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 .
Preliminary Amendments
The applicant/s submitted preliminary amendments on 08/13/2025. Claims 4 and 5 have been cancelled and claims 1 -3 and 6 – 20 are currently pending. The Examiner acknowledges the amendments and examined the claims accordingly.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
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. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“image data generating device configured to…” in claim 15
“a first image data generator configured to…”
“a second image data generator configured to…” and
“a third image data generator configured to…” in claim 16
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. Specifications in para [0053] recites that “the image data generating device 300 may further include a first deep learning model 370 and a second deep learning model 390”.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
(g)(1) during the course of an interference conducted under section 135 or section 291, another inventor involved therein establishes, to the extent permitted in section 104, that before such person’s invention thereof the invention was made by such other inventor and not abandoned, suppressed, or concealed, or (2) before such person’s invention thereof, the invention was made in this country by another inventor who had not abandoned, suppressed, or concealed it. In determining priority of invention under this subsection, there shall be considered not only the respective dates of conception and reduction to practice of the invention, but also the reasonable diligence of one who was first to conceive and last to reduce to practice, from a time prior to conception by the other.
A rejection on this statutory basis (35 U.S.C. 102(g) as in force on March 15, 2013) is appropriate in an application or patent that is examined under the first to file provisions of the AIA if it also contains or contained at any time (1) a claim to an invention having an effective filing date as defined in 35 U.S.C. 100(i) that is before March 16, 2013 or (2) a specific reference under 35 U.S.C. 120, 121, or 365(c) to any patent or application that contains or contained at any time such a claim.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1, 13, 15 and 20 are rejected under 35 U.S.C. 102(a)(1)/(a)(2) as being anticipated by Zhang et al. (See Machine Translation for CN 111257957 A; hereafter referred to as Zhang).
Regarding Claim 1, Zhang teaches:
An image data generating method comprising:
performing operations of emitting electromagnetic waves to an object and receiving scan signals scanning the object, by using an electromagnetic wave transceiver (page 4, para 1 - 4, “A passive terahertz imaging system configured to receive the terahertz wave radiated by the target to be imaged. A focusing lens, which receives and focuses the terahertz wave radiated by the target to be imaged to the polarizing beam splitter… A terahertz wave detection device, which receives a terahertz wave signal and a synchronization signal via a mechanical delay device to transmit to the signal processing and conversion device”);
generating first image data of a low resolution based on the scan signals (page 4, para 1, “A passive terahertz imaging system configured to receive the terahertz wave radiated by the target to be imaged itself to generate a first resolution terahertz wave original image”);
generating second image data of a high resolution associated with a sub-region of the first image data when at least one of a suspicious object and a target object candidate is detected in the sub-region (page 7, para 10, “Generating a network G, which reconstructs and generates a second image of a second resolution based on the original image of the first resolution”; page 8, para 2); and
detecting a final target object from the second image data (page 5, last para - page 6, first para, “Based on the deep learning model, the fusion of image super-resolution recovery technology and the YOL0 target detection algorithm, compared to pure target detection on the original terahertz image, the accuracy of the system for detecting dangerous goods is improved, and the system of dangerous goods statistics database is constantly updated during the detection process”; page 7, para 13, “The recognition unit recognizes and locates the target object in the terahertz reconstructed picture based on the YOLO algorithm”; page 7 last para – page 8, first para, “super-resolution of the obtained original terahertz wave image based on the deep learning model Recover, improve image resolution; use YOLO target detection algorithm to identify and detect dangerous goods on the reconstructed image”).
Regarding Claim 13, Zhang teaches the image data generating method of claim 1, wherein the electromagnetic wave transceiver (Fig. 1, page 4, para 1, “A passive terahertz imaging system configured to receive the terahertz wave radiated by the target to be imaged”) includes:
a transmitter configured to emit the terahertz wave to a scan region (page 4, para 2, “A focusing lens, which receives and focuses the terahertz wave radiated by the target to be imaged to the polarizing beam splitter”); and
a receiver configured to receive the scan signals when the object is present in the scan region (page 4, para 4, “A terahertz wave detection device, which receives a terahertz wave signal and a synchronization signal via a mechanical delay device to transmit to the signal processing and conversion device”).
Regarding Claim 15, Zhang teaches:
An imaging system comprising:
an electromagnetic wave transceiver configured to emit electromagnetic waves to an object and to receive scan signals scanning the object (page 4, para 1 - 4, “A passive terahertz imaging system configured to receive the terahertz wave radiated by the target to be imaged. A focusing lens, which receives and focuses the terahertz wave radiated by the target to be imaged to the polarizing beam splitter… A terahertz wave detection device, which receives a terahertz wave signal and a synchronization signal via a mechanical delay device to transmit to the signal processing and conversion device”); and
an image data generating device configured to generate synthetic image data based on the scan signals (page 4, para 5, “A signal processing conversion device, which filters and reduces noise and processes the terahertz wave signal to generate an original terahertz wave image”),
wherein the image data generating device is configured to:
generate first image data of a low resolution based on the scan signals (page 4, para 1, “A passive terahertz imaging system configured to receive the terahertz wave radiated by the target to be imaged itself to generate a first resolution terahertz wave original image”);
generate second image data of a high resolution associated with a sub-region of the first image data when at least one of a suspicious object and a target object candidate is detected in the sub-region (page 7, para 10, “Generating a network G, which reconstructs and generates a second image of a second resolution based on the original image of the first resolution”; see page 8, para 2); and
detect a final target object from the second image data (page 5, last para - page 6, first para, “Based on the deep learning model, the fusion of image super-resolution recovery technology and the YOL0 target detection algorithm, compared to pure target detection on the original terahertz image, the accuracy of the system for detecting dangerous goods is improved, and the system of dangerous goods statistics database is constantly updated during the detection process”; page 7, para 13, “The recognition unit recognizes and locates the target object in the terahertz reconstructed picture based on the YOLO algorithm”; page 7 last para – page 8, first para, “super-resolution of the obtained original terahertz wave image based on the deep learning model Recover, improve image resolution; use YOLO target detection algorithm to identify and detect dangerous goods on the reconstructed image”).
Regarding Claim 20, Zhang teaches the imaging system of claim 15, wherein the electromagnetic wave transceiver (Fig. 1, page 4, para 1, “A passive terahertz imaging system configured to receive the terahertz wave radiated by the target to be imaged”) includes:
a transmitter configured to emit the terahertz wave to a scan region (page 4, para 2, “A focusing lens, which receives and focuses the terahertz wave radiated by the target to be imaged to the polarizing beam splitter”); and
a receiver configured to receive the scan signals when the object is present in the scan region (page 4, para 4, “A terahertz wave detection device, which receives a terahertz wave signal and a synchronization signal via a mechanical delay device to transmit to the signal processing and conversion device”).
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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 2, 3, 6 – 12, 14 and 16 – 19 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (See Machine Translation for CN 111257957 A; hereafter referred to as Zhang) in view of Jones et al. (US 20210325560 A1; hereafter referred to as Jones).
Regarding Claim 2, Zang teaches the image data generating method of claim 1, but fails to explicitly teach:
wherein the first image data are generated by using one or all of a fast Fourier transform technique and a low-resolution back-projection technique, and
wherein the second image data are generated by using a high-resolution back-projection technique.
In the same field of endeavor, Jones teaches:
wherein the first image data are generated by using one or all of a fast Fourier transform technique and a low-resolution back-projection technique (Jones, [0065] Versatile image reconstruction can be performed using a process called backprojection that is tailored for each specific imaging configuration… style of image reconstruction is referred to as backprojection”; [0069] “The use of the FFT to convert the radar data to the range domain reduces the computational burden … for a substantial increase in efficiency and image reconstruction speed”), and
wherein the second image data are generated by using a high-resolution back-projection technique (Jones, [0061] “processing circuitry 29 performs an image reconstruction method described below that is based on backprojection”; [0065] “style of image reconstruction is referred to as backprojection and can be adapted to focus images from non-uniform apertures of essentially any configuration”).
Zhang and Jones are considered analogous art as they are reasonably pertinent to the same field of endeavor of image processing. Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Zhang with the method of generating image data as taught by Jones to make the invention that generates image data by using one or all of a fast Fourier transform technique and a low-resolution back-projection technique; doing so can generate optimal imaging performance and yield a strong image intensity at that voxel location (Jones [0065]); thus, one of the ordinary skill in the art would have been motivated to combine the references.
Regarding Claim 3, Zhang in view of Jones teaches the image data generating method of claim 2, further comprises:
generating third image data based on the first image data and the second image data when the final target object is detected (Zhang, page 4, para 12, “the image processing module includes a blur filter that converts an original image with a first resolution into a third image with a third resolution smaller than the first resolution to generate The network G generates a second image of the second resolution based on the third image reconstruction”).
Regarding Claim 6, Zhang in view of Jones teaches the image data generating method of claim 3, wherein the generating of the second image data includes:
determining whether the target object candidate is detected from the first image data, by using a first deep learning model (Zhang, page 7, para 3, “A passive terahertz imaging system configured to receive the terahertz wave radiated by the target to be imaged itself to generate a first resolution terahertz wave original image”; Zhang, page 7, para 8, “An image processing module configured to obtain a terahertz wave reconstructed image based on the terahertz wave original image reconstruction, and the image processing module inputs the terahertz wave original image into an SRGAN network for training”), and
wherein the generating of the third image data includes:
determining whether the final target object is detected from the second image data, by using a second deep learning model (Zhang, page 3, summary, “terahertz imaging technology is used to image the measured target, combined with the deep learning model to perform super resolution recovery and dangerous goods identification and detection on the resulting image, and the system performs according to the detection results”; Zhang, page 5, para 3, “The deep convolutional neural network extracts the characteristic information of the predetermined object to obtain the tracking result of the predetermined object”; Zhang, page 6, para 1, “The DeepSort target tracking algorithm is used to track dangerous goods in motion in real time”; Zhang, page 9, last para, “combined Deep learning model, reconstruction of high-resolution terahertz image, the location of the object in the reconstructed image is in strong contrast with the human body, the YOL0 target detection algorithm is used to determine whether the reconstructed image contains dangerous goods”).
Regarding Claim 7, Zhang in view of Jones teaches the image data generating method of claim 6, wherein dangerous objects are classified based on a hierarchical structure so as to be included in an upper category and a lower category (Zhang, page 10, last para, “As shown in Figure 6, the YOLO algorithm is used to identify and locate dangerous goods in the terahertz reconstructed image. The YOLO algorithm mainly uses the convolutional layer to extract features and the fully connected layer to predict category probabilities and coordinates”),
wherein the target object candidate is one of first detailed items included in the upper category (Zhang, page 11, para 1, “Due to the wide variety of dangerous goods, it is necessary to judge the conditional category probability Pr(Classi|Object) of dangerous goods when judging and predicting dangerous goods. That is, if the target dangerous goods are included in the grid, they belong to a certain class of dangerous goods. Assuming that the total category of dangerous goods is N… finally take the largest score among the N scores of each bbox…In a certain category, set the score less than the threshold (0.2) to 0, then arrange the scores according to the high and low scores…If the score is greater than 0, then the bbox is the category corresponding to the score, output the dangerous goods category”), and
wherein the final target object is one of second detailed items included in the lower category (Zhang, page 11, para 1, If the score is less than 0, it means that there is no target dangerous goods in the bbox”).
Regarding Claim 8, Zhang in view of Jones teaches the image data generating method of claim 7, wherein the first deep learning model detects one of the first detailed items as the target object candidate (Zhang, page 4, para 9, “The recognition module is configured to recognize a predetermined object based on the terahertz wave reconstructed image…YOLO detection unit, which recognizes predetermined objects and their positions based on grids and features”), and
wherein the second deep learning model detects one of the second detailed items as the final target object (Zhang, page 11, para 2, “the target dangerous goods are detected by the YOLO algorithm, as shown in Figure 7, the DeepSort algorithm is used to track the target dangerous goods”).
Regarding Claim 9, Zhang in view of Jones teaches the image data generating method of claim 3, wherein the generating of the third image data includes:
composing the first image data and the second image data (page 5, last para, “The present invention proposes a device and method for fusing passive terahertz imaging and visible light imaging… Based on the deep learning model, the fusion of image super-resolution recovery technology and the YOL0 target detection algorithm, compared to pure target detection on the original terahertz image, the accuracy of the system for detecting dangerous goods is improved”).
Regarding Claim 10, Zhang in view of Jones teaches the image data generating method of claim 9, wherein the first image data are generated with respect to an entire scan region where the object is scanned (Zhang, page 3, “The terahertz wave self-radiated by the human body and the object”; Zhang, page 3, last para, “to obtain a picture that can fully reflect the detailed information (entire scan region) and internal characteristics of the target object, and to achieve efficient monitoring and tracking of the target object (sub-region)”), and
wherein the second image data are generated only with respect to the sub-region of the first image data (page 7, para 13, “The recognition unit recognizes and locates the target object (sub-region) in the terahertz reconstructed picture based on the YOLO algorithm”).
Regarding Claim 11, Zhang in view of Jones teaches the image data generating method of claim 10, wherein the third image data are generated by replacing the sub-region of the first image data with the second image data (Zhang, Fig. 6, page 10, last para, “As shown in Figure 6, the YOLO algorithm is used to identify and locate dangerous goods in the terahertz reconstructed image”).
Regarding Claim 12, Zhang teaches the image data generating method of claim 1, wherein the generating of the first image data includes:
generating preprocessed data by performing noise cancellation, error correction, or data alignment with respect to the scan signals (Zhang, page 4, para 5, “A signal processing conversion device, which filters and reduces noise and processes the terahertz wave signal to generate an original terahertz wave image”; Zhang, page 19, para 2, “The amplification unit and the image processing unit perform filtering and noise reduction processing on the acquired terahertz wave signal to obtain the original terahertz wave image”);
However, Zhang fails to explicitly teach:
generating the first image data by processing the preprocessed data by using one or all of a fast Fourier transform technique and a low-resolution back-projection technique.
In the same field of endeavor, Jones teaches:
generating the first image data by processing the preprocessed data by using one or all of a fast Fourier transform technique and a low-resolution back-projection technique (Jones, [0065] Versatile image reconstruction can be performed using a process called backprojection that is tailored for each specific imaging configuration… style of image reconstruction is referred to as backprojection”; [0069] “The use of the FFT to convert the radar data to the range domain reduces the computational burden … for a substantial increase in efficiency and image reconstruction speed”),
Zhang and Jones are considered analogous art as they are reasonably pertinent to the same field of endeavor of image processing. Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Zhang with the method of generating image data as taught by Jones to make the invention that generates image data by using one or all of a fast Fourier transform technique and a low-resolution back-projection technique; doing so can generate optimal imaging performance and yield a strong image intensity at that voxel location (Jones [0065]); thus, one of the ordinary skill in the art would have been motivated to combine the references.
Regarding Claim 14, Zhang in view of Jones teaches the image data generating method of claim 3, wherein each of the first image data, the second image data, and the third image data is three-dimensional (3D) image data (Jones, [0063] “The radar data collected by the active system are three-dimensional… An image reconstruction algorithm is discussed below according to one embodiment to mathematically focus the image in all three spatial dimensions. High-resolution 3D imaging is optimized”).
Regarding Claim 16, Zhang teaches the imaging system of claim 15, wherein the image data generating device includes:
a second image data generator configured to detect a target object candidate from the first image data (Zhang, page 7, para 10, “Generating a network G, which reconstructs and generates a second image of a second resolution based on the original image of the first resolution”; page 8, para 2);
a third image data generator configured to detect a final target object from the second image data and to generate a third image data based on the first image data and the second image data (Zhang, page 4, para 12, “the image processing module includes a blur filter that converts an original image with a first resolution into a third image with a third resolution smaller than the first resolution to generate The network G generates a second image of the second resolution based on the third image reconstruction”; Zhang, page 5, last para, “Based on the deep learning model, the fusion of image super-resolution recovery technology and the YOL0 target detection algorithm, compared to pure target detection on the original terahertz image, the accuracy of the system for detecting dangerous goods is improved”).
However, Zhang fails to explicitly teach:
a first image data generator configured to calculate generate the first image data by applying one or all of a fast Fourier transform technique and a low-resolution back-projection technique to the scan signals; and
to generate the second image data by applying a high-resolution back-projection technique to the sub-region of the first image data;
In the same field of endeavor, Jones teaches:
a first image data generator configured to calculate generate the first image data by applying one or all of a fast Fourier transform technique and a low-resolution back-projection technique to the scan signals (Jones, [0065] Versatile image reconstruction can be performed using a process called backprojection that is tailored for each specific imaging configuration… style of image reconstruction is referred to as backprojection”; [0069] “The use of the FFT to convert the radar data to the range domain reduces the computational burden … for a substantial increase in efficiency and image reconstruction speed”);
to generate the second image data by applying a high-resolution back-projection technique to the sub-region of the first image data (Jones, [0061] “processing circuitry 29 performs an image reconstruction method described below that is based on backprojection”; [0065] “style of image reconstruction is referred to as backprojection and can be adapted to focus images from non-uniform apertures of essentially any configuration”).
Zhang and Jones are considered analogous art as they are reasonably pertinent to the same field of endeavor of image processing. Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Zhang with the method of generating image data as taught by Jones to make the invention that generates image data by using one or all of a fast Fourier transform technique and a low-resolution back-projection technique; doing so can generate optimal imaging performance and yield a strong image intensity at that voxel location (Jones [0065]); thus, one of the ordinary skill in the art would have been motivated to combine the references.
Regarding Claim 17, Zhang in view of Jones teaches the imaging system of claim 16, wherein the image data generating device further includes:
a first deep learning model configured to detect the target object candidate (Zhang, page 4, para 9, “The recognition module is configured to recognize a predetermined object based on the terahertz wave reconstructed image…YOLO detection unit, which recognizes predetermined objects and their positions based on grids and features”); and
a second deep learning model configured to detect the final target object (Zhang, page 11, para 2, “the target dangerous goods are detected by the YOLO algorithm, as shown in Figure 7, the DeepSort algorithm is used to track the target dangerous goods”).
Regarding Claim 18, Zhang in view of Jones teaches the imaging system of claim 17, wherein dangerous objects are classified based on a hierarchical structure so as to be included in an upper category and a lower category (Zhang, page 10, last para, “As shown in Figure 6, the YOLO algorithm is used to identify and locate dangerous goods in the terahertz reconstructed image. The YOLO algorithm mainly uses the convolutional layer to extract features and the fully connected layer to predict category probabilities and coordinates”),
wherein the target object candidate is one of first detailed items included in the upper category (Zhang, page 11, para 1, “Due to the wide variety of dangerous goods, it is necessary to judge the conditional category probability Pr(Classi|Object) of dangerous goods when judging and predicting dangerous goods. That is, if the target dangerous goods are included in the grid, they belong to a certain class of dangerous goods. Assuming that the total category of dangerous goods is N… finally take the largest score among the N scores of each bbox…In a certain category, set the score less than the threshold (0.2) to 0, then arrange the scores according to the high and low scores…If the score is greater than 0, then the bbox is the category corresponding to the score, output the dangerous goods category”), and
wherein the final target object is one of second detailed items included in the lower category (Zhang, page 11, para 1, If the score is less than 0, it means that there is no target dangerous goods in the bbox”).
Regarding Claim 19, Zhang in view of Jones teaches the imaging system of claim 16, wherein the third image data generator is configured to generate the third image data by replacing the sub- region of the first image data with the second image data (Zhang, Fig. 6, page 10, last para, “As shown in Figure 6, the YOLO algorithm is used to identify and locate dangerous goods in the terahertz reconstructed image”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20190244399 A1 SYSTEM AND METHOD FOR REMOVING GIBBS ARTIFACT IN MEDICAL IMAGING SYSTEM
US 20230237803 A1 PEAK LABEL OBJECT DETECTION SYSTEM AND METHOD OF USING
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VAISALI RAO. KOPPOLU
Examiner
Art Unit 2664
/JENNIFER MEHMOOD/Supervisory Patent Examiner, Art Unit 2664