DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Specification
2. Applicant is reminded of the proper content of an abstract of the disclosure.
A patent abstract is a concise statement of the technical disclosure of the patent and should include that which is new in the art to which the invention pertains. The abstract should not refer to purported merits or speculative applications of the invention and should not compare the invention with the prior art.
If the patent is of a basic nature, the entire technical disclosure may be new in the art, and the abstract should be directed to the entire disclosure. If the patent is in the nature of an improvement in an old apparatus, process, product, or composition, the abstract should include the technical disclosure of the improvement. The abstract should also mention by way of example any preferred modifications or alternatives.
Where applicable, the abstract should include the following: (1) if a machine or apparatus, its organization and operation; (2) if an article, its method of making; (3) if a chemical compound, its identity and use; (4) if a mixture, its ingredients; (5) if a process, the steps.
Extensive mechanical and design details of an apparatus should not be included in the abstract. The abstract should be in narrative form and generally limited to a single paragraph within the range of 50 to 150 words in length.
See MPEP § 608.01(b) for guidelines for the preparation of patent abstracts.
3. The abstract of the disclosure is objected to because it exceeds 150 words in length. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
Claim Rejections - 35 USC § 103
4. 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.
5. 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.
6. Claims 1-2, 10-12 are rejected under 35 U.S.C. 103 as being unpatentable over Lutio (Lutio, Riccardo De, et al. "Guided super-resolution as pixel-to-pixel transformation." Proceedings of the IEEE/CVF international conference on computer vision. 2019.), hereinafter Lutio, in view of Liu (US 20220114702 A1), hereinafter Liu .
Regarding claim 1, Lutio teaches a super-resolution method, comprising: obtaining pixel values of respective pixels in a super-resolution frame corresponding to the original frame according to pixel values of respective pixels in the original frame and a preset mapping relationship (Fig. 2, Section 3, wherein finding a function that maps the pixels of an input guide image to the target pixels of the target image is interpreted as finding a preset mapping relationship, and wherein the target high resolution image is interpreted as a super resolution frame, and the guide image is interpreted as the original input frame); wherein the preset mapping relationship is a mapping relationship generated according to pixel values of pixels in an input frame of a super-resolution network model and pixel values of pixels in an output frame of the super-resolution network model (Fig. 2, Section 3, wherein finding an function that maps the pixels of an input guide image to the target pixels of the target image is interpreted as generating a mapping relationship; Fig. 3 wherein the guide pixels from a guide image is interpreted as pixel values of pixels of an input to a super-resolution neural network model, and the target pixel of a target image is interpreted as pixel values of pixels in an output of a super resolution network model), and the super-resolution network model is a model obtained by training a preset machine learning model based on a model training sample (Fig. 3, Section 4 - Evaluation Settings: wherein the super-resolution neural network model is trained on sample image data, which is interpreted as training a preset machine learning model based on model training data.); and generating the super-resolution frame according to the pixel values of the respective pixels in the super-resolution frame (Fig. 2, Section 3, wherein the super resolution neural network outputs a complete target image based on the input guide image, pixel indices, and target pixel values, which is interpreted as generating a super-resolution image frame according to pixel values of the respective pixel in the target super-resolution frame).
Lutio does not teach obtaining an original video frame of a video to be subjected to super-resolution; and generating a super-resolution video frame with a video super-resolution network model.
Liu teaches obtaining an original video frame of a video to be subjected to super-resolution (Fig. 1, paragraph 46, obtaining input frames of a video, paragraph 48 wherein the video frames can be upscaled using a super resolution algorithm, which is interpreted as obtaining an original video frame of a video to be subjected t super-resolution); and generating a super-resolution video frame with a video super-resolution network model (Fig. 1, paragraph 48-49, wherein the neural network can utilize super-resolution to generate an output image frame, which is interpreted as generating a super-resolution frame with a super-resolution network model, which can include video frames).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lutio to incorporate the teachings of Liu for this method of super-resolution. Both Lutio and Liu discuss super-resolution methods for upscaling images at higher resolutions, utilizing neural network models trained for super resolution processes. Both references also discuss mapping pixels from lower resolution images to pixels of higher resolution images as part of the super-resolution process. While Lutio does not explicitly discuss super-resolution in the context of videos, Liu extensively discusses how its super-resolution techniques can be used for upscaling video frames. Additionally, as super-resolution for the purposes of upscaling videos is something that is readily known in the art, it would be obvious to incorporate the teachings for video super-resolution found in Liu with the teachings of Lutio. As both references discuss analogous art in using neural network models for super-resolution in image frames, it would be obvious to combine them.
Regarding claim 2, Lutio in view of Liu discloses the method of claim 1. Additionally, Lutio teaches the method according to claim 1, wherein obtaining the pixel values of the respective pixels in the super-resolution frame corresponding to the original video frame according to the respective pixel values of the respective pixels in the original video frame and the preset mapping relationship comprises: obtaining the pixel values of the respective pixels in the super-resolution video frame corresponding to the original video frame according to the pixel values of the respective pixels in the original video frame and the preset mapping relationship based on a graphics processing unit (GPU) (Section 4, paragraph 5, wherein the super resolution upsampling method can be implemented on a GPU, wherein the super resolution method comprises obtaining pixel values corresponding to the pixels of the original video frame and finding the pixel mapping relationship, and suggests being based on a GPU); or obtaining the pixel values of the respective pixels in the super-resolution video frame corresponding to the original video frame according to the pixel values of the respective pixels in the original video frame and the preset mapping relationship based on a digital signal processor (DSP); or obtaining the pixel values of the respective pixels in the super-resolution video frame corresponding to the original video frame according to the pixel values of the respective pixels in the original video frame and the preset mapping relationship based on a graphics processing unit, a digital signal process, and a preset task assignment policy.
Regarding claim 10, Lutio teaches an electronic device implementing a super-resolution method, comprising: obtaining pixel values of respective pixels in a super-resolution frame corresponding to the original frame according to pixel values of respective pixels in the original frame and a preset mapping relationship (Fig. 2, Section 3, wherein finding a function that maps the pixels of an input guide image to the target pixels of the target image is interpreted as finding a preset mapping relationship, and wherein the target high resolution image is interpreted as a super resolution frame, and the guide image is interpreted as the original input frame); wherein the preset mapping relationship is a mapping relationship generated according to pixel values of pixels in an input frame of a super-resolution network model and pixel values of pixels in an output frame of the super-resolution network model (Fig. 2, Section 3, wherein finding an function that maps the pixels of an input guide image to the target pixels of the target image is interpreted as generating a mapping relationship; Fig. 3 wherein the guide pixels from a guide image is interpreted as pixel values of pixels of an input to a super-resolution neural network model, and the target pixel of a target image is interpreted as pixel values of pixels in an output of a super resolution network model), and the super-resolution network model is a model obtained by training a preset machine learning model based on a model training sample (Fig. 3, Section 4 - Evaluation Settings: wherein the super-resolution neural network model is trained on sample image data, which is interpreted as training a preset machine learning model based on model training data.); and generating the super-resolution frame according to the pixel values of the respective pixels in the super-resolution frame (Fig. 2, Section 3, wherein the super resolution neural network outputs a complete target image based on the input guide image, pixel indices, and target pixel values, which is interpreted as generating a super-resolution image frame according to pixel values of the respective pixel in the target super-resolution frame).
Lutio does not teach a memory and a processor, wherein the memory is configured to store a computer program; the processor is configured to execute the computer program that enables the electronic device to implement a super-resolution method; obtaining an original video frame of a video to be subjected to super-resolution; and generating a super-resolution video frame with a video super-resolution network model.
Liu teaches a memory and a processor, wherein the memory is configured to store a computer program; the processor is configured to execute the computer program that enables the electronic device to implement a super-resolution method (Fig. 6A-6B, paragraph 61-62, wherein super-resolution system is implemented on processors with memory); obtaining an original video frame of a video to be subjected to super-resolution (Fig. 1, paragraph 46, obtaining input frames of a video, paragraph 48 wherein the video frames can be upscaled using a super resolution algorithm, which is interpreted as obtaining an original video frame of a video to be subjected to super-resolution); and generating a super-resolution video frame with a video super-resolution network model (Fig. 1, paragraph 48-49, wherein the neural network can utilize super-resolution to generate an output image frame, which is interpreted as generating a super-resolution frame with a super-resolution network model, which can include video frames).
The motivation to combine would be the same as that set forth for claim 1.
Regarding claim 11, Lutio teaches a computing device to implement a super-resolution method, comprising: obtaining pixel values of respective pixels in a super-resolution frame corresponding to the original frame according to pixel values of respective pixels in the original frame and a preset mapping relationship (Fig. 2, Section 3, wherein finding a function that maps the pixels of an input guide image to the target pixels of the target image is interpreted as finding a preset mapping relationship, and wherein the target high resolution image is interpreted as a super resolution frame, and the guide image is interpreted as the original input frame); wherein the preset mapping relationship is a mapping relationship generated according to pixel values of pixels in an input frame of a super-resolution network model and pixel values of pixels in an output frame of the super-resolution network model (Fig. 2, Section 3, wherein finding an function that maps the pixels of an input guide image to the target pixels of the target image is interpreted as generating a mapping relationship; Fig. 3 wherein the guide pixels from a guide image is interpreted as pixel values of pixels of an input to a super-resolution neural network model, and the target pixel of a target image is interpreted as pixel values of pixels in an output of a super resolution network model), and the super-resolution network model is a model obtained by training a preset machine learning model based on a model training sample (Fig. 3, Section 4 - Evaluation Settings: wherein the super-resolution neural network model is trained on sample image data, which is interpreted as training a preset machine learning model based on model training data.); and generating the super-resolution frame according to the pixel values of the respective pixels in the super-resolution frame (Fig. 2, Section 3, wherein the super resolution neural network outputs a complete target image based on the input guide image, pixel indices, and target pixel values, which is interpreted as generating a super-resolution image frame according to pixel values of the respective pixel in the target super-resolution frame).
Lutio does not teach a non-transient computer-readable storage medium with a computer program stored thereon, wherein the computer program, when being executed by a computing device, enables the computing device to implement a super-resolution method; obtaining an original video frame of a video to be subjected to super-resolution; and generating a super-resolution video frame with a video super-resolution network model.
Liu teaches a non-transient computer-readable storage medium with a computer program stored thereon, wherein the computer program, when being executed by a computing device, enables the computing device to implement a super-resolution method (paragraph 392, wherein processes can be executable in a non-transitory computer-readable medium); obtaining an original video frame of a video to be subjected to super-resolution (Fig. 1, paragraph 46, obtaining input frames of a video, paragraph 48 wherein the video frames can be upscaled using a super resolution algorithm, which is interpreted as obtaining an original video frame of a video to be subjected to super-resolution); and generating a super-resolution video frame with a video super-resolution network model (Fig. 1, paragraph 48-49, wherein the neural network can utilize super-resolution to generate an output image frame, which is interpreted as generating a super-resolution frame with a super-resolution network model, which can include video frames).
The motivation to combine would be the same as that set forth for claim 1.
Regarding claim 12, Lutio in view of Liu discloses the device of claim 10. Additionally, Lutio teaches the device according to claim 10, wherein obtaining the pixel values of the respective pixels in the super-resolution frame corresponding to the original video frame according to the respective pixel values of the respective pixels in the original video frame and the preset mapping relationship comprises: obtaining the pixel values of the respective pixels in the super-resolution video frame corresponding to the original video frame according to the pixel values of the respective pixels in the original video frame and the preset mapping relationship based on a graphics processing unit (GPU) (Section 4, paragraph 5, wherein the super resolution upsampling method can be implemented on a GPU, wherein the super resolution method comprises obtaining pixel values corresponding to the pixels of the original video frame and finding the pixel mapping relationship, and suggests being based on a GPU); or obtaining the pixel values of the respective pixels in the super-resolution video frame corresponding to the original video frame according to the pixel values of the respective pixels in the original video frame and the preset mapping relationship based on a digital signal processor (DSP); or obtaining the pixel values of the respective pixels in the super-resolution video frame corresponding to the original video frame according to the pixel values of the respective pixels in the original video frame and the preset mapping relationship based on a graphics processing unit, a digital signal process, and a preset task assignment policy.
7. Claims 3-4, 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over Lutio in view of Liu as applied to claims 1, 10 above, and further in view of Georgis (Georgis, G., Lentaris, G. & Reisis, D. Acceleration techniques and evaluation on multi-core CPU, GPU and FPGA for image processing and super-resolution. J Real-Time Image Proc 16, 1207–1234 (2019).), hereinafter Georgis.
Regarding claim 3, Lutio in view of Liu disclose the method of claim 2. Additionally, Liu teaches the method according to claim 2, wherein obtaining the pixel values of the respective pixels in the super-resolution video frame corresponding to the original video frame according to the pixel values of the respective pixels in the original video frame and the preset mapping relationship based on the graphics processing unit (GPU) comprises: obtaining the pixel values of the respective pixels in the super-resolution video frame corresponding to the original video frame according to the pixel values of the respective pixels in the original video frame and the preset mapping relationship (Fig. 2, Section 3, finding a function that maps the pixels of an input guide image to the target pixels of the target image is interpreted as finding a preset mapping relationship, and wherein the target image is a super resolution frame, and the guide image is interpreted as the original input frame).
Neither Lutio nor Liu teaches creating an input texture memory; configuring the input texture memory so that an open computing language (OpenCL) of the GPU is capable of accessing the input texture memory; writing the original video frame into the input texture memory; reading the original video frame from the input texture memory through the OpenCL.
Georgis teaches creating an input texture memory (Section 4.2, paragraph 2, wherein memory is allocated for image buffers for both input low resolution and output high resolution images, which is interpreted as including allocating the input texture memory); configuring the input texture memory so that an open computing language (OpenCL) of the GPU is capable of accessing the input texture memory (Section 4.2 wherein the GPU programming model translates an intermediate language to GPU - executable code, and wherein memory is allocated in the executable code, which is interpreted as configuring a computing language of the GPU to be capable of accessing the input texture memory, and suggests that programming language can be an open computing language, which is known in the art); writing the original video frame into the input texture memory (Section 4.2, wherein allocating texture memory for input low resolution images suggests writing the original frame into input texture memory); and reading the original video frame from the input texture memory through the OpenCL (Section 4.2, paragraph 4-6, wherein per-pixel samples from pixel memory are fetched, which includes texture fetches of the input low resolution images, which is interpreted as reading the original video frame from the input texture memory, and is accessible through the computing language).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lutio in view of Liu with the teachings of Georgis for this method of super-resolution. Both Lutio, Liu, and Georgis discuss super-resolution methods for upscaling images at higher resolutions. Lutio and Liu discuss utilizing neural network models trained for super resolution processes in order to more efficiently use those super-resolution algorithms. Meanwhile, Georgis discusses multiple GPU, CPU, and FPGA acceleration techniques in order to accelerate super-resolution processes. All three references also discuss mapping pixels from lower resolution images to pixels of higher resolution images as part of the super-resolution process. While Lutio does not explicitly discuss super-resolution in the context of videos, both Liu and Georgis discuss how their super-resolution techniques can be used for upscaling video frames. Additionally, as super-resolution for the purposes of upscaling videos is something that is readily known in the art, it would be obvious to incorporate the teachings for video super-resolution found in Liu with the teachings of Lutio and Georgis. As both references discuss analogous art in their techniques for accelerating super-resolution upscaling techniques, it would be obvious to combine them.
Regarding claim 4, Lutio in view of Liu and Georgis discloses the method of claim 3. Additionally, Lutio teaches the method according to claim 3, wherein generating the super-resolution video frame according to the pixel values of the respective pixels in the super-resolution video frame comprises: generating the super-resolution video frame according to the pixel values of the respective pixels in the super-resolution video frame (Fig. 2, Section 3, wherein the super resolution neural network outputs a complete target image based on the input guide image, pixel indices, and target pixel values, which is interpreted as generating a super-resolution image frame according to pixel values of the respective pixel in the target super-resolution frame).
Lutio does not teach creating an output texture memory; configuring the output texture memory so that the OpenCL is capable of accessing the input texture memory; writing the pixel values of the respective pixels in the super-resolution video frame into the output texture memory through the OpenCL; outputting the pixel values of the respective pixels in the super-resolution video frame from the output texture memory to a designated memory through a central processing unit CPU, and reading the pixel values of the respective pixels in the super-resolution video frame from the designated memory.
Georgis teaches creating an output texture memory (Section 4.2, paragraph 2, wherein memory is allocated for image buffers for both input low resolution and output high resolution images, which is interpreted as including allocating the output texture memory); configuring the output texture memory so that the OpenCL is capable of accessing the input texture memory (Section 4.2 wherein the GPU programming model translates an intermediate language to GPU - executable code, and wherein memory is allocated in the executable code, which is interpreted as configuring a computing language of the GPU to be capable of accessing the input texture memory, and suggests that programming language can be an open computing language, which is known in the art); writing the pixel values of the respective pixels in the super-resolution video frame into the output texture memory through the OpenCL (Section 4.2, wherein allocating texture memory for output high resolution images suggests writing the pixels of the output super-resolution image frame into input texture memory); outputting the pixel values of the respective pixels in the super-resolution video frame from the output texture memory to a designated memory through a central processing unit CPU (Fig. 2, Section 4.2, wherein as a result of texture memory, buffers containing the output high resolution images is copied onto a device as part of the CPU implementation, which suggests outputting the super-resolution frame from output texture memory to a designated memory through a CPU), and reading the pixel values of the respective pixels in the super-resolution video frame from the designated memory (Fig. 2, Section 4.2, wherein data is fetched from the copied high resolution image buffer that stores an image copied from texture memory, which suggests that the respective pixels in the super-resolution video frame is read from the designated memory).
The motivation to combine would be the same as that set forth for claim 3.
Regarding claim 13, Lutio in view of Liu discloses the device of claim 12. Additionally, Liu teaches the device according to claim 12, wherein obtaining the pixel values of the respective pixels in the super-resolution video frame corresponding to the original video frame according to the pixel values of the respective pixels in the original video frame and the preset mapping relationship based on the graphics processing unit (GPU) comprises: obtaining the pixel values of the respective pixels in the super-resolution video frame corresponding to the original video frame according to the pixel values of the respective pixels in the original video frame and the preset mapping relationship (Fig. 2, Section 3, finding a function that maps the pixels of an input guide image to the target pixels of the target image is interpreted as finding a preset mapping relationship, and wherein the target image is a super resolution frame, and the guide image is interpreted as the original input frame).
Neither Lutio nor Liu teaches creating an input texture memory; configuring the input texture memory so that an open computing language (OpenCL) of the GPU is capable of accessing the input texture memory; writing the original video frame into the input texture memory; reading the original video frame from the input texture memory through the OpenCL.
Georgis teaches creating an input texture memory (Section 4.2, paragraph 2, wherein memory is allocated for image buffers for both input low resolution and output high resolution images, which is interpreted as including allocating the input texture memory); configuring the input texture memory so that an open computing language (OpenCL) of the GPU is capable of accessing the input texture memory (Section 4.2 wherein the GPU programming model translates an intermediate language to GPU - executable code, and wherein memory is allocated in the executable code, which is interpreted as configuring a computing language of the GPU to be capable of accessing the input texture memory, and suggests that programming language can be an open computing language, which is known in the art); writing the original video frame into the input texture memory (Section 4.2, wherein allocating texture memory for input low resolution images suggests writing the original frame into input texture memory); and reading the original video frame from the input texture memory through the OpenCL (Section 4.2, paragraph 4-6, wherein per-pixel samples from pixel memory are fetched, which includes texture fetches of the input low resolution images, which is interpreted as reading the original video frame from the input texture memory, and is accessible through the computing language).
The motivation to combine would be the same as that set forth for claim 3.
Regarding claim 4, Lutio in view of Liu and Georgis discloses the device of claim 3. Additionally, Lutio teaches the device according to claim 3, wherein generating the super-resolution video frame according to the pixel values of the respective pixels in the super-resolution video frame comprises: generating the super-resolution video frame according to the pixel values of the respective pixels in the super-resolution video frame (Fig. 2, Section 3, wherein the super resolution neural network outputs a complete target image based on the input guide image, pixel indices, and target pixel values, which is interpreted as generating a super-resolution image frame according to pixel values of the respective pixel in the target super-resolution frame).
Lutio does not teach creating an output texture memory; configuring the output texture memory so that the OpenCL is capable of accessing the input texture memory; writing the pixel values of the respective pixels in the super-resolution video frame into the output texture memory through the OpenCL; outputting the pixel values of the respective pixels in the super-resolution video frame from the output texture memory to a designated memory through a central processing unit CPU, and reading the pixel values of the respective pixels in the super-resolution video frame from the designated memory.
Georgis teaches creating an output texture memory (Section 4.2, paragraph 2, wherein memory is allocated for image buffers for both input low resolution and output high resolution images, which is interpreted as including allocating the output texture memory); configuring the output texture memory so that the OpenCL is capable of accessing the input texture memory (Section 4.2 wherein the GPU programming model translates an intermediate language to GPU - executable code, and wherein memory is allocated in the executable code, which is interpreted as configuring a computing language of the GPU to be capable of accessing the input texture memory, and suggests that programming language can be an open computing language, which is known in the art); writing the pixel values of the respective pixels in the super-resolution video frame into the output texture memory through the OpenCL (Section 4.2, wherein allocating texture memory for output high resolution images suggests writing the pixels of the output super-resolution image frame into input texture memory); outputting the pixel values of the respective pixels in the super-resolution video frame from the output texture memory to a designated memory through a central processing unit CPU (Fig. 2, Section 4.2, wherein as a result of texture memory, buffers containing the output high resolution images is copied onto a device as part of the CPU implementation, which suggests outputting the super-resolution frame from output texture memory to a designated memory through a CPU), and reading the pixel values of the respective pixels in the super-resolution video frame from the designated memory (Fig. 2, Section 4.2, wherein data is fetched from the copied high resolution image buffer that stores an image copied from texture memory, which suggests that the respective pixels in the super-resolution video frame is read from the designated memory).
The motivation to combine would be the same as that set forth for claim 3.
Allowable Subject Matter
8. Claims 5-8, 15-18 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.
Conclusion
9. Any inquiry concerning this communication or earlier communications from the examiner should be directed to JORDAN W YICK whose telephone number is (571)272-4063. The examiner can normally be reached M-F 8-5.
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/JORDAN WAN YICK/Examiner, Art Unit 2612
/TAMMY GODDARD/Supervisory Patent Examiner, Art Unit 2611
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