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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 7/11/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
Applicant has amended claims 1, 4-9, 11, and 13. Claim 3 is cancelled. Claims 1, 2, and 4-13 are currently being considered. Applicant's arguments filed 8/10/2026 have been fully considered but they are not persuasive. Applicant argues that the prior art Wang in paragraphs [0019]-[0020] fails to expressly or inherently describe, teach, or suggest the claim 1 features of measuring a quality of video clips that have been downscaled in resolution, using a non-references video-based artificial intelligence (AI) model, in order from low to high resolution, and then calculating a quality score difference between video clips of two neighboring resolutions in order from low to high resolution. Examiner disagrees. Wang does disclose these features in Figure 4 where the reference video is transcoded into a plurality of resolution formats with a plurality of transcoding configurations then rescaled into a plurality of different display resolutions followed by a step where quality scores are obtained for each frame of each rescaled transcoded version of the reference video, summarized in paragraphs [0004]-[0006]. The results are shown in Figure 8A with resolutions ranging from low to high. As stated in paragraph [0018], resolutions can be roughly grouped to canonical industry standard resolutions, such as 360p, 480p, 720p, 1080p, 2160p (4k), and so on. Furthermore, as described in paragraphs [0020]-[0022], a set of historical videos is accessed and used to train a machine learning model. Computing a single quality score for a particular video format of a video entails decoding two video streams, and extracting per frame features in order to calculate the overall quality score. Thus, based on this analysis and disclosure, the prior arts are still effective in rejecting all claims as amended.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-2, 4 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Wang et. al. (United States Patent Application Publication US 2023/0054130 A1).
Regarding claim 1, Wang et. al. discloses a method for predicting an original resolution of video contents, the method comprising:
operation (a) of dividing original video contents that are resolution prediction targets into video clips each having a fixed time length; operation (Wang et. al. [0004] transcoding process for videos, Figure 4) (b) of downscaling a resolution of each of the video clips to a
predetermined resolution; operation (c) of measuring a quality of video clips downscaled in
operation (Wang et. al. [0019]-[0020] quality score computation) (b) using a non-reference video-based artificial intelligence (AI) model in order from low to high resolution, and then calculating a quality score difference between the video clips of two neighboring resolutions in order from low to high resolution; operation (d) of predicting a resolution of each of the video clips based on the quality score difference; and operation (e) of aggregating the resolution of each of the video clips predicted in operation (b) to predict the original resolution of the original video contents (Wang et. al. addresses training an AI model using quality scores for a plurality of different resolutions for a content in order to improve video transcoding, [0022], [0023], [0044], [0045], [0050]-[0052], [0066], [0067], [0071]-[0076]).
Regarding dependent claim 2, Wang et. al. recites the method of claim 1, wherein, the resolutions to be downscaled are 360p, 480p, 720p, 1080p, 1440p, and 2160p (Wang et. al. [0018]).
Regarding claim 4, Wang et. al. discloses the method of claim 2, wherein, the operation (d) comprises: operation (d1) of determining whether a current quality score difference (DP), which is a difference between quality scores of the video clips having neighboring resolutions currently being processed, is below a threshold (E); operation (d2) of, if the current quality score difference (Dp) is below the threshold (E), determining whether a previous quality score difference (DB), which is a difference in quality scores between the video clips having neighboring resolutions that have been previously processed, is below the threshold (E); and operation (d3) of, if both the current quality score difference (DP) and the previous quality score difference (DB) are below the threshold (E), predicting a lower resolution used in calculating the previous quality score difference (DB) as the resolution of that one of the video clips (Wang et. al. [0058]-[0061] the system determines an accuracy of each of the one or more trained models and whether one or more of the trained models has an accuracy that meets a threshold accuracy, and then extracts a predicted quality score of the input video at each possible combination of the video format).
Claim Rejections - 35 USC § 103
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter 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 pre-AIA 35 U.S.C. 103(a) 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 5-7 is/are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Wang et. al. (United States Patent Application Publication US 2023/0054130 A1) in view Hu et. al. (United States Patent Application Publication US 2022/0067383 A1).
Regarding claim 5, Wang et. al. discloses the method of claim 4. However, Wang et. al. fails to disclose wherein, the operation (d) comprises: operation (d4) of, if at least one of the current quality score difference (DP) or the previous quality score difference (DB) is above the threshold (E), calculating the quality score difference up to a final resolution, and predicting the resolution at which a maximum quality score difference (Dmax) among the
current quality score difference (DP) and the previous quality score difference (DB), based on the final resolution, occurs as the resolution of that one of the video clips.
Hu et. al. teaches wherein, the operation (d) comprises: operation (d4) of, if at least one of the current quality score difference (DP) or the previous quality score difference (DB) is above the threshold (E), calculating the quality score difference up to a final resolution, and predicting the resolution at which a maximum quality score difference (Dmax) among the
current quality score difference (DP) and the previous quality score difference (DB), based on the final resolution, occurs as the resolution of that one of the video clips (Hu et. al., US2022/00667383 A1, [0031]-[0033], the relative size of scores can be characterized as the differences among relative highlight degrees for the contents of video frames. [0041] The video clip corresponding to the sliding window with the highest score is the highlight clip of the video. [0050] the scoring model is determined based on the data pairs composed of positive clips and negative clips. When the data pairs are obtained, the differences between the positive clips and the negative clips can be clearly distinguished.). This is critical to the claimed invention because it determines how the machine learning model is trained based on the quality score differences. Thus, it would have been obvious to one skilled in the art prior to the effective filing date of the claimed invention to have combined the teachings of Wang et. al. and Hu et. al. to arrive at the solution of the claimed invention.
Regarding claim 6, Wang et. al. and Hu et. al. discloses the method of claim 5, and Wang et. al. further discloses wherein, the AI model used in operation (b) is built by training a number (M) of video training datasets with various resolutions ranging from 360p, 480p, 720p, 1080p, 1440p, and 2160p based on a deep learning method (Wang et. al. [0018]).
Regarding claim 7, Wang et. al. and Hu et. al. discloses the method of claim 6, and Wang et. al. further discloses wherein, the training dataset comprises a YouTube User-Generated Contents dataset (Wang et. al. [0031]-[0032] the content items stored in the content repository may include user-generated media that are uploaded by client machines).
Claim 8-12 is/are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Wang et. al. (United States Patent Application Publication US 2023/0054130 A1) and Hu et. al. (United States Patent Application Publication US 2022/0067383 A1), in further view of Baik et. al. (United States Patent Application Publication US 2017/0347159 A1).
Regarding claim 8, Wang et. al. and Hu et. al. disclose the method of claim 7, however Wang et. al. and Hu et. al. fail to disclose wherein, the number (M) of video training datasets is more than 1,000.
Baik et. al. teaches wherein, the number (M) of video training datasets is more than 1,000 (Baik et. al. US 2017/347159 A1, an exemplary video data set is shown in Table 1, [0070] where the number of videos is 2852). This is important to the claimed invention because the number of training datasets inputted into the machine learning model allows the model to become more accurate. Thus, it would have been obvious to one skilled in the art prior to the effective filing date of the claimed invention to have combined the teachings of Wang et. al., Hu et. al., and Baik et. al. so that this value for the datasets is reached.
Regarding claim 9, Wang et. al., Hu et. al. and Baik et. al. discloses the method of claim 8, and Baik et. al. further discloses wherein, in preparing the training dataset, videos with a high percentage of quality defects are removed by filtering by each defect category up to the maximum floor ((N/100)*M, where N is a natural number) (Baik et. al. US 2017/0347159 A1 [0063]-[0066] a threshold for removing video information may be derived). This is important to the claimed invention because it allows the defects of the parts of the video unwanted by the user to be selectively removed. Thus, it would have been obvious to one skilled in the art prior to the effective filing date of the claimed invention to have combined the teachings of Wang et. al., Hu et. al., and Baik et. al. so that a method for removing certain videos are incorporated into the solution.
Regarding claim 10, Wang et. al., Hu et. al. and Baik et. al. discloses the method of claim 9, and Baik et. al. further discloses wherein, quality defects remove video contents with a high percentage of defects that are (1) too dark, (2) too bright, and (3) too blurry by filtering for each of the defect items in (1), (2), and (3) (Baik et. al. [0065]-[0068] removing video information may be derived by precisely analyzing and modeling the influence of video type. A decision is made as to whether to remove video packets from a video according to the degree of satisfaction set by a user). This is important to the claimed invention because it allows the defects of the parts of the video unwanted by the user to be selectively removed by the user. Thus, it would have been obvious to one skilled in the art prior to the effective filing date of the claimed invention to have combined the teachings of Wang et. al., Hu et. al., and Baik et. al. so that a method for removing certain videos are incorporated into the solution.
Regarding claim 11, Wang et. al., Hu et. al., and Baik et. al. discloses the method of claim 10, and Baik et. al. further discloses wherein, any of the video clips with quality defects are removed by filtering after operation (a) and before operation (b) (Baik et. al. [0066]-[0068], Figure 4). This is important to the claimed invention because it allows the defects of the parts of the video unwanted by the user to be selectively removed by the user in the order specified by the claimed invention. Thus, it would have been obvious to one skilled in the art prior to the effective filing date of the claimed invention to have combined the teachings of Wang et. al., Hu et. al., and Baik et. al. so that a method for removing certain videos are incorporated into the solution.
Regarding claim 12, Wang et. al., Hu et. al., and Baik et. al. discloses the method of claim 11, and Baik et. al. further discloses wherein, for each of the video clips, the quality improvement factor (A) between the lowest and highest resolutions is calculated and averaged to determine the final quality improvement factor for the video contents (Baik et. al. video quality metrics are calculated and are either subjective or objectively quantified using equations, [0089]-[[0107], Figure 4, Figure 6). This is important to the claimed invention because it quantifies the quality of each video based on an expression, objectively classifying the video quality improvement. Thus, it would have been obvious to one skilled in the art prior to the effective filing date of the claimed invention to have combined the teachings of Wang et. al., Hu et. al., and Baik et. al. so that these expressions for quality improvement are included as part of the solution to the claimed invention.
Claim 13 is rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Wang et. al. (United States Patent Application Publication US 2023/0054130 A1) and Hu et. al. (United States Patent Application Publication US 2022/0067383 A1), in further view of Baik et. al. (United States Patent Application Publication US 2017/0347159 A1) as applied to claim 12 above, and further in view of Li et. al. (United States Patent Application Publication US 2018/0167619 A1).
Regarding claim 13, Wang et. al., Hu et. al., and Baik et. al. disclose the method of claim 12, however Wang et. al., Hu et. al., and Baik et. al. fail to disclose wherein, in operation (e), a resolution of the video clip with a highest resolution among the resolutions of each of the video clips is predicted as the resolution of the original video contents.
Li et. al. teaches wherein, in operation (e), a resolution of the video clip with a highest resolution among the resolutions of each of the video clips is predicted as the resolution of the original video contents (Li et. al., US 2018/0167619 A1, [0101] selecting the first model comprises determining a highest spatial resolution included in the plurality of resolutions that is not greater than the first spatial resolution; and identifying a model included in the plurality of models that is associated with the highest resolution). This is important to the claimed invention because it ensures that the output selected is the highest quality video of the plurality of videos trained in the model. Thus, it would have been obvious to one skilled in the art prior to the effective filing date of the claimed invention to have combined the teachings of Wang et. al., Hu et. al., Baik et. al. and Li et. al. so that the resolution predicted among the plurality of training videos is the highest resolution.
Conclusion
Response to Amendment
Examiner has carefully reconsidered all amended claims and performed an updated search. However, after reviewing the prior arts against the amended claims, Examiner has determined that the previous grounds of rejection is still effective in rejecting all amended claims.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JESSICA YIFANG LIN whose telephone number is (571)272-6435. The examiner can normally be reached M-F 7:00am-6:15pm, with optional day off.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Vu Le can be reached at 571-272-7332. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JESSICA YIFANG LIN/Examiner, Art Unit 2668 August 21, 2026
/VU LE/Supervisory Patent Examiner, Art Unit 2668
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