DETAILED ACTION
Claims 1-20 have been examined and are pending.
Claims 1-20 are rejected (Non-Final Rejection).
Notice of 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 .
Claim Rejections - 35 U.S.C. § 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 5, 6, 11, 15 and 16 are rejected under 35 U.S.C. § 103 as being unpatentable over MOYAL et al. (U.S. Patent No. 11,200,045) in view of PATHAK et al. (U.S. Patent Application Publication No. 2023/0076433 A1).
Regarding claim 1, MOYAL discloses a method for updating and optimizing a digital system (digital twins are updated, where each digital twin is continuously updated with the upcoming changes, Col. 1, Lines 47-49, of MOYAL; See also measured service: cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts), Col. 4, Lines 16-22, of MOYAL), the method comprising: creating a digital twin of the digital system (present invention uses digital twin technology to create a virtual hardware replica of an asset with upcoming hardware/firmware changes and software dependencies as input, Col. 2, Lines 58-61, of MOYAL), said creating the digital twin comprising: storing a list of core hardware and software components of the digital system in a catalog on a server (system architecture 200 also includes digital twin replica resources 230, which is the digital twin of the asset to be managed, Col. 7, Lines 44-46 & FIG. 4, of MOYAL; See also upgrade management program 112 includes a list of assets that will become obsolete based on the upcoming changes to hardware, firmware, or software … upgrade management program 112 includes a list of software upgrades that are compatible with the upcoming hardware or firmware changes, Col. 10, Lines 16-21, of MOYAL; See also computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server, Col. 13, Lines 3-8, of MOYAL); detecting, via a plurality of edge sensors: secondary hardware and software components of the digital system (based on the results of the discovery mechanism, upgrade management program 112 stores the information received in step 302 into appropriate repositories for hardware, firmware, and software … in an embodiment, upgrade management program 112 feeds the data stored into the digital twin to generate a replica with the updated hardware and software details, Col. 7, Lines 67 – Col. 8, Line 6, of MOYAL; See also in an example of one possible use case, a smart city with multiple IoT sensors deployed across the city to obtain traffic information requires firmware for each of the interdependent IoT sensors … if an upcoming change is detected in the IoT firmware, the present invention will be able to simulate the effects of the firmware update and prepare the administration beforehand … this can potentially avoid a system-wide shut down if the firmware upgrade has unexpected results, FIG. 1 & Col. 3, Lines 35-42, of MOYAL; [the IOT sensors deployed across the city are interpreted as corresponding to edge sensors]; See also edge servers, Col. 12, Line 54, of MOYAL); and performance metrics of the core and secondary hardware and software components of the digital system (the relevant data received by upgrade management program 112 includes usage metrics that are specific to the usage of an asset, i.e., hardware, software, or firmware, for a specific project by location or business unit, Col. 8, Lines 52-55 & FIG. 3, of MOYAL); storing a list of the secondary hardware and software components (upgrade management program 112 continuously updates the digital twin with changes in hardware, firmware, and software, based on inputs into the system. In an embodiment, once the digital twins resources are up to date with the latest hardware and software information, upgrade management program 112 generates a report detailing the working dependencies and non-working dependencies as well hardware asset dependencies that will be provided to any software and hardware asset management platform, Col. 8, Lines 6-15, of MOYAL) and the performance metrics in the catalog on the server (the present invention will use IoT feeds received from various systems, including server usage metrics and other external and internal data feeds from multiple sources, to simulate their effect on the system for every potential hardware or firmware change found, Col. 8, Lines 28-32, of MOYAL; [IoT Feeds, which include server usage metrics, for the purpose of simulating effects on the system, is interpreted as including performance metrics]); and constructing a digital model to be the digital twin (upgrade management program 112 feeds the data stored into the digital twin to generate a replica with the updated hardware and software details, Col. 8, Lines 1-6, of MOYAL), wherein the digital model: replicates the core and secondary hardware and software components and the performance metrics of the digital system that are stored in the catalog (upgrade management program 112 continuously updates the digital twin with changes in hardware, firmware, and software, based on inputs into the system. In an embodiment, once the digital twins resources are up to date with the latest hardware and software information, upgrade management program 112 generates a report detailing the working dependencies and non-working dependencies as well hardware asset dependencies that will be provided to any software and hardware asset management platform, Col. 8, Lines 6-15, of MOYAL); and is configured to be run on a processor to simulate performance of the digital system (wherein calculate the continued usage metrics for each asset of the one or more assets based on the output of the one or more digital twins, wherein the continued usage metrics are specific to each asset of the one or more assets further comprises one or more of the following program instructions, stored on the one or more computer readable storage media, to: receive one or more server usage metrics; simulate the one or more server usage metrics and the one or more upcoming changes for the one or more assets on the one or more digital twins, Col. 18, Line 63 – Col. 19, Line 6, of MOYAL); receiving, as input, a potential modification to the digital system (upgrade management program 112 receives hardware, firmware, and software requirements (step 302), Col. 8, Lines 27-28 & FIG. 5 (Item 302), of MOYAL); applying the potential modification to the digital twin (upgrade management program 112 generates a digital twin replica with updated requirements data (step 308). In an embodiment, upgrade management program 112 continuously updates the digital twin with changes in hardware, firmware, and software, based on inputs into the system, Col. 9, Lines 10-15 & FIG. 5 (Item 308), of MOYAL); running the digital twin with the potential modification on the processor (the present invention will create simulations of different version upgrades of the software or future releases of the software and map to the hardware or firmware changes identified for future releases to automatically create a list of application software that can adapt to the changing dimensions of the technology in terms of hardware and firmware upgrades, Col. 3, Lines 10-17, of MOYAL); and in response to achieving an improvement in the simulated performance resulting from the running the digital twin with the potential modification, recommending (these recommendations allow the APM system to proactively procure or replace the software or hardware components that will become obsolete by the upcoming changes. In another embodiment, upgrade management program 112 sends the report created in step 310 directly to the user … upgrade management program 112 then ends for this cycle, Col. 10, Lines 54-61, of MOYAL).
MOYAL does not appear to explicitly disclose in response to achieving an improvement in the simulated performance resulting from the running the digital twin with the potential modification, applying the potential modification as a real modification to the digital system.
PATHAK, however, is in the same field of simulating a real world environment (digital twins) (Title & Para. [0001] of PATHAK) and teaches in response to achieving an improvement in the simulated performance resulting from the running the digital twin with the potential modification, applying the potential modification as a real modification to the digital system (the real-time data captured and/or otherwise determined from operation of the real-world environment may be utilized to train one or more model(s) for possible implementation within the real-world environment … using the updated simulation environment, embodiments may test whether such updated trained models are effective and/or sufficiently improve operation of the real-world environment, and more accurately apply trained model(s) to the real-world environment in circumstances where such newly trained models sufficient improve operations, Para. [0047] of PATHAK; See also determining comparison results by comparing the at least one operational metric value associated with operation of the real-world environment with the at least one simulated operational metric value associated with the updated simulated environment; determining the comparison results indicate the at least one simulated operational metric value improve the at least one operational metric compared to the at least one operational metric value associated with the operation of the real-world environment; and in response to determining the comparison results indicate the at least one simulated operational metric value improve the at least one operational metric compared to the at least one operational metric value associated with the operation of the real-world environment: automatically configuring at least one computing device in the real-world environment utilizing the trained model, Para. [0010] of PATHAK; See also Title of PATHAK includes REAL-TIME ENVIRONMENT DIGITAL TWIN SIMULATING, which suggests the real world environments correspond to digital twin(s) in PATHAK).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the invention to modify the digital twin performance based method of MOYAL with the comparison/testing of PATHAK for the purpose of enabling a self-optimizing real world environment (Abstract of PATHAK).
Regarding claim 5, MOYAL as modified by PATHAK teaches the method of claim 1 (as shown above) further comprising, in response to applying the potential modification as a real modification (as discussed above, PATHAK teaches it would be obvious to apply the modification, rather than just recommend the update as in MOYAL) to the digital system: measuring, via the plurality of edge sensors, the performance metrics of the core and secondary hardware and software components of the modified digital system (a smart city with multiple IoT sensors deployed across the city to obtain traffic information requires firmware for each of the interdependent IoT sensors … if an upcoming change is detected in the IoT firmware, the present invention will be able to simulate the effects of the firmware update and prepare the administration beforehand … this can potentially avoid a system-wide shut down if the firmware upgrade has unexpected results, FIG. 1 & Col. 3, Lines 35-42, of MOYAL; [the IOT sensors deployed across the city are interpreted as corresponding to edge sensors]; See also edge servers, Col. 12, Line 54, of MOYAL; See also the relevant data received by upgrade management program 112 includes usage metrics that are specific to the usage of an asset, i.e., hardware, software, or firmware, for a specific project by location or business unit, Col. 8, Lines 52-55 & FIG. 3, of MOYAL); updating the catalog according to the measuring (based on the results of the discovery mechanism, upgrade management program 112 stores the information received in step 302 into appropriate repositories for hardware, firmware, and software … in an embodiment, upgrade management program 112 feeds the data stored into the digital twin to generate a replica with the updated hardware and software details, Col. 7, Lines 67 – Col. 8, Line 6, of MOYAL); and updating the digital twin according to the updated catalog (based on the results of the discovery mechanism, upgrade management program 112 stores the information received in step 302 into appropriate repositories for hardware, firmware, and software … in an embodiment, upgrade management program 112 feeds the data stored into the digital twin to generate a replica with the updated hardware and software details, Col. 7, Lines 67 – Col. 8, Line 6, of MOYAL; See also upgrade management program 112 continuously updates the digital twin with changes in hardware, firmware, and software, based on inputs into the system … in an embodiment, once the digital twins resources are up to date with the latest hardware and software information, upgrade management program 112 generates a report detailing the working dependencies and non-working dependencies as well hardware asset dependencies that will be provided to any software and hardware asset management platform, Col. 8, Lines 6-15, of MOYAL; See also the present invention will use IoT feeds received from various systems, including server usage metrics and other external and internal data feeds from multiple sources, to simulate their effect on the system for every potential hardware or firmware change found, Col. 8, Lines 28-32, of MOYAL).
Regarding claim 6, MOYAL as modified by PATHAK teaches the method of claim 5 (as shown above) wherein, when the measured performance metrics indicate that the real modification to the digital system failed to achieve an actual improvement to the digital system, the method further comprises undoing the real modification (in response to determining the improved threshold is not satisfied, continuing to update the trained model without applying the trained model to any portion of the updated simulated environment, Para. [0012] of PATHAK; See also when the improvement threshold is not satisfied (Step 1204), the real modification is not applied (i.e., undone), Para. [0186] & FIG. 12 of MOYAL).
Regarding claim 11, MOYAL discloses a platform for updating and optimizing a digital system (digital twins are updated, where each digital twin is continuously updated with the upcoming changes, Col. 1, Lines 47-49, of MOYAL; See also measured service: cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts), Col. 4, Lines 16-22, of MOYAL), the platform comprising a processor, a non-transitory memory, and computer-executable instructions that run on the processor (program instructions for upgrade management program 112 may be stored in persistent storage 408, or more generally, any computer readable storage media, for execution by one or more of the respective computer processors 404 via one or more memories of memory 406, Col. 11, Lines 28-32, of MOYAL) and are configured to cause the processor to: create a digital twin of the digital system (present invention uses digital twin technology to create a virtual hardware replica of an asset with upcoming hardware/firmware changes and software dependencies as input, Col. 2, Lines 58-61, of MOYAL), wherein, to create the digital twin, the platform is configured to: store a list of core hardware and software components of the digital system in a catalog on a server (system architecture 200 also includes digital twin replica resources 230, which is the digital twin of the asset to be managed, Col. 7, Lines 44-46 & FIG. 4, of MOYAL; See also upgrade management program 112 includes a list of assets that will become obsolete based on the upcoming changes to hardware, firmware, or software … upgrade management program 112 includes a list of software upgrades that are compatible with the upcoming hardware or firmware changes, Col. 10, Lines 16-21, of MOYAL; See also computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server, Col. 13, Lines 3-8, of MOYAL); detect, via a plurality of edge sensors: secondary hardware and software components of the digital system (based on the results of the discovery mechanism, upgrade management program 112 stores the information received in step 302 into appropriate repositories for hardware, firmware, and software … in an embodiment, upgrade management program 112 feeds the data stored into the digital twin to generate a replica with the updated hardware and software details, Col. 7, Lines 67 – Col. 8, Line 6, of MOYAL; See also in an example of one possible use case, a smart city with multiple IoT sensors deployed across the city to obtain traffic information requires firmware for each of the interdependent IoT sensors … if an upcoming change is detected in the IoT firmware, the present invention will be able to simulate the effects of the firmware update and prepare the administration beforehand … this can potentially avoid a system-wide shut down if the firmware upgrade has unexpected results, FIG. 1 & Col. 3, Lines 35-42, of MOYAL; [the IOT sensors deployed across the city are interpreted as corresponding to edge sensors]; See also edge servers, Col. 12, Line 54, of MOYAL); and performance metrics of the core and secondary hardware and software components of the digital system (the relevant data received by upgrade management program 112 includes usage metrics that are specific to the usage of an asset, i.e., hardware, software, or firmware, for a specific project by location or business unit, Col. 8, Lines 52-55 & FIG. 3, of MOYAL); store a list of the secondary hardware and software components (upgrade management program 112 continuously updates the digital twin with changes in hardware, firmware, and software, based on inputs into the system. In an embodiment, once the digital twins resources are up to date with the latest hardware and software information, upgrade management program 112 generates a report detailing the working dependencies and non-working dependencies as well hardware asset dependencies that will be provided to any software and hardware asset management platform, Col. 8, Lines 6-15, of MOYAL) and the performance metrics in the catalog on the server (the present invention will use IoT feeds received from various systems, including server usage metrics and other external and internal data feeds from multiple sources, to simulate their effect on the system for every potential hardware or firmware change found, Col. 8, Lines 28-32, of MOYAL; [IoT Feeds, which include server usage metrics, for the purpose of simulating effects on the system, is interpreted as including performance metrics]); and construct a digital model to be the digital twin (upgrade management program 112 feeds the data stored into the digital twin to generate a replica with the updated hardware and software details, Col. 8, Lines 1-6, of MOYAL), wherein the digital model: replicates the core and secondary hardware and software components and the performance metrics of the digital system that are stored in the catalog (upgrade management program 112 continuously updates the digital twin with changes in hardware, firmware, and software, based on inputs into the system. In an embodiment, once the digital twins resources are up to date with the latest hardware and software information, upgrade management program 112 generates a report detailing the working dependencies and non-working dependencies as well hardware asset dependencies that will be provided to any software and hardware asset management platform, Col. 8, Lines 6-15, of MOYAL); and is configured to be run on the processor to simulate performance of the digital system (wherein calculate the continued usage metrics for each asset of the one or more assets based on the output of the one or more digital twins, wherein the continued usage metrics are specific to each asset of the one or more assets further comprises one or more of the following program instructions, stored on the one or more computer readable storage media, to: receive one or more server usage metrics; simulate the one or more server usage metrics and the one or more upcoming changes for the one or more assets on the one or more digital twins, Col. 18, Line 63 – Col. 19, Line 6, of MOYAL); receive, as input, a potential modification to the digital system (upgrade management program 112 receives hardware, firmware, and software requirements (step 302), Col. 8, Lines 27-28 & FIG. 5 (Item 302), of MOYAL); apply the potential modification to the digital twin (upgrade management program 112 generates a digital twin replica with updated requirements data (step 308). In an embodiment, upgrade management program 112 continuously updates the digital twin with changes in hardware, firmware, and software, based on inputs into the system, Col. 9, Lines 10-15 & FIG. 5 (Item 308), of MOYAL); run the digital twin with the potential modification on the processor (the present invention will create simulations of different version upgrades of the software or future releases of the software and map to the hardware or firmware changes identified for future releases to automatically create a list of application software that can adapt to the changing dimensions of the technology in terms of hardware and firmware upgrades, Col. 3, Lines 10-17, of MOYAL); and in response to achieving an improvement in the simulated performance resulting from the running the digital twin with the potential modification, recommend (these recommendations allow the APM system to proactively procure or replace the software or hardware components that will become obsolete by the upcoming changes. In another embodiment, upgrade management program 112 sends the report created in step 310 directly to the user … upgrade management program 112 then ends for this cycle, Col. 10, Lines 54-61, of MOYAL).
MOYAL does not appear to explicitly disclose in response to achieving an improvement in the simulated performance resulting from the running the digital twin with the potential modification, apply the potential modification as a real modification to the digital system.
PATHAK, however, is in the same field of simulating a real world environment (digital twins) (Title & Para. [0001] of PATHAK) and teaches in response to achieving an improvement in the simulated performance resulting from the running the digital twin with the potential modification, apply the potential modification as a real modification to the digital system (the real-time data captured and/or otherwise determined from operation of the real-world environment may be utilized to train one or more model(s) for possible implementation within the real-world environment … using the updated simulation environment, embodiments may test whether such updated trained models are effective and/or sufficiently improve operation of the real-world environment, and more accurately apply trained model(s) to the real-world environment in circumstances where such newly trained models sufficient improve operations, Para. [0047] of PATHAK; See also determining comparison results by comparing the at least one operational metric value associated with operation of the real-world environment with the at least one simulated operational metric value associated with the updated simulated environment; determining the comparison results indicate the at least one simulated operational metric value improve the at least one operational metric compared to the at least one operational metric value associated with the operation of the real-world environment; and in response to determining the comparison results indicate the at least one simulated operational metric value improve the at least one operational metric compared to the at least one operational metric value associated with the operation of the real-world environment: automatically configuring at least one computing device in the real-world environment utilizing the trained model, Para. [0010] of PATHAK; See also Title of PATHAK includes REAL-TIME ENVIRONMENT DIGITAL TWIN SIMULATING, which suggests the real world environments correspond to digital twin(s) in PATHAK).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the invention to modify the digital twin performance based method of MOYAL with the comparison/testing of PATHAK for the purpose of enabling a self-optimizing real world environment (Abstract of PATHAK).
Claim 15 has substantially similar limitations as recited in claim 5; therefore, it is rejected under 35 U.S.C. § 103 for the same reasons.
Claim 16 has substantially similar limitations as recited in claim 6; therefore, it is rejected under 35 U.S.C. § 103 for the same reasons.
Claims 2, 3, 12 and 13 are rejected under 35 U.S.C. § 103 as being unpatentable over MOYAL et al. (U.S. Patent No. 11,200,045) in view of PATHAK et al. (U.S. Patent Application Publication No. 2023/0076433 A1), and further in view of KHALIL et al. (U.S. Patent Application Publication No. 2023/0204653).
Regarding claim 2, MOYAL as modified by PATHAK teaches the method of claim 1 (as discussed above) but appears to fail to explicitly disclose further comprising: running predictive analytics, using a machine-learning (ML) engine, to generate a recommended potential modification, wherein the recommended potential modification exceeds a threshold probability score of achieving an improvement to the digital system; and inputting the recommended potential modification as the potential modification.
KHALIL, however, is in the field of digital twin algorithms (Para. [0008] of KHALIL) and teaches running predictive analytics, using a machine-learning (ML) engine, to generate a recommended potential modification, wherein the recommended potential modification exceeds a threshold probability score of achieving an improvement to the digital system (the physics-based reliability model may further be developed through, or include, physics of failure (PoF) modeling, which leverages the knowledge and understanding of the processes and mechanisms that induce failure to predict reliability and improve product performance, Para. [0050] of KHALIL; See also as shown in the graph 600, an increase in the probability of failure increases non-linearly with time, reaching a 6% probability of failure after 560 cycles ( e.g., temperature cycles), whereas a 1 % probability of failure occurs at approximately 325 cycles … if a 1% probability of failure of the component described in graph 600 is considered low risk, and a 6% percent probability of failure of the component described in graph 600 is considered high risk, then the model described in graph 600 may suggest that the component be replaced between 325 and 560 cycles, Para. [0055] of KHALIL; See also the physics-based reliability models represent established empirical correlations and historical failure rate data executed via machine learning (ML) algorithms, Claim 6 of KHALIL); and inputting the recommended potential modification as the potential modification (if a 1% probability of failure of the component described in graph 600 is considered low risk, and a 6% percent probability of failure of the component described in graph 600 is considered high risk, then the model described in graph 600 may suggest that the component be replaced between 325 and 560 cycles, Para. [0055] of KHALIL; See also the physics-based reliability models represent established empirical correlations and historical failure rate data executed via machine learning (ML) algorithms, Claim 6 of KHALIL).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the invention to modify the digital twin performance based method of MOYAL (as modified by PATHAK) with the ML engine of KHALIL for the purpose of ascertaining the reliability of designs under different operating conditions (Para. [0001] of KHALIL).
Regarding claim 3, MOYAL as modified by PATHAK teaches the method of claim 1 (as shown above) further comprising: automatically submitting, to a supplier via an acquisition network, an order for a replacement for the component (these recommendations allow the APM system to proactively procure or replace the software or hardware components that will become obsolete by the upcoming changes, Col. 10, Lines 54-58, of MOYAL) but appears to fail to explicitly disclose analyzing the performance metrics, via a machine-learning (ML) engine running predictive analytics, to identify a component of the digital system that exceeds a threshold likelihood of failing within a predetermined time period; inputting, as the potential modification, replacement of the component.
KHALIL, however, is in the field of digital twin algorithms (Para. [0008] of KHALIL) and teaches analyzing the performance metrics, via a machine-learning (ML) engine running predictive analytics, to identify a component of the digital system that exceeds a threshold likelihood of failing within a predetermined time period (the physics-based reliability model may further be developed through, or include, physics of failure (PoF) modeling, which leverages the knowledge and understanding of the processes and mechanisms that induce failure to predict reliability and improve product performance, Para. [0050] of KHALIL; See also as shown in the graph 600, an increase in the probability of failure increases non-linearly with time, reaching a 6% probability of failure after 560 cycles ( e.g., temperature cycles), whereas a 1 % probability of failure occurs at approximately 325 cycles … if a 1% probability of failure of the component described in graph 600 is considered low risk, and a 6% percent probability of failure of the component described in graph 600 is considered high risk, then the model described in graph 600 may suggest that the component be replaced between 325 and 560 cycles, Para. [0055] of KHALIL; See also the physics-based reliability models represent established empirical correlations and historical failure rate data executed via machine learning (ML) algorithms, Claim 6 of KHALIL); inputting, as the potential modification, replacement of the component (if a 1% probability of failure of the component described in graph 600 is considered low risk, and a 6% percent probability of failure of the component described in graph 600 is considered high risk, then the model described in graph 600 may suggest that the component be replaced between 325 and 560 cycles, Para. [0055] of KHALIL; See also the physics-based reliability models represent established empirical correlations and historical failure rate data executed via machine learning (ML) algorithms, Claim 6 of KHALIL).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the invention to modify the digital twin performance based method of MOYAL (as modified by PATHAK) with the ML engine of KHALIL for the purpose of ascertaining the reliability of designs under different operating conditions (Para. [0001] of KHALIL).
Claim 12 has substantially similar limitations as recited in claim 2; therefore, it is rejected under 35 U.S.C. § 103 for the same reasons.
Claim 13 has substantially similar limitations as recited in claim 3; therefore, it is rejected under 35 U.S.C. § 103 for the same reasons.
Claims 4 and 14 are rejected under 35 U.S.C. § 103 as being unpatentable over MOYAL et al. (U.S. Patent No. 11,200,045) in view of PATHAK et al. (U.S. Patent Application Publication No. 2023/0076433 A1), and further in view of SHOKOOH et al. (U.S. Patent Application Publication No. 2022/0060541).
Regarding claim 4, MOYAL as modified by PATHAK teaches the method of claim 1 (as shown above) but appears to fail to teach further comprising: displaying the digital twin as a three-dimensional rendition on a graphical user interface (GUI) that is accessible to a system administrator via a secure login; and configuring the GUI to receive potential modifications as input from the system administrator.
SHOKOOH, however, is in the field of digital twins (Para. [0002] of SHOOKOH) and teaches displaying the digital twin as a three-dimensional rendition on a graphical user interface (GUI) that is accessible to a system administrator via a secure login (Network projects consist of a digital twin, that represents equipment variables and functions, context and connectivity logic, logical or geospatial network visualization (2D and 3D), three-dimensional or four-dimensional planning models, Para. [0002] of SHOKOOH; See also software does not need to be installed on a specific desktop computer or notebook and may be accessible through Login and Password everywhere through Intranet or Internet, Para. [0066] of SHOKOOH); and configuring the GUI to receive potential modifications as input from the system administrator (ability to view, edit display and transfer protective device information, network fault information and fault location from the physical device to the cloud database and to the desktop client for forensic analysis, Para [0154] of SHOKOOH; See also ability to track changes, history and audit the changes through web interface and reporting, Para. [0155] of SHOKOOH; See also changes may be merged using change management processes (as illustrated in exemplary user interfaces shown in FIG. 9, FIGS. 10 and 1100 in FIG. 11), Para. [0160] of SHOKOOH).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the invention to modify the digital twin performance based method of MOYAL (as modified by PATHAK) with the data visualization of SHOKOOH for the purpose of keeping information synchronized between users (Para. [0002] of SHOKOOH).
Claim 14 has substantially similar limitations as recited in claim 4; therefore, it is rejected under 35 U.S.C. § 103 for the same reasons.
Claims 7 and 17 are rejected under 35 U.S.C. § 103 as being unpatentable over MOYAL et al. (U.S. Patent No. 11,200,045) in view of PATHAK et al. (U.S. Patent Application Publication No. 2023/0076433 A1), and further in view of WOUHAYBI et al. (U.S. Patent Application Publication No. 2020/0310394).
Regarding claim 7, MOYAL as modified by PATHAK teaches the method of claim 1 (as shown above) wherein the performance metrics comprise: memory utilization, central processing unit (CPU) utilization (resource usage can be monitored, controlled, and reported, providing transparency for both the provider and consumer of the utilized service, Col. 4, Lines 20-22 of MOYAL; See also the software nonfunctional requirements details define system attributes such as security, reliability, performance, maintainability, scalability, and usability that are requirements for executing the software”, Col. 9, Lines 45-49 of MOYAL).
However, although MOYAL teaches monitoring resource usage (Col. 4, Lines 20-22 of MOYAL), MOYAL as modified by MADHAVAN and CHHATWAL do not appear to specifically teach wherein the performance metrics comprise: memory utilization, central processing unit (CPU) utilization, CPU temperature, disk swap, processing speed, and transmission latency.
WOUHAYBI, however, is in the field of digital twins (Para. [0412] of WOUHAYBI) and teaches wherein the performance metrics comprise: memory utilization, central processing unit (CPU) utilization, CPU temperature, disk swap, processing speed, and transmission latency (Examples of local resource utilization may be CPU loading, memory utilization, storage, page misses, errors, or the like for reliable operation, Para. [0304] of WOUHAYBI).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the invention to modify the digital twin performance based method of MOYAL (as modified by PATHAK) with the data visualization of WOUHAYBI for the purpose of managing/monitoring various types of architectures (Para. [0072] of WOUHAYBI).
Claim 17 has substantially similar limitations as recited in claim 7; therefore, it is rejected under 35 U.S.C. § 103 for the same reasons.
Claims 8-10 and 18-20 are rejected under 35 U.S.C. § 103 as being unpatentable over MOYAL et al. (U.S. Patent No. 11,200,045) in view of PATHAK et al. (U.S. Patent Application Publication No. 2023/0076433 A1), and further in view of AMARO et al. (U.S. Patent Application Publication No. 2022/0404811).
Regarding claim 8, MOYAL as modified by PATHAK teaches the method of claim 1 (as shown above) but appears to fail to explicitly disclose wherein the digital twin is segmented into a plurality of tiers, each tier representing a different logical layer of the digital system.
AMARO, however, is in the field of digital twins (Para. [0111] of AMARO) and teaches wherein the digital twin is segmented into a plurality of tiers, each tier representing a different logical layer of the digital system (device layer, Para. [0097] of AMARO, operating system layer: in the classification phase, proxy servers 312 may further probe each discovered device to determine the version of its operating system, Para. [0098] of AMARO, a device components layer: in the identification phase, proxy servers 312 may determine specific details about a classified device, Para. [0099] of AMARO, and a status layer: in the exploration phase, proxy servers 312 may determine further details about the operational state of a classified device, Para. [0100] of AMARO; See also Paras. [0095]-[0100] of AMARO).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the invention to modify the digital twin performance based method of MOYAL (as modified by PATHAK) with the segmented digital twin functionality of AMARO for the purpose of decoupling software and hardware of the process control system (Para. [0010] of AMARO).
Regarding claim 9, MOYAL as modified by PATHAK and AMARO teaches the method of claim 8 (as shown above) wherein the different logical layers of the digital system comprise a data layer (in the exploration phase, proxy servers 312 may determine further details about the operational state of a classified device, Para. [0100] of AMARO; See also Paras. [0095]-[0100] of AMARO), a data infrastructure layer (operating system layer: in the classification phase, proxy servers 312 may further probe each discovered device to determine the version of its operating system, Para. [0098] of AMARO, a device components layer: in the identification phase, proxy servers 312 may determine specific details about a classified device, Para. [0099] of AMARO), a security layer (safety routines, Para. [0105] of AMARO), and a container layer (SD application layer software components 235-248 are referred to herein as "containers", Para. [0095] of AMARO).
Regarding claim 10, MOYAL as modified by PATHAK teaches the method of claim 1 (as shown above) but appears to fail to explicitly disclose further comprising, in response to a detection of an overloading of the digital system, transforming a portion of the digital twin into an actual component of the digital system.
AMARO, however, is in the field of digital twins (Para. [0111] of AMARO) and teaches in response to a detection of an overloading of the digital system, transforming a portion of the digital twin into an actual component of the digital system (should the active targets/components fail, the digital twin of the filed targets/components may simply be activated to seamlessly maintain run-time operations of the industrial process plant, Para. [0135] of AMARO).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the invention to modify the digital twin performance based method of MOYAL (as modified by PATHAK) with the segmented digital twin functionality of AMARO for the purpose of decoupling software and hardware of the process control system (Para. [0010] of AMARO).
Claim 18 has substantially similar limitations as recited in claim 8; therefore, it is rejected under 35 U.S.C. § 103 for the same reasons.
Claim 19 has substantially similar limitations as recited in claim 9; therefore, it is rejected under 35 U.S.C. § 103 for the same reasons.
Claim 20 has substantially similar limitations as recited in claim 10; therefore, it is rejected under 35 U.S.C. § 103 for the same reasons.
Conclusion
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JOHN P. HOCKER
Examiner
Art Unit 2189
/JOHN P HOCKER/Examiner, Art Unit 2189
/REHANA PERVEEN/Supervisory Patent Examiner, Art Unit 2189