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 .
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.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 05/20/2026 has been entered.
Status of Claims
Claims 3 and 13 have been cancelled.
Claims 1-2, 4-12 and 14-15 are pending.
Response to Arguments
Applicant’s arguments in the Remarks filed on 05/20/2026 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 4 and 14 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 4 recites the limitation "the global features" in line 3. There is insufficient antecedent basis for this limitation in the claim.
Claim 14 recites the limitation "the global features" in line 3. There is insufficient antecedent basis for this limitation in the claim.
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.
Claims 1-2 and 8-12 are rejected under 35 U.S.C. 103 as being unpatentable over Caballero et al (US 10701394) in view of Wei et al (CN 110415217 A – an English translation version is provided herein).
Regarding claim 1, Caballero discloses a video enhancement method, comprising:
dividing a target video into a plurality of groups of images, the images in a same group belonging to a same scene (Figures 16-17, 19-20 and 25-26; Col 14 line 32 through Col 15 line 27, Col 20 line 64 through Col 21 line 10 and Col 33 lines 34-48 for splitting a target video into a sequence of images which are grouped into scenes having common features);
determining, for each group of images, a matched video enhancement algorithm in a specified set of video enhancement algorithms using a pre-trained model (Col 1 lines 19-22, Col 8 line 47 through Col 9 line 21 and Col 15 lines 54-60 for enhancing a section of lower-quality visual data using an hierarchical algorithm in super-resolution algorithms based on learning (pre-training) neutral network models; Col 15 lines 60-64 for the models can be developed for each scene; Col 13 lines 39-43 for the hierarchical model is used interchangeable with the hierarchical algorithm; and Figures 16-17 and 25-26, Col 17 lines 32-41, Col 18 lines 60-67 and Col 33 line 62 through Col 34 line 15 for each scene (a sequence of images) is mapped to a model/algorithm choosing from a library of a set of pre-trained models in order to enhance lower-quality visual data to high-quality of original visual data);
performing video enhancement processing on the each group of images using the video enhancement algorithm (Figures 16-17, 19-20 and 25-26; Col 37 lines 8-24 for performing image enhancement on frames of scene using selected image enhancement model); and
sequentially splicing video enhancement processing results of all groups of images to obtain video enhancement data of the target video (Figures 16-17, 19-20 and 25-26; Col 37 lines 48-53 and Col 38 lines 7-15 for reconstructing all image enhancing frames of scenes to obtain video enhancement data of the target video);
wherein the determining, for each group of images, the matched video enhancement algorithm comprises: selecting an algorithm from the specified set of video enhancement algorithms as a video enhancement algorithm matched with the currently input group of images according to a strategy of preferentially selecting a high algorithm based on the quality (Col 31 lines 21-28, Col 32 lines 58-65, Col 38 lines 29-40 and Col 39 line 1 through Col 10 line 18 for each current sequence of images is mapped to a model/algorithm choosing from a library of a set of pre-trained models and a most accurate algorithm/model resulting in highest quality of reconstruction is selected in order to enhance lower-quality visual data to high-quality of original visual data).
Caballero is silent about predicting a quality score of each algorithm in a specified set of video enhancement algorithms for performing video enhancement processing on the currently input group of images and selecting a high-score algorithm based on the quality score.
Wei discloses predicting/calculating a quality score of each algorithm in a specified set of video enhancement algorithms for performing video enhancement processing on the currently input group of images and selecting a high-score algorithm based on the quality score (see summary section; and Figure 2 section).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Caballero system with the teaching of Wei, so to provide an alternative way of processing data and selecting the most appropriate algorithm for enhancing video process as a matter of engineering choices.
Regarding claim 2, Caballero in view of Wei discloses the method as discussed in the rejection of claim 1. The combined system further discloses wherein the determining, for each group of images, the matched video enhancement algorithm comprises:
extracting, by the model, image features from a currently input group of images using a deep residual network; generating inter-frame difference information based on the image features output by the deep residual network, performing channel fusion processing on the inter-frame difference information and the image features; extracting global features based on a result of the channel fusion processing; and determining the matched video enhancement algorithm based on a quality score corresponding the global features (taught by Caballero; Figures 7-9 and 28-30 and theirs corresponding description sections).
Regarding claim 8, Caballero discloses the method as discussed in the rejection of claim 1. Caballero further discloses wherein the dividing a target video into a plurality of groups of images comprises: identifying scenes in the target video using a scene boundary detection algorithm; and extracting, for each of the scenes, video frames from a frame sequence corresponding to the each of the scenes using a sliding window, and taking the video frames extracted each time as a group of images, wherein k frames are extracted each time, k is a specified number of frames of a group of images, and based on a number of frames remaining to be extracted in a scene being less than k, a group of images is obtained after supplementing to k frames (Figures 7-8 and 29-31).
Regarding claim 9, Caballero in view of Wei discloses the method as discussed in the rejection of claim 1. The combined system further discloses pre-training the model using specified sample data, wherein a method for constructing the sample data comprises:
performing, for each group of sample images, video enhancement processing on the each group of sample images using each algorithm in a specified set of video enhancement algorithms respectively; and assessing a quality score of a video enhancement processing result of each of the video enhancement algorithms using a specified image quality assessment algorithm or a manual scoring mode, and setting an average value of the quality scores of the video enhancement algorithms as a quality score label of the each group of sample images in corresponding algorithms (taught by Caballero; Col 33 line 23 through Col 34 line 15, Col 37 lines 7-60, Col 38 lines 21-65).
Regarding claim 10, Caballero in view of Wei discloses the method as discussed in the rejection of claim 9. The combined system further discloses wherein a number of the image quality assessment algorithms is greater than 2, and a number of people participating in the manual scoring is greater than 2 (taught by Caballero; Col 1 lines 19-22, Col 8 lines 47-49 and Col 44 lines 51-60).
Regarding claims 11-12, all limitations of claims 11-12 are analyzed and rejected corresponding to claims 1-2.
Claims 4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Caballero et al (US 10701394) in view of Wei et al (CN 110415217 A) as applied to claim 1 above, and further in view of Heo et al (US 2019/0057270).
Regarding claim 4, Caballero in view of Wei discloses the method as discussed in the rejection of claim 1. The combined system is silent about predicting, by a multilayer perceptron (MLP) based on the global features, the quality score of each algorithm.
Heo discloses predicting, by a multilayer perceptron (MLP) based on the global features, the quality score of each algorithm (Figures 5 and 8).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Caballero in view of Wei system with the teaching of Heo, so to provide an alternative way of processing data and selecting the most appropriate algorithm for enhancing video process as a matter of engineering choices.
Regarding claim 14, all limitations of claim 14 are analyzed and rejected corresponding to claim 4.
Allowable Subject Matter
Claims 5-7 and 15 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.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GIGI L DUBASKY whose telephone number is (571)270-5686. The examiner can normally be reached M-F 9:00-5:00.
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, Nathan Flynn can be reached at 571-272-1915. 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.
/GIGI L DUBASKY/Primary Examiner, Art Unit 2421
Prosecution Insights