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 .
Response to Amendment
This Office Action is in response to Applicant’s amendment/response filed on 06/18/2026, which has been entered and made of record. Applicant’s amendments to the Claims have overcome each and every objection previously set forth in the Non-Final (Final) Office Action mailed 03/19/2026.
Claim Objections
Claims 1 is objected to because of the following informalities:
In claim 1, line 19, “the LOS estimate” should read “the LOS estimator”.
Appropriate correction is required.
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.
Claims 1-13 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Miller (US 5881969, hereinafter “Miller”) in view of Tom et al. (US 10445616 B2).
Regarding claim 1, Miller discloses A line-of-sight aimpoint tracking system comprising: (col. 3, lines 15-18, “Once the line-of-sight (LOS) range between the estimated missile and estimated aimpoint falls below the start_range_value, the 3D_LOAL_START stage (2) begins.”; col. 12, lines 24-26, “When the value of the registration reaches some minimum value, 3DLOAL transitions to the terminal tracking stage.”). Note that: a line of sight aimpoint tracking is performed by the lock-on-after launch missile guidance system.
a model projecting module configured to receive a 3D model of an area of interest (col. 2, lines 19-21, “This 3D description is matched to a 3D model of the target area or a 3D description of the target scene is projected into the 2D reconnaissance image and registers in 2D”; col. 1, lines 53-54, “Although the actual prebriefing model is three dimensional in nature and defined in a world coordinate system”). Note that: a 3D model of the target area is a 3D model of an area of interest. to receive an aimpoint in a global reference frame, (col. 2, lines 43-50, “In the case of facet based information, individual objects in the target area are described by 3D polygons (facets) … An aimpoint and trackpoint are described as 3D points referenced to this set of 3D facets”). Note that: an aimpoint is a 3D point related to the 3D facets and can be defined in a world coordinate system (a global reference frame). to receive platform position and attitude measurement information indicating a platform viewpoint (col. 3, lines 35-49, “The missile orientation is used to define the terminal coordinate system as follows: The missile down vector … is mapped to the terminal coordinate system z vector. The component of the missile body vector (the vector which emanates from the missile's center of gravity and points through the nose cone of the missile) parallel to the ground plane is mapped into the terminal coordinate system x vector. The cross product of the terminal coordinate system (xxz) vectors defines the terminal coordinate system y vector. The origin of the terminal coordinate system is calculated as the preplanned trackpoint”). Note that: the missile as a platform is positioned in the terminal coordinate system related to the world coordinate system based on the position and attitude information from the missile down vector and the component of the missile body vector. and to convert the 3D model of the area of interest to a 2D projected image, including the aimpoint, as viewed from the platform viewpoint; and (col. 2, lines 19-21, “This 3D description is matched to a 3D model of the target area or a 3D description of the target scene is projected into the 2D reconnaissance image and registers in 2D”). Note that: (1) the 2D reconnaissance image can be regarded as a 2D projected image; and (2) it is obvious to one having ordinary skills in the art that: (a) the platform viewpoint is used to view the aimpoint when projecting the 3D model of the area of interest to a 2D projected image; and (b) with the same projection from the viewpoint the aimpoint as a 3D point can be projected to a point in the 2D projected image.
a registering/tracking module configured to receive the 2D projected image from the model projecting module, Note that: the 2D projected image converted from the 3D model above can be obtained on the missile to receive an image corresponding to the platform viewpoint, (col. 4, lines 4-5, “In phase (1), for each frame processed, a "2D line_finder" must be executed on the incoming sensor image”). Note that: each frame of the incoming sensor image can be regarded as an image corresponding to the platform viewpoint.
a line-of-sight ("LOS") estimator configured to calculate an estimated LOS vector in terms of the pointing angles, wherein the pointing angles comprise an azimuth and an elevation, from the platform viewpoint to the aimpoint based on the aimpoint pixel location corresponding to the aimpoint, a center of the image, and an instantaneous field of view ("IFOV"), and wherein the LOS estimate is further configured to output the estimated LOS vector for guidance, navigation and control operations when platform position and attitude measurement information is not available. Note that: It is obvious to one having ordinary skill in the art: as shown in the figure on the right:
PNG
media_image1.png
458
592
media_image1.png
Greyscale
an LOS estimator can be created as a part of the line-of-sight aim point tracking system to implement the following mathematical algorithm,
(1) mathematically, the light-of-sight vector AV from the platform viewpoint V to Aimpoint A in the 2D image can be represented by two angles (an azimuth and an elevation) which is equivalent to a 3D vector representation by following the mathematical steps as follows: a) assume Platform viewpoint P’s 3D as (0,0,0) under the Platform coordinate system x0y0z0, Image center C (xc,yc) as the center of the image, and Aimpoint A (xp,yq) projecting to Point Ay and Point Az on the dash lines in the 2D Image through the Image center C and parallel to corresponding image’s local 2D coordinate system yz’s axes y and z, while (xp,yq) and (xc,yc) are in unit of pixel; b) assuming alpha in unit of radian degree to be an angle between Line segments CP and AyP, and beta in unit of radian degree to be an angle between Line segments CP and AzP, and the length of Line segment CP is L, the LOS vector should be calculated as (L,L*tan(alpha),L*tan(beta)) and can be normalized to 3D vector (1, tan(alpha),tan(beta)); c) when an instantaneous field of view ("IFOV" or ifov) is introduced to specify the spatial angle resolution per pixel in the image, alpha and beta can be specified as ifov*(xp- xc) and ifov*(yq- yc), respectively, resulting in the LOS vector of (1, tan(ifov*(xp- xc)), tan(ifov*(yq- yc))); d) when angles alpha and beta is very small, the LOS vector of (1, tan(ifov*(xp- xc)), tan(ifov*(yp- yc))) can be estimated or approximated by (1, ifov*(xp- xc), ifov*(yq- yc)) because of function tan()’s known mathematical characteristics; e) when using an azimuth and an elevation to equivalently represent the 3D LOS vector, the azimuth angle can be calculated as atan(ifov*(xp- xc)/1), and the elevation angle can be calculated as atan(ifov*(yq- yc)/sqrt((ifov*(xp- xc))2+12)), indicating azimuth and elevation based on the aimpoint pixel location corresponding to the aimpoint, a center of the image, and an instantaneous field of view ("IFOV").
(2) the 3D LOS vector in either 3D vector (1, ifov*(xp- xc), ifov*(yp- yc)) or azimuth atan(ifov*(xp- xc)/1) / elevation atan(ifov*(yp- yc)/sqrt((ifov*(xp- xc))2+12)) can be output for GNC applications when the platform position and attitude measurement information is not available.
However, Miller fails to disclose, but in the same art of computer graphics and image processing, Tom discloses
to register the 2D projected image with the image corresponding to the platform viewpoint, (Tom, col. 3, lines 42-47, “a method for registering or matching images having fundamentally different characteristics, wherein the improvement comprises the step of using enhanced phase correlation (EPC) combined with a coarse sensor model to hypothesize a projection and match using a custom match metric to develop a best solution”; FIG. 8: step 808 (“register the test image and the reference image with phase correlation techniques, wherein the phase correlation techniques includes, in order, Equation 1 through Equation 4” and 810 (“register the test image and the reference image with phase correlation techniques, wherein the phase correlation techniques includes, in order, Equation 7 through Equation 8”). Note that: (1) the registration method can register the reference image (2D projected image) and the test image (the image corresponding to the platform viewpoint) while the 2D projected image as a synthetic image and the image corresponding to the platform viewpoint (e.g., infrared image frame) may have different characteristics; and (2) after the registration has been done, the pixel location of the aimpoint in the projected image can be mapped or correlated to the pixel location in the image corresponding to the platform viewpoint, resulting in a determined pixel location corresponding to the aimpoint.
Miller and Tom are in the same field of endeavor, namely computer graphics and image processing. Before the effective filing date of the claimed invention, it would have been obvious to apply registering two images (e.g., one synthetic image, and the other from an infrared video camera) with different characteristics, as taught by Tom into Miller. The motivation would have been “An image registration system and method for matching images having fundamentally different characteristics” (Tom, Abstract). The suggestion for doing so would allow to register two images with different characteristics to locate the aimpoint in the other image based on the location of aimpoint in the synthetic image. Therefore, it would have been obvious to combine Miller with Tom.
Regarding claim 2, Miller in view of Tom discloses The line-of-sight aimpoint tracking system of claim 1, wherein the image corresponding to the platform viewpoint is obtained by a video source configured to capture image data comprising image frames. (Tom, col. 4, lines 2-4, “This approach has been found to be extremely robust for registering or matching SAR, infrared (IR), EO, video, and x-ray imagery.”; col. 6, lines 1-3, “FIG. 5(a) depicts overlaid imagery of a post non-windowed enhanced phase correlation registration of video frame images from the second set of Aerial Video Frames”). Note that: the image corresponding to the platform viewpoint can be one frame image of the frames captured from a video camera mounted on the platform.
The motivation to combine Miller and Tom given in claim 1 is incorporated here.
Regarding claim 3, Miller in view of Tom discloses The line-of-sight aimpoint tracking system of claim 2, wherein the video source is an infrared camera. (Tom, col. 4, lines 2-4, “This approach has been found to be extremely robust for registering or matching SAR, infrared (IR), EO, video, and x-ray imagery.”; col. 4, lines 51-52, “capturing a test image from the camera”). Note that: a test image can be captured from a infrared camera (video source).
The motivation to combine Miller and Tom given in claim 1 is incorporated here.
Regarding claim 4, Miller in view of Tom discloses The line-of-sight aimpoint tracking system of claim 2, wherein the video source is pointed to have, in its field of view, the area of interest wherein the aimpoint is located. (Miller, FIG. 4: line-of-sight “LOS”, “Estimated trackpoint/ aim point”; col. 10, lines 20-23, “The missile orientation at the time of obtaining the frame, such as by camera or other scene reproducing means, is used to define the terminal coordinate system”; col. 2, lines 19-21, “This 3D description is matched to a 3D model of the target area or a 3D description of the target scene is projected into the 2D reconnaissance image and registers in 2D”). Note that: the camera (video source) is used to obtain frames and is pointed to the target area covering the aimpoint.
Regarding claim 5, Miller in view of Tom discloses The line-of-sight aimpoint tracking system of claim 1, wherein the model projecting module is configured to receive or calculate a line-of-sight vector from the platform position to the aimpoint, to calculate the plane that includes the aimpoint and is orthogonal to the line-of-sight vector, Note that: it is obvious to one having skills in the art that: (1) the line-of-sight vector
V
L
O
S
is the difference between the aimpoint
P
a
i
m
and the platform position
P
p
l
a
t
:
V
L
O
S
=
P
a
i
m
-
P
p
l
a
t
; Normalize
V
L
O
S
to get the unit normal vector
n
^
; and (2) The plane contains the aimpoint
P
a
i
m
and is orthogonal to
n
^
. Any point P in this plane satisfies the equation:
P
-
P
a
i
m
∙
n
^
=
0
PNG
media_image2.png
1
1
media_image2.png
Greyscale
to project, onto that plane, vertices from the 3D model of the area of interest, and to in-fill spaces between the projected vertices to form a projected 2D image of the model of the area of interest as if viewed from the viewpoint of the platform. (Miller, col. 2, lines 19-21, “This 3D description is matched to a 3D model of the target area or a 3D description of the target scene is projected into the 2D reconnaissance image and registers in 2D”). Note that: (1) the 2D reconnaissance image can be regarded as a 2D projected image when projecting the 3D model of the area of interest onto the plane above; and (2) it is obvious to one having skills in the art that: for each vertex
V
i
of the 3D model, calculate its perspective projection
V
i
'
:
(
V
i
'
-
P
p
l
a
t
)
.
n
^
=
|
|
P
a
i
m
-
P
p
l
a
t
|
|
and the normalized units of vector
(
V
i
'
-
P
p
l
a
t
)
and vector
(
V
i
-
P
p
l
a
t
)
are the same. In the plane, define two unit vectors u and v, and convert the projected 3D points (vertices)
V
i
'
into 2D coordinates
(
x
i
,
y
i
)
:
x
i
=
(
V
i
'
-
P
a
i
m
)
.
u
,
y
i
=
(
V
i
'
-
P
a
i
m
)
.
v
to form a 2D image. And for the pixels between the projected vertices corresponding pixels, a conventional interpolation method can be performed calculated pixel values to in-fill them.
PNG
media_image2.png
1
1
media_image2.png
Greyscale
PNG
media_image2.png
1
1
media_image2.png
Greyscale
Regarding claim 6, Miller in view of Tom discloses The line-of-sight aimpoint tracking system of claim 1, wherein the registering/tracking module is configured to conduct interframe registration after having performed image registration. (Tom, col. 4, lines 2-4, “This approach has been found to be extremely robust for registering or matching SAR, infrared (IR), EO, video, and x-ray imagery.”; col. 6, lines 1-3, “FIG. 5(a) depicts overlaid imagery of a post non-windowed enhanced phase correlation registration of video frame images from the second set of Aerial Video Frames”; lines 42-47, “a method for registering or matching images having fundamentally different characteristics, wherein the improvement comprises the step of using enhanced phase correlation (EPC) combined with a coarse sensor model to hypothesize a projection and match using a custom match metric to develop a best solution”; FIG. 8: step 808 (“register the test image and the reference image with phase correlation techniques, wherein the phase correlation techniques includes, in order, Equation 1 through Equation 4” and 810 (“register the test image and the reference image with phase correlation techniques, wherein the phase correlation techniques includes, in order, Equation 7 through Equation 8”). Note that: (1) after having performed image registration between the 2D projected image and the image corresponding to the platform viewpoint), a first frame and a second frame captured from a video camera mounted on the platform corresponding to the platform viewpoint can be regarded as two images (the test image and the reference image); and (2) a registration between the first frame and the second can be performed using the registration method by Tom again, resulting in interframe registration to track the determined pixel of the aimpoint in the video frames.
The motivation to combine Miller and Tom given in claim 1 is incorporated here.
Regarding claim 7, Miller in view of Tom discloses The line-of-sight aimpoint tracking system of claim 1, wherein … a projected and in-filled 2D image of the 3D model of the area of interest … an image of the area of interest … (Miller, col. 2, lines 19-21, “This 3D description is matched to a 3D model of the target area or a 3D description of the target scene is projected into the 2D reconnaissance image and registers in 2D”; col. 4, lines 4-5, “In phase (1), for each frame processed, a "2D line_finder" must be executed on the incoming sensor image”). Note that: (1) the 2D reconnaissance image can be regarded as a 2D projected image when projecting the 3D model of the area of interest onto the plane above; (2) it is obvious to one having skills in the art that: for each vertex
V
i
of the 3D model, calculate its perspective projection
V
i
'
:
(
V
i
'
-
P
p
l
a
t
)
.
n
^
=
|
|
P
a
i
m
-
P
p
l
a
t
|
|
and the normalized units of vector
(
V
i
'
-
P
p
l
a
t
)
and vector
(
V
i
-
P
p
l
a
t
)
are the same. In the plane, define two unit vectors u and v, and convert the projected 3D points (vertices)
V
i
'
into 2D coordinates
(
x
i
,
y
i
)
:
x
i
=
(
V
i
'
-
P
a
i
m
)
.
u
,
y
i
=
(
V
i
'
-
P
a
i
m
)
.
v
to form a 2D image. And for the pixels between the projected vertices corresponding pixels, a conventional interpolation method can be performed calculated pixel values to in-fill them, to form a projected and in-filled 2D image of the 3D model of the area of interest; and (3) each frame of the incoming sensor image can be regarded as an image of the area of interest corresponding to the platform viewpoint.
an image of the area of interest acquired by the video source. (Tom, col. 4, lines 2-4, “This approach has been found to be extremely robust for registering or matching SAR, infrared (IR), EO, video, and x-ray imagery.”; col. 6, lines 1-3, “FIG. 5(a) depicts overlaid imagery of a post non-windowed enhanced phase correlation registration of video frame images from the second set of Aerial Video Frames”). Note that: the image corresponding to the platform viewpoint can be one frame image of the frames captured from a video camera mounted on the platform.
… the registering/tracking module is configured to perform image registration to align the projected and in-filled 2D image of the model of the area of interest with an image of the area of interest acquired by the video source. (Tom, col. 3, lines 42-47, “a method for registering or matching images having fundamentally different characteristics, wherein the improvement comprises the step of using enhanced phase correlation (EPC) combined with a coarse sensor model to hypothesize a projection and match using a custom match metric to develop a best solution”; FIG. 8: step 808 (“register the test image and the reference image with phase correlation techniques, wherein the phase correlation techniques includes, in order, Equation 1 through Equation 4” and 810 (“register the test image and the reference image with phase correlation techniques, wherein the phase correlation techniques includes, in order, Equation 7 through Equation 8”). Note that: a registration between the projected and in-filled 2D image of the model of the area of interest and an image of the area of interest acquired by the video source can be performed using the registration method by Tom again, resulting in alignment of two images.
The motivation to combine Miller and Tom given in claim 1 is incorporated here.
Regarding claim 8, Miller in view of Tom discloses The line-of-sight aimpoint tracking system of claim 7, wherein the registering/tracking module is configured to locate a pixel, corresponding to a 3D aimpoint that is within the area of interest, on the image acquired of the area of interest by the video source using such image registration. Note that: (1) a 3D aimpoint within the area of interest is a 3D point within the area of interest and can be mapped to the projected and infilled 2D image (see claim 7) of the 3D model of the area of interest before the image registration has been performed; and (2) after the image registration to align the projected and in-filled 2D image of the 3D model of the area of interest with an image of the area of interest acquired by the video source has been performed, the pixel location of the 3D aimpoint in the projected and in-filled 2D image of the 3D model of the area of interest can be mapped to the pixel location in the image corresponding to the platform viewpoint and acquired by the video source using the image registration, resulting in the pixel location corresponding to a 3D aimpoint that is within the area of interest.
Regarding claim 9, Miller in view of Tom discloses The line-of-sight aimpoint tracking system of claim 1, wherein the registering/tracking module is configured to perform interframe registration to track the aimpoint in a subsequent image from the video source. (Tom, col. 4, lines 2-4, “This approach has been found to be extremely robust for registering or matching SAR, infrared (IR), EO, video, and x-ray imagery.”; col. 6, lines 1-3, “FIG. 5(a) depicts overlaid imagery of a post non-windowed enhanced phase correlation registration of video frame images from the second set of Aerial Video Frames”; lines 42-47, “a method for registering or matching images having fundamentally different characteristics, wherein the improvement comprises the step of using enhanced phase correlation (EPC) combined with a coarse sensor model to hypothesize a projection and match using a custom match metric to develop a best solution”; FIG. 8: step 808 (“register the test image and the reference image with phase correlation techniques, wherein the phase correlation techniques includes, in order, Equation 1 through Equation 4” and 810 (“register the test image and the reference image with phase correlation techniques, wherein the phase correlation techniques includes, in order, Equation 7 through Equation 8”). Note that: (1) the aimpoint is projected on the 2D projected image (see claim 1); (2) after having performed image registration between the 2D projected image and the image corresponding to the platform viewpoint), the aimpoint can be located and tracked in the image corresponding to the platform viewpoint; (3) the image corresponding to the platform viewpoint can regarded as a first frame acquired from a video camera mounted on the platform and a subsequent frame (a second frame) captured from the video camera can be regarded as two images (the test image and the reference image); (4) a registration between the first frame and the second frame can be performed using the registration method by Tom again, resulting in a first interframe registration; and (5) using the first interframe registration, the aimpoint location in the first frame (the image corresponding to the platform viewpoint) is mapped to the pixel location in the second frame, resulting in tracking the aimpoint in the subsequent image (the second frame) from the video source.
The motivation to combine Miller and Tom given in claim 1 is incorporated here.
Regarding claim 10, Miller in view of Tom discloses The line-of-sight aimpoint tracking system of claim 1, wherein the registering/tracking module is configured to keep track of the aimpoint position in a subsequent image by projecting forward an aimpoint pixel location using a transform computed from interframe image registration. (Tom, col. 4, lines 2-4, “This approach has been found to be extremely robust for registering or matching SAR, infrared (IR), EO, video, and x-ray imagery.”; col. 6, lines 1-3, “FIG. 5(a) depicts overlaid imagery of a post non-windowed enhanced phase correlation registration of video frame images from the second set of Aerial Video Frames”; lines 42-47, “a method for registering or matching images having fundamentally different characteristics, wherein the improvement comprises the step of using enhanced phase correlation (EPC) combined with a coarse sensor model to hypothesize a projection and match using a custom match metric to develop a best solution”; FIG. 8: step 808 (“register the test image and the reference image with phase correlation techniques, wherein the phase correlation techniques includes, in order, Equation 1 through Equation 4” and 810 (“register the test image and the reference image with phase correlation techniques, wherein the phase correlation techniques includes, in order, Equation 7 through Equation 8”). Note that: (1) the aimpoint is projected on the 2D projected image (see claim 1); (2) after having performed image registration between the 2D projected image and the image corresponding to the platform viewpoint), the aimpoint can be located and tracked in the image corresponding to the platform viewpoint; (3) the image corresponding to the platform viewpoint can regarded as frame #1 acquired from a video camera mounted on the platform and a subsequent frame (frame #2) captured from the video camera can be regarded as two images (the test image and the reference image); (4) a registration between frame #1 and frame #2 can be performed using the registration method by Tom again, resulting in interframe registration #1; (5) using interframe registration #1, the aimpoint location in frame #1 (the image corresponding to the platform viewpoint) is mapped, projected, or transformed forward to the pixel location in frame #2, resulting in tracking the aimpoint in frame #2 from the video source; (6) repeat the process above starting from n=2: (a) after the pixel location of the aimpoint has been determined or mapped in frame n using interframe registration n-1, acquire a subsequent frame n+1 from the video camera; (b) a registration between frame n and frame n+1 can be performed using the registration method by Tom again, resulting in interframe registration n; (c) using interframe registration n, the aimpoint location in frame #n (the image corresponding to the platform viewpoint) is mapped, projected, or transformed forward to the pixel location in frame n+1, resulting in tracking the aimpoint in frame n+1 from the video source; and (7) In this manner one can keep track of the aimpoint position in a subsequent image by increasing n.
The motivation to combine Miller and Tom given in claim 1 is incorporated here.
Regarding claim 11, Miller in view of Tom discloses
receiving aimpoint position information; (Miller, col. 2, lines 43-50, “In the case of facet based information, individual objects in the target area are described by 3D polygons (facets) … An aimpoint and trackpoint are described as 3D points referenced to this set of 3D facets”). Note that: an aimpoint is a 3D point related to the 3D facets and can be defined in a world coordinate system (a global reference frame).
receiving platform position and attitude measurement information corresponding to a current position of a platform; (Miller, “The missile orientation is used to define the terminal coordinate system as follows: The missile down vector … is mapped to the terminal coordinate system z vector. The component of the missile body vector (the vector which emanates from the missile's center of gravity and points through the nose cone of the missile) parallel to the ground plane is mapped into the terminal coordinate system x vector. The cross product of the terminal coordinate system (xxz) vectors defines the terminal coordinate system y vector. The origin of the terminal coordinate system is calculated as the preplanned trackpoint”). Note that: the missile as a platform is positioned in the terminal coordinate system related to the world coordinate system based on the position and attitude information from the missile down vector and the component of the missile body vector.
obtaining a platform viewpoint image (Miller, col. 4, lines 4-5, “In phase (1), for each frame processed, a "2D line_finder" must be executed on the incoming sensor image”). Note that: each frame of the incoming sensor image can be regarded as an image corresponding to the platform viewpoint.
generating a 2D projected image by projecting at least a portion of a 3D model of an area of interest containing the aimpoint onto a plane from a platform viewpoint based on the received position and attitude measurement information; (Miller, col. 2, lines 19-21, “This 3D description is matched to a 3D model of the target area or a 3D description of the target scene is projected into the 2D reconnaissance image and registers in 2D”). Note that: (1) the 2D reconnaissance image can be regarded as a 2D projected image; and (2) it is obvious to one having ordinary skills in the art that: (a) the platform viewpoint is used to view the aimpoint when projecting the 3D model of the area of interest to a 2D projected image; and (b) with the same projection from the viewpoint the aimpoint as a 3D point can be projected to a point in the 2D projected image.
locating the aimpoint within the 2D projected image; (Miller, col. 2, lines 19-21, “This 3D description is matched to a 3D model of the target area or a 3D description of the target scene is projected into the 2D reconnaissance image and registers in 2D”). Note that: it is obvious to one having ordinary skills in the art that with the same projection from the viewpoint the aimpoint as a 3D point can be projected to a point in the 2D projected image.
A non-transitory computer-readable medium storing a plurality of instructions which when executed by one or more processors causes the one or more processors to perform a method for line-of-sight aimpoint tracking comprising: (Tom, col. 14, lines 4-8, “a non-transitory computer-readable medium in operative communication with the camera and storing a plurality of instructions which when executed by one or more processors causes the one or more processors to perform a method for image registration”).
… from a platform-mounted video source;(Tom, col. 4, lines 2-4, “This approach has been found to be extremely robust for registering or matching SAR, infrared (IR), EO, video, and x-ray imagery.”; col. 6, lines 1-3, “FIG. 5(a) depicts overlaid imagery of a post non-windowed enhanced phase correlation registration of video frame images from the second set of Aerial Video Frames”). Note that: the image corresponding to the platform viewpoint can be one frame image of the frames captured from a video camera mounted on the platform.
using image registration to register the 2D projected image with an image corresponding to the platform viewpoint image; (Tom, col. 3, lines 42-47, “a method for registering or matching images having fundamentally different characteristics, wherein the improvement comprises the step of using enhanced phase correlation (EPC) combined with a coarse sensor model to hypothesize a projection and match using a custom match metric to develop a best solution”; FIG. 8: step 808 (“register the test image and the reference image with phase correlation techniques, wherein the phase correlation techniques includes, in order, Equation 1 through Equation 4” and 810 (“register the test image and the reference image with phase correlation techniques, wherein the phase correlation techniques includes, in order, Equation 7 through Equation 8”). Note that: (1) the registration method can register the reference image (2D projected image) and the test image (the image corresponding to the platform viewpoint) while the 2D projected image as a synthetic image and the image corresponding to the platform viewpoint (e.g., infrared image frame) may have different characteristics; and (2) after the registration has been done, the pixel location of the aimpoint in the projected image can be mapped or correlated to the pixel location in the image corresponding to the platform viewpoint, resulting in a determined pixel location corresponding to the aimpoint.
based on the registration between the image corresponding to the platform viewpoint image and the 2D projected image, locating a pixel position of the aimpoint in the image corresponding to the platform image; Note that: (1) a 3D aimpoint within the area of interest is a 3D point within the area of interest and can be mapped to the projected and infilled 2D image (see claim 7) of the 3D model of the area of interest before the image registration has been performed; and (2) after the image registration to align the projected and in-filled 2D image of the 3D model of the area of interest with an image of the area of interest acquired by the video source has been performed, the pixel location of the 3D aimpoint in the projected and in-filled 2D image of the 3D model of the area of interest can be mapped to the pixel location in the platform viewpoint image acquired by the video source using the image registration, resulting in the pixel location corresponding to a 3D aimpoint that is within the area of interest.
tracking the pixel position of the aimpoint through interframe registration to predict a location of the aimpoint in an image corresponding to a subsequent platform image. (Tom, col. 4, lines 2-4, “This approach has been found to be extremely robust for registering or matching SAR, infrared (IR), EO, video, and x-ray imagery.”; col. 6, lines 1-3, “FIG. 5(a) depicts overlaid imagery of a post non-windowed enhanced phase correlation registration of video frame images from the second set of Aerial Video Frames”; lines 42-47, “a method for registering or matching images having fundamentally different characteristics, wherein the improvement comprises the step of using enhanced phase correlation (EPC) combined with a coarse sensor model to hypothesize a projection and match using a custom match metric to develop a best solution”; FIG. 8: step 808 (“register the test image and the reference image with phase correlation techniques, wherein the phase correlation techniques includes, in order, Equation 1 through Equation 4” and 810 (“register the test image and the reference image with phase correlation techniques, wherein the phase correlation techniques includes, in order, Equation 7 through Equation 8”). Note that: (1) the aimpoint is projected on the 2D projected image; (2) after having performed image registration between the 2D projected image and the image corresponding to the platform viewpoint), the aimpoint can be located and tracked in the image corresponding to the platform viewpoint; (3) the image corresponding to the platform viewpoint can regarded as a first frame acquired from a video camera mounted on the platform and a subsequent frame (a second frame) captured from the video camera can be regarded as two images (the test image and the reference image); (4) a registration between the first frame and the second frame can be performed using the registration method by Tom again, resulting in a first interframe registration; and (5) using the first interframe registration, the aimpoint location in the first frame (the image corresponding to the platform viewpoint) is mapped to the pixel location in the second frame, resulting in tracking the aimpoint in the subsequent image (the second frame) from the video source.
calculating an estimated LOS vector, wherein the LOS vector comprises an azimuth angle and an elevation angle, from a platform viewpoint to the aimpoint based on the pixel position of the aimpoint, a center of the image, and an instantaneous field of view (“IFOV”); and Note that: It is obvious to one having ordinary skill in the art: as shown in the figure on the right:
PNG
media_image1.png
458
592
media_image1.png
Greyscale
mathematically, the light-of-sight vector AV from the platform viewpoint V to Aimpoint A in the 2D image can be represented by two angles (an azimuth and an elevation) which is equivalent to a 3D vector representation by following the mathematical steps as follows: a) assume Platform viewpoint P’s 3D as (0,0,0) under the Platform coordinate system x0y0z0, Image center C (xc,yc) as the center of the image, and Aimpoint A (xp,yq) projecting to Point Ay and Point Az on the dash lines in the 2D Image through the Image center C and parallel to corresponding image’s local 2D coordinate system yz’s axes y and z, while (xp,yq) and (xc,yc) are in unit of pixel; b) assuming alpha in unit of radian degree to be an angle between Line segments CP and AyP, and beta in unit of radian degree to be an angle between Line segments CP and AzP, and the length of Line segment CP is L, the LOS vector should be calculated as (L,L*tan(alpha),L*tan(beta)) and can be normalized to 3D vector (1, tan(alpha),tan(beta)); c) when an instantaneous field of view ("IFOV" or ifov) is introduced to specify the spatial angle resolution per pixel in the image, alpha and beta can be specified as ifov*(xp- xc) and ifov*(yq- yc), respectively, resulting in the LOS vector of (1, tan(ifov*(xp- xc)), tan(ifov*(yq- yc))); d) when angles alpha and beta is very small, the LOS vector of (1, tan(ifov*(xp- xc)), tan(ifov*(yp- yc))) can be estimated or approximated by (1, ifov*(xp- xc), ifov*(yq- yc)) because of function tan()’s known mathematical characteristics; e) when using an azimuth and an elevation to equivalently represent the 3D LOS vector, the azimuth angle can be calculated as atan(ifov*(xp- xc)/1), and the elevation angle can be calculated as atan(ifov*(yq- yc)/sqrt((ifov*(xp- xc))2+12)), indicating azimuth and elevation based on the aimpoint pixel location corresponding to the aimpoint, a center of the image, and an instantaneous field of view ("IFOV").
when current platform position and attitude measurement information is available, outputting guidance, navigation and control operations based on platform position and attitude measurement information; and (Miller, col. 3, lines 35-49, “The missile orientation is used to define the terminal coordinate system as follows: The missile down vector … is mapped to the terminal coordinate system z vector. The component of the missile body vector (the vector which emanates from the missile's center of gravity and points through the nose cone of the missile) parallel to the ground plane is mapped into the terminal coordinate system x vector. The cross product of the terminal coordinate system (xxz) vectors defines the terminal coordinate system y vector. The origin of the terminal coordinate system is calculated as the preplanned trackpoint”; page 65, para. 2, “This phase continues to execute until RLos falls below the blind range, TRANGE:_BLIND. This is the point at which the missile can no longer effectively change its impact point via guidance commands and is missile airframe dependent”). Note that: (1) the missile as a platform is positioned in the terminal coordinate system related to the world coordinate system based on the position and attitude information from the missile down vector and the component of the missile body vector; and (2) when the position and attitude information are available, RLOS is high enough based on platform position and attitude measurement information so that guidance, navigation and control operations can be output for missile.
when current platform position and attitude measurement information is not available, outputting guidance, navigation and control operations based on the estimated LOS vector. Note that: It is obvious to one having ordinary skill in the art: the 3D LOS vector in either 3D vector (1, ifov*(xp- xc), ifov*(yp- yc)) or azimuth atan(ifov*(xp- xc)/1) / elevation atan(ifov*(yp- yc)/sqrt((ifov*(xp- xc))2+12)) can be output for GNC applications when the platform position and attitude measurement information is not available.
The motivation to combine Miller and Tom given in claim 1 is incorporated here.
Regarding claim 12, Miller in view of Tom discloses The non-transitory computer-readable medium of claim 11, wherein generation of the 2D projected image, including the aimpoint, as viewed from the platform viewpoint comprises calculating a line-of-sight vector from the platform position to the aimpoint, calculating the plane that includes the aimpoint and that is orthogonal to the line-of-sight vector, projecting, onto that plane, vertices from the 3D model, and in-filling spaces between the projected vertices. Note that: it is obvious to one having skills in the art that: (1) the line-of-sight vector
V
L
O
S
is the difference between the aimpoint
P
a
i
m
and the platform position
P
p
l
a
t
:
V
L
O
S
=
P
a
i
m
-
P
p
l
a
t
; Normalize
V
L
O
S
to get the unit normal vector
n
^
; and (2) The plane contains the aimpoint
P
a
i
m
and is orthogonal to
n
^
. Any point P in this plane satisfies the equation:
P
-
P
a
i
m
∙
n
^
=
0
PNG
media_image2.png
1
1
media_image2.png
Greyscale
to project, onto that plane, vertices from the 3D model of the area of interest, and to in-fill spaces between the projected vertices to form a projected 2D image of the model of the area of interest as if viewed from the viewpoint of the platform. (Miller, col. 2, lines 19-21, “This 3D description is matched to a 3D model of the target area or a 3D description of the target scene is projected into the 2D reconnaissance image and registers in 2D”). Note that: (1) the 2D reconnaissance image can be regarded as a 2D projected image when projecting the 3D model of the area of interest onto the plane above; (2) it is obvious to one having skills in the art that: for each vertex
V
i
of the 3D model, calculate its perspective projection
V
i
'
:
(
V
i
'
-
P
p
l
a
t
)
.
n
^
=
|
|
P
a
i
m
-
P
p
l
a
t
|
|
and the normalized units of vector
(
V
i
'
-
P
p
l
a
t
)
and vector
(
V
i
-
P
p
l
a
t
)
are the same. In the plane, define two unit vectors u and v, and convert the projected 3D points (vertices)
V
i
'
into 2D coordinates
(
x
i
,
y
i
)
:
x
i
=
(
V
i
'
-
P
a
i
m
)
.
u
,
y
i
=
(
V
i
'
-
P
a
i
m
)
.
v
to form a 2D image. And for the pixels between the projected vertices corresponding pixels, a conventional interpolation method can be performed calculated pixel values to in-fill them.
Regarding claim 13, Miller in view of Tom The non-transitory computer-readable medium of claim 11, wherein tracking the pixel position of the aimpoint through interframe registration is carried out even when platform position and/or attitude information can no longer be received. (Tom, col. 4, lines 2-4, “This approach has been found to be extremely robust for registering or matching SAR, infrared (IR), EO, video, and x-ray imagery.”; col. 6, lines 1-3, “FIG. 5(a) depicts overlaid imagery of a post non-windowed enhanced phase correlation registration of video frame images from the second set of Aerial Video Frames”; lines 42-47, “a method for registering or matching images having fundamentally different characteristics, wherein the improvement comprises the step of using enhanced phase correlation (EPC) combined with a coarse sensor model to hypothesize a projection and match using a custom match metric to develop a best solution”; FIG. 8: step 808 (“register the test image and the reference image with phase correlation techniques, wherein the phase correlation techniques includes, in order, Equation 1 through Equation 4” and 810 (“register the test image and the reference image with phase correlation techniques, wherein the phase correlation techniques includes, in order, Equation 7 through Equation 8”). Note that: (1) the aimpoint is projected on the 2D projected image (see claim 1); (2) after having performed image registration between the 2D projected image and the image corresponding to the platform viewpoint), the aimpoint can be located and tracked in the image corresponding to the platform viewpoint; (3) the image corresponding to the platform viewpoint can regarded as frame #1 acquired from a video camera mounted on the platform and a subsequent frame (frame #2) captured from the video camera can be regarded as two images (the test image and the reference image); (4) a registration between frame #1 and frame #2 can be performed using the registration method by Tom again, resulting in interframe registration #1; (5) using interframe registration #1, the aimpoint location in frame #1 (the image corresponding to the platform viewpoint) is mapped, projected, or transformed forward to the pixel location in frame #2, resulting in tracking the aimpoint in frame #2 from the video source; (6) repeat the process above starting from n=2: (a) after the pixel location of the aimpoint has been determined or mapped in frame n using interframe registration n-1, acquire a subsequent frame n+1 from the video camera; (b) a registration between frame n and frame n+1 can be performed using the registration method by Tom again, resulting in interframe registration n; (c) using interframe registration n, the aimpoint location in frame #n (the image corresponding to the platform viewpoint) is mapped, projected, or transformed forward to the pixel location in frame n+1, resulting in tracking the aimpoint in frame n+1 from the video source; and (7) In this manner one can keep track of the aimpoint position in a subsequent image by increasing n when platform position and/or attitude information can no longer be received since the track of the aimpoint does not need the 2D projected image corresponding to the platform position and/or attitude information.
The motivation to combine Miller and Tom given in claim 1 is incorporated here.
Regarding claim 17, Miller in view of Tom discloses The non-transitory computer-readable medium of claim 11, wherein the 3D model is a wireframe model or a point cloud model. (Miller, col. 9, lines 56-62, “To explain the invention in conjunction with prebriefing option (1) as set forth above, a 3D wire frame model of the target site is initially generated. The wire frame model of the target area including the target is provided and obtained prior to missile launch by prior air reconnaissance or from other sources and stored in a data base, preferably in the missile”). Note that: the 3D model of the target site is 3D wire frame model.
Claims 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Miller in view of Tom and Liu et al. (US 20210192763 A1, hereinafter “Liu”).
Regarding claim 14, Miller in view of Tom discloses The non-transitory computer-readable medium of claim 11, wherein the interframe registration comprises
However, Miller in view of Tom fails to discloses, but in the same aft of image processing, Liu discloses feature-based interframe registration (Liu, page 10, para. [0082], “In another embodiment, a feature-based image registration algorithm may be implemented for image registration … In another example, speeded up robust features (SURF) is used for feature-based registration.”). Note that: (1) frame 1 and frame 2 captured from a video camera can be regarded as two images for registration; and (2) a feature-based image registration algorithm can be implemented to register frame 1 to frame 2, resulting in an interframe registration between frames.
Miller in view of Tom, and Liu, are in the same field of endeavor, namely image processing. Before the effective filing date of the claimed invention, it would have been obvious to apply registering two images using image-feature based registration with Speeded Up Robust Features, as taught by Liu into Miller in view of Tom. The motivation would have been “a feature-based image registration algorithm may be implemented for image registration … In another example, speeded up robust features (SURF) is used for feature-based registration.” (Liu, page 10, para. [0082]). The suggestion for doing so would allow to register two images with image-feature based registration to improve registration performance. Therefore, it would have been obvious to combine Miller, Tom, and Liu.
Regarding claim 15, the combination of Miller, Tom, and Liu discloses The non-transitory computer-readable medium of claim 11, wherein Speeded Up Robust Features are used for interframe registration. (Liu, page 10, para. [0082], “In another embodiment, a feature-based image registration algorithm may be implemented for image registration … In another example, speeded up robust features (SURF) is used for feature-based registration.”). Note that: (1) frame 1 and frame 2 captured from a video camera can be regarded as two images for registration; and (2) a feature-based image registration algorithm can be implemented to register frame 1 to frame 2, resulting in an interframe registration between frames.
The motivation to combine Miller, Tom, and Liu given in claim 14 is incorporated here.
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Miller in view of Tom and Archive_1 (Image registration based on part of the image, archive.org, https://web.archive.org/web/20240415000104/https://www.mathworks.com/matlabcentral/answers/1438099-image-registration-based-on-part-of-the-image, hereinafter “Archive_1”).
Regarding claim 16, Miller in view of Tom discloses The non-transitory computer-readable medium of claim 11, wherein the interframe registration comprises
However, Miller in view of Tom fails to disclose, but in the same art of image processing, Achive_1 discloses matching features that are confined to a local window in the vicinity of the acquired aimpoint. (Archive_1, page 4, para. 1, “In MATLAB, you can perform image registration by specifying a region of interest (ROI) in the images, which allows the registration algorithm to focus on a particular part of the images. This can be especially useful when you have images with large regions that should be excluded from the registration process, such as the black-filled margins in your case.”). Note that: (1) Matlab has a method to define a ROI and use the image features within the ROI for image registration; (2) the ROI that is in the vicinity of the acquired aimpoint can be located at the center of frame 1 and frame 2 captured from a video camera mounted on the platform; and (3) the image registration of frame 1 and frame 2 can result in an interframe registration between frame 1 and frame 2.
Miller in view of Tom, and Archive_1, are in the same field of endeavor, namely image processing. Before the effective filing date of the claimed invention, it would have been obvious to apply registering two images using features within an area of the images, as taught by Archive_1 into Miller in view of Tom. The motivation would have been “In MATLAB, you can perform image registration by specifying a region of interest (ROI) in the images, which allows the registration algorithm to focus on a particular part of the images.” (Archive_1, page 4, para. 1). The suggestion for doing so would allow to register two images with image-feature based registration and the features within an area of the images to improve registration performance. Therefore, it would have been obvious to combine Miller, Tom, and Archive_1.
Claims 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Miller in view of Tom and Sadekar (Understanding Lens Distortion, archive.org, https://web.archive.org/web/20230604010941/https://learnopencv.com/understanding-lens-distortion/, hereinafter “Sadekar”).
Regarding claim 18, Miller in view of Tom discloses The non-transitory computer-readable medium of claim 11, wherein the method for line-of-sight aimpoint tracking further comprises
However, Miller in view of Tom fails to disclose, but in the same art of image processing, Sadekar discloses removing of distortion from the platform viewpoint image based on data regarding distortion in the video source. (Sadekar, page 6, para. 1, “There are three major steps to remove distortion due to lens. 1. Perform camera calibration and get the intrinsic camera parameters. This is what we did in the P-revious P-OSt of this series. (/web/20230604010941 /httP-s ://learnoP-encv.com/camera-calibration-using:QP-encv/) The intrinsic parameters also include the camera distortion parameters. 2. Refine the camera matrix to control the percentage of unwanted pixels in the undistorted image. 3. Using the refined camera matrix to undistort the image.”). Note that: the frame (the platform viewpoint image) from a video camera can be undistorted with the intrinsic camera parameters, resulting in the removal of the distortion from the image.
Miller in view of Tom, and Sadekar, are in the same field of endeavor, namely image processing. Before the effective filing date of the claimed invention, it would have been obvious to apply understanding lens distortion and undistorting the image, as taught by Sadekar into Miller in view of Tom. The motivation would have been “understanding lens distortion” (Sadekar, title) and “use the derived distortion coefficients to un-distort the image” (Sadekar, page 5, para. 4). The suggestion for doing so would allow to understand lens distortion for camera matrix mapping and remove or undistort the distortion from the platform viewpoint image based on data regarding distortion in the video camera. Therefore, it would have been obvious to combine Miller, Tom, and Sadekar.
Regarding claim 19, the combination of Miller, and Sadekar discloses The non-transitory computer-readable medium of claim 11, wherein the method for line-of-sight aimpoint tracking further comprises distorting the 2D projected image based on data regarding distortion in the video source. (Sadekar, page 6, para. 1, “There are three major steps to remove distortion due to lens. 1. Perform camera calibration and get the intrinsic camera parameters. This is what we did in the P-revious P-OSt of this series. (/web/20230604010941 /httP-s ://learnoP-encv.com/camera-calibration-using:QP-encv/) The intrinsic parameters also include the camera distortion parameters. 2. Refine the camera matrix to control the percentage of unwanted pixels in the undistorted image”: Note that: it is obvious to one having ordinary skills in the art that: (1) the camera matrix can be inverted to obtain an inverted matrix mapping a undistorted image to a distorted image mathematically; and (2) the inverted matrix can be applied to the 2D projected image based on data to distort it regarding distortion in the video source.
The motivation to combine Miller, Tom, and Sadekar given in claim 18 is incorporated here.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Miller in view of Tom and Fallah et al. (Intensifying the spatial resolution of 3D thermal models from aerial imagery using deep learning-based image super-resolution, GEOCARTO INTERNATIONAL, 2022, VOL. 37, NO. 26, hereinafter “Fallah”).
Regarding claim 20, Miller in view of Tom discloses The non-transitory computer-readable medium of claim 11, wherein the method for line-of-sight aimpoint tracking further comprises
However, Miller in view of Tom fails to disclose, but in the same art of computer graphics and image processing, Fallah discloses enhancing higher-frequency spatial features of the 3D model and the 2D projected image. (Fallah, page 13518, Abstract, “a method for intensifying 3D thermal model using deep learning-based image super-resolution is presented. In the proposed method, first, the enhanced deep residual super-resolution (EDSR) deep network is re-trained based on thermal aerial images. Second, the resolution of low-resolution thermal images is enhanced using the newly trained network. Finally, the state-of-the-art structures from motion (SfM), semi global matching (SGM) and space intersection are utilized to generate intensified 3D thermal model from the resolution enhanced thermal images.”; page 13534, Figure 14: “the red curve represents the MTF of the Low-resolution 3D model and, the green curve represents the MTF of the intensified 3D model”, indicating that the intensified 3D model has higher MTF and enhancement for high frequency features or details). Note that: (1) the 3D thermal model is intensified with a deep-learning based neural network, resulting in higher MTF of the intensified 3D model for high frequency features; and (2) since the 2D projected images is obtained by the projection process of the 3D model onto a plane, the high frequence features of the 2D projected image are accordingly enhanced.
Miller in view of Tom, and Fallah, are in the same field of endeavor, namely image processing. Before the effective filing date of the claimed invention, it would have been obvious to apply the method intensifying the spatial resolution of 3D model and high frequency features, as taught by Fallah into Miller in view of Tom. The motivation would have been “Intensifying the spatial resolution of 3D thermal models from aerial imagery using deep learning-based image super-resolution” (Fallah, title). The suggestion for doing so would allow to enhance the high frequency features of 3D model and derived 2D image. Therefore, it would have been obvious to combine Miller, Tom, and Fallah.
Response to Arguments
Applicant's arguments with respect to claim rejection 35 U.S.C. 103 have been fully considered but they are not persuasive.
Applicant alleges, “Thus, the amended claims require a distinct downstream estimator and operational fall back: a pixel-to-angular-LOS computation based on IFOV and image center, and use of the resulting estimated LOS vector for GNC in the absence of platform position and attitude measurement information. The Office Action has not identified where Miller, Tom, or their combination teaches or suggests that additional computation and use. Nor has the Office Action articulated why a person of ordinary skill would have modified Miller's GPS/inertial-navigation and sensor-pointing-based guidance architecture with Tom's image-registration technique to arrive at the claimed GPS-denied LOS-vector guidance fallback.” (page 4, lines 15-22). However, Examiner respectfully disagrees about the respective allegations as whole because: this Office Action articulates that it is obvious to one having ordinary skill in the art that the detailed mathematical steps can be implemented using the line-of-sight aim point tracking system for the amendments in claim 1 above. The arguments are not persuasive.
Applicant alleges, “Here, the Office Action's rationale addresses only Tom's registration of different image types. It does not provide an evidence-backed reason to add the claimed IFOV-based azimuth/elevation LOS estimator or to use the resulting LOS vector as the GNC basis when platform position and attitude measurement information is unavailable.” (page 5 , lines 8-12). However, Examiner respectfully disagrees about the respective allegations as whole because: the motivation for combining Miller and Tom in current Office Action is corresponding to the limitations except the amendments in claim 1. The arguments are not persuasive.
Applicant alleges, “Applicant therefore respectfully traverse the rejections under 35 U.S.C. § 103, and requests that the rejection of Claim 11 and all claims dependent thereon withdrawn and that the application be placed in condition for allowance.” (page 5 , lines 13-15). However, Examiner respectfully disagrees about the respective allegations as whole because: (1) independent claim 11 is rejected for the corresponding citations and rationale above; and (2) all claims dependent are rejected for the respective rationale above. The arguments are not persuasive.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BIAO CHEN whose telephone number is (703)756-1199. The examiner can normally be reached M-F 8am-5pm ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kee M Tung can be reached at (571)272-7794. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/Biao Chen/
Patent Examiner, Art Unit 2611
/KEE M TUNG/Supervisory Patent Examiner, Art Unit 2611
Prosecution Insights