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, 10 and 12-15 are pending.
Claim 7, 9 and 11 are cancelled.
Response to Amendment
Applicant’s amendments to the claims 1-6, 8, 10 and 12-15 have overcome each and every 112(b) rejections previously set forth. The 112(b) rejections of the claims 1-6, 8, 10 and 12-15 have been withdrawn.
Examiner thanks Applicant for amending the claims to overcome the 112(b) rejections, and for providing detailed explanations of the amendments.
Response to Arguments
Applicant argues, with respect to the prior art rejections, (see Amendment, Page 17, first paragraph) that “Venne is not available as prior art against the present claims. The present application claims priority to Indian Application No. 201841049091, filed on December 26, 2018. However, filing/priority date of Venne is identified as on February 19, 2019. The Office Action has not established that Venne is entitled to an effective filing date earlier than December 26, 2018, nor has it explained why the presently claimed subject matter lacks support in Applicant's priority application. Accordingly, Applicant respectfully submits that Venne has not been shown to qualify as prior art against the amended claims.”
Examiner respectfully disagrees and submits that Venne reference (US 2021/0003308) is available as prior art against the present claims. Venne reference has a continuity from U.S. Provisional Application No. 62/632,057 filed on February 19, 2018. (emphasis added) February 19, 2018 is an earlier date than the priority date of the instant application of December 26, 2018. Examiner reviewed the specification of the Provisional Application and confirmed that its description supports the description of the published specification of the Venne reference (US 2021/0003308). As such, the Venne reference qualifies as prior art against the amended claims. (see Claim Rejections - 35 USC § 103 section below)
Applicant’s arguments with respect to the 103 rejections of the claims (see Amendment, Pages 25) have been considered but are moot because the arguments do not apply to any of the references being used in the current rejection.
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 1-6, 8, 10 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over VENNE (US 2021/0003308 A1) (“Venne”), in view of Przybylski (US 2016/0313752 A1) (“Przybylski”).
Regarding independent claim 1, Qin teaches:
A system for improving energy management of an integrated HVAC equipment comprising: (Venne: [0034] “The optimization of the operation of the Heating, Ventilating and Air Conditioning (HVAC) system(s) in a building or network of building reduces energy consumption leading to a reduction in building operating costs. Predictive and/or more finely tuned responsive control of the HVAC systems can not only improve efficiency but also improve environmental conditions, comfort and/or reduce management time.”)
a plurality of distributed sensors including sensors associated with a high side equipment including a chiller and a water pump, and a low side equipment including an air handling unit (AHU), of the integrated HVAC equipment, 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; (Venne: [0075] “The system, as illustrated in FIG. 1A and FIG. 1B, is an IoT (Internet of Things) solution to individually control HVAC components 400 of a building management system (BMS) in order to optimize the efficiency of the whole system. The system can advantageously make use of existing sensors 200 (e.g., thermostats which comprise a thermometer) throughout the building, and of actuators or controllers on the various HVAC components 400 to individually control each one of them, at an individual component level.”) (Venne: [0081] “The system according to the invention would have the potential to reduce the inefficiency of the thermal energy in buildings. An edge computing device 100 can be used not only to collect data from sensors 200, but also to connect with every chiller (or any other cooling system component), boiler (or any other heating system component), pump and an entire array of the control points of a building. With this infrastructure capability, advanced algorithms can be deployed by being installed on an easy-to-deploy edge computing device 100 to deliver dynamic optimization of the building base on real-time environmental conditions and the internal load of the building thus keeping all the mechanical systems optimally positioned 24/7. This can be made by connecting the edge computing device 100 with existing equipment (sensors 200, controllers, actuators of HVAC components 400). By putting the intelligence in the edge computing device 100 and not on the equipment (sensors, controllers, actuators), the existing equipment does not need to be replaced and retrofitting is made possible. The deployment of the system according to the invention would eliminate drifting and drastically reduce occupant complaints, equipment alarms and periodic commissioning (physical inspections and maintenance). HVAC systems that are producing and distributing the precise quantity of thermal energy needed in real-time to provide occupant comfort will attain a thermal balance performance level.”) (Venne: [0087] “According to an embodiment, referring to FIG. 3, the edge computing device 100 locally collects and aggregates all the data points from various sensors 200 and sub-systems within the building including, but not limited to, the following data points: fans, electric meters, elevators, occupancy counters, steam meters, internal/external air temperatures, water, and more.”) (Venne: [0111]-[0113] “[0111] Regarding cooling systems, the system is capable of performing the following operations (without limitation): [0112] Modulate the staging of chillers and compressors in function of the load balance along the optimal curve of the chillers. [0113] Modulate the chilled water pumps speed base on the number of persons in the building.”) (Venne: [0156]-[0163] “[0156] Occupancy control. [0157] Automatic switching of ventilation system and lights if the presence of occupants in the area is detected. [0158] Optimum start/stop heating/cooling for each zone. [0159] Space temperature set points and control bands. [0160] Set the optimal range trend and deadband. [0161] Master air handling unit supply air temperature signal. [0162] Modulate master air feed temperature to avoid simultaneous heating and [0163] Cooling.”) (Venne: [0164] “Examples of sensors 200 provided in a building include, without limitation, temperature sensors (often in a thermostat), occupancy sensors, humidity sensors, pressure sensors, and sensors found within the HVAC system such as air speed sensors.”) [All of the sensors within the building associated with the environment condition of the building read on “a plurality of distributed sensors … associated with .. of the integrated HVAC equipment.” The HVAC equipment, such as the chiller and the pumps, read on “a high side equipment including …”. The HVAC equipment, such as the air handing unit, reads on “a low side equipment”. The temperature information reads on “environmental information”. The temperature information from each of the thermostats throughout the building reads on “thermal zone information”, energy meter information reads on “energy consumption information”. The speed data reads on “operational parameter information”.]
a network configured to connect the plurality of distributed sensors; a remote server configured to collect and store data generated using the plurality of distributed sensors through the network; (Venne: [0039] “The systems and methods described herein may provide and enable predictive HVAC control. These systems and methods may utilize advanced data processing and/or artificial intelligence including traditional linear, non-liner regression models, supervised learning, unsupervised learning, deep learning and neural network artificial intelligence technics to provide predictive HVAC control systems and methods that are capable of learning. Additionally, in some embodiments these systems and methods integrate and interconnect devices including sensors, and HVAC components, within existing building infrastructure using wired and/or wireless networks. Some of the features described herein may utilize big data systems, machine learning and artificial intelligence, cloud computing technologies, and cloud services, for example.”) (Venne: [0071] “In one embodiment, referring to FIG. 1A, there is described a system comprising an edge computing device 100 in communication with thermostats of the building, with HVAC components 400 of the building and with a remote server 300. More specifically, there is described a system for managing HVAC components 400 of a building, the system comprising: an edge computing device 100 in communication with sensors 200 of environmental variables in the building and with the HVAC components 400; and a remote server in communication with the edge computing device 100 over a network. …”)
a two-layered hierarchical system, comprising a first layer configured as a sense and connect phase and a second layer configured as an action phase, wherein the sense and connect phase comprises the plurality of distributed sensors and the network, and wherein the action phase comprises the remote server, a hybrid platform, and plurality of power control devices operatively coupled to the high-side equipment and the low-side equipment; and (Venne: [0081] as discussed above) (Venne: [0093] “According to an embodiment, the algorithms are distributed over the edge computing device 100 and the remote server 300 and 310. To optimize the response time in reason of network communication delays, some of the algorithms are executed on the edge computing device 100 and some are executed in the cloud.”) (Venne: [0095]-[0099] “[0095] In one embodiment of the invention, the dynamic thermal equilibrium process algorithms are divided in different subgroups related to their functions, for example, algorithms can be broadly divided into algorithms that specifically control air components of an HVAC, algorithms that specifically control water side components, and algorithms that increase energy efficiency and/or reduce energy costs, and algorithms the optimize control of the HVAC system. For example, in some embodiments, algorithms subgroups specifically instruct particular HVAC components with respect to: [0096] the air systems managing the ventilation, modulation of the air temperature and humidity of the air flowing in the building; [0097] the cooling systems producing and distributing the cooling thermal load in the building, serving in most cases the different air systems; [0098] the heating systems producing and distributing the heating thermal load in the building, serving in most cases the different air systems; and [0099] the control systems.”) [The Sensors 200, as illustrated in FIGS. 1A-1B, reads on “a first layer”. The combination of the remote server 300, HVAC 400, and Electrical equipment 500 reads on “an action phase”. The algorithms distributed over the edge computing device and the remote server reads on “a hybrid platform”. The controllers of the HVAC components that affect the energy reads on “plurality of power control devices”.]
the hybrid platform residing on the remote server comprises a physics-based simulation model and a machine learning model, … wherein the hybrid platform is configured to process sensed values of the set of parameters, comprises: (Venne: [0041] “In some embodiments, artificial intelligence and machine learning utilizes historical data to predict impact of current and/or anticipated environmental conditions on HVAC system operation and control HVAC system operation based on the predicted impact.”) (Venne: [0091] “According to an embodiment, the database, preferably on a remote server 300 (e.g., on the cloud), aggregates all the data in an historic dataset and keeps a fine granularity of the historic time line for each data point. This database become overtime a big data picture of the thermodynamic behavior of the building and is used to extract additional value (e.g., trends) from the data set. Specific algorithms use the dataset to build a real time thermodynamic behavior models of the building including a prediction of the optimal settings of the different HVAC devices in real time.”) [The thermodynamic behavior models, including the prediction model using the machine learning, read on “a physics-based simulation model and a machine learning model”.]
detect, from the sensed values, an irregularity in operation of at least one component of the integrated HVAC equipment; (Venne: [0042] “Optionally, the system and method are configured to detect anomalous or abnormal behavior including HVAC system operation needs outside of predicted parameters. In some embodiments, the detection of anomalous or abnormal behavior triggers the systems and methods of the invention to undergo a period of retraining.”) (Venne: [0091] “According to an embodiment, the database, preferably on a remote server 300 (e.g., on the cloud), aggregates all the data in an historic dataset and keeps a fine granularity of the historic time line for each data point. This database become overtime a big data picture of the thermodynamic behavior of the building and is used to extract additional value (e.g., trends) from the data set. Specific algorithms use the dataset to build a real time thermodynamic behavior models of the building including a prediction of the optimal settings of the different HVAC devices in real time.”) [The anomalous or abnormal behavior in operation reads on “an irregularity in operation”.]
evaluate a plurality of control actions for the integrated HVAC equipment by predicting, for each respective control action, revised energy consumption of the integrated HVAC equipment; select, based on the predicted revised energy consumption and the environmental information in real-time, at least one control action from the plurality of control actions, collectively reduces energy consumption of the integrated HVAC equipment by maintaining zone temperature and relative humidity within comfort conditions; (Venne: [0081] as discussed above) (Venne: [0047] “In embodiments which receive meteorological forecasts, HVAC system operations may be adjusted based on these forecasts. For example, in embodiments where the HVAC system comprises multiple HVAC units, individual units may be turned on or off or adjusted based on anticipated need, optionally allowing for the selection of heating and/or cooling units based on anticipated conditions and allowing for the operation of multiple units at part-load if such operation is more efficient than operation of a single unit at full load. 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: [0063] “Optionally, the system and method may be configured to allow building operators to weight or prioritize one or more factors, for example, maximize energy efficiency and/or cost savings. In such embodiments, a user interface may be provided allowing for selection or ranking of priorities.”) (Venne: [0064] “In some embodiments, the system and method are configured to control HVAC systems to minimize energy expenditure while maintain a minimum comfort in occupied zones and/or areas by taking into account factors that impact perceived comfort level of an occupant including temperature and humidity. For example, set point for temperature and humidity are both adjusted to maintain comfort while reducing energy requirement.”) (Venne: [0069] “In other embodiments, the edge computing device includes an algorithm module configured to calculate both macro and micro parameters. Macro parameters represent the big trends and include forecast and/or prediction values for the next 1 to 8 hours with optionally a +/−15 minutes accuracy. Micro parameters include the forecast values for the next 60 minutes and are the precise value for each command points with optionally an accuracy of +/−2.5 minutes. In such embodiments, the edge computing device can use the macro predictions to define the precise values of each point command in the HVAC system. Optionally, in some embodiment, the edge computing device is configured to keep using the last macro report received until it reaches the end of the 8 hours.”) [Anticipated, forecasted or predicted reads on “predicted …”. The load reads on “energy consumption”.]
generate control commands for at least one selected control action, wherein the control commands comprising a first control command that changes operation of the high-side equipment and a second control command that changes operation of the low-side equipment; and cause the plurality of power control devices to execute the first control command and the second control command in parallel, thereby controlling the high-side equipment and the low-side equipment as the integrated HVAC equipment. (Venne: [0111]-[0113] and [0156]-[0163] as discussed above) (Venne: [0036] “The systems and methods of the invention are configured to extract and monitor all data items, i.e. “points” and considers the thermodynamic properties of both the building as a whole and zones therein to predict the value of all data items, i.e. “points” and control each component of the HVAC system to optimize HVAC performance. Components and operating parameters of the HVAC system controlled by the systems and methods of the invention include set points, fan speed, valve openings, damper position, temperature of the cold water, temperature of the hot water, pipe pressure, pump speed, evaporator rate, and compressor pressure, amongst others.”) (Venne: [0127]-[0135] “[0127] Variable head pressure control (air cooled condensers & water tower). [0128] Adjust condensing pressure base on heat load to be extracted. [0129] Variable head pressure control (water-cooled condensers). [0130] Adjust head pressure of the condenser water base on heat load to be extracted. [0131] Optimized secondary chilled water pumping. [0132] Balance the quantity of chilled water circulated base on the AHU needs (reduce circulation when low request). [0133] Humidity control in function of the outside humidity level. [0134] Predictive cooling production in function of the weather forecast (6 hours). [0135] Balance the chiller temperature base on the evaporator target.”) [Setpoint such as, chiller staging modulation, chiller temperature and the chilled water pump speeds, reads on “a first control command”. Setpoint such as, fan speed, reads on “a second control command”.]
Venne does not expressly teach: wherein the physics-based simulation model comprises a building energy model, a three-dimensional computational fluid dynamics analysis, and one-dimensional system model of the integrated HVAC equipment.
Przybylski teaches:
wherein the physics-based simulation model comprises a building energy model, a three-dimensional computational fluid dynamics analysis, and one-dimensional system model of the integrated HVAC equipment. (Przybylski: [0106] “In some embodiments, the dynamics of thermodynamic block 700 are defined by the dynamics of the systems that thermodynamic block 700 encapsulates (i.e., systems 702-703). Each of systems 702-703 may be modeled using an atomic thermodynamic block (e.g., thermodynamic block 600) and may be defined by specifying the connections and internal system dynamics of each atomic thermodynamic block, as described with reference to FIG. 6. In some embodiments, only the atomic thermodynamic blocks are fully defined with system dynamics in the thermodynamic data model. Higher level assemblies such as thermodynamic block 700 may inherit the combined dynamics of the atomic thermodynamic blocks that the assembly encapsulates. This structure can be used to develop a tree of relationships where the only fully-defined level of the thermodynamic data model is the lowest level consisting of atomic thermodynamic blocks. Any thermodynamic blocks above the lowest level (i.e., thermodynamic blocks that contain one or more atomic thermodynamic blocks) may have properties and/or dynamics that are computationally inferred from the atomic thermodynamic blocks contained therein.”) (Przybylski: [0144] “Referring now to FIGS. 15-18, a sample implementation of the thermodynamic data model described with reference to FIGS. 9-14 is shown, according to an exemplary embodiment. Referring particularly to FIG. 15, a block diagram illustrating a thermodynamic data model 1500 of a chiller plant is shown. Data model 1500 is shown to include several thermodynamic blocks 1502-1518. Each of blocks 1502-1518 may be an instance of thermodynamic block 900, as described with reference to FIG. 9. The blocks in model 1500 that do not encapsulate any other blocks are atomic thermodynamic blocks which model lowest level building equipment and other low level objects. The other blocks in model 1500 are higher level (i.e., non-atomic) thermodynamic blocks which model systems or subsystems containing one or more atomic thermodynamic blocks.”) (Przybylski: [0145] “The atomic thermodynamic blocks are shown to include an electricity supply block 1506, an atmosphere block 1508, a chiller block 1510, a cooling tower block 1512, a first pump block 1514, a second pump block 1516, and a load block 1518. Some of atomic thermodynamic blocks 1506-1518 model individual HVAC devices. For example, chiller block 1510 may model a chiller, tower block 1512 may model a cooling tower, and pump blocks 1514-1516 may model fluid pumps. Other atomic thermodynamic blocks model entities that interact with one or more of thermodynamic blocks 1502-1518. For example, electricity supply block 1506 may model an electric utility provider that supplies electricity to the chiller, cooling tower, and pumps. Atmosphere block 1508 may model the ambient environment (e.g., weather, temperature, humidity, etc.) surrounding the cooling tower. Load block 1518 may model a building or other entity to which chiller plant delivers a chilled fluid. For each of atomic thermodynamic blocks 1506-1518, the sub-blocks attribute 914 may be empty since none of the atomic thermodynamic blocks 1506-1518 contain any sub-blocks.”) (Przybylski: [0166] “In some embodiments, one or more of solvers 1922, 1942, 1962, and 1982 includes a real time solver. The real time solver may use real-world data samples (e.g., time series data) in conjunction with defined thermodynamic blocks to solve for unknown values in BMS 1900. For example, if a thermodynamic block models fluid flow through a length of pipe and the data samples obtained from BMS 1900 provide the input and output temperatures and pressures of the fluid flow, the real time solver can solve for the loss of heat and/or pressure loss in the pipe. As another example, a collection of thermodynamic blocks and connections may model fluid flow from an evaporator to an air handling unit in a HVAC system. If the state of the fluid at the output of the evaporator is known and state of the input fluid to the air handling unit is known, the real time solver can solve the system to determine where, if any, temperature or pressure was lost.”) [The thermodynamic data model reads on “the physics-based simulation model”. Any of the equipment blocks reads on “a one-dimensional system model of the integrated HVAC equipment”. The electric supply block 1506 reads on “a building energy model”. Computationally inferred dynamics of the block that is not an atomic block means that the block is multi-dimensional. The modeling the fluid flow by the block with three internal blocks, such as the chiller plant 1504, as illustrated in FIG. 15, 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 Venne and Przybylski before them, to modify the thermodynamic behavior models of a building to optimize the building management system, to incorporate using hierarchical models starting from the singular equipment level up to the global level that represent the entirety of the building management system.
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 the global model to optimize the overall building management system, and also allow for each equipment to optimize its own operation and performance. (Przybylski: [0158] “In some embodiments, one or more higher level devices in BMS 1900 also store a portion of the thermodynamic data model. For example, HVAC controller 1930 is shown storing a HVAC system model 1940 within the local memory 1930 of HVAC controller 1930. HVAC system model 1940 may be an instance of thermodynamic block 900 representing a HVAC system or portion thereof (e.g., chiller plant block 1504) and may list both chiller block 1510 and pump block 1514 as sub-blocks 914. BMS controller 1910 is shown storing a global model 1920 within the local memory 1918 of BMS controller 1910. Global model 1920 may be an instance of thermodynamic block 900 representing the entirety of BMS 1900 (e.g., global model block 1502) and may list chiller plant block 1504 as a sub-block 914.”) (Przybylski: [0160] One benefit of the distributed thermodynamic data model shown in FIG. 19 is that each BMS device can perform calculations relating to its own operation and/or performance using local processing hardware.”)
Regarding claim 2, Venne and Przybylski teach all the claimed features of claim 1. Vennne further teaches:
wherein the plurality of distributed sensors include environmental sensors configured to sense the environmental information for the relative humidity, carbon dioxide, volatile organic compounds, outdoor weather conditions, building thermal comfort, and air quality conditions, energy consumption sensors configured to sense the energy consumption information of the integrated HVAC equipment including the chiller, (AHU), pumps, and cooling towers … (Venne: [0054] “In some embodiments, historic room occupancy is determined based on fluctuations in room environmental readings from HVAC sensors including temperature, humidity, and CO.sub.2.”) (Venne: [0060] “In some embodiments, the system and method of the invention are configured to manage HVAC systems in response to outside air quality and/or air quality within a specific area or zone. For example, the system and method of the invention are configured to control ventilation rate based on measured pollutant level including level of carbon monoxide and/or nitrous oxides in a particular zone or area such as a car park or loading dock. …”) (Venne: [0087] “According to an embodiment, referring to FIG. 3, the edge computing device 100 locally collects and aggregates all the data points from various sensors 200 and sub-systems within the building including, but not limited to, the following data points: fans, electric meters, elevators, occupancy counters, steam meters, internal/external air temperatures, water, and more. …”) (Venne: [0164] “Examples of sensors 200 provided in a building include, without limitation, temperature sensors (often in a thermostat), occupancy sensors, humidity sensors, pressure sensors, and sensors found within the HVAC system such as air speed sensors.”)
Venne does not expressly teach: … operational parameters sensors configured to monitor the operational parameter information and status of the integrated HVAC equipment including at least the chiller, the AHU, the pumps, and the cooling towers.
Przybylski teaches:
… operational parameters sensors configured to monitor the operational parameter information and status of the integrated HVAC equipment including at least the chiller, the AHU, the pumps, and the cooling towers. (Przybylski: [0004] “One implementation of the present disclosure is a building management system (BMS) including one or more sensors that measure a variable state or condition in the building management system and a plurality of BMS devices that operate to affect the variable state or condition measured by the one or more sensors.”) (Przybylski: [0048] “In some embodiments, the thermodynamic data model is based on the concept of an atomic thermodynamic system (i.e., the smallest thermodynamic system capable of being modeled). For example, each of the lowest level components of the BMS (e.g., individual chillers, pumps, cooling towers, etc.) may be modeled as atomic thermodynamic systems. …”) (Przybylski: [0055] “… VAV units 116 may include dampers or other flow control elements which 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 requiring 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. …”) ([0096] “Referring now to FIGS. 5-18, a linked thermodynamic data model for HVAC equipment is shown, according to an exemplary embodiment. The thermodynamic data model described herein may be used to collect, organize, process, compute, distribute, and manage information in BMS 400. The thermodynamic data model may be capable of mapping all of the information collected by BMS 400 (e.g., sensor data, setpoint data, equipment performance data, etc.) into a single well-defined language that can be applied at any level within BMS 400. In some embodiments, the thermodynamic data model is based on the concept of an atomic thermodynamic system (i.e., the smallest thermodynamic system capable of being modeled). For example, each of the lowest level components of BMS 400 (e.g., individual chillers, pumps, cooling towers, etc.) may be modeled as atomic thermodynamic systems. Each atomic thermodynamic system may be defined by a set of inputs, outputs, and internal system dynamics. Atomic thermodynamic systems can be linked (e.g., connecting the outputs of one system to the inputs of another system), combined into assemblies, nested within higher level thermodynamic systems, or otherwise placed in relation to each other to define the architecture of BMS 400.”) (Przybylski: [0097] “… For example, if an assembly has multiple thermodynamic blocks within it, the overall power consumption of the assembly may be a sum of the individual power consumptions for each thermodynamic block within the assembly.”)
The motivation to combine Venne and Przybylski as described in claim 1 is incorporated herein.
Regarding claim 3, Venne and Przybylski teach all the claimed features of claims 1-2. Venne further teaches:
wherein the plurality of distributed sensors further include one or more field device sensors configured to sense the thermal zone information at one or more points in the integrated HVAC equipment, including an AHU supply line or an AHU return line, a water pump line, a decoupler line, across a chiller evaporator, and a chiller condenser, wherein the plurality of distributed sensors transmit the sensed information to the remote server through the network. (Venne: FIGS. 1A-1B) (Venne: [0052] “The system and method may be configured to control HVAC systems based on room occupancy including controlling heating, cooling and ventilation based on actual or predicted occupancy. Optionally, zones or rooms may be preemptively cooled or heated based on predicted occupancy rates. In some embodiments, usage of supply air fans, hood exhaust and make up fans is dependent on actual or predicted occupancy where usage is reduced in unoccupied rooms or zones. The system and method are optionally figured to automatically switch on ventilation system and/or lights if occupants are detected in the room or zone. Optionally, in addition to occupancy the type of occupancy is considered, for example, occupants involved in strenuous activity will impact environmental conditions to a greater extended then occupants who are not active.) (Venne: [0075] “The system can advantageously make use of existing sensors 200 (e.g., thermostats which comprise a thermometer) throughout the building, and of actuators or controllers on the various HVAC components 400 to individually control each one of them, at an individual component level.”)
Regarding claim 4, Venne and Przybylski teach all the claimed features of claim 1. Venne further teaches:
wherein the plurality of distributed sensors are connected to one or more connecting nodes that collect information from the plurality of distributed sensors. (Venne: [0081] as discussed in claim 1) [The BMS/BAS 150, as illustrated in FIG. 1B, reads on “one or more connecting nodes”.]
Regarding claim 5, Venne and Przybylski teach all the claimed features of claims 1 and 4. Drees further teaches:
wherein the one or more connecting nodes are connected to a base station configured to communicate with the remote server via the network. (Venne: [0081] as discussed in claim 1) [The edge device 100, as illustrated in FIG. 1B, reads on “a base station”.]
Regarding claim 6, Venne and Przybylski 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. (Venne: [0081] as discussed in claim 1) [The edge device delivering dynamic optimization of the building base on real-time environmental conditions and the internal load of the building thus keeping all the mechanical systems optimally positioned reads on “processing the set of parameters … computing an immediate control action”.]
Regarding independent claim 8:
The claim recites similar limitations as corresponding claim 1 and is rejected using the same teachings and rationale.
Regarding claim 10, Venne and Przybylski teach all the claimed features of claim 8. Przybylski further teaches:
wherein the detected irregularity is displayed in a dashboard display, with real-time operating information for the integrated HVAC equipment. (Przybylski: [0093] Fault detection and diagnostics (FDD) layer 416 may be configured to provide on-going fault detection for building subsystems 428, building subsystem devices (i.e., building equipment), and control algorithms used by demand response layer 414 and integrated control layer 418. FDD layer 416 may receive data inputs from integrated control layer 418, directly from one or more building subsystems or devices, or from another data source. FDD layer 416 may automatically diagnose and respond to detected faults. The responses to detected or diagnosed faults may include providing an alert message to a user, a maintenance scheduling system, or a control algorithm configured to attempt to repair the fault or to work-around the fault.”) ([0168] “… If too few pressure samples or constraints are provided in the data model, the batch solver may output a notification that the pressure loop cannot be solved based on the provided information. The batch solver may identify one or more data points that, if measured, would allow the batch solver to solve the pressure loop and present such data points to a user as suggested measurements or system improvements.”)
The motivation to combine Venne and Przybylski as described in claim 8 is incorporated herein.
Regarding claim 15, Venne and Przybylski teach all the claimed features of claim 1. Venne further teaches:
wherein the sense and connect phase comprise the plurality of distributed sensors or IOT devices networked in a wireless manner. (Venne: [0039] “Additionally, in some embodiments these systems and methods integrate and interconnect devices including sensors, and HVAC components, within existing building infrastructure using wired and/or wireless networks. Some of the features described herein may utilize big data systems, machine learning and artificial intelligence, cloud computing technologies, and cloud services, for example.”)
Claims 12-14 is rejected under 35 U.S.C. 103 as being unpatentable over Venne, in view of Przybylski, further in view of LYU et al. (US 2016/0103432 A1) (“Lyu”).
Regarding claim 12, Venne and Przybylski teach all the claimed features of claim 1. Venne further teaches:
wherein the plurality of power control devices actuate the high-side equipment and the low-side equipment in accordance with the first control command and the second control command. (Venne: [0036], [0111]-[0113], [0127]-[0135] and [0156]-[0163] as discussed in claim 1)
Venne and Przybylski do not expressly teach: wherein the a dashboard display displays real-time monitoring information and real-time energy data forecasting information for the integrated HVAC equipment.
Lyu teaches:
wherein the a dashboard display displays real-time monitoring information and real-time energy data forecasting information for the integrated HVAC equipment. (Lyu: [0198] “Herein, as described above, the baseline 64 is a graph showing the energy usage of a facility or equipment to which energy-saving control according to the present invention is not given. For example, if the facility or equipment is a facility or equipment for air-conditioning the target control area, the baseline may be an energy usage-time graph for a facility or equipment whose operation is controlled depending on a temperature set by user input.”) (Lyu: [0216] In an example, as shown in FIGS. 7A and 7B, a bar graph of the actual power consumption of the target facility whose predicted usage has been simulated at predetermined time intervals (e.g., every hour) may be displayed on the screen.”) (Lyu: [0227] “That is, the user may select facilities or equipment according to target control areas or according to groups created according to certain criteria, and the predicted usage or energy reduction rate for the facilities or equipment included in the selected target control area or selected group may be displayed on the screen.”)
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 Venne, Przybylski and Lyu before them, to modify the predictive energy optimization control system, to incorporate displaying of the real time and predicted energy conumption 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 visualize the information and make adjustments. (Lyu: [0292] Accordingly, in the central control apparatus 100, the controller 129 may perform a predetermined process based on the information input through the external terminal 500 and display a processing result externally through the display unit 140 and/or the external terminal 500′s display device.”) (Lyu: [0293] “In addition, the external terminal 500 may create a control command in response to user input and transmit it to the central control apparatus 100 so as to control the facility or equipment through the central control apparatus 100.”)
Regarding claim 13, Venne, Przybylski and Lyu teach all the claimed features of claims 1 and 12. Venne further teaches:
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 the chiller of the integrated HVAC equipment and a part load efficiency, and a third action to decrease fan speed of the AHU of the integrated 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.”)
Regarding claim 14, Venne, Przybylski and Lyu 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 all HVAC equipment read on “a final control action execution includes parallel execution …”.]
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.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kenneth Lo can be reached at (571) 272-9774. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MICHAEL W CHOI/ Primary Examiner, Art Unit 2116
