DETAILED ACTION
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim(s) 6 and 20 is(are) rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 6 and 20 recite computer-implemented functions including, among other limitations, “ a first convolution block to increase a number of channels of the CNN and a second convolutional block to decrease the number of channels of the CNN.”
Applicant(s) is/are respectfully reminded, for computer-implemented functional claims, “examiners should determine whether the specification discloses the computer and the algorithm (e.g., the necessary steps and/or flowcharts) that perform the claimed function in sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor invented the claimed subject matter.” MPEP § 2161.01(I).
As an initial matter, the Examiner notes that claim(s) 6 and 20 is(are) an originally-filed claim. However, originally-filed claim(s) 6 and 20 does(do) not disclose how “a first convolution block to increase a number of channels of the CNN and a second convolutional block to decrease the number of channels of the CNN.” Is constructed to executed, so does not provide the necessary written description support for pending claim 6. Accord Ariad, 598 F.3d at 1349 (indicating original claim language does not necessarily satisfy the written description requirement for the claimed subject matter). That is to say, originally-filed claim 6 itself(themselves) does(do) not provide an algorithm that performs the function “a first convolution block to increase a number of channels of the CNN and a second convolutional block to decrease the number of channels of the CNN.” in sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor invented the claimed subject matter.
Furthermore, Applicant’s specification does not describe an algorithm that performs the function “a first convolution block to increase a number of channels of the CNN and a second convolutional block to decrease the number of channels of the CNN.”
in sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor invented the claimed subject matter. For example, Applicant’s specification discloses “each of the feature extraction blocks include a first convolution block to increase a number of channels of the CNN and a second convolutional block to decrease the number of channels of the CNN” Spec. [0085], [0092], [0095]. However, such disclosure is merely statements, not an algorithm (e.g., the necessary steps and/or flowcharts) that performs the claimed function in sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor invented the claimed subject matter.
Applicant is also reminded, “if the specification does not provide a disclosure of the computer and algorithm in sufficient detail to demonstrate to one of ordinary skill in the art that the inventor possessed the invention including how to program the disclosed computer to perform the claimed function, a rejection under 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph, for lack of written description must be made.” MPEP § 2161.01(I).
Therefore, because an algorithm for the function “a first convolution block to increase a number of channels of the CNN and a second convolutional block to decrease the number of channels of the CNN.” is not disclosed in sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor invented the claimed subject matter, and in accordance with MPEP § 2161.01, claims 6 and 20 are rejected for lack of written description.
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.
Claim(s) 1-5, 9-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tahan (US 20120011194 ) in view of Nayak ( US 20220374714)
Regarding claim 1, Tahan teaches
transmitting, by a first client device, a resolution parameter to a second client device, the resolution parameter indicating a first resolution(810 in Fig. 8);
receiving, by the first client device, one or more downscaled images at the first resolution from the second client device( 830, 835 in Fig. 8) ;
generating a first input sub-image ( 840 in Fig. 8) and a second input sub-image of a respective input image( a portion of the high resolution image at the area of interest in 850 in Fig. 8) ;
outputting, by the first client device and using the machine learning model, one or more images at a second resolution that is higher than the first resolution( composed image in 850 in Fig. 8).
Tahan does not expressly teach
generating, by the first client device, a first set of upscaled images by upscaling the one or more downscaled images using a machine learning model.
displaying, via a user interface of the first client device, the first set of upscaled images.
However, Nayak teaches
generating, by the first client device, a first set of upscaled images by upscaling the one or more downscaled images using a machine learning model ( 506 in FIGURE 5).
displaying, via a user interface of the first client device, the first set of upscaled images( 510 in FIGURE 5) .
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Nayak with Tahan, by enhancing the video frames in the client device in Tahan following the teaching of Nayak, with motivation to “ enabling that content to be upscaled and enhanced at the client device in real time” ( Nayak, abstract)
Regarding claim 2, Tahan in view of NAYAK teaches the method of claim 1, wherein the first resolution is any one of a resolution of 180p, 270p, 360p, 480p and 720p, and the second resolution is 1080p( Tahan, [0084], a high-resolution image of higher resolution than the base resolution (e.g., 1080.times.810 for a 720.times.540 base resolution).
Regarding claim 3, Tahan in view of NAYAK teaches the method of claim 1, further comprising:
receiving additional one or more images from the second client device, the additional one or more images being at the first resolution(Tahan, [0110], a video) or a different resolution;
generating a second set of upscaled images by:
inputting the additional received one or more images( Nayak, 502 in Fig. 5) into the machine learning model( NAYAK,258 in Fig. 2B); and
upscaling the additional received one or more images by the machine learning model( NAYAK, 204 in Fig. 2A); and
outputting by the machine learning model, additional one or more images at the second resolution( NAYAK, 206 in Fig. 2A); and
displaying, via the user interface of the first client device, the second set of upscaled images( NAYAK, 510 in Fig. 5).
Regarding claim 4, Tahan in view of NAYAK teaches the method of claim 1, further comprising:
receiving a video stream from a camera operable with the first client device(Tahan, [0032], input devices (e.g., a camera)), the video stream including multiple images at a resolution of 1080p or 720p([0061], the base-resolution image as a 720);
downscaling the multiple images from the resolution of 1080p or 720p to a lower resolution of 180p, 270p, 360p or 480p(Tahan, [0084], scale down the top-resolution image); and
transmitting, from the first client device, the downscaled multiple images at the lower resolution to the second client device( Tahan, 855 in Figure 8).
Regarding claim 5, Tahan in view of NAYAK teaches the method of claim 1, further comprising:
training the machine learning model ( NAYAK, FIGURE 3) with multiple image pairs, wherein the image pairs include an image at the first resolution and a corresponding image at the second resolution(NAYAK, 302 and Ground Truth Image in FIGURE 3), and
wherein the machine learning model is trained to receive an input of an image at the first resolution and output an image at the second resolution (NAYAK,[0028], comparing an enhanced image against a corresponding ground truth image).
Claims 9-13 recite the system for the method in claims 1-5. Since Tahan also teaches a system ( Fig. 11), claims 9-13 are also rejected.
Claims 14-19 recite the medium for the method in claims 1-5, and are also rejected.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tahan in view of Nayak, further in view of Su ( WO 2020256704)
Regarding claim 7, Tahan in view of NAYAK teaches the method of claim 1.
Tahan in view of NAYAK does not expressly teach further comprising:
decompressing an image included in the received one or more downscaled images;
identifying one or more pixel changing areas and one or more pixel non-changing areas in a decompressed version of the image; and
discarding each respective pixel non-changing area in the decompressed version of the image from a machine learning model input sourced from the image; and
selecting the one or more pixel changing areas as the machine learning model input sourced from the image.
However, official notice is taken that it is well known in the art to decompress an compressed image before further image processing.
Therefore It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to decompress the downscaled image before upsample it, with motivation to minimize compression artifact.
Furthermore, Su teaches
identifying one or more pixel changing areas and one or more pixel non-changing areas in a decompressed version of the image( 614 in Fig. 6, SUBTRACT AN UPSAMPLED VERSION OF THE DOWNSAMPLED lr IMAGE FROM THE CONTRAST-EHHANCED IMAGE); and
discarding each respective pixel non-changing area in the decompressed version of the image from a machine learning model input sourced from the image(614 in Fig. 6, GENERATE A HIGH-RESULUTION(HR) RESIDUAL IMAGE); and
selecting the one or more pixel changing areas as the machine learning model input sourced from the image( 616 in Fig. 6).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Tahan in view of NAYAK with that of Su, by generating training images in Tahan following the teaching for Su, with motivation “ for increasing the image resolution of a digital image”( Su, Abstract).
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tahan in view of Nayak, further in view of ANDREL ( US 20200349681)
Regarding claim 8, Tahan in view of NAYAK teaches the method of claim 1, wherein outputting by the trained machine learning model, one or more images at a second resolution comprises:
applying one or more feature extraction blocks and one or more upsample blocks to the first input sub-image( NAYAK, 210 in FIGURE 2);
generating a composite upsampled image based at least in part on the first and the second input sub-images(Tahan, composed image in 850 in Fig. 8); and
outputting the composite upsampled image( Tahan, 855 in Fig. 8).
Tahan in view of NAYAK does not expressly teach applying a bilinear upsampling process to the second input sub-image;
However, ANDREL teaches applying a bilinear upsampling process to the second input sub-image ([0048], a bilinear upsampler).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Tahan in view of NAYAK with that of ANDREL, by applying ANDREL’s bilinear upsampler to the area of interests in the images in Tahan in view of NAYAK with that of ANDREL, with motivation “that upsamples the image to be enhanced” ( ANDREL, [0048])
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JIANGENG SUN whose telephone number is (571)272-3712. The examiner can normally be reached 8am to 5pm, EST, M-F.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Randolph Vincent can be reached at 571 272 8243. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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JIANGENG SUN
Examiner
Art Unit 2661
/Jiangeng Sun/Examiner, Art Unit 2671