DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Office action is responsive to the communication filed on 03/15/2026. The claim(s) 1-20 is/are pending, of which the claim(s) 1, 8, & 13 is/are in independent form.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Response to Arguments
I) Arguments against the claim Rejections - 35 USC § 103
Applicant’s arguments, see Remarks, filed 03/15/2026, with respect to the amended limitations of the independent claims have been fully considered and are persuasive. Therefore, the 103 rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of discovery of new prior art and its combination with prior cited arts as shown below.
II) Arguments against the claims rejections 35 USC § 101
In light of the received amendments made to the claims and provided arguments, the outstanding 101 rejections are deemed moot and therefore are withdrawn.
Claim Rejections - 35 USC § 103
Claim(s) 1-2 & 4- 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lavid et al. (US 20200166921 A1) in view of Lee (US 20180203440 A1), and in further view of Mizobuchi (US 20210255613 A1). The combination of Lavid, Lee, and Mizobuchi is referred as LLM hereinafter.
Regarding claim 1, Lavid teaches a method [“computer-implemented method for repairing suboptimal operation of an industrial machine”] to control operations in an industrial [facility that includes a machine 170 and sensors 120s],, the method comprising: ([026, 028], claim 1);
monitoring [Fig. 4, Step S410, “server 140 is configured to receive, through the network 110, the preprocessed sensory input data”], by one or more sensors, operating parameters [“collected sensory input data”, e.g., “a temperature parameter may be analyzed… an energy consumption parameter may be analyzed”] of an equipment from amongst a plurality of equipment [machine 170s like turbine, 3D printer] installed in the industrial facility,
identifying [“one machine behavioral pattern that is indicative of at least a sub optimal operation of the machine 170”, “determination that one of the thresholds 310B or 320Bhas been exceeded, an anomaly may be detected”], by a controller [processor of the management server 140], an operating parameter of the equipment to deviate from the corresponding predefined range of values ([038-039, 056, 060]);
identifying [“management server 140 is configured to automatically select a model that optimally indicates anomalies in the sensory input data based on, e.g., a type of one or more portions of the data”] , from a dataset, past instances of deviation in the operating parameter, comparing the identified pattern's characteristics to historical patterns' characteristics of machine behavioral historical patterns… machine failure root cause, a relevant corrective solution, and the like”] of deviation in operating parameters of the plurality of equipment and at least one corrective action taken in respect of each of the past instances of deviation ([041, 060, 067-068]);
selecting, by the controller, a corrective action [“configured to select one or more corrective actions”] from amongst the at least one corrective action in the dataset, wherein the corrective action is designed to correct the deviation in the operating parameter of the equipment ([041, 061]); and
providing [“corrective solution recommendations are usually sent to a user device”], by the controller, a data [“the management server 140 may generate a corrective solution recommendation indicating, e.g., the optimal way of action”] required for implementation of the selected corrective action to an operator to enable the operator to implement the corrective action to modify the operating parameter of the equipment ([010, 042, 062-064]).
Lavid fails to teach:
(1) “wherein a range of values is predefined for each of the operating parameters of the equipment” in monitoring step;
(2) determining, by the controller, a deviation magnitude, wherein the deviation magnitude represents an extent of a deviation of the operating parameter of the equipment from amongst the plurality of equipment installed in the industrial facility;
wherein the past instances of deviation are identified where the deviation magnitude is within a predefined deviation threshold relative to the deviation magnitude of the equipment.
Lee relates to remote monitoring system for monitoring in real time pluralities of the parameters of each equipment [“of driving units(motors) necessary for operation”] of an industrial facility to predict abnormality thereof ([001, 004, 010]). Specifically, Lee teaches a method to control operations in an industrial facility, the method comprising:
monitoring operating parameters [“each of the four monitoring factors”] of an equipment [“each driving unit equipment”] from amongst a plurality of equipment installed in the industrial facility, wherein a range of values is predefined [“a threshold level preset to correspond to each piece of the information”, “upper and lower limits of the normal range may be set”] for each of the operating parameters of the equipment ([011, 037, 050, 070]). Thus, Lee cures 1st deficiency of Lavid.
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to (1) combine Lee and Lavid because they both related to a remote monitoring system monitoring in real-time for abnormality in pluralities of equipment of an industrial facility that generate large amount of data and (2) modify the method/system of Lavid to have a range of values is predefined for each of the operating parameters of the equipment as in Lee to identify risk level differently for the different parameters/factors of the monitored equipment and generate an alarm to notify the user (Lee [070]). Furthermore, doing so would allow individually monitor abnormality level of pluralities of the equipment before an occurrence of a major accident (Lee, [019, 061]).
Lavid in view of Lee still fails to cure the 2nd deficiency of Lavid but is cured by Mizobuchi.
Mizobuchi teaches a system 10 and method for determining a processing result of the measurement data from pluralities of the target devices of a monitored facility including by performing calculation of an abnormality degree on the monitored devices (“cooling fan”) ([004, 040]). Specifically, Mizobuchi teaches a method to control operations in an industrial facility, the method comprising:
determining, by the controller, a deviation magnitude [“performing a calculation of an abnormality degree”, “calculates an abnormality degree of the acquired audio data (S23).”], wherein the deviation magnitude represents an extent of a deviation of the operating parameter [data obtained “using a vibration sensor”] of the equipment from amongst the plurality of equipment installed in the industrial facility ([040- 046, 071]);
identifying [“a matching processing of comparing a current abnormality degree transition with a past abnormality degree transition about…matching rate of the abnormality degree transitions”], from a dataset past instances of deviation in the operating parameter, wherein the past instances of deviation are identified where the deviation magnitude is within a predefined deviation threshold [the matching rate of the abnormality degree transitions is equal to or higher than a predetermined value”] relative to the deviation magnitude of the equipment, wherein the dataset comprises data corresponding to a plurality of past instances of deviation in operating parameters of the plurality of equipment[ ([049-050, 054, 083], Fig. 4).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to (1) combine Mizobuchi and Lavid in view of Lee because they both related to identifying suboptimal operation of the monitored devices/equipment and (2) modify the method/system of Lavid in view of Lee to include 2nd missing limitations as suggested in Mizobuchi. Doing so would allow to easily grasp the sign of the abnormality in the monitored equipment before the abnormality becomes remarkable (Mizobuchi [004]). Furthermore, the “comparing current abnormality degree” with “past abnormality degree transition” of Mizobuchi would be envisioned by PHOSITA as an example of “behavioral pattern that is indicative of at least a sub optimal operation” disclosed in Lavid (Mizobuchi, [049] & Lavid [039]). Accordingly, the combination of Lavid, Lee and Mizobuchi teaches each elements of the claim and renders invention of this claim obvious to PHOSITA.
Regarding claim 2, LLM teaches the method of claim 1, wherein identifying the operating parameter of the equipment to deviate from the corresponding predefined range [“multi-stage threshold level”] of values comprises identifying at least a first stage of deviation and a second stage of deviation, the first stage of deviation being attained when the operating parameter deviates from a mean of the corresponding predefined range of values by at least a first value and the second stage [“deviates from a second threshold level”] of deviation being attained when the operating parameter deviates from the mean of the corresponding predefined range of values by at least a second value, the second value being greater than the first value; and wherein selecting the corrective action from amongst the at least one corrective action in the dataset is based on the identified at least first stage [“machine fault”] of deviation and second stage [“failure”] of deviation (Lavid [056, 041], Lee [071]).
Regarding claim 4, LLM teaches the method of claim 1, wherein implementing the corrective action comprises modifying at least one of the operating parameter of the equipment (Lavid [025, 042]).
Regarding claim 5, LLM teaches the method of claim 1, wherein the corrective action is selected based on a suitability score [“score for a corrective action”] assigned to the at least one corrective action taken in respect of each of the past instances of deviation, wherein the selected corrective action has a highest suitability score (Lavid [061-062, 067-068]).
Regarding claim 6, LLM teaches the method of claim 5, wherein assigning the suitability score comprises employing a machine learning model [“unsupervised machine”] to analyze the dataset for analysing the plurality of past instances of deviation in operating parameters of the plurality of equipment and the corresponding corrective action taken in respect of each of the past instances of deviation to evaluate an outcome associated with the corresponding corrective action taken in respect of each of the past instances of deviation (Lavid [031, 035-036]).
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over LLM as in claim 2 and further in view of Siury et al. (US 20080160650 A1, reference of record).
Regarding claim 3, LLM teaches the method of claim 2, wherein identifying the operating parameter of the equipment to deviate from the corresponding predefined range of values comprises determining a condition of a machine fault,”] of deviation to the second stage of deviation [“the fault is developed into a machine failure”], and wherein selecting the corrective action from amongst the at least one corrective action from the dataset is based on the stage of the deviation
However, LLM fails to teach determining the time taken to reach from first stage of the machine to the second stage as claimed and shown above with strikethrough emphasis.
Siury relates to fault detection and classification mechanism during electrochemical treatment of a surface of a substrate (Abstract). More specifically, Siury teaches A method to control operations in an industrial facility, the method comprising: identifying an operating parameter of the equipment to deviate from the corresponding predefined range of values; wherein identifying the operating parameter of the equipment to deviate from the corresponding predefined range of values comprises assessing time taken [“the unit 160 may indicate to an operator or to a supervising control system a corresponding predicted time to maintenance or time to failure of a specific hardware component” and “ the unit 160 may recognize the degradation of a specific component”, “may monitor the development of the tool status or an indication thereof overtime,”] to move from the first stage of deviation to the second stage of deviation, and wherein selecting the corrective action from amongst the at least one corrective action from the dataset is based on the assessed time ([047, 050-051]).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to (1) combine Siury and LLM because they both related to monitoring and identifying operating parameters of an equipment to select a corrective action and (2) modify the method of LLM to include missing limitations from Siury. Doing so would significantly increase operational reliability and provide fast response in case of failure detection in the industrial facility of LLM (Siury [051]).
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over LLM as in claim 5, and further in view of De Leo et al. (US 9053416 B1, reference of record).
Regarding claim 7, Lavid in view of Lee teaches The method of claim 5, wherein the suitability score [“score for a corrective action”] is assigned to the corrective action was implemented for the past instances of deviation (Lavid [062]).
However, Lavid in view of Lee fails to teach its score assigned based on a number of times the corrective action was implemented as claimed and shown above with strikethrough emphasis.
De Leo teaches the suitability score is assigned based on a number of times [“reliable flag information based on a previous number of times the user 106 has generated flag information”] the corrective action was implemented for the past instances of deviation (Col 4 lines 5-25).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to (1) combine De Leo and Lavid in view of Lee because they both related to generating suitable score for the corrective action (2) modify the system of Lavid in view of Lee to include missing limitations as in De Leo. Doing so would allow the selected actions in the system of Lavid in view of Lee with higher accuracy and reliability (De Leo, Col 4 lines 10-15).
Claim(s) 8-14, 16- 18, & 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lavid et al. (US 20200166921 A1, reference of record) in view of Mizobuchi (US 20210255613 A1).
Regarding claim 8, Lavid teaches a system [item 100] to control an industrial facility [area with machines 170 and sensors 120s] comprising: a processor [server 140 + MMS 130] to: (Fig. 1-2);
identify an operating parameter [“sensory input data related to at least one machine, e.g., the machine 170” are provided to the MMS 130, e.g., temperature parameter, “energy consumption parameter”] of an equipment [machine 170] in the industrial facility to deviate from a predefined range of values ([004, 028, 035, 054-055]);
determine attributes [e.g., temperature, pressure, vibration] of the equipment ([059-060]);
;
identify [“comparing the identified pattern's characteristics to historical patterns' characteristics of machine behavioral historical patterns that were previously analyzed and stored in a database”] from a dataset past instances of deviation in the operating parameters using the attributes of the equipment, , wherein the dataset comprises data corresponding to a plurality of past instances of deviation in operating parameters of a plurality of equipment having attributes similar to the equipment in the industrial facility and one or more corrective actions taken in respect of each past instance of the deviation ([038, 060-068]);
select a corrective action [“At S440, one or more corrective actions are selected “] from amongst the one or more corrective actions, wherein the corrective action is designed to correct the deviation in the operating parameter of the equipment ([061], fig. 5); and
provide a data [“recommendations may be sent by the management server 140 via the network 110 to a client device”] required for implementation of the selected corrective action to an operator to enable the operator to implement the corrective action to modify the operating parameter of the equipment ([041, 062, 064]).
Lavid may not teach limitations shown above with strikethrough emphasis. That is, Lavid may not teach:
determine a deviation magnitude, wherein the deviation magnitude represents an
extent of a deviation of the operating parameter of the equipment from amongst the plurality of equipment installed in the industrial facility and
wherein the past instances of deviation are identified where the deviation magnitude is within a predefined deviation threshold relative to the deviation magnitude of the equipment.
Mizobuchi teaches a system 10 and method for determining a processing result of the measurement data from pluralities of the target devices of a monitored facility including by performing calculation of an abnormality degree on the monitored devices (“cooling fan”) ([004, 040]). Specifically, Mizobuchi teaches a method to control operations in an industrial facility, the method comprising:
determine a deviation magnitude [“performing a calculation of an abnormality degree”, “calculates an abnormality degree of the acquired audio data (S23).”], wherein the deviation magnitude represents an extent of a deviation of the operating parameter of the equipment from amongst the plurality of equipment installed in the industrial facility ([040- 046, 071]);
identify[“a matching processing of comparing a current abnormality degree transition with a past abnormality degree transition about…matching rate of the abnormality degree transitions”], from a dataset past instances of deviation in the operating parameters using the attributes of the equipment, wherein the past instances of deviation are identified where the deviation magnitude is within a predefined deviation threshold [“the matching rate of the abnormality degree transitions is equal to or higher than a predetermined value”] relative to the deviation magnitude of the equipment, wherein the dataset comprises data corresponding to a plurality of past instances of deviation in operating parameters of a plurality of equipment having attributes similar to the equipment in the industrial facility ([049-050, 054, 083], Fig. 4).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to (1) combine Mizobuchi and Lavid Lee because they both related to identifying suboptimal operation of the monitored devices/equipment and (2) modify the method/system of Lavid to include missing limitations as suggested in Mizobuchi. Doing so would allow to easily grasp the sign of the abnormality in the monitored equipment before the abnormality becomes remarkable (Mizobuchi [004]). Furthermore, the “comparing current abnormality degree” with “past abnormality degree transition” of Mizobuchi would be envisioned by PHOSITA as an example of “behavioral pattern related to the industrial machine” disclosed in Lavid (Mizobuchi, [049]). Furthermore, the “comparing current abnormality degree” with “past abnormality degree transition” of Mizobuchi would be understood by PHOSITA as an example of “behavioral pattern that is indicative of at least a sub optimal operation” disclosed in Lavid (Mizobuchi, [049] & Lavid [039]). Accordingly, the combination of Lavid and Mizobuchi teaches each elements of the claim and renders invention of this claim obvious to PHOSITA.
Regarding claim 9, Lavid in view of Mizobuchi teaches the system of claim 8, wherein the processor is to identify the operating parameter of the equipment to deviate from the predefined range of values by identifying at least a first stage of deviation and a second stage of deviation, the first stage of deviation being attained when the operating parameter deviates from a mean of the corresponding predefined range of values by a first value and the second stage of deviation being attained when the operating parameter deviates from the mean of the corresponding predefined range of values by a second value, the second value being greater than the first value; and select the corrective action from amongst the one or more corrective actions in the dataset based on the identified at least first stage of deviation and second stage of deviation (Lavid [041]).
Regarding claim 10, Lavid in view of Mizobuchi teaches the system of claim 8, wherein the plurality of equipment having attributes similar to the equipment in the industrial facility are located at sites other than the industrial facility (Lavid Fig. 1).
Regarding claim 11, Lavid in view of Mizobuchi teaches the system of claim 8, wherein the implementation of the corrective action comprises modifying [“repair or replacement depending on the cause”] the operating parameter of the equipment that is identified to be deviating from the predefined range of values (Lavid [042] & Mizobuchi [064]).
Regarding claim 12, Lavid in view of Mizobuchi teaches the system of claim 8, wherein the dataset further comprises data corresponding to one or more causes of deviation for each of the plurality of past instances [historical patterns] of deviation in the operating parameters of the plurality of equipment (Lavid [060] & Mizobuchi [068]).
Regarding claim 13, Lavid teaches a non-transitory computer readable medium [“computer readable medium”] comprising computer-readable instructions that when executed cause a processing resource of a computing device to: ([069]);
identify a real-time deviation [“abnormal temperature values of the machine may be recognized using temperature indicators” for the sensory inputs] in a key performance indicator (KPI) [“sensory inputs” like temperature values] of an industrial process, with respect to a predetermined value corresponding to the KPI ([058-060]);
identify [“comparing the identified pattern's characteristics to historical patterns' characteristics of machine behavioral historical patterns that were previously analyzed and stored in a database”], from a dataset, instances of deviation in the KPI of the industrial process, wherein the past instances of deviation are selected
select, by the controller, a corrective action from amongst the one or more corrective actions, wherein the corrective action is designed to correct the deviation in the operating parameter of the industrial process ([061-062], fig. 5); and
provide [“recommendations may be sent by the management server 140 via the network 110 to a client device”], by the controller, a data required to implement the selected corrective action to an operator to enable the operator to implement the corrective action to modify the operating parameter of the industrial process ([041, 062, 064]).
Lavid fails to teach the limitations shown above with strikethrough emphasis.
Mizobuchi teaches a system 10 and method for determining a processing result of the measurement data from pluralities of the target devices of a monitored facility including by performing calculation of an abnormality degree on the monitored devices (“cooling fan”) ([004, 040]). Specifically, Mizobuchi teaches A non-transitory computer readable medium comprising computer-readable instructions that when executed cause a processing resource of a computing device to:
determine a deviation magnitude, wherein the deviation magnitude represents an extent of a deviation of the KPI ([040- 046, 071]);
identify [“a matching processing of comparing a current abnormality degree transition with a past abnormality degree transition about…matching rate of the abnormality degree transitions”], from a dataset instances of deviation in the KPI of the industrial process, wherein the past instances of deviation are selected where the deviation magnitude is within a predetermined threshold [“the matching rate of the abnormality degree transitions is equal to or higher than a predetermined value”] relative to the deviation magnitude of the industrial process ([049-050, 054, 083], Fig. 4).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to (1) combine Mizobuchi and Lavid Lee because they both related to identifying suboptimal operation of the monitored devices/equipment and (2) modify the computer readable storage medium of Lavid to include missing limitations as suggested in Mizobuchi. Doing so would allow to easily grasp the sign of the abnormality in the monitored equipment before the abnormality becomes remarkable (Mizobuchi [004]). Furthermore, the “comparing current abnormality degree” with “past abnormality degree transition” of Mizobuchi would be envisioned by PHOSITA as an example of “behavioral pattern that is indicative of at least a sub optimal operation” disclosed in Lavid (Mizobuchi, [049] & Lavid [039]). Accordingly, the combination of Lavid and Mizobuchi teaches each elements of the claim and renders invention of this claim obvious to PHOSITA.
Regarding claim 14, Lavid in view of Mizobuchi teaches the non-transitory computer readable medium of claim 13, wherein the industrial process is configured to execute in at least one mode [normal or suboptimal operation], and wherein the predetermined value of the KPI is defined for each mode, and wherein the computer-readable instructions further cause the processing resource to determine a real-time mode of the industrial process to identify the real-time deviation (Lavid [039, 054-056]).
Regarding claim 16, Lavid in view of Mizobuchi further teaches the non-transitory computer readable medium of claim 13, wherein the corrective action is selected based on a suitability score [“corrective action having a first probability score that is above a first predetermined threshold”] assigned to the each of the one or more corrective actions taken in respect of each of the plurality of past instances of deviation (Lavid, [061-062, 068]).
Regarding claim 17, Lavid in view of Mizobuchi further teaches the non-transitory computer readable medium of claim 16, wherein the computer-readable instructions further cause the processing resource to evaluate an outcome associated with the each of the one or more corrective actions taken in respect of each of the past instances [“the identified pattern's characteristics to historical patterns' characteristics”] of deviation to assign the suitability score (Lavid, [060-062, 068]).
Regarding claim 18, Lavid in view of Mizobuchi further teaches the non-transitory computer readable medium of claim 13, wherein the computer-readable instructions further cause the processing resource to identify at least a first stage [fault] of deviation and a second stage [machine failure] of deviation, the first stage of deviation being attained when the KPI deviates from the predetermined values by a first value and the second stage of deviation being attained when the KPI deviates [“before the fault is developed into a machine failure”] from the predefined values by a second value, the second value being greater than the first value; and wherein the computer-readable instructions further cause the processing resource to select the corrective action from amongst the one or more corrective actions in the dataset based on the identified at least first stage of deviation and second stage of deviation (Lavid [039-041, 054-055, 060]).
Regarding claim 20, Lavid in view of Mizobuchi further teaches the non-transitory computer readable medium of claim 13, wherein implementation of the corrective action comprises modifying one or more parameters of the KPI of the industrial process to bring value of the KPI within the predetermined value ([042]).
Claim(s) 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lavid in view Mizobuchi, and further in view of Perry et al. (US 20220097817 A1, reference of record).
Regarding claim 15, Lavid in view of Mizobuchi teaches the non-transitory computer readable medium of claim 14 wherein the at least one mode of the industrial process comprises a performance mode [“normal machine behavioral patterns”], and wherein a predetermined value of capacity [“adaptive thresholds may be generated based on the determined normal behavior patterns”], availability, or efficiency is defined for the industrial process in the performance mode (Lavid [038, 053-056]).
However, Lavid in view of Mizobuchi fails to teach wherein the at least one mode of the industrial process comprises a sustainability mode, wherein a predetermined value of carbon emission is defined for the industrial process in the sustainability mode as claimed.
Perry relates to operating vessel (engine) in different modes while meeting emission targets set by different jurisdictions. Specifically, Perry teaches a non-transitory computer readable medium for identify key performance indicator (KPI) of an industrial process [vessel ship 10] wherein the industrial process is configured to be operated, using a controller 120, the industrial process in at least one mode [“vessels have may have various operating modes such…carbon emission limits may be different for each operating mode.”], wherein the at least one mode of the industrial process comprises a sustainability mode [larger carbon emission limit depending on the location of “applicable jurisdictional carbon emission regulations”], wherein a predetermined value of carbon emission is defined for the industrial process in the sustainability mode; and a performance mode [smaller carbon emission limits], and wherein a predetermined value of capacity, availability, or efficiency is defined for the industrial process in the performance mode ([021-022, 027-029]).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to (1) combine Perry and Lavid in view of Mizobuchi because they both related to control operating an equipment at different modes to meet strict operating constraints and (2) modify the industrial system of Lavid in view of Mizobuchi to allow operating both in a sustainability mode and performance mode depending on the jurisdiction the industrial process is installed. Doing so would allow operating of the industrial facility and its equipment of Lavid in view of Mizobuchi without being in violation of the carbon emission rules for the given jurisdiction(s) without sacrificing unnecessary performance (Perry [023]).
Claim(s) 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lavid in view Mizobuchi, and further in view of Siury (US 20080160650 A1, reference of record).
Regarding claim 19, Lavid in view of Mizobuchi teaches the non-transitory computer readable medium of claim 18, wherein the computer-readable instructions further cause the processing resource
However, Lavid in view of Mizobuchi fails to teach to assessing time taken to move from the first state to second state and using this assessed time to select the corrective action as claimed and shown above with strikethrough emphasis.
Siury relates to fault detection and classification mechanism during electrochemical treatment of a surface of a substrate (Abstract). More specifically, Siury teaches A method to control operations in an industrial facility, the method comprising: identifying an operating parameter of the equipment to deviate from the corresponding predefined range of values; wherein identifying the operating parameter of the equipment to deviate from the corresponding predefined range of values comprises assessing time taken [“the unit 160 may indicate to an operator or to a supervising control system a corresponding predicted time to maintenance or time to failure of a specific hardware component” and “ the unit 160 may recognize the degradation of a specific component”] to move from the first stage of deviation to the second stage of deviation, and wherein selecting the corrective action from amongst the at least one corrective action from the dataset is based on the assessed time ([047, 050]).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to (1) combine Siury and Lavid in view of Mizobuchi because they both related to monitoring and identifying operating parameters of an equipment to select a corrective action and (2) modify the method of Lavid in view of Lee to include missing limitations from Siury. Doing so would allow to significantly increase operational reliability and provide fast response in case of failure detection in the industrial facility of Lavid in view of Mizobuchi (Siury [051]).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
1) Chen (US 11269718 B1 ) teaches automatically detecting root causes of anomalies occurring in information technology (IT) systems (Abstract).
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/SANTOSH R POUDEL/ Primary Examiner, Art Unit 2115
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