DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
This is a Final Office Action rejection prepared in response to applicant’s amendment for U.S. Patent Application 18/233,068 filed on 03/10/2026.
Claims 1 and 9 are amended.
Claims 2 and 10 are cancelled.
Claims 1, 3-9 and 11-16 are pending and have been considered below.
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, 3-9 and 11-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1 and 3-8 are directed to a method (i.e., process). Claims 9 and 11-16 are directed to a system (i.e., machine, and manufacture). Therefore, these claims fall within the four statutory categories of invention, and thus must be further analyzed at Step 2A to determine if the claims are directed to a judicial exception (See MPEP 2106.03, subsection II).
Step 2A Prong One: Claim 1, recites (i.e., sets forth or describes) an abstract idea. More specifically, the following bolded claim elements recite abstract ideas while the non-bolded claim elements recite additional elements according to MPEP 2106.04(a).
A method for fraud scoring a cryptographic currency transaction using multiple data sets and graphical modeling, comprising:
receiving, by a receiver of a processing server, transaction data for a plurality of past fiat currency-based payment transactions from a first computing system;
receiving, by the receiver of the processing server, transaction data for a plurality of cryptographic currency based blockchain transactions from a second computing system;
receiving, by the receiver of the processing server, node connectivity data for a blockchain network from a third computing system, said node connectivity data including (i) data regarding connections between blockchain nodes in the blockchain network, and (ii) a transaction history of each blockchain node in the blockchain network;
cross-referencing, by a processor the processing server, the transaction data for the plurality of cryptographic currency based blockchain transactions received from the second computing device with the node connectivity data received from the third computing device;
using machine learning, generating, by a processor of the processing server, a fraud detection model by training the fraud detection model using (i) the node connectivity data received from the third computing device, and (ii) past histories regarding a disposition of past blockchain transactions and past fiat-based payment transactions as fraudulent or not fraudulent based on the transaction data for the plurality of past fiat currency-based payment transactions received from the first computing device, and the plurality of cryptographic currency based blockchain transactions received from the second computing device, wherein generating the fraud detection model includes generating a graphical representation of at least the blockchain network including the plurality of blockchain nodes and connections for each blockchain node of the plurality of blockchain nodes to other blockchain nodes of the plurality of blockchain nodes in the blockchain network; wherein generating the graphical representation includes clustering groups of nodes into clusters based on at least shared connections and communication frequency
receiving, by the receiver of the processing server, transaction data for a new blockchain transaction from a computing device;
generating, by the processor of the processing server, a fraud score for the new blockchain transaction via application of the transaction data for the new blockchain transaction to the generated fraud detection model
transmitting, by a transmitter of the processing server, the generated fraud score to the computing device and
further training, by the processor of the processing server, the fraud detection model based on an outcome of new transactions.
Claim 1, recites a method for fraud scoring a cryptographic currency transaction by collecting, analyzing and processing diverse financial transaction data. Particularly, the claim achieves this by receiving plurality of data, (i.e., transaction data, cryptocurrency data and connectivity data) and using it to generate a model that includes a graphical representation of the connectivity data. The model is then use to generate a fraud score for a new transaction and then transmitted to a recipient. Claim 9 is significantly similar to claim 1. As such claim 9 also recites an abstract idea. Specifically, but for the additional elements, the claim under its broadest reasonable interpretation recites limitations grouped within the “certain methods of organizing human activity” grouping of abstract ideas (i.e., fundamental economic principles or practices such as mitigating risk within financial transactions.) and mental process. The examiner submits that the data sets and graphical modeling under the broadest reasonable interpretation to be a mathematical principle.
Step 2A Prong Two: Because the claim recites abstract ideas, the analysis proceeds to
determine whether the claim recites additional elements that recite a practical application of the
abstract ideas. Here, the additional elements of a receiver of a processing server, a first computing system, a second computing system, a third computing system, a computing device and machine learning merely serve as a tool to perform the abstract idea (MPEP § 2106.05(f)). Further, the claim recites “blockchain”, “blockchain node” and “blockchain network” in the context of describing how data is stores or processed which suggest that the terms are being used to describe the data rather than to describe a specific technological implementation. Therefore, the claim as a whole fail to recite a practical application of the abstract ideas.
Step 2B: Determines whether the claim as a whole amount to significantly more than the exception itself. Evaluating additional elements to determine whether they amount to an inventive concept requires considering them both individually and in combination to ensure that they amount to significantly more than the judicial exception itself. Here, the additional elements, taken individually and in combination, do not result in the claim as a whole, amounting to significantly more than the judicial exception. As discussed previously with respect to Step 2A, the additional elements merely serve as a tool to perform an abstract idea. Thus, there is no inventive concept in the claim and thus the claim is not eligible, warranting a rejection for lack of subject matter eligibility and concluding the eligibility analysis.
Dependent Claims: Claims 3-8 and 11-16 have also been analyzed for subject matter
eligibility. However, claims 3-8 and 11-16 also fail to recite patent eligible subject matter for the
following reasons:
Claims 3 and 11 recites the following bolded claim elements as abstract ideas while the non-bolded claim elements recite additional elements according to MPEP 2106.04(a).
the graphical representation of each of the plurality of blockchain nodes included in the graphical representation indicates a likelihood of involvement of the respective blockchain node in fraudulent activity based on the received transaction data for the plurality of cryptographic currency based blockchain transactions.
The claim further recites an abstract idea. In other words, it recites limitations grouped within the “certain methods of organizing human activity” and “mental process” grouping of abstract ideas. The non-bolded additional elements of a plurality of blockchain nodes and blockchain node fail to recite a practical application or significantly more than the abstract idea because they merely serve as tools to perform the abstract idea (MPEP §2106.05(f)). Further, the additional elements, taken individually and in combination, do not result in the claim as a whole, amounting to significantly more than the judicial exception. Thus, there is no inventive concept in the claim and thus the claim is not eligible, warranting a rejection for lack of subject matter eligibility and concluding the eligibility analysis.
Claims 4 and 12 recite the following bolded claim elements as abstract ideas while the non-bolded claim elements recite additional elements according to MPEP 2106.04(a).
the fraud score indicates a higher likelihood of fraud if the transaction data for the new blockchain transaction identifies a blockchain node in the blockchain network with a higher likelihood of fraud based on the node connectivity data for the blockchain network.
The claim further recites an abstract idea. In other words, it recites limitations grouped within the “certain methods of organizing human activity” and “mental process” grouping of abstract ideas. The non-bolded additional elements of a node, a blockchain network and a blockchain node fail to recite a practical application or significantly more than the abstract idea because they merely serve as tools to perform the abstract idea (MPEP §2106.05(f)). Further, the additional elements, taken individually and in combination, do not result in the claim as a whole, amounting to significantly more than the judicial exception. Thus, there is no inventive concept in the claim and thus the claim is not eligible, warranting a rejection for lack of subject matter eligibility and concluding the eligibility analysis.
Claims 5 and 13 recite the following bolded claim elements as abstract ideas while the non-bolded claim elements recite additional elements according to MPEP 2106.04(a).
the higher likelihood of fraud based on the node connectivity data is indicated by the blockchain node or a secondary blockchain node having a direct connection to the blockchain node having a number of connected nodes without additional connections in the blockchain network greater than a predetermined threshold value.
The claim further recites an abstract idea. In other words, it recites limitations grouped within the “certain methods of organizing human activity” and “mental process” grouping of abstract ideas. The non-bolded additional elements of a node, a secondary blockchain node, a blockchain network and a blockchain node fail to recite a practical application or significantly more than the abstract idea because they merely serve as tools to perform the abstract idea (MPEP §2106.05(f)). Further, the additional elements, taken individually and in combination, do not result in the claim as a whole, amounting to significantly more than the judicial exception. Thus, there is no inventive concept in the claim and thus the claim is not eligible, warranting a rejection for lack of subject matter eligibility and concluding the eligibility analysis.
Claims 6 and 14 recite the following bolded claim elements as abstract ideas while the non-bolded claim elements recite additional elements according to MPEP 2106.04(a).
the fraud detection model is further generated using artificial intelligence.
The claim further recites an abstract idea. In other words, it recites limitations grouped within the “certain methods of organizing human activity” and “mental process” grouping of abstract ideas.
Claims 7 and 15 recite the following bolded claim elements as abstract ideas while the non-bolded claim elements recite additional elements according to MPEP 2106.04(a).
modifying, by the processor of the processing server, the generated fraud detection model based on the generated fraud score.
The claim further recites an abstract idea. In other words, it recites limitations grouped within the “certain methods of organizing human activity” and “mental process” grouping of abstract ideas. The non-bolded additional element of a processing server fails to recite a practical application or significantly more than the abstract idea because it merely serves as a tool to perform the abstract idea (MPEP §2106.05(f)). Further, the additional elements, taken individually and in combination, do not result in the claim as a whole, amounting to significantly more than the judicial exception. Thus, there is no inventive concept in the claim and thus the claim is not eligible, warranting a rejection for lack of subject matter eligibility and concluding the eligibility analysis.
Claims 8 and 16 recite the following bolded claim elements as abstract ideas while the non-bolded claim elements recite additional elements according to MPEP 2106.04(a).
receiving, by the receiver of the processing server, a message indicating disposition of the new blockchain transaction, wherein
modifying the generated fraud detection model is further based on the indicated disposition of the new blockchain transaction.
The claim further recites an abstract idea. In other words, it recites limitations grouped within the “certain methods of organizing human activity” and “mental process” grouping of abstract ideas. The non-bolded additional elements of a processing server and blockchain fail to recite a practical application or significantly more than the abstract idea because they merely serve as tools to perform the abstract idea (MPEP §2106.05(f)). Further, the additional elements, taken individually and in combination, do not result in the claim as a whole, amounting to significantly more than the judicial exception. Thus, there is no inventive concept in the claim and thus the claim is not eligible, warranting a rejection for lack of subject matter eligibility and concluding the eligibility analysis.
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 for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 6-9 and 14-16 are rejected under 35 U.S.C. 103 as being unpatentable over Cao (US 20230107703 A1), in view of Jevans (US 2022/0253952 A), and in further view of Liu (US 9817880 B1) in further view of Arrabothu (US 20190385170 A1).
Regarding claims 1 and 9, Cao discloses:
receiving, by the receiver of the processing server, node connectivity data for a blockchain network from a third computing system, (Cao col 3 lines 40-47, A fraud detection system can use heterogeneous information networks to detect fraud. Heterogeneous information networks can include data that can be categorized as different types of nodes (for example, a transaction, an IP address, a user). “Links” or “edges” can indicate sematic relationships between different types of nodes. Cao col 9 lines 9-18, The relationships between types of nodes can be indicated with certain types of links. In the directed graph 400, each link can be assigned a semantic label to indicate its relationship between two types of nodes. For example, the “tranIP” link between a transaction and an IP address indicates that a transaction was executed through the particular IP address. The directed graph 400 shows information received from a heterogeneous information network, which has multiple types of nodes or multiple types of links. Cao col 15 lines 5-13, At blocks 703 and 704, the model generation system can receive or access the selected set of transactions, where the transactions include heterogeneous transaction data. The data can be received from storage devices storing data related to transaction records (for example, billing records 201, network records 203, and application records 205 as shown in FIG. 2). In some embodiments, heterogeneous transaction data is received for both training transactions and test transactions.)
using machine learning, generating, by a processor of the processing server, a fraud detection model by training the fraud detection model using (i) the node connectivity data received from the third computing device, and (ii) past histories regarding a disposition of past blockchain transactions and past fiat-based payment transactions as fraudulent or not fraudulent based on the transaction data for the plurality of past fiat currency-based payment transactions received from the first computing device, and the plurality of cryptographic currency based blockchain transactions received from the second computing device, wherein (Cao Abstract. The fraud detection model can be iteratively refined using machine learning. The fraud detection models can then be applied to new transactions to determine a risk score of the new transaction. Cao col 4 lines 10-13, In some embodiments, machine learning techniques and artificial intelligence can improve the fraud detection model. The models can be iteratively trained using machine learning techniques to improve accuracy. Cao col 6 lines 56-67 & col 7 lines 1-2, A variety of records can be used as the basis for generating models and developing metapaths. A record can include any type of data related to a transaction (for example, as shown in FIG. 4). For example, billing records 201 can include transaction identifiers, transaction amounts, currency used, billing addresses, and the like. Network records 203 can include information identifying IP addresses associated with user names. Application records 205 can include information identifying application sources through which transactions were made, names of items that were purchased, and the like. The records can be collected from a plurality of databases, including other databases and logs (not shown). In some embodiments, the record material can be collected from a single transaction record database. Cao col 7 lines 3-6, A model generation system 299 can generate one or more models 213, 215, 217 according to instructions 206, 207, 208 by processing the records 201, 203, 205 and the training data 211. Cao col 15 lines 5-13, At blocks 703 and 704, the model generation system can receive or access the selected set of transactions, where the transactions include heterogeneous transaction data. The data can be received from storage devices storing data related to transaction records (for example, billing records 201, network records 203, and application records 205 as shown in FIG. 2). In some embodiments, heterogeneous transaction data is received for both training transactions and test transactions. Cao col 17 lines 4-10, At block 740, the model generation system can run the model generator (for example, the classifier) and generate a fraud detection model. In some embodiments, the fraud detection model generated at block 740 can be used to detect fraud (for example, as further discussed below with respect to FIG. 8). Accordingly, block 740 can proceed to block 800a after the model has been generated. Cao col 18 lines 55-58, Each fraud detection model can be generated based on different criteria, for example, different training transactions, different test transactions, different classifiers, different numbers of iterations (or any iterations at all).)
receiving, by the receiver of the processing server, transaction data for a new blockchain transaction from a computing device; (Cao col 8 lines 10-11, A new transaction 301 can be received by the fraud detection system. Cao col 18 lines 36-41, At block 801, the fraud detection system can receive a new transaction. The new transaction can be associated with heterogeneous data. For example, the new transaction may have a transaction ID 099, originate from IP address 99.99.99.99, and come from source (for example, a game title) “title1)
generating, by the processor of the processing server, a fraud score for the new blockchain transaction via application of the transaction data for the new blockchain transaction to the generated fraud detection model; and (Cao col 3 lines 61-63, The fraud detection model can be used to calculate a score indicative of the risk of a new transaction. Cao col 8 lines 24-28, In some embodiments, a plurality of metapath-based features of the new transaction is run through one model. Cao col 8 lines 36-44, In some embodiments, the fraud detection results 303, 305, 307 are numbers that indicate a risk that the new transaction 301 is fraudulent (for example, a number from 0 to 1, where 1 indicates a calculated 100% chance that the transaction is fraudulent). Accordingly, where one of (or where a combination of) the fraud detection results 303, 305, 307 exceed a threshold (for example, greater than 0.5, greater than 0.7, greater than 0.95), the transaction can be denied.” Cao col 18 lines 18-29, At block 750, the model generation system can evaluate the heterogeneous test transaction data processed at block 745 using the model generated at block 740. Accordingly, the model can determine which of the test transactions are fraudulent or the risk of fraud of each test transaction. For example, each test transaction can be scored from 0 to 1, with 1 indicating a 100% calculated chance of being fraudulent. The results of the test transactions can be stored into a memory as labels y.sub.test-N for the test transactions, where “N” indicates the current iteration number. Blocks 755 and 760 can be skipped on a first iteration for the test transactions, and will be discussed further below. Cao col 19 lines 2-7, Each fraud detection model can generate a fraud score (for example, between 0 to 1, where 1 indicates a 100% calculated chance of fraud). Various embodiments can use different numbers of models to evaluate each transaction (for example, 1 model, 5 models, 10 models).
transmitting, by a transmitter of the processing server, the generated fraud score to the computing device. (Cao col 6 lines 9-11, This can include the fraud detection system 109 sending an approval or denial instruction to the transaction processor 111. Cao col 8 lines 44-47, In such a case, the fraud detection system can alert a user (for example, an account supervisor, a transaction processor, credit bureau, legal authorities) that a fraudulent transaction is being attempted. Cao col 19 lines 13-32, At block 830, if the weighted average result exceeds a threshold fraud risk number (for example, greater than 0.7), then the fraud detection system can deny the transaction at block 835. This can include, for example, stopping the transaction, sending a communication or warning to a 3.sup.rd party processing company such as a credit card company, challenging the user who submitted the transaction to provide additional verification information, triggering alternative forms of payment or payment flows, and the like. At block 845, an alert may be generated and sent. This can include warning a system administrator about the fraudulent transaction, flagging the transaction for review, flagging the transaction for a 3.sup.rd party payment system, alerting police or investigators about the fraudulent transaction, alerting the user who initiated the transaction that his transaction is being denied on suspicion of fraud, sending a communication to a registered text number or email address associated with the username who made the transaction, and the like. Cao col 19 lines 41-43, If at block 830, the weighted average result does not exceed a risk threshold, then the fraud detection system can allow the transaction at block 835. This can include approving the transaction, sending a confirmation to a 3.sup.rd party such as a credit card processor, or taking no preventative actions to stop the transaction.
Cao does not disclose, however Jevans teaches:
a blockchain (Jevans ¶0011, The present disclosure pertains to systems and methods for forensically analyzing blockchain/cryptocurrency transactions in combination with fiat transactions to prevent or reduce fraud or other nefarious behavior(s).)
receiving, by a receiver of a processing server, transaction data for a plurality of past fiat currency-based payment transactions from a first computing system; (Jevans ¶0003, obtaining fiat-based transaction data from a bank account; identifying a purchase of a cryptocurrency from fiat-based transaction data and cryptocurrency exchange trade history data from a cryptocurrency exchange where the cryptocurrency was purchased; Jevans ¶0021, It will be understood that the service provider 108 can track three types groups of data. A first type of data can include fiat-based transaction data (obtained from the fiat-based services 106A-106N) which can be obtained, for example from a bank.)
receiving, by the receiver of the processing server, transaction data for a plurality of cryptographic currency based blockchain transactions from a second computing system; (Jevans ¶0003, obtaining cryptocurrency-based transaction data that identifies downstream cryptocurrency transaction data where the cryptocurrency was transferred out of the cryptocurrency exchange; Jevans ¶0021, It will be understood that the service provider 108 can track three types groups of data… A third type of data can include cryptocurrency-based transaction data (obtained from the cryptocurrency endpoints 102A-102N). This can include any type of transaction data where cryptocurrency that was purchased on an exchange is moved off the exchange. The cryptocurrency can be recorded in a blockchain ledger and used to purchase goods or services.
cross-referencing, by a processor the processing server, the transaction data for the plurality of cryptographic currency based blockchain transactions received from the second computing device with the node connectivity data received from the third computing device; (¶0020, The service provider 108 enables forensic analyses of input of data collected from endpoint terminals 102A-102N, cryptocurrency services 104A-104N, fiat-based financial services 106A-106N, and cryptocurrency endpoints 112A-112N. The service provider 108 provides a means to correlate credit and debit card and other payments, such as wire transfers, ACH, SEP, and the like transactions to cryptocurrency addresses across various cryptocurrencies and various exchanges or other cryptocurrency providers (broadly referred to as cryptocurrency services).)
It would have been obvious to one of ordinary skill in the art before the effective filing date of
the claimed invention to combine the features of Cao with the teaching of Jevans. One of ordinary skills in the art would have been motivated to combine these features in order to produce a fraud model that not only analyses regular transaction data but also incorporates cryptographic blockchain transactions to produce a more comprehensive and intelligent fraud model. As well as enhancing the security and accuracy of the data by using a blockchain network.
Further, the recited step of “cross-referencing, by a processor the processing server, the transaction data for the plurality of cryptographic currency based blockchain transactions received from the second computing device with the node connectivity data received from the third computing device” is not functionally related to the remaining steps of the claimed method, as the results of this step are not further use or do not further affect any of the steps of the method. As such this step does not impose a meaningful limitation and on the claimed invention and is given no patentable weight.
Further, the combination of Cao and Jevans do not disclose “said node connectivity data including (i) data regarding connections between blockchain nodes in the blockchain network, and (ii) a transaction history of each blockchain node in the blockchain network;”, however it only describe characteristics of the node connectivity data which are non-functional descriptive material and these characteristics are not processed or used to carry out any functionality that specifically relies on these particular characteristics.
The combination do not disclose, however Liu teaches:
generating the fraud detection model includes generating a graphical representation of at least the blockchain network including a plurality of blockchain nodes and connections for each blockchain node of the plurality of blockchain nodes to other blockchain nodes of the plurality of blockchain nodes in the blockchain network; wherein (Liu abstract, Connections and connection weights between the user and the user's connected users' datacenters are computed. The preferred datacenters for the user's current user data replica is computed based on the location of the connected datacenters and the weights of the connections. Liu col 2 lines 17-47, Creating a social graph may include creating user connections for the user, where each connection is based on communications between the user and one other use in the large-scale distributed system and computing connection weights for each user connection, each weight representing a measurement of the frequency and types of communication between the user and the one other user. Uses may be grouped by users' preferred datacenters to minimize the distance between each user's current user data replica's datacenter and the user's current user data replica's preferred datacenter. Computing preferred datacenters for a user's current user data replica may be based on the user's connected datacenters which contain the user's connected user's current data replicas and the weights of the connections between the user and the connected datacenters. Computing datacenters for a user may be based on the datacenters which contain the user's data replicas. Daily traffic intensity may be used to determine preferred datacenters. Daily traffic levels may be used to determine preferred datacenters. Computing connections and connection weights between a user and the user's connected datacenters may include a social connection decay function. Computing connections and connection weights between a user and the user's connected datacenters may include a communication traffic combination function to compute the weight on a connection between the user and at least one other user, the two users communicating in multiple different ways. Computing connections and connection weights between a user and the user's connected datacenters may also include using a function that takes into account both time-decaying effect and traffic type effect.
generating the graphical representation includes clustering groups of nodes into clusters based on at least shared connections and communication frequency (Liu abstract, A system and method for social-aware clustering of user data replicas in a large-scale distributed computing system is disclosed. An exemplary system finds at least one user's connected users based on communications between the user and other users. Liu col 4 lines 11-18, In order to determine the appropriate cluster in which to assign a user replica, multiple constraints on user replica placement may be considered. An exemplary system may consider a user's social behavior and social activity patterns, including the frequency with which a user communicates with other users and the type of communication medium used for interactions, in order to place the user's user data replicas in datacenters in a way that increases efficiency and performance of the distributed system. Liu col 4 lines 33-37, In an exemplary embodiment, social-aware clustering may place data replicas of users that have a high weighted connection between each other in close proximity to one another. Close proximity may be defined as a continuous or multi-level metric that is measured in terms of network communication delay/latency or bandwidth usage. The values of proximity may indicate the weighted sum of delay and bandwidth on connections among users in the social graph. Liu col 6 lines 55-67 & col 7 lines 1-27, An example of social-aware clustering is illustrated in FIG. 6. FIG. 6 shows two sets of diagrams, one diagram (600a) of two datacenters (601a and 601b) prior to performing social-aware clustering and one diagram (600b) of the two datacenters (601a and 601b) after performing social-aware clustering. Prior to performing social-aware clustering, datacenter (601a) contains three data replicas, 603a, 603b, and 603d. Datacenter (601b) contains three data replicas (603c, 603e, and 603f). For example purposes only, one group of frequently communicating users is denoted by circles and one group of frequently communicating users is denoted by triangles. For this example, it is assumed that each datacenter has the capacity of serving three users. During the social-aware clustering process, the users' current datacenters are identified, i.e. where the users' current user replicas are located. As discussed above, user replicas 603a, 603b, and 603d are located in datacenter 601a. User replicas 603c, 603e, and 603f are located in datacenter 601b. For each user, an exemplary method determines the user's frequently communicating users. In this example, all of the circle users, represented by user replicas 603a, 603b, and 603c, may frequently communicate with one another and all of the triangle users, represented by user replicas 603d, 603e, and 603f may frequently communicate with one another. For each user, an exemplary method may compute the user's communication intensity with each datacenter. Then, the preferred datacenter for the user is determined by identifying the datacenter with the highest communication intensity for the user. In the example depicted in FIG. 6, all of the circle users have the same preferred datacenter, 601a, and all of the triangle users have the same preferred datacenter, 601b. Based on the preferred datacenters, the exemplary method puts the circle users into one group and the triangle users into another. Then, the optimization problem is updated with the newly computed preferred datacenters of the users and the user groups. The updated optimization problem may be solved to move every user towards its preferred datacenter. Every user's current datacenter may be updated according to the result of the optimization as shown in 600b.) See claim 1.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the features of Cao and Jevans with the teaching of Liu. One of ordinary skills in the art would have been motivated to combine these features in order to enhance the system so it can detect fraudulent patterns based on node interactions and communication within the network in addition to transaction and blockchain data.
Further, the claimed limitation recites “… includes …” which is non-functional material that does not move to distinguish over prior art as the generating of a graphical representation and clustering of groups of nodes do not affect the recited steps in the method claim or doesn’t further recite any structure.
Furthermore, the claimed limitation “for…” in “for the blockchain network” and in “for the plurality of past fiat currency-based payment transactions and the plurality of cryptographic currency based blockchain transactions” consist of language disclosing an intended use, so it is considered but given no patentable weight. (see MPEP 2111.05, MPEP 2114 and authorities cited therein). The reference is provided for the purpose of compact prosecution.
The combination do not disclose, however Arrabothu teaches:
further training, by the processor of the processing server, the fraud detection model based on an outcome of new transactions. (Arrabothu ¶0028, In various embodiments, the improvable fraud detection model may be trainable and periodically updated to reflect newly obtained transaction information and patterns, therefore allowing fraud detection to remain accurate in light of new information. Arrabothu ¶0030, In various embodiments, transaction database 110 may receive new transaction information (step 502) associated with new transactions that were not used in a previous update of the improvable fraud detection model. The new transactions and new transaction information ( e.g., provided in real time) may comprise new approved transactions comprising associated approved transaction details and new fraudulent transactions comprising associated fraudulent transaction details. Arrabothu ¶0036, Updating the improvable fraud detection model may occur at any desired interval of time ( e.g., daily, weekly, etc.). Therefore, for example, every week, neural network 160 may receive the newly obtained transaction information, i.e., transaction information not used in the most recent update of the improvable fraud detection model (e.g., comprising new transactions including new approved transaction details and new fraudulent transaction details) and update the improvable fraud detection model as described in relation to method 500. In various embodiments, neural network 160 may be configured to receive and/or process new transaction information to update the improvable fraud detection model in real time (i.e., updating every time new transaction information is received by authorization system 150, or in short intervals (e.g., minutes or hours)). Arrabothu ¶0044, The systems and methods discussed herein improve the functioning of the computer. For example, by including neural network 160 into fraud prediction system 150, the accuracy of authorization system 140 and/or fraud prediction system 150 in detecting and preventing fraud may continually increase by updating fraud detection models with the most recent transaction data (e.g., real time data). Fraudulent trends in recent (i.e., new) transaction information may be detected and used to update the improvable fraud detection model, and the cessation of such a fraudulent trend may also be detected, and the improvable fraud detection model may be updated accordingly.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the features of Cao, Jevans and Liu with the teaching of Arrabothu. One of ordinary skills in the art would have been motivated to combine these features in order to provide a fraud detection model that can be continuously adjusted and refined based on new scores generated from new transaction information. Keeping the system updated with the latest fraud scores help with future predictions and detection of new fraud strategies.
Regarding claims 6 and 14, Cao, Jevans, Liu and Arrabothu teach each and every element of claim 1. Cao further discloses:
the fraud detection model is further generated using artificial intelligence. (Cao col 4 lines 10-11, In some embodiments, machine learning techniques and artificial intelligence can improve the fraud detection model.)
Regarding claims 7 and 15, Cao, Jevans, Liu and Arrabothu teach each and every element of claim 1. The combination further teaches:
modifying, by the processor of the processing server, the generated fraud detection model based on the generated fraud score. (Arrabothu ¶0005, adjusting, by the processor, the improvable fraud detection model based on the first calculated score difference, producing, by the processor, an updated improvable fraud detection model; and/or replacing, by the processor, the improvable fraud detection model in the neural network with the updated improvable fraud detection model. See claim 4 & Fig 5)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the features of Cao, Jevans, Liu and Arrabothu with the additional teaching of Arrabothu. One of ordinary skills in the art would have been motivated to combine these features in order to provide a fraud detection model that can be continuously adjusted and refined based on new scores generated from new transaction information. Keeping the system updated with the latest fraud scores help with future predictions and detection of new fraud strategies.
Regarding claims 8 and 16, Cao, Jevans, Liu and Arrabothu teach each and every element of claim 7. Arrabothu further teaches:
receiving, by the receiver of the processing server, a message indicating disposition of the new blockchain transaction, wherein (Arrabothu ¶0042, In response to analyzing fraud score 154, NN fraud score 169, and/or fraud prediction score 172, authorization system 140 may send an authorization response (step 420). In response to fraud score 154, NN fraud score 169, and/or fraud prediction score 172 being at or above the predetermined fraud prediction score threshold (or otherwise indicating to fraud prediction system 150 that the transaction is fraudulent), fraud prediction system 150 may determine that the transaction is fraudulent, and send an authorization response to merchant system 130 rejecting the transaction. In response to fraud score 154, NN fraud score 169, and/or fraud prediction score 172 being at or below the predetermined fraud prediction score threshold (or otherwise indicating to fraud prediction system 150 that the transaction is legitimate), fraud prediction system 150 may determine that the transaction is legitimate, and send an authorization response to merchant system 130 approving the transaction.)
modifying the generated fraud detection model is further based on the indicated disposition of the new blockchain transaction. (Arrabothu ¶0005, In various embodiments, before receiving the transaction details or after sending the authorization response, the operations may further comprise updating, by the processor, the improvable fraud detection model. Arrabothu ¶0028, In various embodiments, the improvable fraud detection model may be trainable and periodically updated to reflect newly obtained transaction information and patterns, therefore allowing fraud detection to remain accurate in light of new information. Arrabothu ¶0030, In various embodiments, transaction database 110 may receive new transaction information (step 502) associated with new transactions that were not used in a previous update of the improvable fraud detection model. The new transactions and new transaction information ( e.g., provided in real time) may comprise new approved transactions comprising associated approved transaction details and new fraudulent transactions comprising associated fraudulent transaction details. Arrabothu ¶0036, Updating the improvable fraud detection model may occur at any desired interval of time ( e.g., daily, weekly, etc.). Therefore, for example, every week, neural network 160 may receive the newly obtained transaction information, i.e., transaction information not used in the most recent update of the improvable fraud detection model (e.g., comprising new transactions including new approved transaction details and new fraudulent transaction details) and update the improvable fraud detection model as described in relation to method 500. In various embodiments, neural network 160 may be configured to receive and/or process new transaction information to update the improvable fraud detection model in real time (i.e., updating every time new transaction information is received by authorization system 150, or in short intervals (e.g., minutes or hours)). Arrabothu ¶0044, The systems and methods discussed herein improve the functioning of the computer. For example, by including neural network 160 into fraud prediction system 150, the accuracy of authorization system 140 and/or fraud prediction system 150 in detecting and preventing fraud may continually increase by updating fraud detection models with the most recent transaction data (e.g., real time data). Fraudulent trends in recent (i.e., new) transaction information may be detected and used to update the improvable fraud detection model, and the cessation of such a fraudulent trend may also be detected, and the improvable fraud detection model may be updated accordingly. See claim 3)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the features of Cao, Jevans, Liu and Arrabothu with the additional teaching of Arrabothu. One of ordinary skills in the art would have been motivated to combine these features in order to allow the receiver to know if the transaction was flagged as fraud or not and to help refine the fraud detection model so it can be continuously adjusted and refined based on new approved and rejected transactions.
Claims 3 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Cao, Jevans, Liu and Arrabothu as applied to claim 1 above, in further view of Zhang (US 20230107703 A1).
Regarding claims 3 and 11, Cao, Jevans, Liu and Arrabothu teach each and every element of claim 1. The combination does not teach, however Zhang teaches:
the graphical representation of each of the plurality of (Zhang ¶0019, These aspects include but are not limited to which accounts are valid, or normal, which accounts have anomalies or irregular data entries ( e.g. indicate fraud), as well as which accounts may be connected to these irregular accounts or connected to known fraudulent accounts. In at least some aspects, this enhanced network visualization allows more efficient and accurate detection of fraud. Zhang ¶0021, Specifically, the fraud detection engine 110 reviews each of the visualized node clusters containing application data information represented in each node shown as clusters 111 in FIG. 1A and determines whether each cluster may be considered as high likelihood of fraud or low likelihood of fraud. Thus, in at least some aspects, such determination of fraud includes determining whether the data has more than a defined likelihood of fraud ( e.g. categorized as high fraud risk applications 112) or a low likelihood of fraud (e.g. categorized as low fraud risk applications 114). This determination may further include analyzing the occurrence of fraud activity in each of the clusters 111 ( e.g. number of occurrences, types of fraud, etc.) and thereby assigning each cluster to a fraudulent (e.g. high fraud risk applications 112) or non-fraudulent ( e.g. low fraud risk applications 114) determination. Zhang ¶0044, From this, the fraud detection engine 110 may generate a list of transaction requests and corresponding applications with high likelihood of being fraudulent ( e.g. high fraud risk applications 112). For example, clusters may be assigned as being fraudulent based on having more than a defined degree of known prior fraud transactions in the clusters and connections either directly or indirectly from the current transactions in the new application data 104 to the prior fraud transactions. In other aspects, although there may not be a link between the current application data (e.g. new application data 104) and prior application data ( e.g. historical application data 102) which may be known as fraud, such data may have been clustered together by way of having at least some similarity in the underlying data and thus related.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the features of Cao, Jevans, Liu and Arrabothu with the teaching of Zhang. One of ordinary skills in the art would have been motivated to combine these features in order to add an additional layer of intelligence transforming complex data into an intuitive and actionable format and providing a way to quickly interpret and identify high risk nodes on the graphical representation.
Claims 4, 5, 12 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Cao, Jevans, Liu and Arrabothu as applied to claim 1 above, in further view of Arora (US 20190318359 A1).
Regarding claims 4 and 12, Cao, Jevans, Liu and Arrabothu teach each and every element of claim 1. Arora further teaches:
the fraud score indicates a higher likelihood of fraud if the transaction data for the new blockchain transaction identifies a blockchain node in the blockchain network with a higher likelihood of fraud based on the node connectivity data for the blockchain network. (Arora ¶0030, In some cases, the algorithms may be used to generate a fraud score for the payment transaction based on the declined payment transactions and, in some cases, also the transaction details for the proposed payment transaction, where the proposed payment transaction may be declined if the fraud score exceeds a scoring threshold indicative of a high likelihood of fraud. Arora ¶0040, In some cases, the determination may use issuing institution specific fraud algorithms. In some instances, the determination module 216 may generate a fraud score, which may be compared with a score threshold to determine if the proposed payment transaction should be declined. Arora ¶0048, If, in step 308, the determination module 216 determines that the fraud score calculated for the proposed payment transaction exceeds the fraud threshold, then, in step 312, the point of sale device 102 may decline the payment transaction. See claim 8.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the features of Cao, Jevans, Liu and Arrabothu with the teaching of Arora. One of ordinary skills in the art would have been motivated to combine these features in order to adjust the fraud score based on node connectivity patterns and their relationship with transaction data.
Furthermore, in regards to the description of what the fraud score indicates is non-functional descriptive material that does not move to distinguish over prior art.
Regarding claims 5 and 13, Cao, Jevans, Liu and Arrabothu teach each and every element of claim 4 and 12. Arora further teaches:
the higher likelihood of fraud based on the node connectivity data is indicated by the blockchain node or a secondary blockchain node having a direct connection to the blockchain node having a number of connected nodes without additional connections in the blockchain network greater than a predetermined threshold value. (Arora 10036, The receiving device 202 may also be configured to receive data signals electronically transmitted by nodes 112 in a blockchain network 110, which may be superimposed or otherwise encoded with blockchain data, wherein the blockchain data may include data values included in blocks that correspond to declined payment transactions. Arora 40052, In a further embodiment, the transaction data for the payment transaction may include at least the timestamp and a transaction amount. In some embodiments, each data value may further includes a geographic location associated with the corresponding declined payment transaction, the decline of the payment transaction may be further based on a geographic location associated with the point of sale device, and the electronic transmission to the node may further include the geographic location associated with the point of sale device. Arora claim 4, each data value further includes a geographic location associated with the corresponding declined payment transaction, the decline of the payment transaction is further based on a geographic location associated with the point of sale device, and the electronic transmission to the node further includes the geographic location associated with the point of sale device.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the features of Cao, Jevans, Liu and Arrabothu with the additional teaching of Arora. One of ordinary skills in the art would have been motivated to combine these features in order to adjust the fraud score based on node connectivity patterns and their relationship with transaction data.
Furthermore, the claimed limitation “the higher likelihood of fraud based on the node connectivity data is indicated…” is non-functional material that does not move to distinguish over prior art.
Response to Arguments
Claim Rejections – 35 U.S.C. § 101
Applicant presents several assertions/arguments in regards to the 101 rejection in the previous office action.
Applicant asserts that the claim recites a practical application of any alleged abstract idea because the claim reflects an improvement in the functioning of a computer, or an improvement to other technology or technological field and applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment”. The examiner respectfully disagrees with this assertion. As stated in the previous non-final office action, the claims do not improve the functioning of a computer or any underlying technology. The alleged improvement relates only to better fraud-scoring results in blockchain transactions which is considered an improvement to the abstract idea rather than to computer technology. Applying known machine learning and graph techniques to blockchain data represents merely using a computer to execute an abstract idea in a particular environment.
Applicants asserts that the specification identifies a blockchain specific technological problem and the claims recite such technological solution. The examiner finds these assertions not persuasive and respectfully disagrees. The asserted blockchain technological solution is not recited in the amended claim. Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Applicant cites Ex parte Guillaume Desjardins and asserts that "much of the advancement made in computer technology consists of improvements to software that, by their very nature, may not be defined by particular physical features but rather by logical structures and processes.” Although, it is true that technological improvements can be performed in software, it does not mean that any recitation of a “logical architecture” or data model is sufficient to overcome 101 rejection. In Ex parte Guillaume Desjardins the claims were found patent eligible because they explicitly recite an improvement to the machine learning system and its computational efficiency, resulting in a technological improvement in the computing system functionality. In contract, the amended claims recite “(1) construction of a graph-based representation of a distributed blockchain network, (2) cluster formation based on connectivity and communication frequency, (3) integration of that topology into machine learning-based fraud detection, (4) dynamic updating of the graphical model and fraud detection model.” which describe data modeling and analysis of relationships between nodes but do not recite a specific technological mechanism that would change the functioning of the underlining computer technology.
Applicant argues that” the office overlooks the specific structural requirements of the claims” and asserts that the claims require modeling and clustering of blockchain node connectivity data and this data is not generic financial data. The applicant further asserts that claims model the topology and use that topology to influence fraud scoring and modify the fraud detection pipeline to incorporate network structure which is a technological improvement to fraud detection in blockchain networks. The examiner finds this arguments not persuasive and respectfully disagrees. While the node connectivity data may originate from a blockchain and may represent communication relationship between the nodes, the claims remain directed towards analyzing relationship between different data sets. The mere use of blockchain related data does not remove the claim from the realm of abstract idea. Further the applicant suggest that the system utilizes the topology to influence fraud scoring (i.e. (1) model the topology of a distributed computing system, (2) use that topology to influence fraud scoring, and (3) modify the fraud detection pipeline to incorporate network structure.), however the claim do not recite or suggest that. Instead the claim recites the generation of a graphical representation (i.e. topology ) when generating the fraud detection model and not using a graphical representation to generate/train the model (i.e. influence fraud scoring).
Applicant asserts that the amended claims recite a specific implementation that integrate any alleged abstract idea into a practical application according with current USPTO subject matter eligibility guidance. However, the examiner finds this argument not persuasive and respectfully disagrees. The amended claims do not recite an improvement to the functioning of a computer technology or other technology (i.e. blockchain) and do not recite any specific technical implementation. The alleged technical implementation of “(1) multi-source data ingestion, (2) graphical representation of node connectivity, (3) cluster-based modeling tied to network topology, and (4) dynamic model updating based on blockchain outcomes” is merely data gathering, organization and analysis.
Applicant asserts that the claims recite significantly more and provide a technological advancement over known methods. The examiner also finds this assertion not persuasive and respectfully disagrees. The claim as amended recite collecting, analyzing, updating and transferring data which is an abstract idea. The claim does not recite any specific mechanism that improves the computer technology and merely utilizes the additional elements to perform the abstract idea. Therefore, the claim does not amount to significantly more as asserted by the applicant.
As such the claims remain within an abstract idea and rejection is maintained based on the newly amended claims.
Claim Rejections – 35 U.S.C. § 103
Applicant’s arguments regarding claim rejections 35 U.S.C. § 103 have been considered below.
Regarding applicant’s argument that “Cao's disclosure is fundamentally different from the presently claimed "node connectivity data for a blockchain network." Applicant's claims require connectivity data describing connections between blockchain nodes within a blockchain network. Cao does not disclose a blockchain network. Cao does not disclose blockchain nodes participating in consensus or validation. Cao does not disclose communication connectivity between distributed
ledger nodes. Instead, Cao models relationships among transaction-related entities within a fraud detection dataset. The "nodes" in Cao are abstract data entities (e.g., transaction, IP, user), not network-level computing nodes in a distributed ledger architecture..” and “the node connectivity data include a
transaction history of each blockchain node in the blockchain network." , the examiner respectfully disagrees. The claim does not require the disclosure of connectivity data describing connections between blockchain nodes within a blockchain network, blockchain nodes participating in a consensus or validation, communication connectivity between distributed ledger nodes or the node connectivity data to a transaction history of each blockchain node in the blockchain network, as argued by the applicant. As amended, claim 1 recites “said node connectivity data including (i) data regarding connections between blockchain nodes in the blockchain network, and (ii) a transaction history of each blockchain node in the blockchain network;” which only describe characteristics of the node connectivity data which are non-functional descriptive material and these characteristics are not processed or used to carry out any functionality that specifically relies on these particular characteristics.
Further, regarding applicant’s argument that “Jevans discloses obtaining or analyzing fiat-based transaction data and cryptocurrency transaction data for forensic purposes. See, e.g., paragraphs [0003], [0011], [0021]. However, these cited portions describe categories of data, not receipt from distinct upstream computing systems as recited in the claim. Jevans does not disclose or suggest a processing server configured to receive historical fiat-based transaction data from one computing system and historical blockchain transaction data from a separate computing system. The Office's rejection effectively equates the existence of two types of data with the claimed requirement of dual-source receipt from two computing systems. Applicant's claim 1, however, requires more than different data types-it requires a specific system architecture involving separate computing systems supplying distinct transaction histories. Jevans does not disclose or suggest this.”, the examiner finds it not persuasive and respectfully disagrees. Jevans discloses a system (i.e. service provider 108) receiving data from at least three other different computing systems (i.e. endpoint terminals 102A-102N, cryptocurrency services 104A-104N and fiat-based financial services 106A-106N. see fig.1). Specifically, ¶0021 states “A first type of data can include fiat-based transaction data (obtained from the fiat-based services 106A-106N) which can be obtained, for example from a bank. A second type of data can include cryptocurrency exchange trade history data (obtained from the cryptocurrency services 104A-104N) that includes data obtained from a cryptocurrency exchange. For example, a user can buy cryptocurrency and exchange one cryptocurrency for another on the exchange. A third type of data can include cryptocurrency-based transaction data (obtained from the cryptocurrency endpoints 112A-112N).” Therefore, Jevans disclosure supports the system requirements as recited on the claims.
Finally, the applicant asserts that the combination of prior does not disclose “cross-referencing, by a processor the processing server, the transaction data for the plurality of cryptographic currency based blockchain transactions received from the second computing device with the node connectivity data received from the third computing device;”. However, this assertion is moot because it is geared toward the newly added claimed expression in the amendments.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The following prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 11316874 B2 to Lee et al. discloses: Aspects discussed herein relate to the storage of data in graph databases and detecting fraudulent behavior in the stored data. Fraud detection systems may use graph databases to store data, allowing for querying the graph database to obtain data using a variety of graph semantics such as nodes, edges, and properties. Graph databases in accordance with embodiments of the invention may include account nodes and attribute nodes, where nodes of the same type are not directly linked to each other. When a particular node is updated, an updated node may be created with a higher version number than the existing node. Each node may include an indication of the node being associated with fraudulent activity. Fraud indicators may be calculated based on the relationships between the nodes and fraud indicators for the nodes.
US 20160342994 A1 to Davis discloses: A method for linking blockchain transactions to privately verified identities includes: storing account profiles, each profile including data related to a transaction account including an account identifier and account data; receiving a transaction message, the message including a first data element configured to store a personal account number, a second data element configured to store a merchant identifier, and a third data element configured to store a blockchain network identifier; identifying a first account profile that includes the personal account number; identifying a second account profile that includes the merchant identifier; receiving a transaction notification, the notification indicating a transaction processed using a blockchain network associated with the blockchain network identifier and including a transaction identifier and an address identifier associated with the first or second account profile; and storing a linkage between the transaction identifier and the address identifier, the personal account number, and/or the merchant identifier.
KR 202200774327 A to Yoo discloses: The household loan fraud/insolvency audit support system using artificial intelligence according to an embodiment of the present invention provides a credit rating and household loan fraud/insolvency potential when a user submits a prescribed document to apply for a household loan fraud/insolvency In addition to periodic audits on the household loan fraud/insolvency system that conducts household loan fraud/insolvency transactions by investigating the An audit information system that conducts regular audits in consideration of the risk of non-performing transactions and the relevant items, and supports efficient business audits for high-risk groups, and the first database of the household loan fraud/insolvency system through wired and wireless Internet Collects data, performs preprocessing for learning, analyzes, learns, and models data to create and store an abnormal transaction prediction model with the help of an audit terminal, and uses the abnormal transaction prediction model to use the household loan fraud/insolvency system Predicts whether the household loan fraud/non-performing transaction data of It is characterized in that it includes an artificial intelligence audit and analysis system that can efficiently perform regular audits for insolvency.
EP 3282666 A1 to Xu discloses: The present application relates to the field of network technologies, in particular to an address matching-based risk identification method and apparatus, which are used for providing a risk identification solution through address authentication. The address matching-based risk identification method provided by the embodiment of the present application comprises: receiving risk authentication request information, the risk authentication request information comprising identity identification information of a user who requests processing of a service and first address coding information used for identifying a first address; determining stored second address coding information corresponding to the identity identification information and used for identifying a second address; and judging whether the first address is consistent with the second address by matching the first address coding information with the second address coding information, and performing risk identification according to the obtained address matching result.
CN 110308962 B to Lin discloses: The invention relates to a method for managing a block chain network, electronic equipment and a medium. The method for managing the block chain network comprises the steps of displaying a user interface used for managing the block chain network wherein the user interface comprises one or more block chain network viewing icons and one or more block chain network editing icons; in response to receiving a user input for the one or more blockchain network view icons, displaying a blockchain network in the user interface; and in response to receiving a user input for the one or more blockchain network editing icons, editing the one or more blockchain networks, and displaying the edited blockchain network in the user interface.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JANICE LOZA whose telephone number is (571)270-3979. The examiner can normally be reached Monday - Friday 7:30am - 5:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Patrick McAtee can be reached on (571) 272-7575. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/J.L./Examiner, Art Unit 3698 /STEVEN S KIM/Primary Examiner, Art Unit 3698
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