DETAILED ACTION
Claims 1-26 are presented for examination. This action is made in response to the communication filed on March 16, 2026.
Claims 12-22 are rejected under 35 USC 112(a).
Claims 23-26 are rejected under 35 USC 101 as ineligible.
Claims 1-2, 6-7, 10-14, 17-18, and 21-23 are rejected under 35 USC 103 as obvious over Cao in view of Pooya, Immerman, Korn, and Schuster.
Claims 3 and 24 are rejected under 35 USC 103 as obvious over Cao in view of Pooya, Immerman, Korn, Schuster, and Crumer.
Claims 4-5, 8-9, 15-16, 19-20, and 25-26 are rejected under 35 USC 103 as obvious over Cao in view of Pooya, Immerman, Korn, Schuster, and Jeon.
Response to Arguments
35 USC 112(b): The Applicant’s arguments and amendments have been considered and are persuasive. The rejection is withdrawn in light of the amendment to remove the invalid claim language.
35 USC 101: The Applicant’s arguments and amendments have been considered. They are persuasive as to claims 1-22, but are not persuasive as to claims 23-26. The operations of the “adjust” step are so broad as to recite only the idea of a solution or outcome. That is, the claim fails to recite “a restriction on how the result is accomplished” and a “description of the mechanism for accomplishing the result” (See MPEP 2106.05(f)(1)). Also, because the recitation is so broad, the directing control activities could include doing nothing. The “in response to” language is sufficiently broad to encompass that the “operating the chiller” feature merely occurs after the predicted fault or failure. These types of notifications are apply it steps under MPEP 2106.05(f) (e.g., Brown – Cutting hair with scissors after designing the haircut) and are insignificant extra-solution activity under MPEP 2106.05(g) (e.g., printing, data storage, and data selection for analysis and display) and well-understood, routine, and conventional (WURC) activity under MPEP 2106.05(d) (e.g., transmitting data, presenting data).
The Applicant’s arguments will be treated in the order presented.
1. Step 2A, Prong 1 – The Applicant asserts that the claim includes elements that are not abstract ideas. However, the Applicant misconstrues the test at Step 2A, Prong 1. Specifically, the question is not whether the claim recites exclusively an abstract idea, but whether one has been recited at all. The Applicant has failed to allege, let alone demonstrate, that the Office Action’s characterization of specific elements of the claim as being abstract ideas.
The Applicant alleges that the newly amended adjusting steps does not recite an abstract idea. Again, at Step 2A, Prong 1, the question is whether any limitation of the claim recites an abstract idea. The rejection does not include the adjusting step as a step indicated as abstract. Accordingly, the Applicant’s argument is moot. The abstract idea is recited elsewhere in the claim. The Applicant does not dispute this. Therefore, the independent claims recite an abstract idea. The Applicant also asserts that the claims are not directed to the abstract idea under Step 2A, Prong 1. This does not make sense, as the inquiry at Step 2A, Prong 1 is whether the claim recites an abstract idea, not whether the claim is directed to the abstract idea. That is the inquiry of Step 2A, Prong 2. The Applicant is advised to research the distinction between elements of a judicial exception (e.g., an abstract idea) and elements that are additional limitations.
2. Step 2A, Prong 2
The Alleged Technological Improvement: The Applicant refers to the teachings of the specification to attempt to demonstrate a technical improvement. As discussed in the prior action, at least part of the improvement for integration of the abstract idea into the practical application must come from the additional limitations. The Applicant argues that the abstract idea provides many improvements to the accuracy of determinations. The Applicant is reminded that the determinations of the abstract idea must be integrated into the practical application by the additional limitations (e.g., in combination). The Applicant relies on the newly amended features as the additional limitations that, according to the Applicant, would integrate the abstract idea into a practical application. However, (1) the claimed actions of the adjusting step are recited at a high level such that each is very similar to the Brown case that stated cutting hair in accordance with an abstract idea of designing a haircut was an apply it step under MPEP 2106.05(f) and insignificant extra-solution activity under MPEP 2106.05(g). Again, there is no recited mechanism, as required by MPEP 2106.05(f)(1). (2) The adjust step of claim 23 is recited so broadly as to include elements that are clearly generic computing elements/operations, insignificant extra-solution activity, and well-understood, routine, and conventional activity, and are even broad enough to encompass not effectuating the asserted improvements in the Applicant’s response and specification. The mere recitation of a chiller without specifying how the abstract idea is used to operate the chiller differently is clearly insufficient.
While additional limitations can, in some circumstances, combine with the abstract idea to integrate the abstract idea into a practical application, the features of the Applicant’s claim 23 are insufficient to do so.
Step 2B: Contrary to the assertions of the Applicant, claim 23 fails to “recite a particular adjustment applied to a chiller affected by a predicted fault or failure by adjusting a setpoint of the chiller to reduce a demand” and fails to recite “ or even teach “a specific adjustment.” The Applicant’s claim is sufficiently broad to include an adjustment unrelated to the determinations of the abstract idea, which could reasonably be construed to be as broad as having the chiller send a notice that the chiller is operating and continuing to operate under the same setpoints that it would have regardless of the fault detection by the abstract idea. Further still, adjusting the settings of a chiller responsive to the determination that it is likely to fail is WURC based on the references made of record. Accordingly, claim 23 does not provide significantly more than the abstract idea that would confer an inventive concept. This renders claim 23 as ineligible. Also, the recitations of dependent claims 24-26 fail to recite any additional limitations that confer eligibility, so claims 24-26 are also ineligible.
Conclusion: The Applicant’s arguments and amendments are not persuasive. The rejections are maintained.
35 USC 103: The Applicant’s amendments have been considered and require a new rejection for the amended features.
However, the Applicant’s arguments with regard to the calculating and calibrating steps of the independent claims are not convincing. The arguments regarding the independent claims will be addressed in the order presented in the response.
(1) Korn allegedly does not teach or cannot be combined to teach the idle time determination in the claims: Korn is not being relied upon to teach the calculation described. Korn is merely used to teach a simplifying assumption that the time the equipment/chiller runs is based on a threshold temperature. The rationale for combining is that the Cao reference uses simplifying assumptions to reduce downtime and increase energy efficiency. For at least these reasons, the Applicant’s arguments that Korn fails to independently teach the idle time determination of the independent claims is nothing more than attacking the cited references individually rather than in the combination asserted in the Office Action.
(2) Immerman allegedly fails to teach “the idle time is a first subset of the runtime and comprises an amount of time the one or more building devices or building device components are operational but not running” – The Applicant attempts to attack the Immerman reference by demonstrating that the image from Immerman is from an OEE Coach reference, which states:
During the operations time, the equipment is taken out of operation (un-scheduled)
[…]
Reasons to un-schedule the equipment could be:
The product is not needed (over-capacity)
It is not allowed to run (due to Governmental regulations or contracts)
‘Force Majeur’ (catastrophe’s outside of the company)
The equipment is ‘handed over’ to another party”
The Applicant then asserts that the unscheduled time includes times when the equipment is not operational, such as when the equipment cannot be operated due to a force majeure event or catastrophe or when the equipment is handed over to a third party.
The Applicant’s analysis fails to account for:
(1) The claim’s definition of idle time is inclusive, not exclusive. Contrary to the assertion of the Applicant, the unscheduled time includes, as an example, times that the product is not needed, which includes times when the machines are capable of operating but are not operating.
(2) The reference states, “[r]easons to un-schedule the equipment COULD be:” This reference teaches that any one or any combination of the four bulleted options could be included. Accordingly, the reference teaches the un-scheduling applying exclusively to the first bullet, “the product is not needed (over-capacity),” the type of example the Applicant appears to intend to account for in the idle time determination, even if the claim language and, likely, the specification, fails to provide the exclusive language that would be necessary to limit the claim definition of “idle time” to that which the Applicant asserts in the response.
Rather than support the Applicant’s assertions in the response, the Applicant’s OEE coach reference reinforces the claim rejections. Accordingly, the Applicant’s arguments are not persuasive.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 12-23 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Specifically, Regarding independent claim 12, the Applicant relies on the Applicant’s specification [0038]-[0043], [0066]-[0067], [0070], and [0075]. While these paragraphs separately describe control activities and handling of faults, the paragraphs fail to teach the claim features, “compensating for load removed from the chiller affected by the predicted fault or failure by at least one of starting a second chiller, increasing demand load of the second chiller, or utilizing stored energy.” Accordingly, this feature is new matter.
Claims 13-22 are rejected at least based on their dependence from claim 12.
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.
Claims 23-26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to mental processes and mathematical concepts, abstract ideas, without significantly more.
Independent Claims
Claim 1 (Statutory Category – Process)
Step 2A – Prong 1: Judicial Exception Recited?
Yes, the claims recite a mental process and a mathematical concept, which are abstract ideas.
MPEP 2106.04(a)(2)(Ill): “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, Judgments, and opinions. […] The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation.”
MPEP 2106.04(a)(2)(I): “When determining whether a claim recites a mathematical concept (i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations), examiners should consider whether the claim recites a mathematical concept or merely limitations that are based on or involve a mathematical concept […] a mathematical concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). See, e.g., SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163, 127 USPQ2d 1597, 1599 (Fed. Cir. 2018) (holding that claims to a ‘‘series of mathematical calculations based on selected information’’ are directed to abstract ideas); Digitech Image Techs., LLC v. Elecs. for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014) (holding that claims to a ‘‘process of organizing information through mathematical correlations’’ are directed to an abstract idea).
MPEP 2106.04(a)(2)(I)(A): “Mathematical Relationships. A mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols.”
2106.05(I): “RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1327, 122 USPQ2d 1377 (Fed. Cir. 2017) ("Adding one abstract idea (math) to another abstract idea (encoding and decoding) does not render the claim non-abstract")”
2106.04(II)(B): “For example, in a claim that includes a series of steps that recite mental steps as well as a mathematical calculation, an examiner should identify the claim as reciting both a mental process and a mathematical concept for Step 2A Prong One to make the analysis clear on the record. However, if possible, the examiner should consider the limitations together as a single abstract idea for Step 2A Prong Two and Step 2B (if necessary) rather than as a plurality of separate abstract ideas to be analyzed individually”
Claim 1 recites (claim features in italics, abstract ideas in bold, paragraph references are to the Applicant’s specification):
1. A method for generating a reliability model, comprising: (Evaluation/Mental Process, Mathematical Calculation Concept - evaluation and mathematical equation: See equations in paragraph [0090] for calibrating and [0093] for “training” a Weibull model)
[…]
calibrating, using climate data corresponding to a location of the component and performing an operation using the runtime and the idle time to generate a calibrated runtime; wherein the runtime comprises an amount of time the one or more building devices or building device components are operational; the idle time is a first subset of the runtime and comprises an amount of time the one or more building devices or building device components are operational but not running; and the calibrated runtime is a second subset of the runtime and comprises an amount of time the one or more building devices are operational and running; (Evaluation/Mental Process, Mathematical Calculation Concept - See equations in paragraph [0090] that evaluate a calibration by mathematical subtraction, which is performable using a pen, paper, or calculator)
training, (Evaluation/Mental Process, Mathematical Calculation Concept - See evaluation of mathematical equation and mathematical relationships in paragraph [0093] and [0096])
generating a reliability metric indicating a predicted fault or failure of the one or more building devices or building device components using the trained model; (Evaluation/Mental Process, Mathematical Calculation Concept - See evaluations of equations for reliability metrics in [0094]-[0096])
The method steps of claim 1 are elements of an evaluation, a mental process, which can be performed in the mind of a person or with a pen and paper. Further, the method steps of claim 1 as described in the claim and specification include and/or are expressed as mathematical calculations or mathematical relationships, which form a mathematical concept. Being a mental process and a mathematical concept, the method preamble and steps are an abstract idea.
Claim 1 recites an abstract idea.
Step 2A – Prong 2: Integrated into a Practical Solution?
No.
MPEP 2106.04(d): “[A]fter determining that a claim recites a judicial exception in Step 2A Prong One, examiners should evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception in Step 2A Prong Two. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. Whether or not a claim integrates a judicial exception into a practical application is evaluated using the considerations set forth in subsection I below, in accordance with the procedure described below in subsection II.”
MPEP 2106.05(f) Mere Instructions To Apply An Exception: “Another consideration when determining whether a claim integrates a judicial exception into a practical application in Step 2A Prong Two or recites significantly more than a judicial exception in Step 2B is whether the additional elements amount to […] more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer, examiners should explain why they do not meaningfully limit the claim in an eligibility rejection. For example, an examiner could explain that implementing an abstract idea on a generic computer, does not integrate the abstract idea into a practical application in Step 2A Prong Two or add significantly more in Step 2B.
MPEP 2106.05(g): “Another consideration when determining whether a claim integrates the judicial exception into a practical application in Step 2A Prong Two or recites significantly more in Step 2B is whether the additional elements add more than insignificant extra-solution activity to the judicial exception. The term "extra-solution activity" can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent.”
The additional elements:
receiving, historical operating data associated with one or more building devices or building device components;
The receiving step merely gathers existing information (historical operating data) for evaluation. Mere data gathering is insignificant extra solution activity under MPEP 2106.05(g). Under Mere Data Gathering, an analogous example is provided: “iv. Obtaining information about transactions using the Internet to verify credit card transactions, CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011).” Under MPEP 2106.05(g), receiving data for evaluation is not significant in meaningfully limiting the invention, and the receiving of the data is necessary to the evaluations and mathematical operations of the claim. Under MPEP 2106.05(g). The receiving step adds nothing more than insignificant extra solution activity, so it does not integrate the abstract idea into a practical application in Step 2A Prong Two.
[Method step], by a processing circuit,
The computer implementation by a processing circuit is a recitation of a general purpose computer with no specific configurations to execute the claimed method. As such, the computer implementation implements the recited abstract idea on a generic computer, and does not integrate the abstract idea into a practical application in Step 2A Prong Two.
adjusting an operation of the one or more building devices or building device components based on the predicted fault or failure of the one or more building device or building device components, wherein adjusting the operation comprises at least one of attempting to repair the fault or work around the fault, shutting down the one or more building devices or building device components, or directing control activities around the one or more building devices or building device components.
These additional limitations are mere “apply it” steps of the abstract idea under MPEP 2106.05(f), which states, “whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished.” These actions apply the abstract idea at such a high level with no particularity as to the improvement, such that the steps represent a naked “apply it” of the abstract idea. Therefore, under MPEP 2106.05(f), the adjusting step fails to integrate the abstract idea into a practical application at Step 2A, Prong 2.
Further, the adjusting step is insignificant extra-solution activity under MPEP 2106.05(g). This is similar to the example in MPEP 2106.05(g) of insignificant application: “i. Cutting hair after first determining the hair style, In re Brown, 645 Fed. App'x 1014, 1016-1017 (Fed. Cir. 2016)” What else is one going to do with survival data of a machine other than adjust the operating of a machine in some way that improves its operation/survival. Under 2106.05(g), these additional limitations do not integrate the abstract idea into a practical application.
Also, the adjusting step merely limits the abstract to a particular technological field of maintenance control (recited at a high level), which, under MPEP 2106.05(h), fails to integrate the abstract idea into a practical application at Step 2A, Prong 2.
The additional limitations of claim 1 fail to integrate the abstract idea into a practical application at Step 2A, Prong 2.
Claim 1 is directed to the abstract idea.
Step 2B: Claim provides an Inventive Concept?
No.
MPEP 2106.05(I) “An inventive concept "cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself. […] Instead, an "inventive concept" is furnished by an element or combination of elements that is recited in the claim in addition to (beyond) the judicial exception, and is sufficient to ensure that the claim, as a whole, amounts to significantly more than the judicial exception itself.”
MPEP 2106.05(f) Mere Instructions To Apply An Exception: “[I]mplementing an abstract idea on a generic computer, does not integrate the abstract idea into a practical application in Step 2A Prong Two or add significantly more in Step 2B.
MPEP 2106.05(d)(II)(i): “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. […] i. Receiving or transmitting data over a network, e.g., using the Internet to gather data […] iv. Storing and retrieving information in memory”
MPEP 2106.05(g): “As explained by the Supreme Court, the addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood or conventional. Parker v. Flook, 437 U.S. 584, 588-89, 198 USPQ 193, 196 (1978).”
receiving, historical operating data associated with one or more building devices or building device components;
This receiving step is receiving or transmitting data, so it is analogous to the examples cited in MPEP 2106.05(d)(II)(i) (“Receiving or transmitting data”; Storing and retrieving information in memory “) representing a well-understood, routine, and conventional function.
As discussed the additional limitation of computer-implemented method, is mere execution on a generic computer, and the additional limitation of the receiving step is insignificant extra-solution activity and a well-understood, routine, and conventional function, and, therefore, neither of the additional limitations can provide the abstract idea with significantly more to render the combination of the additional limitations an inventive concept, under MPEP 2106.05(d), MPEP 2106.05(f), and MPEP 2106.05(g).
adjusting an operation of the one or more building devices or building device components based on the predicted fault or failure of the one or more building device or building device components, wherein adjusting the operation comprises at least one of attempting to repair the fault or work around the fault, shutting down the one or more building devices or building device components, or directing control activities around the one or more building devices or building device components.
These additional limitations are mere “apply it” steps of the abstract idea under MPEP 2106.05(f), which states, “whether the claim recites only the idea of a solution or outcome i.e., the claims fails to recite details of how a solution to a problem is accomplished.” These actions apply the abstract idea at such a high level with no particularity as to the improvement, such that the steps represent a naked “apply it” of the abstract idea. Therefore, under MPEP 2106.05(f), the adjusting and operating steps fail to combine with other elements of the claim to provide significantly more than the abstract idea at Step 2B.
Further, the adjusting step is WURC: Cao et al., [0063]-[0067]; Yevkin et al., Abstract; Coetzee et al., Abstract; Jeon et al., Conclusion; Pooya Alavian et al., Abstract, Conclusion. Because the adjusting step is WURC and insignificant extra-solution activity, under MPEP 2106.05(d) and 2106.05(g), the adjusting step fails to combine with other elements of the claim to provide significantly more than the abstract idea that would confer an inventive concept at Step 2B.
Also, the adjusting step merely limits the abstract to a particular technological field of maintenance control (recited at a high level), which, under MPEP 2106.05(h), fails to combine with other elements of the claim to provide significantly more than the abstract idea at Step 2B.
Therefore, there are no additional limitations in claim 1 that combine with other elements of claim 1 to furnish claim 1 with an inventive concept to ensure that claim 1, as a whole, amounts to significantly more than the abstract idea.
Claim 1 is ineligible.
Claim 12 (Statutory Category – Machine)
Step 2A – Prong 1: Judicial Exception Recited?
Yes, the claims recite a mental process and a mathematical operation, which are abstract ideas.
MPEP 2106.04(a)(2)(Ill): “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, Judgments, and opinions. […] The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation.”
MPEP 2106.04(a)(2)(I): “When determining whether a claim recites a mathematical concept (i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations), examiners should consider whether the claim recites a mathematical concept or merely limitations that are based on or involve a mathematical concept […] a mathematical concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). See, e.g., SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163, 127 USPQ2d 1597, 1599 (Fed. Cir. 2018) (holding that claims to a ‘‘series of mathematical calculations based on selected information’’ are directed to abstract ideas); Digitech Image Techs., LLC v. Elecs. for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014) (holding that claims to a ‘‘process of organizing information through mathematical correlations’’ are directed to an abstract idea).
MPEP 2106.04(a)(2)(I)(A): “Mathematical Relationships. A mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols.”
Claim 12 recites (claim features in italics, abstract ideas in bold, paragraph references are to the Applicant’s specification):
[…]
calculate a runtime of a chiller of the one or more chillers based on the two or more event dates, wherein the runtime comprises an amount of time the chiller is operational; (Evaluation/Mental Process, Mathematical Calculation Concept - See equations in paragraph [0090] that evaluate a runtime by mathematical subtraction)
calibrate the runtime by (i) determining an idle time associated with the chiller using climate data corresponding to a location of the chiller and (ii) performing an operation using the runtime and the idle time to generate a calibrated runtime; wherein the idle time is a first subset of the runtime and comprises an amount of time the chiller is operational but not running; and the calibrated run time is a second subset of the runtime and comprises an amount of time the chiller is operational and running; (See equations in paragraph [0090] that evaluate an idle time and calibration by mathematical subtraction)
train a chiller reliability model using the calibrated runtime to produce a trained model. (Evaluation/Mental Process, Mathematical Calculation Concept - See evaluation of mathematical equation and mathematical relationships in paragraph [0093])
generate a reliability metric indicating a predicted fault or failure of the chiller using the trained model; (Evaluation/Mental Process, Mathematical Calculation Concept - See evaluations of equations for reliability metrics in [0094]-[0096])
The steps of claim 12 are elements of an evaluation, a mental process, which can be performed in the mind of a person or with a pen and paper. Further, the method steps of claim 12 as described in the claim and specification include and/or are expressed as mathematical calculations or mathematical relationships, which form a mathematical concept. Being a mental process and a mathematical concept, the method preamble and evaluation/mathematical steps are an abstract idea.
Claim 12 recites an abstract idea.
Step 2A – Prong 2: Integrated into a Practical Solution?
No.
MPEP 2106.04(d): “[A]fter determining that a claim recites a judicial exception in Step 2A Prong One, examiners should evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception in Step 2A Prong Two. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. Whether or not a claim integrates a judicial exception into a practical application is evaluated using the considerations set forth in subsection I below, in accordance with the procedure described below in subsection II.”
MPEP 2106.05(f) Mere Instructions To Apply An Exception: “Another consideration when determining whether a claim integrates a judicial exception into a practical application in Step 2A Prong Two or recites significantly more than a judicial exception in Step 2B is whether the additional elements amount to […] more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer, examiners should explain why they do not meaningfully limit the claim in an eligibility rejection. For example, an examiner could explain that implementing an abstract idea on a generic computer, does not integrate the abstract idea into a practical application in Step 2A Prong Two or add significantly more in Step 2B.
MPEP 2106.05(g): “Another consideration when determining whether a claim integrates the judicial exception into a practical application in Step 2A Prong Two or recites significantly more in Step 2B is whether the additional elements add more than insignificant extra-solution activity to the judicial exception. The term "extra-solution activity" can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent.”
The additional elements:
receive historical operating data associated with one or more chillers or chiller components, the historical operating data including two or more event dates associated with the one or more chillers;
The receiving step merely gathers existing information (historical operating data) for evaluation. Mere data gathering is insignificant extra solution activity under MPEP 2106.05(g). Under Mere Data Gathering, an analogous example is provided: “iv. Obtaining information about transactions using the Internet to verify credit card transactions, CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011).” Under MPEP 2106.05(g), receiving data for evaluation is not significant in meaningfully limiting the invention, and the receiving of the data is necessary to the evaluations and mathematical operations of the claim. Under MPEP 2106.05(g). The receiving step adds nothing more than insignificant extra solution activity, so it does not integrate the abstract idea into a practical application in Step 2A Prong Two.
One or more non-transitory computer-readable storage media having instructions stored thereon that, when executed by one or more processors, cause the one or more processors to:
The computer implementation by a processing circuit is a recitation of a general purpose computer with no specific configurations to execute the claimed method. As such, the computer implementation implements the recited abstract idea on a generic computer, and does not integrate the abstract idea into a practical application in Step 2A Prong Two.
adjust an operation of the chiller based on the predicted fault or failure of the chiller, wherein the adjusting the operation comprises at least one of attempting to repair the fault or work around the fault, shutting down the chiller, or directing control activities around the chiller.
The adjust step is a mere “apply it” step of the abstract idea under MPEP 2106.05(f), which states, “whether the claim recites only the idea of a solution or outcome i.e., the claims fails to recite details of how a solution to a problem is accomplished.” These actions apply the abstract idea at such a high level with no particularity as to the improvement, such that the steps represent a naked “apply it” of the abstract idea. Therefore, under MPEP 2106.05(f), the adjusting step fails to integrate the abstract idea into a practical application at Step 2A, Prong 2.
Further, the adjusting step is insignificant extra-solution activity under MPEP 2106.05(g). This is similar to the example in MPEP 2106.05(g) of insignificant application: “i. Cutting hair after first determining the hair style, In re Brown, 645 Fed. App'x 1014, 1016-1017 (Fed. Cir. 2016)” What else is one going to do with survival data of a machine other than adjust the operating of a machine in some way that improves its operation/survival. Under 2106.05(g), these additional limitations do not integrate the abstract idea into a practical application.
Also, the adjust step merely limits the abstract to a particular technological field of maintenance control (recited at a high level), which, under MPEP 2106.05(h), fails to integrate the abstract idea into a practical application at Step 2A, Prong 2.
The additional limitations of claim 12 fail to integrate the abstract idea into a practical application at Step 2A, Prong 2.
Claim 12 is directed to the abstract idea.
Step 2B: Claim provides an Inventive Concept?
No.
MPEP 2106.05(I) “An inventive concept "cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself. […] Instead, an "inventive concept" is furnished by an element or combination of elements that is recited in the claim in addition to (beyond) the judicial exception, and is sufficient to ensure that the claim, as a whole, amounts to significantly more than the judicial exception itself.”
MPEP 2106.05(f) Mere Instructions To Apply An Exception: “[I]mplementing an abstract idea on a generic computer, does not integrate the abstract idea into a practical application in Step 2A Prong Two or add significantly more in Step 2B.
MPEP 2106.05(d)(II)(i): “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. […] i. Receiving or transmitting data over a network, e.g., using the Internet to gather data […] iv. Storing and retrieving information in memory”
MPEP 2106.05(g): “As explained by the Supreme Court, the addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood or conventional. Parker v. Flook, 437 U.S. 584, 588-89, 198 USPQ 193, 196 (1978).”
receive historical operating data associated with one or more chillers or chiller components, the historical operating data including two or more event dates associated with the one or more chillers;
This receiving step is receiving or transmitting data, so it is analogous to the examples cited in MPEP 2106.05(d)(II)(i) (“Receiving or transmitting data”; Storing and retrieving information in memory “)representing a well-understood, routine, and conventional function.
As discussed the additional limitation of one or more non-transitory computer-readable storage media…, is mere execution on a generic computer, and the additional limitation of the receiving step is insignificant extra-solution activity and a well-understood, routine, and conventional function, and, therefore, neither of the additional limitations can provide the abstract idea with significantly more to render the combination of the additional limitations an inventive concept, under MPEP 2106.05(d), MPEP 2106.05(f), and MPEP 2106.05(g).
adjust an operation of the chiller based on the predicted fault or failure of the chiller, wherein the adjusting the operation comprises at least one of attempting to repair the fault or work around the fault, shutting down the chiller, or directing control activities around the chiller.
The adjust step is an “apply it” step of the abstract idea under MPEP 2106.05(f), which states, “whether the claim recites only the idea of a solution or outcome i.e., the claims fails to recite details of how a solution to a problem is accomplished.” These actions apply the abstract idea at such a high level with no particularity as to the improvement, such that the steps represent a naked “apply it” of the abstract idea. Therefore, under MPEP 2106.05(f), the adjusting step fails to combine with other elements of the claim to provide significantly more than the abstract idea at Step 2B.
Further, the adjust step is WURC: Cao et al., [0063]-[0067]; Yevkin et al., Abstract; Coetzee et al., Abstract; Jeon et al., Conclusion; Pooya Alavian et al., Abstract, Conclusion. Because the adjusting step is WURC and insignificant extra-solution activity, under MPEP 2106.05(d) and 2106.05(g), the adjusting step fails to combine with other elements of the claim to provide significantly more than the abstract idea that would confer an inventive concept at Step 2B.
Also, the adjust step merely limits the abstract to a particular technological field of maintenance control (recited at a high level), which, under MPEP 2106.05(h), fails to combine with other elements of the claim to provide significantly more than the abstract idea at Step 2B.
Therefore, there are no additional limitations in claim 12 that furnish claim 12 with an inventive concept to ensure that claim 12, as a whole, amounts to significantly more than the abstract idea at Step 2B.
Claim 12 is ineligible.
Claim 23 (Statutory Category – Machine)
Step 2A – Prong 1: Judicial Exception Recited?
Yes, the claims recite a mental process and a mathematical operation, which are abstract components of an abstract idea.
MPEP 2106.04(a)(2)(Ill): “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, Judgments, and opinions. […] The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation.”
MPEP 2106.04(a)(2)(I): “When determining whether a claim recites a mathematical concept (i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations), examiners should consider whether the claim recites a mathematical concept or merely limitations that are based on or involve a mathematical concept […] a mathematical concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). See, e.g., SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163, 127 USPQ2d 1597, 1599 (Fed. Cir. 2018) (holding that claims to a ‘‘series of mathematical calculations based on selected information’’ are directed to abstract ideas); Digitech Image Techs., LLC v. Elecs. for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014) (holding that claims to a ‘‘process of organizing information through mathematical correlations’’ are directed to an abstract idea).
MPEP 2106.04(a)(2)(I)(A): “Mathematical Relationships. A mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols.”
Claim 23 recites (claim features in italics, abstract ideas in bold, paragraph references are to the Applicant’s specification):
[…]
calculate a runtime of a chiller of the one or more chillers based on the two or more event dates by determining an amount of time between the failure date and the start date, wherein the runtime comprises an amount of time the chiller is operational; (See equations in paragraph [0090] that evaluate a runtime by mathematical subtraction)
calibrate the runtime by determining an idle time associated with the chiller using climate data corresponding to a location of the chiller and subtracting the idle time from the runtime to generate a calibrated runtime; (See equations in paragraph [0090] that evaluate an idle time and calibration by mathematical subtraction)
train a chiller reliability model using the calibrated runtime; and (See evaluation of mathematical equation and mathematical relationships in paragraph [0093])
generate a reliability metric describing a mean time between failures (MTBF) associated with the chiller using the chiller reliability model indicating a predicted fault of the chiller. (See evaluations of mathematical equations and mathematical relationships in paragraphs [0094], including the ones for the purpose of determining an MTBF, a probability density function, a hazard rate function, a time to percentage failure)
The method steps of claim 23 are elements of an evaluation, a mental process, which can be performed in the mind of a person or with a pen and paper. Further, the method steps of claim 23 as described in the claim and specification include and/or are expressed as mathematical calculations or mathematical relationships, which form a mathematical concept. Being a mental process and a mathematical concept, the specified method preamble and evaluation/mathematical steps are an abstract idea.
Claim 23 recites an abstract idea.
Step 2A – Prong 2: Integrated into a Practical Solution?
No.
MPEP 2106.04(d): “[A]fter determining that a claim recites a judicial exception in Step 2A Prong One, examiners should evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception in Step 2A Prong Two. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. Whether or not a claim integrates a judicial exception into a practical application is evaluated using the considerations set forth in subsection I below, in accordance with the procedure described below in subsection II.”
MPEP 2106.05(f) Mere Instructions To Apply An Exception: “Another consideration when determining whether a claim integrates a judicial exception into a practical application in Step 2A Prong Two or recites significantly more than a judicial exception in Step 2B is whether the additional elements amount to […] more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer, examiners should explain why they do not meaningfully limit the claim in an eligibility rejection. For example, an examiner could explain that implementing an abstract idea on a generic computer, does not integrate the abstract idea into a practical application in Step 2A Prong Two or add significantly more in Step 2B.
MPEP 2106.05(g): “Another consideration when determining whether a claim integrates the judicial exception into a practical application in Step 2A Prong Two or recites significantly more in Step 2B is whether the additional elements add more than insignificant extra-solution activity to the judicial exception. The term "extra-solution activity" can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent.”
The additional elements:
receive historical operating data associated with one or more chillers or chiller components, the historical operating data including two or more event dates associated with the one or more chillers, wherein the two or more event dates include a failure date associated with a failure of the chiller and a start date associated with a day when the chiller came online;
The receiving step merely gathers existing information (historical operating data) for evaluation. Mere data gathering is insignificant extra solution activity under MPEP 2106.05(g). Under Mere Data Gathering, an analogous example is provided: “iv. Obtaining information about transactions using the Internet to verify credit card transactions, CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011).” Under MPEP 2106.05(g), receiving data for evaluation is not significant in meaningfully limiting the invention, and the receiving of the data is necessary to the evaluations and mathematical operations of the claim. Under MPEP 2106.05(g). The receiving step adds nothing more than insignificant extra solution activity, so it does not integrate the abstract idea into a practical application in Step 2A Prong Two.
A predictive maintenance system, comprising: a processing circuit including a processor and memory, the memory having instructions stored thereon that, when executed by the processor, cause the processor to:
The computer implementation by a processing circuit is a recitation of a general purpose computer with no specific configurations to execute the claimed method. As such, the computer implementation implements the recited abstract idea on a generic computer, and does not integrate the abstract idea into a practical application in Step 2A Prong Two.
adjust an operation of the chiller based on the predicted fault or failure of the chiller, wherein the adjusting the operation comprises at least one of attempting to repair the fault or work around the fault, shutting down the chiller, or directing control activities around the chiller.
The adjust step is a mere “apply it” step of the abstract idea under MPEP 2106.05(f), which states, “whether the claim recites only the idea of a solution or outcome i.e., the claims fails to recite details of how a solution to a problem is accomplished.” These actions apply the abstract idea at such a high level with no particularity as to the improvement, such that the step represents a naked “apply it” of the abstract idea. Therefore, under MPEP 2106.05(f), the adjust step fails to integrate the abstract idea into a practical application at Step 2A, Prong 2.
Further, the adjust step is insignificant extra-solution activity under MPEP 2106.05(g). This is similar to the example in MPEP 2106.05(g) of insignificant application: “i. Cutting hair after first determining the hair style, In re Brown, 645 Fed. App'x 1014, 1016-1017 (Fed. Cir. 2016)” What else is one going to do with survival data of a machine other than adjust the operating of a machine in some way that improves its operation/survival. Under 2106.05(g), these additional limitations do not integrate the abstract idea into a practical application.
Also, the adjust step merely limits the abstract to a particular technological field of maintenance control (recited at a high level), which, under MPEP 2106.05(h), fails to integrate the abstract idea into a practical application at Step 2A, Prong 2.
The additional limitations of claim 23 fail to integrate the abstract idea into a practical application at Step 2A, Prong 2.
Claim 23 is directed to the abstract idea.
Step 2B: Claim provides an Inventive Concept?
No.
MPEP 2106.05(I) “An inventive concept "cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself. […] Instead, an "inventive concept" is furnished by an element or combination of elements that is recited in the claim in addition to (beyond) the judicial exception, and is sufficient to ensure that the claim, as a whole, amounts to significantly more than the judicial exception itself.”
MPEP 2106.05(f) Mere Instructions To Apply An Exception: “[I]mplementing an abstract idea on a generic computer, does not integrate the abstract idea into a practical application in Step 2A Prong Two or add significantly more in Step 2B.
MPEP 2106.05(d)(II)(i): “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. […] i. Receiving or transmitting data over a network, e.g., using the Internet to gather data […] iv. Storing and retrieving information in memory”
MPEP 2106.05(g): “As explained by the Supreme Court, the addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood or conventional. Parker v. Flook, 437 U.S. 584, 588-89, 198 USPQ 193, 196 (1978).”
receive historical operating data associated with one or more chillers or chiller components, the historical operating data including two or more event dates associated with the one or more chillers;
This receiving step is receiving or transmitting data, so it is analogous to the examples cited in MPEP 2106.05(d)(II)(i) (“Receiving or transmitting data”; “Storing and retrieving information in memory“)representing a well-understood, routine, and conventional function.
As discussed the additional limitation of one or more non-transitory computer-readable storage media…, is mere execution on a generic computer, and the additional limitation of the receiving step is insignificant extra-solution activity and a well-understood, routine, and conventional function, and, therefore, neither of the additional limitations can provide the abstract idea with significantly more to render the combination of the additional limitations an inventive concept, under MPEP 2106.05(d), MPEP 2106.05(f), and MPEP 2106.05(g).
adjust an operation of the chiller based on the predicted fault or failure of the chiller, wherein the adjusting the operation comprises at least one of attempting to repair the fault or work around the fault, shutting down the chiller, or directing control activities around the chiller.
The adjust step is an “apply it” step of the abstract idea under MPEP 2106.05(f), which states, “whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished.” These actions apply the abstract idea at such a high level with no particularity as to the improvement, such that the steps represent a naked “apply it” of the abstract idea. Therefore, under MPEP 2106.05(f), the adjusting step fails to combine with other elements of the claim to provide significantly more than the abstract idea at Step 2B.
Further, the adjusting step is WURC: Cao et al., [0063]-[0067]; Yevkin et al., Abstract; Coetzee et al., Abstract; Jeon et al., Conclusion; Pooya Alavian et al., Abstract, Conclusion. Because the adjusting step is WURC and insignificant extra-solution activity, under MPEP 2106.05(d) and 2106.05(g), the adjusting step fails to combine with other elements of the claim to provide significantly more than the abstract idea that would confer an inventive concept at Step 2B.
Also, the adjusting step merely limits the abstract to a particular technological field of maintenance control (recited at a high level), which, under MPEP 2106.05(h), fails to combine with other elements of the claim to provide significantly more than the abstract idea at Step 2B.
Therefore, there are no additional limitations in claim 23 that furnish claim 23 with an inventive concept to ensure that claim 23, as a whole, amounts to significantly more than the abstract idea.
Claim 23 is ineligible.
Dependent Claims
Dependent claims 2-11, 13-22, and 24-26 are either elements of the abstract idea or provide additional limitations that neither integrate the abstract idea into a practical application or combine with the abstract idea to provide significantly more.
Claims 2 and 13
Claim 2 (and similar claim 13) recites, wherein performing the operation includes subtracting the idle time from the runtime to generate the calibrated runtime. (This is an evaluation of a subtraction mathematical application [0090]). This is an evaluation, which is a mental process practically performable in the mind or with aid of pen, paper, or a calculator, and math equations/relationships/calculations in textual form, which form a mathematical concept, both of which are elements of an abstract idea. This is an element of the abstract idea, and, therefore, is not an additional limitation that can integrate the abstract idea into a practical application or provide significantly more to render the abstract idea an inventive concept. The same applies to claim 13, which recites similar subject matter. Claims 2 and 13 are ineligible subject matter.
Claim 3
Claim 3 recites, wherein training the component reliability model includes training at least one of (i) a Weibull model or (ii) a Cox model using the calibrated runtime to produce the trained model. (This is an evaluation of the mathematical equation and mathematical relationships expressed in [0093]). This is an evaluation, which is a mental process practically performable in the mind or with aid of pen, paper, or a calculator, and math equations/relationships/calculations in textual form, which is a mathematical concept, both of which are elements of an abstract idea. This is an element of the abstract idea, and, therefore, is not an additional limitation that can integrate the abstract idea into a practical application or provide significantly more to render the abstract idea an inventive concept. The same applies to claim 13, which recites similar subject matter. Claim 3 is ineligible subject matter.
Claims 4 and 15
Claim 4 (and similar claim 15) recites, wherein training the component reliability model includes training the component reliability model using: (1) warranty claim data comprising information about building devices having experienced a failure for which a warranty claim has been received; and (2) censored data comprising information about building devices that are in warranty and have not experienced a failure indicated in the warranty claim data. (This merely describes the information used in training/fitting the model, which is an evaluation of the mathematical equation and mathematical relationships expressed in [0093]). This is an evaluation, which is a mental process practically performable in the mind or with aid of pen, paper, or a calculator, and math equations/relationships/calculations in textual form, which form a mathematical concept, both of which are elements of an abstract idea. This is an element of the abstract idea, and, therefore, is not an additional limitation that can integrate the abstract idea into a practical application or provide significantly more to render the abstract idea an inventive concept. The same applies to claim 15, which recites similar subject matter. Claims 4 and 15 are ineligible subject matter.
Claims 5 and 16
Claim 5 (and similar claim 16) recites, wherein training the component reliability model comprises training the component reliability model to estimate a predicted failure time for one or more of the building devices using both the warranty claim data and the censored data. (A mean time between failures is as estimated failure time under the broadest reasonable interpretation, and the Applicant’s specification paragraphs [0093] shows mathematical equations and mathematical relationships to be evaluated to determine the MTBF). This is an evaluation, which is a mental process practically performable in the mind or with aid of pen, paper, or a calculator, and math equations/relationships/calculations in textual form, which form a mathematical concept, both of which are elements of an abstract idea. This is an element of the abstract idea, and, therefore, is not an additional limitation that can integrate the abstract idea into a practical application or provide significantly more to render the abstract idea an inventive concept. The same applies to claim 16, which recites similar subject matter. Claims 5 and 16 are ineligible subject matter.
Claims 6 and 17
Claim 6 (similar claim 17) recites:
generating […] a reliability metric describing a mean time between failures (MTBF) associated with the component based on the trained model. (The Applicant’s specification paragraphs [0093] shows mathematical equations and mathematical relationships to be evaluated to determine the MTBF). This is an evaluation, which is a mental process, and a set of mathematical equations and mathematical relationships, which form a mathematical concept, both of which are elements of an abstract idea. This is an element of the abstract idea, and, therefore, is not an additional limitation that can integrate the abstract idea into a practical application or provide significantly more to render the abstract idea an inventive concept. The same applies to claim 17, which recites similar subject matter. Claims 6 and 17 are ineligible subject matter.
by the processing circuit
As discussed above with respect to claim 1, execution by a general purpose computer neither integrates the abstract idea into a practical application nor combines with the abstract idea to provide significantly more to render an inventive concept.
The additional limitations of claim 6 fail to integrate the abstract idea into a practical application and fail to combine with the abstract idea to provide significantly more to render the claim inventive.
The same applies to claim 17, which recites similar subject matter. Claims 6 and 17 are ineligible subject matter.
Claims 7 and 18
Claim 7 (and similar claim 18) recites, wherein the historical operating data includes two or more event dates that include a failure date associated with a failure of the component and a start date associated with a day when the component came into use, and wherein the method further includes calculating a runtime of the component by determining an amount of time between the failure date and the start date. (This merely qualifies the claimed calculating step, which, by the further limitations of claim 7 remains an evaluation of a mathematical subtraction as illustrated in paragraph [0090]). This is an evaluation, which is a mental process practically performable in the mind or with aid of pen, paper, or a calculator, and math equations/relationships/calculations in textual form, which form a mathematical concept, both of which are elements of an abstract idea. This is an element of the abstract idea, and, therefore, is not an additional limitation that can integrate the abstract idea into a practical application or provide significantly more to render the abstract idea an inventive concept. The same applies to claim 18, which recites similar subject matter. Claims 7 and 18 are ineligible subject matter.
Claims 8, 19, and 25
Claim 8 (and similar claims 19 and 25) recites,
receiving, by the processing circuit, warranty claim data associated with one or more warranty claims associated with the one or more building devices or the building device components; and
The receiving step merely gathers existing information (warranty information) for evaluation. Mere data gathering is insignificant extra solution activity under MPEP 2106.05(g). Under Mere Data Gathering, an analogous example is provided: “iv. Obtaining information about transactions using the Internet to verify credit card transactions, CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011).” Under MPEP 2106.05(g), receiving data for evaluation is not significant in meaningfully limiting the invention, and the receiving of the data is necessary to the evaluations and mathematical operations of the claim. Under MPEP 2106.05(g). The receiving step adds nothing more than insignificant extra solution activity, so it does not integrate the abstract idea into a practical application in Step 2A Prong Two.
This receiving step is receiving or transmitting data, so it is analogous to the examples cited in MPEP 2106.05(d)(II)(i) (“Receiving or transmitting data”; “Storing and retrieving information in memory“) representing a well-understood, routine, and conventional function. The additional limitation of the receiving step is insignificant extra-solution activity and a well-understood, routine, and conventional function, and, therefore, neither of the additional limitations can provide the abstract idea with significantly more to render the combination of the additional limitations an inventive concept, under MPEP 2106.05(d), MPEP 2106.05(f), and MPEP 2106.05(g).
Parsing […] the warranty claim data to identify the historical operating data by generating the start date associated with the component based on at least one of (i) a shipping date associated with a day when the component was shipped to a location of operation or (ii) a manufacture date associated with when the component was manufactured.
Parsing is an evaluation of data to determine the type of data ([0092] discussing processing the data), which is a mental process practically performable in the mind or with the aid of pen, paper, or a calculator, and abstract idea. The parsing step is an element of the abstract idea, so it provides no additional limitations to integrate the abstract idea into a practical application or combine with the abstract idea to add significantly more to render the claim inventive.
, by the processing circuit,
As discussed above with respect to claim 1, execution by a general purpose computer neither integrates the abstract idea into a practical application nor combines with the abstract idea to provide significantly more to render an inventive concept.
The additional limitations of claim 8 fail to integrate the abstract idea into a practical application and fail to combine with the abstract idea to provide significantly more to render the claim inventive.
The same applies to claims 19 and 25, which recite similar subject matter. Claims 8, 19, and 25 are ineligible subject matter.
Claims 9, 20, and 26
Claim 9 recites:
parsing, by the processing circuit, the historical operating data to identify an element in the historical operating data having at least one of (i) a runtime that is below a threshold runtime, (ii) an event date that is before a threshold event date, or (iii) a failure type that is included in a list of failure types that are below a threshold number of failures; and
Parsing is an evaluation of data to determine the type of data ([0092] discussing processing the data), which is a mental process, and abstract idea. The parsing step is an element of the abstract idea, so it provides no additional limitations to integrate the abstract idea into a practical application or combine with the abstract idea to add significantly more to render the claim inventive.
trimming, by the processing circuit, the element from the historical operating data in response.
Trimming to limit the data set is an evaluation of data to limit the type of data ([0092] discussing processing the data) for training/fitting, which is a mental process, and abstract idea. The parsing step is an element of the abstract idea, so it provides no additional limitations to integrate the abstract idea into a practical application or combine with the abstract idea to add significantly more to render the claim inventive.
The parsing and trimming steps are mental processes and form part of the abstract idea. Therefore, claim 9 fails to provide additional limitations that integrate the abstract idea into a practical application or combines with the abstract idea to provide significantly more to render claim 9 inventive. The same applies to claims 20 and 26, which recite similar subject matter. Claims 9, 20, and 26 are ineligible.
Claims 10 and 21
Claim 10 recites,
wherein training the component reliability model to produce the trained model includes determining a shape parameter and a scale parameter of a Weibull model.
The determination of a shape parameter and a scale parameter is an evaluation that can be performed in the mind or with pen and paper (See paragraph [0093]). This is a mental process, which is an element of an abstract idea. Further, the shape parameter and scale parameter of a Weibull model are determined using mathematical operations and mathematical relationships (See paragraph [0093]), which are mathematical concepts, or elements of an abstract idea. Because the wherein clause of claim 10 merely recites elements of the abstract idea that integrate with the abstract idea, claim 10 fails to provide additional limitations that integrate the abstract idea into a practical application or to combine with the abstract idea to provide significantly more to render claim 10 inventive. The same applies to claim 21, which recites similar subject matter. Claims 10 and 21 are ineligible.
Claims 11 and 22
Claim 11 recites,
wherein determining the idle time is based on a climate data corresponding the location of the component.
This qualification of the determining operation in the calibrating step recited in claim 1 is merely an element of the evaluation and, therefore, integral to the abstract idea of claim 1. Accordingly, claim 11 fails to provide additional limitations that integrate the abstract idea into a practical application or that combine with the abstract idea to provide significantly more than the abstract idea. Claims 11 and 22 are ineligible.
Claim 14
Claim 14, recites,
wherein training the chiller reliability model includes training at least one of (i) a Weibull model or (ii) a Cox model using the calibrated runtime to produce the trained model.
Wherein clause qualifies the training step in claim 1 by stating that the training includes an evaluation to fit a Weibull model or a Cox model (See paragraph [0093]). This is a mental process, which is an element of an abstract idea. Further, the shape parameter and scale parameter of a Weibull model are determined using mathematical operations and mathematical relationships (See paragraph [0093]), which are mathematical concepts, or elements of an abstract idea. Because the wherein clause of claim 14 merely recites elements of the abstract idea that integrate with the abstract idea, claim 14 fails to provide additional limitations that integrate the abstract idea into a practical application or to combine with the abstract idea to provide significantly more to render claim 14 inventive. Claim 14 is ineligible.
Claim 24
Claim 24 recites,
wherein training the chiller reliability model includes training at least one of (i) a Weibull model or (ii) a Cox model using the calibrated runtime.
Wherein clause qualifies the training step in claim 23 by stating that the training includes an evaluation to fit a Weibull model or a Cox model (See paragraph [0093]). This is a mental process, which is an element of an abstract idea. Further, the shape parameter and scale parameter of a Weibull model are determined using mathematical operations and mathematical relationships (See paragraph [0093]), which are mathematical concepts, or elements of an abstract idea. Because the wherein clause of claim 24 merely recites elements of the abstract idea that integrate with the abstract idea, claim 24 fails to provide additional limitations that integrate the abstract idea into a practical application or to combine with the abstract idea to provide significantly more to render claim 24 inventive. Claim 24 is ineligible.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-2, 6-7, 10-14, 17-18, and 21-23: Cao, Pooya, Immerman, Korn, and Schuster
Claim(s) 1-2, 6-7, 10-14, 17-18, and 21-23 are rejected under 35 U.S.C. 103 as being unpatentable over US. 2022/0057766 to Cao et al. (Cao) in view of NPL: “The (α, β)-Precise Estimates of MTBF and MTTR: Definitions, Calculations, and Induced Effect on Machine Efficiency Evaluation” by Pooya et al (Pooya), NPL: “OEE, OOE, AND TEEP- WHAT’S THE DIFFERENCE” by Immerman (Immerman), NPL: "Exactly What Is a Full Load Cooling Hour and Does Size Really Matter?" by Korn et al. (Korn), and US 2109/ 0123931 A1 to Schuster et al. (Schuster).
Claim 1
Regarding claim 1, Cao Teaches:
A method for generating a reliability model, comprising: (Cao [0170] “Where traditional SA methods only estimate the averaged RUL of a class of devices, disclosed embodiment can estimate the RUL of a device based on its usage pattern and so is more precise than other approaches. In addition, for device with limited sensor measurement, disclosed embodiments can collect data from associated devices and uses the BN to infer status of the device of interest.)
receiving, by a processing circuit, historical operating data associated with one or more building devices or building device components; (Cao [0045] “Examples of field devices 112 include lights, thermostats, temperature sensors, lighting sensors, fans, damper actuators, heaters, chillers [...] A physical device of the building automation system 100 can include any of these exemplary field devices 112.” [0061] Standard corrective maintenance (CM) and scheduled maintenance (SM) methods are heuristic and imprecise. For example, application engineers may estimate the Mean Time to Failure (MTTF), Mean Time before Failure (MTBF) or Mean Time before Repair (MTBR) for new hardware either from the vendor's manual or from historical log data. The engineer then prescribes either the CM or SM approach.” [0089] “databases can include, for example, time-series databases, SQL and non-SQL databases, graph databases, and any other databases or repositories as may be useful to perform processes as described here. In particular, such data as access runtime and maintenance log data and the time-event tables described above can be stored in data lake 518.” Historical operating data is received. [0048] “ The processor 202 may be configured to execute program instructions or programming software or firmware stored in the instructions 220 of the memory 204,”. A processor and memory to execute instructions.)
and (Cao FIG. 6, [0079] “FIG. 6 illustrates a non-limiting example of historical data 600 that can be used in various embodiments as input data for the survival analysis. In this example, data from multiple machines/devices is shown, with line data indicating lost tracking (plain line with no terminus), that a fault has occurred and when (line terminating in point indicating when fault occurred), and no fault yet (line with arrow indicating that the device is still performing normally).” – Cao determines a determined runtime.)
training, by the processing circuit, a component reliability model (Cao [0105] “The Weibull distribution is a method to describe the failure rate of a device, where a represents a shape parameter, b represents a scale parameter, and k represents an input variable. Weibull distribution is understood by those of skill in the art and is described, at time of filing, at en.wikipedia.org/wiki Weibull distribution. For computing F, the a and b parameters are the same as those in the definition of Weibull distribution. The Gamma function Γ(x) is a standard math function, described, for example, at time of filing at en.wikipedia.org/wiki/Gamma_function.” Train a Weibull function based the on determined runtime.)
generating a reliability metric indicating a predicted fault or failure of the one or more building devices or building device components using the trained model; (Cao [0082] “For the SA operators, the PM engine 508 combines the survival curves of individual components to represent a larger device comprised of the individual components.” [0104] “A system as disclosed herein can perform a probabilistic parametric survival analysis process. Where such parameters as MTTF and/or failure rate F for a device are known, the system can use them to fit in the structured survival curves, such as the Weibull distribution.” [0061] “For example, application engineers may estimate the Mean Time to Failure (MTTF), Mean Time before Failure (MTBF) or Mean Time before Repair (MTBR) for new hardware either from the vendor's manual or from historical log data.” The example Weibull calculation uses MTTF, rather than MTBF for a failure. However, it is stated as an example of a failure rate. The specification further discusses other failure rates including MTBF in paragraph . Further, paragraph [0082] specifies that failures of a subcomponent (e.g., of a chiller), so a collection of determined MTTF’s of subcomponents could be used to calculate an MTBF. NOTE: While in Cao the engineers perform these calculations, POSITA would have found it obvious to have a computer of Cao do this, wherein POSITA would have been motivated to do so because its merely “Automating a manual activity” (MPEP 2144.04(III))) to do the mental process faster with the computer already in Cao.)
adjusting an operation of the one or more building devices or building device components in response to the predicted fault or failure of the one or more building device or building device components, wherein adjusting the operation comprises . (Cao [0003]-[0004] “Building automation systems encompass a wide variety of systems that aid in the monitoring and control of various aspects of building operation. Building automation systems include security systems, fire safety systems, lighting systems, and heating, ventilation, and air conditioning (HVAC) systems. The elements of a building automation system are widely dispersed throughout a facility. For example, an HVAC system may include temperature sensors and ventilation damper controls, as well as other elements that are located in virtually every area of a facility. These building automation systems typically have one or more centralized control stations from which system data may be monitored and various aspects of system operation may be controlled and/or monitored. To allow for monitoring and control of the dispersed control system elements, building automation systems often employ multi-level communication networks to communicate operational and/or alarm information between operating elements, such as sensors and actuators, and the centralized control station. One example of a building automation system is the DXR Controller, available from Siemens Industry, Inc. Building Technologies Division of Buffalo Grove, Ill. (“Siemens”). In this system, several control stations connected via an Ethernet or another type of network may be distributed throughout one or more building locations, each having the ability to monitor and control system operation.” – The system is automated and uses predicted survival time to determine maintenance schedules. [0033] “A building automation system (BAS) such as disclosed herein can operate in an automatic operation mode that helps operate systems in the space efficiently to save energy. The BAS continuously evaluates environmental conditions and energy usage in the space and can determine and indicate to users when the space is being operated most efficiently. Similarly, the BAS can determine and indicate when the systems operate inefficiently, such as due to an occupant overriding the room control because of personal preference or due to weather conditions change drastically. The BAS can automatically, or at the input of a user, adjust the control settings to make the systems operate efficiently again.” – Use the system to save energy. [0035] “Disclosed embodiments include systems and methods for predictive maintenance of HVAC equipment and other physical equipment with using machine learning to ensure proper operation of the BAS or other system.” [0036] “The building automation system 100 is an environmental control system configured to control at least one of a plurality of environmental parameters within a building, such as temperature, humidity, lighting and/or the like. “ [0067] “Disclosed embodiments can use “survival curves” for planned maintenance scheduling. Survival curves project the probability of survival or failure of a device over time.” – Predictive maintenance and control of the devices in the building system is done responsive to determining likely failure times. See Also FIG. 8, which illustrates a replacement valve that fixes the machine)
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Cao does not appear to explicitly teach, but Pooya teaches (in bold):
calibrating, by the processing circuit, a runtime determined from the historical operating data by determining an idle time associated with a component of the building devices or the building device components the component and performing an operation using the runtime and the idle time to generate a calibrated runtime; and […] using the calibrated runtime (Pooya Page 1470, Second Column, under II. Definitions And Problems Formulations “It is noted that the terms up- and downtime imply the time when a machine within a production system does not operate due to technical malfunctions; it does not include the time when it is blocked or starved.” The definition of MTBF removes repair time or other down time, or, idle time.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claims to modify the calculation and calibration steps of Cao with the teachings of Pooya because Cao suggests determining run times for use in determining parameters for a Weibull model, and Pooya shows how to calculate the run times to determine the minimum number of data points needed for a Weibull or other survival functions. (Pooya Page 1470, Second Column, under II. Definitions And Problems Formulations “It is noted that the terms up- and downtime imply the time when a machine within a production system does not operate due to technical malfunctions; it does not include the time when it is blocked or starved.” Page 1472, IV. Evaluating Critical Number of Measurements For Nonexponential Machines, first paragraph: “To investigate the critical number of measurements in the nonexponential case, we consider machines obeying Weibull, gamma, and log-normal reliability models”; Cao [0063] “FIG. 4B is an example of a survival curve 406 for parametric survival analysis using a Weibull estimate as described herein, where band 408 reflects a 95% confidence interval with respect to the survival curve 402 at teach point in the timeline. Survival curve 406 can be produced by using, for example, maintenance log data.” [0134] “The sensor data and failure rate can include historical data, such as runtime data and maintenance log data stored in a data lake repository.” Also, FIG. 6 and paragraph [0079])
Cao and Pooya appear to fail to teach, but Immerman teaches:
determining an idle time associated with the chiller using
[…]
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wherein the runtime comprises an amount of time the one or more building devices or building device components are operational; the idle time is a first subset of the runtime and comprises an amount of time the one or more building devices or building device components are operational but not running; and the calibrated runtime is a second subset of the runtime and comprises an amount of time the one or more building devices are operational and running; (Immerman See Figure on Page 2 (shown on the Right), Fourth Row – Actual production time (claimed calibrated run time) is total operational time (claimed total runtime) minus unscheduled time (claimed idle time during which the machine is operational) minus time losses (non-operational time). To appropriately account for unscheduled time, the actual production time and down time are distinguished. These metrics better represent availability.)
It would have been obvious to a person of ordinary skill in the art to substitute the down time and up time as taught in Pooya with the production time, time losses, and unscheduled time of Immerman because a person of ordinary skill in the art would be motivated to improve the up and down time of Pooya, which attempted to better model effective use times of machinery, with a more accurate reflection of equipment availability provided by Immerman and its use of actual production time relative to time losses and unscheduled time. (Pooya Abstract “The mean time between failures (MTBF) and mean time to repair (MTTR) of manufacturing equipment (e.g., machines) are used in every quantitative method for production systems performance analysis, continuous improvement, and design. Unfortunately, the literature offers no methods for evaluating the smallest number of up- and downtime measurements necessary and sufficient to calculate reliable estimates of these equipment characteristics.” Page 1475, Left Column, Lines 9-14 “The future work on the topic of this article includes the following. 1) Deriving a tighter bound on the observation time required to collect the desired number of event measurements (e.g., up- and downtime realizations, defective parts produced, etc.).; Immerman Page 1, First Three Paragraphs “To review, OEE is measured by combining a machine’s performance, availability, and quality. In this way, OEE helps you to identify potential losses and understand where your process is falling short. But OEE alone is just the first step to fully understanding your performance. Today we will explore two other important and related metrics to help round out your continuous improvement strategy: Overall Operations Effectiveness (OOE) and Total Effective Equipment Performance (TEEP). The major differentiator between these three metrics is in how you define Availability. At their core, the goal of each of these metrics is to determine how much good product was made versus how much could have been made. Deciding which metric to use is really dependent on the time frame you consider. To better illustrate this, let’s think about a year’s production on your shop floor. Should your production be based on 365 days and 24 hrs each day? Or perhaps you should only look at the times when you had a shift scheduled? Or maybe you only want to look at the scheduled shifts the time you actually ran? Clearly there are different ways to think about your Availability and this requires different metrics to address each timeframe.” Page 2, Below The Figure “For example, your company is trying to determine whether or not they need to purchase new equipment in order to meet your production demands. You will first need to confirm that you in fact need the additional capacity. Depending on how you define time, needs to identify how much room there is to increase capacity in order to decide whether to focus on getting more from current equipment or to purchase new equipment. TEEP shows how much potential you have to increase production output with your current equipment. In many cases, reclaiming potential production output is a faster and less expensive alternative to purchasing new equipment. The importance of understanding OEE, OOE, and TEEP is essential so you can accurately forecast, plan and schedule production. Regardless of what the numbers are, if they have been consistent, you can now, with a high degree of accuracy, schedule what you can manufacture for your customers. Once you understand these measures, you can then begin to look at each of the loss categories, speed, quality, operational and planned, and use the appropriate reliability tool to reduce the losses.) Also, adding as a factor the unscheduled time of Immerman in the exponential failure time considerations of Pooyah is a simple substitution of one known element for another under MPEP 2143(I)(B) because the methods differ by a consideration of unscheduled time (i.e., the claimed idle time), the functions of including or excluding unscheduled time are known in the art, a person of ordinary skill in the art would have substituted the exclusion for the inclusion, and the results would be predictable.
Cao teaches that weather is a factor in determining the operation of the equipment and teaches the use of simplifying assumptions to reduce complexity of determinations. (Cao [0033] “Similarly, the BAS can determine and indicate when the systems operate inefficiently, such as due to an occupant overriding the room control because of personal preference or due to weather conditions change drastically.” [0113] “FIGS. 15A and 15B illustrate the use of PCA on sensor values in accordance with disclosed embodiments, for section of key features (sensor values or data). As shown in FIG. 15A, 4 sensor values 1502 are used as the input features (in this example, the discharge air temperature, supply air temperature, reheat valve command, and supply fan status). The system can then fit a health index from these readings using, in this case, a PCA feature engineering process 1504. Assuming the health index h[k] is a linear combination of feature signals s[k], then h[k] A s[k].” Also, the limitation, “corresponds to,” is sufficiently broad to encompass any relationship, whether recognized in data or not.), but does not appear to explicitly teach, but Korn teaches:
calibrating, by the processing circuit, a runtime determined from the historical operating data by determining an idle time associated with a component of the building devices or the building device components using climate data corresponding to a location of the component (Korn Page 1-4, Second Bullet “Variable weather patterns and manual thermostat control. The EFLHC equation is based on the air conditioner running every day that the average daily temperature is above 65°F. Homeowners may wait for days after the first CDD are generated and shut their units off before the last CDD are generated in the fall.” Page 1-1 “To calculate cooling savings, Technical Reference Manuals (TRMs), engineering savings calculations, and evaluation reports frequently rely on full-load cooling hours. Pages 1-1 – 1-2 “Equivalent full-load cooling hours (EFLHC) are the number of hours an air conditioner would have to operate at full load to equal the amount of cooling delivered by the system at a constant thermostat setting over a cooling season.” See Table 1 below; For chillers, an assumption is used to that any summer day that is 65 or below at a location is an idle day (e.g., available for use but not used) and other summer days are calibrated runtime days where the chiller is used. There is an argument that the Immerman reference is not even necessary because the Korn reference teaches the calibration of the runtime based on idle time determined from climate data, so this serves as an alternative rejection on that basis.)
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It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claims to modify the calibration of Cao with the qualification of location in Korn because Cao is concerned with using historical data, such as runtimes to perform survival analysis, and a person skilled in the art would be motivated to look to Korn to better harmonize the data by regularizing the data to make the effect of the run hours more consistent with the effects of run time (e.g., failure or heat use). (Cao [0089] “databases can include, for example, time-series databases, SQL and non-SQL databases, graph databases, and any other databases or repositories as may be useful to perform processes as described here. In particular, such data as access runtime and maintenance log data and the time-event tables described above can be stored in data lake 518.” Also see FIG. 6; Korn Page 1-1 Abstract, second paragraph: “This paper uses actual metering data from 60 homes in a similar climate to address sizing and full-load cooling hours by examining how the air conditioners are actually used and how they actually run.”)
Cao teaches that the automated system responds to predicted issues (Cao [0033] “A building automation system (BAS) such as disclosed herein can operate in an automatic operation mode that helps operate systems in the space efficiently to save energy. The BAS continuously evaluates environmental conditions and energy usage in the space and can determine and indicate to users when the space is being operated most efficiently. Similarly, the BAS can determine and indicate when the systems operate inefficiently, such as due to an occupant overriding the room control because of personal preference or due to weather conditions change drastically. The BAS can automatically, or at the input of a user, adjust the control settings to make the systems operate efficiently again.” – Use the system to save energy. [0035] “Disclosed embodiments include systems and methods for predictive maintenance of HVAC equipment and other physical equipment with using machine learning to ensure proper operation of the BAS or other system.” [0036] “The building automation system 100 is an environmental control system configured to control at least one of a plurality of environmental parameters within a building, such as temperature, humidity, lighting and/or the like.”), but does not appear to explicitly teach, but Schuster teaches:
adjusting an operation of the one or more building devices or building device components in response to the predicted fault or failure of the one or more building device or building device components, wherein adjusting the operation comprises controlling, by the processing circuit, at least one of a pump, a valve, a fan, or a damper to direct one or more fluid flows to bypass the one or more building devices or building device components affected by the predicted fault or failure. (NOTE: The Schuster reference is a prior art reference filed by the Applicant and uses language identical to the language of the Applicant’s support for the amended claim language, as mapped here. The Applicant is estopped, based on the assertion that the paragraphs of the instant application support the amendment, to dispute that the Schuster reference with identical language fails to teach what the Applicant asserts the language teaches on the record. The response states that the support from the amendments come from [0038], [0041]-[0044], [0046]-[0052], and [0075]. The instant application’s [0038], [0041]-[0044], [0046]-[0052], and [0075] are essentially identical to Schuster [0042], [0045]-[0049], [0050]-[0056], and [0079].)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claims to modify the generic automated control of building systems of Cao by the specific building control structures of Schuster because the person of ordinary skill in the art would be motivated by the aims of Cao to reduce maintenance system downtime and improve building energy efficiency to look to Schuster, which provides systems for providing continuous building services with high energy efficiency. (Cao [0033] “A building automation system (BAS) such as disclosed herein can operate in an automatic operation mode that helps operate systems in the space efficiently to save energy. The BAS continuously evaluates environmental conditions and energy usage in the space and can determine and indicate to users when the space is being operated most efficiently. Similarly, the BAS can determine and indicate when the systems operate inefficiently, such as due to an occupant overriding the room control because of personal preference or due to weather conditions change drastically. The BAS can automatically, or at the input of a user, adjust the control settings to make the systems operate efficiently again. [0166] “Disclosed embodiments provide significant advantages over other systems. For example, disclosed processes can reduce HVAC system operating costs by determining the remaining useful life for VAV components, forecast the maintenance budget for facilities management, and prevent downtime by using sensor and meter data to forecast fault occurrences.”; Schuster [0074] “This configuration may advantageously reduce disruptive demand response behavior relative to conventional systems. For example, integrated control layer 418 can be configured to assure that a demand response-driven upward adjustment to the setpoint for chilled water temperature (or another component that directly or indirectly affects temperature) does not result in an increase in fan energy (or other energy used to cool a space) that would result in greater total building energy use than was saved at the chiller.”)
Claim 2
Regarding claim 2, Cao, Pooya, Immerman, Korn, and Schuster teach the method of Claim 1. Pooya further teaches: wherein performing the operation includes subtracting the idle time from the runtime to generate the calibrated runtime. (Immerman Figure on Page 2 (shown above with respect to claim 1), Illustrating that actual production time = total operations time – unscheduled time – time losses)
Claim 6
Regarding claim 6, Cao, Pooya, Immerman, Korn, and Schuster teach the method of Claim 1. Cao further teaches: further comprising generating, by the processing circuit, a reliability metric describing a mean time between failures (MTBF) associated with the component based on the trained model. (Cao [0048] “ The processor 202 may be configured to execute program instructions or programming software or firmware stored in the instructions 220 of the memory 204,”. A processor and memory to execute instructions. [0082] “For the SA operators, the PM engine 508 combines the survival curves of individual components to represent a larger device comprised of the individual components.” [0104] “A system as disclosed herein can perform a probabilistic parametric survival analysis process. Where such parameters as MTTF and/or failure rate F for a device are known, the system can use them to fit in the structured survival curves, such as the Weibull distribution.” [0061] “For example, application engineers may estimate the Mean Time to Failure (MTTF), Mean Time before Failure (MTBF) or Mean Time before Repair (MTBR) for new hardware either from the vendor's manual or from historical log data.” The example Weibull calculation uses MTTF, rather than MTBF for a failure. However, it is stated as an example of a failure rate. The specification further discusses other failure rates including MTBF in paragraph . Further, paragraph [0082] specifies that failures of a subcomponent (e.g., of a chiller), so a collection of determined MTTF’s of subcomponents could be used to calculate an MTBF.)
Claim 7
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Regarding claim 7, Cao, Pooya, Immerman, Korn, and Schuster teach the method of Claim 1. Cao further teaches: wherein the historical operating data includes two or more event dates that include a failure date associated with a failure of the component and a start date associated with a day when the component came into use, and wherein the method further includes calculating a runtime of the component by determining an amount of time between the failure date and the start date. (Cao FIG. 6, [0079] “FIG. 6 illustrates a non-limiting example of historical data 600 that can be used in various embodiments as input data for the survival analysis. In this example, data from multiple machines/devices is shown, with line data indicating lost tracking (plain line with no terminus), that a fault has occurred and when (line terminating in point indicating when fault occurred), and no fault yet (line with arrow indicating that the device is still performing normally). Each line in the line data can be associated with a specific machine or device identifier. Of course, this exemplary illustration does not limit how such data could be recorded, stored, or displayed. The historical data can include any number of samples, including data for hundreds or thousands of devices.” The data represented in FIG. 6 include start dates and failure dates.)
Claim 10
Regarding claim 10, Cao, Pooya, Immerman, Korn, and Schuster teach the method of Claim 1. Cao further teaches: wherein training the component reliability model to produce the trained model includes determining a shape parameter and a scale parameter of a Weibull model. (Cao [0105] “The Weibull distribution is a method to describe the failure rate of a device, where a represents a shape parameter, b represents a scale parameter, and k represents an input variable. Weibull distribution is understood by those of skill in the art and is described, at time of filing, at en.wikipedia.org/wiki Weibull distribution. For computing F, the a and b parameters are the same as those in the definition of Weibull distribution. The Gamma function Γ(x) is a standard math function, described, for example, at time of filing at en.wikipedia.org/wiki/Gamma_function.” Train a Weibull function based on calibrated runtime, the definition and construction of which were demonstrated by Pooya.)
Claim 11
Regarding claim 11, Cao, Pooya, Immerman, Korn, and Schuster teach the method of Claim 1. Korn further teaches: wherein determining the idle time is based on a climate data corresponding the location of the component. (Korn Page 1-1 “To calculate cooling savings, Technical Reference Manuals (TRMs), engineering savings calculations, and evaluation reports frequently rely on full-load cooling hours. Pages 1-1 – 1-2 “Equivalent full-load cooling hours (EFLHC) are the number of hours an air conditioner would have to operate at full load to equal the amount of cooling delivered by the system at a constant thermostat setting over a cooling season.” See Table 1 below; For chillers, it makes sense to adjust or “calibrate” the run hours to account for run hours equivalent at full capacity to regularize the use in different climates.)
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Claim 12
Regarding claim 12, Cao Teaches:
One or more non-transitory computer-readable storage media having instructions stored thereon that, when executed by one or more processors, cause the one or more processors to: (Cao Abstract “Methods for predictive maintenance with using machine learning in a building automation system and corresponding systems and computer-readable mediums.”)
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receive historical operating data associated with one or more chillers or chiller components, (Cao [0045] “Examples of field devices 112 include lights, thermostats, temperature sensors, lighting sensors, fans, damper actuators, heaters, chillers [...] A physical device of the building automation system 100 can include any of these exemplary field devices 112.” [0089] “databases can include, for example, time-series databases, SQL and non-SQL databases, graph databases, and any other databases or repositories as may be useful to perform processes as described here. In particular, such data as access runtime and maintenance log data and the time-event tables described above can be stored in data lake 518.”; FIG. 6, [0079] “FIG. 6 illustrates a non-limiting example of historical data 600 that can be used in various embodiments as input data for the survival analysis. In this example, data from multiple machines/devices is shown, with line data indicating lost tracking (plain line with no terminus), that a fault has occurred and when (line terminating in point indicating when fault occurred), and no fault yet (line with arrow indicating that the device is still performing normally). Each line in the line data can be associated with a specific machine or device identifier. Of course, this exemplary illustration does not limit how such data could be recorded, stored, or displayed. The historical data can include any number of samples, including data for hundreds or thousands of devices.” The data represented in FIG. 6 include start dates and failure dates, which are historical data.)
(Cao FIG. 6, [0079] “FIG. 6 illustrates a non-limiting example of historical data 600 that can be used in various embodiments as input data for the survival analysis. In this example, data from multiple machines/devices is shown, with line data indicating lost tracking (plain line with no terminus), that a fault has occurred and when (line terminating in point indicating when fault occurred), and no fault yet (line with arrow indicating that the device is still performing normally).”)
train a chiller reliability model using the (Cao [0105] “The Weibull distribution is a method to describe the failure rate of a device, where a represents a shape parameter, b represents a scale parameter, and k represents an input variable. Weibull distribution is understood by those of skill in the art and is described, at time of filing, at […]. For computing F, the a and b parameters are the same as those in the definition of Weibull distribution. The Gamma function Γ(x) is a standard math function, described, for example, at time of filing at […].” Train a Weibull function)
generate a reliability metric indicating a predicted fault or failure of the chiller using the trained model; (Cao [0082] “For the SA operators, the PM engine 508 combines the survival curves of individual components to represent a larger device comprised of the individual components.” [0104] “A system as disclosed herein can perform a probabilistic parametric survival analysis process. Where such parameters as MTTF and/or failure rate F for a device are known, the system can use them to fit in the structured survival curves, such as the Weibull distribution.” [0061] “For example, application engineers may estimate the Mean Time to Failure (MTTF), Mean Time before Failure (MTBF) or Mean Time before Repair (MTBR) for new hardware either from the vendor's manual or from historical log data.” The example Weibull calculation uses MTTF, rather than MTBF for a failure. However, it is stated as an example of a failure rate. The specification further discusses other failure rates including MTBF in paragraph . Further, paragraph [0082] specifies that failures of a subcomponent (e.g., of a chiller), so a collection of determined MTTF’s of subcomponents could be used to calculate an MTBF. NOTE: While in Cao the engineers perform these calculations, POSITA would have found it obvious to have a computer of Cao do this, wherein POSITA would have been motivated to do so because its merely “Automating a manual activity” (MPEP 2144.04(III))) to do the mental process faster with the computer already in Cao.)
adjust an operation of the chiller in response to the predicted fault or failure of the chiller, wherein the adjusting the operation comprises. (Cao [0003]-[0004] “Building automation systems encompass a wide variety of systems that aid in the monitoring and control of various aspects of building operation. Building automation systems include security systems, fire safety systems, lighting systems, and heating, ventilation, and air conditioning (HVAC) systems. The elements of a building automation system are widely dispersed throughout a facility. For example, an HVAC system may include temperature sensors and ventilation damper controls, as well as other elements that are located in virtually every area of a facility. These building automation systems typically have one or more centralized control stations from which system data may be monitored and various aspects of system operation may be controlled and/or monitored. To allow for monitoring and control of the dispersed control system elements, building automation systems often employ multi-level communication networks to communicate operational and/or alarm information between operating elements, such as sensors and actuators, and the centralized control station. One example of a building automation system is the DXR Controller, available from Siemens Industry, Inc. Building Technologies Division of Buffalo Grove, Ill. (“Siemens”). In this system, several control stations connected via an Ethernet or another type of network may be distributed throughout one or more building locations, each having the ability to monitor and control system operation.” – The system is automated and uses predicted survival time to determine maintenance schedules. [0033] “A building automation system (BAS) such as disclosed herein can operate in an automatic operation mode that helps operate systems in the space efficiently to save energy. The BAS continuously evaluates environmental conditions and energy usage in the space and can determine and indicate to users when the space is being operated most efficiently. Similarly, the BAS can determine and indicate when the systems operate inefficiently, such as due to an occupant overriding the room control because of personal preference or due to weather conditions change drastically. The BAS can automatically, or at the input of a user, adjust the control settings to make the systems operate efficiently again.” – Use the system to save energy. [0035] “Disclosed embodiments include systems and methods for predictive maintenance of HVAC equipment and other physical equipment with using machine learning to ensure proper operation of the BAS or other system.” [0036] “The building automation system 100 is an environmental control system configured to control at least one of a plurality of environmental parameters within a building, such as temperature, humidity, lighting and/or the like. “ [0067] “Disclosed embodiments can use “survival curves” for planned maintenance scheduling. Survival curves project the probability of survival or failure of a device over time.” – Predictive maintenance and control of the devices in the building system is done responsive to determining likely failure times. See Also FIG. 8, which illustrates a replacement valve that fixes the machine)
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Cao suggests (Cao FIG. 6 and paragraph [0079] illustrating run times) but appears to not explicitly teach (in bold), but Pooya teaches
calculate a runtime of a chiller of the one or more chillers based on the two or more event dates, wherein the runtime comprises an amount of time the chiller is operational; (Pooya Page 1470, Second Column, under II. Definitions And Problems Formulations: “Consider an unreliable machine with au- and downtime being random variables with expected values Tup and Tdown. Obviously, Tup and Tdown are the exact values of MTBF and MTTR; we use these two types of notations interchangeably − depending on the issue at hand. Let tup,i and tdown,i be the durations of the ith occurrence (realization) of up- and downtime, i = 1, 2, . . . , respectively.” Run times are calculated using up and down times to determine MTBF.)
calibrate the runtime by (i) determining an idle time associated with the chiller corresponding to the chiller and (ii) performing an operation using the runtime and the idle time to generate a calibrated runtime; calibrated runtime to produce a trained model. (Pooya Page 1470, Second Column, under II. Definitions And Problems Formulations “It is noted that the terms up- and downtime imply the time when a machine within a production system does not operate due to technical malfunctions; it does not include the time when it is blocked or starved.” The definition of MTBF removes repair time, which is idle time.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claims to modify the calculation and calibration steps of Cao with the teachings of Pooya because Cao suggests determining run times for use in determining parameters for a Weibull model, and Pooya shows how to calculate the run times to determine the minimum number of data points needed for a Weibull or other survival functions. (Pooya Page 1470, Second Column, under II. Definitions And Problems Formulations “It is noted that the terms up- and downtime imply the time when a machine within a production system does not operate due to technical malfunctions; it does not include the time when it is blocked or starved.” Page 1472, IV. Evaluating Critical Number of Measurements For Nonexponential Machines, first paragraph: “To investigate the critical number of measurements in the nonexponential case, we consider machines obeying Weibull, gamma, and log-normal reliability models”; Cao [0134] “The sensor data and failure rate can include historical data, such as runtime data and maintenance log data stored in a data lake repository.” Also, FIG. 6 and paragraph [0079])
Cao and Pooya appear to fail to explicitly teach, but Immerman teaches:
determining an idle time associated with the chiller using
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wherein the runtime comprises an amount of time the one or more building devices or building device components are operational; the idle time is a first subset of the runtime and comprises an amount of time the one or more building devices or building device components are operational but not running; and the calibrated runtime is a second subset of the runtime and comprises an amount of time the one or more building devices are operational and running; (Immerman See Figure on Page 2 (shown on the Right), Fourth Row – Actual production time (claimed calibrated run time) is total operational time (claimed total runtime) minus unscheduled time (claimed idle time during which the machine is operational) minus time losses (non-operational time). To appropriately account for unscheduled time, the actual production time and down time are distinguished. These metrics better represent availability.)
It would have been obvious to a person of ordinary skill in the art to substitute the down time and up time as taught in Pooya with the production time, time losses, and unscheduled time of Immerman because a person of ordinary skill in the art would be motivated to improve the up and down time of Pooya, which attempted to better model effective use times of machinery, with a more accurate reflection of equipment availability provided by Immerman and its use of actual production time relative to time losses and unscheduled time. (Pooya Abstract “The mean time between failures (MTBF) and mean time to repair (MTTR) of manufacturing equipment (e.g., machines) are used in every quantitative method for production systems performance analysis, continuous improvement, and design. Unfortunately, the literature offers no methods for evaluating the smallest number of up- and downtime measurements necessary and sufficient to calculate reliable estimates of these equipment characteristics.” Page 1475, Left Column, Lines 9-14 “The future work on the topic of this article includes the following. 1) Deriving a tighter bound on the observation time required to collect the desired number of event measurements (e.g., up- and downtime realizations, defective parts produced, etc.).; Immerman Page 1, First Three Paragraphs “To review, OEE is measured by combining a machine’s performance, availability, and quality. In this way, OEE helps you to identify potential losses and understand where your process is falling short. But OEE alone is just the first step to fully understanding your performance. Today we will explore two other important and related metrics to help round out your continuous improvement strategy: Overall Operations Effectiveness (OOE) and Total Effective Equipment Performance (TEEP). The major differentiator between these three metrics is in how you define Availability. At their core, the goal of each of these metrics is to determine how much good product was made versus how much could have been made. Deciding which metric to use is really dependent on the time frame you consider. To better illustrate this, let’s think about a year’s production on your shop floor. Should your production be based on 365 days and 24 hrs each day? Or perhaps you should only look at the times when you had a shift scheduled? Or maybe you only want to look at the scheduled shifts the time you actually ran? Clearly there are different ways to think about your Availability and this requires different metrics to address each timeframe.” Page 2, Below The Figure “For example, your company is trying to determine whether or not they need to purchase new equipment in order to meet your production demands. You will first need to confirm that you in fact need the additional capacity. Depending on how you define time, needs to identify how much room there is to increase capacity in order to decide whether to focus on getting more from current equipment or to purchase new equipment. TEEP shows how much potential you have to increase production output with your current equipment. In many cases, reclaiming potential production output is a faster and less expensive alternative to purchasing new equipment. The importance of understanding OEE, OOE, and TEEP is essential so you can accurately forecast, plan and schedule production. Regardless of what the numbers are, if they have been consistent, you can now, with a high degree of accuracy, schedule what you can manufacture for your customers. Once you understand these measures, you can then begin to look at each of the loss categories, speed, quality, operational and planned, and use the appropriate reliability tool to reduce the losses.) Also, adding as a factor the unscheduled time of Immerman in the exponential failure time considerations of Pooyah is a simple substitution of one known element for another under MPEP 2143(I)(B) because the methods differ by a consideration of unscheduled time (i.e., the claimed idle time), the functions of including or excluding unscheduled time are known in the art, a person of ordinary skill in the art would have substituted the exclusion for the inclusion, and the results would be predictable.
Cao teaches that weather is a factor in determining the operation of the equipment and teaches the use of simplifying assumptions to reduce complexity of determinations. (Cao [0033] “Similarly, the BAS can determine and indicate when the systems operate inefficiently, such as due to an occupant overriding the room control because of personal preference or due to weather conditions change drastically.” [0113] “FIGS. 15A and 15B illustrate the use of PCA on sensor values in accordance with disclosed embodiments, for section of key features (sensor values or data). As shown in FIG. 15A, 4 sensor values 1502 are used as the input features (in this example, the discharge air temperature, supply air temperature, reheat valve command, and supply fan status). The system can then fit a health index from these readings using, in this case, a PCA feature engineering process 1504. Assuming the health index h[k] is a linear combination of feature signals s[k], then h[k] A s[k].” Also, the limitation, “corresponds to,” is sufficiently broad to encompass any relationship, whether recognized in data or not.), but does not appear to explicitly teach, but Korn teaches:
calibrate the runtime by (i) determining an idle time associated with the chiller using climate data corresponding to a location of the chiller and (ii) performing an operation using the runtime and the idle time to generate a calibrated runtime; (Korn Page 1-4, Second Bullet “Variable weather patterns and manual thermostat control. The EFLHC equation is based on the air conditioner running every day that the average daily temperature is above 65°F. Homeowners may wait for days after the first CDD are generated and shut their units off before the last CDD are generated in the fall.” Page 1-1 “To calculate cooling savings, Technical Reference Manuals (TRMs), engineering savings calculations, and evaluation reports frequently rely on full-load cooling hours. Pages 1-1 – 1-2 “Equivalent full-load cooling hours (EFLHC) are the number of hours an air conditioner would have to operate at full load to equal the amount of cooling delivered by the system at a constant thermostat setting over a cooling season.” See Table 1 below; For chillers, an assumption is used to that any summer day that is 65 or below at a location is an idle day (e.g., available for use but not used) and other summer days are calibrated runtime days where the chiller is used. There is an argument that the Immerman reference is not even necessary because the Korn reference teaches the calibration of the runtime based on idle time determined from climate data, so this serves as an alternative rejection on that basis.)
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It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claims to modify the calibration of Cao with the qualification of location in Korn because Cao is concerned with using historical data, such as runtimes to perform survival analysis, and a person skilled in the art would be motivated to look to Korn to better harmonize the data by regularizing the data to make the effect of the run hours more consistent with the effects of run time (e.g., failure or heat use). (Cao [0089] “databases can include, for example, time-series databases, SQL and non-SQL databases, graph databases, and any other databases or repositories as may be useful to perform processes as described here. In particular, such data as access runtime and maintenance log data and the time-event tables described above can be stored in data lake 518.” Also see FIG. 6; Korn Page 1-1 Abstract, second paragraph: “This paper uses actual metering data from 60 homes in a similar climate to address sizing and full-load cooling hours by examining how the air conditioners are actually used and how they actually run.”)
Cao teaches that the automated system responds to predicted issues (Cao [0033] “A building automation system (BAS) such as disclosed herein can operate in an automatic operation mode that helps operate systems in the space efficiently to save energy. The BAS continuously evaluates environmental conditions and energy usage in the space and can determine and indicate to users when the space is being operated most efficiently. Similarly, the BAS can determine and indicate when the systems operate inefficiently, such as due to an occupant overriding the room control because of personal preference or due to weather conditions change drastically. The BAS can automatically, or at the input of a user, adjust the control settings to make the systems operate efficiently again.” – Use the system to save energy. [0035] “Disclosed embodiments include systems and methods for predictive maintenance of HVAC equipment and other physical equipment with using machine learning to ensure proper operation of the BAS or other system.” [0036] “The building automation system 100 is an environmental control system configured to control at least one of a plurality of environmental parameters within a building, such as temperature, humidity, lighting and/or the like.”), but does not appear to explicitly teach, but Schuster teaches:
adjust an operation of the chiller in response to the predicted fault or failure of the chiller, wherein the adjusting the operation comprises, by a processing circuit, (i) shutting down the chiller affected by the predicted fault or failure and (ii) compensating for load removed from the chiller affected by the predicted fault or failure by at least one of starting a second chiller, or utilizing stored energy. (NOTE: There is a new matter rejection for these features. That said, the Schuster reference is a prior art reference filed by the Applicant and uses language identical to the language of the Applicant’s support for the amended claim language, as mapped here. The Applicant is estopped, based on the assertion that the paragraphs of the instant application support the amendment, to dispute that the Schuster reference with identical language fails to teach what the Applicant asserts the language teaches on the record. The response states that the support from the amendments come from [0038]-[0043], [0066]-[0067], [0070], and [0075]. The instant application’s [0038]-[0043], [0066]-[0067], [0070], and [0075] are essentially identical to Schuster paragraphs [0042]-[0047], [0070]-[0071], [0074], and [0079].)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claims to modify the generic automated control of building systems of Cao by the specific building control structures of Schuster because the person of ordinary skill in the art would be motivated by the aims of Cao to reduce maintenance system downtime and improve building energy efficiency to look to Schuster, which provides systems for providing continuous building services with high energy efficiency. (Cao [0033] “A building automation system (BAS) such as disclosed herein can operate in an automatic operation mode that helps operate systems in the space efficiently to save energy. The BAS continuously evaluates environmental conditions and energy usage in the space and can determine and indicate to users when the space is being operated most efficiently. Similarly, the BAS can determine and indicate when the systems operate inefficiently, such as due to an occupant overriding the room control because of personal preference or due to weather conditions change drastically. The BAS can automatically, or at the input of a user, adjust the control settings to make the systems operate efficiently again. [0166] “Disclosed embodiments provide significant advantages over other systems. For example, disclosed processes can reduce HVAC system operating costs by determining the remaining useful life for VAV components, forecast the maintenance budget for facilities management, and prevent downtime by using sensor and meter data to forecast fault occurrences.”; Schuster [0074] “This configuration may advantageously reduce disruptive demand response behavior relative to conventional systems. For example, integrated control layer 418 can be configured to assure that a demand response-driven upward adjustment to the setpoint for chilled water temperature (or another component that directly or indirectly affects temperature) does not result in an increase in fan energy (or other energy used to cool a space) that would result in greater total building energy use than was saved at the chiller.”)
Claim 13
Regarding claim 13, Cao, Pooya, Immerman, Korn, and Schuster teach the CRM of Claim 12. Pooya further teaches: The one or more non-transitory computer-readable storage media of Claim 12, wherein performing the operation includes subtracting the idle time from the runtime to generate the calibrated runtime. ; (Immerman See Figure on Page 2 (shown above), Fourth Row – Actual production time (claimed calibrated run time) is total operational time (claimed total runtime) minus unscheduled time (claimed idle time during which the machine is operational) minus time losses (non-operational time). To appropriately account for unscheduled time, the actual production time and down time are distinguished. These metrics better represent availability.)
Claim 14
Regarding claim 14, Cao, Pooya, Immerman, Korn, and Schuster teach the CRM of Claim 12. Cao further teaches: wherein training the chiller reliability model includes training at least one of (i) a Weibull model or (ii) a Cox model using the calibrated runtime to produce the trained model. (Cao [0105] “The Weibull distribution is a method to describe the failure rate of a device, where a represents a shape parameter, b represents a scale parameter, and k represents an input variable. Weibull distribution is understood by those of skill in the art and is described, at time of filing, at en.wikipedia.org/wiki Weibull distribution. For computing F, the a and b parameters are the same as those in the definition of Weibull distribution. The Gamma function Γ(x) is a standard math function, described, for example, at time of filing at en.wikipedia.org/wiki/Gamma_function.” Train a Weibull function based on calibrated runtime, the definition and construction of which were demonstrated by Pooya.)
Claim 17
Regarding claim 17, Cao, Pooya, Immerman, Korn, and Schuster teach the CRM of Claim 12. Cao further teaches: wherein the instructions further cause the one or more processors to generate a reliability metric describing a mean time between failures (MTBF) associated with the chiller based on the trained model. (Cao [0082] “For the SA operators, the PM engine 508 combines the survival curves of individual components to represent a larger device comprised of the individual components.” [0104] “A system as disclosed herein can perform a probabilistic parametric survival analysis process. Where such parameters as MTTF and/or failure rate F for a device are known, the system can use them to fit in the structured survival curves, such as the Weibull distribution.” [0061] “For example, application engineers may estimate the Mean Time to Failure (MTTF), Mean Time before Failure (MTBF) or Mean Time before Repair (MTBR) for new hardware either from the vendor's manual or from historical log data.” The example Weibull calculation uses MTTF, rather than MTBF for a failure. However, it is stated as an example of a failure rate. The specification further discusses other failure rates including MTBF in paragraph . Further, paragraph [0082] specifies that failures of a subcomponent (e.g., of a chiller), so a collection of determined MTTF’s of subcomponents could be used to calculate an MTBF.)
Claim 18
Regarding claim 18, Cao, Pooya, Immerman, Korn, and Schuster teach the CRM of Claim 12. Cao further teaches: wherein the two or more event dates include a failure date associated with a failure of the chiller and a start date associated with a day when the chiller came online, and wherein calculating the runtime of the chiller includes determining an amount of time between the failure date and the start date. (Cao FIG. 6, [0079] “FIG. 6 illustrates a non-limiting example of historical data 600 that can be used in various embodiments as input data for the survival analysis. In this example, data from multiple machines/devices is shown, with line data indicating lost tracking (plain line with no terminus), that a fault has occurred and when (line terminating in point indicating when fault occurred), and no fault yet (line with arrow indicating that the device is still performing normally). Each line in the line data can be associated with a specific machine or device identifier. Of course, this exemplary illustration does not limit how such data could be recorded, stored, or displayed. The historical data can include any number of samples, including data for hundreds or thousands of devices.” The data represented in FIG. 6 include start dates and failure dates.)
Claim 21
Regarding claim 21, Cao, Pooya, Immerman, Korn, and Schuster teach the CRM of Claim 12. Cao further teaches: wherein training the chiller reliability model to produce the trained model includes determining a shape parameter and a scale parameter of a Weibull model. (Cao [0105] “The Weibull distribution is a method to describe the failure rate of a device, where a represents a shape parameter, b represents a scale parameter, and k represents an input variable. Weibull distribution is understood by those of skill in the art and is described, at time of filing, at en.wikipedia.org/wiki Weibull distribution. For computing F, the a and b parameters are the same as those in the definition of Weibull distribution. The Gamma function Γ(x) is a standard math function, described, for example, at time of filing at en.wikipedia.org/wiki/Gamma_function.”)
Claim 22
Regarding claim 22, Cao, Pooya, Immerman, Korn, and Schuster teach the CRM of Claim 12. Korn further teaches:, wherein determining the idle time is based on a climate data corresponding the location of the component. (Korn Page 1-1 “To calculate cooling savings, Technical Reference Manuals (TRMs), engineering savings calculations, and evaluation reports frequently rely on full-load cooling hours. Pages 1-1 – 1-2 “Equivalent full-load cooling hours (EFLHC) are the number of hours an air conditioner would have to operate at full load to equal the amount of cooling delivered by the system at a constant thermostat setting over a cooling season.” See Table 1 below; For chillers, it makes sense to adjust the run hours to account for run hours equivalent at full capacity to regularize the use in different climates.)
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Claim 23
Regarding Claim 23, Cao Teaches:
A predictive maintenance system, comprising: a processing circuit including a processor and memory, the memory having instructions stored thereon that, when executed by the processor, cause the processor to: (Cao [0048] “ The processor 202 may be configured to execute program instructions or programming software or firmware stored in the instructions 220 of the memory 204,”. A processor and memory to execute instructions.)
receive historical operating data associated with one or more chillers or chiller components, (Cao [0045] “Examples of field devices 112 include lights, thermostats, temperature sensors, lighting sensors, fans, damper actuators, heaters, chillers [...] A physical device of the building automation system 100 can include any of these exemplary field devices 112.” [0061] Standard corrective maintenance (CM) and scheduled maintenance (SM) methods are heuristic and imprecise. For example, application engineers may estimate the Mean Time to Failure (MTTF), Mean Time before Failure (MTBF) or Mean Time before Repair (MTBR) for new hardware either from the vendor's manual or from historical log data. The engineer then prescribes either the CM or SM approach.” [0089] “databases can include, for example, time-series databases, SQL and non-SQL databases, graph databases, and any other databases or repositories as may be useful to perform processes as described here. In particular, such data as access runtime and maintenance log data and the time-event tables described above can be stored in data lake 518.” Historical operating data is received.)
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the historical operating data including two or more event dates associated with the one or more chillers, wherein the two or more event dates include a failure date associated with a failure of the chiller and a start date associated with a day when the chiller came online; (Cao FIG. 6, [0079] “FIG. 6 illustrates a non-limiting example of historical data 600 that can be used in various embodiments as input data for the survival analysis. In this example, data from multiple machines/devices is shown, with line data indicating lost tracking (plain line with no terminus), that a fault has occurred and when (line terminating in point indicating when fault occurred), and no fault yet (line with arrow indicating that the device is still performing normally). Each line in the line data can be associated with a specific machine or device identifier. Of course, this exemplary illustration does not limit how such data could be recorded, stored, or displayed. The historical data can include any number of samples, including data for hundreds or thousands of devices.” The data represented in FIG. 6 include start dates and failure dates.)
calculate a runtime of a chiller of the one or more chillers based on the two or more event dates (Cao FIG. 6, [0079] “FIG. 6 illustrates a non-limiting example of historical data 600 that can be used in various embodiments as input data for the survival analysis. In this example, data from multiple machines/devices is shown, with line data indicating lost tracking (plain line with no terminus), that a fault has occurred and when (line terminating in point indicating when fault occurred), and no fault yet (line with arrow indicating that the device is still performing normally). Each line in the line data can be associated with a specific machine or device identifier. Of course, this exemplary illustration does not limit how such data could be recorded, stored, or displayed. The historical data can include any number of samples, including data for hundreds or thousands of devices.” The data represented in FIG. 6 include start dates and failure dates and time before a fault that includes runtime.)
train a chiller reliability model using the (Cao [0105] “The Weibull distribution is a method to describe the failure rate of a device, where a represents a shape parameter, b represents a scale parameter, and k represents an input variable. Weibull distribution is understood by those of skill in the art and is described, at time of filing, at en.wikipedia.org/wiki Weibull distribution. For computing F, the a and b parameters are the same as those in the definition of Weibull distribution. The Gamma function Γ(x) is a standard math function, described, for example, at time of filing at en.wikipedia.org/wiki/Gamma_function.” Train a Weibull function based on determined runtime,)
generate a reliability metric describing a mean time between failures (MTBF) associated with the chiller using the chiller reliability model indicating a predicted fault of the chiller. (Cao [0082] “For the SA operators, the PM engine 508 combines the survival curves of individual components to represent a larger device comprised of the individual components.” [0104] “A system as disclosed herein can perform a probabilistic parametric survival analysis process. Where such parameters as MTTF and/or failure rate F for a device are known, the system can use them to fit in the structured survival curves, such as the Weibull distribution.” [0061] “For example, application engineers may estimate the Mean Time to Failure (MTTF), Mean Time before Failure (MTBF) or Mean Time before Repair (MTBR) for new hardware either from the vendor's manual or from historical log data.” The example Weibull calculation uses MTTF, rather than MTBF for a failure. However, it is stated as an example of a failure rate (predicted fault of the chiller). The specification further discusses other failure rates including MTBF in paragraph . Further, paragraph [0082] specifies that failures of a subcomponent (e.g., of a chiller), so a collection of determined MTTF’s of subcomponents could be used to calculate an MTBF.)
adjust an operation of the chiller in response to the predicted fault or failure of the chiller, wherein the adjusting the operation comprises (Cao [0003]-[0004] “Building automation systems encompass a wide variety of systems that aid in the monitoring and control of various aspects of building operation. Building automation systems include security systems, fire safety systems, lighting systems, and heating, ventilation, and air conditioning (HVAC) systems. The elements of a building automation system are widely dispersed throughout a facility. For example, an HVAC system may include temperature sensors and ventilation damper controls, as well as other elements that are located in virtually every area of a facility. These building automation systems typically have one or more centralized control stations from which system data may be monitored and various aspects of system operation may be controlled and/or monitored. To allow for monitoring and control of the dispersed control system elements, building automation systems often employ multi-level communication networks to communicate operational and/or alarm information between operating elements, such as sensors and actuators, and the centralized control station. One example of a building automation system is the DXR Controller, available from Siemens Industry, Inc. Building Technologies Division of Buffalo Grove, Ill. (“Siemens”). In this system, several control stations connected via an Ethernet or another type of network may be distributed throughout one or more building locations, each having the ability to monitor and control system operation.” – The system is automated and uses predicted survival time to determine maintenance schedules. [0033] “A building automation system (BAS) such as disclosed herein can operate in an automatic operation mode that helps operate systems in the space efficiently to save energy. The BAS continuously evaluates environmental conditions and energy usage in the space and can determine and indicate to users when the space is being operated most efficiently. Similarly, the BAS can determine and indicate when the systems operate inefficiently, such as due to an occupant overriding the room control because of personal preference or due to weather conditions change drastically. The BAS can automatically, or at the input of a user, adjust the control settings to make the systems operate efficiently again.” – Use the system to save energy. [0035] “Disclosed embodiments include systems and methods for predictive maintenance of HVAC equipment and other physical equipment with using machine learning to ensure proper operation of the BAS or other system.” [0036] “The building automation system 100 is an environmental control system configured to control at least one of a plurality of environmental parameters within a building, such as temperature, humidity, lighting and/or the like. “ [0067] “Disclosed embodiments can use “survival curves” for planned maintenance scheduling. Survival curves project the probability of survival or failure of a device over time.” – Predictive maintenance and control of the devices in the building system is done responsive to determining likely failure times.)
Cao suggests (See FIG. 6 and associated paragraph [0079] previously recited for demonstrating run times) but does appear to explicity teach, but Pooya teaches (in bold):
calculate a runtime of a chiller of the one or more chillers based on the two or more event dates by determining an amount of time between the failure date and the start date, wherein the runtime comprises an amount of time the chiller is operational; (Pooya Page 1470, Second Column, under II. Definitions And Problems Formulations: Obviously, Tup and Tdown are the exact values of MTBF and MTTR; we use these two types of notations interchangeably − depending on the issue at hand. Let tup,i and tdown,i be the durations of the ith occurrence (realization) of up- and downtime, i = 1, 2, . . . , respectively. (It is noted that the terms up- and downtime imply the time when a machine within a production system does not operate due to technical malfunctions; it does not include the time when it is blocked or starved.)” Run times are calculated using up and down times and the event dates of Cao.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claims to modify the calculation and calibration steps of Cao with the teachings of Pooya because Cao suggests determining run times for use in determining parameters for a Weibull model, and Pooya shows how to calculate the run times to determine the minimum number of data points needed for a Weibull or other survival functions. (Pooya Page 1470, Second Column, under II. Definitions And Problems Formulations “It is noted that the terms up- and downtime imply the time when a machine within a production system does not operate due to technical malfunctions; it does not include the time when it is blocked or starved.” Page 1472, IV. Evaluating Critical Number of Measurements For Nonexponential Machines, first paragraph: “To investigate the critical number of measurements in the nonexponential case, we consider machines obeying Weibull, gamma, and log-normal reliability models”; Cao [0134] “The sensor data and failure rate can include historical data, such as runtime data and maintenance log data stored in a data lake repository.” Also, FIG. 6 and paragraph [0079])
Cao in view of Pooya does not appear to explicitly teach, but Cao in view of Pooya and Immerman teaches:
determining an idle time associated with the chiller
[…]
calibrate the runtime by determining an idle time associated with the chiller of the chiller and subtracting the idle time from the runtime to generate a calibrated runtime; […] calibrated runtime (Immerman See Figure on Page 2 (shown on the Right), Fourth Row – Actual production time (claimed calibrated run time) is total operational time (claimed total runtime) minus unscheduled time (claimed idle time during which the machine is operational) minus time losses (non-operational time). To appropriately account for unscheduled time, the actual production time and down time are distinguished. These metrics better represent availability.)
It would have been obvious to a person of ordinary skill in the art to substitute the down time and up time as taught in Pooya with the production time, time losses, and unscheduled time of Immerman because a person of ordinary skill in the art would be motivated to improve the up and down time of Pooya, which attempted to better model effective use times of machinery, with a more accurate reflection of equipment availability provided by Immerman and its use of actual production time relative to time losses and unscheduled time. (Pooya Abstract “The mean time between failures (MTBF) and mean time to repair (MTTR) of manufacturing equipment (e.g., machines) are used in every quantitative method for production systems performance analysis, continuous improvement, and design. Unfortunately, the literature offers no methods for evaluating the smallest number of up- and downtime measurements necessary and sufficient to calculate reliable estimates of these equipment characteristics.” Page 1475, Left Column, Lines 9-14 “The future work on the topic of this article includes the following. 1) Deriving a tighter bound on the observation time required to collect the desired number of event measurements (e.g., up- and downtime realizations, defective parts produced, etc.).; Immerman Page 1, First Three Paragraphs “To review, OEE is measured by combining a machine’s performance, availability, and quality. In this way, OEE helps you to identify potential losses and understand where your process is falling short. But OEE alone is just the first step to fully understanding your performance. Today we will explore two other important and related metrics to help round out your continuous improvement strategy: Overall Operations Effectiveness (OOE) and Total Effective Equipment Performance (TEEP). The major differentiator between these three metrics is in how you define Availability. At their core, the goal of each of these metrics is to determine how much good product was made versus how much could have been made. Deciding which metric to use is really dependent on the time frame you consider. To better illustrate this, let’s think about a year’s production on your shop floor. Should your production be based on 365 days and 24 hrs each day? Or perhaps you should only look at the times when you had a shift scheduled? Or maybe you only want to look at the scheduled shifts the time you actually ran? Clearly there are different ways to think about your Availability and this requires different metrics to address each timeframe.” Page 2, Below The Figure “For example, your company is trying to determine whether or not they need to purchase new equipment in order to meet your production demands. You will first need to confirm that you in fact need the additional capacity. Depending on how you define time, needs to identify how much room there is to increase capacity in order to decide whether to focus on getting more from current equipment or to purchase new equipment. TEEP shows how much potential you have to increase production output with your current equipment. In many cases, reclaiming potential production output is a faster and less expensive alternative to purchasing new equipment. The importance of understanding OEE, OOE, and TEEP is essential so you can accurately forecast, plan and schedule production. Regardless of what the numbers are, if they have been consistent, you can now, with a high degree of accuracy, schedule what you can manufacture for your customers. Once you understand these measures, you can then begin to look at each of the loss categories, speed, quality, operational and planned, and use the appropriate reliability tool to reduce the losses.) Also, adding as a factor the unscheduled time of Immerman in the exponential failure time considerations of Pooyah is a simple substitution of one known element for another under MPEP 2143(I)(B) because the methods differ by a consideration of unscheduled time (i.e., the claimed idle time), the functions of including or excluding unscheduled time are known in the art, a person of ordinary skill in the art would have substituted the exclusion for the inclusion, and the results would be predictable.
Cao teaches that weather is a factor in determining the operation of the equipment. (Cao [0033] “Similarly, the BAS can determine and indicate when the systems operate inefficiently, such as due to an occupant overriding the room control because of personal preference or due to weather conditions change drastically.” Also, the limitation, “corresponds to,” is sufficiently broad to encompass any relationship, whether recognized in data or not.), but does not appear to explicitly teach, but Korn teaches:
calibrate the runtime by determining an idle time associated with the chiller using climate data corresponding to a location of the chiller and subtracting the idle time from the runtime to generate a calibrated runtime; (Korn Page 1-4, Second Bullet “Variable weather patterns and manual thermostat control. The EFLHC equation is based on the air conditioner running every day that the average daily temperature is above 65°F. Homeowners may wait for days after the first CDD are generated and shut their units off before the last CDD are generated in the fall.” Page 1-1 “To calculate cooling savings, Technical Reference Manuals (TRMs), engineering savings calculations, and evaluation reports frequently rely on full-load cooling hours. Pages 1-1 – 1-2 “Equivalent full-load cooling hours (EFLHC) are the number of hours an air conditioner would have to operate at full load to equal the amount of cooling delivered by the system at a constant thermostat setting over a cooling season.” See Table 1 below; For chillers, an assumption is used to that any summer day that is 65 or below at a location is an idle day (e.g., available for use but not used) and other summer days are calibrated runtime days where the chiller is used. There is an argument that the Immerman reference is not even necessary because the Korn reference teaches the calibration of the runtime based on idle time determined from climate data, so this serves as an alternative rejection on that basis.)
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It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claims to modify the calibration of Cao with the qualification of location in Korn because Cao is concerned with using historical data, such as runtimes to perform survival analysis, and a person skilled in the art would be motivated to look to Korn to better harmonize the data by regularizing the data to make the effect of the run hours more consistent with the effects of run time (e.g., failure or heat use). (Cao [0089] “databases can include, for example, time-series databases, SQL and non-SQL databases, graph databases, and any other databases or repositories as may be useful to perform processes as described here. In particular, such data as access runtime and maintenance log data and the time-event tables described above can be stored in data lake 518.” Also see FIG. 6; Korn Page 1-1 Abstract, second paragraph: “This paper uses actual metering data from 60 homes in a similar climate to address sizing and full-load cooling hours by examining how the air conditioners are actually used and how they actually run.”)
Cao teaches that the automated system responds to predicted issues (Cao [0033] “A building automation system (BAS) such as disclosed herein can operate in an automatic operation mode that helps operate systems in the space efficiently to save energy. The BAS continuously evaluates environmental conditions and energy usage in the space and can determine and indicate to users when the space is being operated most efficiently. Similarly, the BAS can determine and indicate when the systems operate inefficiently, such as due to an occupant overriding the room control because of personal preference or due to weather conditions change drastically. The BAS can automatically, or at the input of a user, adjust the control settings to make the systems operate efficiently again.” – Use the system to save energy. [0035] “Disclosed embodiments include systems and methods for predictive maintenance of HVAC equipment and other physical equipment with using machine learning to ensure proper operation of the BAS or other system.” [0036] “The building automation system 100 is an environmental control system configured to control at least one of a plurality of environmental parameters within a building, such as temperature, humidity, lighting and/or the like.”), but does not appear to explicitly teach, but Schuster teaches:
adjust an operation of the chiller in response to the predicted fault or failure of the chiller, wherein the adjusting the operation comprises operating the chiller affected by the predicted fault or failure in accordance with one or more adjusted setpoints to reduce demand on the chiller affected by the predicted fault or failure.(NOTE: The Schuster reference is a prior art reference filed by the Applicant and uses language identical to the language of the Applicant’s support for the amended claim language, as mapped here. The Applicant is estopped, based on the assertion that the paragraphs of the instant application support the amendment, to dispute that the Schuster reference with identical language fails to teach what the Applicant asserts the language teaches on the record. The response states that the support from the amendments come from [0068]-[0069] and [0071]-[0072]. The instant application’s [0068]-[0069] and [0071]-[0072] are essentially identical to Schuster [0072]-[0073], and [0075]-[0076].)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claims to modify the generic automated control of building systems of Cao by the specific building control structures of Schuster because the person of ordinary skill in the art would be motivated by the aims of Cao to reduce maintenance system downtime and improve building energy efficiency to look to Schuster, which provides systems for providing continuous building services with high energy efficiency. (Cao [0033] “A building automation system (BAS) such as disclosed herein can operate in an automatic operation mode that helps operate systems in the space efficiently to save energy. The BAS continuously evaluates environmental conditions and energy usage in the space and can determine and indicate to users when the space is being operated most efficiently. Similarly, the BAS can determine and indicate when the systems operate inefficiently, such as due to an occupant overriding the room control because of personal preference or due to weather conditions change drastically. The BAS can automatically, or at the input of a user, adjust the control settings to make the systems operate efficiently again. [0166] “Disclosed embodiments provide significant advantages over other systems. For example, disclosed processes can reduce HVAC system operating costs by determining the remaining useful life for VAV components, forecast the maintenance budget for facilities management, and prevent downtime by using sensor and meter data to forecast fault occurrences.”; Schuster [0074] “This configuration may advantageously reduce disruptive demand response behavior relative to conventional systems. For example, integrated control layer 418 can be configured to assure that a demand response-driven upward adjustment to the setpoint for chilled water temperature (or another component that directly or indirectly affects temperature) does not result in an increase in fan energy (or other energy used to cool a space) that would result in greater total building energy use than was saved at the chiller.”)
Claims 3 and 24: Cao, Pooya, Immerman, Korn, Schuster, Crumer
Claim(s) 3 and 24 are rejected under 35 U.S.C. 103 as being unpatentable over US. 2022/0057766 to Cao et al. (Cao) in view of NPL: “The (α, β)-Precise Estimates of MTBF and MTTR: Definitions, Calculations, and Induced Effect on Machine Efficiency Evaluation” by Pooya et al (Pooya), NPL: “OEE, OOE, AND TEEP- WHAT’S THE DIFFERENCE” by Immerman (Immerman), NPL: "Exactly What Is a Full Load Cooling Hour and Does Size Really Matter?" by Korn et al. (Korn), US 2109/ 0123931 A1 to Schuster et al. (Schuster), and NPL: “COMPARISON BETWEEN WEIBULL AND COX PROPORTIONAL HAZARDS MODELS” by Crumer (Crumer).
Claim 3
Regarding claim 3, Cao, Pooya, Immerman, and Korn teach the method of claim 1. Cao, Pooya, Immerman, and Korn appear to not explicitly teach, but Crumer teaches: wherein training the component reliability model includes training at least one of (i) a Weibull model or (ii) a Cox model using the calibrated runtime to produce the trained model. (Crumer Page ii, Abstract: “The time for an event to take place in an individual is called a survival time. Examples include the time that an individual survives after being diagnosed with a terminal illness or the time that an electronic component functions before failing. A popular parametric model for this type of data is the Weibull model, which is a flexible model that allows for the inclusion of covariates of the survival times. If distributional assumptions are not met or cannot be verified, researchers may turn to the semi-parametric Cox proportional hazards model. This model also allows for the inclusion of covariates of survival times but with less restrictive assumptions.” Cox and Weibull can both be used to describe survival times/failure times, and Crumer uses both.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claims to substitute the Weibull model described in Cao with the Cox model described in Crumer because a person of ordinary skill in the art would be motivated to look to Cao which presents the Weibull model as a mere example of a survival model and a person skilled in the art would look to Crumer, which demonstrates that the Cox model is a better substitute in some circumstances. (Cao [0067] “Disclosed embodiments can use “survival curves” for planned maintenance scheduling. Survival curves project the probability of survival or failure of a device over time.” [0063] “Instead, the valves will fail gradually follow certain distributions, such as Weibull distribution.” Crumer Page ii, Abstract: “ […] A popular parametric model for this type of data is the Weibull model, which is a flexible model that allows for the inclusion of covariates of the survival times. If distributional assumptions are not met or cannot be verified, researchers may turn to the semi-parametric Cox proportional hazards model”)
Claim 24
Regarding claim 24, Cao, Pooya, Immerman, and Korn teach the system of claim 23. Cao, Pooya, Immerman, and Korn appear to not explicitly teach, but Crumer teaches: wherein training the chiller reliability model includes training a Cox model using the calibrated runtime. (Crumer Page ii, Abstract: “The time for an event to take place in an individual is called a survival time. Examples include the time that an individual survives after being diagnosed with a terminal illness or the time that an electronic component functions before failing. A popular parametric model for this type of data is the Weibull model, which is a flexible model that allows for the inclusion of covariates of the survival times. If distributional assumptions are not met or cannot be verified, researchers may turn to the semi-parametric Cox proportional hazards model. This model also allows for the inclusion of covariates of survival times but with less restrictive assumptions.” Cox and Weibull can both be used to describe survival times/failure times.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claims to substitute the Weibull model described in Cao with the Cox model described in Crumer because a person of ordinary skill in the art would be motivated to look to Cao which presents the Weibull model as a mere example of a survival model and a person skilled in the art would look to Crumer, which demonstrates that the Cox model is a better substitute in some circumstances. (Cao [0067] “Disclosed embodiments can use “survival curves” for planned maintenance scheduling. Survival curves project the probability of survival or failure of a device over time.” [0063] “Instead, the valves will fail gradually follow certain distributions, such as Weibull distribution.” Crumer Page ii, Abstract: “ […] A popular parametric model for this type of data is the Weibull model, which is a flexible model that allows for the inclusion of covariates of the survival times. If distributional assumptions are not met or cannot be verified, researchers may turn to the semi-parametric Cox proportional hazards model”)
Claims 4-5, 8-9, 15-16, 19-20, and 25-26: Cao, Pooya, Immerman, Korn, Schuster, and Jeon.
Claim(s) 4-5, 8-9, 15-16, 19-20, and 25-26 are rejected under 35 U.S.C. 103 as being unpatentable over US. 2022/0057766 to Cao et al. (Cao) in view of NPL: “The (α, β)-Precise Estimates of MTBF and MTTR: Definitions, Calculations, and Induced Effect on Machine Efficiency Evaluation” by Pooya et al (Pooya), NPL: “OEE, OOE, AND TEEP- WHAT’S THE DIFFERENCE” by Immerman (Immerman), NPL: "Exactly What Is a Full Load Cooling Hour and Does Size Really Matter?" by Korn et al. (Korn), and NPL: “Product failure pattern analysis from warranty data using association rule and Weibull regression analysis: A case study” by Jeon et al. (Jeon).
Claim 4
Regarding claim 4, Cao, Pooya, Immerman, and Korn teach the method of claim 1. Cao, Pooya, Immerman, and Korn appear to not explicitly teach, but Jeon teaches: wherein training the component reliability model includes training the component reliability model using: (1) warranty claim data comprising information about building devices having experienced a failure for which a warranty claim has been received; and (2) censored data comprising information about building devices that are in warranty and have not experienced a failure indicated in the warranty claim data. (Jeon Page 176, second column, second paragraph: “Generally, warranty data are composed of warranty claims data and supplementary data. Claims data are the data collected during the servicing of claims under warranty, and supplementary data are additional data that are needed for effective warranty management [1]. For this reason, production data for sales should be aggregated with warranty data when probability of survival or expected failure time is needed.” The warranty data includes the failure (“(1)”), and the sales data includes all of the sales, including those that do not have any repairs (“(2)”). Page 178, second column, fourth paragraph: “On the other hand, the longest MTBF is recorded at 1,198,393h when equipment 1 is manufactured in the winter and its diesel engine is fabricated in the spring.” Also, it is clear that the data includes products that have not failed, as one of the MTBF values was over a million hours, which is well over 100 years. None of these parts could have existed for 100 years at the time of publication, as the earliest data came from 2009. This demonstrates that the data included instances where there were no failures.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claims to modify the historical data of Cao to determine a run time demonstrated in Jeon because a person of ordinary skill in the art would be motivated to look to Jeon for sources of run time data and methods for organizing data to be assembled for the Weibull or other survival analysis in Cao because Jeon’s methods for survival predictions improve the determination. (Cao [0005] “Maintenance of building automation systems can be expensive and time-consuming. Device failures can impact production, comfort levels, and facility operations, and can do so without warning. Improved systems are desirable.” [0134] “The sensor data and failure rate can include historical data, such as runtime data and maintenance log data stored in a data lake repository.” Jeon Abstract “The warranty data plays a crucial role in the improvement of the manufactured product. Association rule analysis is an efficient methodology for eliciting useful information from warranty data by defining the relationships between production data and failure data within warranty period. We extract association rules from warranty data of heavy duty diesel engine in order to find significant patterns of failures along with manufacturing information. We also used Weibull regression to identify influential factors that affect the variation in mean time between failures which are identified from extracted association rules. The results from the empirical study for manufacturing firm “D” provide information as to the areas in which improvements should be made. Moreover, the result for specific failure is able to suggest a solution to overcome short-sighted improvement representatively. This study expects to contribute quality improvement to manufacturing industry which is under coarse warranty data with enormous unrevealed information to draw meaningful information with ease.” Page 177, right column, last paragraph: “The warranty data is obtained from firm “D” using failures that occurred in China and were recorded from 2009 to 2012.)
Claim 5
Regarding claim 5, Cao, Pooya, Immerman, Korn, and Jeon teach the method of claim 4. Jeon further teaches: wherein training the component reliability model comprises training the component reliability model to estimate a predicted failure time for one or more of the building devices using both the warranty claim data and the censored data. (Jeon Page 176, second column, second paragraph: “Generally, warranty data are composed of warranty claims data and supplementary data. Claims data are the data collected during the servicing of claims under warranty, and supplementary data are additional data that are needed for effective warranty management [1]. For this reason, production data for sales should be aggregated with warranty data when probability of survival or expected failure time is needed.” The warranty data includes the failure (“(1)”), and the sales data includes all of the sales, including those that do not have any repairs (“(2)”). Page 178, second column, fourth paragraph: “On the other hand, the longest MTBF is recorded at 1,198,393h when equipment 1 is manufactured in the winter and its diesel engine is fabricated in the spring.” Also, it is clear that the data includes products that have not failed, as one of the MTBF values was over a million hours, which is well over 100 years. None of these parts could have existed for 100 years at the time of publication, as the earliest data came from 2009. This demonstrates that the data included instances where there were no failures.)
Claim 8
Regarding claim 8, Cao, Pooya, Immerman, and Korn teach the method of claim 7. Cao, Pooya, Immerman, and Korn appear to fail to teach, but Jeon teaches: further comprising:
receiving, by the processing circuit, warranty claim data associated with one or more warranty claims associated with the one or more building devices or the building device components; and (Jeon Page 177 last Paragraph in right column, Section 3. Case Study: “We applied these methodologies to firm “D”, which is a manufacturer of heavy duty diesel engines and excavators in South Korea. The warranty data is obtained from firm “D” using failures that occurred in China and were recorded from 2009 to 2012.”)
parsing, by the processing circuit, the warranty claim data to identify the historical operating data by generating the start date associated with the component based on at least one of (i) a shipping date associated with a day when the component was shipped to a location of operation or (ii) a manufacture date associated with when the component was manufactured. (Jeon Page 177 last Paragraph in right column, Section 3. Case Study: “We applied these methodologies to firm “D”, which is a manufacturer of heavy duty diesel engines and excavators in South Korea. The warranty data is obtained from firm “D” using failures that occurred in China and were recorded from 2009 to 2012.” […] “Our study indicated that meaningful rules are formed using AR. It was verified that a specific failure is influenced by the manufacturing year, while some failures are influenced by the manufacturing season of sub-part. We were able to highlight significant manufacturing conditions and products through the Weibull regression to the relevant rules and comparisons. These implications provide information as to the areas in which improvements should be made.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claims to modify the historical data of Cao to determine a run time demonstrated in Jeon because a person of ordinary skill in the art would be motivated to look to Jeon for sources of run time data and methods for organizing data to be assembled for the Weibull or other survival analysis in Cao because Jeon’s methods for survival predictions improve the determination. (Cao [0005] “Maintenance of building automation systems can be expensive and time-consuming. Device failures can impact production, comfort levels, and facility operations, and can do so without warning. Improved systems are desirable.” [0134] “The sensor data and failure rate can include historical data, such as runtime data and maintenance log data stored in a data lake repository.” Jeon Abstract “The warranty data plays a crucial role in the improvement of the manufactured product. Association rule analysis is an efficient methodology for eliciting useful information from warranty data by defining the relationships between production data and failure data within warranty period. We extract association rules from warranty data of heavy duty diesel engine in order to find significant patterns of failures along with manufacturing information. We also used Weibull regression to identify influential factors that affect the variation in mean time between failures which are identified from extracted association rules. The results from the empirical study for manufacturing firm “D” provide information as to the areas in which improvements should be made. Moreover, the result for specific failure is able to suggest a solution to overcome short-sighted improvement representatively. This study expects to contribute quality improvement to manufacturing industry which is under coarse warranty data with enormous unrevealed information to draw meaningful information with ease.” Page 177, right column, last paragraph: “The warranty data is obtained from firm “D” using failures that occurred in China and were recorded from 2009 to 2012.)
Claim 9
Regarding claim 9, Cao, Pooya, Immerman, and Korn teach the method of claim 1. Cao, Pooya, Immerman, and Korn appear to fail to teach, but Jeon teaches: further comprising:
parsing, by the processing circuit, the historical operating data to identify an element in the historical operating data having at least one of (i) a runtime that is below a threshold runtime, (ii) an event date that is before a threshold event date, or (iii) a failure type that is included in a list of failure types that are below a threshold number of failures; and (Jeon Page 178, left column, third paragraph: “We focused on the frequency under three times of sequential failures because they account for 93.6% (2,925) of total failures. Failures are classified into 24 different kinds using code from A to X. This is a failure type that is below threshold number of three failures, representing 93.6% of the warranty data.)
trimming, by the processing circuit, the element from the historical operating data in response. (Jeon Page 178, left column, third paragraph: “We focused on the frequency under three times of sequential failures because they account for 93.6% (2,925) of total failures. Failures are classified into 24 different kinds using code from A to X. This is a failure type that is below threshold number of three failures, representing 93.6% of the warranty data, which is trimmed from the data for use.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claims to modify the historical data of Cao to determine a run time demonstrated in Jeon because a person of ordinary skill in the art would be motivated to look to Jeon for sources of run time data and methods for organizing data to be assembled for the Weibull or other survival analysis in Cao because Jeon’s methods for survival predictions improve the determination. (Cao [0005] “Maintenance of building automation systems can be expensive and time-consuming. Device failures can impact production, comfort levels, and facility operations, and can do so without warning. Improved systems are desirable.” [0134] “The sensor data and failure rate can include historical data, such as runtime data and maintenance log data stored in a data lake repository.” Jeon Abstract “The warranty data plays a crucial role in the improvement of the manufactured product. Association rule analysis is an efficient methodology for eliciting useful information from warranty data by defining the relationships between production data and failure data within warranty period. We extract association rules from warranty data of heavy duty diesel engine in order to find significant patterns of failures along with manufacturing information. We also used Weibull regression to identify influential factors that affect the variation in mean time between failures which are identified from extracted association rules. The results from the empirical study for manufacturing firm “D” provide information as to the areas in which improvements should be made. Moreover, the result for specific failure is able to suggest a solution to overcome short-sighted improvement representatively. This study expects to contribute quality improvement to manufacturing industry which is under coarse warranty data with enormous unrevealed information to draw meaningful information with ease.” Page 177, right column, last paragraph: “The warranty data is obtained from firm “D” using failures that occurred in China and were recorded from 2009 to 2012.)
Claim 15
Regarding claim 15, Cao, Pooya, Immerman, and Korn teach the CRM of claim 12. Cao, Pooya, Immerman, and Korn appear to fail to teach, but Jeon teaches: wherein training the component reliability model includes training the component reliability model using:(1) warranty claim data comprising information about building devices having experienced a failure for which a warranty claim has been received; and (2) censored data comprising information about building devices that are in warranty and have not experienced a failure indicated in the warranty claim data. (Cao FIG. 6, [0079] “FIG. 6 illustrates a non-limiting example of historical data 600 that can be used in various embodiments as input data for the survival analysis. In this example, data from multiple machines/devices is shown, with line data indicating lost tracking (plain line with no terminus), that a fault has occurred and when (line terminating in point indicating when fault occurred), and no fault yet (line with arrow indicating that the device is still performing normally). Each line in the line data can be associated with a specific machine or device identifier. Of course, this exemplary illustration does not limit how such data could be recorded, stored, or displayed. The historical data can include any number of samples, including data for hundreds or thousands of devices.” The data represented in FIG. 6 include runtimes for devices with and without faults.) (Jeon Page 176, second column, second paragraph: “Generally, warranty data are composed of warranty claims data and supplementary data. Claims data are the data collected during the servicing of claims under warranty, and supplementary data are additional data that are needed for effective warranty management [1]. For this reason, production data for sales should be aggregated with warranty data when probability of survival or expected failure time is needed.” The warranty data includes the failure (“(1)”), and the sales data includes all of the sales, including those that do not have any repairs (“(2)”). Page 178, second column, fourth paragraph: “On the other hand, the longest MTBF is recorded at 1,198,393h when equipment 1 is manufactured in the winter and its diesel engine is fabricated in the spring.” Also, it is clear that the data includes products that have not failed, as one of the MTBF values was over a million hours, which is well over 100 years. None of these parts could have existed for 100 years at the time of publication, as the earliest data came from 2009. This demonstrates that the data included instances where there were no failures.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claims to modify the historical data of Cao to determine a run time demonstrated in Jeon because a person of ordinary skill in the art would be motivated to look to Jeon for sources of run time data and methods for organizing data to be assembled for the Weibull or other survival analysis in Cao because Jeon’s methods for survival predictions improve the determination. (Cao [0005] “Maintenance of building automation systems can be expensive and time-consuming. Device failures can impact production, comfort levels, and facility operations, and can do so without warning. Improved systems are desirable.” [0134] “The sensor data and failure rate can include historical data, such as runtime data and maintenance log data stored in a data lake repository.” Jeon Abstract “The warranty data plays a crucial role in the improvement of the manufactured product. Association rule analysis is an efficient methodology for eliciting useful information from warranty data by defining the relationships between production data and failure data within warranty period. We extract association rules from warranty data of heavy duty diesel engine in order to find significant patterns of failures along with manufacturing information. We also used Weibull regression to identify influential factors that affect the variation in mean time between failures which are identified from extracted association rules. The results from the empirical study for manufacturing firm “D” provide information as to the areas in which improvements should be made. Moreover, the result for specific failure is able to suggest a solution to overcome short-sighted improvement representatively. This study expects to contribute quality improvement to manufacturing industry which is under coarse warranty data with enormous unrevealed information to draw meaningful information with ease.” Page 177, right column, last paragraph: “The warranty data is obtained from firm “D” using failures that occurred in China and were recorded from 2009 to 2012.)
Claim 16
Regarding claim 16, Cao, Pooya, Immerman, Korn, and Jeon teach the CRM of claim 15. Jeon further teaches: wherein training the component reliability model comprises training the component reliability model to estimate a predicted failure time for one or more of the building devices using both the warranty claim data and the censored data. (Jeon Page 176, second column, second paragraph: “Generally, warranty data are composed of warranty claims data and supplementary data. Claims data are the data collected during the servicing of claims under warranty, and supplementary data are additional data that are needed for effective warranty management [1]. For this reason, production data for sales should be aggregated with warranty data when probability of survival or expected failure time is needed.” The warranty data includes the failure (“(1)”), and the sales data includes all of the sales, including those that do not have any repairs (“(2)”). Page 178, second column, fourth paragraph: “On the other hand, the longest MTBF is recorded at 1,198,393h when equipment 1 is manufactured in the winter and its diesel engine is fabricated in the spring.” Also, it is clear that the data includes products that have not failed, as one of the MTBF values was over a million hours, which is well over 100 years. None of these parts could have existed for 100 years at the time of publication, as the earliest data came from 2009. This demonstrates that the data included instances where there were no failures.)
Claim 19
Regarding claim 19, Cao, Pooya, Immerman, and Korn teach the CRM of claim 18. Cao, Pooya, Immerman, and Korn appear to fail to teach, but Jeon teaches: wherein the instructions further cause the one or more processors to: receive warranty claim data associated with one or more warranty claims associated with the one or more chillers or chiller components; and (Jeon Page 177 last Paragraph in right column, Section 3. Case Study: “We applied these methodologies to firm “D”, which is a manufacturer of heavy duty diesel engines and excavators in South Korea. The warranty data is obtained from firm “D” using failures that occurred in China and were recorded from 2009 to 2012.”)
parse the warranty claim data to identify the historical operating data by generating the start date associated with the chiller based on at least one of (i) a shipping date associated with a day when the chiller was shipped to a location of operation or (ii) a manufacture date associated with when the chiller was manufactured. (Jeon See Table 1 and Page 178, first column, second paragraph: “Although the recorded data include about 130 variables about failures and manufacturing information, 13 representative variables are used to compose the itemset as the transaction data listed in Table 1.” The warranty claim data is parsed based on manufacturing year and season. The manufacturing year and season is also used as a start time.)
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It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claims to modify the historical data of Cao to determine a run time demonstrated in Jeon because a person of ordinary skill in the art would be motivated to look to Jeon for sources of run time data and methods for organizing data to be assembled for the Weibull or other survival analysis in Cao because Jeon’s methods for survival predictions improve the determination. (Cao [0005] “Maintenance of building automation systems can be expensive and time-consuming. Device failures can impact production, comfort levels, and facility operations, and can do so without warning. Improved systems are desirable.” [0134] “The sensor data and failure rate can include historical data, such as runtime data and maintenance log data stored in a data lake repository.” Jeon Abstract “The warranty data plays a crucial role in the improvement of the manufactured product. Association rule analysis is an efficient methodology for eliciting useful information from warranty data by defining the relationships between production data and failure data within warranty period. We extract association rules from warranty data of heavy duty diesel engine in order to find significant patterns of failures along with manufacturing information. We also used Weibull regression to identify influential factors that affect the variation in mean time between failures which are identified from extracted association rules. The results from the empirical study for manufacturing firm “D” provide information as to the areas in which improvements should be made. Moreover, the result for specific failure is able to suggest a solution to overcome short-sighted improvement representatively. This study expects to contribute quality improvement to manufacturing industry which is under coarse warranty data with enormous unrevealed information to draw meaningful information with ease.” Page 177, right column, last paragraph: “The warranty data is obtained from firm “D” using failures that occurred in China and were recorded from 2009 to 2012.)
Claim 20
Regarding claim 20, Cao, Pooya, Immerman, and Korn teach the CRM of claim 12. Cao, Pooya, Immerman, and Korn appear to fail to teach, but Jeon teaches: wherein the instructions further cause the one or more processors to: parse the historical operating data to identify an element in the historical operating data having at least one of (i) a runtime that is below a threshold runtime, (ii) an event date that is before a threshold event date, or (iii) a failure type that is included in a list of failure types that are below a threshold number of failures; and trim the element from the historical operating data in response. (Jeon Page 178, left column, third paragraph: “We focused on the frequency under three times of sequential failures because they account for 93.6% (2,925) of total failures. Failures are classified into 24 different kinds using code from A to X.” This is a failure type that is below threshold number of three failures, representing 93.6% of the warranty data.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claims to modify the historical data of Cao to determine a run time demonstrated in Jeon because a person of ordinary skill in the art would be motivated to look to Jeon for sources of run time data and methods for organizing data to be assembled for the Weibull or other survival analysis in Cao because Jeon’s methods for survival predictions improve the determination. (Cao [0005] “Maintenance of building automation systems can be expensive and time-consuming. Device failures can impact production, comfort levels, and facility operations, and can do so without warning. Improved systems are desirable.” [0134] “The sensor data and failure rate can include historical data, such as runtime data and maintenance log data stored in a data lake repository.” Jeon Abstract “The warranty data plays a crucial role in the improvement of the manufactured product. Association rule analysis is an efficient methodology for eliciting useful information from warranty data by defining the relationships between production data and failure data within warranty period. We extract association rules from warranty data of heavy duty diesel engine in order to find significant patterns of failures along with manufacturing information. We also used Weibull regression to identify influential factors that affect the variation in mean time between failures which are identified from extracted association rules. The results from the empirical study for manufacturing firm “D” provide information as to the areas in which improvements should be made. Moreover, the result for specific failure is able to suggest a solution to overcome short-sighted improvement representatively. This study expects to contribute quality improvement to manufacturing industry which is under coarse warranty data with enormous unrevealed information to draw meaningful information with ease.” Page 177, right column, last paragraph: “The warranty data is obtained from firm “D” using failures that occurred in China and were recorded from 2009 to 2012.)
Claim 25
Regarding claim 25, Cao, Pooya, Immerman, and Korn teach the System of claim 23. Cao, Pooya, Immerman, and Korn appear to fail to teach, but Jeon teaches: wherein the instructions further cause the processor to: receive warranty claim data associated with one or more warranty claims associated with the one or more chillers or chiller components; and (Jeon Page 177 last Paragraph in right column, Section 3. Case Study: “We applied these methodologies to firm “D”, which is a manufacturer of heavy duty diesel engines and excavators in South Korea. The warranty data is obtained from firm “D” using failures that occurred in China and were recorded from 2009 to 2012.”)
parse the warranty claim data to identify the historical operating data by generating the start date associated with the chiller based on at least one of (i) a shipping date associated with a day when the chiller was shipped to a location of operation or (ii) a manufacture date associated with when the chiller was manufactured. (Jeon Page 177 last Paragraph in right column, Section 3. Case Study: “We applied these methodologies to firm “D”, which is a manufacturer of heavy duty diesel engines and excavators in South Korea. The warranty data is obtained from firm “D” using failures that occurred in China and were recorded from 2009 to 2012.” […] “Our study indicated that meaningful rules are formed using AR. It was verified that a specific failure is influenced by the manufacturing year, while some failures are influenced by the manufacturing season of sub-part. We were able to highlight significant manufacturing conditions and products through the Weibull regression to the relevant rules and comparisons. These implications provide information as to the areas in which improvements should be made.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claims to modify the historical data of Cao to determine a run time demonstrated in Jeon because a person of ordinary skill in the art would be motivated to look to Jeon for sources of run time data and methods for organizing data to be assembled for the Weibull or other survival analysis in Cao because Jeon’s methods for survival predictions improve the determination. (Cao [0005] “Maintenance of building automation systems can be expensive and time-consuming. Device failures can impact production, comfort levels, and facility operations, and can do so without warning. Improved systems are desirable.” [0134] “The sensor data and failure rate can include historical data, such as runtime data and maintenance log data stored in a data lake repository.” Jeon Abstract “The warranty data plays a crucial role in the improvement of the manufactured product. Association rule analysis is an efficient methodology for eliciting useful information from warranty data by defining the relationships between production data and failure data within warranty period. We extract association rules from warranty data of heavy duty diesel engine in order to find significant patterns of failures along with manufacturing information. We also used Weibull regression to identify influential factors that affect the variation in mean time between failures which are identified from extracted association rules. The results from the empirical study for manufacturing firm “D” provide information as to the areas in which improvements should be made. Moreover, the result for specific failure is able to suggest a solution to overcome short-sighted improvement representatively. This study expects to contribute quality improvement to manufacturing industry which is under coarse warranty data with enormous unrevealed information to draw meaningful information with ease.” Page 177, right column, last paragraph: “The warranty data is obtained from firm “D” using failures that occurred in China and were recorded from 2009 to 2012.)
Claim 26
Regarding claim 26, Cao, Pooya, Immerman, and Korn teach the System of claim 23. Cao, Pooya, Immerman, and Korn appear to fail to teach, but Jeon teaches: wherein the instructions further cause the processor to: parse the historical operating data to identify an element in the historical operating data having at least one of (i) a runtime that is below a threshold runtime, (ii) an event date that is before a threshold event date, or (iii) a failure type that is included in a list of failure types that are below a threshold number of failures; and (Jeon Page 178, left column, third paragraph: “We focused on the frequency under three times of sequential failures because they account for 93.6% (2,925) of total failures. Failures are classified into 24 different kinds using code from A to X. This is a failure type that is below threshold number of three failures, representing 93.6% of the warranty data.)
trim the element from the historical operating data in response. (Jeon Page 178, left column, third paragraph: “We focused on the frequency under three times of sequential failures because they account for 93.6% (2,925) of total failures. Failures are classified into 24 different kinds using code from A to X. This is a failure type that is below threshold number of three failures, representing 93.6% of the warranty data, which is trimmed from the data for use.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claims to modify the historical data of Cao to determine a run time demonstrated in Jeon because a person of ordinary skill in the art would be motivated to look to Jeon for sources of run time data and methods for organizing data to be assembled for the Weibull or other survival analysis in Cao because Jeon’s methods for survival predictions improve the determination. (Cao [0005] “Maintenance of building automation systems can be expensive and time-consuming. Device failures can impact production, comfort levels, and facility operations, and can do so without warning. Improved systems are desirable.” [0134] “The sensor data and failure rate can include historical data, such as runtime data and maintenance log data stored in a data lake repository.” Jeon Abstract “The warranty data plays a crucial role in the improvement of the manufactured product. Association rule analysis is an efficient methodology for eliciting useful information from warranty data by defining the relationships between production data and failure data within warranty period. We extract association rules from warranty data of heavy duty diesel engine in order to find significant patterns of failures along with manufacturing information. We also used Weibull regression to identify influential factors that affect the variation in mean time between failures which are identified from extracted association rules. The results from the empirical study for manufacturing firm “D” provide information as to the areas in which improvements should be made. Moreover, the result for specific failure is able to suggest a solution to overcome short-sighted improvement representatively. This study expects to contribute quality improvement to manufacturing industry which is under coarse warranty data with enormous unrevealed information to draw meaningful information with ease.” Page 177, right column, last paragraph: “The warranty data is obtained from firm “D” using failures that occurred in China and were recorded from 2009 to 2012.)
Conclusion
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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
(From Prior Office Action)
US 2022/0335547 A1 to Wenzel et al. (Describes determining chiller maintenance schedule based on run hours [0051])
US 2023/0143324 A1 to Coetzee et al. (Uses machine learning to determine maintenance times for a heat exchanger [0084]-[0087])
NPL: “Comparative Analysis of Optimal Maintenance Policies Under General Repair With Underlying Weibull Distributions” by Yevkin et al. (Discloses using a Wiebull model to determine maintenance policies.)
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/J.M.W./Examiner, Art Unit 2188
/RYAN F PITARO/Supervisory Patent Examiner, Art Unit 2188
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