DETAILED ACTION
Status of Claims
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
This action is in reply to Application 19/078,856 filed on 13 March 2025.
Claims 1-20 are currently pending and have been examined.
Information Disclosure Statement
The Information Disclosure Statement filed 17 September 2025 has been considered. An initialed copy of the Form 1449 is enclosed herewith.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
In the instant case, representative method claim 1 is directed towards facilitating tracking and monitoring the paths (e.g., digital wallets) through which digital assets used in a transaction are transferred to. Claim 1 is directed to the abstract idea of utilizing rules and/or instructions for performing the existing commercial practice (e.g., e-commerce) and/or concept of receiving and outputting data/information (transactions corresponding to a transfer of a digital asset between digital wallets) associated with a financial transaction (managing interactions between people), which is grouped under the certain methods of organizing human activity – fundamental economic principles, practices or concepts; sales activity; following set of instructions; commercial interactions; managing interactions between people (including social activities, teachings, following rules or instructions) grouping, in step 2A prong one. Accordingly, for these reasons, the claim recites an abstract idea.
Claim 1 recites: “acquiring information associated with a first transaction that corresponds to a transfer of a first target digital asset from a first digital wallet to a second digital wallet;
based on the information associated with the first transaction, outputting, via a user interface of the apparatus, a first node corresponding to the first digital wallet, a second node corresponding to the second digital wallet, and a first edge corresponding to the first transaction;
outputting, via the user interface, a second edge corresponding to a second transaction satisfying a predetermined condition, among one or more transactions that correspond to one or more transfers of a digital asset from the second digital wallet to a digital wallet different from the second digital wallet; and
outputting, via the user interface, a third node corresponding to a third digital wallet that is a recipient of the second transaction,
wherein the second transaction is a transaction determined to correspond to a transfer of a second target digital asset associated with at least a portion of the first target digital asset.”
Based on the underlined elements above, abstract ideas and/or concepts are identified. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application because, when analyzed under step 2A prong two, the additional elements of the claim such as a “computer device”, “processor”, “user interface”, represent the use of a computer-related devices as a tool (intermediary) to perform an abstract idea and/or does no more than generally apply the abstract idea to a particular field of use. Therefore, the additional elements do not integrate the abstract idea into a practical application as they do no more than represent a computer performing functions that correspond to (i.e. automate) implement the acts of utilizing rules and/or instructions for performing the existing commercial practice (e.g., e-commerce) and/or concept of receiving and outputting data/information (transactions corresponding to a transfer of a digital asset between digital wallets) associated with a financial transaction (managing interactions between people).
When analyzed under step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception itself. Viewed as a whole, the combination of elements recited in the claims merely describe the concept of utilizing rules and/or instructions for performing the existing commercial practice (e.g., e-commerce) and/or concept of receiving and outputting data/information (transactions corresponding to a transfer of a digital asset between digital wallets) associated with a financial transaction (managing interactions between people) using computer computer-related technology and/or devices that merely perform as designed to function. Therefore, the use of these additional elements does no more than employ a computer as a tool to automate and/or implement the abstract idea, which cannot provide significantly more than the abstract idea itself (MPEP 2106.05(I)(A)(f) & (h)). Hence, claim 1 is not patent eligible.
Independent claim 19 recites substantially the same limitations as claim 1 above and is ineligible for the same reasons. The subject matter of claim 19 corresponds to the subject matter of claim 1 in terms of a computer-readable recording medium (e.g., manufacture). Therefore the reasoning provided for claim 1 applies to claim 19 accordingly.
Independent claim 20 recites substantially the same limitations as claim 1 above and is ineligible for the same reasons. The subject matter of claim 20 corresponds to the subject matter of claim 1 in terms of a computer device (e.g., manufacture). Therefore the reasoning provided for claim 1 applies to claim 20 accordingly.
Dependent claims 2-18 add further details and contain limitations that narrow the scope of the invention. However, these details do not result in significantly more than the abstract idea itself. As explained in the December 16, 2014 Interim Eligibility Guidance from the USPTO (in reference to the BuySAFE, Inc. v. Google, Inc. decision), further narrowing the details of an abstract idea does not change the § 101 analysis since a more narrow abstract idea does not make it any less abstract.
The step(s) recited are a further refinement of methods of organizing human activity – fundamental economic principles, practices or concepts; sales activity; following set of instructions; commercial or legal interactions (agreements in the form of contracts; business relations); managing interactions between people (including social activities, teachings, following rules or instructions), because it merely describes intermediate steps and/or rules/instructions of the process.
Viewed individually and in combination, these additional elements do not provide meaningful limitations to transform the abstract idea such that the claims amount to significantly more than the abstraction itself.
Accordingly, the present pending claims are not patent eligible and are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections – 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office Action:
A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Fang et al., US 2022/0067738 A1 (“Fang”), in view of Liu et al., US 2022/0414664 A1 (“Liu”).
Re Claim 1: Fang discloses a method performed by an apparatus, the method comprising:
acquiring information associated with a first transaction that corresponds to a transfer of a first target digital asset from a first digital wallet to a second digital wallet; ([0003] “… method includes receiving blockchain transaction data from a blockchain ledger to a transaction database, the blockchain transaction data including blockchain addresses, transaction identifications, transaction timestamps, and digital assets transferred, receiving intelligence labels from a blockchain ecosystem intelligence database, where the intelligence labels include known behavioral characteristics of entities associated with the blockchain addresses, selecting a blockchain transaction flow comprising blockchain transactions associated with a digital account source address, digital account intermediate addresses, a digital account destination address, and intermediate transactions where the intermediate transactions transfer the digital assets between the digital account source address and the digital account destination address …”; [0039] “Blockchain transaction risk management using machine learning supports the above-disclosed system and method. The risk management machine learning model may generate real time predictions allowing a suspicious cryptocurrency transfer transaction from the user to an unknown wallet address that has observed strong linkage …”)
Regarding the limitation(s), Liu teaches in a related endeavor:
based on the information associated with the first transaction, outputting, via a user interface of the apparatus, a first node corresponding to the first digital wallet, a second node corresponding to the second digital wallet, and a first edge corresponding to the first transaction; ([0160] “…input devices are often connected to the processing unit 1204 through an input device interface 1244”; [0138] “… the analyzing the transaction graph via the at least one machine learning algorithm further can comprise: executing, by the computer system (e.g., 120), an importance-assignment algorithm (e.g., 702) on a first transaction community in the set of transaction communities, wherein the importance assignment algorithm is configured to respectively assign levels of importance ( e.g., 704) to nodes that are within the first transaction community.”; [0123] “… the risk formula 902 can, in various aspects, be equal to and/or otherwise based on a reciprocal exponential of a weighted linear combination of the level of importance of an inputted node, a bit indicating whether the inputted node is the most important in its transaction community, a size of the transaction community of the inputted node, a number of edges leaving the inputted node, a number of edges entering the inputted node, an age of the blockchain address corresponding to the inputted node, and/or a number of tokens held by the blockchain address corresponding to the inputted node.”; [0195] “… transactions may be submitted to the server 1450 via desktop applications, smartphone applications, digital wallet applications”)
outputting, via the user interface, a second edge corresponding to a second transaction satisfying a predetermined condition, among one or more transactions that correspond to one or more transfers of a digital asset from the second digital wallet to a digital wallet different from the second digital wallet; (See fig. 3. [0160] “…input devices are often connected to the processing unit 1204 through an input device interface 1244”; [0138] “… the analyzing the transaction graph via the at least one machine learning algorithm further can comprise: executing, by the computer system (e.g., 120), an importance-assignment algorithm (e.g., 702) on a first transaction community in the set of transaction communities, wherein the importance assignment algorithm is configured to respectively assign levels of importance ( e.g., 704) to nodes that are within the first transaction community.”; [0123] “… the risk formula 902 can, in various aspects, be equal to and/or otherwise based on a reciprocal exponential of a weighted linear combination of the level of importance of an inputted node, a bit indicating whether the inputted node is the most important in its transaction community, a size of the transaction community of the inputted node, a number of edges leaving the inputted node, a number of edges entering the inputted node, an age of the blockchain address corresponding to the inputted node, and/or a number of tokens held by the blockchain address corresponding to the inputted node.”; [0195] “… transactions may be submitted to the server 1450 via desktop applications, smartphone applications, digital wallet applications”)
outputting, via the user interface, a third node corresponding to a third digital wallet that is a recipient of the second transaction, (See fig. 3, [0160] “…input devices are often connected to the processing unit 1204 through an input device interface 1244”; [0138] “… the analyzing the transaction graph via the at least one machine learning algorithm further can comprise: executing, by the computer system (e.g., 120), an importance-assignment algorithm (e.g., 702) on a first transaction community in the set of transaction communities, wherein the importance assignment algorithm is configured to respectively assign levels of importance ( e.g., 704) to nodes that are within the first transaction community.”; [0123] “… the risk formula 902 can, in various aspects, be equal to and/or otherwise based on a reciprocal exponential of a weighted linear combination of the level of importance of an inputted node, a bit indicating whether the inputted node is the most important in its transaction community, a size of the transaction community of the inputted node, a number of edges leaving the inputted node, a number of edges entering the inputted node, an age of the blockchain address corresponding to the inputted node, and/or a number of tokens held by the blockchain address corresponding to the inputted node.”; [0195] “… transactions may be submitted to the server 1450 via desktop applications, smartphone applications, digital wallet applications”)
It would have been obvious to one of ordinary skill in the art at the time of the invention to incorporate the teachings of Liu to the invention of Fang as described above for the motivation of estimating and detecting the level of risk associated with electronic transaction involving digital assets.
Fang further discloses:
wherein the second transaction is a transaction determined to correspond to a transfer of a second target digital asset associated with at least a portion of the first target digital asset. ([0003] “… method includes receiving blockchain transaction data from a blockchain ledger to a transaction database, the blockchain transaction data including blockchain addresses, transaction identifications, transaction timestamps, and digital assets transferred, receiving intelligence labels from a blockchain ecosystem intelligence database, where the intelligence labels include known behavioral characteristics of entities associated with the blockchain addresses, selecting a blockchain transaction flow comprising blockchain transactions associated with a digital account source address, digital account intermediate addresses, a digital account destination address, and intermediate transactions where the intermediate transactions transfer the digital assets between the digital account source address and the digital account destination address …”; [0039] “Blockchain transaction risk management using machine learning supports the above-disclosed system and method. The risk management machine learning model may generate real time predictions allowing a suspicious cryptocurrency transfer transaction from the user to an unknown wallet address that has observed strong linkage …”)
Re Claim 2: Fang in view of Liu discloses the method of claim 1. Fang further discloses:
wherein the acquiring the information associated with the first transaction comprises receiving, via the user interface, a user input that indicates the information associated with the first transaction. ([0003] “… method includes receiving blockchain transaction data from a blockchain ledger to a transaction database, the blockchain transaction data including blockchain addresses, transaction identifications, transaction timestamps, and digital assets transferred, receiving intelligence labels from a blockchain ecosystem intelligence database, where the intelligence labels include known behavioral characteristics of entities associated with the blockchain addresses, selecting a blockchain transaction flow comprising blockchain transactions associated with a digital account source address, digital account intermediate addresses, a digital account destination address, and intermediate transactions where the intermediate transactions transfer the digital assets between the digital account source address and the digital account destination address …”)
Re Claim 3: Fang in view of Liu discloses the method of claim 1. Fang further discloses:
wherein the second transaction is a transaction determined by a machine learning model, by inputting a feature of the third digital wallet into the machine learning model, to transfer a digital asset associated with at least a portion of the first target digital asset to the third digital wallet, and wherein the machine learning model is trained to: ([0041] “… By leveraging machine learning to analyze the behaviors, combining on-blockchain and off-blockchain data sources, the methods and systems of this disclosure may be more accurate, have higher coverage, and may be more preventive.”)
receive a feature regarding a particular recipient that receives a digital asset through a particular transaction from a sender holding a tracking target digital asset; ([0048] “… offline training pipeline may involve feature extraction and transformation, parallel model training, model metric evaluation, and model selection. The online prediction pipeline may involve feature extraction and transformation, model prediction, and result formatting.”)
output an inference result determining whether the particular transaction corresponds to a transfer of a digital asset associated with at least a portion of the tracking target digital asset, based on the feature regarding the particular recipient. ([0279] “An alert timeline may be created in one embodiment. The alert timeline may monitor suspicious blockchain addresses, based on the intelligence labels, with wallets containing funds that have not been transferred. An alert may be generated requesting further investigation of suspicious blockchain addresses with un-transferred funds.”)
Re Claim 4: Fang in view of Liu discloses the method of claim 3. Fang further discloses:
wherein the feature regarding the particular recipient comprises information indicating at least one of a quantity of a digital asset transferred to the particular recipient, a quantity of a digital asset transferred from the particular recipient to another digital wallet, an occurrence time of a transaction that corresponds to transferring a digital asset to the another digital wallet, a type of a digital asset transferred by or transferred to the particular recipient, or whether the particular transaction is an external transaction. ([0003] “… method includes receiving blockchain transaction data from a blockchain ledger to a transaction database, the blockchain transaction data including blockchain addresses, transaction identifications, transaction timestamps, and digital assets transferred, receiving intelligence labels from a blockchain ecosystem intelligence database, where the intelligence labels include known behavioral characteristics of entities associated with the blockchain addresses, selecting a blockchain transaction flow comprising blockchain transactions associated with a digital account source address, digital account intermediate addresses, a digital account destination address, and intermediate transactions where the intermediate transactions transfer the digital assets between the digital account source address and the digital account destination address …”; [0039] “Blockchain transaction risk management using machine learning supports the above-disclosed system and method. The risk management machine learning model may generate real time predictions allowing a suspicious cryptocurrency transfer transaction from the user to an unknown wallet address that has observed strong linkage …”)
Re Claim 5: Fang in view of Liu discloses the method of claim 3. Fang further discloses:
wherein the machine learning model is further trained to output a feature attribution that corresponds to an extent to which the feature regarding the particular recipient contribute to the inference result, and wherein the method further comprises outputting, via the user interface, a feature, of the third digital wallet, that has a feature attribution output by the machine learning model, among features of the third digital wallet, wherein the feature attribution of the output feature of the third digital wallet satisfies a threshold. ([0100] The machine learning model may be utilized to identify exchange behavior addresses. A crypto exchange may have wallet addresses stored as part of their on blockchain infrastructure. These wallets may also include designations categorizing them as cold or hot deposit wallets.”; [0156] “… An example of reaching a two-way classification from a feature vector includes calculating the scalar product between the feature vector and a vector of weights, comparing the result with a threshold, and deciding the class based on the comparison.”; [0048] “… offline training pipeline may involve feature extraction and transformation, parallel model training, model metric evaluation, and model selection. The online prediction pipeline may involve feature extraction and transformation, model prediction, and result formatting.”)
Re Claim 6: Fang in view of Liu discloses the method of claim 5. Fang further discloses:
wherein the outputting the feature of the third digital wallet comprises outputting, via the user interface, the feature attribution satisfying the threshold, together with the feature of the third digital wallet. ([0100] The machine learning model may be utilized to identify exchange behavior addresses. A crypto exchange may have wallet addresses stored as part of their on blockchain infrastructure. These wallets may also include designations categorizing them as cold or hot deposit wallets.”; [0156] “… An example of reaching a two-way classification from a feature vector includes calculating the scalar product between the feature vector and a vector of weights, comparing the result with a threshold, and deciding the class based on the comparison.”; [0048] “… offline training pipeline may involve feature extraction and transformation, parallel model training, model metric evaluation, and model selection. The online prediction pipeline may involve feature extraction and transformation, model prediction, and result formatting.”)
Re Claim 7: Fang in view of Liu discloses the method of claim 1.
wherein, in response to determining that the third digital wallet is an exchange hot wallet, tracking of the digital asset is stopped for an asset transfer path that includes the third digital wallet. ([0100] The machine learning model may be utilized to identify exchange behavior addresses. A crypto exchange may have wallet addresses stored as part of their on blockchain infrastructure. These wallets may also include designations categorizing them as cold or hot deposit wallets.”; [0156] “… An example of reaching a two-way classification from a feature vector includes calculating the scalar product between the feature vector and a vector of weights, comparing the result with a threshold, and deciding the class based on the comparison.”)
Re Claim 8: Fang in view of Liu discloses the method of claim 7. Fang further discloses:
wherein whether the third digital wallet corresponds to the exchange hot wallet is determined based on category information of the third digital wallet, and the category information of the third digital wallet is determined by using a data set that includes a plurality of reference wallets and category information regarding the each of the plurality of reference wallets. ([0100] The machine learning model may be utilized to identify exchange behavior addresses. A crypto exchange may have wallet addresses stored as part of their on blockchain infrastructure. These wallets may also include designations categorizing them as cold or hot deposit wallets.”; [0156] “… An example of reaching a two-way classification from a feature vector includes calculating the scalar product between the feature vector and a vector of weights, comparing the result with a threshold, and deciding the class based on the comparison.”)
Re Claim 9: Fang in view of Liu discloses the method of claim 7. Fang further discloses:
wherein, in response to determining that a number of transactions per unit time, in which the third digital wallet is a recipient or a sender, is at or above a threshold, the third digital wallet is determined to be an exchange hot wallet. ([0100] The machine learning model may be utilized to identify exchange behavior addresses. A crypto exchange may have wallet addresses stored as part of their on blockchain infrastructure. These wallets may also include designations categorizing them as cold or hot deposit wallets.”; [0156] “… An example of reaching a two-way classification from a feature vector includes calculating the scalar product between the feature vector and a vector of weights, comparing the result with a threshold, and deciding the class based on the comparison.”)
Re Claim 10: Fang in view of Liu discloses the method of claim 1. Regarding the limitation feature(s) comprising:
outputting, via the user interface, an occurrence time of the first transaction and an amount of the first target digital asset, on the first edge;
outputting, via the user interface, an occurrence time of the second transaction and an amount of the second target digital asset, on the second edge.
Liu makes these teachings in a related endeavor ([0191] The specific type of cryptographic algorithm being utilized may vary dynamically based on various factors, such as a length of time …”; [0085] “… As another example, a node can be tagged with an age of the blockchain address that corresponds to the node ( e.g., age of a blockchain address can be an amount of time that has elapsed …”). It would have been obvious to one of ordinary skill in the art at the time of the invention to incorporate the teachings of Liu to the invention of Fang as described above for the motivation of estimating and detecting the level of risk associated with electronic transaction involving digital assets.
Re Claim 11: Fang in view of Liu discloses the method of claim 1. Regarding the limitation feature(s) comprising:
wherein the second transaction comprises a plurality of transactions, and wherein the method further comprises:
outputting, via the user interface, a number of the plurality of transactions on the second edge;
receiving, via the user interface, a user input that selects the second edge;
in response to receiving the user input, outputting, via the user interface, a plurality of sub-edges associated with the plurality of transactions; and
outputting, via the user interface, an occurrence time and a transfer amount of each of the plurality of transactions on each of the plurality of sub-edges.
Liu makes these teachings in a related endeavor ([0191] The specific type of cryptographic algorithm being utilized may vary dynamically based on various factors, such as a length of time …”; [0085] “… As another example, a node can be tagged with an age of the blockchain address that corresponds to the node ( e.g., age of a blockchain address can be an amount of time that has elapsed …”). It would have been obvious to one of ordinary skill in the art at the time of the invention to incorporate the teachings of Liu to the invention of Fang as described above for the motivation of estimating and detecting the level of risk associated with electronic transaction involving digital assets.
Re Claim 12: Fang in view of Liu discloses the method of claim 1. Regarding the limitation feature(s) comprising:
receiving, via the user interface, a user input that selects the third node; and
in response to receiving the input that selects the third node, outputting, via the user interface, an activation indicator corresponding to a trading activity level of the third digital wallet.
Liu makes these teachings in a related endeavor ([0191] The specific type of cryptographic algorithm being utilized may vary dynamically based on various factors, such as a length of time …”; [0085] “… As another example, a node can be tagged with an age of the blockchain address that corresponds to the node ( e.g., age of a blockchain address can be an amount of time that has elapsed …”; [0168] “… For example, using a digital currency exchange, a user may buy any value of digital currency or exchange any holdings in digital currencies into worldwide currency or other digital currencies.”). It would have been obvious to one of ordinary skill in the art at the time of the invention to incorporate the teachings of Liu to the invention of Fang as described above for the motivation of estimating and detecting the level of risk associated with electronic transaction involving digital assets.
Re Claim 13: Fang in view of Liu discloses the method of claim 12. Fang further discloses:
wherein the activation indicator is determined based on a number of transactions, in which the third digital wallet is a recipient or a sender, occurring in each of a plurality of time intervals, and a higher weight is assigned to the number of transactions in a time interval closer to a current time point among the plurality of time intervals. ([0158] In common implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some nonlinear function (the activation function) of the sum of its inputs. The connections between artificial neurons are called 'edges' or axons. Artificial neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection …”)
Re Claim 14: Fang in view of Liu discloses the method of claim 1. Fang further discloses:
receiving, via the user interface, a user input that requests summary information of an asset transfer path of the first target digital asset; ([0003] “… method includes receiving blockchain transaction data from a blockchain ledger to a transaction database, the blockchain transaction data including blockchain addresses, transaction identifications, transaction timestamps, and digital assets transferred, receiving intelligence labels from a blockchain ecosystem intelligence database, where the intelligence labels include known behavioral characteristics of entities associated with the blockchain addresses, selecting a blockchain transaction flow comprising blockchain transactions associated with a digital account source address, digital account intermediate addresses, a digital account destination address, and intermediate transactions where the intermediate transactions transfer the digital assets between the digital account source address and the digital account destination address …”
in response to receiving the user input that requests the summary information, outputting, via the user interface, a list enumerating digital wallet information on one or more asset transfer paths along which the first target digital asset has been transferred.
([0003] “… method includes receiving blockchain transaction data from a blockchain ledger to a transaction database, the blockchain transaction data including blockchain addresses, transaction identifications, transaction timestamps, and digital assets transferred, receiving intelligence labels from a blockchain ecosystem intelligence database, where the intelligence labels include known behavioral characteristics of entities associated with the blockchain addresses, selecting a blockchain transaction flow comprising blockchain transactions associated with a digital account source address, digital account intermediate addresses, a digital account destination address, and intermediate transactions where the intermediate transactions transfer the digital assets between the digital account source address and the digital account destination address …”; [0039] “Blockchain transaction risk management using machine learning supports the above-disclosed system and method. The risk management machine learning model may generate real time predictions allowing a suspicious cryptocurrency transfer transaction from the user to an unknown wallet address that has observed strong linkage …”)
Re Claim 15: Fang in view of Liu discloses the method of claim 1. Regarding the limitation feature(s) comprising:
wherein, in response to determining that no transaction that corresponds to a transfer of assets from the third digital wallet to a digital wallet different from the third digital wallet exists, tracking of the digital asset is stopped for an asset transfer path that includes the third digital wallet, wherein whether a third transaction occurs is monitored, wherein the third transaction corresponds to a transfer of a third target digital asset associated with at least a portion of the second target digital asset from the third digital wallet to a digital wallet different from the third digital wallet, and wherein the method further comprises, in response to determining, through the monitoring, that the third transaction has occurred, outputting, via the user interface, a third edge corresponding to the third transaction and a fourth node corresponding to a fourth digital wallet that is a recipient of the third transaction.
Liu makes these teachings in a related endeavor ([0160] “…input devices are often connected to the processing unit 1204 through an input device interface 1244”; [0138] “… the analyzing the transaction graph via the at least one machine learning algorithm further can comprise: executing, by the computer system (e.g., 120), an importance-assignment algorithm (e.g., 702) on a first transaction community in the set of transaction communities, wherein the importance assignment algorithm is configured to respectively assign levels of importance ( e.g., 704) to nodes that are within the first transaction community.”; [0123] “… the risk formula 902 can, in various aspects, be equal to and/or otherwise based on a reciprocal exponential of a weighted linear combination of the level of importance of an inputted node, a bit indicating whether the inputted node is the most important in its transaction community, a size of the transaction community of the inputted node, a number of edges leaving the inputted node, a number of edges entering the inputted node, an age of the blockchain address corresponding to the inputted node, and/or a number of tokens held by the blockchain address corresponding to the inputted node.”; [0195] “… transactions may be submitted to the server 1450 via desktop applications, smartphone applications, digital wallet applications”). It would have been obvious to one of ordinary skill in the art at the time of the invention to incorporate the teachings of Liu to the invention of Fang as described above for the motivation of estimating and detecting the level of risk associated with electronic transaction involving digital assets.
Re Claim 16: Fang in view of Liu discloses the method of claim 1. Fang further discloses:
receiving, via the user interface, a user input associated with a fourth transaction that corresponds to a transfer of a digital asset from the first digital wallet, the second digital wallet, or the third digital wallet to a fifth digital wallet; ([0003] “… method includes receiving blockchain transaction data from a blockchain ledger to a transaction database, the blockchain transaction data including blockchain addresses, transaction identifications, transaction timestamps, and digital assets transferred, receiving intelligence labels from a blockchain ecosystem intelligence database, where the intelligence labels include known behavioral characteristics of entities associated with the blockchain addresses, selecting a blockchain transaction flow comprising blockchain transactions associated with a digital account source address, digital account intermediate addresses, a digital account destination address, and intermediate transactions where the intermediate transactions transfer the digital assets between the digital account source address and the digital account destination address …”)
in response to receiving the user input associated with the fourth transaction, determining that the digital asset transferred to the fifth digital wallet through the fourth transaction is associated with at least a portion of the first target digital asset.
([0070] “… user 120 performs a set of user actions 122 comprising a withdraw 124, a deposit 126, a swap 128, and/or a transfer 130, of funds or cryptocurrency. These actions are understood as digital on blockchain information and digital off blockchain information and are received by a digital asset intake engine 108. The digital asset intake engine 108 extracts data (extracted data 132), which pulls out the digital data and the digital off blockchain information and the digital on blockchain information that includes a blockchain address 134, a transaction identification 136, a user information 138, a device information 140, a business type 146, and a device IP address 142, as well as the exchange or custodian information 144, associated with the user 120's actions”; [0039] Blockchain transaction risk management using machine learning supports the above-disclosed system and method. The risk management machine learning model may generate real time predictions allowing a suspicious cryptocurrency transfer transaction from the user to an unknown wallet address …”; [0269] “… blockchain transaction data may include blockchain addresses, transaction identifications, transaction
timestamps, and digital assets transferred. Digital assets may include cryptocurrency, fiat money, or other currencies represented by digital data capable of moving from one digital
account to another through digital transactions over the Internet or otherwise electronically, by digital transfer.”)
Re Claim 17: Fang in view of Liu discloses the method of claim 16. Regarding the limitation feature(s) comprising:
based on the received user input, outputting, via the user interface, a fourth edge corresponding to the fourth transaction and a fifth node corresponding to the fifth digital wallet.
Liu makes these teachings in a related endeavor ([0191] The specific type of cryptographic algorithm being utilized may vary dynamically based on various factors, such as a length of time …”; [0085] “… As another example, a node can be tagged with an age of the blockchain address that corresponds to the node ( e.g., age of a blockchain address can be an amount of time that has elapsed …”; [0168] “… For example, using a digital currency exchange, a user may buy any value of digital currency or exchange any holdings in digital currencies into worldwide currency or other digital currencies.”). It would have been obvious to one of ordinary skill in the art at the time of the invention to incorporate the teachings of Liu to the invention of Fang as described above for the motivation of estimating and detecting the level of risk associated with electronic transaction involving digital assets.
Re Claim 18: Fang in view of Liu discloses the method of claim 16. Fang further discloses:
wherein information associated with the fourth transaction is included in training data for a machine learning model after being assigned with a correct label corresponding to a transaction that requests transferring a tracking target digital asset, and wherein the machine learning model is trained to: ([0041] “… By leveraging machine learning to analyze the behaviors, combining on-blockchain and off-blockchain data sources, the methods and systems of this disclosure may be more accurate, have higher coverage, and may be more preventive.”)
receive a feature regarding a particular recipient that receives a digital asset through a particular transaction from a sender holding the tracking target digital asset; ([0048] “… offline training pipeline may involve feature extraction and transformation, parallel model training, model metric evaluation, and model selection. The online prediction pipeline may involve feature extraction and transformation, model prediction, and result formatting.”)
output an inference result determining whether the particular transaction corresponds to a transfer of at least a portion of the tracking target digital asset, based on the feature regarding the particular recipient. ([0279] “An alert timeline may be created in one embodiment. The alert timeline may monitor suspicious blockchain addresses, based on the intelligence labels, with wallets containing funds that have not been transferred. An alert may be generated requesting further investigation of suspicious blockchain addresses with un-transferred funds.”)
Re Claim 19: Claim 19, as best understood by the Examiner, encompasses the same or substantially the same scope as claim 1. Accordingly, claim 19 is rejected in the same or substantially the same manner as claim 1.
Re Claim 20: Claim 20, as best understood by the Examiner, encompasses the same or substantially the same scope as claim 1. Accordingly, claim 20 is rejected in the same or substantially the same manner as claim 1.
Conclusion
The prior art(s) made of record and not relied upon is/are considered pertinent to applicant's disclosure.
Thomas et al. (US 12,041,067 B2) discloses behavior detection and verification. Thomas discloses wherein when security-related behavior is detected on an endpoint, e.g., through a local security agent executing on the endpoint, a threat management facility associated with the endpoint can interact with a user via a second local security agent on a second endpoint in order to solicit verification, authorization, authentication or the like related to the behavior. In one aspect, an administrator for an enterprise managed by the threat management facility may verify, authorize, or otherwise approve the detected behavior using this technique. In another aspect, a user of the device may use this infrastructure to approve of a potentially risky behavior on one device by using a verification procedure on a second device associated with the user.
Wu et al. (US 12,217,232 B2) discloses anonymity and traceability of digital property transactions on a distributed transaction consensus network. The present disclosure concerns methods and systems for increasing anonymity and traceability of a digital property management system that manages digital property transactions on a distributed transaction consensus network. The digital property management system comprises a sender's digital property manager and a recipient's digital property
manager. The sender's digital property manager further comprises a sender's module and a sender's transaction node and the recipient's digital property manager further comprises a recipient' module and a recipient's transaction node.
Claims 1-20 are rejected.
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/CLIFFORD B MADAMBA/Primary Examiner, Art Unit 3692
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