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 .
Prior arts cited in this office action:
Dvir et al. (US 20080159639 A1, hereinafter “Dvir”)
Matysik et al. (US 20220417533 A1, hereinafter “Matysik”)
Xu (CN 109698957 A, hereinafter “XU)
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 06/02/2026 has been entered.
Response to Arguments
Applicant's arguments filed 06/02/2026 with regard of rejection under 35 U.S.C. 103 have been fully considered but they are not persuasive.
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.
Claims 1-2, 7-12 are rejected under 35 U.S.C. 103 as being unpatentable over Dvir et al. (US 20080159639 A1, hereinafter “Dvir”) and in view of Matysik et al. (US 20220417533 A1, hereinafter “Matysik”) and in view of Xu (CN 109698957 A, hereinafter “XU).
Regarding claims 1, 11 and 12:
Dvir teaches an image encoding method based on multiple compression levels (Dvir [0004], [0008], where Dvir teaches this invention relates to a method, apparatus, and corresponding receiver for compressing and/or decompressing of images. Some embodiments relate to compressing an image via a video sequence, including an encryption of an image in a video sequence. An aspect of some exemplary embodiments of the invention relates to compressing an image by encoding it as a sequence of video frames, employing spatial and/or temporal compression, and consequent decompressing thereof ), the image encoding method comprising:
obtaining a plurality of Regions of Interest (ROIs) for machine vision from an image (Dvir [0088], where Dvir teaches in exemplary embodiments of the invention, only some of the tiles, or parts thereof, are used for frames 202 in the video sequence 200, for example, responsive to a region of interest, while discarding the others. Optionally, some tiles are compressed to a certain level while others compressed to another level, for example, a region of interest (e.g. center region) is compressed to a lesser extent relative to other regions in order to achieve a better quality in the decoded image for the region of interest);
determining the compression levels for the plurality of the RoIs (Dvir [0031], [0035],[0088], where Dvir teaches the image can be divided into a plurality of parts and each part would have a corresponding center region and other regions. Region of interest (e.g. center region) is compressed lesser extent relative to other regions in order to achieve a better quality in the decoded image for the region of interest);
and
encoding the image based on the compression levels, wherein, based on property information of the RoI, a compression level of the Rol is determined (Dvir [0098], [0125], [0157], where Dvir teaches in exemplary embodiments of the invention, scalable compression is performed, comprising encoding of the same tile (or part thereof) into frames with different compression levels. The scalable compression allows a consequent decoding of the video sequence to reconstruct the image in several quality levels),
wherein the compression level of the Rol is determined as one of a plurality of pre-determined compression level candidates, wherein the property information of the RoI is encoded into a bitstream and includes a location of the ROI or a size of the ROI (Dvir [0035], [0090], [00135], External settings or parameters (610) are provided (602). Optionally, the parameters, or part thereof, are provided by a user such as by configuration file or API. The parameters may comprise the rate (bits per second of the video sequence 200) or the quality level (e.g. as the range of luminance or colors relative to the original),
wherein the compression rate control parameter includes a resolution level of the image (Dvir [0125], [0162], where Dvir teaches in exemplary embodiments of the invention, the coder may accept operation parameters such as compression level, type of compression, number of frames in a GOP or other parameters such as the required bit rate. For example, by setting a configuration file or using API (application programming interface) to set the parameters),
wherein a first compression level of a first RoI of the Rols is different from a second
compression level of a second Rol of the RoIs (Dvir [0088], [0098]-0099], where Dvir teaches Optionally, some tiles are compressed to a certain level while others compressed to another level, for example, a region of interest (e.g. center region) is compressed lesser extent relative to other regions in order to achieve a better quality in the decoded image for the region of interest),
wherein a number of the compression levels of the RoIs is same as a number of the
RoIs (Dvir [0088], [0098]-0099], where Dvir teaches Optionally, some tiles are compressed to a certain level while others compressed to another level, for example, a region of interest (e.g. center region) is compressed lesser extent relative to other regions in order to achieve a better quality in the decoded image for the region of interest), and
wherein the compression level of the RoI is determined as one of a plurality of pre-
determined compression level candidates (Dvir [0090], [0142], where Dvir teaches The DCT coefficients are quantized (626), i.e. some coefficients (high frequencies) of the transform are reduced or eliminated, optionally responsive to rate settings (636). Optionally, the quantization is carried out using a quantization table, and the table is stored for subsequent image restoration. The quantized coefficients are entropy encoded, optionally by arithmetic coding (628) (such as in JPEG compression).
Although Dvir teaches the image can be divided into a plurality of parts and each part would have a corresponding center region and other regions. Region of interest (e.g. center region) is compressed lesser extent relative to other regions in order to achieve a better quality in the decoded image for the region of interest), he fails to explicitly teaches determining a plurality of regions of interests.
However, Matysik explicitly teaches a detection module to detect portions of the image data that contain possible regions of interest. Information indicating the portions that contain the possible regions of interest is then used during a compression process so that the portions that contain the possible regions of interest are compressed using one or more compression algorithms to facilitate further analysis and the remainder are treated differently. the compression process to be used, may not only chose lower or higher quantization factors, but may also switch quantization tables prepared specifically for computer vision algorithms depending on the regions of interest. (Matysik Abstract, [0004]-[0007], [0033], [0044]-[0050], fig. 3).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the application to detect a plurality of regions of interest and compressed and/or encode them based on their content and/or desired bit rate. In order to facilitate transmission, storage, and/or further processing.
The combination fails to teach wherein the plurality of Rols includes a first Rol and a second Rol overlapping the first ROI.
However, Xu teaches an image encoding method wherein first level corresponding to encoding strategy includes encoding the respective areas according to the first quantization parameter value. corresponding to the second level encoding strategy includes encoding the respective areas according to a second quantization parameter value. corresponding to the third level encoding strategy includes encoding the corresponding regions according to the third quantization parameter value. Here, the first quantization parameter value is less than the second quantization parameter value. a second quantization parameter value is less than the third quantization parameter value. further noted that, when some different encoding levels of the region of interest has an overlapping part, the coding scheme of the overlapping part can be determined with the same region of a region of interest.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the application to match each region to a corresponding level and when it is determined that two regions overlaps selecting an encoding/compression that is most appropriate to compress the overlapping region, in order to better able to reproduce the image at the best quality/resolution desired
Regarding claim 2:
Dvir in view of Matysik teaches wherein the compression level of the region of interest (ROI) is determined using a compression rate control algorithm (Dvir [0035], [0075], [0088]; Matysik [0033], [0044]-[0050]).8).
Regarding claim 7:
Dvir in view of Matysik teaches wherein the compression level of the ROI is determined based on a combination of a resolution level and a quantization level of the image (Dvir [0006]-[009], [0017], [0035], [0075], [0088], [0131], [0157]; Matysik [0033], [0044]-[0050]).
Regarding claim 8:
Dvir in view of Matysik teaches wherein, in response of the resolution level having higher priority and the quantization level having lower priority, ROI detection process is performed on compression level candidates to which resolution level candidates belong in an order of quantization level candidates (Dvir [0006]-[009], [0017], [0035], [0075], [0088], [0131], [0157]; Matysik [0033], [0044]-[0050]).
Regarding claim 9:
Dvir in view of Matysik teaches wherein the order of the quantization level candidates is from a maximum quantization level candidate to a minimum quantization level candidate (Dvir [0006]-[009], [0017], [0035], [0075], [0088], [0109], [0131], [0157]; Matysik [0033], [0044]-[0050]).
Regarding claim 10:
Dvir in view of Matysik teaches wherein the order of the quantization level candidates is from a minimum quantization level candidate to a maximum value quantization level candidate (Dvir [0006]-[009], [0017], [0035], [0075], [0088], [0109] [0131], [0157]; Matysik [0033], [0044]-[0050]).
Conclusion
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/WEDNEL CADEAU/Primary Examiner, Art Unit 2632 August 27, 2026
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