-transitory computer-readable storage medium storing instructions that, when executed by at least one processor, cause the at least one processor to perform operations comprising: receiving a first real estate possession record for a first real estate from a first real estate possessor; identifying a value of the first real estate; generating a first digital token by a group of nodes of a blockchain network, the digital token is generated based on the value of the first real estate, the digital token representing ownership of the first real estate; transmitting the generated digital token to a digital token storage associated with the first real estate possessor;recording a lien on the first real estate possession record and a record of the digital token transmitted to the digital token storage associated with the first real estate possessor onto a distributed ledger of the blockchain network; and causing execution of a smart contract by broadcasting one or more functions to the blockchain network and receiving validation from the nodes of the blockchain network, the execution of the smart contract providing a real estate user with access to the first real estate.

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