DETAILED ACTION
Claims 17-18 and 21-24 are pending. Claims 1-16 and 19-20 are cancelled and claims 21-31 are new.
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 4/21/2026 has been entered.
Response to Arguments
Applicant’s arguments, filed 4/21/26, have been fully considered but are not persuasive.
Applicant’s arguments regarding the rejections of the claims under 35 U.S.C. § 101 (pages 6-7) are moot as the claims are no longer rejected under that statute.
Applicant’s arguments regarding 35 U.S.C. § 103 are moot in view of the newly cited reference, Brady.
For at least these reasons, the rejection of the claims is maintained.
Note that claim 25 is objected to but includes allowable subject matter (see below).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 17-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Khalate et al. U.S. Patent Publication No. 20180373234 (hereinafter Khalate) in view of Park et al. U.S. Patent Publication No. 20180232459 (hereinafter Park) and further in view of Bohan U.S. Patent Publication No. 20100298980 (hereinafter Bohan) and Martinez U.S. Patent No. 4966127 (hereinafter Martinez) and further in view of Brady et al. U.S. Patent No. 10041844 (hereinafter Brady).
Regarding claim 17, the combination of Khalate, Park, Bohan, Martinez and Brady teaches all the limitations of the base claims as outlined just below.
Further, Khalate teaches the at least one communications device includes at least one wireless communications device [0051, Fig. 4 — communications interfaces 407 and/or BMS interface 409 can be direct (e.g., local wired or wireless communications) or via a communications network 446 (e.g., a WAN, the Internet, a cellular network, etc.); 0098 — Communications via interface 710 can be direct (e.g., local wired or wireless communications) or via an intermediate communications network 446 (e.g., a WAN, the Internet, a cellular network, etc.).; 0136-0138, Fig. 13 — Communications via interface 1310 can be direct (e.g., local wired or wireless communications) or via an intermediate communications network 446 (e.g., a WAN, the Internet, a cellular network, etc.)… variable monitor 1318 may receive temperature data from one or more temperature sensors of HVAC subsystem 440 of building subsystems 428… monitored variables 1306; 0080-0086, Fig. 5 — 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].
Regarding claim 18, Khalate teaches a system for anticipating shutdown alarms with respect to a boiler by way of machine learning model processing of information [0004-0005, Figs. 1-7 — The building management system includes connected equipment configured to measure a plurality of monitored variables and a predictive diagnostics system; 0033, Fig. 2 — a plurality of heating elements 220 (e.g., boilers, electric heaters, etc.); 0080 — Connected equipment 610 can include connected chillers 612, connected AHUs 614, connected actuators 616, connected controllers 618, or any other type of equipment in a building HVAC system (e.g., boilers, economizers, valves, dampers, cooling towers, fans, pumps, etc.); 0083-0085 — 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. 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. The control panel can generate a data point when a safety shut down or a derate occurs. The data point can include a safety fault code which indicates the reason or condition that triggered the shut down; 0087 -0090, Fig. 6 — If the current operating state is identified as a faulty state or moving toward a faulty state, predictive diagnostics system 502 may generate an alert (alarm) or notification for service technicians 606 to repair the fault or potential fault before it becomes more severe. In some embodiments, predictive diagnostics system 502 uses the current operating state to determine an appropriate control action for connected equipment 610… predictive diagnostics system 502 can predict when connected equipment 610 (including boilers) will next report a safety fault code that triggers a device shut down (that is associated with an alert/alarm); 0123-0130, Figs. 11-12 — At step 1110, predictive diagnostics system 502 is configured to create a machine learning model. By way of example, predictive diagnostics system 502 may be configured to utilize supervised machine learning to produce a model or function from the big data (e.g., the monitored variables history, probability distribution, etc.)… At step 1116, predictive diagnostics system 502 is configured to monitor data (e.g., the monitored variables, etc.) regarding the current operation of connected equipment 610 to predict whether operation of connected equipment 610 is trending towards a fault condition or remaining in a normal condition. At step 1118, predictive diagnostics system 502 is configured to predict a diagnosis for connected equipment 610 in response to the operation of connected equipment trending towards the fault condition] obtained by way of a plurality of internet connected devices [0051, Fig. 4 — communications interfaces 407 and/or BMS interface 409 can be direct (e.g., local wired or wireless communications) or via a communications network 446 (e.g., a WAN, the Internet, a cellular network, etc.); 0098 — Communications via interface 710 can be direct (e.g., local wired or wireless communications) or via an intermediate communications network 446 (e.g., a WAN, the Internet, a cellular network, etc.).; 0136-0138, Fig. 13 — Communications via interface 1310 can be direct (e.g., local wired or wireless communications) or via an intermediate communications network 446 (e.g., a WAN, the Internet, a cellular network, etc.)… variable monitor 1318 may receive temperature data from one or more temperature sensors of HVAC subsystem 440 of building subsystems 428… monitored variables 1306], the system comprising:
a controller of the boiler [0026, Figs. 1-7 — HVAC system 100 is shown to include a chiller 102, a boiler 104; 0039-0043, Fig. 3 — Actuators 324-328 may receive control signals from AHU controller 330 and may provide feedback signals to AHU controller 330. Feedback signals can include, for example, an indication of a current actuator or damper position, an amount of torque or force exerted by the actuator, diagnostic information… AHU controller 330, by BMS controller 366… AHU controller 330 receives a measurement of the supply air temperature from a temperature sensor 362 positioned in supply air duct 312 (e.g., downstream of cooling coil 334 and/or heating coil 336). AHU controller 330 may also receive a measurement of the temperature of building zone 306 from a temperature sensor 364 located in building zone 306.; 0049, Figs. 4 and 7 — 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; 0080 — Connected equipment 610 can include connected chillers 612, connected AHUs 614, connected actuators 616, connected controllers 618, or any other type of equipment in a building HVAC system (e.g., boilers, economizers, valves, dampers, cooling towers, fans, pumps, etc.); 0113, Fig. 7 — 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.)… monitored variables 706];
a plurality of sensors associated with the plurality of internet connected devices for the boiler, wherein the sensors are configured to sense a plurality of parameters regarding the boiler and to provide a plurality of first signals regarding the sensed parameters to the controller [0026, Figs. 1-7 — HVAC system 100 is shown to include a chiller 102, a boiler 104; 0039-0043, Fig. 3 — Actuators 324-328 may receive control signals from AHU controller 330 and may provide feedback signals to AHU controller 330. Feedback signals can include, for example, an indication of a current actuator or damper position, an amount of torque or force exerted by the actuator, diagnostic information… AHU controller 330, by BMS controller 366… AHU controller 330 receives a measurement of the supply air temperature from a temperature sensor 362 positioned in supply air duct 312 (e.g., downstream of cooling coil 334 and/or heating coil 336). AHU controller 330 may also receive a measurement of the temperature of building zone 306 from a temperature sensor 364 located in building zone 306.; 0049, Figs. 4 and 7 — 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; 0080 — Connected equipment 610 can include connected chillers 612, connected AHUs 614, connected actuators 616, connected controllers 618, or any other type of equipment in a building HVAC system (e.g., boilers, economizers, valves, dampers, cooling towers, fans, pumps, etc.); 0113, Fig. 7 — 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.)… monitored variables 706];
a device of the boiler, wherein either the first signals or second signals based at least indirectly upon the first signals are sent to the device [0050-0052, Fig. 4 — BMS controller 366 is shown to include a communications interface 407 (device) and a BMS interface 409. Communications interface 407 may facilitate communications between BMS controller 366 and external applications (e.g., monitoring and reporting applications 422, enterprise control applications 426, remote systems and applications 444… for allowing user control, monitoring, and adjustment to BMS controller 366 and/or subsystems 428; 0080-0086, Figs. 5-6 — BMS 600 is shown to include building 10, network 446, client devices 448, and predictive diagnostics system 502… 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];
at least one communications device by which either the first signals, the second signals, or third signals based at least indirectly upon the first signals or second signals, are sent for receipt by a cloud computing system [0050-0052, Figs. 4 and 6-7 — BMS controller 366 is shown to include a communications interface 407 (device) and a BMS interface 409. Communications interface 407 may facilitate communications between BMS controller 366 and external applications (e.g., monitoring and reporting applications 422, enterprise control applications 426, remote systems and applications 444… for allowing user control, monitoring, and adjustment to BMS controller 366 and/or subsystems 428; 0080-0086, Fig. 5 — 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. For example, predictive diagnostics system 502 can be implemented as part of a PANOPTIX® brand building efficiency platform, as sold by Johnson Controls Inc], for use in either developing one or more machine learning models for predicting the shutdown alarms or generating at least one prediction of one or more of the shutdown alarms by way of the one or more machine learning models [0123-0130, Figs. 11-12 — At step 1110, predictive diagnostics system 502 is configured to create a machine learning model. By way of example, predictive diagnostics system 502 may be configured to utilize supervised machine learning to produce a model or function from the big data (e.g., the monitored variables history, probability distribution, etc.)… At step 1116, predictive diagnostics system 502 is configured to monitor data (e.g., the monitored variables, etc.) regarding the current operation of connected equipment 610 to predict whether operation of connected equipment 610 is trending towards a fault condition or remaining in a normal condition. At step 1118, predictive diagnostics system 502 is configured to predict a diagnosis for connected equipment 610 in response to the operation of connected equipment trending towards the fault condition]; and
a display configured to receive at least one fourth signal indicative of the at least one prediction of the one or more of the shutdown alarms, and to display either the at least one prediction or one or more alerts in response to the at least one prediction [0090 — web interface can be used to… 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] and
at least one actuator associated with the boiler that is controlled by the controller in response to the at least one prediction of the one or more of the shutdown alarms [0066-0067 — Fault detection and diagnostics (FDD) layer 416… FDD layer 416 (or a policy executed by an integrated control engine or business rules engine) may shut-down systems or direct control activities around faulty devices or systems to reduce energy waste, extend equipment life, or assure proper control response; 0083 — 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; 0121 — predictive diagnostics system 502 can automatically cause connected equipment 610 to enter a safety mode (e.g., derated operation, etc.) or shut down when a fault is predicted to occur].
But Khalate fails to clearly specify IoT devices, a controller and sensors supported on or proximate to the boiler and sending data via a gateway device and using an application programming interface (API) and wherein the boiler is a lead boiler that operates with one or more lag boilers; wherein the at least one prediction of the one or more of the alarms that is generated by the way of the one or more machine learning models pertains to the lead boiler, and wherein the one or more machine learning models take into account data patterns concerning interrelated operations between the lead boiler and the one or more other boilers, or between other lead boilers and other lag boilers of other boiler systems.
However, Park teaches IoT devices [0329-0331, Fig. 14 — Internet-of-Things (IoT)… . The IoT environment may include a plurality of devices 1402, 1404, 1406, a cloud-based service 1408… the devices 1402, 1404, 1406 can be sensors, controllers, actuators, sub-systems, thermostats, or any other component within the BMS system capable of communicating to the cloud-based service 1408] and sending data via a gateway device [0331-0335 , Fig. 15 — devices 1402, 1404, 1406 are connected to the Internet via one or more gateways, routers, modems, or other internet connected devices, which provide communication to and from the internet… collator 1512 may be a software element within a local device, such as an internet gateway] and using an application programming interface (API) [0336-0339 — The multi-modal data processing service 1600 includes a timeseries microservice API 1602… The processing layer 1604 may further include a processing service API 1628; 0355-0357 — The mapping used in FIG. 20 can require maintaining mappings and building custom ACID services for each application, which can be expensive and tedious to maintain. These issues can be resolved by building a set of abstractions that provide APIs for application developers and data management applications… telemetry data is received by the service via one or more APIs].
Khalate and Park are analogous art. They relate to HVAC and building management systems.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to simply substitute the known IoT devices of Park for the known devices of Khalate for the predictable result of a system using IoT devices. In addition, it would be obvious to one having ordinary skill in the art to utilize IoT to facilitate communication with the remote/cloud internet connected infrastructure. Furthermore, it would be obvious to one having ordinary skill in the art to utilize a gateway, as taught by Park, to facilitate connecting with the internet and hence the remote/cloud internet connected infrastructure, as suggested by Park [0331-0335]. And one of ordinary skill in the art would have been motivated to perform these functions by way of an API in order to reduce cost and ease maintenance, as suggested by Park [0355-0357].
But the combination of Khalate and Park fails to clearly specify a controller and sensors supported on or positioned proximate to the boiler and wherein the boiler is a lead boiler that operates with one or more lag boilers; wherein the boiler is a lead boiler that operates with one or more lag boilers; wherein the at least one prediction of the one or more of the alarms that is generated by the way of the one or more machine learning models pertains to the lead boiler, and wherein the one or more machine learning models take into account data patterns concerning interrelated operations between the lead boiler and the one or more other boilers, or between other lead boilers and other lag boilers of other boiler systems.
However, Bohan teaches a controller and sensors supported on or positioned proximate to the boiler [0035-0037, Figs. 2-3 — exemplary multi-sensor component 208 is also known herein as TPPS 208, to represent the temperature, pressure and presence sensor functions which this device may perform within a single device housing… multi-sensor component 208, for example, may be threaded into the boiler tank (e.g., 302 of boiler 300 of FIG. 3), while the HVAC controller 200 may be mounted onto sensor TPPS 208, and the case 204 of HVAC controller 200 secured to the exterior of the boiler enclosure (e.g., 305 of boiler 300 of FIG. 3). In this way, TPPS 208 is adapted to make direct contact with the medium (e.g., medium 310, water, water-glycol mix within the boiler 300). TPPS 208, for example, may then utilize a modular plug to electrically interconnect the sensor/detector functions into the HVAC controller 200 as shown in FIG. 2D].
Khalate, Park and Bohan are analogous art. They relate to HVAC and building management systems.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above system, as taught by the combination of Khalate and Park, by incorporating the above limitations, as taught by Bohan.
One of ordinary skill in the art would have been motivated to do this modification so that the sensors are in direct contact with the medium being heated, thus facilitating more direct/accurate measurements, and to enable easy direct connection with the controller, thus facilitating improved communication, as suggested by Bohan [0037]. In addition, the locating the controller proximate to the boiler is an obvious matter of design choice, see MPEP 2144.04.
But the combination of Khalate, Park and Bohan fails to clearly specify that a boiler is a lead boiler that operates with one or more lag boilers; wherein the boiler is a lead boiler that operates with one or more lag boilers; wherein the at least one prediction of the one or more of the alarms that is generated by the way of the one or more machine learning models pertains to the lead boiler, and wherein the one or more machine learning models take into account data patterns concerning interrelated operations between the lead boiler and the one or more other boilers, or between other lead boilers and other lag boilers of other boiler systems.
However, Martinez teaches that a boiler is a lead boiler that operates with one or more lag boilers [col. 4 lines 11-44, Drawing — The direct fired boiler to be turned off is referred to as the lag boiler and the direct fired boiler to be left on is referred to as the lead boiler. For purposes of illustration, direct fired boiler 12 will be the lag boiler, and direct fired boiler 10 will be referred to as the lead boiler].
Khalate, Park, Bohan and Martinez are analogous art. They relate to HVAC and boiler systems.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above system, as taught by the combination of Khalate, Park, and Bohan by incorporating the above limitations, as taught by Martinez.
One of ordinary skill in the art would have been motivated to do this modification in order to save energy, as suggested by Martinez [col. 1 lines 9-11, col. 4 lines 52-60]. In addition, it would have been obvious to a person of ordinary skill in the art to simply substitute the known lead-lag boilers of Martinez for the known boilers of Khalate for the predictable result of a system using a lead-lag boiler arrangement.
But the combination of Khalate, Park, Bohan and Martinez fails to clearly specify that the at least one prediction of the one or more of the alarms that is generated by the way of the one or more machine learning models pertains to the lead boiler, and wherein the one or more machine learning models take into account data patterns concerning interrelated operations between the lead boiler and the one or more other boilers, or between other lead boilers and other lag boilers of other boiler systems.
However, Brady teaches that the at least one prediction of the one or more of the alarms that is generated by the way of the one or more machine learning models pertains to the lead boiler, and wherein the one or more machine learning models take into account data patterns concerning interrelated operations between the lead boiler and the one or more other boilers, or between other lead boilers and other lag boilers of other boiler systems [col. 4 lines 13-21 — A closed-loop thermal energy system (e.g., a comparative boiler or chiller) performance assessment may be performed under one or more defined or standardized situations where buildings operate a system duty/standby arrangement, where one boiler or one chiller takes a lead position (lead boiler) rotated over a selected period of time (e.g., weekly or monthly or the lead position between plant) or regularly alternated at selected time intervals (data patterns concerning interrelated operations between the lead boiler and the one or more standby/other boilers); col. 10 lines 15-36 — FIG. 4 illustrates energy usage anomaly detection and fault diagnosis and training of a machine-learning model… the module/component blocks 400 may also be incorporated into various hardware and software components of a system for accurate temporal event predictive modeling in accordance with the present invention.; col. 12 lines 17-27 — the energy output or fluid flow rate event may be an alert that indicates or displays audibly and/or visually on the GUI 422 “ALERT! Flow rate and energy output of a fluid transfer pump system is high; col. 15 line 32 – col. 16 line 22, Fig. 8 — FIG. 8 is a diagram 800 depicting various user hardware and computing components functioning in a low pressure hot water (LPHW) system in accordance with aspects of the present invention. The LPHW system may include one or more boilers such as, for example, “boiler 1” and “boiler 2” (lead and other/standby boiler), one or more non-intrusive IoT sensors such as, for example, IoT sensor S1, IoT sensor S2 and IoT sensor S3].
Khalate, Park, Bohan, Martinez and Brady are analogous art. They relate to HVAC and boiler systems.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above system, as taught by the combination of Khalate, Park, and Bohan and Martinez by incorporating the above limitations, as taught by Brady.
One of ordinary skill in the art would have been motivated to do this modification in order to account for which boiler is leading and the time intervals during which each boiler is operational.
Claim(s) 21, 27, and 29-31 is/are rejected under 35 U.S.C. 103 as being unpatentable over Khalate in view of Park and further in view of Martinez and Brady.
Regarding claim 21, Khalate teaches a method of anticipating shutdown alarms for a boiler system by way of one or more machine learning models [0004-0005, Figs. 1-7 — The building management system includes connected equipment configured to measure a plurality of monitored variables and a predictive diagnostics system… Another implementation of the present disclosure is a method for performing predictive diagnostics on connected equipment of a building management system; 0033, Fig. 2 — a plurality of heating elements 220 (e.g., boilers, electric heaters, etc.); 0080 — Connected equipment 610 can include connected chillers 612, connected AHUs 614, connected actuators 616, connected controllers 618, or any other type of equipment in a building HVAC system (e.g., boilers, economizers, valves, dampers, cooling towers, fans, pumps, etc.); 0083-0085 — 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. 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. The control panel can generate a data point when a safety shut down or a derate occurs. The data point can include a safety fault code which indicates the reason or condition that triggered the shut down; 0087 -0090, Fig. 6 — If the current operating state is identified as a faulty state or moving toward a faulty state, predictive diagnostics system 502 may generate an alert (alarm) or notification for service technicians 606 to repair the fault or potential fault before it becomes more severe. In some embodiments, predictive diagnostics system 502 uses the current operating state to determine an appropriate control action for connected equipment 610… predictive diagnostics system 502 can predict when connected equipment 610 (including boilers) will next report a safety fault code that triggers a device shut down (that is associated with an alert/alarm); 0123-0130, Figs. 11-12 — At step 1110, predictive diagnostics system 502 is configured to create a machine learning model. By way of example, predictive diagnostics system 502 may be configured to utilize supervised machine learning to produce a model or function from the big data (e.g., the monitored variables history, probability distribution, etc.)… At step 1116, predictive diagnostics system 502 is configured to monitor data (e.g., the monitored variables, etc.) regarding the current operation of connected equipment 610 to predict whether operation of connected equipment 610 is trending towards a fault condition or remaining in a normal condition. At step 1118, predictive diagnostics system 502 is configured to predict a diagnosis for connected equipment 610 in response to the operation of connected equipment trending towards the fault condition] obtained by way of a plurality of internet connected devices [0051, Fig. 4 — communications interfaces 407 and/or BMS interface 409 can be direct (e.g., local wired or wireless communications) or via a communications network 446 (e.g., a WAN, the Internet, a cellular network, etc.); 0098 — Communications via interface 710 can be direct (e.g., local wired or wireless communications) or via an intermediate communications network 446 (e.g., a WAN, the Internet, a cellular network, etc.).; 0136-0138, Fig. 13 — Communications via interface 1310 can be direct (e.g., local wired or wireless communications) or via an intermediate communications network 446 (e.g., a WAN, the Internet, a cellular network, etc.)… variable monitor 1318 may receive temperature data from one or more temperature sensors of HVAC subsystem 440 of building subsystems 428… monitored variables 1306], the method comprising:
providing a boiler system having a controller [0026, Figs. 1-7 — HVAC system 100 is shown to include a chiller 102, a boiler 104; 0039-0043, Fig. 3 — Actuators 324-328 may receive control signals from AHU controller 330 and may provide feedback signals to AHU controller 330. Feedback signals can include, for example, an indication of a current actuator or damper position, an amount of torque or force exerted by the actuator, diagnostic information… AHU controller 330, by BMS controller 366… AHU controller 330 receives a measurement of the supply air temperature from a temperature sensor 362 positioned in supply air duct 312 (e.g., downstream of cooling coil 334 and/or heating coil 336). AHU controller 330 may also receive a measurement of the temperature of building zone 306 from a temperature sensor 364 located in building zone 306.; 0049, Figs. 4 and 7 — 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; 0080 — Connected equipment 610 can include connected chillers 612, connected AHUs 614, connected actuators 616, connected controllers 618, or any other type of equipment in a building HVAC system (e.g., boilers, economizers, valves, dampers, cooling towers, fans, pumps, etc.); 0113, Fig. 7 — 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.)… monitored variables 706];
a plurality of sensors associated with the plurality of internet connected devices for the boiler, wherein the sensors are configured to sense a plurality of parameters regarding the boiler and to provide a plurality of first signals regarding the sensed parameters to the controller [0026, Figs. 1-7 — HVAC system 100 is shown to include a chiller 102, a boiler 104; 0039-0043, Fig. 3 — Actuators 324-328 may receive control signals from AHU controller 330 and may provide feedback signals to AHU controller 330. Feedback signals can include, for example, an indication of a current actuator or damper position, an amount of torque or force exerted by the actuator, diagnostic information… AHU controller 330, by BMS controller 366… AHU controller 330 receives a measurement of the supply air temperature from a temperature sensor 362 positioned in supply air duct 312 (e.g., downstream of cooling coil 334 and/or heating coil 336). AHU controller 330 may also receive a measurement of the temperature of building zone 306 from a temperature sensor 364 located in building zone 306.; 0049, Figs. 4 and 7 — 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; 0080 — Connected equipment 610 can include connected chillers 612, connected AHUs 614, connected actuators 616, connected controllers 618, or any other type of equipment in a building HVAC system (e.g., boilers, economizers, valves, dampers, cooling towers, fans, pumps, etc.); 0113, Fig. 7 — 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.)… monitored variables 706];
causing all of the first signals regarding the sensed parameters regarding the boiler system, or information based upon the first signals, to be stored with at least one memory device, so as to maintain a historical database of the first signals or information based upon the first signals [0050-0052, Fig. 4 — BMS controller 366 is shown to include a communications interface 407 (device) and a BMS interface 409. Communications interface 407 may facilitate communications between BMS controller 366 and external applications (e.g., monitoring and reporting applications 422, enterprise control applications 426, remote systems and applications 444… for allowing user control, monitoring, and adjustment to BMS controller 366 and/or subsystems 428; 0080-0086, Figs. 5-6 — BMS 600 is shown to include building 10, network 446, client devices 448, and predictive diagnostics system 502… 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… ROC 602 can push the monitored variables and equipment status information to a reporting database 604, where the data is stored for reporting and analysis. Predictive diagnostics system 502 can access database 604 to retrieve the monitored variables and the equipment status information (memory device, so as to maintain a historical database of the first signals or information based upon the first signals); 0127 — predictive diagnostics system 502 may be configured to utilize supervised machine learning to produce a model or function from the big data (e.g., the monitored variables history, probability distribution, etc.).];
sending the first signals regarding the sensed parameters, or second signals based upon the first signals, to at least one device of the boiler system; further sending the first signals, the second signals, or third signals based upon the first signals or second signals, for receipt by a cloud computing system [0050-0052, Figs. 4 and 6-7 — BMS controller 366 is shown to include a communications interface 407 (device) and a BMS interface 409. Communications interface 407 may facilitate communications between BMS controller 366 and external applications (e.g., monitoring and reporting applications 422, enterprise control applications 426, remote systems and applications 444… for allowing user control, monitoring, and adjustment to BMS controller 366 and/or subsystems 428; 0080-0086, Fig. 5 — 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. For example, predictive diagnostics system 502 can be implemented as part of a PANOPTIX® brand building efficiency platform, as sold by Johnson Controls Inc] and for use in either developing one or more machine learning models for predicting the shutdown alarms or generating at least one prediction of one or more of the shutdown alarms by way of the one or more machine learning models [0123-0130, Figs. 11-12 — At step 1110, predictive diagnostics system 502 is configured to create a machine learning model. By way of example, predictive diagnostics system 502 may be configured to utilize supervised machine learning to produce a model or function from the big data (e.g., the monitored variables history, probability distribution, etc.)… At step 1116, predictive diagnostics system 502 is configured to monitor data (e.g., the monitored variables, etc.) regarding the current operation of connected equipment 610 to predict whether operation of connected equipment 610 is trending towards a fault condition or remaining in a normal condition. At step 1118, predictive diagnostics system 502 is configured to predict a diagnosis for connected equipment 610 in response to the operation of connected equipment trending towards the fault condition]; and
receiving the at least one prediction of the one or more of the shutdown alarms; displaying, on a display associated with the boiler system, the at least one prediction of the one or more shutdown alarms, or one or more alerts in response to the at least one prediction of the one or more shutdown alarms, by way of a user interface associated with the boiler system, by way of an interface [0090 — web interface can be used to… 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] and
causing an action by at least one actuator associated with the boiler that is controlled by the controller in response to the at least one prediction of the one or more of the shutdown alarms; [0066-0067 — Fault detection and diagnostics (FDD) layer 416… FDD layer 416 (or a policy executed by an integrated control engine or business rules engine) may shut-down systems or direct control activities around faulty devices or systems to reduce energy waste, extend equipment life, or assure proper control response; 0083 — 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; 0121 — predictive diagnostics system 502 can automatically cause connected equipment 610 to enter a safety mode (e.g., derated operation, etc.) or shut down when a fault is predicted to occur].
But Khalate fails to clearly specify IoT devices, sending data via a gateway device and using an application programming interface (API) and wherein the boiler is a lead boiler that operates with one or more lag boilers; wherein the at least one prediction of the one or more of the alarms that is generated by the way of the one or more machine learning models pertains to the lead boiler, and wherein the one or more machine learning models take into account data patterns concerning interrelated operations between the lead boiler and the one or more other boilers, or between other lead boilers and other lag boilers of other boiler systems.
However, Park teaches IoT devices [0329-0331, Fig. 14 — Internet-of-Things (IoT)… . The IoT environment may include a plurality of devices 1402, 1404, 1406, a cloud-based service 1408… the devices 1402, 1404, 1406 can be sensors, controllers, actuators, sub-systems, thermostats, or any other component within the BMS system capable of communicating to the cloud-based service 1408] and sending data via a gateway device [0331-0335 , Fig. 15 — devices 1402, 1404, 1406 are connected to the Internet via one or more gateways, routers, modems, or other internet connected devices, which provide communication to and from the internet… collator 1512 may be a software element within a local device, such as an internet gateway] and using an application programming interface (API) [0336-0339 — The multi-modal data processing service 1600 includes a timeseries microservice API 1602… The processing layer 1604 may further include a processing service API 1628; 0355-0357 — The mapping used in FIG. 20 can require maintaining mappings and building custom ACID services for each application, which can be expensive and tedious to maintain. These issues can be resolved by building a set of abstractions that provide APIs for application developers and data management applications… telemetry data is received by the service via one or more APIs].
Khalate and Park are analogous art. They relate to HVAC and building management systems.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to simply substitute the known IoT devices of Park for the known devices of Khalate for the predictable result of a system using IoT devices. In addition, it would be obvious to one having ordinary skill in the art to utilize IoT to facilitate communication with the remote/cloud internet connected infrastructure. Furthermore, it would be obvious to one having ordinary skill in the art to utilize a gateway, as taught by Park, to facilitate connecting with the internet and hence the remote/cloud internet connected infrastructure, as suggested by Park [0331-0335]. And one of ordinary skill in the art would have been motivated to perform these functions by way of an API in order to reduce cost and ease maintenance, as suggested by Park [0355-0357].
But the combination of Khalate and Park fails to clearly specify that a boiler is a lead boiler that operates with one or more lag boilers; wherein the boiler is a lead boiler that operates with one or more lag boilers; wherein the at least one prediction of the one or more of the alarms that is generated by the way of the one or more machine learning models pertains to the lead boiler, and wherein the one or more machine learning models take into account data patterns concerning interrelated operations between the lead boiler and the one or more other boilers, or between other lead boilers and other lag boilers of other boiler systems.
However, Martinez teaches that a boiler is a lead boiler that operates with one or more lag boilers [col. 4 lines 11-44, Drawing — The direct fired boiler to be turned off is referred to as the lag boiler and the direct fired boiler to be left on is referred to as the lead boiler. For purposes of illustration, direct fired boiler 12 will be the lag boiler, and direct fired boiler 10 will be referred to as the lead boiler].
Khalate, Park and Martinez are analogous art. They relate to HVAC and boiler systems.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above system, as taught by the combination of Khalate and Park, by incorporating the above limitations, as taught by Martinez.
One of ordinary skill in the art would have been motivated to do this modification in order to save energy, as suggested by Martinez [col. 1 lines 9-11, col. 4 lines 52-60]. In addition, it would have been obvious to a person of ordinary skill in the art to simply substitute the known lead-lag boilers of Martinez for the known boilers of Khalate for the predictable result of a system using a lead-lag boiler arrangement.
But the combination of Khalate, Park, and Martinez fails to clearly specify that the at least one prediction of the one or more of the alarms that is generated by the way of the one or more machine learning models pertains to the lead boiler, and wherein the one or more machine learning models take into account data patterns concerning interrelated operations between the lead boiler and the one or more other boilers, or between other lead boilers and other lag boilers of other boiler systems.
However, Brady teaches that the at least one prediction of the one or more of the shutdown alarms that is generated by the way of the one or more machine learning models pertains to the lead boiler, and wherein the one or more machine learning models take into account data patterns concerning interrelated operations between the lead boiler and the one or more boilers, or between other lead boilers and other lag boilers of other boiler systems [col. 4 lines 13-21 — A closed-loop thermal energy system (e.g., a comparative boiler or chiller) performance assessment may be performed under one or more defined or standardized situations where buildings operate a system duty/standby arrangement, where one boiler or one chiller takes a lead position (lead boiler) rotated over a selected period of time (e.g., weekly or monthly or the lead position between plant) or regularly alternated at selected time intervals (data patterns concerning interrelated operations between the lead boiler and the one or more standby/other boilers); col. 10 lines 15-36 — FIG. 4 illustrates energy usage anomaly detection and fault diagnosis and training of a machine-learning model… the module/component blocks 400 may also be incorporated into various hardware and software components of a system for accurate temporal event predictive modeling in accordance with the present invention.; col. 12 lines 17-27 — the energy output or fluid flow rate event may be an alert that indicates or displays audibly and/or visually on the GUI 422 “ALERT! Flow rate and energy output of a fluid transfer pump system is high; col. 15 line 32 – col. 16 line 22, Fig. 8 — FIG. 8 is a diagram 800 depicting various user hardware and computing components functioning in a low pressure hot water (LPHW) system in accordance with aspects of the present invention. The LPHW system may include one or more boilers such as, for example, “boiler 1” and “boiler 2” (lead and other/standby boiler), one or more non-intrusive IoT sensors such as, for example, IoT sensor S1, IoT sensor S2 and IoT sensor S3].
Khalate, Park, Martinez and Brady are analogous art. They relate to HVAC and boiler systems.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above system, as taught by the combination of Khalate, Park, and Martinez by incorporating the above limitations, as taught by Brady.
One of ordinary skill in the art would have been motivated to do this modification in order to account for which boiler is leading and the time intervals during which each boiler is operational.
Regarding claim 27, the combination of Khalate, Park, Martinez and Brady teaches all the limitations of the base claims as outlined above.
Further, Khalate teaches the plurality of parameters concerns one or more of a firing rate, an oxygen level, a fuel valve position, a type of fuel, a stack temperature, a water temperature, or a flame strength of the boiler system [0080-0082 — Connected equipment 610 can include connected chillers 612, connected AHUs 614, connected actuators 616, connected controllers 618, or any other type of equipment in a building HVAC system (e.g., boilers, economizers, valves, dampers, cooling towers, fans, pumps, etc.)… monitored variables can include one or more measured or calculated temperatures (e.g., refrigerant temperatures, cold water supply temperatures, hot water supply temperatures, supply air temperatures, zone temperatures, etc.].
Regarding claim 29, the combination of Khalate, Park, Martinez and Brady teaches all the limitations of the base claims as outlined above.
Further, Khalate teaches an artificial intelligence system renders one or more decisions based on the one or more machine learning models [0004 — determine a boundary for the probability distribution using a supervised machine learning technique to separate normal conditions from faulty conditions indicated by the plurality of monitored variables; separate the faulty conditions into sub-patterns using an unsupervised machine learning technique to generate a fault prediction model, wherein each sub-pattern corresponds with a fault, and wherein each fault is associated with a fault diagnosis; receive a current set of the plurality of monitored variables from the connected equipment; determine whether the current set of the plurality of monitored variables corresponds with one of the sub-patterns of the fault prediction model; 0087-0088, 0096, 0104-0105 — predictive diagnostics system 502 determines whether the current operating state is a normal operating state or a faulty operating state; 0130 — At step 1212, predictive diagnostics system 502 is configured to compute patterns using an unsupervised machine learning algorithm. At step 1214, predictive diagnostics system 502 is configured to predict a diagnosis for the recently predicted faults using an optimal supervised classification machine learning algorithm (e.g., logistic regression, neural networks, etc.)].
Regarding claim 30, the combination of Khalate, Park, Martinez and Brady teaches all the limitations of the base claims as outlined above.
Further, Khalate teaches performing data preprocessing and feature engineering prior to the developing of the one or more machine learning models for predicting the shutdown alarms or the generating of the at least one prediction of the one or more of the shutdown alarms by way of the one or more machine learning models [0102 — the variable monitor 718 addresses missing values in and/or performs various data cleansing processes on past and/or current monitored variables 706 (data preprocessing). Each of the sample matrices can be used by predictive diagnostics system 502 to generate a PCA model (feature engineering) for a different operating state. Once the PCA models are generated, new sample vectors (or samples) can be collected and automatically identified by predictive diagnostics system 502 as belonging to a particular operating state or moving toward a particular operating state using the PCA models; 0126 — data cleansing may include removing erroneous portions of the data that may not be used by predictive diagnostics system 502 and/or scrubbing the data to separate useful portions from erroneous portions; 0005 — a method for performing predictive diagnostics on connected equipment of a building management system; 0080 — Connected equipment 610 can include connected chillers 612, connected AHUs 614, connected actuators 616, connected controllers 618, or any other type of equipment in a building HVAC system (e.g., boilers, economizers, valves, dampers, cooling towers, fans, pumps, etc.); 0083-0085 — 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. 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. The control panel can generate a data point when a safety shut down or a derate occurs. The data point can include a safety fault code which indicates the reason or condition that triggered the shut down; 0087 -0090, Fig. 6 — If the current operating state is identified as a faulty state or moving toward a faulty state, predictive diagnostics system 502 may generate an alert (alarm) or notification for service technicians 606 to repair the fault or potential fault before it becomes more severe. In some embodiments, predictive diagnostics system 502 uses the current operating state to determine an appropriate control action for connected equipment 610… predictive diagnostics system 502 can predict when connected equipment 610 (including boilers) will next report a safety fault code that triggers a device shut down (that is associated with an alert/alarm); 0123-0130, Figs. 11-12 — At step 1110, predictive diagnostics system 502 is configured to create a machine learning model. By way of example, predictive diagnostics system 502 may be configured to utilize supervised machine learning to produce a model or function from the big data (e.g., the monitored variables history, probability distribution, etc.)… At step 1116, predictive diagnostics system 502 is configured to monitor data (e.g., the monitored variables, etc.) regarding the current operation of connected equipment 610 to predict whether operation of connected equipment 610 is trending towards a fault condition or remaining in a normal condition. At step 1118, predictive diagnostics system 502 is configured to predict a diagnosis for connected equipment 610 in response to the operation of connected equipment trending towards the fault condition].
Regarding claim 31, the combination of Khalate, Park, Martinez and Brady teaches all the limitations of the base claims as outlined above.
Further, Khalate teaches the one or more models include a plurality of machine learning models that are trained for to predict the shutdown alarms for a plurality of different types of boilers [0080 — Connected equipment 610 can include connected chillers 612, connected AHUs 614, connected actuators 616, connected controllers 618, or any other type of equipment in a building HVAC system (e.g., boilers, economizers, valves, dampers, cooling towers, fans, pumps, etc.); 0122 — First fault diagnostic model 1002 and second fault diagnostic model 1004 may be for the same connected equipment 610 or different (but similar) connected equipment (e.g., same type of equipment but at different locations, etc.). Predictive diagnostics system 502 may thereby be capable of learning multiple optimal operation subsets (e.g., if the density of normal data points are disseminated among multiple groups rather than an isolated whole, etc.).].
Claim(s) 22-24 and 26 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Khalate, Park, Martinez and Brady in view of Vazquez-Canteli et al. U.S. Patent Publication No. 20220058497 (hereinafter Vazquez).
Regarding claim 22, the combination of Khalate, Park, Martinez and Brady teaches all the limitations of the base claims as outlined above.
Further, Khalate teaches at least one prediction that the one or more the shutdown alarms will occur [0087 -0090, Fig. 6 — If the current operating state is identified as a faulty state or moving toward a faulty state, predictive diagnostics system 502 may generate an alert (alarm) or notification for service technicians 606 to repair the fault or potential fault before it becomes more severe. In some embodiments, predictive diagnostics system 502 uses the current operating state to determine an appropriate control action for connected equipment 610… predictive diagnostics system 502 can predict when connected equipment 610 (including boilers) will next report a safety fault code that triggers a device shut down (that is associated with an alert/alarm); 0123-0130, Figs. 11-12 — At step 1110, predictive diagnostics system 502 is configured to create a machine learning model. By way of example, predictive diagnostics system 502 may be configured to utilize supervised machine learning to produce a model or function from the big data (e.g., the monitored variables history, probability distribution, etc.)… At step 1116, predictive diagnostics system 502 is configured to monitor data (e.g., the monitored variables, etc.) regarding the current operation of connected equipment 610 to predict whether operation of connected equipment 610 is trending towards a fault condition or remaining in a normal condition. At step 1118, predictive diagnostics system 502 is configured to predict a diagnosis for connected equipment 610 in response to the operation of connected equipment trending towards the fault condition].
But the combination of Khalate and Park Khalate, Park, Martinez and Brady fails to clearly specify at least one prediction includes a probability value as to a likelihood that the one or more the shutdown alarms will occur within a first time period.
However, Vazquez teaches at least one prediction includes a probability value as to a likelihood that the one or more the failures will occur within a first time period [0007 — the predictive maintenance engine calculates a probability of failure at any time interval for a device from an annual failure rate of the device; 0088-0093 — the sampling time interval in hours, e.g., for a time interval of 15 minute T.sub.s=0.25. Ns reflects the MTBF in hours… the system can estimate the probability of failure within any given time interval during a year, e.g., a specific 10 hour duration… the fault diagnostics inference engine 608 detects a valve failure with the probability of 90% on day 1].
Khalate, Park, Martinez, Brady and Vazquez are analogous art. They relate to HVAC and building management systems, particularly systems with boilers.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by the combination of Khalate, Park, Martinez and Brady, by incorporating the above limitations, as taught by Vazquez, such that at least one prediction includes a probability value as to a likelihood that the one or more the shutdown alarms will occur within a first time period.
One of ordinary skill in the art would have been motivated to do this modification to improve monitoring/decision making by quantitatively specifying a shutdown prediction for a period of interest to operators.
Regarding claim 23, the combination of Khalate, Park, Martinez, Brady and Vazquez teaches all the limitations of the base claims as outlined above.
Further, Khalate teaches at least one prediction that the one or more of the shutdown alarms will occur [0087 -0090, Fig. 6 — If the current operating state is identified as a faulty state or moving toward a faulty state, predictive diagnostics system 502 may generate an alert (alarm) or notification for service technicians 606 to repair the fault or potential fault before it becomes more severe. In some embodiments, predictive diagnostics system 502 uses the current operating state to determine an appropriate control action for connected equipment 610… predictive diagnostics system 502 can predict when connected equipment 610 (including boilers) will next report a safety fault code that triggers a device shut down (that is associated with an alert/alarm); 0123-0130, Figs. 11-12 — At step 1110, predictive diagnostics system 502 is configured to create a machine learning model. By way of example, predictive diagnostics system 502 may be configured to utilize supervised machine learning to produce a model or function from the big data (e.g., the monitored variables history, probability distribution, etc.)… At step 1116, predictive diagnostics system 502 is configured to monitor data (e.g., the monitored variables, etc.) regarding the current operation of connected equipment 610 to predict whether operation of connected equipment 610 is trending towards a fault condition or remaining in a normal condition. At step 1118, predictive diagnostics system 502 is configured to predict a diagnosis for connected equipment 610 in response to the operation of connected equipment trending towards the fault condition].
Further, Vazquez teaches at least one prediction includes a plurality of predictions regarding whether any of the one or more of the events will occur during each of the first time period, a second time period, or a third time period [0007 — the predictive maintenance engine calculates a probability of failure at any time interval for a device from an annual failure rate of the device; 0088-0093 — the sampling time interval in hours, e.g., for a time interval of 15 minute T.sub.s=0.25. Ns reflects the MTBF in hours… the system can estimate the probability of failure within any given time interval during a year, e.g., a specific 10 hour duration… the fault diagnostics inference engine 608 detects a valve failure with the probability of 90% on day 1].
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by the combination of Khalate, Park, Martinez and Brady, by incorporating the above limitations, as taught by Vazquez, such that at least one prediction includes a plurality of predictions regarding whether any of the one or more of the shutdown alarms will occur during each of the first time period, a second time period, or a third time period. Note that merely specifying multiple/duplicate features is considered obvious, see MPEP 2144.04.
One of ordinary skill in the art would have been motivated to do this modification to improve monitoring/decision making by quantitatively specifying a shutdown prediction for any period of interest to operators.
Regarding claim 24, the combination of Khalate, Park, Martinez, Brady and Vazquez teaches all the limitations of the base claims as outlined above.
Further, Khalate teaches at least one prediction that the one or more of the shutdown alarms will occur [0087 -0090, Fig. 6 — If the current operating state is identified as a faulty state or moving toward a faulty state, predictive diagnostics system 502 may generate an alert (alarm) or notification for service technicians 606 to repair the fault or potential fault before it becomes more severe. In some embodiments, predictive diagnostics system 502 uses the current operating state to determine an appropriate control action for connected equipment 610… predictive diagnostics system 502 can predict when connected equipment 610 (including boilers) will next report a safety fault code that triggers a device shut down (that is associated with an alert/alarm); 0123-0130, Figs. 11-12 — At step 1110, predictive diagnostics system 502 is configured to create a machine learning model. By way of example, predictive diagnostics system 502 may be configured to utilize supervised machine learning to produce a model or function from the big data (e.g., the monitored variables history, probability distribution, etc.)… At step 1116, predictive diagnostics system 502 is configured to monitor data (e.g., the monitored variables, etc.) regarding the current operation of connected equipment 610 to predict whether operation of connected equipment 610 is trending towards a fault condition or remaining in a normal condition. At step 1118, predictive diagnostics system 502 is configured to predict a diagnosis for connected equipment 610 in response to the operation of connected equipment trending towards the fault condition].
Further, Vazquez teaches a first prediction regarding whether a first of the one or more events will occur within the first time period, a second prediction regarding whether the first event will occur within the second time period, and a third prediction regarding whether the first event will occur within the third time period [0007 — the predictive maintenance engine calculates a probability of failure at any time interval for a device from an annual failure rate of the device; 0088-0093 — the sampling time interval in hours, e.g., for a time interval of 15 minute T.sub.s=0.25. Ns reflects the MTBF in hours… the system can estimate the probability of failure within any given time interval during a year, e.g., a specific 10 hour duration… the fault diagnostics inference engine 608 detects a valve failure with the probability of 90% on day 1].
Further, Vazquez teaches time periods between 15 minutes and 1 day [0088-0093 — the sampling time interval in hours, e.g., for a time interval of 15 minute T.sub.s=0.25. Ns reflects the MTBF in hours… the system can estimate the probability of failure within any given time interval during a year, e.g., a specific 10 hour duration… the fault diagnostics inference engine 608 detects a valve failure with the probability of 90% on day 1]. And the time period ranges taught by Vazquez overlap with the 30/60/90 minutes claimed ranges and hence are considered obvious, see MPEP 2144.05.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by the combination of Khalate, Park, Martinez and Brady, by incorporating the above limitations, as taught by Vazquez, such that a first prediction regarding whether a first of the one or more shutdown alarms will occur within the first time period based upon 90 minute interval data, a second prediction regarding whether the first shutdown alarm will occur within the second time period based upon 60 minute interval data, and a third prediction regarding whether the first shutdown alarm will occur within the third time period based upon 30 minute interval data.
One of ordinary skill in the art would have been motivated to do this modification to improve monitoring/decision making by quantitatively specifying a shutdown prediction for 30/60/90 minute periods chosen to be of interest to operators.
Regarding claim 26, the combination of Khalate, Park, Martinez, Brady and Vazquez teaches all the limitations of the base claims as outlined above.
Further, Khalate teaches the first, second, and third predictions are generated by the one or more machine learning models operating in an independent manner [0087 -0090, Fig. 6 — If the current operating state is identified as a faulty state or moving toward a faulty state, predictive diagnostics system 502 may generate an alert (alarm) or notification for service technicians 606 to repair the fault or potential fault before it becomes more severe. In some embodiments, predictive diagnostics system 502 uses the current operating state to determine an appropriate control action for connected equipment 610… predictive diagnostics system 502 can predict when connected equipment 610 (including boilers) will next report a safety fault code that triggers a device shut down (that is associated with an alert/alarm); 0123-0130, Figs. 11-12 — At step 1110, predictive diagnostics system 502 is configured to create a machine learning model. By way of example, predictive diagnostics system 502 may be configured to utilize supervised machine learning to produce a model or function from the big data (e.g., the monitored variables history, probability distribution, etc.)… At step 1116, predictive diagnostics system 502 is configured to monitor data (e.g., the monitored variables, etc.) regarding the current operation of connected equipment 610 to predict whether operation of connected equipment 610 is trending towards a fault condition or remaining in a normal condition. At step 1118, predictive diagnostics system 502 is configured to predict a diagnosis for connected equipment 610 in response to the operation of connected equipment trending towards the fault condition; 0122 — a first fault diagnostic model 1002 to a second fault diagnostic model 1004. First fault diagnostic model 1002 and second fault diagnostic model 1004 may be for the same connected equipment 610 — multiple independent machine learning models]. Note that merely specifying multiple/duplicate features is considered obvious, see MPEP 2144.04.
Claim(s) 28 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Khalate, Park, Martinez and Brady in view of Revilla et al. U.S. Patent Publication No. 20210310904 (hereinafter Revilla).
Regarding claim 28, the combination of Khalate, Park, Martinez and Brady teaches all the limitations of the base claims as outlined above.
But the combination of Khalate, Park, Martinez and Brady fails to clearly specify the action concerns any one or more of checking a pilot flame, checking a burner, checking an ignition status or feature, checking a fuel supply, or checking a communication from a flame safe guard.
However, Revilla teaches the action concerns any one or more of checking a pilot flame, checking a burner, checking an ignition status or feature, checking a fuel supply, or checking a communication from a flame safe guard [0032-0035 — the fifth rule can include a corrective action that includes transmitting a notification to a user device. The notification can include instructions to perform an overall combustion system check and/or to check fuel content… the eighth rule can include a corrective action that includes transmitting a notification to a user device. The notification can include instructions to inspect one or more gas valves, flame sensor integrity, a gas valve filter, an igniter, and/or a hot surface igniter component.].
Khalate, Park, Martinez, Brady and Revilla are analogous art. They relate to HVAC and boiler systems.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by the combination of Khalate, Park, Martinez and Brady, by incorporating the above limitations, as taught by Revilla.
One of ordinary skill in the art would have been motivated to do this modification to enable performance of corrective actions to prevent or mitigate a boiler malfunction, as suggested by Revilla [0002-0003], in particular by guiding a user to diagnose a boiler problem.
Allowable Subject Matter
Claim(s) 25 is/are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
While Khalate, Park, Martinez, Brady and Vazquez each teach elements of dependent claim 24, as outlined above, none of these references taken either alone or in combination with the prior art of record discloses that the second prediction is based at least in part upon the first prediction, and the third prediction is based at least in part upon the second prediction, as generated by the one or more machine learning models operating in a sequential manner, as recited in claim 25.
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Pitonyak et al. U.S. Patent Publication No. 20100006042 discloses a system to optimize multiple boiler plant systems having mixed condensing and non-condensing boiler groups.
Note that any citations to specific, pages, columns, lines, or figures in the prior art references and any interpretation of the reference should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. See MPEP 2123.
Conclusion
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/BERNARD G LINDSAY/
Primary Examiner, Art Unit 2119
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