DETAILED ACTION
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 06/30/2026 has been entered.
This Office Action is in response to claims filed on 06/30/2026.
Claims 1-26 are pending.
Claims 1, 10, 11, 20 and 21 were amended.
Claim Rejections - 35 USC § 112
Applicant's arguments and amendments, see remarks Page 11 filed on 06/30/2026, have been fully considered but they are not persuasive. Examiner notes there were parts of the rejection not argued nor amended. The 35 USC 112(b) rejection is maintained.
Claim Rejections - 35 USC § 103
Applicant's arguments and amendments, see remarks Pages 11-13 filed on 06/30/2026, have been fully considered but they are not persuasive. Applicant argues “combination fails to disclose that “one or more business models comprises at least one of : a fuel management model, or a flight operations model”. Examiner notes, the combination Johnson ’239 and Johnson ‘274 disclose a fuel consumption model (Johnson ‘239, Par 302, fuel consumption model, fig 14, 1416) and a flight operation module (Johnson ‘239, Par 151, Flight operations module, fig 14, 1402). The 35 USC 103 rejection is maintained.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-26 are rejected under 35 USC 112(b).
Claims 1, 11, and 21 recite “a desired rate of variability reduction per unit of computation time.” The specification mentions “a desired rate of variability reduction per unit of computation time” but does not provide a definition for this phrase for a reader to understand what “rate of variability reduction per unit of computation time” means. This phrase renders the claims indefinite.
Claims 11 and 21 recite analogous limitation, so they are rejected under 35 USC 112(b) for the same reasons.
Claims 2-10, 12-20, and 22-26 depend on claims 1, 11, and 21, respectively. They inherit the defects of claims 1, 11, and 21 so are rejected for the same reasons.
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 1-26 are rejected under 35 U.S.C. 103 as being unpatentable over Johnson et al. (US 2017/0323239), hereinafter Johnson239, in view of Johnson et al. (US 2017/0323274), hereinafter Johnson274.
Claim interpretation: limitation “to meet a desired rate of variability reduction per unit of computation time” in claims 1, 11, and 21 is interpreted as intended use, so any teaching of “according to at least one a computation time and a contribution to variance “ would read on to “according to at least one a computation time and a contribution to variance to meet a desired rate of variability reduction per unit of computation time.”
Regarding claim 1, Johnson239 teaches a system comprising:
a plurality of digital twins corresponding to one or more asset systems of an asset (¶ 0038; Johnson239 teaches creating digital twins of asset entities within a system);
one or more business models corresponding to one or more business operations (¶ 0038-0039; Johnson239 teaches using digital twins of subsystems of an asset to model operations of the asset and the business systems to improve financial and operating key performance indicator (KPI) objectives; these operational models of the asset correspond to one or more business models as recited in this limitation); and
an electronic control unit (ECU), wherein the ECU is programmed (¶ 0020, 0032, 0034; Johnson239 teaches a computing system programmed to perform controlling functions) to:
implement an asset optimizer module, wherein implementing the asset optimizer module interconnects the plurality of digital twins for optimization over a time horizon, wherein the plurality of digital twins are selected for optimization by the asset optimizer module according to at least a computation time (¶ 00297, Johnson239 teaches optimizing according to the required convergence time), and a contribution to variance (¶ 0022, 0038-0040, 0061, 0063, 0066, 0122; Johnson239 teaches a computing system modeling subsystems of an asset with interconnected digital twins for optimization over a time period; the function as taught here reads onto an asset optimizer module as recited);
execute the asset optimizer module over the time horizon, wherein the asset optimizer module optimizes one or more parameters of the plurality of digital twins to obtain one or more key process indicators for the one or more asset systems (¶ 0022, 0027, 0038-0040, 0061, 0063, 0066; Johnson239 teaches a computing system modeling subsystems of an asset with interconnected digital twins for optimizing operations over a time period by determining operating parameters to improve financial and operating key performance indicator (KPI) objectives);
implement a system optimizer module, wherein the system optimizer module receives the one or more optimization parameters and the one or more business models (¶ 0027, 0038-0040; Johnson239 teaches performing simulation of an asset modeled with digital twins to generate parameters to optimize operations; these generated optimization parameters in turn are used to manage operations modeled with respect to impacts such as fuel consumption, shop cost, cash flow, maintenance workscopes; the function to perform this function as described is considered a system optimizer module as recited);
execute the system optimizer module, wherein the system optimizer module generates one or more operation protocols for the one or more business models (¶ 0027, 0038-0039; Johnson239 teaches performing simulation of an asset modeled with digital twins to generate parameters to optimize operations; these generated optimization parameters in turn are used to manage operations modeled with respect to impacts such as fuel consumption, shop cost, cash flow, maintenance workscopes; fuel consumption, cash flow, maintenance workscopes plans correspond to one or more operation protocols for one or more business models); and
output, to a user, the one or more operation protocols for implementation in a real-world asset system (¶ 0040; Johnson239 teaches providing maintenance workscopes, scheduling, which correspond to outputting to a user the one or more operation protocols for implementation as recited),
wherein the one or more business models comprises at least one of: a fuel management model (Johnson ‘239, Par 302, fuel consumption model, fig 14, 1416), or a flight operations model (Johnson ‘239, Par 151, Flight operations module, fig 14, 1402).
Johnson239 does not teach:
the plurality of digital twins are selected for optimization by the asset optimizer module according to at least one a computation time and a contribution to variance to meet a desired rate of variability reduction per unit of computation time.
However, Johnson274 teaches:
the plurality of digital twins are selected for optimization by the asset optimizer module according to at least one a computation time and
Johnson239 and Johnson274 are analogous art because they are in the same field of digital twins modeling and simulation for optimization. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Johnson239 and Johnson274. One of ordinary skill in the art would have been motivated to make such a combination because Johnson274’s teachings would have helped increase the specified operating performance criteria in time present and future of the industrial assets or decrease an economic risk associated with the operation of the industrial assets, within a specified probability (Johnson239, Abstract).
Regarding claim 2, Johnson239 and Johnson274 in combination teach the system of claim 1, Johnson239 further teaches wherein the ECU is further programmed to:
output one or more indications of optimization status, wherein the indications define a level of optimization of at least one of the plurality of digital twins or the one or more business models (¶ 0035, 0051, 0053-0055, 0081-0082; Johnson239 teaches optimization of assets comprising contractual services for a given level regarding key components/sub-systems of engines to select the scope, timing, or risk level of maintenance; a given level of maintenance workscope corresponds to a level of optimization of the one or more business models; selection of a scope for a given level corresponds to outputting one or more indications of optimization status).
Regarding claim 3, Johnson239 and Johnson274 in combination teach the system of claim 1, Johnson239 further teaches wherein the ECU is further programmed to:
feedback the one or more operation protocols into one or more of the plurality of digital twins such that the ECU executes another instance of at least one of the asset optimizer module or the system optimizer module to generate an updated set of the one or more operation protocols for implementation in the real-world asset system (¶ 0044; Johnson239 teaches simulator-optimizer receiving and incorporating changes from actual operations, business process updates, operating policy and maintenance service; receiving and incorporating changes and updates into the optimizer correspond to “feedback the one or more operation protocols ...” as recited).
Regarding claim 4, Johnson239 and Johnson274 in combination teach the system of claim 1, Johnson239 further teaches wherein the system optimizer module is configured to optimize each of the one or more business models based on an interplay between the one or more business models and the one or more optimization parameters (¶ 0027, 0038-0039; Johnson239 teaches performing simulation of an asset modeled with digital twins to generate parameters to optimize operations; these generated optimization parameters in turn are used to manage operations modeled with respect to impacts such as fuel consumption, shop cost, cash flow, maintenance workscopes; fuel consumption, cash flow, maintenance workscopes plans correspond to one or more operation protocols for one or more business models; this teaching reads onto this claim as recited).
Regarding claim 5, Johnson239 and Johnson274 in combination teach the system of claim 1, Johnson239 further teaches wherein the ECU is further programmed to:
select one or more of the plurality of digital twins having a computation time less than or equal to the time horizon for a simulated future period according to a limit to compute time duration (¶ 0025, 0033, 0038; Johnson239 teaches using digital twins of asset entities and historical, current, or future time horizons to model operations of the asset and the business systems to simulate over a future time period to generate failure estimations of parts or subsystems or cumulative damage model; this teaching reads onto this limitation).
Regarding claim 6, Johnson239 and Johnson274 in combination teach the system of claim 1, Johnson239 further teaches wherein the ECU is further programmed to:
select one or more of the plurality of digital twins having a computation time less than or equal to the time horizon of a simulated future period according to a desired rate of forecast variability reduction per unit of computation time while concurrently optimizing for the operational forecast KPI objectives (¶ 0025, 0038-0039; Johnson239 teaches using digital twins of asset entities and historical, current, or future time horizons to model operations of the asset and the business systems to simulate over a future time period to optimize the rate of state change to improve financial and operating key performance indicator (KPI) objectives; to optimize the rate of state change over a time period corresponds to variability reduction per unit of computation time, so the whole teaching as discussed above reads onto this claim).
Moreover, Johnson274 teaches that "time steps of data acquisition or virtual sensing are configurable such as to record only changes in sensed values or to record at a time interval", see ¶ 0283) indicating that it was known to consider the rate of forecast variability reduction per unit of computation time, as recited in claim 6.
Regarding claim 7, Johnson239 and Johnson274 in combination teach the system of claim 1, Johnson239 further teaches wherein the asset is an aircraft (¶ 0021-0022).
Regarding claim 8, Johnson239 and Johnson274 in combination teach the system of claim 1, Johnson239 further teaches wherein one or more of the plurality of digital twins comprises one or more first sublevel digital twins corresponding to one or more asset subsystems of the one or more asset systems (¶ 0038-0039; Johnson239 teaches modeling an asset using digital twins at engines level or subsystem level within the engines; depending on point of view, digital twins of engines can be interpreted as one or more first sublevel digital twins corresponding to one or more asset subsystems of the one or more asset systems, where an asset system is an aircraft, or digital twins of subsystems below engines, which are considered asset systems, can be interpreted as one or more first sublevel digital twins as recited).
Regarding claim 9, Johnson239 and Johnson274 in combination teach the system of claim 8, Johnson239 further teaches wherein the one or more first sublevel digital twins comprises one or more component level digital twins corresponding to one or more components of the one or more asset subsystems (¶ 0038-0039; Johnson239 teaches modeling an asset using digital twins at engines level or subsystem level within the engines; Johnson239 further teaches components interchangeable with subsystems; specifically Johnson239 writes “engineering models are generated and fed data pertaining to surface temperature, local oxidation, stress, strain, cracks, and other data needed to estimate the physical state of key components/sub-systems,” see ¶ 0051; hence, these teachings in combination teach this claim as recited).
Regarding claim 10, Johnson239 and Johnson274 in combination teach the system of claim 1, Johnson239 further teaches wherein the one or more business models comprises at least one of:
an asset maintenance scheduling model (¶ 0040; Johnson239 teaches providing maintenance workscopes),
Regarding claim 11, these limitations have already been discussed in claim 1. They are, therefore, rejected for the same reasons.
Regarding claim 12, these limitations have already been discussed in claim 2. They are, therefore, rejected for the same reasons.
Regarding claim 13, these limitations have already been discussed in claim 3. They are, therefore, rejected for the same reasons.
Regarding claim 14, these limitations have already been discussed in claim 4. They are, therefore, rejected for the same reasons.
Regarding claim 15, these limitations have already been discussed in claim 5. They are, therefore, rejected for the same reasons.
Regarding claim 16, these limitations have already been discussed in claim 6. They are, therefore, rejected for the same reasons.
Regarding claim 17, these limitations have already been discussed in claim 7. They are, therefore, rejected for the same reasons.
Regarding claim 18, these limitations have already been discussed in claim 8. They are, therefore, rejected for the same reasons.
Regarding claim 19, these limitations have already been discussed in claim 9. They are, therefore, rejected for the same reasons.
Regarding claim 20, these limitations have already been discussed in claim 10. They are, therefore, rejected for the same reasons.
Regarding claim 21, these limitations have already been discussed in claim 1. They are, therefore, rejected for the same reasons.
Regarding claim 22, these limitations have already been discussed in claim 2. They are, therefore, rejected for the same reasons.
Regarding claim 23, these limitations have already been discussed in claim 3. They are, therefore, rejected for the same reasons.
Regarding claim 24, these limitations have already been discussed in claims 8-9. They are, therefore, rejected for the same reasons.
Regarding claim 25, these limitations have already been discussed in claim 5. They are, therefore, rejected for the same reasons.
Regarding claim 26, these limitations have already been discussed in claim 6. They are, therefore, rejected for the same reasons.
Conclusion
The prior art made of record, listed on PTO-892, and not relied upon is considered pertinent to applicant's disclosure.
Adil Rasheed, NPL, “Digital Twin: Values, Challenges and Enablers From a Modeling Perspective”, discloses a representation of a physical asset for optimization. Digital twins referred as computational megamodel, play a transformative role on operation of cyber physical intelligent systems.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANGEL JAVIER CALLE whose telephone number is (571)272-0463. The examiner can normally be reached Monday - Friday 7:30 a.m. - 5 p.m..
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/A.C./Examiner, Art Unit 2189
/REHANA PERVEEN/Supervisory Patent Examiner, Art Unit 2189
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