DETAILED ACTION
Claims 1, 7, 10, 11, 17 and 20-34 are pending. Claims 2-6, 8-9, 12-16 and 18-19 are cancelled.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 2/24/2026 has been entered.
Response to Arguments
Applicant’s arguments, filed 2/24/26, have been fully considered but are not persuasive, except where noted below.
Applicant’s arguments regarding 35 U.S.C. § 112 (page 1) are persuasive and the claims are no longer rejected under that statute.
Applicant argues, with regard to 35 U.S.C. § 103, that ‘Shi does not teach, suggest or motivate "determining a power flow allocation using a power flow machine learning model" as recited in part by amended claim 1. Furthermore, Applicant respectfully asserts that, therefore, Shi cannot teach, suggest, or motivate "transmitting the command based on the power flow allocation."’ (page 2).
It is respectfully submitted that Shi teaches these limitations because Shi describes that computing device 104 may generate one or more power output (output power flow allocation) recommendations for a local grid operator, power-consuming entity, or the like… Optimization engine 228 may be used to generate recommended courses of action for optimization of carbon output and/or costs and may produce inputs to forecast models 224. Results of optimization may be used as control decisions that are dispatched to energy resources such as power generators, local grid monitors, or other devices and/or entities making decisions affecting power generation and/or power consumption parameters in local grid. Power consumption parameters may include, without limitation, customers' energy storage, electric vehicle charging, diesel generation, fuel cells, flexible loads, and the like [0036, 0072-0076]; that quantities of power produced by a power generator (output power flow quantities) and/or category of fuel as in fuel mix data as described above, in kilowatts, percentage and/or proportion of total power flowing through grid of each quantity produced by each power generator… real-time and forecasted signals may be sent to optimization engine. The outputs of optimization engine may include dispatches sent to control energy resources [0024]; and that computing device 104 may perform one or more processes, as described below, using techniques including machine learning, optimization [0026, 0032, 0046-0048]. See the rejection below under 35 U.S.C. § 103. Applicant’s argument is therefore not persuasive.
Applicant’s arguments regarding Sitton and Snook (page 3) are moot because Sitton and Snook are not needed to cure any alleged deficiencies in Shi. Applicant’s argument is therefore not persuasive.
Applicant’s arguments regarding claim 11 (page 3) that recites similar limitations to claim 1 are not persuasive for similar reasons to those given above regarding claim 1.
Applicant’s arguments regarding the dependent claims and Sitton, Snook, Wang, Sutton and Garcia (page 3-5) are not persuasive because Shi teaches the new limitations in claims 1 and 11, as detailed above, and the other references are therefore not needed to cure any alleged deficiencies in Shi.
For at least these reasons, the rejection of the claims is maintained.
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 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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claim(s) 1, 7, 11, 17, 21-25 and 28-32 and is/are rejected under 35 U.S.C. 103 as being unpatentable over Shi U.S. Patent Publication No. 20200372588 (hereinafter Shi) in view of Sitton U.S. Patent Publication No. 20100249955 (hereinafter Sitton) and further in view of Snook et al. U.S. Patent Publication No. 20200374605 (hereinafter Snook).
Regarding claim 1, Shi teaches an apparatus for generating a projected avoided carbon tonnage in a power grid network [0019-0022, Fig. 1 — a system 100 for machine-learning for prediction grid carbon emissions… first and second grids 112 and 120; 0029 — Processes may calculate projected avoided carbon tonnage, given a projected reduction in carbon consumption and/or marginal emission rate], the apparatus comprises:
at least a processor [0090-0093, Fig. 12 — a processor 1204… Memory 1208 may also include (e.g., stored on one or more machine-readable media) instructions (e.g., software) 1220 embodying any one or more of the aspects and/or methodologies of the present disclosure…storage device 1224 and an associated machine-readable medium 1228 may provide nonvolatile and/or volatile storage of machine-readable instructions, data structures, program modules, and/or other data for computer system 1200]; and
a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor [0090-0093, Fig. 12 — a processor 1204… Memory 1208 may also include (e.g., stored on one or more machine-readable media) instructions (e.g., software) 1220 embodying any one or more of the aspects and/or methodologies of the present disclosure…storage device 1224 and an associated machine-readable medium 1228 may provide nonvolatile and/or volatile storage of machine-readable instructions, data structures, program modules, and/or other data for computer system 1200] to:
receive a sectional power flow datum flowing between a first node and a second node from a specified grid monitoring device communicatively connected to a grid network, wherein the sectional power flow datum comprises a consumption datum and a generation datum [0026 — grid power consumed from a local grid by an entity such as a building, business, one or more items of machinery and/or power storage, or the like (first and second nodes); 0034-0035 — computing device 104 may fetch grid and/or market data from one or more local grid monitoring devices, such as without limitation generation fuel mix, marginal fuel type, system demand (consumption), locational marginal prices (LMPs), historical emissions, and emission data from a reporting service 136 such as the EPA… Computing device 104 may connect with a user's main meter which may measure net power from local power grid in real time and the user's controllable energy resources such as energy storage, EV charging, diesel generation, fuel cells, flexible loads, and the like. User's historical metering data may be imported computing from the user's own metering system or a third party that collects metering data from utilities];
generate a grid modification using the power flow to reduce a carbon footprint of the grid network [0036, 0072 — Optimization engine 228 may be used to generate recommended courses of action for optimization of carbon output and/or costs and may produce inputs to forecast models 224. Results of optimization may be used as control decisions that are dispatched to energy resources such as power generators, local grid monitors, or other devices and/or entities making decisions affecting power generation and/or power consumption parameters in local grid. Power consumption parameters may include, without limitation, customers' energy storage, electric vehicle charging, diesel generation, fuel cells, flexible loads, and the like]; and
transmit a command to a control device located on the grid network to modify the grid using the grid modification [0036, 0072-0076 — Optimization engine 228 may be used to generate recommended courses of action for optimization of carbon output and/or costs and may produce inputs to forecast models 224. Results of optimization may be used as control decisions that are dispatched to energy resources such as power generators, local grid monitors, or other devices and/or entities making decisions affecting power generation and/or power consumption parameters in local grid. Power consumption parameters may include, without limitation, customers' energy storage, electric vehicle charging, diesel generation, fuel cells, flexible loads, and the like… real-time and forecasted signals may be sent to optimization engine. The outputs of optimization engine may include dispatches sent to control energy resources (commands)], wherein the transmitting further comprising: determining a power flow allocation using a power flow machine learning model and transmitting the command based on the power flow allocation [0036, 0072-0076 — computing device 104 may generate one or more power output (output power flow allocation) recommendations for a local grid operator, power-consuming entity, or the like… Optimization engine 228 may be used to generate recommended courses of action for optimization of carbon output and/or costs and may produce inputs to forecast models 224. Results of optimization may be used as control decisions that are dispatched to energy resources such as power generators, local grid monitors, or other devices and/or entities making decisions affecting power generation and/or power consumption parameters in local grid. Power consumption parameters may include, without limitation, customers' energy storage, electric vehicle charging, diesel generation, fuel cells, flexible loads, and the like; 0024 — quantities of power produced by a power generator (output power flow quantities) and/or category of fuel as in fuel mix data as described above, in kilowatts, percentage and/or proportion of total power flowing through grid of each quantity produced by each power generator… real-time and forecasted signals may be sent to optimization engine. The outputs of optimization engine may include dispatches sent to control energy resources; 0026, 0032, 0046-0048 — computing device 104 may perform one or more processes, as described below, using techniques including machine learning, optimization].
But Shi fails to clearly specify determine a carbon flow using the power flow datum and a voltage control device located on the grid network to modify a voltage level between the first node and the second node using the grid modification.
However, Sitton teaches determining a carbon footprint using power flow [0004, 0022 — It is also known to assess the carbon footprint of a national electricity supply.; 0038-0039 — in an environment where changes in energy consumption will appear entirely in the power taken from a national electricity supply, the conversion to carbon footprint can be done simply by using a known conversion factor for the relevant electricity supply].
Shi and Sitton are analogous art. They relate to energy consumption management systems, particularly involving carbon emissions.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above apparatus, as taught by Shi, to determine a carbon flow using the sectional power flow datum, based on the teachings of Sitton.
One of ordinary skill in the art would have been motivated to do this modification in order to facilitate comparing different types of power generation sources by converting each to a common carbon standard, as suggested by the teachings of Sitton [0004-0009].
But the combination of Shi and Sitton fails to clearly specify a voltage control device located on the grid network to modify a voltage level between the first node and the second node using the grid modification.
However, Snook teaches a voltage control device located on the grid network to modify a voltage level between the first node and the second node using the grid modification [0033, 0046 — once the intra-grid sensors are used to reveal where excessive energy levels are occurring within distribution grids, volt/var optimization (i.e., VVO) practices such as but not limited to conservation voltage reduction (CVR) can be implemented to sufficiently regulate voltage levels throughout segments of, and/or the entire distribution grid. Thus, via intra-grid sensors reveal excessive intra-grid voltages, and utilities subsequently applying remediation practices to lessen the excessive energy levels, energy inefficiency/waste is replaced by energy efficiency. Thus, lesser energy levels are then required within the distribution grid, and this transcends upstream to result in a decrease in generation demand. In turn, lesser generation results in reduced GHG].
Shi, Sitton and Snook are analogous art. They relate to energy consumption management systems, particularly involving carbon emissions.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above apparatus, as taught by the combination of Shi and Sitton, by incorporating the above limitations, as taught by Snook.
One of ordinary skill in the art would have been motivated to do this modification in order to reduce greenhouse gas (GHG)/carbon emissions, as suggested by Snook [0033, 0046]. In addition, it would be obvious to one having ordinary skill in the art simply substitute the known voltage control device/level modification of Snook for the known grid control device/modification of Shi and Sitton for the predictable result of an apparatus for generating a projected avoided carbon tonnage that utilizes voltage level modification of the grid.
Regarding claim 7, the combination of Shi, Sitton and Snook teaches all the limitations of the base claims as outlined above.
Further, Shi teaches generate a projected power flow using an objective function [0075 — FIG. 4, an energy resource control problem may be formulated as an optimization problem which may be characterized, in a non-limiting example, as follows:
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preference. Objectives may include 1) minimization of retail energy costs; 2) minimization of energy resource costs; 3) maximization grid service revenues; and/or 4) minimization of carbon emissions. Solving the above problem may produce optimal schedules pi for each energy resource i.; 0026, 0032, 0046-0048 — computing device 104 may perform one or more processes, as described below, using techniques including machine learning, optimization — cf. paragraph 0054 of the instant specification/PGPub that states the objective function 148 may be a machine learning model].
Further, Sitton teaches determining a carbon footprint using power flow [0004, 0022 — It is also known to assess the carbon footprint of a national electricity supply.; 0038-0039 — in an environment where changes in energy consumption will appear entirely in the power taken from a national electricity supply, the conversion to carbon footprint can be done simply by using a known conversion factor for the relevant electricity supply].
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above apparatus, as taught by Shi, to generate a projected carbon flow as a function of a carbon optimization model, based on the teachings of Sitton.
One of ordinary skill in the art would have been motivated to do this modification in order to facilitate comparing different types of power generation sources by converting each to a common carbon standard, as suggested by the teachings of Sitton [0004-0009].
Regarding claim 11, Shi teaches a method for generating a projected avoided carbon tonnage in a power grid network [0004, 0018 — a method of machine-learning for prediction grid carbon emissions ; 0018-0022, Fig. 1 — artificial intelligence and machine-learning methods are used to estimate real-time emission impacts based on grid data… a system 100 for machine-learning for prediction grid carbon emissions… first and second grids 112 and 120; 0029 — Processes may calculate projected avoided carbon tonnage, given a projected reduction in carbon consumption and/or marginal emission rate], the method comprising:
receiving, by at least a processor [0090-0093, Fig. 12 — a processor 1204… Memory 1208 may also include (e.g., stored on one or more machine-readable media) instructions (e.g., software) 1220 embodying any one or more of the aspects and/or methodologies of the present disclosure…storage device 1224 and an associated machine-readable medium 1228 may provide nonvolatile and/or volatile storage of machine-readable instructions, data structures, program modules, and/or other data for computer system 1200], a sectional power flow datum flowing between a first node and a second node from a specific grid monitoring device communicatively connected to a grid network, wherein the sectional power flow datum comprises a consumption datum and a generation datum [0026 — grid power consumed from a local grid by an entity such as a building, business, one or more items of machinery and/or power storage, or the like (first and second nodes); 0034-0035 — computing device 104 may fetch grid and/or market data from one or more local grid monitoring devices, such as without limitation generation fuel mix, marginal fuel type, system demand (consumption), locational marginal prices (LMPs), historical emissions, and emission data from a reporting service 136 such as the EPA… Computing device 104 may connect with a user's main meter which may measure net power from local power grid in real time and the user's controllable energy resources such as energy storage, EV charging, diesel generation, fuel cells, flexible loads, and the like. User's historical metering data may be imported computing from the user's own metering system or a third party that collects metering data from utilities];
generating, by the at least a processor, a grid modification using the power flow to reduce a carbon footprint of the grid network [0036, 0072 — Optimization engine 228 may be used to generate recommended courses of action for optimization of carbon output and/or costs and may produce inputs to forecast models 224. Results of optimization may be used as control decisions that are dispatched to energy resources such as power generators, local grid monitors, or other devices and/or entities making decisions affecting power generation and/or power consumption parameters in local grid. Power consumption parameters may include, without limitation, customers' energy storage, electric vehicle charging, diesel generation, fuel cells, flexible loads, and the like]; and
transmitting, by the at least one processor, a command to a control device located on the grid network to modify the grid using the grid modification [0036, 0072 — Optimization engine 228 may be used to generate recommended courses of action for optimization of carbon output and/or costs and may produce inputs to forecast models 224. Results of optimization may be used as control decisions that are dispatched to energy resources such as power generators, local grid monitors, or other devices and/or entities making decisions affecting power generation and/or power consumption parameters in local grid. Power consumption parameters may include, without limitation, customers' energy storage, electric vehicle charging, diesel generation, fuel cells, flexible loads, and the like], wherein the transmitting further comprising: determining a power flow allocation using a power flow machine learning model and transmitting the command based on the power flow allocation [0036, 0072-0076 — computing device 104 may generate one or more power output (output power flow allocation) recommendations for a local grid operator, power-consuming entity, or the like… Optimization engine 228 may be used to generate recommended courses of action for optimization of carbon output and/or costs and may produce inputs to forecast models 224. Results of optimization may be used as control decisions that are dispatched to energy resources such as power generators, local grid monitors, or other devices and/or entities making decisions affecting power generation and/or power consumption parameters in local grid. Power consumption parameters may include, without limitation, customers' energy storage, electric vehicle charging, diesel generation, fuel cells, flexible loads, and the like; 0024 — quantities of power produced by a power generator (output power flow quantities) and/or category of fuel as in fuel mix data as described above, in kilowatts, percentage and/or proportion of total power flowing through grid of each quantity produced by each power generator… real-time and forecasted signals may be sent to optimization engine. The outputs of optimization engine may include dispatches sent to control energy resources; 0026, 0032, 0046-0048 — computing device 104 may perform one or more processes, as described below, using techniques including machine learning, optimization].
But Shi fails to clearly specify determining a carbon flow as a function of the power flow datum and a voltage control device located on the grid network to modify a voltage level between the first node and the second node using the grid modification.
However, Sitton teaches determining a carbon footprint as function of power flow [0004, 0022 — It is also known to assess the carbon footprint of a national electricity supply.; 0038-0039 — in an environment where changes in energy consumption will appear entirely in the power taken from a national electricity supply, the conversion to carbon footprint can be done simply by using a known conversion factor for the relevant electricity supply].
Shi and Sitton are analogous art. They relate to energy consumption management systems, particularly involving carbon emissions.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by Shi, to determine a carbon flow as a function of the sectional power flow datum and generate the projected avoided carbon tonnage as a function of the carbon flow, based on the teachings of Sitton.
One of ordinary skill in the art would have been motivated to do this modification in order to facilitate comparing different types of power generation sources by converting each to a common carbon standard, as suggested by the teachings of Sitton [0004-0009].
But the combination of Shi and Sitton fails to clearly specify a voltage control device located on the grid network to modify a voltage level between the first node and the second node using the grid modification.
However, Snook teaches a voltage control device located on the grid network to modify a voltage level between the first node and the second node using the grid modification [0033, 0046 — once the intra-grid sensors are used to reveal where excessive energy levels are occurring within distribution grids, volt/var optimization (i.e., VVO) practices such as but not limited to conservation voltage reduction (CVR) can be implemented to sufficiently regulate voltage levels throughout segments of, and/or the entire distribution grid. Thus, via intra-grid sensors reveal excessive intra-grid voltages, and utilities subsequently applying remediation practices to lessen the excessive energy levels, energy inefficiency/waste is replaced by energy efficiency. Thus, lesser energy levels are then required within the distribution grid, and this transcends upstream to result in a decrease in generation demand. In turn, lesser generation results in reduced GHG].
Shi, Sitton and Snook are analogous art. They relate to energy consumption management systems, particularly involving carbon emissions.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by the combination of Shi and Sitton, by incorporating the above limitations, as taught by Snook.
One of ordinary skill in the art would have been motivated to do this modification in order to reduce greenhouse gas (GHG)/carbon emissions, as suggested by Snook [0033, 0046]. In addition, it would be obvious to one having ordinary skill in the art simply substitute the known voltage control device/level modification of Snook for the known grid control device/modification of Shi and Sitton for the predictable result of an method for generating a projected avoided carbon tonnage that utilizes voltage level modification of the grid.
Regarding claim 17, the combination of Shi, Sitton and Snook teaches all the limitations of the base claims as outlined above and this claim is otherwise rejected under the same rationale as claim 7.
Regarding claim 21, the combination of Shi, Sitton and Snook teaches all the limitations of the base claims as outlined above.
Further, Shi teaches generate the projected avoided carbon tonnage based on the projected power flow [0029 — Processes may calculate projected avoided carbon tonnage, given a projected reduction in carbon consumption and/or marginal emission rate; 0036, Fig. 2 — Real-time data streams may be as inputs continuously fed to models, such as real-time models 220 for production of current values such as current carbon intensity and/or cumulative past carbon tonnage and forecast models 224 used for predicting future carbon intensity, past or future carbon tonnage, past or future avoided carbon tonnage, and/or costs; 0076 — both real-time and forecasted signals may be sent to optimization engine. The outputs of optimization engine may include dispatches sent to control energy resources. Future input signals such as price signals, grid service signals, and grid carbon intensities may be input… A load for user may be forecasted].
Further, Sitton teaches determining a carbon footprint as function of power flow [0004, 0022 — It is also known to assess the carbon footprint of a national electricity supply.; 0038-0039 — in an environment where changes in energy consumption will appear entirely in the power taken from a national electricity supply, the conversion to carbon footprint can be done simply by using a known conversion factor for the relevant electricity supply].
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above apparatus, as taught by Shi, to generate the projected avoided carbon tonnage as a function of the projected carbon flow, based on the teachings of Sitton.
One of ordinary skill in the art would have been motivated to do this modification in order to facilitate comparing different types of power generation sources by converting each to a common carbon standard, as suggested by the teachings of Sitton [0004-0009].
Regarding claim 22, the combination of Shi, Sitton and Snook teaches all the limitations of the base claims as outlined above.
Further, Shi teaches determine an energy cost using the objective function [0036-0037 — Real-time data streams may be as inputs continuously fed to models, such as real-time models 220 for production of current values such as current carbon intensity and/or cumulative past carbon tonnage and forecast models 224 used for predicting future carbon intensity, past or future carbon tonnage, past or future avoided carbon tonnage, and/or costs; models may generate outputs that are sent to an optimization engine 228. Optimization engine 228 may be used to generate recommended courses of action for optimization of carbon output and/or costs and may produce inputs to forecast models 224. Results of optimization may be used as control decisions that are dispatched to energy resources such as power generators, local grid monitors, or other devices; 0073-0074 — computing device 104 may generate a power output recommendation minimizing a function of carbon output and cost; 0075 — FIG. 4, an energy resource control problem may be formulated as an optimization problem which may be characterized, in a non-limiting example, as follows:
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preference. Objectives may include 1) minimization of retail energy costs; 2) minimization of energy resource costs; 3) maximization grid service revenues; and/or 4) minimization of carbon emissions. Solving the above problem may produce optimal schedules pi for each energy resource i.; 0026, 0032, 0046-0048 — computing device 104 may perform one or more processes, as described below, using techniques including machine learning, optimization].
Regarding claim 23, the combination of Shi, Sitton and Snook teaches all the limitations of the base claims as outlined above.
Further, Shi teaches the objective function comprises a carbon optimization machine learning model [0073-0074 — computing device 104 may generate a power output recommendation minimizing a function of carbon output and cost; 0075 — FIG. 4, an energy resource control problem may be formulated as an optimization problem which may be characterized, in a non-limiting example, as follows:
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preference. Objectives may include 1) minimization of retail energy costs; 2) minimization of energy resource costs; 3) maximization grid service revenues; and/or 4) minimization of carbon emissions. Solving the above problem may produce optimal schedules pi for each energy resource i.; 0026, 0032, 0046-0048 — computing device 104 may perform one or more processes, as described below, using techniques including machine learning, optimization] ; and
the processor [0090-0093, Fig. 12 — a processor 1204… Memory 1208 may also include (e.g., stored on one or more machine-readable media) instructions (e.g., software) 1220 embodying any one or more of the aspects and/or methodologies of the present disclosure…storage device 1224 and an associated machine-readable medium 1228 may provide nonvolatile and/or volatile storage of machine-readable instructions, data structures, program modules, and/or other data for computer system 1200] is configured to train the carbon optimization machine learning model using a carbon optimization training set [0036-0037 — Real-time data streams may be as inputs continuously fed to models, such as real-time models 220 for production of current values such as current carbon intensity and/or cumulative past carbon tonnage and forecast models 224 used for predicting future carbon intensity, past or future carbon tonnage, past or future avoided carbon tonnage, and/or costs; models may generate outputs that are sent to an optimization engine 228. Optimization engine 228 may be used to generate recommended courses of action for optimization of carbon output and/or costs and may produce inputs to forecast models 224. Results of optimization may be used as control decisions that are dispatched to energy resources such as power generators, local grid monitors, or other devices; 0048 — computing device 104 trains emission projection machine-learning process using the plurality of training data entries. Training may be accomplished using one or more machine-learning algorithms as defined above. Machine-learning algorithms may include, without limitation, supervised machine-learning algorithms. Supervised machine learning algorithms, as defined in this disclosure, include algorithms that receive a training set relating a number of inputs to a number of outputs, and seek to find one or more mathematical relations relating inputs to outputs, where each of the one or more mathematical relations is optimal according to some criterion specified to the algorithm using some scoring function. For instance, a supervised learning algorithm may include inputs described in this disclosure as inputs, including without limitation power output quantities as described above, as well as various alternative or additional exogenous signals such as time, weather, system demand, market conditions, or the like, outputs such as projected carbon emission rates for instance including both average and marginal carbon intensities, current rate of change of carbon emission rates, current carbon emission rates, and/or projected rates of change of carbon emission rates, and a scoring function representing a desired form of relationship to be detected between inputs and outputs; 0073-0074 — computing device 104 may generate a power output recommendation minimizing a function of carbon output and cost; 0073-0074 — computing device 104 may generate a power output recommendation minimizing a function of carbon output and cost; 0075 — FIG. 4, an energy resource control problem may be formulated as an optimization problem which may be characterized, in a non-limiting example, as follows:
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preference. Objectives may include 1) minimization of retail energy costs; 2) minimization of energy resource costs; 3) maximization grid service revenues; and/or 4) minimization of carbon emissions. Solving the above problem may produce optimal schedules pi for each energy resource i.; 0026, 0032, 0046-0048 — computing device 104 may perform one or more processes, as described below, using techniques including machine learning, optimization].
Regarding claim 24, the combination of Shi, Sitton and Snook teaches all the limitations of the base claims as outlined above.
Further, Shi teaches the carbon optimization training set comprises a first set of stored relational rules [0048 — computing device 104 trains emission projection machine-learning process using the plurality of training data entries. Training may be accomplished using one or more machine-learning algorithms as defined above. Machine-learning algorithms may include, without limitation, supervised machine-learning algorithms. Supervised machine learning algorithms, as defined in this disclosure, include algorithms that receive a training set relating a number of inputs to a number of outputs, and seek to find one or more mathematical relations relating inputs to outputs, where each of the one or more mathematical relations is optimal according to some criterion specified to the algorithm using some scoring function. For instance, a supervised learning algorithm may include inputs described in this disclosure as inputs, including without limitation power output quantities as described above, as well as various alternative or additional exogenous signals such as time, weather, system demand, market conditions, or the like, outputs such as projected carbon emission rates for instance including both average and marginal carbon intensities, current rate of change of carbon emission rates, current carbon emission rates, and/or projected rates of change of carbon emission rates, and a scoring function representing a desired form of relationship to be detected between inputs and outputs].
Regarding claim 25, the combination of Shi, Sitton and Snook teaches all the limitations of the base claims as outlined above.
Further, Shi teaches the first set of stored relational rules comprises the sectional power flow datum flowing between the first node and the second node [0026 — grid power consumed from a local grid by an entity such as a building, business, one or more items of machinery and/or power storage, or the like (first and second nodes); 0034-0035 — computing device 104 may fetch grid and/or market data from one or more local grid monitoring devices, such as without limitation generation fuel mix, marginal fuel type, system demand (consumption), locational marginal prices (LMPs), historical emissions, and emission data from a reporting service 136 such as the EPA… Computing device 104 may connect with a user's main meter which may measure net power from local power grid in real time and the user's controllable energy resources such as energy storage, EV charging, diesel generation, fuel cells, flexible loads, and the like. User's historical metering data may be imported computing from the user's own metering system or a third party that collects metering data from utilities; 0048 — computing device 104 trains emission projection machine-learning process using the plurality of training data entries. Training may be accomplished using one or more machine-learning algorithms as defined above. Machine-learning algorithms may include, without limitation, supervised machine-learning algorithms. Supervised machine learning algorithms, as defined in this disclosure, include algorithms that receive a training set relating a number of inputs to a number of outputs, and seek to find one or more mathematical relations relating inputs to outputs, where each of the one or more mathematical relations is optimal according to some criterion specified to the algorithm using some scoring function. For instance, a supervised learning algorithm may include inputs described in this disclosure as inputs, including without limitation power output quantities as described above, as well as various alternative or additional exogenous signals such as time, weather, system demand, market conditions, or the like, outputs such as projected carbon emission rates for instance including both average and marginal carbon intensities, current rate of change of carbon emission rates, current carbon emission rates, and/or projected rates of change of carbon emission rates, and a scoring function representing a desired form of relationship to be detected between inputs and outputs].
Regarding claim 28, the combination of Shi, Sitton and Snook teaches all the limitations of the base claims as outlined above and this claim is otherwise rejected under the same rationale as claim 21.
Regarding claim 29, the combination of Shi, Sitton and Snook teaches all the limitations of the base claims as outlined above and this claim is otherwise rejected under the same rationale as claim 22.
Regarding claim 30, the combination of Shi, Sitton and Snook teaches all the limitations of the base claims as outlined above and this claim is otherwise rejected under the same rationale as claim 23.
Regarding claim 31, the combination of Shi, Sitton and Snook teaches all the limitations of the base claims as outlined above and this claim is otherwise rejected under the same rationale as claim 24.
Regarding claim 32, the combination of Shi, Sitton and Snook teaches all the limitations of the base claims as outlined above and this claim is otherwise rejected under the same rationale as claim 25.
Claim(s) 10 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Shi, Sitton and Snook in view of Wang ‘An Equivalent Circuit-Based Approach for Power and Emission Tracing in Power Networks’ IEEE SYSTEMS JOURNAL, VOL. 16, NO. 2, JUNE 2022, Published April 9, 2021 (hereinafter Wang).
Regarding claim 10, the combination of Shi, Sitton and Snook teaches all the limitations of the base claims as outlined above.
But the combination of Shi, Sitton and Snook fails to clearly specify generating a graphical representation of the carbon flow.
However, Wang teaches generating a graphical representation of the carbon flow [page 2215, Fig. 11 — visualized virtual carbon flow for the 14-bus network].
Shi, Sitton, Snook and Wang are analogous art. They relate to energy consumption management systems, particularly involving carbon emissions.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above apparatus, as taught by the combination of Shi, Sitton and Snook, by incorporating the above limitations, as taught by Wang.
One of ordinary skill in the art would have been motivated to do this modification so that a user could more easily understand the flow of carbon within the system.
Regarding claim 20, the combination of Shi, Sitton and Snook teaches all the limitations of the base claims as outlined above and this claim is otherwise rejected under the same rationale as claim 10.
Claim(s) 26-27 and 33-34 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Shi, Sitton and Snook in view of Garcia et al. U.S. Patent Publication No. 20170025859 (hereinafter Garcia).
Regarding claim 26, the combination of Shi, Sitton and Snook teaches all the limitations of the base claims as outlined above.
But the combination of Shi, Sitton and Snook fails to clearly specify the sectional power flow datum comprises power flow data for a section of the grid network connected to a power plant.
However, Garcia teaches the sectional power flow datum comprises power flow data for a section of the grid network connected to a power plant [0040-0042, Fig. 1 — One or more wind turbine generators 191 are connected to the electrical grid 192 via a WTG connection point 193 and a power connection line 197… grid meter 110 may determine values of voltage, active and reactive power and frequency on basis of measured voltage and current signals supplied by the grid sensor 195. Accordingly, the electrical parameters at the POM 196 may be indicative of an amount of active or reactive electrical power supplied to the grid via the PCC 194].
Shi, Sitton, Snook and Garcia are analogous art. They relate to energy management systems.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to simply substitute the known power flow data for a section of the grid network connected to a power plant, as taught by Garcia, for the known power flow data for a section of the a network, as taught by the combination of Shi, Sitton and Snook, for the predictable result of an apparatus for generating a projected avoided carbon tonnage that utilizes power flow data for a section of the grid network connected to a power plant.
Regarding claim 27, the combination of Shi, Sitton and Snook teaches all the limitations of the base claims as outlined above.
Further, Shi teaches the power plant is a clean power plant [0022, Fig. 1 — photoelectric solar farms, solar collectors, wind farms… geothermal power plants, tidal power plants]
Regarding claim 33, the combination of Shi, Sitton and Snook teaches all the limitations of the base claims as outlined above and this claim is otherwise rejected under the same rationale as claim 26.
Regarding claim 34, the combination of Shi, Sitton and Snook teaches all the limitations of the base claims as outlined above and this claim is otherwise rejected under the same rationale as claim 27.
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Madawala et al. U.S. Patent Publication No. 20140203659 that discloses a control method for a primary side power converter where the power flow amount is controlled using the voltage.
Note that any citations to specific, pages, columns, lines, or figures in the prior art references and any interpretation of the reference should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. See MPEP 2123.
Conclusion
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/BERNARD G LINDSAY/
Primary Examiner, Art Unit 2119
Prosecution Insights