DETAILED ACTION
The instant application having Application No. filed on 09/03/2024 is presented for examination by the examiner.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-24 are rejected under 35 U.S.C. 103 as being unpatentable over Venne (US 2021/0003308 A1) (referenced in the IDS dated 09/03/2024) in view of Patel et al. (US 2018/0004171 A1) hereinafter Patel.
Regarding claim 1
Venne teaches a computer-implemented system for controlling HVAC components of a building, the system comprising: a processor (par. 71); a memory storage device storing a set of instructions, the set of instructions when executed by the processor (par. 71, “a memory and a processor to execute instructions”), cause the processor to: receive one or more user objective indicators, each indicating a corresponding user objective for the HVAC components (see par. 63, “allow building operators to weight or prioritize one or more factors, for example, maximize energy efficiency and/or cost savings.”); receive a plurality of forecasts, each predicting a dynamic state of the building or a usage parameter of one of the HVAC components (see par. 14, “predict future required operational parameters using an artificial intelligence engine trained using historical data from the HVAC components”); receive a plurality of current states of the building (see par. 14, “receiving data from the HVAC components”); maintain a plurality of control modules, each for controlling at least one setting of the HVAC components (see par. 75, “controllers on the various HVAC components 400 to individually control each one of them, at an individual component level.”).
Venne is not specifically disclose upon detecting a change in at least one of the forecasts or at least one of the user objective indicators: perform a plurality of HVAC control simulations, each simulating the performance of a corresponding subset of the control modules based on the plurality of forecasts and the plurality of current states of the building; select a subset of the control modules from among the simulated subsets of the control modules based on results of the simulations and the one or more user objective indicators; and deploy the selected subset of control modules to control the HVAC components.
However, in the same field of endeavor HVAC control, Patel teaches perform a plurality of HVAC control simulations, each simulating the performance of a corresponding subset of the control modules based on the plurality of forecasts and the plurality of current states of the building (see par. 209, “The simulation study involved using model predictive control system 600”); select a subset of the control modules from among the simulated subsets of the control modules based on results of the simulations and the one or more user objective indicators (see par. 209, “Some variables or parameters can be received as input data to control system 600 (e.g., weather data, utility rates, etc.), whereas other variables or parameters can be calculated and/or optimized by control system 600”); and deploy the selected subset of control modules to control the HVAC components (se par. 218).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to provide the simulation study of Patel to the HVAC system of Venne because it would provide for the purpose of reducing the overall cost of energy consumption (see Patel par. 218).
Regarding claim 10
Venne teaches a computer-implemented method for controlling HVAC components of a building, the method comprising: receiving one or more user objective indicators, each indicating a corresponding user objective for the HVAC components (see par. 63, “allow building operators to weight or prioritize one or more factors, for example, maximize energy efficiency and/or cost savings.”); receiving a plurality of forecasts, each predicting a dynamic state of the building or a usage parameter of one of the HVAC components (see par. 14, “predict future required operational parameters using an artificial intelligence engine trained using historical data from the HVAC components”); receiving a plurality of current states of the building (see par. 14, “receiving data from the HVAC components”); maintaining a plurality of control modules, each for controlling at least one setting of the HVAC components (see par. 75, “controllers on the various HVAC components 400 to individually control each one of them, at an individual component level.”);
Venne is not specifically disclose upon detecting a change in at least one of the forecasts or at least one of the user objective indicators: perform a plurality of HVAC control simulations, each simulating the performance of a corresponding subset of the control modules based on the plurality of forecasts and the plurality of current states of the building; select a subset of the control modules from among the simulated subsets of the control modules based on results of the simulations and the one or more user objective indicators; and deploy the selected subset of control modules to control the HVAC components.
However, in the same field of endeavor HVAC control, Patel teaches perform a plurality of HVAC control simulations, each simulating the performance of a corresponding subset of the control modules based on the plurality of forecasts and the plurality of current states of the building (see par. 209, “The simulation study involved using model predictive control system 600”); select a subset of the control modules from among the simulated subsets of the control modules based on results of the simulations and the one or more user objective indicators (see par. 209, “Some variables or parameters can be received as input data to control system 600 (e.g., weather data, utility rates, etc.), whereas other variables or parameters can be calculated and/or optimized by control system 600”); and deploy the selected subset of control modules to control the HVAC components (se par. 218).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to provide the simulation study of Patel to the HVAC system of Venne because it would provide for the purpose of reducing the overall cost of energy consumption (see Patel par. 218).
Regarding claim 18
Venne teaches a computer-implemented system for controlling HVAC components of a building, the system comprising: a building management system (BMS) configured to manage and maintain a plurality of current states of the building (see par. 72); a computing device connected to the BMS and having a processor and a memory storage, the memory storage storing a set of instructions (par. 71, “a memory and a processor to execute instructions”), when executed by the processor, causing the processor to: receive one or more user objective indicators, each indicating a corresponding user objective for the HVAC components (see par. 63, “allow building operators to weight or prioritize one or more factors, for example, maximize energy efficiency and/or cost savings.”); receive a plurality of forecasts, each predicting a dynamic state of the building or a usage parameter of one of the HVAC components (see par. 14, “predict future required operational parameters using an artificial intelligence engine trained using historical data from the HVAC components”); receive the plurality of current states of the building from the BMS (see par. 14, “receiving data from the HVAC components”); maintain a plurality of control modules, each for controlling at least one setting of the HVAC components (see par. 75, “controllers on the various HVAC components 400 to individually control each one of them, at an individual component level.”);
Venne is not specifically disclose upon detecting a change in at least one of the forecasts or at least one of the user objective indicators: perform a plurality of HVAC control simulations, each simulating the performance of a corresponding subset of the control modules based on the plurality of forecasts and the plurality of current states of the building; select a subset of the control modules from among the simulated subsets of the control modules based on results of the simulations and the one or more user objective indicators; and deploy the selected subset of control modules to control the HVAC components.
However, in the same field of endeavor HVAC control, Patel teaches perform a plurality of HVAC control simulations, each simulating the performance of a corresponding subset of the control modules based on the plurality of forecasts and the plurality of current states of the building (see par. 209, “The simulation study involved using model predictive control system 600”); select a subset of the control modules from among the simulated subsets of the control modules based on results of the simulations and the one or more user objective indicators (see par. 209, “Some variables or parameters can be received as input data to control system 600 (e.g., weather data, utility rates, etc.), whereas other variables or parameters can be calculated and/or optimized by control system 600”); and deploy the selected subset of control modules to control the HVAC components (se par. 218).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to provide the simulation study of Patel to the HVAC system of Venne because it would provide for the purpose of reducing the overall cost of energy consumption (see Patel par. 218).
Regarding claim 2
Venne and Patel teach the system of claim 1, Venne further teaches wherein the plurality of forecasts comprise at least one of: a predicted temperature value, a predicted water usage amount, a predicted electricity usage amount, a predicted gas usage amount, a predicted weather, and a predicted humidity level (see par. 36).
Regarding claim 3
Venne and Patel teach the system of claim 1, Venne further teaches wherein at least one of the plurality of control modules comprises a machine learning model (see par. 39).
Regarding claim 4
Venne and Patel teach the system of claim 1, Venne further teaches wherein the plurality of current states of the building comprise at least one of: a number of zones, a temperature measurement, a set point, sensor data, actuator data, occupancy schedule, and occupancy data (see par. 36, “set points … temperature … fan speed”, par 52 “room occupancy”).
Regarding claim 5
Venne and Patel teach the system of claim 4, Venne further teaches wherein the plurality of current states of the building is received from a Building Management System (BMS) (see par. 72).
Regarding claim 6
Venne and Patel teach the system of claim 1, Venne further teaches wherein the one or more user objective indicators comprises at least one of: an operational cost, a power, a water usage amount, an electricity usage amount, a gas usage amount, a humidity level, an emission target, equipment runtime, an equipment cycling rate, a temperature target, and an air quality target (see par. 63, “maximize energy efficiency and/or cost savings”).
Regarding claim 7
Venne and Patel teach the system of claim 1, Venne further teaches wherein deploying the selected subset of control modules to control the HVAC components comprises: executing the selected subset of control modules; and generating one or more operating values for the HVAC components based on the execution of the selected subset of control modules; and transmitting the one or more operating values to the HVAC components (see par. 38).
Regarding claim 8
Venne and Patel teach the system of claim 7, Venne further teaches wherein the system further comprises a display device for displaying the one or more operating values for the HVAC components (see par. 63, “user interface”).
Regarding claim 9
Venne and Patel teach the system of claim 1, Venne further teaches wherein the user objective indicators are weighted (see par. 63. “operators to weight or prioritize one or more factors”).
Regarding claim 11
Venne and Patel teach the method of claim 10, Venne further teaches wherein the plurality of forecasts comprise at least one of: a predicted temperature value, a predicted water usage amount, a predicted electricity usage amount, a predicted gas usage amount, a predicted weather, and a predicted humidity level (see par. 36).
Regarding claim 12
Venne and Patel teach the method of claim 10, Venne further teaches wherein at least one of the plurality of control modules comprises a machine learning model (see par. 39).
Regarding claim 13
Venne and Patel teach the method of claim 10, Venne further teaches wherein the plurality of current states of the building comprise at least one of: a number of zones, a temperature measurement, a set point, sensor data, actuator data, occupancy schedule, and occupancy data (see par. 36, “set points … temperature … fan speed”, par 52 “room occupancy”).
Regarding claim 14
Venne and Patel teach the method of claim 13, Venne further teaches wherein the plurality of current states of the building is received from a Building Management System (BMS) (see par. 72).
Regarding claim 15
Venne and Patel teach the method of claim 10, Venne further teaches wherein the one or more user objective indicators comprises at least one of: an operational cost, a power, a water usage amount, an electricity usage amount, a gas usage amount, a humidity level, an emission target, equipment runtime, an equipment cycling rate, a temperature target, and an air quality target (see par. 63, “maximize energy efficiency and/or cost savings”).
Regarding claim 16
Venne and Patel teach the method of claim 10, Venne further teaches wherein deploying the selected subset of control modules to control the HVAC components comprises: executing the selected subset of control modules; and generating one or more operating values for the HVAC components based on the execution of the selected subset of control modules; and transmitting the one or more operating values to the HVAC components (see par. 38).
Regarding claim 17
Venne and Patel teach the method of claim 10, Venne further teaches wherein the user objective indicators are weighted (see par. 63. “operators to weight or prioritize one or more factors”).
Regarding claim 19
Venne and Patel teach the system of claim 18, Venne further teaches wherein the plurality of forecasts comprise at least one of: a predicted temperature value, a predicted water usage amount, a predicted electricity usage amount, a predicted gas usage amount, a predicted weather, and a predicted humidity level (see par. 36).
Regarding claim 20
Venne and Patel teach the system of claim 18, Venne further teaches wherein at least one of the plurality of control modules comprises a machine learning model (see par. 39).
Regarding claim 21
Venne and Patel teach the system of claim 18, Venne further teaches wherein the plurality of current states of the building comprise at least one of: a number of zones, a temperature measurement, a set point, sensor data, actuator data, occupancy schedule, and occupancy data (see par. 36, “set points … temperature … fan speed”, par 52 “room occupancy”).
Regarding claim 22
Venne and Patel teach the system of claim 18, Venne further teaches wherein the one or more user objective indicators comprises at least one of: an operational cost, a power, a water usage amount, an electricity usage amount, a gas usage amount, a humidity level, an emission target, equipment runtime, an equipment cycling rate, a temperature target, and an air quality target (see par. 63, “maximize energy efficiency and/or cost savings”).
Regarding claim 23
Venne and Patel teach the system of claim 18, Venne further teaches wherein the user objective indicators are weighted (see par. 63. “operators to weight or prioritize one or more factors”).
Regarding claim 24
Venne and Patel teach a non-transitory computer-readable medium having stored thereon machine interpretable instructions which, when executed by a processor, cause the processor to perform the computer-implemented method claimed in claim 10 (see rejection of claim 10 above).
Conclusion
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/THOMAS K PHAM/Supervisory Patent Examiner, Art Unit 2876
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