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 .
Response to Amendments
Claims 1-5, 7-10, and 12-20 have been amended.
Claims 1-20 remain pending in the application.
The amendment filed 02/13/2026 is sufficient to overcome the 35 U.S.C. 101
rejections of claims 1-20. The previous rejections have been withdrawn.
The amendment filed 02/13/2026 is sufficient to overcome the objection made to claim 12. The previous objection has been withdrawn.
Response to Arguments
Argument 1, regarding the claim objection, applicant argues that the objection should be withdrawn in view of amendments made to claim 12. Examiner agrees and the objection has been withdrawn.
Argument 2, regarding the 35 U.S.C. 101 rejections, applicant argues that the rejections should be withdrawn because the claims integrate abstract ideas into the practical application of configuring and training a machine learning model to use a series of user interface interactions that immediately precede an initiation of an online transaction to classify the online transaction. Applicant also argues that the claims provide an improvement to the technical field of machine learning by enabling a machine learning model to analyze a series of user interface interactions by traversing a graph. Examiner agrees and the 35 U.S.C. 101 rejections have been withdrawn.
Argument 3, regarding the prior art rejections, applicant argues that none of the cited art teaches “wherein the first interaction sequence comprises a series of user interface interactions conducted by the first user with the website preceding an initiation of the transaction... embedding data associated with the first interaction sequence and the set of interaction sequences into the first node and the set of nodes of the transaction graph, respectively”. Examiner respectfully disagrees because Mohammed recites a blockchain network that is created using nodes 208, and nodes 208 may comprise a graphical user interface (see Mohammed P0021, P0032). The blockchain network is represented by transactions graphs that show traffic between users and an exchange platform or with other users (see Mohammed P0045). Thus, Mohammed teaches the limitations because the blockchain network represented by nodes 208 may comprise a graphical user interface, and each transaction outlined in the transaction graph may require the user to interact with the graphical user interface. Furthermore, applicant’s arguments are not persuasive because the arguments merely recite portions of the cited references but do not go into detail why said portions do not teach the amended limitations.
The full prior art rejections are outlined below.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 1-5 and 7-19 are rejected under 35 U.S.C. 103 as being unpatentable over Mohammed et al (Pub. No.: US 20240161106 A1), hereafter Mohammed in view of Jayapalan (Pub. No.: US 12236431 B1), hereafter Jayapalan.
Regarding claim 1, Mohammed teaches A system, comprising: a non-transitory memory; and one or more hardware processors coupled with the non-transitory memory and configured to read instructions from the non-transitory memory to cause the system to perform operations comprising: (“The intelligent anomaly detection system 200 further comprises a communication path 202, one or more processors 204, a non-transitory memory component 206,…The intelligent anomaly detection system 200 of FIG. 1 also comprises the processor 204. The processor 204 can be any device capable of executing machine readable instructions”, P0017, P0019) detecting a transaction initiated by a first user (Anomalies based on user interactions on the internet are detected and classified, P0015, P0017. Interactions are conducted using a graphical user interface, P0021);… determining a first interaction sequence associated with the first user and the transaction, wherein the first interaction sequence comprises a series of user interface interactions conducted by the first user preceding an initiation of the interaction (Transaction graphs detail traffic involving a user(s), P0045. The blockchain network is created using nodes 208, and nodes 208 may comprise a graphical user interface, P0021, P0032. The blockchain network is represented by transactions graphs that show traffic between users and an exchange platform or with other users, P0045); traversing a transaction graph representing transactions conducted among different users with the service provider (Transactions between users are outlined on transaction graphs, P0045); identifying, from the traversing, a portion of the transaction graph that includes a first node representing the first user and a set of nodes representing a set of users having conducted transactions with the first user (interactions between users are identified using a transaction graph, fig 4A, P0045); determining a set of interaction sequences associated with the set of users and the transactions (Anomalies associated with the users are identified in the transaction graph, P0045); …providing the portion of the transaction graph with the embedded data as an input to a graph-based machine learning model (GNN model is used to perform image analysis on the transaction graph believed to have an anomaly, P0045), wherein the graph-based machine learning model is trained to predict a classification of the transaction based on a comparison between the first interaction sequence against the set of interaction sequences (GNN model is used to classify transactions based on comparing regular and irregular transaction patterns, P0040, P0045); and classifying the transaction based on an output from the graph-based machine learning model (GNN image analysis is used to determine irregular patterns on the transaction graph, P0045).
Mohammed does not appear to explicitly teach “via a website associated with a service provider… embedding data associated with the first interaction sequence and the set of interaction sequences into the first node and the set of nodes of the transaction graph, respectively”.
Jayapalan teaches via a website associated with a service provider (“When a user has initiated a digital session, on a website, for example, the digital companion could be invoked from within the website”, C5:L55-57)… embedding data associated with the first interaction sequence and the set of interaction sequences into the first node and the set of nodes of the transaction graph, respectively (“Knowledge graph module 440 may also comprise a graph neural network 442 that can be used to learn a suitable embedding of the graph data in graph database 444”, C6:L20-24).
Accordingly, it would have been obvious to a person having ordinary skill in the
art before the effective filing date of the claimed invention, having the teachings of Mohammed and Jayapalan before them, to include Jayapalan’s specific teachings of a website and embedding graph data in Mohammed’s system of real-time identification of an anomaly of a block transactions graph of a blockchain. One would have been motivated to make such a combination of website, embedding graph data (see Jayapalan C5:L55-57, C6:L20-24) and using internet protocols to receive graph parameters and validate block transactions with a graph neural network (see Mohammed P0032).
Regarding claims 8 and 15, Mohammed teaches A method and non-transitory machine-readable medium having stored thereon machine- readable instructions executable to cause a machine to perform operations comprising: (“The intelligent anomaly detection system 200 further comprises a communication path 202, one or more processors 204, a non-transitory memory component 206,…The intelligent anomaly detection system 200 of FIG. 1 also comprises the processor 204. The processor 204 can be any device capable of executing machine readable instructions”, P0017, P0019) detecting, by a computer system, a transaction initiated by a first user via a user interface associated with a service provider (Anomalies based on user interactions on the internet are detected and classified, P0015, P0017. Interactions are conducted using a graphical user interface, P0021); obtaining a first interaction sequence associated with the first user, wherein the first interaction sequence comprises a series of user interface interactions conducted by the first user with the user interface within a predetermined time period from an initiation of the transaction (Transaction graphs detail traffic involving a user(s), P0045. The blockchain network is created using nodes 208, and nodes 208 may comprise a graphical user interface, P0021, P0032. The blockchain network is represented by transactions graphs that show traffic between users and an exchange platform or with other users, P0045); determining that a frequency of interactions between the first user and the service provider does not exceed a threshold (Different events may be any combination of frequency of transaction or financial amounts of transactions. Any value above a predetermined frequency or amount may be determined to be an anomaly, below may not be determined to be an anomaly, P0045); in response to determining that the frequency of interactions does not exceed the threshold: identifying, within a graph representing connections among different users of the service provider, a portion of the graph for analyzing the transaction, wherein the portion of the graph comprises a first node representing the first user and a set of nodes representing a set of users directly connected with the first node (interactions between users are identified using a transaction graph, fig 4A, P0045); determining a set of interaction sequences associated with the set of users (Anomalies associated with the users are identified in the transaction graph, P0045);… and subsequent to the embedding, providing, by the computer system, the portion of the graph as an input to a machine learning model, wherein the machine learning model is trained to predict a classification of the transaction based on a comparison between the first interaction sequence against the set of interaction sequences (GNN model is used to classify transactions based on comparing regular and irregular transaction patterns, P0040, P0045); and classifying, by the computer system, the first event based on an output from the machine learning model (GNN image analysis is used to determine irregular patterns on the transaction graph, P0045).
Mohammed does not appear to explicitly teach “via a user interface associated with a service provider… embedding, by the computer system, the first interaction sequence into the first node of the graph and the set of interaction sequences into respective nodes in the set of nodes of the graph”.
Jayapalan teaches via a user interface associated with a service provider (“When a user has initiated a digital session, on a website, for example, the digital companion could be invoked from within the website”, C5:L55-57)… embedding, by the computer system, the first interaction sequence into the first node of the graph and the set of interaction sequences into respective nodes in the set of nodes of the graph (“Knowledge graph module 440 may also comprise a graph neural network 442 that can be used to learn a suitable embedding of the graph data in graph database 444”, C6:L20-24).
Accordingly, it would have been obvious to a person having ordinary skill in the
art before the effective filing date of the claimed invention, having the teachings of Mohammed and Jayapalan before them, to include Jayapalan’s specific teachings of a website and embedding graph data in Mohammed’s system of real-time identification of an anomaly of a block transactions graph of a blockchain. One would have been motivated to make such a combination of website, embedding graph data (see Jayapalan C5:L55-57, C6:L20-24) and using internet protocols to receive graph parameters and validate block transactions with a graph neural network (see Mohammed P0032).
Regarding claim 2, Mohammed in view of Jayapalan teaches the limitations of claim 1 as outlined above. Mohammed further teaches wherein the operations further comprise: determining that an interaction frequency between the user and the website fails to satisfy a quantity threshold, wherein the traversing of the transaction graph is responsive to the determining that the interaction frequency fails to satisfy the quantity threshold (“an image analysis may be applied to a topology analysis of a transactions graph via the GNN to determine whether an irregular pattern exists, such as when an overlapping amount of clusters exceed a predetermined allowable threshold”, P0037).
Regarding claims 3 and 13, Mohammed in view of Jayapalan teaches the limitations of claims 1 and 8 as outlined above. Jayapalan further teaches wherein the series of user interactions includes at least one of a login request, a navigation on the website, or access of data via the website (user interactions may include a user interacting with a digital interface such as a webpage or web interface, C3:L24-31).
Regarding claim 4, Mohammed in view of Jayapalan teaches the limitations of claim 1 as outlined above. Mohammed further teaches wherein the graph-based machine learning model is trained to classify the transaction further based on relationships between the first user and the set of users according to the transaction graph (“transactions graph 400A shows traffic between legitimate users and an exchange platform for transactions being conducted there between… an image analysis may be applied to a topology analysis of the transactions graph 400B via the GNN model to determine an irregular pattern exists”, P0045).
Regarding claims 5 and 19, Mohammed in view of Jayapalan teaches the limitations of claims 1 and 15 as outlined above. Mohammed further teaches embedding first interaction sequence into the first node representing the first user; and embedding each interaction sequence in the set of interaction sequences into a corresponding node from the set of nodes (“transactions graphs may summarize exchanges between individual addresses (e.g., belonging to user cryptocurrency accounts) ...Transactions between addresses as nodes over time may be represented on the graph”, P0035).
Regarding claim 7, Mohammed in view of Jayapalan teaches the limitations of claim 1 as outlined above. Mohammed further teaches wherein the transaction is associated with a fund transfer between two or more accounts with the service provider (Events are related to block transactions of a blockchain, which are monetary transactions, P0015. “The transactions graphs may summarize exchanges between individual addresses (e.g., belonging to user cryptocurrency accounts)”, P0035).
Regarding claim 9, Mohammed in view of Jayapalan teaches the limitations of claim 8 as outlined above. Mohammed further teaches wherein the set of users is a first set of users, wherein the set of nodes is a first set of nodes, and wherein the portion of the transaction graph further comprises a second set of nodes representing a second set of users and directly connected with the first set of nodes (“transactions graphs may summarize exchanges between individual addresses (e.g., belonging to user cryptocurrency accounts) in which pattern irregularities to identify anomalies will have a distinct construction… Transactions between addresses as nodes over time may be represented on the graph”, P0035).
Regarding claim 10, Mohammed teaches the limitations of claim 8 as outlined above. Mohammed further teaches wherein the machine learning model is configured to (i) generate a first score based on the first interaction sequence embedded into the first node and a second score based on the set of interaction sequences embedded into the set of nodes (Statistical approximations are calculated based on transaction graph patterns. Statistical approximations are also calculated for addresses graph patterns, P0035), and (ii) generate the output based on the first score and the second score (Statistical approximations are used to identify irregular patterns and anomalies, P0035).
Regarding claim 11, Mohammed in view of Jayapalan teaches the limitations of claim 10 as outlined above. Mohammed further teaches wherein the second score is generated further based on a set of connections connecting the first node to the set of nodes (Addresses graphs may be created to single out addresses involved with anomalies. Transactions between addresses as nodes over time may be represented on the graph, P0035).
Regarding claim 12, Mohammed in view of Jayapalan teaches the limitations of claim 8 as outlined above. Mohammed further teaches wherein the series of user interface interactions is arranged in a chronological order in the first interaction sequence (“an artificial intelligence (AI) tool includes a graph neural network (GNN) tool that uses deep learning models and graphics processing units (GPUs) and is trained to analyze graphics for irregular graph patterns based on block(s) over time”, P0015).
Regarding claims 14 and 18, Mohammed in view of Jayapalan teaches the limitations of claims 8 and 15 as outlined above. Mohammed further teaches wherein the machine learning model is trained to classify the transaction further based on the relationships between the first user and the set of users according to the graph (GNN uses transaction graphs to detect and classify events, the graphs having details regarding interactions between users, P0015).
Regarding claim 16, Mohammed in view of Jayapalan teaches the limitations of claim 15 as outlined above. Mohammed further teaches wherein the determining that the interaction frequency of the first user is below the threshold is further based on a volume of interactions in the first interaction sequence (If the amounts of transactions is below a predetermined amount, the event is not considered to be an anomaly, P0045).
Regarding claim 17, Mohammed in view of Jayapalan teaches the limitations of claim 15 as outlined above. Mohammed further teaches wherein the transaction is associated with a payment from an account of the first user (Different events may be any combination of frequency of transaction or financial amounts of transactions. Any value above a predetermined frequency or amount may be determined to be an anomaly, below may not be determined to be an anomaly, P0045. Transactions are associated with addresses exchanging currency, with the addresses being user accounts, P0035).
Claims 6 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Mohammed in view of Jayapalan and further in view of Lu et al (Pub. No.: US 20220107813 A1), hereafter Lu.
Regarding claims 6 and 20, Mohammed in view of Jayapalan teaches the limitations of claim 1 and 15 as outlined above. Mohammed does not appear to explicitly teach “identifying, from the set of users, a second user having a transaction volume exceeding a threshold; and removing a second node representing the second user from the portion of the transaction graph”.
Lu teaches identifying, from the set of users, a second user having a transaction volume exceeding a threshold; and removing a second node representing the second user from the portion of the transaction graph (When a user’s transaction volume exceeds a predetermined threshold, the event is determined to be an outlier and removed from transactional data, P0014, P0020).
Accordingly, it would have been obvious to a person having ordinary skill in the
art before the effective filing date of the claimed invention, having the teachings of Mohammed, Jayapalan and Lu before them, to include Lu’s specific teaching of detecting and removing a node when it is determined to be an outlier based on exceeding a threshold value in Mohammed’s system of real-time identification of an anomaly of a block transactions graph of a blockchain. One would have been motivated to make such a combination of detecting and removing a node when it is determined to be an outlier based on exceeding a threshold value (see Lu P0014, P0020) and analyzing transactions between nodes on a graph to detect anomalies based on a threshold value (see Mohammed P0045, P0048).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ISHAN MOUNDI whose telephone number is (703)756-1547. The examiner can normally be reached 8:30 A.M. - 5 P.M..
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/I.M./Examiner, Art Unit 2141
/MATTHEW ELL/Supervisory Patent Examiner, Art Unit 2141
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