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 .
DETAILED ACTION
This communication is a Final office action in merits. Claims 1-20, after amendment, are presently pending and have been elected and considered below.
Request for Continued Examination
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 3/13/2026 has been entered.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. KR10-2022-0056250, filed on 5/6/2022 and KR10-2022-0056250, filed on 8/11/2022.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 5/4/2023, 10/17/2023, and 7/24/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 of this title, 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 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.
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 1, 4-5, 7-8, 10-11, 14, 17, 20 are rejected under 35 U.S.C. 103 as being unpatentable over US 2008/0317358 A1, Bressan et al. (hereinafter Bressan) in view of US 2023/0239462 A1, Kang et al. (hereinafter Kang) and further in view of US005983251A, Martens et al. (hereinafter Martens).
As to claim 1, Bressan discloses an image processing apparatus comprising: a memory storing one or more instructions; and one or more processors configured to access the memory and execute the one or more instructions stored in the memory to:
obtain a meta model, based on a quality of an input image (Figs 1-3, a model of image enhancement based on input image quality analysis; pars 0015-0016, 0024-0026, 0030, a meta-data associated with the image being used and input to the model), train the meta model by using a training data set corresponding to the input image (pars 0049, 0052-0056, 0060, modeling training with meta-data), and
obtain a quality-processed output image from the input image, based on the trained meta model (Figs 2-3; pars 0032-0033, 0036-0037, 0040, an image output device for outputting image from the model).
Bressan does not expressly disclose a meta model being obtained by combining parameters of two or more pre-trained reference models, and using the combined parameters as parameters of the meta model, each of the one or more pre-trained reference models having been pre-trained with training images having a different quality value.
Kang, in the same or similar field of endeavor, further teaches interpolating one or more reference models which have been trained (e.g. pre-trained) (Figs 12-16; pars 0009-0011, 0052, 0166-0169, 0191, claims 4, 14, trained in advance) and each of the one or more pre-trained reference models having been pre-trained with training images having a different quality value (Figs 12-16; pars 0009-0011, 0052, 0166-0169, 0191, claims 4, 14, note the pre-trained reference models being trained with different training images with reference images with different interpolation rates, representing different training image qualities).
Martens, in the same or similar field of endeavor, additionally teaches a meta model with sets of model parameters being obtained by combining parameters of two or more reference models, and using the combined parameters as parameters of the meta model (col 6, lines 26-40; col 46, lines 12-18; col 49, lines 12-18; col 50, lines 27-31, meta modeling parameters as combined parameters from a plurality of reference models; col 2, lines 6-12; col 12, lines 27-45; col 39, line 60-col 40, line 3, meta modeling parameters as weighted sums of reference models)
Therefore, consider Bressan, Kang, and Martens’s teachings as a whole, it would have been obvious to one of skill in the art before the filing date of invention to incorporate Kang and Martens’s teachings in Bressan’s apparatus to provide variable reference models with different image qualities for providing encoding mechanism with desired performance requirements.
3. (Canceled)
As to claim 4, Bressan as modified discloses the image processing apparatus of claim 1, wherein the different quality value is based on a distribution of quality values of training images in the training data set (Bressan: pars 0010, 0029, 0044-0045, different distribution of quality value).
As to claim 5, Bressan as modified discloses the image processing apparatus of claim 1, wherein the one or more processors are further configured to execute the one or more instructions to search for two or more pre-trained reference models among the plurality of reference models by comparing each of the quality values corresponding to the plurality of reference models with a quality value of the input image to find one or more reference models that have a quality value within a threshold range of the quality value of the input image (Bressan: pars 0012, 0045, 0047, 0070; Baek: pars 0010, 0020, 0052, 0059, 0062, noise or sharpness threshold), and obtain the meta model by combining the parameters of the found two or more pre-trained reference models (Bressan: Figs 3-4; pars 0015-0018; Kang: pars 0009-0011, 0052, 0166-0169; Martens: col 6, lines 26-40; col 12, lines 27-45; col 46, lines 12-18; col 49, lines 12-18; col 50, lines 27-31).
As to claim 7, Bressan as modified discloses the image processing apparatus of claim 1, wherein the one or more processors are further configured to execute the one or more instructions to obtain the quality of the input image, and wherein the quality of the input image comprises at least one of a compression quality (Bressan: pars 0092, 0094), a blur quality (Bressan: pars 0028, 0079, 0097, blurring), a resolution (par 0045, low resolution), or noise for the input image (Bressan: pars 0010, 0029, 0044-0045, noise from various sources).
As to claim 8, Bressan as modified discloses the image processing apparatus of claim 1, wherein the one or more processors are further configured to execute the one or more instructions to: identify a category of the input image, obtain an image belonging to the category (Bressan: pars 0015-0018, 0030, 0047, identifying one or more category of the input image obtained), obtain an image with a degraded quality by processing the image belonging to the category to have a quality corresponding to the quality of the input image, and obtain the training data set including the image belonging to the category and the image with the degraded quality (Bressan: pars 0058-0059, 0079, 0081-0082).
As to claim 10, Bressan as modified discloses the image processing apparatus of claim 8, wherein the one or more processors are further configured to execute the one or more instructions to obtain the image with the degraded quality by performing at least one of compression degradation (Bressan: pars 0081-0082, 0094, compression), blurring degradation (Bressan: pars 0079, 0097-0098), resolution adjustment, or noise addition on the image belonging to the category (Bressan: pars 0010-0011, 0034, 0061, noise filtering).
As to claim 11, Bressan as modified discloses the image processing apparatus of claim 10, wherein the one or more processors are further configured to execute the one or more instructions to perform compression degradation on the image belonging to the identified category by encoding and decoding the image belonging to the category (see rejection in claim 10).
As to claim 14, it is essentially a method claim necessitated claim 1 with more details on parameter combining of reference models. Rejection of claim 1 is therefore incorporated herein. Note that Bressan as modified also teaches a meta model parameters as weighted sum of the reference models (as way of parameter combining) (see rejection in claim 1 above).
As to claim 17, it is rejected with the same reasons as set forth in claim 8.
As to claim 20, it recites a CRM having program stored executed to perform functions of claim 1. Rejection of claim 1 is therefore incorporated herein.
Claims 9, 12, 18 are rejected under 35 U.S.C. 103 as being unpatentable over Bressan in view of in view of Kang and further Martens and US 2022/0335655 A1, Jiang et al. (hereinafter Jiang).
As to claim 9, Bressan as modified discloses the image processing apparatus of claim 8, wherein the one or more processors are further configured to execute the one or more instructions to train the meta model but does not expressly teach for a difference between the image belonging to the category and an image that is output from the meta model by inputting the image with the degraded quality to the meta model is minimized. Jiang, in the same or similar field of endeavor, further teaches the training model to minimize a distortion loss between the input image and the reference image across all training data including categories identified in Bressan as modified (pars 0046, 0055, 0070, 0091). Therefore, consider Bressan as modified and Jiang’s teachings as a whole, it would have been obvious to one of skill in the art before the filing date of invention to incorporate Jiang’s teachings in Bressan as modified’s apparatus to perform meta modeling for images of selected category.
As to claim 12, Bressan as modified discloses the image processing apparatus of claim 1, wherein the one or more processors are further configured to execute the one or more instructions to obtain the meta model and train the obtained meta model each time at least one of a frame (Jiang: par 0046, train the model with image frame), a scene including a plurality of frames, or a content type changes (Bressan: pars 0014-0016, 0024-0027, 0030).
As to claim 18, it is rejected with the same reasons as set forth in claim 9.
Claims 2, 13, 15, 19 are rejected under 35 U.S.C. 103 as being unpatentable over Bressan in view of Kang and further in view of Martens and US 2023/0179768 A1, Besenbruch et al. (hereinafter Besenbruch).
As to claim 2, Bressan discloses the image processing apparatus of claim 1, but does not expressly disclose to obtain an averaged quality value for the input image at a first time point based on quality values of input images obtained at the first time point and at least one past time point in a sequence of input images (Bressan: pars 0260-0261, 0614), and obtain the meta model by combining the parameters of the two or more pre-trained reference models based on the averaged quality value (Bressan: pars 0238, 2051; Kang: pars 0009-0011, 0052, 0166-0169; Martens: col 6, lines 26-40; col 12, lines 27-45; col 46, lines 12-18; col 49, lines 12-18; col 50, lines 27-31).
Besenbruch, in the same or similar field of endeavor, further teaches obtain an averaged quality value for the input image obtained at a first time point and at least one past time point in a sequence of input images and a quality value of an input image obtained at a past time point before the first time point (pars 0260-0261, 0614, note various average or moving average in time domain involves at least two time points, e.g. a present measuring point and a time point before), and obtain the meta model corresponding to the averaged quality value (pars 0238, 0251).
Therefore, consider Bressan as modified and Besenbruch’s teachings as a whole, it would have been obvious to one of skill in the art before the fling date of invention to incorporate Besenbruch’s teachings as described in Bressan’s apparatus to take quality variations of the input images into consideration in meta model training.
As to claim 13, Bressan as modified discloses the image processing apparatus of claim 1, wherein the one or more processors are further configured to execute the one or more instructions to: obtain a first time point exponential moving average model based on both a meta model trained at a first time point and a meta model trained at a past time point before the first time point (Besenbruch: pars 0261, 0614, 0619), and input the input image to the first time point exponential moving average model and obtain the quality-processed output image as on output from the first time point exponential moving average model (Besenbruch: Figs 1-4; pars 0238, 0251, 0261, 0614, 0619).
As to claim 15, it is rejected with the same reason as set forth in claim 2.
As to claim 19, it is rejected with the same reasons as set forth in claim 13.
Allowable Subject Matter
Claims 6 and 16 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 and overcoming any 35 USC 112 rejection.
Reasons for Allowance
Prior art of record (Bressan, Kang, Martens, Jiang, and Besenbruch), neither discloses alone nor teaches in combination functions and features recited in claim 6 and claim 16, respectively.
Response to Arguments
Applicant’s arguments have been considered but they are moot in light of new ground of rejection.
Conclusion
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.
Examiner’s Note
Examiner has cited particular column, line number, paragraphs and/or figure(s) in the reference(s) as applied to the claims for the convenience of the Applicant. Although the specified citations are representative of the teachings of the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the reference(s) in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to QUN SHEN whose telephone number is (571)270-7927. The examiner can normally be reached on Mon-Fri 8:30-5:50 PT.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Amandeep Saini can be reached on 571-272-3382. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/QUN SHEN/
Primary Examiner, Art Unit 2662
Prosecution Insights