DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 03/06/2026 was in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Status of the claims
The argument received on March, 6 2026 has been acknowledged and entered. Claims 1, 6, 13-15, and 18 are amended. Claim 5 is cancelled Thus, claims 1-4 and 6-20 are currently pending.
Response to Arguments
Applicant’s arguments filed on March 6, 2026 with respect to claims 1-4 and 6-20 under 35 U.S.C. 101 have been considered but are moot because the new ground of rejection.
Applicant’s arguments filed on March 6, 2026, 2026 with respect to claims 1-4 and 6-20 under 35 U.S.C. 103 have been considered but are moot because the new ground of rejection.
Claim Objections
Claim 1 is objected to because of the following informalities:
In claim 20, lines 31-35, the limitation of “outputting, via a user interface, one or more notifications generated by the updated Fl model responsive to a value of the Fl function satisfying a respective Fl threshold value of the one or more Fl threshold values, wherein each of the one or more notifications identifies a condition associated with a physical condition of at least one additional instance of the too” should be deleted.
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 1-4 and 6-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Specifically, representative Claim 1 recites:
A method comprising:
storing, by a processing device, a failure index (Fl) model comprises:
an FI function computing, an input into the FI function comprising a plurality of weighted statistical characteristics of run-time sensor data collected during one or more operations of a tool of a manufacturing system, each statistical characteristic of the plurality of weighted statistical characteristics weighted with a respective weight of a plurality of weights of the failure index function, and
one or more FI threshold values for the FI function, wherein each of the one or more FI threshold values is associated with at least one of a present condition of the tool or a projected condition of the tool, wherein the plurality of weights and the one or more failure index threshold values are initially set, prior to first five failures of the tool, based on input received from a user;
collecting run-time sensor data for one or more instances of the tool;
applying, by the processing device, the FI model to the [[new ]]run-time sensor data to identify one or more conditions associated with each of the one or more instances of the tool;
responsive to one or more tool failures of the one or more instances of the tool, updating the FI model using the run-time sensor data, wherein updating the FI model comprises modifying at least one or more of:
at least one weight of the plurality of weights dependence of the FI function on the run-time sensor data, or at least one FI threshold value of the one or more FI threshold values.
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements.”
Step 1: under the Step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category (Process).
Step 2A, Prong One: under the Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject matter Eligibility Guidance, it falls into the groupings of subject matter when recited as such in a claim limitation that falls into the grouping of subject matter when recited as such in a claim limitation, that covers mathematical concepts - mathematical relationships, mathematical formulas or equations, mathematical calculations.
For example, the limitations of “a failure index (Fl) model comprises:
an FI function computing, an input into the FI function comprising a plurality of weighted statistical characteristics of run-time sensor data collected during one or more operations of a tool of a manufacturing system, each statistical characteristic of the plurality of weighted statistical characteristics weighted with a respective weight of a plurality of weights of the failure index function (see para. [0077] of instant application), and
one or more FI threshold values for the FI function, wherein each of the one or more FI threshold values is associated with at least one of a present condition of the tool or a projected condition of the tool, wherein the plurality of weights and the one or more failure index threshold values are initially set, prior to first five failures of the tool, based on input received from a user (see paras. [0048], [0077], [0082] of instant application);
applying, by the processing device, the FI model to the run-time sensor data to identify one or more conditions associated with each of the one or more instances of the tool (see paras. [0053]-[0054], [0064] of instant application);
responsive to one or more tool failures of the one or more instances of the tool, updating the FI model using the run-time sensor data, wherein updating the FI model comprises modifying at least one or more of:
at least one weight of the plurality of weights dependence of the FI function on the run-time sensor data, or at least one FI threshold value of the one or more FI threshold values (see paras. [0081]-[0082] of instant application)” are mathematical calculations.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mathematical concepts, then it falls within the “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Similar limitations comprise the abstract ideas of Claims 13 and 15.
Step 2A, Prong Two: under the Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application. In this step, we evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception. This judicial exception is not integrated into a practical application. Therefore, none of the additional elements indicate a practical application.
Therefore, the claims are directed to a judicial exception and require further analysis under the Step 2B.
Step 2B:
The above claims comprise the following additional elements:
In Claim 1: a method (preamble); step of storing, by a processing device, collecting run-time sensor data for one or more instances of the tool, and outputting, via a user interface, one or more notifications generated by the updated FI model responsive to a value of the FI function satisfying a respective FI threshold value of the one or more FI threshold values, wherein each of the one or more notifications identifies a condition associated with a physical condition of at least one additional instances of the tool;
In Claim 13: a method (preamble); step of storing, by a processing device, collecting run-time sensor data for one or more instances of the tool; and outputting, via a user interface, one or more notifications generated by the updated FI model responsive to a value of the FI function satisfying a respective FI threshold value of the one or more FI threshold values, wherein each of the one or more notifications identifies a condition associated with a physical condition of at least one additional instances of the tool;
In Claim 15: a system (preamble); a memory; a processing device; step of storing, by a processing device, collecting run-time sensor data for one or more instances of the tool, and outputting, via a user interface, one or more notifications generated by the updated FI model responsive to a value of the FI function satisfying a respective FI threshold value of the one or more FI threshold values, wherein each of the one or more notifications identifies a condition associated with a physical condition of at least one additional instances of the tool.
The additional elements such as a method, system, memory, and storing by a processor are recited at a high-level of generality without descriptions of its specific structure/features. See MPEP 2106.05(d). Further, note that the additional elements of “collecting run-time sensor data for one or more instances of the tool is insignificant (gathering data) extra-solution activity to perform abstract idea (i.e., applying the FI model to collected routine data). See MPEP 2106.05(g)). Further, the additional element of “outputting, via a user interface, one or more notifications generated by the updated FI model responsive to a value of the FI function satisfying a respective FI threshold value of the one or more FI threshold values, wherein each of the one or more notifications identifies a condition associated with a physical condition of at least one additional instances of the tool” is insignificant post-solution activity merely after performing abstract idea (i.e. the updated FI model responsive to a value of the FI function satisfying a respective FI threshold value of the one or more FI threshold values) and notification still amounts to nothing more than mere data processing (i.e. do not contain inventive step beyond the abstract idea). see MPEP 2106.05(g).
As discussed above, with respect to integration of the abstract idea into a practical application, using a computer system to perform the processes of "an FI function computing, an input into the FI function comprising a plurality of weighted statistical characteristics of run-time sensor data collected during one or more operations of a tool of a manufacturing system, each statistical characteristic of the plurality of weighted statistical characteristics weighted with a respective weight of a plurality of weights of the failure index function, and one or more FI threshold values for the FI function, wherein each of the one or more FI threshold values is associated with at least one of a present condition of the tool or a projected condition of the tool, wherein the plurality of weights and the one or more failure index threshold values are initially set, prior to first five failures of the tool, based on input received from a user” and “applying, by the processing device, the FI model to the run-time sensor data to identify one or more conditions associated with each of the one or more instances of the tool; responsive to one or more tool failures of the one or more instances of the tool, updating the FI model using the run-time sensor data, wherein updating the FI model comprises modifying at least one or more of: at least one weight of the plurality of weights dependence of the FI function on the run-time sensor data, or at least one FI threshold value of the one or more FI threshold values" amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept cannot provide statutory eligibility. Therefore, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Claim 1 is not patent eligible.
Regarding claims 2 and 16,
The additional elements of “the run-time sensor data is collected prior to a first failure of the tool” are well-understood, routine, and conventional in the relevant based on the prior art of record (para. [0041] of Trinh; paras. [0006]-[0008],[0023]-[0025] of Kaushal). Therefore, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because these additional elements/steps are well-understood, routine, and conventional in the relevant based on the prior art of record.
Regarding claim 11,
The additional elements of “generating one or more notifications to a user, wherein each of the one or more notifications is generated responsive to a value of the time series of Fl function values satisfying a respective threshold condition of one or more threshold conditions” is insignificant (post-solution activity) extra-solution activity (MPEP 2106.05(g)).
Regarding claims 3-4, 6-10, 12, 14, and 17-20,
All features recited in these claims are abstract ideas, as all features found in these claims are directed towards mathematical calculations or mathematical calculation/mental process steps. The explanation for the rejection of Claims 3-10, 12, 14, and 17-20 therefore are incorporated herein and applied to Claims 1, 13, and 15. These claims therefore stand rejected for similar reasons as explained in above Claims 1, 13, and 15.
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 for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3-4, 8-9, 13-15, 17, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Ferry et al. (US 2018/0267521 A1, hereinafter referred to as “Ferry”).
Regarding claim 1, Ferry teaches a method comprising:
storing, by a processing device, a failure index (FI) model (para. [0022]: several samples are taken over time, each sample comprising several parts made by a manufacturing device; para. [0063]: he at least one statistical indicator is a capability index and/or a centring coefficient) comprises:
an FI function (paras. [0021], [0063]: statistical indicator) computing a plurality of weighted statistical characteristics of run-time sensor data collected during one or more operations of a tool of a manufacturing system (para. [0021]: at least one statistical indicator representative of a characteristic dimension of parts; para. [0022]: several samples are taken over time, each sample comprising several parts made by a manufacturing device; para. [0024]: a weighted average and a weighted standard deviation of the characteristic dimension according to an exponential weighting are calculated for each sample taken),
each statistical characteristic of the plurality of weighted statistical characteristics weighted with a respective weight (paras. [0021], [0022], [0024]: see above) of a plurality of weights of the FI function (para. [0063]: statistical indicator; para. [0024]: see above), and
one or more FI threshold values (para. [0012]: Reference can be made for example to the process capability index Cp which represents the aptitude of a manufacturing process to produce parts precisely and repeatably, note that since Ferry teaches reference can be mode for example to the process capability index Cp, one or more FI threshold values would be an inherent functional property or an obvious variation of such method) for the FI function (para. [0063]: statistical indicator),
wherein each of the one or more FI threshold values is associated with at least one of a present condition of the tool or a projected condition of the tool (para. [0021]: at least one statistical indicator representative of a characteristic dimension of parts; para. [0022]: several samples are taken over time, each sample comprising several parts made by a manufacturing device; para. [0024]: a weighted average and a weighted standard deviation of the characteristic dimension according to an exponential weighting are calculated for each sample taken),
wherein the plurality of weights (paras. [0022], [0024]: see above) and the one or more FI threshold values are initially set (para. [0012]: see above), prior to first five failures of the tool, based on input received from a user (para. [0021]: at least one statistical indicator representative of a characteristic dimension of parts; para. [0022]: several samples are taken over time, each sample comprising several parts made by a manufacturing device; para. [0024]: a weighted average and a weighted standard deviation of the characteristic dimension according to an exponential weighting are calculated for each sample taken, note that since Ferry teaches several samples are taken over time, each sample comprising several parts made by a manufacturing device (see para. [0022]), the feature of “prior to first five failures of the tool” would be an obvious variation of such method);
collecting run-time sensor data for one or more instances of the tool (para. [0021]: at least one statistical indicator representative of a characteristic dimension of parts; para. [0022]: several samples are taken over time, each sample comprising several parts made by a manufacturing device);
applying, by the processing device, the FI model to the run-time sensor data to identify one or more conditions associated with each of the one or more instances of the tool (para. [0021]-[0022]: see above; para. [0063]: the at least one statistical indicator is a capability index and/or a centring coefficient; para. [0064]: a capability index Cpk calculated according to the formula is selected as statistical indicator, note that the above feature of “statistical indicator” in para. [0063] and “a capability index Cpk” in para. [0064] reads on “FI model”);
responsive to one or more tool failures of the one or more instances of the tool, updating the FI model using the run-time sensor data (para. [0021]-[0022], [0063]-[0064]: see above), wherein updating the FI model comprises modifying one or more of: at least one weight of the plurality of weight of the FI function, or at least one FI threshold value of the one or more FI threshold values (paras. [0012], [0063]-[0064] :see above; para. [0082]: it is proposed to calculate for each sample taken a weighted average and a weighted standard deviation of the characteristic dimension according to exponential weighting; para. [0083]: This calculation is preferably based on the average and the standard deviation of characteristic dimensions measured on the parts of the sample taken, as well as on the weighted averages and the weighted standard deviations of the characteristic dimension which have been calculated for samples taken previously, note that the above feature of paras. [0012], [0063]-[0064], [0082] and “the weighted averages and the weighted standard deviations of the characteristic dimension which have been calculated for samples taken previously” in para. [0083] reads on “updating the FI model comprising at least one weight of the plurality of weight of the FI function or at least one FI threshold value of the one or more FI threshold values); and
outputting (Fig. 1: the evolution of the capability index Cpk over time for several sampling type), via a user interface (paras. [0111]-[0112]: display), one or more notifications generated by the updated FI model responsive to a value of the FI function satisfying a respective FI threshold value of the one or more FI threshold values (para. [0012], [0063]-[0064], [0083]:see above) , wherein each of the one or more notifications identifies a condition associated with a physical condition of at least one additional instances of the tool (para. [0022]: several samples are taken over time, each sample comprising several parts made by a manufacturing device; para. [0064]: capability index Cpk calculated according to the formula is selected as statistical indicator).
Regarding claim 3, Ferry teaches all the limitation of claim 1. In addition Ferry teaches that that updating the Fl model is responsive to a first failure of the one or more instances of the tool (para. [0021]: at least one statistical indicator representative of a characteristic dimension of parts; para. [0022]: several samples are taken over time, each sample comprising several parts made by a manufacturing device; para. [0063]: the at least one statistical indicator is a capability index and/or a centring coefficient; para. [0064]: a capability index Cpk calculated according to the formula is selected as statistical indicator; para. [0083]: This calculation is preferably based on the average and the standard deviation of characteristic dimensions measured on the parts of the sample taken, as well as on the weighted averages and the weighted standard deviations of the characteristic dimension which have been calculated for samples taken previously, note that since Ferry teaches the updating Fi model in samples taken over time (see paras. [0021]-[0022], [0063]-[0064], [0083]), the above feature of “updating the Fl model is responsive to a first failure of the one or more instances of the tool” would be an inherent functional property or obvious variation of such method).
Regarding claim 4, Ferry teaches all the limitation of claim 1, in addition, Ferry teaches further comprising:
collecting additional run-time sensor data for one or more additional instances of the tool (para. [0021]: at least one statistical indicator representative of a characteristic dimension of parts; para. [0022]: several samples are taken over time, each sample comprising several parts made by a manufacturing device); and
applying the updated Fl model to the additional run-time sensor data to identify one or more conditions of the one or more additional instances of the tool (para. [0021]: at least one statistical indicator representative of a characteristic dimension of parts; para. [0022]: several samples are taken over time, each sample comprising several parts made by a manufacturing device; para. [0063]: the at least one statistical indicator is a capability index and/or a centring coefficient; para. [0064]: a capability index Cpk calculated according to the formula is selected as statistical indicator; para. [0083]: This calculation is preferably based on the average and the standard deviation of characteristic dimensions measured on the parts of the sample taken, as well as on the weighted averages and the weighted standard deviations of the characteristic dimension which have been calculated for samples taken previously).
Regarding claim 8, Ferry teaches all the limitation of claim 1, in addition, Ferry teaches that applying the Fl model (paras. [0021], [0063]: statistical indicator) to the run-time sensor data collected for a first instance of the tool of the one or more instances of the tool (para. [0021]: at least one statistical indicator representative of a characteristic dimension of parts; para. [0022]: several samples are taken over time, each sample comprising several parts made by a manufacturing device) comprises:
computing, using the run-time sensor data collected for the first instance of the tool, a time series of Fl function values (paras. [0021], [0022]: see above; para. [0063]: the at least one statistical indicator is a capability index and/or a centring coefficient; para. [0064]: a capability index Cpk calculated according to the formula is selected as statistical indicator); and
estimating, using the time series of Fl function values (para. [0021]: at least one statistical indicator representative of a characteristic dimension of parts; para. [0022]: several samples are taken over time, each sample comprising several parts made by a manufacturing device ), a time to a threshold condition (TTC) (Fig. 1 and para. [0012]: Reference can be made for example to the process capability index Cp which represents the aptitude of a manufacturing process to produce parts precisely and repeatably) for the first instance of the tool (para. [0063]: the at least one statistical indicator is a capability index and/or a centring coefficient; para. [0064]: a capability index Cpk calculated according to the formula is selected as statistical indicator).
Regarding 9. Ferry teaches all the limitation of claim 1, in addition, Ferry teaches that applying the Fl model (paras. [0021], [0063]: statistical indicator) to the run-time sensor data collected for a second instance of the tool of the one or more instances of the tool (para. [0021]: at least one statistical indicator representative of a characteristic dimension of parts; para. [0022]: several samples are taken over time, each sample comprising several parts made by a manufacturing device, note that the above feature of “at least one statistical indicator representative of a characteristic dimension of parts” in para. [0021] and “several samples are taken over time” in para. [0022] reads on “ second instance of the tool”) comprises:
computing, using the run-time sensor data collected for the second instance of the tool, one or more Fl function values for the second instance of the tool (paras. [0021], [0022]: see above; para. [0063]: the at least one statistical indicator is a capability index and/or a centring coefficient; para. [0064]: a capability index Cpk calculated according to the formula is selected as statistical indicator); and
estimating, using the one or more Fl function values (para. [0021]: at least one statistical indicator representative of a characteristic dimension of parts; para. [0022]: several samples are taken over time, each sample comprising several parts made by a manufacturing device) computed for the second instance of the tool (paras. [0021]-[0022]: see above), a quality of a maintenance operation performed for the second instance of the tool. (para. [0063]: the at least one statistical indicator is a capability index and/or a centring coefficient; para. [0064]: a capability index Cpk calculated according to the formula is selected as statistical indicator).
Regarding claim 13, it is a method type claim having similar limitations as of claim 1 above. Therefore, it is rejected under the same rational as of parts of claim 1 above.
Regarding claim 14, it is dependent on claim 13 and has similar limitations as of claim 1 above. Therefore, it is rejected under the same rational as of parts of claim 1 above.
Regarding claim 15, it is a system type claim having similar limitations as of claim 1 above. Therefore, it is rejected under the same rational as of claim 1 above.
Regarding claim 17, it is dependent on claim 15 and has similar limitations as of claim 3 above. Therefore, it is rejected under the same rational as of claim 3 above.
Regarding claim 19, it is dependent on claim 15 and has similar limitations as of claim 8 above. Therefore, it is rejected under the same rational as of claim 8 above.
Claims 2, 6-7, 10-11, 16, 18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Ferry in view of Trinh et al. (US 2020/0379454 A1, hereinafter referred to as “Trinh”) (cited in IDS dated January 30, 2025).
Regarding claim 2, Ferry teaches all the limitation of claim 1. Ferry does not specifically teach that the run-time sensor data is collected prior to a first failure of the tool.
However, Trinh teaches that the run-time sensor data is collected prior to a first failure of the tool (para. [0041]: the data generated by a sensor 154 may be in any suitable format such as a time-series format; para. [0044]: the predictive maintenance server 110 may train one or more machine learning models that assign anomaly scores to a piece of equipment 150; para. [0062]: the predictive maintenance server 110 may collect the sensor data of the first period of time (e.g., a month of sensor data) as training data 422).
Ferry and Trinh are both considered to be analogous art to the claimed invention because they are in the similar filed of detecting anomalies in equipment. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the run-time sensor data collected prior to a first failure of the tool such as is described in Trinh into Ferry in order to provide various predictive maintenance approaches that can detect anomalies of the equipment (Trinh, para. [0033]).
Regarding claim 6, Ferry teaches all the limitation of claim 1, in addition, Ferry teaches that the Fl model is initially configured to use operation para. [0021]: at least one statistical indicator representative of a characteristic dimension of parts; para. [0022]: several samples are taken over time, each sample comprising several parts made by a manufacturing device) and constructing the Fl function using a weighted combination of the one or more identified features (para. [0021]: at least one statistical indicator representative of a characteristic dimension of parts; para. [0022]: several samples are taken over time, each sample comprising several parts made by a manufacturing device; para. [0024]: a weighted average and a weighted standard deviation of the characteristic dimension according to an exponential weighting are calculated for each sample taken), wherein the weighted combination is initially set based on the input received from a user (para. [0026]: weighted averages and weighted standard deviations of the characteristic dimension calculated for samples taken previously).
Ferry does not specifically teach identifying, by the processing device, one or more features in the collected run-time sensor data, wherein the one or more features comprise at least one of: a departure of a sensed quantity from a normal operating range for the sensed quantity, or a departure of a derivative of the sensed quantity from a normal operating range for the derivative of the sensed quantity.
However, Trinh teaches identifying, by the processing device (Fig. 1, 160), one or more features in the collected run-time sensor data (Fig 1, 154) (para. [0040]: collect the data from various sensors 154 of different pieces of equipment 150 of a facility site 140; para. [0044]: the predictive maintenance server 110 may train one or more), wherein the one or more features comprise at least one of:
a departure of a sensed quantity from a normal operating range for the sensed quantity, or a departure of a derivative of the sensed quantity from a normal operating range for the derivative of the sensed quantity (para. [0068]: A subset of measurements of the sensors of various components of a piece of equipment 150 should predict fairly well another subset of measurements if the equipment is operating normally. Put differently, the measurements of different sensors of a piece of normal equipment 150 may often have a correlation with each other, note that the above feature of para. [0068] reads on “a departure of a derivative of the sensed quantity from a normal operating range for the derivative of the sensed quantity”).
Ferry and Trinh are both considered to be analogous art to the claimed invention because they are in the similar filed of detecting anomalies in equipment. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the FI model such as is described in Trinh into Ferry in order to provide various predictive maintenance approaches that can detect anomalies of the equipment (Trinh, para. [0033]).
Regarding claim 7, Ferry in view of Trinh teaches all the limitation of claim 6, in addition, Ferry further comprising:
providing, via a user interface, the constructed Fl function to a user (para. [0021]: at least one statistical indicator representative of a characteristic dimension of parts; para. [0022]: several samples are taken over time, each sample comprising several parts made by a manufacturing device); and
responsive to a user input (paras [0021]-[0022]: see above), modifying, by the processing device (para. [0032]: computer), the Fl function by changing the weighted combination of the one or more identified features (para. [0024]: a weighted average and a weighted standard deviation of the characteristic dimension according to an exponential weighting are calculated for each sample taken; para. [0082]: it is proposed to calculate for each sample taken a weighted average and a weighted standard deviation of the characteristic dimension according to exponential weighting; para. [0083]: This calculation is preferably based on the average and the standard deviation of characteristic dimensions measured on the parts of the sample taken, as well as on the weighted averages and the weighted standard deviations of the characteristic dimension which have been calculated for samples taken previously).
Regarding claim 10, Ferry teaches all the limitation of claim 8. Ferry does not specifically teach estimating the TTC for the first instance of the tool comprises estimating at least one of: a most probable number of operations that the first instance of the tool is projected to support before a reference event; an average number of operations that the first instance of the tool is projected to support before the reference event; a range of a number of operations that the first instance of the tool is projected to support, before the reference event, with a first probability; a minimum number of operations that the first instance of the tool is projected to process, before the reference event, with a second probability; or a third probability that the first instance of the tool is projected to support at least a threshold minimum number of operations before the reference event.
However, Trinh teaches estimating the TTC for the first instance of the tool (paras. [00041]-[0043] and [0043]: see claim 8 above) comprises estimating at least one of:
a most probable number of operations that the first instance of the tool is projected to support before a reference event (para. [0049]: a regression model may provide a prediction of the score that corresponds to the likelihood of a piece of equipment (or a component thereof) is abnormal; para. [0052]: the maintenance recommendation engine 270 may provide one or more alerts (e.g., in the form of recommendations) for inspecting or repairing of pieces of equipment 15, note that the above feature of “a regression model” and “one or more alerts” reads on “a most probable number of operations that the first instance of the tool is projected to support before a reference event” because these feature notify user a most probable number of operation before a reference event (i.e., maintenance of the tool));
an average number of operations that the first instance of the tool is projected to support before the reference event;
a range of a number of operations that the first instance of the tool is projected to support, before the reference event, with a first probability;
a minimum number of operations that the first instance of the tool is projected to process, before the reference event, with a second probability; or
a third probability that the first instance of the tool is projected to support at least a threshold minimum number of operations before the reference event.
Ferry and Trinh are both considered to be analogous art to the claimed invention because they are in the similar filed of detecting anomalies in equipment. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the estimating TTC such as is described in Trinh into Ferry in order to provide various predictive maintenance approaches that can detect anomalies of the equipment (Trinh, para. [0033]).
Regarding claim 11, Ferry teaches all the limitation of claim 8. Ferry does not specifically teaches generating one or more notifications to a user, wherein each of the one or more notifications is generated responsive to a value of the time series of Fl function values satisfying a respective threshold condition of one or more threshold conditions.
However Trinh teaches further comprising: generating one or more notifications to a user, wherein each of the one or more notifications is generated responsive to a value of the time series of Fl function values satisfying a respective threshold condition of one or more threshold conditions (para. [0052]: The maintenance recommendation engine 270 may provide one or more alerts (e.g., in the form of recommendations) for inspecting or repairing of pieces of equipment 15; para. [0066]: if the anomaly score exceeds a threshold value for more than a threshold number of instances, the system sends an alert).
Ferry and Trinh are both considered to be analogous art to the claimed invention because they are in the similar filed of detecting anomalies in equipment. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the generating one or more notifications to a user such as is described in Trinh into Ferry in order to provide various predictive maintenance approaches that can detect anomalies of the equipment (Trinh, para. [0033]).
Regarding claim 16, it is dependent on claim 15 and has similar limitations as of claim 2 above. Therefore, it is rejected under the same rational as of claim 2 above.
Regarding claim 18, it is dependent on claim 15 and has similar limitations as of claim 6 above. Therefore, it is rejected under the same rational as of claim 6 above.
Regarding claim 20, it is dependent on claim 19 and has similar limitations as of claim 10 above. Therefore, it is rejected under the same rational as of claim 10 above.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Ferry in view of Das et al. (US 8,781,982 B1, hereinafter referred to as “Das”).
Regarding claim 12, Ferry teaches all the limitation of claim 8. Ferry teaches the timeseries of FI values is an time series (para. [0021]: at least one statistical indicator representative of a characteristic dimension of parts; para. [0022]: several samples are taken over time, each sample comprising several parts made by a manufacturing device).
Ferry does not specifically teach an isotonic time series.
However, Das teaches an isotonic time series (col. 7, lines 15-17: Time domain data statistics include such things as root mean square (RMS), crest factor, variance, skewness, and kurtosis, note that the above feature of time domain data statistics reads on an isotonic time series because the time domain data statistics is fitted to a monotonic *non-decreasing or non-increasing) trend).
Ferry and Das are both considered to be analogous art to the claimed invention because they are in the similar filed of prognosis and predictive maintenance. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the an isotonic time series such as is described in Das into Ferry in order to provide predictive estimates of Remaining useful life (RUL) for machines, machine components, equipment and the like using conditioned data obtained from peer systems and estimate the RUL of a machine component in a new or unseen environment based upon limited, observable, data (Das, col. 1, lines 58-64).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Mojtahedzadeh et al. (US 2020/0210968 A1) teaches that systems and methods for generating a sensor driven optimized maintenance program are provided. A first maintenance plan for an apparatus type is received; the first maintenance plan defining maintenance tasks for the apparatus type and defining intervals at which the maintenance tasks are to be performed. Historical sensor data, historical scheduled maintenance data, and historical unscheduled maintenance data are received and correlated to determine a predictive value of the sensor data.
Ferry et al. (US 20180267519) teaches that the invention pertains to a method of manufacturing parts produced with a manufacturing device, based on the analysis of at least one statistical indicator representative of a characteristic dimension of the parts, according to which: a) in the course of time several samples are collected, each sample comprising several parts produced with the manufacturing device; b) the characteristic dimension of each part of the sample is measured; c) for each sample collected a mean μ and a standard deviation σ of the characteristic dimension measured are calculated, and then a value of a statistical indicator I3C defined according to formula (I) is calculated for each sample collected.
Rosca et al. (CN 109791401 B) teaches that a computer-implemented method for detecting a fault and event associated with a system, comprising receiving sensor data from a plurality of sensors associated with the system. The hierarchical fault model of the system uses (i) sensor data, (ii) fault detector data, (iii) a prior knowledge of the system variable and state and (iii) one or more statistical description of the system to construct. The fault model includes a plurality of diagnostic variables associated with the system and their relationships.
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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/SANGKYUNG LEE/Examiner, Art Unit 2858
/LEE E RODAK/Supervisory Patent Examiner, Art Unit 2858