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 .
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 11-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 11 recites:
ascertaining, according to a predetermined analysis method, quasi-steady-state time intervals of the operating period that meet a predetermined criterion for quasi-steady-state time intervals,
generating quasi-steady-state operating data points for the quasi-steady-state time intervals according to a predetermined averaging method, the quasi-steady-state operating data points including averaged measured operating values of the measured operating values of the particular operating parameters detected during the particular quasi-steady-state time intervals;
ascertaining, according to a predetermined expected value ascertainment method, particular expected data points for the quasi-steady-state operating data points including particular expected operating values of the particular operating parameters; ascertaining particular measured operating value residuals of the particular operating parameters describing deviations between the expected operating values and the averaged measured operating values of the particular operating parameters; and
checking the measured operating value residuals of the particular quasi-steady-state operating data points for compliance with predetermined anomaly criteria for an anomaly detection with regard to predefined nominal values of the measured operating value residuals.
The above identified abstract ideas fall into the abstract idea grouping of mental concepts. These limitations define, under the broadest reasonable interpretation, observing the overall operating period, using a predefined methodology to evaluate stability (i.e., whether the state is quasi-steady) and selecting only the time intervals that meet your specific stability thresholds. The examiner considers the limitations directed towards generating the data points, under the BRI, as merely recognizing specific intervals where the system's operating conditions are mostly stable (quasi-steady-state), gathering the continuous, high-frequency measured values of operating parameters during these intervals and reducing those time-series values into single, averaged representative data points based on a specific mathematical rule. Lastly, the steps directed towards ascertaining particular expected data point and particular measured values to perform a check involves taking expected values to generate a baseline comparing a difference between the baseline to measured values for the purpose of an anomaly check. Therefore, under the BRI, these steps are considered to be capable of being performed in the human mind, with the aid of pen and paper.
This judicial exception is not integrated into a practical application because the turbomachine and aircraft turbine read as additional elements that merely link the abstract idea to a field of use; as neither the performance or result of the abstract idea improves the turbine or aircraft. MPEP 2106.05(h)
The recited step of repeatedly detecting measured operating values of particular operating parameters of a turbomachine during an operating period of the turbomachine, using sensors of the turbomachine covers additional elements that merely define the insignificant pre-solution activity of data gathering without integrating the abstract idea into practical application. MPEP 2106.05(g)
The step of transferring an anomaly indicator including a violated anomaly criterion of the anomaly criteria for an anomaly detection and a point in time of the violation, to a machine monitoring device merely reads as mere instruction to apply the exception; as the transferred indication does nothing to the turbine or the machine monitoring device. Therefore, the identified additional element fails to integrate the abstract idea into a practical application.
Lastly, the additional elements of an analysis device and machine monitoring device merely read as tools for performing the abstract idea, as there generically claimed computing devices merely perform the abstract ideas in a computer environment without integrating the abstract idea into a practical application. MPEP 2106.05(a)
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the identified additional elements of the turbine of an aircraft are neither improved or bettered by the performance of the abstract idea. The sensors merely feed the abstract idea the needed data to perform the abstract idea in a generic computer environment while generically applying the exception without providing significantly more or integrating the abstract idea into a practical application.
Claim 12 further defines the abstract idea by defining how the analysis device ascertains nominal values of the measured values according to predetermined standard ascertainment method, not defined by the claim. Therefore, under BRI, the further defined abstract idea is capable of being performed in the human mind when observing the measured operating values. Therefore, the claim fails to integrate the abstract idea into a practical application or provide significantly more; as none of the identified additional elements are improved or bettered by the abstract idea result.
Claim 13 further defines the abstract idea by reciting how quasi-steady-state operating data points are added to a time series. The further defined abstract idea does not integrate the abstract idea into a practical application or provide significantly more there; as it neither improves or betters the analysis device itself.
Claim 14 recites wherein for the particular quasi-steady- state operating data points, the analysis device ascertains particular quality parameters, and only those quasi-steady-state operating data points whose quality parameters meet a predetermined quality criterion are added to a time series. The limitation further defines the abstract idea falling into the abstract idea grouping of mental concepts, as under the BRI and insofar as how “ascertains” is structurally defined, the limitations of only ascertaining data that meets quality parameters is capable of being performed in the human mind with the aid of pen and paper. Further, the analysis device, as claimed, merely acts as a tool to perform the abstract idea in a computer environment; as the device is neither improved or bettered by the result of the abstract idea.
Claim 15 further defines the additional element step of transferring without providing significantly more or integrating the abstract idea into a practical application; as the machine monitoring device merely acts as a tool and is neither improved or bettered by the result of the abstract idea.
Claim 16 recites the machine monitoring device ascertains the quasi-steady-state operating data point in whose quasi-steady-state time interval the point in time of the anomaly indicator falls, and the anomaly indicator is assigned at least to this quasi-steady-state operating data point. The limitation further defines the abstract idea falling into the abstract idea grouping of mental concepts, as under the BRI and insofar as how “ascertains” is structurally defined, the limitations of assigning data is capable of being performed in the human mind with the aid of pen and paper. Further, the monitoring device, as claimed, merely acts as a tool to perform the abstract idea in a computer environment; as the device is neither improved or bettered by the result of the abstract idea.
Claim 17 recites the machine monitoring device assigns the anomaly indicator at least to the quasi-steady-state operating data points whose time intervals lie after the point in time. The limitation further defines the abstract idea falling into the abstract idea grouping of mental concepts, as under the BRI, assigning data is capable of being performed in the human mind with the aid of pen and paper. Further, the monitoring device, as claimed, merely acts as a tool to perform the abstract idea in a computer environment; as the device is neither improved or bettered by the result of the abstract idea.
Claim 18 recites the machine monitoring device examines, according to a predetermined error diagnosis method, at least the quasi-steady-state operating data points of the time series to which the anomaly indicator is assigned. The limitation further defines the abstract idea falling into the abstract idea grouping of mental concepts, as under the BRI and insofar as how “examines, according to a predetermined error diagnosis method” is structurally defined, the limitations of examining data according to a predetermined error diagnosis method is capable of being performed in the human mind with the aid of pen and paper. Further, the monitoring device, as claimed, merely acts as a tool to perform the abstract idea in a computer environment; as the device is neither improved or bettered by the result of the abstract idea.
Claim 19 recites an additional element step of “provided maintenance to the turbomachine based on the anomaly”. The limitation amount to a mere instruction to apply the exception without providing significantly more or integrating the abstract idea into a practical application; as the limitation generically recites an effect of the judicial exception, claiming all forms of maintenance accomplishing that effect.
Claim 20 recites:
ascertain, according to a predetermined analysis method, quasi-steady-state time intervals of the operating period that meet a predetermined criterion for quasi-steady-state time intervals,
generate quasi-steady-state operating data points for the quasi-steady-state time intervals according to a predetermined averaging method, the quasi-steady-state operating data points including averaged measured operating values of the measured operating values of the particular operating parameters detected during the particular quasi-steady-state time intervals;
ascertain, according to a predetermined expected value ascertainment method, particular expected data points for the quasi-steady-state operating data points including particular expected operating values of the particular operating parameters; ascertain particular measured operating value residuals of the particular operating parameters describing deviations between the expected operating values and the averaged measured operating values of the particular operating parameters; and
check the measured operating value residuals of the particular quasi-steady-state operating data points for compliance with predetermined anomaly criteria for an anomaly detection with regard to predefined nominal values of the measured operating value residuals.
The above identified abstract ideas fall into the abstract idea grouping of mental concepts. These limitations define, under the broadest reasonable interpretation, observing the overall operating period, using a predefined methodology to evaluate stability (i.e., whether the state is quasi-steady) and selecting only the time intervals that meet your specific stability thresholds. The examiner considers the limitations directed towards generating the data points, under the BRI, as merely recognizing specific intervals where the system's operating conditions are mostly stable (quasi-steady-state), gathering the continuous, high-frequency measured values of operating parameters during these intervals and reducing those time-series values into single, averaged representative data points based on a specific mathematical rule. Lastly, the steps directed towards ascertaining particular expected data point and particular measured values to perform a check involves taking expected values to generate a baseline comparing a difference between the baseline to measured values for the purpose of an anomaly check. Therefore, under the BRI, these steps are considered to be capable of being performed in the human mind, with the aid of pen and paper.
This judicial exception is not integrated into a practical application because the step of transferring an anomaly indicator including a violated anomaly criterion of the anomaly criteria for an anomaly detection and a point in time of the violation, to a machine monitoring device merely reads as mere instruction to apply the exception; as the transferred indication does nothing to analysis device or the machine monitoring device. Therefore, the identified additional element fails to integrate the abstract idea into a practical application.
Lastly, the additional elements of an analysis device and machine monitoring device merely read as tools for performing the abstract idea, as there generically claimed computing devices merely perform the abstract ideas in a computer environment without integrating the abstract idea into a practical application. MPEP 2106.05(a)
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the identified additional elements are neither improved or bettered by the performance of the abstract idea. In addition, generically applying the exception without providing significantly more or integrating the abstract idea into a practical application fails to integrate the abstract idea or provide significantly more.
Claim 21 recites:
ascertain the quasi-steady-state operating data point in whose quasi-steady-state time interval the point in time of the anomaly indicator falls, and to assign the anomaly indicator at least to this quasi-steady-state operating data point; and
examine, according to a predetermined error diagnosis method, at least the quasi-steady- state operating data points of the time series to which the anomaly indicator is assigned.
The above identified abstract ideas fall into the abstract idea grouping of mental concepts. These limitations define, under the broadest reasonable interpretation, observing quasi-steady-state data and assigning the anomaly indicator and examining according to an undefined diagnosis method to the assigned anomaly indicator. The identified abstract idea is capable of being performed in the human mind, as observing data, assigning a label, and then applying an examining method to that data is broad enough to occur in the human mind.
This judicial exception is not integrated into a practical application because
The recited step receiving a time series including quasi-steady-state operating data points for particular quasi-steady-state time intervals of an operating period, the quasi-steady-state operating data points including averaged measured operating values of measured operating values of particular operating parameters detected during the particular quasi-steady-state time intervals, receiving an anomaly indicator including a violated anomaly criterion and a point in time of the violation covers additional elements that merely define the insignificant pre-solution activity of data gathering without integrating the abstract idea into practical application. MPEP 2106.05(g)
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the identified additional element of receiving data merely defines data gathering to perform the abstract idea without providing significantly more or integrating the abstract idea into a practical application.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 11-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lacaille et al. (2011/0288836) in view of Applicant’s Admitted Prior Art (AAPA).
With respect to claim 11, Lacaille et al. teaches a method in Fig. 3 for recognizing an anomaly in measured operating values of a turbomachine (1; [0001]), in particular an aircraft turbine (1), including at least the steps: repeatedly detecting measured operating values of particular operating parameters of a turbomachine (1) during an operating period of the turbomachine (i.e. during a current flight regime; E6), using sensors (3a-3e) of the turbomachine (1); ascertaining (at E6), according to a predetermined analysis method (i.e. a predetermined analysis method, insofar as structurally defined, being a comparison method used to identify the same flight routine that matches the same portion of a reference behavior model; [0099]), quasi-steady-state time intervals of the operating period that meet a predetermined criterion for quasi-steady-state time intervals (as [0099] teaches the computational process of comparing operating parameters during periods of stability that match predetermined quasi-steady flight regime intervals), using an analysis device (5); generating quasi-steady-state operating data points for the quasi-steady-state time intervals (E7, which generates quasi-steady operating data points in a current behavior model which defines the same flight regime for comparison); ascertaining (via a reference behavior model), according to a predetermined expected value ascertainment method (i.e. a bench test expected value ascertainment method; [0086]), particular expected data points for the quasi-steady-state operating data points (i.e. data pointed during R3 which define steady interval or stages defined by quasi-steady measurements of speed of rotation; [0092]) including particular expected operating values of the particular operating parameters (as the expected operating values reflect past behaviors and functions of other measurements; [0094]); ascertaining (E8) particular measured operating value residuals of the particular operating parameters describing deviations between the expected operating values and measured operating values of the particular operating parameters (as the processor means 5 is configured to estimate a behavior distance between the current behavior model and the reference behavior model corresponding to the same flight regime, for example the quasi-steady-state; [0100]); checking (E9) the measured operating value residuals of the particular quasi-steady-state operating data points for compliance with predetermined anomaly criteria for an anomaly detection with regard to predefined nominal values of the measured operating value residuals (as processor means 5 are configured to detect a behavior anomaly of the control means 21 when the behavior distance is greater than a predetermined normality threshold); and transferring an anomaly indicator (i.e. an alert) including a violated anomaly criterion of the anomaly criteria for an anomaly detection and a point in time of the violation, to a machine monitoring device (as in the event of a normality indicator, Lacaille et al. teaches issuing the crossing of one of these thresholds which includes logic that can then be implemented in order to manage the occurrence time of these events during the flight regime and to manage alarms [0120-0121]; the examiner considers a portion of 5 that perform E9 to read on “a machine monitoring device”, insofar as how the machine monitoring device is structurally defined).
Lacaille et al. remains silent regarding generating quasi-steady-state operating data points according to a predetermined averaging method, the quasi-steady-state operating data points including averaged measured operating values of the measured operating values of the particular operating parameters detected during the particular quasi-steady-state time intervals.
AAPA teaches on page 2, lines 12-21 and page 4, lines 11-13 teaches using a predetermined averaging method of Simon, D.L. and Litt, J.S on operating parameters.
It would have been obvious to one of ordinary skill in the art before the effective filing of the instant invention modify the analysis device of Lacaille et al. to utilized the predetermined averaging method of the AAPA because such a modification aids in saving time by stabilizing erratic data points, thereby establishing a consistent process for evaluating operations.
Therefore, such a modification would improve Lacaille et al. by stabilizing data erratic data points, thereby improving anomaly detection.
With respect to claim 12, Lacaille et al. as modified teaches the method wherein the analysis device (5) ascertains the predetermined nominal values of the measured operating value residuals according to a predetermined standard ascertainment method (Lacaille et al. teaches ascertaining nominal values according to a function of past behaviors and as a function of other measurements both past and present; [0094]), based on measured operating value residuals of quasi-steady-state operating data points of previous operating periods of the turbomachine (1) that are stored in the analysis device (5; [0094]).
With respect to claim 13, Lacaille et al. as modified teaches the method wherein the particular quasi-steady-state operating data points (i.e. those data points taught to define quasi-steady-state flight regime) are added to a time series (to define a portion of the different flight stages in; R3, Fig. 3) by the analysis device (5).
With respect to claim 14, Lacaille et al. as modified teaches the method wherein for the particular quasi-steady-state operating data points, the analysis device (5) ascertains particular quality parameters (as Lacaille et al. teaches when selecting data points, the flight regimes, including the quasi-steady-state, is identified using interval forms part of a specific class of the flight regime and tolerance values; [0093], Lacaille et al. also teaches using a comparison criterion to monitor the quality of the model; [0110]), and only those quasi-steady-state operating data points whose quality parameters meet a predetermined quality criterion are added to a time series (thereby ensuring accurate representation of the different flight regimes, including quasi-steady-state; [0111]).
With respect to claim 15, Lacaille et al. as modified teaches the method wherein the analysis device (5) transfers the time series (i.e. the data points selected to define the quasi-steady-state flight regime used to compare against the baseline data) to the machine monitoring device (i.e. the portion of 5 that performs E9).
With respect to claim 16, Lacaille et al. as modified teaches the method wherein the machine monitoring device (i.e. the portion of 5 that performs E9) ascertains the quasi-steady-state operating data point in whose quasi-steady-state time interval the point in time of the anomaly indicator falls (Lacaille et al. teaches in E9, using the quasi-steady-state operating point falling within the steady state flight regime, the data point is compared to a predetermined normality; [0101]), and the anomaly indicator is assigned at least to this quasi-steady-state operating data point (Lacaille et al. continues to teach step E9 monitors the data point relative the threshold and if it falls outside the threshold, an anomaly is assigned to the data point; [0101]).
With respect to claim 17, Lacaille et al. as modified teaches the method wherein the machine monitoring device (i.e. the portion of 5 that performs E9) assigns the anomaly indicator at least to the quasi-steady-state operating data points whose time intervals lie after the point in time (as step E9 is capable of assigning data points whose time interval lie after the point in time when those addition points fall outside the threshold of normality).
With respect to claim 18, Lacaille et al. as modified teaches the method wherein the machine monitoring device (i.e. the portion of 5 that performs E9) examines, according to a predetermined error diagnosis method (via a scoring method; [0184]), at least the quasi-steady-state operating data points of the time series to which the anomaly indicator is assigned (as Lacaille et al. teaches, prior to being flagged, the data points defining the quasi-steady-state flight regime are classified prior to flagging; therefore, the identified quasi-steady-state operating data points of the time series flagged with the anomaly indicator have been examined prior by a predetermined error diagnosis method).
With respect to claim 19, Lacaille et al. as modified teaches method for providing maintenance using the method as recited in claim 15 (as rejected above), the method comprising provided maintenance to the turbomachine based on the anomaly (as Lacaille teaches in [0015], the taught method steps allow for targeted or conditional maintenance).
With respect to claim 20, Lacaille et al. teaches an analysis device (5), the analysis device (5) being configured to ascertain (at E6), according to a predetermined analysis method (i.e. a predetermined analysis method, insofar as structurally defined, being a comparison method used to identify the same flight routine that matches the same portion of a reference behavior model; [0099]), quasi-steady-state time intervals of the operating period that meet a predetermined criterion for quasi-steady-state time intervals (as [0099] teaches the computational process of comparing operating parameters during periods of stability that match predetermined quasi-steady flight regime intervals); generate quasi-steady-state operating data points for the quasi-steady-state time intervals (E7, which generates quasi-steady operating data points in a current behavior model which defines the same flight regime for comparison); ascertain (via a reference behavior model), particular expected data points for the quasi-steady-state operating data points (i.e. data pointed during R3 which define steady interval or stages defined by quasi-steady measurements of speed of rotation; [0092]) according to a predetermined expected value ascertainment method (i.e. a bench test expected value ascertainment method; [0086]), the expected data point including particular expected operating values of the particular operating parameters (as the expected operating values reflect past behaviors and functions of other measurements; [0094]); ascertain (E8) particular measured operating value residuals of the particular operating parameters describing deviations between the expected operating values and measured operating values of the particular operating parameters (as the processor means 5 is configured to estimate a behavior distance between the current behavior model and the reference behavior model corresponding to the same flight regime, for example the quasi-steady-state; [0100]); check (E9) measured operating value residuals of the particular quasi-steady-state operating data points for compliance with predetermined anomaly criteria for an anomaly detection with regard to predefined nominal values of the measured operating value residuals (as processor means 5 are configured to detect a behavior anomaly of the control means 21 when the behavior distance is greater than a predetermined normality threshold); and transfer an anomaly indicator (i.e. an alert) including a violated anomaly criterion of the anomaly criteria for an anomaly detection and a point in time of the violation, to a machine monitoring device (as in the event of a normality indicator, Lacaille et al. teaches issuing the crossing of one of these thresholds which includes logic that can then be implemented in order to manage the occurrence time of these events during the flight regime and to manage alarms [0120-0121]; the examiner considers a portion of 5 that perform E9 to read on “a machine monitoring device”, insofar as how the machine monitoring device is structurally defined).
Lacaille et al. remains silent regarding generating quasi-steady-state operating data points according to a predetermined averaging method, the quasi-steady-state operating data points including averaged measured operating values of the measured operating values of the particular operating parameters detected during the particular quasi-steady-state time intervals.
AAPA teaches on page 2, lines 12-21 and page 4, lines 11-13 teaches a predetermined averaging method of Simon, D.L. and Litt, J.S used on operating data points.
It would have been obvious to one of ordinary skill in the art before the effective filing of the instant invention modify the analysis device of Lacaille et al. to utilized the predetermined averaging method of the AAPA because such a modification aids in saving time by stabilizing erratic data points thereby establishing a consistent process for evaluating operations.
Therefore, such a modification would improve Lacaille et al. by stabilizing data erratic data points, thereby improving anomaly detection.
With respect to claim 21, Lacaille et al. teaches a machine monitoring device (i.e. a portion of 5) configured to receive a time series including quasi-steady-state operating data points for particular quasi-steady-state time intervals of an operating period (via E6 which uses the current data set to calculate an estimate of the regime indicators to identify the current flight regime specific to the predetermined time period, for example a quasi-steady-state; [0098]), receive an anomaly indicator including a violated anomaly criterion and a point in time of the violation (as the taught processor means 5 is configured to detect a behavior anomaly of the control means 21 when the behavior distance is greater than a predetermined normality threshold and then receive the indicator flagging the alert); ascertain the quasi-steady-state operating data point in whose quasi-steady-state time interval the point in time of the anomaly indicator falls (as Lacaille et al. teaches in E9, using the quasi-steady-state operating point falling within the steady state flight regime, the data point is compared to a predetermined normality; [0101]), and to assign the anomaly indicator at least to this quasi-steady-state operating data point (Lacaille et al. teaches if the data point falls outside the threshold, an anomaly is assigned to the data point); and examine, according to a predetermined error diagnosis method (via a scoring method; [0184]), at least the quasi-steady-state operating data points of the time series to which the anomaly indicator is assigned (as Lacaille et al. teaches, prior to being flagged, the data points defining the quasi-steady-state flight regime are classified prior to flagging; therefore, the identified quasi-steady-state operating data points of the time series flagged with the anomaly indicator have been examined prior by a predetermined error diagnosis method).
Lacaille et al. remains silent regarding the quasi-steady-state operating data points including averaged measured operating values of measured operating values of particular operating parameters detected during the particular quasi-steady-state time intervals.
AAPA teaches on page 2, lines 12-21 and page 4, lines 11-13 teaches using a predetermined averaging method of Simon, D.L. and Litt, J.S of measured operating values.
It would have been obvious to one of ordinary skill in the art before the effective filing of the instant invention modify the analysis device of Lacaille et al. to utilized the predetermined averaging method of the AAPA because such a modification aids in saving time by stabilizing erratic data points thereby establishing a consistent process for evaluating operations.
Therefore, such a modification would improve Lacaille et al. by stabilizing data erratic data points, thereby improving anomaly detection.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
La Pierre (5,951,611) which teaches a diagnostic test to analyze engine trends.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW G MARINI whose telephone number is (571)272-2676. The examiner can normally be reached Monday-Friday 8am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Stephen Meier can be reached at 571-272-2149. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MATTHEW G MARINI/ Primary Examiner, Art Unit 2853
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