DETAILED ACTION
This office action is in response to the application filed August 6, 2024.
Claims 1-20 are pending.
This application claims benefit of the provisional application 63/070,460 filed August 26, 2020, and US Patent 12,120,838 filed August 23, 2021 and is examined herein as entitled to that earlier filing date.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on August 21, 2024 was filed after the mailing date of the application on August 6, 2024. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-19 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1-14,17-20, 16, and 8 respectively of U.S. Patent No. 12,120,838. Although the claims at issue are not identical, they are not patentably distinct from each other because the pending claims are obvious in view of the patented claims as aligned below.
Application 18/795,854
U.S. Patent 12,120,838
1. A system comprising: a controller, display screen, and devices, the controller comprising computer hardware and memory, the controller operationally able to control the devices;
a digital twin comprising a neural network with nodes modeling devices in a physical space, the nodes arranged with reference to connections between at least some of the devices;
wherein the controller is operationally able to track device errors;
wherein at least one of the device errors is tracked through more than one device using the digital twin;
and wherein at least one node within the digital twin has an actor type and is acted on by a quanta type to model behavior of an associated device.
2. The system of claim 1, wherein the digital twin is operationally able to simulate the devices from a first time to a second time, and wherein the display screen is operationally able to display simulated physical space behavior from the first time to the second time.
3. The system of claim 2, wherein an event associated with at least one of the devices comprises: a state change associated with at least one of the devices and a state change action that is a reason for the state change.
4. The system of claim 1, wherein the at least one node uses at least one property and at least two computed properties to model behavior of the associated device.
5. The system of claim 1, wherein the digital twin utilizes historical data of the devices to simulate the devices from a current time to a future time.
6. The system of claim 1, wherein the actor type comprises a producer, a consumer, a transformer, a transporter, a store, a router, a mixer, a path, or a branch.
7. The system of claim 1, wherein an actor of an actor type interacts with a quanta.
8. The system of claim 4, further comprising the devices comprising a first device connected to a second device connected to a third device;
the nodes comprising a first node representing the first device connected to a second node representing the second device, the second node connected to a third node representing the third device;
and wherein the connection between the first device and the third device is determined through the connection between the first node and the second node.
9. The system of claim 8, wherein the connection between the first device and the third device can be determined through the connection between the first node and the second node and the connection between the second node and the third node.
10. The system of claim 9, wherein the connection between the first node and the second node is represented by a quanta.
11. The system of claim 9, further comprising a second property of the first node and wherein the at least one property of the first node is used to calculate a first computed property of the first node, and the second property of the first node is used to calculate a second computed property of the first node.
12. The system of claim 11, wherein the first device is a boiler and wherein the first node has an efficiency coefficient property.
13. The system of claim 12, wherein the at least one property and at least one of the at least two computed properties of the second node is interrogated to determine a fault in the first device.
14. The system of claim 13, wherein the at least one property and at least one of the at least two computed properties of the third node is interrogated to determine a fault in the first device.
15. A method comprising:accessing a digital twin, the digital twin comprising a neural network with nodes modeling devices in a physical space, the nodes arranged with reference to connections -3- between the devices, at least one of the nodes comprising at least one property and at least two computed properties;
tracking device errors of the devices;
wherein at least one of the device errors is tracked through more than one of the devices using the digital twin;
and wherein at least one node within the digital twin has an actor type and is acted on by a quanta type to model behavior of an associated device.
16. The method of claim 15, wherein the digital twin utilizes historical data of at least one of the devices to simulate the at least one of the devices from a current time to a future time.
17. The method of claim 16, further comprising:the devices comprising a first device connected to a second device connected to a third device;
the nodes comprising a first node representing the first device connected to a second node representing the second device, the second node connected to a third node representing the third device;
and wherein the connection between the first device and the third device is determined through the connection between the first node and the second node.
18. The method of claim 17, further comprising a second property of the first node and wherein the at least one property of the first node is used to calculate a first computed property of the first node, and the second property is used to calculate a second computed property of the first node.
19. A non-transitory computer readable storage medium configured with data and instructions that upon execution by at least one processor with programmable memory -4- in a controller computing system having a devices attached thereto, the programmable memory storing instructions for using a digital twin of the devices, when the instructions, when executed by a processor, cause the processor to perform steps including: instructions for accessing the digital twin, the digital twin comprising a neural network with nodes modeling devices in a physical space, the nodes arranged with reference to connections between the devices, at least one of the nodes comprising at least one property and at least two computed properties;
instructions for tracking errors of the devices;
wherein at least one of the errors of the devices is tracked through more than one of the devices using the digital twin;
and wherein at least one node within the digital twin has an actor type and is acted on by a quanta type to model behavior of an associated device.
20. The non-transitory computer readable storage medium of claim 19, further comprising:the devices comprising a first device connected to a second device connected to a third device;
the nodes comprising a first node representing the first device connected to a second node representing the second device, the second node connected to a third node representing the third device;
and wherein the connection between the first device and the third device is determined through the connection between the first node and the second node.
1. A system comprising: a controller, display screen, and devices, the controller comprising computer hardware and memory, the controller operationally able to control the devices, the controller operationally able to track the devices;
a computer program in the memory configured to display, on the display screen, at least one of: state of the devices over a given time, an event associated with at least one of the devices, length of time of the event, an action associated with the event, or detail associated with the event;
a digital twin comprising a neural network with nodes modeling devices in a physical space, the nodes arranged with reference to connections between the devices, at least one of the nodes comprising at least one property and at least two computed properties;
wherein the controller operationally able to track the devices comprises tracking device errors;
wherein at least one of the device errors is tracked through more than one device using the digital twin;
and wherein at least one node within the digital twin has an actor type and is acted on by a quanta type to model behavior of an associated device.
2. The system of claim 1, wherein the digital twin is opcrationally able to simulate the devices from a first time to a second time, and wherein the display screen is operationally able to display simulated physical space behavior from the first time to the second time.
3. The system of claim 2, wherein the event associated with at least one of the devices comprises: a state change associated with at least one of the devices and a state change action that is a reason for the state change.
4. The system of claim 1, wherein the at least one node uses the at least one property and the at least two computed properties to model behavior of the associated device.
5. The system of claim 1, wherein the digital twin uses machine learning methods utilizing utilizes historical data of the devices to simulate the devices from a current time to a future time.
6. The system of claim 1, wherein the actor comprises a producer, a consumer, a transformer, a transporter, a store, a router, a mixer, a path, or a branch.
7. The system of claim 1, wherein the actor interacts with a quanta.
8. The system of claim 1, further comprising the devices comprising a first device connected to a second device connected to a third device;
the nodes comprising a first node representing the first device connected to a second node representing the second device, the second node connected to a third node representing the third device;
and wherein the connection between the first device and the third device is determined through the connection between the first node and the second node.
9. The system of claim 8, wherein the connection between the first device and the third device can be determined through the connection between the first node and the second node and the connection between the second node and the third node.
10. The system of claim 9, wherein the connection between the first node and the second node is represented by a quanta.
11. The system of claim 9, further comprising a second property of the first node and wherein the property of the first node is used to calculate a first computed property of the first node, and the second property of the first node is used to calculate a second computed property of the first node.
12. The system of claim 9, wherein the first device is a boiler and wherein the first node has an efficiency coefficient property.
13. The system of claim 9, wherein the property and at least one of the computed properties of the second node is interrogated to determine a fault in the first device.
14. The system of claim 9, wherein the property and at least one of the computed properties of the third node is interrogated to determine a fault in the first device.
17. A method comprising: accessing a digital twin, the digital twin comprising a neural network with nodes modeling devices in a physical space, the nodes arranged with reference to connections between the devices, at least one of the nodes comprising at least one property and at least two computed properties;
running, using a processor, a computer program stored in a memory, the computer program displaying on a display screen, at least one of: state of the devices over a given time, an event associated with at least one of the devices, length of time of the event, an action associated with the event, or detail associated with the event;
tracking errors of the devices;
wherein at least one of the device errors is tracked through more than one of the devices using the digital twin;
and wherein at least one node within the digital twin has an actor type and is acted on by a quanta type to model behavior of an associated device.
18. The method of claim 17, wherein the digital twin utilizes historical data of the device to simulate the device from a current time to a future time.
19. The method of claim 17, further comprising: the devices comprising a first device connected to a second device connected to a third device;
the nodes comprising a first node representing the first device connected to a second node representing the second device, the second node connected to a third node representing the third device;
and wherein the connection between the first device and the third device is determined through the connection between the first node and the second node.
20. The method of claim 19, further comprising a second property of the first node and wherein the property of the first node is used to calculate a first computed property of the first node, and the second property is used to calculate a second computed property of the first node.
16. A non-transitory computer readable storage medium configured with data and instructions that upon execution by at least one processor with programmable memory in a controller computing system having a devices attached thereto, the programmable memory storing instructions for using a digital twin of the devices, when the instructions, when executed by a processor, cause the processor to perform steps including: instructions for accessing the digital twin, the digital twin comprising a neural network with nodes modeling devices in a physical space, the nodes arranged with reference to connections between the devices, at least one of the nodes comprising at least one property and at least two computed properties;
instructions for running a computer program stored in a memory, the computer program displaying on a display screen, at least one of: state of the devices over a given time, an event associated with at least one of the devices, length of time of the event, an action associated with the event, or detail associated with the event;
instructions for running tracking errors of the devices;
wherein at least one of the device errors is tracked through more than one of the devices using the digital twin;
and wherein at least one node within the digital twin has an actor type and is acted on by a quanta type to model behavior of an associated device.
8. The system of claim 1, further comprising the devices comprising a first device connected to a second device connected to a third device;
the nodes comprising a first node representing the first device connected to a second node representing the second device, the second node connected to a third node representing the third device;
and wherein the connection between the first device and the third device is determined through the connection between the first node and the second node.
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.
Claim(s) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over “VAZQUEZ-CANTELI” (US PG Pub 2022/0058497) as applied above and further in view of “Sharma” (US PG Pub 2020/0142365) and further in view of “Riedmiller” (US Patent 12,499,359)
Regarding Claim 1, VAZQUEZ-CANTELI teaches:
1. A system comprising:
a controller, display screen, and devices, the controller comprising computer hardware and memory, the controller operationally able to control the devices;(See.e.g Fig. 1, ¶¶28-30)
a digital twin (VAZQUEZ-CANTELI ¶¶7,75 82,96,100 – teaches a set of digital twin modesl for building equipment items which include observed input data (“properties”) from the real world system and mathematical models for determining fault causes and predicting future states; see further e.g. fig. 8 & 9)
wherein the controller is operationally able to track device errors;(VAZQUEZ-CANTELI ¶¶28-30,57,104 describes a set of controllers which track events associated with faults in the equipment) )
wherein at least one of the device errors is tracked through more than one device using the digital twin; (VAZQUEZ-CANTELI ¶¶¶¶7,100, Fig. 9, teach a digital twin that takes in the event data from the controller system and user it to determine fault causes and predict future states)
Vazquez-Canteli does not teach, but Sharma teaches:
a digital twin comprising a neural network (Sharma ¶159 teaches creating digital twins of devices in a building management system including neural networks)
and wherein at least one node within the digital twin has an actor type and is acted on by a quanta type to model behavior of an associated device. (Sharma ¶¶131-132 teaches creating digital twin of BMS devices to predict behavior, including various devices (chillers, AHUs etc in ¶151) which are acted on by water, air etc wherein the digital twin is used to predict the devices and systems behaviors )
In addition, it would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the application to combine the teachings of VAZQUEZ-CANTELI and Sharma as each is directed to modeling of real world systems and Sharma recognized the need to “generate a predicted future performance of the physical building device based on the building device digital twin; and generate a recommendation based on the predicted future performance of the physical building device, the recommendation indicating one or more changes to implement on the physical building device.” (¶6).
Vazquez-Canteli does not teach, but Reidmiler teaches:
Neural network with nodes modeling devices in a physical space, the nodes arranged with reference to connections between at least some of the devices; (Reidmiller Col. 1, Ln 60 to Col. 2, Ln 12 teaches a graph neural network for representing any physical system where the nodes represent bodies in the physical system and edges represent the connections between them)
In addition, it would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the application to combine the teachings of VAZQUEZ-CANTELI and Reidmiller as each is directed to modeling of real world systems and Reidmiller recognized “general implementations of the system can be accurate, robust and generalizable and can thus be used for planning and control in challenging physical settings.” (Col. 7, Ln 8-10).
Regarding Claim 2, VAZQUEZ-CANTELI teaches:
2. The system of claim 1, wherein the digital twin is operationally able to simulate the devices from a first time to a second time, and wherein the display screen is operationally able to display simulated physical space behavior from the first time to the second time. (See e.g. VAZQUEZ-CANTELI, ¶43 and ¶100 describing UIs for the system and further e.g. Fig. 7, ¶94 presenting an example maintenance curve prediction from the digital twin models over time)
Regarding Claim 3, VAZQUEZ-CANTELI teaches:
3. The system of claim 2, wherein an event associated with at least one of the devices comprises: a state change associated with at least one of the devices and a state change action that is a reason for the state change. . (VAZQUEZ-CANTELI ¶¶98-100 teaches using the event data to determine causes and display the data related to the faults to the user for remediation).
Regarding Claim 4, VAZQUEZ-CANTELI teaches:
4. The system of claim 1, wherein the at least one node uses at least one property and at least two computed properties to model behavior of the associated device. (VAZQUEZ-CANTELI ¶¶7,75 82,96,100 – teaches a set of digital twin modesl for building equipment items which include observed input data (“properties”) from the real world system and mathematical models for determining fault causes and predicting future states; see further e.g. fig. 8 & 9)
Regarding Claim 5, VAZQUEZ-CANTELI teaches:
5. The system of claim 1, wherein the digital twin utilizes historical data of the devices to simulate the devices from a current time to a future time. (VAZQUEZ-CANTELI e.g. ¶108 teaches using data from the failures in the system to feed a machine-learning system to predict probabilities of failure using the digital twin system). and using the mathematical model (“computed property”) to model the cause of faults or predict a future state).
Regarding Claim 6, Vazquez-Canteli does not teach, but Sharma teaches:6. The system of claim 1, wherein the actor type comprises a producer, a consumer, a transformer, a transporter, a store, a router, a mixer, a path, or a branch. (Sharma ¶¶131-132 teaches creating digital twin of BMS devices to predict behavior, including various devices (chillers, AHUs etc in ¶151) which are acted on by water, air etc wherein the digital twin is used to predict the devices and systems behaviors )
In addition, it would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the application to combine the teachings of VAZQUEZ-CANTELI and Sharma as each is directed to modeling of real world systems and Sharma recognized the need to “generate a predicted future performance of the physical building device based on the building device digital twin; and generate a recommendation based on the predicted future performance of the physical building device, the recommendation indicating one or more changes to implement on the physical building device.” (¶6).
Regarding Claim 7, Vazquez-Canteli does not teach, but Sharma teaches:7. The system of claim 1, wherein an actor of an actor type interacts with a quanta. (Sharma ¶¶131-132 teaches creating digital twin of BMS devices to predict behavior, including various devices (chillers, AHUs etc in ¶151) which are acted on by water, air etc wherein the digital twin is used to predict the devices and systems behaviors )
In addition, it would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the application to combine the teachings of VAZQUEZ-CANTELI and Sharma as each is directed to modeling of real world systems and Sharma recognized the need to “generate a predicted future performance of the physical building device based on the building device digital twin; and generate a recommendation based on the predicted future performance of the physical building device, the recommendation indicating one or more changes to implement on the physical building device.” (¶6).
Regarding Claim 7, Vazquez-Canteli does not teach, but Sharma teaches:
8. The system of claim 4, further comprising the devices comprising a first device connected to a second device connected to a third device;(Sharma e.g. Fig. 2 teaches multiple physical devices in BMS system ) In addition, it would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the application to combine the teachings of VAZQUEZ-CANTELI and Sharma as each is directed to modeling of real world systems and Sharma recognized the need to “generate a predicted future performance of the physical building device based on the building device digital twin; and generate a recommendation based on the predicted future performance of the physical building device, the recommendation indicating one or more changes to implement on the physical building device.” (¶6).
Vazquez-Canteli does not teach, but Reidmiller teaches:
the nodes comprising a first node representing the first device connected to a second node representing the second device, the second node connected to a third node representing the third device;(Reidmiller e.g. 1b teaches a plurality of nodes in graph neural network representing physical objects )
and wherein the connection between the first device and the third device is determined through the connection between the first node and the second node. (Reidmiller e.g. 1b teaches a plurality of nodes in graph neural network representing physical objects, including an indirect connection fe to fg) In addition, it would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the application to combine the teachings of VAZQUEZ-CANTELI and Reidmiller as each is directed to modeling of real world systems and Reidmiller recognized “general implementations of the system can be accurate, robust and generalizable and can thus be used for planning and control in challenging physical settings.” (Col. 7, Ln 8-10).
Regarding Claim 9, Vazquez-Canteli does not teach, but Reidmiller teaches:
9. The system of claim 8, wherein the connection between the first device and the third device can be determined through the connection between the first node and the second node and the connection between the second node and the third node. (Reidmiller e.g. 1b teaches a plurality of nodes in graph neural network representing physical objects, including an indirect connection fe to fg) In addition, it would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the application to combine the teachings of VAZQUEZ-CANTELI and Reidmiller as each is directed to modeling of real world systems and Reidmiller recognized “general implementations of the system can be accurate, robust and generalizable and can thus be used for planning and control in challenging physical settings.” (Col. 7, Ln 8-10).
10. The system of claim 9, wherein the connection between the first node and the second node is represented by a quanta. . (Sharma ¶¶131-132 teaches creating digital twin of BMS devices to predict behavior, including various devices (chillers, AHUs etc in ¶151) which are acted on by water, air etc wherein the digital twin is used to predict the devices and systems behaviors )
In addition, it would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the application to combine the teachings of VAZQUEZ-CANTELI and Sharma as each is directed to modeling of real world systems and Sharma recognized the need to “generate a predicted future performance of the physical building device based on the building device digital twin; and generate a recommendation based on the predicted future performance of the physical building device, the recommendation indicating one or more changes to implement on the physical building device.” (¶6).
Regarding Claim 11, Vazquez-Canteli does not teach, but Reidmiller teaches:
11. The system of claim 9, further comprising a second property of the first node and wherein the at least one property of the first node is used to calculate a first computed property of the first node, and the second property of the first node is used to calculate a second computed property of the first node. (See e.g. Reidmiller Col 8, Ln 56 to Col. 9, Ln 10 teaches nodes representing physical object and edges the connections between them In addition, it would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the application to combine the teachings of VAZQUEZ-CANTELI and Reidmiller as each is directed to modeling of real world systems and Reidmiller recognized “general implementations of the system can be accurate, robust and generalizable and can thus be used for planning and control in challenging physical settings.” (Col. 7, Ln 8-10).
Regarding Claim 12, Vazquez-Canteli does not teach, but Sharma teaches:
12. The system of claim 11, wherein the first device is a boiler and wherein the first node has an efficiency coefficient property. (Sharma e.g. ¶¶61 teaches a boiler as a device in the BMS/digital twin system and ¶91 describes efficiency as a property to be optimized in the BMS system.) In addition, it would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the application to combine the teachings of VAZQUEZ-CANTELI and Sharma as each is directed to modeling of real world systems and Sharma recognized the need to “generate a predicted future performance of the physical building device based on the building device digital twin; and generate a recommendation based on the predicted future performance of the physical building device, the recommendation indicating one or more changes to implement on the physical building device.” (¶6).
Regarding Claim 13, VAZQUEZ-CANTELI teaches:
13. The system of claim 12, wherein the at least one property and at least one of the at least two computed properties of the second node is interrogated to determine a fault in the first device. (VAZQUEZ-CANTELI ¶¶28-30,57,104 describes a set of controllers which track events associated with faults in the equipment)
Regarding Claim 14, VAZQUEZ-CANTELI teaches:
14. The system of claim 13, wherein the at least one property and at least one of the at least two computed properties of the third node is interrogated to determine a fault in the first device. (VAZQUEZ-CANTELI ¶¶28-30,57,104 describes a set of controllers which track events associated with faults in the equipment)
Regarding Claim 15, Vazquez-Canteli teaches:
15. A method comprising:
accessing a digital twin, … at least one of the nodes comprising at least one property and at least two computed properties; (VAZQUEZ-CANTELI ¶¶7,75 82,96,100 – teaches a set of digital twin modesl for building equipment items which include observed input data (“properties”) from the real world system and mathematical models for determining fault causes and predicting future states; see further e.g. fig. 8 & 9)
tracking device errors of the devices;( VAZQUEZ-CANTELI ¶¶28-30,57,104 describes a set of controllers which track events associated with faults in the equipment)
wherein at least one of the device errors is tracked through more than one of the devices using the digital twin; (VAZQUEZ-CANTELI ¶¶¶¶7,100, Fig. 9, teach a digital twin that takes in the event data from the controller system and user it to determine fault causes and predict future states).
Vazquez-Canteli does not teach, but Sharma teaches:
the digital twin comprising a neural network (Sharma ¶159 teaches creating digital twins of devices in a building management system including neural networks)
and wherein at least one node within the digital twin has an actor type and is acted on by a quanta type to model behavior of an associated device. (Sharma ¶¶131-132 teaches creating digital twin of BMS devices to predict behavior, including various devices (chillers, AHUs etc in ¶151) which are acted on by water, air etc wherein the digital twin is used to predict the devices and systems behaviors )
In addition, it would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the application to combine the teachings of VAZQUEZ-CANTELI and Sharma as each is directed to modeling of real world systems and Sharma recognized the need to “generate a predicted future performance of the physical building device based on the building device digital twin; and generate a recommendation based on the predicted future performance of the physical building device, the recommendation indicating one or more changes to implement on the physical building device.” (¶6).
Vazquez-Canteli does not teach, but Reidmiler teaches:
with nodes modeling devices in a physical space, the nodes arranged with reference to connections between the devices; (Reidmiller Col. 1, Ln 60 to Col. 2, Ln 12 teaches a graph neural network for representing any physical system where the nodes represent bodies in the physical system and edges represent the connections between them)
In addition, it would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the application to combine the teachings of VAZQUEZ-CANTELI and Reidmiller as each is directed to modeling of real world systems and Reidmiller recognized “general implementations of the system can be accurate, robust and generalizable and can thus be used for planning and control in challenging physical settings.” (Col. 7, Ln 8-10).
Regarding Claim 19, Vazquez-Canteli teaches:
19. A non-transitory computer readable storage medium configured with data and instructions that upon execution by at least one processor with programmable memory -4- in a controller computing system having a devices attached thereto, the programmable memory storing instructions for using a digital twin of the devices, when the instructions, when executed by a processor, ,(See.e.g Fig. 1, ¶¶28-30) cause the processor to perform steps including: instructions for accessing the digital twin… at least one of the nodes comprising at least one property and at least two computed properties;(VAZQUEZ-CANTELI ¶¶7,75 82,96,100 – teaches a set of digital twin modesl for building equipment items which include observed input data (“properties”) from the real world system and mathematical models for determining fault causes and predicting future states; see further e.g. fig. 8 & 9)
instructions for tracking errors of the devices; ; (See VAZQUEZ-CANTELI e.g. ¶¶28-30 teaches controllers as part of a building system which control equipment and use digital twins of the equipment for root cause analysis systems)
wherein at least one of the errors of the devices is tracked through more than one of the devices using the digital twin; (VAZQUEZ-CANTELI ¶¶¶¶7,100, Fig. 9, teach a digital twin that takes in the event data from the controller system and user it to determine fault causes and predict future states)
Vazquez-Canteli does not teach, but Sharma teaches:
the digital twin comprising a neural network (Sharma ¶159 teaches creating digital twins of devices in a building management system including neural networks)
and wherein at least one node within the digital twin has an actor type and is acted on by a quanta type to model behavior of an associated device. (Sharma ¶¶131-132 teaches creating digital twin of BMS devices to predict behavior, including various devices (chillers, AHUs etc in ¶151) which are acted on by water, air etc wherein the digital twin is used to predict the devices and systems behaviors )
In addition, it would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the application to combine the teachings of VAZQUEZ-CANTELI and Sharma as each is directed to modeling of real world systems and Sharma recognized the need to “generate a predicted future performance of the physical building device based on the building device digital twin; and generate a recommendation based on the predicted future performance of the physical building device, the recommendation indicating one or more changes to implement on the physical building device.” (¶6).
Vazquez-Canteli does not teach, but Reidmiler teaches:
Neural network with nodes modeling devices in a physical space, the nodes arranged with reference to connections between the devices, (Reidmiller Col. 1, Ln 60 to Col. 2, Ln 12 teaches a graph neural network for representing any physical system where the nodes represent bodies in the physical system and edges represent the connections between them)
In addition, it would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the application to combine the teachings of VAZQUEZ-CANTELI and Reidmiller as each is directed to modeling of real world systems and Reidmiller recognized “general implementations of the system can be accurate, robust and generalizable and can thus be used for planning and control in challenging physical settings.” (Col. 7, Ln 8-10).
Claims 16, 17, 18 and 20 are rejected on the same basis as claims 5, 8, 11 and 8 respectively above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The Prior Art cited in the PTO-892 form includes prior art relevant to applicant’s disclosures related to systems for building automation management, including those applying digital twin models.
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MJB
9/18/2026
/MATTHEW J BROPHY/Primary Examiner, Art Unit 2191
