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 .
Allowable Subject Matter
Claims 42 and 52 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. The subject matter of claims 42 and 52, including the combination of limitations, further define over the application of newly cited prior art for associating oil deficiencies with predicted HVAC components degradation/corrosion in addition to the basis for applying corrective actions, the basis based on the severity metric and oil deficiency fault condition. Claims 43-44 and 53-54 dependent upon an allowable base claim.
Response to Arguments
Applicant’s arguments have been fully considered and persuasive regarding the instant amendment. However, a new ground of rejection is applied.
The applied combination of prior art teaches predicting that component characteristics such as degrading or corroding components are associated with oil deficiencies for taking remedial actions, see Tashman in view over Schieke (e.g. learning that degradation and/or corrosion are associated with oil leaks, which when applied to HVAC components (e.g. Wang, as modified) provides an inference that degraded HVAC components are likely to leak necessitating corrective action).
The limitations do not appear to describe particular steps for determining oil deficiencies or define the type of characteristics of each component. It is suggested to define over corrosion or degradation classification as well as define association because the limitation ‘associated’ provides a general relationship or causal link with the characteristics opposed to a definite determination or direct consequence of the analyzing the claimed characteristics.
As discussed below, one of ordinary skill in the art before the effective filing date of the claimed invention applying the teachings of Tashman, namely applying machine learning to predict machine conditions associated with component failure as well as classifying component degradation, to the teachings Schieke, namely associating degradation states/corrosion of components with oil leaks, to the teachings of Bailey, as modified, namely identifying applying machine learning to predict HVAC component[s] failure associated with oil deficiency, would achieve an expected and predictable result of applying machine learning to identify conditions of HVAC components leading to oil deficiency. Tashman teaches applying machine learning to generate predicted degradation states of pieces of equipment (e.g. identify, based on the characteristics of devices) while Wang, as modified, teaches the HVAC devices). Schieke further teaches predicting component states associated with oil deficiency (e.g. predicting that corrosion will leak to oil leaks (e.g. As such, a failure in the piping system may cause leakage to the atmosphere and/or exposure to plant personnel). Accordingly, one of ordinary skill in the art applying the teachings of Tashman to Schieke to Wang, as modified, would achieve an expected and predictable result of identifying HVAC components having characteristics (e.g. degradation/corrosion) associated with oil deficiency because degradation and/or corrosion compromise the integrity of HVAC components.
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, and 58 are rejected under 35 USC 1093 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 Tashman et al. (PG/PUB 20200265331).
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.
However, the combination of limitations does not expressly teach the underlined limitations described below. Tashman et al. teaches the underlined limitations described below.
a machine learning model to predict characteristics of HVAC devices (e.g. as interpreted, a characteristic is a degraded state indicative of potential leaks) of the HVAC system indicating a fault classification for the HVAC system, the fault classification identifying an oil deficiency fault condition affecting the HVAC system (Tashman et al., ABSTRACT, claim 1, 0033-34, 0039 e.g. see machine learning predicting and classifying degradation states of pieces of equipment and applying corrective actions, supra applied combination of prior art for implementing corrective actions based on predicting HVAC fault conditions of pieces of HVAC equipment indicative of oil deficiency. The Examiner takes official notice that degraded equipment states are associated/indicative of oil leaks, see Schieke, PG/PUB 20250076179, ABSTRACT, 0006)
identify, based on the characteristics of the HVAC devices (e.g. identifying degradation states or potential degradation for HVAC components/pieces), a HVAC device of the HVAC devices of the HVAC system associated with the oil deficiency fault condition (e.g. based on analyzing potential degradation states, identify the piece of equipment that is degraded requiring corrective action, see Tashman et al., ABSTRACT, claim 1, 0033-34, 0039 e.g. see machine learning predicting and classifying degradation states of equipment and applying corrective actions, see also root cause analysis for identifying supra applied combination of prior art for implementing corrective actions based on predicting HVAC fault conditions including oil leaks. The Examiner takes official notice that degraded equipment states are associated with oil leaks, see also Schieke, PG/PUB 20250076179, ABSTRACT, 0006)
One of ordinary skill in the art before the effective filing date of the claimed invention applying the teachings of Tashman, namely applying machine learning to predict machine conditions associated with component failure as well as classifying component degradation, to the teachings Schieke, namely associating degradation states/corrosion of components with oil leaks, to the teachings of Bailey, as modified, namely identifying applying machine learning to predict HVAC component[s] failure associated with oil deficiency, would achieve an expected and predictable result of applying machine learning to identify conditions of HVAC components leading to oil deficiency. Tashman teaches applying machine learning to generate predicted degradation states of pieces of equipment (e.g. identify, based on the characteristics of devices) while Wang, as modified, teaches the HVAC devices). Schieke further teaches predicting component states associated with oil deficiency (e.g. predicting that corrosion will leak to oil leaks (e.g. As such, a failure in the piping system may cause leakage to the atmosphere and/or exposure to plant personnel). Accordingly, one of ordinary skill in the art applying the teachings of Tashman to Schieke to Wang, as modified, would achieve an expected and predictable result of identifying HVAC components having characteristics (e.g. degradation/corrosion) associated with oil deficiency because degradation and/or corrosion compromise the integrity of HVAC components.
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,
However, the combination of limitations does not expressly teach the underlined limitations described below. Tashman et al. teaches the underlined limitations described below.
a machine learning model to predict characteristics of HVAC devices (e.g. as interpreted, a characteristic is a degraded state indicative of potential leaks) of the HVAC system indicating a fault classification for the HVAC system, the fault classification identifying an oil deficiency fault condition affecting the HVAC system (Tashman et al., ABSTRACT, claim 1, 0033-34, 0039 e.g. see machine learning predicting and classifying degradation states of pieces of equipment and applying corrective actions, supra applied combination of prior art for implementing corrective actions based on predicting HVAC fault conditions of pieces of HVAC equipment indicative of oil deficiency. The Examiner takes official notice that degraded equipment states are associated/indicative of oil leaks, see Schieke, PG/PUB 20250076179, ABSTRACT, 0006)
identify, based on the characteristics of the HVAC devices (e.g. identifying degradation states or potential degradation for HVAC components/pieces), a HVAC device of the HVAC devices of the HVAC system associated with the oil deficiency fault condition (e.g. based on analyzing potential degradation states, identify the piece of equipment that is degraded requiring corrective action, see Tashman et al., ABSTRACT, claim 1, 0033-34, 0039 e.g. see machine learning predicting and classifying degradation states of equipment and applying corrective actions, see also root cause analysis for identifying supra applied combination of prior art for implementing corrective actions based on predicting HVAC fault conditions including oil leaks. The Examiner takes official notice that degraded equipment states are associated with oil leaks, see also Schieke, PG/PUB 20250076179, ABSTRACT, 0006)
One of ordinary skill in the art before the effective filing date of the claimed invention applying the teachings of Tashman, namely applying machine learning to predict machine conditions associated with component failure as well as classifying component degradation, to the teachings Schieke, namely associating degradation states/corrosion of components with oil leaks, to the teachings of Bailey, as modified, namely identifying applying machine learning to predict HVAC component[s] failure associated with oil deficiency, would achieve an expected and predictable result of applying machine learning to identify conditions of HVAC components leading to oil deficiency. Tashman teaches applying machine learning to generate predicted degradation states of pieces of equipment (e.g. identify, based on the characteristics of devices) while Wang, as modified, teaches the HVAC devices). Schieke further teaches predicting component states associated with oil deficiency (e.g. predicting that corrosion will leak to oil leaks (e.g. As such, a failure in the piping system may cause leakage to the atmosphere and/or exposure to plant personnel). Accordingly, one of ordinary skill in the art applying the teachings of Tashman to Schieke to Wang, as modified, would achieve an expected and predictable result of identifying HVAC components having characteristics (e.g. degradation/corrosion) associated with oil deficiency because degradation and/or corrosion compromise the integrity of HVAC components.
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
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 Tashman et al. (PG/PUB 20200265331). 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 Tashman et al. (PG/PUB 20200265331). 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 Tashman et al. (PG/PUB 20200265331). 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 Tashman et al. (PG/PUB 20200265331). 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 1 relevancy
20230384781 , claim 1 relevancy for identifying component characteristics indicative of leaking, 0021, ABSTRACT : A system for monitoring potential failure in a machine or a component thereof, the system including: at least one optical sensor configured to be fixed on or in vicinity of the machine or the component thereof, at least one processor in communication with the sensor, the processor being executable to: receive signals from the at least one optical sensor, obtain data associated with characteristics of at least one mode of failure of the machine or the component thereof, identify at least one change in the received signals, for an identified change in the received signals, apply the at least one identified change to an algorithm configured to analyze the identified change in the received signals and to classify whether the identified change in the received signals is associated with a mode of failure of the machine or the component thereof, thereby labeling the identified change as a fault, based, at least in part, on the obtained data, and for an identified change is classified as being associated with a mode of failure, outputting a signal indicative of the identified change associated with the mode of failure.
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
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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.
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/DARRIN D DUNN/Patent Examiner, Art Unit 2117
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