DETAILED ACTION
Claims 1-4, 6-21 are pending.
Claim 5 is canceled.
Claims 1-4, 6-21 are rejected.
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 Arguments
Applicant’s arguments with respect to the 35 U.S.C. 103 rejections (Remarks pp. 8-11) have been fully considered but are unpersuasive.
1. The applicant argues that Vozar does not teach “comparing, by the one or more processors, one or more values corresponding to the first component of the abstracted virtual model to a portion of enterprise metadata and performance metrics, wherein the enterprise metadata and performance metrics indicate target metrics of the performance of the first component and historical outputs of a first physical system that maps to the first component of the abstracted virtual model” as recited in amended independent claims 1, 11, and 18.
The Examiner respectfully disagrees with this statement. Vozar does teach generating, by the one or more processors, health scores corresponding to the plurality of components of the abstracted virtual model (
Vozar discloses, “Explicitly, S220 may function to use one or more health monitors associated with the autonomous agent to perform an introspection of onboard data sources, per se, and/or data streams originating from onboard data sources of the autonomous agent and derive quality metrics (health metrics or health metadata values) for each of the onboard data sources and/or onboard data streams based on reprocessing directly on data,” ¶ 0082.
Vozar teaches to generate health scores that correspond to onboard data sources and/or onboard data streams.
Chapin already teaches an abstracted virtual model with components, disclosing, “According to an embodiment, a system includes a digital twin modeling component, a digital twin scoring component, and an asset scoring component. The digital twin modeling component generates a set of digital twin models indicative of a set of virtual representations for a set of physical components associated with an asset,” ¶ 0004, “In an embodiment, the asset data 114 can be generated by and/or associated with a set of physical components of an asset,” ¶ 0025, and “Further yet, one or more components and/or sub-components may be combined into a single component providing aggregate functionality,” ¶ 0055.
The claimed “abstracted virtual model” is mapped to the disclosed virtual model of the “digital twin”.
After the combination of Chapin in view of Contreras, with Vozar, the onboard data sources and/or onboard data streams from Vozar are replaced with the plurality of components of the abstracted virtual model from Chapin in view of Contreras, so that the health scores correspond to the plurality of components of the abstracted virtual model.),
wherein generating a first health score corresponding to a first component of the abstracted virtual model comprises:
comparing, by the one or more processors, one or more values corresponding to the first component of the abstracted virtual model to a portion of enterprise metadata and performance metrics (
Vozar discloses, “Explicitly, S220 may function to use one or more health monitors associated with the autonomous agent to perform an introspection of onboard data sources, per se, and/or data streams originating from onboard data sources of the autonomous agent and derive quality metrics (health metrics or health metadata values) for each of the onboard data sources and/or onboard data streams based on reprocessing directly on data,” ¶ 0082, and “Preferably, for each health monitor, the expected health values or the expect health datasets may specify minimum/maximum criteria that data sources, data processes, data streams, or the like should (or must) meet for particular runlevels or for executing behavioral policies or the like. For instance, the expected health values may include minimum, maximum, and/or optimal metrics and/or operational ranges for varying components and/or processes associated with an autonomous agent,” ¶ 0087.
Here, a value corresponding to a data source is compared with minimum/maximum criteria to determine whether said value satisfies the criteria or not.
After the combination of Chapin in view of Contreras, with Vozar, the onboard data sources and/or onboard data streams from Vozar are replaced with the plurality of components of the abstracted virtual model from Chapin in view of Contreras, so that the values being compared with the minimum/maximum criteria are associated with components of the abstracted virtual model.),
wherein the enterprise metadata and performance metrics indicate target metrics of the performance of the first component and historical outputs of a first physical system that maps to the first component of the abstracted virtual model (
Vozar discloses, “Preferably, for each health monitor, the expected health values or the expect health datasets may specify minimum/maximum criteria that data sources, data processes, data streams, or the like should (or must) meet for particular runlevels or for executing behavioral policies or the like. For instance, the expected health values may include minimum, maximum, and/or optimal metrics and/or operational ranges for varying components and/or processes associated with an autonomous agent,” ¶ 0087, and “the expected health values may be based on a historical running averages associated with a normal or standard operation of a data source and thus, some expected health values may be statistically defined (e.g., a reference expected rate or the like),” ¶ 0088.
Here, Vozar discloses metadata and metrics that indicate target metrics of performance for a data source such as minimum/maximum/optimal metrics, and historical outputs of an operation of a data source such as historical running averages. The operation of a data source is analogous to a physical system because it outputs values that can be collected and saved into a group for comparison with future values, akin to how a physical system outputs values that are compared with later values from the digital twin that models the physical system.
Chapin already teaches a physical system, disclosing, “The digital twin modeling component generates a set of digital twin models indicative of a set of virtual representations for a set of physical components associated with an asset,” ¶ 0004.);
and generating, by one or more processors, the first heath score based on the comparison (
Vozar discloses, “S220 preferably includes evaluating the data streams against autonomous operation health standards (e.g., comparing streams of data to autonomous operation health standards),” ¶ 0082, and “…an output of each health monitor may be selected from a spectrum or continuum of health scores, health grades, or generally health values. Accordingly, a health monitor may be able to output one of a plurality of health values (e.g., three or more) selected from a reference health spectrum or health continuum for a given stream of data. For instance, a health continuum may include the selectable values of ‘low’, ‘medium’, and ‘high’ for a given stream of data that relates to a speed or rate of communication between onboard and offboard devices associated with an autonomous agent,” ¶ 0086.).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 11, 18-19, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Chapin (US 20200118053 A1) in view of Contreras (US 11526638 B1) and Vozar (US 20200035098 A1).
Regarding Claim 1, Chapin teaches a method for creating and leveraging digital twins of physical systems of enterprises (
Chapin discloses, “This disclosure relates generally to asset management systems, and more specifically, to an analytics system for one or more assets,” ¶ 0001, and “According to an embodiment, a method provides for generating, by a system comprising a processor, a set of digital twin models indicative of a set of virtual representations for a set of physical components associated with an asset,” ¶ 0005, and “Referring initially to FIG. 1, there is illustrated an example system 100 that manages asset performance, in accordance with one or more embodiments described herein. The system 100 can be implemented on or in connection with a network of servers associated with an enterprise application,” ¶ 0023.), the method comprising:
generating, by one or more processors, an abstracted virtual model of a computing system of an enterprise (
Chapin discloses, “According to an embodiment, a system includes a digital twin modeling component, a digital twin scoring component, and an asset scoring component. The digital twin modeling component generates a set of digital twin models indicative of a set of virtual representations for a set of physical components associated with an asset,” ¶ 0004.
The claimed “abstracted virtual model” is mapped to the disclosed virtual model of the “digital twin”.), wherein:
the abstracted virtual model corresponds to a plurality of physical systems of the enterprise that are communicatively coupled via one or more networks (
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Chapin discloses, “The digital twin modeling component generates a set of digital twin models indicative of a set of virtual representations for a set of physical components associated with an asset,” ¶ 0004.
The claimed “physical system” is mapped to the disclosed “physical component” associated with an asset.
Fig. 4 shows that the assets are connected to each other via a network, thus linking the physical components of each asset as well.),
the abstracted virtual model is logically organized as a monolithic system comprising a plurality of components that are mapped to the plurality of physical systems (
Chapin discloses, “The digital twin modeling component generates a set of digital twin models indicative of a set of virtual representations for a set of physical components associated with an asset,” ¶ 0004, and “In an embodiment, the asset data 114 can be generated by and/or associated with a set of physical components of an asset,” ¶ 0025, and “Further yet, one or more components and/or sub-components may be combined into a single component providing aggregate functionality,” ¶ 0055.),
and the abstracted virtual model defines relationships, dependencies, and attributes corresponding to the plurality of components (
Chapin discloses, “For example, the asset data 114 can be generated by and/or associated with one or more assets, one or more devices, one or more machines and/or one or more types of equipment. In an embodiment, the asset data 114 can be generated by and/or associated with a set of physical components of an asset. For example, the set of physical components can be one or more subcomponents of an asset. The asset data 114 can be, for example, time-series data. The asset data 114 can also be, for example, parametric data that includes one or more parameters and corresponding data values,” ¶ 0025, and “The set of physical components 604.sub.1-N can be one or more physical components of the asset 602. For example, the set of physical components 604.sub.1-N can be one or more subcomponents of the asset 602. In a non-limiting example, the asset 602 can be an engine. Furthermore, the set of physical components 604.sub.1-N can include a turbine blade of the asset 602, a compressor of the asset 602, a component of the asset 602 with high operating temperature, a high speed component of the asset 602, a high stress component of the asset 602, and/or another physical component of the asset 602,” ¶ 0046.
The claimed “relationships” is mapped to the disclosed association of physical components that are in the same set.
The claimed “dependencies” is mapped to the disclosed association of physical components that are “subcomponents” of an asset. The disclosed Paragraph 46 gives an example of this, with the component in question being an engine and dependencies of the component being a turbine blade, a compressor, and other components that are used to comprise the engine. Here, it teaches at least hierarchical dependency.
The claimed “attributes” is mapped to the disclosed “parametric data that includes one or more parameters and corresponding data values”.);
obtaining, by the one or more processors, monitoring data from the plurality of physical systems (
Chapin discloses, “The asset performance component 102 (e.g., the digital twin modeling component 104) can receive asset data 114… In an embodiment, the asset data 114 can be generated by and/or associated with a set of physical components of an asset,” ¶ 0025, and “For example, the amount of the asset data 114 processed, the speed of processing of the asset data 114 and/or the data types of the asset data 114 analyzed by the asset performance component 102 over a certain period of time can be respectively greater, faster and different than the amount, speed and data type that can be processed by a single human mind over the same period of time,” ¶ 0042.
The claimed “monitoring data” is mapped to the disclosed “asset data 114”.);
mapping, by the one or more processors, the monitoring data to input data (
Chapin discloses, “The asset scoring component 108 can generate digital twin scoring data 116 for the asset associated with the asset data 114,” ¶ 0030.
The claimed “input data” is mapped to the disclosed “digital twin scoring data”.);
and outputting, by the one or more processors, health scores corresponding to one or more components of the abstracted virtual model (
Chapin discloses, “For example, the digital twin score for the asset associated with the asset data 114 can be indicative of a health state (e.g., a predicted health state) for the asset associated with the asset data 114,” ¶ 0030.
The claimed “health score” is mapped to the disclosed “predicted health state” value.).
Chapin does not teach mapping, by the one or more processors, the monitoring data to input data to update the plurality of components of the abstracted virtual model, and outputting, by the one or more processors, health scores corresponding to one or more components of the abstracted virtual model after the update.
Chapin also does not teach generating, by the one or more processors, health scores corresponding to the plurality of components of the abstracted virtual model, wherein generating a first health score corresponding to a first component of the abstracted virtual model comprises: comparing, by the one or more processors, one or more values corresponding to the first component of the abstracted virtual model to a portion of enterprise metadata and performance metrics, wherein the enterprise metadata and performance metrics indicate target metrics of the performance of the first component and historical outputs of a first physical system that maps to the first component of the abstracted virtual model; and generating, by one or more processors, the first heath score based on the comparison.
However, Contreras teaches mapping, by the one or more processors, the monitoring data to input data to update the plurality of components of the abstracted virtual model, and outputting, by the one or more processors, health scores corresponding to one or more components of the abstracted virtual model after the update (
Contreras discloses, “Based on performing the one or more evaluations and/or analysis, the DQS may generate one or more correction to be applied to the digital twin. In addition, the DQS may generate an ‘as is score’ (baseline score) representing the original state of the digital twin and a ‘post auto-correction score’ (updated score) representing an updated state of the digital twin if the corrections are applied,” Col 1, Lines 63-67 and Col 2, Lines 1-2.).
Chapin and Contreras are both considered to be analogous to the claimed invention because they are in the same field of digital twins. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Chapin to incorporate the teachings of Contreras and provide mapping, by the one or more processors, the monitoring data to input data to update the plurality of components of the abstracted virtual model, and outputting, by the one or more processors, health scores corresponding to one or more components of the abstracted virtual model after the update. Doing so would help allow incremental improvement of the model. (Contreras discloses, “If the updated score meets or is above a threshold value, the DQS automatically applies and save the corrections to the digital twin. If the updated score does not meet the threshold value, the DQS presents a failure notification and one or more graphical representations of the utility network such that incremental corrections can be made,” Abstract.).
Chapin in view of Contreras does not teach generating, by the one or more processors, health scores corresponding to the plurality of components of the abstracted virtual model, wherein generating a first health score corresponding to a first component of the abstracted virtual model comprises: comparing, by the one or more processors, one or more values corresponding to the first component of the abstracted virtual model to a portion of enterprise metadata and performance metrics, wherein the enterprise metadata and performance metrics indicate target metrics of the performance of the first component and historical outputs of a first physical system that maps to the first component of the abstracted virtual model; and generating, by one or more processors, the first heath score based on the comparison.
However, Vozar teaches generating, by the one or more processors, health scores corresponding to the plurality of components of the abstracted virtual model (
Vozar discloses, “Explicitly, S220 may function to use one or more health monitors associated with the autonomous agent to perform an introspection of onboard data sources, per se, and/or data streams originating from onboard data sources of the autonomous agent and derive quality metrics (health metrics or health metadata values) for each of the onboard data sources and/or onboard data streams based on reprocessing directly on data,” ¶ 0082.
Vozar teaches to generate health scores that correspond to onboard data sources and/or onboard data streams.
After the combination of Chapin in view of Contreras, with Vozar, the onboard data sources and/or onboard data streams from Vozar are replaced with the plurality of components of the abstracted virtual model from Chapin in view of Contreras, so that the health scores correspond to the plurality of components of the abstracted virtual model.),
wherein generating a first health score corresponding to a first component of the abstracted virtual model comprises:
comparing, by the one or more processors, one or more values corresponding to the first component of the abstracted virtual model to a portion of enterprise metadata and performance metrics (
Vozar discloses, “Explicitly, S220 may function to use one or more health monitors associated with the autonomous agent to perform an introspection of onboard data sources, per se, and/or data streams originating from onboard data sources of the autonomous agent and derive quality metrics (health metrics or health metadata values) for each of the onboard data sources and/or onboard data streams based on reprocessing directly on data,” ¶ 0082, and “Preferably, for each health monitor, the expected health values or the expect health datasets may specify minimum/maximum criteria that data sources, data processes, data streams, or the like should (or must) meet for particular runlevels or for executing behavioral policies or the like. For instance, the expected health values may include minimum, maximum, and/or optimal metrics and/or operational ranges for varying components and/or processes associated with an autonomous agent,” ¶ 0087.
Here, a value corresponding to a data source is compared with minimum/maximum criteria to determine whether said value satisfies the criteria or not.
After the combination of Chapin in view of Contreras, with Vozar, the onboard data sources and/or onboard data streams from Vozar are replaced with the plurality of components of the abstracted virtual model from Chapin in view of Contreras, so that the values being compared with the minimum/maximum criteria are associated with components of the abstracted virtual model.),
wherein the enterprise metadata and performance metrics indicate target metrics of the performance of the first component and historical outputs of a first physical system that maps to the first component of the abstracted virtual model (
Vozar discloses, “Preferably, for each health monitor, the expected health values or the expect health datasets may specify minimum/maximum criteria that data sources, data processes, data streams, or the like should (or must) meet for particular runlevels or for executing behavioral policies or the like. For instance, the expected health values may include minimum, maximum, and/or optimal metrics and/or operational ranges for varying components and/or processes associated with an autonomous agent,” ¶ 0087, and “the expected health values may be based on a historical running averages associated with a normal or standard operation of a data source and thus, some expected health values may be statistically defined (e.g., a reference expected rate or the like),” ¶ 0088.
Here, Vozar discloses metadata and metrics that indicate target metrics of performance for a data source such as minimum/maximum/optimal metrics, and historical outputs of an operation of a data source such as historical running averages. The operation of a data source is analogous to a physical system because it outputs values that can be collected and saved into a group for comparison with future values, akin to how a physical system outputs values that are compared with later values from the digital twin that models the physical system.
Chapin already teaches a physical system, disclosing, “The digital twin modeling component generates a set of digital twin models indicative of a set of virtual representations for a set of physical components associated with an asset,” ¶ 0004.);
and generating, by one or more processors, the first heath score based on the comparison (
Vozar discloses, “S220 preferably includes evaluating the data streams against autonomous operation health standards (e.g., comparing streams of data to autonomous operation health standards),” ¶ 0082, and “…an output of each health monitor may be selected from a spectrum or continuum of health scores, health grades, or generally health values. Accordingly, a health monitor may be able to output one of a plurality of health values (e.g., three or more) selected from a reference health spectrum or health continuum for a given stream of data. For instance, a health continuum may include the selectable values of ‘low’, ‘medium’, and ‘high’ for a given stream of data that relates to a speed or rate of communication between onboard and offboard devices associated with an autonomous agent,” ¶ 0086.).
Chapin in view of Contreras, and Vozar are both considered to be analogous to the claimed invention because they are in the same field of computer applications. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Chapin in view of Contreras to incorporate the teachings of Vozar and provide generating, by the one or more processors, health scores corresponding to the plurality of components of the abstracted virtual model, wherein generating a first health score corresponding to a first component of the abstracted virtual model comprises: comparing, by the one or more processors, one or more values corresponding to the first component of the abstracted virtual model to a portion of enterprise metadata and performance metrics, wherein the enterprise metadata and performance metrics indicate target metrics of the performance of the first component and historical outputs of a first physical system that maps to the first component of the abstracted virtual model; and generating, by one or more processors, the first heath score based on the comparison. Doing so would help provide more useful information to the user for determining if there is an error or failure based on a low score (Vozar discloses, “In some embodiments, S220 may function to implement one or more health monitors to evaluate the data streams and further, detect errors, deficiencies, degradation, failings, and/or the like in the data source based the evaluation of the data streams,” ¶ 0082.).
Claims 11 and 18 are a system and non-transitory storage medium claim (¶ 0081 of Chapin.), respectively corresponding to the method Claim 1. Therefore, Claims 11 and 18 are rejected for the same reasons set forth in the rejection of Claim 1.
Regarding Claim 2, Chapin in view of Contreras and Vozar teaches the method of claim 1, wherein the abstracted virtual model represents a digital twin of a monolithically organized computing system of an enterprise that corresponds to the plurality of physical systems (
Chapin discloses, “According to an embodiment, a system includes a digital twin modeling component, a digital twin scoring component, and an asset scoring component. The digital twin modeling component generates a set of digital twin models indicative of a set of virtual representations for a set of physical components associated with an asset,” ¶ 0004, and “In an embodiment, the asset data 114 can be generated by and/or associated with a set of physical components of an asset,” ¶ 0025.).
Claim 19 is a non-transitory storage medium claim (¶ 0081 of Chapin.) corresponding to the method Claim 2. Therefore, Claim 19 is rejected for the same reasons set forth in the rejection of Claim 2.
Regarding Claim 21, Chapin in view of Contreras and Vozar teaches the method of claim 1, wherein comparing the one or more values corresponding to the first component of the abstracted virtual model to the portion of enterprise metadata and performance metrics comprises: comparing a first value that maps to the first component during a first time period to one or more thresholds of the first component of the abstracted virtual model to determine the first health score (
Vozar discloses, “Preferably, for each health monitor, the expected health values or the expect health datasets may specify minimum/maximum criteria that data sources, data processes, data streams, or the like should (or must) meet for particular runlevels or for executing behavioral policies or the like. For instance, the expected health values may include minimum, maximum, and/or optimal metrics and/or operational ranges for varying components and/or processes associated with an autonomous agent,” ¶ 0087.
The claimed “one or more thresholds” is mapped to the disclosed “minimum/maximum criteria” that data sources, data processes, etc., must meet in order to have an optimal health metric.
Here, a first value that maps to a component is compared with minimum/maximum/optimal metrics in order to determine whether the value satisfies the metrics or not.
After the combination of Chapin in view of Contreras, with Vozar, the first value being compared with one or more thresholds from Vozar, now corresponds to the first component of the abstracted virtual model from Chapin in view of Contreras.).
Chapin in view of Contreras, and Vozar are both considered to be analogous to the claimed invention because they are in the same field of computer applications. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Chapin in view of Contreras to incorporate the teachings of Vozar and provide wherein comparing the one or more values corresponding to the first component of the abstracted virtual model to the portion of enterprise metadata and performance metrics comprises: comparing a first value that maps to the first component during a first time period to one or more thresholds of the first component of the abstracted virtual model to determine the first health score. Doing so would help provide more useful information to the user for determining if there is an error or failure based on a low score (Vozar discloses, “In some embodiments, S220 may function to implement one or more health monitors to evaluate the data streams and further, detect errors, deficiencies, degradation, failings, and/or the like in the data source based the evaluation of the data streams,” ¶ 0082.).
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Chapin (US 20200118053 A1) in view of Contreras (US 11526638 B1), Vozar (US 20200035098 A1), and Mason (US 20220035988 A1).
Regarding Claim 3, Chapin in view of Contreras and Vozar teaches the method of claim 1. Chapin in view of Contreras and Vozar does not teach wherein the abstracted virtual model comprises an application programming interface (API) layer configured to update the plurality of components based on the input data.
However, Mason teaches wherein the abstracted virtual model comprises an application programming interface (API) layer configured to update the plurality of components based on the input data (
Mason discloses, “In some embodiments, at least one of the one or more modular items is automatically updated based on an input information obtained via an application programming interface (API),” ¶ 0010.
After the combination of Chapin in view of Contreras and Vozar, with Mason, the abstracted virtual model from Chapin in view of Contreras and Vozar now contains the API from Mason that is used to update a plurality of components based on input data.).
Chapin in view of Contreras and Vozar, and Mason are both considered to be analogous to the claimed invention because they are in the same field of computer applications. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Chapin in view of Contreras and Vozar, to incorporate the teachings of Mason and provide wherein the abstracted virtual model comprises an application programming interface (API) layer configured to update the plurality of components based on the input data. Doing so would help eliminate the need for manually updating the components with user intervention. (Mason discloses, “In some embodiments, at least one of the one or more modular items is automatically updated based on an input information obtained via an application programming interface (API),” ¶ 0010).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Chapin (US 20200118053 A1) in view of Contreras (US 11526638 B1), Vozar (US 20200035098 A1), and Alcorn (US 20120029977 A1).
Regarding Claim 4, Chapin in view of Contreras and Vozar teaches the method of claim 1. Chapin in view of Contreras and Vozar does not teach further comprising outputting an enterprise report based on the abstracted virtual model, the enterprise report comprising system availability information, enterprise key performance indicators (KPIs), technical KPIs, enterprise transactions, or a combination thereof.
However, Alcorn teaches further comprising outputting an enterprise report based on the abstracted virtual model, the enterprise report comprising system availability information, enterprise key performance indicators (KPIs), technical KPIs, enterprise transactions, or a combination thereof (
Alcorn discloses, “This information may be communicated in an event sent to the business system monitor 310 and the business system monitor 310 may extract the necessary KPI information based on the business system model 320 and KPI definitions 330, thereafter storing the extracted information in the database 355. Thereafter, if a user desires to find out the average duration of the loan approval process for a specified period of time, or the average credit score for applicants whose loans are approved, then this information may be retrieved from the database 355 by the KPI report generation engine 365 to generate a corresponding report,” ¶ 0061.).
Chapin in view of Contreras and Vozar, and Alcorn are both considered to be analogous to the claimed invention because they are in the same field of computer applications. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Chapin in view of Contreras and Vozar to incorporate the teachings of Alcorn and provide further comprising outputting an enterprise report based on the abstracted virtual model, the enterprise report comprising system availability information, enterprise key performance indicators (KPIs), technical KPIs, enterprise transactions, or a combination thereof. Doing so would help provide more useful information to the user (Alcorn discloses, “This event may specify, among other things, the start time and end time of the process, the credit score associated with the applicant, and the like,” ¶ 0061.).
Claims 6 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Chapin (US 20200118053 A1) in view of Contreras (US 11526638 B1), Vozar (US 20200035098 A1), and James Stephen (US 20220335151 A1).
Regarding Claim 6, Chapin in view of Contreras and Vozar teaches the method of claim 1. Chapin in view of Contreras and Vozar does not teach further comprising outputting, by the one or more processors, a health graphical user interface (GUI) that includes the health scores and that represents relationships between the plurality of components.
However, James Stephen teaches further comprising outputting, by the one or more processors, a health graphical user interface (GUI) that includes the health scores and that represents relationships between the plurality of components (
James Stephen discloses, “Dashboard component 310 generates a graphical user interface (GUI) to help convey priorities in a fast and effective way. In one example embodiment, the dashboard component 310 shows the geographical location of different components of a Flow, overall Flow scores, and ranked Flows along each dimension. In one example embodiment, sensitive data elements are grouped by geography and presented on a world map to provide an at-a-glance view of where privacy related sensitive data resides and how that data is flowing. In another example embodiment, Flows are compared to a threshold, and the GUI issues alerts to administrators in response to one or more Flows exceeding the threshold,” ¶ 0070, and “The sensitive data risk identification engine parses inputs described above with reference to blocks 402-405 to identify entities that correspond to vertices and relations that correspond to edges (block 406). The sensitive data risk identification engine then constructs the knowledge graph (block 407) and identifies Flows in the graph (block 408). A Flow is a path from a sensitive data element to an endpoint. Any vertex label that logically represents the final step of data movement can be an endpoint. The sensitive data risk identification engine scores and ranks the identified Flows (block 409),” ¶ 0073.).
Chapin in view of Contreras and Vozar, and James Stephen are both considered to be analogous to the claimed invention because they are in the same field of computer applications. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Chapin in view of Contreras and Vozar to incorporate the teachings of James Stephen and provide further comprising outputting, by the one or more processors, a health graphical user interface (GUI) that includes the health scores and that represents relationships between the plurality of components. Doing so would help efficiently provide more useful information to the user (James Stephen discloses, “Dashboard component 310 generates a graphical user interface (GUI) to help convey priorities in a fast and effective way,” ¶ 0070.).
Claim 20 is a non-transitory storage medium claim (¶ 0081 of Chapin.) corresponding to the method Claim 6. Therefore, Claim 20 is rejected for the same reasons set forth in the rejection of Claim 6.
Claims 7-8 are rejected under 35 U.S.C. 103 as being unpatentable over Chapin (US 20200118053 A1) in view of Contreras (US 11526638 B1), Vozar (US 20200035098 A1), and Ghosh (US 20180040073 A1).
Regarding Claim 7, Chapin in view of Contreras and Vozar teaches the method of claim 1. Chapin in view of Contreras and Vozar does not teach further comprising: identifying, by the one or more processors, transactions indicated by the input data and the abstracted virtual model during a time period; determining, by the one or more processors, correlation scores for pairs of the transactions; determining, by the one or more processors, relationship scores between the pairs of the transactions and an overall health score; and identifying, by the one or more processors, one or more highly correlated transaction pairs having correlation scores that satisfy a first threshold and relationship scores that satisfy a second threshold.
However, Ghosh teaches further comprising:
identifying, by the one or more processors, transactions indicated by the input data and the abstracted virtual model during a time period (
Ghosh discloses, “payment card transaction records data including at least one of customer identifiers, merchant identifiers, merchant category codes, and transaction purchase amounts corresponding to product purchase transactions of the taxable entity over a given time period,” ¶ 0007.);
determining, by the one or more processors, correlation scores for pairs of the transactions (
Ghosh discloses, “correlating the one or more spending profiles of the entity with one or more other aggregated spending profiles representative of historical spending by additional entities whose reported income is within a predetermined (close) range of the entity reported income to generate a correlation score,” ¶ 0007.);
determining, by the one or more processors, relationship scores between the pairs of the transactions and an overall health score (
Ghosh discloses, “a correlation index indicative of the degree of correlation between the entity profile spending and the aggregated profile spending; and on the condition that the correlation index deviates from a predetermined threshold indicative of a high degree of uncorrelation, generating a signal indicative of a request for secondary review of a tax filing of the entity,” ¶ 0007.
The claimed “relationship score” is mapped to the disclosed correlation index, which indicates the degree of correlation between the entity profile spending and the aggregate profile spending.
The claimed “health score” is mapped to the disclosed deviation value between the correlation index and the predetermined threshold indicative of a high degree of uncorrelation. If the deviation value is high, it can indicate that the health of the transaction is infested by fraud or error.);
and identifying, by the one or more processors, one or more highly correlated transaction pairs having correlation scores that satisfy a first threshold and relationship scores that satisfy a second threshold (
Ghosh discloses, “comparing the correlation score with a threshold correlation score value to generate a correlation index indicative of the degree of correlation between the entity profile spending and the aggregated profile spending; and on the condition that the correlation index deviates from a predetermined threshold indicative of a high degree of uncorrelation, generating a signal indicative of a request for secondary review of a tax filing of the entity,” ¶ 0007.
There are two thresholds, one being the “threshold correlation score value” for the correlation score, and the “predetermined threshold indicative of a high degree of uncorrelation” for the correlation index.).
Chapin in view of Contreras and Vozar, and Ghosh are both considered to be analogous to the claimed invention because they are in the same field of computer applications. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Chapin in view of Contreras and Vozar to incorporate the teachings of Ghosh and provide further comprising: identifying, by the one or more processors, transactions indicated by the input data and the abstracted virtual model during a time period; determining, by the one or more processors, correlation scores for pairs of the transactions; and identifying, by the one or more processors, one or more highly correlated transaction pairs having correlation scores that satisfy a first threshold. Doing so would help determine which pairs are the closest in terms of correlation to each other, and filter out the other pairs that are not needed.
Regarding Claim 8, Chapin in view of Contreras, Vozar, and Ghosh teaches the method of claim 7, further comprising outputting, by the one or more processors, one or more insights based on the one or more highly correlated transaction pairs (
Ghosh discloses, “analyzing the payment card transaction records data of the taxable entity to generate one or more spending profiles… generating a signal indicative of a request for secondary review of a tax filing of the entity; and otherwise generating a signal indicative of a validated tax filing of the entity,” ¶ 0007.
The claimed “insight” is mapped to whether a transaction is compliant. If the transaction is not compliant, a secondary review is requested for the entity.).
Chapin in view of Contreras and Vozar, and Ghosh are both considered to be analogous to the claimed invention because they are in the same field of computer applications. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Chapin in view of Contreras and Vozar to incorporate the teachings of Ghosh and provide further comprising outputting, by the one or more processors, one or more insights based on the one or more highly correlated transaction pairs. Doing so would allow stronger validation of the transactions. (Ghosh discloses, “generating a signal indicative of a request for secondary review of a tax filing of the entity; and otherwise generating a signal indicative of a validated tax filing of the entity,” ¶ 0007.).
Claims 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Chapin (US 20200118053 A1) in view of Contreras (US 11526638 B1), Vozar (US 20200035098 A1), Ghosh (US 20180040073 A1) and Fox (US 20200175083 A1).
Regarding Claim 9, Chapin in view of Contreras, Vozar, and Ghosh teaches the method of claim 8. Chapin in view of Contreras, Vozar, Ghosh does not teach wherein generating a first insight of the one or more insights comprises performing, by the one or more processors, natural language processing (NLP) on a first transaction of a first highly correlated transaction pair and a second transaction of the first highly correlated transaction pair to generate text that indicates a relationship between the first transaction and the second transaction.
However, Fox teaches wherein generating a first insight of the one or more insights comprises performing, by the one or more processors, natural language processing (NLP) on a first transaction of a first highly correlated transaction pair and a second transaction of the first highly correlated transaction pair to generate text that indicates a relationship between the first transaction and the second transaction (
Fox discloses, “analyzing the text of the draft social media message using natural language processing to determine at least one topic of the draft social media message, using linear discriminant analysis or linear regression to correlate each of the at least one topics to the classifications stored in the repository 412, generating a ranked list of the correlations, and selecting the data associated with the highest ranking correlation as data that is related to the draft social media message,” ¶ 0067.).
Chapin in view of Contreras, Vozar, Ghosh, and Fox are both considered to be analogous to the claimed invention because they are in the same field of computer applications. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Chapin in view of Contreras, Vozar, and Gosh to incorporate the teachings of Fox and provide wherein generating a first insight of the one or more insights comprises performing, by the one or more processors, natural language processing (NLP) on a first transaction of a first highly correlated transaction pair and a second transaction of the first highly correlated transaction pair to generate text that indicates a relationship between the first transaction and the second transaction. Doing so would help eliminate the need for a user to manually write or type the text.
Regarding Claim 10, Chapin in view of Contreras, Vozar, Ghosh, and Fox teaches the method of claim 8, wherein generating a second insight of the one or more insights comprises applying, by the one or more processors, a second highly correlated transaction pair to one or more insight templates to generate text that indicates a relationship between a first transaction of the second highly correlated transaction pair and a second transaction of the second highly correlated transaction pair (
Ghosh discloses, “comparing the correlation score with a threshold correlation score value to generate a correlation index indicative of the degree of correlation between the entity profile spending and the aggregated profile spending; and on the condition that the correlation index deviates from a predetermined threshold indicative of a high degree of uncorrelation, generating a signal indicative of a request for secondary review of a tax filing of the entity; and otherwise generating a signal indicative of a validated tax filing of the entity,” ¶ 0007, and “output a signal to cause a taxing authority to initiate a secondary review of the tax filing of the entity when the correlation indicator is greater than a threshold value,” ¶ 0009.
The disclosed process template to generate insight is mapped to the claimed “insight template.”.
The generated text is the “outputting of the signal indicative of a request” that causes a secondary review to be initiated in response to the correlation index.).
Chapin in view of Contreras and Vozar, and Ghosh are both considered to be analogous to the claimed invention because they are in the same field of computer applications. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Chapin in view of Contreras and Vozar to incorporate the teachings of Ghosh and provide wherein generating a second insight of the one or more insights comprises applying, by the one or more processors, a second highly correlated transaction pair to one or more insight templates to generate text that indicates a relationship between a first transaction of the second highly correlated transaction pair and a second transaction of the second highly correlated transaction pair. Doing so would help eliminate the need for manually outputting the text for indication.
If there is concern that Ghosh’s “signal indicative of a request for secondary review” is not text, Fox discloses messages/request could be a text, stating “analyzing the text of the draft social media message.” ¶ 0067. It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Chapin in view of Contreras, Vozar, and Ghosh to incorporate the teachings of Fox on text representation of information. Doing so would help a person to understand the request, which would be in text.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Chapin (US 20200118053 A1 in view of Contreras (US 11526638 B1), Vozar (US 20200035098 A1), and Unnikrishnan (US 20220067626 A1).
Regarding Claim 12, Chapin in view of Contreras and Vozar teaches the system of claim 11. Chapin in view of Contreras and Vozar does not teach wherein, to map the monitoring data to the input data, the one or more processors are configured to provide the monitoring data to a machine learning (ML) model configured to map fields of the monitoring data to fields of the input data.
However, Unnikrishnan teaches wherein, to map the monitoring data to the input data, the one or more processors are configured to provide the monitoring data to a machine learning (ML) model configured to map fields of the monitoring data to fields of the input data (
Unnikrishnan discloses, “In one or more embodiments, the feature similarity supervised model 908 is configured to analyze and/or identity data characteristics related to the source data 914. Additionally or alternatively, in one or more embodiments, the feature similarity supervised model 908 determines feature matrix similarity between the source data 914 and the target data 916. In one or more embodiments, the feature similarity supervised model 908 provides a mapping recommendation 918. … the mapping recommendation 918 includes a predicted column name data field of a format structure for the source data 914,” ¶ 0107.).
Chapin in view of Contreras and Vozar, and Unnikrishnan are both considered to be analogous to the claimed invention because they are in the same field of computer applications. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Chapin in view of Contreras and Vozar to incorporate the teachings of Unnikrishnan and provide wherein, to map the monitoring data to the input data, the one or more processors are configured to provide the monitoring data to a machine learning (ML) model configured to map fields of the monitoring data to fields of the input data. Doing so would eliminate the need for manually mapping the fields with user intervention.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Chapin (US 20200118053 A1 in view of Contreras (US 11526638 B1), Vozar (US 20200035098 A1), Unnikrishnan (US 20220067626 A1), and Jurek (“A novel ensemble learning approach to unsupervised record linkage”).
Regarding Claim 13, Chapin in view of Contreras, Vozar, and Unnikrishnan teaches the system of claim 12, wherein the ML model or a recommendation from an association ML model (
data value ML model: Unnikrishnan discloses, “In one or more embodiments, the text similarity supervised model 906 determines text similarity between data field names and/or data field descriptions of the target format structure and the source format structure,” ¶ 0108,
similarity ML model: Unnikrishnan discloses, “…the feature similarity supervised model 908 determines feature matrix similarity between the source data 914 and the target data 916. In one or more embodiments, the feature similarity supervised model 908 provides a mapping recommendation 918.
association ML model: Unnikrishnan discloses, “In one or more embodiments, the ground truth model 902 maps context vocabulary generated from historical data. … In one or more embodiments, to enhance the ground truth model 902, valid tokens and/or invalid tokens are defined using historical mapping information and/or by analyzing trained model results. In one or more embodiments, valid tokens are used to recommend possible similar mappings for a field. In one or more embodiments, invalid tokens are used to eliminate model recommendations that show the same data characteristics or similar data characteristics,” ¶ 0110.).
Chapin in view of Contreras and Vozar, and Unnikrishnan are both considered to be analogous to the claimed invention because they are in the same field of computer applications. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Chapin in view of Contreras and Vozar to incorporate the teachings of Unnikrishnan and provide wherein the ML model configured to output a mapping recommendation based on a recommendation from a data value ML model, a recommendation from a similarity ML model, or a recommendation from an association ML model. Doing so would eliminate the need for manually mapping the fields with user intervention by providing a variety of solutions.
Chapin in view of Contreras, Vozar, and Unnikrishnan does not teach wherein the ML model comprises an ensemble model based on recommendations from various ML models.
However, Jurek teaches the ML model comprises an ensemble model based on recommendations from various ML models. (
“A novel ensemble learning approach to unsupervised record linkage.” Jurek Title.
Jurek discloses, “In this paper we propose a new approach to unsupervised record linkage based on a combination of ensemble learning and enhanced automatic self-learning. In the proposed approach an ensemble of automatic self-learning models is generated with different similarity measure schemes.” Abstract; and “The second step of the RL process is the selection of similarity measure schemes. In this step we search the whole space of all possible similarity measure schemes in order to select the most diverse subset of it. In the third step (seed selection with field weighting) each of the selected similarity measure schemes is first used to generate a set of similarity vectors. Then the automatic seed selection process is performed on each set of similarity vectors. As the output of this step different sets of seeds are selected. In the fourth step (Selecting the most diverse sets of seeds), the diversity between sets of seeds is measured using the proposed technique referred to as Seed Q Statistics. Only those most diverse sets of seeds are selected. In the fifth step the self-learning algorithm is applied with each of the selected sets of seeds. In the last step the proposed contribution ratios of BCs are used to eliminate the weakest BCs from the final ensemble,” Page 4.
After the combination of Chapin in view of Contreras, Vozar, and Unnikrishnan with Jurek, the ensemble model from Jurek is used to combine the “feature similarity supervised model” from Chapin in view of Contreras, Vozar, and Unnikrishnan, with the “text similarity supervised model” and “ground truth model”, also from Chapin in view of Contreras, Vozar, and Unnikrishnan, so that each of the models outputs a mapping recommendation to produce an overall combined recommendation.).
Chapin in view of Contreras, Vozar, and Unnikrishnan, and Jurek are both considered to be analogous to the claimed invention because they are in the same field of machine learning. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Chapin in view of Contreras, Vozar, and Unnikrishnan to incorporate the teachings of Jurek and provide wherein the ML model comprises an ensemble model configured to output a mapping recommendation based on a recommendation from a data value ML model, a recommendation from a similarity ML model, and a recommendation from an association ML model. Doing so would help combine the advantages of each model (Jurek discloses, “The goal of ensemble learning [11,14] is to train and combine a number of different classification models to obtain better performance than any of those individual classifiers,” Page 2.).
Claims 14-16 are rejected under 35 U.S.C. 103 as being unpatentable over Chapin (US 20200118053 A1 in view of Contreras (US 11526638 B1), Vozar (US 20200035098 A1), Unnikrishnan (US 20220067626 A1), and Huang (US 20200327450 A1).
Regarding Claim 14, Chapin in view of Contreras, Vozar, and Unnikrishnan teaches the system of claim 12. Chapin in view of Contreras, Vozar, and Unnikrishnan does not teach wherein the one or more processors are further configured to provide one or more outputs based on the abstracted virtual model as input to an agent configured to determine an action to take to improve the mapping performed by the ML model based on a reward function.
However, Huang teaches wherein the one or more processors are further configured to provide one or more outputs based on the abstracted virtual model as input to an agent configured to determine an action to take to improve the mapping performed by the ML model based on a reward function (
“In at least an implementation, the ALA technique learns to adjust the loss function using reinforcement learning (RL) at the same time as the model weights are being learned using a gradient descent technique,” ¶ 0020, and “In other words, the ALA controller 230 collects the validation statistics S(f.sub.wΦt, D.sub.val) only for class pairs at each time step t, in order to construct state s.sub.t for updating the corresponding loss parameter Φ.sub.t(i, j),” ¶ 0064.).
Chapin in view of Contreras, Vozar, and Unnikrishnan, and Huang are both considered to be analogous to the claimed invention because they are in the same field of computer applications. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Chapin in view of Contreras, Vozar, and Unnikrishnan, to incorporate the teachings of Huang and provide wherein the one or more processors are further configured to provide one or more outputs based on the abstracted virtual model as input to an agent configured to determine an action to take to improve the mapping performed by the ML model based on a reward function. Doing so would simplify the training of the ML model and optimizing machine learning models. (Huang discloses, “This approach helps align the loss function to the evaluation metric cumulatively over successive training iterations. ALA as described herein differs from other techniques by optimizing the evaluation metric directly via a sample efficient RL policy that iteratively adjusts the loss function,” ¶ 0020.).
Regarding Claim 15, Chapin in view of Contreras, Vozar, Unnikrishnan, and Huang teaches the system of claim 14, wherein the agent comprises a reinforcement learning (RL) model (
Huang discloses, “In at least an implementation, the ALA technique learns to adjust the loss function using reinforcement learning (RL) at the same time as the model weights are being learned using a gradient descent technique,” ¶ 0020.).
Chapin in view of Contreras, Vozar, and Unnikrishnan, and Huang are both considered to be analogous to the claimed invention because they are in the same field of computer applications. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Chapin in view of Contreras, Vozar, and Unnikrishnan to incorporate the teachings of Huang and provide wherein the agent comprises a reinforcement learning (RL) model. Doing so would help allow simple iterative adjustment of the reward function and optimizing machine learning models. (Huang discloses, “This approach helps align the loss function to the evaluation metric cumulatively over successive training iterations. ALA as described herein differs from other techniques by optimizing the evaluation metric directly via a sample efficient RL policy that iteratively adjusts the loss function,” ¶ 0020.).
Regarding Claim 16, Chapin in view of Contreras, Vozar, Unnikrishnan, and Huang teaches the system of claim 14, wherein the reward function is based on calculation error associated with the one or more outputs, missing data associated with the one or more outputs, validation error associated with the one or more outputs, coverage scope associated with the one or more outputs, or a combination thereof (
Huang discloses, “ALA uses the default reward based on the validation error metric, and improves both validation loss and test metric by addressing the loss-metric mismatch (both before and after the loss/metric drop due to learning rate change),” ¶ 0081.).
Chapin in view of Contreras, Vozar, and Unnikrishnan, and Huang are both considered to be analogous to the claimed invention because they are in the same field of computer applications. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Chapin in view of Contreras, Vozar, and Unnikrishnan to incorporate the teachings of Huang and provide wherein the reward function is based on calculation error associated with the one or more outputs, missing data associated with the one or more outputs, validation error associated with the one or more outputs, coverage scope associated with the one or more outputs, or a combination thereof. Doing so would help allow for optimizing machine learning models. (Huang discloses, “This approach helps align the loss function to the evaluation metric cumulatively over successive training iterations. ALA as described herein differs from other techniques by optimizing the evaluation metric directly via a sample efficient RL policy that iteratively adjusts the loss function,” ¶ 0020.)
Claims 17 are rejected under 35 U.S.C. 103 as being unpatentable over Chapin (US 20200118053 A1 in view of Contreras (US 11526638 B1), Vozar (US 20200035098 A1), Unnikrishnan (US 20220067626 A1), Huang (US 20200327450 A1), and Basu (US 20220342900 A1).
Regarding Claim 17, Chapin in view of Contreras, Vozar, Unnikrishnan, and Huang teaches the system of claim 14. Chapin in view of Contreras, Vozar, Unnikrishnan, and Huang does not teach wherein the action comprises one of adding a mapping of a field of the monitoring data to a field of the input data, removing the mapping of the field of the monitoring data to the field of the input data, modifying the mapping of the field of the monitoring data to the field of the input data, or taking no action.
However, Basu teaches wherein the action comprises one of adding a mapping of a field of the monitoring data to a field of the input data, removing the mapping of the field of the monitoring data to the field of the input data, modifying the mapping of the field of the monitoring data to the field of the input data, or taking no action (
Basu discloses, “In this regard, a user may view the traversal mapping rules, and edit one or more of such traversal mapping rules, add to the traversal mapping rules, delete a traversal mapping rule, and/or otherwise modify the traversal mapping rules…,” ¶ 0186.).
Chapin in view of Contreras, Vozar, Unnikrishnan, and Huang, and Basu are both considered to be analogous to the claimed invention because they are in the same field of computer applications. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Chapin in view of Contreras, Vozar, Unnikrishnan, and Huang to incorporate the teachings of Basu and provide wherein the action comprises one of adding a mapping of a field of the monitoring data to a field of the input data, removing the mapping of the field of the monitoring data to the field of the input data, modifying the mapping of the field of the monitoring data to the field of the input data, or taking no action. Doing so would help allow for changing the set of mappings to account for a mistake (Basu discloses, “In this regard, a user may view the traversal mapping rules, and edit one or more of such traversal mapping rules, add to the traversal mapping rules, delete a traversal mapping rule, and/or otherwise modify the traversal mapping rules upon determination that such modifications are appropriate,” ¶ 0186.).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
DeLuca et al. (US 20220198404 A1): Method For Assigning Health Scores To Physical Assets Based On Digital Twin Resources, Involves Predicting Health Score Of Physical Asset Based On Asset Health Formula Associated With Digital Twin By The Processors, Where An Asset Group Is Created For Grouping The Physical Assets That Are Similar
Higgins et al. (US 20230161661 A1): Utilizing Topology-centric Monitoring to Model a System and Correlate Low Level System Anomalies and High Level System Impacts
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.
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/ANDREW NMN SUN/Examiner, Art Unit 2195
/Aimee Li/Supervisory Patent Examiner, Art Unit 2195