DETAILED ACTION
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 .
Information Disclosure Statement
The information disclosure statements (IDSs) were submitted on 7/26/2023. The submissions are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
The current 35 USC 101 analysis is based on the current guidance (Federal Register vol. 79, No. 241. pp. 74618-74633). The analysis follows several steps. Step 1 determines whether the claim belongs to a valid statutory class. Step 2A prong 1 identifies whether an abstract idea is claimed. Step 2A prong 2 determines whether any abstract idea is integrated into a practical application. If the abstract idea is integrated into a practical application the claim is patent eligible under 35 USC 101. Last, step 2B determines whether the claims contain something significantly more than the abstract idea. In most cases the existence of a practical application predicates the existence of an additional element that is significantly more.
The 35 USC 101 analysis between each element of claims and its combination is presented in the table below
Claim number and elements
Judicial exception (Step 2A Prong one)
Practical application (Step 2A Prong two)/ Significantly more (Step 2B)
Claim 1
Step 1: Yes, statutory class
Step 2A Prong two: No / Step 2B: No
A method for affecting operation of building equipment, comprising:
providing a plurality of reliability models that model failure probabilities of components of the building equipment as functions of equipment runtime;
Step2A Prong one: Yes
abstract idea
mathematical concept or mental process
“providing a plurality of reliability models ~” is
insignificant extra-solution activity to execute the models to collect routine data (i.e., the reliability models for the components), which is performed by a generic computer functions/programs of a generic computer.
providing associations of the components with a plurality of subsystems of the building equipment;
abstract idea
mathematical concept or mental process
“providing associations of the components ~” is insignificant extra-solution activity to collect routine data (i.e., the associations) to thereby perform a mathematical calculation.
calculating, for the plurality of subsystems of the building equipment, probabilities of subsystem failure based on the reliability models for the components and the associations; and
abstract idea
mathematical concept or mental process
“calculating, for the plurality of subsystems of the building equipment, probabilities of subsystem failure ~” is a math process based on the collected routine data.
initiating an automated action to affect operation of the building equipment based on the probabilities of subsystem failure.
abstract idea
mathematical concept
“initiating an automated action …” is an insignificant post-solution activity.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 1-20 are directed to an abstract idea. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception as addressed below and presented in the above table.
Step 2A: Prong One
Regarding Claim 1, the limitations recited in Claim 1, as drafted, are processes that, under its broadest reasonable interpretation, cover performance of the limitation in the mathematical calculations and/or the mind, as presented in the above table. Nothing in the claim elements precludes the step from practically being performed in the mind and/or the mathematical calculations. For example, “calculating, for the plurality of subsystems of the building equipment, probabilities of subsystem failure based on the reliability models for the components and the associations” in the context of this claim may encompass manually calculating or inferring the probabilities of subsystem failure based on the collected data (i.e., the reliability models for the components and the associations) (see at least paragraphs 0102, 0148-0151). (MPEP 2106.04(a)(2)).
Step 2A: Prong Two
This judicial exception is abstract ideal itself and not integrated into a practical application. In particular, the specification details use of a computer processor to perform mathematical calculations or mental processes of “calculating, for the plurality of subsystems of the building equipment, probabilities of subsystem failure based on the reliability models for the components and the associations”. The limitations of “providing a plurality of reliability models that model failure probabilities of components of the building equipment as functions of equipment runtime” and “providing associations of the components with a plurality of subsystems of the building equipment” are insignificant extra-solution activities necessary to merely collect routine data (the reliability models for the components), where the reliability models are indicative of mathematical algorithms to perform mathematical calculations and the associations of the components are indicative of mathematical concepts/relationship/values. See MPEP 2106.05(g). The limitation of “initiating an automated action to affect operation of the building equipment based on the probabilities of subsystem failure” is an insignificant post-solution activity necessary to merely perform a generic computer function of a generic computer component. See MPEP 2106.05(g). Claim 1 does not present tangible or physical elements/components and/or integration of improvements to be indicative of specific features/structure/acts how and or with what to provide the reliability models for the components and the associations of the components as well as initiate the automated action. (See MPEP 2106.04(d)). Claim 1 does not present a technical solution to a technical problem by providing an improvement to the functioning of computer, or to any other technology or technical field related to provide the reliability models for the components and the associations of the components as well as initiate the automated action. (See MPEP 2106.04(d)). Therefore, there is no showing of integration into a practical application such as an improvement to the functioning of a computer, or to any other technology or technical field, or use of a particular machine.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The limitations of “providing a plurality of reliability models that model failure probabilities of components of the building equipment as functions of equipment runtime” and “providing associations of the components with a plurality of subsystems of the building equipment” are insignificant extra-solution activities necessary to merely collect routine data (the reliability models for the components), where the reliability models are indicative of mathematical algorithms to perform mathematical calculations and the associations of the components are indicative of mathematical concepts/relationship/values. See MPEP 2106.05(g). The limitation of “initiating an automated action to affect operation of the building equipment based on the probabilities of subsystem failure” is an insignificant post-solution activity necessary to merely perform a generic computer function of a generic computer component. See MPEP 2106.05(g). As discussed above, with respect to integration of the abstract idea into a practical application, using a computer system to perform “providing a plurality of reliability models that model failure probabilities of components of the building equipment as functions of equipment runtime”, “providing associations of the components with a plurality of subsystems of the building equipment”, “calculating, for the plurality of subsystems of the building equipment, probabilities of subsystem failure based on the reliability models for the components and the associations” and “initiating an automated action to affect operation of the building equipment based on the probabilities of subsystem failure” amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept cannot provide statutory eligibility. Claim 1 is not patent eligible.
Regarding Claims 2-14, the limitations are further directed to an abstract idea, as described in claim 1. The limitations of “generating a first recommendation based on the probabilities of subsystem failure; and generating a second recommendation based on age of the building equipment; wherein the automated action is initiated based on both the first recommendation and the second recommendation” in Claim 2 may encompass manually calculating or inferring the first and second recommendations based on the mathematical calculation (i.e., the probabilities of subsystem failure), where “the automated action is initiated …” is insignificant extra-solution activity to be performed by a generic computer function of a generic computer. (MPEP 2106.04(a)(2)).
The limitation of “identifying parameter values for the plurality of reliability models based on warranty data indicating at least one of historical instances of component or subsystem failure” in Claim 3 may encompass manually calculating or inferring the parameter values for the plurality of reliability models. (MPEP 2106.04(a)(2)). The limitations of calculating a risk by multiplying at least one of the probabilities of subsystem failure by a cost of subsystem failure” in Claim 4 may encompass manually calculating or inferring the risk using the mathematical calculation. (MPEP 2106.04(a)(2)). The limitations of “initiating the automated action is performed in response to the risk exceeding a threshold” and “initiating the automated action is performed …” in Claims 5 and 6 are insignificant post-solution activities necessary to merely perform a generic computer function of a generic computer component. See MPEP 2106.05(g). The limitations of “estimating the cost of subsystem failure based on historical data” in Claim 7, “identifying a subset of the subsystems for which the probabilities of subsystem failure exceed a threshold; wherein the automated action comprises causing performance of maintenance on the subset of the subsystems” in Claim 10, “repeatedly updating the probabilities of failure over time based on runtime data provided by the building equipment” in Claim 11 may encompass manually calculating or inferring the cost of subsystem failure, the subset of the subsystems and the probabilities of failure based on the mathematical calculations. (MPEP 2106.04(a)(2)). The limitations of “structuring a service offering for the building equipment based on the probabilities of subsystem failure” in Claim 8 and “… allocating resources to the service offering based on the probabilities of system failure” in Claim 9 may encompass manually calculating or inferring the service offering to allocate the resources based on the mathematical calculations. The limitation related to “the automated action is predicted to reduce …” in Claim 12 may encompass manually calculating or inferring the prediction of the automated action based on the mathematical calculation. The Weibull models in Claim 14 are indicative of mathematical algorithms (see paragraphs 0100).
Regarding Claim 15, it is a non-transitory computer-readable media claim having similar limitations as of claim 2 above. Therefore, it is rejected under the same rationale as of claim 2 above. The additional limitation of “providing a combined output based on the first service recommendation and the second service recommendation” is an insignificant post-solution activity necessary to output the calculated or inferred result which is performed by a generic computer function of a generic computer component. See MPEP 2106.05(g).
Regarding Claims 16-17, the limitations are further directed to an abstract idea, as described in claim 1-2 and 15. The limitation of “causing performance of an action influencing operation of the subsystem” in Claim 16 is an insignificant post-solution activity necessary to perform the action based on the calculated or inferred result which is performed by a generic computer function of a generic computer component. See MPEP 2106.05(g). Claim 17 is dependent on Claim 15 and has similar limitation as of claim 1 above. Therefore, it is rejected under the same rationale as of claim 1 above. For the reasons described above with respect to Claims 15-17, the judicial exceptions are not meaningfully integrated into a practical application, or amount to significantly more than the abstract idea.
Regarding Claim 18, it is a system type claim having similar limitations as of claim 1 above. Therefore, it is rejected under the same rationale as of claim 1 above. The additional elements of the memory and the processor are merely recited at a high-level of generality to perform a generic computer function. Mere nominal recitation of a generic computer system or component does not take the claim out of the mathematical concepts and the mental process grouping. Thus, the claim recites an abstract idea.
Regarding Claims 19-20, the limitations are further directed to an abstract idea, as described in claims 2, 4 and 5. Therefore, the claims are rejected under the same rationale as of claims 2, 4 and 5 above.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-13 and 15-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Amores et al. (US 20210191378 A1, hereinafter referred to as “Amores”).
Regarding Claim 1, Amores teaches a method for affecting operation of building equipment (Abstract, “a method of generating a fault determination in a building management system”), comprising:
providing a plurality of reliability models (the number of fault detection models) that model failure probabilities (fault score) of components of the building equipment as functions of equipment runtime (At least paragraphs 0001-0004 and 0101 teach control, monitor, and manage equipment in or around a building or building area by using/providing the fault score which indicates the probability that a parameter of the building management system (BMS) has a specific value by using the fault detection models, “generate a probability score (e.g., a probability that the specific fault property”);
providing associations of the components with a plurality of subsystems of the building equipment (At least paragraph 0004 teaches generating/providing components associated data in a building management (BMS), “generating, by the first subset of fault detection models, a first fault indication of the number of fault indications using a first subset of the signal data associated with a first component of the BMS and generating, by the second subset of fault detection models, a second fault indication of the number of fault indications using a second subset of the signal data associated with a second component of the BMS.”);
calculating, for the plurality of subsystems of the building equipment, probabilities (fault score) of subsystem failure based on the reliability models for the components and the associations (At least paragraphs 0003-0004 teach generating/calculating fault score of components, which indicative of probabilities in a building management (BMS), “generating, using a number of fault detection models, a number of fault indications based on the signal data, generating, using a weighting function, based on the number of fault indications, a fault score, … the fault score indicates a level of confidence that the fault exists. In some embodiments, the fault score indicates the probability that a parameter of the building management system (BMS) has a specific value.”); and
initiating an automated action to affect operation of the building equipment based on the probabilities of subsystem failure (At least paragraphs 0004, 0056 -0057 teach determining control actions and generating control signals to building subsystem to thereby optimize building performance , “in response to determining the existence of the fault, sending an indication of the fault to a building management (BMS) operator … optimize building performance (e.g., efficiency, energy use, comfort, or safety) based on inputs received”).
Regarding Claim 2, Amores teaches generating a first recommendation (optimal actions and controls signals) based on the probabilities of subsystem failure (At least paragraph 0056 teaches generating optimal actions and controls signals to be sent to the building subsystem, “determine optimal control actions for building subsystems 428 based on the inputs, generate control signals based on the optimal control actions, and provide the generated control signals to building subsystems 428”); and
generating a second recommendation based on age of the building equipment (an estimation of remaining system lifetime) (At least paragraphs 0056 and 0100 teaches generating optimal actions and controls signals to be sent to the building subsystem considering an estimation of remaining system lifetime, “determine optimal control actions for building subsystems 428 based on the inputs, generate control signals based on the optimal control actions, and provide the generated control signals to building subsystems 428 … generate a specific fault property. For example, fault properties may include a time-of-onset of a fault, a cause of a fault, a fault severity, a risk of system shutdown, an estimation of remaining system lifetime.”);
wherein the automated action is initiated based on both the first recommendation and the second recommendation. (At least paragraphs 0004, 0056 -0057 and 0100 teaches determining control actions and generating control signals to building subsystem to thereby optimize building performance, “in response to determining the existence of the fault, sending an indication of the fault to a building management (BMS) operator … optimize building performance (e.g., efficiency, energy use, comfort, or safety) based on inputs received”).
Regarding Claim 3, Amores teaches identifying parameter values for the plurality of reliability models based on warranty data indicating at least one of historical instances of component or subsystem failure (At least paragraph 0004, 0007-0008 teaches generating/identifying detection models each having specific parameter value considering historical data elements in BMS, “the fault score indicates the probability that a parameter of the building management system (BMS) has a specific value … the number of fault detection models is associated with detecting a second type of fault, wherein generating the number of fault indications comprises generating, by the first subset of fault detection models, a first fault indication of the number of fault indications using a first subset of the signal data associated with a first component and generating, by the second subset of fault detection models, a second fault indication of the number of fault indications using a second subset of the signal data associated with a second component … the probability that a parameter associated with a system including the first and second components has a specific value”; para 0109, “generate an FDD model 622 where h is the number of historical data elements”).
Regarding Claim 4, Amores teaches calculating a risk by multiplying at least one of the probabilities of subsystem failure by a cost of subsystem failure (At least paragraph 0004, 0007-0008, 0056-0057, 0059 and 0061 teach generating a risk of system shutdown and detection models each having specific parameter value consider a cost and energy efficiency, “generate a specific fault property. For example, fault properties may include a time-of-onset of a fault, a cause of a fault, a fault severity, a risk of system shutdown, an estimation of remaining system lifetime, and/or any other system property. … may determine that a fault exists and further determine an individual estimate of a time-of-onset of the fault.”; Para 0056-0057, “optimize building performance (e.g., efficiency, energy use, comfort, or safety) based on inputs received”; Para 0059, 0061, “optimize resource usage (e.g., electricity use, natural gas use, water use, etc.) and/or the monetary cost of such resource usage in response to satisfy the demand of building 10. The optimization can be based on time-of-use prices, curtailment signals, energy availability, …. a control module configured to actively initiate control actions (e.g., automatically changing setpoints) which minimize energy costs based on one or more inputs representative of or based on demand (e.g., price, a curtailment signal, a demand level, etc.)”).
Regarding Claim 5, Amores teaches initiating the automated action is performed in response to the risk exceeding a threshold (At least paragraphs 0096, 0102, 0105 and 0109, teach determining if the fault score (i.e., risk) exceeds a threshold to thereby determine sending control actions; Para 0096, “threshold circuit 628 determines a fault exists (e.g., there is a fault, etc.) if the fault score φ exceeds the threshold T”; Para 0105, “FDD circuit 620 may be configured to detect a fault if either the temperature measurements or the energy consumption measurements exceed a threshold. The thresholds may be associated with a state of the monitored system. For example, a first set of thresholds may be associated with an occupied state of the monitored room and a second set of thresholds may be associated with an unoccupied state of the monitored room”; Para 0109“the first prediction based FDD model 622 may generate a fault indication if the result exceeds a threshold. As a further example, a second deep neural network FDD model 622 may generate a model based on the input vector x and/or historical input data.”).
Regarding Claim 6, Amores teaches initiating the automated action is performed in response to the risk exceeding an expected cost of mitigating the risk (At least paragraph 0004, 0007-0008 and 0056-0056, 0059, 0061, 0096, 0102, 0105 and 0109 teach determining if the fault score (i.e., risk) exceeds a threshold to thereby determine sending control actions considering a cost and energy efficiency, “generate a specific fault property. For example, fault properties may include a time-of-onset of a fault, a cause of a fault, a fault severity, a risk of system shutdown, an estimation of remaining system lifetime, and/or any other system property. … may determine that a fault exists and further determine an individual estimate of a time-of-onset of the fault.”; Para 0056-0057, “optimize building performance (e.g., efficiency, energy use, comfort, or safety) based on inputs received”; Para 0059, 0061, “optimize resource usage (e.g., electricity use, natural gas use, water use, etc.) and/or the monetary cost of such resource usage in response to satisfy the demand of building 10. The optimization can be based on time-of-use prices, curtailment signals, energy availability, …. a control module configured to actively initiate control actions (e.g., automatically changing setpoints) which minimize energy costs based on one or more inputs representative of or based on demand (e.g., price, a curtailment signal, a demand level, etc.)”).
Regarding Claim 6, Amores teaches initiating the automated action is performed in response to the risk exceeding an expected cost of mitigating the risk (At least paragraph 0004, 0007-0008 and 0056-0056, 0059, 0061, 0096, 0102, 0105 and 0109 teach determining if the fault score (i.e., risk) exceeds a threshold to thereby determine sending control actions considering a cost and energy efficiency, “generate a specific fault property. For example, fault properties may include a time-of-onset of a fault, a cause of a fault, a fault severity, a risk of system shutdown, an estimation of remaining system lifetime, and/or any other system property. … may determine that a fault exists and further determine an individual estimate of a time-of-onset of the fault.”; Para 0056-0057, “optimize building performance (e.g., efficiency, energy use, comfort, or safety) based on inputs received”; Para 0059, 0061, “optimize resource usage (e.g., electricity use, natural gas use, water use, etc.) and/or the monetary cost of such resource usage in response to satisfy the demand of building 10. The optimization can be based on time-of-use prices, curtailment signals, energy availability, …. a control module configured to actively initiate control actions (e.g., automatically changing setpoints) which minimize energy costs based on one or more inputs representative of or based on demand (e.g., price, a curtailment signal, a demand level, etc.)”).
Regarding Claim 7, Amores teaches estimating the cost of subsystem failure based on historical data (At least paragraph 0056-0056, 0059, 0061, 0108-0109 teach optimizing monetary cost of such resource usage in response to satisfy the demand of building based on historical data; Para 0056-0057, “optimize building performance (e.g., efficiency, energy use, comfort, or safety) based on inputs received”; Para 0059, 0061, “optimize resource usage (e.g., electricity use, natural gas use, water use, etc.) and/or the monetary cost of such resource usage in response to satisfy the demand of building 10. The optimization can be based on time-of-use prices, curtailment signals, energy availability, …. a control module configured to actively initiate control actions (e.g., automatically changing setpoints) which minimize energy costs based on one or more inputs representative of or based on demand (e.g., price, a curtailment signal, a demand level, etc.)”; Para 0108-0109, “determined based on historical data … generate an FDD model 622 where h is the number of historical data elements … generate a model based on the input vector x and/or historical input data”).
Regarding Claim 8, Amores teaches structuring a service offering for the building equipment based on the probabilities of subsystem failure (Under the broadest reasonable interpretation, this limitation of “structuring a service offering” is indicative of sending control actions/signal when the system failure is determined, which is taught by Amores at least at paragraphs 0004, 0056 -0057, “in response to determining the existence of the fault, sending an indication of the fault to a building management (BMS) operator … optimize building performance (e.g., efficiency, energy use, comfort, or safety) based on inputs received”).
Regarding Claim 9, Amores teaches structuring the service offering comprises allocating resources to the service offering based on the probabilities of system failure (Under the broadest reasonable interpretation, this limitation of “allocating resources to the service offering” is indicative of sending control actions/signal when the system failure is determined, which is taught by Amores at least at paragraphs 0004, 0056 -0057 and 0083, “in response to determining the existence of the fault, sending an indication of the fault to a building management (BMS) operator … optimize building performance (e.g., efficiency, energy use, comfort, or safety) based on inputs received”; Para 0083 “conserves computing resources by reducing or eliminating elaborate model generation and thereby reducing computing resources dedicated to processing large datasets.).
Regarding Claim 10, Amores teaches identifying a subset of the subsystems for which the probabilities of subsystem failure exceed a threshold; wherein the automated action comprises causing performance of maintenance on the subset of the subsystems (At least paragraphs 0096, 0102, 0105 and 0109, teach determining if the fault score of a subset of the subsystem exceeds a threshold to thereby determine sending control actions; Para 0096, “threshold circuit 628 determines a fault exists (e.g., there is a fault, etc.) if the fault score φ exceeds the threshold T”; Para 0105, “FDD circuit 620 may be configured to detect a fault if either the temperature measurements or the energy consumption measurements exceed a threshold. The thresholds may be associated with a state of the monitored system. For example, a first set of thresholds may be associated with an occupied state of the monitored room and a second set of thresholds may be associated with an unoccupied state of the monitored room”; Para 0109“the first prediction based FDD model 622 may generate a fault indication if the result exceeds a threshold. As a further example, a second deep neural network FDD model 622 may generate a model based on the input vector x and/or historical input data.”).
Regarding Claim 11, Amores teaches repeatedly updating the probabilities of failure over time based on runtime data provided by the building equipment (At least paragraphs 0004-0005 teach timeseries data related to components which are used for generating/updating probabilities of failure (i.e., fault score); Para 0004-0005, “the signal data is timeseries data … the signal data associated with a first component of the BMS and generating, by the second subset of fault detection models, a second fault indication of the number of fault indications using a second subset of the signal data associated with a second component of the BMS …. generate, using a number of fault detection models, a number of fault indications based on the signal data, generate, using a weighting function, based on the number of fault indications, a fault score, compare the fault score to a fault value, and determine, based on the comparison, an existence of a fault.”).
Regarding Claim 12, Amores teaches wherein the automated action is predicted to reduce the failure probabilities of components of the building equipment or the probabilities of subsystem failure before occurrence of a failure (At least paragraphs 0082 teach reducing fault probabilities to thereby optimize building performance; Para 0082-0083, “use multiple parameter values and/or multiple models in parallel to significantly improve the robustness of fault determinations and reduce the sensitivity to individual parameter values …. reduces the number of false positives that mask legitimate fault determinations”; Para 0057, “optimize building performance (e.g., efficiency, energy use, comfort, or safety) based on inputs received”).
Regarding Claim 13, Amores teaches wherein the automated action comprises a control adjustment for the building equipment predicted to delay occurrence of the subsystem failure (At least paragraphs 0003-0005 teach controlling operation to be allowed for adjustment to BMS controller 366 and/or subsystems; Para 0051, “allowing user control, monitoring, and adjustment to BMS controller 366 and/or subsystems 428”).
Regarding Claim 15, it is a non-transitory computer-readable media claim having similar limitations as of claim 2 above. Therefore, it is rejected under the same rationale as of claim 2 above. The additional limitation of “providing a combined output based on the first service recommendation and the second service recommendation” is taught by Amores at least at paragraph 0068 and 0088, “output a specific identification of the faulty component or cause of the fault (e.g., loose damper linkage))”.
Regarding Claim 16, Amores teaches wherein providing the combined output comprises causing performance of an action influencing operation of the subsystem (Under the broadest reasonable interpretation, this limitation of “causing performance of an action influencing operation of the subsystem” is indicative of causing control actions and generating control signals to building subsystem to thereby optimize building performance, which is taught at least paragraphs 0004, 0056 -0057 teach determining control actions and generating control signals to building subsystem to thereby optimize building performance , “in response to determining the existence of the fault, sending an indication of the fault to a building management (BMS) operator … optimize building performance (e.g., efficiency, energy use, comfort, or safety) based on inputs received”).
Regarding Claim 17, it is dependent on claim 15 and has similar limitations as of claim 1 above. Therefore, it is rejected under the same rationale as of claim 1 above.
Regarding Claim 18, it is a system type claim and has similar limitations as of claim 1 above. Therefore, it is rejected under the same rationale as of claim 1 above. The additional elements of the subsystem, the processors and the non-transitory computer readable media are taught at least Fig. 6, building subsystems 428, memory 612 and processor 610, and paragraphs 0084-0085.
Regarding Claim 19, it is a system type claim and has similar limitations as of a part of claim 2 above. Therefore, it is rejected under the same rationale as of claim 2 above.
Regarding Claim 20, it is a system type claim and has similar limitations as of claims 4 and 5 above. Therefore, it is rejected under the same rationale as of claims 4 and 5 above.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 14 is rejected under 35 U.S.C. 103 as being unpatentable over Amores in view of GARRITY et al. (US 20200084601 A1, hereinafter referred to as “US 20200084601 A1”).
Regarding Claim 14, Amores fails to explicitly disclose, GARRITY but teaches wherein the plurality of reliability models are Weibull models (Para 0051, “Analytics module 122 can alternatively use parametric models as opposed to the constant-hazard model, such as Weibull or log-normal models; a Weibull model assumes the hazard is a polynomial:.”).
Amores and GARRITY are both considered to be analogous to the claimed invention because they are in the same field of predicting failure probabilities using a Weibull model. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Amores to incorporate the teachings of GARRITY by providing Weibull models to training data to thereby predict failure probabilities, taught by GARRITY at least at paragraph 0051.
Citation of Pertinent Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ettl et al. (US 20160146493 A1) teaches “a heat transfer model of a building, and additional steps include supplying a predictive model, based on the predicted values of the parameters for the future time period, to a building heating, ventilating, and air conditioning system model predictive fault detection and diagnosis tool 1395; and troubleshooting the building heating, ventilating, and air conditioning system 1393 in accordance with the predictive model”.
Conclusion
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/BYUNG RO LEE/Examiner, Art Unit 2858
/LEE E RODAK/Supervisory Patent Examiner, Art Unit 2858