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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on April 15, 2024, is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Amendment
Applicant’s Amendments filed on October 27, 2025, has been entered and made of record.
Currently pending Claim(s) 1-23
Independent Claim(s) 1 and 19
Canceled Claim(s) 24-40
Specification
The disclosure is objected to because the Specification refers to a label that is not present in the figures. On page 10, line 17, the Specification refers to label 48 in Fig. 4. Appropriate correction is required.
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 1-3, 5-9, and 14-21 are rejected under 35 U.S.C. 103 as being unpatentable over Yarkoni et al. (US 2022/0265165 A1), hereafter Yarkoni, in view of Ahmed (US 2015/0293221 A1).
Regarding claim 1, Yarkoni teaches a surface determination system ([0003] “…a system is introduced for scanning the body of a subject.”) comprising:
processing circuitry ([0003] “The system may comprise: a radar unit comprising, a preprocessor unit, and a processor unit... the preprocessor unit may be configured and operable to receive the raw data from the at least one receiver unit and operable to generate filtered point cloud data.”) configured to:
access a three-dimensional complex-valued image volume of a target, wherein the three-dimensional complex-valued image volume comprises a plurality of voxels ([0029] “The raw data generated by the receivers is typically a set of magnitude and phase measurements corresponding to the waves scattered back from the objects in front of the array. Spatial reconstruction processing is applied to the measurements to reconstruct the amplitude (scattering strength) at the three-dimensional coordinates of interest within the target region. Thus each three dimensional section of the volume within the target region may represented by a voxel defined by four values corresponding to an x-coordinate, a y-coordinate, a z-coordinate, and an amplitude value.”);
define a plurality of first locations of the three-dimensional complex-valued image volume using a first dimension and a second dimension of the three-dimensional complex-valued image volume (Referring to Fig. 3, [0041] “The scanning arrangement generates a set of raw data representing a three-dimensional amplitude map 402 indicating objects within the region including the subject. The three-dimensional amplitude matrix 402 may be filtered by removing outlying voxels which are unconnected with the subject.” Thus, a clean map 404 is produced containing the “first locations.” Additionally, Fig. 4 shows preprocessing steps that involves selecting voxels that are relevant to the surface of the person being scanned by filtering out irrelevant (low-amplitude) voxels.); for each of the first locations,
identify a plurality of voxels along a third dimension of the three-dimensional complex-valued image volume that correspond to the respective one of the first locations for each of the first locations, select one of the identified voxels that corresponds to the respective one of the first locations as having an increased complex amplitude compared with others of the identified voxels that correspond to the respective one of the first locations (Voxels which have a high complex amplitude are selected as corresponding to the location of the target. [0004] “Optionally, the preprocessor unit comprises an amplitude filter operable to generate filtered data by selecting from the raw data voxels having amplitudes above a required threshold. Where appropriate, the preprocessor unit comprises a voxel selector operable to further reduce voxel number in the filtered data thereby generating the point cloud data. Variously, the voxel selector is operable to sample the filtered data and/or to cluster neighboring voxels. Accordingly, the amplitude may be further operable to set the value of each voxel having an amplitude above the required threshold to ONE, and the value of each voxel having an amplitude below the required threshold to ZERO.”);
Yarkoni teaches selecting the voxels with high amplitude to represent the surface of the human being scanned, and the points at “second locations” are determined from the high-amplitude voxels to create a point cloud [0010]. These points are later refined and can be moved by comparing the point cloud to a parametric model [0032-0036]. Thus, Yarkoni’s method generates second locations (point in a point cloud representing the target surface) which are in a different location from the third locations (the selected high amplitude voxels), but this method relies on a parametric model rather than phase information. Therefore, Yarkoni fails to teach for each of the selected voxels, use phase information of the three-dimensional complex-valued image volume to identify a second location in the third dimension for the respective one of the selected voxels that corresponds to a location of a surface of the target and that is different than a third location of the respective one of the selected voxels.
However, Ahmed teaches for each of the selected voxels, use phase information of the three-dimensional complex-valued image volume to identify a second location in the third dimension for the respective one of the selected voxels that corresponds to a location of a surface of the target and that is different than a third location of the respective one of the selected voxels (See Fig. 6 and [0103-0115]. Ahmed teaches coherent imaging, which requires both amplitude and phase information for determining points representing the surface of the target. [0114] “Phase values may be derived 614 from the coherent subimages represented by complex number pixels in stage 612. The phase values may be accepted by a phase estimator 618 to estimate a phase for each of the voxels in the reconstruction volume 620, for example for a predefined time point at the center or at the end of the total measurement time, i.e. the data acquisition phase. An interpolation algorithm known as such may be employed, for example a linear interpolation or a spline interpolation.” Thus, when combining the images into a reconstructed image volume, the phase information is used to correct voxel locations which are incoherent. This creates a final volume of second locations.).
Yarkoni and Ahmed are directly analogous in the art to the claimed invention, because both Yarkoni and Ahmed teach an antennae array for transmitting and receiving electromagnetic waves for generating a 3D representation of the surface of a human target. Therefore, it would have been obvious to one of ordinary skill in the art to modify Yarkoni’s invention by utilizing fully coherent image reconstruction. This modification would allow for image interpolation utilizing phase information to be used for correcting incoherent voxels when different frequencies or antennae clusters and/or subgroups for scanning a target are used ([Ahmed 0110] “Depending on available processing capabilities at LPU 126 and CPU 110, it can be preferred to perform coherent addition of intermediate images of various transmission antennas, or various Tx clusters, for one Tx subgroup at the CPU.” [Ahmed 0112] “Further then, the CPU 110 is operable to coherently add together 610 all intermediate (interpolated, weighted) images belonging to a given Tx subgroup 602, which includes images from all LPUs 126 involved in the associated Rx subgroup 604, to generate one coherent subimage per Tx subgroup”). Different antennae apertures could result in varying voxel positions, thus requiring correction for coherent location ([Ahmed 0079] “However, it is noted that the coherent subimages are generated from various different effective apertures of the various Tx/Rx subgroups and may therefore differ in aperture, image size, pixel/voxel positions, etc., such that prior to subimage adding an interpolation procedure has to be performed, as will be discussed further below.”).
Examiner note to Applicant: Regarding the independent claims 1 and 19, using phase information to interpolate and keep high-amplitude points locations coherent is interpreted broadly as using phase information to determine second locations (points) different from the third locations (high-amplitude voxels). Additionally, utilizing phase information to increase accuracy for distance measurements of waves is also well-established in the art and could be interpreted as using phase information to determine the second locations. For example, Piotrowsky (NPL reference #28 from the IDS dated 4/15/2024; Also see the ISR dated 4/15/2024 discussing this reference applied to claim 1.) utilizes phase information for obtaining sub-wavelength distance measurements for continuous wave systems. Many of these systems utilize determining a point location based on the phase value but are not limited to choosing points where the phase is zero. Thus, the Examiner recommends incorporating the Allowable Subject Matter into the independent claims to be more specific about second locations being at zero-phase locations and selecting the nearest zero-phase location to a high-amplitude voxel for the second location(s) (Claims 10 and 11).
Regarding claim 2, Yarkoni and Ahmed teach the system of claim 1. Yarkoni further teaches wherein the processing circuitry is configured to use the second locations of the selected voxels to generate a representation of the surface of the target (Yarkoni creates a representation of the surface of the target by determining the high-amplitude voxels, which correspond to the “first locations,” and determining a point for the point cloud representation from each voxel. [0044-0045] “The scanning arrangement may scan a subject may be in the target region in front of the array 1008, thereby generating amplitude data for the region 1010. The amplitude matrix is sent to a preprocessing unit 1012, which generates a filtered point cloud 1014. Optionally, the data may be processed by removing data below a threshold amplitude value 1016, removing outlying data 1018 and downsampling voxels 1020.”).
Regarding claim 3, Yarkoni and Ahmed teach the system of claim 2. Yarkoni further teaches wherein the representation of the surface of the target is a point cloud ([0045] “The amplitude matrix is sent to a preprocessing unit 1012, which generates a filtered point cloud 1014.”).
Regarding claim 5, Yarkoni and Ahmed teach the system of claim 1. Yarkoni further teaches wherein the processing circuitry is configured to compare the complex amplitude of each of the identified voxels with respect to a threshold, and to determine that some of the identified voxels do not correspond to the surface of the target as a result of the comparison ([0004] “Optionally, the preprocessor unit comprises an amplitude filter operable to generate filtered data by selecting from the raw data voxels having amplitudes above a required threshold. Where appropriate, the preprocessor unit comprises a voxel selector operable to further reduce voxel number in the filtered data thereby generating the point cloud data.” [0044-0045] “The scanning arrangement may scan a subject may be in the target region in front of the array 1008, thereby generating amplitude data for the region 1010. The amplitude matrix is sent to a preprocessing unit 1012, which generates a filtered point cloud 1014. Optionally, the data may be processed by removing data below a threshold amplitude value 1016, removing outlying data 1018 and down sampling voxels 1020.“).
Regarding claim 6, Yarkoni and Ahmed teach the system of claim 5. Yarkoni further teaches wherein the processing circuitry is configured to only select the identified voxels having complex amplitudes greater than the threshold (As shown in [0004] and [0044-0045] in the rejection to claim 5 above, Yarkoni teaches selecting voxels which contain an amplitude above a threshold for representing the surface of the target. Also see Fig. 3 showing the voxel selection process.).
Regarding claim 7, Yarkoni and Ahmed teach the system of claim 1. Ahmed further teaches wherein the second locations of the identified voxels correspond to the surface of the target with increased resolution in the third dimension compared with a resolution of the voxels in the third dimension (In [0079], Ahmed teaches that coherent images comprising the complex image volume may have low accuracy if different antennae apertures or frequencies are used. Thus, using the method in Fig. 6 and [0103-0115], the interpolation utilizes phase information to maintain coherence between multiple images, which involves using the interpolated points for correcting the incoherent voxels.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yarkoni’s invention by using phase information to obtain second locations (points which are determined from high amplitude voxels) with higher resolution rather than simply obtaining points at the voxel locations. This modification would consider maintaining image coherence when different frequencies or antennae clusters and/or subgroups for scanning a target are used ([Ahmed 0079] “However, it is noted that the coherent subimages are generated from various different effective apertures of the various Tx/Rx subgroups and may therefore differ in aperture, image size, pixel/voxel positions, etc., such that prior to subimage adding an interpolation procedure has to be performed,” [Ahmed 0112] “Further then, the CPU 110 is operable to coherently add together 610 all intermediate (interpolated, weighted) images belonging to a given Tx subgroup 602, which includes images from all LPUs 126 involved in the associated Rx subgroup 604, to generate one coherent subimage per Tx subgroup”).
Regarding claim 8, Yarkoni and Ahmed teach the system of claim 1. Yarkoni further teaches wherein the three- dimensional complex-valued image volume is a rectangular cuboid, the first and second dimensions correspond to a face of the rectangular cuboid, and the third dimension is in a depth direction of the rectangular cuboid that is perpendicular to the face of the rectangular cuboid ([0029] “The raw data generated by the receivers is typically a set of magnitude and phase measurements corresponding to the waves scattered back from the objects in front of the array. Spatial reconstruction processing is applied to the measurements to reconstruct the amplitude (scattering strength) at the three-dimensional coordinates of interest within the target region. Thus each three dimensional section of the volume within the target region may represented by a voxel defined by four values corresponding to an x-coordinate, a y-coordinate, a z-coordinate, and an amplitude value.”);
Regarding claim 9, Yarkoni and Ahmed teach the system of claim 1. Ahmed further teaches wherein the processing circuitry is configured to, for each of the selected voxels, identify the second location as a result of phase information of the second location being a given phase value (Ahmed teaches receiving phase information for interpolation to keep the high-amplitude voxels coherent across images in the volume, which would involve determining a second location based on a selected voxel and given phase value(s). [0114] “Phase values may be derived 614 from the coherent subimages represented by complex number pixels in stage 612. The phase values may be accepted by a phase estimator 618 to estimate a phase for each of the voxels in the reconstruction volume 620, for example for a predefined time point at the center or at the end of the total measurement time, i.e. the data acquisition phase. An interpolation algorithm known as such may be employed, for example a linear interpolation or a spline interpolation.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yarkoni’s invention by using phase values to obtain second locations (points which are determined from high amplitude voxels). This modification would consider maintaining image coherence when different frequencies or antennae clusters and/or subgroups for scanning a target are used ([Ahmed 0079] “However, it is noted that the coherent subimages are generated from various different effective apertures of the various Tx/Rx subgroups and may therefore differ in aperture, image size, pixel/voxel positions, etc., such that prior to subimage adding an interpolation procedure has to be performed,” [Ahmed 0112] “Further then, the CPU 110 is operable to coherently add together 610 all intermediate (interpolated, weighted) images belonging to a given Tx subgroup 602, which includes images from all LPUs 126 involved in the associated Rx subgroup 604, to generate one coherent subimage per Tx subgroup”).
Regarding claim 14, Yarkoni and Ahmed teach the system of claim 1. Ahmed further teaches wherein each of the identified second locations is between a plurality of the voxels in the third dimension for the respective one of the first locations (Ahmed teaches receiving phase information for interpolation to keep the high-amplitude voxels coherent across images in the volume, which would involve determining a second location based on a selected voxel and given phase value(s). This interpolation produces second locations using sub-wavelength accuracy, which could fall between the identified voxels of high-amplitude (first locations). [0114] “Phase values may be derived 614 from the coherent subimages represented by complex number pixels in stage 612. The phase values may be accepted by a phase estimator 618 to estimate a phase for each of the voxels in the reconstruction volume 620, for example for a predefined time point at the center or at the end of the total measurement time, i.e. the data acquisition phase. An interpolation algorithm known as such may be employed, for example a linear interpolation or a spline interpolation.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yarkoni’s invention by using phase values to obtain second locations (points which are determined from high amplitude voxels). This modification would consider maintaining image coherence when different frequencies or antennae clusters and/or subgroups for scanning a target are used ([Ahmed 0079] “However, it is noted that the coherent subimages are generated from various different effective apertures of the various Tx/Rx subgroups and may therefore differ in aperture, image size, pixel/voxel positions, etc., such that prior to subimage adding an interpolation procedure has to be performed,” [Ahmed 0112] “Further then, the CPU 110 is operable to coherently add together 610 all intermediate (interpolated, weighted) images belonging to a given Tx subgroup 602, which includes images from all LPUs 126 involved in the associated Rx subgroup 604, to generate one coherent subimage per Tx subgroup”).
Regarding claim 15, Yarkoni and Ahmed teach the system of claim 1. Yarkoni further teaches wherein the processing circuitry is configured to select the selected voxels having the increased complex amplitude as a result of the selected voxels having maximum complex amplitudes corresponding to each of the first locations ([0031] “Accordingly, where appropriate, a preprocessing unit 114B may include an amplitude filter 116B operable to select voxels having amplitude above a required threshold and a voxel selector 117B operable to reduce the number of voxels in the filtered data, for example by sampling the data or clustering neighboring voxels. In this manner the filtered point cloud may be output 118B to a processor 120B. It is further note that the filtered point cloud may further be simplified by setting the amplitude value of each voxel to ONE when the amplitude is above the threshold and to ZERO when the amplitude is below the threshold.”).
Regarding claim 16, Yarkoni and Ahmed teach the system of claim 1. Yarkoni further teaches wherein the processing circuitry is configured to control emission of electromagnetic energy to process electromagnetic energy reflected from the surface of the target to generate the image volume (Fig. 1 shows an array which emits and receives electromagnetic energy for creating an image volume. Fig. 3 shows the resulting point cloud.).
Although Yarkoni teaches emitting electromagnetic energy, Yarkoni does not mention the exact frequency range. However, Ahmed teaches within a frequency range of 1-100 GHz towards the target ([0064] “For illumination, the antennas 104 emit (transmit) millimeter-wave and/or micrometer-wave radiation, i.e. electromagnetic radiation in a sub-Terahertz (THz) and/or THz range, e.g. in a range between 1 Gigahertz (GHz) and 1 Terahertz (THz). As a specific example, transmission frequencies may be selected in a range between about 70 Gigahertz (GHz) and about 80 GHZ.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yarkoni’s invention to use a frequency range of 1-100 GHz, because such a modification would have been obvious to try. This modification would produce a predictable and ascertainable result that is typical of millimeter wave screening devices as seen in Ahmed [0064], Sheen [Section 2], and many other prior art sources. For further NPL examples, see Section 4 of NPL #27 and Section 6 of NPL #28 from the IDS dated April 15, 2024.).
Regarding claim 17, Yarkoni and Ahmed teach the system of claim 1. Ahmed further teaches wherein the second locations more accurately correspond to actual locations of the surface of the target compared with the third locations of the selected voxels having the increased complex amplitudes (See Fig. 6 and [0103-0115], Ahmed teaches determining coherent points across the images in the complex image volume by interpolating the voxels using phase information; thus, the coherent image volume will contain more accurate location of the surface of the target).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yarkoni’s invention by using phase information to obtain second locations (points which are determined from high amplitude voxels) with higher resolution (which would better correspond the surface of the target) rather than simply obtaining points at the voxel locations. This modification would consider maintaining image coherence when different frequencies or antennae clusters and/or subgroups for scanning a target are used ([Ahmed 0079] “However, it is noted that the coherent subimages are generated from various different effective apertures of the various Tx/Rx subgroups and may therefore differ in aperture, image size, pixel/voxel positions, etc., such that prior to subimage adding an interpolation procedure has to be performed,” [Ahmed 0112] “Further then, the CPU 110 is operable to coherently add together 610 all intermediate (interpolated, weighted) images belonging to a given Tx subgroup 602, which includes images from all LPUs 126 involved in the associated Rx subgroup 604, to generate one coherent subimage per Tx subgroup”).
Regarding claim 18, Yarkoni and Ahmed teach the system of claim 1. Yarkoni further teaches wherein the second locations correspond to respective ones of the first locations in the third dimension (Fig. 3 label 404 shows voxels which are selected as the “first locations” as being relevant to the location of the target. Label 408 shows a point cloud of “second locations” derived from the high-amplitude voxels which correspond to the “first locations.”).
Regarding claim 19, Yarkoni teaches a surface determination system ([0003] “…a system is introduced for scanning the body of a subject.”) comprising:
an antenna system configured to emit electromagnetic energy towards a target and to receive electromagnetic energy reflected from the target (See Fig. 1. [0028] “The radar typically includes at least one array of radio frequency transmitter antennas and at least one array of radio frequency receiver antennas. The radio frequency transmitter antennas are connected to an oscillator (radio frequency signal source) and are configured and operable to transmit electromagnetic waves towards the target region. The radio frequency receiver antennas are configured to receive electromagnetic waves reflected back from objects within the target region.”);
processing circuitry ([0003] “The system may comprise: a radar unit comprising, a preprocessor unit, and a processor unit... the preprocessor unit may be configured and operable to receive the raw data from the at least one receiver unit and operable to generate filtered point cloud data.”) configured to:
use the received electromagnetic energy to generate a three-dimensional complex-valued image volume of the target, wherein the three-dimensional complex-valued image volume comprises a plurality of complex values associated with a plurality of voxels ([0029] “The raw data generated by the receivers is typically a set of magnitude and phase measurements corresponding to the waves scattered back from the objects in front of the array. Spatial reconstruction processing is applied to the measurements to reconstruct the amplitude (scattering strength) at the three-dimensional coordinates of interest within the target region. Thus each three dimensional section of the volume within the target region may represented by a voxel defined by four values corresponding to an x-coordinate, a y-coordinate, a z-coordinate, and an amplitude value.” Phase information is also collected in the raw data.).
Yarkoni teaches selecting the voxels with high amplitude to represent the surface of the human being scanned, and points are determined from the high-amplitude voxels to create a point cloud [0010]. These points are later refined and can be moved by comparing the point cloud to a parametric model [0032-0036]. Thus, Yarkoni relies on a parametric model rather than phase information. Therefore, Yarkoni fails to teach using amplitude information and phase information of the complex values of the three-dimensional complex-valued image volume to generate a representation of a surface of the target.
However, Ahmed teaches processing circuitry to use amplitude information and phase information of the complex values of the three-dimensional complex-valued image volume to generate a representation of a surface of the target (See Fig. 6 and [0103-0115]. Ahmed teaches coherent imaging, which requires both amplitude and phase information for determining points representing the surface of the target. [0114] “Phase values may be derived 614 from the coherent subimages represented by complex number pixels in stage 612. The phase values may be accepted by a phase estimator 618 to estimate a phase for each of the voxels in the reconstruction volume 620, for example for a predefined time point at the center or at the end of the total measurement time, i.e. the data acquisition phase. An interpolation algorithm known as such may be employed, for example a linear interpolation or a spline interpolation.”).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Yarkoni’s invention by utilizing fully coherent image reconstruction. This modification would allow for image interpolation utilizing phase information to be used for correcting incoherent voxels when different frequencies or antennae clusters and/or subgroups for scanning a target are used ([0110] “Depending on available processing capabilities at LPU 126 and CPU 110, it can be preferred to perform coherent addition of intermediate images of various transmission antennas, or various Tx clusters, for one Tx subgroup at the CPU.” [Ahmed 0112] “Further then, the CPU 110 is operable to coherently add together 610 all intermediate (interpolated, weighted) images belonging to a given Tx subgroup 602, which includes images from all LPUs 126 involved in the associated Rx subgroup 604, to generate one coherent subimage per Tx subgroup”). Different antennae apertures could result in varying voxel positions, thus requiring correction for coherent location ([Ahmed 0079] “However, it is noted that the coherent subimages are generated from various different effective apertures of the various Tx/Rx subgroups and may therefore differ in aperture, image size, pixel/voxel positions, etc., such that prior to subimage adding an interpolation procedure has to be performed, as will be discussed further below.”).
Regarding claim 20, Yarkoni and Ahmed teaches the system of claim 19. Yarkoni further teaches wherein the representation of the surface of the target is a point cloud ([0045] “The amplitude matrix is sent to a preprocessing unit 1012, which generates a filtered point cloud 1014.”).
Regarding claim 21, Yarkoni and Ahmed teach the system of claim 19. Yarkoni further teaches wherein the processing circuitry is configured to: use the amplitude information to select some of the voxels of the image volume ([0004] “Optionally, the preprocessor unit comprises an amplitude filter operable to generate filtered data by selecting from the raw data voxels having amplitudes above a required threshold. Where appropriate, the preprocessor unit comprises a voxel selector operable to further reduce voxel number in the filtered data thereby generating the point cloud data.” [0044-0045] “The scanning arrangement may scan a subject may be in the target region in front of the array 1008, thereby generating amplitude data for the region 1010. The amplitude matrix is sent to a preprocessing unit 1012, which generates a filtered point cloud 1014. Optionally, the data may be processed by removing data below a threshold amplitude value 1016, removing outlying data 1018 and down sampling voxels 1020.”).
Yarkoni teaches selecting the voxels with high amplitude to represent the surface of the human being scanned and determining points from the high-amplitude voxels to create a point cloud [0010], but Yarkoni fails to teach processing circuitry to use the selected voxels to identify a plurality of locations in the image volume as a result of the phase information for the locations being a given phase value; and use the identified locations to generate the representation of the surface of the target.
However, Ahmed teaches processing circuitry to use the selected voxels to identify a plurality of locations in the image volume as a result of the phase information for the locations being a given phase value; and use the identified locations to generate the representation of the surface of the target (Ahmed teaches receiving phase information for interpolation to keep the high-amplitude voxels coherent across images in the volume, which would involve determining a plurality of locations based on a selected voxel and given phase value(s). [0114] “Phase values may be derived 614 from the coherent subimages represented by complex number pixels in stage 612. The phase values may be accepted by a phase estimator 618 to estimate a phase for each of the voxels in the reconstruction volume 620, for example for a predefined time point at the center or at the end of the total measurement time, i.e. the data acquisition phase. An interpolation algorithm known as such may be employed, for example a linear interpolation or a spline interpolation.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yarkoni’s invention by using phase values to obtain second locations (points which are determined from high amplitude voxels). This modification would consider maintaining image coherence when different frequencies or antennae clusters and/or subgroups for scanning a target are used ([Ahmed 0079] “However, it is noted that the coherent subimages are generated from various different effective apertures of the various Tx/Rx subgroups and may therefore differ in aperture, image size, pixel/voxel positions, etc., such that prior to subimage adding an interpolation procedure has to be performed,” [Ahmed 0112] “Further then, the CPU 110 is operable to coherently add together 610 all intermediate (interpolated, weighted) images belonging to a given Tx subgroup 602, which includes images from all LPUs 126 involved in the associated Rx subgroup 604, to generate one coherent subimage per Tx subgroup”).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Yarkoni (US 2022/0265165 A1) and Ahmed (US 2015/0293221 A1), and further in view of Sheen et al. (High-resolution 3D microwave imaging of a moving target using optical motion capture. Proceedings Volume 10994, Passive and Active Millimeter-Wave Imaging XXII.)
Regarding claim 4, Yarkoni and Ahmed teach the system of claim 2. Ahmed teaches using a motion tracker to determine relative positioning between images of a moving object for point cloud construction [0155], but Ahmed does not provide a detailed explanation. Thus, Yarkoni and Ahmed do not effectively teach using another three-dimensional complex-valued image volume to generate a second representation of the surface of the target and registering the first and second representations of the surface of the target with respect to one another to determine movement of the surface of the target.
However, Sheen teaches wherein the representation of the surface of the target is a first representation, and the processing circuitry is configured to: use another three-dimensional complex-valued image volume to generate a second representation of the surface of the target (Sheen teaches continuous scanning of a moving target for image reconstruction and surface detection. [Section 3] “…a hardware trigger simultaneously and synchronously triggers the radar to collect data at a matched frame rate. The marker position data are therefore time-aligned with the radar data and can be used directly by the image reconstruction algorithm. Markers on the target are used to calculate the homogeneous CT from a reference frame to each frame in the animation that is used in the image reconstruction.”) and
register the first and second representations of the surface of the target with respect to one another to determine movement of the surface of the target (Fig. 1 shows a first and second representation of a moving target being scanned by a linear array. The movement of a target voxel is tracked between representations to determine movement of the surface of the target.).
Yarkoni, Ahmed, and Sheen are analogous in the art to the claimed invention, because all teaches methods of utilizing an antennae array for transmitting and receiving electromagnetic waves for determining a 3D representation of the surface of a target. Therefore, it would have been obvious to one of ordinary skill in the art to modify Yarkoni and Ahmed’s invention by tracking the target between two representations. This modification would allow for antennae array to move when scanning the target, which would be required for handheld scanners or other scanners smaller than the target ([Sheen Abstract] “For some applications, it is more convenient to manually translate a linear array over the scene of interest, or equivalently, move the target in front of the linear array to scan an effective aperture.”)
Allowable Subject Matter
Claims 10-13 and 22-23 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Regarding these claims, the closest prior art of record, Yarkoni, teaches a surface determination system [0003] utilizing an antennae array [0028] and processing circuitry [0003] for creating a complex-valued image volume by transmitting and receiving electromagnetic waves to a target [Fig. 3]. First locations related to the surface of the target are selected, and high-amplitude voxels which correspond to those locations are selected [Fig. 3]. From the high-amplitude voxels, points (second locations) are determined, and these points create an accurate representation of the surface of the target. Yarkoni further refines the points using a parametric model rather than phase information, so Ahmed was relied upon for teaching the use of phase information to refine the points. Ahmed teaches coherent imaging, which requires phase information for keeping the images coherent to one another [Fig. 6]; thus, a more accurate representation of the surface is derived using amplitude and phase information.
However, claims 10 and 22 introduce a limitation where the points (second locations) are selected only at locations where the phase value is zero. Ahmed teaches using the phase for interpolation, but neither Ahmed or Sheen specify choosing points with zero-phase.
Claims 11 and 23 specify that a point (second location) selected is closest to one of the selected voxels with high amplitude (corresponding to the target surface). Similarly, claim 12 requires that multiple fourth locations with the same phase value are selected, and the point (second location) is chosen by selecting the fourth location that is closest to a selected voxel with high amplitude. Yarkoni teaches downsampling the selected voxels to obtain points and does not use the phase information in this process. Additionally, Ahmed’s interpolation method determines points using the phase value to ensure coherence between images. This method does not mention that the new point (second location) is selected by being the closest to a specific selected voxel.
Claim 13 is dependent on claim 12 and contains allowable subject matter for the same reasons.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Cooper et al. (US 7,773,205 B2) teaches a three-dimensional imaging radar which detects surfaces of objects by examining peak amplitudes returned by the FMCW waves. A compensation signal is used to focus on small, distant targets for increased resolution.
Evers et al. (US 2012/0038666 A1) teaches a method for observing a person using electromagnetic waves, determining a 3D image volume of the surface of the person for detecting any devices which may be hidden under clothing, and mapping the 3D image volume to a simplified human model to display an indication of detected device(s).
Rogan (US 9,110,163 B2) teaches recognizing movement of a target which is represented within a three-dimensional complex-valued image volume containing voxels. The method involves determining the voxels associated with the target and tracking movement of the target across multiple volumes over time.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ERIC JAMES SHOEMAKER whose telephone number is (571)272-6605. The examiner can normally be reached Monday through Friday from 8am to 5pm ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner' s supervisor, JENNIFER MEHMOOD, can be reached at (571)272-2976. The fax phone number for the organization where this application or proceeding is assigned is (571)273-8300.
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/JENNIFER MEHMOOD/Supervisory Patent Examiner, Art Unit 2664