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 fully considered and persuasive regarding the instant amendment. However, a new ground of rejection is applied addressing the oil recovery limitations described below, see also cited prior art directed to recovering and/or removing oil (e.g. as interpreted, replacing or purifying bad oil from the system). The 35 USC 101 rejection is withdrawn.
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) 41, 48, 51-52, and 58 are rejected under 35 USC 103 as being unpatentable over Bailey et al. (PG/PUB 20190264936) in view over Pal et al. (PG/PUB 20160245279) in view over Wang (PG/PUB 20200141619).
Claim 41.
Bailey et al. teaches a controller for predicting faults in a heating, ventilation, or air conditioning (HVAC) system, but does not expressly teach the oil deficiency fault condition and corrective actions described below. Pal teaches the oil deficiency fault condition and Wang teaches the corrective actions described below
the controller comprising a processing circuit configured to:
analyze operating data for the HVAC system using a machine learning model to predict a fault classification for the HVAC system, the fault classification identifying an oil deficiency fault condition affecting the HVAC system (Bailey, 0042, 0150-0151, 0156-0165, 0215, Figure 2 e.g. see classifying faults types based on HVAC time series data using machine learning, see Pal et al for oil deficiency fault detection based on machine learning (ABSTRACT, e.g. “The blower sensor data in association with the motor sensor data is analyzed based on machine learning to detect one of a deficient oil level and a deficient oil structure,” see also severity classification, as “bad and/or low oil level…clean, old, leaked, overfilled classes, 0008, 0051-52)
identify a HVAC device of the HVAC system associated with the oil deficiency fault condition ((0042, 0150-0151, 0156-0165, 0215, Figure 2 e.g. see classifying faults types based on HVAC time series data using machine learning, Pal e.g. see associated system for oil deficiency fault condition, ABSTRACT, Figure 1)
One of ordinary skill in the art before the effective filing date of the claimed invention applying the teachings of Pal, namely employing machine learning to identify deficient oil levels for machines, to the teachings of Bailey, namely employing machine learning to classify HVAC fault conditions, would achieve an expected and predictable result of employing machine learning to identify deficient oil levels of HVAC systems based on applying the teachings of Pal. Pal is reasonably pertinent to a problem of using machine learning to classify abnormal oil including but not limited to oil levels and would commend itself for automatically determining when HVAC oils are deficient for purposes of maintenance as described, ABSTRACT.
Bailey, as modified by Pal, does not teach the automatic corrective actions described below. Wang teaches the automatic corrective actions descried below
automatically initiate a corrective action by remedying the fault condition responsive to identifying the HVAC device and the fault condition (Figure 2-212-218, 0032-33, 0043, 0075-76, 0202-0212 e.g. see implementing remedial actions in response to failure types, including alerting users, requesting engineers, and/or disabling HVAC components), the corrective action comprising at least one of removing oil from the HVAC system, or operating the HVAC system to return oil to the HVAC device associated with the oil deficiency fault condition from one or more other devices of the HVAC system (Wang, ABSTRACT, 0008-0012, claim 1 e.g. see providing oil from a tank (e.g. other devices) to an HVAC component having insufficient oil), see also Pal for applying machine learning to determine low oil levels, 0008-0009, 0032, 0041, 0065)
One of ordinary skill in the art before the effective filing date of the claimed invention applying the teachings of Wang, namely returning to a component from at least a storage tank, to the teachings of Bailey, as modified by Pal, namely automatically determining low oil levels for an HVAC system, would achieve an expected and predictable result if refilling oil levels responsive to deficient oil levels. Wang is reasonably pertinent to a problem of implementing corrective actions for improving machine health and would commend itself to ensuring the HVAC system has sufficient oil levels, as described, ABSTRACT, summary of invention.
One of ordinary skill in the art before the effective filing date of the claimed invention apply the teachings of Bailey, namely quantifying the severity levels of HVAC faults (e.g. critical vs. non-critical), to the teachings of Pal, namely classifying oil levels such as bad, good, overfilled, would achieve an expected and predictable result of quantifying the oil levels of Pal as either one of critical or non-critical. One of ordinary skill in the art would be motivated to quantify the degree of influence each oil level has on machine operation and for determining maintenance.
Claim 48.
Bailey teaches the controller of claim 41, wherein the fault classification identifies a plurality of fault conditions affecting the HVAC system, the plurality of fault conditions associated with a plurality of HVAC devices of the HVAC system (0031-33, 0035, 0150 e.g. see performance conditions)
Claim 51.
Bailey teaches a method for predicting faults in a heating, ventilation, or air conditioning (HVAC) system, the method comprising:
analyzing operating data for the HVAC system using a machine learning model to predict a fault classification for the HVAC system, the fault classification identifying a oil deficiency fault condition affecting the HVAC system, supra claim 41
identifying a HVAC device of the HVAC system associated with the oil deficiency fault condition, supra claim 41, and
automatically initiating a corrective action to address the fault condition responsive to identifying the HVAC device and the fault condition, the corrective action comprising at least one of adding oil to the HVAC system, removing oil from the HVAC system, or operating the HVAC system to return oil to the HVAC device associated with the oil deficiency fault condition from one or more other devices of the HVAC system (supra claim 41, see also Pal for applying machine learning to determine low oil levels, 0008-0009, 0032, 0041, 0065) supra claim 41,
Claim 58.
Bailey teaches the method of claim 51, wherein the fault classification identifies a plurality of fault conditions affecting the HVAC system, the plurality of fault conditions associated with a plurality of HVAC devices of the HVAC system, supra claim 48
Claim 42 and 52 are rejected under 35 USC 103 as being unpatentable over Bailey et al. (PG/PUB 20190264936) in view over Pal et al. (PG/PUB 20160245279) in view over Wang (PG/PUB 20200141619) in view over Weber (USPN 5099437)
Claim 42.
Bailey teaches the controller of claim 41 but does not expressly teach the corrective action is based on the fault condition and severity level described below. Weber teaches a corrective action is based on the fault condition and severity described below
the oil deficiency fault classification includes a severity metric associated with the fault condition, the severity metric indicating a degree of influence that the fault condition has on the HVAC system (Bailey, 0165, 0169-0171, 0206-0208 e.g. see critical vs. non-critical failures as a severity level and degree of influence, supra claim 1)
the corrective action is determined based on both the fault condition (e.g. low oil level) and the severity metric (e.g. critical) associated with the oil deficiency fault condition (Bailey Figure 2-212-218, 0032-33, 0043, 0075-76, 0202-0212 e.g. see implementing remedial actions in response to failure types, including alerting users, requesting engineers, and/or disabling HVAC components, supra claim 1, see Weber for the corrective action determined of type and severity, ABSTRACT, Col 1 lines 35-40, Col 2 lines 37-45, col 6 lines 35-67)
One of ordinary skill in the art before the effective filing date of the claimed invention applying the teachings of Weber, namely determining a corrective action based on severity and fault type, to the teachings of Bailey, as modified, namely using machine learning to determine the severity of an oil fault of a HVAC, would achieve an expected and predictable result of automatically scheduling maintenance or performing maintenance based on oil fault severity. Weber is pertinent to remedying faults and would commend itself to prioritizing corrective actions based on severity as described.
Claim 52.
Bailey teaches the method of claim 51, wherein:
the fault classification includes a severity metric associated with the oil deficiency fault condition, the severity metric indicating a degree of influence that the oil deficiency fault condition has on the HVAC system, supra claim 41; and
the corrective action is determined based on both the oil deficiency fault condition and the severity metric associated with the fault condition, supra claim 41
Claim(s) 43-44 and 53-54 are rejected under 35 U.S.C. 103 as being unpatentable over Bailey et al. (PG/PUB 20190264936) Pal et al. (PG/PUB 20160245279) in view over Wang (PG/PUB 20200141619) in view over Weber (USPN 5099437) in view over Bieda et al. (PG/PUB 20030171897).
Claim 43.
Bailey teaches the controller of claim 42 but does not expressly teach the above and below severity thresholds described below. Bieda teaches the above/ below severity levels described below
wherein the processing circuit is configured to:
automatically initiate a first corrective action in response to a value of the severity metric being below a severity threshold (Bieda, 0021-22, 0059, 0070-71 e.g. see comparing severity/risk to a threshold level , and see Bailey as determining an alert, message, or engineer notification as a corrective action based on severity level, Figure 2-212-218, 0032-33, 0043, 0075-76, 0202-0212 e.g. see implementing remedial actions in response to failure types, including alerting users, requesting engineers, and/or disabling HVAC components
automatically initiate a second corrective action in response to the value of the severity metric being above the severity threshold (Bieda, 0021-22, 0059 e.g. see comparing severity/risk to a threshold level, and if exceeded, implement immediate action, and see corrective action of Bailey as disabling the HVAC system based on severity level, Figure 2-212-218, 0032-33, 0043, 0075-76, 0202-0212 e.g. see implementing remedial actions in response to failure types, including alerting users, requesting engineers, and/or disabling HVAC components)
One of ordinary skill in the art before the effective filing date of the claimed invention applying the teachings of Bieda, namely implementing first and second corrective actions in response to comparing severity to a threshold, to the teachings of Bailey, namely implementing first and second corrective actions in response to a severity level, would achieve an expected and predictable result of employing thresholds to quantify a severity level for determining a level of corrective action.
One of ordinary skill in the art given corrective actions comprising user alerts, notifications, automated maintenance, and disabling, in light of the fault severity, would be motivated to first provide a user notification for smaller severity faults while notifying engineers in response to high severity faults given the finite and quantifiable faults types and corrective actions. Bieda is reasonably pertinent to a problem of addressing failures using thresholds and would commend itself to the teachings of Bailey for determining when to take particular corrective actions.
Claim 44.
Bailey, as modified by Bieda, teaches the controller of claim 43, wherein:
the first corrective action comprises providing a notification to a user device, supra claim 43 for user notification; and
the second corrective action comprises scheduling maintenance for the HVAC system or replacement of the HVAC device associated with the oil deficiency fault condition, supra claim 43 for engineer maintenance)
Claim 53.
Bailey, as modified, supra claim 43, teaches the method of claim 52, comprising:
automatically initiating a first corrective action in response to a value of the severity metric being below a severity threshold, supra claim 43, ; and
automatically initiating a second corrective action in response to the value of the severity metric being above the severity threshold, supra claim 43
Claim 53 is rejected under the same rationale and prior art set forth in claim 43
Claim 54.
Bailey, as modified, teaches the method of claim 53, wherein:
the first corrective action comprises providing a notification to a user device, supra claim 43, and
the second corrective action comprises scheduling maintenance for the HVAC system or replacement of the HVAC device associated with the oil deficiency fault condition, supra claim 42
Claim 54 is rejected under the same rationale and prior art set forth in claim 43
Claims 45 and 55 are rejected under 35 U.S.C. 103 as being unpatentable over Bailey et al. (PG/PUB 20190264936) Pal et al. (PG/PUB 20160245279) in view over Wang (PG/PUB 20200141619) in view over Weber (USPN 5099437) in view over Bisht et al. (PG/PUB 20230015709)
Claim 45.
Bailey teaches the controller of claim 42 but does not expressly teach taking no action described below. Bisht et al. teaches taking no action described below
wherein the corrective action comprises taking no action in response to a value of the severity metric being below a severity threshold (0037 e.g. see no corrective action is needed based on a threshold comparison, supra claim 42 for severity level)
One of ordinary skill in the art before the effective filing date of the claimed invention applying the teachings of Bisht, namely determining no action is needed when a severity is beneath a threshold, to the teachings of Bailey, namely performing corrective actions based on severity levels, would achieve an expected and predictable result of not implementing a corrective action a severity level is less than a threshold. Bisht is reasonably pertinent to a problem of selecting corrective actions and would commend itself to the teachings of Bailey for selecting different types of corrective actions based on severity.
Claim 55.
Bailey, as modified, supra claim 45, teaches the method of claim 52, wherein the corrective action comprises taking no action in response to a value of the severity metric being below a severity threshold, supra claim 45 reasoning and applied prior art,
Claims 46 and 56 are rejected under 35 U.S.C. 103 as being unpatentable over Bailey et al. (PG/PUB 20190264936) Pal et al. (PG/PUB 20160245279) in view over Wang (PG/PUB 20200141619) in view over Sun et al. (PG/PUB 20200241514).
Claim 46.
Bailey et al. teaches the controller of claim 41 but does not teach the RNN limitations described below. Sun teaches the RNN limitations described below, wherein
the machine learning model is a recurrent neural network (RNN) model
analyzing the operating data comprises providing a time series of values of the operating data as an input to the RNN model and obtaining a prediction of the fault classification as an output of the RNN model (Sun ,0011, 0029, 0044, 0079 e.g. see classifying component health based on the application of RNN)
One of ordinary skill in the art before the effective filing date of the claimed invention applying the teachings of Sun, namely predicting fault classification for system performance using RNN, to the teachings of Bailey namely predicting fault classes for HVAC components, would achieve an expected and predictable result via combining said elements using known methods. Sun is pertinent to a problem of fault classification and would commend itself to the fault classification of Bailey with a benefit of reducing false positive, as described, 0005.
Claim 56.
Bailey, as modified, teaches the method of claim 51, wherein
the machine learning model is a recurrent neural network (RNN) model, supra claim 46; and
analyzing the operating data comprises providing a time series of values of the operating data as an input to the RNN model and obtaining a prediction of the fault classification as an output of the RNN model, supra claim 46.
Claims 47 and 57 are rejected under 35 U.S.C. 103 as being unpatentable over Bailey et al. (PG/PUB 20190264936) Pal et al. (PG/PUB 20160245279) in view over Wang (PG/PUB 20200141619) in view over HE (PG/PUB 20200387785)
Claim 47.
Bailey teaches the controller of claim 41 but does not teach simulated training data described below. HE teaches simulated training data described below
the processing circuit further configured to generate the machine learning model using a set of simulated training data obtained from a simulation model of the HVAC system (0065 e.g. see training using real data after training using simulated data)
One of ordinary skill in the art before the effective filing date of the claimed invention applying the teachings of He, namely using simulated data to train a neural network, to the teachings of Bailey namely predicting fault classes for HVAC components, would achieve an expected and predictable result of training a neural network using simulated data and fine tuned using real world data as described in HE. HE is reasonably pertinent to training a neural network for fault detection as described.
Claim 57.
Bailey, as modified, teaches the method of claim 51, comprising generating the machine learning model using a set of simulated training data obtained from a simulation model of the HVAC system, supra claim 47
Claims 49 and 59 are rejected under 35 U.S.C. 103 as being unpatentable over Bailey et al. (PG/PUB 20190264936) Pal et al. (PG/PUB 20160245279) in view over Wang (PG/PUB 20200141619) in view over Umehara (USPN 4940965)
Claim 49
Bailey teaches the controller of claim 41 but does not expressly teach the fault conditions (oil deficiency fault) described below. Umehara teaches one of the oil deficiency fault conditions described below.
wherein the oil deficiency fault condition comprises at least one of:
leakage of a refrigerant
frosting of an outdoor unit
clogging of an indoor fan;
clogging of an indoor filter (Col 1 lines 45-46, Col 7 lines 56-67 e.g. see association between oil levels and clogged oil filters)
clogging of a heat exchanger;
clogging of an outdoor fan;
demagnetization of a motor;
One of ordinary skill in the art before the effective filing date of the claimed invention applying the teachings of Umehara, namely determining oil deficiency fault conditions due to clogged indoor filter (e.g. indoor refers to an internal location), to the teachings of Bailey, namely applying machine learning for determining oil deficiency fault conditions, would achieve an expected and predictable result of identifying at least an oil deficiency due to a clogged indoor filter of an HVAC system. Umehara is reasonably pertinent to correlating oil deficiencies to a clogged indoor filter and would commend itself for classifying the type of oil deficiency condition as described, ABSTRACT, summary of invention.
Claim 59.
Bailey, as modified, supra claim 49, teaches the method of claim 51, wherein the fault condition comprises at least one of:
leakage of a refrigerant;
frosting of an outdoor unit
clogging of an indoor fan;
clogging of an indoor filter; supra claim 49
clogging of a heat exchanger;
clogging of an outdoor fan;
demagnetization of a motor; or
Claims 50 and 60 are rejected under 35 U.S.C. 103 as being unpatentable over Bailey et al. (PG/PUB 20190264936) Pal et al. (PG/PUB 20160245279) in view over Wang (PG/PUB 20200141619) in view over Chen (PG/PUB 20220296930)
Claim 50.
Bailey teaches the controller of claim 41 but does not expressly teach a second machine learning model to predict the severity described below. Chen teaches a second machine learning model to predict the severity described below
wherein the machine learning model is a first machine learning model and the processing circuit is configured to:
use the first machine learning model to predict the fault classification for the HVAC system (Bailey, supra claim 41); and
use a second machine learning model to predict a severity of the fault condition identified by the fault classification (Chen, ABSTRACT, 0011, 0023, 0137 e.g. see second model for determining severity levels based on fault types, see “In some examples, the DL model may be trained using the process 700A to classify a DLG fault into one of a plurality of fault severity levels. The fault severity can be based on a trend of a DLG metric. For example, a fault is identified as a “severe” fault if the DLG metric value exceeds a specific threshold, or as a “slight” fault if the DLG metric value is below said specific threshold. “)
One of ordinary skill in the art before the effective filing date of the claimed invention applying the teachings of Chen, namely determining a severity of fault type using a second machine learning model, to the teachings of Bailey, namely determining fault types using a first learning model, would achieve an expected and predictable result of employing multiple learning models for faulty classification and fault severity. Chen is reasonably pertinent to fault classification as described, ABSTRACT, summary of invention.
Claim 60.
Bailey, as modified, supra claim 50, teaches the method of claim 51, wherein the machine learning model is a first machine learning model and the method comprises:
using the first machine learning model to predict the fault classification for the HVAC system, supra claim 50. and
using a second machine learning model to predict a severity of the oil deficiency fault condition identified by the fault classification, supra claim 50.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Claim 41 relevancy
20220187815 20190264936 20080033674 11739964 20220342411 20210381861 20210096555 20200379454 20200240662 20160370799 20160203036 20120072029 20080033674 20220296930 20030171897 – risk and actions threshold. 20220004182 2003005579
Claim 45 relevancy
20210302275 20130304239 20050210337 20120173299 9223644
Claim 46 relevancy
20030055798 20220004182
Claim 49 relevancy
20160370026
Claim 50 relevancy
16440654 11494295 20220296930 6853920 6442511 20200103894
Corrective Actions
20200272139 20210271237 20200167736
20170045052 -0067 20140130539 20130308674-0157
See automatically applying oil/lubrication
6123174 20180306616
claim 1 oil return
*5415003 6604371 20220120727 11280527 20200141619 5970942 5749339
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/DARRIN D DUNN/ Patent Examiner, Art Unit 2117