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 .
Priority
Application 17/929,835, filed on 09/06/2022, claims priority to JAPAN 2022-033726, filed on 03/04/2022.
Response to Amendment
This office action is in response to amendments submitted on 10/23/2025 wherein claims 1-2, 4-5, 8-11, and 13-14 are pending and ready for examination. Claims 3, 6, and 7 were previously canceled and claim 12 is newly canceled.
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.
Claims 1-2, 11, and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Behnia et al., “Advanced structural health monitoring of concrete structures with the aid of acoustic emission” downloaded from https://doi.org/10.1016/j.conbuildmat.2014.04.103 in view of Colombo et al., “Assessing Damage of Reinforced Concrete Beam Using ‘b-value’ Analysis of Acoustic Emission Signals” downloaded from https://www.researchgate.net/publication/228559897 in view of Andrews, U.S. Pub. No. 2007/0056374 A1.
Regarding Independent claim 1 Behnia teaches:
“A structure evaluation system” (Behnia, Abstract) comprising:
“a plurality of sensors configured to detect elastic waves generated from a structure and processing circuitry” (Behnia, § 1. Introduction: Behnia teaches using sensors to detect elastic waves due to acoustic emission (§ 1. 1st paragraph). Additionally, Behnia teaches using an “advanced computational processor” for analysis (§ 2.2 Signal waveform analysis)).
“acquire detection information for an evaluation target period in which information on at least an amplitude of each elastic wave detected by each of the plurality of sensors is associated with time information on the time when each elastic wave is detected” (Behnia, Table 1, fig. 1, § 2. Potential structural assessment approaches based on AE and motivations of this study, § 2.1. Parametric analysis, § 2.2. Signal waveform analysis: Behnia teaches the “AE (acoustic emission) technique is extensively used for real-time damage monitoring” (§ 2.) disclosing the sensors must collect data in real time in order to monitor in real time. Parameters collected include amplitude (Table 1, fig 1) where these parameters are acquired (§ 2.2)).
“calculate an evaluation value, which is a slope of an amplitude scale-based frequency distribution of the elastic waves, based on the acquired detection information for the evaluation target period” (Behnia, § 5.6. Improved b-value analysis: Behnia teaches “The calculation of Ib-value (an “evaluation value”) is based upon the slope of the peak amplitude distribution of AE signals” and “it was observed that the AE amplitude values vary with time” (§ 5.6.) disclosing b-values also vary with time. Fig 15 depicts b-value vs. time graphs for 1 complete load cycle where 1 complete load cycle discloses an “evaluation target period”).
While Behnia teaches b-values vary with time, Behnia does not explicitly teach the period is a “predetermined period.”
Colombo teaches the total number of events during a loading cycle are divided into groups (Analysis and Results) where an event is related to the amplitude of the acoustic emission (AE) (Introduction), where the different loading cycles correspond to different applied loads (fig 1) and each loading cycle has an associated period (fig. 1). The b-value (evaluation value) is determined for each of the groups (fig 4). Each loading cycle is divided into groups of 70, 100, and 130 events (fig. 5) where the groups have an associated period as each period contains 70, 100, or 130 events thereby disclosing “each evaluation value” is “calculated every predetermined period.”
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system for monitoring structural health using acoustic emissions as taught by Behnia by including a predetermined period for calculating the evaluation value, the b-value, as taught by Colombo in order to provide a system where “the monitoring procedure gave real confidence regarding its accuracy re: deterioration progress” (Colombo, § Practical Significance, pg 285).
“estimate a trend component which represents the component for evaluating a long-term damage trend of a structure using time-series data of each evaluation value; and evaluate a deterioration state of the structure in each of the estimated trend component”
(Behnia, fig 10, fig 14, § 5.2. Use of AE amplitude to compute AE based b-value, § 5.6. Improved b-value analysis, § 5.7. Shifted b-value analysis: Behnia teaches b-value analysis where the b-value varies as the damage level varies (§ 5.6., § 5.7.) where statistical crack classification through b-value analysis occurs with an increasing trend in micro-crack stages and a decreasing trend as macro-cracks start to open (§ 5.2.) The trend components are depicted as Micro-cracks nucleation (green and increasing) and Macro-cracks opening (red and decreasing) of fig 10 where increasing, decreasing, and steady (fig. 10, blue, macro-cracks formation) disclose an estimated “trend component” that is evaluated for damage disclosing “evaluate a deterioration state of the structure” (§ 5.7,see fig. 14) moreover “a decreasing trend in b-value can be known as a serious damage alert” (§ 5.2, see fig 10) where “a serious damage alert” discloses “long-term damage” and the “decreasing trend in b-value” discloses the “component for evaluating a long-term damage trend.” Additionally, Behnia teaches evaluating “time-series data” (see § 9.3, fig 8, fig. 11, fig. 15)).
“the processing circuitry evaluates that damage is progressing in the structure when the trend component is on a downward trend” (Behnia, fig. 10, § 5.6. Improved b-value analysis, § 5.7. Shifted b-value analysis: Behnia teaches b-value analysis where the b-value varies as the damage level varies (§ 5.6., § 5.7.) where statistical crack classification through b-value analysis occurs with an increasing trend in micro-crack stages and a decreasing trend as macro-cracks start to open where the “decreasing trend in b-value can be known as a serious damage alert” (§ 5.2.) disclosing the “damage is progressing” as load steps progress as depicted in fig. 10. The damage progresses from micro-cracks nucleation to macro-cracks formation to macro-cracks opening. The trend components are depicted as Micro-cracks nucleation (green, increasing), macro-cracks formation (blue, steady stage), and Macro-cracks opening (red, decreasing) of fig 10.
Behnia does not teach
“a seasonality component which represents the component for evaluating a seasonal damage variation.”
“the processing circuitry evaluates that low-progressive damage is inside the structure when the seasonality component fluctuates periodically.”
“wherein the evaluation target period is a period of 2 years or more,
wherein the processing circuitry calculates time series data of evaluation values for each year, compares the calculated time series data of the evaluation values for each year, and when the time series data of the evaluation values for each year show similar fluctuations, determines that the seasonality component is fluctuating periodically.”
Andrews teaches collecting temperature data to identify seasonal changes when evaluating structural integrity (¶ 0063) disclosing “a seasonality component” where the seasonal changes in temperature are used to reduce or eliminate false alarms concerning structural damage (¶ 0063) thereby disclosing “the component for evaluating a seasonal damage variation.” Additionally, the “frequency of testing at any given location can be chosen to be as high as several times an hour or lower than one a week” (¶ 0009) disclosing “time-series data.” Therefore the combination of Andrew’s seasonal data with Behnia’s trend analysis discloses the limitation “estimate a trend component which represents the component for evaluating a long-term damage trend of a structure and a seasonality component which represents the component for evaluating a seasonal damage variation using time-series data of each evaluation value and evaluate a deterioration state of the structure in each of the estimated trend component and seasonality component.”
Behnia and Andrews both monitor systems for structural damage using acoustic signals therefore it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system for monitoring structural using acoustic emissions as taught by Behnia as modified by including seasonal data as taught by Andrews as identifying seasonal and using seasonal data improves “the training of the artificial neural networks” in order to make the system “more robust against temperature changes over long period of time, such as daily and seasonal periods of time” (Andrews ¶ 0073).
Andrews teaches:
“the processing circuitry evaluates that low-progressive damage is inside the structure when the seasonality component fluctuates periodically” (Andrews, ¶ 0009,
¶ 0055, ¶ 0063, ¶ 0073: Andrews teaches a monitoring system that detects “structural defects in the interior of the structure” (¶ 0009). Moreover, “a range of ways of training the decision-making algorithms to detect changes with particular characteristics related to the seasons, time of day, loading patterns, so there is flexibility in how the system is used” (¶ 0073) where the temperature or seasonal changes are accounted for to eliminate these changes from causing “significant changes in structural integrity” (¶ 0063) disclosing temperature or seasonal changes cause “low-progressive damage” “inside the structure.” Andrews teaches “processing circuitry” (¶ 0055)).
“wherein the evaluation target period is a period of 2 years or more (Andrews ¶ 0023: : Andrews teaches monitoring a structure for “a period of time that is equal to or greater than the desired monitoring period, which might be as short as a few minutes but equally could be as long as many years” (¶ 0023) disclosing “the evaluation target period is a period of 2 years or more.”)
“wherein the processing circuitry calculates time series data of evaluation values for each year, compares the calculated time series data of the evaluation values for each year” (Andrews, claim 1, ¶ 0009: Andrews teaches the frequency of testing by the monitoring system “at any location can be chosen to be as high as several times an hour or lower than once a week” (¶ 0009) disclosing “time series data” where the monitored information (the signal from a monitoring location) is compared with other “processed or archived information in order to decide is any significant change in the mechanical integrity, operational worthiness and safety of the structure being monitored has happened” (claim 1) disclosing the time series data is calculated by processing circuitry and is compared with other “processed or archived information” where the “processed or archived information” is from a period that “could be as long as many years” (see above) therefore Andrews discloses comparing “the calculated time series data of the evaluation values for each year.”
“when the time series data of the evaluation values for each year show similar fluctuations, determines that the seasonality component is fluctuating periodically” (Andrews, ¶ 0063, ¶ 0073: Andrews teaches improving the training of the neural network in order to “make a system that is more robust against temperature changes over long period of time, such as daily and seasonal periods of time” (¶ 0073) where seasonal “temperature changes can alter the material properties of surface layers of structures, causing received information signals to be changes” and “a well-trained decision-making algorithm compensates for the variation in temperature so that the frequency of these false alarms of significant change in structural integrity is reduced or eliminated” (¶ 0063) therefore Andrews teaches identifying seasonal fluctuations due to temperature in order to identify significant changes in structural integrity as the system is trained to “detect changes with particular characteristics related to the seasons, . . .” (¶ 0073).)
Both Behnia and Andrews use acoustic signals to analyze structural health therefore it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system for monitoring structural health using acoustic emissions as taught by Behnia as modified by including accounting for damage done by temperature or seasonal changes disclosed by Andrews in order to provide a system where false alarms due to variation in temperature are reduced or eliminated (Andrews, ¶ 0063).
Regarding claim 2 Behnia as modified teaches:
“the processing circuitry calculates a plurality of candidate evaluation values, and calculates a representative value of the plurality of candidate evaluation values as the evaluation value” (Behnia, fig. 15, § 5.8. Minimum b-value approach in bridges: Behnia teaches determining, in step 1, “the lowest b-values from one complete load event for each of the AE sensors” and then computing the “sensor network mean and standard deviation for all values obtained from step 1”(§ 5.8.)).
While Behnia teaches b-values vary with time (see claim 1 above), Behnia does not explicitly teach the period is a “predetermined period.”
Colombo teaches the total number of events during a loading cycle are divided into groups (Analysis and Results) where an event is related to the amplitude of the acoustic emission (AE) (Introduction), where the different loading cycles correspond to different applied loads (fig 1) and each loading cycle has an associated period (fig. 1). The b-value (evaluation value) is determined for each of the groups (fig 4). Each loading cycle is divided into groups of 70, 100, and 130 events (fig. 5) where the groups have an associated period as each period contains 70, 100, or 130 events thereby disclosing “each evaluation value” is “calculated every predetermined period.”
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system for monitoring structural health using acoustic emissions as taught by Behnia as modified by including a predetermined period for calculating the evaluation value, the b-value, as taught by Colombo in order to provide a system where “the monitoring procedure gave real confidence regarding its accuracy re: deterioration progress” (Colombo, § Practical Significance, pg 285).
Regarding claim 11 Behnia as modified does not teach:
“a display configured to display information, wherein the processing circuitry generates a two-dimensional map using the evaluation value as a representative value at an installation position of each sensor, and displays the generated two-dimensional map on the display.”
Colombo teaches:
“a display configured to display information, wherein the processing circuitry generates a two-dimensional map using the evaluation value as a representative value at an installation position of each sensor, and displays the generated two-dimensional map on the display” (Colombo fig 2, fig 5, § Test Description, § Analyses and Results, 1st-2nd paragraph, Colombo teaches calculating and plotting the trend of the b-value (evaluation value) for each cycle and for each channel (sensor) where the location of each sensor is shown in fig. 2. Fig. 5 is a display of the trend of be-values for channel 2. A person of ordinary skill in the art would understand a modern computer capable of calculating and plotting the trend of b-values would have a monitor to display such 2 dimensional graphs. Colombo teaches using Matlab to “carry out a b-value analysis” (§ Analysis and Results, 1st paragraph). A person of ordinary skill in the art would understand using Matlab requires a computer which would have “processing circuitry.”)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system for monitoring structural health using acoustic emissions as taught by Behnia as modified by including displaying 2-dimensional maps of b-values over time as taught by Colombo in order to provide a system with visual results where one can more easily identify trends where “the monitoring procedure gave real confidence regarding its accuracy re: deterioration progress” (Colombo, § Practical Significance, pg 285).
Regarding claim 13 Behnia teaches:
“A structure evaluation method” (Behnia, Abstract).
“detecting, by a plurality of sensors, elastic waves generated from a structure” (Behnia, § 1 Introduction: Behnia teaches using sensors to detect elastic waves due to acoustic emission in a structure (§ 1. 1st paragraph)).
“acquiring detection information for an evaluation target period in which information on at least an amplitude of each elastic wave detected by each of the plurality of sensors that detects elastic waves generated from the structure is associated with time information on the time when each elastic wave is detected” (Behnia, Table 1, fig. 1, § 2. Potential structural assessment approaches based on AE and motivations of this study, § 2.1. Parametric analysis, § 2.2. Signal waveform analysis: Behnia teaches the “AE (acoustic emission) technique is extensively used for real-time damage monitoring” (§ 2.) disclosing the sensors must collect data in real time in order to monitor in real time. Parameters collected include amplitude (Table 1, fig 1) where these parameters are acquired (§ 2.2)).
“calculating an evaluation value, which is a slope of an amplitude scale-based frequency distribution of the elastic waves, based on the acquired detection information for the evaluation target period” (Behnia, § 5.6. Improved b-value analysis: Behnia teaches “The calculation of Ib-value (an “evaluation value”) is based upon the slope of the peak amplitude distribution of AE signals” and “it was observed that the AE amplitude values vary with time” (§ 5.6.) disclosing b-values also vary with time. 15. Fig 15 depicts b-value vs. time graphs for 1 complete load cycle where 1 complete load cycle discloses an “evaluation target period”).
While Behnia teaches b-values vary with time, Behnia does not explicitly teach the period is a “predetermined period.”
Colombo teaches the total number of events during a loading cycle are divided into groups (Analysis and Results) where an event is related to the amplitude of the acoustic emission (AE) (Introduction), where the different loading cycles correspond to different applied loads (fig 1) and each loading cycle has an associated period (fig. 1). The b-value (evaluation value) is determined for each of the groups (fig 4). Each loading cycle is divided into groups of 70, 100, and 130 events (fig. 5) where the groups have an associated period as the cycle which contains the groups has an associated period thereby disclosing “each evaluation value” is “calculated every predetermined period.”
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system for monitoring structural health using acoustic emissions as taught by Behnia by including a predetermined period for calculating the evaluation value, the b-value, as taught by Colombo in order to provide a system where “the monitoring procedure gave real confidence regarding its accuracy re: deterioration progress” (Colombo, § Practical Significance, pg 285).
“estimating a trend component which represents the component for evaluating a long-term damage trend of a structure using time-series data of each evaluation value; and evaluating a deterioration state of the structure in each of the estimated trend component”
(Behnia, fig 10, fig 14, § 5.2. Use of AE amplitude to compute AE based b-value, § 5.6. Improved b-value analysis, § 5.7. Shifted b-value analysis: Behnia teaches b-value analysis where the b-value varies as the damage level varies (§ 5.6., § 5.7.) where statistical crack classification through b-value analysis occurs with an increasing trend in micro-crack stages and a decreasing trend as macro-cracks start to open (§ 5.2.) The trend components are depicted as Micro-cracks nucleation (green and increasing) and Macro-cracks opening (red and decreasing) of fig 10 where increasing, decreasing, and steady (fig. 10, blue, macro-cracks formation) disclose an estimated “trend component” that is evaluated for damage disclosing “evaluate a deterioration state of the structure” (§ 5.7,see fig. 14) moreover “a decreasing trend in b-value can be known as a serious damage alert” (§ 5.2, see fig 10) where “a serious damage alert” discloses “long-term damage” and the “decreasing trend in b-value” discloses the “component for evaluating a long-term damage trend.” Additionally, Behnia teaches evaluating “time-series data” (see § 9.3, fig 8, fig. 11, fig. 15)).
Behnia does not teach
“a seasonality component which represents the component for evaluating a seasonal damage variation.”
“the processing circuitry evaluates that low-progressive damage is inside the structure when the seasonality component fluctuates periodically.”
“wherein the evaluation target period is a period of 2 years or more, wherein the structure evaluation method further comprises calculating time series data of evaluation values for each year, comparing the calculated time series data of the evaluation values for each year, and when the time series data of the evaluation values for each year show similar fluctuations, determining that the seasonality component is fluctuating periodically.”
Andrews teaches collecting temperature data to identify seasonal changes when evaluating structural integrity (¶ 0063) disclosing “a seasonality component” where the seasonal changes in temperature are used to reduce or eliminate false alarms concerning structural damage (¶ 0063) thereby disclosing “the component for evaluating a seasonal damage variation.” Additionally, the “frequency of testing at any given location can be chosen to be as high as several times an hour or lower than one a week” (¶ 0009) disclosing “time-series data.” Therefore the combination of Andrew’s seasonal data with Behnia’s trend analysis discloses the limitation “estimating a trend component which represents the component for evaluating a long-term damage trend of a structure and a seasonality component which represents the component for evaluating a seasonal damage variation using time-series data of each evaluation value and evaluating a deterioration state of the structure in each of the estimated trend component and seasonality component.”
Behnia and Andrews both monitor systems for structural damage using acoustic signals therefore it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system for monitoring structural using acoustic emissions as taught by Behnia as modified by including seasonal data as taught by Andrews as identifying seasonal and using seasonal data improves “the training of the artificial neural networks” in order to make the system “more robust against temperature changes over long period of time, such as daily and seasonal periods of time” (Andrews ¶ 0073).
Andrews teaches:
“the processing circuitry evaluates that low-progressive damage is inside the structure when the seasonality component fluctuates periodically” (Andrews, ¶ 0009,
¶ 0055, ¶ 0063, ¶ 0073: Andrews teaches a monitoring system that detects “structural defects in the interior of the structure” (¶ 0009). Moreover, “a range of ways of training the decision-making algorithms to detect changes with particular characteristics related to the seasons, time of day, loading patterns, so there is flexibility in how the system is used” (¶ 0073) where the temperature or seasonal changes are accounted for to eliminate these changes from causing “significant changes in structural integrity” (¶ 0063) disclosing temperature or seasonal changes cause “low-progressive damage” “inside the structure.” Andrews teaches “processing circuitry” (¶ 0055)).
“wherein the evaluation target period is a period of 2 years or more (Andrews ¶ 0023: : Andrews teaches monitoring a structure for “a period of time that is equal to or greater than the desired monitoring period, which might be as short as a few minutes but equally could be as long as many years” (¶ 0023) disclosing “the evaluation target period is a period of 2 years or more.”)
“wherein the processing circuitry calculates time series data of evaluation values for each year, compares the calculated time series data of the evaluation values for each year” (Andrews, claim 1, ¶ 0009: Andrews teaches the frequency of testing by the monitoring system “at any location can be chosen to be as high as several times an hour or lower than once a week” (¶ 0009) disclosing “time series data” where the monitored information (the signal from a monitoring location) is compared with other “processed or archived information in order to decide is any significant change in the mechanical integrity, operational worthiness and safety of the structure being monitored has happened” (claim 1) disclosing the time series data is calculated by processing circuitry and is compared with other “processed or archived information” where the “processed or archived information” is from a period that “could be as long as many years” (see above) therefore Andrews discloses comparing “the calculated time series data of the evaluation values for each year.”
“when the time series data of the evaluation values for each year show similar fluctuations, determines that the seasonality component is fluctuating periodically” (Andrews, ¶ 0063, ¶ 0073: Andrews teaches improving the training of the neural network in order to “make a system that is more robust against temperature changes over long period of time, such as daily and seasonal periods of time” (¶ 0073) where seasonal “temperature changes can alter the material properties of surface layers of structures, causing received information signals to be changes” and “a well-trained decision-making algorithm compensates for the variation in temperature so that the frequency of these false alarms of significant change in structural integrity is reduced or eliminated” (¶ 0063) therefore Andrews teaches identifying seasonal fluctuations due to temperature in order to identify significant changes in structural integrity as the system is trained to “detect changes with particular characteristics related to the seasons, . . .” (¶ 0073).)
Both Behnia and Andrews use acoustic signals to analyze structural health therefore it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system for monitoring structural health using acoustic emissions as taught by Behnia as modified by including accounting for damage done by temperature or seasonal changes disclosed by Andrews in order to provide a system where false alarms due to variation in temperature are reduced or eliminated (Andrews, ¶ 0063).
Claims 4-5 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Behnia as modified by Colombo and Andrews as applied to claim 1 above, and further in view of Beaver et al., U.S. Pub. 2020/0210393.
Regarding claim 4 Behnia as modified does not teach:
“estimates the trend component and the seasonality component by applying an additive regression model.”
Beaver teaches:
“the estimator estimates the trend component and the seasonality component by applying an additive regression model” (Beaver, ¶ 0096-0099: Beaver teaches a “Facebook Prophet” that is a “additive regression model” which includes modeling a “trend” and a “seasonality” component.
Behnia and Beaver use statistical analysis to determine damage or anomalies in data therefore it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system for monitoring structural health using acoustic emissions as taught by Behnia as modified by including the well-known additive regression model as taught by Beaver as an additive regression model generates predictions by combining contributions from multiple models thereby providing a system where anomalies are “detected as accurately and efficiently as possible, while minimizing false positives to avoid alarm fatigue” where “alarm fatigue can lead to a serious alert being overlooked and wasted time in checking for problems when there are none” (Beaver ¶ 0010).
Behnia teaches “processing circuitry” see claim 1 above.
Regarding claim 5 Behnia as modified does not teach:
“estimates the trend component by an interval linear model and the seasonality component by a Fourier series.”
Beaver teaches:
“the estimator estimates the trend component by an interval linear model and the seasonality component by a Fourier series” (Beaver, ¶ 0096-0099: Beaver teaches a “Facebook Prophet” that is a “additive regression model” which includes modeling a “trend” and a “seasonality” component using a “piecewise linear or logistic growth curve trend” and a “Fourier series” (¶ 0097)).
Behnia and Beaver use statistical analysis to determine damage or anomalies in data therefore it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system for monitoring structural health using acoustic emissions as taught by Behnia as modified by including using an using a piecewise linear model and Fourier series as taught by Beaver as using an piecewise linear model is advantageous when data does not fit a single line and using Fourier series is advantageous for statistical analysis as it is a sum of sine and cosine functions and not more complicated functions thereby providing a system where anomalies are “detected as accurately and efficiently as possible, while minimizing false positives to avoid alarm fatigue” where “alarm fatigue can lead to a serious alert being overlooked and wasted time in checking for problems when there are none” (Beaver ¶ 0010).
Behnia teaches “processing circuitry” see claim 1 above.
Regarding claim 8 Behnia as modified does not teach:
“estimates the trend component and the seasonality component using the additive regression model in which a term proportional to sensor data of a predetermined physical quantity is added in addition to the trend component and the seasonality component.”
Beaver teaches:
“the estimator estimates the trend component and the seasonality component using an additive regression model in which a term proportional to sensor data of a predetermined physical quantity is added in addition to the trend component and the seasonality component” (Beaver, ¶ 0096-0099: Beaver teaches using an “additive regression model” of the form
y
t
=
g
t
+
s
t
+
h
t
+
E
t
(¶ 0097) where
E
t
is an error term disclosing “a term proportional to sensor data”).
Behnia and Beaver use statistical analysis to determine damage or anomalies in data therefore it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system for monitoring structural health using acoustic emissions as taught by Behnia as modified by including a proportional term to the well-known additive regression model as taught by Beaver as an additive regression model generates predictions by combining contributions from multiple models thereby providing a system where anomalies are “detected as accurately and efficiently as possible, while minimizing false positives to avoid alarm fatigue” where “alarm fatigue can lead to a serious alert being overlooked and wasted time in checking for problems when there are none” (Beaver ¶ 0010).
Behnia teaches “processing circuitry” see claim 1 above.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Behnia as modified by Colombo, Andrews, and Beaver as applied to claim 8 above, and evidenced by Luo et al., “Traffic Flow Prediction during the Holidays Based on DFT and SVR” downloaded from https://doi.org/10.1155/2019/6461450.
Regarding claim 9 Behnia as modified does not teach:
“estimates the trend component and the seasonality component using then additive regression model in which a term proportional to at least one of a temperature, a humidity, and a traffic volume is added in addition to the trend component and the seasonality component.”
Beaver teaches:
“the estimator estimates the trend component and the seasonality component using an additive regression model in which a term proportional to at least one of a temperature, a humidity, and a traffic volume is added in addition to the trend component and the seasonality component.” (Beaver, ¶ 0096-0099: Beaver teaches using an “additive regression model” of the form
y
t
=
g
t
+
s
t
+
h
t
+
E
t
(¶ 0097) where
h
t
is a “holiday term,”
Behnia and Beaver use statistical analysis to determine damage or anomalies in data therefore it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system for monitoring structural health using acoustic emissions as taught by Behnia as modified by including a holiday term to the well-known additive regression model as taught by Beaver as an additive regression model generates predictions by combining contributions from multiple models thereby providing a system where anomalies are “detected as accurately and efficiently as possible, while minimizing false positives to avoid alarm fatigue” where “alarm fatigue can lead to a serious alert being overlooked and wasted time in checking for problems when there are none” (Beaver ¶ 0010).
While Behnia as modified by Beaver teaches a “holiday term” Behnia as modified by Beaver does not explicitly teach the “holiday term” includes “traffic volume.”
Luo teaches “For holidays, the trend component of traffic flow data changes” (Lou, § 2.2. Prediction of the Common Trend, page 3) disclosing holidays produce changes in traffic flow. It would have been obvious to have included a change in traffic flow to the holiday term as disclosed by Behnia as modified by Beaver to more accurately account for the stress load when monitoring the health of a structure.
Behnia teaches “processing circuitry” see claim 1 above.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Behnia as modified by Colombo and Andrews as applied to claim 1 above, and further in view of Sekine, U.S. Pub. NO. 2019/0005433 A1.
Regarding claim 10 Behnia as modified does not teach:
“the calculator calculates the evaluation value on a daily basis based on the detection information for a period of at least 2 years among the detection information for a period of 2 years or more.”
Sekine teaches:
“the calculator calculates the evaluation value on a daily basis based on the detection information” (Sekine, fig. 10, ¶ 0069-0071: Sekine teaches comparing reference data and detection object data (detection information) using comparison data in single-day units (¶ 0047) where the comparison data is used to determine a non-normal state, a fault, disclosing determining an “evaluation value on a daily basis.”
Behnia and Sekine use statistical analysis to determine damage or anomalies in data therefore it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system for monitoring structural health using acoustic emissions as taught by Behnia as modified by including evaluating data daily as taught by Sekine in order to provide a system that takes into consideration “the periodicity of time series data at a normal time that was obtained in the past” such that “a non-normal state can be detected with higher accuracy” (Sekine, ¶ 0067).
While Sekine teaches time series data collected for 1 year (fig 10, ¶ 0069) Sekine does not teach collecting data for 2 years. However, it would have been obvious for one of ordinary skill in the art to have increased the time frame for collecting data to two years in order to increase the amount of data to provide more accurate results.
Behnia teaches “processing circuitry” see claim 1 above.
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Behnia as modified by Colombo and Andrews as applied to claim 1 above, and further in view of Breed, U.S. Pub. No. 2014/0067284 A1.
Regarding claim 14 Behnia as modified does not teach:
“comprising an activation control device that detects an approach of a vehicle and outputs an activation signal for putting the system into an operating mode when the approach of a vehicle is detected.”
Breed teaches:
“comprising an activation control device that detects an approach of a vehicle and outputs an activation signal for putting the system into an operating mode when the approach of a vehicle is detected” (Breed, ¶ 0016, ¶ 0029-¶ 0032: Breed teaches a wake-up sensor coupled to a monitoring system that causes the monitoring system to activate when predetermined conditions are detected (¶ 0016) where the monitoring system monitors a structure such as a bridge and the predetermined conditions deal with the motion of vehicles (¶ 0032)).
Behnia and Breed both monitor systems for structural damage therefore it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system for monitoring structural damage as taught by Behnia as modified by including an activation control device such as a wake up sensor as taught by Breed as wake-up sensors detect specific events to “wake-up” a device only when needed avoiding unnecessary power consumption due to continuous operation thereby extending the life of the battery in order to more reliably detect “potentially damaging vibrations in a part of the bridge” (Breed, ¶ 0030).
Response to Arguments
Applicant’s arguments (remarks) filed on 10/23/2025 have been fully considered.
Regarding the rejection of Claim 12 under 35 U.S.C. § 101 page 7 of Applicant’s remarks, Examiner acknowledges the cancellation of claim 12 and the 35 U.S.C. § 101 rejection has been withdrawn.
Regarding the rejection of Claim 1 under U.S.,C. § 103, page 9-11 of Applicant’s remarks, Applicant argues “On the other hand, Behnia does not have the above configuration. In particular, Behnia is not intended for long-term monitoring as in the present invention, so evaluation using time-series data over two years or more is not anticipated. Since Behnia can be evaluated with short-term time-series data, there is no motivation to modify veneer as in Amended Claim 1” (remarks, page 9).
Examiner respectfully disagrees. Behnia teaches “a variety of approaches by various authors to pick the onset time include Short Term Average (STA) and Long Term Average (LTA) picker by Baer and Kradolfer” (§ 4.1 1st paragraph) and “Felicity ratio characterizes the damage of all different types of structures, with a challenge of defining the onset of significant acoustic emission” ( 4.3.3 1st paragraph) where “Felicity ratio is defined as load ratio at onset of significant AE activity in the current load cycle to maximum load in the previous loading history” (§ 4.3.3.1) and where “The onset of significant emission is defined as the first time the historic index during the reloading portion exceeds 1.4. The load corresponding to that time is the load at onset of significant AE” (§ 4.3.3.1) thereby disclosing “long term monitoring.”
Applicant argues “The examiner has determined that the configuration of ‘the processing circuitry evaluates that low-progressive damage is inside the structure when the seasonality component fluctuates periodically’ in amended Claim I is described in Andrews.
However, Andrews does not specify the conditions for determining whether seasonality component fluctuate periodically, as in Amended Claim 1” (remarks page 9) and “Andrews only states that temperature and seasonal variations affect the evaluation. Therefore, Andrews does not provide any description or suggestion regarding the condition for determining whether the seasonality component mentioned above fluctuate periodically. In other words, the Andrews does not describe any configuration as in amended Claim 1, the processing circuitry calculates time series data of evaluation values for each year, compare the calculated time series data of the evaluation values for each year, and when the time series data of the evaluation values for each year show similar fluctuations, determines that the seasonality component is fluctuating periodically” (remarks page 10-11).
Examiner respectfully disagrees. Andrews teaches monitoring a structure for “a period of time that is equal to are greater than the desired monitoring period, which might be as short as a few minutes but equally could be as long as many years” (¶ 0023) Additionally, Andrews teaches improving the training of the neural network in order to “make a system that is more robust against temperature changes over long period of time, such as daily and seasonal periods of time” (¶ 0073) where the training enables the system to detect changes that happen slowly or quickly. “Consequently, there is a range of ways of training the decision-making algorithms to detect changes with particular characteristics related to the seasons . . .” (¶ 0073) where “changes with particular characteristics related to the seasons” discloses fluctuations due to seasonal changes.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Ra, et al., JP2006010595A, teaches determining structure damage using acoustic emission sound.
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/DENISE R KARAVIAS/Examiner, Art Unit 2857
/MICHAEL J DALBO/Primary Examiner, Art Unit 2857