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 .
Claim Rejections - 35 USC § 102
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.
Claim Rejections - 35 USC § 102
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.
Claim(s) 20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Hu et al., US 20220030232 A1 “Hu”.
A bit stream generated by a method, the method comprising… is a product by process claim limitation where the product is the bit stream and the process is the method steps to generate the bitstream. MPEP §2113 recites “Product-by-Process claims are not limited to the manipulations of the recited steps, only the structure implied by the steps”. Thus, the scope of the claim is the storage medium storing the bitstream (with the structure implied by the method steps). The structure includes the information and samples manipulated by the steps.
“To be given patentable weight, the printed matter and associated product must be in a functional relationship. A functional relationship can be found where the printed matter performs some function with respect to the product to which it is associated”. MPEP §2111.05(I)(A). When a claimed “computer-readable medium merely serves as a support for information or data, no functional relationship exists. MPEP §2111.05(III). The storage medium storing the claimed bitstream in claim 20 merely services as a support for the storage of the bitstream and provides no fictional relationship between the stored bitstream and storage medium. Therefor the structure bitstream, which scope is implied by the method steps, is non-functional descriptive material and given no patentable weight. MPEP §2111.05(III). Thus, the claim scope is just a storage medium storing data and is anticipated by REFERENCE which recites a storage medium storing a bitstream (Hu, ¶ 32).
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) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over CHEN, et a. US pat. No.: 20220109860, “CHEN” in view of LIM et al. KR102205708B1 (IDS) “LIM”
Regarding claim 1, CHEN discloses a method for video processing (CHEN, abstract ¶ 30, Fig. 2), comprising: performing a conversion between a current video unit of a video and a bitstream of the video (as cited above, i.e. generating a bitstream… encoded video data), wherein a neural network filter is applied to the current video unit (CHEN, ¶ 74), wherein the neural network filter has a target purpose (as cited above, i.e. ¶ 76, i.e. downsampled) comprising one of, an implementation of a machine vision task, or an image or video processing (as cited above), and/or wherein a neural network filter is applied to the current video unit (as cited above), an output of the neural network filter comprising a set of color components (CHEN, ¶ 96), wherein an indication of a usage of the set of color components is included in the bitstream (CHEN, ¶ 39, i.e. Input interface 122 of destination device 116 receives an encoded video bitstream from computer-readable medium 110 (e.g., a communication medium, storage device 112, file server 114, or the like). The encoded video bitstream may include signaling information defined by video encoder 200, which is also used by video decoder 300, such as syntax elements having values that describe characteristics and/or processing of video blocks or other coded units (e.g., slices, pictures, groups of pictures, sequences, or the like). Display device 118 displays decoded pictures of the decoded video data to a user. Display device 118 may represent any of a variety of display devices such as a liquid crystal display (LCD), a plasma display, an organic light emitting diode (OLED) display, or another type of display device).
It is noted that CHEN is silent about Visual quality improvement as claimed.
However, LIM discloses Visual quality improvement(LIM) (LIM, Pg. 2-3 description, see machine translation: The apparatus for providing a heavy rain removal image according to an embodiment of the present application includes a preprocessor that obtains a high-frequency region image and a low-frequency region image through filtering on a single frame, and a first layer extracted through a neural network that has learned the high-frequency region image. A feature extraction unit for extracting feature information by deep learning information and second layer information extracted through the neural network that has learned the low-frequency region image through the neural network, and the single frame, the high-frequency region image, and the feature information. And an image enhancement unit for deep learning through the neural network to output a heavy rain improvement image, and outputting a clean image for the heavy rain improvement image through physical modeling using a transmittance map estimated according to the neural network).
Both CHEN and LIM teach systems with AI application to visual information, and those systems are comparable to that of the instant application. Because the two cited references are analogous to the instant application, it 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, to include in the CHEN disclosure, image improvement, as taught by LIM. Such inclusion would have increased the usefulness of the system by an algorithm for improving the input image degraded by rain and fog will improve autonomous vehicles, and would have been consistent with the rationale of combining prior art elements according to known methods to yield predictable results to show a prima facie case of obviousness (MPEP 2143(I)(A)) under KSR International Co. v. Teleflex Inc., 127 S. Ct. 1727, 82 USPQ2d 1385, 1395-97 (2007).
Regarding claim 2, CHEN/LIM, for the same motivation of combination, further discloses the method of claim 1, wherein the neural network filter comprises a neural network post-processing filter (LIM, ¶ 34, post loop filter).
Regarding claim 3, CHEN/LIM, for the same motivation of combination, further discloses the method of claim 1, wherein the target purpose is determined from a set of purposes based on an indication of the target purpose included in the bitstream, the set of purposes comprising the visual quality improvement, the implementation of the machine vision task and the image or video processing (see rejection of claim 1, i.e. LIM, visual quality improvement citation)
Regarding claim 4, CHEN/LIM, for the same motivation of combination, further discloses the method of claim 3, wherein the indication of the target purpose is included in a neural network post-filter characteristics (NNPFC) supplemental enhancement information (SEI) message (LIM, para 46, i.e. HEVC) in the bitstream (CHEN, ¶ 64).
Regarding claim 5, CHEN/LIM, for the same motivation of combination, further discloses the method of claim 1, wherein the visual quality improvement comprises at least one of: an image or video dehazing, or an image or video de-raining (see LIM, citation above, i.e. de-raining).
Regarding claim 6, CHEN/LIM, for the same motivation of combination, further discloses the method of claim 1, wherein the machine vision task comprises at least one of: an enhancement task or an improvement of the enhancement task, wherein the enhancement task comprises an edge enhancement task, an analysis task or an improvement of the analysis task, wherein the analysis or detection or recognition task comprises at least one of: a face recognition task, an object recognition task, an object tracking task, or an object segmentation task, a detection task or an improvement of the detection task, a recognition task or an improvement of the recognition task, an image or video processing task or an improvement of the image or video processing task, an image or video understanding task or an improvement of the image or video understanding task, or a further machine vision task (LIM, as cited above, i.e. image improve device for operating a camera).
Regarding claim 7, CHEN/LIM, for the same motivation of combination, further discloses the method of claim 1, wherein the image or video processing comprises at least one of: an image or video style transfer, or an image or video object removal (See LIM, citation above, i.e. removing the raining).
Regarding claim 8, CHEN/LIM, for the same motivation of combination, further discloses the method of claim 1, wherein the set of color components comprises at least one of: a luma component, a first chroma component, or a second chroma component (see CHEN, color and other citation, i.e. ¶ 75).
Regarding claim 9, CHEN/LIM, for the same motivation of combination, further discloses the method of claim 1, wherein the indication of the usage of the set of color components is included in a neural network post-filter characteristics (hNPFC) supplemental enhancement information (SEI) message in the bitstream (see CHEN citation about post-filer, such filter by HEVC standard is signal in SEI).
Regarding claim 10, CHEN/LIM, for the same motivation of combination, further discloses the method of claim 1, wherein the indication of the usage of the set of color components indicates that at least one color component in the set of color components is not used for the conversion, wherein the neural network filter is applied based on a set of input color components, and at least one input color component in the set of input color components corresponding to the at least one color component is unchanged after applying the neural network filter (see CHEN, color citation, and ¶ 77).
Regarding claim 11, CHEN/LIM, for the same motivation of combination, further discloses the method of claim 1, wherein the indication of the usage of the set of color components indicates that at least one color component (CHEN, ¶ 96) in the set of color components is used for the conversion (CHEN, ¶ 78).
Regarding claim 12, CHEN/LIM, for the same motivation of combination, further discloses the method of claim 11, wherein the set of color components comprises a first chroma component and a second chroma component, and the at least one color component comprises the first chroma component, or the second chroma component, or both the first and second chroma components (CHEN, ¶ 80).
Regarding claim 13, CHEN/LIM, for the same motivation of combination, further discloses the method of claim 11, wherein the set of color components comprises a luma component, a first chroma component and a second chroma component (CHEN, ¶ 81).
Regarding claim 14, CHEN/LIM, for the same motivation of combination, further discloses the method of claim 13, wherein the at least one color component comprises one of the luma component, the first chroma component, or the second chroma component, or wherein the at least one color component comprises the first chroma component and the second chroma component, or wherein the at least one color component comprises a combination of at least two color components in the set of color components (See color citation, see also CHEN, ¶ 78).
Regarding claim 15, CHEN/LIM, for the same motivation of combination, further discloses the method of claim 11, wherein the neural network filter is applied based on a set of input color components, the set of color components further comprises at least one remaining color component not used, and the at least one remaining color component is replaced with at least one input color component in the set of input color components corresponding to the at least one remaining color component (CHEN, ¶ 79).
Regarding claim 16, CHEN/LIM, for the same motivation of combination, further discloses the method of claim 1, wherein the current video unit comprises a picture or a slice (CHEN, ¶ 30).
Regarding claim 17, CHEN/LIM, for the same motivation of combination, further discloses the method of claim 1, wherein the conversion includes encoding the current video unit into the bitstream, or wherein the conversion includes decoding the current video unit from the bitstream (CHEN, ¶ 65, see the citation above).
Regarding claim 18, CHEN/LIM, for the same motivation of combination, discloses an apparatus for video processing comprising a processor and a non-transitory memory with instructions thereon, wherein the instructions upon execution by the processor, cause the processor to: perform a conversion between a current video unit of a video and a bitstream of the video (see the rejection of claim 1), wherein a neural network filter is applied to the current video unit, wherein the neural network filter has a target purpose comprising one of: visual quality improvement, an implementation of a machine vision task, or an image or video processing, and/or wherein a neural network filter is applied to the current video unit, an output of the neural network filter comprising a set of color components, wherein an indication of a usage of the set of color components is included in the bitstream (This claim recited similar limitation as claim 1, see the rejection of claim 1).
Regarding claim 19, CHEN/LIM, for the same motivation of combination, discloses a non-transitory computer-readable storage medium storing instructions that cause a processor to perform a method comprising: performing a conversion between a current video unit of a video and a bitstream of the video (see the rejection of claim 1), wherein a neural network filter is applied to the current video unit (see the rejection of claim 1), wherein the neural network filter has a target purpose comprising one of (see the rejection of claim 1): visual quality improvement, an implementation of a machine vision task, or an image or video processing, and/or wherein a neural network filter is applied to the current video unit, an output of the neural network filter comprising a set of color components, wherein an indication of a usage of the set of color components is included in the bitstream (see the rejection of claim 1).
Regarding claim 20, CHEN/LIM, for the same motivation of combination, discloses a non-transitory computer-readable recording medium storing a bitstream of a video which is generated by a method performed by an apparatus for video processing, wherein the method comprises: generating the bitstream of the video (see the rejection of claim 1), wherein a neural network filter is applied to a current video unit of the video, wherein the neural network filter has a target purpose comprising one of: visual quality improvement (see the rejection of claim 1), and/or wherein a neural network filter is applied to a current video unit of the video, an output of the neural network filter comprising a set of color components, wherein an indication of a usage of the set of color components is included in the bitstream (see the rejection of claim 1).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 20250008100 A1 EXTERNAL ATTENTION IN NEURAL NETWORK-BASED VIDEO CODING
US 20240348809 A1 Neural Network-Based In-Loop Filter With Residual Scaling For Video Coding
US 20240276020 A1 Unified Neural Network In-Loop Filter Signaling
US 20240137574 A1 ADAPTIVE BILATERAL FILTER IN VIDEO CODING
US 20240056570 A1 Unified Neural Network Filter Model
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FRANK F HUANG whose telephone number is (571)272-0701. The examiner can normally be reached Monday-Friday, 8:30 am - 6:00 pm (Eastern Time), Federal Alternative First Friday Off.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jay Patel can be reached at (571)272-2988.. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/FRANK F HUANG/Primary Examiner, Art Unit 2485
Prosecution Insights