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 .
This office action is in response to remarks filed July 21, 2026.
Claims 1-20 are pending.
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.
Claims 1-3, 10, 12-15, and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Sinha et al. (US20200241482A1) in further view of Wei et al. (US20130274940A1).
Regarding claim 1, Sinha teaches a method for operating a local Building Management System (BMS) of a local building, the method comprising:
one or more edge devices located at the local building (As shown in FIG. 5, spaces 510, 512, 514, 516, and 518 are each associated with one or more edge devices 530)([0165]; Figure 5 – edge devices located within a building is shown):
controlling one or more components of the local BMS based at least in part on one or more control setpoints via one or more controllers of the local BMS (Referring now to FIG. 7, a block diagram of an edge device 700 of a building system (e.g., building system 100) is shown … Edge controller 702 is shown to include a communications interface 710. Interface 710 may facilitate communications between edge controller 702 and ... one or more actuators 714 ... process 1000 can be used to control the operation of one or more of the edge devices 700 ... Process 1000 can continuously control actuator 714 to achieve and/or maintain a desired state (e.g., a temperature set point, a humidity set point, a desired range of light intensities, etc.))([0176], [0177], [0192], and [0220]; an edge controller controls components (e.g., actuators) to maintain a setpoint (e.g., temperature));
collecting real time data from a plurality of field sensors of the local BMS of the local building (Interface 710 may facilitate communications between edge controller 702 and … one or more sensors 712 .. In step 1002, edge controller 702 receives input data … the input data may include sensor data from a sensor 712 that measures a characteristic of the space associated with edge device 700)([0177], [0194], and [0195]; real time sensor data is collected);
storing at least some of the collected real time data over time resulting in a collection of historical data for the local BMS (Historical data manager 750 records (e.g., generates and stores) historical data 752 using the input data)([0186]);
controlling one or more components of the local BMS based on … via one or more controllers of the local BMS ((Local control scheme 730 can control how edge controller 702 responds to certain input data (e.g., what commands edge controller 702 provides to actuators 714 based on the input data received, control logic that determines the commands, etc.) ... In step 1018, command generator 732 controls edge device 700 by generating a command for actuator 714 based on local control scheme 730)([0183] and [0216]; actuator control is generated based on real time sensor data);
a remote server located remote from the local building (Referring again to FIG. 5, the edge devices 530 are shown in communication with cloud device 104 ... cloud device 104 is an off-site device that is located remote from buildings 502, 504, and 506 ... may include one or more cloud servers located within a remote datacenter)([0166]; Figure 6 – a remote cloud device (e.g., server) is shown):
receiving data collected by at least some of the plurality of field sensors of the local BMS of the local building (Referring now to FIG. 6, a block diagram a cloud device 600 of a building system (e.g., building system 100) is shown … a cloud controller 602 is implemented within cloud device 600 ... Cloud controller 602 can receive information (e.g., sensor data) provided by sensors 712 of the edge devices 700 … By way of example, the edge device 700 providing the processing request may be associated with a first space and a first building equipment domain ... Cloud controller 602 may use information provided by edge devices 700 associated the first building equipment domain but associated with a second space)([0169], [0205], and [0207]; sensor data of other locations within a building are received);
receiving data collected by at least some field sensors of remote Building Management Systems of each of a plurality of remote buildings (Cloud controller 602 may additionally ... utilize information relating to the first space but associated with a second building domain)([0207]; sensor data of other buildings are received);
performing data analytics on the data collected by at least some of the plurality of field sensors of the local BMS of the local building and the data collected by at least some field sensors of the remote Building Management Systems of each of the plurality of remote buildings to determine an updated local building model for the local BMS of the local building (Edge control adaptation command generator 640 may be configured to analyze the input data provided by the edge devices 700 to determine if a local control scheme (e.g., local control scheme 730) of an edge device 700 should be modified. If edge control adaptation command generator determines that the local control scheme should be modified, edge control adaptation command generator generates an edge control adaptation command for edge controller 702 outlining the changes to the local control scheme)([0175]; sensor data is analyzed to determine an updated building model (i.e., changes to control scheme)); and
sending the updated local building model to one or more of the edge devices located at the local building for use by one or more of the edge devices located at the local building … (in step 1014, cloud controller 602 transfers the edge control adaptation command to edge controller 702. In step 1016, in response to receiving the edge control adaptation command, command generator 732 of edge controller 702 updates local control scheme 730)([0216]; the changes to control scheme are transmitted to edge controllers to update local control scheme).
Sinha differs from the claim in that Sinha fails to teach performing energy optimization of a building based on a building model and historical data, updating control setpoints based on the energy optimization to optimize energy consumption while maintaining comfort requirements, and controlling components based on the updated control setpoints.
However, performing energy optimization of a building based on a building model and historical data, updating control setpoints based on the energy optimization to optimize energy consumption while maintaining comfort requirements, and controlling components based on the updated control setpoints is taught by Wei (an occupant may request that the temperature in a zone in the building be set to a specific temperature value … Referring to FIG. 3, the energy simulator 204 simulates different EMC strategies and determines which strategy is the most energy efficient ... The energy simulator 204 may communicate with the optimization tool/libraries module 303 of the EMC run-time module 203 to determine an optimized strategy … For example, the load 10 shifting control strategy includes pre-cooling or pre-heating zones of a building prior to the peak load time. A heuristic search based optimization process may be implemented to determine the optimal start time and the duration of the pre-cooling or pre-heating .. The EMC system 100 receives the occupancy data 103, facility manager data 106 and energy price data 104 along with associated historical data, and a physical makeup or model of an associated building … The EMC system 100 performs an optimization calculation … provides a proactive control strategy wherein an optimized schedule is generated that is based on prior knowledge)([0017], [0027], [0034], [0040], and [0041]; updated setpoints (e.g., time and duration of cooling or heating) for comfort requirements (e.g., specified temperature) are determined based on optimization calculation, the optimization calculation uses building model and historical data).
The examiner notes Sinha and Wei teach controlling building components. As such, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Sinha to include the performing, the updating, and the controlling of Wei such that energy optimization of a building based on a building model and historical data is performed, control setpoints based on the energy optimization is updated to optimize energy consumption while maintaining comfort requirements, and components are controlled based on the updated control setpoints. One would be motivated to make such a combination to provide the advantage of decreasing energy consumption while increasing occupant comfort ([0016]; Wei).
Regarding claim 2, Sinha-Wei teach the method of claim 1, wherein the energy optimization for the local BMS of the local building is configured to optimize a cost of energy consumed by the local BMS and is based at least in part on the local building model of the local building, at least part of the collection of historical data of the local BMS, and local energy tariff information that is applicable to the local building (Wei - The EMC system 100 receives the occupancy data 103, facility manager data 106 and energy price data 104 along with associated historical data, and a physical makeup or model of an associated building ... The EMC system 100 performs an optimization calculation … provides a proactive control strategy wherein an optimized schedule is generated that is based on prior knowledge)([0040] and [0041]; an optimized plan is based on model of building, historical data, and energy tariff (i.e., price)).
Regarding claim 3, Sinha-Wei teach the method of claim 1, wherein the energy optimization for the local BMS of the local building comprises optimizing energy consumption to minimize energy costs (Wei - The EMC system 100 then sends the most suitable schedule in terms of optimized energy consumption and cost to the controllers 12)([0040]; optimization is minimizing energy cost).
Regarding claim 10, Sinha-Wei teach the method of claim 1, wherein the plurality of field sensors are configured to sense one or more of temperature, humidity, noise, fumes, and physical disturbance (Sinha - Sensors 712 can include, but are not limited to ... chemical sensors ... environment sensors, weather sensors, moisture sensors, humidity sensors ... thermal sensors, heat sensors, temperature sensors, proximity sensors, presence sensors, and/or any other type of sensors or sensing systems)([0179]).
Regarding claim 12, Sinha-Wei teach the method of claim 1, wherein the energy optimization is based at least in part on: the local building model of the local building; at least part of the collected real time data of the local BMS; and at least part of the collection of historical data of the local BMS (Wei - The architecture 130 includes a data server 132 that stores historical data, building modeling data ... the data server 132 stores sensor and actuator data collected by the controllers 12 ... generates optimized schedules and settings in terms of energy usage and cost)([0042] and [0043]).
Regarding claim 13, Sinha-Wei teach the method of claim 1 comprising sending the data collected by at least some of the plurality of field sensors of the local BMS of the local building from one or more of the edge devices located at the local building to the remote server via an intervening IoT hub (Sinha - In some embodiments, building system 100 further includes an intermediate control layer 106 including a secondary control device, shown as intermediate control device 108 ... “edge devices” refers to one or more pieces of building equipment or other devices in communication with the cloud device 104 ... indirectly through one or more intermediate control devices 108)([0122] and [0123]).
Regarding claim 14, Sinha teaches an energy optimizer implemented by one or more edge devices located at a local building, the energy optimizer operatively coupled to a local BMS of the local building, the energy optimizer comprising:
an I/O (Referring now to FIG. 7, a block diagram of an edge device 700 ... Edge controller 702 is shown to include a communications interface 710. Interface 710 may facilitate communications)([0176] and [0177]);
a memory (Still referring to FIG. 7, edge controller 702 is shown to include ... a memory 724)([0181]);
a controller operatively coupled to the I/O and the memory (Still referring to FIG. 7, edge controller 702 is shown to include processing circuit 720 including a processor 722 … Processing circuit 720 can be communicably connected to interface 710 … memory 724 is communicably connected to processor 722 via processing circuit 720)([0181] and [0182]), the controller configured to:
receive real time data from a plurality of field sensors of the local BMS of the local building via the I/O (Interface 710 may facilitate communications between edge controller 702 and … one or more sensors 712 .. In step 1002, edge controller 702 receives input data … the input data may include sensor data from a sensor 712 that measures a characteristic of the space associated with edge device 700)([0177], [0194], and [0195]; real time sensor data is collected);
store at least some of the collected real time data over time in the memory, resulting in a collection of historical data for the local BMS (Historical data manager 750 records (e.g., generates and stores) historical data 752 using the input data)([0186]);
send at least some of the data collected by at least some of the plurality of field sensors of the local BMS of the local building to a remote server via the I/O (transfers the input data to cloud controller 602 along with the processing request ... The processing request and the input data may travel through communications interface 710, across network 112, and through communications interface 610 to reach cloud controller 602)([0203]);
receive an updated local building model from the remote server via the I/O, the updated local building model is based at least in part on data analytics performed by the remote server on the data collected by at least some of the plurality of field sensors of the local BMS and sent to the remote server and on data collected by at least some field sensors of one or more remote Building Management Systems of each of a plurality of remote buildings (Referring now to FIG. 6, a block diagram a cloud device 600 of a building system (e.g., building system 100) is shown … a cloud controller 602 is implemented within cloud device 600 … Edge control adaptation command generator 640 may be configured to analyze the input data provided by the edge devices 700 to determine if a local control scheme (e.g., local control scheme 730) of an edge device 700 should be modified. If edge control adaptation command generator determines that the local control scheme should be modified, edge control adaptation command generator generates an edge control adaptation command for edge controller 702 outlining the changes to the local control scheme … in step 1014, cloud controller 602 transfers the edge control adaptation command to edge controller 702. In step 1016, in response to receiving the edge control adaptation command, command generator 732 of edge controller 702 updates local control scheme 730)([0169], [0175], and [0216]; sensor data from buildings is analyzed to determine an updated building model (i.e., changes to control scheme), the changes to control scheme are received by edge controllers).
Sinha differs from the claim in that Sinha fails to teach performing energy optimization of a building based on a building model and historical data, determining updated control setpoints based on the energy optimization to optimize energy consumption while maintaining comfort requirements, and sending the updated control setpoints to control components.
However, performing energy optimization of a building based on building model and historical data, determining updated control setpoints based on the energy optimization to optimize energy consumption while maintaining comfort requirements, and sending the updated control setpoints to control components is taught by Wei (an occupant may request that the temperature in a zone in the building be set to a specific temperature value … Referring to FIG. 3, the energy simulator 204 simulates different EMC strategies and determines which strategy is the most energy efficient ... The energy simulator 204 may communicate with the optimization tool/libraries module 303 of the EMC run-time module 203 to determine an optimized strategy … For example, the load 10 shifting control strategy includes pre-cooling or pre-heating zones of a building prior to the peak load time. A heuristic search based optimization process may be implemented to determine the optimal start time and the duration of the pre-cooling or pre-heating .. The EMC system 100 receives the occupancy data 103, facility manager data 106 and energy price data 104 along with associated historical data, and a physical makeup or model of an associated building … The EMC system 100 then sends the most suitable schedule in terms of optimized energy consumption and cost to the controllers 12 … The EMC system 100 performs an optimization calculation … provides a proactive control strategy wherein an optimized schedule is generated that is based on prior knowledge)([0017], [0027], [0034], [0040], and [0041]; updated setpoints (e.g., time and duration of cooling or heating) for comfort requirements (e.g., specified temperature) are determined based on optimization calculation, the optimization calculation uses building model and historical data).
The examiner notes Sinha and Wei teach controlling building components. As such, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Sinha to include the performing, the determining, and the sending of Wei such that energy optimization of a building based on a building model and historical data is performed, updated control setpoints based on the energy optimization is determined to optimize energy consumption while maintaining comfort requirements, and components are controlled based on sent updated control setpoints. One would be motivated to make such a combination to provide the advantage of decreasing energy consumption while increasing occupant comfort ([0016]; Wei).
Regarding claim 15, Sinha-Wei teach the energy optimizer of claim 14, wherein the energy optimization for the local BMS of the local building is based at least in part on the updated local building model of the local building, at least part of the collection of historical data of the local BMS and local energy tariff information that is applicable to the local building (Wei - The EMC system 100 receives the occupancy data 103, facility manager data 106 and energy price data 104 along with associated historical data, and a physical makeup or model of an associated building ... The EMC system 100 performs an optimization calculation … provides a proactive control strategy wherein an optimized schedule is generated that is based on prior knowledge)([0040] and [0041]; an optimized plan is based on model of building, historical data, and energy tariff (i.e., price)).
Regarding claim 17, Sinha-Wei teach the energy optimizer of claim 14, wherein the energy optimization is based at least in part on: the updated local building model of the local building; at least part of the collected real time data of the local BMS; and at least part of the collection of historical data of the local BMS (Wei - The architecture 130 includes a data server 132 that stores historical data, building modeling data ... the data server 132 stores sensor and actuator data collected by the controllers 12 ... generates optimized schedules and settings in terms of energy usage and cost)([0042] and [0043]).
Regarding claim 18, Sinha teach A cloud service, comprising:
an I/O (Referring now to FIG. 6, a block diagram a cloud device 600 ... Cloud controller 602 is shown to include a communications interface 610)([0169] and [0170]);
a memory (Still referring to FIG. 6, cloud controller 602 is shown to include ... a memory 624)([0172]);
a controller operatively coupled to the I/O and the memory (Still referring to FIG. 6, cloud controller 602 is shown to include a processing circuit 620 including a processor 622 … Processing circuit 620 can be communicably connected to interface 610 … memory 624 is communicably connected to processor 622 via processing circuit 620)([0172] and [0173]), the controller configured to:
receive, via the I/O, data collected by a plurality of field sensors at each of a plurality Building Management Systems of a plurality of buildings (Cloud controller 602 can be configured to receive information from any edge devices 700 in communication with cloud controller 602 ... Cloud controller 602 can receive information (e.g., sensor data) provided by sensors 712 of the edge devices 700)([0205]; sensor data is received);
perform data analytics on the data collected by at least some of the plurality of field sensors of the plurality Building Management Systems of the plurality of buildings to determine an updated building model for a particular one of the plurality of buildings (Edge control adaptation command generator 640 may be configured to analyze the input data provided by the edge devices 700 to determine if a local control scheme (e.g., local control scheme 730) of an edge device 700 should be modified. If edge control adaptation command generator determines that the local control scheme should be modified, edge control adaptation command generator generates an edge control adaptation command for edge controller 702 outlining the changes to the local control scheme)([0175]; sensor data is analyzed to determine an updated building model (i.e., changes to control scheme)); and
send via the I/O the updated building model to one or more edge devices at the particular one of the plurality of buildings for use by the one or more edge devices at the particular one of the plurality of buildings … (in step 1014, cloud controller 602 transfers the edge control adaptation command to edge controller 702. In step 1016, in response to receiving the edge control adaptation command, command generator 732 of edge controller 702 updates local control scheme 730)([0216]; the changes to control scheme are transmitted to edge controllers to update local control scheme).
Sinha differs from the claim in that Sinha fails to teach performing energy optimization of a building to optimize energy consumption while maintaining comfort requirements.
However, performing energy optimization of a building to optimize energy consumption while maintaining comfort requirements is taught by Wei (an occupant may request that the temperature in a zone in the building be set to a specific temperature value … Referring to FIG. 3, the energy simulator 204 simulates different EMC strategies and determines which strategy is the most energy efficient ... The energy simulator 204 may communicate with the optimization tool/libraries module 303 of the EMC run-time module 203 to determine an optimized strategy … For example, the load 10 shifting control strategy includes pre-cooling or pre-heating zones of a building prior to the peak load time. A heuristic search based optimization process may be implemented to determine the optimal start time and the duration of the pre-cooling or pre-heating .. The EMC system 100 receives the occupancy data 103, facility manager data 106 and energy price data 104 along with associated historical data, and a physical makeup or model of an associated building … The EMC system 100 performs an optimization calculation … provides a proactive control strategy wherein an optimized schedule is generated that is based on prior knowledge)([0017], [0027], [0034], [0040], and [0041]; updated setpoints (e.g., time and duration of cooling or heating) for comfort requirements (e.g., specified temperature) are determined based on optimization calculation).
The examiner notes Sinha and Wei teach controlling building components. As such, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Sinha to include the performing of Wei such that energy optimization of a building is performed to optimize energy consumption while maintaining comfort requirements. One would be motivated to make such a combination to provide the advantage of decreasing energy consumption while increasing occupant comfort ([0016]; Wei).
Claims 7-9 are rejected under 35 U.S.C. 103 as being unpatentable over Sinha, Wei, and in further view of Carty et al. (US20120232701A1).
Regarding claim 7, Sinha-Wei teach the method as applied above, wherein data analytics is performed on collected data from local and remote building to determine an updated building model (i.e., changes to control scheme)(Sinha - Edge control adaptation command generator 640 may be configured to analyze the input data provided by the edge devices 700 to determine if a local control scheme (e.g., local control scheme 730) of an edge device 700 should be modified. If edge control adaptation command generator determines that the local control scheme should be modified, edge control adaptation command generator generates an edge control adaptation command for edge controller 702 outlining the changes to the local control scheme)([0175]). Sinha-Wei differs from the claim in that Sinha-Wei fails to teach generating insights (e.g., recommendations).
However, generating insights based on analyzing data is taught by Carty (After the optimization engine 216 optimizes the demand models, the analytical engine 214 is operative to receive real-time inputs and generate predictions based on the optimized demand models ... the analytical engine 200 may simply generate recommendations 226 and display such recommendations to an operator or building manager via a graphical user interface)([0037]).
The examiner notes Sinha, Wei, and Carty teach controlling building components. As such, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Sinha-Wei to include the generating of Carty such that insights are generated. One would be motivated to make such a combination to provide the advantage of providing real-time feedback to a user.
Regarding claim 8, Sinha-Wei-Carty teach the method of claim 7, wherein the one or more insights include one or more of: performance monitoring; performing trending; fault detection; diagnostics; and improvement recommendations (Carty - the analytical engine 200 may simply generate recommendations 226 and display such recommendations to an operator or building manager via a graphical user interface … a method 1100 analyzes sensor data and equipment status data and generates trends on sensor and equipment status ... the method 1100 may notify the appropriate building control system and the optimizer (see, e.g., FIG. 8) of the failure, step 1122)([0037], [0078], and [0081]).
Regarding claim 9, Sinha-Wei-Carty teach the method of claim 7, comprising sending one or more of the insights to a portal and/or mobile application for visualization (Carty - The system further comprises a graphical user interface operating on a client device operative to display recommendations)([0014]); a portal is a type of graphical user interface).
Claims 11 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Sinha, Wei, and in further view of Henze et al. (US20130013121A1).
Regarding claim 11, Sinha-Wei teach the method as applied above, wherein control is optimized to maintain conform requirements in a building (Wei - The EMC system 100 then sends the most suitable schedule in terms of optimized energy consumption and cost to the controllers 12. Thus, the EMC system 100 provides a proactive and predictive control strategy)([0040]). Sinha-Wei differs from the claim in that Sinha-Wei fails to teach predicting a demand of a building based on a building model and historical data and optimizing control to meet the predicted demand.
However, predicting a demand of a building based on a building model and historical data and optimizing control to meet the predicted demand is taught by Henze (a model of the building is used to predict building behavior going forward, and these predictions are then used to decide now on a control strategy ... A software model 10 of a building simulates building behavior given a set of conditions including a) past building performance over a properly chosen recent past ... c) controllable conditions, e.g., temperature set points on one or more thermostats in the building or HVAC system set points. The output of the building model represents the predicted response of the building to those conditions ... The building model outputs are provided to an optimizer 20 which explores a number of different possible control signal sets ... After evaluating a large number of options and choosing the best one, this optimized control signal set for the current control period)([0037] and [0038]).
The examiner notes Sinha, Wei, and Henze teach controlling building components. As such, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Sinha-Wei to include the predicting and optimizing the of Henze such a demand of a building is predicted based on a building model and historical data and control is optimized to meet the predicted demand. One would be motivated to make such a combination to provide the advantage of decreasing significant financial loss.
Regarding claim 16, Sinha-Wei teach the energy optimizer as applied above, wherein control is optimized to maintain conform requirements in a building (Wei - The EMC system 100 then sends the most suitable schedule in terms of optimized energy consumption and cost to the controllers 12. Thus, the EMC system 100 provides a proactive and predictive control strategy)([0040]). Sinha-Wei differs from the claim in that Sinha-Wei fails to teach predicting a demand of a building based on a building model and historical data and optimizing control to meet the predicted demand.
However, predicting a demand of a building based on a building model and historical data and optimizing control to meet the predicted demand is taught by Henze (a model of the building is used to predict building behavior going forward, and these predictions are then used to decide now on a control strategy ... A software model 10 of a building simulates building behavior given a set of conditions including a) past building performance over a properly chosen recent past ... c) controllable conditions, e.g., temperature set points on one or more thermostats in the building or HVAC system set points. The output of the building model represents the predicted response of the building to those conditions ... The building model outputs are provided to an optimizer 20 which explores a number of different possible control signal sets ... After evaluating a large number of options and choosing the best one, this optimized control signal set for the current control period)([0037] and [0038]).
The examiner notes Sinha, Wei, and Henze teach controlling building components. As such, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Sinha-Wei to include the predicting and optimizing the of Henze such a demand of a building is predicted based on a building model and historical data and control is optimized to meet the predicted demand. One would be motivated to make such a combination to provide the advantage of decreasing significant financial loss.
Allowable Subject Matter
Claims 4-6 and 19-20 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.
Response to Arguments
Applicant's arguments filed July 21, 2026 have been fully considered but they are not persuasive.
Regarding claim 1, 14, and 18, applicant argues the combination of Sinha and Wei is improper; the examiner respectfully disagrees.
The examiner recognizes a proposed modification would render the prior art invention being modified unsatisfactory for its intended purpose, then there is no suggestion or motivation to make the proposed modification. In re Gordon, 733 F.2d 900, 221 USPQ 1125 (Fed. Cir. 1984)( "The question is not whether a patentable distinction is created by viewing a prior art apparatus from one direction and a claimed apparatus from another, but, rather, whether it would have been obvious from a fair reading of the prior art reference as a whole to turn the prior art apparatus upside down").
The combination of Sinha and Wei would not be inoperable. No feature is destroyed in the combination of Sinha and Wei, instead features of Sinha would be improved utilizing the features of Wei. That is, Sinha features controlling building components “the present disclosure is a method of controlling equipment of a building”. In particular, Sinha controls by analyzing data “the method includes ... a request to a cloud controller to analyze the data ... modifying, by the edge controller, the first neural network based on the response from the cloud controller and analyzing, by the edge controller, the data using the first neural network)” ([0003] and [0009]).
Wei similarly features controlling building components “A method of controlling energy consumption in a building is disclosed ... control signals are simulated to determine an optimized control signal based on optimized energy use or optimized cost” ([0005]). In particular, Wei controls by performing energy optimization of a building based on a building model and historical data, updating control setpoints (e.g., time and duration of cooling or heating) based on the energy optimization to optimize energy consumption while maintaining comfort requirements (e.g., specified temperature), and controlling components based on the updated control setpoints “an occupant may request that the temperature in a zone in the building be set to a specific temperature value … Referring to FIG. 3, the energy simulator 204 simulates different EMC strategies and determines which strategy is the most energy efficient ... The energy simulator 204 may communicate with the optimization tool/libraries module 303 of the EMC run-time module 203 to determine an optimized strategy … For example, the load 10 shifting control strategy includes pre-cooling or pre-heating zones of a building prior to the peak load time. A heuristic search based optimization process may be implemented to determine the optimal start time and the duration of the pre-cooling or pre-heating .. The EMC system 100 receives the occupancy data 103, facility manager data 106 and energy price data 104 along with associated historical data, and a physical makeup or model of an associated building … The EMC system 100 performs an optimization calculation … provides a proactive control strategy wherein an optimized schedule is generated that is based on prior knowledge” ([0017], [0027], [0034], [0040], and [0041]).
The examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007).
In this case, as noted above, Sinha and Wei both teach controlling building components. As such, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Sinha to include the performing, the updating, and the controlling of Wei such that energy optimization of a building based on a building model and historical data is performed, control setpoints based on the energy optimization is updated to optimize energy consumption while maintaining comfort requirements, and components are controlled based on the updated control setpoints. One would be motivated to make such a combination to provide the advantage of decreasing energy consumption while increasing occupant comfort ([0016]; Wei).
Conclusion
The prior art made of record on form PTO-892 and not relied upon is considered pertinent to applicant's disclosure. Applicant is required under 37 C.F.R. § 1.111(c) to consider the reference fully when responding to this action. The document cited therein and enumerated below teaches a method and apparatus for managing energy usage.
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THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Yongjia Pan whose telephone number is (571)270-1177. The examiner can normally be reached Monday - Friday, 9:00 AM - 5:00 PM EST.
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/YONGJIA PAN/Primary Examiner, Art Unit 2118
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