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 1-4, and 6-8 are pending.
Claims 5, and 9 are cancelled.
Response to Amendment
The amendment filed December 10th, 2025 has been entered. Claims 1-4, and 6-8 remain pending in the application.
Response to Arguments
Applicant’s arguments filed 12/10/2025 have been fully considered but they are not persuasive.
Applicant’s arguments on page 6, applicant argues, “The current rejection of Independent Claim 1 as previously presented argues that Fife teaches a first optimization at "EO using generalized optimization algorithm to determine optimal set of values for control parameter set of value to determine control variable" (Office Action, Page 6). Then, the second optimization is identified as "preparing lasting solution using control parameters and control variables" (Id.). These are not separate optimizations, but rather two steps of a single optimization. In Fife, the EOESC 510 first uses the economic optimizer 530 to determine values for the control parameter set. HSC 540 then essentially translates these control parameters into control variables communicated to the electric system 502 (Fife, Paragraph 131). It does not optimize using a second optimization variable or objective function”.
Examiner respectfully disagrees because Fife teaches in Paragraph [0131] that “The EO 530 may determine a set of values for a control parameter set X and provide the set of values and/or the control parameter set X to the HSC 540. The EO 530 uses a generalized optimization algorithm to determine an optimal set of values for the control parameter set X.sub.opt. The HSC 540 utilizes the set of values for the control parameter set X (e.g., an optimal control parameter set X.sub.opt) to determine the control variables to communicate to the electrical system 502… The two part approach of the EOESC 510, namely the EO 530 determining control parameters and then the HSC 540 determining the control variables, enables generation of a lasting set of controls, or a control solution (or plan) that can be used into the future rather than a single control to be applied at the present”, wherein under the broadest reasonable interpretation, examiner interpreted determining optimal set of values for control parameter set X as the first optimization of calculating multiple optimum solutions of the values regarding first optimization variable. Examiner interpreted determining of control parameter set X as first optimization, and determination of control variables as a second optimization. Therefore, examiner believes both steps can be interpreted as separate optimizations regarding their unique optimization variables, and so, Fife does teach first and second optimization, and does teach optimizing using a second optimization variable or objective function.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 3, and 6-8 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Fife USPGPUB 2017/0285111 (hereinafter “Fife”).
Regarding claim 1, Fife teaches a method for controlling energy conversion, energy storage, energy transportation, and/or energy consumption of multiple energy installations of an energy system and/or of multiple consumers flexible with regard to their load ([Abstract] “The present disclosure is directed to systems and methods for economically optimal control of an electrical system”), the method comprising:
optimizing values of variables for control by calculation ([Abstract] “Some embodiments employ generalized multivariable constrained continuous optimization techniques to determine an optimal control sequence over a future time domain in the presence of any number of costs, savings opportunities (value streams), and constraints”, wherein examiner interpreted generalized multivariable constrained optimization techniques to determine optimal control sequence for optimal control of an electrical system as optimizing values of variables for control by calculation);
wherein the optimization is based on a first optimization variable and a second optimization variable (Paragraph [0118] “A controller according to some embodiments of the present disclosure may use multi-variable optimization to determine the control variables”, wherein examiner interpreted multi-variable optimization as including first and second optimization variable);
wherein the first optimization variable includes an economic factor (Paragraph [0098] “An objective of optimization may be economic optimization, or determining economically optimal control variables to effectuate one or more changes to the electrical system to achieve economic efficiency (e.g., to operate the electrical system at as low a cost as may be possible, given the circumstances)”, wherein examiner interpreted objective optimization being an economic optimization as first optimization variable includes an economic factor);
calculating multiple solutions of the values, wherein the multiple solutions are optimum with regard to the first optimization variable, by way of a first optimization (Paragraph [0131] “The EO 530 uses a generalized optimization algorithm to determine an optimal set of values for the control parameter set X.sub.opt. The HSC 540 utilizes the set of values for the control parameter set X (e.g., an optimal control parameter set X.sub.opt) to determine the control variables to communicate to the electrical system 502”, wherein examiner interpreted EO using generalized optimization algorithm to determine optimal set of values for control parameter set of values to determine control variables as calculating multiple solutions of the values, wherein the multiple solutions are optimum with regard to the first optimization variable by way of a first optimization);
ascertaining one of the calculated solutions as optimum with regard to the second optimization variable as values of the variables for the control by way of a second optimization (Paragraph [0131] “The HSC 540 in some embodiments is also presumed to have ability to measure or obtain a current date and time. The two part approach of the EOESC 510, namely the EO 530 determining control parameters and then the HSC 540 determining the control variables, enables generation of a lasting set of controls, or a control solution (or plan) that can be used into the future rather than a single control to be applied at the present. Preparing a lasting control solution can be useful if the optimization algorithm takes a significant amount of time to execute. Preparing a lasting control solution can also be useful if there is a communication interruption between the calculating of the control parameter values and the processor interpreting the control parameters and sending control variables to the electrical system 502”, wherein examiner interpreted preparing lasting solution using control parameters and control variables as ascertaining one of the calculated solutions as optimum with regard to the second optimization variable as values of the variables for the control by way of a second optimization);
wherein the second optimization variable includes a technical factor (Paragraph [0042], Paragraph [0062-0073] “Process variables are typically measurements of the electrical system state and are used by the controller 110 to, among other things, determine how well its objectives are being met. These process variables may be read and used by the controller 110 to generate new control variable values. The rate at which process variables are read and used by the controller 110 depends upon the application but typically ranges from once per millisecond to once per hour. For battery energy storage system applications, the rate is often between 10 times per second and once per 15 minutes. Examples of process variables may include: [0063] Unadjusted net power [0064] Unadjusted demand [0065] Adjusted net power [0066] Demand [0067] Load (e.g., load energy consumption for one or more loads) [0068] Generation for one or more loads [0069] Actual ESS charge or generation rate for one or more ESS [0070] Frequency [0071] Energy storage device state of charge (SoC) (%) for one or more ESS [0072] Energy storage device temperature (deg. C.) for one or more ESS [0073] Electrical meter outputs such as kilowatt-hours (kWh) or demand”, Paragraph [0154] “The HSC process 601b determines 626 the values for the control variables by using the optimal control parameter set X.sub.opt in conjunction with a control law. The control laws specify how to determine the control variables from X (or X.sub.opt) and the process variables. Stated another way, the control law enforces the definition of X. For example, for a control parameter set X defined such that a particular element, X.sub.i, is an upper bound on demand to be applied at the present time, the control law may compare process variables such as the unadjusted demand to X.sub.i. If unadjusted building demand exceeds X.sub.i, the control law may respond with a command (in the form of a control variable) to instruct the ESS to discharge at a rate that will make the adjusted demand equal to or less than X.sub.i”, wherein examiner interpreted process variables that are measuremetns of electrical system related to power, demand, and ESS as the second optimization variable including a technical factor);
wherein the first optimization and the second optimization each depend on different associatedfunctions (Paragraph [0131] “The two part approach of the EOESC 510, namely the EO 530 determining control parameters and then the HSC 540 determining the control variables, enables generation of a lasting set of controls, or a control solution (or plan) that can be used into the future rather than a single control to be applied at the present”, Paragraphs [0087-0090] “The outputs of the controller 110 are the control variables that can affect the electrical system behavior. Examples of control variables are: [0088] ESS power command (kW or %). For example, an ESS power command of 50 kW would command the ESS to charge at a rate of 50 kW, and an ESS power command of −20 kW would command the ESS to discharge at a rate of 20 kW. [0089] Building or subsystem net power increase or reduction (kW or %) [0090] Renewable energy increase or curtailment (kW or %). For example a photovoltaic (PV) system curtailment command of −100 kW would command a PV system to limit generation to no less than −100 kW. Again, the negative sign is indicative of the fact that that the value is generative (non-consumptive)”, Paragraph [0122] “In certain embodiments, a control parameter set X can be defined (in conjunction with a control law) that is to be applied to the electrical system, how they should behave, and at what times in the future time domain they should be applied. In some embodiments, the cost function can be evaluated by performing a simulation of electrical system operation with a provided set X of control parameters. The control laws specify how to use X and the process variables to determine the control variables. The cost function can then be prepared or otherwise developed to consider the control parameter set X”, and Paragraph [0123], Paragraph [0290], wherein examiner interpreted determining control parameters set x, and determining control variables as using different associated-objective functions); and
using the optimum calculated solution for controlling the energy system (Paragraph [0129] “FIG. 5 is a control diagram of an electrical system 500, according to one embodiment of the present disclosure, including an EOESC 510. Stated otherwise, FIG. 5 is a diagram of a system architecture of the electrical system 500 including the EOESC 510, according to one embodiment. The electrical system 500 comprises a building electrical system 502 that is controlled by the EOESC 510. The building electrical system 502 includes one or more loads 522, one or more generators 524, an energy storage system (ESS) 526, and one or more sensors 528 (e.g., meters) to provide measurements or other indication(s) of a state of the building electrical system 502”, Paragraph [0130] “The EOESC 510 receives or otherwise obtains a configuration of the electrical system, external inputs, and process variables and produces control variables to be sent to the electrical system 502 to effectuate a change to the electrical system toward meeting a controller objective for economical optimization of the electrical system, for example during an upcoming time domain”, and Fig. 6, Paragraph [0136], wherein examiner interpreted controlling electrical system using EOESC produced control variables as using the optimum calculated solution for controlling the energy system).
Regarding claim 3, Fife teaches wherein the second optimization variable includes a degree of use of an electricity grid, the peak power, generation peaks and/or load peaks, prioritization according to type of load, prioritization according to instability of a load, an availability of one or more energy installations, and/or or an emission (Paragraph [0129] “FIG. 5 is a control diagram of an electrical system 500, according to one embodiment of the present disclosure, including an EOESC 510. Stated otherwise, FIG. 5 is a diagram of a system architecture of the electrical system 500 including the EOESC 510, according to one embodiment. The electrical system 500 comprises a building electrical system 502 that is controlled by the EOESC 510. The building electrical system 502 includes one or more loads 522, one or more generators 524, an energy storage system (ESS) 526, and one or more sensors 528 (e.g., meters) to provide measurements or other indication(s) of a state of the building electrical system 502. The building electrical system 502 is coupled to an electrical utility distribution system 550, and therefore may be considered on-grid”, and Paragraph [0130] “The EOESC 510 receives or otherwise obtains a configuration of the electrical system, external inputs, and process variables and produces control variables to be sent to the electrical system 502 to effectuate a change to the electrical system toward meeting a controller objective for economical optimization of the electrical system, for example during an upcoming time domain”, and Paragraph [0131], wherein examiner interpreted EOESC optimizing electrical system as second optimization variable including a degree of use of an electricity grid, the peak power, generation peaks and/or load peaks, prioritization according to type of load, prioritization according to instability of a load, an availability of one or more energy installations, and/or or an emission).
Regarding claim 6, Fife teaches wherein: the energy system comprises multiple electric vehicles as flexible consumers within a time range (Paragraph [0042] “A battery is a familiar example of a chemical energy storage device. For example, in one embodiment of the present disclosure, one or more electric vehicle batteries is connected to an electrical system and can be used to store energy for later use by the electrical system”);
the first optimization variable includes the total charging energy within the time range (Paragraph [0130] “The EOESC 510 receives or otherwise obtains a configuration of the electrical system, external inputs, and process variables and produces control variables to be sent to the electrical system 502 to effectuate a change to the electrical system toward meeting a controller objective for economical optimization of the electrical system, for example during an upcoming time domain. The EOESC 510 may include electronic hardware and software to process the inputs (e.g., the configuration of the electrical system, external inputs, and process variables) to determine values for each of the control variables”, and Paragraph [0091] “consider that an objective of the controller 110 may be to reduce demand charges while preserving battery life. In this example, only the ESS may be controlled. To accomplish this objective, the controller should have knowledge of a configuration of the electrical system 102, such as the demand rates and associated time windows, the battery capacity, the battery type and arrangement, etc. Other external inputs may also be used to help the controller 110 meet its objectives, such as a forecast of upcoming load and/or forecast of upcoming weather (e.g., temperature, expected solar irradiance, wind). Process variables from the electrical system 102 that may be used may provide information concerning a net electrical system power or energy consumption, demand, a battery SoC, an unadjusted building load, and an actual battery charge or discharge power. In this one illustrative example, the control variable may be a commanded battery ESS's charge or discharge power” wherein examiner interpreted EOESC processing inputs that include configuration of electrical system which includes battery charge or discharge power as first optimization variable to include the total charging energy within the time range);
the second optimization variable includes minimizing the total power (Paragraph [0054] “the controller 110 may attempt to meet certain objectives by changing a value associated with one or more control variables, if necessary. The objectives may be predefined, and may also be dependent on time, on any external inputs, on any process variables that are obtained from the building electrical system 102, and/or on the control variables themselves. Some examples of controller objectives for different applications are: [0055] Minimize demand (kW) over a prescribed time interval; [0056] Minimize demand charges ($) over a prescribed time interval; [0057] Minimize total electricity charges ($) from the grid; [0058] Reduce demand (kW) from the grid by a prescribed amount during a prescribed time window; and [0059] Maximize the life of the energy storage device”, and Paragraph [0130-0131] wherein examiner interpreted minimizing demand over prescribed time interval as the second optimization variable includes minimizing the total power);
the first optimization includes minimizing the total charging energy (Paragraph [0130] “The EOESC 510 receives or otherwise obtains a configuration of the electrical system, external inputs, and process variables and produces control variables to be sent to the electrical system 502 to effectuate a change to the electrical system toward meeting a controller objective for economical optimization of the electrical system, for example during an upcoming time domain. The EOESC 510 may include electronic hardware and software to process the inputs (e.g., the configuration of the electrical system, external inputs, and process variables) to determine values for each of the control variables”, and Paragraph [0091] “consider that an objective of the controller 110 may be to reduce demand charges while preserving battery life. In this example, only the ESS may be controlled. To accomplish this objective, the controller should have knowledge of a configuration of the electrical system 102, such as the demand rates and associated time windows, the battery capacity, the battery type and arrangement, etc. Other external inputs may also be used to help the controller 110 meet its objectives, such as a forecast of upcoming load and/or forecast of upcoming weather (e.g., temperature, expected solar irradiance, wind). Process variables from the electrical system 102 that may be used may provide information concerning a net electrical system power or energy consumption, demand, a battery SoC, an unadjusted building load, and an actual battery charge or discharge power. In this one illustrative example, the control variable may be a commanded battery ESS's charge or discharge power” wherein examiner interpreted EOESC processing inputs that include configuration of electrical system which includes battery charge or discharge power that optimizes battery charging as first optimization that includes minimizing the total charging energy); and
the second optimization includes minimizing the total power (Paragraph [0054] “the controller 110 may attempt to meet certain objectives by changing a value associated with one or more control variables, if necessary. The objectives may be predefined, and may also be dependent on time, on any external inputs, on any process variables that are obtained from the building electrical system 102, and/or on the control variables themselves. Some examples of controller objectives for different applications are: [0055] Minimize demand (kW) over a prescribed time interval; [0056] Minimize demand charges ($) over a prescribed time interval; [0057] Minimize total electricity charges ($) from the grid; [0058] Reduce demand (kW) from the grid by a prescribed amount during a prescribed time window; and [0059] Maximize the life of the energy storage device”, and Paragraph [0130-0131] wherein examiner interpreted minimizing demand over prescribed time interval as the second optimization variable includes minimizing the total power).
Regarding claim 7, Fife teaches wherein: the first optimization and the second optimization are performed by a local energy market platform (Paragraph [0130] “The EOESC 510 receives or otherwise obtains a configuration of the electrical system, external inputs, and process variables and produces control variables to be sent to the electrical system 502 to effectuate a change to the electrical system toward meeting a controller objective for economical optimization of the electrical system, for example during an upcoming time domain”, and Paragraph [0131], wherein examiner interpreted EOESC as a local energy market platform);
the local energy market platform transmits a control signal intended for the control to the energy system (Paragraph [0130] “The EOESC 510 receives or otherwise obtains a configuration of the electrical system, external inputs, and process variables and produces control variables to be sent to the electrical system 502 to effectuate a change to the electrical system toward meeting a controller objective for economical optimization of the electrical system, for example during an upcoming time domain”, and Paragraph [0131], wherein examiner interpreted energy market platform transmitting control variable to electrical system as transmitting a control signal intended for control to the energy system); and
the control signal is based on the optimum solution (Paragraph [0131] “The two part approach of the EOESC 510, namely the EO 530 determining control parameters and then the HSC 540 determining the control variables, enables generation of a lasting set of controls, or a control solution (or plan) that can be used into the future rather than a single control to be applied at the present. Preparing a lasting control solution can be useful if the optimization algorithm takes a significant amount of time to execute. Preparing a lasting control solution can also be useful if there is a communication interruption between the calculating of the control parameter values and the processor interpreting the control parameters and sending control variables to the electrical system 502”, wherein sending control variables based on the optimization of creating a lasting control solution as the control signal based on the optimum solution).
Regarding claim 8, Fife teaches wherein the energy system transmits technical data related to energy installations and/or the flexible consumers to the local energy market platform for the first optimization and/or the second optimization (Paragraph [0130] “The EOESC 510 receives or otherwise obtains a configuration of the electrical system, external inputs, and process variables and produces control variables to be sent to the electrical system 502 to effectuate a change to the electrical system toward meeting a controller objective for economical optimization of the electrical system, for example during an upcoming time domain. The EOESC 510 may include electronic hardware and software to process the inputs (e.g., the configuration of the electrical system, external inputs, and process variables) to determine values for each of the control variables”, and wherein Paragraph [0129] “The electrical system 500 comprises a building electrical system 502 that is controlled by the EOESC 510. The building electrical system 502 includes one or more loads 522, one or more generators 524, an energy storage system (ESS) 526, and one or more sensors 528 (e.g., meters) to provide measurements or other indication(s) of a state of the building electrical system 502. The building electrical system 502 is coupled to an electrical utility distribution system 550, and therefore may be considered on-grid”, wherein examiner interpreted receiving or obtaining configuration of electrical system as energy system transmitting technical data related to energy installations and/or the flexible consumers to the local energy market platform for the first optimization and/or the second optimization).
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.
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 2, and 4 are rejected under 35 U.S.C. 103 as being unpatentable over Fife USPGPUB 2017/0285111 (hereinafter “Fife”) as applied to claims 1, 3, and 6-8 above, in view of Zhou et al. USPGPUB 2017/0308968 (hereinafter “Zhou”).
Regarding claim 2, Fife teaches all of the features with respect to claim 1 as outlined above.
Fife does not explicitly teach wherein the first optimization variable and the second optimization variable are defined according to a fixed priority.
However, Zhou teaches wherein the first optimization variable and the second optimization variable are defined according to a fixed priority (Paragraph [0012] “an optimal energy scheduling process may be pre-performed by determining operation priorities of the multi-type energy supply devices and an operation mode of the CCHP 10 unit at each time interval in a scheduling period. In such a way, it could greatly facilitate the result searching of optimal solutions of the device capacity determination process”, wherein examiner interpreted optimal energy scheduling process being performed by determining priorities of the multi-type energy supply devices as first and second optimization variable being deigned according to a fixed priority).
Fife, and Zhou are analogous art because they are from the same field of endeavor and contain overlapping structural and functional similarities. They relate to optimization.
Therefore, before the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above system for controlling an energy system as taught by Fife, and incorporating a fixed priority, as taught by Zhou.
One of ordinary skill in the art would have been motivated to improve Paragraph [0012] “searching of optimal solutions of the device capacity determination process”, as suggested by Zhou.
Regarding claim 4, Fife teaches all of the features with respect to claim 1 as outlined above.
Fife does not explicitly teach wherein ascertaining the optimum solution includes a calculation based on Pareto Principles.
However, Zhou teaches wherein ascertaining the optimum solution includes a calculation based on Pareto Principles (Paragraph [0203] “In a multi-objective optimization, usually there is no unique globally optimal solution, but there exists a Pareto optimum set, composed by a group of mutually non-dominant solutions. Consequently, in the MOPSO implementation, a container is constructed to hold the Pareto non-dominant solutions, and the changes of the particles' speeds are guided by these non-dominant solutions. Hereinafter, an example process will be described to explain the approach to searching the optimal solution with reference to FIG. 8”, wherein examiner interpreted searching for optimal solution using Pareto optimum set as ascertaining the optimum solution includes a calculation based on Pareto Principles).
Fife, and Zhou are analogous art because they are from the same field of endeavor and contain overlapping structural and functional similarities. They relate to optimization.
Therefore, before the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above system for controlling an energy system as taught by Fife, and incorporating Pareto Principles, as taught by Zhou.
One of ordinary skill in the art would have been motivated to improve Paragraph [0012] “searching of optimal solutions of the device capacity determination process”, as suggested by Zhou.
Citation of Pertinent Prior Art
The prior art made of record and on the attached PTO Form 892 but not relied upon is considered pertinent to applicant's disclosure.
KUMAR et al. [USPGPUB 2019/0079473] teaches a controller that is configured to control the equipment to achieve the optimal allocation of energy resource.
Ozog et al. [USPGPUB 2011/0231028] teaches systems and methods for energy optimization.
Sarker et al. [USPGPUB 2019/0139159] teaches a system that is provided to identify energy opportunities for an entity (e.g., supplier or consumer) to help ensure than an objective of the entity is me
Conclusion
THIS ACTION IS MADE FINAL. 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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/D.P./ Examiner, Art Unit 2119
/MOHAMMAD ALI/ Supervisory Patent Examiner, Art Unit 2119