CA2933407A1 - System and method for rating a transaction history - Google Patents
System and method for rating a transaction history Download PDFInfo
- Publication number
- CA2933407A1 CA2933407A1 CA2933407A CA2933407A CA2933407A1 CA 2933407 A1 CA2933407 A1 CA 2933407A1 CA 2933407 A CA2933407 A CA 2933407A CA 2933407 A CA2933407 A CA 2933407A CA 2933407 A1 CA2933407 A1 CA 2933407A1
- Authority
- CA
- Canada
- Prior art keywords
- rating
- peer
- digital currency
- account
- transaction
- Prior art date
- Legal status (The legal status 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 status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 29
- 230000002596 correlated effect Effects 0.000 claims description 32
- 238000004590 computer program Methods 0.000 claims description 4
- 238000010586 diagram Methods 0.000 description 23
- 238000004364 calculation method Methods 0.000 description 15
- 230000006870 function Effects 0.000 description 12
- 238000012546 transfer Methods 0.000 description 9
- 238000004422 calculation algorithm Methods 0.000 description 8
- 238000004891 communication Methods 0.000 description 8
- 230000000875 corresponding effect Effects 0.000 description 8
- 230000002431 foraging effect Effects 0.000 description 8
- 238000012545 processing Methods 0.000 description 8
- 238000013500 data storage Methods 0.000 description 6
- 230000007423 decrease Effects 0.000 description 6
- 230000000694 effects Effects 0.000 description 5
- 230000032683 aging Effects 0.000 description 4
- 238000012986 modification Methods 0.000 description 4
- 230000004048 modification Effects 0.000 description 4
- 230000008520 organization Effects 0.000 description 4
- 230000003993 interaction Effects 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 2
- 230000007246 mechanism Effects 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000002093 peripheral effect Effects 0.000 description 2
- 238000010200 validation analysis Methods 0.000 description 2
- 241000531116 Blitum bonus-henricus Species 0.000 description 1
- 235000008645 Chenopodium bonus henricus Nutrition 0.000 description 1
- 101100321409 Rattus norvegicus Zdhhc23 gene Proteins 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000036528 appetite Effects 0.000 description 1
- 235000019789 appetite Nutrition 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 230000000295 complement effect Effects 0.000 description 1
- 230000008602 contraction Effects 0.000 description 1
- 235000014510 cooky Nutrition 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 238000013479 data entry Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 239000010931 gold Substances 0.000 description 1
- 229910052737 gold Inorganic materials 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 238000005192 partition Methods 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 239000000758 substrate Substances 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Classifications
-
- 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
- G06Q20/00—Payment architectures, schemes or protocols
- G06Q20/04—Payment circuits
- G06Q20/06—Private payment circuits, e.g. involving electronic currency used among participants of a common payment scheme
-
- 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
- G06Q20/00—Payment architectures, schemes or protocols
- G06Q20/22—Payment schemes or models
- G06Q20/223—Payment schemes or models based on the use of peer-to-peer networks
-
- 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
- G06Q20/00—Payment architectures, schemes or protocols
- G06Q20/38—Payment protocols; Details thereof
- G06Q20/389—Keeping log of transactions for guaranteeing non-repudiation of a transaction
-
- 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
- G06Q20/00—Payment architectures, schemes or protocols
- G06Q20/38—Payment protocols; Details thereof
- G06Q20/40—Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check credit lines or negative lists
- G06Q20/401—Transaction verification
- G06Q20/4016—Transaction verification involving fraud or risk level assessment in transaction processing
-
- 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
- G06Q40/02—Banking, e.g. interest calculation or account maintenance
Abstract
-30- ABSTRACT OF THE DISCLOSURE Described herein is a rating system for rating a transaction history of a digital currency, comprising: a storage system for storing transaction information of the digital currency; an interface for receiving an identifier of at least one account associated with the digital currency and a request for rating the transaction history of the at least one account; a processor communicative with the storage system and the interface; the processor identifying transactions of the at least one account from the transaction information stored in the storage system and assessing the destination of the identified transactions to generate a rating for the at least one account. The rating system may further assess the amount and age of the identified transactions. The rating system may be useful, for example, for peer-to-peer digital currencies such as Bitcoin. Methods for rating a transaction history of a digital currency and computer readable media for providing the same are also described herein.
Description
SYSTEM AND METHOD FOR RATING A TRANSACTION HISTORY
BACKGROUND OF THE INVENTION
Field of the Invention The present invention relates to generating a rating of a transaction history in electronic commerce, and more specifically rating a transaction history of a digital currency.
Description of the Related Art The internet age has fostered the development and use of digital currencies.
A digital currency is electronic money that acts as alternative currency.
Currently, digital currencies are not produced by government-endorsed central banks nor necessarily backed by national currency.
Digital currencies are typically used in transactions with all goods and services and are not limited to electronic media such as Internet distribution of movies and games.
Many digital currencies have been developed and found favor among users for several years, but are no longer active. For example, non-cryptocurrencies e-Gold and e-Bullion, and cryptocurrencies SolidCoin, BBQCoin, Fairbrix, and GeistGeld had varying degrees of use in the past, but are no longer active.
Many digital currencies such as non-cryptocurrency Ven, and cryptocurrencies Bitcoin, Litecoin, PPcoin (Peer-to-peer coin), Freicoin, Namecoin, Terracoin, and Feathercoin are in active use. The appetite for digital currencies among internet users ensures that further digital currencies will continue to be developed.
Mainstream monetary transactions are typically based on trust. Identification of each party in a two party transaction is a significant part of building trust.
Digital currencies such as Bitcoin and Bitcoin based currencies use proof-of-work, as well as proof-of-stake for PPcoin, as validators instead of traditional mechanisms for establishing trust. Moreover, with digital currencies such as Bitcoin, transactions are often carried out in anonymity without any exchange of identification other than a user's account identifier. Thus, digital currency transactions are susceptible to identity fraud or scams.
Accordingly, there is a need for alternative mechanisms to help establish trust among users of digital currencies.
SUMMARY OF THE INVENTION
In an aspect there is provided, a rating system for rating a transaction history of a digital currency, comprising:
a storage system for storing transaction information of the digital currency;
an interface for receiving an identifier of at least one account associated with the digital currency and a request for rating the transaction history of the at least one account;
a processor communicative with the storage system and the interface;
the processor identifying transactions of the at least one account from the transaction information stored in the storage system and assessing the amount, date, and destination of the identified transactions to generate a rating for the at least one account.
In another aspect there is provided, a computer-implemented method for rating a transaction history of a digital currency, comprising:
storing transaction information of the digital currency in a storage system;
receiving over a network via an interface an identifier of at least one account associated with the digital currency and a request for rating the transaction history of the at least one account;
identifying transactions of the at least one account from the transaction information stored in the storage system; and assessing the amount, date, and destination of the identified transactions to generate a rating for the at least one account.
In yet another aspect there is provided, a computer readable medium embodying a computer program for rating a transaction history of a digital currency, comprising:
computer readable code for storing transaction information of the digital currency in a storage system;
computer readable code for receiving over a network via an interface an identifier of at least one account associated with the digital currency and a request for rating the transaction history of the at least one account;
computer readable code for identifying transactions of the at least one account from the transaction information stored in the storage system; and
BACKGROUND OF THE INVENTION
Field of the Invention The present invention relates to generating a rating of a transaction history in electronic commerce, and more specifically rating a transaction history of a digital currency.
Description of the Related Art The internet age has fostered the development and use of digital currencies.
A digital currency is electronic money that acts as alternative currency.
Currently, digital currencies are not produced by government-endorsed central banks nor necessarily backed by national currency.
Digital currencies are typically used in transactions with all goods and services and are not limited to electronic media such as Internet distribution of movies and games.
Many digital currencies have been developed and found favor among users for several years, but are no longer active. For example, non-cryptocurrencies e-Gold and e-Bullion, and cryptocurrencies SolidCoin, BBQCoin, Fairbrix, and GeistGeld had varying degrees of use in the past, but are no longer active.
Many digital currencies such as non-cryptocurrency Ven, and cryptocurrencies Bitcoin, Litecoin, PPcoin (Peer-to-peer coin), Freicoin, Namecoin, Terracoin, and Feathercoin are in active use. The appetite for digital currencies among internet users ensures that further digital currencies will continue to be developed.
Mainstream monetary transactions are typically based on trust. Identification of each party in a two party transaction is a significant part of building trust.
Digital currencies such as Bitcoin and Bitcoin based currencies use proof-of-work, as well as proof-of-stake for PPcoin, as validators instead of traditional mechanisms for establishing trust. Moreover, with digital currencies such as Bitcoin, transactions are often carried out in anonymity without any exchange of identification other than a user's account identifier. Thus, digital currency transactions are susceptible to identity fraud or scams.
Accordingly, there is a need for alternative mechanisms to help establish trust among users of digital currencies.
SUMMARY OF THE INVENTION
In an aspect there is provided, a rating system for rating a transaction history of a digital currency, comprising:
a storage system for storing transaction information of the digital currency;
an interface for receiving an identifier of at least one account associated with the digital currency and a request for rating the transaction history of the at least one account;
a processor communicative with the storage system and the interface;
the processor identifying transactions of the at least one account from the transaction information stored in the storage system and assessing the amount, date, and destination of the identified transactions to generate a rating for the at least one account.
In another aspect there is provided, a computer-implemented method for rating a transaction history of a digital currency, comprising:
storing transaction information of the digital currency in a storage system;
receiving over a network via an interface an identifier of at least one account associated with the digital currency and a request for rating the transaction history of the at least one account;
identifying transactions of the at least one account from the transaction information stored in the storage system; and assessing the amount, date, and destination of the identified transactions to generate a rating for the at least one account.
In yet another aspect there is provided, a computer readable medium embodying a computer program for rating a transaction history of a digital currency, comprising:
computer readable code for storing transaction information of the digital currency in a storage system;
computer readable code for receiving over a network via an interface an identifier of at least one account associated with the digital currency and a request for rating the transaction history of the at least one account;
computer readable code for identifying transactions of the at least one account from the transaction information stored in the storage system; and
-2-
3 PCT/CA2013/050967 computer readable code for assessing the amount, date, and destination of the identified transactions to generate a rating for the at least one account.
In still another aspect there is provided, a rating system for rating a transaction history of a peer-to-peer digital currency, comprising:
a storage system for storing transaction information of the peer-to-peer digital currency;
an interface for receiving an identifier of at least one account associated with the peer-to-peer digital currency and a request for rating the transaction history of the at least one account, a processor communicative with the storage system and the interface;
the processor identifying transactions of the at least one account from the transaction information stored in the storage system and assessing the destination of the identified transactions to generate a rating for the at least one account.
In a further aspect there is provided, a computer-implemented method for rating a transaction history of a peer-to-peer digital currency, comprising:
storing transaction information of the peer-to-peer digital currency in a storage system;
receiving over a network via an interface an identifier of at least one account associated with the peer-to-peer digital currency and a request for rating the transaction history of the at least one account;
identifying transactions of the at least one account from the transaction information stored in the storage system; and assessing the amount, date, and destination of the identified transactions to generate a rating for the at least one account.
In an even further aspect there is provided, a computer readable medium embodying a computer program for rating a transaction history of a peer-to-peer digital currency, comprising:
computer readable code for storing transaction information of the peer-to-peer digital currency in a storage system;
computer readable code for receiving over a network via an interface an identifier of at least one account associated with the peer-to peer digital currency and a request for rating the transaction history of the at least one account;
computer readable code for identifying transactions of the at least one account from the transaction information stored in the storage system; and computer readable code for assessing the amount, date, and destination of the identified transactions to generate a rating for the at least one account.
BRIEF DESCRIPTION OF THE DRAWINGS
Figure 1 shows a block diagram describing a flow example of a user interaction with a transaction history rating system;
Figure 2 shows a block diagram describing a flow example of the transaction history rating system processing the user request shown in Figure 1;
Figure 3 shows a block diagram describing a flow example of a user interaction with a transaction history rating system for Bitcoin transactions;
Figure 4 shows a block diagram describing a flow example of the Bitcoin transaction history rating system processing the user request shown in Figure 3;
Figure 5 shows a block diagram describing a flow example of the Bitcoin transaction history rating system generating a reference value;
Figure 6 shows a block diagram describing a flow example of the Bitcoin transaction history rating system generating a score characterizing the transaction history of a specified account;
Figure 7 shows a block diagram describing a flow example of the Bitcoin transaction history rating system generating an averaged score characterizing the transaction history of a specified account;
Figure 8 shows a block diagram describing a flow example of the Bitcoin transaction history rating system generating a projected score characterizing the transaction history of a specified account;
Figure 9 shows a block diagram describing a flow example of an aggressive projection for generating the projected score shown in Figure 8;
In still another aspect there is provided, a rating system for rating a transaction history of a peer-to-peer digital currency, comprising:
a storage system for storing transaction information of the peer-to-peer digital currency;
an interface for receiving an identifier of at least one account associated with the peer-to-peer digital currency and a request for rating the transaction history of the at least one account, a processor communicative with the storage system and the interface;
the processor identifying transactions of the at least one account from the transaction information stored in the storage system and assessing the destination of the identified transactions to generate a rating for the at least one account.
In a further aspect there is provided, a computer-implemented method for rating a transaction history of a peer-to-peer digital currency, comprising:
storing transaction information of the peer-to-peer digital currency in a storage system;
receiving over a network via an interface an identifier of at least one account associated with the peer-to-peer digital currency and a request for rating the transaction history of the at least one account;
identifying transactions of the at least one account from the transaction information stored in the storage system; and assessing the amount, date, and destination of the identified transactions to generate a rating for the at least one account.
In an even further aspect there is provided, a computer readable medium embodying a computer program for rating a transaction history of a peer-to-peer digital currency, comprising:
computer readable code for storing transaction information of the peer-to-peer digital currency in a storage system;
computer readable code for receiving over a network via an interface an identifier of at least one account associated with the peer-to peer digital currency and a request for rating the transaction history of the at least one account;
computer readable code for identifying transactions of the at least one account from the transaction information stored in the storage system; and computer readable code for assessing the amount, date, and destination of the identified transactions to generate a rating for the at least one account.
BRIEF DESCRIPTION OF THE DRAWINGS
Figure 1 shows a block diagram describing a flow example of a user interaction with a transaction history rating system;
Figure 2 shows a block diagram describing a flow example of the transaction history rating system processing the user request shown in Figure 1;
Figure 3 shows a block diagram describing a flow example of a user interaction with a transaction history rating system for Bitcoin transactions;
Figure 4 shows a block diagram describing a flow example of the Bitcoin transaction history rating system processing the user request shown in Figure 3;
Figure 5 shows a block diagram describing a flow example of the Bitcoin transaction history rating system generating a reference value;
Figure 6 shows a block diagram describing a flow example of the Bitcoin transaction history rating system generating a score characterizing the transaction history of a specified account;
Figure 7 shows a block diagram describing a flow example of the Bitcoin transaction history rating system generating an averaged score characterizing the transaction history of a specified account;
Figure 8 shows a block diagram describing a flow example of the Bitcoin transaction history rating system generating a projected score characterizing the transaction history of a specified account;
Figure 9 shows a block diagram describing a flow example of an aggressive projection for generating the projected score shown in Figure 8;
-4-Figure 10 shows a block diagram describing a flow example of a conservative projection for generating the projected score shown in Figure 8;
Figure 11 shows a block diagram describing an alternative flow example, to that shown in Figure 5, of the Bitcoin transaction history rating system generating a reference value;
Figure 12 shows a block diagram describing an alternative flow example, to that shown in Figure 6, of the Bitcoin transaction history rating system generating a score characterizing the transaction history of a specified account.
Figure 13 is a system map showing an implementation of the rating system, Figure 14 is a system map showing an alternative implementation of the rating system.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT
Referring to the drawings, an example of the system, and method for providing the same, will be described in the context of a user requesting and receiving a score for a desired digital currency account for illustrative purposes.
Figure 1 shows a flow diagram describing an example of a user inputing an account identifier and requesting a score for the account identifier within the system. The user may perform the steps shown in Figure 1 using an application installed on a personal computing device or using a website interface for an online application connected to a server computer.
Furthermore, the application installed on a personal computing device may be used offline with local processor and memory or may be used online in connection with a server computer and database. For convenience the steps are described in the context of an application installed on the user's personal computing device with an online connection to a server computer. Typically, upon start-up of the user's computing device or by intended selection by the user, an end-user interface application software previously installed on the computing device will start (110) and initiate a networked communication with a server computer of the system. The server computer will typically require login information (120) that may be provided by the application software in the form of a stored electronic data packet such as an electronic cookie. In the absence of automated login information provided by the application
Figure 11 shows a block diagram describing an alternative flow example, to that shown in Figure 5, of the Bitcoin transaction history rating system generating a reference value;
Figure 12 shows a block diagram describing an alternative flow example, to that shown in Figure 6, of the Bitcoin transaction history rating system generating a score characterizing the transaction history of a specified account.
Figure 13 is a system map showing an implementation of the rating system, Figure 14 is a system map showing an alternative implementation of the rating system.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT
Referring to the drawings, an example of the system, and method for providing the same, will be described in the context of a user requesting and receiving a score for a desired digital currency account for illustrative purposes.
Figure 1 shows a flow diagram describing an example of a user inputing an account identifier and requesting a score for the account identifier within the system. The user may perform the steps shown in Figure 1 using an application installed on a personal computing device or using a website interface for an online application connected to a server computer.
Furthermore, the application installed on a personal computing device may be used offline with local processor and memory or may be used online in connection with a server computer and database. For convenience the steps are described in the context of an application installed on the user's personal computing device with an online connection to a server computer. Typically, upon start-up of the user's computing device or by intended selection by the user, an end-user interface application software previously installed on the computing device will start (110) and initiate a networked communication with a server computer of the system. The server computer will typically require login information (120) that may be provided by the application software in the form of a stored electronic data packet such as an electronic cookie. In the absence of automated login information provided by the application
-5-software, the user is prompted to manually enter the login information (122) such as a login name and password. Once in a logged in environment the user can access a list (130), for example as a pull down menu, of predetermined score types. The system may provide the user with a brief description of each score type. The user selects a desired score type (140) and inputs an identifier for a digital currency account (150). The server computer calculates the score for the specified account. The user then receives the requested score (160).
Figure 2 shows a flow diagram describing an example of processing steps performed by the server computer to generate the requested score. The server computer receives (210) the selected score type and the specified account identifier from the user's computing device.
The server computer accesses the transaction history of the digital currency (220) and based on the account identifier information provided by the user (150) the server computer can filter the transaction history of the digital currency to analyze the transactions of the specified account (230). Depending upon the score type selected by the user (140) the server computer analyzes the transactions of the specified account with reference to one or more relevant parameters such as transaction amount (232), transaction frequency (234), account age (236), or transaction destination (238). The one or more relevant parameters may be any single or combination of quantifiable factors that can define a digital currency transaction. The server computer executes algorithm(s) based on the one or more relevant parameters to calculate a score (240). The score is sent to the user's computing device (250) and received by the application software, displayed to the user and optionally stored in memory.
Figure 3 shows a flow diagram providing an example of the steps similar to those shown in Figures 1 and 2 applied to generation of a score for a Bitcoin digital currency account. A user wishing to obtain a score for a Bitcoin account inputs information containing a unique identifier of the account, typically a Bitcoin address or a hash of the address (310).
The user may choose a type of score (312) selecting from options (314) such as Current, Projected, Average, or Projected Average. Furthermore, the user may choose an output format (316) selecting from options (318) such as XML, plain text or CSV. The information containing the unique identifier, the choice of score type and the choice of output format is submitted to a processor for score calculation. The processor calculates the requested score with predetermined algorithms corresponding to the chosen score type (322, 326) and by
Figure 2 shows a flow diagram describing an example of processing steps performed by the server computer to generate the requested score. The server computer receives (210) the selected score type and the specified account identifier from the user's computing device.
The server computer accesses the transaction history of the digital currency (220) and based on the account identifier information provided by the user (150) the server computer can filter the transaction history of the digital currency to analyze the transactions of the specified account (230). Depending upon the score type selected by the user (140) the server computer analyzes the transactions of the specified account with reference to one or more relevant parameters such as transaction amount (232), transaction frequency (234), account age (236), or transaction destination (238). The one or more relevant parameters may be any single or combination of quantifiable factors that can define a digital currency transaction. The server computer executes algorithm(s) based on the one or more relevant parameters to calculate a score (240). The score is sent to the user's computing device (250) and received by the application software, displayed to the user and optionally stored in memory.
Figure 3 shows a flow diagram providing an example of the steps similar to those shown in Figures 1 and 2 applied to generation of a score for a Bitcoin digital currency account. A user wishing to obtain a score for a Bitcoin account inputs information containing a unique identifier of the account, typically a Bitcoin address or a hash of the address (310).
The user may choose a type of score (312) selecting from options (314) such as Current, Projected, Average, or Projected Average. Furthermore, the user may choose an output format (316) selecting from options (318) such as XML, plain text or CSV. The information containing the unique identifier, the choice of score type and the choice of output format is submitted to a processor for score calculation. The processor calculates the requested score with predetermined algorithms corresponding to the chosen score type (322, 326) and by
-6-accessing the transaction history database or Blockchain of the Bitcoin (324) or a database derived from the Blockchain information (328). The calculated score is returned to the user's personal computing device and displayed to the user.
Figure 4 shows a flow diagram providing an example of steps of a calculation (322).
Calculation (322) is initiated by receipt of a request for a score calculation (340) with the request comprising information relating to an account identifier, and optionally a score type and an output format (342). The received information and more particularly the account identifier information may be validated, for example by checksum analysis, to ensure that it is in an appropriate form (344) with an invalid format resulting in an error message returned to the user (346) and a valid format proceeding to access Blockchain information stored in a local memory (348). The locally stored Blockchain information is assessed (350), and if it is up to date the processor selects an algorithm (354) corresponding to the score type specified in the request (342) and executes the selected algorithm (356, 357, 358 or 359). If the Blockchain information is not up to date a networked connection is initiated to update the Blockchain information (352) and to calculate reference values relating to the total, average or mean of an accumulated value of transactions in the Blockchain. The reference values are used to normalize scores to facilitate comparison of scores for different accounts at different time points.
Figure 5 shows a flow diagram providing an example of steps to update the Blockchain information and reference values (352). A networked connection to a realtime bitcoin Blockchain database is initiated and compared to a record of the last updated Blockchain transaction (370) to determine a starting point and size of the update. The update can involve updating a Bitcoin Blockchain database stored in local memory as well as parameter weight factors database also stored in a local memory and used for calculating scores. A reference value Cm is updated (374) in view of updated Blockchain transactions.
Calculation of the reference value Cm comprises three components: a total transaction amount, a negative weighting penalizing longer intervals (ie., lower frequency), and a negative weighting penalizing greater account age. The reference value Cm can be updated in iterative fashion (378) by considering each subsequent updated transaction until the current transaction is reached (376) resulting in an updated Cm reference value.
Figure 4 shows a flow diagram providing an example of steps of a calculation (322).
Calculation (322) is initiated by receipt of a request for a score calculation (340) with the request comprising information relating to an account identifier, and optionally a score type and an output format (342). The received information and more particularly the account identifier information may be validated, for example by checksum analysis, to ensure that it is in an appropriate form (344) with an invalid format resulting in an error message returned to the user (346) and a valid format proceeding to access Blockchain information stored in a local memory (348). The locally stored Blockchain information is assessed (350), and if it is up to date the processor selects an algorithm (354) corresponding to the score type specified in the request (342) and executes the selected algorithm (356, 357, 358 or 359). If the Blockchain information is not up to date a networked connection is initiated to update the Blockchain information (352) and to calculate reference values relating to the total, average or mean of an accumulated value of transactions in the Blockchain. The reference values are used to normalize scores to facilitate comparison of scores for different accounts at different time points.
Figure 5 shows a flow diagram providing an example of steps to update the Blockchain information and reference values (352). A networked connection to a realtime bitcoin Blockchain database is initiated and compared to a record of the last updated Blockchain transaction (370) to determine a starting point and size of the update. The update can involve updating a Bitcoin Blockchain database stored in local memory as well as parameter weight factors database also stored in a local memory and used for calculating scores. A reference value Cm is updated (374) in view of updated Blockchain transactions.
Calculation of the reference value Cm comprises three components: a total transaction amount, a negative weighting penalizing longer intervals (ie., lower frequency), and a negative weighting penalizing greater account age. The reference value Cm can be updated in iterative fashion (378) by considering each subsequent updated transaction until the current transaction is reached (376) resulting in an updated Cm reference value.
-7-Figure 6 shows a flow diagram providing an example of steps to calculate a Bitcoin account score extending (356) from the steps shown in Figure 4. The Blockchain and weight factor databases (372), updated as shown in Figure 5, are accessed for transaction information relevant to the specified account identifier for which a score has been requested as shown in Figure 3 (310). Value Cid, an accumulated value of all the transactions of Bitcoin transfers carried out by the specified account, is then calculated (390). Similar to reference value Cm, calculation of value Cid comprises three components (390): a total transaction amount, a negative weighting penalizing longer intervals (ie., lower frequency), and a negative weighting penalizing greater account age. The Bitcoin account score is then calculated as a comparison, more specifically a ratio, of the value Cid over the reference value Cm to yield the score Sid (394) which is then sent (396) and displayed to the user to complete the user's request (320).
From Figures 5 and 6:
Sid ¨ Cid/ Cm Cid ¨ E(di)*ai ¨ E wfi*(di ¨ (11.1 raj + EwaNcli-dorai positive weight for aging, id's accumulated negative weight for calculated value decreases transactions interval with age C = E(di)*Ai ¨ E Wfi*(di ¨ di_i )*Ai +EI/Vai*( Di-Dõ)*Ai positive weight for aging, all accumulated negative weight for calculated value decreases transactions interval with age e.i. 50% counts only half wfi = 0=> ,<=1 of frequency contribution 1 / ( d -d0)2 Wai ¨
1(1¨ Cm / CO
From Figures 5 and 6:
Sid ¨ Cid/ Cm Cid ¨ E(di)*ai ¨ E wfi*(di ¨ (11.1 raj + EwaNcli-dorai positive weight for aging, id's accumulated negative weight for calculated value decreases transactions interval with age C = E(di)*Ai ¨ E Wfi*(di ¨ di_i )*Ai +EI/Vai*( Di-Dõ)*Ai positive weight for aging, all accumulated negative weight for calculated value decreases transactions interval with age e.i. 50% counts only half wfi = 0=> ,<=1 of frequency contribution 1 / ( d -d0)2 Wai ¨
1(1¨ Cm / CO
-8-Or as an alternative (not shown in the Figures):
Cid ¨ E(di)*ai ¨ E wfi*(di ¨ d1_1 raj ¨ Ewai*(di-do)*ai id's accumulated negative weight for negative weight for aging transactions interval C = E(di)*Ai ¨ E Wfi*(di ¨ d1 )*Ai ¨ EWai*( Di-Do)*Ai all accumulated negative weight for negative weight for aging transactions interval where, Id identifier of a digital currency account, may also be identification number of a report, also is the address in Bitcoin Cid Accumulated value of transactions associated with id.
di date i do first transaction date of id de current date ai amount i wfi weight factor of frequency Wai weight factor of aging Accumulated value of all transactions Ai amount i Wft-id weight factor of frequency for date i for an id Wm-id weight factor of aging for date i for an id Sid Credit score of id Cm Average value of C
Co Reference date's Cm, a date prior to the date of Cm.
Li Score Index on date d, it indicates the trends of average score
Cid ¨ E(di)*ai ¨ E wfi*(di ¨ d1_1 raj ¨ Ewai*(di-do)*ai id's accumulated negative weight for negative weight for aging transactions interval C = E(di)*Ai ¨ E Wfi*(di ¨ d1 )*Ai ¨ EWai*( Di-Do)*Ai all accumulated negative weight for negative weight for aging transactions interval where, Id identifier of a digital currency account, may also be identification number of a report, also is the address in Bitcoin Cid Accumulated value of transactions associated with id.
di date i do first transaction date of id de current date ai amount i wfi weight factor of frequency Wai weight factor of aging Accumulated value of all transactions Ai amount i Wft-id weight factor of frequency for date i for an id Wm-id weight factor of aging for date i for an id Sid Credit score of id Cm Average value of C
Co Reference date's Cm, a date prior to the date of Cm.
Li Score Index on date d, it indicates the trends of average score
-9-Figure 7 shows a flow diagram providing an example of steps to calculate a system index score extending (357) from the steps shown in Figure 4. The score index Id is calculated (400) as a comparison, or more specifically a ratio of a Cm value at a specified date to a Cm value at a prior reference date, designated Co. The index score can then be returned (402) and displayed to the user. The index score is useful for assessing overall or top level trends of the system between two or more dates. For example, the Index score may reflect overall expansion or contraction of the system between a queried date and a reference date. As a more specific example, the index score may be used for month-over-month or year-over-year comparisons.
Figure 8 shows a flow diagram providing an example of steps to calculate a projected Bitcoin account score extending (358) from the steps shown in Figure 4. The Blockchain and weight factor databases (372), updated as shown in Figure 5, are accessed for transaction information relevant to the specified account identifier for which a score has been requested as shown in Figure 3 (310). Value Cdp, a projected accumulated value of all the transactions of Bitcoin transfers carried out by the specified account, is then calculated (410). The value Cdp is for projected day dp. A projected reference value Cp is also calculated (414). Cp is the projected average value considering all accounts/addresses of Bitcoin as predicted for projected day dp. The Bitcoin account score is then calculated as a comparison, more specifically a ratio, of the projected account value Cdp over the projected reference value Cp to yield the projected score Sdp (416) for projected day dp which is then sent (418) and displayed to the user to complete the user's request (320). In blocks 410 and 414 fp is a predictive function that is applied to a data set comprising a plurality of existing Cid values and their corresponding dates. The predictive function may be applied independent of the parameters considered to obtain the data set of Cid values. Many different predictive algorithms may be applied including Lagrange interpoloation, polynomial regression, trigonometric functions, complex number functions, exponential functions, logarithmic functions and the like.
Figures 9 and 10 show alternative calculations or predictive algorithms for obtaining the projected score shown in Figure 8, an aggressive projection (420) in Figure 9 based on a
Figure 8 shows a flow diagram providing an example of steps to calculate a projected Bitcoin account score extending (358) from the steps shown in Figure 4. The Blockchain and weight factor databases (372), updated as shown in Figure 5, are accessed for transaction information relevant to the specified account identifier for which a score has been requested as shown in Figure 3 (310). Value Cdp, a projected accumulated value of all the transactions of Bitcoin transfers carried out by the specified account, is then calculated (410). The value Cdp is for projected day dp. A projected reference value Cp is also calculated (414). Cp is the projected average value considering all accounts/addresses of Bitcoin as predicted for projected day dp. The Bitcoin account score is then calculated as a comparison, more specifically a ratio, of the projected account value Cdp over the projected reference value Cp to yield the projected score Sdp (416) for projected day dp which is then sent (418) and displayed to the user to complete the user's request (320). In blocks 410 and 414 fp is a predictive function that is applied to a data set comprising a plurality of existing Cid values and their corresponding dates. The predictive function may be applied independent of the parameters considered to obtain the data set of Cid values. Many different predictive algorithms may be applied including Lagrange interpoloation, polynomial regression, trigonometric functions, complex number functions, exponential functions, logarithmic functions and the like.
Figures 9 and 10 show alternative calculations or predictive algorithms for obtaining the projected score shown in Figure 8, an aggressive projection (420) in Figure 9 based on a
-10-Lagrange interpolation and a conservative projection (450) in Figure 10 based on a polynomial regression.
Figure 9 provides a Lagrange interpolation for fp shown in Figure 8. Data sets for a plurality of corresponding Cid and di data points can be obtained (422) and treated with a Lagrange interpolation to obtain a Cdp value (424) and a Cp value (426). The Lagrange expression comprises the requested projected date - d, the date of transaction i ¨ di, and all dates for which Cid has been calculated that are not equal to di ¨ dj. The Cdp value is obtained for the projected date ¨ d. The projected reference value Cp for the projected date d is obtained by summing all Cdp values for all Bitcoin addresses for the projected date d divided by the number of total addresses. The projected score is then calculated as a comparison, more specifically a ratio, of the Cdp value over the Cp value to yield the projected score Sdp (428) for the projected date d which is then sent (430) and displayed to the user.
Figure 10 provides a polynomial regression for fp shown in Figure 8.
Conventionally, a polynomial regression can be expressed in matrix form and solved using a least squares method. Data sets for a plurality of corresponding Cid and di data points can be obtained (452) and treated with a polynomial regression to obtain a Cdp value (456) and a Cp value (458) for the projected date d. The projected score is then calculated as a comparison, more specifically a ratio, of the Cdp value over the Cp value to yield the projected score Sdp (460) for the projected date d which is then sent (462) and displayed to the user.
Figure 11 shows a flow diagram providing an alternative example, to that shown in Figure 5, of steps to update the Blockchain information and reference values (352). Figure 11 differs from Figure 5 by assessing an additional parameter of transaction destination information. A networked connection to a realtime bitcoin Blockchain database is initiated and compared to a record of the last updated Blockchain transaction to determine a starting point and size of the update and access relevant information for calculation of reference values (470). The update can involve updating a Bitcoin Blockchain database stored in local niemory as well as parameter weight factors database also stored in a local memory and used for calculating scores (372). A reference value Cmk(id,idd) for each unique account/destination pair is updated (474) in view of updated Blockchain transactions.
Figure 9 provides a Lagrange interpolation for fp shown in Figure 8. Data sets for a plurality of corresponding Cid and di data points can be obtained (422) and treated with a Lagrange interpolation to obtain a Cdp value (424) and a Cp value (426). The Lagrange expression comprises the requested projected date - d, the date of transaction i ¨ di, and all dates for which Cid has been calculated that are not equal to di ¨ dj. The Cdp value is obtained for the projected date ¨ d. The projected reference value Cp for the projected date d is obtained by summing all Cdp values for all Bitcoin addresses for the projected date d divided by the number of total addresses. The projected score is then calculated as a comparison, more specifically a ratio, of the Cdp value over the Cp value to yield the projected score Sdp (428) for the projected date d which is then sent (430) and displayed to the user.
Figure 10 provides a polynomial regression for fp shown in Figure 8.
Conventionally, a polynomial regression can be expressed in matrix form and solved using a least squares method. Data sets for a plurality of corresponding Cid and di data points can be obtained (452) and treated with a polynomial regression to obtain a Cdp value (456) and a Cp value (458) for the projected date d. The projected score is then calculated as a comparison, more specifically a ratio, of the Cdp value over the Cp value to yield the projected score Sdp (460) for the projected date d which is then sent (462) and displayed to the user.
Figure 11 shows a flow diagram providing an alternative example, to that shown in Figure 5, of steps to update the Blockchain information and reference values (352). Figure 11 differs from Figure 5 by assessing an additional parameter of transaction destination information. A networked connection to a realtime bitcoin Blockchain database is initiated and compared to a record of the last updated Blockchain transaction to determine a starting point and size of the update and access relevant information for calculation of reference values (470). The update can involve updating a Bitcoin Blockchain database stored in local niemory as well as parameter weight factors database also stored in a local memory and used for calculating scores (372). A reference value Cmk(id,idd) for each unique account/destination pair is updated (474) in view of updated Blockchain transactions.
-11-Calculation of the reference value Cmk(id,idd) comprises three components: a total transaction amount, a negative weighting penalizing longer intervals (ie., lower frequency), and a negative weighting penalizing greater account age. The reference value Cmk(id,idd) can be updated in iterative fashion (478) for each unique account/destination pair by considering each subsequent updated transaction until the current transaction is reached (476) resulting in an updated Cmk(id,idd) reference value. A factor k(id,idd) is then calculated based on the number of repeated transactions for each unique account/destination pair (480).
The product of factor k(id,idd) and corresponding Cmk(id,idd) reference value for each unique account/destination pair is then summed until all account/destination pairs are considered (484) to yield reference value Cmi (482). The alternative example shown in Figure 11 rewards greater numbers of unique account/destination pair as the Cmk(id,idd) reference values for each unique account/destination pair are summed. However, repeated transactions within the same account/destination pair are not rewarded as the number of transactions for each account/destination pair is placed in the denominator for calculation of factor k(id,idd).
Figure 12 shows a flow diagram providing an alternative example, to that shown in Figure 6, of steps to calculate a Bitcoin account score extending (356) from the steps shown in Figure 4. Figure 12 differs from Figure 6 by assessing an additional parameter of transaction destination information. The Blockchain and weight factor databases (372), updated as shown in Figure 11, are accessed for transaction information (570) relevant to the specified account identifier for which a score has been requested as shown in Figure 3 (310).
Value Ck(id,idd) characterizing accumulated transactions for each unique destination for Bitcoin transfers from the specified account is calculated (574) in view of updated Blockchain transaction information. Calculation of Ck(id,idd) comprises three components: a total transaction amount, a negative weighting penalizing longer intervals (ie., lower frequency), and a negative weighting penalizing greater account age.
Ck(id,idd) can be calculated in iterative fashion (578) for each unique destination from the specified account by considering each subsequent transaction until the current transaction is reached (576). A
factor k(id,idd) is then calculated based on the number of repeated transactions for each unique destination for Bitcoin transfer from the specified account (580). The product of
The product of factor k(id,idd) and corresponding Cmk(id,idd) reference value for each unique account/destination pair is then summed until all account/destination pairs are considered (484) to yield reference value Cmi (482). The alternative example shown in Figure 11 rewards greater numbers of unique account/destination pair as the Cmk(id,idd) reference values for each unique account/destination pair are summed. However, repeated transactions within the same account/destination pair are not rewarded as the number of transactions for each account/destination pair is placed in the denominator for calculation of factor k(id,idd).
Figure 12 shows a flow diagram providing an alternative example, to that shown in Figure 6, of steps to calculate a Bitcoin account score extending (356) from the steps shown in Figure 4. Figure 12 differs from Figure 6 by assessing an additional parameter of transaction destination information. The Blockchain and weight factor databases (372), updated as shown in Figure 11, are accessed for transaction information (570) relevant to the specified account identifier for which a score has been requested as shown in Figure 3 (310).
Value Ck(id,idd) characterizing accumulated transactions for each unique destination for Bitcoin transfers from the specified account is calculated (574) in view of updated Blockchain transaction information. Calculation of Ck(id,idd) comprises three components: a total transaction amount, a negative weighting penalizing longer intervals (ie., lower frequency), and a negative weighting penalizing greater account age.
Ck(id,idd) can be calculated in iterative fashion (578) for each unique destination from the specified account by considering each subsequent transaction until the current transaction is reached (576). A
factor k(id,idd) is then calculated based on the number of repeated transactions for each unique destination for Bitcoin transfer from the specified account (580). The product of
-12-WO 2015/085393 PCT/CA2013/(15(1967 factor k(id,idd) and corresponding Ck(id,idd) reference value for each unique destination for the specified account is then summed (582) until all destinations for the specified account are considered (584) to yield value Cid (586). The alternative example shown in Figure 12 rewards greater numbers of unique destinations from the specified account as Ck(id,idd) values are summed. However, repeated transactions to the same destination are not rewarded as the number of transactions for each destination from the specified account is placed in the denominator for calculation of factor k(id,idd).
Similar to the alternative method for obtaining reference value Cm shown in Figure 11, the alternative calculation of value Cid shown in Figure 12 comprises five components: a total transaction amount, a negative weighting penalizing longer intervals (ie., lower frequency), a negative weighting penalizing greater account age, a positive summation rewarding greater numbers of unique destinations, and a factor that penalizes multiple transactions to the same destination. The Bitcoin account score Sid may then be calculated as shown in Figure 6, as a comparison, more specifically a ratio, of the value Cid over the reference value Cm to yield the score Sid (394) which is then sent (396) and displayed to the user to complete the user's request (320). From Figures 11 and 12:
Sid ¨ Cid/ Cm Ck (id,idd) =Edi * a ¨ Ewl(d) * (di ¨ )*ai + Ewa(d) * (di-do)*ai positive weight id's accumulated for aging with same negative weight for interval transactions with destination idd, but with same destination idd same destination idd calculated value decreases with age k(id,idd) = ( 1 / Thad )2 Cid = E k(id, idd) * Ck(id, idd) Cink(id,idd) = E (di)*Ai ¨ Ewf(d)*(di ¨d11 )*A. + Ewa(d)*( Di-Do)*
Ai all accumulated positive weight negative weight for interval transactions with for aging with same with same destination same destination destination idd, but
Similar to the alternative method for obtaining reference value Cm shown in Figure 11, the alternative calculation of value Cid shown in Figure 12 comprises five components: a total transaction amount, a negative weighting penalizing longer intervals (ie., lower frequency), a negative weighting penalizing greater account age, a positive summation rewarding greater numbers of unique destinations, and a factor that penalizes multiple transactions to the same destination. The Bitcoin account score Sid may then be calculated as shown in Figure 6, as a comparison, more specifically a ratio, of the value Cid over the reference value Cm to yield the score Sid (394) which is then sent (396) and displayed to the user to complete the user's request (320). From Figures 11 and 12:
Sid ¨ Cid/ Cm Ck (id,idd) =Edi * a ¨ Ewl(d) * (di ¨ )*ai + Ewa(d) * (di-do)*ai positive weight id's accumulated for aging with same negative weight for interval transactions with destination idd, but with same destination idd same destination idd calculated value decreases with age k(id,idd) = ( 1 / Thad )2 Cid = E k(id, idd) * Ck(id, idd) Cink(id,idd) = E (di)*Ai ¨ Ewf(d)*(di ¨d11 )*A. + Ewa(d)*( Di-Do)*
Ai all accumulated positive weight negative weight for interval transactions with for aging with same with same destination same destination destination idd, but
-13-calculated value decreases with age ¨ 1 / 1N_Itk U k(id, idd) * C,,k(id, idd) Wa¨ I d ¨ do )2 Or, as an alternative (not shown in the Figures):
Ck (id,idd) = Ed; * a; ¨ Ewf(d) * (d; ¨ d1_1 )*a; ¨ Ewa(d) * (d,-dõ)*a, id's accumulated negative weight for negative weight transactions with interval with same for aging with same same destination idd destination idd destination idd k(id,idd) = ( 1 / nidd Cid ¨ k(id, idd) * Ck(id, idd) C,õk(id,idd) = E (d;)*A, ¨ Ewf(d)*(d; ¨ d1 )*Ai ¨ Ewa(d)*(1);-Do)*A;
All accumulated negative weight for negative weight transactions with interval with same for aging with same same destination destination destination idd C,õ ¨ 1 / N.dLI k(id, idd) * Coik(id, idd) where, id Identifier of a digital currency account, may also be identification number of a report, also is the address in Bitcoin the number I transaction of Id! all transactions Cid Accumulated value of transactions associated with id.
d, date and time of transaction i current date and time do/ Do first transaction date of id I all transactions a; / Ao transaction amount of transaction i of the Id! all transactions wt(d) current (time d) weight factor of frequency
Ck (id,idd) = Ed; * a; ¨ Ewf(d) * (d; ¨ d1_1 )*a; ¨ Ewa(d) * (d,-dõ)*a, id's accumulated negative weight for negative weight transactions with interval with same for aging with same same destination idd destination idd destination idd k(id,idd) = ( 1 / nidd Cid ¨ k(id, idd) * Ck(id, idd) C,õk(id,idd) = E (d;)*A, ¨ Ewf(d)*(d; ¨ d1 )*Ai ¨ Ewa(d)*(1);-Do)*A;
All accumulated negative weight for negative weight transactions with interval with same for aging with same same destination destination destination idd C,õ ¨ 1 / N.dLI k(id, idd) * Coik(id, idd) where, id Identifier of a digital currency account, may also be identification number of a report, also is the address in Bitcoin the number I transaction of Id! all transactions Cid Accumulated value of transactions associated with id.
d, date and time of transaction i current date and time do/ Do first transaction date of id I all transactions a; / Ao transaction amount of transaction i of the Id! all transactions wt(d) current (time d) weight factor of frequency
-14-wa(d) current (time d) weight factor of aging Cm average value of Accumulated value of all transactions Sid score of address id idd/IDD number i transaction of the same destination address in id/all transactions Cmk(idk) number i transaction accumulated value of the same destination address in id Cmk(idk) number i transaction accumulated value of the same destination address in id k(id,idd) factor of same destination address transaction in id naid total number of transactions with same destination address from source id Figure 13 shows a system map describing an example of an interne cloud (620) based implementation of the system (600) for generating ratings in respect of Bitcoin transaction histories, where functions for analyzing transaction histories and generating scores are carried out by server computers (602) operably linked to data storage systems (604) that are isolated from the cloud (620) by an electronic interface device (606). The electronic interface device has processing power, optionally including a firewall, to receive via the internet (620) requests from client computing devices for a score relating to an account identifier and then to return a score for the account identifier back to the client computing device. The electronic interface device (606) shields the server computers (602) and data storage systems (604) from direct interaction with the Internet or from direct communication with client computers: Upon receiving a request from a client computing device the electronic interface device (606) logs the request, optionally performing a validation check of the request, and then sends a request for a score including account identifier information to the server computers (602). Once a score is generated, the server computers (602) return the score to the electronic interface device for subsequent return to the client computing device. Bitcoin transaction histories termed Blockchain are publically available from many sources including Bitcoin electronic exchanges and connection of server computers (602) with the Bitcoin system (610), may be established either directly or through an electronic interface device at any desired frequency to ensure that the transaction history information stored within data storage system (604) is up to date. The computer architecture shown in Figure 13 may be simplified or elaborated depending upon the desired application. In a simplified implementation (500) the system
-15-comprises a plurality of users (630) each with at least one computing device.
User computing devices (630) through either a web browser or a locally installed application communicate over a network (620) with the scoring system (600). The scoring system (600) may be independent and shielded from the Bitcoin system or may be embedded in the Bitcoin system.
In a simplified form the scoring system (600) may be hosted on a single server with a processor operably linked to a memory. Memory may include volatile memory such as various RAM types, and non-volatile such as ROM, magnetic storage systems, optical storage systems and the like.
Figure 14 shows a system map describing another example of an implementation (700) of the system. User computing devices 710 including smartphones, tablets, laptops, desktops and the like may communicate through web browsers or locally installed applications with a digital currency system comprising a component for conducting transactions (730) and a component for recording each transaction and its related information (732). The user computing devices 710 may also communicate through web browsers or locally installed applications with a system for rating transactional histories of the digital currency. Corporate entity computers (712) such as financial institutions, commodity and currency exchanges, transaction processors and the like may also communicate with the digital currency system and/or the rating system through an application programming interface (API). Typically, the user computing devices (710) will communicate with the rating system through a network (720) such as the Internet, while corporate computers (712) may have a more direct electronic linkage. The rating system receives requests for ratings including an account identifier over the network (720) through website applications (740) hosted on a load balanced web server farm (742). Corporate computers (712) may send requests directly to the web server farm (742) based on an API. The web server farm is communicative with load balanced transaction history servers (750) and score calculation servers (752) which are in turn communicative with a load balanced database server farm (760, 762, 764, 766, 768, 770) which maintain, update, and access records from a data storage system (660). The transaction history servers (750) are communicative with the score calculation servers (752). Furthermore, the transaction history servers (750) or the web
User computing devices (630) through either a web browser or a locally installed application communicate over a network (620) with the scoring system (600). The scoring system (600) may be independent and shielded from the Bitcoin system or may be embedded in the Bitcoin system.
In a simplified form the scoring system (600) may be hosted on a single server with a processor operably linked to a memory. Memory may include volatile memory such as various RAM types, and non-volatile such as ROM, magnetic storage systems, optical storage systems and the like.
Figure 14 shows a system map describing another example of an implementation (700) of the system. User computing devices 710 including smartphones, tablets, laptops, desktops and the like may communicate through web browsers or locally installed applications with a digital currency system comprising a component for conducting transactions (730) and a component for recording each transaction and its related information (732). The user computing devices 710 may also communicate through web browsers or locally installed applications with a system for rating transactional histories of the digital currency. Corporate entity computers (712) such as financial institutions, commodity and currency exchanges, transaction processors and the like may also communicate with the digital currency system and/or the rating system through an application programming interface (API). Typically, the user computing devices (710) will communicate with the rating system through a network (720) such as the Internet, while corporate computers (712) may have a more direct electronic linkage. The rating system receives requests for ratings including an account identifier over the network (720) through website applications (740) hosted on a load balanced web server farm (742). Corporate computers (712) may send requests directly to the web server farm (742) based on an API. The web server farm is communicative with load balanced transaction history servers (750) and score calculation servers (752) which are in turn communicative with a load balanced database server farm (760, 762, 764, 766, 768, 770) which maintain, update, and access records from a data storage system (660). The transaction history servers (750) are communicative with the score calculation servers (752). Furthermore, the transaction history servers (750) or the web
-16-servers (742) may be communicative with a publically accessible or privately held electronic record or data base of the transaction histories of the digital currency.
In use, the rating system described herein provides users with a rating, score, ranking and the like to be able to assess the activity of a targeted account of a digital currency.
Currently, several currencies use a proof-of-work concept as a validator of a transaction.
Proof-of-work inherently requires time to lapse after a transfer of currency.
Furthermore, proof-of-work is difficult to understand for most users. The rating system can provide a score characterizing a queried account's transaction activity in advance of agreeing to a transaction.
Thus, it may complement existing validators such as proof-of-work or proof-of-stake to provide confidence or trust in a digital currency system.
The rating system may be used by users that are expecting to enter into a transaction using a digital currency or by corporate entities such as financial institutions or transaction processors that may be tasked with monitoring a digital currency system.
Taking the individual user as an example, before agreeing to a transaction in a digital currency, a user can obtain the account address or identifier of the opposing party. The user can then input the account identifier in a data entry box in a web application and send a request over the intern&
for a rating corresponding to the account identifier. The request is received and a rating is calculated and returned to be displayed to the user through the web application. The rating may be presented in any manner of ways including numerical score, star rating, percentile ranking, 2D graphs, distribution curves, and the like. Once the user receives the rating, the user can assess the risk of the potential transaction in view of the rating.
The rating reflects the ability of the account to fulfill a transaction of the digital currency at a current date or a target projected date queried by the user.
An example of the system and several illustrative variants have been described above.
Further illustrative variants and modifications will now be described. Still further variants, modifications or combinations thereof will be recognized by the person of skill in the art.
The system is typically used for rating a transaction history of a digital currency. The transaction history of the digital currency may be publically accessible or privately held. For example, the Bitcoin transaction history is publically accessible, while a significant portion of the transaction history of Amazon's digital currency, Amazon coins, is privately held.
In use, the rating system described herein provides users with a rating, score, ranking and the like to be able to assess the activity of a targeted account of a digital currency.
Currently, several currencies use a proof-of-work concept as a validator of a transaction.
Proof-of-work inherently requires time to lapse after a transfer of currency.
Furthermore, proof-of-work is difficult to understand for most users. The rating system can provide a score characterizing a queried account's transaction activity in advance of agreeing to a transaction.
Thus, it may complement existing validators such as proof-of-work or proof-of-stake to provide confidence or trust in a digital currency system.
The rating system may be used by users that are expecting to enter into a transaction using a digital currency or by corporate entities such as financial institutions or transaction processors that may be tasked with monitoring a digital currency system.
Taking the individual user as an example, before agreeing to a transaction in a digital currency, a user can obtain the account address or identifier of the opposing party. The user can then input the account identifier in a data entry box in a web application and send a request over the intern&
for a rating corresponding to the account identifier. The request is received and a rating is calculated and returned to be displayed to the user through the web application. The rating may be presented in any manner of ways including numerical score, star rating, percentile ranking, 2D graphs, distribution curves, and the like. Once the user receives the rating, the user can assess the risk of the potential transaction in view of the rating.
The rating reflects the ability of the account to fulfill a transaction of the digital currency at a current date or a target projected date queried by the user.
An example of the system and several illustrative variants have been described above.
Further illustrative variants and modifications will now be described. Still further variants, modifications or combinations thereof will be recognized by the person of skill in the art.
The system is typically used for rating a transaction history of a digital currency. The transaction history of the digital currency may be publically accessible or privately held. For example, the Bitcoin transaction history is publically accessible, while a significant portion of the transaction history of Amazon's digital currency, Amazon coins, is privately held.
-17-The system may be used in conjunction with any digital currency and may function independently of a digital currency or may be embedded within the digital currency computing infrastructure.
While the rating system may be used to assess transactions histories of any digital currency, it may have particular benefit for peer-to-peer digital currency as these currencies suffer from reports of fraud and double spending. Bitcoin, Litecoin, PPcoin (Peer-to-peer coin), Freicoin, or Namecoin are examples of a peer-to-peer digital currency.
A peer-to-peer digital currency is a decentralized currency managed by peer-to-peer networked computing technology (P2P), without a central authority. Consistent with P2P technology, the networked distributed computing application architecture partitions tasks or workloads among peers or nodes. Functions of currency issuance, transaction processing and verification are carried out collectively by the network, without a central supervisor or agency to oversee operations.
The system typically requires a memory, an interface and a processor. The types and arrangements of memory, interface and processor may be varied according to implementations. For example, the interface may include a software interlace such as a web application that launces a web browser and internet connection on an end-user computing device. The interface may also include a physical electronic device configured to receive requests or queries from an end-user.
The rating system can represent a rating in any convenient graphical or text format.
For example, the rating system may generate a numerical score that characterizes the transaction history of an account of a digital currency. The numerical score may represent a total value for the accumulated transactions of the account, per day average, a per transaction average, a per destination average, or any other convenient representation.
The numerical score may be compared to a reference value to normalize the numerical score.
The reference value may characterize the total accumulated transaction information of the digital currency.
The reference value may be represented as a total, average, mean or modal (most frequently observed) value or any other convenient representation characterizing the accumulated transaction information of the digital currency.
The rating system will typically assess at least one of the parameters of amount, date and destination of transactions that make up the transaction history of an account of the
While the rating system may be used to assess transactions histories of any digital currency, it may have particular benefit for peer-to-peer digital currency as these currencies suffer from reports of fraud and double spending. Bitcoin, Litecoin, PPcoin (Peer-to-peer coin), Freicoin, or Namecoin are examples of a peer-to-peer digital currency.
A peer-to-peer digital currency is a decentralized currency managed by peer-to-peer networked computing technology (P2P), without a central authority. Consistent with P2P technology, the networked distributed computing application architecture partitions tasks or workloads among peers or nodes. Functions of currency issuance, transaction processing and verification are carried out collectively by the network, without a central supervisor or agency to oversee operations.
The system typically requires a memory, an interface and a processor. The types and arrangements of memory, interface and processor may be varied according to implementations. For example, the interface may include a software interlace such as a web application that launces a web browser and internet connection on an end-user computing device. The interface may also include a physical electronic device configured to receive requests or queries from an end-user.
The rating system can represent a rating in any convenient graphical or text format.
For example, the rating system may generate a numerical score that characterizes the transaction history of an account of a digital currency. The numerical score may represent a total value for the accumulated transactions of the account, per day average, a per transaction average, a per destination average, or any other convenient representation.
The numerical score may be compared to a reference value to normalize the numerical score.
The reference value may characterize the total accumulated transaction information of the digital currency.
The reference value may be represented as a total, average, mean or modal (most frequently observed) value or any other convenient representation characterizing the accumulated transaction information of the digital currency.
The rating system will typically assess at least one of the parameters of amount, date and destination of transactions that make up the transaction history of an account of the
-18-digital currency. More typically, the rating system will assess at least two of the parameters of amount, date and destination. For example, assessing amounts alone is a measure of currency volume. Assessing dates alone can provide a rating based on frequency and/or age of transactions. Assessing destination identifiers alone can provide a rating based on numbers of unique destinations and/or repetition of the same destination. Thus, each parameter of amount, date, and destination taken alone may be able to provide a useful rating, but taken together with at least two of the three, or all three, co-operate to provide an increasingly robust rating.
The parameters of amount, date, and destination, or parameters derived therefrom may be negatively or positively correlated with a rating depending on the rating scheme and the algorithms for generating a rating. Correlations may encompass linear relationships as well as non-linear relationships. A correlation may be considered for any two or more variables that depart from independence. Typically, amount is positively correlated with the rating such that an increase in amount is correlated with an increase in rating. An increase amount may be rewarded as it reflects currency volume and provides precedent for a transaction level. Transaction frequency may be derived from transaction dates, for example by calculating intervals between each transaction, with transaction frequency typically being positively correlated with rating, such that an increase in transaction frequency is correlated with an increase in the rating. The rating system may be configured to reward increase frequency as higher successful active density means more creditable contributions. Age of transactions or age of the account can also be derived from transaction dates, with age typically being negatively correlated with the rating, such that a decrease in transaction age is correlated with an increase in the rating. The rating system may be configured to penalize an increase in age as a contribution erodes by time or aging, with more recent activity providing a better indicator. The number of unique transaction destinations may be derived from transaction destination information with unique transaction destinations typically being positively correlated with the rating, such that an increase in the number of unique transaction destinations is correlated with an increase in the rating. The rating system may be configured to reward an increase in unique destinations to reward diversity as more unique destinations means more diverse activities to different receivers and increased exposure to detection of
The parameters of amount, date, and destination, or parameters derived therefrom may be negatively or positively correlated with a rating depending on the rating scheme and the algorithms for generating a rating. Correlations may encompass linear relationships as well as non-linear relationships. A correlation may be considered for any two or more variables that depart from independence. Typically, amount is positively correlated with the rating such that an increase in amount is correlated with an increase in rating. An increase amount may be rewarded as it reflects currency volume and provides precedent for a transaction level. Transaction frequency may be derived from transaction dates, for example by calculating intervals between each transaction, with transaction frequency typically being positively correlated with rating, such that an increase in transaction frequency is correlated with an increase in the rating. The rating system may be configured to reward increase frequency as higher successful active density means more creditable contributions. Age of transactions or age of the account can also be derived from transaction dates, with age typically being negatively correlated with the rating, such that a decrease in transaction age is correlated with an increase in the rating. The rating system may be configured to penalize an increase in age as a contribution erodes by time or aging, with more recent activity providing a better indicator. The number of unique transaction destinations may be derived from transaction destination information with unique transaction destinations typically being positively correlated with the rating, such that an increase in the number of unique transaction destinations is correlated with an increase in the rating. The rating system may be configured to reward an increase in unique destinations to reward diversity as more unique destinations means more diverse activities to different receivers and increased exposure to detection of
-19-fraudulent activity. The number of repeated transactions to the same destination may be derived from the transaction destination information with the number of repeated transactions to the same destination being negatively correlated with the rating such that a decrease in repeated transactions to the same destination is correlated with an increase in the rating. The rating system may be configured to penalize increase in repeated transaction to the same destination to promote diversity and to limit contributions from potential cheat or fake transactions for the purpose of gaming the rating system to increase a rating by simply shuttling currency back and forth between accounts. Destinations are typically derived from the transaction information by extracting destination account identifiers.
The components of the system may be administered by a single organization or a plurality of partnering organizations. The tracking and validation of transaction histories of a digital currency, for example, may be administered by an organization at arm's length from the organization administering the rest of the system. Such an arm's length organization may be a financial institution, accounting firm or payment transaction processor.
The system may accommodate any type of end-user computing device or client computing device provided the computing device can be networked to the system and is configured to display website interfaces and/or graphical interface elements for performing the various functions of the system such as inputting an account identifier, displaying a rating or updating a transaction history database that may be locally stored in the computing device.
For example, the computing device may be a desktop, laptop, notebook, tablet, personal digital assistant (PDA), PDA phone or smartphone, gaming console, portable media player, and the like. The computing device may be implemented using any appropriate combination of hardware and/or software configured for wired and/or wireless communication over the network.
The server computer may be any combination of hardware and software components used to store, process and/or provide scores or ratings for transactions from one or more accounts using a digital currency, and monitoring and analyzing such transactions. The server computer components such as storage systems, processors, interface devices, input/output ports, bus connections, switches, routers, gateways and the like may be geographically centralized or distributed. The server computer may be a single server computer or any
The components of the system may be administered by a single organization or a plurality of partnering organizations. The tracking and validation of transaction histories of a digital currency, for example, may be administered by an organization at arm's length from the organization administering the rest of the system. Such an arm's length organization may be a financial institution, accounting firm or payment transaction processor.
The system may accommodate any type of end-user computing device or client computing device provided the computing device can be networked to the system and is configured to display website interfaces and/or graphical interface elements for performing the various functions of the system such as inputting an account identifier, displaying a rating or updating a transaction history database that may be locally stored in the computing device.
For example, the computing device may be a desktop, laptop, notebook, tablet, personal digital assistant (PDA), PDA phone or smartphone, gaming console, portable media player, and the like. The computing device may be implemented using any appropriate combination of hardware and/or software configured for wired and/or wireless communication over the network.
The server computer may be any combination of hardware and software components used to store, process and/or provide scores or ratings for transactions from one or more accounts using a digital currency, and monitoring and analyzing such transactions. The server computer components such as storage systems, processors, interface devices, input/output ports, bus connections, switches, routers, gateways and the like may be geographically centralized or distributed. The server computer may be a single server computer or any
-20-combination of multiple physical and/or virtual servers including for example, a web server, a transaction server, an application server, a bus server, an integration server, a user profile server, an algorithm server, a weighting factor server, a log server, an accounting server and the like.
Any conventional computer architecture may be used to implement the system including for example a memory, a mass storage device, a processor (CPU), a Read-Only Memory (ROM), and a Random-Access Memory (RAM) generally connected to a system bus of data-processing apparatus. Memory can be implemented as a ROM, RAM, a combination thereof, or simply a general memory unit. Software modules in the form of routines and/or subroutines for carrying out features of the rating system can be stored within memory and then retrieved and processed via processor to perform a particular task or function. Similarly, one or more of the flow diagrams shown in Figures 1-12 may be encoded as a program component, stored as executable instructions within memory and then retrieved and processed via a processor. A user input device, such as a keyboard, mouse, or another pointing device, can be connected to PCI (Peripheral Component Interconnect) bus. The software will typically provide an environment that represents programs, files, options, and so forth by means of graphically displayed icons, menus, and dialog boxes on a computer monitor screen.
A data-process apparatus can include CPU, ROM, and RAM, which are also coupled to a PCI (Peripheral Component Interconnect) local bus of data-processing apparatus through PCI Host Bridge. The PCI Host Bridge can provide a low latency path through which processor may directly access PCI devices mapped anywhere within bus memory and/or input/output (I/0) address spaces. PCI Host Bridge can also provide a high bandwidth path for allowing PCI devices to directly access RAM.
A communications adapter, a small computer system interface (SCSI), and an expansion bus-bridge may also be attached to PCI local bus. The communications adapter can be utilized for connecting data-processing apparatus to a network. SCSI can be utilized to control a high-speed SCSI disk drive. An expansion bus-bridge, such as a PCI-to-ISA bus bridge, may be utilized for coupling ISA bus to PCI local bus. PCI local bus can be connected
Any conventional computer architecture may be used to implement the system including for example a memory, a mass storage device, a processor (CPU), a Read-Only Memory (ROM), and a Random-Access Memory (RAM) generally connected to a system bus of data-processing apparatus. Memory can be implemented as a ROM, RAM, a combination thereof, or simply a general memory unit. Software modules in the form of routines and/or subroutines for carrying out features of the rating system can be stored within memory and then retrieved and processed via processor to perform a particular task or function. Similarly, one or more of the flow diagrams shown in Figures 1-12 may be encoded as a program component, stored as executable instructions within memory and then retrieved and processed via a processor. A user input device, such as a keyboard, mouse, or another pointing device, can be connected to PCI (Peripheral Component Interconnect) bus. The software will typically provide an environment that represents programs, files, options, and so forth by means of graphically displayed icons, menus, and dialog boxes on a computer monitor screen.
A data-process apparatus can include CPU, ROM, and RAM, which are also coupled to a PCI (Peripheral Component Interconnect) local bus of data-processing apparatus through PCI Host Bridge. The PCI Host Bridge can provide a low latency path through which processor may directly access PCI devices mapped anywhere within bus memory and/or input/output (I/0) address spaces. PCI Host Bridge can also provide a high bandwidth path for allowing PCI devices to directly access RAM.
A communications adapter, a small computer system interface (SCSI), and an expansion bus-bridge may also be attached to PCI local bus. The communications adapter can be utilized for connecting data-processing apparatus to a network. SCSI can be utilized to control a high-speed SCSI disk drive. An expansion bus-bridge, such as a PCI-to-ISA bus bridge, may be utilized for coupling ISA bus to PCI local bus. PCI local bus can be connected
-21-to a monitor, which functions as a display (e.g., a video monitor) for displaying data and information for an operator and also for interactively displaying a graphical user interface.
A database can contain information on a variety of matters such as data relating to digital currency transactions, transfers, or conversions. For example, a database may contain user profiles, user preferences, transaction history, score history, and/or history of prior score requests. A user profile may include, but is not limited to, a user identifier such as login name, a password, contact information, mailing information, billing information, saved product searches, and/or user preferences for use in searching database and/or displaying product searches. Although it will be recognized that currently many users of digital currencies try to maintain anonymity.
The network may be a single network or a combination of multiple networks. For example, the network may include the internet and/or one or more intranets, landline networks, wireless networks, and/or other appropriate types of communication networks. In another example, the network may comprise a wireless telecommunications network (e.g., cellular phone network) adapted to communicate with other communication networks, such as the Internet. Typically, the network will comprise a computer network that makes use of a TCP/IP protocol (including protocols based on TCP/IP protocol, such as HTTP, HTTPS or FTP).
The system may be adapted to follow any computer communication standard including Extensible Markup Language (XML), Comma-Separated Values (CSV), Hypertext Transfer Protocol (HTTP), Java Message Service (JMS), Simple Object Access Protocol (SOAP), Representational State Transfer (REST), Lightweight Directory Access Protocol (LDAP), Simple Mail Transfer Protocol (SMTP) and the like.
The system may represent a score graphically or by images and may accordingly accommodate any type of still or moving image file including JPEG, PNG, GIF, PDF, RAW, BMP, TIFF, MP3, WAV, WMV, MOV, MPEG, AVI, FLV, WebM, 3GPP, SVI and the like.
The system may guide or prompt user attempts to input account identifiers and request various score types by any convenient form of user interface element including, for example, a window, a tab, a text box, a button, a hyperlink, a drop down list, a list box, a check box, a radio button box, a cycle button, a datagrid or any combination thereof.
A database can contain information on a variety of matters such as data relating to digital currency transactions, transfers, or conversions. For example, a database may contain user profiles, user preferences, transaction history, score history, and/or history of prior score requests. A user profile may include, but is not limited to, a user identifier such as login name, a password, contact information, mailing information, billing information, saved product searches, and/or user preferences for use in searching database and/or displaying product searches. Although it will be recognized that currently many users of digital currencies try to maintain anonymity.
The network may be a single network or a combination of multiple networks. For example, the network may include the internet and/or one or more intranets, landline networks, wireless networks, and/or other appropriate types of communication networks. In another example, the network may comprise a wireless telecommunications network (e.g., cellular phone network) adapted to communicate with other communication networks, such as the Internet. Typically, the network will comprise a computer network that makes use of a TCP/IP protocol (including protocols based on TCP/IP protocol, such as HTTP, HTTPS or FTP).
The system may be adapted to follow any computer communication standard including Extensible Markup Language (XML), Comma-Separated Values (CSV), Hypertext Transfer Protocol (HTTP), Java Message Service (JMS), Simple Object Access Protocol (SOAP), Representational State Transfer (REST), Lightweight Directory Access Protocol (LDAP), Simple Mail Transfer Protocol (SMTP) and the like.
The system may represent a score graphically or by images and may accordingly accommodate any type of still or moving image file including JPEG, PNG, GIF, PDF, RAW, BMP, TIFF, MP3, WAV, WMV, MOV, MPEG, AVI, FLV, WebM, 3GPP, SVI and the like.
The system may guide or prompt user attempts to input account identifiers and request various score types by any convenient form of user interface element including, for example, a window, a tab, a text box, a button, a hyperlink, a drop down list, a list box, a check box, a radio button box, a cycle button, a datagrid or any combination thereof.
-22-Furthermore, the user interface elements may provide a graphic label such as any type of symbol or icon, a text label or any combination thereof. Any desired spatial pattern or timing pattern of appearance of user interface elements may be accommodated by the system.
The system described herein and each variant, modification or combination thereof may also be implemented as a method or code on a computer readable medium (i.e. a substrate). The computer readable medium is a tangible data storage device that can store data, which can thereafter, be read by a computer system. Examples of a computer readable medium include read-only memory, random-access memory, CD-ROMs, magnetic tape, optical data storage devices and the like. The computer readable medium may be geographically localized or may be distributed over a network coupled computer system so that the computer readable code is stored and executed in a distributed fashion.
Still further variants, modifications or combinations thereof will be recognized by the person of skill in the art.
The system described herein and each variant, modification or combination thereof may also be implemented as a method or code on a computer readable medium (i.e. a substrate). The computer readable medium is a tangible data storage device that can store data, which can thereafter, be read by a computer system. Examples of a computer readable medium include read-only memory, random-access memory, CD-ROMs, magnetic tape, optical data storage devices and the like. The computer readable medium may be geographically localized or may be distributed over a network coupled computer system so that the computer readable code is stored and executed in a distributed fashion.
Still further variants, modifications or combinations thereof will be recognized by the person of skill in the art.
-23-
Claims (56)
1 A rating system for rating a transaction history of a digital currency, comprising a storage system for storing transaction information of the digital currency, an interface for receiving an identifier of at least one account associated with the digital currency and a request for rating the transaction history of the at least one account, a processor communicative with the storage system and the interface, the processor identifying transactions of the at least one account from the transaction information stored in the storage system and assessing the amount, date, and destination of the identified transactions to generate a rating for the at least one account
2 The rating system of claim 1, wherein the rating is a numerical score.
3 The rating system of claim 2, wherein the numerical score is compared to a reference value to normalize the score
4 The rating system of claim 3, wherein the reference value is a total, average, mean or modal value characterizing the accumulated transaction information of the digital currency
The rating system of claim 1, wherein the amount is positively correlated with the rating
6 The rating system of claim 1, wherein transaction frequency is positively correlated with the rating
7 The rating system of claim 1, wherein transaction age is negatively correlated with the rating
8 The rating system of claim 1, wherein the number of unique transaction destinations is positively correlated with the rating
9 The rating system of claim 1, wherein the number of repeated transactions to the same destination is negatively correlated with the rating
The rating system of claim 1, wherein the digital currency is a peer-to-peer digital currency
11, The rating system of claim 10, wherein the peer-to-peer digital currency is Bitcoin, Litecoin, PPcoin (Peer-to-peer coin), Freicoin, or Namecoin
12 The rating system of claim 1, wherein the rating reflects the ability of the account to fulfill a transaction of the digital currency
13. The rating system of claim 1, wherein the processor, the interface and the storage system are located within the same computing device.
14. The rating system of claim 1, wherein the processor, the interface and the storage system are each located on a different computing device.
15. A computer-implemented method for rating a transaction history of a digital currency, comprising:
storing transaction information of the digital currency in a storage system;
receiving over a network via an interface an identifier of at least one account associated with the digital currency and a request for rating the transaction history of the at least one account;
identifying transactions of the at least one account from the transaction information stored in the storage system; and assessing the amount, date, and destination of the identified transactions to generate a rating for the at least one account.
storing transaction information of the digital currency in a storage system;
receiving over a network via an interface an identifier of at least one account associated with the digital currency and a request for rating the transaction history of the at least one account;
identifying transactions of the at least one account from the transaction information stored in the storage system; and assessing the amount, date, and destination of the identified transactions to generate a rating for the at least one account.
16. The method of claim 15, wherein the rating is a numerical score.
17. The method of claim 16, further comprising comparing the numerical score to a reference value to normalize the score.
18. The method of claim 17, wherein the reference value is a total, average, mean or modal value characterizing the accumulated transaction information of the digital currency.
19. The method of claim 15, wherein the amount is positively correlated with the rating.
20. The method of claim 15, wherein transaction frequency is positively correlated with the rating.
21. The method of claim 15, wherein transaction age is negatively correlated with the rating.
22. The method of claim 15, wherein the number of unique transaction destinations is positively correlated with the rating.
23. The method of claim 15, wherein the number of repeated transactions to the same destination is negatively correlated with the rating.
24. The method of claim 15, wherein the digital currency is a peer-to-peer digital currency.
25. The method of claim 24, wherein the peer-to-peer digital currency is Bitcoin, Litecoin, PPcoin (Peer-to-peer coin), Freicoin, or Namecoin.
26. The method of claim 15, wherein the rating reflects the ability of the account to fulfill a transaction of the digital currency.
27. A computer readable medium embodying a computer program for rating a transaction history of a digital currency, comprising:
computer readable code for storing transaction information of the digital currency in a storage system;
computer readable code for receiving over a network via an interface an identifier of at least one account associated with the digital currency and a request for rating the transaction history of the at least one account;
computer readable code for identifying transactions of the at least one account from the transaction information stored in the storage system; and computer readable code for assessing the amount, date, and destination of the identified transactions to generate a rating for the at least one account.
computer readable code for storing transaction information of the digital currency in a storage system;
computer readable code for receiving over a network via an interface an identifier of at least one account associated with the digital currency and a request for rating the transaction history of the at least one account;
computer readable code for identifying transactions of the at least one account from the transaction information stored in the storage system; and computer readable code for assessing the amount, date, and destination of the identified transactions to generate a rating for the at least one account.
28. The computer readable medium of claim 27, wherein the rating is a numerical score.
29. The computer readable medium of claim 28, further comprising computer readable code for comparing the numerical score to a reference value to normalize the score.
30. The computer readable medium of claim 29, wherein the reference value is a total, average, mean or modal value characterizing the accumulated transaction information of the digital currency.
31. The computer readable medium of claim 27, wherein the amount is positively correlated with the rating.
32. The computer readable medium of claim 27, wherein transaction frequency is positively correlated with the rating.
33. The computer readable medium of claim 27, wherein transaction age is negatively correlated with the rating.
34. The computer readable medium of claim 27, wherein the number of unique transaction destinations is positively correlated with the rating.
35. The computer readable medium of claim 27, wherein the number of repeated transactions to the same destination is negatively correlated with the rating.
36. The computer readable medium of claim 27, wherein the digital currency is a peer-to-peer digital currency.
37, The computer readable medium of claim 36, wherein the peer-to-peer digital currency is Bitcoin, Litecoin, PPcoin (Peer-to-peer coin), Freicoin, or Namecoin.
38. The computer readable medium of claim 27, wherein the rating reflects the ability of the account to fulfill a transaction of the digital currency.
39. A rating system for rating a transaction history of a peer-to-peer digital currency, comprising:
a storage system for storing transaction information of the peer-to-peer digital currency;
an interface for receiving an identifier of at least one account associated with the peer-to-peer digital currency and a request for rating the transaction history of the at least one account;
a processor communicative with the storage system and the interface;
the processor identifying transactions of the at least one account from the transaction information stored in the storage system and assessing the destination of the identified transactions to generate a rating for the at least one account.
a storage system for storing transaction information of the peer-to-peer digital currency;
an interface for receiving an identifier of at least one account associated with the peer-to-peer digital currency and a request for rating the transaction history of the at least one account;
a processor communicative with the storage system and the interface;
the processor identifying transactions of the at least one account from the transaction information stored in the storage system and assessing the destination of the identified transactions to generate a rating for the at least one account.
40. The rating system of claim 39, wherein the rating is a numerical score.
41. The rating system of claim 40, wherein the numerical score is compared to a reference value to normalize the score.
42. The rating system of claim 41, wherein the reference value is a total, average, mean or modal value characterizing the accumulated transaction information of the peer-to-peer digital currency.
43. The rating system of claim 39, wherein the number of unique transaction destinations is positively correlated with the rating.
44. The rating system of claim 39, wherein the number of repeated transactions to the same destination is negatively correlated with the rating.
45. A computer-implemented method for rating a transaction history of a peer-to-peer digital currency, comprising:
storing transaction information of the peer-to-peer digital currency in a storage system;
receiving over a network via an interface an identifier of at least one account associated with the peer-to-peer digital currency and a request for rating the transaction history of the at least one account;
identifying transactions of the at least one account from the transaction information stored in the storage system; and assessing the amount, date, and destination of the identified transactions to generate a rating for the at least one account.
storing transaction information of the peer-to-peer digital currency in a storage system;
receiving over a network via an interface an identifier of at least one account associated with the peer-to-peer digital currency and a request for rating the transaction history of the at least one account;
identifying transactions of the at least one account from the transaction information stored in the storage system; and assessing the amount, date, and destination of the identified transactions to generate a rating for the at least one account.
46. The method of claim 45, wherein the rating is a numerical score.
47. The method of claim 46, further comprising comparing the numerical score to a reference value to normalize the score.
48. The method of claim 47, wherein the reference value is a total, average, mean or modal value characterizing the accumulated transaction information of the peer-to-peer digital currency.
49. The method of claim 45, wherein the number of unique transaction destinations is positively correlated with the rating.
50. The method of claim 45, wherein the number of repeated transactions to the same destination is negatively correlated with the rating.
51. A computer readable medium embodying a computer program for rating a transaction history of a peer-to-peer digital currency, comprising:
computer readable code for storing transaction information of the peer-to-peer digital currency in a storage system;
computer readable code for receiving over a network via an interface an identifier of at least one account associated with the peer-to peer digital currency and a request for rating the transaction history of the at least one account;
computer readable code for identifying transactions of the at least one account from the transaction information stored in the storage system; and computer readable code for assessing the amount, date, and destination of the identified transactions to generate a rating for the at least one account.
computer readable code for storing transaction information of the peer-to-peer digital currency in a storage system;
computer readable code for receiving over a network via an interface an identifier of at least one account associated with the peer-to peer digital currency and a request for rating the transaction history of the at least one account;
computer readable code for identifying transactions of the at least one account from the transaction information stored in the storage system; and computer readable code for assessing the amount, date, and destination of the identified transactions to generate a rating for the at least one account.
52. The computer readable medium of claim 51, wherein the rating is a numerical score.
53. The computer readable medium of claim 52, further comprising computer readable code for comparing the numerical score to a reference value to normalize the score.
54. The computer readable medium of claim 53, wherein the reference value is a total, average, mean or modal value characterizing the accumulated transaction information of the peer-to-peer digital currency.
55. The computer readable medium of claim 51, wherein the number of unique transaction destinations is positively correlated with the rating.
56. The computer readable medium of claim 51, wherein the number of repeated transactions to the same destination is negatively correlated with the rating.
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| PCT/CA2013/050967 WO2015085393A1 (en) | 2013-12-13 | 2013-12-13 | System and method for rating a transaction history |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CA2933407A1 true CA2933407A1 (en) | 2015-06-18 |
Family
ID=53370394
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CA2933407A Pending CA2933407A1 (en) | 2013-12-13 | 2013-12-13 | System and method for rating a transaction history |
Country Status (2)
| Country | Link |
|---|---|
| CA (1) | CA2933407A1 (en) |
| WO (1) | WO2015085393A1 (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20200027089A1 (en) * | 2018-07-20 | 2020-01-23 | Coral Protocol | Blockchain transaction safety using smart contracts |
| US11188918B1 (en) * | 2015-06-26 | 2021-11-30 | Wells Fargo Bank, N.A. | Systems and methods for expediting math-based currency transactions |
Families Citing this family (68)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10354325B1 (en) | 2013-06-28 | 2019-07-16 | Winklevoss Ip, Llc | Computer-generated graphical user interface |
| US10269009B1 (en) | 2013-06-28 | 2019-04-23 | Winklevoss Ip, Llc | Systems, methods, and program products for a digital math-based asset exchange |
| US9892460B1 (en) | 2013-06-28 | 2018-02-13 | Winklevoss Ip, Llc | Systems, methods, and program products for operating exchange traded products holding digital math-based assets |
| US10068228B1 (en) | 2013-06-28 | 2018-09-04 | Winklevoss Ip, Llc | Systems and methods for storing digital math-based assets using a secure portal |
| US11282139B1 (en) | 2013-06-28 | 2022-03-22 | Gemini Ip, Llc | Systems, methods, and program products for verifying digital assets held in a custodial digital asset wallet |
| US20150302400A1 (en) * | 2014-04-18 | 2015-10-22 | Ebay Inc. | Distributed crypto currency reputation system |
| US10275772B2 (en) * | 2014-06-16 | 2019-04-30 | Bank Of America Corporation | Cryptocurrency risk detection system |
| US20150363777A1 (en) * | 2014-06-16 | 2015-12-17 | Bank Of America Corporation | Cryptocurrency suspicious user alert system |
| KR101955833B1 (en) * | 2014-07-11 | 2019-03-07 | 로얄 코퍼레이션 | Distributed ledger protocol to incentivize transactional and non-transactional commerce |
| US9853977B1 (en) | 2015-01-26 | 2017-12-26 | Winklevoss Ip, Llc | System, method, and program product for processing secure transactions within a cloud computing system |
| US10158480B1 (en) | 2015-03-16 | 2018-12-18 | Winklevoss Ip, Llc | Autonomous devices |
| US10915891B1 (en) | 2015-03-16 | 2021-02-09 | Winklevoss Ip, Llc | Autonomous devices |
| CN107615317A (en) | 2015-03-31 | 2018-01-19 | 纳斯达克公司 | The system and method for block chain transaction record |
| US10979410B1 (en) | 2015-05-04 | 2021-04-13 | United Services Automobile Association (Usaa) | Systems and methods for utilizing cryptology with virtual ledgers in support of transactions and agreements |
| US10909510B1 (en) * | 2015-06-26 | 2021-02-02 | Wells Fargo Bank, N.A. | Systems and methods for expediting math-based currency transactions |
| US10097356B2 (en) | 2015-07-02 | 2018-10-09 | Nasdaq, Inc. | Systems and methods of secure provenance for distributed transaction databases |
| WO2017011601A1 (en) * | 2015-07-14 | 2017-01-19 | Fmr Llc | Computationally efficient transfer processing, auditing, and search apparatuses, methods and systems |
| US10339523B2 (en) | 2015-07-14 | 2019-07-02 | Fmr Llc | Point-to-point transaction guidance apparatuses, methods and systems |
| US11636471B2 (en) | 2017-12-15 | 2023-04-25 | Fmr Llc | Social data tracking datastructures, apparatuses, methods and systems |
| US11436598B2 (en) | 2017-12-15 | 2022-09-06 | Fmr Llc | Social data tracking datastructures, apparatuses, methods and systems |
| US11488147B2 (en) | 2015-07-14 | 2022-11-01 | Fmr Llc | Computationally efficient transfer processing and auditing apparatuses, methods and systems |
| US10504179B1 (en) | 2015-12-08 | 2019-12-10 | Fmr Llc | Social aggregated fractional equity transaction partitioned acquisition apparatuses, methods and systems |
| US10644885B2 (en) | 2015-07-14 | 2020-05-05 | Fmr Llc | Firmware extension for secure cryptocurrency key backup, restore, and transaction signing platform apparatuses, methods and systems |
| US10992469B2 (en) | 2015-07-14 | 2021-04-27 | Fmr Llc | Seed splitting and firmware extension for secure cryptocurrency key backup, restore, and transaction signing platform apparatuses, methods and systems |
| US10778439B2 (en) | 2015-07-14 | 2020-09-15 | Fmr Llc | Seed splitting and firmware extension for secure cryptocurrency key backup, restore, and transaction signing platform apparatuses, methods and systems |
| US11915332B2 (en) | 2015-10-02 | 2024-02-27 | Loyyal Holdings Incorporated | System and process for tokenization and management of liability |
| CA3002034A1 (en) | 2015-10-14 | 2017-04-20 | Cambridge Blockchain, LLC | Systems and methods for managing digital identities |
| US20170132625A1 (en) * | 2015-11-05 | 2017-05-11 | Mastercard International Incorporated | Method and system for use of a blockchain in a transaction processing network |
| US10108812B2 (en) | 2016-01-28 | 2018-10-23 | Nasdaq, Inc. | Systems and methods for securing and disseminating time sensitive information using a blockchain |
| EP3411824B1 (en) | 2016-02-04 | 2019-10-30 | Nasdaq Technology AB | Systems and methods for storing and sharing transactional data using distributed computer systems |
| US9855785B1 (en) | 2016-04-04 | 2018-01-02 | Uipco, Llc | Digitally encoded seal for document verification |
| US10643203B2 (en) | 2016-04-12 | 2020-05-05 | Digicash Pty Ltd. | Secure transaction controller for value token exchange systems |
| US10333705B2 (en) | 2016-04-30 | 2019-06-25 | Civic Technologies, Inc. | Methods and apparatus for providing attestation of information using a centralized or distributed ledger |
| US11854011B1 (en) | 2016-07-11 | 2023-12-26 | United Services Automobile Association (Usaa) | Identity management framework |
| US11455642B1 (en) | 2016-09-19 | 2022-09-27 | United Services Automobile Association (Usaa) | Distributed ledger based interchange |
| US11050763B1 (en) | 2016-10-21 | 2021-06-29 | United Services Automobile Association (Usaa) | Distributed ledger for network security management |
| US10438170B2 (en) | 2017-01-05 | 2019-10-08 | International Business Machines Corporation | Blockchain for program code credit and programmer contribution in a collective |
| CN116993331A (en) | 2017-01-31 | 2023-11-03 | 区块链控股有限公司 | Computer-implemented system and method for generating and extracting user-related data stored on a blockchain |
| US11392947B1 (en) | 2017-02-27 | 2022-07-19 | United Services Automobile Association (Usaa) | Distributed ledger for device management |
| US10693954B2 (en) | 2017-03-03 | 2020-06-23 | International Business Machines Corporation | Blockchain-enhanced mobile telecommunication device |
| US10375105B2 (en) | 2017-03-03 | 2019-08-06 | International Business Machines Corporation | Blockchain web browser interface |
| US10611474B2 (en) | 2017-03-20 | 2020-04-07 | International Business Machines Corporation | Unmanned aerial vehicle data management |
| CN107103471B (en) * | 2017-03-28 | 2020-06-30 | 上海瑞麒维网络科技有限公司 | Method and device for determining transaction validity based on block chain |
| US10713963B2 (en) | 2017-05-25 | 2020-07-14 | International Business Machines Corporation | Managing lifelong learner events on a blockchain |
| US11038682B2 (en) | 2017-05-26 | 2021-06-15 | Cloudminds (Shanghai) Robotics Co., Ltd. | Communication method, apparatus and system, electronic device, and computer readable storage medium |
| US10380812B2 (en) | 2017-06-23 | 2019-08-13 | International Business Machines Corporation | Vehicle transaction validation |
| US10713677B2 (en) * | 2017-12-13 | 2020-07-14 | Mastercard Asia/Pacific Pte. Ltd. | Method and system for social savings platform via blockchain |
| US11170092B1 (en) | 2017-12-14 | 2021-11-09 | United Services Automobile Association (Usaa) | Document authentication certification with blockchain and distributed ledger techniques |
| US11308487B1 (en) | 2018-02-12 | 2022-04-19 | Gemini Ip, Llc | System, method and program product for obtaining digital assets |
| US11909860B1 (en) | 2018-02-12 | 2024-02-20 | Gemini Ip, Llc | Systems, methods, and program products for loaning digital assets and for depositing, holding and/or distributing collateral as a token in the form of digital assets on an underlying blockchain |
| US10373158B1 (en) | 2018-02-12 | 2019-08-06 | Winklevoss Ip, Llc | System, method and program product for modifying a supply of stable value digital asset tokens |
| US11522700B1 (en) | 2018-02-12 | 2022-12-06 | Gemini Ip, Llc | Systems, methods, and program products for depositing, holding and/or distributing collateral as a token in the form of digital assets on an underlying blockchain |
| US10438290B1 (en) | 2018-03-05 | 2019-10-08 | Winklevoss Ip, Llc | System, method and program product for generating and utilizing stable value digital assets |
| US10540654B1 (en) | 2018-02-12 | 2020-01-21 | Winklevoss Ip, Llc | System, method and program product for generating and utilizing stable value digital assets |
| US10373129B1 (en) | 2018-03-05 | 2019-08-06 | Winklevoss Ip, Llc | System, method and program product for generating and utilizing stable value digital assets |
| US11475442B1 (en) | 2018-02-12 | 2022-10-18 | Gemini Ip, Llc | System, method and program product for modifying a supply of stable value digital asset tokens |
| US11200569B1 (en) | 2018-02-12 | 2021-12-14 | Winklevoss Ip, Llc | System, method and program product for making payments using fiat-backed digital assets |
| US10929842B1 (en) | 2018-03-05 | 2021-02-23 | Winklevoss Ip, Llc | System, method and program product for depositing and withdrawing stable value digital assets in exchange for fiat |
| US11139955B1 (en) | 2018-02-12 | 2021-10-05 | Winklevoss Ip, Llc | Systems, methods, and program products for loaning digital assets and for depositing, holding and/or distributing collateral as a token in the form of digital assets on an underlying blockchain |
| US11334883B1 (en) | 2018-03-05 | 2022-05-17 | Gemini Ip, Llc | Systems, methods, and program products for modifying the supply, depositing, holding and/or distributing collateral as a stable value token in the form of digital assets |
| US10853812B2 (en) | 2018-03-07 | 2020-12-01 | Coral Protocol | Blockchain transaction safety |
| US10380685B1 (en) | 2018-05-18 | 2019-08-13 | Capital One Services, Llc | Secure system |
| AU2019282043A1 (en) * | 2018-06-04 | 2021-01-28 | Strong Force TX Portfolio 2018, LLC | Decentralized safeguard against fraud |
| US11507928B2 (en) | 2018-06-05 | 2022-11-22 | International Business Machines Corporation | Blockchain and cryptocurrency for real-time vehicle accident management |
| CN108985644B (en) * | 2018-07-27 | 2021-02-09 | 创新先进技术有限公司 | Rights and interests distribution method and device and electronic equipment |
| US11055412B2 (en) | 2018-12-20 | 2021-07-06 | At&T Intellectual Property I, L.P. | Method and system for stake-based event management with ledgers |
| US11501370B1 (en) | 2019-06-17 | 2022-11-15 | Gemini Ip, Llc | Systems, methods, and program products for non-custodial trading of digital assets on a digital asset exchange |
| WO2023215933A1 (en) * | 2022-05-11 | 2023-11-16 | Pocket Venture Holdings Pty Ltd | Blockchain comparison systems and methods |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8527418B2 (en) * | 2011-11-22 | 2013-09-03 | The Western Union Company | Risk analysis of money transfer transactions |
| US20130325701A1 (en) * | 2012-06-05 | 2013-12-05 | International Business Machines Corporation | E-currency validation and authorization services platform |
-
2013
- 2013-12-13 WO PCT/CA2013/050967 patent/WO2015085393A1/en active Application Filing
- 2013-12-13 CA CA2933407A patent/CA2933407A1/en active Pending
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11188918B1 (en) * | 2015-06-26 | 2021-11-30 | Wells Fargo Bank, N.A. | Systems and methods for expediting math-based currency transactions |
| US11928687B1 (en) | 2015-06-26 | 2024-03-12 | Wells Fargo Bank, N.A. | Systems and methods for expediting math-based currency transactions |
| US20200027089A1 (en) * | 2018-07-20 | 2020-01-23 | Coral Protocol | Blockchain transaction safety using smart contracts |
Also Published As
| Publication number | Publication date |
|---|---|
| WO2015085393A1 (en) | 2015-06-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CA2933407A1 (en) | System and method for rating a transaction history | |
| US11328366B2 (en) | System and method for account transaction and balance prediction | |
| CA3065807C (en) | System and method for issuing a loan to a consumer determined to be creditworthy | |
| CN109074389B (en) | Crowdsourcing of confidence indicators | |
| CA3014995C (en) | Searching for entities based on trust score and geography | |
| US10055466B2 (en) | Extrapolating trends in trust scores | |
| US10230764B2 (en) | Inferential analysis using feedback for extracting and combining cyber risk information | |
| US20190378219A1 (en) | Calculating a trust score | |
| EP3155549B1 (en) | Analyzing facial recognition data and social network data for user authentication | |
| US8676684B2 (en) | System and method for evaluating risk in fraud prevention | |
| US20150026120A1 (en) | Systems and methods for visualizing social graphs | |
| WO2021167858A1 (en) | Transaction card system having overdraft capability | |
| US11652604B2 (en) | Blockchain data compression and storage | |
| US20150348166A1 (en) | System and method for providing enhanced financial services based on social signals | |
| US20160019552A1 (en) | System and method for using social media information to identify and classify users | |
| US20230139364A1 (en) | Generating user interfaces comprising dynamic base limit value user interface elements determined from a base limit value model | |
| Batool et al. | Block-RACS: Towards Reputation-Aware Client Selection and Monetization Mechanism for Federated Learning | |
| Ouyang et al. | FedCrowdSensing: Incentive Mechanism for Crowdsensing Based on Reputation and Federated Learning | |
| US20230031309A1 (en) | Method and System for Providing Users with an Open Opinion Marketplace Analysis | |
| US11966887B1 (en) | Bridging network transaction platforms to unify cross-platform transfers | |
| US20230306530A1 (en) | Determining base limit values for contacts based on inter-network user interactions | |
| US20230316393A1 (en) | Determining recognized user activities for a third-party risk generator integrated within an application | |
| US20240144201A1 (en) | Bridging network transaction platforms to unify cross-platform transfers | |
| US20240144246A1 (en) | System and method for proof-of-stake validated serving of stake-prioritized utility | |
| CN112580916A (en) | Data evaluation method and device, computer equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| EEER | Examination request |
Effective date: 20181212 |
|
| EEER | Examination request |
Effective date: 20181212 |
|
| EEER | Examination request |
Effective date: 20181212 |
|
| EEER | Examination request |
Effective date: 20181212 |
|
| EEER | Examination request |
Effective date: 20181212 |
|
| EEER | Examination request |
Effective date: 20181212 |
|
| EEER | Examination request |
Effective date: 20181212 |