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 .
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, 4-8, and 13-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
The claim(s) recite(s) a mental process directed to optimize the operation of the HVAC system based upon the current thermal load and an expected upcoming thermal load on the area of the building where the server is received to reduce energy usage by the HVAC system, claim 1; optimizing operation of the HVAC system based upon the current thermal load on the area of the building where the at least one computer server is housed to reduce energy usage by the HVAC system., claim 6; to optimize the operation of the HVAC system based upon the current thermal load on the area of the building where the server is received to reduce energy usage by the HVAC system., claim 16; predicting an upcoming thermal load in the optimization., claim 20
This judicial exception is not integrated into a practical application because the following combination of limitations represent insignificant extra solution activity:
to receive information from a building housing at least one computer server, with the information including at least a current thermal load, claim 1; operable to control an associated HVAC system for at least an area of the building where the at least one computer server is housed;, claim 1 (e.g. “operable to” represents a descriptive function); receiving information from a building housing at least one computer server, with the information including at least current thermal load;, claim 6; to control an associated HVAC system for at least an area of the building where the at least one computer server is housed; clam 6 (e.g. “to control” represents and intended function)l including the step of using historic data in the optimization., claim 13 -14; receiving information from an operator associated with the area of the building receiving the plurality of computer servers and controlling the HVAC system based upon the information received from the operator., claim 15 ; the processing circuitry being operable to receive information from the building, with the information including at least a current thermal load, the processing circuitry being operable to control the HVAC system for at least an area of the building where the at least one computer server is housed; , claim 16; the processing circuitry being operable to control the HVAC system for at least an area of the building where the at least one computer server is housed, claim 16 (e.g. “to control” represents an intended function)
Moreover, the processor and memory represent mere instructions, MPEP 2106.05(f)
This judicial exception is not integrated into a practical application because the following combination of limitations generally link the abstract idea to the field of energy wherein there are a plurality of the computer servers, and there are a plurality of areas in the building housing the plurality of computer servers., claim 4; the thermal optimization control further having a memory being provided with historic thermal load information from the building; wherein the HVAC system includes a plurality of water chillers for cooling air to be delivered from an air hanging unit into the area of the building that houses the at least one computer server., claim 6; wherein there are a plurality of the computer servers and a plurality of areas in the building receiving the plurality of computer servers., claim 7 l an area housing at least one computer server;
a HVAC system for the building, claim 16l wherein the HVAC system includes a plurality of water chillers for cooling air to be delivered into the area of the building that houses the at least one computer server, claim 17; The building as set forth in claim 17, wherein there are a plurality of the computer servers, and there are a plurality of areas in the building receiving the plurality of computer servers, claim 18; The building as set forth in claim 16, wherein the thermal optimization control also having a memory with historic data on the thermal load, claim 19
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the insignificant extra solution activity is well understood, conventional, and routine, MPEP 2106.05 (d). Moreover, the processor and memory represent mere instructions, MPEP 2106.05(f)
**It is suggested to provide an affirmative control vs a descriptive/intended control
Claim Rejections - 35 USC § 102
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 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.
Claim(s) 1-6 and 13-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Albinger et al. (PG/PUB 20230075122).
Claim 1. Albinger et al. teaches a thermal optimization control (ABSTRACT, summary of invention) comprising:
processing circuitry (0027, 0083, Figure 2-110 “Still referring to FIG. 2 , processing circuit 106 is shown to include a processor 110 and memory 112. Processor 110 may be a general purpose or specific purpose processor, an application specific integrated circuit (ASIC), one or more field programmable gate arrays (FPGAs), a group of processing components, or other suitable processing components. Processor 110 may be configured to execute computer code or instructions stored in memory 112 or received from other computer readable media”
the processing circuitry being operable to receive information from a building housing at least one computer server, with the information including at least a current thermal load (0011, 0036, 0082, 0089, 0102-0103, 0201 Figure 3)
the processing circuitry being operable to control an associated HVAC system for at least an area of the building where the at least one computer server is housed (0002, 0007, 0017, 0024, 0058, 0076-78, 0080, 0257-0258, 0267-0268, 0271-0273 “Communications interface 104 may be a network interface configured to facilitate electronic data communications between central plant controller 102 and various external systems or devices (e.g., BAS 108, subplants 12-22, etc.). For example, central plant controller 102 may receive information from BAS 108 indicating one or more measured states of the controlled building (e.g., temperature, humidity, electric loads, etc.) and one or more states of subplants 12-22 (e.g., equipment status, power consumption, equipment availability, etc.). Communications interface 104 may receive inputs from BAS 108 and/or subplants 12-22 and may provide operating parameters (e.g., on/off decisions, setpoints, etc.) to subplants 12-22 via BAS 108. The operating parameters may cause subplants 12-22 to activate, deactivate, or adjust a setpoint for various devices of equipment 60.
the processing circuity being operable to optimize the operation of the HVAC system based upon the current thermal load and an expected upcoming thermal load on the area of the building where the server is received to reduce energy usage by the HVAC system (0002, 0007, 0017, 0024, 0058, 0076-78, 0080, 0257-0258, 0267-0268, 0271-0273, see also 0024, 0161, 0163, 0268, 0273 , claim 12 “. The system of claim 11, wherein the controller is configured to generate the setpoints by performing an optimization process that minimizes an overall resource usage of the servers and the equipment over a time horizon.”
Claim 2. The control as set forth in claim 1, wherein the optimization includes controlling a staging of a plurality of HVAC systems between on/off conditions and/or full load/partial load (0060, 0076-77, 0079-0080, 0082, 0086, 0094-95, 0099 “In some embodiments, the central plant optimization is a cascaded optimization process including a high level optimization and a low level optimization. The high level optimization may determine an optimal distribution of energy loads across the various subplants. For example, the high level optimization may determine a thermal energy load to be produced by each of the subplants at each time element in an optimization period. In some embodiments, the high level optimization includes optimizing a high level cost function that expresses the monetary cost of operating the subplants as a function of the resources consumed by the subplants at each time element of the optimization period. The low level optimization may use the optimal load distribution determined by the high level optimization to determine optimal operating statuses for individual devices within each subplant. Optimal operating statuses may include, for example, on/off states and/or operating setpoints for individual devices of each subplant. The low level optimization may include optimizing a low level cost function that expresses the energy consumption of a subplant as a function of the on/off states and/or operating setpoints for the individual devices of the subplant.”
Claim 3. The control as set forth in claim 2, wherein the optimization further including controlling a volumetric flow and temperature of water delivered from a water chillers to an air handling unit to optimize and reduce energy consumption (0090-91, 0117, 0119, 0130-0132, 0143-0144)
Claim 4. The control as set forth in claim 1, wherein there are a plurality of the computer servers, and there are a plurality of areas in the building housing the plurality of computer servers (Figure 2, Figure 12 “Advantageously, such a model may take advantage of data rooms where certain areas, walls, sub-rooms, racks, etc. have higher cooling capacity than other areas in the data center to optimize heat contribution and control across all areas of the room”
Claim 5. The control as set forth in claim 1, wherein the thermal optimization control further having a memory being provided with historic thermal load information from the building (0282, 0285-0286, 0291 “As another example, repeating patterns of the heat generated by the data equipment may be ascertained, for example based on time-of-day, day-of-week, holiday/workday, etc. In some embodiments, a machine-learning algorithm is trained on historical data to predict the amount of heat that will be generated by data center equipment based on inputs such as day of the week, time of day, season, weather, economic conditions, special events, or other domain-specific factors (i.e., relating to the field of use of the data center). Predictions of the amount of thermal energy generated by the data equipment may thereby generated at step 1704.”
Claim 6. A method of optimizing thermal conditioning comprising:
receiving information from a building housing at least one computer server, with the information including at least current thermal load, supra claim 1
using thermal load information from the building to control an associated HVAC system for at least an area of the building where the at least one computer server is housed; supra claim 1
optimizing operation of the HVAC system based upon the current thermal load on the area of the building where the at least one computer server is housed to reduce energy usage by the HVAC system, supra claim 1, 0002)
Claim 13. The method as set forth in claim 6, further including the step of using historic data in the optimization ((0282, 0285-0286, 0291 “As another example, repeating patterns of the heat generated by the data equipment may be ascertained, for example based on time-of-day, day-of-week, holiday/workday, etc. In some embodiments, a machine-learning algorithm is trained on historical data to predict the amount of heat that will be generated by data center equipment based on inputs such as day of the week, time of day, season, weather, economic conditions, special events, or other domain-specific factors (i.e., relating to the field of use of the data center). Predictions of the amount of thermal energy generated by the data equipment may thereby generated at step 1704.”
Claim 14. The method as set forth in claim 13, wherein the historic data includes prior load information ((0282, 0285-0286, 0291 “As another example, repeating patterns of the heat generated by the data equipment may be ascertained, for example based on time-of-day, day-of-week, holiday/workday, etc. In some embodiments, a machine-learning algorithm is trained on historical data to predict the amount of heat that will be generated by data center equipment based on inputs such as day of the week, time of day, season, weather, economic conditions, special events, or other domain-specific factors (i.e., relating to the field of use of the data center). Predictions of the amount of thermal energy generated by the data equipment may thereby generated at step 1704.”
Claim 15. The method as set forth in claim 14, receiving information from an operator associated with the area of the building receiving the plurality of computer servers and controlling the HVAC system based upon the information received from the operator (0098, 0186, 0193-0194 “n planning tool 902, high level optimization circuit 130 may receive planned loads and utility rates for the entire simulation period. The planned loads and utility rates may be defined by input received from a user via a client device 922 (e.g., user-defined, user selected, etc.) and/or retrieved from a plan information database 926. High level optimization circuit 130 uses the planned loads and utility rates in conjunction with subplant curves from low level optimization circuit 132 to determine optimal subplant loads (i.e., an optimal dispatch schedule) for a portion of the simulation period.”)
Claim 16. A building comprising:
an area housing at least one computer server, supra claim 1
a HVAC system for the building; supra claim 1
a thermal optimization control having processing circuitry, the processing circuitry being operable to receive information from the building, with the information including at least a current thermal load, the processing circuitry being operable to control the HVAC system for at least an area of the building where the at least one computer server is housed; supra claim 1
the processing circuity being operable to optimize the operation of the HVAC system based upon the current thermal load on the area of the building where the server is received to reduce energy usage by the HVAC system, supra claim 1
Claim 17. The building as set forth in claim 16, wherein the HVAC system includes a plurality of water chillers for cooling air to be delivered into the area of the building that houses the at least one computer server, 0059 0306 “A central plant is one type of system that could be used to heat or cool a data center. A central plant may include may include various types of equipment configured to serve the thermal energy loads of a building or campus (i.e., a system of buildings). For example, a central plant may include heaters, chillers, heat recovery chillers, cooling towers, or other types of equipment configured to provide heating or cooling for the building or campus. The central plant equipment may be divided into various groups configured to perform a particular function. Such groups of central plant equipment are referred to herein as subplants. For example, a central plant may include a heater subplant, a chiller subplant, a heat recovery chiller subplant, “)
Claim 18. The building as set forth in claim 17, wherein there are a plurality of the computer servers, and there are a plurality of areas in the building receiving the plurality of computer servers (0015-0018, 0289, 0307, 0315 “Advantageously, such a model may take advantage of data rooms where certain areas, walls, sub-rooms, racks, etc. have higher cooling capacity than other areas in the data center to optimize heat contribution and control across all areas of the room.”
Claim 19. The building as set forth in claim 16, wherein the thermal optimization control also having a memory with historic data on the thermal load , supra claim 13, 0282, 0285-0286, 0291 “At step 1702, a model is provided that predicts thermal behavior of the data center. The model uses an amount of thermal energy generated by the data center equipment as an input. In some embodiments, the model uses different amounts of thermal energy generated by different portions of the data center equipment as multiple inputs. In some embodiments, the model is a grey-box model that can be identified using historical data, for example as described above with reference to FIGS. 12-14 “
Claim 20. The building as set forth in claim 16, wherein the thermal optimization control predicting an upcoming thermal load in the optimization, supra claim 1, see load prediction, 0020, 0122, 0131 In some embodiments, the predicted thermal energy loads include a predicted hot water thermal energy load Hot,k and a predicted cold water thermal energy load
COld,k for each time step k. The predicted hot water thermal energy load
Hot,k may be satisfied by the combination of heat recovery chiller subplant 14, heater subplant 12, and hot TES subplant 20. The predicted cold water thermal energy load
Cold,k may be satisfied by the combination of heat recovery chiller subplant 14, chiller subplant 16, and cold TES subplant 22.)
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.
Claim(s) 7-12 are rejected under 35 U.S.C. 103 as being unpatentable over Albinger in view over Malone et al. (USPN 9313929)
Claim 7. The method as set forth in claim 6 but does not expressly teach the air hanging unit limitations. Malone teaches the air hanging unit limitations described below, wherein the HVAC system includes a plurality of water chillers for cooling air to be delivered from an air hanging unit into the area of the building that houses the at least one computer server (figure 3-324, figure 1A)
One of ordinary skill in the art before the effective filing date of the claimed invention applying the teachings of Malone for overhead airflow distribution via multiple chillers, to the teachings of Albinger, namely optimizing HVAC control based on thermal loads, would achieve an expected and predictable result via combining said elements using known methods. Malone is in the same field of endeavor and would commend itself to data center cooling as described, ABSTRACR, summary of invention
Claim 8. The method as set forth in claim 7, wherein there are a plurality of the computer servers and a plurality of areas in the building receiving the plurality of computer servers (Albinger 0015-0018, 0289, 0307, 0315)
Claim 9. The method as set forth in claim 8, wherein the optimization includes controlling a staging of the plurality of water chillers between on/off conditions and/or full load/partial load (Albinger, 0060, 0076-77, 0079-0080, 0082, 0086, 0094-95, 0099)
Claim 10. The method as set forth in claim 9, wherein the optimization further including controlling a volumetric flow and temperature of water delivered from the water chillers to the air handling unit to optimize and reduce energy consumption (albinger 0090-91, 0117, 0119, 0130-0132, 0143-0144)
Claim 11. The method as set forth in claim 10 but does not expressly teach the fan limitations. Malone teaches the fan limitations described below
wherein a volume of air delivered into the building is controlled by controlling a fan for delivering air into the building to reduce energy consumptions (claim 17 ‘he system further includes a control system communicably coupled to the fan and the pump and configured to operate the air handling system in a first operation mode by controlling the fan to circulate outside airflow from an outside environment into the airflow path through the outside air intake; controlling the pump to circulate the cooling fluid to the direct evaporative cooling module to cool the outside airflow to reduce a dry bulb temperature of the outside airflow; and controlling the fan to circulate the cooled outside airflow to the data center without substantial mixing of the cooled outside airflow with another airflow. The control system is configured to receive an indication of a measured air contaminant level of the outside airflow greater than a setpoint contaminant level, and based on the received indication, switch from operating the air handling system in the first operation mode to operating the air handling system in a second operation mode by controlling the fan to circulate the return airflow from the data center to the air handling system; controlling the pump to circulate the cooling fluid to the indirect evaporative cooling module to cool the return airflow to reduce a dry bulb temperature and a wet bulb temperature of the return airflow; and controlling the fan to circulate the cooled return airflow to the data center without substantial mixing of the cool’)
One of ordinary skill in the art before the effective filing date of the claimed invention applying the teachings of Malone for overhead airflow distribution via fans associated with multiple chillers, to the teachings of Albinger, namely optimizing HVAC control based on thermal loads, would achieve an expected and predictable result via combining said elements using known methods. Malone is in the same field of endeavor and would commend itself to data center cooling as described, ABSTRACR, summary of invention
Claim 12. The method as set forth in claim 11, wherein air flow is managed at a rack level within the plurality of servers to reduce energy consumption (Albinger , 0308-0309)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
20210034024 see optimizing energy based on load - ‘A heating, ventilation, or air conditioning (HVAC) system for a building includes HVAC equipment configured to provide heating or cooling to one or more building spaces and one or more controllers. The one or more controllers include one or more processing circuits configured to generate energy targets for the one or more building spaces using a thermal capacitance of the one or more building spaces to which the heating or cooling is provided by the HVAC equipment, generate setpoints for the HVAC equipment using the energy targets for the one or more building spaces to which the heating or cooling is provided by the HVAC equipment, and operate the HVAC equipment using the setpoints to provide the heating or cooling to the one or more building spaces.
20200041965 see optimizing energy usage based on load ‘A building HVAC system includes an airside system having a plurality of airside subsystems, a high-level controller, and a plurality of low-level airside controllers. Each airside subsystem includes airside HVAC equipment configured to provide heating or cooling to one or more building spaces. The high-level controller is configured to generate a plurality of airside subsystem energy targets, each airside subsystem energy target corresponding to one of the plurality of airside subsystems and generated based on a thermal capacitance of the one or more building spaces to which heating or cooling is provided by the corresponding airside subsystem. Each low-level airside controller corresponds to one of the airside subsystems and is configured to control the airside HVAC equipment of the corresponding airside subsystem in accordance with the airside subsystem energy target for the corresponding airside subsystem.
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/DARRIN D DUNN/Patent Examiner, Art Unit 2117
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