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 Remarks
The Office Action has been made issued in response to amendment filed November 26, 2025. Claims 1-2, 5-10 and 13-16 are pending. Applicant’s arguments have been carefully and respectfully considered in light of the instant amendment, and are not persuasive. Accordingly, this action has been made FINAL.
Claim Rejections – 35 USC section § 103
Applicant's arguments with respect to the limitation “media when an aesthetic score of the selected media fails to meet a predefined threshold, and, wherein the arrangement order of the at least one AI-based media enhancement model maximizes the aesthetic score of the selected media” have been considered but are moot in view of the new ground(s) of rejection.
Claim Rejections - 35 USC § 103
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 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 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.
Claims 1, 3, 5-6, 9, 11 and 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over Mech (Pub No.: 20180268533) in view of Safonov et al (NPL titled: Image Enhancement Pipeline Based on EXIF Metadata) in view of Zheng et al (NPL titled: Learning Image-adaptive 3D Lookup Tables for High Performance Photo Enhancement in Real-time) in view of Cerosaletti et al (US20110074966A1).
As to independent claim 1, Mech discloses a method for enhancing selected media (image defect identification and correction method – see [p][0004]), comprising:
selecting selected media to be enhanced (note that digital image 112 is capture by a camera – see [p][0029]); detecting at least one artifact included in the selected media (training digital images 202 , for instance, may be tagged to identify a defect type 204 included in the image, such as exposure, white balance, saturation, noise, haze, blur, or composition – see [p][0034]); identifying at least one artificial intelligence (AI)-based selected media enhancement model, from among a plurality of AI-based selected media enhancement models based on the type of the at least one artifact included in the selected media ([a]s previously described, each of the plurality of defect type identification models 214(1)-214(N) corresponds to a respective one of plurality of different defects types – see [p][0039]), the at least one AI-based selected media enhancement model being configured to enhance the at least one artifact detected in the selected media ([t]he plurality of defect type identification models 214 (1) - 214 (N) are then provided by the model generation module 208 to a defect identification module 216 to identify a defect within a digital image -see [p][0038]); and enhancing the selected media by applying the at least one AI-based selected media enhancement model (“[a] correction is generated by the image defect correction system 120 to the identified at least one defect ( block 412 ) based on the identification of the defect 114” – see [p][0041]); however, Mech does no expressly disclose wherein the detecting including tag information indicating the at least one artifact included in the selected media.
Safonov discloses an image enhancement pipeline including wherein the detecting including wherein the tag information is stored with the selected media as metadata (standard for storing information about a digital image into an image file during photo acquisition (EXIF Version 2.2 2002) - see section 3.1, [p][001]) of the selected media in a selected media file format (see section 3.2.2 and Table 3.1).
Mech and Safonov are combinable because they are from the same field of endeavor. It would have been obvious to one of ordinary skill in the art before the effective filling date of the invention to have incorporated image enhancement pipeline of Safonov into the image defect identification and correction method of Mech in order to implement an automatic image enhancement pipeline, based on the analysis of EXIF tags (see section 3.4.1). Such a modification is the result of combining prior art elements according to known methods, they would have performed as expected, and the results would have been predictable.
However, the combination of Safonov and Mech as a whole does not expressly disclose determining of the at least one AI-based selected media enhancement model related to the least one artifact included in the selected media and wherein an arrangement order of the at least one AI-based media enhancement mode defined in the determine pipeline in the enhancing step.
Zheng discloses a learning image-adaptive 3D lookup tables including determining an arrangement order of the at least one AI-based selected media enhancement model related to the least one artifact included in the selected media and wherein an arrangement order of the at least one AI-based media enhancement mode defined in the determine pipeline in the enhancing step (we can learn one or multiple LUTs by optimizing some objective function. Once learned, those LUTs can be used to automatically enhance the given images. To address the second limitation, the model should be content-aware and adaptive to the input, and we propose to learn image-adaptive 3D LUTs to achieve this goal. An intuitive idea is to learn a classifier to perform scene classification, and then use different 3D LUTs to enhance different images. This strategy is adopted by many camera devices and image editing tools. Suppose that N 3D LUTs, denoted by {φn}n₌₁,...,N , are learned. The classifier outputs N probabilities {pn}n₌₁,...,N for scene classification. This 3D LUT – see section3.2, [p][002]).
Mech, Zheng and Safonov are combinable because they are from the same field of endeavor. It would have been obvious to one of ordinary skill in the art before the effective filling date of the invention to have incorporated the learning image-adaptive 3D lookup tables of Safonov into the image defect identification and correction method of Mech as modified by Safonov for performing automatic photo enhancement by implementing a learning architecture which can learn image-adaptive 3D LUTs to achieve intelligent and high performance photo enhancement (see section 1, [p][007]). Such a modification is the result of combining prior art elements according to known methods, they would have performed as expected, and the results would have been predictable.
However, the combination of Safonov, Zheng and Mech as a whole does not expressly disclose media when an aesthetic score of the selected media fails to meet a predefined threshold, and, wherein the arrangement order of the at least one AI-based media enhancement model maximizes the aesthetic score of the selected media
Cerosaletti discloses an automatically determining the aesthetic quality media when an aesthetic score of the selected media fails to meet a predefined threshold (satisfy a threshold aesthetic quality criteria – see [p][0103]), and, wherein the arrangement order of the at least one AI-based media enhancement model maximizes the aesthetic score of the selected media (the photographer with respect to a particular image by determining which features could be changed to produce the largest improvement to the computed aesthetic quality parameter – see [p][0107]).
Mech, Cerosaletti, Zheng and Safonov are combinable because they are from the same field of endeavor. It would have been obvious to one of ordinary skill in the art before the effective filling date of the invention to have incorporated the automatically determining the aesthetic quality media of Cerosaletti into the image defect identification and correction method of Mech as modified by Safonov and Zheng for producing an indication of the photographer's progress toward producing images with a high level of aesthetic quality using the aesthetic quality parameters for each digital image in the set and the corresponding associated capture times (see [p][0015]). Such a modification is the result of combining prior art elements according to known methods, they would have performed as expected, and the results would have been predictable.
As to claim 3, Mech teaches the method, wherein the determining of the type of the at least one artifact comprises determining whether an aesthetic score of the selected media (see [p][0040] - image defect identification system 118 , for instance , may base this determination on a threshold set for each of the defect type scores 302 (1) - 302 (N) individually , an aggregate score calculated from the defect type scores 302 (1) - 302 ( N ) together , and so on); however, Mech does not expressly fails to meet a predefined threshold.
Safonov discloses an image enhancement pipeline including the selected media fails to meet a predefined threshold (If C1>C2, then the image is affected by JPEG artefact compression; if C1<C2, then post-processing is unnecessary – see section 3.3.5)
Therefore, combining Mech, Cerosaletti Zheng and Safonov would meet the claim limitations for the same reasons as previously discussed in claim 1.
As to claim 5, Mech teaches the method, the determining of the arrangement order comprises determining a type of the at least one AI-based selected media enhancement model (see [p][0040]); however, Mech does not expressly teaches an order of the at least one AI-based selected media enhancement model.
Safonov discloses an image enhancement pipeline including an order of the at least one AI-based selected media enhancement model ([t]ypical defects of photos are estimated and corrected (if necessary) in the following order: (a) JPEG artefacts; (b) noise; (c) exposure problems; (d) sharpness and (e) red eye artefacts – see section 3.4.1). Therefore, combining Mech, Cerosaletti, Zheng and Safonov would meet the claim limitations for the same reasons as previously discussed in claim 1.
As to claim 6, Mech teaches the method, wherein: the identifying the at least one AI-based selected media enhancement model further comprises determining a plurality of AI-based selected media enhancement models for enhancing the at least one artifact detected in the selected media (see [p][0041]); and the applying the at least one AI-based selected media enhancement model comprises applying the plurality of AI-based selected media enhancement models to the selected media (see [p][0041]); however, Mech according to a predetermined order.
Safonov discloses an image enhancement pipeline including applying enhancement model according to a predetermined order ([t]ypical defects of photos are estimated and corrected (if necessary) in the following order: (a) JPEG artefacts; (b) noise; (c) exposure problems; (d) sharpness and (e) red eye artefacts – see section 3.4.1).
Therefore, combining Mech, Cerosaletti, Zheng and Safonov would meet the claim limitations for the same reasons as previously discussed in claim 1.
As to independent claim 9, this claim differs from claim 1 only in that claim 1 is method whereas claim 31 is an electronic device and the electronic device comprising: a memory; one or more processors communicatively connected to the memory are additively recited. Mech in combination discloses a system including a memory (1112 – see Fig 11 of Mech); one or more processors (see Fig 11 of Mech) communicatively connected to the memory. Therefore, combining Mech, Cerosaletti, Zheng and Safonov would meet the claim limitations for the same reasons as previously discussed in claim 1.
Claims 11 and 13-14 are rejected for the same reasons as set forth in the rejection of the claims 3 and 5-6 are method claims 3 and 5-6 for the electronic device claimed in claims 11 and 13-14.
Claims 2 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over
Mech in view of Safonov et al in view of Zheng et al in view of Cerosaletti as applied to claim 1 further in view of Wijayanto et al (NPL titled: Encryption EXIF Metadata for Protection Photographic Image of Copyright Piracy).
As to claim 2, note the discussion above; the combination of Mech, Cerosaletti Zheng and Safonov as a whole does not expressly disclose encrypting the tag information regarding the selected media. Wijayanto discloses a method of protection photographic image of copyright piracy encrypting the tag information regarding the selected media (see section 3, [p][004] - encryption process is done after reading EXIF metadata of digital images is completed).
Mech, Wijayanto, Cerosaletti Zheng and Safonov are combinable because they are from the same field of endeavor. It would have been obvious to one of ordinary skill in the art before the effective filling date of the invention to method of protection photographic image of copyright piracy of Wijayanto into the image defect identification and correction method of Mech as modified by Safonov and Zheng in order to protect the copyright of a digital image using common cryptographic algorithms to secure data or information from copyright piracy using the EXIF metadata information contained therein (see abstract). Such a modification is the result of combining prior art elements according to known methods, they would have performed as expected, and the results would have been predictable.
Claim 10 is rejected for the same reasons as set forth in the rejection of the claim 2, as claim 2 is method claim for the electronic device claimed in claim 10.
Allowable Subject Matter
Claims 7-8 and 15-16 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter: None of the prior art of record teaches or fairly suggests: wherein the identifying the at least one AI-based selected media enhancement model comprises: determining a type of a reference AI-based selected media enhancement model and an order of the reference AI-based selected media enhancement model, the reference AI-based selected media enhancement model being configured to enhance a reference selected media; storing, in a database, the type and the order of the reference AI-based selected media enhancement model; obtaining feature vectors of the selected media; and determining a type of the at least one AI-based selected media enhancement model and an order of the at least one AI-based selected media enhancement model according to the type and the order of the reference AI-based selected media enhancement model , wherein the reference selected media has similar feature vectors with the selected media.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Fisher et al (NPL titled: NICER: Aesthetic Image Enhancement with Humans in the Loop) discloses proposes the Neural Image Correction & Enhancement Routine
(NICER), a neural network based approach to no-reference image enhancement in a fully-, semi-automatic or fully manual process that is interactive and user-centered. NICER iteratively adjusts image editing parameters in order to maximize an
aesthetic score based on image style and content. Users can modify these parameters at any time and guide the optimization process towards a desired direction. This interactive workflow is a novelty in the field of human-computer interaction for
image enhancement tasks.
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.
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Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDRAE S ALLISON whose telephone number is (571)270-1052. The examiner can normally be reached on Monday-Friday 9am-5pm EST.
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/ANDRAE S ALLISON/Primary Examiner, Art Unit 2663 February 9, 2026