DETAILED ACTION
This action is filed in response to the application filed on 3/11/2024.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of
matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the
conditions and requirements of this title.
Claims 19-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Under Step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: process, machine, manufacture, or composition of matter.
Claims 19 and 20 recite "a server". The broadest reasonable interpretation of a claim drawn to a server typically covers forms of non-transitory tangible media and transitory propagating signals per se in view of the ordinary and customary meaning.
As currently claimed, the language of “a server” does not specify if the computer readable medium is "transitory" or "non-transitory" and therefore Claims 19 and 20 are considered to be non-statutory under 35 U.S.C. 101 (See In re Nuijten, 500 F.3d 1346, 1356-57 (Fed. Cir. 2007 (transitory embodiments are not directed to statutory subject matter) and Interim Examination
Instructions for Evaluating Subject Matter Eligibility Under 35 U.S.C. § 101, Aug. 24, 2009; p.2).
In order to overcome this rejection, language similar to the following is suggested: “A non-transitory computer readable medium having computer-executable components …”
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 3-5, 8-9, 12, and 14-16 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Khalate (US20210223768 A1).
Regarding Claim 1, Khalate teaches a monitoring device (e.g. see [0069] “Referring now to FIG. 5, a block diagram of another building management system (BMS) 500 is shown, according to some embodiments. BMS 500 can be used to monitor and control the devices of HVAC system 100”) comprising: a communication interface configured to communicate with one or more sensors coupled to a refrigeration loop of a heating, ventilation, and air conditioning (HVAC) system (e.g. see [0050] “BMS interface 409 may facilitate communications between BMS controller 366 and building subsystems 428 (e.g., HVAC, lighting security, lifts, power distribution, business, etc.),” and [0049] “For example, HVAC subsystem 440 can include a chiller, a boiler, any number of air handling units, economizers, field controllers, supervisory controllers, actuators, temperature sensors, thermostats, and other devices for controlling the temperature, humidity, airflow, or other variable conditions within building 10”);
electrical sensing circuitry coupled to at least one electro-mechanical device associated with the HVAC system (e.g. see [0081] “Connected equipment 610 can be outfitted with sensors to monitor particular conditions of the connected equipment 610. For example, chillers 612 can include sensors configured to monitor chiller variables such as chilled water return temperature, chilled water supply temperature, chilled water flow status (e.g., mass flow rate, volume flow rate, etc.), condensing water return temperature, condensing water supply temperature, motor amperage (e.g., of a compressor, etc.), variable speed drive (VSD) output frequency, and refrigerant properties (e.g., refrigerant pressure, refrigerant temperature, condenser pressure, evaporator pressure, etc.)”);
one or more processors (e.g. see [0045] “In an integrated implementation, AHU controller 330 can be a software module configured for execution by a processor of BMS controller 366”); non-transitory memory; and one or more programs stored in the non-transitory memory, which, when executed by the one or more processors, cause the monitoring device (e.g. see [0053] “According to some embodiments, memory 408 is communicably connected to processor 406 via processing circuit 404 and includes computer code for executing (e.g., by processing circuit 404 and/or processor 406) one or more processes described herein”) to:
obtain, via the communication interface, readings from the one or more sensors (e.g. see [0113] “Building controller 744 may receive inputs from sensory devices (e.g., temperature sensors, pressure sensors, flow rate sensors, humidity sensors, electric current sensors, cameras, radio frequency sensors, microphones, etc.), user input devices (e.g., computer terminals, client devices, user devices, etc.) or other data input devices via communications interface 710”);
obtain a set of electrical operating characteristics associated with the at least one electro-mechanical device from the electrical sensing circuitry (e.g. see [0081] “Connected equipment 610 can be outfitted with sensors to monitor particular conditions of the connected equipment 610. For example, chillers 612 can include sensors configured to monitor chiller variables such as chilled water return temperature, chilled water supply temperature, chilled water flow status (e.g., mass flow rate, volume flow rate, etc.), condensing water return temperature, condensing water supply temperature, motor amperage (e.g., of a compressor, etc.), variable speed drive (VSD) output frequency, and refrigerant properties (e.g., refrigerant pressure, refrigerant temperature, condenser pressure, evaporator pressure, etc.))”;
determine an operating profile based on the readings from the one or more sensors and the set of electrical operating characteristics associated with the at least one electro-mechanical device (e.g. see [0083] “Connected equipment 610 can also report equipment status information. Equipment status information can include, for example, the operational status of the equipment, an operating mode (e.g., low load, medium load, high load, etc.), an indication of whether the equipment is running under normal or abnormal conditions, a safety fault code, and/or any other information that indicates the current status of connected equipment 610. In some embodiments, each device of connected equipment 610 includes a control panel (e.g., control panel 660 shown in FIG. 6B). The control panel can use the sensor data to shut down the device if the control panel determines that the device is operating under unsafe conditions”); and
generate a notification in response to determining that the operating profile satisfies a trigger condition (e.g. see [0085] “Network control engine 608 can broadcast the monitored variables and the equipment status information to a remote operations center (ROC) 602. ROC 602 can provide remote monitoring services and can send an alert to building 10 in the event of a critical alarm”).
Regarding Claim 3, Khalate teaches the limitations of Claim 1. Khalate further discloses wherein the trigger condition is satisfied when the operating profile exceeds a safety tolerance relative to a nominal operating profile. (e.g. see [0083] “For example, the control panel can compare the sensor data (or a value derived from the sensor data) to predetermined thresholds. If the sensor data or calculated value crosses a safety threshold, the control panel can shut down the device and/or operate the device at a derated setpoint”).
Regarding Claim 4, Khalate teaches the limitations of Claim 1. Khalate further discloses wherein the trigger condition is satisfied when the operating profile indicates a probable failure relative to a nominal operating profile (e.g. see [0105] “For example, fault detector 1124 may access a stored list, database, or other mapping that indicates which operating states are normal and which operating states are faulty. If the identified operating state is a normal operating state, fault detector 724 may not output a fault detection 734. However, if the identified operating state is a faulty operating state, fault detector 724 may output a fault detection 734.”).
Regarding Claim 5, Khalate teaches the limitations of Claim 3. Khalate further discloses wherein the nominal operating profile corresponds to a learned model for at least one of the HVAC system, the one or more sensors, and the one or more electro-mechanical devices. (e.g. see [0092-0093] “Chiller 650 can be configured to operate in multiple different operating states. For example, chiller 650 can be operated in a low load state, a medium load state, a high load state, and/or various states therebetween. The operating states may represent the normal operating states or conditions of chiller 650…Predictive diagnostics system 502 may build principal component analysis (PCA) models of the operating states by collecting samples of the monitored variables.”).
Regarding Claim 8, Khalate teaches the limitations of Claim 1. Khalate further discloses wherein the communication interface enables external communication, and wherein the notification corresponds to an SMS, email, an alert within a dashboard, or the like that is sent to a service provider and/or the owner/operator of the HVAC system (e.g. see [0097] “Predictive diagnostics system 502 is shown to include a communications interface 710 and a processing circuit 712. Communications interface 710 may facilitate communications between predictive diagnostics system 502 and various external systems or devices. For example, predictive diagnostics system 502 may receive the monitored variables from connected equipment 610 and provide control signals to connected equipment 610 via communications interface 710. Communications interface 710 may also be used to communicate with remote systems and applications 444, client devices 448, and/or any other external system or device. For example, predictive diagnostics system 502 may provide fault detections, diagnoses, and fault predictions to remote systems and applications 444, client devices 448, service technicians 606, or any other external system or device via communications interface 710”).
Regarding Claim 9, Khalate teaches the limitations of Claim 1. Khalate further discloses wherein the notification includes a time-to-event probability estimate to give early alerting capability for when preventative maintenance can be applied (e.g. see [0022] “Fault predictions may identify a particular fault, a particular device of the connected equipment in which the fault is predicted to occur, and/or an estimated time at which the fault is estimated to occur”).
Regarding Claim 12, Khalate teaches a monitoring method comprising: at a monitoring device (e.g. see [0069] “Referring now to FIG. 5, a block diagram of another building management system (BMS) 500 is shown, according to some embodiments. BMS 500 can be used to monitor and control the devices of HVAC system 100”) including a communication interface (e.g. see [0050] “BMS interface 409 may facilitate communications between BMS controller 366 and building subsystems 428 (e.g., HVAC, lighting security, lifts, power distribution, business, etc.)”), configured to communicate with one or more sensors coupled to a refrigeration loop of a heating, ventilation, and air conditioning (HVAC) system (e.g. see [0049] “For example, HVAC subsystem 440 can include a chiller, a boiler, any number of air handling units, economizers, field controllers, supervisory controllers, actuators, temperature sensors, thermostats, and other devices for controlling the temperature, humidity, airflow, or other variable conditions within building 10”);
electrical sensing circuitry coupled to at least one electro-mechanical device associated with the HVAC system (e.g. see [0081] “Connected equipment 610 can be outfitted with sensors to monitor particular conditions of the connected equipment 610. For example, chillers 612 can include sensors configured to monitor chiller variables such as chilled water return temperature, chilled water supply temperature, chilled water flow status (e.g., mass flow rate, volume flow rate, etc.), condensing water return temperature, condensing water supply temperature, motor amperage (e.g., of a compressor, etc.), variable speed drive (VSD) output frequency, and refrigerant properties (e.g., refrigerant pressure, refrigerant temperature, condenser pressure, evaporator pressure, etc.)”);
one or more processors (e.g. see [0045] “In an integrated implementation, AHU controller 330 can be a software module configured for execution by a processor of BMS controller 366”), a non-transitory memory; and one or more programs stored in the non-transitory memory, which, when executed by the one or more processors, cause the monitoring device (e.g. see [0053] “According to some embodiments, memory 408 is communicably connected to processor 406 via processing circuit 404 and includes computer code for executing (e.g., by processing circuit 404 and/or processor 406) one or more processes described herein”) to perform any of the operations including:
obtain, via the communication interface, readings from the one or more sensors (e.g. see [0113] “Building controller 744 may receive inputs from sensory devices (e.g., temperature sensors, pressure sensors, flow rate sensors, humidity sensors, electric current sensors, cameras, radio frequency sensors, microphones, etc.), user input devices (e.g., computer terminals, client devices, user devices, etc.) or other data input devices via communications interface 710”);
obtain a set of electrical operating characteristics associated with the at least one electro-mechanical device from the electrical sensing circuitry (e.g. see [0081] “Connected equipment 610 can be outfitted with sensors to monitor particular conditions of the connected equipment 610. For example, chillers 612 can include sensors configured to monitor chiller variables such as chilled water return temperature, chilled water supply temperature, chilled water flow status (e.g., mass flow rate, volume flow rate, etc.), condensing water return temperature, condensing water supply temperature, motor amperage (e.g., of a compressor, etc.), variable speed drive (VSD) output frequency, and refrigerant properties (e.g., refrigerant pressure, refrigerant temperature, condenser pressure, evaporator pressure, etc.)”);
determine an operating profile based on the readings from the one or more sensors and the set of electrical operating characteristics associated with the at least one electro-mechanical device (e.g. see [0083] “Connected equipment 610 can also report equipment status information. Equipment status information can include, for example, the operational status of the equipment, an operating mode (e.g., low load, medium load, high load, etc.), an indication of whether the equipment is running under normal or abnormal conditions, a safety fault code, and/or any other information that indicates the current status of connected equipment 610. In some embodiments, each device of connected equipment 610 includes a control panel (e.g., control panel 660 shown in FIG. 6B). The control panel can use the sensor data to shut down the device if the control panel determines that the device is operating under unsafe conditions”); and
generate a notification in response to determining that the operating profile satisfies a trigger condition (e.g. see [0085] “Network control engine 608 can broadcast the monitored variables and the equipment status information to a remote operations center (ROC) 602. ROC 602 can provide remote monitoring services and can send an alert to building 10 in the event of a critical alarm”).
Regarding Claim 14, Khalate teaches the limitations of Claim 12. Khalate further discloses wherein the trigger condition is satisfied when the operating profile exceeds a safety tolerance relative to a nominal operating profile. (e.g. see [0083] “For example, the control panel can compare the sensor data (or a value derived from the sensor data) to predetermined thresholds. If the sensor data or calculated value crosses a safety threshold, the control panel can shut down the device and/or operate the device at a derated setpoint”).
Regarding Claim 15, Khalate teaches the limitations of Claim 12. Khalate further discloses wherein the trigger condition is satisfied when the operating profile indicates a probable failure relative to a nominal operating profile (e.g. see [0105] “For example, fault detector 1124 may access a stored list, database, or other mapping that indicates which operating states are normal and which operating states are faulty. If the identified operating state is a normal operating state, fault detector 724 may not output a fault detection 734. However, if the identified operating state is a faulty operating state, fault detector 724 may output a fault detection 734.”).
Regarding Claim 16, Khalate teaches the limitations of Claim 15. Khalate further discloses wherein the nominal operating profile corresponds to a learned model for at least one of the HVAC system, the one or more sensors, and the one or more electro-mechanical devices. (e.g. see [0092-0093] “Chiller 650 can be configured to operate in multiple different operating states. For example, chiller 650 can be operated in a low load state, a medium load state, a high load state, and/or various states therebetween. The operating states may represent the normal operating states or conditions of chiller 650…Predictive diagnostics system 502 may build principal component analysis (PCA) models of the operating states by collecting samples of the monitored variables.”).
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 2 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Khalate (US20210223768 A1) in view of Dempsey (US20210302043 A1).
Regarding Claim 2, Khalate teaches the limitations of Claim 1. Khalate does not explicitly disclose wherein the trigger condition is satisfied when a degradation trend associated with the operating profile exceeds a threshold rate of change.
In the same field of endeavor, Dempsey teaches wherein the trigger condition is satisfied when a degradation trend associated with the operating profile exceeds a threshold rate of change (e.g. see [0013] “The monitoring system can then analyze the adjusted IATR values in reference to the HVAC system performance values at one or more prescribed intervals. These adjusted IATR values can be plotted over a timeline to monitor for any potential degradation of the system. Additionally, if the adjusted IATR value reaches a certain threshold value, the monitoring system can initiate an alert to a user using any suitable method. In some embodiments, the alert can be a text, email, alarm, or other notification. In some embodiment, the alert can be transmitted to a user devices or graphical display, such as a smart phone, tablet or computer. In addition to being obviously visible to an HVAC contractor monitoring the system, an alert signal can also be generated and/or communicated if the if the IATR value falls outside a predetermined performance tolerance threshold value,” and [0014] “The monitoring system controller can then obtain environmental data from at least one sensor or database communicatively coupled to the monitoring system controller. The monitoring system controller can then generate a rate of temperature change (IATR) value during a pre-determined baseline time interval. The monitoring system controller can then generate an adjusted rate of temperature change (IATRadj) values at pre-determined time intervals during the performance evaluation cycle using one or more performance analysis algorithms and at least one of the following: obtained environmental data; historical system performance data; or system performance product data. The monitoring system controller can then alert a user when the IATRadj value reaches a pre-determined value threshold”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the monitoring device of Khalate with the rate of change embodiment of Dempsey for the purpose of determining faults in HVAC systems with the advantage of additional data to ensure the accuracy of the determination.
Regarding Claim 13, Khalate teaches the limitations of Claim 12. Khalate does not explicitly disclose wherein the trigger condition is satisfied when a degradation trend associated with the operating profile exceeds a threshold rate of change.
In the same field of endeavor, Dempsey teaches wherein the trigger condition is satisfied when a degradation trend associated with the operating profile exceeds a threshold rate of change (e.g. see [0013] “The monitoring system can then analyze the adjusted IATR values in reference to the HVAC system performance values at one or more prescribed intervals. These adjusted IATR values can be plotted over a timeline to monitor for any potential degradation of the system. Additionally, if the adjusted IATR value reaches a certain threshold value, the monitoring system can initiate an alert to a user using any suitable method. In some embodiments, the alert can be a text, email, alarm, or other notification. In some embodiment, the alert can be transmitted to a user devices or graphical display, such as a smart phone, tablet or computer. In addition to being obviously visible to an HVAC contractor monitoring the system, an alert signal can also be generated and/or communicated if the if the IATR value falls outside a predetermined performance tolerance threshold value,” and [0014] “The monitoring system controller can then obtain environmental data from at least one sensor or database communicatively coupled to the monitoring system controller. The monitoring system controller can then generate a rate of temperature change (IATR) value during a pre-determined baseline time interval. The monitoring system controller can then generate an adjusted rate of temperature change (IATRadj) values at pre-determined time intervals during the performance evaluation cycle using one or more performance analysis algorithms and at least one of the following: obtained environmental data; historical system performance data; or system performance product data. The monitoring system controller can then alert a user when the IATRadj value reaches a pre-determined value threshold”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the monitoring device of Khalate with the rate of change embodiment of Dempsey for the purpose of determining faults in HVAC systems with the advantage of additional data to ensure the accuracy of the determination.
Claims 6-7, 10 and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Khalate (US20210223768 A1) in view of Stewart (US20160217674 A1).
Regarding Claim 6, Khalate teaches the limitations of Claim 3. While Khalate teaches a web interface (e.g. see [0090] “In some embodiments, predictive diagnostics system 502 provides a web interface which can be accessed by service technicians 606, client devices 448, and other systems or devices. The web interface can be used to access the raw data in reporting database 604, view the results of the predictive diagnostics, identify which equipment is in need of preventative maintenance, and otherwise interact with predictive diagnostics system 502. Service technicians 606 can access the web interface to view a list of equipment for which faults are predicted by predictive diagnostics system 502. Service technicians 606 can use the predicted faults to proactively repair connected equipment 610 before a fault and/or an unexpected shut down occurs. These and other features of predictive diagnostics system 502 are described in greater detail below”), Khalate does not explicitly disclose wherein the communication interface enables collection of federated data from other HVAC systems to generate the nominal operating profile.
In the same field of endeavor, Stewart teaches wherein the communication interface enables collection of federated data from other HVAC systems to generate the nominal operating profile (e.g. see [0018-0019] “The methods of the present disclosure can be applied to monitor a plurality of heat, ventilation and air conditioning systems to determine whether any of the equipment within any of those systems requires service and/or maintenance. [0019] In embodiments, the server also collects sensor data associated with a common type of the equipment or a common mode of equipment from a plurality of heating, ventilation and air conditioning systems. The collected data associated with the common type or common mode of the equipment can then be analyzed to determine a set of rules for calculating a probability of an existing fault and/or a likelihood of an occurrence of a particular fault occurring in the equipment”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the HVAC fault detection device of Khalate with the data from other HVAC systems as taught by Stewart for the purpose of identifying faults in the system with the advantage of additional data for comparison to enhance the accuracy of fault determinations.
Regarding Claim 7, Khalate teaches the limitations of Claim 3. Khalate further discloses wherein the communication interface enables collection of aggregated data from similar HVAC systems to generate the nominal operating profile (e.g. see [0124] “At step 1102, predictive diagnostics system 502 is configured to receive big data. The big data may include data specific to connected equipment 610 associated with predictive diagnostics system 502. The big data may also include data for similar equipment not associated with predictive diagnostics system 502 (e.g., same model/type of equipment in another location or same location, etc.)”).
In the same field of endeavor, Stewart also teaches wherein the communication interface enables collection of aggregated data from similar HVAC systems to generate the nominal operating profile (e.g. see [0018-0019] “The methods of the present disclosure can be applied to monitor a plurality of heat, ventilation and air conditioning systems to determine whether any of the equipment within any of those systems requires service and/or maintenance.[0019] In embodiments, the server also collects sensor data associated with a common type of the equipment or a common mode of equipment from a plurality of heating, ventilation and air conditioning systems. The collected data associated with the common type or common mode of the equipment can then be analyzed to determine a set of rules for calculating a probability of an existing fault and/or a likelihood of an occurrence of a particular fault occurring in the equipment”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the HVAC fault detection device of Khalate with the data from similar HVAC systems as taught by Stewart for the purpose of identifying faults in the system with the advantage of additional data for comparison to enhance the accuracy of fault determinations.
Regarding Claim 10, Khalate teaches the limitations of Claim 1. Khalate does not explicitly disclose wherein the one or programs further cause the monitoring device to: generate diagnostic rules for fault detection and diagnosis that is useable by a technician.
In the same field of endeavor, Stewart teaches wherein the one or programs further cause the monitoring device to: generate diagnostic rules for fault detection and diagnosis that is useable by a technician (e.g. see [0029] “In some embodiments, the server is further configured to collect data associated with a common type or common mode of equipment from a plurality of heating, ventilation and air conditioning systems. The server then analyzes the collected data associated with the common type or common mode of equipment to determine a set of rules for calculating the probability of a particular fault occurring in the equipment associated with the common type or common mode”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the fault detection device of Khalate with the rules of Stewart for the purpose of diagnosing faults in HVAC systems with the advantage of creating a streamlined process to diagnose additional faults in the future for more efficient and accurate detection.
Regarding Claim 17, Khalate teaches the limitations of Claim 15. While Khalate teaches a web interface (e.g. see [0090] “In some embodiments, predictive diagnostics system 502 provides a web interface which can be accessed by service technicians 606, client devices 448, and other systems or devices. The web interface can be used to access the raw data in reporting database 604, view the results of the predictive diagnostics, identify which equipment is in need of preventative maintenance, and otherwise interact with predictive diagnostics system 502. Service technicians 606 can access the web interface to view a list of equipment for which faults are predicted by predictive diagnostics system 502. Service technicians 606 can use the predicted faults to proactively repair connected equipment 610 before a fault and/or an unexpected shut down occurs. These and other features of predictive diagnostics system 502 are described in greater detail below”), Khalate does not explicitly disclose wherein the communication interface enables collection of federated data from other HVAC systems to generate the nominal operating profile.
In the same field of endeavor, Stewart teaches wherein the communication interface enables collection of federated data from other HVAC systems to generate the nominal operating profile (e.g. see [0018-0019] “The methods of the present disclosure can be applied to monitor a plurality of heat, ventilation and air conditioning systems to determine whether any of the equipment within any of those systems requires service and/or maintenance. [0019] In embodiments, the server also collects sensor data associated with a common type of the equipment or a common mode of equipment from a plurality of heating, ventilation and air conditioning systems. The collected data associated with the common type or common mode of the equipment can then be analyzed to determine a set of rules for calculating a probability of an existing fault and/or a likelihood of an occurrence of a particular fault occurring in the equipment”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the HVAC fault detection device of Khalate with the data from other HVAC systems as taught by Stewart for the purpose of identifying faults in the system with the advantage of additional data for comparison to enhance the accuracy of fault determinations.
Regarding Claim 18, Khalate teaches the limitations of Claim 15. While Khalate further discloses wherein the communication interface enables collection of aggregated data from similar HVAC systems to generate the nominal operating profile (e.g. see [0124] “At step 1102, predictive diagnostics system 502 is configured to receive big data. The big data may include data specific to connected equipment 610 associated with predictive diagnostics system 502. The big data may also include data for similar equipment not associated with predictive diagnostics system 502 (e.g., same model/type of equipment in another location or same location, etc.)”).
In the same field of endeavor, Stewart also teaches wherein the communication interface enables collection of aggregated data from similar HVAC systems to generate the nominal operating profile (e.g. see [0018-0019] “The methods of the present disclosure can be applied to monitor a plurality of heat, ventilation and air conditioning systems to determine whether any of the equipment within any of those systems requires service and/or maintenance.[0019] In embodiments, the server also collects sensor data associated with a common type of the equipment or a common mode of equipment from a plurality of heating, ventilation and air conditioning systems. The collected data associated with the common type or common mode of the equipment can then be analyzed to determine a set of rules for calculating a probability of an existing fault and/or a likelihood of an occurrence of a particular fault occurring in the equipment”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the HVAC fault detection device of Khalate with the data from similar HVAC systems as taught by Stewart for the purpose of identifying faults in the system with the advantage of additional data for comparison to enhance the accuracy of fault determinations.
Regarding Claim 19, Khalate teaches a server (e.g. see [0045] “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 can 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. B”)comprising:
a communication interface configured to communicate with a plurality of heating, ventilation, and air conditioning (HVAC) system monitoring devices (e.g. see [0050] “BMS interface 409 may facilitate communications between BMS controller 366 and building subsystems 428 (e.g., HVAC, lighting security, lifts, power distribution, business, etc.),” and [0086] “In other embodiments, predictive diagnostics system 502 can be a component of a remote computing system or cloud-based computing system configured to receive and process data from one or more building management systems”),
electrical sensing circuitry coupled to at least one electro-mechanical device associated with the HVAC system (e.g. see [0081] “Connected equipment 610 can be outfitted with sensors to monitor particular conditions of the connected equipment 610. For example, chillers 612 can include sensors configured to monitor chiller variables such as chilled water return temperature, chilled water supply temperature, chilled water flow status (e.g., mass flow rate, volume flow rate, etc.), condensing water return temperature, condensing water supply temperature, motor amperage (e.g., of a compressor, etc.), variable speed drive (VSD) output frequency, and refrigerant properties (e.g., refrigerant pressure, refrigerant temperature, condenser pressure, evaporator pressure, etc.)”);
one or more processors (e.g. see [0045] “In an integrated implementation, AHU controller 330 can be a software module configured for execution by a processor of BMS controller 366”); non-transitory memory; and one or more programs stored in the non-transitory memory, which, when executed by the one or more processors,(e.g. see [0053] “According to some embodiments, memory 408 is communicably connected to processor 406 via processing circuit 404 and includes computer code for executing (e.g., by processing circuit 404 and/or processor 406) one or more processes described herein”) cause the server to:
obtain, via the communication interface, readings from the one or more sensors coupled to a refrigeration loop of an HVAC system; (e.g. see [0138] “By way of example, variable monitor 1318 may receive temperature data from one or more temperature sensors of HVAC subsystem 440 of building subsystems 428 regarding the temperature in one or more rooms of building 10”);
obtain via the communication interface a set of electrical operating characteristics associated with at least one electro-mechanical device associated with the HVAC system from electrical sensing circuitry of a monitoring device associated with the HVAC system (e.g. see [0081] “Connected equipment 610 can be outfitted with sensors to monitor particular conditions of the connected equipment 610. For example, chillers 612 can include sensors configured to monitor chiller variables such as chilled water return temperature, chilled water supply temperature, chilled water flow status (e.g., mass flow rate, volume flow rate, etc.), condensing water return temperature, condensing water supply temperature, motor amperage (e.g., of a compressor, etc.), variable speed drive (VSD) output frequency, and refrigerant properties (e.g., refrigerant pressure, refrigerant temperature, condenser pressure, evaporator pressure, etc.)”);
generate a nominal operating profile based on the readings and the set of electrical operating characteristics associated with the HVAC system (e.g. see [0115] “classify a normal condition from a faulty condition, predictive diagnostics system 502 may be configured to form a boundary 820 using a supervised machine learning technique. The boundary may be linear or non-linear. To form boundary 820, predicative diagnostics system 502 may be configured to use one or more functions, such as kernel functions. The kernel functions may be configured to take in the various attributes or parameters of data points 810 and find a correlation between data points 810. Additionally, the kernel functions may be configured to convert data points 810 into probability distribution graph 1000. With the probability distribution graph 800 generated, predictive diagnostics system 502 may be able to autonomously determine boundary 820 to determine which data points 810 correspond with a normal condition 830”) and similar data from other HVAC systems (e.g. see [0124] “At step 1102, predictive diagnostics system 502 is configured to receive big data. The big data may include data specific to connected equipment 610 associated with predictive diagnostics system 502. The big data may also include data for similar equipment not associated with predictive diagnostics system 502 (e.g., same model/type of equipment in another location or same location, etc.)”); and
transmit via the communication interface the nominal operating profile to the monitoring device (e.g. see [0087] “Predictive diagnostics system 502 may report the current operating state and/or the predicted faults to client devices 448, service technicians 606, building 10, and/or any other system and/or device. Communications between predictive diagnostics system 502 and other systems and/or devices can be direct and/or via an intermediate communications network, such as network 446”).
In the same field of endeavor, Stewart also discloses generate a nominal operating profile based on the readings and the set of electrical operating characteristics associated with the HVAC system and similar data from other HVAC systems (e.g. see (e.g. see [0018-0019] “The methods of the present disclosure can be applied to monitor a plurality of heat, ventilation and air conditioning systems to determine whether any of the equipment within any of those systems requires service and/or maintenance.[0019] In embodiments, the server also collects sensor data associated with a common type of the equipment or a common mode of equipment from a plurality of heating, ventilation and air conditioning systems. The collected data associated with the common type or common mode of the equipment can then be analyzed to determine a set of rules for calculating a probability of an existing fault and/or a likelihood of an occurrence of a particular fault occurring in the equipment”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the HVAC fault detection device of Khalate with the data from similar HVAC systems as taught by Stewart for the purpose of identifying faults in the system with the advantage of additional data for comparison to enhance the accuracy of fault determinations.
Regarding Claim 20, Khalate and Stewart teach the limitations of Claim 19. Khalate further discloses wherein the trigger condition is satisfied when a degradation trend associated with the operating profile exceeds a threshold rate of change, when the operating profile exceeds a safety tolerance relative to a nominal operating profile (e.g. see [0083] “For example, the control panel can compare the sensor data (or a value derived from the sensor data) to predetermined thresholds. If the sensor data or calculated value crosses a safety threshold, the control panel can shut down the device and/or operate the device at a derated setpoint”), or when the operating profile indicates a probable failure relative to a nominal operating profile (e.g. see [0105] “For example, fault detector 1124 may access a stored list, database, or other mapping that indicates which operating states are normal and which operating states are faulty. If the identified operating state is a normal operating state, fault detector 724 may not output a fault detection 734. However, if the identified operating state is a faulty operating state, fault detector 724 may output a fault detection 734.”).
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Khalate (US20210223768 A1) in view of Zimmerman (WO2012162360 A2).
Regarding Claim 11, Khalate teaches the limitations of Claim 1. Khalate does not explicitly disclose wherein the one or programs further cause the monitoring device to: generate diagnostic rules for unknown or new faults that is useable by a technician.
In the same field of endeavor, Zimmerman teaches wherein the one or programs further cause the monitoring device to: generate diagnostic rules for unknown or new faults that is useable by a technician (e.g. see [0007] “in order to reduce computation time and cost involved with detecting and diagnosing a fault in a system, simplified representations of components of the system are used to estimate valid intervals for state variables at the components. Generic failure rules are configured to compare the estimated valid intervals to related intervals for the same state variables, from either observations or propagations, for overlap. Failure output vectors are generated based on the comparison, and the failure output vectors are compared to diagnostic matrices to determine a source of the fault”)
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the fault detection device of Khalate with the rules of Zimmerman for the purpose of diagnosing faults in HVAC systems with the advantage of creating a streamlined process to diagnose additional faults in the future for more efficient and accurate detection.
Conclusion
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/NYLA GAVIA/Examiner, Art Unit 2857
/Catherine T. Rastovski/Supervisory Primary Examiner, Art Unit 2857
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