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 .
Response to Amendment
This Office action is responsive to Applicant’s ENGLISH TRANSLATION AND PRELIMINARY AMENDMENT, filed January 4, 2024. Claims 1-9 and 11-21 are pending.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
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 (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.
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.
Claims 1, 2, 7, 11, 12, 14 and 19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Chinese Application Publication CN 110399848 A (hereinafter “Huang”).
Regarding claim 1, Huang discloses a method for selecting a video cover, comprising:
- acquiring a video data with un-selected cover, the video data comprising multiple video frames (parse the target video to obtain multiple video frame images (paragraph [0067]); multiple video frame images can be processed using object detection to select the video frame images containing the target object (paragraph [0073]));
- performing quality quantization processing on each of the video frames to obtain quality quantization data of each video frame, the quality quantization data comprising at least one of an imaging quality quantization value and a composition quality quantization value (based on the type and number of target objects contained in the target object information, determine the quality score of each video frame image (paragraph [0076]); after object detection is performed on multiple video frames, the quality of each video frame image can be evaluated based on the type and number of target objects detected on each video frame, and a quality score can be obtained for each video frame image (paragraph [0077]));
- selecting a target video frame from the video data according to the quality quantization data of each of the video frames (based on the quality score of each video frame image, select the target video frame image as the video cover from the plurality of video frame images (paragraph [0080])), and
- generating a cover of the video data based on the target video frame (method for generating video cover images (paragraph [0065])).
Regarding claim 2, Huang discloses wherein the step of performing quality quantization processing on each of the video frames to obtain quality quantization data of each video frame comprises:
- inputting each of the video frames into a pre-trained imaging quality prediction model to obtain the imaging quality quantization value of each video frame, the imaging quality quantization value comprising at least one of a brightness quality quantization value, a definition quality quantization value, a contrast quality quantization value, a colorfulness quantization value and an aesthetic index quantization value (pre-trained CNN convolutional neural network to score the quality of the video frame with the highest quality score among multiple video frames, the quality scoring process can comprehensively evaluate multiple aspects, including image quality and color (paragraph [0103])).
Regarding claim 7, Huang discloses wherein the step of generating a cover of the video data based on the target video frame comprises:
- when the target video frame is a two-dimensional image, clipping the target video frame according to the position of the target object in the target video frame (performing a cropping operation on the target video frame image, so as to preserve multiple target objects present in the video frame (paragraph [0106])); and
- taking the clipped target video frame as the cover of the video data (using the cropped target video frame image as the video cover (paragraph [0106])).
Regarding claim 11, Huang discloses a computer device comprising a memory and a processor (electronic device 60 includes at least one processor and a memory communicatively connected to the at least one processor (paragraphs [0126]-[0128])), the memory storing a computer program, wherein the processor implements a method for selecting a video cover, when executing the computer program (memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the video cover generation method (paragraph [0129])); wherein the method for selecting a video cover comprises following steps:
- acquiring a video data with un-selected cover, the video data comprising multiple video frames (parse the target video to obtain multiple video frame images (paragraph [0067]); multiple video frame images can be processed using object detection to select the video frame images containing the target object (paragraph [0073]));
- performing quality quantization processing on each of the video frames to obtain quality quantization data of each video frame, the quality quantization data comprising at least one of an imaging quality quantization value and a composition quality quantization value (based on the type and number of target objects contained in the target object information, determine the quality score of each video frame image (paragraph [0076]); after object detection is performed on multiple video frames, the quality of each video frame image can be evaluated based on the type and number of target objects detected on each video frame, and a quality score can be obtained for each video frame image (paragraph [0077]));
- selecting a target video frame from the video data according to the quality quantization data of each of the video frames (based on the quality score of each video frame image, select the target video frame image as the video cover from the plurality of video frame images (paragraph [0080])), and
- generating a cover of the video data based on the target video frame (method for generating video cover images (paragraph [0065])).
Regarding claim 12, Huang discloses a computer-readable non-volatile storage medium with a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of a method for selecting a video cover (computer program product includes a computer program stored on a non-transitory computer-readable storage medium, the computer program including program instructions that, when executed by a computer, cause the computer to perform the video cover generation method (paragraph [0131])), wherein the method for selecting a video cover comprises following steps:
- acquiring a video data with un-selected cover, the video data comprising multiple video frames (parse the target video to obtain multiple video frame images (paragraph [0067]); multiple video frame images can be processed using object detection to select the video frame images containing the target object (paragraph [0073]));
- performing quality quantization processing on each of the video frames to obtain quality quantization data of each video frame, the quality quantization data comprising at least one of an imaging quality quantization value and a composition quality quantization value (based on the type and number of target objects contained in the target object information, determine the quality score of each video frame image (paragraph [0076]); after object detection is performed on multiple video frames, the quality of each video frame image can be evaluated based on the type and number of target objects detected on each video frame, and a quality score can be obtained for each video frame image (paragraph [0077]));
- selecting a target video frame from the video data according to the quality quantization data of each of the video frames (based on the quality score of each video frame image, select the target video frame image as the video cover from the plurality of video frame images (paragraph [0080])), and
- generating a cover of the video data based on the target video frame (method for generating video cover images (paragraph [0065])).
Regarding claim 14, Huang discloses wherein the step of performing quality quantization processing on each of the video frames to obtain quality quantization data of each video frame comprises:
- inputting each of the video frames into a pre-trained imaging quality prediction model to obtain the imaging quality quantization value of each video frame, the imaging quality quantization value comprising at least one of a brightness quality quantization value, a definition quality quantization value, a contrast quality quantization value, a colorfulness quantization value and an aesthetic index quantization value (pre-trained CNN convolutional neural network to score the quality of the video frame with the highest quality score among multiple video frames, the quality scoring process can comprehensively evaluate multiple aspects, including image quality and color (paragraph [0103])).
Regarding claim 19, Huang discloses wherein the step of generating a cover of the video data based on the target video frame comprises:
- when the target video frame is a two-dimensional image, clipping the target video frame according to the position of the target object in the target video frame (performing a cropping operation on the target video frame image, so as to preserve multiple target objects present in the video frame (paragraph [0106])); and
- taking the clipped target video frame as the cover of the video data (using the cropped target video frame image as the video cover (paragraph [0106])).
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 3 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Huang as applied to claims 1 and 11 above, and further in view of U.S. Patent Application Publication US 2022/0147741 A1 (hereinafter “Wang”).
Regarding claims 3 and 15, Huang discloses wherein the step of performing quality quantization processing on each of the video frames to obtain quality quantization data of each video frames comprises:
- inputting each of the video frames into a pre-trained target detection model to obtain an output result (pre-trained CNN convolutional neural network to score the quality of the video frame with the highest quality score among multiple video frames (paragraph [0103])).
Huang does not expressly disclose when the output result comprises a position information of at least one target object in the video frame, obtaining the composition quality quantization value of the video frame according to the position information.
Wang disclose a video cover determining method, wherein a video cover image is determined from a plurality of image frames to be processed according to a sorting result (Abstract). An object scoring network may be pre-trained based on a mathematical model for scoring each target object in each image frame to be processed to obtain an object feature score (paragraph [0046]). The object feature may be the location of a person image in an image frame to be processed (paragraph [0047]). The object feature score is calculated, based at least in part on the location score of a person image in an image frame to be processed (paragraphs [0062]-[0063]). In view of Wang, one of ordinary skill would have recognized the importance of the location of a target object in an image frame in determining whether the image frame is a good candidate for being selected as a cover image, for if the location of the target object is not excellent, the finally selected cover image may show poor content (paragraph [0055]). Therefore, it would have been obvious for one of ordinary skill in the art to have modified the teaching of Hwang by providing a pre-trained object scoring network which factors in location information of a target object, such as taught by Wang.
Allowable Subject Matter
11. Claims 4-6, 8, 9, 13, 16-18, 20 and 21 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.
12. The following is a statement of reasons for the indication of allowable subject matter:
Regarding claims 4 and 16, the cited prior art fails to disclose Applicant’s method according to claim 3, or computer device according to claim 15, wherein the step of obtaining the composition quality quantization value of the video frame according to the position information comprises:
- obtaining a position coordinates of a center point of the video frame;
- obtaining a target distance between the target object and the center point of, according to the position information and the position coordinates of the center point;
- obtaining the composition quality quantization value according to the target distance.
Claim 5 depends from claim 4.
Claim 17 depends from claim 16.
Regarding claims 6 and 18, the cited prior art fails to disclose or suggest
Applicant’s method according to claim 3, or computer device according to claim 15, wherein the method further comprises:
- when the output result excludes the position information of the target object, taking the composition quality quantization value of the video frame as a preset composition quality quantization value, wherein the preset composition quality quantization value is related to the composition quality quantization value of at least one video frame containing the target object in the video data.
Regarding claims 8 and 20, Huang does not expressly disclose wherein the step of generating a cover of the video data based on the target video frame comprises:
- when the target video frame is a panoramic image, rendering the target video frame according to a preset rendering mode; and
- taking the rendered target video frame as the cover of the video data.
Regarding claims 9, 13 and 21, the cited prior art fails to disclose or suggest Applicant’s method according to claim 1, computer-readable non-volatile storage medium according to claim 12, or computer device according to claim 11, wherein the quality quantization data comprises an imaging quality quantization value and a composition quality quantization value, and the step of selecting a target video frame from the video data according to the quality quantization data of each of the video frames comprises:
calculating a difference between the imaging quality quantization value and the composition quality quantization value of each video frame, and taking the difference as a comprehensive quality quantization value of each video frame;
taking the video frame with the largest comprehensive quality quantization value among the video frames as the target video frame.
13. Any inquiry concerning this communication or earlier communications from the examiner should be directed to THOMAS D LEE whose telephone number is (571)272-7436. The examiner can normally be reached Mon-Fri 7:30AM-5:00PM.
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/THOMAS D LEE/Primary Examiner, Art Unit 2683