DETAILED ACTION Notice of Pre-AIA or AIA Status The present application is being examined under the pre-AIA first to invent provisions. Claim Rejections - 35 USC § 101 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. In reference to claim 1 : Step 1 - Is the claim to a process, machine, manufacture or composition of matter? Yes, the claim is directed to a manufacture Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon? “ identifying, from a graph datastore, a graph subpart associated with a user identifier; ” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could identify a graph subpart associated with a user identifier . “generating, based on the identified graph subpart, a feature vector;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could generate a feature vector based on identified graph subpart. “processing, [using a machine learning model], the feature vector to generate a set of reputation metrics;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could process feature vectors to generate a set of reputation metrics. Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application? “ A system comprising: at least one processor; and memory storing instructions that, when executed by the at least one processor, cause the system to perform a set of operations, the set of operations comprising: ” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). “using a machine learning model” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). “and providing an indication of the generated set of reputation metrics to a third-party service.” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g)) The claim does not include additional elements that are integrated into a practical application. Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception? “A system comprising: at least one processor; and memory storing instructions that, when executed by the at least one processor, cause the system to perform a set of operations, the set of operations comprising:” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). “using a machine learning model” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). “and providing an indication of the generated set of reputation metrics to a third-party service.” (well-understood, routine, conventional MPEP 2106.05(d)) The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. In reference to claim 2: Claim 2 is directed to a judicial exception from claim(s) depended on and does not recite additional elements that integrate the judicial exception into a practical application and amount to significantly more than the judicial exception. In reference to claim 3: Claim 3 is directed to a judicial exception from claim(s) depended on and does not recite additional elements that integrate the judicial exception into a practical application and amount to significantly more than the judicial exception. In reference to claim 4: Claim 4 is directed to a judicial exception from claim(s) depended on and does not recite additional elements that integrate the judicial exception into a practical application and amount to significantly more than the judicial exception. In reference to claim 5: Claim 5 is directed to a judicial exception from claim(s) depended on and does not recite additional elements that integrate the judicial exception into a practical application and amount to significantly more than the judicial exception. In reference to claim 6: Claim 6 is directed to a judicial exception from claim(s) depended on and does not recite additional elements that integrate the judicial exception into a practical application and amount to significantly more than the judicial exception. In reference to claim 7: Claim 7 is directed to a judicial exception from claim(s) depended on and does not recite additional elements that integrate the judicial exception into a practical application and amount to significantly more than the judicial exception. In reference to claim 8: Step 1 - Is the claim to a process, machine, manufacture or composition of matter? Yes, the claim is directed to a process Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon? “preprocessing identification information of the transaction information;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could preprocess identification information of the transaction information. “generating, within the graph datastore, a transaction node including a set of properties based on the transaction information;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could generate a transaction node on a mental graph that has a set of properties based on transaction information like other nodes it is connected to. “and generating an edge between the generated transaction node and a user identifier node for the user identifier, wherein the edge includes at least a part of the preprocessed identification information.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could generate an edge/connection between a transaction node an a user identifier node. Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application? “A method for maintaining a graph datastore, comprising: obtaining transaction information associated with a user identifier;” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g)) The claim does not include additional elements that are integrated into a practical application. Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception? “A method for maintaining a graph datastore, comprising: obtaining transaction information associated with a user identifier;” (well-understood, routine, conventional MPEP 2106.05(d)) The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. In reference to claim 9: Step 1 - Is the claim to a process, machine, manufacture or composition of matter? Yes, the claim is directed to a process Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon? “The method of claim 8, further comprising: determining whether the graph datastore includes the user identifier node for the user identifier;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could determine whether the graph datastore includes the user identifier node for the user identifier . “and based on determining the graph datastore does not include the user identifier node, generating the user identifier node for the user identifier.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could generate the user identifier node the user identifier based on the determining. Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application? No Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception? No In reference to claim 10: Step 1 - Is the claim to a process, machine, manufacture or composition of matter? Yes, the claim is directed to a process Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon? “The method of claim 8, wherein preprocessing the identification information comprises one or more of: processing a name of the identification information to omit a suffix, to omit a character, or to replace a character; processing an email address of the identification information to omit a domain name of the email address or to perform partial matching using a part of the email address; processing a phone number of the identification information to omit a part of the phone number or to determine whether to omit the phone number for inclusion in the graph datastore when the phone number is invalid; or processing a mailing address of the identification information to determine whether to omit the mailing address for inclusion in the graph datastore when a shipping provider indicates the mailing address is invalid.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could process a name, an email address , a phone number, or a mailing address to omit unneeded aspects or determine to not include in database . Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application? No Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception? No In reference to claim 11: Claim 11 is directed to a judicial exception from claim(s) depended on and does not recite additional elements that integrate the judicial exception into a practical application and amount to significantly more than the judicial exception. In reference to claim 12: Step 1 - Is the claim to a process, machine, manufacture or composition of matter? Yes, the claim is directed to a process Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon? “identifying, from a graph datastore, a graph subpart associated with a user identifier;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could identify a graph subpart associated with a user identifier. “generating, based on the identified graph subpart, a feature vector;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could generate a feature vector based on identified graph subpart. “processing, [using a machine learning model], the feature vector to generate a set of reputation metrics;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could process feature vectors to generate a set of reputation metrics. Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application? “The method of claim 8, further comprising: receiving, from a third-party service, a request for a set of reputation metrics associated with a given user identifier;” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g)) “using a machine learning model” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). “and providing, to the third-party service, an indication of the generated set of reputation metrics.” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g)) The claim does not include additional elements that are integrated into a practical application. Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception? “The method of claim 8, further comprising: receiving, from a third-party service, a request for a set of reputation metrics associated with a given user identifier;” (well-understood, routine, conventional MPEP 2106.05(d)) “using a machine learning model” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). “and providing, to the third-party service, an indication of the generated set of reputation metrics.” (well-understood, routine, conventional MPEP 2106.05(d)) The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. In reference to claim 13: Claim 13 is directed to a judicial exception from claim(s) depended on and does not recite additional elements that integrate the judicial exception into a practical application and amount to significantly more than the judicial exception. In reference to claim 14: Step 1 - Is the claim to a process, machine, manufacture or composition of matter? Yes, the claim is directed to a process Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon? “identifying, from a graph datastore, a graph subpart associated with a user identifier;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could identify a graph subpart associated with a user identifier. “generating, based on the identified graph subpart, a feature vector;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could generate a feature vector based on identified graph subpart. “processing, [using a machine learning model], the feature vector to generate a set of reputation metrics;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could process feature vectors to generate a set of reputation metrics. Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application? “using a machine learning model” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). “and providing an indication of the generated set of reputation metrics to a third-party service.” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g)) The claim does not include additional elements that are integrated into a practical application. Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception? “using a machine learning model” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). “and providing an indication of the generated set of reputation metrics to a third-party service.” (well-understood, routine, conventional MPEP 2106.05(d)) The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. In reference to claim 15: Claim 15 is directed to a judicial exception from claim(s) depended on and does not recite additional elements that integrate the judicial exception into a practical application and amount to significantly more than the judicial exception. In reference to claim 16: Claim 16 is directed to a judicial exception from claim(s) depended on and does not recite additional elements that integrate the judicial exception into a practical application and amount to significantly more than the judicial exception. In reference to claim 17: Claim 17 is directed to a judicial exception from claim(s) depended on and does not recite additional elements that integrate the judicial exception into a practical application and amount to significantly more than the judicial exception. In reference to claim 18: Claim 18 is directed to a judicial exception from claim(s) depended on and does not recite additional elements that integrate the judicial exception into a practical application and amount to significantly more than the judicial exception. In reference to claim 19: Claim 19 is directed to a judicial exception from claim(s) depended on and does not recite additional elements that integrate the judicial exception into a practical application and amount to significantly more than the judicial exception. In reference to claim 20: Claim 20 is directed to a judicial exception from claim(s) depended on and does not recite additional elements that integrate the judicial exception into a practical application and amount to significantly more than the judicial exception Claim Rejections - 35 USC § 102 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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. Claim(s) 1, 6-7, 1 4 , and 19-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Parker Erickson et al; US 20210406917 A1 filed on Jun 30, 2020 (hereinafter “Erickson”). Regarding claim 1, Erickson anticipates A system comprising: at least one processor; and memory storing instructions that, when executed by the at least one processor, cause the system to perform a set of operations, the set of operations comprising: (Erickson Paragraph 0005; “In accordance with yet another aspect, an apparatus comprising at least one processor and at least one memory including computer program code is provided. In one embodiment, the at least one memory and the computer program code may be configured to, with the processor, cause the apparatus to”) identifying, from a graph datastore, a graph subpart associated with a user identifier; (Erickson Paragraph 0055; “the graph convolutional neural network computing entity 106 identifies a graph database including a plurality of graph database objects.” Erickson Paragraph 0057; “in a graph-based transactional record database that records transactions performed by particular IP addresses as relationships between transaction nodes and IP-address nodes and transactions performed at particular times between particular user profiles as relationships between transaction nodes and user profile nodes” Examiner notes that neural network computing entity 106 identifies a graph subpart from a graph datastore (identifying a graph database; a graph is a subgraph of itself) associated with a user identifier (IP-address is present within graph database)) generating, based on the identified graph subpart, a feature vector; (Erickson Paragraph 0061; “the graph convolutional neural network computing entity 106 generates related graph feature data for the predictive entity based on the related graph database input data for the predictive entity. In some embodiments, the related graph feature data includes a feature vector for each related graph database object of one or more related graph database objects associated with the predictive entity.” Examiner notes that neural network computing entity 106 generates a feature vector (related graph feature data which includes a feature vector) based on the identified graph subpart (related graph feature data is related to graph database)) processing, using a machine learning model, the feature vector to generate a set of reputation metrics; (Erickson Paragraph 0068; “the graph convolutional neural network computing entity 106 processes each feature vector for a related graph database object of the one or more related graph database objects using a first graph convolutional neural network of the graph convolutional neural network model to generate an anomaly presence likelihood for the predictive entity and an anomaly absence likelihood for the predictive entity.” Examiner notes that neural network computing entity 106 processes the feature vector using a machine learning model (graph convolutional neural network model) to generate a set of reputation metrics (an anomaly presence likelihood for the predictive entity and an anomaly absence likelihood for the predictive entity)) and providing an indication of the generated set of reputation metrics to a third-party service. (Erickson Paragraph 0031; “The architecture 100 includes an anomaly detection 101 configured to receive anomaly detection requests from external computing entities 102, process the anomaly detection requests to generate anomaly detection outputs, provide the anomaly detection systems to the external computing entities 102” Examiner notes an indication of the generated set of reputation metrics (anomaly detection outputs) is provided to a third-party service (external computing entities 102)) Regarding claim 6, Erickson anticipates The system of claim 1, wherein the set of reputation metrics comprises at least one of: a stability metric for the user identifier; or a supplemental information for the user identifier. (Erickson Paragraph 0068; “the graph convolutional neural network computing entity 106 processes each feature vector for a related graph database object of the one or more related graph database objects using a first graph convolutional neural network of the graph convolutional neural network model to generate an anomaly presence likelihood for the predictive entity and an anomaly absence likelihood for the predictive entity.” Examiner notes that supplemental information for the user identifier is anomaly presence likelihood for the predictive entity and an anomaly absence likelihood for the predictive entity) Regarding claim 7, Erickson anticipates The system of claim 1, wherein the indication of the generated set of reputation metrics is provided in response to a request from the third-party service. (Erickson Paragraph 0031; “The architecture 100 includes an anomaly detection 101 configured to receive anomaly detection requests from external computing entities 102, process the anomaly detection requests to generate anomaly detection outputs, provide the anomaly detection systems to the external computing entities 102” Examiner notes that the indication of the generated set of reputation metrics (anomaly detection outputs) is provided in response to a request from the third-party service (receive anomaly detection requests from external computing entities 102)) Regarding claim 1 4 , Erickson anticipates A method, comprising: identifying, from a graph datastore, a graph subpart associated with a user identifier; (Erickson Paragraph 0055; “the graph convolutional neural network computing entity 106 identifies a graph database including a plurality of graph database objects.” Erickson Paragraph 0057; “in a graph-based transactional record database that records transactions performed by particular IP addresses as relationships between transaction nodes and IP-address nodes and transactions performed at particular times between particular user profiles as relationships between transaction nodes and user profile nodes” Examiner notes that neural network computing entity 106 identifies a graph subpart from a graph datastore (identifying a graph database; a graph is a subgraph of itself) associated with a user identifier (IP-address is present within graph database)) generating, based on the identified graph subpart, a feature vector; (Erickson Paragraph 0061; “the graph convolutional neural network computing entity 106 generates related graph feature data for the predictive entity based on the related graph database input data for the predictive entity. In some embodiments, the related graph feature data includes a feature vector for each related graph database object of one or more related graph database objects associated with the predictive entity.” Examiner notes that neural network computing entity 106 generates a feature vector (related graph feature data which includes a feature vector) based on the identified graph subpart (related graph feature data is related to graph database)) processing, using a machine learning model, the feature vector to generate a set of reputation metrics; (Erickson Paragraph 0068; “the graph convolutional neural network computing entity 106 processes each feature vector for a related graph database object of the one or more related graph database objects using a first graph convolutional neural network of the graph convolutional neural network model to generate an anomaly presence likelihood for the predictive entity and an anomaly absence likelihood for the predictive entity.” Examiner notes that neural network computing entity 106 processes the feature vector using a machine learning model (graph convolutional neural network model) to generate a set of reputation metrics (an anomaly presence likelihood for the predictive entity and an anomaly absence likelihood for the predictive entity)) and providing an indication of the generated set of reputation metrics to a third-party service. (Erickson Paragraph 0031; “The architecture 100 includes an anomaly detection 101 configured to receive anomaly detection requests from external computing entities 102, process the anomaly detection requests to generate anomaly detection outputs, provide the anomaly detection systems to the external computing entities 102” Examiner notes an indication of the generated set of reputation metrics (anomaly detection outputs) is provided to a third-party service (external computing entities 102)) Regarding claim 19, Erickson anticipates The method of claim 14, wherein the set of reputation metrics comprises at least one of: a stability metric for the user identifier; or a supplemental information for the user identifier. (Erickson Paragraph 0068; “the graph convolutional neural network computing entity 106 processes each feature vector for a related graph database object of the one or more related graph database objects using a first graph convolutional neural network of the graph convolutional neural network model to generate an anomaly presence likelihood for the predictive entity and an anomaly absence likelihood for the predictive entity.” Examiner notes that supplemental information for the user identifier is anomaly presence likelihood for the predictive entity and an anomaly absence likelihood for the predictive entity) Regarding claim 20, Erickson anticipates The method of claim 14, wherein the indication of the generated set of reputation metrics is provided in response to a request from the third-party service. (Erickson Paragraph 0031; “The architecture 100 includes an anomaly detection 101 configured to receive anomaly detection requests from external computing entities 102, process the anomaly detection requests to generate anomaly detection outputs, provide the anomaly detection systems to the external computing entities 102” Examiner notes that the indication of the generated set of reputation metrics (anomaly detection outputs) is provided in response to a request from the third-party service (receive anomaly detection requests from external computing entities 102)) 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. Claim(s) 2 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Parker Erickson et al; US 20210406917 A1 filed on Jun 30, 2020 (hereinafter “Erickson”) in view of Lishui Cheng et al; US 11599840 B2 filed on Feb 25, 2019 (hereinafter “Cheng”) in further view of Caleb Noble et al; “Graph-based Anomaly Detection” available online on Nov 27, 2020 (hereinafter “Noble”) . Regarding claim 2, Erickson does not teach The system of claim 1, wherein the machine learning model is trained based on a set of annotated feature vectors generated from a training graph datastore, [ wherein the training graph datastore includes a normal graph subpart and an abnormal graph subpart. ] However, Cheng does teach The system of claim 1, wherein the machine learning model is trained based on a set of annotated feature vectors generated from a training graph datastore, [wherein the training graph datastore includes a normal graph subpart and an abnormal graph subpart.] (Cheng Column 15 line 20; “the method 1500 may include computing, based on the generated graph, a set of features and/or corresponding labels for the plurality of entities… the method 1500 may proceed to step 1508, which may include training a machine learning model using the computed set of features and the corresponding labels.” Examiner notes the machine learning model is trained based on a set of annotated feature vectors (set of features and the corresponding labels) from a training graph datastore (graph)) It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Erickson and Cheng . Erickson teaches a method for graph convolutional anomaly detection . Cheng teaches a method for detecting hidden correlation relationships among entities for risk analysis . One of ordinary skill would have motivation to combine Erickson and Cheng to effectively discover hidden relationships before negative impacts are felt “ Such enhancements and improvements may provide for improved quality of service, improved interactions between entities and users, increased efficiencies, increased access to meaningful data, and substantially-improved decision-making abilities for entities, particularly when hidden correlation relationships are effectively discovered before negative impacts are felt by the entities. ” ( Cheng Column 2 Line 2 ). Erickson in view of Cheng does not teach wherein the training graph datastore includes a normal graph subpart and an abnormal graph subpart. However, Noble does teach wherein the training graph datastore includes a normal graph subpart and an abnormal graph subpart. ( Noble Page 3 Paragraph 10; “The rationale for our method lies in the idea that subgraphs containing many common substructures are generally less anomalous than subgraphs with few common substructures. This is related to the underlying idea behind anomalous substructure detection – that common substructures are, in a loose sense, the “opposite” of anomalous substructures.” Examiner notes that the training graph datastore includes a normal graph subpart (common substructures) and an abnormal graph subpart (anomalous substructures) ) It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Erickson , Cheng, and Noble . Erickson teaches a method for graph convolutional anomaly detection. Cheng teaches a method for detecting hidden correlation relationships among entities for risk analysis. Noble teaches graph-based anomaly detection . One of ordinary skill would have motivation to combine Erickson , Cheng , and Noble to leverage graph-based data for encouraging results in finding anomalies “ We hypothesize that these methods will prove useful both for finding anomalies, and for determining the likelihood of successful anomaly detection within graph-based data. ” ( Noble Abstract ) “These approaches have been implemented and tested using the Subdue system, with encouraging results.” (Noble Conclusion) Regarding claim 15, Erickson does not teach The method of claim 14, wherein the machine learning model is trained based on a set of annotated feature vectors generated from a training graph datastore, [wherein the training graph datastore includes a normal graph subpart and an abnormal graph subpart.] However, Cheng does teach The method of claim 14, wherein the machine learning model is trained based on a set of annotated feature vectors generated from a training graph datastore, [wherein the training graph datastore includes a normal graph subpart and an abnormal graph subpart.] (Cheng Column 15 line 20; “the method 1500 may include computing, based on the generated graph, a set of features and/or corresponding labels for the plurality of entities… the method 1500 may proceed to step 1508, which may include training a machine learning model using the computed set of features and the corresponding labels.” Examiner notes the machine learning model is trained based on a set of annotated feature vectors (set of features and the corresponding labels) from a training graph datastore (graph)) It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Erickson and Cheng . Erickson teaches a method for graph convolutional anomaly detection. Cheng teaches a method for detecting hidden correlation relationships among entities for risk analysis. One of ordinary skill would have motivation to combine Erickson and Cheng to effectively discover hidden relationships before negative impacts are felt “Such enhancements and improvements may provide for improved quality of service, improved interactions between entities and users, increased efficiencies, increased access to meaningful data, and substantially-improved decision-making abilities for entities, particularly when hidden correlation relationships are effectively discovered before negative impacts are felt by the entities.” (Cheng Column 2 Line 2). Erickson in view of Cheng does not teach wherein the training graph datastore includes a normal graph subpart and an abnormal graph subpart. However, Noble does teach wherein the training graph datastore includes a normal graph subpart and an abnormal graph subpart. (Noble Page 3 Paragraph 10; “The rationale for our method lies in the idea that subgraphs containing many common substructures are generally less anomalous than subgraphs with few common substructures. This is related to the underlying idea behind anomalous substructure detection – that common substructures are, in a loose sense, the “opposite” of anomalous substructures.” Examiner notes that the training graph datastore includes a normal graph subpart (common substructures) and an abnormal graph subpart (anomalous substructures)) It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Erickson , Cheng, and Noble . Erickson teaches a method for graph convolutional anomaly detection. Cheng teaches a method for detecting hidden correlation relationships among entities for risk analysis. Noble teaches graph-based anomaly detection. One of ordinary skill would have motivation to combine Erickson , Cheng, and Noble to leverage graph-based data for encouraging results in finding anomalies “We hypothesize that these methods will prove useful both for finding anomalies, and for determining the likelihood of successful anomaly detection within graph-based data.” (Noble Abstract) “These approaches have been implemented and tested using the Subdue system, with encouraging results.” (Noble Conclusion) Claim(s) 3-5 and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Parker Erickson et al; US 20210406917 A1 filed on Jun 30, 2020 (hereinafter “Erickson”) in view of Ronnie J. Morris et al; US 11710033 B2 filed on Jun 12, 2018 (hereinafter “Morris”) Regarding claim 3, Erickson does not teach The system of claim 1, wherein the identified graph subpart includes at least one user identifier node, at least one transaction node, and an edge node associating a user identifier node and a transaction node. However, Morris does teach The system of claim 1, wherein the identified graph subpart includes at least one user identifier node, at least one transaction node, and an edge node associating a user identifier node and a transaction node. ( Morris Column 4 Line 56; “the first entity type is a device entity type and the second entity type is a customer entity type, and where the edge inserted linking the first node to the second node is in response to the ensemble detecting a smartphone device corresponding to the first node is associated with a fraudulent transaction reported by a customer corresponding to the second node.” Examiner notes that the identified graph subpart includes at least one user identifier node (second node corresponding to a customer), at least one transaction node (first node associated with a fraudulent transaction) and an edge associating a user identifier node and transaction node (edge linking first node to the second node) ) It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Erickson , and Morris . Erickson teaches a method for graph convolutional anomaly detection. Morris teaches a method for detecting fraudulent transactions in a graph structure . One of ordinary skill would have motivation to combine Erickson and Morris to overcome the difficulty in detecting fraudulent transactions “ The complexity and speed of financial transactions makes fraud particularly difficult to detect and to act upon... But such human-designed conditions are slow and undesirably inaccurate, particularly given the speed and near real-time streaming of financial transactions. The situation is further exacerbated given the complexity of modern financial transactions that make determining complex associations between seemingly unrelated sets of data extremely difficult and nearly impossible for humans without a machine learning system, such as the one illustrated in FIG. 6 . At least one approach described herein to address various shortcoming in a hot file system involves implementing the hot file in a graph structure in computer memory, then enhancing the graph structure with one or more machine learning technologies described herein. ” ( Morris Column 28 Line 20 ) Regarding claim 4, Erickson does not teach The system of claim 3, wherein the edge node is associated with a property that includes identification information for a user of the user identifier. However, Morris does teach The system of claim 3, wherein the edge node is associated with a property that includes identification information for a user of the user identifier. ( Morris Column 4 Line 56; “the first entity type is a device entity type and the second entity type is a customer entity type, and where the edge inserted linking the first node to the second node is in response to the ensemble detecting a smartphone device corresponding to the first node is associated with a fraudulent transaction reported by a customer corresponding to the second node.” Morris Column 31 Line 48; “the transaction data may comprise merchant data (e.g., name, identifier, merchant type, Boolean value), location data (e.g., IP address, ISP, MAC address, device identifier, UUID), amount data (e.g., monetary amount, currency type, tender type (e.g., credit card, mobile payment, debit card, online payment merchant, cryptocurrency)), and/or other characteristics.” Examiner notes that the edge (edge inserted linking the first to the second node) is associated/linking with a property (transaction node) that includes identification information (location data) for a user of the user identifier (merchant data) ) It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Erickson , and Morris . Erickson teaches a method for graph convolutional anomaly detection. Morris teaches a method for detecting fraudulent transactions in a graph structure. One of ordinary skill would have motivation to combine Erickson and Morris to overcome the difficulty in detecting fraudulent transactions “The complexity and speed of financial transactions makes fraud particularly difficult to detect and to act upon... But such human-designed conditions are slow and undesirably inaccurate, particularly given the speed and near real-time streaming of financial transactions. The situation is further exacerbated given the complexity of modern financial transactions that make determining complex associations between seemingly unrelated sets of data extremely difficult and nearly impossible for humans without a machine learning system, such as the one illustrated in FIG. 6 . At least one approach described herein to address various shortcoming in a hot file system involves implementing the hot file in a graph structure in computer memory, then enhancing the graph structure with one or more machine learning technologies described herein.” (Morris Column 28 Line 20) Regarding claim 5, Erickson does not teach The system of claim 1, wherein the user identifier is a first user identifier associated with a user and the identified graph subpart includes a first node for the first user identifier and a second node for a second user identifier that is also associated with the user. However, Morris does teach The system of claim 1, wherein the user identifier is a first user identifier associated with a user and the identified graph subpart includes a first node for the first user identifier and a second node for a second user identifier that is also associated with the user. (Morris Fig 3 C and Column 31 Line 41; “The credit card 210 may be associated with a credit card number, personal identification number (PIN), and other similar information.” Morris Column 34 line 15; “The electronic device may be device J, 308 illustrated in FIG. 3C. The system may, retrieve the MAC address in step 506 (and/or other device information in step 507) of the electronic device.” Examiner notes that the user identifier (MAC address of device 308) is a first user identifier associated/connected with a user (Customer B 304) and the identified graph subpart (Graph shown in 3C) includes a first node for the first user identifier (Node 308) and a second node (Node 303) for a second user identifier (credit card number of first card 303) that is also associated/connected with the user (customer B 304) ) It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Erickson , and Morris . Erickson teaches a method for graph convolutional anomaly detection. Morris teaches a method for detecting fraudulent transactions in a graph structure. One of ordinary skill would have motivation to combine Erickson and Morris to overcome the difficulty in detecting fraudulent transactions “The complexity and speed of financial transactions makes fraud particularly difficult to detect and to act upon... But such human-designed conditions are slow and undesirably inaccurate, particularly given the speed and near real-time streaming of financial transactions. The situation is further exacerbated given the complexity of modern financial transactions that make determining complex associations between seemingly unrelated sets of data extremely difficult and nearly impossible for humans without a machine learning system, such as the one illustrated in FIG. 6 . At least one approach described herein to address various shortcoming in a hot file system involves implementing the hot file in a graph structure in computer memory, then enhancing the graph structure with one or more machine learning technologies described herein.” (Morris Column 28 Line 20) Regarding claim 16, Erickson does not teach The method of claim 14, wherein the identified graph subpart includes at least one user identifier node, at least one transaction node, and an edge node associating a user identifier node and a transaction node. However, Morris does teach The method of claim 14, wherein the identified graph subpart includes at least one user identifier node, at least one transaction node, and an edge node associating a user identifier node and a transaction node. (Morris Column 4 Line 56; “the first entity type is a device entity type and the second entity type is a customer entity type, and where the edge inserted linking the first node to the second node is in response to the ensemble detecting a smartphone device corresponding to the first node is associated wit