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 .
Allowable Subject Matter
Claims 4 and 9-12 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 6-7 and 12 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 6 and 12 recite maintaining an average value of the respective temperature prediction values “within the predetermined range.” However, neither claim 6 nor claim 12, nor the claims from which they depend, previously introduces or defines a “predetermined range.” Accordingly, it is unclear what temperature range is required by the claims and how the boundaries of the claimed range are determined. Claims 6 and 12 further recite giving “greater weight” to the respective temperature prediction value “and/or” the net energy consumption “closer to the current time.” This language is unclear because it does not distinctly identify whether the temperature prediction values, the net energy-consumption values, or both are assigned greater weight. It is also unclear whether “closer to the current time” modifies the prediction values, the net energy-consumption values, the weighting operation, or some combination thereof. Further, the claims do not provide an objective standard for determining how much greater the weight must be or how temporal proximity to the current time affects the assigned weight. Therefore, the metes and bounds of the claimed weighting operation cannot be determined with reasonable certainty.
Claim 7 recites that the computer uses data collected from an external weather system and/or data derived using a stored formula “instead of at least some of the real-time data measured from at least any one of the external environment, room, and photovoltaic power generation system.” It is unclear which real-time data is replaced by the externally collected or formula-derived data and whether the substituted data must correspond to the same physical parameter as the replaced data. The phrase “instead of at least some” also fails to establish whether any portion, a particular subset, or all of the identified real-time data may be replaced. Further, claim 7 depends from claim 1, which requires the computer to collect real-time data measured from at least one of the heat pump, external environment, room, and photovoltaic power-generation system. Claim 7 permits replacement of at least some of the measured data from the external environment, room, and photovoltaic system, but does not clearly establish what measured data must remain after the substitution. Accordingly, it is uncertain whether claim 7 continues to require collection of measured real-time data from one of the recited sources or permits the computer to rely entirely on external or formula-derived data.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception, namely an abstract idea, without reciting additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception. Claim(s) 1 “recites” abstract ideas under the 2019 PEG:
Step 2A, Prong One – Judicial Exception
Claim 1 recites the following limitations:
collecting real-time data measured from at least one of a heat pump, an external environment, a room, and a photovoltaic power-generation system;
inputting real-time data from before a preset time into multiple dynamic-behavior prediction models;
deriving an indoor-air-temperature prediction value, a heat-pump-electricity-consumption prediction value, and a photovoltaic-power-generation prediction value for after the preset time; and
searching for an optimal control variable of the heat pump using the derived prediction values.
The limitations of inputting data into prediction models, deriving predicted values, and searching for an optimal variable using the predicted values recite mathematical concepts. In particular, the limitations encompass mathematical calculations and relationships used to model future system behavior and optimize a control variable based on the calculated prediction values.
The limitation of collecting real-time data merely gathers the information upon which the mathematical prediction and optimization operations are performed and therefore constitutes insignificant data-gathering activity ancillary to the recited mathematical concepts.
Step 2A, Prong Two – Integration into a Practical Application
The claim is evaluated as a whole to determine whether the additional elements integrate the identified abstract idea into a practical application.
The additional elements include:
a computer comprising a processor and memory;
the heat pump, external environment, room, and photovoltaic power-generation system as sources of measured data; and
the characterization of the result of the optimization as an “optimal control variable of the heat pump.”
The computer, processor, and memory are recited at a high level of generality and merely provide a computing environment in which the abstract mathematical prediction and optimization operations are performed. The processor executes stored code, while the memory stores the code and prediction models. These elements therefore merely apply the abstract idea using a computer.
The recited heat pump, external environment, room, and photovoltaic power-generation system do not meaningfully limit the abstract idea. These elements merely identify the technological environment from which the data is collected and the field in which the prediction and optimization calculations are used.
Although the result of the mathematical operations is characterized as an “optimal control variable of the heat pump,” claim 1 does not require the computer to:
transmit the optimal control variable to the heat pump;
apply the optimal control variable to a heat-pump controller;
change a heat-pump set temperature or airflow rate;
cause the heat pump to operate according to the optimal control variable; or
otherwise produce a physical change in the heat pump or building.
Rather, the claim ends with searching for the optimal control variable. Thus, the claim merely calculates or identifies information that could subsequently be used to control the heat pump, without requiring that the calculated result actually be applied.
Step 2B – Inventive Concept
The claim is next evaluated to determine whether any additional element, individually or in combination, amounts to significantly more than the abstract idea.
Individually, the computer, processor, and memory perform their ordinary functions of storing instructions, receiving data, and processing data. The specification describes the claimed functions as being implementable using a computer, processor, memory, software, and communication network without identifying any specialized or unconventional computer architecture. See paragraphs 78 and 116–124.
The collection of measured data from the heat pump, external environment, room, and photovoltaic power-generation system merely supplies the data used in the abstract prediction and optimization process. This data collection is insignificant extra-solution activity and does not add an inventive concept to the abstract idea.
The identification of the calculated output as an optimal heat-pump control variable also does not amount to significantly more because the claim does not require the control variable to be implemented or used to control the heat pump. Instead, this limitation merely limits the abstract mathematical operations to a particular field of use.
Accordingly, claim 1 is directed to mathematical prediction and optimization, which constitute an abstract idea. The additional elements do not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea. Therefore, claim 1 is ineligible under 35 U.S.C. 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 5 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Ward (US 2016/0327295 A1) in view of Dong (US 2020/0059098 A1).
Regarding claim 1, Ward teaches an integrated control system for heat pump of a building, comprising a computer comprising a processor and a memory within which code for execution by the processor is stored, the computer configured to collect real-time data measured from at least any one of the heat pump, an external environment, and a room, where interactions occur in a dynamic behavior of the heat pump. In particular, Ward teaches a computer-implemented HVAC supervisory control system that interfaces with a building management system and receives HVAC plant data, including zone temperatures, chiller and fan setpoints, HVAC power, external temperature, and weather information (paras. 45, 53–58). Ward further teaches that the HVAC model represents building thermal performance, HVAC-system behavior, and building thermal loads and learns the relationship between external thermal conditions, HVAC power consumption, and zone conditions (paras. 54–57).
Ward teaches input the real-time data of before pre-set time into multiple dynamic behavior prediction models to derive an indoor air temperature prediction value and heat pump electricity consumption prediction value of after pre-set time. Ward teaches loading historical power and corresponding temperature profiles and using the historical data to form a dynamic thermal model that predicts future internal building temperature based on time, expected external weather, and HVAC power (paras. 61, 67). Ward further teaches a thermal-modeling loop, a power-planning loop, and a setpoint-determination loop that generate and continuously update forward-looking indoor-temperature and HVAC power-consumption profiles (paras. 62, 66–69). Ward’s model expressly represents dynamic responses to ambient temperature, HVAC cooling power, and HVAC heating power (paras. 78–85).
Ward further teaches search for an optimal control variable of the heat pump using the derived indoor air temperature prediction value and heat pump electricity consumption prediction value. Ward teaches using the fitted thermal model in an optimization loop that evaluates a range of possible HVAC control actions to identify an appropriate control strategy (para. 55). Ward’s optimization routine considers numerous possible HVAC power profiles and selects an optimal profile based on predicted occupant comfort, HVAC power consumption, operating cost, and emissions, and translates the selected profile into zone-temperature setpoints for controlling the HVAC plant (paras. 62–69, 72).
Ward does not expressly teach that the building includes a photovoltaic power-generation system or that the computer derives a photovoltaic power generation prediction value of after pre-set time and uses the photovoltaic power-generation prediction value when searching for the optimal heat-pump control variable.
Dong teaches an integrated building-energy-management and optimization system including photovoltaic generation, a solar-energy forecasting model, a building-load forecasting model, an HVAC model, and model-predictive control (paras. 33–40). Dong teaches forecasting future photovoltaic power generation and future building-load patterns and using the forecasts in an optimization system to compute an optimized control strategy (paras. 36, 39, 41–43). Dong further teaches that, based on real-time updated renewable-energy and building-load forecasts, the optimization engine computes optimal HVAC-system and component-level setpoints, including zone-temperature setpoints, fan-stage commands, supply-air-temperature setpoints, and airflow rates (paras. 43–44). Thus, Dong teaches derive a photovoltaic power generation prediction value of after pre-set time and search for an optimal control variable of the heat pump using the derived photovoltaic power generation prediction value.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ward’s predictive HVAC control system to include Dong’s photovoltaic power-generation forecasting model and to use the predicted photovoltaic generation together with Ward’s predicted indoor-air temperature and HVAC electricity consumption when determining the optimal HVAC control variable, in order to coordinate controllable HVAC demand with anticipated on-site photovoltaic generation, improve utilization of locally generated renewable energy, reduce electricity purchased from the utility grid, and reduce overall building energy cost. Dong expressly teaches integrating renewable-generation forecasting, building-load forecasting, HVAC modeling, and model-predictive control to compute optimal HVAC setpoints based on updated renewable-generation and building-demand forecasts (paras. 33, 36, 41–48).
Regarding claim 5, Dong further teaches wherein the computer is configured to derive a net energy consumption calculated from a difference between the heat pump electricity consumption prediction value and a photovoltaic electricity generation prediction value, and to search for the optimal control variable using the derived net energy consumption. Dong teaches an integrated building-energy optimization formulation in which predicted building electricity consumption, including HVAC electricity consumption, is offset by predicted renewable energy generated by the photovoltaic system to determine the amount of power required from or supplied to the utility grid. Dong’s power-balance equations account for HVAC power consumption and renewable power generated by the photovoltaic system, and Dong optimizes the building control variables based on the resulting net grid-energy requirement and associated energy cost (Dong, paras. 43, 48–53).
In particular, Dong teaches that the optimization engine uses updated renewable-energy-generation forecasts and building-load forecasts to compute optimal HVAC-system and component-level setpoints (Dong, para. 43). Dong further defines HVAC power as power consumed by the HVAC system and renewable power as power generated by photovoltaic or solar panels and incorporates these values into the building power-balance and objective functions (Dong, paras. 49–53). Thus, Dong teaches calculating net building energy consumption by offsetting predicted HVAC electricity consumption with predicted photovoltaic electricity generation and using the resulting net energy requirement when determining the optimal HVAC control variable.
Regarding claim 7, Ward further teaches wherein the computer is configured to use data collected from an external weather system and/or data derived using a formula stored in the memory, instead of at least some of the real-time data measured from at least any one of the external environment, room, and photovoltaic power generation system. Ward teaches that its HVAC supervisory control system receives outside data including weather forecasts and combines the weather-forecast data with HVAC plant data and a learned building thermal model to determine an HVAC operating plan and corresponding zone-temperature setpoints (Ward, paras. 53–58). In particular, Ward teaches software interfaces configured to obtain weather-prediction data from an external meteorological data provider, such as the Australian Bureau of Meteorology (Ward, para. 58).
Ward further teaches that the thermal-modeling loop predicts future indoor temperature using expected external weather and HVAC power, and that the power-planning loop determines a future weather forecast and uses the forecast to produce a future HVAC power-consumption plan through optimization (Ward, paras. 67–68). Thus, rather than requiring future external-environment conditions to be directly measured at the building, Ward uses weather data obtained from an external weather system as a substitute for at least some external-environment measurement data when predicting future building conditions and determining the HVAC control variable.
Ward also teaches that the building thermal model may be implemented as a polynomial model derived from historical building data and that the polynomial model uses HVAC power, ambient temperature, and an identified thermal-baseload profile to estimate average zone temperature (Ward, paras. 54–60). Accordingly, Ward further teaches using values derived through a stored mathematical formula in place of directly measured future room-condition data when predicting building temperature and determining the HVAC operating plan.
Claim 2-3 are rejected under 35 U.S.C. 103 as being unpatentable over Ward (US 2016/0327295 A1) in view of Dong (US 2020/0059098 A1), and further in view of Burton (US 2013/0261810 A1).
Regarding claim 2, Ward further teaches wherein the real-time data comprises at least one of a set temperature of the heat pump, which is a control variable, and an outdoor air temperature, indoor air temperature, and heat pump electricity consumption, which are state variables. Ward receives zone-temperature setpoints and measured zone temperatures, ambient or outside temperature, and HVAC heating and cooling power information for use in its predictive HVAC-control system (Ward, paras. 53–60 and 78–85).
Dong further teaches airflow rate of the heat pump, which is a control variable; and solar radiation and photovoltaic electricity generation, which are state variables. Dong identifies HVAC control variables including zone-temperature setpoints, fan-stage commands, supply-air-temperature setpoints, supply-airflow rates, damper positions, valve positions, and pump speeds (Dong, paras. 43–44). Dong also teaches a building thermal model using solar heat gain and a renewable-energy forecasting model that forecasts photovoltaic power generation for use in building-energy optimization (Dong, paras. 39–44 and 54–58).
Ward and Dong do not expressly teach collecting both heat pump supply air temperature and heat pump return air temperature as real-time state variables of the HVAC equipment.
Burton teaches an HVAC control system that continuously receives a return-air-temperature signal and a supply-air-temperature signal from respective sensors and uses the received signals to control operation of the HVAC unit (Burton, paras. 19–22 and 32–35). Burton teaches that the return-air sensor measures the temperature of air entering the HVAC unit and the supply-air sensor measures the temperature of air exiting the HVAC unit after being processed by the HVAC unit (Burton, paras. 20 and 34). Burton further teaches that the temperature signals may be transmitted automatically every 10–15 seconds, stored as historical sensor data, and received continuously in real time (Burton, paras. 20, 33, and 51).
Burton also teaches a set temperature of the heat pump and airflow rate of the heat pump, which are control variables. Burton receives and stores user-configurable return-air and supply-air setpoint temperatures and controls HVAC operation by comparing measured temperatures with the corresponding setpoints (Burton, paras. 21–22, 35, and 47–52). Burton further controls fan speed using a variable-speed drive, thereby varying the volume or airflow rate supplied by the HVAC unit (Burton, paras. 40–41).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the predictive HVAC-control system of Ward and Dong to further collect and use Burton’s real-time supply-air temperature, return-air temperature, HVAC setpoint, and variable fan-airflow information, in order to provide the predictive controller with additional measured operating-state and control-variable information representative of the actual thermal performance of the HVAC equipment, thereby improving temperature regulation, reducing inefficient HVAC operation, and permitting more accurate determination of the HVAC control settings. Burton expressly teaches that using supply-air and return-air temperatures together with temperature setpoints and fan-speed control improves HVAC efficiency, minimizes power consumption, and avoids inefficient load hopping (Burton, paras. 6–9 and 46–55).
Regarding claim 3, Dong further teaches wherein the multiple dynamic behavior prediction models consist of an indoor air temperature prediction model, a heat pump electricity consumption prediction model, and a photovoltaic electricity generation prediction model, stored in the memory. In particular, Dong’s optimization system includes a building or building-cluster model for predicting zone temperature, an HVAC model for predicting HVAC system response and power consumption, and a solar-energy forecasting model for predicting photovoltaic generation (Dong, paras. 33, 36, 39, 43, 54–61, and 71–82; Fig. 2). Dong further teaches a data store containing historical information and optimal controls and a computer implementation including a processor and memory for storing and executing the models (Dong, paras. 34 and 84–89; Figs. 1, 2, and 10).
Dong further teaches the heat pump electricity consumption prediction model derives the heat pump electricity consumption prediction value using the indoor air temperature prediction value predicted by the indoor air temperature prediction model. Dong teaches a state-space building model that predicts zone temperature as a building state and an HVAC response model that predicts the power consumption of rooftop units and heat-pump systems. Dong explains that rooftop-unit and heat-pump power consumption is modeled using an energy-input-ratio function dependent on zone-air temperature and outdoor-air temperature, such that the predicted zone temperature generated by the building model is used in predicting future HVAC electricity consumption (Dong, paras. 54–61). Dong further teaches integrating the building model and HVAC model into a model-predictive-control formulation over a future prediction horizon, thereby using predicted building temperature states to derive corresponding future HVAC power demand (Dong, paras. 43 and 46–49).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Ward (US 2016/0327295 A1) in view of Dong (US 2020/0059098 A1), and further in view of Pavlovski (US 2016/0305678 A1) and Ponullak (US 2013/0073080 A1).
Regarding claim 6, Ward in view of Dong teaches the integrated control system of claim 5, including deriving net energy consumption based on predicted HVAC electricity consumption and predicted photovoltaic electricity generation and searching for an optimal HVAC control variable using the derived net energy consumption.
Pavlovski further teaches wherein the computer is configured to search for the optimal control variable that minimizes a total sum of the net energy consumption of after pre-set time while maintaining an average value of the indoor air temperature prediction values of after pre-set time within the predetermined range. Pavlovski teaches predicting building energy consumption and thermal-zone temperature over a future optimization horizon for each candidate schedule of HVAC setpoints and selecting an optimal setpoint schedule that minimizes predicted building energy consumption or energy cost while satisfying thermal-comfort requirements (Pavlovski, paras. 74, 79, and 101–105; Fig. 5). Pavlovski further teaches excluding candidate control schedules that cause predicted zone temperature to fall outside a desired comfort range and minimizing an objective function that includes energy, demand, and comfort components over the future horizon (Pavlovski, paras. 101–103).
Pavlovski does not expressly teach giving greater weight to the indoor air temperature prediction value and/or the net energy consumption, closer to the current time.
Ponullak teaches an intelligent control system applicable to an HVAC plant in which a critic evaluates control performance over a finite future horizon and the actor modifies HVAC control parameters, such as heater power and fan speed, to minimize the evaluated performance measure (Ponullak, paras. 8–11 and 17–21).
Ponullak further teaches a cost-to-go function expressed as:
V
(
t
)
=
∑
k
=
0
N
γ
k
J
(
t
+
k
)
,
where
0
<
γ
<
1
,
k
is the future time-step index, and
J
(
t
+
k
)
is the performance measure or local cost at the respective future time (Ponullak, paras. 12–17). Because
γ
is less than one, the weight
γ
k
progressively decreases as the future time step
k
increases. Accordingly, Ponullak teaches giving greater weight to the indoor air temperature prediction value and/or the net energy consumption, closer to the current time, because performance values occurring closer to the current time are assigned a larger weighting factor than performance values occurring farther into the future.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the predictive HVAC optimization system of Ward and Dong to perform Pavlovski’s future-horizon optimization that minimizes accumulated predicted energy consumption while maintaining predicted indoor temperature within a desired comfort range, and to further apply Ponullak’s finite-horizon discount factor to the temperature and/or net-energy performance values, in order to account for the greater reliability and immediate operational importance of nearer-term predictions, reduce the influence of increasingly uncertain predictions farther into the future, and produce HVAC control settings that more effectively balance energy consumption and occupant comfort over the prediction horizon.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Ward (US 2016/0327295 A1) in view of Dong (US 2020/0059098 A1), and further in view of Burton (US 2013/0261810 A1) and Pavlovski (US 2016/0305678 A1).
Regarding claim 8, Ward further teaches wherein the weather data comprises at least one of an outdoor air temperature prediction value, rainfall prediction value, rainfall condition prediction value, and cloud condition prediction value. Ward teaches obtaining weather-prediction data from an external meteorological system and using expected external weather, including forecast ambient or outdoor-air temperature, in predicting future building temperature and determining a future HVAC operating plan (Ward, paras. 58 and 67–68). Because claim 8 requires at least one of the listed weather-data values, Ward’s forecast outdoor-air temperature teaches the recited weather data.
Ward and Dong do not expressly teach using the heat pump return air temperature as the indoor air temperature.
Burton teaches receiving a return-air-temperature signal from a sensor positioned to measure air entering an HVAC unit. Burton explains that the measured return-air temperature “is likely to be the ambient temperature of the room or the air near each unit” and uses the return-air temperature to control the HVAC unit and maintain the desired room temperature (Burton, paras. 19–20). Thus, Burton teaches using the heat-pump return-air temperature as representative of the indoor-air temperature.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the predictive HVAC control system of Ward and Dong to use Burton’s measured return-air temperature as the indoor-air temperature, in order to obtain an HVAC-unit-specific measurement that represents the temperature of the room air entering the unit and thereby provide the controller with a readily available indication of the indoor thermal condition used for prediction and control.
Ward, Dong, and Burton do not expressly teach deriving a solar radiation prediction value using the stored formula.
Pavlovski teaches a predictive building-control system that receives forecast ambient weather information, including forecast solar irradiance, and calculates solar irradiance and resulting surface-temperature effects using mathematical equations implemented by the predictive controller (Pavlovski, paras. 91–100). Pavlovski expressly provides a formula for solar irradiance on an external building surface:
I
(
t
)
=
d
i
r
(
t
)
(
1
-
P
S
A
(
t
)
)
+
d
i
f
(
t
)
S
V
F
,
where
d
i
r
(
t
)
is direct solar irradiance,
d
i
f
(
t
)
is diffuse solar irradiance,
P
S
A
(
t
)
represents the shaded portion of the surface, and
S
V
F
is the sky-view factor (Pavlovski, paras. 95–98). Pavlovski applies the equations to external-surface cells and uses the resulting solar-radiation information in its building-response and predictive-control models (Pavlovski, paras. 97–100). Thus, Pavlovski teaches deriving a solar-radiation prediction value using a formula stored and executed by the predictive building-control computer.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the predictive HVAC control system of Ward, Dong, and Burton to derive the solar-radiation prediction value using Pavlovski’s stored solar-irradiance formula, in order to account for direct and diffuse solar radiation, shading, and surface exposure when predicting the building’s future thermal load and photovoltaic-energy availability, thereby improving the accuracy of the indoor-temperature, energy-consumption, and optimal-control determinations.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Lee et al (US 2023/0228446) abstract, para 0090-0092
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/OMEED ALIZADA/Primary Examiner, Art Unit 2686
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