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 .
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 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.
Priority
Receipt is acknowledged of papers submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record in the file.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 06/10/2024 and 04/22/2025 have been considered by the examiner. The submissions are in compliance with the provisions of 37 CFR 1.97.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1 – 9 and 11 – 17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by LANCELLE M. et al. ("Controlling Motion Blur in Synthetic Long Time Exposures" [Note: reference provided by applicant on IDS filed on 06/10/2024]).
Regarding claim 1, LANCELLE M. et al. teaches a method of providing motion blur on an image viewed on a mobile device (page 394; a short video, e.g. captured by a hand-held compact camera or a smartphone) comprising: measuring at least one sensor output from the mobile device (page 394 paragraph staring with “Motion blur generation […]”; stabilization methods exist to compensate for rolling shutter artifacts, either blind [GKCE12] or using additional sensor information); generating motion blur parameters based on the at least one sensor output and metadata provided with the image (pages 394 - 395; international defocus blur based on parameters and image data); selecting, from a filter bank and based on the motion blur parameters, at least one blur kernel for a corresponding pixel of the image (figures 14 – 15 also page 394 paragraph staring with “Motion blur generation […]”, page 397 paragraph staring with “Soft brush to […]”, page 400 paragraph staring with “The results from […]”, 400 paragraph staring with “While Liu et al. also […]”, blur kernel based on motion); applying blur on the image to produce a blurred image by using the at least one blur kernel at each corresponding pixel (figures 14 – 15; applying blur); performing image composition on the blurred image to produce an output image (figures 14 – 15; output image); and presenting the output image on the mobile device, wherein the at least one sensor output comprises velocity data related to a movement of the mobile device (figure 17 also page 397 – 398 video output).
Regarding claim 2, as mentioned above in the discussion of claim 1, LANCELLE M. et al. teaches all of the limitations of the parent claim. Additionally, LANCELLE M. et al. teaches repeating processing steps in claim 1 to make the output image responsive to changes in the at least one sensor output over time (page 397 3.7 Video output; The presented method can be applied repeatedly to produce frames of a video. Different styles can be achieved).
Regarding claim 3, as mentioned above in the discussion of claim 1, LANCELLE M. et al. teaches all of the limitations of the parent claim. Additionally, LANCELLE M. et al. teaches wherein the applying blur comprises using an angle map to adjust blur direction at the corresponding pixel (figure 14 angle via rotating camera and blur).
Regarding claim 4, as mentioned above in the discussion of claim 1, LANCELLE M. et al. teaches all of the limitations of the parent claim. Additionally, LANCELLE M. et al. teaches wherein the applying blur further comprises using a depth map to adjust blur strength at each corresponding pixel (figure 11 depth of field and blur).
Regarding claim 5, as mentioned above in the discussion of claim 1, LANCELLE M. et al. teaches all of the limitations of the parent claim. Additionally, LANCELLE M. et al. teaches using a mask map to separate foreground objects from a background object, the applying blur being selectively performed on foreground and background objects based on the metadata (figure 13; masks for foreground and background object and blurring).
Regarding claim 6, as mentioned above in the discussion of claim 1, LANCELLE M. et al. teaches all of the limitations of the parent claim. Additionally, LANCELLE M. et al. teaches determining the number of radial blur centers from the metadata and, if there are more than one radial blur center, generating multiple blur vector maps, deriving blur vector map weights, and aggregating the multiple blur vector maps to create an aggregate blur vector map used for the applying blur (figures 10 – 12; area is changed based on seizes of subject(s) and blur; also figures 14 – 15 also page 394 paragraph staring with “Motion blur generation […]”, page 397 paragraph staring with “Soft brush to […]”, page 400 paragraph staring with “The results from […]”, 400 paragraph staring with “While Liu et al. also […]”, blur kernel based on motion).
Regarding claim 7, as mentioned above in the discussion of claim 1, LANCELLE M. et al. teaches all of the limitations of the parent claim. Additionally, LANCELLE M. et al. teaches deriving a radial blur center based on the metadata (figures 11 – 12; radial blur center based on the international defocus blur based on parameters and image data).
Regarding claim 8, as mentioned above in the discussion of claim 7, LANCELLE M. et al. teaches all of the limitations of the parent claim. Additionally, LANCELLE M. et al. teaches generating a blur vector map based on the radial blur center (figures 10 – 12; area is changed based on seizes of subject(s) and blur; also figures 14 – 15 also page 394 paragraph staring with “Motion blur generation […]”, page 397 paragraph staring with “Soft brush to […]”, page 400 paragraph staring with “The results from […]”, 400 paragraph staring with “While Liu et al. also […]”, blur kernel based on motion).
Regarding claim 9, as mentioned above in the discussion of claim 1, LANCELLE M. et al. teaches all of the limitations of the parent claim. Additionally, LANCELLE M. et al. teaches determining, from the metadata, if an entirety of the image is to be blurred, only background regions of the image are to be blurred, or if foreground images are to be blurred (figures 11 – 12; determine if an entirety of the image is to be blurred, only background regions of the image are to be blurred, or if foreground images are to be blurred).
Regarding claim 11, as mentioned above in the discussion of claim 1, LANCELLE M. et al. teaches all of the limitations of the parent claim. Additionally, LANCELLE M. et al. teaches generating a strength and direction of motion blur map based on the metadata (page 400 paragraph staring with “The results from […]”; blur direction).
Regarding claim 12, as mentioned above in the discussion of claim 1, LANCELLE M. et al. teaches all of the limitations of the parent claim. Additionally, LANCELLE M. et al. teaches modifying the blur kernel to have coefficients only in a background region of the image (page 400 5.3. Discussion; produces high-quality images with a sharp object of interest and a blurred background).
Regarding claim 13, as mentioned above in the discussion of claim 1, LANCELLE M. et al. teaches all of the limitations of the parent claim. Additionally, LANCELLE M. et al. teaches receiving a mask map of a background region of the image and one or more foreground images, the mask map being identified in the metadata (figure 13; masks for foreground and background object and blurring).
Regarding claim 14, as mentioned above in the discussion of claim 1, LANCELLE M. et al. teaches all of the limitations of the parent claim. Additionally, LANCELLE M. et al. teaches wherein generating the motion blur parameters includes using a mask map masking between foreground objects and background in the image (figure 13; masks for foreground and background object and blurring).
Regarding claim 15, as mentioned above in the discussion of claim 1, LANCELLE M. et al. teaches all of the limitations of the parent claim. Additionally, LANCELLE M. et al. teaches wherein generating the motion blur parameters includes using a depth map providing blur weighting values (figure 11 depth of field and blur).
Regarding claim 16, as mentioned above in the discussion of claim 3, LANCELLE M. et al. teaches all of the limitations of the parent claim. Additionally, LANCELLE M. et al. teaches wherein the angle map is used to apply scaling factors that control sensitivity of the blur in each direction (figures 14-15 angle via rotating camera and blur and zooming also (page 402 5.4. User Study; linear or zoom blur).
Regarding claim 17, as mentioned above in the discussion of claim 7, LANCELLE M. et al. teaches all of the limitations of the parent claim. Additionally, LANCELLE M. et al. teaches computing vectors from the radial blur center to each pixel location (figures 10 – 12; area is changed based on seizes of subject(s) and blur; also figures 14 – 15 also page 394 paragraph staring with “Motion blur generation […]”, page 397 paragraph staring with “Soft brush to […]”, page 400 paragraph staring with “The results from […]”, 400 paragraph staring with “While Liu et al. also […]”, blur kernel based on motion).
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:
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 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.
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 20, 22, and 24 – 25 are rejected under 35 U.S.C. 103 as being unpatentable over LANCELLE M. et al. ("Controlling Motion Blur in Synthetic Long Time Exposures" [Note: reference provided by applicant on IDS filed on 06/10/2024]) in view of Wang (US PgPub No. 20150147047).
Regarding claim 20, as mentioned above in the discussion of claim 1, LANCELLE M. et al. teaches all of the limitations of the parent claim.
Additionally, LANCELLE M et al. teaches perform the method of claim 1 (please see claim 1 above).
However, LANCELLE M. et al. fails to clearly teach a decoder configured to perform, said decoder comprising: an image decoder; a motion blur estimation module; an image composition module; a filter bank; and a blur application module. Wang, on the other hand teaches a decoder configured to perform, said decoder comprising: an image decoder; a motion blur estimation module; an image composition module; a filter bank; and a blur application module.
More specifically, Wang teaches a decoder (figure 1 item 100) configured to perform (figure 1 item 100 and 2 item 112), said decoder comprising: an image decoder (figure 1 item 104); a motion blur estimation module (figure 2 item 215); an image composition module (figure 1 item 108); a filter bank (figure 3 items 220 - 230); and a blur application module (figure 1 item 116).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention (AIA ) to incorporate the teachings of Wang with the teachings of LANCELLE M. et al. to have a system for improved image processing thereby improving the system of LANCELLE M. et al.
Regarding claim 22, as mentioned above in the discussion of claim 20, LANCELLE M. et al. in view of Wang teach all of the limitations of the parent claim. Additionally, Wang teaches an encoder (figure 3) configured to provide an image and metadata to the decoder of claim 20 (please see claim 20 above), the encoder configured to: encode the image to an encoded image (figure 3 items 312 – 318); generate a depth map, a mask map, and/or an angle map for the image based on preferences (figure 3 items 309 and/or item 315); multiplex the encoded image, the depth mask, the mask map, and/or the angle map as output to the decoder ( (figure 3 items 324 and/or 327); and provide metadata related to the blurring to the decoder (figure 3 item 321 and 339).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention (AIA ) to incorporate the teachings of Wang with the teachings of LANCELLE M. et al. to have a system for improved image processing thereby improving the system of LANCELLE M. et al.
Regarding claim 24, as mentioned above in the discussion of claim 22, LANCELLE M. et al. in view of Wang teach all of the limitations of the parent claim. Additionally, Wang teaches the encoder being further configured to convert the depth map to depth weight metadata using a transfer function (figure 3 item 321 and 339).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention (AIA ) to incorporate the teachings of Wang with the teachings of LANCELLE M. et al. to have a system for improved image processing thereby improving the system of LANCELLE M. et al.
Regarding claim 25, as mentioned above in the discussion of claim 24, LANCELLE M. et al. in view of Wang teach all of the limitations of the parent claim. Additionally, Wang teaches wherein the transfer function is one of linear, cosine square, or exponential (figure 3 item 321 and 339 and 4 - 7).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention (AIA ) to incorporate the teachings of Wang with the teachings of LANCELLE M. et al. to have a system for improved image processing thereby improving the system of LANCELLE M. et al.
Allowable Subject Matter
Claims 10 and 18 - 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 a statement of reasons for the indication of allowable subject matter:
The following is a statement of reasons for the indication of allowable subject matter for claim 10: “generating an aggregated blur vector map based on the blur vector map and derived blur vector map weights” in combination with the other limitations in the claim and the parent claim is not discussed or suggested in any of the prior art that was searched.
The following is a statement of reasons for the indication of allowable subject matter for claim 18: “wherein a strength of each blur kernel of the at least one blur kernel is computed as a combination of a normalized filter strength of the corresponding pixel, a velocity magnitude, and a maximum blur value” in combination with the other limitations in the claim and the parent claim is not discussed or suggested in any of the prior art that was searched.
Regarding claim 19, the claim is also objected to as being dependent from objected claim 18.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Jelinek (US PgPub No. 20120033096) teaches an imaging system with blur kernels.
Thumpudi (US PgPub No. 20180035058) teaches an imaging system with blur processing.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Usman A Khan whose telephone number is (571)270-1131. The examiner can normally be reached on M - Th 5:30 AM - 2 PM, F 5:30 AM - Noon.
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Usman Khan
/USMAN A KHAN/Primary Examiner, Art Unit 2637
06/04/2026
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