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.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 05/04/2026 has been entered.
Response to Amendment
This office action is in response to a communication submitted on 06/03/2026 wherein claims 1-2, 4-5, 8-11, and 13-14 are pending and ready for examination. Claims 3, 6, 7, and 12 were previously canceled.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 14 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 14: Claim 14 recites the limitation “a vehicle” (line 4).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-2, 4-5, 8-11 and 13-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite an abstract idea as discussed below. This abstract idea is not integrated into a practical application for the reasons discussed below. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception for the reasons discussed below.
Step 1 of the 2019 Guidance requires the examiner to determine if the claims are to one of the statutory categories of invention. Applied to the present application, the claims belong to one of the statutory classes of a process or product as a computer implemented method or a computer system/product.
Step 2A of the 2019 Guidance is divided into two Prongs. Prong 1 requires the examiner to determine if the claims recite an abstract idea, and further requires that the abstract idea belong to one of three enumerated groupings: mathematical concepts, mental processes, and certain methods of organizing human activity.
Claim 1 is copied below, with the limitations belonging to an abstract idea being underlined.
A structure evaluation system comprising:
a plurality of sensors configured to detect elastic waves generated from a structure and processing circuitry configured to
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 and store the acquired detection information for the evaluation target period in a storage;
calculate an evaluation value, which is a slope of an amplitude scale-based frequency distribution of the elastic waves, every predetermined period based on the acquired detection information for the evaluation target period;
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,
wherein
the processing circuitry evaluates that damage is progressing in the structure when the trend component is on a downward trend, wherein
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 uses the detection information for the evaluation target period stored in the storage to calculate time series data of evaluation values on an annual basis, compares the calculated time series data of the evaluation values on an annual basis, and when the time series data of the evaluation values on an annual basis show similar fluctuations, determines that the seasonality component is fluctuating periodically.
Claim 13 is copied below, with the limitations belonging to an abstract idea being underlined.
A structure evaluation method comprising:
detecting, by a plurality of sensors, elastic wave generated from a structure;
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 the elastic waves generated from the structure is associated with time information on the time when each elastic wave is detected;
storing the acquired detection information for the evaluation target period in a storage;
calculating an evaluation value, which is a slope of an amplitude scale-based frequency distribution of the elastic waves, every predetermined period based on the acquired detection information for the evaluation target period;
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 and seasonality component,
wherein
the evaluating that damage is progressing in the structure when the trend component is on a downward trend, wherein
the evaluating 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 using the detection information for the evaluation target period stored in the storage to calculate time series data of evaluation values on an annual basis, comparing the calculated time series data of the evaluation values on an annual basis, and when the time series data of the evaluation values on an annual basis show similar fluctuations, determining that the seasonality component is fluctuating periodically.
The limitations underlined can be considered to describe a series of mathematical concepts where “calculate/calculating,” “estimate/estimating,” “evaluate/evaluates/evaluating,” “uses/using,” “compares/comparing” and “determine/determining” may include a series of calculations leading to one or more numerical results or answers, obtained by a sequence of mathematical operations on numbers. The lack of a specific equation in the claim merely points out that the claim would monopolize all possible appropriate equations/two-group significance tests for accomplishing this purpose in all possible systems. These steps recited by the claim therefore amount to a series of mental and/or mathematical steps, making these limitations amount to an abstract idea.
Regarding the underlined limitation calculate/calculating an evaluation value, which is a slope of an amplitude scale-based frequency distribution of the elastic waves, every predetermined period based on the acquired detection information for the evaluation target period (claim 1 and 13), it is an abstract idea as it is a set of programming routines and patterns for calculating a value. It is an algorithm or program which is a mathematical routine.
Regarding the underlined limitation estimate/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 (claim 1 and 13), it is an abstract idea as it is a set of programming routines and patterns for estimating a trend component. It is an algorithm or program which is a mathematical routine.
Regarding the underlined limitation evaluate/evaluating a deterioration state of the structure in each of the estimated trend component (claim 1 and 13), it is an abstract idea as it is a set of programming routines and patterns for evaluating a deterioration state of a structure. It is an algorithm or program which is a mathematical routine.
Regarding the underlined limitation evaluates/evaluating that damage is progressing in the structure when the trend component is on a downward trend (claim 1 and 13), it is an abstract idea as it is a set of programming routines and patterns for evaluating damage is progressing when a condition is met. It is an algorithm or program which is a mathematical routine.
Regarding the underlined limitation uses/using the detection information for the evaluation target period stored in the storage to calculate time series data of evaluation values on an annual basis, compares the calculated time series data of the evaluation values on an annual basis, and when the time series data of the evaluation values on an annual basis show similar fluctuations, determines that the seasonality component is fluctuating periodically it is an abstract idea as it is a set of programming routines and patterns for using detection information to calculate time series data and compare said data on an annual basis and determine the seasonality component is fluctuating when the annual data comparisons show similar fluctuations. It is an algorithm or program which is a mathematic routine.
In summary, the highlighted steps in the claims above therefore recite an abstract idea at Prong 1 of the 101 analysis.
The additional elements in the claim have been left in normal font. This judicial exception is not integrated into a practical application because the additional elements within the claims only amount to instructions to implement the judicial exception using a computer [MPEP 2106.05(f)].
Claims 1 and 13 recite memory for storage and one or more processors. The specification on page 13 line 9-18 supports a general purpose computer which would include a processor and memory in addition to circuitry that may be well known application specific integrated circuit, programmable logic device, or field programmable gate array. Each of these elements is only recited as a tool for performing steps of the abstract idea, such as the use of the storage mediums to store data and data processing devices to apply the algorithm. These additional elements therefore only amount to mere instructions to perform the abstract idea using a computer and are not sufficient to amount to significantly more than the abstract idea (MPEP 2016.05(f) see for additional guidance on the “mere instructions to apply an exception”).
Regarding the “sensors.” The claims recite generic sensors for detecting data, elastic waves.
The above claims, as a whole, are therefore directed to an abstract idea.
Step 2b of the 2019 Guidance requires the examiner to determine whether the additional elements cause the claim to amount to significantly more than the abstract idea itself. The considerations for this particular claim are essentially the same as the considerations for Prong 2 of Step 2a, and the same analysis leads to the conclusion that the claim does not amount to significantly more than the abstract idea.
The claims do not integrate the abstract idea into a practical application. Various considerations are used to determine whether the additional elements are sufficient to integrate the abstract idea into a practical application. The claims does not recite a particular machine applying or being used by the abstract idea. The claims do not affect a real-world transformation or reduction of any particular article to a different state or thing. (Manipulating data from one form to another or obtaining a mathematical answer using input data does not qualify as a transformation in the sense of Prong 2.)
The claims do not contain additional elements which describe the functioning of a computer, or which describe a particular technology or technical field, being improved by the use of the abstract idea. (This is understood in the sense of the claimed invention from Diamond v Diehr, in which the claim as a whole recited a complete rubber-curing process including a rubber-molding press, a timer, a temperature sensor adjacent the mold cavity, and the steps of closing and opening the press, in which the recited use of a mathematical calculation served to improve that particular technology by providing a better estimate of the time when curing was complete. Here, the claim does not recite carrying out any comparable particular technological process.) In all of these respects, the claim fails to recite additional elements which might possibly integrate the claim into a particular practical application. Instead, based on the above considerations, the claim would tend to monopolize the abstract idea itself, rather than integrate the abstract idea into a practical application.
Therefore, claims 1 and 13 are rejected under 35 U.S.C. 101 as directed to an abstract idea without significantly more.
Dependent claims 2-9, 11-13, and 15-18 are similarly ineligible. The dependent claims merely add limitations which further detail or limit the abstract idea with limitations such as:
“processing circuitry calculates a plurality of candidate evaluation values every predetermined period, and calculates a representative value of the plurality of candidate evaluation values every predetermined period as the evaluation value” (claim 2),
“estimates the trend component and the seasonality component by applying an additive regression model” (claim 4),
“estimates the trend component by an interval linear model and the seasonality component by a Fourier series” (claim 5),
“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” (claim 8),
“estimates the trend component and the seasonality component using the 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” (claim 9),
“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” (claim 10),
“generates a two-dimensional map using the evaluation value as a representative value at an installation position of each sensor” (claim 11),
which do not help to integrate the claim into a practical application or make it significant more than the abstract idea (which is recited in slightly more detail, but not in enough detail to be considered to narrow the claim to a particular practical application itself).
Claims 2, 4-5, 8-9, and 11 recite processing circuitry. The specification on page 13 line 9-18 supports a general purpose computer which would include a processor and memory in addition to circuitry that may be well known application specific integrated circuit, programmable logic device, or field programmable gate array. Additionally, claim 11 recites a display. The specification is silent on the form of the display, however, it does support a general purpose computer which would include a display. Each of these elements is only recited as a tool for performing steps of the abstract idea, such as the use of the storage mediums to store data and data processing devices to apply the algorithm. These additional elements therefore only amount to mere instructions to perform the abstract idea using a computer and are not sufficient to amount to significantly more than the abstract idea (MPEP 2016.05(f) see for additional guidance on the “mere instructions to apply an exception”).
Claim 14 recites an activation control device which is, according to the specification, is a generic sensor such as an accelerometer (Specification page 11 line 2-15).
Considering all the limitations individually and in combination, the additional elements claimed in the dependent claims do not show any inventive concept to applying algorithms such as improving the performance of a computer or any technology, and do not meaningfully limit the performance of the application.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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 and store the acquired detection information for the evaluation target period in a storage” (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, § 2.4 Types of AE sensors: 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 where “resonant AE-sensors” are used to collect data including “amplitude” (§ 2.4). Table 1 depicts the parameters collected including amplitude. Fig. 1 depicts the collected data as a graph of amplitude vs time (§ 2.1). Additionally Behnia teaches “there are possibilities to have high record of data, in addition to high data speed storage so that fast visualization of data would be facilitated” and “AE classical method would be highly applicable for real time monitoring” (§ 2.3) disclosing “store the acquired detection information for the evaluation target period in a storage” where data is analyzed for loading cycles (§ 5.4) disclosing an “evaluation target period.”)
“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).
Behnia teaches:
“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 uses the detection information for the evaluation target period stored in the storage to calculate time series data of evaluation values on an annual basis, compares the calculated time series data of the evaluation values on an annual basis, and when the time series data of the evaluation values on an annual basis 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” and are evaluated. 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 uses the detection information for the evaluation target period stored in the storage to calculate time series data of evaluation values on an annual basis, compares the calculated time series data of the evaluation values on an annual basis, and when the time series data of the evaluation values on an annual basis show similar fluctuations, determines that the seasonality component is fluctuating periodically” (Andrews, claim 1, ¶ 0009, ¶ 0063, ¶ 0073: 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 if 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) disclosing annually, therefore Andrews discloses comparing “the calculated time series data of the evaluation values on an annual basis.” Additionally, Andrews teaches temperature values are included with echo-wave information (¶ 0063) where the monitoring devices “store or archive information derived from echo-wave signals” and “analysing or interpreting some or all of the information pertaining to echo-waves . . . to compare the information, possible but not essentially after some processing, with other processed or archived information in order to decide if any significant change in the mechanical integrity, operational worthiness and safety of the structure being monitored has happened” (claim 1) disclosing “uses the detection information for the evaluation target period stored in the storage.” Moreover, a neural network is trained 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 make the system “more robust against temperature changes over long period of time, such as daily and seasonal periods of time” in order to “detect changes with particular characteristics related to (among other things) the seasons, . . .” (¶ 0073) disclosing “determines that the seasonality component is fluctuating periodically”.)
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; storing the acquired detection information for the evaluation target period in a storage” (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, § 2.4 Types of AE sensors: 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 where “resonant AE-sensors” are used to collect data including “amplitude” (§ 2.4). Table 1 depicts the parameters collected including amplitude. Fig. 1 depicts the collected data as a graph of amplitude vs time (§ 2.1). Additionally Behnia teaches “there are possibilities to have high record of data, in addition to high data speed storage so that fast visualization of data would be facilitated” and “AE classical method would be highly applicable for real time monitoring” (§ 2.3) disclosing “storing the acquired detection information for the evaluation target period in a storage” where data is analyzed for loading cycles (§ 5.4) disclosing an “evaluation target period.”)
“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).
Behnia teaches:
“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)).
“evaluating 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.”
“evaluating 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 using the detection information for the evaluation target period stored in the storage to calculate time series data of evaluation values on an annual basis, comparing the calculated time series data of the evaluation values on an annual basis, and when the time series data of the evaluation values on an annual basis 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:
“evaluating 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.”)
“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.”)
“using the detection information for the evaluation target period stored in the storage to calculate time series data of evaluation values on an annual basis, comparing the calculated time series data of the evaluation values on an annual basis, and when the time series data of the evaluation values on an annual basis show similar fluctuations, determines that the seasonality component is fluctuating periodically” (Andrews, claim 1, ¶ 0009, ¶ 0063, ¶ 0073: 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 if 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 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) disclosing annually, therefore Andrews discloses comparing “the calculated time series data of the evaluation values on an annual basis.” Additionally, Andrews teaches temperature values are included with echo-wave information (¶ 0063) where the monitoring devices “store or archive information derived from echo-wave signals” and “analysing or interpreting some or all of the information pertaining to echo-waves . . . to compare the information, possible but not essentially after some processing, with other processed or archived information in order to decide if any significant change in the mechanical integrity, operational worthiness and safety of the structure being monitored has happened” (claim 1) disclosing “using the detection information for the evaluation target period stored in the storage.” Moreover, a neural network is trained 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 make the system “more robust against temperature changes over long period of time, such as daily and seasonal periods of time” in order to “detect changes with particular characteristics related to (among other things) the seasons, . . .” (¶ 0073) disclosing “determines that the seasonality component is fluctuating periodically”.)
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 05/04/2026 have been fully considered.
Regarding the rejection of Claim 1 under 35 U.S.C. § 103 page 7-10 of Applicant’s remarks.
Examiner finds Applicant’s arguments persuasive with respect to the amendments. New grounds for rejection are necessitated by the amendments and are presented above.
Applicant argues “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 Behnia as in Amended Claim 1” (remarks page 9).
Examiner respectfully disagrees. Behnia states “no change of the amplitude distribution can be observed the beam (Fig. 15b) which was frequently loaded at only 75% until 2,500, 000 cycles” (§ 5.4) where Fig. 15b depicts “one complete load cycle” as approximately 360 seconds therefore Behnia teaches long term monitoring as 2,500,000 cycles at 360 seconds per cycle would amount to long term monitoring.
Applicant argues “In the Office Action, the Examiner determined that a configuration relating to "long- term monitoring"-similar to that of the independent claim-was disclosed in Andrews.
However, Andrews does not specify the conditions for determining whether seasonality component fluctuate periodically, as in amended Claim 1” (Remarks, page 9).
Examiner respectfully disagrees. Andrews teaches, in ¶ 0073 detecting “changes with particular characteristics related to (among other things) the seasons” (¶ 0073) where the “characteristics” are seasonal temperature changes (¶ 0063).
Applicant argues “In Andrews, the artificial neural network trained using historical data is fed the received signal to obtain evaluation results; thus, this configuration differs from the annual basis comparison described in Amended Claim 1. In particular, Andrews does not calculate time series data of the evaluation values on an annual basis using detection information for the evaluation target period stored in the storage. Therefore, it is not possible to compare the time series data of the evaluation values on an annual basis, as described in Amended Claim 1. Thus, Andrews does not explicitly disclose a method that involves comparing time series data of the evaluation values on an annual basis to determine whether seasonal components are fluctuating periodically” (remarks page 9-10).
Examiner respectfully disagrees. Andrews teaches comparing processed or archived data with time series data which is data that has been collected at a chosen frequency (¶ 0063) where the monitoring period could be as long as many years (¶ 0023) which would include a monitoring period that includes seasonal changes. The data is compared with other “processed or archived information” looking for changes in “the mechanical integrity, operational worthiness and safety of the structure being monitored” (claim 1). Andrews trains a machine learning model to “detect changes with particular characteristics related to (among other things) the seasons” (¶ 0073) in order to reduce false alarms mistaking long-term seasonal changes for “significant changes in structural integrity” (¶ 0063).
Applicant argues “Furthermore, the examiner determined that Andrews satisfies the configuration of the claimed invention based on the fact that it performs "collecting data over a long period". However, "collecting data over a long period" is technically distinct from "evaluating periodic fluctuations by comparing time series data of the evaluation values on an annual basis" as described in amended Claim 1” (Remarks page 10).
Examiner respectfully disagrees. With regard to "collecting data over a long period" this is mere allegations. MPEP states the Examiner should consider “whether, on balance, the applicant has met the burden of proof to show nonequivalence. However, under no circumstance Should an examiner accept as persuasive a bare statement or opinion that the element shown in the prior art is not an equivalent embraced by the claim limitation. Moreover, if an applicant argues that the means- (or step-) plus-function language in a claim is limited to certain specific structural or additional functional characteristics (as opposed to "equivalents" thereof) where the specification does not describe the invention as being only those specific characteristics, the claim should not be allowed until the claim is amended to recite those specific structural or additional functional characteristics” (MPEP 2184.II]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Sohn eta l., U.S. 2009/0301198A1, teaches damage detection and detection of the state of a structure or material using acoustics.
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/DENISE R KARAVIAS/ Examiner, Art Unit 2857
/ARLEEN M VAZQUEZ/ Supervisory Patent Examiner, Art Unit 2857
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