DETAILED ACTION
This office action is in response to communication filed on April 16, 2026.
Response to Amendment
Amendments filed on April 16, 2026 have been entered.
Claims 1-2 and 7 have been amended.
Claims 1-7 have been examined.
Response to Arguments
Applicant’s arguments, see Remarks (p. 4), filed on 04/16/2026, with respect to the objections to the claims have been fully considered. In view of the amendments to the claims addressing the informalities raised in the previous office action, the objections to the claims have been withdrawn.
Applicant’s arguments, see Remarks (p. 4-6), filed on 04/16/2026, with respect to the rejection of claims 1-7 under 35 U.S.C. 101 have been fully considered but are not persuasive.
Applicant argues (p. 5) that claim 1 clarifies the technical feature of the present invention that improves data collection and broadcast functionality, i.e., functionality of the computer itself. Applicant also argues (p. 6) that More than a generic computer, claim 1 recites how the edge application operates on the industrial IoT platform that the processor to calculate a feature value for the instrumentation data from the data instrumentation unit and broadcast the feature value to the industrial IoT platform. Under Step 2A - Prong 2, claim 1 as currently recited is integrated into a practical application of data collection and broadcast functionality that enables adjustments to be made through relatively simple customizations of the edge application without requiring a change in the industrial IoT platform and to ensure the real-timeness of processing, which makes it possible to reduce the volume of data collected and broadcasted by the industrial IoT platform. For example, as noted in the Specification as filed (page 6), because a feature (value) required in the diagnosis performed on the edge application is calculated on the side of an edge, the execution of unnecessary processing or an unused volume of files transferred can be eliminated to thereby ensure the real-timeness of processing.
These arguments are not persuasive.
The examiner submits that according to the specification:
“The data diagnosis unit DA is configured with a central processing unit (CPU), a read-only memory (ROM), a random access memory (RAM), etc., (none of which is shown) … The data diagnosis unit DA is operated, for example, on a supervision system at a production facility, a production site or the like. As discussed earlier, the data diagnosis unit DA includes an industrial IoT platform 60 and an edge application 40 that operates in cooperation with the industrial IoT platform 60. Typically, the industrial IoT platform 60 is a software to be installed to an industrial computer incorporated in a supervision system at a production facility or the like … Typically, just like the industrial IoT platform 60, the edge application 40 is also a software to be installed to an industrial computer” (see p. 8, lines 7-25).
Based on this, the examiner submits that, under the broadest reasonable interpretation in light of the specification, using software (i.e., IoT platform, edge application) running on a generic computer, to perform an abstract idea (e.g., data collection and analysis) does not integrate the judicial exception into a practical application 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 integrate a judicial exception into a practical application or provide significantly more” (see MPEP 2106.05(f)).
Applicant further argues (p. 6) that The recited features of claim 1 cannot be performed using mental processes and are not directed to mathematical concepts.
This argument is not persuasive.
First, the examiner submits that as indicated in the October 2019 Patent Eligibility Guidance Update:
“A claim that recites a mathematical calculation will be considered as falling within the “mathematical concepts” grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word “calculating” in order to be considered a mathematical calculation. For example, a step of “determining” a variable or number using mathematical methods or “performing” a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation” (p. 4, section “iii. “Mathematical Calculations””, par. 1); and
“claims do recite a mental process when they contain limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions. Examples of claims that recite mental processes include:• a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group, LLC v. Alstom, S.A.; • claims to “comparing BRCA sequences and determining the existence of alterations,” where the claims cover any way of comparing BRCA sequences such that the comparison steps can practically be performed in the human mind, University of Utah Research Foundation v. Ambry Genetics Corp.;• a claim to collecting and comparing known information (claim 1), which are steps that can be practically performed in the human mind, Classen Immunotherapies, Inc. v. Biogen IDEC” (p. 7-8).
Based on these guidelines, the examiner submits that, as indicated in the rejection, the features “use the instrumentation data to perform a diagnosis process to diagnose the conditions of the piece of equipment” and “calculate a feature value for the instrumentation data”, under the broadest reasonable interpretation in light of the specification, cover performance of the limitations using mental processes and/or mathematical concepts to manipulate and compare data (e.g., calculating a value (i.e., feature value) from collected data (see specification at p. 5, 9-10; see also claims 6-7) and compare it to thresholds (i.e., to perform diagnosis; see specification at p. 11, 13-14)).
Applicant’s arguments, see Remarks (p. 6), filed on 04/16/2026, with respect to the rejection of claims 1-7 under 35 U.S.C. 103 have been fully considered but are moot in view of new grounds of rejection.
Applicant argues (p. 6) that Contrary to Hoyte, Maturana and Takada, the claimed edge application which “operates on the industrial IoT platform” makes adjustments possible through simple customizations of the edge application without requiring a change in the industrial IoT platform. Reference Maturana does not disclose or suggest the above feature or effects.
This argument is not persuasive.
In response to applicant’s argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., “edge application which “operates on the industrial IoT platform” makes adjustments possible through simple customizations of the edge application without requiring a change in the industrial IoT platform”) are not recited in the rejected claims. Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Also, the fact that the inventor has recognized another advantage which would flow naturally from following the suggestion of the prior art cannot be the basis for patentability when the differences would otherwise be obvious. See Ex parte Obiaya, 227 USPQ 58, 60 (Bd. Pat. App. & Inter. 1985).
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 04/16/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-7 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 machine, 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 “a data diagnosis unit configured to receive the instrumentation data from the data instrumentation unit and use the instrumentation data to perform a diagnosis process to diagnose the conditions of the piece of equipment, the data diagnosis unit including an edge application and an industrial Internet-of-Things (IoT) platform, wherein the edge application operates on the industrial IoT platform and the edge application includes a data collection and analysis module configured to calculate a feature value for the instrumentation data from the data instrumentation unit and broadcast the feature value to the industrial IoT platform” 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 manipulate and compare data (e.g., calculating a value (i.e., feature value) from collected data (see specification at p. 5, 9-10; see also claims 6-7) and compare it to thresholds (i.e., to perform diagnosis; see specification at p. 11, 13-14)). Except for the recitation of the extra-solution activities (e.g., source/type of data being evaluated, data gathering/outputting), the particular technological environment or field of use (e.g., diagnosing conditions of equipment), and the computer elements (i.e., data diagnosis unit including an edge application and an industrial Internet-of-Things (IoT) platform, data instrumentation unit, edge application including a data collection and analysis module, see p. 7-8) used to facilitate the application of the judicial exception, the limitation in the context of the claim mainly refers to performing mental evaluations and/or applying mathematical concepts to manipulate and compare data.
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):
“A condition monitoring system for monitoring conditions of a piece of equipment” generally links the use of the judicial exception to a particular technological environment or field of use (e.g., diagnosing conditions of equipment; see MPEP 2106.05(h)), while adding computer components (e.g., conditioning monitoring system; see specification at p. 6-7) as tools to perform an abstract idea (see MPEP 2106.05(f));
“a sensor configured to be installed to the piece of equipment” adds a machine (i.e., a sensor, see specification at p. 7; see also claim 2) recited at a high level of generality and used for extra-solution activities (e.g., mere data gathering, source/type of data to be manipulated; see MPEP 2106.05(b));
“a data instrumentation unit configured to receive a sensing signal from the sensor to acquire instrumentation data from the sensing signal under a predefined instrumentation condition” adds computer components (i.e., data instrumentation unit, see specification at p. 6) as tools and used for extra-solution activities (e.g., to receive data, see specification at p. 7; see MPEP 2106.05(f)); and
“a data diagnosis unit configured to receive the instrumentation data from the data instrumentation unit and use the instrumentation data to perform a diagnosis process to diagnose the conditions of the piece of equipment, the data diagnosis unit including an edge application and an industrial Internet-of-Things (IoT) platform, wherein the edge application operates on the industrial IoT platform and the edge application includes a data collection and analysis module configured to calculate a feature value for the instrumentation data from the data instrumentation unit and broadcast the feature value to the industrial IoT platform” adds extra-solution activities (e.g., source/type of data being evaluated, data gathering/outputting; see MPEP 2106.05(g)), a particular technological environment or field of use (e.g., diagnosing conditions of equipment; see MPEP 2106.05(h)), and computer elements (i.e., data diagnosis unit including an edge application and an industrial Internet-of-Things (IoT) platform, data instrumentation unit, edge application including a data collection and analysis module, see p. 7-8) used as tools 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., diagnosing conditions of equipment), 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));
add a machine (i.e., a sensor; see specification at p. 7; see also claim 2) recited at a high level of generality and used for extra-solution activities (e.g., mere data gathering, source/type of data to be manipulated), which according to the MPEP: “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 integrate a judicial exception or provide significantly more” (see MPEP 2106.05(b));
recite extra-solution activities (e.g., mere data gathering/outputting, selecting a particular data source/type to be manipulated), 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” (see MPEP 2106.05(g)); and
append generic computer components (i.e., data instrumentation unit, data diagnosis unit including an edge application and an industrial Internet-of-Things (IoT) platform, edge application including a data collection and analysis module) used to facilitate the application of the abstract idea (i.e., mere computer implementation), 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), item 2).
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.
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 claim by additional limitations (Claims 4-7), 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 machines/computer components and/or field of use (Claims 2-3), 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 1-6 are rejected under 35 U.S.C. 103 as being unpatentable over Hoyte (US 20050240289 A1, IDS reference), hereinafter ‘Hoyte’, in view of Oyekanlu (E. Oyekanlu, “Predictive edge computing for time series of industrial IoT and large scale critical infrastructure based on open-source software analytic of big data,” 2017 IEEE International Conference on Big Data (Big Data), Boston, MA, USA, 2017, pp. 1663-1669, doi: 10.1109/BigData.2017.8258103), hereinafter ‘Oyekanlu’.
Regarding claim 1.
Hoyte discloses:
A condition monitoring system for monitoring conditions of a piece of equipment ([0005]: an integrated monitoring and control system for a plant having a plurality of equipment is presented, the integrated monitoring and control system including sensors for monitoring plant operations and assessing equipment conditions), the condition monitoring system comprising:
a sensor configured to be installed to the piece of equipment ([0005], [0012]: industrial plant includes sensors installed on equipment for monitoring conditions (see [0015]-[0016], [0018]-[0019]); examiner notes that sensors need to be installed in order to monitor equipment);
a data instrumentation unit (Fig. 1, item 76 – “controller”) configured to receive a sensing signal from the sensor to acquire instrumentation data from the sensing signal under a predefined instrumentation condition ([0018]: a (skid) controller receives signals from sensors and processes the signals according to predetermined algorithms (see also [0013] regarding a local diagnostic module, [0015] regarding field modules or preprocessing modules, [0020]-[0021] regarding other control systems, and [0033], [0045] regarding I/O device and data collection devices)); and
a data diagnosis unit (Fig. 1, item 20 – “distributed control system (DCS)”) configured to receive the instrumentation data from the data instrumentation unit ([0013], [0017]-[0021]: a distributed control system collects process parameter data from controller (or local diagnostic module or field modules; see also [0046])) and use the instrumentation data to perform a diagnosis process to diagnose the conditions of the piece of equipment ([0013]; [0037]-[0038]: the DCS analyzes the data to determine health of the machines), the data diagnosis unit including an application ([0037]: DCS includes a plurality of hardware layers (Fig. 4, item 402) performing various functions (applications)) and an industrial platform ([0036]: DCS includes a continuous integrated machinery monitoring system (CIMMS) (see also [0021], [0040])),
and the application includes a data collection and analysis module (Fig. 4, items 404 and 410 – “communications layer” and “data analyzer layer”) configured to calculate a feature value for the instrumentation data from the data instrumentation unit ([0038]-[0039]: data analyzer layer processes the data received by the DCS through communications layer and performs signal processing such as average and peak detection (feature value)) and broadcast the feature value to the industrial platform ([0040]-[0041]: CIMMS uses data available in the DCS hardware layers, which implies that the signal processing results determined by the data analyzer layer are broadcasted to the CIMMS).
Hoyte does not disclose (see italic text):
the data diagnosis unit including an edge application and an industrial Internet-of-Things (IoT) platform, wherein the edge application operates on the industrial IoT platform.
Oyekanlu teaches:
“The Industrial Internet of Things (IIoT) is quite different from the general IoT in terms of latency, bandwidth, cost, security and connectivity. Most existing IoT platforms are designed for general IoT needs, and thus cannot handle the specificities of IIoT. With the anticipated big data generation in IIoT, an open source platform capable of minimizing the amount of data being sent from the edge and at the same time, that can effectively monitor and communicate the condition of the largescale engineering system by doing efficient real-time edge analytics is sorely needed. In this work, an industrial machine condition-monitoring open-source software database, equipped with a dictionary and small enough to fit into the memory of edge data-analytic devices is created. The database-dictionary system will prevent excessive industrial and smart grid machine data
from being sent to the cloud since only fault report and requisite recommendations, sourced from the edge dictionary and database will be sent … Statistical analysis at the network edge using well known industrial methods such as kurtosis and skewness reveal
significant differences between generated machine signal and reference signal” (Abstract: an IIoT condition monitoring edge-analytics configuration (analogous to the edge application operating on the industrial IoT platform) is employed at an edge analytic device (see p. 1664, col. 1, par. 2) to effectively monitor the condition of engineering systems using statistical analysis, while reducing the amount of data being transmitted to the cloud (see also Fig. 4)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Hoyte in view of Oyekanlu to incorporate the data diagnosis unit including an edge application and an industrial Internet-of-Things (IoT) platform, wherein the edge application operates on the industrial IoT platform, in order to satisfy the need for efficient real-time data analytics for timely decision making, as described by Oyekanlu (see p. 1664, col. 1, par. 1).
Regarding claim 2.
Hoyte in view of Oyekanlu discloses all the features of claim 1 as described above.
Hoyte further discloses:
the sensor includes at least one of a vibration sensor, a temperature sensor, a pressure sensor, a strain sensor, a load sensor, or an acoustic emission (AE) sensor ([0013], [0015], [0034]: industrial plant includes sensors such as pressure sensors, temperature sensors, vibration sensors (see also [0029], [0041])).
Regarding claim 3.
Hoyte in view of Oyekanlu discloses all the features of claim 1 as described above.
Hoyte further discloses:
the application includes, in addition to the data collection and analysis module, a data diagnosis module (Fig. 4, item 406 – “data processing layer”; [0038]: a data processing layer compares data to predetermined limits, check for faulty instruments and sensors), a supervision and control module ([0013]: DSC includes software code segment (supervision and control module) configured to control processor (Fig. 1, item 24)), and a data display module (Fig. 4, item 408 – “archive layer”; [0038]: archive layer is used to output data to output devices).
Hoyte does not disclose (see italic text):
the edge application.
Oyekanlu teaches:
“The Industrial Internet of Things (IIoT) is quite different from the general IoT in terms of latency, bandwidth, cost, security and connectivity. Most existing IoT platforms are designed for general IoT needs, and thus cannot handle the specificities of IIoT. With the anticipated big data generation in IIoT, an open source platform capable of minimizing the amount of data being sent from the edge and at the same time, that can effectively monitor and communicate the condition of the largescale engineering system by doing efficient real-time edge analytics is sorely needed. In this work, an industrial machine condition-monitoring open-source software database, equipped with a dictionary and small enough to fit into the memory of edge data-analytic devices is created. The database-dictionary system will prevent excessive industrial and smart grid machine data
from being sent to the cloud since only fault report and requisite recommendations, sourced from the edge dictionary and database will be sent … Statistical analysis at the network edge using well known industrial methods such as kurtosis and skewness reveal
significant differences between generated machine signal and reference signal” (Abstract: an IIoT condition monitoring edge-analytics configuration is employed at an edge analytic device (see p. 1664, col. 1, par. 2) to effectively monitor the condition of engineering systems using statistical analysis, while reducing the amount of data being transmitted to the cloud (see also Fig. 4)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Hoyte in view of Oyekanlu to incorporate an edge application, in order to satisfy the need for efficient real-time data analytics for timely decision making, as described by Oyekanlu (see p. 1664, col. 1, par. 1).
Regarding claim 4.
Hoyte in view of Oyekanlu discloses all the features of claim 1 as described above.
Hoyte does not explicitly disclose:
the data collection and analysis module of the edge application is configured to calculate the feature value as a function of a type of the sensor.
However, Hoyte teaches:
“A data analyzer layer 410 may be used to provide signal processing of data received through communications layer 404. Such signal processing may include, but is not limited to average, standard deviation, peak detection, correlation, fast fourier transform (FFT), and demodulation” ([0038]: data analyzer layer processes the data received by the DCS through communications layer and performs signal processing such as peak detection (feature value); examiner submits that signal analysis must be performed based on the type of signal (or type of sensor) in order to extract relevant information while removing noise (see claim 1 for “edge application” feature)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Hoyte in view of Oyekanlu to configure the data collection and analysis module of the edge application to calculate the feature value as a function of a type of the sensor, in order to implement extract relevant information from the signal while also filtering noise to improve analysis.
Regarding claim 5.
Hoyte in view of Oyekanlu discloses all the features of claim 1 as described above.
Hoyte does not explicitly disclose:
the data collection and analysis module of the edge application is configured to calculate a single scalar quantity for each sensor per single retrieval.
However, Hoyte teaches:
“A data analyzer layer 410 may be used to provide signal processing of data received through communications layer 404. Such signal processing may include, but is not limited to average, standard deviation, peak detection, correlation, fast fourier transform (FFT), and demodulation” ([0038]: data analyzer layer processes the data received by the DCS through communications layer and performs signal processing such as average (analogous to single scalar quantity) (see claim 1 for “edge application” feature)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Hoyte in view of Oyekanlu to configure the data collection and analysis module of the edge application to calculate a single scalar quantity for each sensor per single retrieval, in order to implement reduced and more comprehensive representations of data that result in less computational cost during analysis.
Regarding claim 6.
Hoyte in view of Oyekanlu discloses all the features of claim 1 as described above.
Hoyte further discloses:
the data collection and analysis module of the application is configured to calculate at least one of an effective value, an overall value, a peak value, a crest factor, a kurtosis value, or a skewness value ([0038]: data analyzer layer processes the data received by the DCS through communications layer and performs signal processing such as peak detection (feature value)).
Hoyte does not explicitly disclose:
when a type of the sensor is a vibration sensor.
However, Hoyte teaches:
“Additional sensors that may be used include, but are not limited to, vibration sensors, which may be embodied in an accelerometer 324, other vibration sensors may be proximity sensors, such as a pump outboard proximity sensor 326, a pump inboard proximity sensor 328, a transducer a once-per-revolution event, such as a Keyphasor®330, and a thrust sensor 332” ([0034]: vibration sensors are implemented to collect data for analysis (see also [0013])).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Hoyte in view of Oyekanlu to configure the data collection and analysis module of the application to calculate at least one of an effective value, an overall value, a peak value, a crest factor, a kurtosis value, or a skewness value, when a type of the sensor is a vibration sensor, in order to incorporate well known signal analysis techniques implemented on vibration data for identification of equipment conditions.
Hoyte does not disclose (see italic text):
the edge application.
Oyekanlu teaches:
“The Industrial Internet of Things (IIoT) is quite different from the general IoT in terms of latency, bandwidth, cost, security and connectivity. Most existing IoT platforms are designed for general IoT needs, and thus cannot handle the specificities of IIoT. With the anticipated big data generation in IIoT, an open source platform capable of minimizing the amount of data being sent from the edge and at the same time, that can effectively monitor and communicate the condition of the largescale engineering system by doing efficient real-time edge analytics is sorely needed. In this work, an industrial machine condition-monitoring open-source software database, equipped with a dictionary and small enough to fit into the memory of edge data-analytic devices is created. The database-dictionary system will prevent excessive industrial and smart grid machine data
from being sent to the cloud since only fault report and requisite recommendations, sourced from the edge dictionary and database will be sent … Statistical analysis at the network edge using well known industrial methods such as kurtosis and skewness reveal
significant differences between generated machine signal and reference signal” (Abstract: an IIoT condition monitoring edge-analytics configuration is employed at an edge analytic device (see p. 1664, col. 1, par. 2) to effectively monitor the condition of engineering systems using statistical analysis, while reducing the amount of data being transmitted to the cloud (see also Fig. 4)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Hoyte in view of Oyekanlu to incorporate an edge application, in order to satisfy the need for efficient real-time data analytics for timely decision making, as described by Oyekanlu (see p. 1664, col. 1, par. 1).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Hoyte, in view of Oyekanlu, and in further view of Takada (US 20170108407 A1, IDS reference), hereinafter ‘Takada’
Regarding claim 7.
Hoyte in view of Oyekanlu discloses all the features of claim 2 as described above.
Hoyte further discloses:
the data collection and analysis module of the application is configured to calculate at least one of an effective value, a peak value, a cumulative peak count, or an energy-converted value ([0038]: data analyzer layer processes the data received by the DCS through communications layer and performs signal processing such as peak detection (peak value)).
Hoyte does not disclose (see italic text):
the edge application.
when a type of the sensor is the AE sensor.
Regarding “the edge application”, Oyekanlu teaches:
“The Industrial Internet of Things (IIoT) is quite different from the general IoT in terms of latency, bandwidth, cost, security and connectivity. Most existing IoT platforms are designed for general IoT needs, and thus cannot handle the specificities of IIoT. With the anticipated big data generation in IIoT, an open source platform capable of minimizing the amount of data being sent from the edge and at the same time, that can effectively monitor and communicate the condition of the largescale engineering system by doing efficient real-time edge analytics is sorely needed. In this work, an industrial machine condition-monitoring open-source software database, equipped with a dictionary and small enough to fit into the memory of edge data-analytic devices is created. The database-dictionary system will prevent excessive industrial and smart grid machine data
from being sent to the cloud since only fault report and requisite recommendations, sourced from the edge dictionary and database will be sent … Statistical analysis at the network edge using well known industrial methods such as kurtosis and skewness reveal
significant differences between generated machine signal and reference signal” (Abstract: an IIoT condition monitoring edge-analytics configuration is employed at an edge analytic device (see p. 1664, col. 1, par. 2) to effectively monitor the condition of engineering systems using statistical analysis, while reducing the amount of data being transmitted to the cloud (see also Fig. 4)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Hoyte in view of Oyekanlu to incorporate an edge application, in order to satisfy the need for efficient real-time data analytics for timely decision making, as described by Oyekanlu (see p. 1664, col. 1, par. 1).
Regarding “when a type of the sensor is the AE sensor”, Takada teaches:
“A machine component diagnosis system according to a first aspect of the present invention relates to a system for diagnosing malfunction of a diagnosis subject (a subject to be diagnosed) 1 composed of a machine component, the system comprising: … an additional sensor 4 configured to measure one characteristic of the diagnosis subject 1, the additional sensor 4 being a temperature sensor 41 or an AE sensor 42, the temperature sensor 41 being configured to measure, as the one characteristic, temperature of the diagnosis subject 1, the AE sensor 42 being configured to measure, as the one characteristic, an acoustic emission wave (AE wave)” ([0013]-[0017]: AE sensors are implemented for diagnosing malfunctioning of equipment (see [0032])).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Hoyte in view of Oyekanlu, and in further view of Takada, to configure the data collection and analysis module of the edge application to calculate at least one of an effective value, a peak value, a cumulative peak count, or an energy-converted value, when a type of the sensor is the AE sensor, in order to implement well known signal analysis techniques implemented on AE data while enhancing accuracy of malfunction diagnosis.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Craig; Jason et al., US 20140244192 A1, SYSTEM AND METHOD FOR PROVIDING MONITORING OF INDUSTRIAL EQUIPMENT
Reference discloses monitoring industrial equipment based on power measurements.
Furem, Ken et al., US 20050085973 A1, System and method for remotely analyzing machine performance
Reference discloses improving machine operation and/or predicting failure by determining relationships related to collected data.
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.
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/LINA CORDERO/Primary Examiner, Art Unit 2857
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