DETAILED ACTION
This office action is in response to application filed on February 21, 2024
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 02/21/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign(s) mentioned in the description:
Fig. 1, item 100, as described in the specification ([0020], [0041], [0054]).
Fig. 3, item 300, as described in the specification ([0040]).
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The disclosure is objected to because it includes multiple informalities (see below) that require appropriate correction. The examiner has made an effort suggesting some language to clarify the described subject matter. Applicant’s cooperation is kindly requested.
[0021]: Language “… A specific nature or a type of the facility (or a room of the facility) can determine types of data sources(e.g., sensors) …” should read “… A specific nature or a type of the facility (or a room of the facility) can determine types of data sources (e.g., sensors) …” in order to correct minor informalities (e.g., add space before parenthesis).
[0025]: Language “The predictive maintenance engine 102 can include data collectors 122 to collect and store in memory (e.g., the memory 112) the equipment datal18 over time for the equipment 104” should read “The predictive maintenance engine 102 can include data collectors 122 to collect and store in memory (e.g., the memory 112) the equipment data 118 over time for the equipment 104” in order to correct minor informalities (e.g., add space).
[0027]: Language “… The analyzer 128 can provide analytics data 130 which can indicating whether the equipment 104 needs maintenance … In a non-limiting examples, the training algorithm 132 can be configured to observe a current room temperature and required supplied air temperature with regard to required time to reach to a predefined set-point temperature … The training data 134 can represents a variance between …” should read “… The analyzer 128 can provide analytics data 130 which can indicate whether the equipment 104 needs maintenance … In [[a]] non-limiting examples, the training algorithm 132 can be configured to observe a current room temperature and required supplied air temperature with regard to required time to reach [[to]] a predefined set-point temperature … The training data 134 can represent a variance between …” in order to correct minor informalities.
[0027]: Paragraph should end with a period.
[0028]: Language “… The correlator 136 can determine if there an correlation between different sensor readings … an anomaly indicative of a negative correlation between two more different attributes of the physical attributes …” should read “… The correlator 136 can determine if there [[an]] is a correlation between different sensor readings … an anomaly indicative of a negative correlation between two or more different attributes of the physical attributes …” in order to correct minor informalities.
[0032]: Language “… Further received aggregate equipment data can be processed to determine a trend and the baseline trend can be compared by the correlator 136 to the baseline …” should read “… Further received aggregate equipment data can be processed to determine a trend and the
[0035]: Language “Thus, the remediator 140 can issue the remedial action 142 in response to the machine learning model 138 determining that the equipment 104 needs maintenance and/or the correlator determining that the equipment data contains the anomaly, which can indicative of that the equipment needs maintenance …” should read “Thus, the remediator 140 can issue the remedial action 142 in response to the machine learning model 138 determining that the equipment 104 needs maintenance and/or the correlator determining that the equipment data contains the anomaly, which can be indicative of that the equipment needs maintenance …” in order to correct minor informalities.
[0036]: Language “In some examples, the analyzer 128 can analyze the aggregated data to suggest the one or more actions for initiating or causing maintenance of the equipment 104. n . For example … The remediator 140 can select a most aggressive remedial action as the remedial action 142 from the one or more remedial actions in response to receiving the alert 144 and the analytics data 130, as this can be indicative of immediate maintenance action is required for the equipment 104 …” should read “In some examples, the analyzer 128 can analyze the aggregated data to suggest the one or more actions for initiating or causing maintenance of the equipment 104. [[n .]] For example … The remediator 140 can select a most aggressive remedial action as the remedial action 142 from the one or more remedial actions in response to receiving the alert 144 and the analytics data 130, as this can be indicative of immediate maintenance action [[is]] required for the equipment 104 …” in order to correct minor informalities.
[0038]: Language “… The RTUs 204-210 can be distributed across AITD sites in order to gather the needed data of facilities systems and can transmit the gathered through a network …” should read “… The RTUs 204-210 can be distributed across AITD sites in order to gather the needed data of facilities systems and can transmit the gathered data through a network …” in order to correct minor informalities.
[0040]: Language “FIG. 3 is an example of a system 300 with am RTU 302 … The RTU 302 can be configured to receive data from one or input device, which can include sensors, as shown in the example of FIG. 3. In the example of FIG. 3, the sensors are identified as “Sensor 1”, ”Sensor 2”, ”Sensor 3”, ”Sensor 4”, and ”Sensor 5” ...” should read “FIG. 3 is an example of a system 300 with [[am]]a RTU 302 … The RTU 302 can be configured to receive data from one or more input devices, which can include sensors, as shown in the example of FIG. 3. In the example of FIG. 3, the sensors are identified as “Sensor 1”, ”Sensor 2”, ”Sensor 3”, ”Sensor 4”, and “Sensor 6”…” in order to correct minor informalities and in accordance with the details of Figure 3 (e.g., six sensors are illustrated).
[0046]: Language “… The data collector 122 can collect data from different sources and send the collected data to the predictor 124, as disclosed herein, which can use the collected data (the equipment data 118) to make a failure prediction and provides suggestions (or actions) for initiating maintenance …” should read “… The data collector 122 can collect data from different sources and send the collected data to the predictor 124, as disclosed herein, which can use the collected data (the equipment data 118) to make a failure prediction and provide suggestions (or actions) for initiating maintenance …” in order to correct minor informalities.
[0048]: Language “… This can include, for example, temperature readings from HV AC systems, smoke or heat levels from fire systems, and other performance-related data.) …” should read “… This can include, for example, temperature readings from HV AC systems, smoke or heat levels from fire systems, and other performance-related data[[.]]) …” in order to correct minor informalities.
[0050]: Language “In some examples, the predictive maintenance engine 406 can communicate with and a preventative maintenance system (or platform) 418 … to send and receive data from the preventative maintenance system 418 … the preventative maintenance system 418 through use of the bidirectional interface. The preventative maintenance system 418 can be implemented … In some examples, the preventative maintenance system 418 can be software … Example systems of the preventative maintenance system 418 …” should read “In some examples, the predictive maintenance engine 406 can communicate with [[and]] a predictive maintenance system (or platform) 418 … to send and receive data from the predictive maintenance system 418 … the predictive maintenance system 418 through use of the bidirectional interface. The predictive maintenance system 418 can be implemented … In some examples, the predictive maintenance system 418 can be software … Example systems of the predictive maintenance system 418 …” in order to correct minor informalities and in accordance with the details of Figure 4 (e.g., item 418 is labeled “predictive maintenance system”).
[0051]: Language “… For example, the predictive maintenance engine 406 can cause the preventive maintenance system 418 to schedule maintenance tasks, dispatch personnel (e.g., by causing the preventative maintenance system 418 to send an alert, an email, message, etc. to a device used by personnel), or cause the preventive maintenance system 418 to order parts for replacement … For example, an “X” work order is scheduled for a particular data the predictive maintenance engine 406 can cause it to be reschedule to a different date” should read “… For example, the predictive maintenance engine 406 can cause the predictive maintenance system 418 to schedule maintenance tasks, dispatch personnel (e.g., by causing the predictive maintenance system 418 to send an alert, an email, message, etc. to a device used by personnel), or cause the predictive maintenance system 418 to order parts for replacement … For example, an “X” work order [[is]] scheduled for a particular date can be rescheduled by the predictive maintenance engine 406
[0053]: Language “… The correlation module correlates the analysis results received from the analysis module and looks for consistency and draw the big picture …” should read “… The correlation module correlates the analysis results received from the analysis module [[and]], looks for consistency and draws the big picture …” in order to correct minor informalities.
[0056]: Language “… While, for purposes of simplicity of explanation, the example method of FIG. 5 are shown and described as executing serially …” should read “… While, for purposes of simplicity of explanation, the example method of FIG. 5 [[are]]is shown and described as executing serially …” in order to correct minor informalities.
[0057]: Language “… At 506, analyzing using a correlator (e.g., the correlator 136, as shown in FIG. 1) the equipment data to determine whether the equipment data contains an anomaly indicative of a negative correlation between two more different attributes of the physical attributes …” should read “… At 506, analyzing using a correlator (e.g., the correlator 136, as shown in FIG. 1) the equipment data to determine whether the equipment data contains an anomaly indicative of a negative correlation between two or more different attributes of the physical attributes …” in order to correct minor informalities.
[0061]: Language “… A basic input/output system (BIOS) 614 can reside in ROM 612 containing the basic routines that help to transfer information among elements within computer system 600” should read “… A basic input/output system (BIOS) 614 can reside in ROM [[612]]610 containing the basic routines that help to transfer information among elements within computer system 600” in accordance with the details of Figure 6 (e.g., item 610 is labeled ‘ROM’).
[0062]: Language “… A number of program modules may be stored in drives and RAM 610 … Thus, in some examples, the application programs 634 can include the predictive maintenance engine 102 (or one or more of its modules, as shown in FIG. 1” should read “… A number of program modules may be stored in drives and RAM [[610]]612 … Thus, in some examples, the application programs 634 can include the predictive maintenance engine 102 (or one or more of its modules, as shown in FIG. 1)” in accordance with the details of Figure 6 (e.g., item 612 is labeled ‘RAM’) and to correct minor informalities (e.g., add closing parenthesis at the end of the paragraph).
[0066]: Language “… This cloud model may include at least five characteristics, at least three service models (e.g., software as a service (SaaS, platform as a service (PaaS), and/or infrastructure as a service (IaaS)) and at least four deployment models (e.g., private cloud, community cloud, public cloud, and/or hybrid cloud) …” should read “… This cloud model may include at least five characteristics, at least three service models (e.g., software as a service (SaaS, platform as a service (PaaS), and/or infrastructure as a service (IaaS))) and at least four deployment models (e.g., private cloud, community cloud, public cloud, and/or hybrid cloud) …” in order to correct minor informalities (e.g., add closing parenthesis).
[0067]: Language “… The devices 704-708, as shown in FIG. 7, are intended to be illustrative and that computing nodes 702 and cloud computing environment 700 can communicate …” should read “… The devices 704-708, as shown in FIG. 7, are intended to be illustrative, and [[that]] computing nodes 702 and cloud computing environment 700 can communicate …” in order to correct minor informalities.
[0068]: Language “… It is to be understood that the cloud computing environment 700 need not provide all of the one or more functional abstraction layers …” should read “… It is to be understood that the cloud computing environment 700 need not to provide all of the one or more functional abstraction layers …” in order to correct minor informalities.
Appropriate correction is required.
Claim Objections
Claim 1 is objected to because of the following informalities:
Claim language “receiving equipment data for equipment comprising data points relating to physical attributes of the equipment or environmental conditions for the equipment” should read “receiving equipment data for equipment, the equipment data comprising data points relating to physical attributes of the equipment or environmental conditions for the equipment” in order to clarify the recited subject matter.
Claim language “analyzing using a correlator the equipment data to determine whether the equipment data contains an anomaly indicative of a negative correlation between two more different attributes of the physical attributes and two or more different environmental conditions of the environmental conditions” should read “analyzing, using a correlator, the equipment data to determine whether the equipment data contains an anomaly indicative of a negative correlation between two or more different attributes of the physical attributes and two or more different environmental conditions of the environmental conditions” in order to correct minor informalities.
Appropriate correction is required.
Claim 2 is objected to because of the following informalities:
Claim language should read “The method of claim 1, wherein a standby state, or a reduced operational state” in order to correct minor informalities.
Appropriate correction is required.
Claim 3 is objected to because of the following informalities:
Claim language should read “The method of claim 1, wherein the equipment data is first equipment data and second equipment data, the method further comprising aggregating the equipment data to provide the equipment data in a predefined format” in order to correct minor informalities.
Appropriate correction is required.
Claim 5 is objected to because of the following informalities:
Claim language should read “The method of claim 1, further comprising: identifying one or more data points that are outliers in the data points; and excluding the .
Appropriate correction is required.
Claim 6 is objected to because of the following informalities:
Claim language should read “The method of claim 5, .
Appropriate correction is required.
Claim 10 is objected to because of the following informalities:
Claim language should read “The method of claim 1, wherein the ML model is configured to provide one or [[or]]more recommendations for one or more remedial actions, and the method further comprising selecting one of the one or more remedial actions based on previous remedial actions and the .
Appropriate correction is required.
Claim 11 is objected to because of the following informalities:
Claim language should read “The method of claim 10, wherein the previous remedial actions comprise replacing a specific part, performing a certain type of maintenance and adjusting operational parameters to avoid [[the]] failure” in order to correct minor informalities and provide appropriate antecedence basis.
Appropriate correction is required.
Claim 12 is objected to because of the following informalities:
Claim language “a data aggregator configured to aggregate equipment data from one or more data sources to provide aggregated equipment data, the equipment data comprising data points relating to physical attributes of the equipment or environmental conditions for the equipment” should read “a data aggregator configured to aggregate equipment data for equipment from one or more data sources to provide aggregated equipment data, the equipment data comprising data points relating to physical attributes of the equipment or environmental conditions for the equipment” in order to provide appropriate antecedence basis.
Claim language “a correlator configured to analyze the aggregated equipment data to determine whether the aggregated equipment data contains an anomaly indicative of a negative correlation between two more different attributes of the physical attributes and two or more different environmental conditions of the environmental conditions” should read “a correlator configured to analyze the aggregated equipment data to determine whether the aggregated equipment data contains an anomaly indicative of a negative correlation between two or more different attributes of the physical attributes and two or more different environmental conditions of the environmental conditions” in order to correct minor informalities.
Appropriate correction is required.
Claim 13 is objected to because of the following informalities:
Claim language should read “The system of claim 12, wherein the remediator is configured to generate a command as the remedial action and provide the command to the equipment to cause the equipment to transition from an operational state into one of a shutdown state, a standby state, or a reduced operational state” in order to correct minor informalities.
Appropriate correction is required.
Claim 15 is objected to because of the following informalities:
Claim language “exclude the identified outliers from the aggregated equipment data to provide filtered equipment data” should read “exclude the .
Appropriate correction is required.
Claim 17 is objected to because of the following informalities:
Claim language should read “The system of claim 12, wherein the ML model is configured to provide one or [[or]]more recommendations for one or more remedial actions, and the remediator is further configured to select one of the one or more remedial actions based on previous remedial actions and the .
Appropriate correction is required.
Claim 18 is objected to because of the following informalities:
Claim language should read “The system of claim 17, wherein the previous remedial actions comprise replacing a specific part, performing a certain type of maintenance and adjusting operational parameters to avoid [[the]] failure” in order to correct minor informalities and provide appropriate antecedence basis.
Appropriate correction is required.
Claim 19 is objected to because of the following informalities:
Claim language “receive equipment data for equipment comprising data points relating to physical attributes of the equipment or environmental conditions for the equipment” should read “receive equipment data for equipment, the equipment data comprising data points relating to physical attributes of the equipment or environmental conditions for the equipment” in order to clarify the recited subject matter.
Claim language “analyze using a correlator the equipment data to determine whether the equipment data contains an anomaly indicative of a negative correlation between two more different attributes of the physical attributes and two or more different environmental conditions of the environmental conditions” should read “analyze, using a correlator, the equipment data to determine whether the equipment data contains an anomaly indicative of a negative correlation between two or more different attributes of the physical attributes and two or more different environmental conditions of the environmental conditions” in order to correct minor informalities.
Appropriate correction is required.
Claim 20 is objected to because of the following informalities:
Claim language should read “The system of claim 19, wherein the ML model is configured to provide one or [[or]]more recommendations for one or more remedial actions, and select one of the one or more remedial actions based on previous remedial actions and the wherein the previous remedial actions comprise replacing a specific part, performing a certain type of maintenance and adjusting operational parameters to avoid [[the]] failure” in order to correct minor informalities and provide appropriate antecedence basis.
Appropriate correction is required.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Regarding claim 1, the examiner submits that under Step 1 of the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence (see also 2019 Revised Patent Subject Matter Eligibility Guidance) for evaluating claims for eligibility under 35 U.S.C. 101, the claim is to a process, which is one of the statutory categories of invention.
Continuing with the analysis, under Step 2A - Prong One of the test (see italic text for abstract idea):
the limitation “analyzing the equipment data using a machine learning (ML) model to determine whether the equipment needs maintenance” is a process that, under its broadest reasonable interpretation in light of the specification, covers performance of the limitation using mental processes and/or mathematical concepts to evaluate data and obtain a result (i.e., whether the equipment needs maintenance; see specification at [0027]-[0028]). Except for the recitation of the extra-solution activities (e.g., source/type of data being evaluated, see specification at [0021]-[0024]), the particular technological environment or field of use, and/or the generic computer implementation (i.e., a machine learning (ML) model, see specification at [0027]), the limitation in the context of the claim mainly refers to performing a mental evaluation and/or applying mathematical concepts to manipulate data and obtain additional information.
the limitation “analyzing using a correlator the equipment data to determine whether the equipment data contains an anomaly indicative of a negative correlation between two more different attributes of the physical attributes and two or more different environmental conditions of the environmental conditions” is a process that, under its broadest reasonable interpretation in light of the specification, covers performance of the limitation using mental processes and/or mathematical concepts to evaluate data and obtain a result (i.e., whether the equipment data contains an anomaly; see specification at [0028]-[0029], [0032]). Except for the recitation of the extra-solution activities (e.g., source/type of data being evaluated, see specification at [0021]-[0024]), the particular technological environment or field of use, and/or the generic computer elements (i.e., a correlator, see specification at [0020], [0060]), the limitation in the context of the claim mainly refers to performing a mental evaluation and/or applying mathematical concepts to compare data and obtain additional information.
Therefore, the claim recites a judicial exception under Step 2A - Prong One of the test.
Furthermore, under Step 2A - Prong Two of the test, this judicial exception is not integrated into a practical application when considering the claim as a whole. In particular, the additional elements recited in the claim (see non-italic text for additional elements):
“receiving equipment data for equipment comprising data points relating to physical attributes of the equipment or environmental conditions for the equipment” adds extra-solution activities (e.g., mere data gathering, source/type of data to be manipulated, see specification at [0021]-[0024]) using elements recited at a high level of generality (i.e., equipment, see specification at [0021]-[0024]) (see MPEP 2106.05(g));
“analyzing the equipment data using a machine learning (ML) model to determine whether the equipment needs maintenance” adds extra-solution activities (e.g., source/type of data being evaluated, see specification at [0021]-[0024]) (see MPEP 2106.05(g)), a particular technological environment or field of use (e.g., equipment maintenance, see specification at [0001]) (see MPEP 2106.05(h)), and/or generic computer implementation (i.e., a machine learning (ML) model, see specification at [0027]) used to facilitate the application of the judicial exception (see MPEP 2106.05(f));
“analyzing using a correlator the equipment data to determine whether the equipment data contains an anomaly indicative of a negative correlation between two more different attributes of the physical attributes and two or more different environmental conditions of the environmental conditions” adds extra-solution activities (e.g., source/type of data being evaluated, see specification at [0021]-[0024]) (see MPEP 2106.05(g)), a particular technological environment or field of use (e.g., equipment maintenance, see specification at [0001]) (see MPEP 2106.05(h)), and/or generic computer elements (i.e., a correlator, see specification at [0020], [0060]) used to facilitate the application of the judicial exception (see MPEP 2106.05(f)); and
“issuing a remedial action in response to the ML model determining that the equipment needs maintenance and/or the correlator determining that the equipment data contains the anomaly” appends a transformation at a high level of generality such that substantially all practical applications of the judicial exception(s) are covered (see specification at [0033]-[0036]; see also MPEP 2106.05(c)), a particular technological environment or field of use (e.g., equipment maintenance, see specification at [0001]) (see MPEP 2106.05(h)), and/or generic computer implementation (i.e., ML model, see specification at [0027]) used to facilitate the application of the judicial exception (see MPEP 2106.05(f)).
Accordingly, these additional elements, when considered individually and in combination, do not integrate the judicial exception into a practical application because they do not impose any meaningful limits on practicing the abstract idea when considering the claim as a whole. The claim is directed to a judicial exception under Step 2A of the test.
Additionally, under Step 2B of the test, the claim, when considered as a whole, does not include additional elements that, when considered individually and in combination, are sufficient to amount to significantly more than the judicial exception because the additional elements:
generally link the use of the judicial exception to a particular technological environment or field of use (e.g., equipment maintenance, see specification at [0001]), which as indicated in the MPEP: “As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible “simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use.” Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application” (see MPEP 2106.05(h));
recite extra-solution activities (i.e., mere data gathering by selecting a particular data source/type to be manipulated) using elements (i.e., equipment, see specification at [0021]-[0024]) specified at a high level of generality, which as indicated in the MPEP: “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” (see MPEP 2106.05(g)); “Use of a machine that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not provide significantly more” (see MPEP 2106.05(b));
append generic computer components (i.e., a correlator, see specification at [0020], [0060]) used to facilitate the application of the abstract idea (i.e., mere computer implementation, ML model, see specification at [0027]), which as indicated in the MPEP: “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not provide significantly more” (see MPEP 2106.05(f)); and
append transformations at a high level of generality such that substantially all practical applications of the judicial exception(s) are covered (i.e., issuing a remedial action), which as indicated in the MPEP: “A transformation applied to a generically recited article or to any and all articles would likely not provide significantly more than the judicial exception” (see MPEP 2106.05(c)).
The claim, when considered as a whole, does not provide significantly more under Step 2B of the test.
Based on the analysis, the claim is not patent eligible.
Similarly, independent claims 12 and 19 are directed to a judicial exception (abstract idea, Step 2A – Prong One) without integrating the judicial exception into a practical application (Step 2A – Prong Two) and/or without providing significantly more (Step 2B) when considering the claimed invention as a whole as explained above with regards to claim 1.
With regards to the dependent claims they are also directed to the non-statutory subject matter because:
they just extend the abstract idea of the independent claims by additional limitations (Claims 3, 5-6, 9-10, 15-17 and 20), that under the broadest reasonable interpretation in light of the specification, cover performance of the limitations using mental processes and/or mathematical concepts, and
the additional elements recited in the dependent claims, when considered individually and in combination, refer to extra-solution activities (e.g., mere data gathering using a data type or source), generic computer components, a field of use, and/or transformations at a high level of generality such that substantially all practical applications of the judicial exception(s) are covered (Claims 2-4, 7-11, 13-14, 16-18 and 20), which as indicated in the Office’s guidance does not integrate the judicial exception into a practical application (Step 2A – Prong Two) and/or does not provide significantly more (Step 2B) when considering the claimed invention as a whole.
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.
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.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3-12 and 14-20 are rejected under 35 U.S.C. 103 as being unpatentable over Lavid (US 20180348747 A1), hereinafter ‘Lavid’, in view of Deeg (US 20230359191 A1), hereinafter ‘Deeg’.
Regarding claim 1.
Lavid discloses:
A method (Fig. 5; [0002], [0059]: identification of machine failures and root causes is presented (see also [0013], [0026])) comprising:
receiving equipment data for equipment (Fig. 1, item 170 –‘machine’) comprising data points (Figs. 3A-B, 4 and 7-8) relating to physical attributes of the equipment or environmental conditions for the equipment ([0033]: a root cause analyzer (Fig. 1, item 140) receives sensory inputs (see [0030]-[0031]) associated with a machine (see also [0067] regarding attribution dataset including environmental variables related to the operation of the machine during collection of sensory inputs; see also [0073]));
analyzing the equipment data using a machine learning (ML) model to determine whether the equipment needs maintenance (Fig. 5, item S510; [0035], [0039], [0060]: root cause analyzer determines a machine failure (equipment needs maintenance to avoid failure, see [0036], [0038]) and its cause by analyzing the sensory inputs using unsupervised machine learning (see also [0054])); and
issuing a remedial action in response to the ML model determining that the equipment needs maintenance and/or the correlator determining that the equipment data contains the anomaly (Fig. 5, item S570; [0036], [0038]: the root cause analyzer generates recommendations to avoid failure (see also [0044], [0054], [0068], [0070])).
Lavid does not disclose:
analyzing using a correlator the equipment data to determine whether the equipment data contains an anomaly indicative of a negative correlation between two more different attributes of the physical attributes and two or more different environmental conditions of the environmental conditions.
Deeg teaches:
“During the execution of the correlation model, the model ascertains at least one correlation between the machine parameters, the filtered environmental parameters and the operating anomaly. This means that the computing facility checks whether a correlation with the operating anomaly can be found between the machine parameters and the filtered environmental parameters and, if such a correlation is found, the cause of the operating anomaly can be derived therefrom” (Fig. 2, item S16; [0014]: a computing facility (correlator) checks whether a correlation with an operating anomaly can be found between the machine parameters (equipment data) and the environmental parameters, and if a correlation is found, the cause of the operating anomaly is determined and appropriate actions are derived (see [0038]-[0039]; see also [0042]-[0043] regarding different correlation examples, such as when flooding (environmental parameter) is present, machine temperature (equipment data) can decrease, which represents a negative correlation)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Lavid in view of Deeg to analyze using a correlator the equipment data to determine whether the equipment data contains an anomaly indicative of a negative correlation between two more different attributes of the physical attributes and two or more different environmental conditions of the environmental conditions, in order to diagnose damage on a machine influenced not only by machine operation but also by environmental conditions, as discussed by Deeg ([0002]-[0003]).
Regarding claim 3.
Lavid in view of Deeg discloses all the features of claim 1 as described above.
Lavid further discloses:
the equipment data is first equipment data and second equipment data ([0028], [0030]: different sensors are used to collect sensory inputs data (see also [0067] regarding attribution dataset including environmental variables related to the operation of the machine during collection of sensory inputs)).
Lavid does not explicitly disclose:
the method further comprising aggregating the equipment data to provide the equipment data in predefined format.
However, Lavid teaches:
“The curve 720 represents aggregated behavior of the sensory inputs over time. The aggregated behavior may be continuously monitored to identify failures” ([0078]: sensory inputs are aggregated over time to be continuously monitored to identify faults; examiner notes that data needs to be in a predefined format for proper aggregation).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Lavid in view of Deeg to incorporate the method further comprising aggregating the equipment data to provide the equipment data in predefined format, in order to easily perform monitoring and analysis of multiple data sources.
Regarding claim 4.
Lavid in view of Deeg discloses all the features of claim 1 as described above.
Lavid does not explicitly disclose:
training the ML model based on training data representing a variance between real-time equipment data and predefined equipment data.
However, Laved teaches:
“In the example simulation shown in FIG. 3, the curve 310A represents an aggregated behavior of the sensory inputs over time. During operation of a machine (e.g., the machine 170, FIG. 1), the aggregated behavior represented by the curve 310A may be continuously monitored for repeated sequences such as repeated sequences 320A and 330A. Upon determination of, for example, the repeated sequence 320A, the repeated sequence 330A, or both, a model of a normal behavior pattern of the machine is generated. It should be noted that continuous monitoring of, e.g., two or more cycles of behavior may be useful for determining more accurate patterns. As monitoring and, consequently, learning, continue, the normal behavior model may be updated accordingly. The models of normal behavior patterns may be utilized to determine root causes of machine failures ... FIG. 3B is an example simulation 300B illustrating generation of adaptive thresholds. Based on one or more repeated sequences (e.g., the repeated sequences 320A and 330A), a maximum threshold 310B and a minimum threshold 320B are determined. The thresholds 310B and 320B may be determined in real-time and regardless of past machine behavior … The point 330B represents an indicator, i.e., a data point that is above the maximum threshold 310B or below the minimum threshold 320B. Upon determination that one of the thresholds 310B or 320B has been exceeded, an anomaly may be detected” ([0056]-[0057]: models of normal behavior patterns (analogous to predefined equipment data) are learned during monitoring using aggregated behavior of sensory inputs over time (analogous to real-time equipment data) and implemented to determine thresholds (analogous to variance) for identification of anomalies).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Lavid in view of Deeg to train the ML model based on training data representing a variance between real-time equipment data and predefined equipment data, in order to improve accuracy during identification of anomalies in equipment by learning actual normal machine conditions while accounting for realistic variations in the machine standard behavior.
Regarding claim 5.
Lavid in view of Deeg discloses all the features of claim 1 as described above.
Lavid further discloses:
identifying one or more data points that are outliers in the data points ([0031], [0073]: sensory inputs data are preprocessed including noise filtered, which implies the noise (outliers) to be first identified); and
excluding the identified outliers from the equipment data to provide filtered equipment data ([0031], [0073]: noise is filtered in the sensory inputs data).
Regarding claim 6.
Lavid in view of Deeg discloses all the features of claim 5 as described above.
Lavid does not disclose:
wherein the analyzing using the correlator comprises analyzing the filtered equipment data to determine whether the filtered equipment data contains the anomaly.
Deeg further teaches:
“In an advantageous embodiment of the present invention, it is provided that the at least one correlation is ascertained by statistical analysis. In particular, new unknown correlations can be sought by means of statistical analysis. The statistical analysis can preferably comprise outlier detection, cluster analysis, classification of the machine parameters and the environmental parameters association analysis and/or regression analysis” ([0019]: correlations are obtained by statistical analysis, which includes outlier detection (see also [0020] regarding filtering data)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Lavid in view of Deeg to incorporate the analyzing using the correlator comprising analyzing the filtered equipment data to determine whether the filtered equipment data contains the anomaly, in order to improve accuracy of the identification of anomalies in equipment.
Regarding claim 7.
Lavid in view of Deeg discloses all the features of claim 1 as described above.
Lavid further discloses:
generating an alert in response to determining that the equipment data contains the anomaly ([0044]: when anomalies are found, a notification is generated indicating the anomalous activity).
Regarding claim 8.
Lavid in view of Deeg discloses all the features of claim 7 as described above.
Lavid further discloses:
the remedial action is issued based on the alert ([0044]: notification is configured to automatically mitigate failures).
Regarding claim 9.
Lavid in view of Deeg discloses all the features of claim 1 as described above.
Lavid further discloses:
a type of the remedial action issued is based on a type of the equipment ([0036]: root cause analyzer determines type of failure, which is used to generate recommendations to avoid failure (e.g., type of equipment to be replaced, see [0054]; see also [0038], [0044])).
Regarding claim 10.
Lavid in view of Deeg discloses all the features of claim 1 as described above.
Lavid does not explicitly disclose:
the ML model is configured to provide one or more recommendations for one or more remedial actions, and the method further comprising selecting one of the remedial actions based on previous remedial actions and the provided one or more recommendations.
However, Lavid teaches:
“The unsupervised machine learning analysis may further include modeling the sensory inputs and detecting indicators in the sensory inputs. The modeling may include generating meta-models for each component, module, or portion of the machine. The meta-models are monitored to detect indicators therein. Based on the indicators, root causes of the machine failures may be determined. In a further embodiment, a behavioral model utilized for predicting machine failures may be generated based on the determined root causes” ([0027]: machine learning is used to detect anomalies, their causes, and generate recommendations to avoid re-occurrence of failure (see [0026]) with models being generated based on learning from previous data); and
“In another embodiment, the root cause analyzer 140 may be configured to identify a type of the failure … The type of failure may be utilized to, e.g., generate
recommendations for avoiding failure” ([0036]: the root cause analyzer generates recommendations to avoid failure (see also [0044], [0054], [0068], [0070]); examiner notes that recommendations may also be based on personnel experience (analogous to previous remedial actions)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Lavid in view of Deeg to configure the ML model to provide one or more recommendations for one or more remedial actions, and the method further comprising selecting one of the remedial actions based on previous remedial actions and the provided one or more recommendations, in order to provide a robust response for addressing machine failures based on previous knowledge and current equipment behavior.
Regarding claim 11.
Lavid in view of Deeg discloses all the features of claim 10 as described above.
Lavid does not explicitly disclose:
the previous remedial actions comprises replacing a specific part, performing a certain type of maintenance and adjusting operational parameters to avoid the failure.
However, Lavid further teaches:
“In another embodiment, the root cause analyzer 140 may be configured to generate at least one recommendation for avoiding failure based on the at least one analytics. Each recommendation may be, e.g., a recommendation for preventing the root cause of the failure. As a non-limiting example, when the root cause is an anomaly occurring during parallel operation of machines, the recommendation may indicate that the machines should not operate in parallel” ([0038]: when root anomaly happens during parallel machine operation, the recommendation may indicate not parallel operation (analogous to performing a certain type of maintenance and adjusting operational parameters to avoid the failure)); and
“In a further embodiment, the machine learning analyzer 250 is also configured to determine at least one recommendation for avoiding future failures based on the determined root causes. As a non-limiting example, the at least one recommendation may indicate that an exhaust pipe on the machine 170 should be replaced with a new exhaust pipe after a period of time to avoid failure” ([0054]: recommendation may include replacing a specific part on the machine).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Lavid in view of Deeg to incorporate the previous remedial actions comprising replacing a specific part, performing a certain type of maintenance and adjusting operational parameters to avoid the failure, in order to provide a robust response including all appropriate actions in response to specific failures.
Regarding claim 12.
Lavid discloses:
A system (Fig. 1, item 100; [0002], [0026]: a system for identification of machine failures and root causes is presented (see also [0015])) comprising:
a predictive maintenance engine (Fig. 1, items 130 and 140 – “MMS” and “root cause analyzer”; [0028]: the system includes a machine monitoring system (MMS) and a root cause analyzer (see also [0045])) comprising:
a data aggregator (Fig. 1, item 130 – “MMS”) configured to aggregate equipment data from one or more data sources (Fig. 1, items 120-1 … 120-n – “sensor”) to provide aggregated equipment data ([0031], [0078]: the machine monitoring system preprocess sensory inputs, which are aggregated over time for continuously monitoring to identify faults), the equipment data comprising data points (Figs. 3A-B, 4 and 7-8) relating to physical attributes of the equipment or environmental conditions for the equipment ([0033]: a root cause analyzer (Fig. 1, item 140) receives sensory inputs (see [0030]-[0031]) associated with a machine (see also [0067] regarding attribution dataset including environmental variables related to the operation of the machine during collection of sensory inputs; see also [0073]));
an analyzer (Fig. 1, item 140 – “root cause analyzer”) comprising a machine learning (ML) model configured to analyze the aggregated equipment data to determine whether the equipment needs maintenance (Fig. 5, item S510; [0035], [0039], [0060]: root cause analyzer determines a machine failure (equipment needs maintenance to avoid failure, see [0036], [0038]) and its cause by analyzing the sensory inputs using unsupervised machine learning (see also [0048], [0054]), the data being aggregated (see [0078])); and
a remediator (Fig. 1, item 140 – “root cause analyzer”) configured to issuing a remedial action in response to the ML model determining that the equipment needs maintenance and/or the correlator determining that the equipment data contains the anomaly (Fig. 5, item S570; [0036], [0038]: the root cause analyzer generates recommendations to avoid failure (see also [0044], [0054], [0068], [0070])).
Lavid does not disclose:
a correlator configured to analyze the aggregated equipment data to determine whether the aggregated equipment data contains an anomaly indicative of a negative correlation between two more different attributes of the physical attributes and two or more different environmental conditions of the environmental conditions.
Deeg teaches:
“During the execution of the correlation model, the model ascertains at least one correlation between the machine parameters, the filtered environmental parameters and the operating anomaly. This means that the computing facility checks whether a correlation with the operating anomaly can be found between the machine parameters and the filtered environmental parameters and, if such a correlation is found, the cause of the operating anomaly can be derived therefrom” (Fig. 2, item S16; [0014]: a computing facility (correlator) checks whether a correlation with an operating anomaly can be found between the machine parameters (equipment data) and the environmental parameters, and if a correlation is found, the cause of the operating anomaly is determined and appropriate actions are derived (see [0038]-[0039]; see also [0042]-[0043] regarding different correlation examples, such as when flooding (environmental parameter) is present, machine temperature (equipment data) can decrease, which represents a negative correlation)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Lavid in view of Deeg to incorporate a correlator configured to analyze the aggregated equipment data to determine whether the aggregated equipment data contains an anomaly indicative of a negative correlation between two more different attributes of the physical attributes and two or more different environmental conditions of the environmental conditions, in order to diagnose damage on a machine influenced not only by machine operation but also by environmental conditions, as discussed by Deeg ([0002]-[0003]).
Regarding claim 14.
Lavid in view of Deeg discloses all the features of claim 12 as described above.
Lavid does not explicitly disclose:
a ML training algorithm configured to train the ML model based on training data representing a variance between real-time equipment data and predefined equipment data.
However, Laved teaches:
“In the example simulation shown in FIG. 3, the curve 310A represents an aggregated behavior of the sensory inputs over time. During operation of a machine (e.g., the machine 170, FIG. 1), the aggregated behavior represented by the curve 310A may be continuously monitored for repeated sequences such as repeated sequences 320A and 330A. Upon determination of, for example, the repeated sequence 320A, the repeated sequence 330A, or both, a model of a normal behavior pattern of the machine is generated. It should be noted that continuous monitoring of, e.g., two or more cycles of behavior may be useful for determining more accurate patterns. As monitoring and, consequently, learning, continue, the normal behavior model may be updated accordingly. The models of normal behavior patterns may be utilized to determine root causes of machine failures ... FIG. 3B is an example simulation 300B illustrating generation of adaptive thresholds. Based on one or more repeated sequences (e.g., the repeated sequences 320A and 330A), a maximum threshold 310B and a minimum threshold 320B are determined. The thresholds 310B and 320B may be determined in real-time and regardless of past machine behavior … The point 330B represents an indicator, i.e., a data point that is above the maximum threshold 310B or below the minimum threshold 320B. Upon determination that one of the thresholds 310B or 320B has been exceeded, an anomaly may be detected” ([0056]-[0057]: models of normal behavior patterns (analogous to predefined equipment data) are learned during monitoring using aggregated behavior of sensory inputs over time (analogous to real-time equipment data) and implemented to determine thresholds (analogous to variance) for identification of anomalies).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Lavid in view of Deeg to incorporate a ML training algorithm configured to train the ML model based on training data representing a variance between real-time equipment data and predefined equipment data, in order to improve accuracy during identification of anomalies in equipment by learning actual normal machine conditions while accounting for realistic variations in the machine standard behavior.
Regarding claim 15.
Lavid in view of Deeg discloses all the features of claim 12 as described above.
Lavid further discloses:
the correlator is further configured to:
identify one or more data points that are outliers in the data points ([0031], [0073]: sensory inputs data are preprocessed including noise filtered, which implies the noise (outliers) to be first identified); and
exclude the identified outliers from the aggregated equipment data to provide filtered equipment data ([0031], [0073]: noise is filtered in the sensory inputs data).
Lavid does not disclose:
analyze the filtered equipment data to determine whether the filtered equipment data contains the anomaly.
Deeg further teaches:
“In an advantageous embodiment of the present invention, it is provided that the at least one correlation is ascertained by statistical analysis. In particular, new unknown correlations can be sought by means of statistical analysis. The statistical analysis can preferably comprise outlier detection, cluster analysis, classification of the machine parameters and the environmental parameters association analysis and/or regression analysis” ([0019]: correlations are obtained by statistical analysis, which includes outlier detection (see also [0020] regarding filtering data)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Lavid in view of Deeg to analyze the filtered equipment data to determine whether the filtered equipment data contains the anomaly, in order to improve accuracy of the identification of anomalies in equipment.
Regarding claim 16.
Lavid in view of Deeg discloses all the features of claim 12 as described above.
Lavid further discloses:
a type of the remedial action issued is based on a type of the equipment ([0036]: root cause analyzer determines type of failure, which is used to generate recommendations to avoid failure (e.g., type of equipment to be replaced, see [0054]; see also [0038], [0044])).
Regarding claim 17.
Lavid in view of Deeg discloses all the features of claim 12 as described above.
Lavid does not explicitly disclose:
the ML model is configured to provide one or more recommendations for one or more remedial actions, and the remediator is further configured to select one of the remedial actions based on previous remedial actions and the provided one or more recommendations.
However, Lavid teaches:
“The unsupervised machine learning analysis may further include modeling the sensory inputs and detecting indicators in the sensory inputs. The modeling may include generating meta-models for each component, module, or portion of the machine. The meta-models are monitored to detect indicators therein. Based on the indicators, root causes of the machine failures may be determined. In a further embodiment, a behavioral model utilized for predicting machine failures may be generated based on the determined root causes” ([0027]: machine learning is used to detect anomalies, their causes, and generate recommendations to avoid re-occurrence of failure (see [0026]) with models being generated based on learning from previous data); and
“In another embodiment, the root cause analyzer 140 may be configured to identify a type of the failure … The type of failure may be utilized to, e.g., generate
recommendations for avoiding failure” ([0036]: the root cause analyzer generates recommendations to avoid failure (see also [0044], [0054], [0068], [0070]); examiner notes that recommendations may also be based on personnel experience (analogous to previous remedial actions)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Lavid in view of Deeg to configure the ML model to provide one or more recommendations for one or more remedial actions, and to configure the remediator to select one of the remedial actions based on previous remedial actions and the provided one or more recommendations, in order to provide a robust response for addressing machine failures based on previous knowledge and current equipment behavior.
Regarding claim 18.
Lavid in view of Deeg discloses all the features of claim 17 as described above.
Lavid does not explicitly disclose:
the previous remedial actions comprises replacing a specific part, performing a certain type of maintenance and adjusting operational parameters to avoid the failure.
However, Lavid further teaches:
“In another embodiment, the root cause analyzer 140 may be configured to generate at least one recommendation for avoiding failure based on the at least one analytics. Each recommendation may be, e.g., a recommendation for preventing the root cause of the failure. As a non-limiting example, when the root cause is an anomaly occurring during parallel operation of machines, the recommendation may indicate that the machines should not operate in parallel” ([0038]: when root anomaly happens during parallel machine operation, the recommendation may indicate not parallel operation (analogous to performing a certain type of maintenance and adjusting operational parameters to avoid the failure)); and
“In a further embodiment, the machine learning analyzer 250 is also configured to determine at least one recommendation for avoiding future failures based on the determined root causes. As a non-limiting example, the at least one recommendation may indicate that an exhaust pipe on the machine 170 should be replaced with a new exhaust pipe after a period of time to avoid failure” ([0054]: recommendation may include replacing a specific part on the machine).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Lavid in view of Deeg to incorporate the previous remedial actions comprising replacing a specific part, performing a certain type of maintenance and adjusting operational parameters to avoid the failure, in order to provide a robust response including all appropriate actions in response to specific failures.
Regarding claim 19.
Lavid discloses:
A system (Fig. 1, item 100; [0002], [0026]: a system for identification of machine failures and root causes is presented) comprising one or more computing platforms ([0015]: the system includes computer components) configured to:
receive equipment data for equipment (Fig. 1, item 170 –‘machine’) comprising data points (Figs. 3A-B, 4 and 7-8) relating to physical attributes of the equipment or environmental conditions for the equipment ([0033]: a root cause analyzer (Fig. 1, item 140) receives sensory inputs (see [0030]-[0031]) associated with a machine (see also [0067] regarding attribution dataset including environmental variables related to the operation of the machine during collection of sensory inputs; see also [0073]));
analyze the equipment data using a machine learning (ML) model to determine whether the equipment needs maintenance (Fig. 5, item S510; [0035], [0039], [0060]: root cause analyzer determines a machine failure (equipment needs maintenance to avoid failure, see [0036], [0038]) and its cause by analyzing the sensory inputs using unsupervised machine learning (see also [0048], [0054])); and
issue a remedial action in response to the ML model determining that the equipment needs maintenance (Fig. 5, item S570; [0036], [0038]: the root cause analyzer generates recommendations to avoid failure (see also [0044], [0054], [0068], [0070])).
Lavid does not disclose:
analyze using a correlator the equipment data to determine whether the equipment data contains an anomaly indicative of a negative correlation between two more different attributes of the physical attributes and two or more different environmental conditions of the environmental conditions; and
issue a remedial action in response to the correlator determining that the equipment data contains the anomaly.
Deeg teaches:
“During the execution of the correlation model, the model ascertains at least one correlation between the machine parameters, the filtered environmental parameters and the operating anomaly. This means that the computing facility checks whether a correlation with the operating anomaly can be found between the machine parameters and the filtered environmental parameters and, if such a correlation is found, the cause of the operating anomaly can be derived therefrom” (Fig. 2, item S16; [0014]: a computing facility (correlator) checks whether a correlation with an operating anomaly can be found between the machine parameters (equipment data) and the environmental parameters, and if a correlation is found, the cause of the operating anomaly is determined and appropriate actions are derived (see [0038]-[0039]; see also [0042]-[0043] regarding different correlation examples, such as when flooding (environmental parameter) is present, machine temperature (equipment data) can decrease, which represents a negative correlation)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Lavid in view of Deeg to analyze using a correlator the equipment data to determine whether the equipment data contains an anomaly indicative of a negative correlation between two more different attributes of the physical attributes and two or more different environmental conditions of the environmental conditions; and to issue a remedial action in response to the correlator determining that the equipment data contains the anomaly, in order to diagnose damage on a machine influenced not only by machine operation but also by environmental conditions, as discussed by Deeg ([0002]-[0003]).
Regarding claim 20.
Lavid in view of Deeg discloses all the features of claim 19 as described above.
Lavid does not explicitly disclose:
the ML model is configured to provide one or more recommendations for one or more remedial actions, and the method further comprising selecting one of the remedial actions based on previous remedial actions and the provided one or more recommendations, and the previous remedial actions comprises replacing a specific part, performing a certain type of maintenance and adjusting operational parameters to avoid the failure.
Regarding “the ML model is configured to provide one or more recommendations for one or more remedial actions, and the method further comprising selecting one of the remedial actions based on previous remedial actions and the provided one or more recommendations”, Lavid teaches:
“The unsupervised machine learning analysis may further include modeling the sensory inputs and detecting indicators in the sensory inputs. The modeling may include generating meta-models for each component, module, or portion of the machine. The meta-models are monitored to detect indicators therein. Based on the indicators, root causes of the machine failures may be determined. In a further embodiment, a behavioral model utilized for predicting machine failures may be generated based on the determined root causes” ([0027]: machine learning is used to detect anomalies, their causes, and generate recommendations to avoid re-occurrence of failure (see [0026]) with models being generated based on learning from previous data); and
“In another embodiment, the root cause analyzer 140 may be configured to identify a type of the failure … The type of failure may be utilized to, e.g., generate
recommendations for avoiding failure” ([0036]: the root cause analyzer generates recommendations to avoid failure (see also [0044], [0054], [0068], [0070]); examiner notes that recommendations may also be based on personnel experience (analogous to previous remedial actions)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Lavid in view of Deeg to configure the ML model to provide one or more recommendations for one or more remedial actions, and the method further comprising selecting one of the remedial actions based on previous remedial actions and the provided one or more recommendations, in order to provide a robust response for addressing machine failures based on previous knowledge and current equipment behavior.
Regarding “the previous remedial actions comprises replacing a specific part, performing a certain type of maintenance and adjusting operational parameters to avoid the failure”, Lavid further teaches:
“In another embodiment, the root cause analyzer 140 may be configured to generate at least one recommendation for avoiding failure based on the at least one analytics. Each recommendation may be, e.g., a recommendation for preventing the root cause of the failure. As a non-limiting example, when the root cause is an anomaly occurring during parallel operation of machines, the recommendation may indicate that the machines should not operate in parallel” ([0038]: when root anomaly happens during parallel machine operation, the recommendation may indicate not parallel operation (analogous to performing a certain type of maintenance and adjusting operational parameters to avoid the failure)); and
“In a further embodiment, the machine learning analyzer 250 is also configured to determine at least one recommendation for avoiding future failures based on the determined root causes. As a non-limiting example, the at least one recommendation may indicate that an exhaust pipe on the machine 170 should be replaced with a new exhaust pipe after a period of time to avoid failure” ([0054]: recommendation may include replacing a specific part on the machine).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Lavid in view of Deeg to incorporate the previous remedial actions comprising replacing a specific part, performing a certain type of maintenance and adjusting operational parameters to avoid the failure, in order to provide a robust response including all appropriate actions in response to specific failures.
Claims 2 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Lavid, in view of Deeg, and in further view of Priyadarsini (US 20210382470 A1), hereinafter ‘Priyadarsini’.
Regarding claim 2.
Lavid in view of Deeg discloses all the features of claim 1 as described above.
Lavid does not disclose:
said issuing the remedial action comprises generating a command and providing the command to the equipment to cause the equipment to transition from an operational state into one of a shutdown state, standby state, or a reduced operational state.
Priyadarsini teaches:
“Additionally, to enable the next level of improvement in industrial plant performance, one or more embodiments include automated methods to perform the following. First, an embodiment can predict lead indications of abnormal events (e.g., process stress). The embodiment that deals with these abnormal events is robust (e.g., increased strength of protection and control systems). Second, the embodiment can estimate future values of key performance indicators (KPIs) to enable the corrective actions that should be taken to address the problem(s). Third, the embodiment can provide real-time guidance to operators to ensure optimal operation of the plant and/or to ensure appropriate responses to events” ([0016]: abnormal events in an industrial plant are predicted to enable corrective action (see also [0014], [0019])); and
“An emergency shutdown is part of a plant safeguarding system and its purpose is to keep the plant processes within design limits and to prevent the escalation of abnormal conditions into a major hazardous event. The possible causes of an emergency shutdown
include equipment failure, process upsets/trips, process/operational constraints like pipeline choking, flooding/weeping in columns, runaway conditions, over pressurization,
and process control issues” ([0025]: emergency shutdowns are caused by issues such as equipment failure (see also [0041], [0048])).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Lavid in view of Deeg, and in further view of Priyadarsini, to incorporate said issuing the remedial action comprising generating a command and providing the command to the equipment to cause the equipment to transition from an operational state into one of a shutdown state, standby state, or a reduced operational state, in order to avoid plant damage and to manage safety, as discussed by Priyadarsini ([0013]).
Regarding claim 13.
Lavid in view of Deeg discloses all the features of claim 12 as described above.
Lavid does not disclose:
the remediator is configured to generate a command as the remedial action and provide the command to the equipment to cause the equipment to transition from an operational state into one of a shutdown state, standby state, or a reduced operational state.
Priyadarsini teaches:
“Additionally, to enable the next level of improvement in industrial plant performance, one or more embodiments include automated methods to perform the following. First, an embodiment can predict lead indications of abnormal events (e.g., process stress). The embodiment that deals with these abnormal events is robust (e.g., increased strength of protection and control systems). Second, the embodiment can estimate future values of key performance indicators (KPIs) to enable the corrective actions that should be taken to address the problem(s). Third, the embodiment can provide real-time guidance to operators to ensure optimal operation of the plant and/or to ensure appropriate responses to events” ([0016]: abnormal events in an industrial plant are predicted to enable corrective action (see also [0014], [0019])); and
“An emergency shutdown is part of a plant safeguarding system and its purpose is to keep the plant processes within design limits and to prevent the escalation of abnormal conditions into a major hazardous event. The possible causes of an emergency shutdown
include equipment failure, process upsets/trips, process/operational constraints like pipeline choking, flooding/weeping in columns, runaway conditions, over pressurization,
and process control issues” ([0025]: emergency shutdowns are caused by issues such as equipment failure (see also [0041], [0048])).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Lavid in view of Deeg, and in further view of Priyadarsini, to incorporate the remediator configured to generate a command as the remedial action and provide the command to the equipment to cause the equipment to transition from an operational state into one of a shutdown state, standby state, or a reduced operational state, in order to avoid plant damage and to manage safety, as discussed by Priyadarsini ([0013]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Cantrell; Michael, US 20200117177 A1, Computer System and Method of Defining a Set of Anomaly Thresholds for an Anomaly Detection Model
Reference discloses anomaly detection model thresholds based on updating training data.
Leitch; Robert Michael et al., US 20220024607 A1, PREDICTIVE MAINTENANCE MODEL DESIGN SYSTEM
Reference discloses generating predictive maintenance models based on historical data to display metric evaluations.
Nowak; Matthew Louis et al., US 20230126147 A1, REMEDIATION ACTION SYSTEM
Reference discloses using machine learning models to predict remediation actions to mitigate reoccurrence of incidents.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LINA CORDERO whose telephone number is (571)272-9969. The examiner can normally be reached 9:30 am - 6:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, ANDREW SCHECHTER can be reached at 571-272-2302. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/LINA CORDERO/Primary Examiner, Art Unit 2857
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