Computer system and method for programmatic collateralization services

Information

  • Patent Grant
  • 12008526
  • Patent Number
    12,008,526
  • Date Filed
    Saturday, March 26, 2022
    2 years ago
  • Date Issued
    Tuesday, June 11, 2024
    18 days ago
Abstract
A computer system processes a digital representation of an illiquid asset, to confirm accuracy of the digital representation and value of the asset, and generates an asset-information table indicating where the various sections of the digital representation are stored. The system also generates an asset-value token, financial-return tokens, and a data structure that includes a self-executing program. The program associates the asset-value token with the financial-return tokens, so that the illiquid asset becomes a collateral for a decentralized loan transaction implemented using blockchain technology. The use of blockchain technology for decentralized loan transactions enables immutable data traceability.
Description
FIELD OF THE DISCLOSURE

The present invention relates to decentralized computer systems using self-executing programs for enabling digitized financial transactions.


BACKGROUND

Rapid digitization of the global economy has led to a proliferation of digital currencies, e.g., Bitcoin, Ethereum, Dogecoin, stablecoins, etc., also known as cryptocurrencies. Today, cryptocurrencies are used to buy various goods and services in online transactions. The underlying technology in these processes is called blockchain, which is a decentralized, digital database storing all forms of data, such as transactions, using lists of records, called blocks, that are linked together using cryptography. Each block contains a cryptographic hash of the previous block, time stamp, and data. The time stamp proves that the data in the block existed when the block was published on a decentralized computer system with its hash. As each block includes information about the preceding block, they form a chain, with each additional block reinforcing the ones before. Because the hash value of each block uniquely corresponds to the data in the block, once recorded, the data in any given block cannot be altered retroactively without also altering all the subsequent blocks. As a result, blockchains are resilient to data modifications.


Cryptocurrencies have also been used in financial lending transactions. In the traditional financial landscape, loans are secured by some asset, which acts as a collateral. When the asset is illiquid (e.g., larger commercial office building) and is worth many millions of dollars, the loan can typically be supported only by large financial institutions with a massive amount of capital. Moreover, the due diligence involved in approving such a loan often takes months or even years.


Lending using blockchain technology on decentralized computer systems, i.e., decentralized lending, has provided a new mechanism for processing and distribution of loans. Not only does it allow expediting the lending process, but it also removes the need to have a financial institution facilitate it. Present day decentralized lending, however, requires collateral to be liquid, which essentially restricts the collateral to cryptocurrencies.


In addition, because cryptocurrencies are highly volatile, loans using them as collateral can quickly become under-collateralized and subject to automatic liquidation.


As a result, billions in illiquid assets, such as artwork, residential, commercial, and industrial real estate, private equity, etc., cannot be used as collateral for decentralized lending, which means they are relegated to serving as collateral in traditional lending only. The lack of technology that would allow illiquid assets to be used in decentralized lending greatly limits the scope of this lending mechanism.


What is needed is a computer system and method that can use illiquid or substantially illiquid assets as collateral in automated digital lending transactions on decentralized systems.


What is also needed is a computer system and method for on-demand access to verified private data.


What is also needed is a computer system and method that enables both payment and value to be commuted during a transaction that involves illiquid or substantially illiquid assets.


What is also needed is a computer system and method that enables an investment mechanism that provides up to date information about the value of a collateralized illiquid asset.


What is also needed is a computer system and method that generates a digital special purpose vehicle that uses a self-executing program to lock in (use) an illiquid asset as a collateral for a loaned amount of cryptocurrency.


What is also needed is a computer system and method that generates a digital special purpose vehicle that uses a self-executing program to lock in a portion of an illiquid asset (e.g., one building out of a multi-building portfolio) as a collateral for a loaned amount of cryptocurrency.


What is also needed is a computer system and method that enables digital decentralized-lending transactions, using self-executing programs, in which the loaned amounts are provided by multiple tiers of funders.


What is also needed is a computer system and method that enables digital decentralized-lending transactions, using self-executing programs, in which the loaned amounts are supplied by multiple tiers of funders and where the funder(s) in at least one tier can affect the rules under which the digital decentralized lending transactions are executed.


SUMMARY

The present invention involves a novel decentralized computer network and method capable of using illiquid or substantially illiquid assets as collateral in automated decentralized digital lending transactions implemented using blockchain technology. The system facilitates automated transaction processing of complete, verified, and valued data, and real-time performance monitoring, enabling private markets to transact with greater velocity. The use of blockchain technology for decentralized loan transactions enables immutable data traceability. As a result, the invention creates a paradigm shift from traditional lending to a decentralized, digital lending platform for many types of illiquid or substantially illiquid assets. The resulting digital financial instrument may represent not only proportionate asset ownership but is also an auditable source of performance data, providing a platform having both know-your-customer and know-your-asset features.


The invented computer system and method involve receiving, by a first server, electronic documents and information comprising a digital representation of an illiquid asset, the digital representation indicating (showing) borrower's financial right in the asset. In one embodiment, the digital representation is recorded on a first blockchain and in an asset-information table, identifying the various sections of the digital representation and their respective locations on the blockchain. The digital representation and asset-information table are further updated (supplemented) based on additional data that confirms accuracy of the digital representation and/or value of the illiquid asset. (This way, the asset-information table is updated with at least one of digital representation of the illiquid asset and asset-valuation data of the illiquid asset.) A second server of the invented system generates an asset-value token and a data structure comprising a self-executing program for facilitating a decentralized loan transaction. The asset-value token is linked to the digital representation of the illiquid asset via the asset-information table in the first server. Using a predetermined set of rules, the second server determines an amount of digital currency needed for an automated digital loan transaction in which the illiquid asset serves as a collateral and, in return for an investment of digital currency by one or more funders, the second server issues one or more financial-return tokens. The data structure associates the issued financial-return tokens with the asset-value token and, through this association further associates the financial-return tokens with the digital representation of the illiquid asset via the asset-information table.


In another embodiment, the invented computer system and method involve receiving, by a first server, electronic documents and information comprising a digital representation of an illiquid asset, the digital representation indicating (showing) borrower's financial right in the asset. In one embodiment, the digital representation is hashed, and the resulting hash values are recorded on a blockchain and in an asset-information table, identifying the various sections of the digital representation and their respective hashes' locations on the blockchain. The digital representation is made accessible for valuation to relevant experts, such as data auditors, appraisers, pricing specialists, etc. The auditing, appraising, and/or pricing functions can be performed by automated digital expert systems, thus reducing or eliminating the need for human involvement. The digital representation and asset-information table are further updated (supplemented) based on additional data received from the experts that confirms accuracy of the digital representation and/or value of the illiquid asset. (This way, the asset-information table is updated with at least one of digital representation of the illiquid asset and asset-valuation data of the illiquid asset.) A second server, communicatively coupled to the first server, generates an asset-value token and a data structure comprising a self-executing program for facilitating a decentralized loan transaction. The asset-value token is linked to the digital representation of the illiquid asset via the asset-information table in the first server. Using a predetermined set of rules, the second server determines the amount of digital currency needed for an automated digital loan transaction in which the illiquid asset serves as a collateral and, in return for an investment of digital currency from one or more funders, issues one or more financial-return tokens. The data structure associates the issued financial-return tokens with the asset-value token and, through this association further associates the financial-return tokens with the digital representation of the illiquid asset via the asset-information table.


A system for performing a collateralized loan transaction includes a first server configured to: receive an electronic document including a digital representation of a illiquid asset, the digital representation identifying a financial interest in the illiquid asset by a borrower; hash the digital representation of the illiquid asset to generate a first hash value; record the first hash value on a first blockchain; store the digital representation of the illiquid asset at a first memory location; generate an asset-information table that associates a location of the first hash value on the first blockchain with the first memory location; receive asset-valuation data; store the asset-valuation data at a second memory location; hash the asset-valuation data to generate a second hash value; record the second hash value on the first blockchain; and update the asset-information table to associate a location of the second hash value with the second memory location. A second server is communicatively coupled to the first server and is configured to: generate an asset-value token comprising an association with the updated asset-information table; record the asset-value token on a second blockchain; determine an amount of digital currency for an automated digital transaction based on a programmed set of rules using the asset-value as a parameter; generate a data structure comprising a self-executing digital program for performing the automated digital transaction, the data structure including the asset-value token; and record the data structure on the second blockchain, wherein the automated digital transaction involves loaning the amount of digital currency at a rate of interest determined using the programmed set of rules, and wherein the illiquid asset, via the asset-value token, constitutes a collateral for the amount of digital currency being loaned.


The first server may periodically update the digital representation or valuation of the illiquid asset and the asset-information table before or after the amount of digital currency has been loaned. The second server is further configured to generate financial-return tokens, which can include governance tokens and return-interest tokens. The return-interest token could be a senior return-interest token or a subordinate return-interest token. The same may apply to governance tokens. The second server is further configured to provide access to the asset-information table in the first server to an entity possessing a financial-return token(s).


An automated method of the present invention for collateralizing an illiquid asset includes the steps of: receiving, by a first server, an electronic document including a digital representation of the illiquid asset and identifying a financial interest in the illiquid asset by a borrower; hashing, by the first server, the digital representation of the illiquid asset to generate a first hash value; recording, by the first server, the first hash value on a first blockchain; storing, by the first server, the digital representation of the illiquid asset at a first memory location; generating, by the first server, an asset-information table that associates a location of the first hash value on the first blockchain with the first memory location; receiving, by the first server, an asset-valuation data; storing, by the first server, the asset-valuation data at a second memory location; hashing, by the first server, the asset-valuation data, to generate a second hash value; recording, by the first server, the second hash value on the first blockchain; updating, by the first server, the asset-information table to associate a location of the second hash value with the second memory location; generating, by a second server, an asset-value token comprising an association with the updated asset-information table; recording, by the second server, the asset-value token on a second blockchain; determining, by the second server, an amount of digital currency for an automated digital transaction based on a programmed set of rules using the asset-value as a parameter; generating, by the second server, a data structure comprising a self-executing digital program for performing the automated digital transaction, the data structure including the asset-value token; and recording the data structure on the second blockchain; wherein the automated digital transaction involves loaning the amount of digital currency at a rate of interest determined using the programmed set of rules, and wherein the illiquid asset, via the asset-token, constitutes a collateral for the amount of digital currency being loaned.


In another embodiment, because hash values are unique, instead of the asset-information table storing a location of the hash values on the first blockchain, the asset-information table may store the hash values themselves.


As explained above, instead of recording on a blockchain a hashed value of the digital representation, the method includes recording on the blockchain the digital representation itself and storing in an asset-information table a location of the digital representation on the blockchain.





BRIEF DESCRIPTION OF THE FIGURES

The accompanying figures, where like reference numerals refer to identical or functionally similar elements throughout the separate views, together with the detailed description below, are incorporated in and form a part of the specification, and serve to further illustrate embodiments of concepts that include the claimed invention and explain various principles and advantages of those embodiments.



FIG. 1 shows a system in accordance with some embodiments of the present invention.



FIG. 2 is another view of a system in accordance with some embodiments of the present invention.



FIG. 3 shows an asset-information table in accordance with an embodiment of the present invention.



FIG. 4 shows an asset-information table in accordance with another embodiment of the present invention.



FIG. 5 shows a conceptual diagram of the asset-information server in accordance with some embodiments of the present invention.



FIGS. 6(a), 6(b), and 6(c) show a flow chart of a method in accordance with some embodiments of the invention.



FIG. 7 shows a server in accordance with some embodiments of the invention.





The apparatus and method components have been represented where appropriate by conventional symbols in the drawings, showing only those specific details that are pertinent to understanding the embodiments of the present invention so as not to obscure the disclosure with details that will be readily apparent to those of ordinary skill in the art having the benefit of the description herein.


DETAILED DESCRIPTION

The following detailed description discloses some embodiments of the system and method of the present invention for programmatic collateralization of illiquid assets.



FIG. 1 shows a decentralized computer system for programmatic collateralization of illiquid assets in accordance with some embodiments of the present invention for decentralized loan processing and distribution. The system 1 includes an asset-information server(s) 12, a borrower's computer 16, an asset-related information source(s) 18, an expert system 20, a database 28, a funding system 44, a collateralization server(s) 14 that operates a blockchain on a blockchain-platform A. The system has other server(s) 15 that operate another blockchain on blockchain-platform B. In an alternative embodiment, server(s) 15 could operate a blockchain on the same platform as the collateralization server 14. The recited system components communicate with each other over a communication network 8, such as the Internet, via their respective communication links L1 though L8, which can be wired or wireless. Link L6 represents a communication channel used for recording and accessing information on an asset-information blockchain 34. Link L7 represents a communication channel used for recording and accessing information on a collateralization blockchain 38.



FIG. 2 shows a decentralized computer system 1 of FIG. 1 in more detail. (The communication network 8 in FIG. 1 is also present in the system illustrated in FIG. 2 but is not shown in FIG. 2.) FIG. 2 depicts an illiquid asset 10 in which a borrower has a financial right (interest), and which will be used as a collateral for a decentralized loan transaction. The process starts by having the borrower use his or her computer 16 to connect to the asset-information server 12 via the network 8. The borrower's computer 16 may already include a borrowing application (APP) stored on the device, or the asset-information server 12 may transmit a web page for the borrower to enter information regarding the asset and requested loan. Either way, having connected to the asset-information server 12, the borrower, via the borrower's computer 16, transmits to the asset-information server 12 a document(s) 22 describing the illiquid asset 10 and asset-related information 24. The document 22 may exist in any digital format, such as PDF, WORD, EXCEL, HTML, etc., constituting a digital representation 25 of the illiquid asset. The digital representation 25 includes information confirming the borrower's financial interest in the asset 10 (e.g., title, lease income, ownership, etc.), as well as any other asset-related information 24 that the borrower may need/wish to provide to the system for asset verification, asset valuation, and decentralized loan purposes. The digital representation may also include metadata about the electronic document(s) and asset-related information.


In addition to receiving the electronic document(s) 22, including asset-related information 24 from the borrower, the asset-information server 12 may also receive additional asset-related information 26 in any format from other source(s) 18.


The asset-information server 12 may process the received digital representation of the illiquid asset (digital data representing the illiquid asset) 25, such as by an artificial intelligence engine or other methods to extract certain data, create a summary, or metadata associated with the received information.


The asset-information server 12 may then record (publish) the digital representation of the illiquid asset on the asset-information blockchain 34 of server(s) 15. Blockchains and blockchain recordation are known in the art and will not be described here in detail. For example, blockchains and blockchain recordation are explained in U.S. Pat. No. 10,873,457, the disclosure of which is incorporated herein it its entirety. After recording the digital representation of the illiquid asset on the asset-information blockchain 34, the asset-information server 12 generates an asset-information table that associates each of the various sections of the digital representation with its respective location on the blockchain 34 at which the corresponding portion is stored. FIG. 3 illustrates one such table.



FIG. 3 shows an asset-information table that associates the various sections of the illiquid assets' digital representations to their respective locations on the asset-information blockchain 34. For example, the table in FIG. 3 shows that for an illiquid asset A (reference numeral 300) the server 12 has collected four documents and/or pieces of information that form the digital representation of asset A; the title 302, rent roll information 304, zoning information 306, and liabilities 308. The table further illustrates that digital representation of the title to asset A is located on the blockchain 34 at block 1, location 1 (reference numeral 312); digital representation of the rent roll information for asset A is located at block 1, location 2 (reference numeral 314); digital representation of zoning information is located at block 2, location 10 (reference numeral 316); and digital representation of borrower's liabilities is located at block 2, location 14 (reference numeral 318). As also shown in FIG. 3, the illiquid asset B (reference numeral 320) has its own mapping between the different sections of the digital representation of asset B and the respective locations on the asset-information blockchain 34 that contain the actual digital representations. Thus, the asset-information table can be thought of as an identification structure in which the left column designates directories of files, the middle column designates file names, and the right column designates locations on a blockchain at which the corresponding file contents are present. In addition to recording the digital representation of the illiquid asset on a blockchain, the server may also separately save the information in a database 28, central or distributed, or in its own memory.


In another embodiment, instead of recording the digital representation of an illiquid asset on the asset-information blockchain 34, the asset-information server 12 first hashes the digital representation of the illiquid asset using a hashing algorithm and then records the calculated hash values on the blockchain 34. Hashing algorithms are known in the art, and any hashing algorithm, e.g., SHA-256 algorithm, could be used to obtain a hash value of the digital representation of the illiquid asset. After recording the hash value(s) of the digital representation of an illiquid asset on the blockchain 34, the asset-information server 12 stores the digital representation either in its memory or in a database, central or distributed. (Note however, storage of the digital representation may precede recordation of its hash value on the blockchain.) Afterward, the asset-information server 12 generates an asset-information table that associates each of the various sections (portions) of the digital representation of the illiquid asset with a location on the blockchain 34 at which the corresponding hash value is stored, and also associates each section with a memory location in the database 28 storing the corresponding digital representation that was used to generate the hash value. FIG. 4 illustrates one such table.



FIG. 4 shows an asset-information table that associates the various sections of an illiquid asset's digital representation to locations on the asset-information blockchain 34 that contain the hash values of the corresponding sections, and also associates them with the respective memory locations in the database 28 where the corresponding digital representations that were used to generate those hash values are stored. For example, the table in FIG. 4 shows that for the illiquid asset A (reference numeral 300) digital representation includes four pieces of information; title 302, rent roll information 304, zoning information 306, and liabilities 308. The table also illustrates that hash value of the digital representation of title to asset A is located on the blockchain 34 at block 1, location 1 (reference numeral 412); hash value of the digital representation of the rent roll information for asset A is located at block 1, location 2 (reference numeral 414); hash value of the digital representation of zoning information is located at block 2, location 10 (reference numeral 416), and hash value of the digital representation of borrower's liabilities is located at block 2, location 14 (reference numeral 418). As further shown in FIG. 4, the table also associates each section of the digital representation of asset A with a corresponding location in the database where the respective portion of the digital representation itself is stored. For example, digital representation of title is shown as being stored in the database 28 at location 100 (reference numeral 422); digital representation of rent roll information is shown as being stored in the database 28 at location 101 (reference numeral 424); digital representation of zoning information is shown as being stored in the database 28 at location 102 (reference numeral 426); and digital representation of borrower's liabilities is shown as being stored in the database 28 at location 103 (reference numeral 428). As also shown in FIG. 4, the illiquid asset B (reference numeral 320) has its own mapping between the various sections of the digital representation of asset B, locations of their respective hash values on the asset-information blockchain 34, and memory location in the database 28 containing the actual digital representations. Instead of storing the digital representations of illiquid asset on the database 28, the server may save the information in its own memory. Memory locations in the rightmost column in FIG. 4 may also be Uniform Resource Locator (URL) addresses, or any other kind of addresses pointing to local or remote storage locations. In another embodiment, in addition to recording on the blockchain 34 hash values of the various pieces (portions) of the digital representation of an illiquid asset, the system may also record on the blockchain 34 the memory locations at which those corresponding pieces are stored.


Recording a hash value of a digital representation on the asset-information blockchain 34, as opposed to recording the digital representation itself, has several advantages. One advantage relates to the size of the resulting blockchain. Specifically, because hash values typically occupy less memory space than the corresponding digital representations of the illiquid assets, a blockchain that records the hash value of the digital representation of illiquid asset occupies less memory space than a blockchain that records the digital representation itself. And, because in decentralized computer networks a copy of the blockchain is stored on multiple network nodes, having a blockchain of smaller size saves memory space on each such node. Furthermore, reducing the size of the blockchain allows for faster transmission of the blockchain across the network.


Another advantage of recording a hash value is that it allows the system to confirm that the digital representation has not been changed from the time its hash value was generated, while precluding unwanted disclosure of the corresponding content of the digital representation. Specifically, because disclosure of a hash value does not lead to disclosure of the data that was used to generate it, there is no danger in publishing hash values of digital representations. Yet, when a party requests access to a specific portion of the digital representation of the illiquid asset, e.g., title (FIG. 4, Ref. 302) of the illiquid asset A (FIG. 4, Ref. 300), the asset-information server 12 can retrieve the digital representation of title from location 100 in the database 28 (FIG. 4, Ref. 422) and can also retrieve the corresponding hash value from location 1 in block 1 of blockchain 34 (FIG. 4, Ref. 412). At that point, the asset-information server 12 can hash the retrieved digital representation (digital data) of title and compare the resulting hash value to the hash value previously recorded on the asset-information blockchain 34. If the hash values match, it signifies that the retrieved digital representation of the title is authentic, i.e., it has not been changed from the time its hash value was recorded on the blockchain, at which point the retrieved digital representation of the title can be provided to the requesting party. Authenticity of the digital representation can thus be confirmed without having to disclose its content. If, however, the hash values do not match, it signifies that the original digital representation of the title was tempered with, in which case the retrieved digital representation may be flagged for further system analysis.


In another embodiment, the invention may use a modified version of the asset-information table of FIG. 4. Specifically, because various sections of the asset's digital representation have different binary (“Os” and “Is”) content, each section will have a different, unique, hash value. As a result, instead of listing in the table the hash values' blockchain locations, the table may list the hash values themselves. In such a scenario, the system could then scan the blockchain 34 to confirm authenticity of a specific hash value in the table. Once the hash value has been authenticated, i.e., the hash value in the table is also present on the blockchain 34, the system may then retrieve the corresponding digital representation from a memory location identified in the table, hash the retrieved digital representation, and compare the resulting hash value to the hash value in the table. If the hash values match, then the retrieved digital representation is authentic. Otherwise, the retrieved digital representation has been tempered with, in which case the data would be flagged for further analysis.


Once the digital representation of the illiquid asset or its hash value has been recorded on the blockchain 34, access to the digital representation can be protected by the asset-information server via an access-rights mechanism, such as those using public-private encryption method. Access rights may be free, or may be provided for a fee, either in fiat currency or in cryptocurrency, or based on some other consideration.


For example, when a party requests access to a document or information concerning a particular illiquid asset, the server 12 may require the party to go through an authentication process, such as requiring the party to enter its login and password information, as is typically done with many software applications today. If the login and password match the login and password information in the system, the party is granted access to the data sought. Instead of a single factor authentication, a multi-factor authentication, known in the art, may be used.


In another embodiment, instead of using a login and password, access to information may be controlled using authentication approaches known in the art, such as the Web3 authentication. A party's (public) wallet address can be whitelisted and the party can prove its identity by signing a transaction using its private key, which would prove possession of the private key, thus confirming the party's identity.


The system may implement multi-tiered access rights. For example, parties with low-tier access, may only view (read) the digital representation, but may not download or supplement it. Parties (users) with a middle-tier access may read and download the digital representation but not supplement it. And parties with high-tier access may download, read, and supplement the digital representation. In addition, the ability to modify a digital representation may depend not only on the access-tier level but also on the type of data in question. For example, while rent-roll information may be supplemented by a party (user) with the high-tier of access rights, title information may not be supplemented regardless of the party's access-tier level.


In one embodiment, access rights may be implemented on a blockchain via cryptographic tokens. For example, when recording on the blockchain either digital representations of illiquid assets or their respective hash values, the server 12 may also record a cryptographic token that would control access rights to the digital representation. Such a token may also be associated with a self-executing program that would control access rights based on various specified conditions. FIG. 2 illustrates access rights to the digital representation of an illiquid asset being implemented via an asset-information token 30.


Once the asset-information server 12 receives digital representation of an illiquid asset and generates an asset-information table, the system proceeds to verify the information and to determine accurate market value of the corresponding illiquid asset. This may be accomplished by having an automated expert system(s) 20 (human experts may also be used) access the asset's digital representation, via an access-rights mechanism discussed above, and then analyze the data. For example, an audit expert system 20, or Deloitte or KPMG accountants, may audit the digital representation of the illiquid asset to confirm veracity of the information. The audit data 32a and the results of the audit are then received by the server 12.


In an embodiment where the server records asset's digital representation on the asset-information blockchain 34 and uses the asset-information table in FIG. 3, the received audit data and audit results for the particular asset are also recorded on the asset-information blockchain, acting as a certification of accuracy of the information contained in the digital representation. The asset-information table would then get updated to reflect the additional information. For example, once audit information regarding the digital representation of the illiquid asset A (FIG. 3, Ref. 300) has been recorded on the information blockchain 34, asset-information table of FIG. 3 in the server 12 is updated. Specifically, a row would be added in the middle and right columns of the table for asset A. In the middle column, the added cell could refer to documents and information labeled “audit information,” and the added cell in the right column could include a location on the blockchain 34 where the digital representation of the audit information is stored.


In an embodiment where the server records hash values of the asset's digital representation on the asset-information blockchain 34 and uses the asset-information table shown in FIG. 4, the received digital representation of the audit data and audit results for the particular asset would be hashed and the hash values recorded on the blockchain 34, with the digital representation of the audit data 32a and results being saved in the database 28 (or server's memory). The asset-information table would then get updated to reflect the additional information. For example, once the hash of digital representation of audit information for the illiquid asset A (FIG. 4, Ref 300) has been recorded on the information blockchain 34, asset-information table in the server 12 is updated accordingly. Specifically, a row would be added in each of the three rightmost columns of the table for asset A. In the “Asset Related Documents & Information” column, the added cell could refer to documents and information labeled “audit information.” In the “Location of Hash Value on Blockchain 34” column, the added cell would include a location of the hash value of the digital representation of audit information. In the rightmost column of the table in FIG. 4, the added cell would list the location in the database 28 where the digital representation of the audit information is stored.


The expert system 20 may also analyze the digital representation of the illiquid asset to determine its market value. For example, for a commercial real estate asset, a real estate appraisal expert system, or human appraiser from such a firm as Cushman & Wakefield, may be used to perform an appraisal. The appraisal data is then also recorded on the asset-information blockchain 34, acting as a certification of the asset's appraised value.


In an embodiment where the server records on the asset-information blockchain 34 asset's digital representation and uses the asset-information table in FIG. 3, the received digital representation of the appraisal data and appraisal results for the particular asset are also recorded on the asset-information blockchain 34, acting as a certification of the asset's value. The asset-information table would then get updated to reflect the additional information. For example, once an appraisal information regarding the digital representation of the illiquid asset A (FIG. 3, Ref. 300) has been recorded on the asset-information blockchain 34, asset-information table of FIG. 3 in the server 12 is updated. Specifically, a row would be added in the middle and right columns of the table for asset A. In the middle column, the added cell could refer to documents and information labeled “appraisal information,” and the added cell in the right column could include a location on the blockchain 34 where the digital representation of this appraisal information is stored.


In an embodiment where the server records hash values of the asset's digital representation on the asset-information blockchain 34 and uses the asset-information table shown in FIG. 4, the received appraisal data and appraisal results for the particular asset would be hashed first, the hash values recorded on the asset-information blockchain 34, with the appraisal information being saved as digital representation in the database 28 (or server's memory). The asset-information table would then get updated to reflect the appraisal information. For example, once a hash value of the digital representation of appraisal information for the illiquid asset A (FIG. 4, Ref. 300) has been recorded on the asset-information blockchain 34, asset-information table in the server 12 is updated. Specifically, a row would be added in each of the three rightmost columns of the table for asset A. In the “Asset Related Documents & Information” column, the added cell could refer to documents and information labeled “appraisal information.” In the “Location of Hash Value on Blockchain 34” column, the added cell would include a location of the hash value of the digital representation of appraisal information. In the rightmost column of the table in FIG. 4, the added cell would list the location in the database 28 where the digital representation of the appraisal information is stored.


In addition, or as an alternative to an appraisal expert, the invention may use a pricing expert system. The pricing expert system or a human pricing agent may be used to further estimate the asset's market value. This valuation data is then recorded on the asset-information blockchain 34, acting as a certification of the asset's value.


In an embodiment where the server records on the asset-information blockchain 34 asset's digital representation and uses the asset-information table of FIG. 3, the digital representation of the pricing data and pricing results for the particular asset are also recorded on the asset-information blockchain, acting as a certification of asset's market value. (Appraisal and pricing data can each be referred to as “valuation” data 32b.) The asset-information table would then get updated to reflect the additional information. For example, once digital representation of pricing information concerning the illiquid asset A (FIG. 3, Ref 300) has been recorded on the asset-information blockchain 34, asset-information table of FIG. 3 in the server 12 is updated. Specifically, as shown by arrow 307 in FIG. 3, a row would be added in the middle and right columns of the table for asset A. In the middle column, the added cell 310 could refer to documents and information labeled “Valuation” and the added cell 320 in the right column would list a location, e.g., Block 3, location 26, on the blockchain 34 where the digital representation of the valuation data is stored.


In an embodiment where the server records on the asset-information blockchain 34 hash values of the asset's digital representation and uses the asset-information table shown in FIG. 4, the received pricing data and pricing results for the particular asset would be hashed first, the hash values recorded on the asset-information blockchain 34, with the digital representation of the pricing data and results being saved in the database 28 (or server's memory). The asset-information table would then get updated to reflect pricing information. For example, once a hash value of the digital representation of pricing information for the illiquid asset A (FIG. 4, Ref. 300) has been recorded on the asset-information blockchain 34, asset-information table in the server 12 is updated. Specifically, as shown by arrow 407 in FIG. 4, a row would be added in each of the three rightmost columns of the table for asset A. In the “Asset Related Documents & Information” column, the added cell 310 could refer to documents and information labeled “Valuation.” In the “Location of Hash Value on Blockchain 34” column, the added cell 420 would list a location, e.g., Block 3, location 26, of the hash value of the digital representation of valuation information on the blockchain 34. In the rightmost column of the table of FIG. 4, the added cell 430 would list the location, e.g., Location 104, in the database 28 where the digital representation of valuation information is stored.


The modified version of asset-information table in FIG. 4, where instead of recording locations of hash values, the hash values themselves are recorded, would be updated similarly. Except, instead of supplementing the table with the blockchain locations storing hash values of digital representations of audit, appraisal, and/or pricing, the system would supplement the table with the actual hash values of those digital representations. Although each of FIGS. 3 and 4 shows a single asset-information table generated for multiple assets, the invention also includes an embodiment in which a separate asset-information table is generated for each asset.


Once asset-information table has been updated with the audit and appraisal, and/or pricing data, the asset-information server 12 provides the collateralization server 14 with access rights to the asset-information table and its associated information.


Access rights could be credentialed via a username and password login method. Alternatively, access rights could be credentialed by providing the collateralization server 14 with an asset-information token 30 for the asset-information blockchain 34. Asset-information token may not only include access-rights information, but may also link to an asset-information table in the asset-information server 12. In another embodiment, instead of using a login and password, access to information may be controlled using authentication approaches known in the art, such as the Web3 authentication. A party's (public) wallet address can be whitelisted and the party can prove its identity by signing a transaction using its private key, which would prove possession of the private key, thus confirming the party's identity.


Once the collateralization server 14 receives access to an asset-information table, via the asset-information token 30 or otherwise, it generates an asset-value token 36 and records it on an asset-collateralization blockchain 38, which is running on a collateralization server 14 and its associated decentralized server system.


The asset-value token 36 incorporates a mechanism for accessing the asset-information table in the asset-information server 12 and the table-associated data.


For example, the asset-value token 36 may include a link to the asset-information token 30, i.e., the blockchain address (digital identifier) of the asset-information token 30 on the asset-information blockchain 34, or it may incorporate information from the received asset-information token 30.


The asset-value token 36 may be a digital rights management (DRM) token that provides access rights and points to the asset-information table, or part thereof, in the asset-information server 12.


In another embodiment, the asset-token 36 may include the audit and valuation data in addition to including DRM-type access to the corresponding asset-information table.


In yet another embodiment, the asset-token 36 may include the audit and valuation data and the blockchain address (digital identifier) of the corresponding asset-information token 30 on the asset-information blockchain 34.


The asset-value token 36 may be associated with a self-executing digital program for automated execution based on various specified conditions, such as periodically checking for any updates in the asset-information table or requesting that the asset-information table be updated with current data. Thus, while an asset-information table in the asset-information server 12 may be updated from time to time by the asset-information server 12 or a self-executing program associated with the asset-information token 30, table updates may also be initiated by the collateralization server 14 or the asset-value token 36.


In FIG. 2, the asset-information blockchain 34 and the asset-collateralization blockchain 38 are shown as two separate blockchains, running on different blockchain platforms. For example, while the asset-collateralization blockchain 38 may run on Ethereum platform, the asset-information blockchain 34 may run on another blockchain platform. In other embodiments, however, the two blockchains may not only run on the same blockchain platform, e.g., Ethereum, but they may also be the same blockchain. In another embodiment, functions of the asset-information server 12 and of asset-collateralization server 14 may be performed by a single server. Moreover, in another embodiment, the asset-information token 30 and the asset-value token 36 may be the same token, i.e., one and the same.


Having generated the asset-value token 36, the collateralization server 14 uses a digitally programmed set of rules incorporating the determined asset value as a parameter to calculate an amount of digital currency for which the illiquid asset could serve as a collateral in a decentralized loan transaction. The rules may also take into account other parameters, such as the loan duration, interest rate, risk level, etc. The rules may also set escrow parameters for the decentralized loan transaction.


Once the possible loan amount has been determined, using digitally programmed set of rules, the collateralization server 14 generates a software object, referred to herein as a decentralized special purpose vehicle (“DSPV”) 40, that ties or associates the determined loan amount to the asset-value token 36, which in turn is associated either directly or indirectly with the digital representation of the illiquid asset, including its audit and valuation data. The software object may be a smart contract (self-executing program) programmed on a blockchain platform, e.g., Ethereum platform.


The asset-collateralization server 14 collects liquid funds for the loan and programmatically ties or associates them in the DSPV 40 to the illiquid asset, via the asset's asset-value token 36, which results in the illiquid asset being programmatically secured as collateral for the upcoming loan.


As shown in FIGS. 1 and 2, the invention includes a funding system 44. The funding system 44 represents devices through which funders (depositors, creditors, sponsors) provide liquid funds 42, whether in cryptocurrencies or in fiat currencies, for the collateralization server to use in the decentralized loan transaction. If the funders provide funds in fiat currencies, the funds are first converted to a cryptocurrency, e.g., stablecoins (which are tied to assets like fiat currencies). The asset-collateralization server 14 uses a digitally programmed set of rules to generate various types of financial-return tokens that will provide financial returns to the various funders. Thus, as shown in FIG. 2, the funding system 44 provides liquid funds 42 to the DSPV 40, and the collateralization server 14, through DSPV, issues financial-return tokens, such as return-interest token(s) 46 and/or governance token(s) 47 (explained below), to funders 44. Each return-interest token 46 may be associated either with 100% of the loan amount or with a fraction of the loan amount. As a result, various return-interest tokens may entitle their holders to unequal rates of interest income in return. All of this may be determined by the set of programmed rules in the collateralization server 14 and effectuated via a self-executing program in the DSPV 40. As explained below, return-interest tokens may have various seniority levels (tranches) with each tranche presumably having different interest rates. Thus, the DSVP 40 not only includes the asset-value token 36, which links and collateralizes the digital representation of the illiquid asset, but also associates the asset-value token 36 to financial-return tokens.


In one embodiment, liquid funds may be available in the asset-collateralization server 14, as part of a credit pool, even prior to generating the asset-value token 36. In another embodiment, the funds start being collected from depositors once the asset-value token gets generated in the asset-collateralization server 14 and the DSPV data structure is created. This way, potential depositors can evaluate the digital representation of the illiquid asset being collateralized and decide whether to invest their funds in a particular loan transaction. The DSPV can be used to crowdfund a particular loan project by raising funds from a large number of people over the Internet. The information in the DSPV, including the asset-value token 36 and the issued financial-returns tokens, is being recorded on the asset-collateralization blockchain 38 on an ongoing basis, as the depositors and other funders contribute funds for the decentralized loan transaction.


Once a sufficient amount of liquid funds for the loan has been collected and the DSPV is recorded on the blockchain 38, the loan is ready for distribution to the borrower's computer or his digital wallet. In another embodiment, the loaned amount of digital currency is converted to fiat currency, which is then provided to the borrower. FIG. 2 diagrammatically illustrates loaned digital funds 48 being distributed to the borrower, via borrower's computer 16 (his digital wallet), and repayments 50 being transmitted back to the DSPV 40 in the collateralization server 14, for further distribution to the funders as return interest payments through their respective interest tokens and governance tokens. All of this takes place on the asset-collateralization blockchain 38. The distribution of the loaned funds to the borrower and any repayments by the borrower are also automatically recorded on the blockchain 38 as they occur.


The present invention also contemplates updating and supplementing of the digital representation, auditing, and valuation of the illiquid asset. Such updates could be periodic or non-periodic. For example, supplemental asset-related information can be received and added to the information about the illiquid asset that is already present on the asset-information blockchain 34. Such supplemental information could come from the borrower himself/herself or it can come from other sources. In addition, the asset-information server 12 may be programmed to scan the web, including social media platforms and Internet-of-Things sensor data using web bots (also referred to as web crawlers) for any asset-related information, and record the newly found data to the asset-information blockchain 34. The data may also be crowdsourced. This way, the digital representation of an illiquid asset stays current over time, and the asset may be periodically re-valued by the expert system 20, to make sure that the loan does not become under-collateralized. The supplementation and re-valuation not only result in additional recording on the asset-information blockchain 34, but also result in the corresponding asset-information table in the server 12 being updated. Having the ability to update information about an illiquid asset in a way that guarantees data's immutability helps to monitor the collateralized value of the illiquid asset during the life of the loan and to propagate changes to the asset-value token and potential actions by the DSPV. For example, if the asset value increases, the DSPV may allow for additional funds to be borrowed. On the other hand, if the asset value decreases, funds from a sponsor may be used to cover (provide a backstop for) the loan.


Updates to the digital representation of the illiquid asset can also be initiated either by the self-executing digital program in the DSPV 40 or by the self-executing program in the asset-value token 36 itself, which forms a part of the DSPV 40. In FIG. 2, this feature is shown via an update input 39 received by the asset-information server 12. Regardless of how the updates are initiated, the updated digital representation of the illiquid asset is available to the DSPV 40, and thus funders, for further processing and analysis.



FIG. 5 shows a conceptual diagram of the asset-information server 12 in accordance with some embodiments of the present invention. The asset-information server 12 includes a number of structural elements that perform various functions. An intake circuit 500 is communicatively coupled to the network 8 (see FIG. 1). The intake circuit is responsible for receiving digital electronic documents, asset-related information, output(s) of the expert system 20, outputs from collateralization server 14, etc. The digital representation may be received in various formats, such as PDF, WORD, EXCEL, HTML, bitmap, graphical, audio, etc. The invention also contemplates a natural language processing engine (NLP), which enables processing of incoming data that is unstructured. During the receiving step, the intake circuit 500 identifies the format of an incoming digital representation, so that the format information could be passed to an extraction circuit 502. Once the digital representation passes to the extraction circuit 502, the extraction circuit 502 processes it to extract relevant data, such as metadata. This may involve using an artificial intelligence engine, which may be integrated into the extraction circuit 502. After the data has been extracted, it is provided to a summary circuit 504, which integrates the extracted data into a digital representation of the illiquid asset. The digital representation can be supplemented by data from a valuation circuitry 506, which integrates the audit, appraisal, and pricing data and results from the expert system 20 into the asset's digital representation.


An output-and-recordation circuit 508 is responsible for storing the digital representation of the illiquid asset in a database 28 (or in the internal memory of the asset-information server 12), recording data on blockchains, generating asset-information token 30, as well as periodically generating signals for searching the network 8 for additional asset-related information, e.g., by generating web crawlers, etc. Although structural elements 500, 502, 504, 506, and 508 have been described as circuits, they may be implemented in hardware, software, firmware, or their combinations.


As mentioned above, the DSPV may have different funders. Some funders may be depositors whose contributions are limited to providing liquid assets for a particular DSPV loan based on a set of rules. Other funders may act as creditors, e.g., retail and institutional investors that combine their respective contributions into a pool of liquid assets that is then available for the collateralization server 14 to allocate to one or more DSPVs under a predetermined set of rules. These rules may be the same or different from the rules applied concerning depositors' investments. A creditor-funder may be entitled to a larger interest than a depositor-funder.


The invention also contemplates having a standby credit facility, where liquid funds might be obtained through banks, insurance companies, flexible deposits, and matched deposits.


Yet other funders may be referred to as sponsors. These funders may not only provide liquid funds at the start of the decentralized loan transaction but may also provide funds to cover the loan in case it might become undercollateralized in the future. Because a sponsor-funder acts as a backstop for the loan, taking on greater risk, the sponsor-funder may be entitled to a higher interest rate than a depositor-funder or a creditor-funder.


As a result, a DSPV 40 may have several tiers of return-interest tokens 46, with each tier being associated with its own percentage of return interest. For example, a first tier may be assigned to depositors, a second tier may be assigned to creditors, and a third tier may be assigned to sponsors.


Moreover, even within the same tier, there may be distinct token levels (tranches). For example, return-interest tokens 46 for depositor-funders may include senior return-interest tokens (senior level return-interest tokens) and subordinate return-interest tokens (junior level return-interest tokens), representing different positions in the DSPV 40. For example, a depositor-funder with a senior level return-interest token would be entitled to collect interest before a depositor-funder with a junior level return-interest token.


In addition to having separate tiers of interest tokens, the rules under which the asset-collateralization server 14 operates and generates DSPV 40 may be controlled by governance tokens (FIG. 2, Ref. 47). For example, creditor-funders and/or sponsor-funders may not only have rights to collect interest income but may also have a voice in setting the rules for a decentralized loan and the types of collateral that would qualify for the loan. As a result, instead of being issued return-interest tokens 46, some funders, such as creditor-funders and/or sponsor-funders, may be issued governance tokens 47. Importantly, governance tokens 47 may represent only governance or they may represent both governance and financial-return interest.



FIGS. 6(a), 6(b) and 6(c) show a flow chart of a method in accordance with some embodiments of the invention. The process starts at Step 604 in FIG. 6(a), with the asset-information server 12, referred to in the figure as a first server, receiving an electronic document and/or asset-related information comprising a digital representation of an illiquid asset that confirms borrowing party's financial interest in the asset. Next, at Step 606, the asset-information server 12 uses a hashing algorithm to generate a hash value of the digital representation. (If the received document and/or asset related information has multiple sections, the invention contemplates hashing the digital representations of each section, thus generating several corresponding hash values.) At Step 608, the hash value(s) is stored on an asset-information blockchain 34, referred to in the FIG. 6(a) as a first blockchain. At Step 610, the asset-information server 12 stores the digital representation of the illiquid asset at a first location (or a set of locations) in memory storage. As discussed above, the memory can be a central or distributed database or it can be memory storage in the server 12 itself. Next, at Step 612, the system generates an asset-information table, such as the ones discussed above with reference to FIGS. 3 and 4. At Step 614, the asset-information server 12 receives asset-valuation (audit, appraisal and/or pricing) data, in the form of a digital representation, from the expert system 20.


Proceeding to FIG. 6(b), at Step 616, the received asset-valuation data is also stored in memory, at a second location. At Step 618, the hashing algorithm is used to hash the digital representation of the asset-valuation data, and at Step 620, the hash value(s) is stored on the asset-information blockchain 34. Then, at Step 622, the asset-information table in the asset-information server 12 is updated with the digital representation of the asset-valuation data for the asset in question. Next, at Step 624, the asset-information server 12 provides the asset-collateralization server 14 (referred to in the figure as a second server) with access to the asset-information table, which in turn provides access to the underlying digital representation for the asset in question. Having received access to the asset-information table, at Step 626 the asset-collateralization server 14 generates an asset-value token 36. At Step 628, the asset-collateralization server 14 records the asset-value token 36 on an asset-collateralization (loan) blockchain 38, referred to in the figure as a second blockchain. At step 630, the asset-collateralization server 14 generates a data structure 40, referred to in the preceding paragraphs as a DSPV, which includes a self-executing digital program and the asset-value token 36.


Proceeding to FIG. 6(c), at Step 632, based on a set of programmed rules, the asset-collateralization server 14, directly or via the DSPV 40, determines an amount of digital currency for an automated digital loan transaction. Then, at Step 634, the amount of digital currency needed for the loan transaction is collected from funders, and the financial-return tokens are generated. Next, at Step 636, the DSPV 40 data structure, including the asset-value token and the financial-return tokens, is recorded on the asset-collateralization blockchain 38. Finally, at Step 638, the system completes the automated digital loan transaction. This includes providing the loaned amount of digital currency to the borrower's computer, activating the rights represented by the financial-return tokens and in return automatically receiving (from the borrower) interest payments on a predetermined schedule, distributing the received interest payments to funders based on their tokens, and periodically reevaluating the loan.


While the method steps disclosed in FIGS. 6(a) through 6(c) are shown in a certain sequence, as understood by a person of ordinary skill in the art (POSITA), the order of many of the steps can be changed. For example, the order of Steps 608 and 610 can be reversed. In fact, the actions of Step 610 could be taken prior to the actions in Step 606. The same can be done with the actions in Steps 616, 618, and 620.


Also, depending on the granularity of the process, token generation and token recordation could be considered a single step in general. Moreover, Steps 626 through 638 could be viewed as a part of a single method step of generating and recording a DSPV 40 data structure, comprising a self-executing program that ties an asset-value token to financial-return tokens (return-interest tokens and governance tokens).



FIG. 7 shows a server 700, whether asset-information server or asset-collateralization server, in accordance with an embodiment of the present invention. The server 700 includes a system controller 702 that is coupled to a memory subsystem 704, a processor 706, a graphics subsystem 708 and a peripheral bus controller 710. The peripheral bus controller 710 may be coupled to some elements directly and to other elements via a communication bus 722. For example, the peripheral bus controller may be coupled directly to a memory 726 via an enhanced integrated drive electronics (EIDE) interface, to a USB interface port 728, and to an audio subsystem 712. At the same time, the bus 722 may couple the peripheral bus controller 710 to a super input/output (SIO) circuit 713, flash memory 724, Ethernet port 730, a small computer system interface (SCSI) 732, an external device 734, and a wireless interface 736, such as WiFi, Bluetooth, cellular, etc. The SIO circuit 713, in turn, may be coupled to a keyboard port 714, a mouse port 716, a serial interface port 718, and a parallel interface port 720.


Exemplary embodiments may be applied to any processor-controlled device operating in the wired or radio-frequency domain. Exemplary embodiments may be applied to any processor-controlled device utilizing a distributed computing network, such as the Internet (sometimes alternatively known as the “World Wide Web”), an intranet, a local-area network (LAN), and/or a wide-area network (WAN). Exemplary embodiments may be applied to any processor-controlled device utilizing power line technologies, in which signals are communicated via electrical wiring. Indeed, exemplary embodiments may be applied regardless of physical componentry, physical configuration, or communications standard(s).


Exemplary embodiments may utilize any processing component, configuration, or system. Any processor could be multiple processors, which could include distributed processors or parallel processors in a single machine or multiple machines. The processor can be used in supporting a virtual processing environment. The processor could include a state machine, application specific integrated circuit (ASIC), programmable gate array (PGA) including a Field PGA, or state machine. When any of the processors execute instructions to perform “operations,” this could include the processor performing the operations directly and/or facilitating, directing, or cooperating with another device or component to perform the operations.


Exemplary embodiments may packetize. The servers may have network interfaces to the various communications networks, thus allowing transmittal and retrieval of information. The information may be received as packets of data according to a packet protocol (such as the Internet Protocol). The packets of data contain bits or bytes of data describing the contents, or payload, of a message. A header of each packet of data may contain routing information identifying an origination address and/or a destination address.


The present invention can operate with escrow bots that function as self-executing digital programs that release/loan digital funds when required conditions, such as third party and crowdsourced data from a variety of sources, are met. These sources can be mobile phone, social media, and Internet-of-Things sensors.


While the foregoing descriptions disclose specific values, any other values may be used to achieve similar results. Furthermore, the various features of the foregoing embodiments may be selected and combined to produce numerous variations of improved systems.


In the foregoing specification, specific embodiments have been described. However, one of ordinary skill in the art appreciates that various modifications and changes can be made without departing from the scope of the invention as set forth in the claims below. Accordingly, the specification and figures are to be regarded as illustrative rather than restrictive, and all such modifications are intended to be included within the scope of present teachings.


Moreover, in this document, relational terms such as first and second, top and bottom, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any such specific relationship or order between such entities or actions. The terms “comprises,” “comprising,” “has”, “having,” “includes”, “including,” “contains”, “containing” or any other variation thereof, are intended to cover a non-exclusive implementation, such that a process, method, article, or apparatus that comprises, has, includes, contains a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element preceded by “comprises . . . a”, “has . . . a”, “includes . . . a”, “contains . . . a” does not, without more constraints, preclude the existence of additional identical elements in the process, method, article, or apparatus that comprises, has, includes, contains the element. The terms “a” and “an” are defined as one or more unless explicitly stated otherwise herein. The terms “substantially”, “essentially”, “approximately”, “about” or any other version thereof, are defined as being close to as understood by one of ordinary skill in the art. The term “coupled” as used herein is defined as connected, although not necessarily directly and not necessarily mechanically. A device or structure that is “configured” in a certain way is configured in at least that way but may also be configured in ways that are not listed.


The Abstract of the Disclosure is provided to allow the reader to quickly ascertain the nature of the technical disclosure. It is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims. In addition, in the foregoing Detailed Description, it can be seen that various features are grouped together in various embodiments for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claimed embodiments require more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive subject matter lies in less than all features of a single disclosed embodiment. Thus, the following claims are hereby incorporated into the Detailed Description, with each claim standing on its own as a separately claimed subject matter.

Claims
  • 1. An automated method of collateralizing an illiquid asset, the method comprising the steps of: receiving, by a first server, an electronic document comprising a digital representation of the illiquid asset, the digital representation identifying a financial interest in the illiquid asset by a borrower;hashing, by the first server, the digital representation of the illiquid asset to generate a first hash value;recording, by the first server, the first hash value on a first blockchain;storing, by the first server, the digital representation of the illiquid asset at a first memory location;generating, by the first server, an asset-information table that associates a location of the first hash value on the first blockchain with the first memory location;receiving, by the first server, an asset-valuation data concerning the illiquid asset;storing, by the first server, the asset-valuation data at a second memory location;hashing, by the first server, the asset-valuation data to generate a second hash value;recording, by the first server, the second hash value on the first blockchain;updating, by the first server, the asset-information table to associate a location of the second hash value on the first blockchain with the second memory location;generating, by a second server, an asset-value token comprising an association with the updated asset-information table;recording, by the second server, the asset-value token on a second blockchain;determining, by the second server, an amount of digital currency for an automated digital transaction based on a programmed set of rules using the asset-value as a parameter;
  • 2. The method of claim 1, further comprising a step of periodically updating at least one of the digital representation of the illiquid asset and the asset-valuation data of the illiquid asset after the amount of digital currency has been loaned.
  • 3. The method of claim 2, wherein the step of periodically updating at least one of the digital representation of the illiquid asset and the asset-valuation data of the illiquid asset after the amount of digital currency has been loaned comprises updating the asset-information table.
  • 4. The method of claim 1, wherein the step of generating a data structure comprises generating a financial-return token.
  • 5. The method of claim 4, further comprising a step of having the second server provide access to the asset-information table, in the first server, to an entity possessing the financial-return token.
  • 6. The method of claim 4, wherein the financial-return token is selected from a set comprising a return-interest token and a governance token.
  • 7. The method of claim 6, wherein the return-interest token is selected from a set comprising a senior return-interest token and a subordinate return-interest token.
  • 8. The method of claim 1, wherein the first blockchain and the second blockchain are one and the same.
  • 9. A system for performing a collateralized loan transaction, comprising: a first server including a first hardware processor and configured to: receive an electronic document comprising a digital representation of an illiquid asset, the digital representation identifying a financial interest in the illiquid asset by a borrower;hash the digital representation of he illiquid asset to generate a first hash value;record the first hash value on a first blockchain;store the digital representation of the illiquid asset at a first memory location;generate an asset-information table that associates a location of the first hash value on the first blockchain with the first memory location;receive an asset-valuation data concerning the illiquid asset;store the asset-valuation data at a second memory location;hash the asset-valuation data to generate a second hash value;record the second hash value on the first blockchain; andupdate the asset-information table to associate a location of the second hash value on the first blockchain with the second memory location; anda second server, communicatively coupled to the first server, the second server including a second hardware processor and configured to: generate an asset-value token comprising an association with the updated asset-information table;record the asset-value token on a second blockchain;determine an amount of digital currency for an automated digital transaction based on a programmed set of rules using the asset-value as a parameter;generate a data structure comprising a self-executing digital program for performing the automated digital transaction, the data structure including the asset-value token; andrecord the data structure on the second blockchain,
  • 10. The system of claim 9, wherein the first server is further configured to periodically update at least one of the digital representation of the illiquid asset and the asset-valuation data of the illiquid asset.
  • 11. The system of claim 10, wherein the first server is further configured to periodically update the asset-information table.
  • 12. The system of claim 9, wherein the second server is further configured to generate a financial-return token.
  • 13. The system of claim 12, wherein the second server is further configured to provide access to the asset-information table in the first server to an entity possessing the financial-return token.
  • 14. The system of claim 12, wherein the financial-return token is selected from a set comprising a return-interest token and a governance token.
  • 15. The system of claim 14, wherein the return-interest token is selected from a set comprising a senior return-interest token and a subordinate return-interest token.
  • 16. The system of claim 9, wherein the first blockchain and the second blockchain are one and the same.
  • 17. An automated method of collateralizing an illiquid asset, the method comprising the steps of: hashing, by a first server, a received digital representation of the illiquid asset to generate a first hash value, the digital representation identifying a financial interest in the illiquid asset by a borrower;recording, by the first server, the first hash value on a first blockchain;storing, by the first server, the digital representation of the illiquid asset at a first memory location;generating, by the first server, an asset-information table that associates the first hash value on the first blockchain with the first memory location;receiving, by the first server, an asset-valuation data;storing, by the first server, the asset-valuation data at a second memory location;hashing, by the first server, the asset-valuation data, to generate a second hash value;recording, by the first server, the second hash value on the first blockchain;updating, by the first server, the asset-information table to associate the second hash value with the second memory location;generating, by a second server, an asset-value token comprising an association with the updated asset-information table;recording, by the second server, the asset-value token on a second blockchain;determining, by the second server, an amount of digital currency for an automated digital transaction based on a programmed set of rules using the asset-value as a parameter;generating, by the second server, a data structure comprising a self-executing digital program for performing the automated digital transaction, the data structure including the asset-value token; andrecording the data structure on the second blockchain;
  • 18. The method of claim 17, further comprising a step of periodically updating at least one of the digital representation of the illiquid asset and the asset-valuation data of the illiquid asset after the amount of digital currency has been loaned.
  • 19. The method of claim 18, wherein the step of periodically updating at least one of the digital representation of the illiquid asset and the asset-valuation data of the illiquid asset after the amount of digital currency has been loaned comprises updating the asset-information table.
  • 20. The method of claim 17, wherein the step of generating a data structure comprises generating a financial-return token.
  • 21. The method of claim 20, further comprising a step of having the second server provide access to the asset-information table, in the first server, to an entity possessing the financial-return token.
  • 22. The method of claim 20, wherein the financial-return token is selected from a set comprising a return-interest token and a governance token.
  • 23. The method of claim 17, wherein the first blockchain and the second blockchain are one and the same.
REFERENCE TO PRIOR APPLICATIONS

This application claims the benefit of the following U.S. provisional applications, each of which is hereby incorporated by reference in its entirety: 1) U.S. Provisional Application Ser. No. 63/166,344, filed on Mar. 26, 2021; 2) U.S. Provisional Application Ser. No. 63/170,006, filed on Apr. 2, 2021; and 3) U.S. Provisional Application Ser. No. 63/224,431, filed on Jul. 22, 2021.

US Referenced Citations (435)
Number Name Date Kind
4309569 Merkle Jan 1982 A
5499294 Friedman Mar 1996 A
5606609 Houser Feb 1997 A
5862218 Steinberg Jan 1999 A
5920629 Rosen Jul 1999 A
5966446 Davis Oct 1999 A
6363481 Hardjono Mar 2002 B1
7028263 Maguire Apr 2006 B2
7212808 Engstrom May 2007 B2
7272179 Siemens Sep 2007 B2
7572179 Choi Aug 2009 B2
7729950 Mendizabal Jun 2010 B2
7730113 Payette Jun 2010 B1
8245038 Golle Aug 2012 B2
8266439 Haber Sep 2012 B2
8359361 Thornton Jan 2013 B2
8442903 Zadoorian May 2013 B2
8560722 Gates Oct 2013 B2
8612477 Becker Dec 2013 B2
8706616 Flynn Apr 2014 B1
8712887 Degroeve Apr 2014 B2
8867741 Mccorkindale Oct 2014 B2
8943332 Horne Jan 2015 B2
8990322 Cai Mar 2015 B2
9124423 Jennas, II Sep 2015 B2
9378343 David Jun 2016 B1
9396006 Kundu Jul 2016 B2
9398018 Macgregor Jul 2016 B2
9407431 Bellare Aug 2016 B2
9411524 O'Hare Aug 2016 B2
9411976 Irvine Aug 2016 B2
9411982 Dippenaar Aug 2016 B1
9424576 Vandervort Aug 2016 B2
9436923 Sriram Sep 2016 B1
9436935 Hudon Sep 2016 B2
9472069 Roskowski Oct 2016 B2
9489827 Quinn Nov 2016 B2
9584493 Leavy Feb 2017 B1
9588790 Wagner Mar 2017 B1
9647977 Levasseur May 2017 B2
9722790 Ebrahimi Aug 2017 B2
9818109 Loh Nov 2017 B2
9830580 Macgregor Nov 2017 B2
9875510 Kasper Jan 2018 B1
9876646 Ebrahimi Jan 2018 B2
9882918 Ford Jan 2018 B1
10025941 Griffin Jul 2018 B1
10046228 Tran Aug 2018 B2
10102265 Madisetti Oct 2018 B1
10102526 Madisetti Oct 2018 B1
10108954 Dunlevy Oct 2018 B2
10135607 Roets Nov 2018 B1
10163080 Chow Dec 2018 B2
10270599 Nadeau Apr 2019 B2
10346815 Glover Jul 2019 B2
10355869 Bisti Jul 2019 B2
10366204 Tanner, Jr. Jul 2019 B2
10373129 James Aug 2019 B1
10411897 Paolini-Subramanya Sep 2019 B2
10419225 Deery Sep 2019 B2
10438285 Konstantinides Oct 2019 B1
10476847 Smith Nov 2019 B1
10532268 Tran Jan 2020 B2
10586270 Reddy Mar 2020 B2
10628268 Baruch Apr 2020 B1
10685399 Snow Jun 2020 B2
10693652 Nadeau Jun 2020 B2
10749848 Voell Aug 2020 B2
10764752 Avetisov Sep 2020 B1
10783164 Snow Sep 2020 B2
10817873 Paolini-Subramanya Oct 2020 B2
10826685 Campagna Nov 2020 B1
10855446 Ow Dec 2020 B2
10873457 Beaudoin Dec 2020 B1
10915895 Fogg Feb 2021 B1
10929842 Arvanaghi Feb 2021 B1
10949926 Call Mar 2021 B1
10956973 Chang Mar 2021 B1
10958418 Ajoy Mar 2021 B2
10997159 Iwama May 2021 B2
11042871 Snow Jun 2021 B2
11044095 Lynde Jun 2021 B2
11044097 Snow Jun 2021 B2
11044100 Deery Jun 2021 B2
11063770 Peng Jul 2021 B1
11075744 Tormasov Jul 2021 B2
11093933 Peng Aug 2021 B1
11134120 Snow Sep 2021 B2
11164250 Snow Nov 2021 B2
11164254 Gordon, III Nov 2021 B1
11170366 Snow Nov 2021 B2
11171782 Tang Nov 2021 B2
11205172 Snow Dec 2021 B2
11276056 Snow Mar 2022 B2
11295296 Snow Apr 2022 B2
11296889 Snow Apr 2022 B2
11328290 Snow May 2022 B2
11334874 Snow May 2022 B2
11347769 Snow May 2022 B2
11348097 Snow May 2022 B2
11348098 Snow May 2022 B2
11423398 Mullins Aug 2022 B1
20010029482 Tealdi Oct 2001 A1
20030018563 Kilgour Jan 2003 A1
20040085445 Park May 2004 A1
20050206741 Raber Sep 2005 A1
20060075228 Black Apr 2006 A1
20060184443 Erez Aug 2006 A1
20070027787 Tripp Feb 2007 A1
20070094272 Yeh Apr 2007 A1
20070174630 Shannon Jul 2007 A1
20070296817 Ebrahimi Dec 2007 A1
20080010466 Hopper Jan 2008 A1
20080028439 Shevade Jan 2008 A1
20080059726 Rozas Mar 2008 A1
20090025063 Thomas Jan 2009 A1
20090287597 Bahar Nov 2009 A1
20100049966 Kato Feb 2010 A1
20100058476 Isoda Mar 2010 A1
20100161459 Kass Jun 2010 A1
20100228798 Kodama Sep 2010 A1
20100241537 Kass Sep 2010 A1
20110061092 Bailloeul Mar 2011 A1
20110161674 Ming Jun 2011 A1
20120203670 Piersol Aug 2012 A1
20120264520 Marsland Oct 2012 A1
20130142323 Chiarella Jun 2013 A1
20130222587 Roskowski Aug 2013 A1
20130275765 Lay Oct 2013 A1
20130276058 Buldas Oct 2013 A1
20140022973 Kopikare Jan 2014 A1
20140201541 Paul Jul 2014 A1
20140229738 Sato Aug 2014 A1
20140282852 Vestevich Sep 2014 A1
20140289802 Lee Sep 2014 A1
20140297447 O'Brien Oct 2014 A1
20140344015 Puértolas-Montañés Nov 2014 A1
20150052615 Gault Feb 2015 A1
20150193633 Chida Jul 2015 A1
20150206106 Yago Jul 2015 A1
20150242835 Vaughan Aug 2015 A1
20150244729 Mao Aug 2015 A1
20150309831 Powers Oct 2015 A1
20150332256 Minor Nov 2015 A1
20150363769 Ronca Dec 2015 A1
20150378627 Kitazawa Dec 2015 A1
20150379484 Mccarthy Dec 2015 A1
20160002923 Alobily Jan 2016 A1
20160012240 Smith Jan 2016 A1
20160021743 Pai Jan 2016 A1
20160071096 Rosca Mar 2016 A1
20160098578 Hincker Apr 2016 A1
20160119134 Hakoda Apr 2016 A1
20160148198 Kelley May 2016 A1
20160162897 Feeney Jun 2016 A1
20160217436 Brama Jul 2016 A1
20160239653 Loughlin-Mchugh Aug 2016 A1
20160253663 Clark Sep 2016 A1
20160260091 Tobias Sep 2016 A1
20160267472 Lingham Sep 2016 A1
20160267558 Bonnell Sep 2016 A1
20160275294 Irvine Sep 2016 A1
20160283920 Fisher Sep 2016 A1
20160292396 Akerwall Oct 2016 A1
20160292672 Fay Oct 2016 A1
20160292680 Wilson, Jr. Oct 2016 A1
20160294783 Piqueras Jover Oct 2016 A1
20160300200 Brown Oct 2016 A1
20160300234 Moss-Pultz Oct 2016 A1
20160321675 Mccoy Nov 2016 A1
20160321751 Creighton, IV Nov 2016 A1
20160321769 Mccoy Nov 2016 A1
20160328791 Parsells Nov 2016 A1
20160330031 Drego Nov 2016 A1
20160330244 Denton Nov 2016 A1
20160337119 Hosaka Nov 2016 A1
20160342977 Lam Nov 2016 A1
20160342989 Davis Nov 2016 A1
20160344737 Anton Nov 2016 A1
20160371771 Serrano Dec 2016 A1
20170000613 Lerf Jan 2017 A1
20170005797 Lanc Jan 2017 A1
20170005804 Zinder Jan 2017 A1
20170033933 Haber Feb 2017 A1
20170053249 Tunnell Feb 2017 A1
20170061396 Melika Mar 2017 A1
20170075938 Black Mar 2017 A1
20170103167 Shah Apr 2017 A1
20170124534 Savolainen May 2017 A1
20170124535 Juels May 2017 A1
20170134162 Code May 2017 A1
20170148016 Davis May 2017 A1
20170161439 Raduchel Jun 2017 A1
20170177898 Dillenberger Jun 2017 A1
20170178237 Wong Jun 2017 A1
20170213287 Bruno Jul 2017 A1
20170221052 Sheng Aug 2017 A1
20170228731 Sheng Aug 2017 A1
20170236123 Ali Aug 2017 A1
20170243208 Kurian Aug 2017 A1
20170243289 Rufo Aug 2017 A1
20170244757 Castinado Aug 2017 A1
20170330279 Ponzone Nov 2017 A1
20170344983 Muftic Nov 2017 A1
20170346693 Dix Nov 2017 A1
20170352031 Collin Dec 2017 A1
20170353309 Gray Dec 2017 A1
20170359374 Smith Dec 2017 A1
20170364642 Bogdanowicz Dec 2017 A1
20170373859 Shors Dec 2017 A1
20180005186 Hunn Jan 2018 A1
20180048599 Arghandiwal Feb 2018 A1
20180075239 Boutnaru Mar 2018 A1
20180075527 Nagla Mar 2018 A1
20180082043 Witchey Mar 2018 A1
20180088928 Smith Mar 2018 A1
20180091524 Setty Mar 2018 A1
20180097779 Karame Apr 2018 A1
20180101701 Barinov Apr 2018 A1
20180101842 Ventura Apr 2018 A1
20180108024 Greco Apr 2018 A1
20180117446 Tran May 2018 A1
20180123779 Zhang May 2018 A1
20180139042 Binning May 2018 A1
20180144292 Mattingly May 2018 A1
20180157700 Roberts Jun 2018 A1
20180158034 Hunt Jun 2018 A1
20180167201 Naqvi Jun 2018 A1
20180173906 Rodriguez Jun 2018 A1
20180176017 Rodriguez Jun 2018 A1
20180181768 Leporini Jun 2018 A1
20180182042 Vinay Jun 2018 A1
20180189333 Childress Jul 2018 A1
20180189781 Mccann Jul 2018 A1
20180204213 Zappier Jul 2018 A1
20180219683 Deery Aug 2018 A1
20180219685 Deery Aug 2018 A1
20180225640 Chapman Aug 2018 A1
20180225649 Babar Aug 2018 A1
20180241565 Paolini-Subramanya Aug 2018 A1
20180260888 Paolini-Subramanya Sep 2018 A1
20180260889 Paolini-Subramanya Sep 2018 A1
20180268162 Dillenberger Sep 2018 A1
20180268382 Wasserman Sep 2018 A1
20180268504 Paolini-Subramanya Sep 2018 A1
20180276270 Bisbee Sep 2018 A1
20180276668 Li Sep 2018 A1
20180276745 Paolini-Subramanya Sep 2018 A1
20180285879 Gadnis Oct 2018 A1
20180285970 Snow Oct 2018 A1
20180285971 Rosenoer Oct 2018 A1
20180288022 Madisetti Oct 2018 A1
20180315051 Hurley Nov 2018 A1
20180316502 Nadeau Nov 2018 A1
20180356236 Lawrenson Dec 2018 A1
20180365201 Hunn Dec 2018 A1
20180365686 Kondo Dec 2018 A1
20180365764 Nelson Dec 2018 A1
20180367298 Wright Dec 2018 A1
20190012637 Gillen Jan 2019 A1
20190013948 Mercuri Jan 2019 A1
20190018947 Li Jan 2019 A1
20190028273 Harras Jan 2019 A1
20190034459 Qiu Jan 2019 A1
20190036887 Miller Jan 2019 A1
20190036957 Smith Jan 2019 A1
20190043048 Wright Feb 2019 A1
20190044727 Scott Feb 2019 A1
20190050855 Martino Feb 2019 A1
20190057382 Wright Feb 2019 A1
20190065709 Salomon Feb 2019 A1
20190073666 Ortiz Mar 2019 A1
20190080284 Kim Mar 2019 A1
20190081793 Martino Mar 2019 A1
20190081796 Chow Mar 2019 A1
20190087446 Sharma Mar 2019 A1
20190123889 Schmidt-Karaca Apr 2019 A1
20190132350 Smith May 2019 A1
20190188699 Thibodeau Jun 2019 A1
20190197532 Jayachandran Jun 2019 A1
20190205563 Gonzales, Jr. Jul 2019 A1
20190236286 Scriber Aug 2019 A1
20190251557 Jin Aug 2019 A1
20190253240 Treat Aug 2019 A1
20190253258 Thekadath Aug 2019 A1
20190268141 Pandurangan Aug 2019 A1
20190268163 Nadeau Aug 2019 A1
20190281259 Palazzolo Sep 2019 A1
20190287107 Gaur Sep 2019 A1
20190287199 Messerges Sep 2019 A1
20190287200 Schuler Sep 2019 A1
20190288832 Dang Sep 2019 A1
20190296915 Lancashire Sep 2019 A1
20190303623 Reddy Oct 2019 A1
20190303887 Wright Oct 2019 A1
20190306150 Letz Oct 2019 A1
20190311357 Madisetti Oct 2019 A1
20190324867 Tang Oct 2019 A1
20190332691 Beadles Oct 2019 A1
20190333054 Cona Oct 2019 A1
20190334715 Gray Oct 2019 A1
20190334912 Sloane Oct 2019 A1
20190340586 Sheng Nov 2019 A1
20190340607 Lynn Nov 2019 A1
20190342422 Li Nov 2019 A1
20190347444 Lowagie Nov 2019 A1
20190347628 Al-Naji Nov 2019 A1
20190349190 Smith Nov 2019 A1
20190349426 Smith Nov 2019 A1
20190354606 Snow Nov 2019 A1
20190354607 Snow Nov 2019 A1
20190354611 Snow Nov 2019 A1
20190354724 Lowagie Nov 2019 A1
20190354725 Lowagie Nov 2019 A1
20190354964 Snow Nov 2019 A1
20190356733 Snow Nov 2019 A1
20190361917 Tran Nov 2019 A1
20190372770 Xu Dec 2019 A1
20190378128 Moore Dec 2019 A1
20190385165 Castinado Dec 2019 A1
20190386940 Hong Dec 2019 A1
20190391540 Westervelt Dec 2019 A1
20190391858 Studnicka Dec 2019 A1
20190394044 Snow Dec 2019 A1
20190394048 Deery Dec 2019 A1
20200004263 Dalla Libera Jan 2020 A1
20200004946 Gilpin Jan 2020 A1
20200005290 Madisetti Jan 2020 A1
20200019937 Edwards Jan 2020 A1
20200034571 Fett Jan 2020 A1
20200034813 Calinog Jan 2020 A1
20200042635 Douglass Feb 2020 A1
20200042960 Cook Feb 2020 A1
20200042982 Snow Feb 2020 A1
20200042983 Snow Feb 2020 A1
20200042984 Snow Feb 2020 A1
20200042985 Snow Feb 2020 A1
20200042986 Snow Feb 2020 A1
20200042987 Snow Feb 2020 A1
20200042988 Snow Feb 2020 A1
20200042990 Snow Feb 2020 A1
20200042995 Snow Feb 2020 A1
20200044827 Snow Feb 2020 A1
20200044856 Lynde Feb 2020 A1
20200044857 Snow Feb 2020 A1
20200065761 Tatchell Feb 2020 A1
20200067907 Avetisov Feb 2020 A1
20200075056 Yang Mar 2020 A1
20200089690 Qiu Mar 2020 A1
20200099524 Schiatti Mar 2020 A1
20200099534 Lowagie Mar 2020 A1
20200104712 Katz Apr 2020 A1
20200118068 Turetsky Apr 2020 A1
20200127812 Schuler Apr 2020 A1
20200134760 Messerges Apr 2020 A1
20200145219 Sebastian May 2020 A1
20200151709 Bryan May 2020 A1
20200167870 Isaacson May 2020 A1
20200175506 Snow Jun 2020 A1
20200184555 Gleizer Jun 2020 A1
20200195441 Suen Jun 2020 A1
20200211011 Anderson Jul 2020 A1
20200234386 Blackman Jul 2020 A1
20200258061 Beadles Aug 2020 A1
20200265515 Runnels Aug 2020 A1
20200279324 Snow Sep 2020 A1
20200279325 Snow Sep 2020 A1
20200279326 Snow Sep 2020 A1
20200280447 Snow Sep 2020 A1
20200302433 Green Sep 2020 A1
20200314648 Cao Oct 2020 A1
20200320097 Snow Oct 2020 A1
20200320514 Snow Oct 2020 A1
20200320521 Snow Oct 2020 A1
20200320522 Snow Oct 2020 A1
20200320620 Snow Oct 2020 A1
20200374129 Dilles Nov 2020 A1
20200382480 Isaacson Dec 2020 A1
20200389294 Soundararajan Dec 2020 A1
20210035092 Pierce Feb 2021 A1
20210042758 Durvasula Feb 2021 A1
20210044976 Avetisov Feb 2021 A1
20210073212 Conley Mar 2021 A1
20210073750 Ledford Mar 2021 A1
20210090076 Wright Mar 2021 A1
20210097602 Eichel Apr 2021 A1
20210119785 Ben-Reuven Apr 2021 A1
20210144149 Simons May 2021 A1
20210174353 Snow Jun 2021 A1
20210200653 Jetzfellner Jul 2021 A1
20210201321 Studnitzer Jul 2021 A1
20210201328 Gunther Jul 2021 A1
20210226769 Snow Jul 2021 A1
20210226773 Snow Jul 2021 A1
20210241282 Gu Aug 2021 A1
20210248514 Cella Aug 2021 A1
20210266167 Lohe Aug 2021 A1
20210266174 Snow Aug 2021 A1
20210272103 Snow Sep 2021 A1
20210273810 Lynde Sep 2021 A1
20210273816 Deery Sep 2021 A1
20210326815 Brody Oct 2021 A1
20210328804 Snow Oct 2021 A1
20210342836 Cella Nov 2021 A1
20210366586 Ryan Nov 2021 A1
20220006641 Snow Jan 2022 A1
20220012731 Derosa-Grund Jan 2022 A1
20220019559 Snow Jan 2022 A1
20220020001 Snow Jan 2022 A1
20220023742 Tran Jan 2022 A1
20220027893 Snow Jan 2022 A1
20220027897 Snow Jan 2022 A1
20220027994 Snow Jan 2022 A1
20220027995 Snow Jan 2022 A1
20220027996 Snow Jan 2022 A1
20220029805 Snow Jan 2022 A1
20220030054 Snow Jan 2022 A1
20220034004 Snow Feb 2022 A1
20220040557 Tran Feb 2022 A1
20220043831 Douglass Feb 2022 A1
20220058622 Snow Feb 2022 A1
20220058623 Snow Feb 2022 A1
20220083991 Kemper Mar 2022 A1
20220103341 Snow Mar 2022 A1
20220103343 Snow Mar 2022 A1
20220103344 Snow Mar 2022 A1
20220103364 Snow Mar 2022 A1
20220141231 Simons May 2022 A1
20220156737 Wright May 2022 A1
20220172207 Cella Jun 2022 A1
20220173893 Basu Jun 2022 A1
20220198554 Filter Jun 2022 A1
20220215389 Balaraman Jul 2022 A1
20220245626 Sewell Aug 2022 A1
20230185783 Haddad Jun 2023 A1
Foreign Referenced Citations (23)
Number Date Country
107392618 Nov 2017 CN
110392052 Oct 2019 CN
110599147 Dec 2019 CN
112329041 Feb 2021 CN
10128728 Jan 2003 DE
3726438 Oct 2020 EP
3862947 Aug 2021 EP
S5383297 Jul 1978 JP
2021152931 Sep 2021 JP
100653512 Dec 2006 KR
1747221 May 2017 KR
101747221 Jun 2017 KR
0049797 Aug 2000 WO
2007069176 Jun 2007 WO
2015077378 May 2015 WO
2017190795 Nov 2017 WO
2018013898 Jan 2018 WO
2018109010 Jun 2018 WO
2018127923 Jul 2018 WO
2018127923072018 Jul 2018 WO
2019180702 Sep 2019 WO
2019207504 Oct 2019 WO
2020125839 Jun 2020 WO
Non-Patent Literature Citations (39)
Entry
M. Nandi, R. K. Bhattacharjee, A. Jha and F. A. Barbhuiya, “A secured land registration framework on Blockchain,” 2020 Third ISEA Conference on Security and Privacy (ISEA-ISAP), Guwahati, India, 2020, pp. 130-138, doi: 10.1109/ISEA-ISAP49340.2020.235011 (Year: 2020).
Merkle Mountain Ranges (MMRs)-Grin Documentation, https://quentinlesceller.github.io/grin-docs/technical/building-plocks/merkle-mountain-ranges/, 5 pages, printed Jun. 1, 2022.
Merkle Mountain Ranges, https://github.com/opentimestamps/opentimestamps-server/blob/master/doc/merkle-mountain-range.md, 3 pages, printed Jun. 1, 2022.
Michelson, Kyle, et al., “Accumulate: An identity-based blockchain protocol with cross-chain support, human-readable addresses, and key management capabilities”, Accumulate Whitepaper, v1.0, Jun. 12, 2022, 28 pages.
MOF-BC: A Memory Optimized and Flexible BlockChain for Large Scale Networks. lle:///C:/Users/eoussir/Documents/e-Red%20Folder/16905961/NPL_MOF_BC_A%20Memory%20Optimized%20and%20Flexible%20Blockchain.pdf (Year:2018) 43 pages.
On blockchain and its integration with IoT. Challenges and opportunities. file:///C:/Users/eoussir/Downloads/1-s2.0S0167739X17329205-main%20(1). pdf (Year: 2018) 18 pages.
“Money in programmable applications: Cross-sector perspectives from the German economy”, Deutsche Bundesbank Eurosystem, https://www.bundesbank.de, 18 pages, 2020.
Al-Naji, Nader et al., “Basis: A Price-Stable Cryptocurrency with an Algorithmic Central Bank” www.basis.io Jun. 20, 2017, 27 pages.
Alsolami, Fahad, and Terrance E. Boult. “CloudStash: using secret-sharing scheme to secure data, not keys, in multi-clouds.” Information Technology: New Generations (ITNG), 2014 11th International Conference on. IEEE, 2014.
Ana Reyna et al.; On blockchain and its integration with IoT. Challenges and opportunities. Future generation computer systems. vol. 88, Nov. 2018, pp. 173-190. https://www.sciencedirect.com/science/article/pii/S0167739X17329205 (Year: 2018).
Casey, “BitBeat: Factom Touts Blockchain Tool for Keeping Record Keepers Honest”, Wall Street Journal, Nov. 5, 2014.
Chakravorty, Antorweep, and Chunming Rong, “Ushare: user controlled social media based on blockchain.” Proceedings of the 11th International Conference on Ubiquitous Information Management and Communication. ACM, 2017.
Chen, Zhixong, and Yixuan Zhu. “Personal Archive Service System using Blockchain Technology: Case Study, Promising and Challenging.” AI & Mobile Services (AIMS), 2017 IEEE International Conference on. IEEE, 2017.
Crosby, Michael et al., “BlockChain Technology, Beyond Bitcoin”, Sutardja Center for Entrepreneurship & Technology, Berkeley Engineering, Oct. 16, 2015, 35 pages.
Dai et al. TrialChain: A Blockchain-Based Platform to Validate Data Integrity in Large, Biomedical Research Studies arXiv: 1807.03662 Jul. 10, 2018 (Year: 2018).
Eberhardt et al., “ZoKrates—Scalable Privacy-Preserving Off-Chain Computations,” https://ieeeexplore.ieee.org/stamp/JSP?tp:::&armumber:::8726497. (Year:2018).
Feng and Luo, “Evaluating Memory-Hard Proof-of-Work Algorithms on Three Processors,” PVLDB, 13(6): 898-911, 2020.
Fernandez-Carames et al.; A Review on the Use of Blockchain for the Internet of Things. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8370027 (Year: 2018). 23 pages.
Haarmann, et al., “DMN Decision Execution on the Ethereum Blockchain,” Hasso Plattner Institute, University of Potsdam, 15 pages.
Iddo Bentov, Bitcoin and Secure Computation with Money, May 2016 (Year: 2016).
Kim et al., “A Perspective on Blockchain Smart Contracts,” Schulich School of Business, York University, Toronto, Canada, 6 pages.
Kroeger, T. et al., The Case for Distributed Data Archival Using Secret Splitting with Percival, 6th International Symposium on Resilient Control Systems (available at IEEE Xplore), p. 204-209 (Year: 2013).
Krol, Michal et al., “SPOC: Secure Payments for Outsourced Computations” https://arxiv.org/pdf/1807.06462.pdf. (Year: 2018).
Luther, “Do We Need A “Fedcoin” Cryptocurrency?,” ValueWalk, Newstex Global Business Blogs, Dec. 30, 2015 (Year: 2015).
Luu et al., Making Smart Contracts Smarter, 2016.
Menezes, Alfred. J., et al. “Handbook of Applied Cryptography,” 1997, CRC Press, p. 527-28.
Muhamed et al. EduCTX: A Blockchain-Based Higher Education Credit Platform, https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8247166. (Year: 2017). 16 pages.
Sokolowski, R. (2011). Signed, sealed, delivered: EMortgages are protected from unauthorized alteration by something called a tamper seal. Mortgage Banking, 71(6), 108(4). Retrieved from https://dialog.proquest.com/professional/docview/1068158815? accountid=131444 (Year: 2011).
United States: New Generation cryptocurrency, USDX Protocol, Offers Crypto Advantages and Fiat Pegging, Apr. 2, 2018 (Year: 2018).
Unknown, “Federated Learning: Collaborative Machine Learning without Centralized Training Data” Apr. 6, 2017, 11 pages.
Unknown, “Midex”, https://promo.midex.com/Midex_EN.pdf, 25 pages.
Unknown, Xtrade White Paper, https://xtrade1-9649.kxcdn.com/wp-content/uploads/2017/09/xtrade-whitepaper.pdf Feb. 7, 2018, 37 pages.
Watanabe, Hiroki, et al. “Blockchain contract: Securing a blockchain applied to smart contracts.” 2016 IEEE International Conference on Consumer Electronics (ICCE). IEEE, 2016.
White, Ron, “How Computers Work,” Oct. 2003, QUE, Seventh Edition (Year: 2003), 23 pages.
Why offchain storage is needed for blockchain_V4_1 FINAL (Year: 2018), by IBM, 13 pages.
Written Opinion in PCT/US2021/040207, Inventor Snow, dated Oct. 7, 2021, 14 pages.
ZoKrates—Scalable Privacy-Preserving Off-Chain Computations, by Jacob Eberhardt, Stefan Tai , 8 pages, Nov. 3, 2011 (Year: 2011).
Office Action (Non-Final Rejection) dated Feb. 29, 2024 for U.S. Appl. No. 17/979,097 (pp. 1-15).
P. Sood, P. Palsania, S. Ahuja, S. Kumar, K. Khatter and A. Mishra, “Decentralised & Collaborative DocuPad Using Blockchain,” 2022 IEEE Delhi Section Conference (DELCON), New Delhi, India, 2022, pp. 1-8, doi: 10.1109/DELCON54057.2022.9752853. ( Year: 2022).
Related Publications (1)
Number Date Country
20220309479 A1 Sep 2022 US
Provisional Applications (3)
Number Date Country
63224431 Jul 2021 US
63170006 Apr 2021 US
63166344 Mar 2021 US