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 .
Priority
Foreign Priority is acknowledged from Korean application KR10-2023-0171436 with a filing date of 11/30/2023.
Information Disclosure Statement
The information disclosure statement (“IDS”) filed on 09/26/2024 was reviewed and the listed references were noted.
Drawings
The 28-page drawings have been considered and placed on record in the file.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 5 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention. Examiner respectfully requests that appropriate corrections be made to clarify the scope of the claims.
Claim 5 recites the limitations “…FD1(u,v) indicates a data value of coordinates (u, v) in Fourier transform of the reference image data, FD2(x,y) indicates a data value of the coordinates (u, v) in the Fourier transform of one of the plurality of crop image data…” and “M and N are natural numbers of 2 or more”. There is insufficient antecedent basis for these limitations in the claim such as FD1, FD2, M, and N.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim 1 is rejected under 35 U.S.C. 103 as being unpatentable over Feng et al. ("Generating aligned pseudo-supervision from non-aligned data for image restoration in under-display camera") in view of Shechtman et al. (US 20140105499 A1).
Regarding Claim 1, Feng teaches "An image preprocessing method generating an image data pair for image restoration training, the image preprocessing method comprising: receiving first image data and second image data"; (Feng, Section 3 and Figure 2, teaches a given Under-Display Camera image ID and a high-quality reference image IR of the same scene to generate image IP with finer texture and details from image IR and preserves the content of ID wherein the pseudo pair of the UDC image and the reference image is well-aligned and can serve as a training sample to provide better supervision for subsequent image restoration networks, i.e., generate image pair for restoration training comprising receiving first and second image data being the UDC image and the reference image as a training sample image pair for restoration networks);
"generating reference image data based on the first image data"; (Feng, Abstract and Figure 1 and Section 4, teaches capturing stereo image pairs by an Under-Display Camera and a high-end camera in which the key idea is to copy details from the high-quality reference image and paste them on the UDC image to generate training pairs for image restoration and wherein each pair of full-resolution images are cropped into 512x512 patches for training, i.e., generate reference image data based on the first image data being the cropped image patches of the high-quality reference images).
"
"
"and outputting the reference image data and the selected crop image data as the image data pair"; (Feng, Section 4, teaches each pair of full-resolution images are cropped into 512x512 patches for training, i.e., reference image data and selected crop image data is output as the data pair for training being the cropped patch of the high-quality reference image and the cropped patch of the UDC image).
However, Feng does not explicitly teach "generating a plurality of crop image data based on the second image data; selecting crop image data based on a smallest loss function value among loss function values generated based on each of the reference image data and the plurality of crop image data”.
In an analogous field of endeavor, Shechtman teaches "generating a plurality of crop image data based on the second image data"; (Shechtman, FIG. 1 and Paras. 31 and 36, teaches finding one or more corresponding patches in the image T under a patch distance function for each patch in the image S, i.e., generating a plurality of crop image data based on the second image data being the plurality of corresponding patches based on the image T);
"selecting crop image data based on a smallest loss function value among loss function values generated based on each of the reference image data and the plurality of crop image data"; (Shechtman, FIG. 1 and Paras. 31-32 and 49 , teaches finding one or more corresponding patches in the image T under a patch distance function for each patch in the image S wherein the best mapping from image S to T in the arg minimum of the patch distance function between the patch in image S and the patch in image T and wherein offsets are ranked with regard to patch distance in which patch distance is compared to the worst offset and is determined to be better if it has less patch distance, i.e., select the crop image data being the patch in image T based on a smallest loss function among loss function values generated based on each of the reference image data and the plurality of crop image data being the patch of image T having the smallest or minimum patch distance to the patch in image S).
It would have been obvious to one having ordinary skill in the art before the effective filing date to modify the invention of Feng by including the generation of a plurality of crop image regions based on the second image data and selecting the region based on a smallest loss taught by Shechtman. One of ordinary skill in the art would be motivated to combine the references since it improves mapping between the patches (Shechtman, Para. 48, teaches the motivation of combination to be to improve the mapping between patches).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date.
Claims 2 and 5 are rejected under 35 U.S.C. 103 as being unpatentable over Feng in view of Shechtman and Yu et al. (US 20200311871 A1).
Regarding Claim 2, the combination of references of Feng in view of Shechtman does not explicitly teach "The image preprocessing method of claim 1, wherein the loss function values are calculated by Equation 1 below:
PNG
media_image1.png
75
386
media_image1.png
Greyscale
wherein D1 indicates the reference image data, D2 indicates one of the plurality of crop image data, M indicates a height of the reference image data, N indicates a width of the reference image data, D1(x,y) indicates a data value of coordinates (x, y) in the reference image data, D2(x,y) indicates a data value of the coordinates (x, y) in one of the plurality of crop image data, L1(D1,D2) indicates a loss function value generated based on one of the reference image data and the plurality of crop image data, and M and N are natural numbers of 2 or more".
In an analogous field of endeavor, Yu teaches Regarding Claim 2, the combination of references of Feng in view of Shechtman does not explicitly teach "The image preprocessing method of claim 1, wherein the loss function values are calculated by Equation 1 below:
PNG
media_image1.png
75
386
media_image1.png
Greyscale
wherein D1 indicates the reference image data, D2 indicates one of the plurality of crop image data, M indicates a height of the reference image data, N indicates a width of the reference image data, D1(x,y) indicates a data value of coordinates (x, y) in the reference image data, D2(x,y) indicates a data value of the coordinates (x, y) in one of the plurality of crop image data, L1(D1,D2) indicates a loss function value generated based on one of the reference image data and the plurality of crop image data, and M and N are natural numbers of 2 or more"; (Yu, Paras. 63-64 and Equation 1-2, teaches a pixel mean square error loss between the reconstructed image and a high-resolution image corresponding to the input low-resolution image wherein L1 is the pixel mean square error loss, F and H are respectively pixel values of a width and a height of the image, I1 x, y is a pixel value, at a location x, y of the high-resolution image corresponding to the input low-resolution image, and I2, x, y is a pixel value, at the location x, y of the reconstructed image of the low-resolution image).
It would have been obvious to one having ordinary skill in the art before the effective filing date to modify the invention of Feng and Shechtman by including the use of a pixel mean square error loss between the images taught by Yu. One of ordinary skill in the art would be motivated to combine the references since it improves the quality of the reconstructed image.
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date.
Regarding Claim 5, the combination of references of Feng in view of Shechtman and Yu teaches "The image preprocessing method of claim 1, wherein the loss function values are calculated by Equation 4 below:
PNG
media_image2.png
46
526
media_image2.png
Greyscale
wherein D1 indicates the reference image data, D2 indicates one of the plurality of crop image data, M indicates a height of the reference image data, N indicates a width of the reference image data, D1(x,y) indicates a data value of coordinates (x, y) in the reference image data, D2(x,y) indicates a data value of the coordinates (x, y) in one of the plurality of crop image data, FD1(u,v) indicates a data value of coordinates (u, v) in Fourier transform of the reference image data, FD2(x,y) indicates a data value of the coordinates (u, v) in the Fourier transform of one of the plurality of crop image data, L3(D1,D2) indicates a loss function value generated based on one of the reference image data and the plurality of crop image data, M and N are natural numbers of 2 or more, and each of λ1, λ2, and λ3 is a selected real number"; (Yu, Paras. 101-103 and Equation 1-8, teaches the error loss is L=λ1L1+λ2L2+λ.3L3, i.e., error loss is the weighted sum of the three previously evaluated loss terms using λ per loss term).
The proposed combination as well as the motivation for combining the Feng, Shechtman, and Yu references presented in the rejection of Claim 2, applies to claim 5. Thus, the method recited in claim 5 is met by Feng in view of Shechtman and Yu.
Claims 3 and 4 are rejected under 35 U.S.C. 103 as being unpatentable over Feng in view of Shechtman and Fuoli et al. ("Fourier space losses for efficient perceptual image super-resolution").
Regarding Claim 3, the combination of references of Feng in view of Shechtman does not explicitly teach "The image preprocessing method of claim 1, wherein the loss function values are calculated by Equation 2 below:
PNG
media_image3.png
84
377
media_image3.png
Greyscale
wherein D1 indicates the reference image data, D2 indicates one of the plurality of crop image data, M indicates a height of the reference image data, N indicates a width of the reference image data, FD1(u,v) indicates a data value of coordinates (u, v) in Fourier transform of the reference image data, FD2(x,y) indicates a data value of the coordinates (u, v) in the Fourier transform of one of the plurality of crop image data, L2(D1,D2) indicates a loss function value generated based on one of the reference image data and the plurality of crop image data, and M and N are natural numbers of 2 or more".
In an analogous field of endeavor, Fuoli teaches "The image preprocessing method of claim 1, wherein the loss function values are calculated by Equation 2 below:
PNG
media_image3.png
84
377
media_image3.png
Greyscale
wherein D1 indicates the reference image data, D2 indicates one of the plurality of crop image data, M indicates a height of the reference image data, N indicates a width of the reference image data, FD1(u,v) indicates a data value of coordinates (u, v) in Fourier transform of the reference image data, FD2(x,y) indicates a data value of the coordinates (u, v) in the Fourier transform of one of the plurality of crop image data, L2(D1,D2) indicates a loss function value generated based on one of the reference image data and the plurality of crop image data, and M and N are natural numbers of 2 or more"; (Fuoli, Section 3.3 and Equation 7, teaches a Fourier space loss for a ground truth image and a generated image wherein both images are transformed into Fourier space by applying the fast Fourier transform and calculate amplitude and phase of all frequency components in which loss of amplitude difference is evaluated as:
PNG
media_image4.png
61
287
media_image4.png
Greyscale
).
It would have been obvious to one having ordinary skill in the art before the effective filing date to modify the invention of Feng and Shechtman by including the Fourier space loss between images to calculate amplitude and phase of all frequency components taught by Fuoli. One of ordinary skill in the art would be motivated to combine the references since it provides global guidance during training (Fuoli, Section 3.3, teaches the motivation of combination to be to provide global guidance during training).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date.
Regarding Claim 4, the combination of references of Feng in view of Shechtman and Fuoli teaches "The image preprocessing method of claim 1, wherein the loss function values are calculated by Equation 3 below:
PNG
media_image5.png
70
393
media_image5.png
Greyscale
wherein D1 indicates the reference image data, D2 indicates one of the plurality of crop image data, M indicates a height of the reference image data, N indicates a width of the reference image data, FD1(u,v) indicates a data value of coordinates (u, v) in Fourier transform of the reference image data, FD2(x,y) indicates a data value of the coordinates (u, v) in the Fourier transform of one of the plurality of crop image data, L3(D1,D2) indicates a loss function value generated based on one of the reference image data and the plurality of crop image data, and M and N are natural numbers of 2 or more"; (Fuoli, Section 3.3 and Equation 7, teaches a Fourier space loss for a ground truth image and a generated image wherein both images are transformed into Fourier space by applying the fast Fourier transform and calculate amplitude and phase of all frequency components in which loss of phase difference is evaluated as:
PNG
media_image6.png
57
295
media_image6.png
Greyscale
).
The proposed combination as well as the motivation for combining the Feng, Shechtman, and Fuoli references presented in the rejection of Claim 3, applies to claim 4. Thus, the method recited in claim 4 is met by Feng in view of Shechtman and Fuoli.
Claims 6-7 are rejected under 35 U.S.C. 103 as being unpatentable over Feng in view of Shechtman, Pati et al. (US 20230169666 A1), and Chen et al. (US 5721595 A).
Regarding Claim 6, the combination of references of Feng in view of Shechtman does not explicitly teach “The image preprocessing method of claim 1, wherein the selecting of the crop image data based on the smallest loss function value among the loss function values generated based on each of the reference image data and the plurality of crop image data comprises: initializing a temporary loss function value, a horizontal movement values, and a vertical movement value; calculating the loss function value based on the reference image data and crop image data corresponding to given horizontal movement value and vertical movement value among the plurality of crop image data; and determining whether the loss function value is less than the temporary loss function value”.
In an analogous field of endeavor, Pati teaches "The image preprocessing method of claim 1, wherein the selecting of the crop image data based on the smallest loss function value among the loss function values generated based on each of the reference image data and the plurality of crop image data comprises: initializing a temporary loss function value, a horizontal movement values, and a vertical movement value"; (Pati, Paras. 19 and 69, teaches calculating loss between an output layer and the ground truth transformation matrix using two or more loss function including location based loss wherein the output layer may be a transformation matrix comprising horizontal and vertical shifts, i.e., temporary loss function for training including horizontal and vertical movement values);
"calculating the loss function value based on the reference image data and crop image data corresponding to given horizontal movement value and vertical movement value among the plurality of crop image data"; (Pati, Para. 19, teaches training pairs comprising a first image with a region of interest and a second image with a second region of interest in which the deep learning model is trained to output an accurate transformation matrix even in the presence of large lateral or vertical motion between images and wherein the transformation matrix may include horizontal shifts, vertical shifts, rotation, skew, and zoom in which loss is calculated by comparing the transformation matrix to the associated ground truth, i.e., calculate loss function based on reference image data and crop image data corresponding to horizontal and vertical movement of the images being the loss calculated based on the transformation matrix including horizontal and vertical shifts).
It would have been obvious to one having ordinary skill in the art before the effective filing date to modify the invention of Feng and Shechtman by including the use of a temporary loss function using movement values among image data taught by Pati. One of ordinary skill in the art would be motivated to combine the references since it reduces the differences and increases correspondence between the two images (Pati, Para. 38, teaches the motivation of combination to be to reduce differences and increase correspondence between the two images).
However, the combination of references of Feng in view of Shechtman and Pati does not explicitly teach "and determining whether the loss function value is less than the temporary loss function value".
In an analogous field of endeavor, Chen teaches "and determining whether the loss function value is less than the temporary loss function value"; (Chen, Col. 3 lines 31-39, teaches determining the value of the absolute error function F(k,I) was smaller than the value of err at that time, i.e., determine the loss function value being the absolute error function is smaller than the value of the err or temporary loss function).
It would have been obvious to one having ordinary skill in the art before the effective filing date to modify the invention of Feng, Shechtman, and Pati by including the determination of a loss function being smaller than the temporary loss function taught by Chen. One of ordinary skill in the art would be motivated to combine the references since it reduces load (Chen, Abstract, teaches the motivation of combination to be to reduce computational load).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date.
Regarding Claim 7, the combination of references of Feng in view of Shechtman, Pati, and Chen teaches "The image preprocessing method of claim 6, wherein the selecting of the crop image data based on the smallest loss function value among the loss function values generated based on each of the reference image data and the plurality of crop image data further comprises: updating the temporary loss function value with the calculated loss function value in case that the calculated loss function value is less than the temporary loss function value"; (Chen, Col. 3 lines 31-39, teaches the current value of the absolute error function F(k,l) replaces the current value of the minimum absolute error variable err since the value of F(k,l) was smaller than the value of err at that time, i.e., updating the temporary loss being the err value with the calculated loss being the current absolute error function in the case that the calculated loss is less than the temporary loss);
"and designating a horizontal movement value and a vertical movement value corresponding to the calculated loss function value as a temporary position value"; (Chen, Col. 3 lines 31-39 and Col. 4 lines 1-12, teaches the shift of the current compared image block with respect to its corresponding original image block should be taken as the new motion vector MV = (k,I) wherein the image block is organized in N vertical columns and N horizontal rows and the plurality of compared image blocks have shifts (k,I), i.e., designate horizontal and vertical movement corresponding to the loss function as a temporary position being the horizontal and vertical shifts corresponding to the error as the new motion vector).
The proposed combination as well as the motivation for combining the Feng, Shechtman, Pati, and Chen references presented in the rejection of Claim 6, applies to claim 7. Thus, the method recited in claim 7 is met by Feng in view of Shechtman, Pati, and Chen.
Claims 8-12 are rejected under 35 U.S.C. 103 as being unpatentable over Feng in view of Shechtman, Pati, Chen, and Tee et al. (US 20040179604 A1).
Regarding Claim 8, the combination of references of Feng in view of Shechtman, Pati, and Chen does not explicitly teach “The image preprocessing method of claim 7, wherein the selecting of the crop image data based on the smallest loss function value among the loss function values generated based on each of the reference image data and the plurality of crop image data further comprises: increasing the horizontal movement value by a first unit length in case that the horizontal movement value does not reach a maximum value; increasing the vertical movement value by a unit length; and calculating the loss function value based on the reference image data and crop image data corresponding to given horizontal movement value and vertical movement value among the plurality of crop image data”.
In an analogous field of endeavor, Tee teaches "The image preprocessing method of claim 7, wherein the selecting of the crop image data based on the smallest loss function value among the loss function values generated based on each of the reference image data and the plurality of crop image data further comprises: increasing the horizontal movement value by a first unit length in case that the horizontal movement value does not reach a maximum value"; (Tee, FIG. 2A and Paras. 36, 43, and 51, teaches a search position is one pixel away from a previous search position wherein the distance between the previous search position and the current search position is one pixel horizontally or vertically wherein the vertical and horizontal iteration is repeated until the whole search window has been scanned, i.e., increase horizontal movement by a unit length in the case that horizontal is not a maximum);
"increasing the vertical movement value by a unit length"; (Tee, FIG. 2A and Paras. 36 and 43, teaches a search position is one pixel away from a previous search position wherein the distance between the previous search position and the current search position is one pixel horizontally or vertically, i.e., vertical movement is increased by a unit length);
"and calculating the loss function value based on the reference image data and crop image data corresponding to given horizontal movement value and vertical movement value among the plurality of crop image data"; (Tee, FIG. 2A and Paras. 36 and 43, teaches calculating an absolute error for each search position, i.e., calculating the loss function value based on the image data corresponding to the given movement values).
It would have been obvious to one having ordinary skill in the art before the effective filing date to modify the invention of Feng, Shechtman, Pati, and Chen wherein loss is between a reference image and a plurality of crop image data by including the increase of horizontal movement to not reach a maximum, the increase of vertical movement, and calculating the loss function corresponding to the new movement values taught by Tee. One of ordinary skill in the art would be motivated to combine the references since it increases speed of calculation and reduces calculations and comparisons (Tee, Para. 24, teaches the motivation of combination to be to increase speed of calculation and reduce multiple calculations and comparisons).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date.
Regarding Claim 9, the combination of references of Feng in view of Shechtman, Pati, Chen, and Tee teaches "The image preprocessing method of claim 7, wherein the selecting of the crop image data based on the smallest loss function value among the loss function values generated based on each of the reference image data and the plurality of crop image data further comprises: initializing the horizontal movement value in case that the horizontal movement value reaches a maximum value"; (Tee, Para. 50, teaches a scanning order running from left-to-right and top-to-down, when the scanning of the first row of search positions is completed, a new vertical iteration scan is executed, and it scans the next row of search positions, starting at the same horizontal position, i.e., horizontal movement initialized in case that the horizontal movement reaching a maximum value being the reset to the starting at the same horizontal position).
The proposed combination as well as the motivation for combining the Feng, Shechtman, Pati, Chen, and Tee references presented in the rejection of Claim 8, applies to claim 9. Thus, the method recited in claim 9 is met by Feng in view of Shechtman, Pati, Chen, and Tee.
Regarding Claim 10, the combination of references of Feng in view of Shechtman, Pati, Chen, and Tee teaches "The image preprocessing method of claim 9, wherein the selecting of the crop image data based on the smallest loss function value among the loss function values generated based on each of the reference image data and the plurality of crop image data further comprises: increasing the vertical movement value by a second unit length in case that the vertical movement value does not reach a maximum value"; (Tee, FIG. 2A and Paras. 36, 43, and 50, teaches a search position is one pixel away from a previous search position wherein the distance between the previous search position and the current search position is one pixel horizontally or vertically wherein vertical iteration is repeated until the bottom of the search window is reached, i.e., vertical movement is increased by a second unit length being the increase in distance by a pixel vertically in the case that the vertical movement does not reach a maximum);
"and calculating the loss function value based on the reference image data and crop image data corresponding to given horizontal movement value and vertical movement value among the plurality of crop image data"; (Tee, FIG. 2A and Paras. 36, 43, and 57, teaches calculating an absolute error for each search position and is used to determine the best match block, i.e., calculating the loss function value based on the image data corresponding to the given movement values).
The proposed combination as well as the motivation for combining the Feng, Shechtman, Pati, Chen, and Tee references presented in the rejection of Claim 8, applies to claim 10. Thus, the method recited in claim 10 is met by Feng in view of Shechtman, Pati, Chen, and Tee.
Regarding Claim 11, the combination of references of Feng in view of Shechtman, Pati, Chen, and Tee teaches "The image preprocessing method of claim 9, wherein the selecting of the crop image data based on the smallest loss function value among the loss function values generated based on each of the reference image data and the plurality of crop image data further comprises: selecting crop image data corresponding to the temporary position value in case that the vertical movement value reaches a maximum value"; (Tee, Paras. 50 and 57, teaches vertical iteration is repeated until the bottom of the search window is reached in which absolute errors and the distances with reference to the preferred point of the new row of search position is calculated and wherein the absolute error value of every search position is used to determine the best match block wherein the smallest absolute error value is kept and defined as the smallest after comparison with the incoming error, i.e., selecting image data corresponding to the temporary position in the case the vertical movement reaches a maximum being the vertical iteration being repeated until the bottom is reached).
The proposed combination as well as the motivation for combining the Feng, Shechtman, Pati, Chen, and Tee references presented in the rejection of Claim 8, applies to claim 11. Thus, the method recited in claim 11 is met by Feng in view of Shechtman, Pati, Chen, and Tee.
Regarding Claim 12, the combination of references of Feng in view of Shechtman, Pati, Chen, and Tee teaches "The image preprocessing method of claim 11, wherein the selecting of the crop image data based on the smallest loss function value among the loss function values generated based on each of the reference image data and the plurality of crop image data comprises: initializing a temporary loss function value, a rotation angle, a horizontal movement value, and a vertical movement value"; (Pati, Paras. 19 and 69, teaches calculating loss between an output layer and the ground truth transformation matrix using two or more loss function including location based loss wherein the output layer may be a transformation matrix comprising horizontal and vertical shifts as well as a rotation function, i.e., temporary loss function for training including horizontal and vertical movement values as well as a rotation angle);
"calculating the loss function value based on the reference image data and crop image data corresponding to given rotation angle, horizontal movement value, and vertical movement value among the plurality of crop image data"; (Pati, Para. 19, teaches training pairs comprising a first image with a region of interest and a second image with a second region of interest in which the deep learning model is trained to output an accurate transformation matrix even in the presence of large lateral or vertical motion between images and wherein the transformation matrix may include horizontal shifts, vertical shifts, rotation, skew, and zoom in which loss is calculated by comparing the transformation matrix to the associated ground truth, i.e., calculate loss function based on reference image data and crop image data corresponding to horizontal and vertical movement of the images as well as rotation being the loss calculated based on the transformation matrix including horizontal and vertical shifts as well as rotation);
"and determining whether the calculated loss function value is less than the temporary loss function value"; (Chen, Col. 3 lines 31-39, teaches determining the value of the absolute error function F(k,I) was smaller than the value of err at that time, i.e., determine the loss function value being the absolute error function is smaller than the value of the err or temporary loss function).
The proposed combination as well as the motivation for combining the Feng, Shechtman, Pati, Chen, and Tee references presented in the rejection of Claim 8, applies to claim 12. Thus, the method recited in claim 12 is met by Feng in view of Shechtman, Pati, Chen, and Tee.
Allowable Subject Matter
Claims 13-19 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. The following is the examiner’s stated reason for indication of allowable subject matter: none of the cited prior art references, alone or in combination, provides a motivation to teach the ordered combination of limitations recited in Claim 13. Regarding Claim 13, Pati, Para. 19, teaches training pairs comprising a first image with a region of interest and a second image with a second region of interest in which the deep learning model is trained to output an accurate transformation matrix even in the presence of large lateral or vertical motion between images and wherein the transformation matrix may include horizontal shifts, vertical shifts, rotation, skew, and zoom in which loss is calculated by comparing the transformation matrix to the associated ground truth as opposed to explicitly designating the rotation and movement values corresponding to the loss function as a temporary position value. Additionally, Roth (US 20070064990 A1), Paras. 65, 67, and 74, teaches the manipulation of coordinates include an orthogonal offset of image coordinates in the x and y directions as well as rotating coordinates of one or more images wherein the computed values of aggregate error or mean square differences for each of the different composite images may be evaluated to identify the offset as the translation and rotation values that resulted in the minimum error for a composite image in which the identified values are saved for each non-reference image as opposed to explicitly designating the rotation angle, a horizontal movement value, and a vertical movement value that corresponds to the calculated loss function as a temporary position value. Claims 14-19 are dependent upon Claim 13 and therefore contain the above indicated allowable subject matter.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDREW STEVEN BUDISALICH whose telephone number is (703)756-5568. The examiner can normally be reached Monday - Friday 8:30am-5:00pm EST.
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/ANDREW S BUDISALICH/Examiner, Art Unit 2662
/AMANDEEP SAINI/Supervisory Patent Examiner, Art Unit 2662
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