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 .
Priority
Receipt is acknowledged of certified copies of papers submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record in the file.
Response to Amendment
Applicant’s remarks, filed 08/05/2026, regarding the objections made to the claims submitted in the non-final office action dated 05/05/2026 are withdrawn due to the amendments made to the claims.
Response to Arguments
Applicant’s arguments see remarks, filed 08/05/2026, with respect to the claims 1-2, 4, and 8-14 have been fully considered but are moot because the arguments do not apply to the current combinations of references being used in the current 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 (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 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, 4, 9, and 12-14 are rejected under 35 U.S.C. 103 as being unpatentable over OJIMA et al. (US 20080002910 A1), hereinafter referenced as OJIMA, in view of HAGIWARA (US 20210383508 A1), hereinafter referenced as HAGIWARA, and further in view of YAMADA et al. (US 20160156851 A1), hereinafter referenced as YAMADA.
Regarding claim 1, OJIMA explicitly teaches a derivation device that derives a correction amount related to white balance correction for correcting a color of image data obtained by imaging a subject (Figs. 1-3. Paragraph [0094]-OJIMA discloses FIG. 1 shows a block diagram of an image processor 100 according to the first embodiment of the present invention (wherein the image processor #100 performs color correction).), the derivation device comprising:
a processor (Fig. 1, #100 called image processor. Paragraph [0215]-OJIMA discloses the above devices specifically are computer systems made from a microprocessor, ROM, and RAM, for instance. The RAM stores a computer program. The microprocessor operates in accordance with the computer program, allowing each device to achieve its function. Here, in order to achieve a predetermined function, the computer program is arrived at by combining a plural number of command codes for indicating an order to the computer.),
wherein the processor is configured to execute (Fig. 1, #100 called image processor. Paragraph [0215]-OJIMA discloses the above devices specifically are computer systems made from a microprocessor, ROM, and RAM, for instance. The RAM stores a computer program. The microprocessor operates in accordance with the computer program, allowing each device to achieve its function. Here, in order to achieve a predetermined function, the computer program is arrived at by combining a plural number of command codes for indicating an order to the computer.):
first derivation processing of deriving first correction information without using a machine-learned model (Fig. 3. Paragraph [0107]-OJIMA discloses the color saturation correction unit 233 receives the image signal output from the selection unit 237 and the color saturation correction gain output from the color saturation correction gain setting unit 236, and performs, based on the color saturation correction gain, color saturation correction on the image signal output from the selection unit 237, and outputs the result to the image data output unit 240 (wherein color saturation correction is first correction information).),
wherein the processor is further configured to (Fig. 1, #100 called image processor. Paragraph [0215]-OJIMA discloses the above devices specifically are computer systems made from a microprocessor, ROM, and RAM, for instance. The RAM stores a computer program. The microprocessor operates in accordance with the computer program, allowing each device to achieve its function. Here, in order to achieve a predetermined function, the computer program is arrived at by combining a plural number of command codes for indicating an order to the computer.):
execute selection processing (Fig. 3, #237 called selection unit. Paragraph [0102]-OJIMA discloses a selection unit 237 that selects, based on the depth data output from the depth data input unit 210 and the depth degree output from the depth degree input unit 220, one of the image signal output from the contour correction unit 231 and the image signal output from blur correction unit 232, and a color saturation correction unit 233 that performs color saturation on the image signal output from the selection unit 237.) of selecting one type of processing from among the first derivation processing (Fig. 3, #231 called contour correction unit.), the second derivation processing (Fig. 3. #232 called blur correction unit.), and the third derivation processing (Fig. 3, #233 called color saturation correction unit.) based on subject information of the subject (Fig. 3. Paragraph [0103]-OJIMA discloses the depth feeling correction unit 230 includes, as shown in FIG. 3, a contour correction unit 231 that performs contour correction on the input image signal, a blur correction unit 232 that performs blur correction on the input image signal, a selection unit 237 that selects, based on the depth data output from the depth data input unit 210 and the depth degree output from the depth degree input unit 220, one of the image signal output from the contour correction unit 231 and the image signal output from blur correction unit 232, and a color saturation correction unit 233 that performs color saturation on the image signal output from the selection unit 237 (wherein depth data is subject information).),
execute correction amount derivation processing of deriving the correction amount (Fig. 3. Paragraph [0107]-OJIMA discloses based on the color saturation correction gain, color saturation correction on the image signal output from the selection unit 237, and outputs the result to the image data output unit 240 (wherein the outputs are correction amounts).) based on information obtained by the processing selected by the selection processing (Fig. 3. Paragraph [0095]-OJIMA discloses an image data output unit 240 that changes the image signal corrected by the depth feeling correction unit 230 to a predetermined format for output (wherein correction unit 230 does selection processing and changes to the image signal is information obtained by selection processing).),
OJIMA fails to explicitly teach second derivation processing of deriving second correction information using the image data and the machine-learned model, and third derivation processing of executing the first derivation processing and the second derivation processing, the subject information including brightness information or color information calculated based on the image data; and.
However, HAGIWARA explicitly teaches second derivation processing of deriving second correction information using the image data (Fig. 3. Paragraph [0085]-HAGIWARA discloses when the derived difference is the first threshold or more, the derivation module 20H outputs a correction instruction for the pixel value of the pixel used for the derivation of the difference among the pixels included in the third image 56 to the first correction module 20I. The correction instruction includes the pixel position of the pixel the difference of which is the first threshold or more and a signal indicating the correction of the pixel value of the pixel (wherein pixels are image data, wherein first correction is second derivation processing, and wherein correction instruction is second correction information).) and the machine-learned model (Fig. 3. Paragraph [0037]-HAGIWARA discloses the learning unit 16 performs processing using a learned model based on an output image received from the information processing apparatus 10 and outputs processing result information. The processing result information is information derived from the image and is correct answer information derived by the processing using the learned model, for example.), and
third derivation processing (Fig. 3, derivation module 20H.) of executing the first derivation processing and the second derivation processing (Fig. 3. Paragraph [0114]-HAGIWARA discloses the derivation module 20H derives the difference between the first characteristic point information extracted at Step S108 and the second characteristic point information extracted at Step S112 (Step S114). The derivation module 20H determines whether the difference derived at Step S114 is the first threshold or more (Step S116) (wherein derivation module 20H conducts third derivation processing, wherein first derivation processing is the information extracted at Step S108, and wherein the second derivation processing is the information extracted at Step S112).),
the subject information including brightness information (Fig. 4E. Paragraph [0073]-HAGIWARA discloses the histogram 60 of brightness of the first image 52 includes both a peak 60A indicating the object P1 and a peak 60B indicating the object P2.) or color information calculated based on the image data (Fig. 3. Paragraph [0058]-HAGIWARA discloses the change ratio of the pixel value indicates a change ratio of brightness of a color represented by the pixel value. The pixel value represents a color element such as red (R), green (G), or blue (B) by a color value (a gray scale value of 0 to 255, for example).); and.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of OJIMA of a derivation device that derives a correction amount related to white balance correction for correcting a color of image data obtained by imaging a subject, the derivation device comprising: a processor, wherein the processor is configured to execute: first derivation processing of deriving first correction information without using a machine-learned model, with the teachings of HAGIWARA of second derivation processing of deriving second correction information using the image data and the machine-learned model, and third derivation processing of executing the first derivation processing and the second derivation processing, the subject information including brightness information or color information calculated based on the image data; and.
Wherein having OJIMA’s image correction processor having second derivation processing of deriving second correction information using the image data and the machine-learned model, and third derivation processing of executing the first derivation processing and the second derivation processing, the subject information including brightness information or color information calculated based on the image data; and.
The motivation behind the modification would have been to obtain a color correction device that enhances the visual appeal and vividness of an image. Since both OJIMA and HAGIWARA relate to image processing and correction, wherein OJIMA visually, the user can feel pixels in the near view area on the image more vividly, thereby improving the depth feeling in the processing image, while HAGIWARA provides an information processing apparatus and an information processing method that can provide an image with a characteristic point loss inhibited. Please see OJIMA et al. (US 20080002910 A1), Paragraph [0031], and HAGIWARA (US 20210383508 A1), Paragraph [0005].
OJIMA in view of HAGIWARA fail to explicitly teach wherein the third derivation processing includes: calculating, in the second derivation processing, light source determination information related to a type of a light source as the second correction information; determining reliability of the first correction information based on the light source determination information; and calculating the correction amount related to white balance correction based on information obtained by correcting the first correction information based on the light source determination information.
However, YAMADA explicitly teaches wherein the third derivation processing includes (Fig. 3. Paragraph [0069]-YAMADA discloses the candidate list selection unit 313 selects candidates of correction information (candidate list) from sets of correction information, in relation to a plurality of light-source types, based on the shooting condition when taking an image with an image capture optical system 100 and the imaging element 200.):
calculating, in the second derivation processing, light source determination information related to a type of a light source as the second correction information (Fig. 3. Paragraph [0052]-YAMADA discloses for each light-source, the correction-information selection unit 316 totalizes the number of blocks in each of the classes, and selects correction information corresponding to a light-source that leads to aggregated values of the plurality of classes including the highest maximum value, as the correction information that leads to a suitable color shading-correction result (wherein the highest maximum value is light source determination information).);
determining reliability (Fig. 3. Paragraph [0060]-YAMADA discloses the shading correction unit 317 evaluates the reliability of the correction information selected by the correction-information selection unit 316 as follows.) of the first correction information based on the light source determination information (Fig. 3. Paragraph [0043]-YAMADA discloses the correction-information storage unit 311 stores sets of correction information, each of which sets is associated with any of a plurality of light-source types as shooting conditions. Further in paragraph [0052]-YAMADA discloses for each light-source, the correction-information selection unit 316 totalizes the number of blocks in each of the classes, and selects correction information corresponding to a light-source that leads to aggregated values of the plurality of classes including the highest maximum value, as the correction information that leads to a suitable color shading-correction result.); and
calculating the correction amount related to white balance correction based on information obtained by correcting the first correction information based on the light source determination information (Fig. 3. Paragraph [0052]-YAMADA discloses based on the results of the color shading correction, the correction-information selection unit 316 sorts the blocks into a plurality of classes related to values of a color component. Then, for each light-source, the correction-information selection unit 316 totalizes the number of blocks in each of the classes, and selects correction information corresponding to a light-source that leads to aggregated values of the plurality of classes including the highest maximum value, as the correction information that leads to a suitable color shading-correction result. This method corresponds to histogram aggregation of correction results for each light-source, and selection of correction information associated with a light-source that leads to the highest maximum frequency (wherein correction information is the correction amount, and first correction information is color shading correction).).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of OJIMA in view of HAGIWARA a derivation device that derives a correction amount related to white balance correction for correcting a color of image data obtained by imaging a subject, the derivation device comprising: a processor, wherein the processor is configured to execute: first derivation processing of deriving first correction information without using a machine-learned model, with the teachings of YAMADA wherein the third derivation processing includes: calculating, in the second derivation processing, light source determination information related to a type of a light source as the second correction information; determining reliability of the first correction information based on the light source determination information; and calculating the correction amount related to white balance correction based on information obtained by correcting the first correction information based on the light source determination information.
Wherein having OJIMA’s image correction processor having wherein the third derivation processing includes: calculating, in the second derivation processing, light source determination information related to a type of a light source as the second correction information; determining reliability of the first correction information based on the light source determination information; and calculating the correction amount related to white balance correction based on information obtained by correcting the first correction information based on the light source determination information.
The motivation behind the modification would have been to obtain a color correction device that enhances the visual appeal and vividness of an image. Since both OJIMA and YAMADA relate to image processing and color analysis, wherein OJIMA visually, the user can feel pixels in the near view area on the image more vividly, thereby improving the depth feeling in the processing image, while YAMADA allows favorable shading correction for shading which varies depending on the light-source. Please see OJIMA et al. (US 20080002910 A1), Paragraph [0031], and YAMADA et al. (US 20160156851 A1), Paragraph [0003].
Regarding claim 4, OJIMA in view of HAGIWARA and further in view of YAMADA explicitly teach the derivation device according to claim 1,
OJIMA further explicitly teaches wherein the processor (Fig. 1, #100 called image processor. Paragraph [0215]-OJIMA discloses the above devices specifically are computer systems made from a microprocessor, ROM, and RAM, for instance. The RAM stores a computer program. The microprocessor operates in accordance with the computer program, allowing each device to achieve its function. Here, in order to achieve a predetermined function, the computer program is arrived at by combining a plural number of command codes for indicating an order to the computer.) is configured to execute the first derivation processing and the third derivation processing (Fig. 3. Paragraph [0103]-OJIMA discloses the depth feeling correction unit 230 includes, as shown in FIG. 3, a contour correction unit 231 that performs contour correction on the input image signal, a blur correction unit 232 that performs blur correction on the input image signal, a selection unit 237 that selects, based on the depth data output from the depth data input unit 210 and the depth degree output from the depth degree input unit 220, one of the image signal output from the contour correction unit 231 and the image signal output from blur correction unit 232, and a color saturation correction unit 233 that performs color saturation on the image signal output from the selection unit 237 (wherein the contour correction is first derivation processing and color saturation correction is third derivation correction).), and
in the selection processing (Fig. 3, #237 called selection unit. Paragraph [0102]-OJIMA discloses a selection unit 237 that selects, based on the depth data output from the depth data input unit 210 and the depth degree output from the depth degree input unit 220, one of the image signal output from the contour correction unit 231 and the image signal output from blur correction unit 232, and a color saturation correction unit 233 that performs color saturation on the image signal output from the selection unit 237.), select any one piece of processing from the first derivation processing or the third derivation processing (Fig. 3. Paragraph [0103]-OJIMA discloses the depth feeling correction unit 230 includes, as shown in FIG. 3, a contour correction unit 231 that performs contour correction on the input image signal, a blur correction unit 232 that performs blur correction on the input image signal, a selection unit 237 that selects, based on the depth data output from the depth data input unit 210 and the depth degree output from the depth degree input unit 220, one of the image signal output from the contour correction unit 231 and the image signal output from blur correction unit 232, and a color saturation correction unit 233 that performs color saturation on the image signal output from the selection unit 237 (wherein the signals output from the contour correction unit, blur correction unit, or color saturation correction unit are pieces of processing from first and third derivation processing).).
Regarding claim 9, OJIMA in view of HAGIWARA and further in view of YAMADA explicitly teach the derivation device according to claim 1,
OJIMA further explicitly teaches wherein, in the first derivation processing, reference information including an evaluation value corresponding to the brightness information and the color information is used (Fig. 6. Paragraph [0125-0126]-OJIMA discloses the color saturation correction unit 233 mainly includes, as shown in FIG. 6, an HSV conversion unit 2331, a delay unit 2332, a color saturation addition unit 2333, and an inverse HSV conversion unit 2334. Further in paragraph [0128]-OJIMA discloses the inverse HSV conversion unit 2334 reconstructs the H (Hue) signal and V (Value (brightness)) signal output from the delay unit 2332 and the S (Saturation (color saturation)) signal to which the color saturation is added by the color saturation addition unit 2333 into the image signal in the original format (image signal which has not been HSV-converted by the HSV conversion unit 2331 yet), i.e., the inverse HSV conversion is performed, and outputs it to the image data output unit 240. The H (Hue) signal output from the delay unit 2332 is one that has the same H (Hue) as that of the original signal (image signal output from the selection unit 237), and the V (Value (brightness)) signal output from the delay unit 2332 is one that has the same V (Value (brightness)) as that of the original signal (image signal output from the selection unit 237) (wherein the V(Value (brightness) is brightness information, the H (Hue) is color information, wherein the brightness and color information are evaluation values and color saturation correction is first derivation processing).), and
the processor is configured to acquire, as the first correction information, the evaluation value corresponding to the subject information (Fig. 3. Paragraph [0050]-OJIMA discloses it should be noted that "feature quantity" is physical quantity that can be idealized for each of pixels or areas in the image, for example, and includes brightness (luminance), lightness, color saturation, hue, contrast, brightness contrast, and color contrast, for example (wherein an area is a subject).) based on the reference information in the first derivation processing (Fig. 6. Paragraph [0125-0126]-OJIMA discloses the color saturation correction unit 233 mainly includes, as shown in FIG. 6, an HSV conversion unit 2331, a delay unit 2332, a color saturation addition unit 2333, and an inverse HSV conversion unit 2334. Further in paragraph [0128]-OJIMA discloses the inverse HSV conversion unit 2334 reconstructs the H (Hue) signal and V (Value (brightness)) signal output from the delay unit 2332 and the S (Saturation (color saturation)) signal to which the color saturation is added by the color saturation addition unit 2333 into the image signal in the original format (image signal which has not been HSV-converted by the HSV conversion unit 2331 yet), i.e., the inverse HSV conversion is performed, and outputs it to the image data output unit 240. The H (Hue) signal output from the delay unit 2332 is one that has the same H (Hue) as that of the original signal (image signal output from the selection unit 237), and the V (Value (brightness)) signal output from the delay unit 2332 is one that has the same V (Value (brightness)) as that of the original signal (image signal output from the selection unit 237) (wherein the V(Value (brightness) is brightness information, the H (Hue) is color information, wherein the brightness and color information are evaluation values, and wherein color saturation correction is first correction processing).).
Regarding claim 12, OJIMA in view of HAGIWARA and further in view of YAMADA explicitly teach the derivation device according to claim 1,
OJIMA further explicitly teaches wherein, in the selection processing, any one piece of processing of the first derivation processing in which the second correction information is not combined (Fig. 3. Paragraph [0102]-OJIMA discloses a selection unit 237 that selects, based on the depth data output from the depth data input unit 210 and the depth degree output from the depth degree input unit 220, one of the image signal output from the contour correction unit 231 and the image signal output from blur correction unit 232, and a color saturation correction unit 233 that performs color saturation on the image signal output from the selection unit 237 (wherein second correction information is blur correction which is not combined).), the second derivation processing in which the first correction information is not combined (Fig. 3. Paragraph [0102]-OJIMA discloses a selection unit 237 that selects, based on the depth data output from the depth data input unit 210 and the depth degree output from the depth degree input unit 220, one of the image signal output from the contour correction unit 231 and the image signal output from blur correction unit 232, and a color saturation correction unit 233 that performs color saturation on the image signal output from the selection unit 237 (wherein first correction information is color saturation correction which is not combined).), or the third derivation processing is selected based on the subject information (Fig. 3. Paragraph [0102]-OJIMA discloses a selection unit 237 that selects, based on the depth data output from the depth data input unit 210 and the depth degree output from the depth degree input unit 220, one of the image signal output from the contour correction unit 231 and the image signal output from blur correction unit 232, and a color saturation correction unit 233 that performs color saturation on the image signal output from the selection unit 237 (wherein depth data is subject information and wherein third derivation processing is contour correction).).
Regarding claim 13, OJIMA explicitly teaches a derivation method of deriving a correction amount related to white balance correction for correcting a color of image data obtained by imaging a subject (Fig. 1. Paragraph [0129]-OJIMA discloses in the image processor 100, the depth feeling correction is performed as the contour correction, the blur correction and the color saturation correction (wherein the processor performs the method).), the derivation method comprising:
executing first derivation processing of deriving first correction information without using a machine-learned model (Fig. 3. Paragraph [0107]-OJIMA discloses the color saturation correction unit 233 receives the image signal output from the selection unit 237 and the color saturation correction gain output from the color saturation correction gain setting unit 236, and performs, based on the color saturation correction gain, color saturation correction on the image signal output from the selection unit 237, and outputs the result to the image data output unit 240 (wherein color saturation correction is first correction information).),
executing a selection processing of selecting (Fig. 3, #237 called selection unit. Paragraph [0102]-OJIMA discloses a selection unit 237 that selects, based on the depth data output from the depth data input unit 210 and the depth degree output from the depth degree input unit 220, one of the image signal output from the contour correction unit 231 and the image signal output from blur correction unit 232, and a color saturation correction unit 233 that performs color saturation on the image signal output from the selection unit 237.) one type of processing from among the first derivation processing (Fig. 3, #231 called contour correction unit.), the second derivation processing (Fig. 3. #232 called blur correction unit.), and the third derivation processing (Fig. 3, #233 called color saturation correction unit.) based on subject information of the subject (Fig. 3. Paragraph [0103]-OJIMA discloses the depth feeling correction unit 230 includes, as shown in FIG. 3, a contour correction unit 231 that performs contour correction on the input image signal, a blur correction unit 232 that performs blur correction on the input image signal, a selection unit 237 that selects, based on the depth data output from the depth data input unit 210 and the depth degree output from the depth degree input unit 220, one of the image signal output from the contour correction unit 231 and the image signal output from blur correction unit 232, and a color saturation correction unit 233 that performs color saturation on the image signal output from the selection unit 237 (wherein depth data is subject information).),
executing a correction amount derivation processing of deriving the correction amount (Fig. 3. Paragraph [0107]-OJIMA discloses based on the color saturation correction gain, color saturation correction on the image signal output from the selection unit 237, and outputs the result to the image data output unit 240 (wherein the outputs are correction amounts).) based on information obtained by the processing selected by the selection processing (Fig. 3. Paragraph [0095]-OJIMA discloses an image data output unit 240 that changes the image signal corrected by the depth feeling correction unit 230 to a predetermined format for output (wherein correction unit 230 does selection processing and changes to the image signal is information obtained by selection processing).),
OJIMA fails to explicitly teach executing second derivation processing of deriving second correction information using the image data and the machine-learned model, and executing third derivation processing of executing the first derivation processing and the second derivation processing, the subject information including brightness information or color information calculated based on the image data; and.
However, HAGIWARA explicitly teaches executing second derivation processing of deriving second correction information using the image data (Fig. 3. Paragraph [0085]-HAGIWARA discloses when the derived difference is the first threshold or more, the derivation module 20H outputs a correction instruction for the pixel value of the pixel used for the derivation of the difference among the pixels included in the third image 56 to the first correction module 20I. The correction instruction includes the pixel position of the pixel the difference of which is the first threshold or more and a signal indicating the correction of the pixel value of the pixel (wherein pixels are image data, wherein first correction is second derivation processing, and wherein correction instruction is second correction information).) and the machine-learned model (Fig. 3. Paragraph [0037]-HAGIWARA discloses the learning unit 16 performs processing using a learned model based on an output image received from the information processing apparatus 10 and outputs processing result information. The processing result information is information derived from the image and is correct answer information derived by the processing using the learned model, for example.), and
executing third derivation processing (Fig. 3, derivation module 20H.) of executing the first derivation processing and the second derivation processing (Fig. 3. Paragraph [0114]-HAGIWARA discloses the derivation module 20H derives the difference between the first characteristic point information extracted at Step S108 and the second characteristic point information extracted at Step S112 (Step S114). The derivation module 20H determines whether the difference derived at Step S114 is the first threshold or more (Step S116) (wherein derivation module 20H conducts third derivation processing, wherein first derivation processing is the information extracted at Step S108, and wherein the second derivation processing is the information extracted at Step S112).),
the subject information including brightness information (Fig. 4E. Paragraph [0073]-HAGIWARA discloses the histogram 60 of brightness of the first image 52 includes both a peak 60A indicating the object P1 and a peak 60B indicating the object P2.) or color information calculated based on the image data (Fig. 3. Paragraph [0058]-HAGIWARA discloses the change ratio of the pixel value indicates a change ratio of brightness of a color represented by the pixel value. The pixel value represents a color element such as red (R), green (G), or blue (B) by a color value (a gray scale value of 0 to 255, for example).); and
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of OJIMA of a derivation method of deriving a correction amount related to white balance correction for correcting a color of image data obtained by imaging a subject, the derivation method comprising: executing first derivation processing of deriving first correction information without using a machine-learned model, with the teachings of HAGIWARA of executing second derivation processing of deriving second correction information using the image data and the machine-learned model, and executing third derivation processing of executing the first derivation processing and the second derivation processing, the subject information including brightness information or color information calculated based on the image data; and.
Wherein having OJIMA’s image correction processing having executing second derivation processing of deriving second correction information using the image data and the machine-learned model, and executing third derivation processing of executing the first derivation processing and the second derivation processing, the subject information including brightness information or color information calculated based on the image data; and.
The motivation behind the modification would have been to obtain a color correction processing method that enhances the visual appeal and vividness of an image. Since both OJIMA and HAGIWARA relate to image processing and correction, wherein OJIMA visually, the user can feel pixels in the near view area on the image more vividly, thereby improving the depth feeling in the processing image, while HAGIWARA provides an information processing apparatus and an information processing method that can provide an image with a characteristic point loss inhibited. Please see OJIMA et al. (US 20080002910 A1), Paragraph [0031], and HAGIWARA (US 20210383508 A1), Paragraph [0005].
OJIMA in view of HAGIWARA fail to explicitly teach wherein the third derivation processing includes: calculating, in the second derivation processing, light source determination information related to a type of a light source as the second correction information; determining reliability of the first correction information based on the light source determination information; and calculating the correction amount related to white balance correction based on information obtained by correcting the first correction information based on the light source determination information.
However, YAMADA explicitly teaches wherein the third derivation processing includes (Fig. 3. Paragraph [0069]-YAMADA discloses the candidate list selection unit 313 selects candidates of correction information (candidate list) from sets of correction information, in relation to a plurality of light-source types, based on the shooting condition when taking an image with an image capture optical system 100 and the imaging element 200.):
calculating, in the second derivation processing, light source determination information related to a type of a light source as the second correction information (Fig. 3. Paragraph [0052]-YAMADA discloses for each light-source, the correction-information selection unit 316 totalizes the number of blocks in each of the classes, and selects correction information corresponding to a light-source that leads to aggregated values of the plurality of classes including the highest maximum value, as the correction information that leads to a suitable color shading-correction result (wherein the highest maximum value is light source determination information).);
determining reliability (Fig. 3. Paragraph [0060]-YAMADA discloses the shading correction unit 317 evaluates the reliability of the correction information selected by the correction-information selection unit 316 as follows.) of the first correction information based on the light source determination information (Fig. 3. Paragraph [0043]-YAMADA discloses the correction-information storage unit 311 stores sets of correction information, each of which sets is associated with any of a plurality of light-source types as shooting conditions. Further in paragraph [0052]-YAMADA discloses for each light-source, the correction-information selection unit 316 totalizes the number of blocks in each of the classes, and selects correction information corresponding to a light-source that leads to aggregated values of the plurality of classes including the highest maximum value, as the correction information that leads to a suitable color shading-correction result.); and
calculating the correction amount related to white balance correction based on information obtained by correcting the first correction information based on the light source determination information (Fig. 3. Paragraph [0052]-YAMADA discloses based on the results of the color shading correction, the correction-information selection unit 316 sorts the blocks into a plurality of classes related to values of a color component. Then, for each light-source, the correction-information selection unit 316 totalizes the number of blocks in each of the classes, and selects correction information corresponding to a light-source that leads to aggregated values of the plurality of classes including the highest maximum value, as the correction information that leads to a suitable color shading-correction result. This method corresponds to histogram aggregation of correction results for each light-source, and selection of correction information associated with a light-source that leads to the highest maximum frequency (wherein correction information is the correction amount, and first correction information is color shading correction).).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of OJIMA in view of HAGIWARA a derivation device that derives a correction amount related to white balance correction for correcting a color of image data obtained by imaging a subject, the derivation device comprising: a processor, wherein the processor is configured to execute: first derivation processing of deriving first correction information without using a machine-learned model, with the teachings of YAMADA wherein the third derivation processing includes: calculating, in the second derivation processing, light source determination information related to a type of a light source as the second correction information; determining reliability of the first correction information based on the light source determination information; and calculating the correction amount related to white balance correction based on information obtained by correcting the first correction information based on the light source determination information.
Wherein having OJIMA’s image correction processor having wherein the third derivation processing includes: calculating, in the second derivation processing, light source determination information related to a type of a light source as the second correction information; determining reliability of the first correction information based on the light source determination information; and calculating the correction amount related to white balance correction based on information obtained by correcting the first correction information based on the light source determination information.
The motivation behind the modification would have been to obtain a color correction device that enhances the visual appeal and vividness of an image. Since both OJIMA and YAMADA relate to image processing and color analysis, wherein OJIMA visually, the user can feel pixels in the near view area on the image more vividly, thereby improving the depth feeling in the processing image, while YAMADA allows favorable shading correction for shading which varies depending on the light-source. Please see OJIMA et al. (US 20080002910 A1), Paragraph [0031], and YAMADA et al. (US 20160156851 A1), Paragraph [0003].
Regarding claim 14, OJIMA explicitly teaches a non-transitory computer-readable storage medium storing a program (Fig. 1. Paragraph [0218]-OJIMA discloses it is possible for these methods to be a computer program that is achieved by a computer, or a digital signal that is made from the computer program. The invention also may be a computer-readable recording medium, such as a flexible disk, a hard disk, a CD-ROM, a MO, a DVD, a DVD-ROM, a DVD-RAM, a BD (Blu-ray Disc), or a semiconductor memory, on which the computer program or the digital signal is stored.) causing a computer to execute derivation processing of deriving a correction amount related to white balance correction for correcting a color of image data obtained by imaging a subject (Fig. 1. Paragraph [0129]-OJIMA discloses in the image processor 100, the depth feeling correction is performed as the contour correction, the blur correction and the color saturation correction.), the program causing the computer to execute:
first derivation processing of deriving first correction information without using a machine-learned model (Fig. 3. Paragraph [0107]-OJIMA discloses the color saturation correction unit 233 receives the image signal output from the selection unit 237 and the color saturation correction gain output from the color saturation correction gain setting unit 236, and performs, based on the color saturation correction gain, color saturation correction on the image signal output from the selection unit 237, and outputs the result to the image data output unit 240 (wherein color saturation correction is first correction information).),
selection processing of selecting (Fig. 3, #237 called selection unit. Paragraph [0102]-OJIMA discloses a selection unit 237 that selects, based on the depth data output from the depth data input unit 210 and the depth degree output from the depth degree input unit 220, one of the image signal output from the contour correction unit 231 and the image signal output from blur correction unit 232, and a color saturation correction unit 233 that performs color saturation on the image signal output from the selection unit 237.) one type of processing from among the first derivation processing (Fig. 3, #231 called contour correction unit.), the second derivation processing (Fig. 3. #232 called blur correction unit.), and the third derivation processing (Fig. 3, #233 called color saturation correction unit.) based on subject information of the subject (Fig. 3. Paragraph [0103]-OJIMA discloses the depth feeling correction unit 230 includes, as shown in FIG. 3, a contour correction unit 231 that performs contour correction on the input image signal, a blur correction unit 232 that performs blur correction on the input image signal, a selection unit 237 that selects, based on the depth data output from the depth data input unit 210 and the depth degree output from the depth degree input unit 220, one of the image signal output from the contour correction unit 231 and the image signal output from blur correction unit 232, and a color saturation correction unit 233 that performs color saturation on the image signal output from the selection unit 237 (wherein depth data is subject information).),
correction amount derivation processing of deriving the correction (Fig. 3. Paragraph [0107]-OJIMA discloses based on the color saturation correction gain, color saturation correction on the image signal output from the selection unit 237, and outputs the result to the image data output unit 240 (wherein the outputs are correction amounts).) based on information obtained by the processing selected by the selection processing (Fig. 3. Paragraph [0095]-OJIMA discloses an image data output unit 240 that changes the image signal corrected by the depth feeling correction unit 230 to a predetermined format for output (wherein correction unit 230 does selection processing and changes to the image signal is information obtained by selection processing).),
OJIMA fails to explicitly teach second derivation processing of deriving second correction information using the image data and the machine-learned model, third derivation processing of executing the first derivation processing and the second derivation processing, the subject information including brightness information or color information calculated based on the image data; and.
However, HAGIWARA explicitly teaches second derivation processing of deriving second correction information using the image data (Fig. 3. Paragraph [0085]-HAGIWARA discloses when the derived difference is the first threshold or more, the derivation module 20H outputs a correction instruction for the pixel value of the pixel used for the derivation of the difference among the pixels included in the third image 56 to the first correction module 20I. The correction instruction includes the pixel position of the pixel the difference of which is the first threshold or more and a signal indicating the correction of the pixel value of the pixel (wherein pixels are image data, wherein first correction is second derivation processing, and wherein correction instruction is second correction information).) and the machine-learned model (Fig. 3. Paragraph [0037]-HAGIWARA discloses the learning unit 16 performs processing using a learned model based on an output image received from the information processing apparatus 10 and outputs processing result information. The processing result information is information derived from the image and is correct answer information derived by the processing using the learned model, for example.),
third derivation processing (Fig. 3, derivation module 20H.) of executing the first derivation processing and the second derivation processing (Fig. 3. Paragraph [0114]-HAGIWARA discloses the derivation module 20H derives the difference between the first characteristic point information extracted at Step S108 and the second characteristic point information extracted at Step S112 (Step S114). The derivation module 20H determines whether the difference derived at Step S114 is the first threshold or more (Step S116) (wherein derivation module 20H conducts third derivation processing, wherein first derivation processing is the information extracted at Step S108, and wherein the second derivation processing is the information extracted at Step S112).),
the subject information including brightness information (Fig. 4E. Paragraph [0073]-HAGIWARA discloses the histogram 60 of brightness of the first image 52 includes both a peak 60A indicating the object P1 and a peak 60B indicating the object P2.) or color information calculated based on the image data (Fig. 3. Paragraph [0058]-HAGIWARA discloses the change ratio of the pixel value indicates a change ratio of brightness of a color represented by the pixel value. The pixel value represents a color element such as red (R), green (G), or blue (B) by a color value (a gray scale value of 0 to 255, for example).); and
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of OJIMA of a non-transitory computer-readable storage medium storing a program causing a computer to execute derivation processing of deriving a correction amount related to white balance correction for correcting a color of image data obtained by imaging a subject, the program causing the computer to execute: first derivation processing of deriving first correction information without using a machine-learned model, with the teachings of HAGIWARA of a second derivation processing of deriving second correction information using the image data and the machine-learned model, third derivation processing of executing the first derivation processing and the second derivation processing, the subject information including brightness information or color information calculated based on the image data; and.
Wherein having OJIMA’s image correction processor having second derivation processing of deriving second correction information using the image data and the machine-learned model, and third derivation processing of executing the first derivation processing and the second derivation processing.
The motivation behind the modification would have been to obtain a color correction device that enhances the visual appeal and vividness of an image. Since both OJIMA and HAGIWARA relate to image processing and correction, wherein OJIMA visually, the user can feel pixels in the near view area on the image more vividly, thereby improving the depth feeling in the processing image, while HAGIWARA provides an information processing apparatus and an information processing method that can provide an image with a characteristic point loss inhibited. Please see OJIMA et al. (US 20080002910 A1), Paragraph [0031], and HAGIWARA (US 20210383508 A1), Paragraph [0005].
OJIMA in view of HAGIWARA fail to explicitly teach wherein the third derivation processing includes: calculating, in the second derivation processing, light source determination information related to a type of a light source as the second correction information; determining reliability of the first correction information based on the light source determination information; and calculating the correction amount related to white balance correction based on information obtained by correcting the first correction information based on the light source determination information.
However, YAMADA explicitly teaches wherein the third derivation processing includes (Fig. 3. Paragraph [0069]-YAMADA discloses the candidate list selection unit 313 selects candidates of correction information (candidate list) from sets of correction information, in relation to a plurality of light-source types, based on the shooting condition when taking an image with an image capture optical system 100 and the imaging element 200.):
calculating, in the second derivation processing, light source determination information related to a type of a light source as the second correction information (Fig. 3. Paragraph [0052]-YAMADA discloses for each light-source, the correction-information selection unit 316 totalizes the number of blocks in each of the classes, and selects correction information corresponding to a light-source that leads to aggregated values of the plurality of classes including the highest maximum value, as the correction information that leads to a suitable color shading-correction result (wherein the highest maximum value is light source determination information).);
determining reliability (Fig. 3. Paragraph [0060]-YAMADA discloses the shading correction unit 317 evaluates the reliability of the correction information selected by the correction-information selection unit 316 as follows.) of the first correction information based on the light source determination information (Fig. 3. Paragraph [0043]-YAMADA discloses the correction-information storage unit 311 stores sets of correction information, each of which sets is associated with any of a plurality of light-source types as shooting conditions. Further in paragraph [0052]-YAMADA discloses for each light-source, the correction-information selection unit 316 totalizes the number of blocks in each of the classes, and selects correction information corresponding to a light-source that leads to aggregated values of the plurality of classes including the highest maximum value, as the correction information that leads to a suitable color shading-correction result.); and
calculating the correction amount related to white balance correction based on information obtained by correcting the first correction information based on the light source determination information (Fig. 3. Paragraph [0052]-YAMADA discloses based on the results of the color shading correction, the correction-information selection unit 316 sorts the blocks into a plurality of classes related to values of a color component. Then, for each light-source, the correction-information selection unit 316 totalizes the number of blocks in each of the classes, and selects correction information corresponding to a light-source that leads to aggregated values of the plurality of classes including the highest maximum value, as the correction information that leads to a suitable color shading-correction result. This method corresponds to histogram aggregation of correction results for each light-source, and selection of correction information associated with a light-source that leads to the highest maximum frequency (wherein correction information is the correction amount, and first correction information is color shading correction).).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of OJIMA in view of HAGIWARA a non-transitory computer-readable storage medium storing a program causing a computer to execute derivation processing of deriving a correction amount related to white balance correction for correcting a color of image data obtained by imaging a subject, the program causing the computer to execute: first derivation processing of deriving first correction information without using a machine-learned model, with the teachings of YAMADA wherein the third derivation processing includes: calculating, in the second derivation processing, light source determination information related to a type of a light source as the second correction information; calculating the correction amount related to white balance correction based on information obtained by correcting the first correction information based on the light source determination information.
Wherein having OJIMA’s image correction processing having wherein the third derivation processing includes: calculating, in the second derivation processing, light source determination information related to a type of a light source as the second correction information; calculating the correction amount related to white balance correction based on information obtained by correcting the first correction information based on the light source determination information.
The motivation behind the modification would have been to obtain a color correction processing method that enhances the visual appeal and vividness of an image. Since both OJIMA and YAMADA relate to image processing and color analysis, wherein OJIMA visually, the user can feel pixels in the near view area on the image more vividly, thereby improving the depth feeling in the processing image, while YAMADA allows favorable shading correction for shading which varies depending on the light-source. Please see OJIMA et al. (US 20080002910 A1), Paragraph [0031], and YAMADA et al. (US 20160156851 A1), Paragraph [0003].
Claims 2 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over OJIMA et al. (US 20080002910 A1), hereinafter referenced as OJIMA, in view of HAGIWARA (US 20210383508 A1), hereinafter referenced as HAGIWARA, and further in view of YAMADA et al. (US 20160156851 A1), hereinafter referenced as YAMADA, and further in view of TANAKA (US 20190130591 A1), hereinafter referenced as TANAKA.
Regarding claim 2, OJIMA in view of HAGIWARA and further in view of YAMADA explicitly teach the derivation device according to claim 1,
OJIMA further explicitly teaches wherein the subject information is one or more pieces of information selected from color information of the subject (Fig. 3. Paragraph [0050]-OJIMA discloses it should be noted that "feature quantity" is physical quantity that can be idealized for each of pixels or areas in the image, for example, and includes brightness (luminance), lightness, color saturation, hue, contrast, brightness contrast, and color contrast, for example (wherein an area is a subject). Further in paragraph [0128]-OJIMA discloses the inverse HSV conversion unit 2334 reconstructs the H (Hue) signal and V (Value (brightness)) signal output from the delay unit 2332 and the S (Saturation (color saturation)) signal to which the color saturation is added by the color saturation addition unit 2333 into the image signal in the original format (image signal which has not been HSV-converted by the HSV conversion unit 2331 yet), i.e., the inverse HSV conversion is performed, and outputs it to the image data output unit 240.), brightness information of the subject (Fig. 3. Paragraph [0050]-OJIMA discloses it should be noted that "feature quantity" is physical quantity that can be idealized for each of pixels or areas in the image, for example, and includes brightness (luminance), lightness, color saturation, hue, contrast, brightness contrast, and color contrast, for example (wherein an area is a subject). Further in paragraph [0128]-OJIMA discloses the inverse HSV conversion unit 2334 reconstructs the H (Hue) signal and V (Value (brightness)) signal output from the delay unit 2332 and the S (Saturation (color saturation)) signal to which the color saturation is added by the color saturation addition unit 2333 into the image signal in the original format (image signal which has not been HSV-converted by the HSV conversion unit 2331 yet), i.e., the inverse HSV conversion is performed, and outputs it to the image data output unit 240.), and
OJIMA in view of HAGIWARA and further in view of YAMADA fail to explicitly teach subject recognition information.
However, TANAKA explicitly teaches subject recognition information (Figs. 5A-5D. Paragraph [0047]-TANAKA discloses the semantic segment is a method in which a known-shape portion in the image signal S1 for detection is recognized and the region is extracted. While various methods are available, a method of learning in advance from a database and performing identification is commonly used. In recent years, deep learning is used to perform highly-accurate identification. FIG. 5C illustrates the case where a white line is recognized, as an example, and. FIG. 5D illustrates the case where a traffic sign is recognized (the white line and the traffic sign are both displayed in oblique lines), as an example.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of OJIMA in view of HAGIWARA and further in view of YAMADA of a derivation device that derives a correction amount related to white balance correction for correcting a color of image data obtained by imaging a subject, the derivation device comprising: a processor, wherein the processor is configured to execute: first derivation processing of deriving first correction information without using a machine-learned model, with the teachings of TANAKA of subject recognition information.
Wherein having OJIMA’s image correction processor having subject recognition information.
The motivation behind the modification would have been to obtain a color correction device that enhances the visual appeal and vividness of an image. Since both OJIMA and TANAKA relate to image processing and color analysis, wherein OJIMA visually, the user can feel pixels in the near view area on the image more vividly, thereby improving the depth feeling in the processing image, while TANAKA reduces the effect of temporal errors. Please see OJIMA et al. (US 20080002910 A1), Paragraph [0031], and TANAKA (US 20190130591 A1), Paragraph [0016].
Regarding claim 10, OJIMA in view of HAGIWARA and further in view of YAMADA explicitly teach the derivation device according to claim 1,
OJIMA in view of HAGIWARA and further in view of YAMADA fail to explicitly teach wherein the processor is configured to, in the selection processing, select one piece of processing based on subject recognition information as the subject information.
TANAKA explicitly teaches wherein the processor is configured to, in the selection processing, select one piece of processing based on subject recognition information as the subject information (Figs. 5A-5D. Paragraph [0070]-TANAKA discloses an external environment recognition unit (not illustrated) performs transformation into external environment recognition data using the acquired corrected distance information Icd. An action plan generation unit (not illustrated) generates an action plan based on the external environment recognition data and an aim provided in advance (wherein generating an action plan is a piece of processing and wherein environment recognition is subject recognition).).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of OJIMA in view of HAGIWARA and further in view of YAMADA of a derivation device that derives a correction amount related to white balance correction for correcting a color of image data obtained by imaging a subject, the derivation device comprising: a processor, wherein the processor is configured to execute: first derivation processing of deriving first correction information without using a machine-learned model, with the teachings of TANAKA wherein the processor is configured to, in the selection processing, select one piece of processing based on subject recognition information as the subject information.
Wherein having OJIMA’s image correction processor wherein the processor is configured to, in the selection processing, select one piece of processing based on subject recognition information as the subject information.
The motivation behind the modification would have been to obtain a color correction device that enhances the visual appeal and vividness of an image. Since both OJIMA and TANAKA relate to image processing and color analysis, wherein OJIMA visually, the user can feel pixels in the near view area on the image more vividly, thereby improving the depth feeling in the processing image, while TANAKA reduces the effect of temporal errors. Please see OJIMA et al. (US 20080002910 A1), Paragraph [0031], and TANAKA (US 20190130591 A1), Paragraph [0016].
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over OJIMA et al. (US 20080002910 A1), hereinafter referenced as OJIMA, in view of HAGIWARA (US 20210383508 A1), hereinafter referenced as HAGIWARA, and further in view of YAMADA et al. (US 20160156851 A1), hereinafter referenced as YAMADA, and further in view of OKAZAKI et al. (US 6795115 B1), hereinafter referenced as OKAZAKI.
Regarding claim 8, OJIMA in view of HAGIWARA and further in view of YAMADA explicitly teach the derivation device according to claim 1,
OJIMA in view of HAGIWARA and further in view of YAMADA fail to explicitly teach wherein the color information is integration information obtained by integrating pixel signals for each color with respect to a plurality of areas of the image data.
However, OKAZAKI explicitly teaches wherein the color information is integration information obtained by integrating pixel signals for each color with respect to a plurality of areas of the image data (Fig. 1. Col. 3, Lines [44-47]-OKAZAKI discloses the pixel signals are converted into digital signals, or pixel data, by an A/D converter 18. The pixel data is temporarily stored in a RAM 20 by a memory controller 22. Further in Col. 4, Lines [8-12]-OKAZAKI discloses the integration circuit 32 integrates R' data, G data and B' data on an each-divisional-area basis and on a same-color basis. This provides 256 integration values I.sub.R', 256 integration values, I.sub.G and 256 integration values I.sub.B' in the 1-frame period.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of OJIMA in view of HAGIWARA and further in view of YAMADA of a derivation device that derives a correction amount related to white balance correction for correcting a color of image data obtained by imaging a subject, the derivation device comprising: a processor, wherein the processor is configured to execute: first derivation processing of deriving first correction information without using a machine-learned model, with the teachings of OKAZAKI of wherein the color information is integration information obtained by integrating pixel signals for each color with respect to a plurality of areas of the image data.
Wherein having OJIMA’s image correction processor wherein the color information is integration information obtained by integrating pixel signals for each color with respect to a plurality of areas of the image data.
The motivation behind the modification would have been to obtain a color correction device that enhances the visual appeal and vividness of an image. Since both OJIMA and OKAZAKI relate to image processing and color analysis, wherein OJIMA visually, the user can feel pixels in the near view area on the image more vividly, thereby improving the depth feeling in the processing image, while OKAZAKI enable proper adjustment of white balance while the color evaluation value with another exposure amount is outside the draw-in area resulting in poor adjustment of white balance. Please see OJIMA et al. (US 20080002910 A1), Paragraph [0031], and OKAZAKI et al. (US 6795115 B1), Paragraph Col. 1, Lines [30-44].
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over OJIMA et al. (US 20080002910 A1), hereinafter referenced as OJIMA, in view of HAGIWARA (US 20210383508 A1), hereinafter referenced as HAGIWARA, and further in view of YAMADA et al. (US 20160156851 A1), hereinafter referenced as YAMADA, and further in view of HAYASHI et al. (US 20060029287 A1), hereinafter referenced as HAYASHI.
Regarding claim 11, OJIMA in view of HAGIWARA and further in view of YAMADA explicitly teach the derivation device according to claim 1,
OJIMA fails to explicitly teach wherein the processor is configured to repeatedly execute the first derivation processing and the second derivation processing in the third derivation processing, and.
However, HAGIWARA explicitly teaches wherein the processor is configured to repeatedly execute (Fig. 3. Paragraph [0097]-HAGIWARA discloses the derivation module 20H repeats the processing to output the correction instruction for the pixel value to the first correction module 20I for the pixel at the pixel position in the expanded image 55 until determining that this difference is less than the first threshold.) the first derivation processing and the second derivation processing in the third derivation processing (Fig. 3. Paragraph [0114]-HAGIWARA discloses the derivation module 20H derives the difference between the first characteristic point information extracted at Step S108 and the second characteristic point information extracted at Step S112 (Step S114). The derivation module 20H determines whether the difference derived at Step S114 is the first threshold or more (Step S116) (wherein derivation module 20H conducts third derivation processing, wherein first derivation processing is the information extracted at Step S108, and wherein the second derivation processing is the information extracted at Step S112).), and
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of OJIMA of a derivation device that derives a correction amount related to white balance correction for correcting a color of image data obtained by imaging a subject, the derivation device comprising: a processor, wherein the processor is configured to execute: first derivation processing of deriving first correction information without using a machine-learned model, with the teachings of HAGIWARA of wherein the processor is configured to repeatedly execute the first derivation processing and the second derivation processing in the third derivation processing, and.
Wherein having OJIMA’s image correction processor wherein the processor is configured to repeatedly execute the first derivation processing and the second derivation processing in the third derivation processing, and.
The motivation behind the modification would have been to obtain a color correction device that enhances the visual appeal and vividness of an image. Since both OJIMA and HAGIWARA relate to image processing and correction, wherein OJIMA visually, the user can feel pixels in the near view area on the image more vividly, thereby improving the depth feeling in the processing image, while HAGIWARA provides an information processing apparatus and an information processing method that can provide an image with a characteristic point loss inhibited. Please see OJIMA et al. (US 20080002910 A1), Paragraph [0031], and HAGIWARA (US 20210383508 A1), Paragraph [0005].
OJIMA in view of HAGIWARA and further in view of YAMADA fail to explicitly teach a frequency at which the second derivation processing is executed is lower than a frequency at which the first derivation processing is executed.
However, HAYASHI explicitly teaches a frequency at which the second derivation processing is executed is lower than a frequency at which the first derivation processing is executed (Fig. 10. Paragraph [0087]-HAYASHI discloses the color correction processing also is executed so as to be suited to a frequency band for every frequency band of the image data. Further, the color correction is applied to three different items of frequency-component data, namely low-, intermediate- and high-frequency components (wherein second derivation processing is color correction on the intermediate frequency component and wherein first derivation processing is color correction on the low frequency components).).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of OJIMA in view of HAGIWARA and further in view of YAMADA of a derivation device that derives a correction amount related to white balance correction for correcting a color of image data obtained by imaging a subject, the derivation device comprising: a processor, wherein the processor is configured to execute: first derivation processing of deriving first correction information without using a machine-learned model, with the teachings of HAYASHI of wherein a frequency at which the second derivation processing is executed is lower than a frequency at which the first derivation processing is executed.
Wherein having OJIMA’s image correction wherein a frequency at which the second derivation processing is executed is lower than a frequency at which the first derivation processing is executed.
The motivation behind the modification would have been to obtain a color correction device that enhances the visual appeal and vividness of an image. Since both OJIMA and HAYASHI relate to image processing and correction, wherein OJIMA visually, the user can feel pixels in the near view area on the image more vividly, thereby improving the depth feeling in the processing image, while HAYASHI is to improve image quality. Please see OJIMA et al. (US 20080002910 A1), Paragraph [0031], and HAYASHI et al. (US 20060029287 A1), Paragraph [0006].
Conclusion
Listed below are the prior arts made of record and not relied upon but are considered pertinent to applicant’s disclosure.
FUJIWARA et al. (US 20170094241 A1) – An image processing device includes a first image acquisition unit that acquires each of a first imaging signal indicating a flash emission image and a second imaging signal indicating a flash non-emission image, a second image acquisition unit that acquires a third imaging signal indicating a difference between the first imaging signal and the second imaging signal that have been acquired, a third image acquisition unit that acquires a fourth imaging signal obtained by multiplying the acquired third imaging signal by a white balance gain for a flash for removing an influence due to color of the flash light, a color signal acquisition unit that acquires a color signal indicating a first color of each area in an imaging screen on the basis of the acquired fourth imaging signal, and a first white balance gain calculation unit…Abstract, Fig. 3.
MINAGAWA et al. (US 20160027190 A1) – An image processing apparatus is provided including a storage unit for storing relationships of color coordinates between a plurality of reference colors under each of a plurality of different light sources for each light source, with respect to the plurality of reference colors set in a color space and being different from one another; and a calculation unit for detecting color coordinates of the plurality of reference colors from an object image to be processed, and identifying a light source used in capturing of the object image according to a similarity between relationships of the detected color coordinates and the relationships of color coordinates stored in the storage unit for each light source…Abstract, Fig. 1.
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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/ETHAN N WOLFSON/Examiner, Art Unit 2673
/CHINEYERE WILLS-BURNS/Supervisory Patent Examiner, Art Unit 2673
