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 .
Claims 1-17 are pending.
Claim Objections
Claim 8 is objected to because of the following informalities:
The term “outcome of the change generic use” in line 6 should read “outcome of the changed generic use”.
The term “such changed maintenance schedule” in line 7 should read “the changed maintenance schedule”.
Appropriate correction is required.
Claim 13 is objected to because of the following informalities: The phrase "A prediction unit to be utilized in a method according to claim 1, comprising: a processing unit and a data storage comprising the neural network being an autoencoder, wherein the processing unit is adapted to communicate with the at least two time series databases, wherein the at least two time series databases comprise at least an electricity load time series database and a weather forecast time series database" should read "A prediction unit to be utilized in the method according to claim 1, comprising: the processing unit and a data storage comprising the neural network being an autoencoder, wherein the processing unit is adapted to communicate with the at least two time series databases, wherein the at least two time series databases comprise at least the electricity load time series database and the weather forecast time series database". Appropriate correction is required.
Claim Rejections - 35 USC § 112(b)
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.
Claim(s) 5, 11 is/are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claims 5 and 11 each describe the exemplary language "preferably". This language renders the claim indefinite, as it does not clearly set forth the metes and bounds of the claim, rendering the intended scope of the claim unclear.
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 non-obviousness.
Claim(s) 1, 4, 6-7, 9-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wichmann et al, US Patent Pub US 20160146118 A1 (hereinafter Wichmann) in view of Shouxiang et al, Chinese Patent Num CN111144643A (hereinafter Shouxiang).
Claim 1
Wichmann teaches a method of demand based optimizing of a power generation in a power generation and distribution network (Wichmann, para 9 – A control method for optimizing an operation of a power plant having generating units during a selected operating period subdivided so to include regular intervals.) , comprising: utilizing at least two time series databases, wherein the at least two time series databases comprise at least an electricity load time series database and a weather forecast time series database (Wichmann, para 43, 83, 87, 96 – Stored data including forecast weather data for future hours/”time series” and load forecast over intervals/”time series”.), processing the at least two time series databases by a processing unit utilizing a neural network (Wichmann, para 59, 63-64 – Processing the data using a processor and a neural network.), providing by the processing unit a predicted power demand profile for further processing, to a user interface and/or a power generation control unit. (Wichmann, para 93, 121 – Providing the optimization over a prediction horizon/”predicted power demand profile” for adjustment/”further processing” to control setpoints of the power plant or a display/”user interface”.)
But Wichmann fails to specify the neural network is an autoencoder.
However Shouxiang teaches the neural network is an autoencoder. (Shouxiang, lines 80-90 – Using a neural network with an encoder to determine power load prediction.)
Wichmann and Shouxiang are analogous art because they are from the same field of endeavor. They relate to power generation load prediction systems.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the above power generation load prediction system, as taught by Wichmann, and incorporating the above limitations, as taught by Shouxiang.
One of ordinary skill in the art would have been motivated to do this modification in order to improve the load predictions by incorporating the above limitations, as suggested by Shouxiang (abstract).
This rejection also applies to claims 13 and 14.
Claim 4
The combination of Wichmann and Shouxiang teaches all the limitations of the base claims as outlined above.
The combination of Wichmann and Shouxiang further teaches the electricity load time series database is a historic electricity load time series database. (Wichmann, para 43 – Stored data including historical load data.)
Claim 6
The combination of Wichmann and Shouxiang teaches all the limitations of the base claims as outlined above.
The combination of Wichmann and Shouxiang further teaches the at least two time series databases also comprise a power demand time series database. (Wichmann, para 116 – Power demand data over various time periods.)
Claim 7
The combination of Wichmann and Shouxiang teaches all the limitations of the base claims as outlined above.
The combination of Wichmann and Shouxiang further teaches utilizing a power generation device characteristics database to retrieve and evaluate maintenance schedule of at least one power generation device (Wichmann, para 78 – Maintenance database for power generation equipment.), wherein the predicted power demand profile is utilized to simulate an outcome of a changed generic use of the at least one power generation device (Wichmann, para 116 – Running scenarios/simulations based on the demand requirements using a changed configuration/”generic use” of a power generating device.), wherein the changed generic use changes the wear of the at least one power generation device (Wichmann, para 92 - Modeling parts of the power plant to estimate wear based on various parameters.), wherein optionally an adapted maintenance schedule is provided when required (Wichmann, para 84 – Balancing performance and cost based on a maintenance schedule.), wherein the simulated outcome of the changed generic use and optionally the adapted maintenance schedule when required is forwarded to a user interface and/or control unit of a power generation device. (Wichmann, para 113 – Forwarding model/simulation, changed usage, and maintenance schedules to a user on a user interface of the power generation device.)
Claim 9
The combination of Wichmann and Shouxiang teaches all the limitations of the base claims as outlined above.
The combination of Wichmann and Shouxiang further teaches wherein a safety processing unit retrieves the predicted power demand profile and a measured power demand profile, wherein the safety processing unit evaluates deviations of the measured power demand profile and the corresponding elements of the predicted power demand profile, wherein the deviations are subjected to an error calculation to identify relevant deviations, wherein relevant deviations are provided to a user interface and/or a safety control unit, wherein the safety control unit triggers diagnosis actions to identify an origin of the relevant deviations. (Wichmann, para 99 - Discrepancies between the model and the measured data may indicate errors in the data, and a performance module/”safety processing unit” uses plant equipment models to predict/diagnosis the expected performance of major plant components and equipment, and the performance module may track degradation over time so that performance problems having the most significant effect on plant performance are identified/”origin of the relevant deviations”.)
Claim 10
The combination of Wichmann and Shouxiang teaches all the limitations of the base claims as outlined above.
The combination of Wichmann and Shouxiang further teaches the safety processing unit is a remote safety processing unit or is connected to a remote database or a remote distributed database. (Wichmann, para 74 - Aspects of the computer system may be located at the power plant, while other aspects maybe remote and connected via communications network.)
Claim 11
The combination of Wichmann and Shouxiang teaches all the limitations of the base claims as outlined above.
The combination of Wichmann and Shouxiang further teaches automatically adjusting the controls of at least one power generating device, preferably a continuous flow engine or a gas turbine, based on the predicted power demand profile. (Wichmann, para 6-7, 93, 98 – Automatically adjusting the power generating device, such as a gas turbine or other thermal engine/”continuous flow engine”, based on the optimization/”demand profile”.)
Claim 12
The combination of Wichmann and Shouxiang teaches all the limitations of the base claims as outlined above.
The combination of Wichmann and Shouxiang further teaches the predicted power demand profile is utilized to optimize the controls of at least two different power generation devices. (Wichmann, para 6-7, 93, 98 – Automatically adjusting the power generating device, such as a gas turbine or other thermal engine/”continuous flow engine”, based on the predicted optimization/”demand profile”.)
Claim 15
The combination of Wichmann and Shouxiang teaches all the limitations of the base claims as outlined above.
The combination of Wichmann and Shouxiang further teaches a computer program product, tangibly embodied in a machine-readable storage medium, comprising: instructions stored thereon and operable to cause a computing entity to execute a method. (Wichmann, para 46 – A computer system having a processor that executes program code to control the operation of the gas turbine system.)
Claim 16
The combination of Wichmann and Shouxiang teaches all the limitations of the base claims as outlined above.
The combination of Wichmann and Shouxiang further teaches optimizing the power generation in the power generation and distribution network based on the predicted power demand profile. (Wichmann, para 55, 93 – Optimizing the power generation based on the optimization over a prediction horizon/”predicted power demand profile”.)
Claim 17
The combination of Wichmann and Shouxiang teaches all the limitations of the base claims as outlined above.
The combination of Wichmann and Shouxiang further teaches controlling the power generation in the power generation and distribution network via the power generation control unit based on the predicted power demand profile. (Wichmann, para 55, 93 – Controlling the power generation based on the optimization over a prediction horizon/”predicted power demand profile”.)
Claim(s) 2-3, 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wichmann et al, US Patent Pub US 20160146118 A1 (hereinafter Wichmann) in view of Shouxiang et al, Chinese Patent Num CN111144643A (hereinafter Shouxiang) as applied to claims 1, 4, 6-7, 9-17 above, in further view of Ahn et al, US Patent Pub US 20210049460 A1 (hereinafter Ahn).
Claim 2
The combination of Wichmann and Shouxiang teaches all the limitations of the base claims as outlined above.
But the combination of Wichmann and Shouxiang fails to specify the autoencoder is a sequential autoencoder.
However Ahn teaches the autoencoder is a sequential autoencoder. (Ahn, para 99 - Sequentially-learned variational auto-encoded features.)
Wichmann, Shouxiang, and Ahn are analogous art because they are from the same field of endeavor. They relate to machine learning methods.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the above machine learning method, as taught by Wichmann and Shouxiang, and incorporating the above limitations, as taught by Ahn.
One of ordinary skill in the art would have been motivated to do this modification in order to maximize the relevant key performance indicators by incorporating the above limitations, as suggested by Ahn (abstract).
Claim 3
The combination of Wichmann, Shouxiang, and Ahn teaches all the limitations of the base claims as outlined above.
Ahn further teaches wherein a high dimensional vector of the sequential autoencoder is utilized by the processing unit to provide the predicted power demand profile. (Ahn, para 94-95 - Stochastic processes involving high-dimensional causally-correlated relationships among actions, conditions, sensors, and target observations to determine a resulting state/”predicted power demand profile”.)
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the above machine learning method, as taught by Wichmann, Shouxiang, and Ahn, and incorporating the above limitations, as taught by Ahn.
One of ordinary skill in the art would have been motivated to do this modification in order to maximize the relevant key performance indicators by incorporating the above limitations, as suggested by Ahn (abstract).
Claim 5
The combination of Wichmann, Shouxiang, and Ahn teaches all the limitations of the base claims as outlined above.
Ahn further teaches the sequential autoencoder is utilized to identify relevant data in the at least two time series databases, wherein preferably a high dimensional vector of the sequential autoencoder is utilized. (Ahn, para 27-28, 94-95 - Stochastic processes involving high-dimensional causally-correlated relationships among actions, conditions, sensors, and target observations to determine a resulting state/”predicted power demand profile” used to maximize the relevant key performance indicators (KPIs) relying on the predicted values of sensor and target outputs for different actions and conditions over the near future.)
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the above machine learning method, as taught by Wichmann, Shouxiang, and Ahn, and incorporating the above limitations, as taught by Ahn.
One of ordinary skill in the art would have been motivated to do this modification in order to maximize the relevant key performance indicators by incorporating the above limitations, as suggested by Ahn (abstract).
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wichmann et al, US Patent Pub US 20160146118 A1 (hereinafter Wichmann) in view of Shouxiang et al, Chinese Patent Num CN111144643A (hereinafter Shouxiang) as applied to claims 1, 4, 6-7, 9-17 above, in further view of Long et al, US Patent Pub US 20110282500 A1 (hereinafter Long).
Claim 8
The combination of Wichmann and Shouxiang teaches all the limitations of the base claims as outlined above.
But the combination of Wichmann and Shouxiang fails to specify requesting a changed maintenance schedule of a first power generation device, wherein based on at least the predicted power demand profile, a maintenance schedule of at least one second power generation device, and the simulated outcome of the change generic use resulting from the changed maintenance schedule of the first power generation device an evaluation of such changed maintenance schedule is provided.
However Long teaches requesting a changed maintenance schedule of a first power generation device (Long, para 48 – A changed maintenance schedule for a power generation device.), wherein based on at least the predicted power demand profile, a maintenance schedule of at least one second power generation device, and the simulated outcome of the change generic use resulting from the changed maintenance schedule of the first power generation device an evaluation of such changed maintenance schedule is provided. (Long, para 32, 46-48 – Adjusting the maintenance schedule based on the outcome of a simulation of a power generation unit based on an evaluation of maintenance needs versus economic costs.)
Wichmann, Shouxiang, and Long are analogous art because they are from the same field of endeavor. They relate to power generation load prediction systems.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the above power generation load prediction system, as taught by Wichmann and Shouxiang, and incorporating the above limitations, as taught by Long.
One of ordinary skill in the art would have been motivated to do this modification in order to generate an enhancement of the power generation system by incorporating the above limitations, as suggested by Long (abstract).
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Tiwari et al, US Patent Pub US 20160258363 A1 relates to claims regarding power plant control including receiving operating parameters measured by sensors for each component of a combined cycle power plant, power demand, and gas turbines.
Graber et al, US Patent Pub US 20110040550 A1 relates to claims regarding power generating systems with power production and demand forecasting using a neural network forecaster.
Li et al, US Patent Pub US 20190312457 A1 relates to claims regarding a power generating system, an artificial neural network trained to generate a forecasted transmission level load, real-time or near real-time historical and current electricity consumption and generator data, weather forecasts, and auto-encoder neural networks.
Conclusion
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/DAVID EARL OGG/
Primary Examiner, Art Unit 2119
Prosecution Insights