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 .
DETAILED ACTION
This action is in response to application filed 09/30/2024.
Claims 1-20 are pending in this application.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-18 of U.S. Patent No. 12,107,882 B2 . Although the claims at issue are not identical, they are not patentably distinct from each other because claims 1-20 of the current application perform the same steps or limitations recited by claims 1-18 of U.S. Patent No. 12,107,882 B2 as detailed below by the examiner.
Claim 1-20
Current Application
Claim 1-18
Patent Case No. 12,107,882 B2
Claim 1. A system for identifying patterns in blockchain activities based on multi-modal data using artificial intelligence models that compensate for training data featuring a high proportion of missing data points, the system comprising:
one or more processors; and one or more non-transitory, computer-readable media comprising instructions that when executed by the one or more processors cause operations comprising: receiving a target blockchain activity and blockchain activity record data for a plurality of blockchain activities involving a plurality of blockchain accounts;
generating for display, in a user interface, a visualization of the target blockchain activity using an artificial intelligence model, wherein the artificial intelligence model is trained to identify serial relationships of related blockchain activities corresponding to inputted target blockchain activities based on proportions of digital assets at subsets of blockchain accounts of the plurality of blockchain accounts, and wherein the visualization comprises a subset of serial blockchain activities that correspond to the target blockchain activity;
determining that a first blockchain activity in the subset of serial blockchain activities corresponds to a first type of a plurality of types of blockchain activities;
retrieving a filtering criterion for the visualization;
applying the filtering criterion to the first type; and
in response to determining that the first type does not corresponds to the filtering criterion, generating for display, in the visualization, a first icon corresponding to the first blockchain activity.
Claim 1. A system for identifying patterns in blockchain activities based on multi-modal data using artificial intelligence models that compensate for training data featuring a high proportion of missing data points, the system comprising:
cloud-based storage circuitry configured to store an artificial intelligence model, wherein the artificial intelligence model is trained to identify serial relationships of related blockchain activities corresponding to inputted target blockchain activities based on proportions of digital assets at subsets of blockchain accounts of the plurality of blockchain accounts;
cloud-based control circuitry configured to: receive blockchain activity record data for a plurality of blockchain activities involving a plurality of blockchain accounts, wherein the blockchain activity record data comprises first data corresponding to a first blockchain network, and second data corresponding to a second blockchain network;
receive a first user input selecting a target blockchain activity;
generate a feature input based on the blockchain activity record data and the target blockchain activity;
input the feature input into an artificial intelligence model; and
receive an output from the artificial intelligence model; and
cloud-based input/output circuitry configured to: generate for display, in a user interface, a visualization of the target blockchain activity based on the output, wherein the visualization comprises a subset of serial blockchain activities that correspond to the target blockchain activity, and wherein the subset of serial blockchain activities comprise a first subset of blockchain activities corresponding to the first blockchain network and a second subset of blockchain activities corresponding to the second blockchain network; and
generate for display, in the visualization, a first icon corresponding to the first blockchain activity in response to:
determining that a first blockchain activity in the subset of serial blockchain activities corresponds to a first type of a plurality of types of blockchain activities;
retrieving a filtering criterion for the visualization;
applying the filtering criterion to the first type; and
determining that the first type does not corresponds to the filtering criterion.
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 of this title, 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-2, 4, 9-12, 14, 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Koenig et al. (US 2020/0311646 A1) in view of Galka (US 2021/0383395 A1).
Regarding claim 1, Koenig discloses a system for identifying patterns in blockchain activities based on multi-modal data using artificial intelligence models that compensate for training data featuring a high proportion of missing data points ([0039]: classifying one or more of the plurality of actions performed by users as a malicious activity. The actions may be classified as the malicious activity based on the analysis of the hash value and the timestamp and, optionally, the analysis of historical data, external data, user activity, browsing history, and so forth to identify certain patterns which might trigger a warning of malicious activity. [0041], Claim 2: the recommendations being provided based on machine learning and artificial intelligence (AI) trained on the historical data), the system comprising:
one or more processors; and one or more non-transitory, computer-readable media comprising instructions that when executed by the one or more processors cause operations comprising: receiving a target blockchain activity and blockchain activity record data for a plurality of blockchain activities involving a plurality of blockchain accounts ([0041]-[0042]: collecting, by a machine learning module, historical data related to the work performance of the one or more users. The machine learning module may provide recommendations to at least one of the one or more users to improve the work performance of the at least one of the one or more users. The recommendations may be provided based on machine learning and AI trained on historical data sets. the machine learning module may be further configured to access external data provided by third parties. The machine learning module may analyze the external data and the historical data. The recommendations for the at least one of the one or more users may be selected based on the analysis of the external data and the historical data);
wherein the artificial intelligence model is trained to identify serial relationships of related blockchain activities corresponding to inputted target blockchain activities based on proportions of digital assets at subsets of blockchain accounts of the plurality of blockchain accounts ([0035]: The method 300 may continue with creating, by the activity tracking module, a timestamp for each of the plurality of actions at operation 304. The method 300 may further include generating, by a processor, a hash value for each of the plurality of actions at operation 306. The method 300 may further include operation 308, at which the processor may store the hash value and the timestamp associated with each of the plurality of actions to a record on a blockchain. The blockchain may be configured to store a plurality of records. [0041]: collecting, by a machine learning module, historical data related to the work performance of the one or more users. The machine learning module may provide recommendations to at least one of the one or more users to improve the work performance of the at least one of the one or more users).
However, Koenig does not disclose generating for display, in a user interface, a visualization of the target blockchain activity using an artificial intelligence model, and wherein the visualization comprises a subset of serial blockchain activities that correspond to the target blockchain activity; determining that a first blockchain activity in the subset of serial blockchain activities corresponds to a first type of a plurality of types of blockchain activities; retrieving a filtering criterion for the visualization; applying the filtering criterion to the first type; and in response to determining that the first type does not corresponds to the filtering criterion, generating for display, in the visualization, a first icon corresponding to the first blockchain activity.
In an analogous art, Galka discloses generating for display, in a user interface, a visualization of the target blockchain activity using an artificial intelligence model ([0007]: visualize blockchain data analytics in an optimal and user-friendly manner [0074]: the controller 130 may generate, upon user request or automatically for example via a machine learning engine. [0058]: the controller 130 generates a graphical user interface (GUI) based on the node data and edge data as follows), and wherein the visualization comprises a subset of serial blockchain activities that correspond to the target blockchain activity ([0087]: GUIs presented on display 120, wherein certain nodes such as nodes which are not attributed to any known actors or are not of interest to the user may be condensed or concealed from view, according to one or more embodiments. To facilitate easier viewing, in some embodiments, transactional data or blockchain nodes which are not attributed to a source or particular entity (for example, nodes that are not attributed to any known actors or of interest to the user) may be condensed in order to improve the visibility of nodes of interest to the user); determining that a first blockchain activity in the subset of serial blockchain activities corresponds to a first type of a plurality of types of blockchain activities; retrieving a filtering criterion for the visualization; applying the filtering criterion to the first type ([0081]-[0082]: A filter input element 610 may also be implemented on the GUI 600, which enables a user to filter or modify the nodes and edges displayed based on a parameter, for example, time, risk values, source values generated according to the disclosure, and so forth. The GUI 600 enables a user to zoom in and out and view further or less details using a zoom control element 615. The GUI 600 may also include a toolbar 620 presented on display 120. In some embodiments, the graphical depiction may show all transactions over a time period between all of the nodes depicted. In this manner, via the GUI 600, a user may be able to quickly and easily visualize relationships at a high level between a set of nodes. The GUI 600 in some embodiments may permit a user to select a node in order to view additional information about the node, including information that may not have been depicted when the graph was originally generated); and in response to determining that the first type does not corresponds to the filtering criterion, generating for display, in the visualization, a first icon corresponding to the first blockchain activity ([0082]: graph is generated and a node data set is initially depicted on the display 120, a user may be able to select and change the nodes and edges of the node data set that were previously displayed in the report, for example, by determining a minimum transfer amounts (e.g. minimum edge weights) or by determining a minimum dilution and/or maximum hops as described above with respect to FIG. 5. By setting a minimum transfer amount using the minimum transfer amount input element 770, a user may be able to filter out smaller or minor transactions that are not of interest to the user).
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Koenig to comprise “generating for display, in a user interface, a visualization of the target blockchain activity using an artificial intelligence model, and wherein the visualization comprises a subset of serial blockchain activities that correspond to the target blockchain activity; determining that a first blockchain activity in the subset of serial blockchain activities corresponds to a first type of a plurality of types of blockchain activities; retrieving a filtering criterion for the visualization; applying the filtering criterion to the first type; and in response to determining that the first type does not corresponds to the filtering criterion, generating for display, in the visualization, a first icon corresponding to the first blockchain activity” taught by Galka.
One of ordinary skilled in the art would have been motivated because it would have enabled visualize blockchain data analytics in an optimal and user-friendly manner (Galka, [0007]).
Regarding claim 4, Koenig-Galka discloses the method of claim 2, further comprising: generating a feature input based on the blockchain activity record data and the target blockchain activity (Koenig, [0035]: The method 300 may continue with creating, by the activity tracking module, a timestamp for each of the plurality of actions at operation 304. The method 300 may further include generating, by a processor, a hash value for each of the plurality of actions at operation 306. The method 300 may further include operation 308, at which the processor may store the hash value and the timestamp associated with each of the plurality of actions to a record on a blockchain. The blockchain may be configured to store a plurality of records); and inputting the feature input into the artificial intelligence model to generate an output (Koenig, [0041]-[0042]: collecting, by a machine learning module, historical data related to the work performance of the one or more users. The machine learning module may provide recommendations to at least one of the one or more users to improve the work performance of the at least one of the one or more users. The recommendations may be provided based on machine learning and AI trained on historical data sets. the machine learning module may be further configured to access external data provided by third parties. The machine learning module may analyze the external data and the historical data. The recommendations for the at least one of the one or more users may be selected based on the analysis of the external data and the historical data).
Regarding claim 9, Koenig-Galka discloses the method of claim 2, wherein the artificial intelligence model is further trained based on non-blockchain record data for a plurality of entities linked to one or more of the plurality of blockchain accounts, and wherein a feature input for the artificial intelligence model comprises information retrieved from a third party source (Koenig, [0041]-[0042]: collecting, by a machine learning module, historical data related to the work performance of the one or more users. The machine learning module may provide recommendations to at least one of the one or more users to improve the work performance of the at least one of the one or more users. The recommendations may be provided based on machine learning and AI trained on historical data sets. the machine learning module may be further configured to access external data provided by third parties. The machine learning module may analyze the external data and the historical data. The recommendations for the at least one of the one or more users may be selected based on the analysis of the external data and the historical data).
Regarding claim 10, Koenig-Galka discloses the method of claim 2, wherein generating the visualization further comprises: generating for display, in the user interface, a plurality of icons corresponding to blockchain accounts corresponding to the subset of serial blockchain activities (Galka, [0044]: The wallet may contain multiple cryptocurrency addresses associated with a common owner or entity, for example, an exchange, a business entity, an individual owner, or any other entity associated with one or more accounts in the wallet); and generating a plurality of lines connecting the plurality of icons, wherein the plurality of lines represent characteristics of the subset of serial blockchain activities (Galka, fig. 6-7, [0080]: FIG. 6 depicts a GUI 600 presenting a graphical depiction of blockchain nodes and transactions between the nodes after a report has been generated as described above, according to one or more embodiments. The graph may reflect data contained in one or more reports that have been requested by a user and generated using the methods and systems described above with respect to FIGS. 2-4. In some embodiments, each edge is represented by an arrow that originates from a source or input node or wallet and terminates at a target node or wallet, and may further include a value that corresponds to an edge weight. For example, edge 670 is depicted with an edge weight of $68 in FIG. 6).
Regarding claim 11, Koenig-Galka discloses the method of claim 2, wherein the subset of serial blockchain activities comprise a first subset of blockchain activities on a first blockchain network, and a second subset of blockchain activities on a second blockchain network (Koenig, [0012], claim 1: receiving, by the one or more first processors executing instructions for a first blockchain-based decentralized analytics application, from a plurality of computing devices executing instructions of a blockchain-based decentralized analytics application, a plurality of blockchain transactions, including a first blockchain transaction, wherein the first blockchain transaction comprises transaction data associated with a first aggregated analytics operation executed by an analytics blockchain smart contract. [0015], claim 4: receiving, by one or more second processors of the second computing device, via the first blockchain-based decentralized analytics application executing at the second computing device, from the first computing device of the plurality of computing devices, the second blockchain transaction).
Regarding claims 2, 12; the claim is interpreted and rejected for the same reason as set forth in claim 1.
Regarding claim 14; the claim is interpreted and rejected for the same reason as set forth in claim 4.
Regarding claim 19; the claim is interpreted and rejected for the same reason as set forth in claim 9.
Regarding claim 20; the claim is interpreted and rejected for the same reason as set forth in claim 10.
Claims 3, 5, 13, 15 are rejected under 35 U.S.C. 103 as being unpatentable over Koenig in view of Galka, as applied to claim 2, in view of Gaur et al. (US 2023/0070625 A1).
Regarding claim 3, Koenig-Galka discloses the method of claim 2.
However, Koenig-Galka does not disclose further comprising: determining, using the artificial intelligence model, a respective proportion of digital assets corresponding to each blockchain activity in the plurality of blockchain activities; and based on the respective proportion, determining, using the artificial intelligence model, that the first blockchain activity is included in the subset of serial blockchain activities.
In an analogous art, Gaur discloses determining, using the artificial intelligence model, a respective proportion of digital assets corresponding to each blockchain activity in the plurality of blockchain activities; and based on the respective proportion, determining, using the artificial intelligence model, that the first blockchain activity is included in the subset of serial blockchain activities (fig. 4B-4B, [0051]: A graph modeling and correlational analysis module 177 may employs the graph and generative modeling based on asset class, asset type, and wallet and custodian holder data that is stored in the data store 180 to generate an output 178 that includes graphs and DeFi shopping stake (DSS) of the token. [0083]: a content area 424 may provide a list of events that show how the asset moved between the nodes in the path illustrated in content area 422 such as exchanges, sales, trades, integrations, etc. The path analytics area 422 may also include risk identification variables that classify path parts as riskier, less risker, moderate risk, severe risk, low risk, etc. The risk may be detected from attributes of the digital token itself such as the blockchain network that created the digital token, a current network where the asset is held, a geographic location of the asset, a type of the asset (e.g., stable coin, liquidity pool, financial instrument, etc.), and various other factors).
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Koenig-Galka to comprise “determining, using the artificial intelligence model, a respective proportion of digital assets corresponding to each blockchain activity in the plurality of blockchain activities; and based on the respective proportion, determining, using the artificial intelligence model, that the first blockchain activity is included in the subset of serial blockchain activities” taught by Gaur.
One of ordinary skilled in the art would have been motivated because it would have enabled to identify a digital token issued via a blockchain, and determine an asset that is linked to the digital token, a current custodian of the asset that is linked to the digital token, and a risk value associated with the asset based on attributes of the asset, and generate a graph that comprises a path between a user that owns the digital token and the current custodian of the asset (Gaur, [0002]).
Regarding claim 5, Koenig-Galka-Gaur discloses the method of claim 3, wherein determining that the first blockchain activity is included in the subset of serial blockchain activities further comprising: determining that the first blockchain activity corresponds to a first blockchain account; determining that the first blockchain account corresponds to a first type of a plurality of types of blockchain accounts (Galka, [0044]: the wallet may contain multiple cryptocurrency addresses associated with a common owner or entity, for example, an exchange, a business entity, an individual owner, or any other entity associated with one or more accounts in the wallet. The node data set also comprises edges, wherein each edge may have edge data indicating a source address (from which a transaction originates), a target edge (where the transaction terminates) and an edge weight, which may be, for example, an indicator representing a value of a transaction); in response to determining that the first blockchain account corresponds to the first type of the plurality of types of blockchain accounts, retrieving a second filtering criterion for the visualization (Galka, [0081]: A filter input element 610 may also be implemented on the GUI 600, which enables a user to filter or modify the nodes and edges displayed based on a parameter, for example, time, risk values, source values generated according to the disclosure, and so forth); applying the second filtering criterion to the first type of the plurality of types of blockchain accounts; and in response to determining that the first type does not correspond to the filtering criterion, generating for display, in the visualization, a second icon corresponding to the first blockchain activity (Galka, [0082]: graph is generated and a node data set is initially depicted on the display 120, a user may be able to select and change the nodes and edges of the node data set that were previously displayed in the report, for example, by determining a minimum transfer amounts (e.g. minimum edge weights) or by determining a minimum dilution and/or maximum hops as described above with respect to FIG. 5. By setting a minimum transfer amount using the minimum transfer amount input element 770, a user may be able to filter out smaller or minor transactions that are not of interest to the user).
Regarding claim 13; the claim is interpreted and rejected for the same reason as set forth in claim 3.
Regarding claim 15; the claim is interpreted and rejected for the same reason as set forth in claim 5.
Claims 6-8, 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Koenig in view of Galka, as applied to claim 2, in further view of Fang et al. (US 2022/0067738 B1).
Regarding claim 6, Koenig-Galka discloses the method of claim 2,
However, Koenig-Galka does not disclose determining, using the artificial intelligence model, a subset of blockchain accounts of the plurality of blockchain accounts corresponding to the target blockchain activity; determining, using the artificial intelligence model, a respective proportion of digital assets corresponding to each blockchain account in the subset of blockchain accounts; and based on the respective proportion, determining, using the artificial intelligence model, that a first blockchain account in the subset of blockchain accounts corresponds to the target blockchain activity.
In an analogous art, Fang discloses determining, using the artificial intelligence model, a subset of blockchain accounts of the plurality of blockchain accounts corresponding to the target blockchain activity; determining, using the artificial intelligence model, a respective proportion of digital assets corresponding to each blockchain account in the subset of blockchain accounts; and based on the respective proportion, determining, using the artificial intelligence model, that a first blockchain account in the subset of blockchain accounts corresponds to the target blockchain activity ([0072]: the auto tracing system 154 may trace the flow of digital assets through a number of digital accounts, and may generate tracing reports 156 providing visual representations of asset flow. These tracing reports 156 may be used to assist in identifying illicit activities such as money laundering, fraud, and other activities of interest to cybersecurity blockchain forensic specialists. [0284]: The tracing begins with four transactions of $100 each sent by the digital account source address 1602 on Jul. 15, 2020 to the addresses in Entity A 1612. Entity A 1612 may comprise a set of digital accounts that have been identified as having a known relationship, and have been tagged with the same cluster IDs as described with respect to the address clustering labels 1416 of FIG. 14.).
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Koenig-Galka to comprise “determining, using the artificial intelligence model, a subset of blockchain accounts of the plurality of blockchain accounts corresponding to the target blockchain activity; determining, using the artificial intelligence model, a respective proportion of digital assets corresponding to each blockchain account in the subset of blockchain accounts; and based on the respective proportion, determining, using the artificial intelligence model, that a first blockchain account in the subset of blockchain accounts corresponds to the target blockchain activity” taught by Fang.
One of ordinary skilled in the art would have been motivated because it would have enabled to effectively and accurately trace money flow between entities, represented by digital account addresses, from data stored in a transaction database, using artificial intelligence and machine learning. (Fang, [0031]).
Regarding claim 7, Koenig-Galka-Fang discloses the method of claim 6, further comprising: in response to determining that the first blockchain account in the subset of blockchain accounts corresponds to the target blockchain activity, retrieving a filtering criterion for the visualization (Fang, [0003]: receiving input trace parameters, where the input trace parameters include at least one of an objective directional setting, a tracing constraint, and a transaction filter, the transaction filter based on the transaction timestamps and the digital assets transferred, applying the intelligence labels to the digital account source address, the digital account intermediate addresses, and the digital account destination address, thereby creating labeled account addresses, applying an artificial intelligence graph search algorithm to the blockchain transaction flow based on the input trace parameters); applying the filtering criterion to a characteristic of the first blockchain account; and in response to determining that the characteristic does not correspond to the filtering criterion, generating for display, in the visualization, a first icon corresponding to the first blockchain account (Fang, [0277]: The interactive visualization may include topological sorting to graph layout by the intermediate transactions between the digital account source address and the digital account destination address. The interactive visualization may show time-bound tracing, where the trace path is restricted by time or hops after a specific transaction. The visualization may show value-bound tracing, where the trace path is based on the transaction values in the trace, with at least a portion of the trace path highlighted). The same rationale applies as in claim 6.
Regarding claim 8, Koenig-Galka-Fang discloses the method of claim 7, further comprising: determining an amount of a digital asset corresponding to the target blockchain activity; and determining the filtering criterion based on a percentage of the amount (Fang, [0099]: sequential feature category may include items such as the time interval between 2 consecutive transactions of one address, the percentage of transactions that have the same dollar amount within each rolling time window. [0261]-[0263]: Tracing destination summary statistics may display percentage by label: [0262] Example: 67% funding going to BINANCE™ exchange; 13% funding going to dark web markets; 20% remains unknown. [0263] Visualization may illustrate all money paths from an original address in an expandable interactive view). The same rationale applies as in claim 6.
Regarding claim 16; the claim is interpreted and rejected for the same reason as set forth in claim 6.
Regarding claim 17; the claim is interpreted and rejected for the same reason as set forth in claim 7.
Regarding claim 18; the claim is interpreted and rejected for the same reason as set forth in claim 8.
Additional References
The prior art made of record and not relied upon is considered pertinent to applicants disclosure.
Yong, US 2019/0282906 A1: Secure Decentralized Video Game Transaction Platform.
Fletcher, US 2020/0213085 A1: Systems and Methods for Addressing Security-Related Vulnerabilities Arising in Relation to Off-Blockchain Channels in the Event of Failures in a Network.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JUAN C TURRIATE GASTULO whose telephone number is (571)272-6707. The examiner can normally be reached Monday - Friday 8 am-4 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Brian J Gillis can be reached at 571-272-7952. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/J.C.T/Examiner, Art Unit 2446
/BRIAN J. GILLIS/Supervisory Patent Examiner, Art Unit 2446