WO2023215933A1 - Systèmes et procédés de comparaison de chaîne de blocs - Google Patents

Systèmes et procédés de comparaison de chaîne de blocs Download PDF

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Publication number
WO2023215933A1
WO2023215933A1 PCT/AU2023/050349 AU2023050349W WO2023215933A1 WO 2023215933 A1 WO2023215933 A1 WO 2023215933A1 AU 2023050349 W AU2023050349 W AU 2023050349W WO 2023215933 A1 WO2023215933 A1 WO 2023215933A1
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WO
WIPO (PCT)
Prior art keywords
entities
blockchain
blockchain system
score
attributes
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Application number
PCT/AU2023/050349
Other languages
English (en)
Inventor
Nicholas CROW
Benjamin MEMISEVIC
Matthys GROBBELAAR
Original Assignee
Pocket Venture Holdings Pty Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from AU2022901256A external-priority patent/AU2022901256A0/en
Application filed by Pocket Venture Holdings Pty Ltd filed Critical Pocket Venture Holdings Pty Ltd
Publication of WO2023215933A1 publication Critical patent/WO2023215933A1/fr

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Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
    • H04L9/50—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols using hash chains, e.g. blockchains or hash trees
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/018—Certifying business or products
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0609—Buyer or seller confidence or verification
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/03—Credit; Loans; Processing thereof
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/04—Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q2220/00—Business processing using cryptography

Definitions

  • the present invention relates to blockchain systems and methods.
  • the present invention relates to blockchain comparison systems and methods, which may be used to compare reputations or other characteristics of different entities.
  • a blockchain is a decentralized, distributed, and generally public, digital ledger consisting of records, called blocks, that are used to record transactions. Rather than being controlled by a central authority, the blockchain is recorded across many computers, allowing participants to verify and audit transactions independently.
  • a problem with blockchain systems of the prior art is that, despite the data thereon being public and accessible, the data is difficult to interpret beyond a number of limited use cases (such as verifying a specific cryptocurrency transaction).
  • the present invention relates to blockchain systems and methods, which may at least partially overcome at least one of the abovementioned disadvantages or provide the consumer with a useful or commercial choice.
  • the present invention in one form, resides broadly in a blockchain system including: a plurality of entities, configured to interact with one or more blockchains; a plurality of blocks defined on the one or more blockchains, each of the blocks relating to the interaction between an entity of the plurality of entities and the blockchain; a comparison module, configured to retrieve data from the one or more blockchains relating to interactions with the plurality of entities and the one or more blockchains, and generate a score relating to each of the plurality of entities.
  • the blockchain system provides a simple means for evaluating entities, such as for comparison or suitability purposes. Furthermore, as the score is generated according to interactions on the one or more blockchains, the score is accurate and objective (e.g. without bias).
  • At least one smart contract is defined by code on a blockchain of the one or more blockchains, wherein the plurality of entities are configured to interact with the at least one smart contract.
  • the entity includes parties of or associated with the contract.
  • the parties may include natural persons.
  • the score comprises a credit score associated with the entity, the credit score an indicator of creditworthiness of the entity.
  • the attributes may include one or more historical repayment attributes.
  • the attributes may include one or more asset attributes.
  • the asset attributes may include a liquid asset attribute.
  • the attributes may relate to an interaction with at least one smart contract.
  • the score is generated from a plurality of attributes associated with the entity.
  • the plurality of attributes include attributes having different units.
  • the comparison module is configured to convert each of the attributes to a relative sub-score, the relative sub-score indicative of a performance of the entity relative to other entities with reference to the attribute.
  • the sub-score is defined according to a distribution of entities relative to a pre-defined scale.
  • the pre-defined scale may comprise a score between 0 and 100.
  • the distribution to the pre-defined scale comprises dynamic range adjustment, such that the sub-scores (at least substantially) fill the dynamic range of the predefined scale.
  • each of the sub-scores is defined according to the same pre-defined scale. This enables the sub-scores to be considered with one score drowning other scores.
  • the score is generated using a weighted average of the plurality of subscores.
  • the weighted average is configurable.
  • the weighted average may be configured to each of a plurality of different scenarios.
  • the comparison module is further configured to compare scores of two or more of the plurality of entities, and select an entity at least in part according to a result of the comparison.
  • the attributes may be generated from data from the one or more blockchains.
  • the attributes may be generated at least in part according to a difference between a value and a desired value.
  • the attributes may be generated according to an average of differences.
  • the entity may comprise an oracle feed representing an off-chain data feed.
  • the attributes may comprise one or more performance attributes.
  • the attributes may comprise one or more accuracy attributes.
  • the system may be configured to compare oracles feeds and automatically select oracle feeds.
  • the invention resides broadly in a blockchain method including: retrieving data from one or more blockchains relating to an interaction between a plurality of entities and the one or more blockchains; generating a score relating to each of the plurality of entities according to the retrieved data.
  • the one or more blockchains define at least one smart contract, wherein the interaction between the entities and the one or more blockchains is by the at least one smart contract.
  • the data retrieved from the one or more blockchains relates to a plurality of interactions for each of the plurality of entities.
  • the step of generating a score comprises transforming the retrieved data into a plurality of sub-scores for each of the entities.
  • Each of the sub-scores may comprise an attribute relating to the entity, and may be represented in a pre-defined scale, e.g. 0-100.
  • the sub-score may be indicative of a performance of the entity relative to other entities with reference to the attribute.
  • the step of generating the score may include generating the score using a weighted combination of the sub-scores associated with the respective entities.
  • Figure 1 illustrates a simplified schematic of a blockchain system, according to an embodiment of the present invention.
  • Figure 2 illustrates a simplified schematic of a reputation score generating portion of the blockchain system of Figure 1 , according to an embodiment of the present invention.
  • Figure 3a illustrates an example distribution (in the form of a histogram) of average response time attributes of a plurality of oracle feeds.
  • Figure 3b illustrates an example sub-score mapping of the response time attributes of Figure 3a, according to an embodiment of the present invention.
  • Figure 4 illustrates a blockchain method, according to an embodiment of the present invention.
  • Embodiments of the present invention provide various blockchain methods and systems, which are described below, which are particularly useful in analysing and comparing entities interacting with the blockchain.
  • the entity is an oracle feed
  • the methods and systems enable a “reputation” score of the oracle to be determined, thereby enabling better comparison and selection of oracle feeds for use based upon their past behaviour.
  • the methods and systems may be used to generate a credit score based upon actual interactions on the blockchain by that person.
  • the methods and systems have, however, much broader application, as will be apparent from the description below.
  • FIG. 1 illustrates a simplified schematic of a blockchain system 100, according to an embodiment of the present invention.
  • the blockchain system 100 enables simple and efficient analysis of data on the blockchain for generating scores, such as reputation and credit scores, for one or more entities.
  • scores such as reputation and credit scores
  • the comparison is quantifiable and based on quantifiable characteristics.
  • the blockchain system 100 includes a plurality of smart contract instances 105, which receive data from a plurality of oracle feeds 110.
  • the smart contract instances 105 are associated with user accounts 1 15, e.g. defining a financial contract between two or more of the user accounts 1 15, as will be readily understood by the skilled addressee.
  • the price of gold may be provided by an oracle feed 110, which can be used by a smart contract 1 15 relating to gold futures between first and second user accounts 1 15, which require the changing price of gold as input.
  • an oracle feed 110 which can be used by a smart contract 1 15 relating to gold futures between first and second user accounts 1 15, which require the changing price of gold as input.
  • a smart contract 1 15 relating to gold futures between first and second user accounts 1 15, which require the changing price of gold as input.
  • any suitable input, or combination of inputs may be used by any suitable smart contract 1 15.
  • Each of the oracle feeds 110 is associated with a reputation score 120, which is automatically calculated and updated based upon data in the blockchain.
  • the reputation score 120 provides a simple way for users (or contracts) to choose between, or choose a selection of, oracle feeds 110 based upon performance.
  • Each of the user accounts 1 15 is similarly associated with a reputation score, the reputation score functioning as a credit score for an associated natural person or legal entity.
  • the credit score (reputation score) associated with the user accounts functions in a similar way to the reputation score associated with the oracles, and is useful when providing credit (e.g. in the context of leveraged trading) as it enables the creditworthiness of a user to be evaluated quickly and objectively.
  • Figure 2 illustrates a simplified schematic of a reputation score generating portion 100a of the blockchain system 100, according to an embodiment of the present invention.
  • the oracle feeds 1 10 engage with a plurality of blockchains 205a-205c, e.g. when settling a smart contract instance 105. This enables the oracle feeds 1 10 to be used in a wide range of instances.
  • a plurality of streamers 210 engage with the blockchains 205a-205c to obtain data therefrom relating to the oracle feeds 1 10, and provides same to a reputation module 215.
  • the reputation module 215 then generates the reputation scores 120 relating to each of the oracle feeds 1 10.
  • the reputation scores 120 are generated from a plurality of attributes, such as average response time, accuracy and operating time.
  • attributes such as average response time, accuracy and operating time.
  • the skilled addressee will, however, appreciate that any suitable attribute or combination of attributes may be used.
  • the attributes need not be defined directly on the blockchain, and as such, may be calculated by the streamers 210 according to data on the blockchain.
  • a delay attribute may not be defined on the blockchain, but instead request and response times may be recorded.
  • the streamer 210 may determine the delay attribute by subtracting the request time from the response time.
  • the streamers 210 may take data from the blockchain in a particular format, and convert it to a more easily processed format (e.g. from a number to date or time).
  • the streamers 210 may similarly generate averages of large amounts of data, or aggregates of data.
  • the streamers may first determine a plurality of delay values by subtracting the request time from the response time, and subsequently average the delay values.
  • the values are averaged in a time-weighted fashion, thus giving higher weight to recent values.
  • the streamer 210 may initially determine a target value for each of a plurality of time points, and then compare data from the oracle feed 110 with the target value.
  • the target value may be determined by averaging a number of other feeds, or by any suitable metric.
  • the attributes are generally expressed in different units of measurement (e.g. the average response time may be measured in seconds, and accuracy may be expressed as an average percentage deviation). Even when measured in the same unit of measurement, different data ranges for the attributes can result in the data being incomparable. As an illustrative example, response time and operating time (i.e. how long an oracle feed has been operating) may be orders of magnitude different, meaning comparison is difficult despite being expressed in the same units.
  • the reputation module 215 first transforms the attributes into a comparable form. This is performed by mapping the raw data of the attributes to a pre-defined scale (e.g. between 0 and 100). Such mapping generally comprises a dynamic range adjustment, such that the subscores (at least substantially) fill the dynamic range of the pre-defined scale.
  • a pre-defined scale e.g. between 0 and 100.
  • Figure 3a illustrates an example distribution (in the form of a histogram) of average response time attributes of a plurality of oracle feeds.
  • the lowest average response time is 0.2 seconds, and the highest response time is 3.2s. As such, only 3 seconds separates the fastest and slowest oracle feeds.
  • the average response time is transformed linearly to a score between 0 and 100, with the lowest response time mapped to the score 100, the highest response time mapped to the score 0, and the remaining response times mapped between 0 and 100 according to their distance between the highest and lowest respond times.
  • Figure 3b illustrates an example sub-score mapping of the response time attributes of Figure 3a.
  • the sub-score mapping provides a relative performance ranking where 100 is best and 0 is worst, and that utilises the entire range (or substantially entire range) of 0 to 100.
  • response time a value that is a low as possible is desirable. As such, the lowest response times are mapped to the highest scores (and vice versa). However, if a high value is desirable, the mapping of high values to high scores may be provided.
  • the reputation module 215 may map absolute differences between the value and the desired value to scores between 0 and 100.
  • non-linear mappings may be used.
  • delay values may be mapped in a manner that penalises high values more than low values. In one embodiment, this may be achieved by applying a non-linear function to the values prior to mapping (e.g. X 2 ).
  • the various attributes are mapped to scores between 0 and 100, the various attributes may be combined, e.g. using weighted averages, without one of the attributes drowning the others.
  • a weight of 0.8 may be provided to accuracy, and a weight of only 0.2 be provided to response time.
  • attributes may include time operational, number of daily submissions, average gas price, total gas used, and average gas used.
  • the resultant scores of each of the oracle feeds enable simple comparison of oracle feeds, as oracle feeds with higher scores are “better” than oracle feeds with lower scores, at least with reference to the chosen attributes and weights.
  • the resultant scores may in turn be easily ranked.
  • the scores are particularly useful in generating credit scores for entities, such as natural persons or legal entities.
  • Credit scores for entities are calculated in a similar manner to the oracle feeds, described above, but using data relating to historical repayments, historical loan amounts, total assets, liquid assets and the like.
  • the streamers retrieve data relating to the natural person or legal entity, from a variety of blockchains 205a-205c.
  • This data may relate to the trading activity of the natural person or legal entity over time, as well as information regarding on-chain assets.
  • the credit scores have a variety of uses, but are particularly useful in relation to leveraged trading, where the natural person or legal entity may essentially utilise credit to enable leveraged trading.
  • funding rates may be determined at least in part according to credit score
  • liquidation thresholds may be determined at least in part according to credit score.
  • scores may be generated based upon products, services or companies, e.g. using customer data.
  • a score may be generated based upon customer numbers, return rates, average referral rate, days between returns, and operation time.
  • the score may be much more reliable than existing online customer reviews, which are heavily manipulable by providers of the product or service.
  • scores may be generated to enable comparison of different blockchains according to one or more criteria.
  • metrics include: number of validators, number of user accounts, number of dApps, transactions per second, cost of transaction, and number of attacks to the network. Any suitable characteristics may, however, be used.
  • scores may be generated to enable comparison of different decentralized autonomous organizations (DAOs) and protocols.
  • metrics may include participation, profitability, stability, statistics around use and level of external interaction.
  • scores may be generated for tokens, using metrics such as total volume of circulation, total value (e.g. in Eth or USD), and total number of unique addresses transacting the token.
  • web3 users can be scored to measure their activity and level of sophistication, e.g. through data relating to their historical activity.
  • health scores may be generated for persons, e.g. for use by insurance companies when calculating premiums or determining whether to take on a client.
  • health monitoring devices e.g. smartwatches
  • the streamers 210 may then retrieve this data, process it and provide it to the reputation module 215, where it is processed to generate a health score.
  • any suitable type of score may be generated based upon on-chain data, including personal scores, e.g. based upon height, weight, income, net worth and IQ, for use in the context of dating or on dating sites, market related scores (e.g. a fear and greed index, based upon price momentum, volatility, options, etc), or any other type of score.
  • the reputation module 215 is configurable to provide weights that are customisable according to input from a user or smart contract. Such configuration enables users (or smart contracts) to make selections according to their own priorities.
  • one lender may prioritise repayment history when calculating a credit score, whereas another may prioritise assets.
  • a dating website may use input from individuals when calculating date ability, as different individuals will generally prioritise characteristics of their potential partners differently.
  • streamers 210 that gather data from the blockchain
  • oracles are specifically set up to provide off-chain data to the streamers 210.
  • This can include health data from third-party health systems, personal data (e.g. height, weight), or any suitable data from any source.
  • the different streamers 210 focus on different attributes or different types of data. This enables the system 100 to function with partial data should one of the streamers 210 fail, or data of a particular type be unavailable.
  • FIG. 4 illustrates a blockchain method 400, according to an embodiment of the present invention.
  • the blockchain method 400 may be similar or identical to the method performed by the system 100.
  • step 405 data is retrieved from one or more blockchains relating to interactions between a plurality of entities and at least one smart contract.
  • Each of the at least one smart contracts is defined by code on a blockchain of the one or more blockchains.
  • the retrieved data is transformed into a plurality of sub-scores for each of the entities.
  • Each of the sub-scores is an attribute relating to the entity, and is represented in a pre-defined scale, e.g. 0-100.
  • the sub-score is indicative of a performance of the entity relative to other entities with reference to the attribute.
  • a score is generated relating to each of the plurality of entities according to a weighted combination of the sub-scores associated with the respective entities.
  • the score can then be used to compare and rank entities, e.g. oracle feeds, natural persons or legal entities, in a simple and objective manner.
  • entities e.g. oracle feeds, natural persons or legal entities
  • the weighted combination of sub-scores are mapped to a pre-defined scale, e.g. 0-100, according to a relative performance of the entities. This enables a score to be interpreted without reference to other scores, as its position on the pre-defined scale is relative to the other scores.
  • mapping function is used to generate the score (/?): where A/ is the set of all entities, N x is the specific entity for which the score is created, M, is the set of raw data for attribute /, M x is the raw data for entity x and attribute /, w, is the weighting for metric /, and ⁇ p is the mapping function.
  • the systems and methods disclosed herein provide various benefits over the prior art.
  • the systems and methods enable entities to be easily and objectively compared, based upon data on the blockchain. This in turn enables better decisions to be made around use of entities.

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Abstract

L'invention concerne un système et un procédé de chaîne de blocs qui simplifie l'évaluation et la comparaison d'entités à l'aide de données en chaîne. Le système de chaîne de blocs comprend : une pluralité d'entités, configurées pour interagir avec une ou plusieurs chaînes de blocs ; une pluralité de blocs définis sur la ou les chaînes de blocs, chacun des blocs se rapportant à l'interaction entre une entité de la pluralité d'entités et la chaîne de blocs ; et un module de comparaison, configuré pour récupérer des données à partir de la ou des chaînes de blocs concernant des interactions avec la pluralité d'entités et la ou les chaînes de blocs, et générer un score relatif à chacune de la pluralité d'entités.
PCT/AU2023/050349 2022-05-11 2023-04-28 Systèmes et procédés de comparaison de chaîne de blocs WO2023215933A1 (fr)

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AU2022901256A AU2022901256A0 (en) 2022-05-11 Blockchain comparison systems and methods
AU2022901256 2022-05-11

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