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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 06/22/2026 has been entered.
Response to Amendment
Applicant has submitted the following:
Claims 1-16 and 18-21 are pending examination;
Claims 1, 7, 11, and 18 are newly amended; and
Claims 17 and 22-23 remain cancelled.
Response to Arguments
Applicant's arguments filed 04/28/2026 have been fully considered but they are not persuasive.
Applicant argues that none of the prior art teaches the limitations of newly amended independent claims 1, 7, 11, and 18. Specifically, Applicant argues that none of the prior art, alone or in combination, teaches the recited claims, including automatically disabling the unreliable physical sensor for future measurements while other physical sensors in the group of physical sensors remain enabled.
Examiner respectfully disagrees. Previously cited Grichnik (US 20140012791 A1) teaches identifying an unreliable physical sensor from a group of sensors (see [0041]). Further, previously cited Kim (US 20130073261 A1) teaches determining the unreliability of each physical sensor in a group of physical sensors (see [0062] and [0075]), thereby selectively identifying each sensor within the group of sensors. Previously cited Debeurre (US 20140266246 A1) teaches disabling the unreliable physical sensor (Fig. 3, steps 214 and 222; [0035] lines 1-6, “In a seventh operation 214, microcontroller 40 makes a determination, based on the results of the comparison of operation 212, as to whether or not sensor system 30 (FIG. 2) and/or sensor-responsive system 10 (FIG. 1) should be disabled. If sensor system 30 and/or sensor-responsive system 10 are to be disabled, they are disabled in operation 222.”; [0036] lines 7-11, “in addition to providing the ability to disable systems in which MEMS devices cannot be re-calibrated or are no longer functional, the ability to notify users of systems employing MEMS devices of problems with the MEMS devices is provided.”). Therefore, it would be obvious to disable the selectively identified unreliable sensor from the group of sensors, thereby leaving the remaining sensors enabled.
Claim Rejections - 35 USC § 103
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) 1-16 and 18-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Grichnik et al. (US 20140012791 A1, previously cited) in view of Kim et al. (US 20130073261 A1, previously cited) and Debeurre et al. (US 20140266246 A1, previously cited)
Regarding claim 1, Grichnik teaches a method for sensor measurement processing (Abstract), comprising:
collecting measurements (Fig. 3A, Input parameter values 310) from a group of physical sensors (Fig. 1, physical sensors 140 and 142; [0021] lines 1-3, “Physical sensor 140 may include one or more sensors provided for measuring certain parameters related to machine 100 and providing corresponding parameter values.”), wherein different physical sensors in the group monitor different physical processes ([0022] lines 7-16, “For example, a physical NO.sub.x emission sensor may measure the NO.sub.x emission level of machine 100 and provide parameter values of the NO.sub.x emission level to other components, such as ECM 120. A virtual NO.sub.x emission sensor may provide calculated parameter values of the NO.sub.x emission level to ECM 120 based on other measured or calculated parameters, such as compression ratios, turbocharger efficiencies, aftercooler characteristics, temperature values, pressure values, ambient conditions, fuel rates, engine speeds, etc.”);
estimating, based on the measurements, initial true states of the various physical processes ([0037] lines 1-6, “Input parameter values 310 may include any appropriate type of data associated with NO.sub.x emission levels. For example, input parameter values 310 may be values of parameters used to control various response characteristics of engine 110 and/or values of parameters associated with conditions corresponding to the operation of engine 110.”; lines 16-19, “Further, input parameter values 310 may be measured by certain physical sensors, such as physical sensor 142, and/or generated by other control systems such as ECM 120.”);
repeating until convergence: calculating, based on the estimated true states of the physical processes, reliability scores (confidence value .beta) of each sensor of the group of sensors ([0052] lines 8-14, “For each reading in the time series, sensor state estimation module 123 may calculate an effective normalized sensor reading difference d(i). The calculation of d(i) is discussed in greater detail below. Sensor state estimation module 123 may also calculate a current weighted average D(i) of the different d(i) values”; [0053] lines 1-3, “Sensor state estimation module 123 may use the current weighted average D(i) to determine the current physical sensor confidence value .beta.”). The calculation of the confidence value, which is calculated from each reading in the time series of physical sensor comprising one or more sensors, is the reliability scores being calculated for each sensor, wherein a higher score represents a more reliable sensor measurement ([0051] “Sensor state estimation module 123 may receive the virtual sensor value x.sub.v, the physical sensor value x.sub.s, and the virtual sensor confidence value m, and may generate the physical sensor confidence value .beta. by comparing the outputs of the physical sensor with those of the virtual sensor. In certain embodiments, sensor state estimation module 123 may compare the values at several different times and may determine that those comparisons where the virtual sensor confidence value m is high are to be assigned greater weight than those comparisons where the virtual sensor confidence value m is low. Sensor state estimation module 123 may send the generated physical sensor confidence value .beta. to one or more of sensor error detection and compensation system 121 and sensor state estimation module 123.”; Fig. 7, sensor confidence value β). One of ordinary skill in the art would recognize that the step of comparing and assigning weights would result in a convergence to a value.; and
estimating, based on the calculated reliability scores, true states of the various physical processes (aggregated sensor value x.sub.a); and
identifying an unreliable physical sensor from the group of physical sensors and automatically servicing the unreliable physical sensor for future measurements ([0041] lines 20-28, “Sensor error detection and compensation system 121 may also output a replace physical sensor signal R.sub.s that indicates whether physical sensor 140 has failed and should be replaced. It should be noted that "replacing" a physical sensor may include complete replacement of the sensor (i.e., removing the old physical sensor and introducing a new physical sensor) or servicing the existing physical sensor in some manner without complete replacement.”).
Grichnik does not teach the method, comprising:
automatically disabling the unreliable physical sensor for future measurements, while other physical sensors in the group of physical sensors remain enabled.
Kim teaches an analogous method for determining reliability of physical sensors, comprising:
automatically changing the operation of the unreliable physical sensor for future measurements ([0062] “The sensor operation setting unit 260 receives operation rule data or software (or firmware) update data for each sensor in the sensor group 100, according to the results of abnormality diagnosis on sensor data and/or setting modification of the sensor, etc., from the sensor data management apparatus 300, through the transceiver 250, and provides the received data to the corresponding sensor. Or, the sensor operation setting unit 260 sets a reliability value of each sensor, received from the sensor data analyzer 230, to the corresponding sensor.”; [0045] “Also, the sensor data processing apparatus 200 provides each sensor with an execution command and a command related to setting modification, based on the result of determination on the reliability of the sensor, thereby correcting any error of the sensor or controlling the operation of the sensor.”), while other physical sensors in the group of physical sensors remain enabled ([0075] The post-processor 237 analyzes the received sensor data to determine whether the sensor data corresponds to data (that is, abnormal data) indicating an abnormality situation (or an abnormality state) or data (normal data) indicating a normal situation (or a normal state), based on the sensor data classified by the main processor 235, predicts a flow of states in consideration of the currently analyzed data and analysis data previously analyzed and stored, and diagnoses abnormality. Also, the post-processor 237 checks the result of abnormality diagnosis on the sensor signal, based on the analysis data of the sensor signal, and determines the reliability of the corresponding sensor that has transmitted the sensor signal, based on the result of the abnormality diagnosis.). The selective change in the operation (i.e. operation settings, rules, and commands) of the unreliable sensor (abnormality diagnosis) is the change in operation of the unreliable sensor, while other physical sensors in the group remain enabled (normal state).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Grichnik to include the reliable sensors remaining enabled of Kim because it would yield predictable results, such as only the identified unreliable sensor having a change in operation based on its unreliability.
Grichnik in view of Kim does not teach the method, comprising:
automatically disabling the unreliable physical sensor for future measurements.
Debeurre teaches an analogous method of processing sensors measurements (Abstract), comprising:
automatically disabling the unreliable physical sensor for future measurements (Fig. 3, steps 214 and 222; [0035] lines 1-6, “In a seventh operation 214, microcontroller 40 makes a determination, based on the results of the comparison of operation 212, as to whether or not sensor system 30 (FIG. 2) and/or sensor-responsive system 10 (FIG. 1) should be disabled. If sensor system 30 and/or sensor-responsive system 10 are to be disabled, they are disabled in operation 222.”; [0036] lines 7-11, “in addition to providing the ability to disable systems in which MEMS devices cannot be re-calibrated or are no longer functional, the ability to notify users of systems employing MEMS devices of problems with the MEMS devices is provided.”). The sensors and/or sensor system that cannot be re-calibration or are no longer function are the unreliable physical sensor, and the disabling operation is the automatic disabling.
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Grichnik in view of Kim to include the automatic disabling of the unreliable physical sensor of Debeurre because it would yield predictable and advantageous results of removing unreliable sensor measurement information from analysis, thereby reducing the inaccuracy of a set of sensors.
Even if Grichnik in view of Kim and Debeurre does not teach automatically disabling the unreliable physical sensor for future measurements, it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to make the disabling step automatic since it has been held that broadly providing a mechanical or automatic means to replace manual activity which has accomplished the same result involves only routine skill in the art. In re Venner, 120 USPQ 192.
Regarding claim 2, Grichnik in view of Kim and Debeurre teaches the method according to claim 1, further comprising, before repeating until convergence, constructing, for each of the physical processes, a respective soft sensor by calculating measurement based on measurement by an additional sensor other than the sensor monitoring the physical process in the group of sensor, to enlarge the group of sensors (Grichnik: [0041] lines 10-20, the virtual sensor value x.sub.v may be a NO.sub.x emissions value determined by virtual sensor model 334 based on input parameter values 310, and the physical sensor value x.sub.s may be a NO.sub.x emissions value measured directly by a physical emissions sensor, e.g., physical sensor 140. Sensor error detection and compensation system 121 may also determine an aggregated sensor value x.sub.a that represents a combination of the virtual sensor value x.sub.v and physical sensor value x.sub.s and may output the aggregated sensor value x.sub.a to a control system, such as various components of ECM 120 in order to control machine 100 and/or engine 110.).
Regarding claim 3, Grichnik in view of Kim and Debeurre teaches the method according to claim 2, wherein calculating reliability scores for the individual sensors of the group of sensors comprises calculating reliability scores of each sensor in the group of sensors, such that the more reliable a sensor is, the higher penalty if the measurement by the sensor is far away from the estimated true state of the respective physical process (Grichnik: Equations 9-11; [0052] lines 15-20, D(i) represents a weighted average of the effective normalized sensor reading differences d(t) that is weighted by the virtual sensor confidence values m(t). This way, the bigger m(t), the more confident the system is in the readings from the virtual sensor and the more it contributes to the determination of the current weighted average D(i).; [0053] lines 1-3, Sensor state estimation module 123 may use the current weighted average D(i) to determine the current physical sensor confidence value .beta..).
Regarding claim 4, Grichnik in view of Kim and Debeurre teaches the method according to claim 1, wherein calculating reliability scores of a sensor comprises calculating reliability scores of each sensor in the group of sensors to provide a sum of sensor reliability-weighted distance between estimated true state of a physical process and measurement by the sensor monitoring the physical process in a predefined time step and among the physical processes and among the group of sensors is minimum (Grichnik: [0047] lines 1-4, sensor output aggregation module 122 calculates and outputs the aggregated sensor value x.sub.a as an ordered weighted average (OWA) of the virtual sensor value x.sub.v and the physical sensor value x.sub.s.; [0059] lines 6-9, sensor state estimation module 123 may also discount earlier readings in the time series, e.g., using a windowing method or an exponential smoothing method.).
Regarding claim 5, Grichnik in view of Kim and Debeurre teaches the method according to claim 1, wherein estimating true states of physical processes monitored by the group of sensors comprises calculating true states of the physical processes which make sum of sensor reliability-weighted distance between estimated true state of a physical process and measurement by the sensor monitoring the physical process in predefined at least onetime step and among the physical processes and among the group of sensors is minimum (Grichnik: aggregated sensor value x.sub.a, Equations 3, 6, 9; [0059] lines 6-9, sensor state estimation module 123 may also discount earlier readings in the time series, e.g., using a windowing method or an exponential smoothing method.).
Regarding claim 6, Grichnik in view of Kim and Debeurre teaches the method according to claim 1, wherein estimating true states of the physical processes comprises estimating true states of the physical processes such that estimated true states of the physical processes monitored by the group of sensors in two consecutive discrete time steps are smooth (Grichnik: [0059] lines 6-9, sensor state estimation module 123 may also discount earlier readings in the time series, e.g., using a windowing method or an exponential smoothing method.).
Regarding claim 7, Grichnik teaches A method for sensor measurement processing (Abstract), the method comprising: gathering measurements (Fig. 3A, Input parameter values 310) from a group of physical sensors (Fig. 1, physical sensors 140 and 142; [0021] lines 1-3, “Physical sensor 140 may include one or more sensors provided for measuring certain parameters related to machine 100 and providing corresponding parameter values.”), wherein different physical sensors monitor different physical processes ([0022] lines 7-16, “ For example, a physical NO.sub.x emission sensor may measure the NO.sub.x emission level of machine 100 and provide parameter values of the NO.sub.x emission level to other components, such as ECM 120. A virtual NO.sub.x emission sensor may provide calculated parameter values of the NO.sub.x emission level to ECM 120 based on other measured or calculated parameters, such as compression ratios, turbocharger efficiencies, aftercooler characteristics, temperature values, pressure values, ambient conditions, fuel rates, engine speeds, etc.”);
acquiring reliability scores (confidence value .beta) for each sensor in the group of sensors ([0052] lines 8-14, “For each reading in the time series, sensor state estimation module 123 may calculate an effective normalized sensor reading difference d(i). The calculation of d(i) is discussed in greater detail below. Sensor state estimation module 123 may also calculate a current weighted average D(i) of the different d(i) values”; [0053] lines 1-3, “Sensor state estimation module 123 may use the current weighted average D(i) to determine the current physical sensor confidence value .beta.”). The calculation of the confidence value, which is calculated from each reading in the time series of physical sensor comprising one or more sensors, is the reliability scores being calculated for each sensor, the reliability scores based on the measurements from the group of physical sensors (Fig. 7, steps 710 and 720);
conducting sensor fusion based on the acquired reliability scores, to estimate true states of the physical processes monitored by the group of physical sensors such that a true state of a physical process should be closer to the measurements by physical sensors with higher reliability scores (Fig. 4 sensor output aggregation module 122, aggregated sensor value x.sub.a); and
identifying an unreliable physical sensor from the group of physical sensors and automatically servicing the unreliable physical sensor for future measurements ([0041] lines 20-28, “Sensor error detection and compensation system 121 may also output a replace physical sensor signal R.sub.s that indicates whether physical sensor 140 has failed and should be replaced. It should be noted that "replacing" a physical sensor may include complete replacement of the sensor (i.e., removing the old physical sensor and introducing a new physical sensor) or servicing the existing physical sensor in some manner without complete replacement.”).
Grichnik does not teach the method, comprising:
automatically disabling the unreliable physical sensor for future measurements, while other physical sensors in the group of physical sensors remain enabled.
Kim teaches an analogous method for determining reliability of physical sensors, comprising:
automatically changing the operation of the unreliable physical sensor for future measurements ([0062] “The sensor operation setting unit 260 receives operation rule data or software (or firmware) update data for each sensor in the sensor group 100, according to the results of abnormality diagnosis on sensor data and/or setting modification of the sensor, etc., from the sensor data management apparatus 300, through the transceiver 250, and provides the received data to the corresponding sensor. Or, the sensor operation setting unit 260 sets a reliability value of each sensor, received from the sensor data analyzer 230, to the corresponding sensor.”; [0045] “Also, the sensor data processing apparatus 200 provides each sensor with an execution command and a command related to setting modification, based on the result of determination on the reliability of the sensor, thereby correcting any error of the sensor or controlling the operation of the sensor.”), while other physical sensors in the group of physical sensors remain enabled ([0075] The post-processor 237 analyzes the received sensor data to determine whether the sensor data corresponds to data (that is, abnormal data) indicating an abnormality situation (or an abnormality state) or data (normal data) indicating a normal situation (or a normal state), based on the sensor data classified by the main processor 235, predicts a flow of states in consideration of the currently analyzed data and analysis data previously analyzed and stored, and diagnoses abnormality. Also, the post-processor 237 checks the result of abnormality diagnosis on the sensor signal, based on the analysis data of the sensor signal, and determines the reliability of the corresponding sensor that has transmitted the sensor signal, based on the result of the abnormality diagnosis.). The selective change in the operation (i.e. operation settings, rules, and commands) of the unreliable sensor (abnormality diagnosis) is the change in operation of the unreliable sensor, while other physical sensors in the group remain enabled (normal state).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Grichnik to include the reliable sensors remaining enabled of Kim because it would yield predictable results, such as only the identified unreliable sensor having a change in operation based on its unreliability.
Grichnik in view of Kim does not teach the method, comprising:
automatically disabling the unreliable physical sensor for future measurements.
Debeurre teaches an analogous method of processing sensors measurements (Abstract), comprising:
automatically disabling the unreliable physical sensor for future measurements (Fig. 3, steps 214 and 222; [0035] lines 1-6, “In a seventh operation 214, microcontroller 40 makes a determination, based on the results of the comparison of operation 212, as to whether or not sensor system 30 (FIG. 2) and/or sensor-responsive system 10 (FIG. 1) should be disabled. If sensor system 30 and/or sensor-responsive system 10 are to be disabled, they are disabled in operation 222.”; [0036] lines 7-11, “in addition to providing the ability to disable systems in which MEMS devices cannot be re-calibrated or are no longer functional, the ability to notify users of systems employing MEMS devices of problems with the MEMS devices is provided.”). The sensors and/or sensor system that cannot be re-calibration or are no longer function are the unreliable physical sensor, and the disabling operation is the automatic disabling.
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Grichnik in view of Kim to include the automatic disabling of the unreliable physical sensor of Debeurre because it would yield predictable and advantageous results of removing unreliable sensor measurement information from analysis, thereby reducing the inaccuracy of a set of sensors.
Even if Grichnik in view of Kim and Debeurre does not teach automatically disabling the unreliable physical sensor for future measurements, it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to make the disabling step automatic since it has been held that broadly providing a mechanical or automatic means to replace manual activity which has accomplished the same result involves only routine skill in the art. In re Venner, 120 USPQ 192.
Regarding claim 8, Grichnik in view of Kim and Debeurre teaches the method according to claim 7, wherein conducting sensor fusion based on the acquired reliability scores to estimate true states of the physical processes monitored by the group of sensors comprises conducting sensor fusion such that estimated true states of the physical processes monitored by the group of sensors in two consecutive discrete time steps are smooth (Grichnik: [0047] lines 1-4, sensor output aggregation module 122 calculates and outputs the aggregated sensor value x.sub.a as an ordered weighted average (OWA) of the virtual sensor value x.sub.v and the physical sensor value x.sub.s.; [0059] lines 6-9, sensor state estimation module 123 may also discount earlier readings in the time series, e.g., using a windowing method or an exponential smoothing method.).
Regarding claim 9, Grichnik in view of Kim and Debeurre teaches the method according to claim 7, wherein: gathering measurements by the sensors comprises getting at time step t, measurements by the group of sensors (Grichnik: [0059] lines 6-9, sensor state estimation module 123 may also discount earlier readings in the time series, e.g., using a windowing method or an exponential smoothing method.);
acquiring reliability scores of the sensors comprises calculating reliability scores of the sensors based on: measurements by the group of sensors got at each time step from t-L to t (Grichnik: Fig. 7, sensor confidence value β, [0059] “window method”), and estimated true states of the physical processes at each time step from t-L to t (Grichnik: Fig. 7, aggregated sensor value x.sub.a, [0059] “window method”).
Regarding claim 10, Grichnik in view of Kim and Debeurre teaches the method according to claim 7, further comprising constructing for each physical process a soft sensor by measurement by a soft sensor based on measurement by at least one sensor other than the sensor monitoring the physical process in the group of sensors to enlarge the group of sensors (Grichnik: [0041] lines 10-20, the virtual sensor value x.sub.v may be a NO.sub.x emissions value determined by virtual sensor model 334 based on input parameter values 310, and the physical sensor value x.sub.s may be a NO.sub.x emissions value measured directly by a physical emissions sensor, e.g., physical sensor 140. Sensor error detection and compensation system 121 may also determine an aggregated sensor value x.sub.a that represents a combination of the virtual sensor value x.sub.v and physical sensor value x.sub.s and may output the aggregated sensor value x.sub.a to a control system, such as various components of ECM 120 in order to control machine 100 and/or engine 110.).
Regarding claim 11, Grichnik teaches an apparatus for sensor measurements processing (Fig. 1; Abstract), comprising: a measurement module (electronic control module 120) configured to gather measurements (Fig. 3A, Input parameter values 310) by each physical sensor in a group of sensors (Fig. 1, physical sensors 140 and 142; [0021] lines 1-3, “Physical sensor 140 may include one or more sensors provided for measuring certain parameters related to machine 100 and providing corresponding parameter values.”), wherein different physical sensors monitor different physical processes ([0022] lines 7-16, “ For example, a physical NO.sub.x emission sensor may measure the NO.sub.x emission level of machine 100 and provide parameter values of the NO.sub.x emission level to other components, such as ECM 120. A virtual NO.sub.x emission sensor may provide calculated parameter values of the NO.sub.x emission level to ECM 120 based on other measured or calculated parameters, such as compression ratios, turbocharger efficiencies, aftercooler characteristics, temperature values, pressure values, ambient conditions, fuel rates, engine speeds, etc.”);
an estimation module configured to estimate based on the measurements initial true states of the physical processes monitored by the group of physical sensors ([0037] lines 1-6, “Input parameter values 310 may include any appropriate type of data associated with NO.sub.x emission levels. For example, input parameter values 310 may be values of parameters used to control various response characteristics of engine 110 and/or values of parameters associated with conditions corresponding to the operation of engine 110.”; lines 16-19, “Further, input parameter values 310 may be measured by certain physical sensors, such as physical sensor 142, and/or generated by other control systems such as ECM 120.”);
a calculation module configured to repeat until convergence: calculating, based on the estimated true states of the physical processes, reliability scores (confidence value .beta) of the group of physical sensors ( [0021] lines 1-3, “Physical sensor 140 may include one or more sensors provided for measuring certain parameters related to machine 100 and providing corresponding parameter values.”) wherein the higher the score is, the more reliable a physical sensor is, such that a more reliable physical sensor should be more likely to provide measurements which are closer to real state of the physical process monitored by the physical sensor ([0051] Sensor state estimation module 123 may receive the virtual sensor value x.sub.v, the physical sensor value x.sub.s, and the virtual sensor confidence value m, and may generate the physical sensor confidence value .beta. by comparing the outputs of the physical sensor with those of the virtual sensor. In certain embodiments, sensor state estimation module 123 may compare the values at several different times and may determine that those comparisons where the virtual sensor confidence value m is high are to be assigned greater weight than those comparisons where the virtual sensor confidence value m is low. Sensor state estimation module 123 may send the generated physical sensor confidence value .beta. to one or more of sensor error detection and compensation system 121 and sensor state estimation module 123.; Fig. 7, sensor confidence value β) One of ordinary skill in the art would recognize that the step of comparing and assigning weights would result in a convergence to a value.; and
estimating, based on the calculated reliability scores, true states of the physical processes, such that the real state of a physical process should be closer to measurements by a more reliable physical sensor (aggregated sensor value x.sub.a) ); and
identifying an unreliable physical sensor from the group of physical sensors and automatically servicing the unreliable physical sensor for future measurements ([0041] lines 20-28, “Sensor error detection and compensation system 121 may also output a replace physical sensor signal R.sub.s that indicates whether physical sensor 140 has failed and should be replaced. It should be noted that "replacing" a physical sensor may include complete replacement of the sensor (i.e., removing the old physical sensor and introducing a new physical sensor) or servicing the existing physical sensor in some manner without complete replacement.”).
Grichnik does not teach the apparatus, comprising:
automatically disabling the unreliable physical sensor for future measurements, while other physical sensors in the group of physical sensors remain enabled.
Kim teaches an analogous apparatus for determining reliability of physical sensors, comprising:
automatically changing the operation of the unreliable physical sensor for future measurements ([0062] “The sensor operation setting unit 260 receives operation rule data or software (or firmware) update data for each sensor in the sensor group 100, according to the results of abnormality diagnosis on sensor data and/or setting modification of the sensor, etc., from the sensor data management apparatus 300, through the transceiver 250, and provides the received data to the corresponding sensor. Or, the sensor operation setting unit 260 sets a reliability value of each sensor, received from the sensor data analyzer 230, to the corresponding sensor.”; [0045] “Also, the sensor data processing apparatus 200 provides each sensor with an execution command and a command related to setting modification, based on the result of determination on the reliability of the sensor, thereby correcting any error of the sensor or controlling the operation of the sensor.”), while other physical sensors in the group of physical sensors remain enabled ([0075] The post-processor 237 analyzes the received sensor data to determine whether the sensor data corresponds to data (that is, abnormal data) indicating an abnormality situation (or an abnormality state) or data (normal data) indicating a normal situation (or a normal state), based on the sensor data classified by the main processor 235, predicts a flow of states in consideration of the currently analyzed data and analysis data previously analyzed and stored, and diagnoses abnormality. Also, the post-processor 237 checks the result of abnormality diagnosis on the sensor signal, based on the analysis data of the sensor signal, and determines the reliability of the corresponding sensor that has transmitted the sensor signal, based on the result of the abnormality diagnosis.). The selective change in the operation (i.e. operation settings, rules, and commands) of the unreliable sensor (abnormality diagnosis) is the change in operation of the unreliable sensor, while other physical sensors in the group remain enabled (normal state).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the apparatus of Grichnik to include the reliable sensors remaining enabled of Kim because it would yield predictable results, such as only the identified unreliable sensor having a change in operation based on its unreliability.
Grichnik in view of Kim does not teach the apparatus, comprising:
automatically disabling the unreliable physical sensor for future measurements.
Debeurre teaches an analogous apparatus for processing sensors measurements (Abstract), comprising:
automatically disabling the unreliable physical sensor for future measurements (Fig. 3, steps 214 and 222; [0035] lines 1-6, “In a seventh operation 214, microcontroller 40 makes a determination, based on the results of the comparison of operation 212, as to whether or not sensor system 30 (FIG. 2) and/or sensor-responsive system 10 (FIG. 1) should be disabled. If sensor system 30 and/or sensor-responsive system 10 are to be disabled, they are disabled in operation 222.”; [0036] lines 7-11, “in addition to providing the ability to disable systems in which MEMS devices cannot be re-calibrated or are no longer functional, the ability to notify users of systems employing MEMS devices of problems with the MEMS devices is provided.”). The sensors and/or sensor system that cannot be re-calibration or are no longer function are the unreliable physical sensor, and the disabling operation is the automatic disabling.
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the apparatus of Grichnik in view of Kim to include the automatic disabling of the unreliable physical sensor of Debeurre because it would yield predictable and advantageous results of removing unreliable sensor measurement information from analysis, thereby reducing the inaccuracy of a set of sensors.
Even if Grichnik in view of Kim and Debeurre does not teach automatically disabling the unreliable physical sensor for future measurements, it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to make the disabling step automatic since it has been held that broadly providing a mechanical or automatic means to replace manual activity which has accomplished the same result involves only routine skill in the art. In re Venner, 120 USPQ 192.
Regarding claim 12, Grichnik in view of Kim and Debeurre teaches the apparatus according claim 11, further comprising a construction module configured to before the calculation module repeats until convergence, construct, for each of the physical processes, a soft sensor by calculating measurement by a soft sensor based on measurement by at least one sensor other than the sensor monitoring the physical process in the group of sensors, to enlarge the group of sensors (Grichnik: Figs. 3A and 3B, [0032] Sensor input interface 302 may include any appropriate interface, such as an I/O interface or a data link configured to obtain information from various physical sensors (e.g., physical sensors 140 and 142) and/or from ECM 120. The information may include values of input or control parameters of the physical sensors, operational status of the physical sensors, and/or values of output parameters of the physical sensors. The information may also include values of input parameters from ECM 120 that may be sent to replace parameter values otherwise received from physical sensors 140 and 142. Further, the information may be provided to sensor input interface 302 as input parameter values 310.).
Regarding claim 13, Grichnik in view of Kim and Debeurre teaches the apparatus according to claim 12, wherein the calculation module is further configured to calculate reliability scores of the group of sensors, such that the more reliable a sensor is, the higher penalty if the measurement by the sensor is far away from the estimated true state of the respective physical process (Grichnik: Equations 9-11, [0052] lines 15-20, D(i) represents a weighted average of the effective normalized sensor reading differences d(t) that is weighted by the virtual sensor confidence values m(t). This way, the bigger m(t), the more confident the system is in the readings from the virtual sensor and the more it contributes to the determination of the current weighted average D(i).; [0053] lines 1-3, Sensor state estimation module 123 may use the current weighted average D(i) to determine the current physical sensor confidence value .beta..).
Regarding claim 14, Grichnik in view of Kim and Debeurre teaches the apparatus according to claim 11, wherein the calculation module is further configured to calculate out reliability scores of the group of sensors which make sum of differences between estimated true state of a physical process and measurement by the sensor monitoring the physical process in predefined at least on time step and among the physical processes and among the group of sensors is minimum (Grichnik: Fig. 4, [0047] lines 1-4, sensor output aggregation module 122 calculates and outputs the aggregated sensor value x.sub.a as an ordered weighted average (OWA) of the virtual sensor value x.sub.v and the physical sensor value x.sub.s.; [0059] lines 6-9, sensor state estimation module 123 may also discount earlier readings in the time series, e.g., using a windowing method or an exponential smoothing method.).
Regarding claim 15, Grichnik in view of Kim and Debeurre teaches the apparatus according to claim 1, wherein the calculation module is further configured to calculate out true states of the physical processes which make sum of differences between estimated true state of a physical process and measurement by the sensor monitoring the physical process in predefined at least on time step and among the physical processes and among the group of sensors is minimum (Grichnik: aggregated sensor value x.sub.a, Equations 3, 6, 9; [0059] lines 6-9, sensor state estimation module 123 may also discount earlier readings in the time series, e.g., using a windowing method or an exponential smoothing method.).
Regarding claim 16, Grichnik in view of Kim and Debeurre teaches the apparatus according to claim 11, wherein the calculation module is further configured to estimate true states of the physical processes such that estimated true states of the physical processes monitored by the group of sensors in two consecutive discrete time steps are smooth (Grichnik: [0059] lines 6-9, sensor state estimation module 123 may also discount earlier readings in the time series, e.g., using a windowing method or an exponential smoothing method.).
Regarding claim 18, Grichnik teaches An apparatus for sensor measurement processing (Fig. 1; Abstract), the apparatus comprising: a measurement module configured to get measurements (Fig. 3A, Input parameter values 310) by a group of physical sensors (Fig. 1, physical sensors 140 and 142; [0021] lines 1-3, “Physical sensor 140 may include one or more sensors provided for measuring certain parameters related to machine 100 and providing corresponding parameter values.”), wherein different physical sensors monitor different physical processes ([0022] lines 7-16, “ For example, a physical NO.sub.x emission sensor may measure the NO.sub.x emission level of machine 100 and provide parameter values of the NO.sub.x emission level to other components, such as ECM 120. A virtual NO.sub.x emission sensor may provide calculated parameter values of the NO.sub.x emission level to ECM 120 based on other measured or calculated parameters, such as compression ratios, turbocharger efficiencies, aftercooler characteristics, temperature values, pressure values, ambient conditions, fuel rates, engine speeds, etc.”);
an acquisition module configured (Fig. 4, sensor output aggregation module 122) to acquire reliability scores (confidence value .beta) of the group of physical sensors ([0021] lines 1-3, “Physical sensor 140 may include one or more sensors provided for measuring certain parameters related to machine 100 and providing corresponding parameter values.”), the reliability scores based on the measurements from the group of physical sensors (Fig. 7, step 720); and
an fusion module configured to estimate, based on the acquired reliability scores, true states of the physical processes monitored by the group of physical sensors such that a true state of a physical process should be closer to the measurements by physical sensors with higher reliability scores ([0051] “Sensor state estimation module 123 may receive the virtual sensor value x.sub.v, the physical sensor value x.sub.s, and the virtual sensor confidence value m, and may generate the physical sensor confidence value .beta. by comparing the outputs of the physical sensor with those of the virtual sensor. In certain embodiments, sensor state estimation module 123 may compare the values at several different times and may determine that those comparisons where the virtual sensor confidence value m is high are to be assigned greater weight than those comparisons where the virtual sensor confidence value m is low. Sensor state estimation module 123 may send the generated physical sensor confidence value .beta. to one or more of sensor error detection and compensation system 121 and sensor state estimation module 123.”; Fig. 7, sensor confidence value β) and identifying an unreliable physical sensor from the group of physical sensors and automatically servicing the unreliable physical sensor for future measurements ([0041] lines 20-28, “Sensor error detection and compensation system 121 may also output a replace physical sensor signal R.sub.s that indicates whether physical sensor 140 has failed and should be replaced. It should be noted that "replacing" a physical sensor may include complete replacement of the sensor (i.e., removing the old physical sensor and introducing a new physical sensor) or servicing the existing physical sensor in some manner without complete replacement.”).
Grichnik does not teach the apparatus, comprising:
automatically disabling the unreliable physical sensor for future measurements, while other physical sensors in the group of physical sensors remain enabled.
Kim teaches an analogous apparatus for determining reliability of physical sensors, comprising:
automatically changing the operation of the unreliable physical sensor for future measurements ([0062] “The sensor operation setting unit 260 receives operation rule data or software (or firmware) update data for each sensor in the sensor group 100, according to the results of abnormality diagnosis on sensor data and/or setting modification of the sensor, etc., from the sensor data management apparatus 300, through the transceiver 250, and provides the received data to the corresponding sensor. Or, the sensor operation setting unit 260 sets a reliability value of each sensor, received from the sensor data analyzer 230, to the corresponding sensor.”; [0045] “Also, the sensor data processing apparatus 200 provides each sensor with an execution command and a command related to setting modification, based on the result of determination on the reliability of the sensor, thereby correcting any error of the sensor or controlling the operation of the sensor.”), while other physical sensors in the group of physical sensors remain enabled ([0075] The post-processor 237 analyzes the received sensor data to determine whether the sensor data corresponds to data (that is, abnormal data) indicating an abnormality situation (or an abnormality state) or data (normal data) indicating a normal situation (or a normal state), based on the sensor data classified by the main processor 235, predicts a flow of states in consideration of the currently analyzed data and analysis data previously analyzed and stored, and diagnoses abnormality. Also, the post-processor 237 checks the result of abnormality diagnosis on the sensor signal, based on the analysis data of the sensor signal, and determines the reliability of the corresponding sensor that has transmitted the sensor signal, based on the result of the abnormality diagnosis.). The selective change in the operation (i.e. operation settings, rules, and commands) of the unreliable sensor (abnormality diagnosis) is the change in operation of the unreliable sensor, while other physical sensors in the group remain enabled (normal state).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the apparatus of Grichnik to include the reliable sensors remaining enabled of Kim because it would yield predictable results, such as only the identified unreliable sensor having a change in operation based on its unreliability.
Grichnik in view of Kim does not teach the apparatus, comprising:
automatically disabling the unreliable physical sensor for future measurements.
Debeurre teaches an analogous apparatus for processing sensors measurements (Abstract), comprising:
automatically disabling the unreliable physical sensor for future measurements (Fig. 3, steps 214 and 222; [0035] lines 1-6, “In a seventh operation 214, microcontroller 40 makes a determination, based on the results of the comparison of operation 212, as to whether or not sensor system 30 (FIG. 2) and/or sensor-responsive system 10 (FIG. 1) should be disabled. If sensor system 30 and/or sensor-responsive system 10 are to be disabled, they are disabled in operation 222.”; [0036] lines 7-11, “in addition to providing the ability to disable systems in which MEMS devices cannot be re-calibrated or are no longer functional, the ability to notify users of systems employing MEMS devices of problems with the MEMS devices is provided.”). The sensors and/or sensor system that cannot be re-calibration or are no longer function are the unreliable physical sensor, and the disabling operation is the automatic disabling.
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the apparatus of Grichnik to include the automatic disabling of the unreliable physical sensor of Debeurre because it would yield predictable and advantageous results of removing unreliable sensor measurement information from analysis, thereby reducing the inaccuracy of a set of sensors.
Even if Grichnik in view of Kim and Debeurre does not teach automatically disabling the unreliable physical sensor for future measurements, it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to make the disabling step automatic since it has been held that broadly providing a mechanical or automatic means to replace manual activity which has accomplished the same result involves only routine skill in the art. In re Venner, 120 USPQ 192.
Regarding claim 19, Grichnik in view of Kim and Debeurre teaches the apparatus according to claim 18, wherein the fusion module is further configured to conduct the sensor fusion such that estimated true states of the physical processes monitored by the group of sensors in two consecutive discrete time steps are smooth (Grichnik: [0047] lines 1-4, sensor output aggregation module 122 calculates and outputs the aggregated sensor value x.sub.a as an ordered weighted average (OWA) of the virtual sensor value x.sub.v and the physical sensor value x.sub.s.; [0059] lines 6-9, sensor state estimation module 123 may also discount earlier readings in the time series, e.g., using a windowing method or an exponential smoothing method.).
Regarding claim 20, Grichnik in view of Kim and Debeurre teaches the apparatus according to claim 18, wherein: the measurement module is further configured to get at time step t, measurements by a group of sensors (Grichnik: [0059] lines 6-9, sensor state estimation module 123 may also discount earlier readings in the time series, e.g., using a windowing method or an exponential smoothing method.); and
the acquisition module is further configured to calculate reliability scores of the group of sensors based on: measurements by the group of sensors got at each time step from t-L to t, and estimated true states of the physical at each time step from t-L to t (Grichnik: Fig. 7, sensor confidence value β; [0052] lines 8-14, “For each reading in the time series, sensor state estimation module 123 may calculate an effective normalized sensor reading difference d(i). The calculation of d(i) is discussed in greater detail below. Sensor state estimation module 123 may also calculate a current weighted average D(i) of the different d(i) values”; [0053] lines 1-3, “Sensor state estimation module 123 may use the current weighted average D(i) to determine the current physical sensor confidence value .beta.”).
Regarding claim 21, Grichnik in view of Kim and Debeurre teaches the apparatus according to claim 18, further comprising a construction module configured to, before the acquisition module acquires reliability scores of the group of sensors, construct, for each physical process, a soft sensor by calculating measurement by a soft sensor based on measurement by at least one sensor other than the sensor monitoring the physical process in the group of sensors, to enlarge the group of sensors (Grichnik: [0041] lines 10-20, the virtual sensor value x.sub.v may be a NO.sub.x emissions value determined by virtual sensor model 334 based on input parameter values 310, and the physical sensor value x.sub.s may be a NO.sub.x emissions value measured directly by a physical emissions sensor, e.g., physical sensor 140. Sensor error detection and compensation system 121 may also determine an aggregated sensor value x.sub.a that represents a combination of the virtual sensor value x.sub.v and physical sensor value x.sub.s and may output the aggregated sensor value x.sub.a to a control system, such as various components of ECM 120 in order to control machine 100 and/or engine 110.).
Conclusion
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/B.B.G./Examiner, Art Unit 2857
/Catherine T. Rastovski/Supervisory Primary Examiner, Art Unit 2857
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