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 .
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.
Status of Claims
In the amendment filed on January 20th, 2026, claims 12 and 39 have been amended, no claims 1-11, 14-16, 18-38 have been cancelled and no new claims have been added. Therefore, claims 12, 13, 17, and 39-42 are pending for examination.
Election/Restrictions
Restriction to one of the following inventions is required under 35 U.S.C. 121:
I. Claims 12-17, drawn to relates to airfield ground lights operable to receive data representative of sensor data, the sensor data selected from a group consisting of vibration signals from the plurality of airfield lighting fixtures and data representative of a plurality of temperatures from the plurality of airfield lighting fixtures, further using vibration signals to determine the status of lighting fixtures classified in H05B47/22 or H05B45/58.
II. Claims 21-38, drawn to airfield ground lights operable to receive data representative of sensor data, sensor data such as temperature data and pressure data for the purpose of determining a leak through the changes in temperature data and pressure data, classified in G01M3/3272 or F21V23/0457
During a telephone conversation with Larry B. Donovan on February 27th, 2025 a provisional election was made with traverse to prosecute the invention of # Group 1, claims 12-17. Affirmation of this election must be made by applicant in replying to this Office action. Claims 21-28 are withdrawn from further consideration by the examiner, 37 CFR 1.142(b), as being drawn to a non-elected invention.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 12, 13 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chawda et al. (US 2021280040 A1) in view of J.V Candey et al., “Anomaly Detection For A Vibrating Structure; A Subspace Identification/Tracking Approach” The Journal Of The Acousical Society of America, Volume 142, Issue 2, August 2017
In regards to claim 12, Chawda teaches an apparatus, comprising a controller operable to communicate with a plurality of airfield lighting fixtures, the controller comprises logic comprising a processor operable to receive data representative of sensor data, the sensor data vibration signals from the plurality of airfield lighting fixtures (Paragraphs 15, 26, 27, 32, 33), i.e.
airfield luminaire vibration monitoring, in accordance with the present disclosure, can allow for remote vibration monitoring to determine a bolt status of an airfield luminaire. The sensor 106 may be located on the base 108, among other locations that may be suitable for detecting vibrations caused by airfield traffic that are experienced by the airfield luminaire 102. The sensor 106 can be a piezoelectric sensor. As used herein, the term “piezoelectric sensor” refers to a sensor that utilizes the piezoelectric effect to measure changes (e.g., pressure, acceleration, strain, force, etc.) in its surrounding environment. For instance, the piezoelectric sensor can detect vibrations caused by airfield traffic that are experienced by the airfield luminaire 102. The airfield luminaire 102 can be connected to the computing device 114. The computing device 114 can receive a vibration signal from the sensor 106. For example, in response to vibrations experienced by the airfield luminaire 102 as a result of airfield traffic around the airfield luminaire 102, the sensor 106 can generate a signal and transmit the signal to the computing device 114 for analysis. The computing device 114 can analyze a vibration signal received from the sensor 106. As used herein, the term “vibration signal” refers to a signal generated by a sensor in response to detecting a vibration in its surrounding environment. The vibration signal can be generated in response to the sensor 106 experiencing vibrations caused by airfield traffic that are experienced by the airfield luminaire 102.
Furthermore, Chawda teaches determine a status of a selected one of the plurality if lighting fixtures, the status being a structural integrity based on a comparison of the plurality of vibration signals and determining whether the vibration signal from the selected one of the plurality of lighting fixture indicates the selected one airfield lighting fixture is vibrating more than other airfield lighting fixtures from the plurality of airfield lighting fixtures (Paragraph 40-42, 59-61), i.e.
in order to determine whether one of the bolts 112-1, 112-2 is unsecure, the computing device 114 can compare the determined frequency corresponding to the vibration signal to a calibration frequency included in a vibration profile. As used herein, the term “vibration profile” refers to a plurality of calibration frequencies corresponding to the airfield luminaire 102. The plurality of calibration frequencies can each correspond to a particular harmonic. For example, each harmonic can include a corresponding calibrated frequency. The calibrated frequency can be the determined frequency of the airfield luminaire 102 when it is known that both bolts. the frequency of the vibration signal can be determined to be 831 Hz at a first harmonic. The frequency of the vibration signal can be compared to a calibration frequency of 1,389 Hz at the first harmonic included in the vibration profile for the airfield luminaire 102. The comparison can include determining the difference between the determined frequency and the calibration frequency. For example, the computing device 114 can determine the difference between the determined frequency (e.g., 831 Hz at the first harmonic) and the calibration frequency (e.g., 1,389 Hz at the first harmonic) to be a difference of 558 Hz. each airfield luminaire 202-1, 202-2, 202-N can include a vibration profile. For example, each airfield luminaire 202-1, 202-2, 202-N can include calibration frequencies corresponding to different harmonics against which frequencies can be compared. Accordingly, a calibration procedure can be performed to determine the calibration frequencies.
Chawda fails to teach comparing the vibration from the plurality of airfield lighting fixtures with each other, such that determining a status structural integrity of a selected one of the plurality of lighting fixtures has deteriorated.
Candy on the other hand teaches determining outlier deterioration of structural devices by comparing sequential vibration signals such that the threshold comparison is made of the real time vibrational reading to calculated “normal” threshold from the previous sequential readings (Page 15, Sequential Anomaly detection), i.e.
Pre-processing of the acquired vibrational data is a crucial first step in the signal processing. It is designed to extract the “targeted” frequencies and remove outliers, sample (2.25×Nyquist frequency) to minimize the rate, enhance or equalize the modal frequencies, filter any disturbances outside the band of interest, and normalize the filtered data to scale the data for identification.
Outlier detection/correction is based on the median absolute deviation (MAD) statistic because of its inherent robustness property relative to the usual mean/standard deviation approach.26,27 MAD is defined as
γ MAD(t):=γ Mt(|Yt−Mi(Yi)|),
where Yt:={y(0),…,y(t)} is the set of discrete-time data up to time t, Mt is the median of the data, and γγ=1.4826 is a constant based on the normalized (assumed Gaussian) data. The outliers are detected using the bounds
ββMt−β×MAD(t)<Yt<Mt+β×MAD(t),
where β is a threshold equivalent to a confidence limit—we selected β = 4 for our data sets. Due to the limited amount of estimated modal frequency samples available in our application, we replaced the detected outlier with the median amplitude of the data as shown in Fig. 3, usually less than 1% of the samples/window. [Pg 9, Pre-Processing]
A reasonable approach to this problem of making a reliable decision with high confidence in a timely manner is to develop a sequential detection processor. At each measurement Sample y(tk), we sequentially update the decision function and compare it to the thresholds to perform the detection, “sample-by-sample” as illustrated in Fig. 10. Here, as each sample is collected producing the measurement sequence, the detector processes that measurement and attempts to “decide” whether or not it has evolved from the “normal” vibration signature. Therefore, for each measurement the decision function is “sequentially” updated and compared to the detection thresholds obtained from a receiver operating characteristic (ROC) curve operating point enabling a rapid decision.31,32 Once the threshold is crossed, the decision (normal or anomaly) is made and the measurement is processed [Pg 15, Sequential Anomaly Detection]
Here, Candy discloses the use of comparing lie readings of vibrational signals with calculated previous sequential “normal” vibration readings in order to detect, outlier deterioration or abnormality. When applying by substitution this method of comparing and detecting outlier damage of structural integrity to Chowda’s comparison method of a stored threshold, then one of ordinary skill in the art may have an outlier detection via using previous sequential data from the light fixtures as the comparison threshold. Hence, it would be obvious to one of ordinary skill in the art to combine Candy’s teaching with Chowda’s teaching in order to effectively use a cost effective adaptive comparison threshold to determine abnormalities in the structural integrity of the light fixtures specifically in an environment with continuous levels of strain, for the purpose of improving the accuracy of detecting structural integrity.
The applicant has amended the claim language to focus on “associated with a most recent event from the plurality of airfield lighting fixtures, the sensor data from the plurality of airfield lighting fixtures comprises vibration signals from each of the plurality of airfield lighting fixtures; compare the vibration signal associated with only the most recent event from a selected one of the plurality of airfield lighting fixtures with each vibration signals associated with the only the most recent event from other of the plurality of lighting fixtures; and determine a structural integrity of the selected one of the plurality of lighting fixtures has deteriorated based on determining whether a vibration signal associated with only the most recent event from the selected one of the plurality of lighting fixture differs from vibration signals associated with the event from other of the plurality of lighting fixtures by a predefined amount”
Though the applicant’s invention is now amended to specify the received data representative of sensor data is associated with an event from the plurality of airfield fixtures, comparing the vibration signal from a selected one of the plurality of airfield lighting fixtures is still met by Candy’s teaching of determining outlier deterioration of structural devices by comparing sequential vibration signals such that the threshold comparison is made of the real time vibrational reading to calculated “normal” threshold from the previous sequential readings (Page 15, Sequential Anomaly detection), i.e.
Pre-processing of the acquired vibrational data is a crucial first step in the signal processing. It is designed to extract the “targeted” frequencies and remove outliers, sample (2.25×Nyquist frequency) to minimize the rate, enhance or equalize the modal frequencies, filter any disturbances outside the band of interest, and normalize the filtered data to scale the data for identification.
Outlier detection/correction is based on the median absolute deviation (MAD) statistic because of its inherent robustness property relative to the usual mean/standard deviation approach.26,27 MAD is defined as
γ MAD(t):=γ Mt(|Yt−Mi(Yi)|),
where Yt:={y(0),…,y(t)} is the set of discrete-time data up to time t, Mt is the median of the data, and γγ=1.4826 is a constant based on the normalized (assumed Gaussian) data. The outliers are detected using the bounds
ββMt−β×MAD(t)<Yt<Mt+β×MAD(t),
where β is a threshold equivalent to a confidence limit—we selected β = 4 for our data sets. Due to the limited amount of estimated modal frequency samples available in our application, we replaced the detected outlier with the median amplitude of the data as shown in Fig. 3, usually less than 1% of the samples/window. [Pg 9, Pre-Processing]
A reasonable approach to this problem of making a reliable decision with high confidence in a timely manner is to develop a sequential detection processor. At each measurement Sample y(tk), we sequentially update the decision function and compare it to the thresholds to perform the detection, “sample-by-sample” as illustrated in Fig. 10. Here, as each sample is collected producing the measurement sequence, the detector processes that measurement and attempts to “decide” whether or not it has evolved from the “normal” vibration signature. Therefore, for each measurement the decision function is “sequentially” updated and compared to the detection thresholds obtained from a receiver operating characteristic (ROC) curve operating point enabling a rapid decision.31,32 Once the threshold is crossed, the decision (normal or anomaly) is made and the measurement is processed [Pg 15, Sequential Anomaly Detection]
the applicant establishes the event as “the most recent event”. Thereby using broadest reasonable interpretation, a most recent event may entail a sequence of intermittent vibrations over a recent timeframe, to which the most recent could be an event that happened two hours ago, a week ago or a minute ago. The historical data of a most recent event may influence the signal(s) used in the comparison step of light fixtures. With that scenario of a most recent event, Candy’s teaching still reads on the applicant’s invention.
In regards to claim 13, Chawda modified teaches determining the structure integrity of the selected one of the plurality of light fixtures has deteriorated based on determining the vibration signal from the selected one of the plurality of lighting fixture indicates the selected one airfield lighting fixture is vibrating more than other airfield lighting fixtures from the plurality of airfield lighting fixtures based on a measurement of one of a group consisting of frequency of vibration, length of time vibrating, and amplitude of vibration signal (Paragraphs 40-42, 48, Chawda), i.e.
in order to determine whether one of the bolts 112-1, 112-2 is unsecure, the computing device 114 can compare the determined frequency corresponding to the vibration signal to a calibration frequency included in a vibration profile. As used herein, the term “vibration profile” refers to a plurality of calibration frequencies corresponding to the airfield luminaire 102. The plurality of calibration frequencies can each correspond to a particular harmonic. For example, each harmonic can include a corresponding calibrated frequency. The calibrated frequency can be the determined frequency of the airfield luminaire 102 when it is known that both bolts 112-1, 112-2 are secure.
In regards to claim 17, Chawda modified teaches at least one circuit providing power to the plurality of airfield lighting fixtures and the controller is coupled with least one circuit providing power to the plurality of airfield lighting fixtures and operable to communicate with the plurality of airfield lighting fixtures via the circuit (Paragraphs 65, 67, Chawda), i.e.
the airfield luminaires 302-1, 302-2, 302-3, 302-4, 302-N can be connected to the airfield ground lighting circuit 330 via series isolation transformers 334-1, 334-2, 334-3, 334-4, 334-N, respectively. As used herein, the term “series isolation transformer” refers to a device to transfer electrical power from a power source to a load. For example, the series isolation transformers 334-1, 334-2, 334-3, 334-4, 334-N can transfer electrical power from an AC mains 342 to each of the airfield luminaires 302-1, 302-2, 302-3, 302-4, 302-N, respectively. The AC mains 342 can provide power to the airfield ground lighting circuit 330 via the constant current regulator 332. As used herein, the term “constant current regulator” refers to a device to regulate an AC power source. For example, the constant current regulator 332 can regulate current from the AC mains 342 by providing current to the airfield ground lighting circuit 330 in the range of 2.8 A to 6.6 A, as well as provide isolation between the AC mains 342 and the rest of the airfield ground lighting circuit 330 in the event of an electrical power surge.
Claim(s) 39 and 40 is/are rejected under 35 U.S.C. 103 as being unpatentable over in view of Chemel et al. (US 11193652 B2) in view of Bo Sheng et al. “Outlier Detection In Sensor Networks” Department of Computer Science, College of William and Mary, 09/09/2007
In regards to claim 39, Chemel teaches an apparatus, comprising: a controller operable to communicate with a plurality of airfield lighting fixtures, the controller comprises logic comprising a processor operable to receive data representative of sensor data, the data representative of a plurality of temperatures from the plurality of airfield lighting fixtures and a fixture malfunction based on a comparison of temperature data from the selected one of the plurality of airfield lighting fixtures with other airfield lighting fixtures of the plurality of airfield lighting fixtures (Column 14, lines 20-25; Column 15, lines 1-10; Column 70, lines 47-65), i.e.
a method may include powering a plurality of light emitting diode (LED) light bars with a power management unit. The method may also include determining temperature associated with at least one of the plurality of LED light bars. Furthermore, the method may include assessing the temperature to identify a lighting power management parameter to be adjusted to facilitate temperature protection of an LED light bar. The management systems 134 modules such as measurement and verification module 170 may compare these measurement values received from the sensors 120, with at least one of the stored lighting parameters. Based on the comparison the management systems 134 may make an adjustment to at least one of the lighting systems 120. The power measurement system may include a temperature sensor, located in proximity to the Zener diode that measures a temperature of the system and communicates the temperature to a temperature compensation system. The Digital Light Agent DLA 4512 may also receive information regarding physical conditions of the environment such as temperature of a particular device of the LED light engines 4520, pressure within tubing and ducting of the fixture or frame of the LED light engines, rate of heat dissipation within the LED light engines and the like. The DLA 4512 may accordingly decide if the sensed physical conditions are within the discretion set by the rules and parameters and may adaptively control the flow of current through the LED light engines 4520. For example, in an illustrative scenario, a temperature sensor may detect overheating of the ducting (malfunction) associated with the frame of the LED light engines 4520 due to reduced heat dissipation
Chemel however fails to teach comparing the temperature from the plurality of airfield lighting fixtures with each other, such that determining a status structural integrity of a selected one of the plurality of lighting fixtures has deteriorated. Bo Sheng on the other hand teaches comparing real time temperature readings with previously recorded sequential temperature readings that establish the “normal” threshold, to which outlier/ abnormal readings that indicate faults may be detected. (Page 226, 7.1 (Network Settings and Datasets), 7.21 (Outlier Detection); Page 227, 7.2.2 (Outlier Detection)),
In our simulation, we select the entire temperature records on two dates (03/01 and 03/20) as two datasets. The dataset for 03/01 represents a regular temperature distribution with mean value around 24 degrees. The dataset for 03/20, however, displays a large deviation from the average value. In the latter dataset, for some reason, 50 degrees is reported for many times, and a lot of data are sparsely scattered between 35 degrees and 50 degrees. In this simulation, we use precision 0.01 to round temperature values and scale them by 100 times in order to obtain integer values.[ Page 226, 7.1 (Network Settings and Datasets)]
To compare the basic scheme and enhanced scheme with different parameters, we vary d and k separately. First, we f ix k = 100 and vary d from 20 to 70 for the 03/01 dataset and from 50 to 450 for the 03/20 dataset. Fig. 4 shows the numbers of outliers with various d. We find the two datasets differ dramatically. For the 03/01 dataset, when we set d = 70 (i.e., 0.7 degree in original data), no outlier exists in the entire set. For the 03/20 dataset, however, when we use a large distance with d = 100 (1 degree in the original data), 126 outliers appear. We keep increasing d to 400 (4 degree), we still find one outlier. This figure indicates that the 03/20 dataset contains more scattered data points and yields more outliers for a certain (d,k) setting.[ Page 226, 7.2.1 (Outlier Detection)]
In this simulation, we set k=100, and vary n from 10 to 80 for both datasets. The initial bucket width is set to a large value of 1500, i.e.,Winit=1500. Fig. 8 shows the values for Dk(pn) and the communication cost is presented in Fig.9. The simulation results show that our approach is cost efficient for the O(n,k) outlier detection. Compared with the centralized solution, our approach significantly reduces the communication cost. For the abnormal 03/20 dataset, it takes less than1% of the cost to find all top-80 outliers. For the normal 03/01 dataset, our scheme consumes less than 1.5% of the cost in all the cases.[ Page 227, 7.2.2 (Outlier Detection)]
When applying by substitution this method of comparing and detecting outlier data to Chemel’s method of a stored threshold, then one of ordinary skill in the art may have an outlier detection via using previous sequential data from the light fixtures as the comparison threshold. Hence, it would be obvious to one of ordinary skill in the art to combine Bo Sheng’s teaching with Chemel’s teaching in order to have a more cost effective use an adaptive comparison threshold to determine abnormalities in the structural integrity of the light fixtures specifically in an environment with continuous levels of strain, for the purpose of improving the accuracy of detecting structural integrity.
Though the applicant’s invention is now amended to specify the received data representative of sensor data is associated with a most recent event from the plurality of airfield lighting fixtures, the sensor data from the plurality of airfield lighting fixtures, comparing the temperature from a selected one of the plurality of airfield lighting fixtures is still met by Bo Sheng’s teaching of comparing real time temperature readings with previously recorded sequential temperature readings that establish the “normal” threshold, to which outlier/ abnormal readings that indicate faults may be detected. (Page 226, 7.1 (Network Settings and Datasets), 7.21 (Outlier Detection); Page 227, 7.2.2 (Outlier Detection)),
In our simulation, we select the entire temperature records on two dates (03/01 and 03/20) as two datasets. The dataset for 03/01 represents a regular temperature distribution with mean value around 24 degrees. The dataset for 03/20, however, displays a large deviation from the average value. In the latter dataset, for some reason, 50 degrees is reported for many times, and a lot of data are sparsely scattered between 35 degrees and 50 degrees. In this simulation, we use precision 0.01 to round temperature values and scale them by 100 times in order to obtain integer values.[ Page 226, 7.1 (Network Settings and Datasets)]
To compare the basic scheme and enhanced scheme with different parameters, we vary d and k separately. First, we f ix k = 100 and vary d from 20 to 70 for the 03/01 dataset and from 50 to 450 for the 03/20 dataset. Fig. 4 shows the numbers of outliers with various d. We find the two datasets differ dramatically. For the 03/01 dataset, when we set d = 70 (i.e., 0.7 degree in original data), no outlier exists in the entire set. For the 03/20 dataset, however, when we use a large distance with d = 100 (1 degree in the original data), 126 outliers appear. We keep increasing d to 400 (4 degree), we still find one outlier. This figure indicates that the 03/20 dataset contains more scattered data points and yields more outliers for a certain (d,k) setting.[ Page 226, 7.2.1 (Outlier Detection)]
In this simulation, we set k=100, and vary n from 10 to 80 for both datasets. The initial bucket width is set to a large value of 1500, i.e.,Winit=1500. Fig. 8 shows the values for Dk(pn) and the communication cost is presented in Fig.9. The simulation results show that our approach is cost efficient for the O(n,k) outlier detection. Compared with the centralized solution, our approach significantly reduces the communication cost. For the abnormal 03/20 dataset, it takes less than1% of the cost to find all top-80 outliers. For the normal 03/01 dataset, our scheme consumes less than 1.5% of the cost in all the cases.[ Page 227, 7.2.2 (Outlier Detection)]
the applicant establishes the event as “the most recent event”. Thereby using broadest reasonable interpretation, a most recent event may entail a sequence of intermittent temperature readings over a recent timeframe, to which the most recent could be an event that happened two hours ago, a week ago or a minute ago. The historical data of a most recent event may influence the signal(s) used in the comparison step of light fixtures. With that scenario of a most recent event, BoSheng’s teaching still reads on the applicant’s invention.
In regards to claim 40, Chemel modified via Bo Sheng, teaches a fixture malfunction is determined when the temperature of the selected one of the plurality of lighting fixtures is greater than the other of the plurality of lighting fixtures by a predetermined amount (Page 226, 7.2.1 (Outlier Detection))
To compare the basic scheme and enhanced scheme with different parameters, we vary d and k separately. First, we fix k = 100 and vary d from 20 to 70 for the 03/01 dataset and from 50 to 450 for the 03/20 dataset. Fig. 4 shows the numbers of outliers with various d. We find the two datasets differ dramatically. For the 03/01 dataset, when we set d = 70 (i.e., 0.7 degree in original data), no outlier exists in the entire set. For the 03/20 dataset, however, when we use a large distance with d = 100 (1 degree in the original data), 126 outliers appear. We keep increasing d to 400 (4 degree), we still find one outlier. This figure indicates that the 03/20 dataset contains more scattered data points and yields more outliers for a certain (d,k) setting.[ Page 226, 7.2.1 (Outlier Detection)]
Claim(s) 41 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chemel et al. (US 11193652 B2) in view of C Bo Sheng et al. “Outlier Detection In Sensor Networks” Department of Computer Science, College of William and Mary, (09/09/2007) as applied to claim 39 above, and further in view of Carson et al. (IT 102020000029486 B1).
In regards to claim 41, Chemel modified fails to teach a fixture malfunction is determined when the temperature of the selected one the plurality of lighting fixtures is less than other of the plurality of lighting fixtures by a predetermined amount.
Carson on the other hand teaches a fixture malfunction is determined when the temperature of the selected one the plurality of lighting fixtures is less than other of the plurality of lighting fixtures by a predetermined amount (Page 9, Paragraph 2), i.e.
the controller 251 is configured to identify a rise or fall in the operating temperature beyond the lower threshold and upper threshold values, respectively - events indicative of a malfunction of the lighting body 30 under consideration.
It would have been obvious to a person of ordinary skill in the art before the effective filing of the invention to combine Carson’s teaching with Chemel modified’s teaching in order to enable the monitoring and adequate performance of various lighting fixtures.
Claim(s) 42 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chemel et al. (US 11193652 B2) in view of Bo Sheng et al. “Outlier Detection In Sensor Networks” Department of Computer Science, College of William and Mary, (09/09/2007) as applied to claim 39 above, and further in view of Chawda et al. (US 2021280040 A1).
In regards to claim 42, Chemel fails to teach at least one circuit providing power to the plurality of airfield lighting fixtures and the controller is coupled with least one circuit providing power to the plurality of airfield lighting fixtures and operable to communicate with the plurality of airfield lighting fixtures via the circuit.
Chawda teaches at least one circuit providing power to the plurality of airfield lighting fixtures and the controller is coupled with least one circuit providing power to the plurality of airfield lighting fixtures and operable to communicate with the plurality of airfield lighting fixtures via the circuit (Paragraphs 65, 67,), i.e.
the airfield luminaires 302-1, 302-2, 302-3, 302-4, 302-N can be connected to the airfield ground lighting circuit 330 via series isolation transformers 334-1, 334-2, 334-3, 334-4, 334-N, respectively. As used herein, the term “series isolation transformer” refers to a device to transfer electrical power from a power source to a load. For example, the series isolation transformers 334-1, 334-2, 334-3, 334-4, 334-N can transfer electrical power from an AC mains 342 to each of the airfield luminaires 302-1, 302-2, 302-3, 302-4, 302-N, respectively. The AC mains 342 can provide power to the airfield ground lighting circuit 330 via the constant current regulator 332. As used herein, the term “constant current regulator” refers to a device to regulate an AC power source. For example, the constant current regulator 332 can regulate current from the AC mains 342 by providing current to the airfield ground lighting circuit 330 in the range of 2.8 A to 6.6 A, as well as provide isolation between the AC mains 342 and the rest of the airfield ground lighting circuit 330 in the event of an electrical power surge.
It would have been obvious to a person of ordinary skill in the art before the effective filing of the invention to combine Chawda’s teaching with Chemel’s teaching in order to effectively detect the signs leading to malfunction and damage to an airfield lighting fixture(s) and further prevent any damage to the lighting accordingly.
Response to Arguments
Examiner acknowledges applicant’s amendments regarding the comparison of the light fixture signals, being predicated on a most recent event, and has addressed them in the rejection above.
Regarding the applicants arguments pertaining to claim 12, the aforementioned shortcomings of Chawda and are not remedied by any teaching of Candey. Candey uses sequential measurements from the same device ("At each measurement sample, we sequentially update the decision function" emphasis added; Section V. Sequential ASnomaly Detection, p. 15). In fact, Candey removes outliers which actually teaches away from what is recited in claim 12 ("Pre-processing of the acquired vibrational data is a crucial first step in the signal processing. It is designed to extract the "targeted" frequencies and remove outliers, sample ( ) to minimize the rate, enhance or equalize the model frequencies ..." emphasis added; Section 3A. Pre-Processing, p. 9).
The applicant’s argument regarding sequence of events taught by Candey, may be rebutted by the applicant establishing the event as “the most recent event”. Thereby using broadest reasonable interpretation, a most recent event may entail a sequence of intermittent vibrations over a most recent timeframe, to which the most recent could be an event that happened two hours ago, a week ago or a minute ago. The historical data of a most recent event may influence the signal(s) used in the comparison step of light fixtures. With that scenario of a most recent event, Candy’s teaching still reads on the applicant’s invention.
Furthermore, regarding claim 39’s arguments, “Bo Sheng compares data sets from the same device, for example uses datasets from two different dates (see e.g., § 7.1, p. 226; "In our simulation, we select the entire temperature records on two dates (03.01 and 03/20) as two datasets."). Thus, Bo Sheng does not compare data associated with a most recent event from a plurality of fixtures.
Much like claim 12’s rebuttal, by Bo Sheng’s teaching of comparing real time temperature readings with previously recorded sequential temperature readings that establish the “normal” threshold, to which outlier/ abnormal readings that indicate faults may be detected. (Page 226, 7.1 (Network Settings and Datasets), 7.21 (Outlier Detection); Page 227, 7.2.2 (Outlier Detection)), and the applicant establishing the event as “the most recent event”, using broadest reasonable interpretation, a most recent event may entail a sequence of intermittent temperature readings over a recent timeframe, to which the most recent could be an event that happened two hours ago, a week ago or a minute ago. The historical data of a most recent event may influence the signal(s) used in the comparison step of light fixtures. With that scenario of a most recent event, BoSheng’s teaching still reads on the applicant’s invention.
To overcome the historical comparison still reading over the claim language relative description of a most recent event, the claim language should be closely directed to a real-time event, and a real time comparison event, to overcome the historical aspect being applicable to the “most recent event” claimed
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.
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/ANTHONY D AFRIFA-KYEI/Examiner, Art Unit 2686
/BRIAN A ZIMMERMAN/Supervisory Patent Examiner, Art Unit 2686