DETAILED ACTION
This action is in response to the initial filing filed on February 8, 2024.
Claims 1, 8, 11-12, 14, 19, 27-29, 31-32, and 34 are amended.
Claims 2-7, 9-10, 13, 15-18, 20 and 3-81 have been cancelled.
Claims 1, 8, 11-12, 14, 19, and 21-34 have been examined in this application.
Information Disclosure Statement
The Information Disclosure Statement (IDS) filed on 2/8/2024 have been acknowledged.
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 .
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 8, 11-12, 14, 19, 21-22, 27, and 34 are rejected under 35 U.S.C. 103 as being unpatentable over McMakin et al (US 2004/0090359 A1) in view of Su et al (ArXiv, 2015).
Regarding Claim 1, McMakin teaches a radar system for scanning a target subject and detecting concealed objects [0010-0011 for scanning and detecting specific concealed objects in the 200MHz to 1THz range (radar is usually 77-80 GHz so within specified range) and 0034 for using the millimeter bands],
the system comprising a radar-based sensor unit, a pre-processing unit and a processing unit, wherein [0009-0011 for multiple processing units, 0036-0037]:
the radar-based sensor unit comprises: an array of transmitters configured to transmit a beam of electromagnetic radiations towards the target subject [0034 for an array of transmitters using an electromagnetic radiation];
and an array of receivers configured to receive a beam of electromagnetic radiations reflected from the target subject [0034 for using receivers with an electromagnetic radiation],
wherein the electromagnetic radiations received by the receiver comprise a raw complex image [0029 for having an image of what in underneath clothing with 0055-0056 for using complex valued spatial frequencies in the Fourier space];
the pre-processing unit is configured to receive the raw complex image from the receivers and generate a plurality of convoluted slices [0047 for getting interrogation data from transceiver (raw) and accumulating imaging data with 0056-0057 for using feature extraction].
McMakin teaches cross sectional and cylindrical slicing [004-0052] however, McMakin fails to explicitly teach and the processing unit configured to receive the convoluted slices from the pre- processing unit and detect the concealed object within the target subject.
Su has a standard CNN architecture trained to recognize the shapes’ rendered views independently of each other (page 1, abstract) and teaches and the processing unit configured to receive the convoluted slices from the pre- processing unit and detect the concealed object within the target subject [page 1, right column, second paragraph, with page 5, left column, second paragraph].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the concealed object imaging techniques, as disclosed by McMakin, further including the convolutional slicing calculations as taught by Su for the purpose of optimal classification and retrieval performance (Su, page 5, left column, second paragraph).
Regarding Claim 8, McMakin teaches a database configured to store one or more of the raw complex images received by the receiver, the convoluted slices generated by the pre-processing unit and an identification of the detected concealed object [0053 for identifying conceal object feature and subregion, with 0057-0058].
Regarding Claim 11, McMakin teaches a communicator configured to transmit a notification of the detected concealed object to one or more concerned authorities through a communication network [0063 for having a communication subsystem].
Regarding Claim 12, McMakin teaches an anomaly detector which is configured to detect deviation of the detected concealed object from a standard identification stored in the database [0057 for determining suspicious/non-suspicious (standard) objects].
Regarding Claim 14, McMakin teaches the radar-based sensor unit is selected from at least one of a group consisting of portable hand devices, full body scanners, walk-through scanners and combinations thereof [0071 for scanning booth and figure 9].
Regarding Claim 19, McMakin teaches the radar-based sensor unit comprises one or more of the processing unit, a database [0059],
a communicator and an anomaly detector are a part of the radar-based sensor unit [0055 for using logic subsystem to classify image portions of the suspected object].
Regarding Claim 21, McMakin teaches a method for scanning a target subject and detecting concealed objects, the method comprising [0010-0011 for scanning and detecting specific concealed objects in the 200MHz to 1THz range (radar is usually 77-80 GHz so within specified range) and 0034 for using the millimeter bands]:
transmitting, by an array of transmitters, a beam of electromagnetic radiations towards the target subject [0034 for an array of transmitters using an electromagnetic radiation];
receiving, by an array of receivers, a beam of electromagnetic radiations reflected from the target subject [0034 for using receivers with an electromagnetic radiation],
wherein the received electromagnetic radiations comprise a raw complex image [0029 for having an image of what in underneath clothing with 0055-0056 for using complex valued spatial frequencies in the Fourier space];
receiving, by a pre-processing unit, the raw complex image from the receivers and generating a plurality of convoluted slices [0047 for getting interrogation data from transceiver (raw) and accumulating imaging data with 0056-0057 for using feature extraction].
McMakin teaches cross sectional and cylindrical slicing [004-0052] however, McMakin fails to explicitly teach and processing, by a processing unit, the convoluted slices for detecting the concealed object within the target subject.
Su has a standard CNN architecture trained to recognize the shapes’ rendered views independently of each other (page 1, abstract) and teaches and processing, by a processing unit, the convoluted slices for detecting the concealed object within the target subject [page 1, right column, second paragraph, with page 5, left column, second paragraph].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the concealed object imaging techniques, as disclosed by McMakin, further including the convolutional slicing calculations as taught by Su for the purpose of optimal classification and retrieval performance (Su, page 5, left column, second paragraph).
Regarding Claim 22, McMakin teaches the raw complex image comprises a 3D matrix of voxels [0049 for cylindrical (3D) images with image data].
Regarding Claim 27, McMakin fails to explicitly teach the concealed objects are detected by the processing unit from the convoluted slices using a Convolutional neural network.
Su has a standard CNN architecture trained to recognize the shapes’ rendered views independently of each other (page 1, abstract) and teaches the concealed objects are detected by the processing unit from the convoluted slices using a Convolutional neural network [page 2, right column, last paragraph, with page 5, left column, second paragraph].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the concealed object imaging techniques, as disclosed by McMakin, further including the convolutional slicing calculations as taught by Su for the purpose of optimal classification and retrieval performance (Su, page 5, left column, second paragraph).
Regarding Claim 28, McMakin teaches storing, in a database, one or more of the raw complex images received by the receiver, the convoluted slices generated by the pre-processing unit and an identification of the detected concealed object [0049-0050 for using overlapping arc segments to created a 3D body surface].
Regarding Claim 29, McMakin teaches training the processing unit for detecting the concealed objects using the information stored in the database [0081].
Regarding Claim 30, McMakin teaches the training of the processing unit for detecting the concealed objects is done using a Machine Learning (ML) algorithm [0057].
Regarding Claim 31, McMakin teaches transmitting, by a communicator, a notification of the detected concealed object to one or more concerned authorities through a communication network [0054 for alert signals].
Regarding Claim 32, McMakin teaches detecting, by an anomaly detector, deviation of the detected concealed object from a standard identification stored in the database [0057 for determining suspicious/non-suspicious (standard) objects].
Regarding Claim 33, McMakin teaches the detected deviation is indicative of detection of non-specific concealed objects [0057 for determining suspicious/non-suspicious (standard) objects].
Regarding Claim 34, McMakin teaches detecting, by the processing unit, at least one of a position of the concealed object within the target subject, a size of the concealed object, and a shape of the concealed object [figure 8, for getting object shape].
Claims 23-14, and 26 are rejected under 35 U.S.C. 103 as being unpatentable over McMakin et al (US 2004/0090359 A1) in view of Su et al (ArXiv, 2015), as applied to Claim 22 above, and further in view of Buyanovski (US 2007/0040833 A1).
Regarding Claim 23, McMakin fails to explicitly teach the convoluted slices are maximum intensity slices, wherein the maximum intensity slice is a matrix of the energy levels of the voxels with the highest intensity for each pair of orthogonal coordinates.
Buyanovski has an adaptive MIP ray casting system first fragments a 3-D dataset into multiple sub-volumes (abstract) and teaches the convoluted slices are maximum intensity slices, wherein the maximum intensity slice is a matrix of the energy levels of the voxels with the highest intensity for each pair of orthogonal coordinates [0005, 0042, and 0063].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the concealed object imaging techniques, as disclosed by McMakin, further including the intensity calculations as taught by Buyanovski for the purpose of determining image value is indicative of the 3-D characteristics of the objects embedded within the image volume encountered by the ray path (Buyanovski, 0005).
Regarding Claim 24, McMakin fails to explicitly teach the convoluted slices are range slices, wherein the range slice is a matrix of argument values of the voxels with the highest energy values for each pair of orthogonal coordinates.
Buyanovski has an adaptive MIP ray casting system first fragments a 3-D dataset into multiple sub-volumes (abstract) and teaches the convoluted slices are maximum intensity slices, wherein the maximum intensity slice is a matrix of the energy levels of the voxels with the highest intensity for each pair of orthogonal coordinates [0005, 0042, and 0063].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the concealed object imaging techniques, as disclosed by McMakin, further including the intensity calculations as taught by Buyanovski for the purpose of determining image value is indicative of the 3-D characteristics of the objects embedded within the image volume encountered by the ray path (Buyanovski, 0005).
Regarding Claim 26, McMakin fails to explicitly teach the convoluted slices are median value slices, wherein the median value slice is a matrix of average energy values for each voxel.
Buyanovski has an adaptive MIP ray casting system first fragments a 3-D dataset into multiple sub-volumes (abstract) and teaches teach the convoluted slices are median value slices, wherein the median value slice is a matrix of average energy values for each voxel [0005, 0042, and 0063].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the concealed object imaging techniques, as disclosed by McMakin, further including the intensity calculations as taught by Buyanovski for the purpose of determining image value is indicative of the 3-D characteristics of the objects embedded within the image volume encountered by the ray path (Buyanovski, 0005).
Claims 25 are rejected under 35 U.S.C. 103 as being unpatentable over McMakin et al (US 2004/0090359 A1) in view of Su et al (ArXiv, 2015), as applied to Claim 22 above, and further in view of Du et al (BMC Bioinformatics, 2013).
Regarding Claim 25, McMakin fails to explicitly the convoluted slices are Laplacian slices, wherein the Laplacian slice is a matrix of phase values of a z-plane containing the voxel having the highest intensity.
Du has an adaptive MIP ray casting system first fragments a 3-D dataset into multiple sub-volumes (abstract) and teaches the convoluted slices are maximum intensity slices, wherein the maximum intensity slice is a matrix of the energy levels of the voxels with the highest intensity for each pair of orthogonal coordinates [page 8, left column, second paragraph and page 12, right column, first two paragraphs].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the concealed object imaging techniques, as disclosed by McMakin, further including the voxel calculations as taught by Du for the purpose to minimize the area and topology distortions of the surface net in the mapping (Du, page 8, left column, second paragraph).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Testar et al (US 2015/0285898 A1) has a handheld Screening device including: an antenna array including a plurality of antennas.
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/SAMARINA MAKHDOOM/
Examiner, Art Unit 3648