DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-6, 8 and 10-15 are pending.
Claim 7 and 9 are cancelled.
Response to Arguments
Applicant’s arguments with respect to the 103 rejections of the claims have been considered but are moot because the arguments do not apply to any of the references being used in the current rejection.
Claim Objections
The following claims are objected to for informalities, lack of antecedent support, or for redundancies. The Examiner recommends the following changes:
Claim 1, line 20, replace “select one of the physics-based simulation model or the machine learning model” with either “select the physics-based simulation model or the machine learning model” or “select one of the physics-based simulation model and the machine learning model”.
Claim 8, line 18, replace “select one of the physics-based simulation model or the machine learning model” with either “select the physics-based simulation model or the machine learning model” or “select one of the physics-based simulation model and the machine learning model”.
Appropriate correction is respectfully requested.
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.
Claims 1, 4-6, 8 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over QIN (US 2019/0236446 A1) (“Qin”), in view of Dweik et al. (US 2018/0260503 A1) (“Dweik”), further in view of Drees (US 2024/0045385 A1) (“Drees”).
Regarding independent claim 1, Qin teaches:
A system for energy management of HVAC equipment comprising: (Qin: [0038] “The disclosed energy audit and optimization control technology employs sophisticated artificial intelligence computer models and analysis processes to assess and optimize the energy utilization efficiency of an energy consuming structure, such as a building having a heating ventilation and air conditioning (HVAC) system (HVAC) and optionally other electrically controlled occupant life support and convenience systems, including lighting systems.”)
a plurality of distributed sensors for sensing, in real time, a set of parameters including environmental information, thermal zone information, energy consumption information and operational parameter information associated with a building; (Qin: [0047] “Without the benefit of the disclosed optimization system, the HVAC system would affect control over heating and cooling by controlling whether the chiller is on or off and by manipulating the settings of various dampers, with the goal of providing a constant temperature within a particular zone controlled by a particular thermostat, such as thermostat 40a, or 40b. By way of example, in a typical HVAC control system to achieve a zone temperature of 72 degrees F., the HVAC control system might set the zone damper to a 15% open setting and command the chiller to be turned on. This might produce a supply air temperature from the chiller at 57 degrees F. at a static pressure of 350 pa, with the VFD 36a controlling the supply air being set to 95%.”) (Qin: [0100] “Referring to FIG. 3, the structure 10 under audit and optimization control includes energy consuming equipment, such as the HVAC system 12. Associated with the energy consuming equipment are a plurality of sensors 50 and actuators 52. In general, the sensors 50 measure relevant conditions such as temperature, pressure, humidity, airflow, electric current, energy and heat flow and the operating states of various components of the energy consuming equipment. The actuators 52 perform functional changes to the energy consuming equipment, such as turning components on and off and changing variable settings, such as damper settings. In the illustrated embodiment, these actuators and sensors are coupled to a communication computer 130 that is programmed to collect and compile sensor readings and actuator instructions, and to communicate sensor readings to the audit computer 100 and to receive actuator instructions from the optimization computer 120. If desired the communication computer 130 may be implemented as a set of functions within a smart device, such as a smart power meter that meters electrical power being supplied to the structure.”) (Qin: [0212] “FIG. 17C illustrates a wireless embodiment where the HVAC system is controlled by a wireless sensor network comprising small wirelessly communicating sensor nodes that may be deployed in suitable controllers, such as thermostat units 40.”) [The structure 10 reads on “a building”.]
a network configured to connect the plurality of distributed sensors; (Qin: [0040] “To do this a plurality of sensors are deployed within or in proximity to the structure. These sensors are equipped with communication links and report collected data to an audit computer that is programmed to perform a set of audit functions on the collected data and thereby compute both real time energy utilization statistics and aggregated historical statistics. The details of the set of audit functions are discussed below.”) (Qin: [0102] “To support the audit stage, the sensors 50 associated with energy consuming systems within structure 10 may be equipped with wired or wireless communication links, allowing locally measured data to be collected and sent to the audit computer 100 via the communication computer 130. To support the optimization stage, at least some of the actuators 52 may be equipped with electronic control circuits, allowing them to be controlled by the optimization computer 120.”) (Qin: [0213] “In larger applications, it can be desirable to deploy the optimization control system on a computer 60 that interfaces with the electronic controllers and actuators within the structure, in a manner that utilizes the structures existing wiring and network topology. An example of this has been illustrated in FIG. 18. In FIG. 18 the sensors 50, actuators 52, room control thermostats 40, and other HVAC components such as the chiller 22, fan coils, VAV box and the like, are coupled to communicate over a LON works or BACnet bus, as illustrated. Automation level controllers 66 provide the local control over these HVAC components and these controllers 66 are in turn responsive to computer 60, which provides control strategies to be carried out by the automation level controllers 66. If needed, a router or gateway 68 provides connectivity between computer 60 and certain HVAC devices that may not be accessible through the automation level controllers.”)
a server configured to collect and store data generated using the plurality of distributed sensors through the network; and (Qin: [0100] as discussed above) (Qin: [0101] “While the audit computer 100 and optimization computer 120 have been depicted as separate computer devices in FIG. 2, these functions of these two computers can be implemented on a single computer device. Also while the audit computer 100 and optimization computer 120 have been depicted in a physical location outside structure 10, if desired these computers can be located within structure 10.”; (Qin: [0117] “In addition to the model-based parameters (developed using EnergyPlus), the audit computer also captures real-time data from the sensors deployed within the structure, as described above. If desired, these real-time data can be aggregated into sets of historical control parameter data.”) (Qin: [0210] “FIGS. 17A, 17B and 17C illustrate three different ways of deploying the optimization control system with a computer 60. As illustrated, the computer 60 is performing both the functions of the audit computer 100 and the optimization computer 120 (FIG. 3).”) [The combination of the audit computer 100 and optimization computer 120 or the computer 60 reads on “a server”. Capturing and aggregating into sets of historical data read on “to collect and store data…”.]
a platform, including a physics-based simulation model … and a machine learning model, residing on the server and configured to: (Qin: Abstract “A simulation processor generates and stores a simulation model based on conditions associated with a physical structure, such as a building. A neural network processor implements a neural network, having an input layer coupled to receive sensor data from the structure and having an output layer coupled to supply control signals to the at least one electrically operable environmental control device. The neural network is trained using the simulation model.”) (Qin: [0051] “The disclosed energy audit and optimization control system uses sophisticated computer models for certain aspects in both the energy audit stage and the optimization stage. By way of brief introduction, FIG. 2 illustrates how heat flow can be modeled in much the same way as electrical circuits can be modeled, where heat flow is analogous to electric current flow and temperature is analogous to voltage.”) (Qin: [0181] “Referring to FIG. 7, the neural network 142 is trained using the simulation output 146 generated by the simulation model 144 (as described in connection with FIG. 4). The neural network, so trained, is a now able to make predictions relative to the four models established when developing the simulation model 144. These are the four key structure models used to perform energy audits, namely, the thermal load model, the envelope model, the HVAC model and the occupants model.”) [The simulation model based on conditions associated with the physical structure reads on “a physics-based simulation model”. The neural network trained using the simulation model reads on “a machine learning model”. The optimization computer with the simulation processor and the neural network processor reads on “a platform, including … , residing on the server”.]
predict an amount of energy consumption of the HVAC equipment in the building based at least on the stored data, the prediction generated using both the physics-based simulation model and the machine learning model; (Qin: [0048] “When the disclosed optimization system is used to instruct the HVAC controller, a far more energy efficient control strategy becomes possible. Using energy audit and optimization techniques more fully discussed below, the same structure might be controlled to achieve a zone temperature of 72 degrees F. (the same as in the example above), where the chiller system 22 is commanded to be shut off, and the zone damper is set to 85.22% open. With the chiller off, the supply air temperature might be 64.4 degrees F. at a static pressure of 196.6 psi, with the VFD 36a being set to 74.5%.”) (Qin: [0189] “The second training mechanism provides dynamic information about the structure. To perform the second training mechanism, the simulation models are fed with a series of time-varying conditions that cause the model to predict different responsive behaviors and these behaviors are used to further train the neural network 142. The second training mechanism thus provides the neural network with information about predicted energy consumption levels under time-varying dynamic conditions.”) (Qin: [0190] “By way of example the second training mechanism might model the performance of an economizer such as that illustrated in FIG. 9. The second training process generates simulation results showing economizer performance under different environmental conditions as damper and fan settings are changed.”)
detect irregularities … based at least on the stored data; (Qin: [0114] “The HVAC model thus models the on/off states of HVAC system components, and their operational settings. Lastly, the structure occupancy behavior model captures properties about how building occupants move throughout the building both positionally and as a function of time.”) (Qin: [0115] “Collectively, the data captured in these four models represents a complex parameter space, describing how energy is utilized as a result of conditions external to the structure (e.g., weather, and qualities of the structure envelope), and conditions internal to the structure (e.g., caused by occupant behavior) and as a result of the operation of the HVAC systems associated with the structure.”) (Qin: [0117] “In addition to the model-based parameters (developed using EnergyPlus), the audit computer also captures real-time data from the sensors deployed within the structure, as described above. If desired, these real-time data can be aggregated into sets of historical control parameter data. The audit computer uses these two ingredients—the model-based parameters and the control parameter data—to train a neural network that effectively learns how the control parameters within the actual physical structure correlate to the model-based parameters. The trained neural network thus acquires a powerful understanding of how control parameters that can be physically monitored, relate to theoretical (model-based) parameters that are grounded in principles of thermodynamics. To appreciate the significance, the trained neural network model can predict how the HVAC systems within the structure will behave under certain weather and occupancy patterns, even if those have not actually been experienced before. The trained neural network model can also predict how changes in certain control parameter settings (i.e., different HVAC equipment settings or operating conditions) will affect other systems when certain weather and occupancy patterns occur.”) [Determining the certain weather and occupancy patterns that have not been experienced before reads on “detect irregularities”.]
determine a plurality of control commands to perform based at least on the detected irregularities, the stored data, and the predicted amount of energy consumption of the HVAC equipment; and cause parallel execution of the determined plurality of control commands using the HVAC equipment. (Qin: [0098] “In addition to modeling the structure, the disclosed energy audit and optimization control system is also able to generate control instructions that may be fed back to the structure to induce energy optimization. In one embodiment these control instructions are fed back as control parameters that instruct the HVAC control system (and optionally other electronic control systems) to make changes to the HVAC component and other system settings to improve energy efficiency. In such an automated embodiment, the energy audit and optimization control system can either supply explicit commands to specific actuators within the structure (e.g., actuators that control heating and cooling equipment, adjust damper settings, change thermostat settings, and the like).”) (Qin: [0208] “The DNN 142a works in conjunction with a Q table 206 that stores values for every state vs every action possible in the environment. This Q table is also mapped to DNN 142b, which can be a separate neural network, or an instance of DNN 142a. DNN 142b is used to drive the settings of coupled HVAC equipment, such as to manage the damper positions as illustrated at 208. The DNN 142b is also coupled to the simulation models 144 which in turn regulate the environment (i.e., energy consumption) by the structure 10.”) (Qin: [0214] “In the embodiment of FIG. 18, the optimization control strategy, developed by the neural networks and particle swarm optimization functions, is supplied by computer 60 to the automation level controllers 66 as a control regimen which the controllers 66 carry out. If certain components cannot be accessed through the automation level controllers 66, computer 60 can effect direct control over those components by sending instructions through the router or gateway 68.”) [The commands carrying out changes to the specific actuators of the HVAC control system read on “cause parallel execution of the determined plurality of control commands …”.]
Qin does not expressly teach: a platform, including a physics-based simulation model comprising at least a three-dimensional computational fluid dynamics analysis and a machine learning model …; detect irregularities in functioning of the HVAC equipment based at least on the stored data; wherein the platform is configured to select one of the physics-based simulation model or the machine learning model based on defined parameters, wherein the selected model has priority over the other model in computing the plurality of control commands.
Dweik teaches:
a platform, including a physics-based simulation model comprising at least a three-dimensional computational fluid dynamics analysis and a machine learning model … (Dweik: [0025] “Systems and techniques that facilitate optimization of prototype and machine design within a three dimensional (3D) fluid modeling environment are presented. For example, as compared to conventional analysis of a fluid system that involves human interpretation of two-dimensional (2D) data and/or human trial and error with respect to a fluid system, the subject innovations provide for a three-dimensional (3D) design environment and/or a probabilistic simulation environment that can facilitate optimization of prototype and machine design. In an aspect, physics modeling data associated with a degree of fluid flow can be rendered on a 3D model of a device and optimized to facilitate optimization of a design for the device. In one example, visual characteristics of the physics modeling data can be dynamic based on the degree of fluid flow and/or optimization of the physics modeling data. Various systems and techniques disclosed herein can be related to cloud-based services, a heating, ventilation and air conditioning (HVAC) system, a medical system, an automobile, an aircraft, a water craft, a water filtration system, a cooling system, pumps, engines, diagnostics, prognostics, optimized machine design factoring in cost of materials in real-time, explicit and/or implicit training of models through real-time aggregation of data, etc. …”) (Dweik: [0055] “Additionally, the system 500 can include a physics 3D model 506. The physics 3D model 506 can be associated with the 3D model 502. The physics 3D model 506 can also include physics modeling data (e.g., PHYSICS MODELING DATA shown in FIG. 5) generated by the machine learning process 504. The physics modeling data can be indicative of information associated with fluid dynamics, thermal dynamic and/or combustion dynamics For instance, the physics modeling data can be rendered on the physics 3D model 506 to represent fluid flow, thermal characteristics, combustion characteristics and/or physics behavior for a device associated with the physics 3D model 506. In one example, the physics modeling data can simulate physical phenomena such as, but not limited to, compressible fluid flow, incompressible fluid flow, buoyancy driven flow, rotating cavity system flow, conduction heat transfer, convection heat transfer, radiation heat transfer, combustion equilibrium-chemistry, species transport, and/or other physics behavior.”) [3D model for fluid dynamics that simulates optimized physical phenomena reads on “a three-dimensional computational fluid dynamics analysis”.]
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Qin and Dweik before them, to modify using physics model-based parameters to simulate control parameters, to incorporate using the dynamic 3D model.
One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to do this modification because it would result in less interpretation error compared to the two-dimensional model. (Dweik: [0003])
Qin and Dweik do not expressly teach: detect irregularities in functioning of the HVAC equipment based at least on the stored data; wherein the platform is configured to select one of the physics-based simulation model or the machine learning model based on defined parameters, wherein the selected model has priority over the other model in computing the plurality of control commands.
Drees teaches:
detect irregularities in functioning of the HVAC equipment based at least on the stored data; (Drees: [0072] “FDD layer 416 may be configured to store or access a variety of different system data stores (or data points for live data). FDD layer 416 may use some content of the data stores to identify faults at the equipment level (e.g., specific chiller, specific AHU, specific terminal unit, etc.) and other content to identify faults at component or subsystem levels. For example, building subsystems 428 may generate temporal (i.e., time series) data indicating the performance of BMS 400 and the various components thereof. The data generated by building subsystems 428 may include measured or calculated values that exhibit statistical characteristics and provide information about how the corresponding system or process (e.g., a temperature control process, a flow control process, etc.) is performing in terms of error from its setpoint. These processes can be examined by FDD layer 416 to expose when the system begins to degrade in performance and alert a user to repair the fault before it becomes more severe.”) (Drees: [0090] “Process 600 concludes at step 616 as combined prediction calculator 518 utilizes the regression model prediction (i.e., [y.sub.j].sub.pred,reg) and the ANN model prediction (i.e., [y.sub.j].sub.pred,ANN) to determine an ADL prediction. In various embodiments, combined prediction calculator 518 uses any suitable technique (e.g., Kalman filters, linear combinations, non-linear combinations) to determine the combined prediction from the regression model prediction and the ANN model prediction. In various embodiments, the combined prediction calculator 518 may utilize the regression model prediction, one ANN model prediction, multiple ANN predictions, or any combination thereof to determine the ADL prediction. The ADL prediction may be utilized to modify an operating characteristic of the physical plant 502. For example, the ADL prediction may be used to optimize control of the equipment in HVAC system 100, waterside system 200, or airside system 300. In other embodiments, the ADL prediction can be used to perform fault detection tasks, fault diagnostic tasks, or other tasks related to analytics.”) [Performing the fault detection tasks or fault diagnostic tasks reads on “detect irregularities in functioning of the HVAC equipment”.]
wherein the platform is configured to select one of the physics-based simulation model or the machine learning model based on defined parameters, wherein the selected model has priority over the other model in computing the plurality of control commands. (Drees: [0090] as discussed above) (Drees: [0003] “Building management systems may utilize models to predict the relationships between physical plant inputs and outputs. These relationships may be utilized in physical plant optimization, control optimization, fault detection and diagnosis, and various other building management analytics. Regression and ANN modeling techniques have complementary advantages and disadvantages when utilized to predict these relationships. For example, regression model predictions can be made immediately upon startup of the physical plant before a large set of operational data is collected, but tend to be less accurate than ANN model predictions. ANN model predictions require access to a larger data set, but result in more accurate predictions once the data set has been collected. A building management system that leverages the advantages of both modeling techniques would therefore be useful.”) (Drees: [0005] “In some embodiments, the combined prediction calculator uses a weighted average or a Kalman filter to determine the combined prediction.”) (Drees: [0014] “Yet another implementation of the present disclosure is a method of making an augmented deep learning model prediction. The method includes receiving plant input data and plant output data from a physical plant, performing a cluster analysis technique to identify trust regions, calculating a regression model prediction using a regression model technique based on plant input data and plant output data, and calculating a distance metric. The method further includes calculating an artificial neural network prediction using an artificial neural network technique based on plant input data, plant output data, and the distance metric, determining a combined prediction based on the distance metric and at least one of the regression model prediction or the artificial neural network prediction, modifying a characteristic of the physical plant according to the combined prediction.”) (Drees: [0028] “Referring generally to the FIGURES, various systems and methods for making augmented deep learning (ADL) predictions using combined regression and artificial neural network (ANN) modeling techniques in the operation of a building management system are shown. The combination of the modeling techniques leverages the advantages of both: predictions resulting from regression models are utilized in early operational stages when a lack of sufficient data makes ANN predictions impossible or inadvisable, while more accurate ANN predictions are utilized once a sufficient body of operational data has been collected. In some cases, regression model predictions are provided as input to the ANN model and vice versa, increasing the quality of the predictions from both the regression and ANN models.”) [Giving more weight onto one of the models based on the distances or the sufficiency of data for the two models in combining the prediction reads on “to select … based on defined parameters, wherein the selected model has priority over the other model”, where the distances or the sufficiency of data reads on “defined parameters”.]
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Qin, Dweik and Drees before them, to modify using both simulation model associated with the physical structure and the neural network model for the prediction, to incorporate using a combined prediction of the two models based on sufficiency of the operational data.
One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to do this modification because it would result in an efficient usage of the models by combining the results with respective weights based on changes in model accuracy. (Drees: [0003])
Regarding claim 4, Qin, Dweik and Drees teach all the claimed features of claim 1. Qin further teaches:
wherein the plurality of distributed sensors are connected to connecting nodes that collect information from the plurality of distributed sensors. (Qin: [0212] FIG. 17C illustrates a wireless embodiment where the HVAC system is controlled by a wireless sensor network comprising small wirelessly communicating sensor nodes that may be deployed in suitable controllers, such as thermostat units 40.”) [The sensor nodes read on “connecting nodes”.]
Regarding claim 5, Qin, Dweik and Drees teach all the claimed features of claims 1 and 4. Drees further teaches:
wherein the connecting nodes are connected to a base station which communicates with the server via the network. (Drees: [0049] “Still referring to FIG. 3, airside system 300 is shown to include a building management system (BMS) controller 366 and a client device 368. BMS controller 366 may include one or more computer systems (e.g., servers, supervisory controllers, subsystem controllers, etc.) that serve as system level controllers, application or data servers, head nodes, or master controllers for airside system 300, waterside system 200, HVAC system 100, and/or other controllable systems that serve building 10. BMS controller 366 may communicate with multiple downstream building systems or subsystems (e.g., HVAC system 100, a security system, a lighting system, waterside system 200, etc.) via a communications link 370 according to like or disparate protocols (e.g., LON, BACnet, etc.). In various embodiments, AHU controller 330 and BMS controller 366 may be separate (as shown in FIG. 3) or integrated. In an integrated implementation, AHU controller 330 may be a software module configured for execution by a processor of BMS controller 366.) (Drees: [0052] “Referring now to FIG. 4, a block diagram of a building management system (BMS) 400 is shown, according to an exemplary embodiment. BMS 400 may be implemented in building 10 to automatically monitor and control various building functions. BMS 400 is shown to include BMS controller 366 and a plurality of building subsystems 428. Building subsystems 428 are shown to include a building electrical subsystem 434, an information communication technology (ICT) subsystem 436, a security subsystem 438, a HVAC subsystem 440, a lighting subsystem 442, a lift/escalators subsystem 432, and a fire safety subsystem 430. In various embodiments, building subsystems 428 can include fewer, additional, or alternative subsystems. For example, building subsystems 428 may also or alternatively include a refrigeration subsystem, an advertising or signage subsystem, a cooking subsystem, a vending subsystem, a printer or copy service subsystem, or any other type of building subsystem that uses controllable equipment and/or sensors to monitor or control building 10. In some embodiments, building subsystems 428 include waterside system 200 and/or airside system 300, as described with reference to FIGS. 2-3.”) [The Any one of the AHU controller or the BMS controller reads on “a base station”. The data servers reads on “the server”.]
The motivation to combine Qin, Dweik and Drees as described in claim 1 is incorporated herein.
Regarding claim 6, Qin, Dweik and Drees teach all the claimed features of claims 1 and 4-5. Drees further teaches:
wherein the base station includes the platform for collecting, storing, and processing the set of parameters from the plurality of distributed sensors and computing an immediate control action. (Drees: [0059] “Still referring to FIG. 4, memory 408 is shown to include an enterprise integration layer 410, an automated measurement and validation (AM&V) layer 412, a demand response (DR) layer 414, a fault detection and diagnostics (FDD) layer 416, an integrated control layer 418, and a building subsystem integration later 420. Layers 410-420 may be configured to receive inputs from building subsystems 428 and other data sources, determine optimal control actions for building subsystems 428 based on the inputs, generate control signals based on the optimal control actions, and provide the generated control signals to building subsystems 428. The following paragraphs describe some of the general functions performed by each of layers 410-420 in BMS 400.”) (Drees: [0061] “Building subsystem integration layer 420 may be configured to manage communications between BMS controller 366 and building subsystems 428. For example, building subsystem integration layer 420 may receive sensor data and input signals from building subsystems 428 and provide output data and control signals to building subsystems 428. Building subsystem integration layer 420 may also be configured to manage communications between building subsystems 428. Building subsystem integration layer 420 translate communications (e.g., sensor data, input signals, output signals, etc.) across a plurality of multi-vendor/multi-protocol systems.”)
The motivation to combine Qin, Dweik and Drees as described in claim 1 is incorporated herein.
Regarding independent claim 8:
The claim recites similar limitations as corresponding claim 1 and is rejected using the same teachings and rationale.
Regarding claim 11, Qin, Dweik and Drees teach all the claimed features of claim 1. Qin further teaches:
wherein the physics-based simulation model further comprises at least an energy model for the building and a one-dimensional system model of the HVAC equipment. (Qin: [0114] “The structure envelope model captures the relevant properties about how the envelope of the structure is defined. The envelope defines the boundary between inside and outside of the structure (e.g., walls, roof, doors, windows, etc.). The thermal model captures the relevant properties about how heat flows within the structure and through the structure. This includes how radiant heat from the sun is absorbed and transferred into the interior of the structure and how heat convectively flows through the envelope of the structure. The structure HVAC model captures the relevant properties concerning the operating parameters of the HVAC system within the structure. The HVAC model thus models the on/off states of HVAC system components, and their operational settings. Lastly, the structure occupancy behavior model captures properties about how building occupants move throughout the building both positionally and as a function of time. The structure occupancy behavior model would this record when occupants enter or exit the building, whether windows have been opened, and the extent to which different rooms are occupied throughout the day and night.”) (Qin: [0208] “The DNN 142a works in conjunction with a Q table 206 that stores values for every state vs every action possible in the environment. This Q table is also mapped to DNN 142b, which can be a separate neural network, or an instance of DNN 142a. DNN 142b is used to drive the settings of coupled HVAC equipment, such as to manage the damper positions as illustrated at 208. The DNN 142b is also coupled to the simulation models 144 which in turn regulate the environment (i.e., energy consumption) by the structure 10.”)
Claims 2-3 are rejected under 35 U.S.C. 103 as being unpatentable over Qin, in view of Dweik, further in view of Drees, further in view of Cirino (US 2019/0146441 A1) (“Cirino”).
Regarding claim 2, Qin, Dweik and Drees teach all the claimed features of claim 1. Qin further teaches:
wherein the plurality of distributed sensors include environmental sensors to get the environmental information for at least relative humidity, … building thermal comfort, and air quality conditions, energy consumption sensors to detect the energy consumption information of the HVAC equipment including at least chiller, air handling unit (AHU), pumps, and cooling towers, and operational parameters sensors to monitor the operational parameter information and status of the HVAC equipment including at least the chiller, the AHU, the pumps, and the cooling towers. (Qin: [0100] as discussed in claim 1) (Qin: [0043] “Illustrated in FIG. 1 is an exemplary structure 10, which in this example represents a commercial building, together with an exemplary HVAC system shown generally at 12. While the details of the HVAC system can vary depending on the requirements, the illustrated HVAC system 12 includes an air conditioning and ventilation portion 12a and a water cooling and heating portion 12b. The air conditioning and ventilation portion 12a comprises a forced air system that provides supply air to the structure through a supply air duct 14 and that receives return air from the structure through a return air duct 16. One or more air blowers 18a and 18b, positioned as shown, cause the air to circulate from the supply air side, through the structure and thence through the return air side where the air is reconditioned before circulating back through the structure.”) [See the chiller 22, the air handling unit provision with supply air 14 and return air 16 with VAV boxes, cooling tower 28 and pumps 26a-26b, as illustrated in FIG. 1.]
Qin does not expressly teach: wherein the plurality of distributed sensors include environmental sensors to get the environmental information for at least … carbon dioxide, volatile organic compounds, outdoor weather conditions …
Drees teaches:
wherein the plurality of distributed sensors include environmental sensors to get the environmental information for at least … carbon dioxide … (Drees: [0062] “Demand response layer 414 may be configured to optimize resource usage (e.g., electricity use, natural gas use, water use, etc.) and/or the monetary cost of such resource usage in response to satisfy the demand of building 10. The optimization may be based on time-of-use prices, curtailment signals, energy availability, or other data received from utility providers, distributed energy generation systems 424, from energy storage 427 (e.g., hot TES 242, cold TES 244, etc.), or from other sources. Demand response layer 414 may receive inputs from other layers of BMS controller 366 (e.g., building subsystem integration layer 420, integrated control layer 418, etc.). The inputs received from other layers may include environmental or sensor inputs such as temperature, carbon dioxide levels, relative humidity levels, air quality sensor outputs, occupancy sensor outputs, room schedules, and the like. The inputs may also include inputs such as electrical use (e.g., expressed in kWh), thermal load measurements, pricing information, projected pricing, smoothed pricing, curtailment signals from utilities, and the like.”)
The motivation to combine Qin, Dweik and Drees as described in claim 1 is incorporated herein.
Qin, Dweik and Drees do not expressly teach: wherein the plurality of distributed sensors include environmental sensors to get the environmental information for at least …volatile organic compounds, outdoor weather conditions …
Cirino teaches:
wherein the plurality of distributed sensors include environmental sensors to get the environmental information for at least … volatile organic compounds, outdoor weather conditions … (Cirino: [0024] “By way of example, the sensors may optionally include one or more of the following: [0025] Lock sensor; [0026] Sunlight intensity sensor (e.g., a multi-channel light sensor configured to detect UV-light, visible light and infrared light); [0027] UV sensor; [0028] Infrared sensor; [0029] Visible light sensor; [0030] Pressure sensor (e.g., to detect pressure of a noble gas between panes of glass); [0031] Humidity sensor; [0032] Indoor temperature sensor; [0033] Outdoor temperature sensor; [0034] Rain sensor; [0035] Barometric pressure sensor; [0036] Air quality sensor (e.g., criteria pollutant sensors, sensors to detect volatile organic compounds (VOCs), particle sensors, and/or the like); [0037] Pollen count sensor; [0038] Dust sensor; [0039] Flex sensor (e.g., to detect flex in window or door panes); [0040] Sound sensor (e.g., microphone); [0041] Vibration sensor; [0042] Accelerometer; [0043] Tilt sensor; [0044] Position sensor (e.g., window or shade open, closed, or an intermediate position); [0045] Smoke detector; [0046] Liquid detector; [0047] Heat detector; [0048] Carbon monoxide detector; [0049] Natural gas detector; [0050] Location sensor (e.g., GPS radio); [0051] Direction sensor (e.g., compass); [0052] Breakage sensor; [0053] Motion detector (exterior); [0054] Motion detector (interior); [0055] Proximity detector, [0056] Camera (interior); and/or [0057] Camera (exterior).”) (Cirino: [0139] “By way of yet further example, a window may be used to enhance the comfort, health or convenience of a structure or user. For example, a window may monitor air quality (e.g., continuously or periodically monitor air quality) via one or more sensors (e.g., rain sensors, pollen count sensors, dust sensors, and/or the like), and/or weather conditions via one or more sensors (e.g., rain sensors, outdoor temperature sensors, sunlight intensity sensors, and/or the like). In response to an unfavorable change in home exterior conditions that satisfy certain criteria (e.g., where one or more designated sensors indicate that one or more sensed condition exceed a respective threshold), the window may perform one or more of the following acts”)
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Qin, Dweik, Drees and Cirino before them, to modify obtaining environmental conditions of the space in order to optimize the conditions, to incorporate outdoor environment measurement sensors and indoor air quality sensors.
One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to do this modification because it would allow evaluating if outdoor environment condition may affect the indoor environment condition. (Cirino: [0141]-[0142])
Regarding claim 3, Qin, Dweik, Drees and Cirino teach all the claimed features of claims 1-2. Drees further teaches:
wherein the plurality of distributed sensors further include one or more field device sensors to read the thermal zone information at one or more points in the HVAC equipment starting from at least the AHU supply or return line to water pump lines, decoupler lines, across chiller evaporator, and condenser. (Drees: [0032] Airside system 130 may deliver the airflow supplied by AHU 106 (i.e., the supply airflow) to building 10 via air supply ducts 112 and may provide return air from building 10 to AHU 106 via air return ducts 114. In some embodiments, airside system 130 includes multiple variable air volume (VAV) units 116. For example, airside system 130 is shown to include a separate VAV unit 116 on each floor or zone of building 10. VAV units 116 may include dampers or other flow control elements that can be operated to control an amount of the supply airflow provided to individual zones of building 10. In other embodiments, airside system 130 delivers the supply airflow into one or more zones of building 10 (e.g., via supply ducts 112) without using intermediate VAV units 116 or other flow control elements. AHU 106 may include various sensors (e.g., temperature sensors, pressure sensors, etc.) configured to measure attributes of the supply airflow. AHU 106 may receive input from sensors located within AHU 106 and/or within the building zone and may adjust the flow rate, temperature, or other attributes of the supply airflow through AHU 106 to achieve setpoint conditions for the building zone.”)
The motivation to combine Qin, Dweik, Drees and Cirino as described in claims 1-2 is incorporated herein.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Qin, in view of Dweik, further in view of Drees, further in view of Camilus et al. (US 2019/0378020 A1) (“Camilus”).
Regarding claim 12, Qin, Dweik and Drees teach all the claimed features of claim 1. Qin further teaches:
power control devices that are configured to receive at least one control command of the plurality of control commands from the platform via a base station and branch nodes, wherein the power control devices send at least the one control command to actuate control actions in the HVAC equipment, and (Qin: FIG. 18) [As illustrated in FIG. 18, the management level computing device reads on “a base station”, and the automation level controllers read on “branch nodes”. The field level controllers read on “power control devices” that control the HVAC equipment and receive sensor information.]
Qin, Dweik and Drees do not expressly teach: wherein the power control devices are connected to a dashboard display that displays real-time monitoring and real-time energy data forecasting.
Camilus teaches:
wherein the power control devices are connected to a dashboard display that displays real-time monitoring and real-time energy data forecasting. (Camilus: [0007] “In some embodiments, the instructions cause the one or more processors to record an electric load amount associated with the optimal equipment setting, generate a plot of the electric load amount associated with the optimal equipment setting and other electric load amounts associated with other equipment settings, and cause a user device to display the plot.”) (Camilus: [0110] “Referring now to FIG. 12, an interface 1200 is shown for receiving input from a user and displaying output to a user for the building energy system 400, according to an exemplary embodiment.”) (Camilus: [0111] “The interface 1200 is shown to include a building model editor 1204 and a control model performance 1206. The building model editor 1204 can be an interactive editor that allows a user to define a building. The building model editor 1204 can allow a user to define a model e.g., the model shown in FIG. 11. In some embodiments, the building model editor 1204 is a graphical model editor. In some embodiments, the building model editor 1204 is a text based (e.g., code based) model editor for defining the model. The control model performance 1206 may provide a user with an example of their energy usage. For example, the control model performance 1206 may indicate energy usage for one or more times based on constant temperature setpoints and/or temperature setpoints generated by the building energy system 400. Control model performance 1206 may be the same as and/or similar to the chart 1000 of FIG. 10.”) (Camilus: [0112] “In some embodiments, the building energy system 400 collects energy usage data indicating an electric load from operating at the optimal setpoint and/or setpoints of the setpoint schedule 1218. In this regard, the building energy system 400 can record the electric load overtime and plot the electric load so that a user can review the energy usage of the building for various hours, days, weeks, and/or months.”)
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Qin, Dweik, Drees and Camilus before them, to modify the predictive energy optimization control system, to incorporate displaying of the real time and predicted data to the user.
One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to do this modification because it would allow for allowing user to receive the information and make adjustments. (Camilus: [0115])
Claims 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over Qin, in view of Dweik, further in view of Drees, further in view of Camilus, further in view of VENNE (US 2021/0003308 A1) (“Venne”).
Regarding claim 13, Qin, Dweik, Drees and Camilus teach all the claimed features of claims 1 and 12. Qin, Dweik, Drees and Camilus do not expressly teach the recitations of claim 13.
Venne teaches:
wherein the platform is further configured to generate the control actions based on at least on the one control command of the plurality of control commands, wherein the control actions include at least: a first action to increase chilled water temperature, a second action to determine chiller staging levels based on real time health of chiller of the HVAC equipment and part load efficiency, and a third action to decrease fan speed of an AHU of the HVAC equipment or reduce water flow rate. (Venne: [0047] “In some embodiments, where multiple chillers serve the building the systems and methods may provide for chiller staging. Optionally, chiller staging considers equipment configuration and type, refrigerating capacity, chilled water flow rates, power consumption by water condensers and water tower fans.”) (Venne: [0058] “The systems and methods of the invention optionally further provide for variable or floating temperature set points of heating hot water (HHW), chilled water (CHW) and condenser water (CW) whereby the temperature (or grade) of the thermal energy is dynamically adjusted (or reset) to minimize the energy consumption of the associated HVAC equipment. For example, the methods and systems are optionally configured to provide the coolest possible water for heating; the warmest possible water for cooling and/or the coolest possible CW for cooling of refrigeration equipment.”) (Venne: [0112] “Modulate the staging of chillers and compressors in function of the load balance along the optimal curve of the chillers.”) (Venne: [0154] “Reduce fan speed in proportion of VAV modulation.”)
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Qin, Dweik, Drees, Camilus and Venne before them, to modify controlling of the HVAC equipment that includes chiller, to incorporate controlling chiller temperature, chiller staging and fan speed..
One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to do this modification because it would allow for minimizing energy consumption by optimizing the chiller temperature, the chiller staging and the fan speed. (Venne: Abstract, [0059])
Regarding claim 14, Qin, Dweik, Drees, Camilus and Venne teach all the claimed features of claims 1 and 12-13. Venne further teaches:
wherein a final control action execution includes parallel execution of the first action, the second action, and the third action. (Venne: [0092] “Referring to FIG. 5A to 13, a collection of algorithms combines the thermodynamic model derived from the dataset with real-time value of the data point, the number of persons in each zone and the outside weather parameters condition in real-time and the forecast of the next few hours. The results of these algorithms calculation are a series of orders sent to the different controllers in the building that will dictate the modulation of all the HVAC devices to maintain the desire temperature and humidity level in the building at all time. These orders are the optimal settings for the next time interval (example: 5 minutes), once the time interval has elapse, a new calculation will be triggered to produce a new series of orders. This process run in continues mode and self-adjust base on the results of the previous order on the behavior of the HVAC equipment's.”) (Venne: [0126] For example, in the embodiment shown in FIG. 2, the edge computing device using various algorithms dictate the control of the following parameters in the building without requiring physical modification of the building:”) (Venne: [0126]-[0165]) [As described in the paragraphs [0126]-[0165] and the examples that follow in the specification, the optimized algorithms that consider all of the cooling production, distribution, ventilation and system controls or the process running in continuous mode on the behavior of the HVAC equipments read on “a final control action execution includes parallel execution …”.]
The motivation to combine Qin, Dweik, Drees, Camilus and Venne as described in claims 1 and 12 is incorporated herein.
Claims 10 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Qin, in view of Dweik, further in view of Drees, further in view of Quam et al. (US 2015/0127168 A1) (“Quam”).
Regarding claim 10, Qin, Dweik and Drees teach all the claimed features of claim 8. Qin, Dweik and Drees do not expressly teach the recitations of claim 10.
Quam teaches:
wherein the detected irregularities are displayed in a dashboard display. (Quam: [0115] “In some examples, the customer report card 60 may provide or call out points of interest configured by the controller 111 and provided to the contractor that are particularly relevant to the customer and/or locations associated with the report card 60 being viewed. For example, the report card 60 may include a call out section 68, such as shown in FIG. 16B, that indicates data analysis has uncovered a potential issue/abnormality with an associated HVAC system 130, one or more potential remedies for the issue/abnormality, and locations of potential parts needed for the remedies (e.g., a potential solution). In some instances, the system 100 may search one or more databases to find solutions to problems identified through data analysis and presented in the call out section 68.”)
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Qin, Dweik, Drees and Quam before them, to modify the determining of the anomaly or abnormal behavior of the HVAC operations, to incorporate displaying of such determination.
One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to do this modification because it would allow for allowing user to receive presented anomaly or abnormal behaviors, as well as, recommendations. (Quam: [0114])
Regarding claim 15, Qin, Dweik and Drees teach all the claimed features of claim 1. Qin, Dweik and Drees do not expressly teach the recitations of claim 15.
Quam teaches:
a dashboard display to detect or monitor the determined irregularities of the HVAC equipment, wherein the dashboard display collects the determined irregularities directly from a sense and connect phase including the plurality of distributed sensors in a wireless manner. (Quam: [0102] “The controller 111 of the remote computing device 110 may be configured to, via the communications port 113, send and/or receive data related to the operation of one or more HVAC controllers each associated with an HVAC system 130 controlling an environment of a space of a building, and to store the data in the memory 112 or other memory. The received data may be collected with sensors existing in HVAC systems 130 of clients and/or from one or more sensors added to the HVAC systems 130. Illustratively, the received data may include, but is not limited to, ambient temperature gains and losses sensed by a temperature sensor of the HVAC system 130 (e.g., temperature gains and losses with numerical precision of 1.0 degree Fahrenheit (F) and greater, 0.5 degree F. and greater, 0.2 degree F. and greater, 0.1 degree F. and greater, or smaller temperature gains and losses), a change in ambient temperature sensed by a temperature sensor of the HVAC system 130 at various time intervals (e.g., time intervals of up 10 minutes or greater, 5 minutes or greater, 2 minutes or greater, 1 minute or greater, forty-five seconds or greater, thirty seconds or greater, twenty seconds or greater, or at time intervals faster than twenty seconds), indoor temperature, outdoor temperature, temperature set points (e.g., temperature set points relative to time and otherwise), relay status (e.g., relay open/closed status relative to time or otherwise), indoor humidity, outdoor humidity, heating activation (e.g., activation of a furnace), cooling activation (e.g., activation of an air conditioning cooling unit), of which some or all may be collected with existing sensors of existing HVAC systems 130.”) (Quam: [0115] “In some examples, the customer report card 60 may provide or call out points of interest configured by the controller 111 and provided to the contractor that are particularly relevant to the customer and/or locations associated with the report card 60 being viewed. For example, the report card 60 may include a call out section 68, such as shown in FIG. 16B, that indicates data analysis has uncovered a potential issue/abnormality with an associated HVAC system 130, one or more potential remedies for the issue/abnormality, and locations of potential parts needed for the remedies (e.g., a potential solution). In some instances, the system 100 may search one or more databases to find solutions to problems identified through data analysis and presented in the call out section 68.”)
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Qin, Dweik, Drees and Quam before them, to modify the determining of the anomaly or abnormal behavior of the HVAC operations, to incorporate displaying of such determination.
One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to do this modification because it would allow for allowing user to receive presented anomaly or abnormal behaviors, as well as, recommendations. (Quam: [0114])
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL W CHOI whose telephone number is (571)270-5069. The examiner can normally be reached Monday-Friday 8am-5pm.
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/MICHAEL W CHOI/ Primary Examiner, Art Unit 2116