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 .
This Office Action is in response to claims filed on 12/10/2025
Claims 1-15 are pending.
Claims 1, 5 and 15 were amended.
Specification
Applicant’s amendments to the specification, see specification, filed 12/10/2025, with respect to the objection has been fully considered and is persuasive. The objection to the specification has been withdrawn.
Claim Rejections - 35 USC § 112
Applicant’s arguments and amendments, see remarks Page 8, filed 12/10/2025, with respect to 35 USC 112(b) rejections have been fully considered and are persuasive. The 35 USC 112(b) rejection of claims 5 and 12 have been withdrawn.
Claim Rejections - 35 USC § 101
Applicant’s arguments and amendments, see remarks Page 9, filed 12/10/2025, with respect to 35 USC 101 rejection has been fully considered and are persuasive. The 35 USC 101 rejection of claim 15 has been withdrawn.
Claim Rejections - 35 USC § 103
Applicant’s arguments and amendments, see remarks Page 10-12, filed 12/10/2025, with respect to 35 USC 103 rejection has been fully considered and are persuasive. Therefore, the 35 USC 103 rejections of claims 1-15 have been withdrawn.
However, upon further consideration, a new ground(s) of rejection necessitated by claim amendments is made in view of Jonas Landahl, NPL, “Towards Adopting Digital Twins to Support Design Reuse during Platform Concept Development”, Published: August 14, 2018 hereinafter (Landahl).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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 1-15 are rejected under 35 U.S.C. 103 as being unpatentable over Qiuchen Lu, NPL “Digital twin-enabled anomaly detection for built asset monitoring in operation and maintenance”, Autonomation in Construction, Published: 25 May 2020, (hereafter Lu), in views of D. J. Wagg, NPL, “Digital Twins: State -of-the-Art and Future Directions for Modeling and Simulation in Engineering Dynamics Applications”, ASCE, Published May 12, 2020 (hereafter Wagg), in further views of Jonas Landahl, NPL, “Towards Adopting Digital Twins to Support Design Reuse during Platform Concept Development”, Published: August 14, 2018 (hereafter Landahl).
Regarding claim 1. Lu teaches a method of determining an operational configuration of an asset (Page 1, abstract, monitoring data that carries diagnostic information on the operational condition of assets is extracted from building DTs.), the method comprising:
receiving, by a processing unit, a set of operating parameters associated with the asset (Page 9, sec 5.1, fig 6, ifcOperationaand MantenanceProcess, receiving data)(Page 11, fig 9, data acquisition layer, asset management system), wherein the set of operating parameters correspond to initial operating conditions of the asset (Page 10, fig 8, operation condition)(Page 7, Table 1, information requirements, Initial values);
identifying data associated with the asset based on the received set of operating parameters (Page 10, sec 5.1, fig 7, service related asset, Matching table), wherein the data is stored in a first knowledge database comprising information pertaining to a first digital twin of the asset (Page 10, sec 5.1, fig 7, service related asset, Matching table)(Page 11, fig 9, Information transmission and integration, Data model integration layer);
configuring a second digital twin of the asset based on the data identified from the first knowledge database, (Page 4, sec 4.2, DTs integrate their sub-DTs and intelligent functions to create digital models) (Page 15, sec 7, col 1, a new DT-based anomaly detection process flow, asset behavioral changes by normal operating condition variations or true anomalies);
and
determining the operational configuration associated with the variant of the asset based on results of the simulation (Page 1, abstract, extracted from the building DTs, operational condition of the asset).
Lu does not explicitly teach simulating a behavior of the asset based on the configured second digital twin in a simulation environment.
However, Wagg teaches simulating a behavior of the asset based on the configured second digital twin in a simulation environment (Wagg, Page 4, col 1, par 2, incorporates both supervision and operation into its processes as well as simulation. Enhanced the predigital twin capabilities by adding the ability to simulate).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Lu to incorporate the teachings of Wagg to simulate the behavior of the asset based on the configured second digital twin in a simulation environment because it improves the decision making which would allow to have better more realistic prediction and improved decision capabilities (Wagg, Page 14, col 2, par 3)
Lu and Wagg do not explicitly teach wherein the first digital twin represents a generic replica of the asset which represents a normalized cumulative model for the asset, wherein the second digital twin is derived from the first digital twin based on the data identified from the first knowledge database and depicts a variant of the asset.
Landahl teaches wherein the first digital twin represents a generic replica of the asset which represents a normalized cumulative model for the asset (Landahl, Page 7, sec 4.1, par 2, high fidelity simulation models of the digital state that are then fed back to optimize performance of the existing physical assets, Digital State) (Landahl, Page 8, Fig 4, High Fidelity Digital Twins), wherein the second digital twin is derived from the first digital twin based on the data identified from the first knowledge database and depicts a variant of the asset (Landahl, Page 8, sec 4.1, par 1, design reuse of existing platform and its variants can be enabled, Future physical state) (Landahl, Page 8, Fig 4, High Fidelity Feasible Designs).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Lu and Wagg to incorporate the teachings of Landahl to have two digital twins representing a generic and a variant, where the variant is derived from the generic because it allows abstracting functionalities of existing assets, inserting new functionalities and technologies in the function structure to ultimately support the generation and evaluation of new functional concepts, thus supporting generation and evaluation of solutions during the conceptual stages (Landahl, abstract)
Regarding claim 2. Lu, Wagg and Landahl teach the method according to claim 1, wherein identifying data associated with the asset comprises:
determining a configuration from a plurality of predetermined configurations of the asset based on the set of operating parameters (Lu, Page 4, col 1, Par 1, DT integrates multiple fragmented data sources and thus greatly enhances the data availability for buildings),
wherein each predetermined configuration is associated with a respective variant of the asset (Lu, Page 4, sec 4.2, DTs constructed based on definitions, integrate their sub-DTs and intelligent functions).
Regarding claim 3. Lu, Wagg and Landahl teach the method according to claim 2 further comprising: storing the identified data associated with the respective variant of the asset in a second knowledge database (Lu, Page 9, fig 6, IFC MaintenanceHistory, IFCInspection History, IFCSpareRecord),
wherein the second knowledge database is communicatively coupled to the first knowledge database (Lu, Page 10, fig 7, Service related Assets, IFCDistributionFlowElement, Monitor and control related Assets, communicating with element, having their matching tables).
Regarding claim 4. Lu, Wagg and Landahl teach the method according to claim 1, wherein simulating the behavior of the asset based on the second digital twin in a simulation environment (Wagg, Page 4, col2, simulate future scenarios, update the geometry of the digital twin) comprises:
generating a simulation instance based on the configured second digital twin (Wagg, Page 4, col 2, simulation set control processes for the physical twin, hardware-in-the-loop); and
executing the simulation instance in a simulation environment using a simulation model for generating simulation results indicative of a behavior of one or more components of the asset (Wagg, Page 4 col 2, quantify the level of confidence (trust) that can be given to simulation output).
Regarding claim 5. Lu, Wagg and Landahl teach the method according to claim 4, further comprising:
receiving, by the processing unit, operational data pertaining to the asset (Lu, Page 11, fig 9, data acquisition layer, asset management system),
wherein the operational data is indicative of performance of the asset in real-time (Lu, Page 2, col 2, par 1, integrated unit, and further supports real-time data collection and storage);
computing at least one value for the set of operating parameters of the asset based on the received operational data and the second knowledge database (Lu, Page 13, fig 17, vibration frequency, time/days) (Page 12, fig 11, sensor data); and
executing the simulation instance in the simulation environment to simulate the behavior of the asset based on the computed at least one value for the set of operating parameters of the asset (Wagg, Page 4 col 2, quantify the level of confidence (trust) that can be given to simulation output).
Regarding claim 6. Lu, Wagg and Landahl teach the method according to claim 1, further comprising: reconfiguring the second digital twin of the asset based on results of the simulation (Wagg, Page 4, col 2, sec 4.1, digital twin will need to be regularly revalidated throughout its life, in order to ensure that it can continue to deliver highly trusted output)(Wagg, Page 5, fig 3, Digital twin design validation, is iterated ).
Regarding claim 7. Lu, Wagg and Landahl teach the method according to claim 6, wherein reconfiguring the second digital twin of the asset based on results of the simulation (Wagg, Page 4, col 2, sec 4.1, digital twin will need to be regularly revalidated throughout its life, in order to ensure that it can continue to deliver highly trusted output) comprises:
comparing a simulated performance of the asset from the results of the simulation model with a threshold value (Lu, Page 12, col 1, anomaly indicative frequency before reaching the determined threshold); and
reconfiguring the second digital twin of the asset based on the results from the simulation if the simulated performance of the asset is above the threshold value (Wagg, Page 4, col 2, sec 4.1, digital twin will need to be regularly revalidated throughout its life, in order to ensure that it can continue to deliver highly trusted output)(Wagg, Page 10, col 2, iterated in such a way that succeeding generations of the population contain better solutions).
Regarding claim 8. Lu, Wagg and Landahl teach the method according to claim 6, wherein determining the operational configuration associated with the variant of the asset based on the results of the simulation comprises:
identifying the at least one value for the set of operating parameters based on the reconfigured second digital twin (Lu, Page 13, fig 17, vibration frequency, time/days)(Lu, Page 12, fig 11, sensor data); and
determining the operational configuration for the variant of the asset using the at least one value for the set of operating parameters (Lu, Page 1, abstract, extracted from the building DTs, operational condition of the asset).
Regarding claim 9. Lu, Wagg and Landahl teach the method according to claim 1, further comprising: updating a second knowledge database based on a reconfigured second digital twin of the asset (Wagg, Page 4, col 2, sec 4.1, digital twin will need to be regularly revalidated throughout its life, in order to ensure that it can continue to deliver highly trusted output)(Wagg, Page 10, col 2, iterated in such a way that succeeding generations of the population contain better solutions); and
augmenting the updated second knowledge database to the first knowledge database (Wagg, Page 14, sec 7.1, updating models and databases).
Regarding claim 10. Lu, Wagg and Landahl teach the method according to claim 8, further comprising:
generating one or more recommendations for optimizing a performance of the asset based on the determined operational configuration of the variant of the asset (Wagg, Page 10, col 2, par 1, optimization procedures, a population of possible solutions, succeeding generations of the population contain better solutions).
Regarding claim 11. Lu, Wagg and Landahl teach the method according to claim 1, wherein the first digital twin is executed on a cloud server and the second digital twin is executed on an edge server (Lu, Page 2, col 2, Par 1, network, cloud based services, designer can chose in which device it would execute).
Regarding claim 12. Lu, Wagg and Landahl teach the method according to claim 1, further comprising: generating a remaining useful life model based on a reconfigured second digital twin and a second knowledge database (Lu, Page 5, fig 3, estimated lifetime remaining)(Lu, Page 7, table 1, estimated lifetime remaining)(Wagg, Page 15, sec 8, structural life prediction)(Wagg, Page 2, sex 2.1, structural life prediction).
Regarding claim 13. Lu, Wagg and Landahl teach an apparatus for optimizing performance of an asset (Lu, Page 2, sec 2.2, raised anomalies for optimizing building operations), the apparatus comprising:
one or more processing units; and
a memory unit communicatively coupled to the one or more processing units (Lu, Page 3, fig 1, Hardware, computer related capacity, thus for a computer to operate it requires to have a processor and memory coupled to the processor),
wherein the memory unit comprises a configuration determination module stored in the form of machine-readable instructions executable by the one or more processing units (Lu, Page 3, fig 1, Hardware, computer related capacity, thus for a computer to operate it requires to have memory with executable instructions being executed with the processor),
wherein the performance optimization module is configured to perform the method according to claim 1, (Rejected under the same ground as claim 1).
Regarding claim 14. Lu, Wagg and Landahl teach a system for optimizing performance of an asset (Lu, Page 2, sec 2.2, raised anomalies for optimizing building operations), the system comprising:
one or more sources configured for providing operational data associated with the asset, (Page 5, fig 3, Asset Management system record, BMS record, Sensors Record)(Page 7, Table 1); and
the apparatus according to claim 13 (Rejected under the same ground as claim 13), communicatively coupled to the one or more sources (Lu, Page 11, fig 9, coupled, data acquisition layer, data/model integration layer),
wherein the apparatus is configured for configuring the operational configuration of the asset (Lu, Page 4, sec 4.2, DTs integrate their sub-DTs and intelligent functions to create digital models).
Regarding claim 15. Lu, Wagg and Landahl teach a computer program product, comprising a non-transitory computer readable storage medium having computer-readable instructions stored therein (Lu, Page 3, fig 1, Hardware, computer related capacity, thus for a computer to operate it requires to have memory with executable instructions being executed with the processor),
which when executed by one or more processing units, cause the processing units to perform the method according to claim 1 (Rejected under the same ground as claim 1).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/A.C./Examiner, Art Unit 2189
/REHANA PERVEEN/Supervisory Patent Examiner, Art Unit 2189
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