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
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 02/03/2025 and 10/01/2025 were filed and are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
Claims 18-20 are 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 18, the claim recites: “processing the image in which the noise removal process is performed through a pre-trained deep learning model in a memory unit of the server”. The limitation is open to the interpretation of “using a pre-trained deep learning model for noise removal”, and the interpretation of “processing the noise removed image with a pre-trained deep learning model”. Since the claim is open to interpretation that conflicts with each other, claim 18 is rejected as an indefinite claim. For the purpose of this rejection, the limitation is interpreted as “using a pre-trained deep learning model for noise removal”.
Regarding claim 19 and 20, a similar reasoning is used for the limitation of “segmenting the image” and “detecting at least one object in the image”. It is unclear which function within the claim is performed by a pre-trained deep learning model.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 4, 8-9, 11, and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Ilhan et al. (Offloading Deep Learning Empowered Image Segmentation from UAV to Edge Server, hereinafter Ilhan) in view of KIM (US 2022/0083797 A1, hereinafter Kim).
Regarding claim 1, Ilhan discloses
A system, comprising: an air vehicle having an image sensor to acquire images, a control unit to process the images acquired by the image sensor, and a communication unit to transmit the images processed by the control unit to a remote server by at least one base station (P. 2 Section IV: “The image is taken by the UAV’s camera is a colored (3-channel RGB) image where each pixel color is a discrete value and saved by 8-bits for each channel… Once the received image is re-obtained on eNB, it is transmitted over a wired environment to the edge server where the pretrained deep learning algorithm is used.”);
wherein the control unit is configured to modulate the images and send the images to the at least one base station by the communication unit (P. 2 Section IV: “The next step focuses on coding and modulation to be transmitted over the wireless channel. The binary data streams
b
are coded, modulated and mapped into LTE frame. The LTE frame
x
is transmitted over the noisy fading channel and at the end of the channel
x
^
is received on the eNB site.”);
the at least one base station is configured to demodulate the images coming from the air vehicle and to transmit the images to the remote server (P. 2 Section IV: “The received data is threaded by using channel estimation and equalization techniques to eliminate the channel noise and distortion effects. The corrected signal is then decoded, demodulated and remapped into binary data streams. The decoded binary stream
b
is converted back to decimal-matrix format to form the received image. Once the received image is re-obtained on eNB, it is transmitted over a wired environment to the edge server where the pretrained deep learning algorithm is used.”);
the remote server comprises a memory unit containing a pre-trained deep learning model to process the images and a processor unit configured to process the images coming from the air vehicle through the deep learning model in the memory unit (P. 1 Section 1: “In this work, we aim to study the performance of a standard and pre-trained deep learning based object segmentation algorithm, when the image segmentation task is offloaded from a UAV to a remote (edge) server under different conditions”, P. 2 Section IV: “ The goal in our system is transmitting (offloading) the deep learning-based image segmentation task from UAV to the remote edge server.”, P. 5 Section VII: “In this paper, we studied the effect of offloading a deep learning-based image segmentation task from a UAV to a remote server over an LTE wireless communication system.”).
However, Ilhan does not explicitly disclose
the processor unit is configured to perform a noise removal process to reduce noise generated during a transfer of the images to the at least one base station before processing the images acquired by the air vehicle.
Kim teaches
the processor unit is configured to perform a noise removal process to reduce noise generated during a transfer of the images to the at least one base station before processing the images acquired by the air vehicle (Para [0011]: “The apparatus may include a preprocessing module configured to receive an image including the object, and output a preprocessed image by performing at least one image enhancement processing among denoise operation processing, deblur operation processing, high dynamic range operation processing, color tone mapping operation processing, defog operation processing, brightness operation processing, contrast operation processing, auto white balance operation processing, back light compensation operation processing, or decompression operation processing, on the received image, so as to increase a recognition rate of the object included in the received image; and an object recognition module configured to recognize the object included in the image by inputting the preprocessed image to an input layer of an artificial neural network for object recognition.”).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ilhan with denoising an image and performing object recognition within the same apparatus and other aspects of Kim, which is in the same field of endeavor of image denoising and object recognition, to effectively increase the accuracy of object recognition.
Regarding claim 2, dependent upon claim 1, Ilhan in view of Kim teaches every limitation regarding claim 1.
Ilhan further discloses
the remote server comprises the memory unit containing the pre-trained deep learning model to segment the images and the processor unit configured to segment the images coming from the air vehicle through the deep learning model in the memory unit (P. 1 Section I: “In this work, we aim to study the performance of a standard and pre-trained deep learning based object segmentation algorithm, when the image segmentation task is offloaded from a UAV to a remote (edge) server under different conditions”);
Kim further teaches
the processor unit is configured to perform the noise removal process to reduce the noise generated during the transfer of the images to the at least one base station before segmenting the images acquired by the air vehicle (Para [0011]: “The apparatus may include a preprocessing module configured to receive an image including the object, and output a preprocessed image by performing at least one image enhancement processing among denoise operation processing, deblur operation processing, high dynamic range operation processing, color tone mapping operation processing, defog operation processing, brightness operation processing, contrast operation processing, auto white balance operation processing, back light compensation operation processing, or decompression operation processing, on the received image, so as to increase a recognition rate of the object included in the received image; and an object recognition module configured to recognize the object included in the image by inputting the preprocessed image to an input layer of an artificial neural network for object recognition.”).
Regarding claim 3, dependent upon claim 1, Ilhan in view of Kim teaches every limitation regarding claim 1.
Kim further teaches
the remote server comprises the memory unit containing the pre-trained deep learning model to detect at least one object in the images and the processor unit configured to detect the at least one object in the images coming from the air vehicle through the deep learning model in the memory unit; the processor unit is configured to perform the noise removal process to reduce the noise generated during the transfer of the images to the at least one base station before detecting an object in the images acquired by the air vehicle (Para [0011]: “The apparatus may include a preprocessing module configured to receive an image including the object, and output a preprocessed image by performing at least one image enhancement processing among denoise operation processing, deblur operation processing, high dynamic range operation processing, color tone mapping operation processing, defog operation processing, brightness operation processing, contrast operation processing, auto white balance operation processing, back light compensation operation processing, or decompression operation processing, on the received image, so as to increase a recognition rate of the object included in the received image; and an object recognition module configured to recognize the object included in the image by inputting the preprocessed image to an input layer of an artificial neural network for object recognition.”).
Regarding claim 4, dependent upon claim 1, Ilhan in view of Kim teaches every limitation regarding claim 1.
Ilhan further discloses
wherein the control unit is configured to convert the images into a binary vector (P. 2 Section IV: “The first step in our system is converting the taken image into a large binary vector”).
Regarding claim 8, dependent upon claim 1, Ilhan in view of Kim teaches every limitation regarding claim 1.
Ilhan further discloses
wherein the deep learning model is trained with the PASCAL visual object classes dataset (P. 2 Section III: “…its pretrained models are also available to be used on mobile platforms. MobileNetV2 is trained on PASCAL VOC 2012 [19] image segmentation dataset.”).
Regarding claim 9, dependent upon claim 1, Ilhan in view of Kim teaches every limitation regarding claim 1.
Ilhan further discloses
wherein the communication unit is a Long Term Evolution (LTE) module (P. 2 Section IV: “The next step focuses on coding and modulation to be transmitted over the wireless channel. The binary data streams
b
are coded, modulated and mapped into LTE frame. The LTE frame
x
is transmitted over the noisy fading channel and at the end of the channel
x
^
is received on the eNB site.”); and
the communication unit is configured to demodulate the images acquired by the at least one base station under LTE standards (P. 2 Section IV: “The next step focuses on coding and modulation to be transmitted over the wireless channel. The binary data streams
b
are coded, modulated and mapped into LTE frame. The LTE frame
x
is transmitted over the noisy fading channel and at the end of the channel
x
^
is received on the eNB site.”, P. 3 Section V: “Each symbol is mapped into an LTE frame structure that consists of 100 resource blocks (RB) with the demodulation reference signal (DSR).”).
Regarding claim 11, dependent upon claim 1, Ilhan in view of Kim teaches every limitation regarding claim 1.
wherein the air vehicle is an unmanned air vehicle (P. 1 Section 1: “In this work, we aim to study the performance of a standard and pre-trained deep learning based object segmentation algorithm, when the image segmentation task is offloaded from a UAV to a remote (edge) server under different conditions”).
Regarding claim 15, dependent upon claim 1, Ilhan in view of Kim teaches every limitation regarding claim 1.
Ilhan further discloses
wherein the image sensor is a camera in such a way that each pixel color is a separate value, and the camera takes a color image recorded with 8 bits for each channel (P. 2 Section IV: “The image is taken by the UAV’s camera is a colored (3-channel RGB) image where each pixel color is a discrete value and saved by 8-bits for each channel.”).
Regarding claim 16, dependent upon claim 1, Ilhan in view of Kim teaches every limitation regarding claim 1.
Ilhan further discloses
a power unit positioned on the air vehicle to provide electrical energy to the air vehicle (P. 1 Abstract: “However, a constraint on UAVs is having limited battery power”).
Regarding claim 17, dependent upon claim 1, Ilhan in view of Kim teaches every limitation regarding claim 1.
Ilhan further discloses
the at least one base station is configured to demodulate the images coming from the air vehicle (P. 2 Section IV: “The received data is threaded by using channel estimation and equalization techniques to eliminate the channel noise and distortion effects. The corrected signal is then decoded, demodulated and remapped into binary data streams.”) and
to transmit the images to the remote server through a cable (P. 2 Section IV: “Once the received image is re-obtained on eNB, it is transmitted over a wired environment to the edge server where the pretrained deep learning algorithm is used.”).
Regarding claim 18, Ilhan discloses
A method for processing an image acquired by an air vehicle having an image sensor to acquire the image, and a control unit to process the image acquired by the said image sensor, wherein the method comprises the following steps: - acquiring the image through the image sensor (P. 2 Section IV: “The image is taken by the UAV’s camera is a colored (3-channel RGB) image where each pixel color is a discrete value and saved by 8-bits for each channel.”),
- modulating the image by the control unit to obtain a modulated image for wireless transmission thereof (P. 2 Section IV: “The next step focuses on coding and modulation to be transmitted over the wireless channel. The binary data streams
b
are coded, modulated and mapped into LTE frame.”),
- transmitting the modulated image to a base station by a communication unit (“The LTE frame
x
is transmitted over the noisy fading channel and at the end of the channel
x
^
is received on the eNB site”),
- demodulating the modulated image transmitted to the base station to obtain a demodulated image (P. 2 Section IV: “The received data is threaded by using channel estimation and equalization techniques to eliminate the channel noise and distortion effects. The corrected signal is then decoded, demodulated and remapped into binary data streams.”),
- transmitting the demodulated image to a server through a cable (P. 2 Section IV: “Once the received image is re-obtained on eNB, it is transmitted over a wired environment to the edge server where the pretrained deep learning algorithm is used.”).
However, Ilhan does not explicitly disclose
- performing a noise removal process to reduce the noise generated during a transmission of the image to the base station by a processor unit of the server
- processing the image in which the noise removal process is performed through a pre-trained deep learning model in a memory unit of the server.
Kim teaches
- performing a noise removal process to reduce the noise generated during a transmission of the image to the base station by a processor unit of the server (Para [0011]: “The apparatus may include a preprocessing module configured to receive an image including the object, and output a preprocessed image by performing at least one image enhancement processing among denoise operation processing, deblur operation processing, high dynamic range operation processing, color tone mapping operation processing, defog operation processing, brightness operation processing, contrast operation processing, auto white balance operation processing, back light compensation operation processing, or decompression operation processing, on the received image, so as to increase a recognition rate of the object included in the received image; and an object recognition module configured to recognize the object included in the image by inputting the preprocessed image to an input layer of an artificial neural network for object recognition.”),
- processing the image in which the noise removal process is performed through a pre-trained deep learning model in a memory unit of the server (Para [0056]: “An image preprocessing technique may include at least one of an image signal processing technique, a technique for preprocessing an image using a machine learning method, and a technique for preprocessing an image using a deep learning method.”).
Regarding claim 19, dependent upon claim 18, Ilhan in view of Kim teaches every limitation regarding claim 18.
Ilhan further discloses
the step of segmenting the image in which the noise removal process is performed through the trained deep learning model in the said memory unit (P. 1 Section I: “In this work, we aim to study the performance of a standard and pre-trained deep learning based object segmentation algorithm, when the image segmentation task is offloaded from a UAV to a remote (edge) server under different conditions”).
Kim further teaches
in which the noise removal process is performed through the trained deep learning model in the said memory unit (Para [0056]: “An image preprocessing technique may include at least one of an image signal processing technique, a technique for preprocessing an image using a machine learning method, and a technique for preprocessing an image using a deep learning method.”).
Regarding claim 20, dependent upon claim 18, Ilhan in view of Kim teaches every limitation regarding claim 18.
the step of detecting at least one object in the image in which the noise removal process is performed through the trained deep learning model in the said memory unit (Para [0011]: “The apparatus may include a preprocessing module configured to receive an image including the object, and output a preprocessed image by performing at least one image enhancement processing among denoise operation processing, deblur operation processing, high dynamic range operation processing, color tone mapping operation processing, defog operation processing, brightness operation processing, contrast operation processing, auto white balance operation processing, back light compensation operation processing, or decompression operation processing, on the received image, so as to increase a recognition rate of the object included in the received image; and an object recognition module configured to recognize the object included in the image by inputting the preprocessed image to an input layer of an artificial neural network for object recognition.”, Para [0056]: “An image preprocessing technique may include at least one of an image signal processing technique, a technique for preprocessing an image using a machine learning method, and a technique for preprocessing an image using a deep learning method).
Claims 5-7 are rejected under 35 U.S.C. 103 as being unpatentable over Ilhan et al. (Offloading Deep Learning Empowered Image Segmentation from UAV to Edge Server, hereinafter Ilhan) in view of KIM (US 2022/0083797 A1, hereinafter Kim) and DELBRACIO et al. (US 2024/0013350 A1, hereinafter Delbracio).
Regarding claim 5, dependent upon claim 1, Ilhan in view of Kim teaches every limitation regarding claim 1.
However, Ilhan in view of Kim does not explicitly teach
wherein the processor unit is configured to perform median filtering in the noise removal process.
Delbracio teaches
wherein the processor unit is configured to perform median filtering in the noise removal process (Para [0034]: “In some embodiments, the pre-filter module 204 can use other noise removal techniques to generate a noise reduced image. In some examples, the noise removal techniques can include AIM Engine Denoise-Upscale-Down scale, median filtering, Anisotropic diffusion, geometric mean filtering, local means filtering, bilateral filtering, block-matching and three-dimensional (3D) filtering, local pixel grouping, filtering using a Wiener filter, any other suitable noise filtering technique, and/or any combination thereof.”).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ilhan in view of Kim with median filtering and other types of filtering for denoising image of Delbracio, which is in the same field of endeavor of image denoising, to effectively increase quality of an image.
Regarding claim 6, dependent upon claim 1, Ilhan in view of Kim teaches every limitation regarding claim 1.
However, Ilhan in view of Kim does not explicitly teach
wherein the processor unit is configured to perform average filtering in the noise removal process.
Delbracio teaches
wherein the processor unit is configured to perform average filtering in the noise removal process (Para [0034]: “In some embodiments, the pre-filter module 204 can use other noise removal techniques to generate a noise reduced image. In some examples, the noise removal techniques can include AIM Engine Denoise-Upscale-Down scale, median filtering, Anisotropic diffusion, geometric mean filtering, local means filtering, bilateral filtering, block-matching and three-dimensional (3D) filtering, local pixel grouping, filtering using a Wiener filter, any other suitable noise filtering technique, and/or any combination thereof.”).
Regarding claim 7, dependent upon claim 1, Ilhan in view of Kim teaches every limitation regarding claim 1.
However, Ilhan in view of Kim does not explicitly teach
wherein the processor unit is configured to perform block-matching and three-dimensional filtering in the noise removal process.
Delbracio teaches
wherein the processor unit is configured to perform block-matching and three-dimensional filtering in the noise removal process (Para [0034]: “In some embodiments, the pre-filter module 204 can use other noise removal techniques to generate a noise reduced image. In some examples, the noise removal techniques can include AIM Engine Denoise-Upscale-Down scale, median filtering, Anisotropic diffusion, geometric mean filtering, local means filtering, bilateral filtering, block-matching and three-dimensional (3D) filtering, local pixel grouping, filtering using a Wiener filter, any other suitable noise filtering technique, and/or any combination thereof.”).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Ilhan et al. (Offloading Deep Learning Empowered Image Segmentation from UAV to Edge Server, hereinafter Ilhan) in view of KIM (US 2022/0083797 A1, hereinafter Kim) and Huang et al. (Research on Channel Feedback Algorithm in UAV Inspection Communication Subsystem of Smart Grid, hereinafter Huang).
Regarding claim 10, dependent upon claim 1, Ilhan in view of Kim teaches every limitation regarding claim 1.
However, Ilhan in view of Kim does not explicitly teach
wherein the communication unit is a 5G module; and the communication unit is configured to demodulate the images acquired by the at least one base station under 5G standards.
Huang teaches
wherein the communication unit is a 5G module; and the communication unit is configured to demodulate the images acquired by the at least one base station under 5G standards (P. 1. Section 1: “The 5G technology of large bandwidth, high reliability and low delay communication can meet the demand.”, P.2 Section 3A: “As shown in Fig. 2, the encoder is composed of two modules, feature extraction and feature compression, in series. The feature extraction module is composed of Convolutional LSTM[6l (ConvLSTM) and Pseudo-3D (P3D) whose convolution kernel is two matrices.”, P. 4 Section 3B: “The decoder side includes feature decompression and channel recovery parts, and the decompression module is similar to the compression module” ).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ilhan in view of Kim with using 5G as the signal standard for transmitting information between the UAV and base station of Huang, which is in the same field of endeavor of signal transmission between UAV and base station, to effectively increase the efficiency of transmitting signals.
Claims 12-14 are rejected under 35 U.S.C. 103 as being unpatentable over Ilhan et al. (Offloading Deep Learning Empowered Image Segmentation from UAV to Edge Server, hereinafter Ilhan) in view of KIM (US 2022/0083797 A1, hereinafter Kim) and Schwaerzler (US 6,377,875 B1, hereinafter Schwaerzler).
Regarding claim 12, dependent upon claim 11, Ilhan in view of Kim teaches every limitation regarding claim 11.
However, Ilhan in view of Kim does not explicitly teach
wherein the unmanned air vehicle is configured to have remote flight control.
Schwaerzler teaches
wherein the unmanned air vehicle is configured to have remote flight control (Abstract: “To avoid an uncontrolled flight of a remotely controlled unmanned air vehicle (UAV), upon loss of radio contact between a control station (6) and the UAV (1), the UAV (1) flies on a preprogrammed safety route (3), as required the (2) UAV is guided to a flight path that is remote-controlled from the control station (6), and, in the event of an interruption of the radio contact, the UAV will fly on a substitute route calculated with on-board equipment, without active intervention from the remote control station.”).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ilhan in view of Kim with controlling flying and other aspects of UAV of Schwaerzler, which is in the same field of endeavor of remote control and autonomous flying of aerial vehicle, to effectively reduce the risk of crashing a UAV.
Regarding claim 13, dependent upon claim 11, Ilhan in view of Kim teaches every limitation regarding claim 11.
However, Ilhan in view of Kim does not explicitly teach
wherein the unmanned air vehicle is configured to fly along a predetermined route.
Schwaerzler teaches
wherein the unmanned air vehicle is configured to fly along a predetermined route (Abstract: “To avoid an uncontrolled flight of a remotely controlled unmanned air vehicle (UAV), upon loss of radio contact between a control station (6) and the UAV (1), the UAV (1) flies on a preprogrammed safety route (3), as required the (2) UAV is guided to a flight path that is remote-controlled from the control station (6), and, in the event of an interruption of the radio contact, the UAV will fly on a substitute route calculated with on-board equipment, without active intervention from the remote control station.”).
Regarding claim 14, dependent upon claim 11, Ilhan in view of Kim teaches every limitation regarding claim 11.
However, Ilhan in view of Kim does not explicitly teach
wherein the unmanned air vehicle is configured to fly autonomously.
Schwaerzler teaches
wherein the unmanned air vehicle is configured to fly autonomously (Abstract: “To avoid an uncontrolled flight of a remotely controlled unmanned air vehicle (UAV), upon loss of radio contact between a control station (6) and the UAV (1), the UAV (1) flies on a preprogrammed safety route (3), as required the (2) UAV is guided to a flight path that is remote-controlled from the control station (6), and, in the event of an interruption of the radio contact, the UAV will fly on a substitute route calculated with on-board equipment, without active intervention from the remote control station.”).
Relevant Prior Art Directed to State of Art
Yun et al. (US 2017/0201714 Al, hereinafter Yun) is prior art not applied in the rejection(s) above. Yun discloses an electronic device for generating video data. The electronic device may include a communication unit configured to wirelessly receive a video stream captured by a camera, wherein the camera is located in an unmanned aerial vehicle. The electronic device may also include at least one sound sensor configured to receive an input sound stream. In addition, the electronic device may include an audio control unit configured to generate an audio stream associated with the video stream based on the input sound stream. Further, the electronic device may include a synthesizer unit configured to generate the video data based on the video stream and the audio stream.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSHUA CHEN whose telephone number is (703)756-5394. The examiner can normally be reached M-Th: 9:30 am - 4:30pm ET F: 9:30 am - 2:30pm ET.
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/J. C./Examiner, Art Unit 2665
/Stephen R Koziol/Supervisory Patent Examiner, Art Unit 2665
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