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 .
Comments
The Amendment – After Non-Final Rejection filed on August 10, 2026 has been entered and made of record.
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, 4, 12, and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Koishi (U.S. Pub. No. 2011/0001843) in view of Yusuke et al. (U.S. Pub. No. 2023/0351570).
As to claims 1, 12 and 13, Koishi teaches an image processing apparatus (i.e., “image processing apparatus 10”, Paragraph [0023])/image processing method to be executed in an image processing apparatus/non-transitory computer-readable storage medium (i.e., “computer-readable storage medium”, Paragraph [0021]) which stores a program for causing a computer to execute a method, the method comprising:
a content acquisition unit configured to acquire/acquiring first image content (i.e., “The image acquiring processing by the image acquisition unit 11 is processing for acquiring an image in accordance with an operation by the user”, Paragraph [0025]);
a degree acquisition unit configured to acquire/acquiring a degree of fluctuation of a fluctuation element of the first image content, the fluctuation element being an element having fluctuation as a state variation, out of elements constituting an image (See for example, “This processing is part of the processing by the appearance reproduction unit 15. In this processing, the ambient lighting Sr in the visual environment of the scene, defined by CIECAM02, is found”, Paragraph [0031]);
an intent acquisition unit configured to acquire/acquiring information indicating a shooting intent of a user (i.e., “the depth of field and angle of view determined at step S5 and S6 are used to judge the intent of the photographer when shooting the shot image (whether the photographer is focusing on a person or other main object) in accordance with predetermined conditions”, Paragraph [0042]); and
a generation unit configured to generate/generating second image content having a different degree of fluctuation of a fluctuation element of the image content from the first image content (i.e., “This appearance reproduction processing is processing for preparing an output image converted so that the appearance of the image when viewed by the input visual environment (visual environment of scene) is reproduced when viewing it by the scheduled output visual environment for viewing the image”, Paragraph [0044]), the second image content in which the degree of fluctuation acquired from the first image content corresponds to the information indicating the shooting intent (See for example, “it is deduced that the photographer is focusing on a person or other main object, so the ambient lighting condition of the input visual environment determined at step S4 is corrected to a one-level darker condition. For example, when the depth of field is shallow (d
≤
1 m), if the ambient lighting condition of the input visual environment determined at step S4 is "average lighting", it is corrected to "dim", if when the ambient lighting condition of the input visual environment determined at step S4 is "dim", it is corrected to "dark".”, Paragraph [0042]).
However, Koishi et al. does not explicitly disclose generating the second image content using a trained learning model, and wherein the trained learning model generates the second image content in which the degree of fluctuation acquired from the first image content corresponds to the information indicating the shooting intent.
Yusuke et al. teaches generating second image content using a trained learning model (i.e., “the discriminator is a pre-trained neural network”, Paragraph [0010]), and wherein the trained learning model generates the second image content (i.e., “processes the first image according to a first relighting algorithm to obtain a second image”, Paragraph [0190]; and “adjusting one or more lighting parameters in the first relighting algorithm at a first rate according to an optimization algorithm to obtain a first adjustment lighting parameter”, Paragraph [0222]) in which the degree of fluctuation acquired from the first image content corresponds to the information indicating the shooting intent (See for example, “the target image obtained in D may reflect an intention of the user”, Paragraph [0271]).
Koishi et al. and Yusuke et al. are analogous art because they are from the field of digital image processing.
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify Koishi et al. by incorporating the generating of the second image content using a trained learning model, and the trained learning model generates the second image content in which the degree of fluctuation acquired from the first image content corresponds to the information indicating the shooting intent, as taught by Yusuke et al.
The suggestion/motivation for doing so would have been to improve the user experience when relighting image content.
Therefore, it would have been obvious to combine Yusuke et al. with Koishi et al. to obtain the invention as specified in claims 1, 12 and 13.
As to claim 2, Koishi et al. teaches wherein the intent acquisition unit acquires the information indicating the shooting intent based on image information of the first image content (i.e., “the depth of field and angle of view determined at step S5 and S6 are used to judge the intent of the photographer when shooting the shot image (whether the photographer is focusing on a person or other main object) in accordance with predetermined conditions”, Paragraph [0042]) or based on at least one of text information input by the user, operation history information, action history information, and sound information, the information indicating the shooting intent being information associated with the first image content.
As to claim 4, Koishi et al. teaches a determination unit configured to determine whether the information indicating the shooting intent corresponds to a settable degree, out of degrees of fluctuation of a fluctuation element (i.e., “(“average lighting”, “dim”, or “dark”)”, Paragraph [0031]), and generating the second image content in which the degree of fluctuation of the fluctuation element extracted from the first image content corresponds to the information indicating the shooting intent, in a case where the information indicating the shooting intent corresponds to the settable degree, out of the degrees of fluctuation of the fluctuation element (i.e., “This appearance reproduction processing is processing for preparing an output image converted so that the appearance of the image when viewed by the input visual environment (visual environment of scene) is reproduced when viewing it by the scheduled output visual environment for viewing the image”, Paragraph [0044]).
However, Koishi et al. does not explicitly disclose the trained learning model generating the second image content.
Yusuke et al. teaches a trained learning model generating the second image content (i.e., “the discriminator is a pre-trained neural network”, Paragraph [0010]; “processes the first image according to a first relighting algorithm to obtain a second image”, Paragraph [0190]; and “adjusting one or more lighting parameters in the first relighting algorithm at a first rate according to an optimization algorithm to obtain a first adjustment lighting parameter”, Paragraph [0222]).
Therefore, in view of Yusuke et al., it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Koishi et al. by incorporating the trained learning model generating the second image content, as taught by Yusuke et al., in order to improve the user experience when relighting image content.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Koishi et al. in view of Yusuke et al. as applied to claim 1 above, and further in view of Shibagami (U.S. Pub. No. 2012/0044384). The teachings of Koishi et al. and Yusuke et al. have been discussed above.
As to claim 11, Koishi et al. and Yusuke et al. do not explicitly disclose wherein the fluctuation element of the image content includes at least one of a facial expression or a posture of a person in the image content, a composition of the image content, the weather found in the image content, and clothing of a subject found in the image content.
Shibagami teaches a fluctuation element of the image content that includes at least one of a facial expression or a posture of a person in the image content, a composition of the image content, the weather found in the image content, and clothing of a subject found in the image content (i.e., “The smile detection module 115 calculates a face smile level for the face detected by the face detection module 114. Specifically, the smile detection module 115 obtains feature amounts required to calculate the degree of smiling such as contours of face constituents which constitute a face and include eyes, nose and mouth, and positions of the face constituents such as inner corners of eyes, outer corners of eyes, nostrils, corner of mouth, and lip”, Paragraph [0031]).
Koishi et al., Yusuke et al. and Shibagami are analogous art because they are from the field of digital image processing.
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to further modify Koishi et al. and Yusuke et al. by incorporating the fluctuation element of the image content includes at least one of a facial expression or a posture of a person in the image content, a composition of the image content, the weather found in the image content, and clothing of a subject found in the image content.
The suggestion/motivation for doing so would have been to.
Therefore, it would have been obvious to combine Shibagami with Koishi et al. and Yusuke et al. to obtain the invention as specified in claim 11.
Allowable Subject Matter
With respect to claims 1, 2, 4, and 11-13, upon further consideration, the claims are rejected in view of Koishi et al., Yusuke et al. and Shibagami as state above, claims 3 and 5-10 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.
Response to Arguments
Claim Objections
With respect to claims 1, 4, 5, 8-10, 12, and 13, Applicant has amended the claims in order to correct for minor informalities. Therefore, the objections have been withdrawn.
Claim Rejections - 35 USC § 112
With respect to claims 2 and 3, Applicant has amended the claims in order to particularly point out and distinctly claim the subject matter which the inventor regards as the invention. Therefore, the rejections have been withdrawn.
Conclusion
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/JOSE M TORRES/Examiner, Art Unit 2664 09/30/2026
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