DETAILED ACTION
The following is a Non-Final Office Action upon examination of the above-identified application on the merits. Claims 1-20 are pending in this application.
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 .
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.
Information Disclosure Statement
The examiner has considered the information disclosure statements (IDS) submitted on 10 November 2025, 9 September 2024, and 28 April 2023.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(4) because reference characters “134” (figure 1) and “136” ([0032]) have both been used to designate historical data. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Allowable Subject Matter
The indicated allowability of claims 1-20 is withdrawn in view of US Pub. No. 2021/0180891 A1 (USPN 11,732,967 B2) to Rousselet et al. Rejection based on the reference follows.
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(1)/102(a)(2) as being anticipated by US Pub. No. 2021/0180891 A1 (USPN 11,732,967 B2) to Rousselet et al.
As per claim 1, the Rousselet et al. reference discloses a computing system comprising: a processor (Paragraph 60); and memory (Paragraph 60) storing an energy efficiency optimization application that, when executed by the processor, causes the energy efficiency optimization application to perform acts (Paragraph 54, the master controller 50, the cooling subsystem controller 52, and/or the server computer 54 perform a method 80 that includes determining 86 one or more optimal control settings or operating parameters, such as one or more set points and a cooling tower operating mode, to achieve a particular target optimization criterion of the cooling subsystem 14. The target optimization criterion of the cooling subsystem 14 may include, for example, minimizing energy consumption, minimizing water consumption, minimizing chemical water treatment, and/or minimizing operating costs and maintenance of the cooling subsystem 14) comprising: receiving a first energy consumption metric for a heating, ventilation, and air conditioning (HVAC) system configured to regulate an indoor environment of a building, wherein the first energy consumption metric is indicative of the energy consumed by the HVAC system during operation (Paragraph 56, the method 80 includes aggregating 82 data from sensors of the cooling subsystem 14 and providing 84 a plurality of potential operating parameters of the cooling subsystem 14 to at least one machine learning algorithm for estimating energy and water consumption based on the provided potential parameters. The method 80 further includes determining 86 a recommended or optimal operating parameter of the cooling system 10 based at least in part upon the estimated energy and water consumption. The optimal parameter may include one or more optimal setpoints and/or an optimal operating mode of one or more components of the cooling system 10.); receiving sensor data generated by a sensor, wherein the sensor data is indicative of an air tonnage of the HVAC system (Paragraph 57, The sensor data and set points may include, for example, one or more variables representative of a cooling load (such as building load), chiller, water-source heat pump (WSHP), compressor, pumps, and heat rejection equipment. ); receiving electrical grid data from an electrical grid resource (Paragraph 76, Examples in this regard include adjusting energy consumption to correspond to the available supply of a renewable energy source (e.g., solar power) and adjusting water consumption during a drought. In one embodiment, the master controller 50 may receive a communication from a utility provider indicative of available power and/or water.); providing the sensor data and the electrical grid data as input into a computer-implemented machine learning model that has learned parameters assigned thereto (Paragraph 58, The providing 84 operation may include providing 94 the cooling subsystem variables and the environmental variables to one or more machine learning models of the cooling subsystem 14. ), wherein the learned parameters are based on historical data for the HVAC system (Paragraph 53, In one embodiment, the processor 70 utilizes reinforcement learning and self-tuning to modify the one or more machine learning models 151 over time and make the machine learning models 151 more accurate as more historical data is collected from the cooling subsystem 14 and from cooling subsystems in other installations.), wherein the machine learning model is configured to determine a setpoint modification for the HVAC system based upon the sensor data and the electrical grid data (Paragraph 63, The determining 86 may include providing 98 at least one optimal operating parameter of the cooling subsystem 14, such as set points and/or an operating mode of one or more components of the cooling subsystem 14, according to the target optimization criterion. In one form, determining 86 includes selecting at least one optimal operating parameter of the cooling subsystem 14 from one or more optimal operating parameters of the cooling subsystem 14 the machine learning model predicts would meet the cooling demands of the cooling subsystem 14. The selection may include selecting at least one optimal operating parameter based on the target optimization criterion.), wherein the setpoint modification, when applied to the HVAC system, results in a second energy consumption metric for the HVAC system, wherein the second energy consumption metric is less than the first energy consumption metric (Paragraph 75, As another example, minimizing operating costs for the cooling subsystem 14 may result in a higher energy consumption of the components of the cooling subsystem 14 during an earlier time of day when energy is cheaper and less energy consumption later in the day when energy is more expensive.); transmitting the setpoint modification to a building management system associated with the HVAC system; and causing the setpoint modification to be applied to the HVAC system by the building management system (Paragraph 63, The cooling subsystem controller 52 may then implement 99 the optimal parameter(s). The implementing 99 may involve adjusting one or more of the components of the cooling subsystem 14 to operate according to the provided optimal parameter.).
As per claim 2, the Rousselet et al. reference discloses the setpoint modification comprises an adjustment to a discharge air temperature setpoint (Paragraph 29, an optimal temperature of the process fluid leaving the heat rejection apparatus) or a static pressure setpoint (Paragraph 29, an optimal pressure of the process fluid leaving the heat rejection apparatus).
As per claim 3, the Rousselet et al. reference discloses the setpoint modification comprises an adjustment to a discharge air temperature setpoint (Paragraph 29, an optimal temperature of the process fluid leaving the heat rejection apparatus) and a static pressure setpoint (Paragraph 29, an optimal pressure of the process fluid leaving the heat rejection apparatus).
As per claim 4, the Rousselet et al. reference discloses causing the setpoint modification (Paragraph 54, determining 86 one or more optimal control settings or operating parameters) to be applied to the HVAC system by the building management system comprises transmitting instructions to the building management system that, when executed by the building management system, cause the building management system to control one or more dampers associated with the HVAC system (Paragraph 63, implement 99 the optimal parameter(s). The implementing 99 may involve adjusting one or more of the components of the cooling subsystem 14 to operate according to the provided optimal parameter.).
As per claim 5, the Rousselet et al. reference discloses the acts further comprising: receiving electrical pricing data (paragraph [0067], pricing data 119 for energy, water, and/or chemicals) from the electrical grid resource, wherein the electrical pricing data comprises a price per kilowatt hour (kWh) for electricity consumption; providing the electrical pricing data as input into the computer-implemented machine learning model (Paragraph 63, one or more machine learning models), wherein the machine learning model is configured to determine a second setpoint modification (Paragraph 79, minimize the CO.sub.2 or greenhouse gas emissions) for the HVAC system based upon the electrical pricing data, wherein the second setpoint modification, when applied to the HVAC system, results in a third energy consumption metric (Paragraph 80, real-time, scheduled, and/or predicted cost of water and/or energy), wherein the third energy consumption metric is less than the first energy consumption metric; transmitting the third setpoint modification to the building management system associated with the HVAC system; and causing the third setpoint modification to be applied to the HVAC system by the building management system (Paragraph 63, implement 99 the optimal parameter(s). The implementing 99 may involve adjusting one or more of the components of the cooling subsystem 14 to operate according to the provided optimal parameter.).
As per claim 6, the Rousselet et al. reference discloses the electrical grid data comprises a grid load metric indicative of total grid load (Paragraph 79, energy sources providing power to the grid).
As per claim 7, the Rousselet et al. reference discloses the historical data is generated by a digital twin simulator (Paragraph 94, weighted k-nearest neighbors regression (w-k-NN) 400, neural network regression (NN) 450), wherein the digital twin simulator is configured to simulate the behavior of the HVAC system in the building.
As per claim 8, the Rousselet et al. reference discloses the sensor data is received in real-time or near real-time (Paragraph 31, performs optimization in real-time using live, historical, and/or predicted future data).
As per claim 9, the Rousselet et al. reference discloses a method for modifying energy consumption by a building, the method comprising: receiving a first energy consumption metric for a heating, ventilation, and air conditioning (HVAC) system configured to regulate an indoor environment of a building, wherein the first energy consumption metric is indicative of the energy consumed by the HVAC system during operation (Paragraph 56, the method 80 includes aggregating 82 data from sensors of the cooling subsystem 14 and providing 84 a plurality of potential operating parameters of the cooling subsystem 14 to at least one machine learning algorithm for estimating energy and water consumption based on the provided potential parameters. The method 80 further includes determining 86 a recommended or optimal operating parameter of the cooling system 10 based at least in part upon the estimated energy and water consumption. The optimal parameter may include one or more optimal setpoints and/or an optimal operating mode of one or more components of the cooling system 10.); receiving sensor data generated by a sensor, wherein the sensor data is indicative of a mixed air temperature (Paragraph 57, The environmental variables may include, for example, air dry bulb temperature, relative humidity, wet bulb temperature, date, time, utility cost (e.g., electricity and water), and/or cost of water treatment chemicals used in the cooling subsystem 14.); receiving electrical grid data from an electrical grid resource (Paragraph 76, Examples in this regard include adjusting energy consumption to correspond to the available supply of a renewable energy source (e.g., solar power) and adjusting water consumption during a drought. In one embodiment, the master controller 50 may receive a communication from a utility provider indicative of available power and/or water.); providing the sensor data and the electrical grid data as input into a computer-implemented machine learning model that has learned parameters assigned thereto (Paragraph 58, The providing 84 operation may include providing 94 the cooling subsystem variables and the environmental variables to one or more machine learning models of the cooling subsystem 14. ), wherein the learned parameters are based on historical data for the HVAC system (Paragraph 53, In one embodiment, the processor 70 utilizes reinforcement learning and self-tuning to modify the one or more machine learning models 151 over time and make the machine learning models 151 more accurate as more historical data is collected from the cooling subsystem 14 and from cooling subsystems in other installations.), wherein the machine learning model is configured to determine a setpoint modification for the HVAC system based upon the sensor data and the electrical grid data (Paragraph 63, The determining 86 may include providing 98 at least one optimal operating parameter of the cooling subsystem 14, such as set points and/or an operating mode of one or more components of the cooling subsystem 14, according to the target optimization criterion. In one form, determining 86 includes selecting at least one optimal operating parameter of the cooling subsystem 14 from one or more optimal operating parameters of the cooling subsystem 14 the machine learning model predicts would meet the cooling demands of the cooling subsystem 14. The selection may include selecting at least one optimal operating parameter based on the target optimization criterion.), wherein the setpoint modification, when applied to the HVAC system, results in a second energy consumption metric for the HVAC system, wherein the second energy consumption metric is less than the first energy consumption metric (Paragraph 75, As another example, minimizing operating costs for the cooling subsystem 14 may result in a higher energy consumption of the components of the cooling subsystem 14 during an earlier time of day when energy is cheaper and less energy consumption later in the day when energy is more expensive.); transmitting the setpoint modification to a building management system associated with the HVAC system; and causing the setpoint modification to be applied to the HVAC system by the building management system (Paragraph 63, The cooling subsystem controller 52 may then implement 99 the optimal parameter(s). The implementing 99 may involve adjusting one or more of the components of the cooling subsystem 14 to operate according to the provided optimal parameter.).
As per claim 10, the rejection of claim 2 is incorporated and further claim 10 contains limitations recited in claim 2; therefore claim 10 are rejected under the same rational as claim 2.
As per claim 11, the rejection of claim 3 is incorporated and further claim 11 contains limitations recited in claim 3; therefore claim 11 are rejected under the same rational as claim 3.
As per claim 12, the rejection of claim 4 is incorporated and further claim 12 contains limitations recited in claim 4; therefore claim 12 are rejected under the same rational as claim 4.
As per claim 13, the rejection of claim 5 is incorporated and further claim 13 contains limitations recited in claim 5; therefore claim 13 are rejected under the same rational as claim 5.
As per claim 14, the rejection of claim 6 is incorporated and further claim 14 contains limitations recited in claim 6; therefore claim 14 are rejected under the same rational as claim 6.
As per claim 15, the rejection of claim 7 is incorporated and further claim 15 contains limitations recited in claim 7; therefore claim 15 are rejected under the same rational as claim 7.
As per claim 16, the rejection of claim 8 is incorporated and further claim 16 contains limitations recited in claim 8; therefore claim 16 are rejected under the same rational as claim 8.
As per claim 17, the Rousselet et al. reference discloses computer-readable storage medium comprising an energy efficiency application that, when executed by a processor, cause the energy efficiency application to perform acts comprising: receiving a first energy consumption metric for a heating, ventilation, and air conditioning (HVAC) system configured to regulate an indoor environment of a building, wherein the first energy consumption metric is indicative of the energy consumed by the HVAC system during operation (Paragraph 56, the method 80 includes aggregating 82 data from sensors of the cooling subsystem 14 and providing 84 a plurality of potential operating parameters of the cooling subsystem 14 to at least one machine learning algorithm for estimating energy and water consumption based on the provided potential parameters. The method 80 further includes determining 86 a recommended or optimal operating parameter of the cooling system 10 based at least in part upon the estimated energy and water consumption. The optimal parameter may include one or more optimal setpoints and/or an optimal operating mode of one or more components of the cooling system 10.); receiving sensor data generated by a sensor, wherein the sensor data is indicative of an outside air temperature (Paragraph 57, The environmental variables may include, for example, air dry bulb temperature, relative humidity, wet bulb temperature, date, time, utility cost (e.g., electricity and water), and/or cost of water treatment chemicals used in the cooling subsystem 14.); receiving electrical grid data from an electrical grid resource (Paragraph 76, Examples in this regard include adjusting energy consumption to correspond to the available supply of a renewable energy source (e.g., solar power) and adjusting water consumption during a drought. In one embodiment, the master controller 50 may receive a communication from a utility provider indicative of available power and/or water.); providing the sensor data and the electrical grid data as input into a computer-implemented machine learning model that has learned parameters assigned thereto (Paragraph 58, The providing 84 operation may include providing 94 the cooling subsystem variables and the environmental variables to one or more machine learning models of the cooling subsystem 14. ), wherein the learned parameters are based on historical data for the HVAC system (Paragraph 53, In one embodiment, the processor 70 utilizes reinforcement learning and self-tuning to modify the one or more machine learning models 151 over time and make the machine learning models 151 more accurate as more historical data is collected from the cooling subsystem 14 and from cooling subsystems in other installations.), wherein the machine learning model is configured to determine a setpoint modification for the HVAC system based upon the sensor data and the electrical grid data (Paragraph 63, The determining 86 may include providing 98 at least one optimal operating parameter of the cooling subsystem 14, such as set points and/or an operating mode of one or more components of the cooling subsystem 14, according to the target optimization criterion. In one form, determining 86 includes selecting at least one optimal operating parameter of the cooling subsystem 14 from one or more optimal operating parameters of the cooling subsystem 14 the machine learning model predicts would meet the cooling demands of the cooling subsystem 14. The selection may include selecting at least one optimal operating parameter based on the target optimization criterion.), wherein the setpoint modification, when applied to the HVAC system, results in a second energy consumption metric for the HVAC system, wherein the second energy consumption metric is less than the first energy consumption metric (Paragraph 75, As another example, minimizing operating costs for the cooling subsystem 14 may result in a higher energy consumption of the components of the cooling subsystem 14 during an earlier time of day when energy is cheaper and less energy consumption later in the day when energy is more expensive.); transmitting the setpoint modification to a building management system associated with the HVAC system; and causing the setpoint modification to be applied to the HVAC system by the building management system (Paragraph 63, The cooling subsystem controller 52 may then implement 99 the optimal parameter(s). The implementing 99 may involve adjusting one or more of the components of the cooling subsystem 14 to operate according to the provided optimal parameter.).
As per claim 18, the rejection of claim 5 is incorporated and further claim 18 contains limitations recited in claim 5; therefore claim 18 are rejected under the same rational as claim 5.
As per claim 19, the rejection of claim 4 is incorporated and further claim 19 contains limitations recited in claim 4; therefore claim 19 are rejected under the same rational as claim 4.
As per claim 20, the rejection of claim 3 is incorporated and further claim 20 contains limitations recited in claim 3; therefore claim 20 are rejected under the same rational as claim 3.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
The following references are cited to further show the state of the art with respect to modifying energy consumption by HVAC system in general:
US 12305874 B2 to Shinde et al.
US 12203671 B2 to Brahme et al.
US 11713894 B2 to Rigg et al.
US 11675322 B2 to Du et al.
US 11268996 B2 to Vitullo et al.
US 9470430 B2 to Stefanski et al.
US 8406929 B2 to Duncan
US 20230120453 A1 to Rao et al.
US 20210123771 Al to Vega et al.
WO 2024031177 Al to MCDONALD et al.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Crystal J Barnes-Bullock whose telephone number is (571)272-3679. The examiner can normally be reached Monday - Friday 8 am - 5 pm.
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/CRYSTAL J BARNES-BULLOCK/Primary Examiner, Art Unit 2117 14 November 2025