DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in response to the claims filed on 01/29/2026. Wherein, claims 1, 5, 7, 8, 12, 14, 15, 18, and 20 are amended and claims 2, 9, and 16 are cancelled. Claims 1, 3-8, 10-15, and 17-20 are rejected.
Information Disclosure Statement
The information Disclosure Statements filed on 01/29/2026 and 04/04/2025 have been considered. An initialed copy of form 1449 for each is enclosed herewith.
Response to Arguments
Applicant’s arguments, see REMARKS, filed 01/29/2026, with respect to the rejection of claims 1-20, under 35 USC §101, have been fully considered and are persuasive. Therefore, the previous rejections under 35 USC §101 have been withdrawn.
Applicant’s arguments, with respect to the rejection of claims 1-20, under 35 USC §103, have been fully considered but are not persuasive. Therefore, the previous rejections under 35 USC §103 have been maintained.
With respect to the rejection of claims 1-20, under 35 USC §103, the Applicant argues:
Claim 1, as amended, is patentable over the applied references at least because the applied references do not teach or suggest the features of "generate a two-dimensional (2D) coherent processing interval (CPI) heatmap image based on at least a portion of the radar return signals corresponding to at least a volume of space along a forward flight vector of the aircraft." The Office admitted that Lue does not teach or suggest "generate a two-dimensional (2D) coherent processing interval (CPI) heatmap image." Sun is directed to "target occlusion by clutter with range-Doppler two-dimensional ambiguity during airborne radar downlooking target detection." That is, the airborne radar in Sun operates in a down-looking mode (see Figure 1 of Sun) and thus does not appear to receive radar return signals "corresponding to at least a volume of space along a forward flight vector of the aircraft," as recited by amended claim 1. Therefore, the CPI heatmap images disclosed by Sun cannot be "generated ... based on at least a portion of the radar return signals corresponding to at least a volume of space along a forward flight vector of the aircraft," as recited in amended claim 1. For at least this reason, amended claim 1 is patentable over the applied references.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. Here, the claim language states “…corresponding to at least a volume of space along a forward flight vector of the aircraft…” The specification is silent on the definition of a “forward flight vector”. Thus the plan and ordinary meaning is applied. The term “forward flight vector” can be broadly interpreted as any vector that exists somewhere in front of the aircraft as it is in flight.
Under this interpretation both Lue and Sun disclose implementing radar return signals corresponding to at least a volume of space along a forward flight vector of the aircraft. Lue, as shown in Fig. 1, provides for radar signals from the nose of the aircraft that capture the forward vectors between 104 and 106. While, Sun discloses sending and receiving radar signals forward and downward of the aircraft, e.g., r1 and r2 in Figure 1.
While Lue discloses the use of maps, it does not disclose the use of CPI heatmap images. However, Sun discloses the use of CPI heatmap images, for example Fig. 13 (a). Therefore, the combination of Lue and Sun teach the above limitation(s) and the Examiner finds this argument unpersuasive.
Applicant further submits that modifying Lue with the teachings of Sun would change the principle of operation of Lue and would render Lue unsatisfactory for its intended purpose. In the Office Action, the Office proposed that "Lue discloses radar signal processing, including 2-D, to identify and classify targets in the environment. Lue does not explicitly disclose the use of 2-D CPI heatmap images. However, Sun discloses airborne radar staggered PRF coherent processing method for down-looking target detection and teaches using CPI heatmap images to solve the ambiguities of target occlusion by clutter with range-Doppler two-dimensional ambiguity during airborne radar down-looking target detection. Therefore it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the systems and methods of intelligent weather sensing using deep learning convolutional neural networks of Lue to provide for the airborne radar staggered PRF coherent processing method for down-looking target detection, as taught in Sun, to effectively extend the range of the radar and increase the probability of detecting low SNR signals. (At Sun §5 Conclusions)." Applicant respectfully disagrees.
Applicant notes that "[P]rior art references that address different problems may not, depending on the art and circumstances, support an inference that the skilled artisan would consult both of them simultaneously." Lue addresses "interpretation and evaluation of radar data to detect weather and other threats using radar." In particular, the techniques of Lue are directed to "determining whether the radar data detected by the weather radar circuit 512 represents entities such as threats (e.g., high altitude ice crystals, turbulence, windshear, lightning, hail, convection), and similarly to distinguish ground clutter."
In contrast, Sun is directed to the problem of "target occlusion by clutter with range-Doppler two-dimensional ambiguity during airborne radar downlooking target detection" where "The range-Doppler (RD) results of ground clutter in airborne radar may overlap the range-Doppler results of the targets, causing target occlusion and rendering them undetectable." As can be seen, targets as described in Sun appear to be airborne objects such as airplanes (see, e.g., Figure 1 of Sun). To address this problem Sun teaches "detection of airborne radar down-looking targets based on orthogonal signals combined with pulse repetition frequency (PRF) staggered transmission" where "The range-Doppler two-dimensional ambiguity of targets is resolved using multiple sets of PRF, and coherent processing is performed on the corresponding echoes using the transmission intervals, maximizing the utilization of radar transmission energy," and thereby "enhances the signal-to-noise ratio (SNR) and improves the detection performance and parameter estimation accuracy, particularly for low SNR targets."
The problem of distinguishing between weather threats and ground clutter using a weather radar that typically scans ahead of the aircraft, as discussed in Lue, is different from the problem in Sun of ground clutter in airborne radar (operating in the down-looking mode) that may overlap the range-Doppler results of the targets, causing target occlusion and rendering the targets undetectable.
As stated by the Applicant above, Lue is directed towards “determining whether the radar data detected by weather radar circuit 512 represents entities such as threats (e.g., high altitude crystals, turbulence, windshear, lighting, hail, convection), and similarly to distinguish ground clutter.” Examiner would add that Lue further teaches that the radar data can be used to identify terrain or “other platforms” as well. Here, other platforms are other aircraft. (Col. 7, ln. 47-60)
The Applicant further states that Sun “is directed to the problem of "target occlusion by clutter with range-Doppler two-dimensional ambiguity during airborne radar down looking target detection" where "The range-Doppler (RD) results of ground clutter in airborne radar may overlap the range-Doppler results of the targets, causing target occlusion and rendering them undetectable." As can be seen, targets as described in Sun appear to be airborne objects such as airplanes (see, e.g., Figure 1 of Sun).” In other words, Sun is determining ground clutter and distinguishing it from other aircraft by processing radar signals using CPI.
In both Lue and Sun an objective of the inventions is to identify ground clutter and other aircraft in the environment using radar signals. With Sun providing a specific advantage in distinguishing aircraft from ground clutter, i.e., enhances the signal-to-noise ratio (SNR) and improves the detection performance and parameter estimation accuracy, particularly for low SNR targets.
For the above reasons the Examiner finds these arguments unpersuasive.
The specific solution in Sun arises from the problem of previous approaches to "mitigate ambiguity in radar echo signals from targets and ground clutter" to identify targets with a radar operating in a down-looking mode. As described in Sun, a previous approach "involves employing multiple signal groups with different medium PRFs (MultiMPRF) for detection. By varying the PRF, which affects both the maximum unambiguous range and velocity, the ambiguous region of the clutter spectrum can be adjusted." As described by Sun, "the conventional Multi-PRF approach independently detects targets in each PRF signal group, leading to degraded target detection performance in low SNR conditions due to inefficient use of energy within the main beam time"" (emphasis added) To overcome these issues in conventional Multi-PRF approach, Sun proposes "interleaved transmission of multiple low PRF signal groups, each utilizing orthogonal discrete frequency coded (DFC) signals," which enhances target detection performance in low SNR conditions by obtaining high SNR results and improving target detection performance."
As such, the specific problem addressed in Sun is "degraded target detection performance in low SNR conditions due to inefficient use of energy within the main beam time" of "conventional Multi-PRF approach independently detects targets in each PRF signal group" for airborne radar operating in down-looking mode to "mitigate ambiguity in radar echo signals from targets and ground clutter." Meanwhile, the problem addressed in Lue is distinguishing between weather threats and ground clutter in weather radar scanning ahead of the aircraft.
Therefore, because Lue and Sun address different problems and, in particular, because Sun addresses a problem specific to "employing multiple signal groups with different medium PRFs" to detect targets while operating in a down-looking mode, which is not mentioned as a technical problem in Lue, it is improper to conclude, as was done in the Office Action, that one of ordinary skill in the art would have consulted both Lue and Sun simultaneously.
This argument is substantially similar to the argument above and the Examiner finds it unpersuasive for the reasons above.
Further, Applicant respectfully submits that the Office erred in alleging that Sun "teaches using CPI heatmap images to solve the ambiguities of target occlusion by clutter with range-Doppler two-dimensional ambiguity during airborne radar down-looking target detection." As described above, Sun teaches interleaved transmission of multiple low PRF signal groups, each utilizing orthogonal discrete frequency coded (DFC) signals" to solve problems with the conventional Multi-PRF approach. Even assuming Figures 12 and 13 of Sun teaches CPI heatmap images, such images are shown to present visualizations of data (e.g., numerical range-doppler data) that are processed as part of the solution in Sun.
However, there is no teaching or suggestion in Sun that the CIP heatmap images depicted in Sun, rather than the underlying range-doppler data visualized in the images, are processed or used in any way "to solve the ambiguities of target occlusion by clutter with range-Doppler two dimensional ambiguity during airborne radar down-looking target detection," as alleged by the Office (emphasis added). Applicant invites the Office to examine Section 3, "The Flow Design of Multilayer Detection and Blind Zone Optimization" in pp. 9-12 of Sun, which appear to describe the algorithm of the solution, which does not appear to perform any processing of CPI heatmap images as part of the algorithm. If the Office continues to allege that Sun teaches "using CPI heatmap images to solve the ambiguities of target occlusion by clutter," Applicant respectfully requests the Office to cite the specific portions of Sun that are alleged to describe how the actual CPI heatmap images, rather than the underlying numerical range-doppler data visualized in the images, are processed as part of the technical solution described in Sun.
Thus, because Sun does not teach that CPI heatmap images are used to solve the
ambiguities of target occlusion by clutter, and because there is no teaching in Sun or Lue that "multiple signal groups with different medium PRFs," as described in Sun, is used to distinguish between weather threats and ground clutter, as described in Lue, a person of ordinary skill in the art would not have considered the techniques described Sun to be applicable to Lue.
In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
In the office action, Sun is not used to teach the image processing step. Instead, Lue discloses a machine learning circuit and training database which maps radar data to entity data. The machine learning circuit may provide the input from the training data base in an image-based format (e.g., computed radar values mapped in spatial dimensions. (See pg. 13 of the Non-Final Rejection 09/29/2025). Lue does not explicitly teach that these image-based computed radar values mapped in spatial dimensions includes a heatmap. However, Sun discloses generating a heatmap based on radar data. Therefore, it would be obvious to use the heatmap image format of Sun as an input for the image processing in Lue.
For the above reasons, the Examiner finds the above arguments unpersuasive.
Further, a person of ordinary skill in the art would not have consulted both Lue and Sun simultaneously, and would not have modified "the systems and methods of intelligent weather sensing using deep learning convolutional neural networks of Lue to provide for the airborne radar staggered PRF coherent processing method for down-looking target detection, as taught in Sun, to effectively extend the range of the radar and increase the probability of detecting low SNR signals," as alleged by the Examiner, as there is no teaching or suggestion that such a technique of interleaved transmission of multiple low PRF signal groups, each utilizing orthogonal discrete frequency coded (DFC) signals, when applied to the techniques of Lue, would "effectively extend the range of the radar and increase the probability of detecting low SNR signals," as alleged by the Examiner. For these reasons, it is improper to conclude, as was done in the Office Action, that one of ordinary skill in the art would have consulted both Lue and Sun simultaneously.
In response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, both Lue and Sun are directed towards identifying other aircraft and ground clutter in using radar data. With Sun providing a specific advantage in distinguishing aircraft from ground clutter, i.e., enhances the signal-to-noise ratio (SNR) and improves the detection performance and parameter estimation accuracy, particularly for low SNR targets.
Therefore, the Examiner finds the above arguments unpersuasive.
For at least the reasons discussed above, independent claims 1, 8, and 15, as amended, are patentable over the applied references. The dependent claims incorporate the requirements of the respective independent claims. Accordingly, the dependent claims are likewise patentable. Applicant has canceled claims 2, 9, and 16, obviating the rejections of those claims.
For the above reasons the Examiner finds this argument unpersuasive.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 3, 6, 8, 10, 13, 15, 17, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Lue et al. (US 11,181,634 B1, “Lue”) in view of Sun et al. (Airborne Radar Staggered PRF Coherent Processing Method for Down-Looking Target Detection, “Sun”).
Regarding claims 1, 8, and 15, Lue discloses systems and methods of intelligent weather sensing using deep learning convolutional neural networks and teaches:
A computing system, the system comprising: (Radar system 300 contains processor (DSP) 324, i.e., a computer system – See at least Col. 7, ln. 16-20 and Fig. 3)
a memory; and (Radar system 300 contains memory 328 – See at least Col. 7, ln. 16-20 and Fig. 3)
processing circuitry operatively coupled to the memory, the processing circuitry configured to: (Memory 328 is communicably connected to processor 326 and includes computer code or instruction modules for executing one or more processes described herein. The memory 328 includes various circuits, software engines, and/or modules that cause the processor 324 to execute the systems and methods described herein – See at least Col. 7, ln. 26-33 and Fig. 3)
receive radar return signals from a weather radar antenna installed on an aircraft; (the radar system 300 can transmit a radar signal, and receive a radar return signal corresponding to the transmitted radar signal (e.g., a reflection of the transmitted radar signal reflected by objects in the sky, such as clouds or other platforms) – See at least Col. 6, ln. 26-31; the radar system uses an antenna system – See at least Col. 4, ln. 21-23)
generate a two-dimensional (2D) [] image based on at least a portion of the radar return signals (the radar system 300 can electronically merge a plurality of radar return signals from different tilt angles to form a composite image for display on an electronic flight display (e.g., flight displays 20 described with reference to Fig. 1) – See at least Col. 6, ln. 35-39; For example, the flight display 20 may include a display configured to display a two-dimensional (2-d) image – See at least Col. 5, ln. 36-41) corresponding to at least a volume of space along a forward flight vector of the aircraft; (As shown in Fig. 2, the radar signals correspond to a volume of space along the forward flight vector of the aircraft, e.g., the space between lines 104 and 106.)
perform image processing on the [] image including analyzing the [image] using a neural network based classification model, (The radar system 300 includes machine learning circuit 328 and a training database 332. The training database 332 can map historical radar data to entity data, i.e., processing data – See at least Col. 7, ln. 47-60; The machine learning circuit 328 an provide the input from the training database 332 in an image-based format (e.g., computed radar values mapped in spatial dimensions), i.e., image processing – See at least Col. 9, ln. 30-34; Examiner notes that the system implements deep learning convolutional neural networks – See at least Col. 3, ln. 41-42) wherein the neural network based classification model is trained to identify latent patterns of [] data and classify the features of the [image] (The radar detection model 330 can include various machine learning models that the machine learning circuit 328 can train using the training database 332. The machine learning circuit 328 can execute supervised learning to train the radar detection model 330 using the training database 332. In some embodiments, the radar detection model includes a classification model – See at least Col. 9, ln. 6-9) based on a maximum probability score; (The indication of the detected entity can include a likelihood (e.g., a probability score) of the received radar data corresponding to one or more entities. For example, given particular values of received radar data, the radar detection model 330 can output a likelihood that the radar data represents a particular type of weather, another platform, terrain, or other feature – See at least Col. 8, ln. 44-51)
based on the image processing, classify the features of the [] image as at least weather or clutter; and (In some embodiments, the training database 332 includes radar mapped to ground clutter, enabling the machine learning circuit 328 to distinguish ground clutter from weather and other entities for detection – See at least Col. 8, ln. 52-60)
based on the classification, determine whether wind shear is present in a field of regard for the weather radar antenna. (the training database 332 can include a plurality of data components, each data components assigning a type of detected entity (e.g., a type of cloud, lightning; thunder; rain; hail; turbulence; windshear; ice; a remote platform; a terrain feature) to one or more corresponding radar values that were detected when the type of detected entity was detected – See at least Col. 7, ln. 51-57)
Lue does not explicitly teach that the generated two-dimensional (2D) is a coherent processing interval heatmap image. However, Sun discloses airborne radar staggered PRF coherent processing method for down-looking target detection and teaches:
generate a two-dimensional (2D) coherent processing interval (CPI) heatmap image based on the radar return signals; (The invention is directed towards resolving ambiguities of target occlusion by clutter with range-Doppler two-dimensional ambiguity during airborne radar down-looking target detection – See at least Abstract. As shown in the figures below, the system creates a 2-D CPI heatmap image and processes the data from that image to determine cluttering.)
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[] heatmap image [] (As shown in the figures above, the system generates a 2-D heatmap image showing the doppler signatures of the radar scanning.)
In summary, Lue discloses radar signal processing, including 2-D, to identify and classify targets in the environment. Lue does not explicitly disclose the use of 2-D CPI heatmap images. However, Sun discloses airborne radar staggered PRF coherent processing method for down-looking target detection and teaches using CPI heatmap images to solve the ambiguities of target occlusion by clutter with range-Doppler two-dimensional ambiguity during airborne radar down-looking target detection.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the systems and methods of intelligent weather sensing using deep learning convolutional neural networks of Lue to provide for the airborne radar staggered PRF coherent processing method for down-looking target detection, as taught in Sun, to effectively extend the range of the radar and increase the probability of detecting low SNR signals. (At Sun §5 Conclusions)
Regarding claims 3, 10, and 17, Lue further teaches:
wherein the antenna is a first antenna, wherein the weather radar system further comprises two or more radar antenna, and wherein the processing circuitry is configured to process source data received from the two or more antenna to classify the features of the [] image. (Referring now to FIG. 3, the radar system 300 according to an exemplary embodiment of the inventive concepts disclosed herein includes a radar receiver 304 (e.g., a radio frequency (RF) receiver) and a base assembly 320. The radar receiver 304 is coupled to one or more antennas 316 (e.g., a radio frequency antenna) that can transmit and/or receive radio frequency signals, such as to transmit a radar signal and receive a radar return signal corresponding to the transmitted radar signal – See at least Col. 6, ln. 52-60)
Lue does not explicitly teach, but Sun further teaches:
[] heatmap image (As shown in the figures above, the system generates a 2-D heatmap image showing the doppler signatures of the radar scanning.)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the systems and methods of intelligent weather sensing using deep learning convolutional neural networks of Lue to provide for the airborne radar staggered PRF coherent processing method for down-looking target detection, as taught in Sun, to effectively extend the range of the radar and increase the probability of detecting low SNR signals. (At Sun §5 Conclusions)
Regarding claim 6, 13, and 19, Lue further teaches:
wherein the processing circuitry is further configured to determine whether specified weather phenomena are present in a field of regard for the radar system based on the classification, wherein the specified weather phenomena comprise one or more of:
thunderstorms, hail, turbulence, rain, and snow. (the training database 332 can include a plurality of data components, each data components assigning a type of detected entity (e.g., a type of cloud, lightning; thunder; rain; hail; turbulence; windshear; ice; a remote platform; a terrain feature) to one or more corresponding radar values that were detected when the type of detected entity was detected – See at least Col. 7, ln. 51-57)
Claim(s) 4 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Lue in view of Sun, as applied to claims 1 and 8, and in further view of Dana et al. (US 11,828,836 B1, “Dana”).
Regarding claims 4 and 11, the combination of Lue and Sun discloses identifying and classifying targets based on the image processing. The combination does not explicitly teach wherein the processing circuitry is further configured to identify and classify low radar cross section (RCS) targets based on the image processing. However, Dana discloses radar detection and discrimination of quadcopters using measured doppler signatures systems and methods and teaches:
wherein the processing circuitry is further configured to identify and classify low radar cross section (RCS) targets based on the image processing. (The non-transitory memory 102 may include a plurality of signatures representative of an associated plurality of UAS models to enable the system for detecting and identifying a UAS 100 to identify the exact model of UAS 150. The controller 110 may query, at a step 310, with a comparison of each of UAS parameters including the micro-Doppler spectra, the RCS and the radial speed of the at least one UAS to a signature stored within the non-transitory memory 102. Should the comparison result in a non-match and a result of query 310 be negative, the logic may pass to a step 312 with storing the unidentified UAS parameters to a newly detected signature definition within the memory 102. Should the result of query 310 be positive, the controller 110 may identify a type of UAS based on the comparison at a step 314, and send, at a step 316, a detection indication and an identification indication to the user interface. Should the system for detecting and identifying a UAS 100 be manned, the controller 110 may display the UAS ID on a user display viewable by a user at a step 318 – See at least Col. 9, ln. 10-29; Examiner notes that the RCS includes low RCS targets – See at least Col. 8, ln. 19-24)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the systems and methods of intelligent weather sensing using deep learning convolutional neural networks of Lue and Sun to provide for the radar detection and discrimination of quadcopters using measured doppler signatures system and method, as taught in Dana, to a provide a novel solution to detection and identification of a UAS by a currently deployed radar system offering an additional level of protection to the locations and machines on which the radars are employed. (At Dana Col. 1, ln. 39-44)
Claim(s) 5, 12, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Lue in view of Sun, as applied to claims 1 and 8, and in further view of Zhao et al. (Attentional Feature Refinement and Alignment Network for Aircraft Detection in SAR Imagery, “Zhao”).
Regarding claims 5, 12, and 18, Lue further teaches:
wherein the processing circuitry is further configured to:
receive images from one or more cameras, (The one or more sensors 420 can detect, generate, and output sensor data regarding the platform (e.g., platform state data) and an environment about/60 around the platform/antenna, including altitude and energy state variables, such as speed (e.g., vertical speed) and acceleration (e.g., vertical acceleration). The one or more sensors 420 can include various sensors, including but not limited to…a vision system ( e.g., a camera, an infrared image sensor, a LIDAR)… - See at least Col. 11, ln. 2-3)
infrared image sensor, a LIDAR)) synthesize a 2D CPI [] based on the received [data]; (the radar system 300 can electronically merge a plurality of radar return signals from different tilt angles to form a composite image for display on an electronic flight display (e.g., flight displays 20 described with reference to Fig. 1) – See at least Col. 6, ln. 35-39; For example, the flight display 20 may include a display configured to display a two-dimensional (2-d) image – See at least Col. 5, ln. 36-41)
perform image processing on the image; (The radar system 300 includes machine learning circuit 328 and a training database 332. The training database 332 can map historical radar data to entity data, i.e., processing data – See at least Col. 7, ln. 47-60; The machine learning circuit 328 an provide the input from the training database 332 in an image-based format (e.g., computed radar values mapped in spatial dimensions), i.e., image processing – See at least Col. 9, ln. 30-34)
based on the image processing, classify features of the [] image. (In some embodiments, the training database 332 includes radar mapped to ground clutter, enabling the machine learning circuit 328 to distinguish ground clutter from weather and other entities for detection – See at least Col. 8, ln. 52-60)
Lue does not explicitly teach, but Sun further teaches:
[] heatmap image [] (As shown in the figures above, the system generates a 2-D heatmap image showing the doppler signatures of the radar scanning.)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the systems and methods of intelligent weather sensing using deep learning convolutional neural networks of Lue to provide for the airborne radar staggered PRF coherent processing method for down-looking target detection, as taught in Sun, to effectively extend the range of the radar and increase the probability of detecting low SNR signals. (At Sun §5 Conclusions)
The combination of Lue and Sun does not explicitly teach receive images from one or more cameras, synthesize a 2D CPI heatmap image based on the received camera images. However, Zhao discloses attentional feature refinement and alignment network for aircraft detection in SAR imagery and teaches:
synthesize a 2D CPI heatmap image based on the received images; (SAR systems fuse data from optical sensors, e.g., images, with radar data. Zhao expands on the baseline system: To further investigate evolution of features maps acquired by the baseline equipped with the three sub-modules progressively, aircraft detection results and heat maps of specific fine-grained feature maps including P3 and P4, are provided in Fig. 14 (reproduced below) – See at least pg. 9)
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In summary, Lue discloses receiving image data and radar data from a system to determine information about its environment. Sun discloses generating a 2-D CPI image based on radar data. The combination of Lue and Sun does not explicitly teach synthesize a 2D CPI heatmap image based on the received camera images. However, attentional feature refinement and alignment network for aircraft detection in SAR imagery and teaches combining radar and optical data to generate a 2-D CPI image based on the fused data.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the systems and methods of intelligent weather sensing using deep learning convolutional neural networks of Lue and Sun to provide for the attentional feature refinement and alignment network for aircraft detection in SAR imagery, as taught in Zhao, because Synthetic Aperture Radar (SAR) has attractive imaging capabilities in nearly all weather and illumination conditions. (At Zhao § 1. Introduction)
Claim(s) 7, 14, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Lue in view of Sun, as applied to claims 1, 8, and 15, and in further view of Crosmer et al. (US 9,223,020 B1, “Crosmer”).
Regarding claims 7, 14, and 20, Lue further teaches:
wherein the weather radar system is configured to be installed on the aircraft; (As shown in Fig. 2 the weather radar system is installed on an aircraft, i.e., a first aircraft.)
wherein the processing circuitry is further configured to:
receive weather information from a [external source]; (In some embodiments, the flight displays 20 may provide an output based on data received from a system external to an aircraft, such as a ground-based weather radar system, satellite-based system, or from a system of another aircraft – See at least Col. 5, ln. 25-27)
generate a 2D [] image based on the received weather information; (the radar system 300 can electronically merge a plurality of radar return signals from different tilt angles to form a composite image for display on an electronic flight display (e.g., flight displays 20 described with reference to Fig. 1) – See at least Col. 6, ln. 35-39; For example, the flight display 20 may include a display configured to display a two-dimensional (2-d) image – See at least Col. 5, ln. 36-41)
perform image processing on the [] image; (The radar system 300 includes machine learning circuit 328 and a training database 332. The training database 332 can map historical radar data to entity data, i.e., processing data – See at least Col. 7, ln. 47-60; The machine learning circuit 328 an provide the input from the training database 332 in an image-based format (e.g., computed radar values mapped in spatial dimensions), i.e., image processing – See at least Col. 9, ln. 30-34)
based on the image processing, classify features of the [] image. (In some embodiments, the training database 332 includes radar mapped to ground clutter, enabling the machine learning circuit 328 to distinguish ground clutter from weather and other entities for detection – See at least Col. 8, ln. 52-60)
Lue does not explicitly teach, but Sun further teaches:
generate a 2D CPI heatmap image [] (The invention is directed towards resolving ambiguities of target occlusion by clutter with range-Doppler two-dimensional ambiguity during airborne radar down-looking target detection – See at least Abstract. As shown in the figures above, the system creates a 2-D CPI heatmap image and processes the data from that image to determine cluttering.)
[] heatmap image; (As shown in the figures above, the system generates a 2-D heatmap image showing the doppler signatures of the radar scanning.)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the systems and methods of intelligent weather sensing using deep learning convolutional neural networks of Lue to provide for the airborne radar staggered PRF coherent processing method for down-looking target detection, as taught in Sun, to effectively extend the range of the radar and increase the probability of detecting low SNR signals. (At Sun §5 Conclusions)
The combination of Lue and Sun does not explicitly teach receiving weather information from a second aircraft. However, Crosmer discloses system and method for weather detection using more than one source of radar data and teaches:
receive weather information from a second aircraft (With reference to FIG. 8, composite weather data 900 can include a portion 902 from a source 906, a portion 904 from a first data source 908 and a second data source 910, and a portion 912 from a data source 914. For example, source 906 can be aircraft 110, source 908 can be from aircraft 344 and Source 910 can be from aircraft 348. Source 914 can be from radar 360. Sources 906, 908,910 and 914 can be airborne or non-airborne Sources (e.g., satellite or ground radar) – See at least Col. 11, ln. 53-60)
In summary, Lue discloses receiving weather data from a ground based weather system and general data from other aircraft. The combination of Lue and Sun does not explicitly teach receiving weather information from a second aircraft. However, Crosmer discloses system and method for weather detection using more than one source of radar data and teaches receiving weather data from multiple planes to determine weather activity and other information about the environment the aircraft is traveling through.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the systems and methods of intelligent weather sensing using deep learning convolutional neural networks of Lue and Sun to provide for the system and method for weather detection using more than one source of radar data, as taught in Crosmer, to increase accuracy of weather threat assessment, extent, and/or detection range. (At Crosmer Col. 1, ln. 39-44)
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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/C.L.C./Examiner, Art Unit 3662
/ANISS CHAD/Supervisory Patent Examiner, Art Unit 3662
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