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 .
Response to Arguments
Applicant’s arguments have been considered and persuasive; however, a new ground of rejection is applied addressing a common data bus within HVAC systems. The Examiner suggests defining the particular data sets; particular data routing within the common data bus; and the particular configurations of the air conditioner components. More importantly, the common data bus is not further defined and represents a means for data sharing/routing. The claim language generally recites data exchange within a rooftop system including protocol translation and common data formats. The applied prior art, in combination, teaches a common data bus within a rooftop unit for sharing data. The pertinent data sharing function provides access not only between the air conditioner component but also acts as sources of data for FDD and predictive model analysis. Furthermore, Wallaert et al., infra application, teaches an integrated data bus within a HVAC system, which when viewed in association with Figure 4 of Majewski et al., which was further modified to include a machine learning model, provides a basis for sharing data between system components. The data exchange and/or sharing data, in light of module requirements for fault prediction or pattern analysis, employs a common data bus opposed to distributed data sources,
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 (i.e., changing from AIA to pre-AIA ) 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, 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 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) 1, 5, 8, 15-16, 19, 25, 36, and 39-40 are rejected under 35 U.S.C. 103 as being unpatentable over Majewski et al. (PG/PUB 20140172400) in view over Poumohammad et al. (PG/PUB 20200380387) in view over Wallaert et al. (PG/PUB 20100106323).
Claim 1.
Majewski teaches a rooftop unit (Figure 2-11, Figure 5) but does not expressly teach the machine learning algorithm and common data bus limitations described below. Poumohammad et al. teaches the machine learning algorithm and Wallaert teaches the common data bus limitations described below, comprising:
a housing (Figure 2-11, 0059)
air conditioning components coupled to the housing (Figure 2-11, 0059); and
circuitry enclosed within and/or coupled to the housing and programmed to execute a control logic for the air conditioning components (Figure 2-26, 0020, 0059, 0076-82 e.g. see computer coupled to rooftop unit), an expression-based event processing logic (0028, 0034, 0045, 0059, Figure 6, Figure 6 e.g. see fault identification algorithms and native control logic for processing) , and a machine learning algorithm (Poumohammad et al , Figure 6-646, 0088, 0092* (e.g. see embedded algorithm, 0118, 0126, Figure 7 e.g. “The machine learning model 646 can include one or more algorithms (e.g., supervised learning, unsupervised learning, reinforcement learning, etc.) to learn.”)
One of ordinary skill in the art before the effective filing date of the claimed invention applying the teachings of Poumohammad et al, namely providing machine learning models (e.g. including learning algorithms) to a computing device for fault prediction, to the teachings of Majewski , namely performing fault detection and analysis via a computer coupled to the rooftop unit, would achieve an expected and predictable result via combining said elements using known methods. One of ordinary skill in the art adapting the rooftop unit computer to integrate the computing device components, including the machine learning models comprising learning algorithms of Poumohammad et al., would achieve an expected and predictable result of proactively scheduling maintenance based on predicting anomalies and/or faults as well providing model updates for fault remediation. Moreover, one of ordinary skill in the art given the data connections between the FDD and associated data loggers, as per Figure 4, and given the machine learning model data connections of Poumohammad, would achieve an expected and predictable result of integrating an additional analysis module (e.g. machine learning) as part of the FDD analysis to provide predictive fault analysis. Poumohammad et al. is in the same field of endeavor and reasonably pertinent to a problem of predicting air conditioner faults, see summary of Invention.
However, the applied combination does not expressly teach the common data bus limitations described below. Wallaert et al. teaches the common data bus limitations described below
wherein the circuitry provides a common data bus onboard the rooftop unit (Majewski, 0059, Figure 3, Figure 4, claim 1 e.g. see computing system mounted internally with RTU, the computing system comprising multiple data connections between, supra above modification, for expanded data connections of the RTU to include the machine learning model, FDD, data logger, vibration sensors, load aggregations, etc., and see Wallaert for the “common data bus” , Figure 2-180, 0034-36, 0037 e.g. see common bus for data exchange or sharing between HVAC components, see also broadcasting data on the bus, see also publishing messages. 0069. Accordingly, the data bus provides for data exchange and potentially sharing.
the expression-based event processing logic performs pattern recognition on data on the common data bus (Majewski et al., 0027, 0039 0044, 0059, 0076-77 0083, Figure 6 e.g. see identifying vibration patterns for potential faults via the algorithms based on received time series data, see RTU computer having FDD algorithm configured to receive data logger/external data in addition to vibration sensor data by the circuitry, and see Wallaert for the “common data bus” , Figure 2-180, 0034-35, 0037 e.g. see common bus for data exchange between HVAC components)
the control logic adjusts operation of the mechanical component based on the data on the common data bus (Majewski et al., claim 13, 0067, and see Wallaert for the “common data bus” , Figure 2-180, 0034-35, 0037 e.g. see common bus for data exchange between HVAC )components, and see sensor data as a basis for control on the common data bus_
the machine learning algorithm uses inputs from the data on the common data bus (see above modification, e.g. see embedded algorithm, 0118, 0126, Figure 7 e.g. “The machine learning model 646 can include one or more algorithms (e.g., supervised learning, unsupervised learning, reinforcement learning, etc.) to learn.”, i.e., the machine learning model are part of the data connections, and see Wallaert for the “common data bus” , Figure 2-180, 0034-35, 0037 e.g. see common bus for data exchange between HVAC components)
One of ordinary skill in the art before the effective filing date of the claimed invention applying the teachings of Wallaert, namely providing a common data bus between components of the HVAC system for data sharing, to the teachings of Majewski et al., as modified, namely providing a data connections between RTU components including but not limited to FDD, data logging, control signals, supervisory controllers, and machine learning models, would achieve an expected and predictable result of providing an integrated, common data bus between RTU system modules for providing both predictive and actual fault diagnosis. Whether the system modules are separate or integral with the RTU, an expected and predictable result is realized. Majewski et al. expressly suggest embedded or integrated components of the RTU, 0059, in additional to providing data connections between modules for both control and fault detection purposes, Figure 4. Accordingly, the application of a common data bus to the modified data connections (e.g. additional machine learning model), would achieve an expected and predictable result given that predictive fault analysis follows identifying actual faults for purposes of proactive control to limit equipment damage. As applied, the RTU of the rooftop unit comprises a common bus linking the multiple data sources or equivalent of Figure 4. Moreover, the machine learning and FDD components of the prior require data sources associated with the rooftop air conditioner. The common data bus functions as a data source via its connection to the data logger, sensors, and network so as to relay necessary data. Whether the data sourced to each model, even based logic, etc. is from a common data bus existing between the components or separate, an expected and predictable result of receiving data is realized.
Claim 5.
The rooftop unit of claim 1, wherein the expression-based event processing logic diagnoses occurring fault conditions and the machine learning algorithm predicts future fault conditions ((Majewski et al. 0034, 0053, 0081, supra claim 1 for inclusion of machine learning algorithms for use by the predictive learning models for fault prediction in addition to the fault detection functions of Majewski)
Claim 8.
The rooftop unit of claim 1 but does not expressly teach ingest from an external analytics system. Majewski et al. teaches an external analytics system and Majewski, as modifies, teaches ingesting or placing data on a common data bus,
wherein the circuitry is configured to ingest streaming sensor data onto the common databus from an external sensor (supra claim 1, 0032 see vibration sensor, Figure 4-36) and to ingest additional data onto the common data bus from an external analytics system, 0033 e.g. supervisory controller, supra claim 1 e.g. see common data bus between supervisory controller, sensors, FDD, data loggers, predictive model)
One of ordinary skill in the art before the effective filing date of the claimed invention applying the teachings of Majewski et al., as modified, namely providing or ingesting data onto a common data bus, to the teachings of Majewski et al., namely having a supervisory controller provide data to a data connection, would achieve an expected and predictable result of sharing data of ingesting additional data via using a supervisory controller. The motivation is to enable the supervisory controller to communicate with the RTU via the common data bus.
Claim 15.
(Majewski et al., as modified, teaches a unit of building equipment comprising:
Heating, ventilation, or cooling component controllable to affect a condition of a building; and
circuitry packaged with the mechanical component and programmed to , supa claim 1
provide a common data bus onboard the unit of the building equipment; supra claim 1
execute, by reading data from the common data bus onboard the unit of building equipment, supra claim 1,
a control logic for the heating, ventilation, or cooling component, an expression-based event processing logic, and a machine learning algorithm, supra claim 1
Claim 16.
The unit of building equipment of claim 15 ,
wherein the circuitry provides a common data bus (Majewski, see computer coupled to a first data source or a communication medium, 0076-77, 0058, supra claim 1 for common data bus)
the expression-based event processing logic performs pattern recognition on data read from the common data bus (Majewski, see FDD algorithm within computer accessing data logger/first data source over a communication medium, Figure 6-46 -> 49, supra claim 1 for common data bus
the control logic adjust operation of the heating, ventilation or cooling component based on data read from the common data bus (Majewski et al., Figure 5 -26-> sensors ->control signals over a communication medium accessing a second data source/sensors, supra claim 1 for common data bus)
the machine learning algorithm uses, as inputs, data from the common data bus (Majewski et al. supra claim 1, see computer comprising a machine learning algorithm accessing data sets over a communication medium, supra claim 1 for common data bus)
Claim 19.
The unit of building equipment of claim 15, wherein the expression-based event processing logic diagnoses occurring fault conditions and the machine learning algorithm predicts future fault conditions, supra claim 5
Claim 25.
The unit of building equipment of claim 15, wherein the control logic for the heating, ventilation, or cooling component is a native control logic, and wherein the expression-based event processing logic comprises a modification of the native control logic, supra claim 11
Claim 36.
Majewski et al. teaches a method comprising:
providing a package comprising a heating, ventilation, or cooling component and onboard circuitry; supra claim 1
providing, by the onboard circuitry, a common data bus locally on the onboard circuitry
executing, by the onboard circuitry, control logic to control the heating, ventilation, or cooling component using the data on the common data bus; supra claim 1
executing, by the onboard circuitry, an expression-based event processing logic using the data on the common data bus; supra claim 1and
executing, by the onboard circuitry, a machine learning algorithm using input from the data on the common data bus, supra claim 1
Claim 39.
The method of claim 36, wherein executing the expression-based event processing logic diagnoses an occurring fault condition and wherein executing the machine learning algorithm predicts a future fault condition, supra claim 5
Claim 40.
The method of claim 36, further comprising receiving, at the onboard circuitry and from a cloud system, a set of expressions and a machine learning model;
wherein executing the expression-based event processing logic comprises using the set of expressions and executing the machine learning model comprises using the machine learning model, supra claim 1
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Majewski et al. (PG/PUB 20140172400) in view over Poumohammad et al. (PG/PUB 20200380387) in view over Wallaert et al. (PG/PUB 20100106323) in view over Ritmanich (PG/PUB 20170317915)
Claim 2.
The rooftop unit of claim 1 but does not teach the data ingestion layer described below. Ritmanich teaches the data ingestion layer described below
wherein the circuitry provides a data ingestion layer configured to ingest the data from multiple sources received using multiple data protocols(e.g. as interpreted, data sources having different protocols) and provide the data onto the common data bus , wherein the multiple sources comprise the air conditioning components and one or more external data sources (Ritmanich 0037, 0109, 0112, 0163 Figure 5-502 e.g. see smart controller as reading on data ingestion layer, see also multiple components of Majewski et al., supra claim 1, 0039 e.g. see sensors such as vibration sensors attached to RTU, infra claim 8 for additional external sources, see also Majewski et al., 0027, 0039 0044, 0059, 0076-77 0083, Figure 6 e.g. see identifying vibration patterns for potential faults via the algorithms based on received time series data, see RTU computer having FDD algorithm configured to receive data logger/external data in addition to vibration sensor data by the circuitry, see also data logger as additional data source)
One of ordinary skill in the art before the effective filing date of the claimed invention applying the teachings of Ritmanich, namely providing a data ingestion layer for providing translation between multiple data sources having different communication protocols for output, to the teachings of Majewski et al., as modified, namely providing a common data bus between multiple data sources including but not limited to sensors, equipment, FDD, predictive models, data logger, would achieve an expected and predictable result of at least providing translation between multiple sources having different communication protocols prior to outputting to a common data bus to enable communications between multiple data sources having disparate protocols.
Claims 3, 17, and 22-23 are rejected under 35 U.S.C. 103 as being unpatentable over Majewski et al. (PG/PUB 20140172400) in view over Poumohammad et al. (PG/PUB 20200380387) in view over Wallaert et al. (PG/PUB 20100106323) in view over Darrah et al. (PG/PUB 20220414526).
Claim 3.
(Majewski et al. teaches the rooftop unit of claim 1 but does not expressly teach the model trained at the cloud system. Darrah teaches a model trained at the cloud system described below,
wherein the machine learning algorithm is based on a machine learning model trained at a cloud system remote from the rooftop unit (Darrah, ABSTRACT, Figure 3A, Figure 3B, 0008, 0077-81, Figure 2C. see also the cloud server and Figure 7, supra claim 1, for training machine learning models although the training location is not expressly taught (local or remote is not definitely explained)
One of ordinary skill in the art before the effective filing date of the claimed invention applying the teachings of Darrah, namely training a machine learning model at a cloud server, to the teachings of Poumohammad et al as modified, namely providing machine learning models from a cloud server to a local computer adapted to perform fault detection and prediction for a rooftop unit, would achieve an expected and predictable result via combining said elements using known methods. One of ordinary skill in the art would be motivated to remotely training a fault prediction learning model to offload local computing resources while providing a combined benefit of deploying updated machine learning models to fault analysis. Darrah is reasonably pertinent to fault prediction.
Claim 17.
The unit of building equipment of claim 15, wherein the machine learning algorithm is based on a machine learning model trained at a cloud system remote from the unit of building equipment, supra claim 3
Claim 22.
The unit of building equipment of claim 15 but does not expressly teach using both data sets as inputs described below. Darrah teaches training using both data sets described below
wherein the circuitry receives a first data set from the mechanical component (e.g. rooftop sensor associated with mechanical component) and a second data set from an external sensor (e.g. vibration sensor) and provides the first data set and the second data set onto the common data bus, , wherein the machine learning algorithm uses the first data set and the second data set as inputs, supra claim 3 (Darrah, ABSTRACT, Figure 3A, Figure 3B, 0008, 0077-81, Figure 2C. see also the cloud server and Figure 7, supra claim 1, for training machine learning models although the training location is not expressly taught (local or remote is not definitely explained)
One of ordinary skill in the art before the effective filing date of the claimed invention applying the teachings of Majewski et al., as modified by Darrah, namely training machine learning model using multiple data sets, to the teachings of Majewski, namely providing multiple data sets, would achieve an expected and predicable result of employing first and second data sets as training data for predictive analysis.
Claim 23.
The unit of building equipment of claim 22, wherein the external sensor is an indoor air quality sensor, supra claim 3, Darrah, see claim 1, 0061
Claims 4 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Majewski et al. (PG/PUB 20140172400) in view over Poumohammad et al. (PG/PUB 20200380387) in view over Wallaert et al. (PG/PUB 20100106323). in view over Darrah et al. (PG/PUB 20220414526) in view over Ningho et al. (PG/PUB 20230103149)
Claim 4.
The rooftop unit of claim 3 but does not expressly teach the modified version limitations described below. Ningbo teaches the modified version limitations described below
wherein the machine learning algorithm comprises a modified version of the machine learning model trained at the cloud system that is configured to execute on more limited processing resources of the circuitry relative to the cloud system (Ningbo, 0015 e.g. see model adaptation/compression based on limited computing/edge device capabilities)
One of ordinary skill in the art before the effective filing date of the claimed invention applying the teachings of Ningbo, namely adapting a model based on computing limitations, to the teachings of Majewski et as modified , namely uploading machine learning models (e.g. including learning algorithms) for fault prediction to a rooftop computer, would achieve an expected and predictable result via combining said elements using known methods for a purpose of providing a modified version of the learning algorithm by virtue of providing a simplified/compressed model in light of limited capabilities. The adaption of the learning algorithm within the machine learning model with the compression function of Ningbo accounts for limited computing capabilities and is reasonably pertinent to a problem of model deployment.
Claim 18.
The unit of building equipment of claim 17, wherein the machine learning algorithm comprises a modified version of the machine learning model trained at the cloud system that is configured to execute on more limited processing resources of the circuitry relative to the cloud system, supra claim 4
Claims 6, 11-13, 20 and 26-27 are rejected under 35 USC 103 as being unpatentable over Majewski et al. (PG/PUB 20140172400) in view over Poumohammad et al. (PG/PUB 20200380387) in view over Wallaert et al. (PG/PUB 20100106323). in view over Kawai (PG/PUB 20220113048)
Claim 6.
The rooftop unit of claim 1 but does not expressly teach the remote updates limitation described below. Kawai teaches the remote update limitation described below
wherein the circuitry is programmed to modify the expression-based event processing logic in response to remote updates received at the circuitry (ABSTRACT e.g. see adapter for performing model updates and control program updates, supra claim 1 for control programs/expression)
One of ordinary skill in the art before the effective filing date of the claimed invention applying the teachings of Kawai, namely implementing an adapter for performing model and program updates, to the teachings of Majewski et as modified , namely uploading machine learning models for fault prediction to a rooftop computer as well as providing fault detection algorithms/programs, would achieve an expected and predictable result via combining said elements using known methods for remotely updating control programs/expression logic for an air conditioning system. Kawai is in the same field of endeavor and would commend itself to improving remote updates as described, 0010.
Claim 11.
The rooftop unit of claim 1 but does not teach the modification described below. Kawai teaches the modification described below. wherein the control logic for the air conditioning components is a native control logic (Majewski, see computer functions, 0076-82 for localized/native processing), and wherein the expression-based event processing logic comprises a modification of the native control logic (see expression logic as fault detection, 0034-35, 0076-82, supra claim 1 e.g. see additional or modified functions such as data logging and additional analysis functions, see Kawai, ABSTRACT e.g. see adapter for performing model/program updates, supra claim 1 for control programs/expression)
One of ordinary skill in the art before the effective filing date of the claimed invention applying the teachings of Kawai, namely implementing an adapter for performing model and program updates, to the teachings of Majewski et as modified , namely uploading machine learning models for fault prediction to a rooftop computer as well as providing fault detection algorithms/programs, would achieve an expected and predictable result via combining said elements using known methods for remotely updating control programs for an air conditioning system. Kawai is in the same field of endeavor and would commend itself to improving remote updates as described, 0010.
Claim 12.
The rooftop unit of claim 11 but does not expressly teach the modification limitations described below. Kawai teaches the modification limitations described below,
wherein the modification of the native control logic is received from a cloud system or another computing system external from the rooftop unit via a network connection (ABSTRACT e.g. see adapter for performing model/program updates, supra claim 1 for control programs/expression)
One of ordinary skill in the art before the effective filing date of the claimed invention applying the teachings of Kawai, namely implementing an adapter for performing model and program updates, to the teachings of Majewski et as modified , namely uploading machine learning models for fault prediction to a rooftop computer as well as providing fault detection algorithms/programs, would achieve an expected and predictable result via combining said elements using known methods for remotely updating control programs for an air conditioning system. Kawai is in the same field of endeavor and would commend itself to improving remote updates as described, 0010.
Claim 13.
The rooftop unit of Claim 12, wherein the modification of the native control logic is received after installation of the rooftop unit while the rooftop unit is connected to the network connection and operational, supra claim 12 for updating an installed air conditioning system, supra claim 1 for the installed rooftop system in communication with the cloud server)
Claim 20.
The unit of building equipment of claim 15, wherein the circuitry is programmed to modify the expression-based event processing logic in response to remote updates received at the circuitry, supra claim 6
Claim 26.
The unit of building equipment of claim 25, wherein the modification of the native control logic is received from a cloud system or another computing system external from the unit via a network connection, supra claim 12
Claim 27. The unit of building equipment of claim 26, wherein the modification of the native control logic is received after installation of the unit while the unit is connected to the network connection and operational, supra claim 13
Claims 7 and 21 are rejected under 35 USC 103 as being unpatentable over Majewski et al. (PG/PUB 20140172400) in view over Poumohammad et al. (PG/PUB 20200380387) in view over Wallaert et al. (PG/PUB 20100106323). in view over Gray (PG/PUB 20210294624).
Claim 7.
The rooftop unit of claim 1 but does not expressly teach the memory footprint described below. Gray teaches the memory footprint described below
wherein the expression-based event processing logic and the machine learning algorithm have a combined memory footprint of less than 256 MB (Gray, 0017, 0035, 0054)
One of ordinary skill in the art before the effective filing date of the claimed invention applying the teachings of Gray, namely providing a memory footprint of less than 256, to the teachings of Majewski et al., namely providing a memory store and/or application memory, would achieve an expected and predictable result via combining said elements using known methods in light of the finite and quantifiable memory sizes. Gray pertains to allocating memory for embedded firmware applications and would commend itself to the embedded application programs of Majewski et al for minimizing memory footprints.
Claim 21.
The unit of building equipment of claim 15, wherein the expression-based event processing logic and the machine learning algorithm have a combined memory footprint of less than 256 MB, supra claim 7
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Majewski et al. (PG/PUB 20140172400) in view over Poumohammad et al. (PG/PUB 20200380387) in view over Wallaert et al. (PG/PUB 20100106323) in view over Bhattacharya (USPN 11727335)
Claim 9.
The rooftop unit of claim 1 but does not expressly teach the common format described below. Bhattacharya teaches the common format described below
wherein the circuitry is configured to translate the data into a common data format before providing the data onto provides a common data base such that the control logic, the expression-based event processing, and the machine learning algorithm read data from the common data bus (Col 7 lines 25-40)
One of ordinary skill in the art before the effective filing date of the claimed invention applying the teachings of Bhattacharya, namely translating disparate data sources into a common data format, to the teachings of Majewski, namely providing a common data bus for multiple data sources, would achieve an expected and predictable result via combining said element using known methods. Bhattacharya is in the same field of endeavor, as described, ABSTRACT, summary of invention.
Claims 10 and 24 are rejected under 35 USC 103 as being unpatentable over Majewski et al. (PG/PUB 20140172400) in view over Poumohammad et al. (PG/PUB 20200380387) in view over Wallaert et al. (PG/PUB 20100106323) in view over Gust (PG/PUB 20130310986)
Claim 10.
The rooftop unit of claim 1 but does not expressly teach the interruptions described below. Gust teaches the interruptions described below,
wherein:
the circuitry is configured to established communications with a cloud system (supra claim 1, see Gust, Figure 1); and
the control logic for the air conditioning components, the expression-based event processing logic, and the machine learning algorithm are functional during interruptions of the communications with the cloud system (supra claim 1, Gust, 0014 e.g. see maintaining control despite communication loss with the server/”maintaining minimla level of control or operational control , supra claim 1 for cloud communications of Poumohammad et al., 0015
One of ordinary skill in the art before the effective filing date of the claimed invention applying the teachings of Gust, namely providing a minimal level of control in light of communication loss, to the teachings of Majewski et al., namely providing the control logic , would achieve an expected and predictable result via maintaining control in light of a communication loss. Gust is in the same field of endeavor and recognizes the need for internal control in light of communication losses as described.
Claim 24.
The unit of building equipment of claim 15, wherein:
the circuitry is configured to established communications with a cloud system; and
the control logic for the mechanical component, the expression-based event processing logic, and the machine learning algorithm are functional during interruptions of the communications with the cloud system, supra claim 10
Claims 14 and 24 are rejected under 35 USC 103 as being unpatentable over Majewski et al. (PG/PUB 20140172400) in view over Poumohammad et al. (PG/PUB 20200380387) in view over Wallaert et al. (PG/PUB 20100106323) in view over Kar et al. (PG/PUB 20180004180)
Claim 14.
The rooftop unit of claim 1 but does not expressly teach the second set limitations described below. Kar teaches the second set limitations described below (e.g. see providing additional fault detection logic via a server) wherein the control logic comprises a first set of one or more fault detection and/or diagnostics rules (supra claim 1 for FDD algorithms), and wherein the expression-based event processing logic comprises a second set of one or more fault detection and/or diagnostics rules that supplement (e.g. second or additional expressions or machine learning models, Kar ABSTRACT, 0008, Figure 3, 0018, 0028, 0037, 0057) or modify the first set of one or more fault detection and/or diagnostics rules, the second set of one or more fault detection and/or diagnostics rules received from a cloud system or another source remote from the rooftop unit (Kar ABSTRACT, 0008, Figure 3, 0018, 0028, 0037, 0057) supra claim 1 for receiving learning models for predicting faults that are in addition to the FDD algorithms) and defined according to an expression-based language (supra claim 1, see expression-based language as machine learning/training, supra claim 1)
One of ordinary skill in the art before the effective filing date of the claimed invention applying the teachings of Kar, namely providing a software application for providing additional fault detection via a cloud server, to the teachings of Majewski, as modified, namely providing a rooftop unit coupled to a cloud server for receiving fault detection machine learning algorithms for fault prediction while providing fault detection algorithms, would achieve an expected and predictable result of receiving a second set of fault detection logic for redundancy as well as expanding upon the capabilities of existing fault detection logic. Kar is reasonably pertinent to fault detection and would commend itself to expanding upon the fault detection capabilities of rooftop units as described.
Claim 28. The unit of building equipment of claim 15, wherein the control logic comprises a first set of one or more fault detection and/or diagnostics rules, and wherein the expression-based event processing logic comprises a second set of one or more fault detection and/or diagnostics rules that supplement or modify the first set of one or more fault detection and/or diagnostics rules, the second set of one or more fault detection and/or diagnostics rules received from a cloud system or another source remote from the unit and defined according to an expression-based language, supra claim 14
Claim 28 is rejected under the same rationale and prior art set forth in claim 14.
Claim 37 is rejected under 35 USC 103 as being unpatentable over Majewski et al. (PG/PUB 20140172400) in view over Poumohammad et al. (PG/PUB 20200380387) in view over Wallaert et al. (PG/PUB 20100106323) in view over Darrah in view over Hicks (PG/PUB 20220405568)
claim 37.
The method of claim 36 but does not expressly teach the neural network and historical data sets limitations described below. Hicks teaches the neural network and Darrah teaches historical data sets limitations described below, wherein the method further comprises:
training, by a computing system remote from the onboard circuitry, a neural network on a training data set comprises historical data from at least one of the heating, ventilation, or cooling component or other heating, ventilation, or cooling components (supra claim 3 for historical data sets for training a learning model for fault prediction, and see Hicks for determining an optimal learning algorithm from a trained neural network, 0003, 0004-0006, figure 2)
generating the machine learning algorithm to transmit to the onboard circuitry using the neural network (Hicks, Figure 2, ABSTRACT e.g. see determining the learning algorithm from multiple learning algorithms based on the trained neural network, and see model deployment of Poumohammad et al., supra claim 1)
One of ordinary skill in the art before the effective filing date of the claimed invention applying the teachings of Hicks, namely training a neural network, to the teachings of Majewski et al., as modified by Darrah, supra teachings employing historical training data obtained from environmental and HVAC components, would achieve an expected and predictable result of training a neural network using historical data including but not limited to air quality and vibration parameters for fault prediction.
The combination does not expressly teach generating the machine learning algorithm from the trained neural network, but Hicks teaches generating the machine learning algorithm from the trained neural network.
Accordingly, one of ordinary skill in the art before the effective filing date of the claimed invention applying the teachings of Hicks, namely generating the learning algorithm from the neural network, to the teachings of Majewski, as modified, namely training a neural network for fault prediction, would achieve an expected and predictable result of determining which learning algorithm of multiple is best suited for machine learning to minimize training time while maximizing accuracy. Hicks is reasonably pertinent to a problem of training neural networks and would commend itself to the training methods of Majewski, as modified, supra claim 1.
Claim 38 is rejected under 35 USC 103 as being unpatentable over Majewski et al. (PG/PUB 20140172400) in view over Poumohammad et al. (PG/PUB 20200380387) in view over Wallaert et al. (PG/PUB 20100106323) in view over Darrah (PG/PUB 20220414526)
Claim 38.
The method of Claim 36 but does not expressly teach a pattern recognition applied to another data source. Darrah teaches pattern recognition for another data source
wherein executing the expression- based event processing logic provides recognition of patterns in the data (supra claim 1 for learning model pattern, Majewski et al., 0027, 0039 0044, 0059, 0076-77 0083, Figure 6 e.g. see identifying vibration patterns for potential faults via the algorithms based on received time series data, see RTU computer having FDD algorithm configured to receive data logger/external data in addition to vibration sensor data by the circuitry)
the data received at the onboard circuitry from the heating, ventilation, or cooling component (supra claim 1 for common data bus within RTU) and another data source and streamed onto the common data bus (Darrah, ABSTRACT, 0041)
One of ordinary skill in the art before the effective filing date of the claimed invention applying the teachings of Darrah, namely identify HVAC faults based on additional data, to the teachings of Majewski, namely determining HVAC faults based on vibration patterns, would achieve an expected and predictable result of using another data source to determine an HVAC fault as described.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
20190391573 “Methods, mediums, and systems include use of a system manger application in a data processing system for fault detection a building automation system using deep learning, to receive point data for a hardware being analyzed, where the received point data is contaminated data, train a deep learning model for the hardware being analyzed, generate predicted data based on the deep learning model, analyze the predicted data and the received point data, identify a fault in the hardware being analyzed according to the received point data and the predicted data, and produce a fault report according to the identified fault.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DARRIN D DUNN whose telephone number is (571)270-1645. The examiner can normally be reached M-Sat (10-8) PST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Robert Fennema can be reached at 571-272-2748. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/DARRIN D DUNN/Patent Examiner, Art Unit 2117
Prosecution Insights