DETAILED ACTION
Claims 1-16 are pending in this application and have been examined under the priority date of 12/26/2023 in accordance with the applicant’s claim to foreign priority.
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 required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 12/18/2024 and 8/20/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Objections
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed. The examiner respectfully suggests the following title based upon the specification paragraph [0015] “IMAGE PROCESSING METHOD, IMAGE PROCESSING APPARATUS, IMAGE PICKUP APPARATUS, AND STORAGE MEDIUM FOR IMAGE SHARPENING”.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea, mental process or step of mere data gathering without significantly more.
Regarding claims 1 and 11 the claims recite the following limitations which are drawn to an abstract idea, mental process or step of mere data gathering without significantly more as noted by the examiner;
An image processing method comprising: determining, by a processor, a specific region in a first image; (Mental process of determination)
acquiring, by the processor, at least one second image corresponding to some of a plurality of partial images of the first image; (Abstract idea consisting of a step which a human could manually perform the collection of a second image which has parts of a first)
generating, by the processor, a third image corresponding to the second image by inputting the second image into a machine learning model; (Abstract idea consisting of a step which a human could manually input an image into a model to generate an output)
and generating, by the processor, a fourth image based on the first image and the third image, wherein the second image includes at least a part of the specific region. (Abstract idea consisting of a step which a human could manually input an image into a model to generate an output)
Under step 2A, prong 1, the judicial exceptions recited in claims 1 and 11 are not integrated into a practical application because the steps recited above are drawn to steps of mathematical analysis or image generation which could be practically performed by a human manually. Under step 2A, prong 1, the claims recite the additional elements of a processor and a machine learning model, however these elements are recited with a high level of generality and do not translate the claim into practical application or amount to significantly more than an abstract idea. Under step 2B, the claims do not include additional elements or limitations that are sufficient to amount to significantly more than the judicial exception. (See MPEP 2106)
Dependent claims 2-9, 13, and 15-16 do not add limitations which translate the aforementioned abstract idea into practical application and therefore are similarly rejected.
Regarding claim 2, the claim adds limitations which are drawn to the mental process or abstract idea of determining a region in an image based on mathematical operations without significantly more.
Regarding claim 3, the claim adds limitations which are drawn to the mental process or abstract idea of determining a region in an image based on mathematical operations without significantly more.
Regarding claim 4, the claim adds limitations which are drawn to the abstract idea of the inclusion of a focus map in the distance information without significantly more.
Regarding claim 5, the claim adds limitations which are drawn to the mental process of manually dividing an image into regions without significantly more.
Regarding claim 6, the claim adds limitations which are drawn to the abstract idea of generating an image based on another image without significantly more.
Regarding claim 7, the claim adds limitations which are drawn to the abstract idea of adjusting the processing intensity of a model based on image areas without significantly more.
Regarding claim 8 the claim adds limitations which are drawn to the abstract idea of adjusting the processing intensity of a model based on image areas without significantly more.
Regarding claim 9 the claim adds limitations which are drawn to the abstract idea of obtaining an image using general image processing parameters without significantly more.
Regarding claim 13, the claim adds the additional elements of an image pickup apparatus and an image sensor, both of which are recited with a high level of generality and fail to translate the claim into practical application.
Regarding claim 15, the claim adds the additional element of a non-transitory computer readable medium, which is recited with a high level of generality and fails to translate the claim into practical application.
Regarding claim 16, the claim adds the additional element of a non-transitory computer readable medium, which is recited with a high level of generality and fails to translate the claim into practical application.
Regarding claim 10 and claim 12, the claims recite the following limitations which are drawn to an abstract idea, mental process or step of mere data gathering without significantly more as noted by the examiner;
An image processing method comprising: determining, by a processor, a specific region in a first image; (Mental process of determination)
acquiring, by the processor, a second image corresponding to the specific region; (Abstract idea consisting of a step which a human could manually perform the collection of a second image which has parts of a first)
generating, by the processor, a third image corresponding to the second image by inputting the second image into a machine learning model; (Abstract idea consisting of a step which a human could manually input an image into a model to generate an output)
generating, by the processor, a fourth image based on the first image and the third image, (Abstract idea consisting of a step which a human could manually input an image into a model to generate an output)
wherein the specific region is determined based on at least one of information regarding a distance corresponding to the first image, semantic area division information, cropping information, image circle information, and an optical performance. (Mental process or abstract idea of determining a region in an image based on mathematical operations)
Under step 2A, prong 1, this judicial exception is not integrated into a practical application because the steps recited above are drawn to steps of mathematical analysis or image generation which could be practically performed by a human manually. Under step 2A, prong 1, the claims recite the additional elements of a processor, a memory (claim 12) and a machine learning model, however these elements are recited with a high level of generality and do not translate the claim into practical application or amount to significantly more than an abstract idea. Under step 2B, the claims do not include additional elements or limitations that are sufficient to amount to significantly more than the judicial exception. (See MPEP 2106)
Dependent claim 14 does not add limitations which translate the abstract idea into practical application and therefore is similarly rejected.
Regarding claim 14, the claim adds additional elements of an image pickup apparatus and an image sensor, both of which are recited with a high level of generality and fail to translate the claim into practical application.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-8 and 10-16 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Sakurabu (US 20230326170 A1).
Regarding claim 1 Sakurabu discloses; An image processing method comprising: determining, by a processor (Sakurabu, [0268] a processor implements the functions of the entire system), a specific region in a first image (Sakurabu, [0137] the captured image (first image) is divided into a plurality of regions and this division is used to generate multiple virtual frames including specific regions in the first image as shown in figure 8, [0138-139] the image is divided into a plurality of cells and an anchor box is used to generate a specific region which generates the virtual frames);
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acquiring, by the processor (Sakurabu, [0268] a processor implements the functions of the entire system), at least one second image corresponding to some of a plurality of partial images of the first image (Sakurabu, [0138-139] the image (first image) is divided into a plurality of cells and an anchor box is used to generate a specific region (specific region of the first image) which generates the virtual frames (at least a second image), denoted as 112(a-c) in figure 8, where a virtual frame (second image), denoted as 112(a) in figure 8, is generated including multiple virtual cells (114, in figure 8) of the first image, the multiple virtual cells being the partial images of the first image);
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generating, by the processor (Sakurabu, [0268] a processor implements the functions of the entire system), a third image corresponding to the second image by inputting the second image into a machine learning model (Sakurabu, [0146] the virtual frames/second virtual frame (second image) are analyzed by a machine learning model to generate a bounding box around the subject of the image based on the virtual frame, this becomes an area frame (third image) as shown in figure 9, [0007] the system uses image inputs into a trained machine learning model to generate the outputs);
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and generating, by the processor (Sakurabu, [0268] a processor implements the functions of the entire system), a fourth image based on the first image and the third image (Sakurabu, [0146] the area frame (third image), denoted as 134 in figure 9, and the captured image (first image), denoted as 108 in figure 9, are used to generate a subject image/person image (fourth image)), wherein the second image includes at least a part of the specific region (Sakurabu, [0138-139] the image (first image) is divided into a plurality of cells and an anchor box is used to generate a specific region (specific region of the first image) which generates the virtual frames (at least a second image), denoted as 112(a-c) in figure 8, where a virtual frame (second image), denoted as 112(a) in figure 8, is generated including multiple virtual cells (114, in figure 8) of the first image, the multiple virtual cells being the partial images of the first image, where the specific region is at least partially included in the virtual frames as shown in figure 8).
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Regarding claim 2 Sakurabu discloses; The image processing method according to claim 1, wherein the specific region is determined based on at least one of information regarding a distance corresponding to the first image (Sakurabu, [0097]-[0098] the system uses two modes, manual focus (MF) and auto-focus (AF) to focus on the subject in the distance, this may include using a focus position calculation to focus the image on a subject based on the subject distance, which in [0146] is used to determine the focus area (specific region) in the captured image (first image)), semantic area division information (Sakurabu, [0140] the anchor box/focus area in the image (specific region) is set and scored based on a probability that a person is included in the box/region, which is analogous to the semantic division information applicant defined in paragraph [0065] of the applicant’s specification), cropping information (Sakurabu, [0133] and [0142] the bounding box/anchor box (specific region) information is also determined in part by position information specifying the width and height of the region (cropping information)), image circle information (Sakurabu, [0077] the image sensor is configured to capture an image within an imaging range including a subject, therefore this is analogous to what applicant defines as the image circle information in paragraph [0067] of the applicant’s specification, where the applicant notes that the image circle information is information about the area of the image which is picked up by the optical system/camera lens because the system is set up to capture images within the range that the system can pick up), and an optical performance (Sakurabu, [0191] the focus area (specific region) is in part determined by a calculated focusing position with respect to the distance and depth information to calculated focus needed in the image, which satisfies the definition of optical performance set in paragraph [0068] of the applicant’s specification notes that this has to do with the area being set based on the requirement of the area for sharpening, or blur correction).
Regarding claim 3 Sakurabu discloses; The image processing method according to claim 1, wherein the specific region is determined based on at least one of a content of the first image (Sakurabu, [0140] the anchor box/focus area in the image (specific region) is set and scored based on a probability that a person is included in the box/region), changeable image processing setting (Sakurabu, [0133] and [0142] the bounding box/anchor box (specific region) information is also determined in part by position information specifying the width and height of the region, which is cropping information designating an area as an image processing target, therefore this satisfies the definition of the “image processing setting” the applicant states in paragraph [0070] of the applicant’s specification), and changeable image pickup setting (Sakurabu, [0022] the specific focus area (specific region) is set based on focus settings of the camera to assure the region selected is prioritized for focus, which is an image capture setting pertaining to lens focus, therefore this satisfies the definition of the “image pickup setting” the applicant states in paragraph [0070] of the applicant’s specification).
Regarding claim 4, Sakurabu discloses; The image processing method according to claim 2, wherein the information regarding the distance includes a focus map (Sakurabu, [0191] the system generates autofocus details, which uses a distance measurement using the parallax from the pair of images of a stereo cameras, this auto-focus measures uses the parallax between two stereo cameras to determine which regions to auto-focus on, according to paragraph [0024] of applicant’s specification, the focus map is a map indicating object areas that are in focus based on parallax shift information, therefore the auto-focus control of Sakurabu [0191] would be analogous to this).
Regarding claim 5 Sakurabu discloses; The image processing method according to claim 1, wherein the plurality of partial images are acquired by equally dividing the first image (Sakurabu,[0139] the captured image (first image) is divided in multiple virtual cells, [0142] the cell width and height are specified and then in [0143] this specification information is used to apply a bounding box, from the formula given the cells would be equal in size).
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Regarding claim 6 Sakurabu discloses; The image processing method according to claim 1, wherein the fourth image is an image based only on an area included in the specific region in the third image (Sakurabu, [0146] the area frame (third image), denoted as 134 in figure 9, and the captured image (first image), denoted as 108 in figure 9, are used to generate a subject image/person image (fourth image), the subject image (fourth image) includes on the subject of interest in the area frame (third image) as shown in figure 9).
Regarding claim 7 Sakurabu discloses; The image processing method according to claim 1, wherein the third image or the fourth image is generated by processing such that processing intensity of the machine learning model for an area not included in the specific region is lower than processing intensity of the machine learning model for an area included in the specific region (Sakurabu, [0171] the system performs detection on the captured image such that the areas where the subject of focus is (specific region) are set to be higher values and the areas where the subject of the image is not are set to be “0”, the regions which belong to the object (specific areas) then undergo processing to determine and adjust the area frame (third image) to include subject region (specific region), further [0239] the area frame may be adjusted to only include the object of interest (specific region) such that autofocus correction may be prioritized there to support image processing, [0242] focused correction is applied to prioritized areas (higher intensity processing), according to paragraph [0073] of the applicant’s specification, the processing intensity is lowered by changing the pixel values such that the correction applied to those areas is smaller, prioritizing the areas of interest, therefore the method of Sakurabu fulfills this by the method of prioritizing the focus correction of the subject area in the image, thereby reducing processing intensity) .
Regarding claim 8 Sakurabu discloses; The image processing method according to claim 1, wherein the second image includes a first area and a second area (Sakurabu, [0138-139] the image (first image) is divided into a plurality of cells and an anchor box is used to generate a specific region (specific region of the first image) which generates the virtual frames (at least a second image), denoted as 112(a-c) in figure 8, where a virtual frame (second image), denoted as 112(a) in figure 8, is generated including multiple virtual cells (114, in figure 8) of the first image, the multiple virtual cells being the partial images of the first image, the virtual frames (second image) consist of multiple cells, each of which are scored such that some contain the subject of interest (first area, specific region) and some do not (second area)),
the first area including the specific region (Sakurabu, [0138-139] the image (first image) is divided into a plurality of cells and an anchor box is used to generate a specific region (specific region of the first image) which generates the virtual frames (at least a second image), denoted as 112(a-c) in figure 8, the virtual frames (second image) consist of multiple cells, each of which are scored such that some contain the subject of interest (first area, specific region) and some do not (second area)),
the second area not including the specific region (Sakurabu, [0138-139] the image (first image) is divided into a plurality of cells and an anchor box is used to generate a specific region (specific region of the first image) which generates the virtual frames (at least a second image), denoted as 112(a-c) in figure 8, the virtual frames (second image) consist of multiple cells, each of which are scored such that some contain the subject of interest (first area, specific region) and some do not (second area)),
wherein processing intensity of the machine learning model for an area corresponding to the first area in the third image is set to first intensity (Sakurabu, [0171] the system performs detection on the captured image such that the areas where the subject of focus is (specific region) are set to be higher values and the areas where the subject of the image is not are set to be “0”, the regions which belong to the object (specific areas) then undergo processing to determine and adjust the area frame (third image) to include subject region (specific region), further [0239] the area frame may be adjusted to only include the object of interest (specific region) such that autofocus correction may be prioritized there to support image processing, [0242] focused correction is applied to prioritized areas (higher intensity processing for a first area), according to paragraph [0073] of the applicant’s specification, the processing intensity is lowered by changing the pixel values such that the correction applied to those areas is smaller, prioritizing the areas of interest, therefore the method of Sakurabu fulfills this by the method of prioritizing the focus correction of the subject area in the image, thereby reducing processing intensity),
wherein the processing intensity for an area corresponding to the second area in the third image is set to second intensity smaller than the first intensity (Sakurabu, [0160] the system adjusts the number of divisions as well as the focus correction based on whether or not the area in the image contains the object (specific region) [0171] the system performs detection on the captured image such that the areas where the subject of focus is (specific region) are set to be higher values and the areas where the subject of the image is not are set to be “0”, the regions which belong to the object (specific areas) then undergo processing to determine and adjust the area frame (third image) to include subject region (specific region), [0172] when the region is determined as being part of the subject (specific region/first area) it undergoes further processing (higher intensity/ first intensity), when the area is determined as not containing the subject, it is sent to division processing and either excluded or not prioritized (second, lower intensity processing), further [0239] the area frame may be adjusted to only include the object of interest (specific region) such that autofocus correction may be prioritized there to support image processing, [0242] focused correction is applied to prioritized areas (higher intensity processing for a first area), according to paragraph [0073] of the applicant’s specification, the processing intensity is lowered by changing the pixel values such that the correction applied to those areas is smaller, prioritizing the areas of interest, therefore the method of Sakurabu fulfills this by the method of prioritizing the focus correction of the subject area in the image, thereby reducing processing intensity),
and wherein the fourth image is generated by using the area set to the first intensity and the area set to the second intensity in the third image (Sakurabu, [0146] the area frame (third image), denoted as 134 in figure 9, and the captured image (first image), denoted as 108 in figure 9, are used to generate a subject image/person image (fourth image), where the area frame contains both regions processed as described above in [0160], [0171]-[0172], [0239] and [0242]).
Regarding claim 10 Sakurabu discloses; An image processing method comprising: determining, by a processor (Sakurabu, [0268] a processor implements the functions of the entire system), a specific region in a first image (Sakurabu, [0137] the captured image (first image) is divided into a plurality of regions and this division is used to generate multiple virtual frames including specific regions in the first image as shown in figure 8, [0138-139] the image is divided into a plurality of cells and an anchor box is used to generate a specific region which generates the virtual frames);
acquiring, by the processor (Sakurabu, [0268] a processor implements the functions of the entire system), a second image corresponding to the specific region (Sakurabu, [0138-139] the image (first image) is divided into a plurality of cells and an anchor box is used to generate a specific region (specific region of the first image) which generates the virtual frames (at least a second image), denoted as 112(a-c) in figure 8, where a virtual frame (second image), denoted as 112(a) in figure 8, is generated including multiple virtual cells (114, in figure 8) of the first image, the multiple virtual cells being the partial images of the first image, where the specific region is at least partially included in the virtual frames as shown in figure 8);
generating, by the processor (Sakurabu, [0268] a processor implements the functions of the entire system), a third image corresponding to the second image by inputting the second image into a machine learning model (Sakurabu, [0146] the virtual frames/second virtual frame (second image) are analyzed by a machine learning model to generate a bounding box around the subject of the image based on the virtual frame, this becomes an area frame (third image) as shown in figure 9);
generating, by the processor (Sakurabu, [0268] a processor implements the functions of the entire system), a fourth image based on the first image and the third image (Sakurabu, [0146] the area frame (third image), denoted as 134 in figure 9, and the captured image (first image), denoted as 108 in figure 9, are used to generate a subject image/person image (fourth image)),
wherein the specific region is determined based on at least one of information regarding a distance corresponding to the first image (Sakurabu, [0097]-[0098] the system uses two modes, manual focus (MF) and auto-focus (AF) to focus on the subject in the distance, this may include using a focus position calculation to focus the image on a subject based on the subject distance, which in [0146] is used to determine the focus area (specific region) in the captured image (first image)), semantic area division information (Sakurabu, [0140] the anchor box/focus area in the image (specific region) is set and scored based on a probability that a person is included in the box/region, which is analogous to the semantic division information applicant defined in paragraph [0065] of the applicant’s specification), cropping information (Sakurabu, [0133] and [0142] the bounding box/anchor box (specific region) information is also determined in part by position information specifying the width and height of the region (cropping information)), image circle information (Sakurabu, [0077] the image sensor is configured to capture an image within an imaging range including a subject, therefore this is analogous to what applicant defines as the image circle information in paragraph [0067] of the applicant’s specification, where the applicant notes that the image circle information is information about the area of the image which is picked up by the optical system/camera lens because the system is set up to capture images within the range that the system can pick up), and an optical performance (Sakurabu, [0191] the focus area (specific region) is in part determined by a calculated focusing position with respect to the distance and depth information to calculated focus needed in the image, which satisfies the definition of optical performance set in paragraph [0068] of the applicant’s specification notes that this has to do with the area being set based on the requirement of the area for sharpening, or blur correction).
Regarding claim 11 Sakurabu discloses; An image processing apparatus comprising: memory storing instructions (Sakurabu, [0165] the CPU executes instructions stored on a memory);
and at least one processor that executes the instructions to (Sakurabu, [0268] a processor implements the functions of the entire system):
determine a specific region in a first image (Sakurabu, [0137] the captured image (first image) is divided into a plurality of regions and this division is used to generate multiple virtual frames including specific regions in the first image as shown in figure 8, [0138-139] the image is divided into a plurality of cells and an anchor box is used to generate a specific region which generates the virtual frames);
acquire at least one second image corresponding to some of a plurality of partial images of the first image (Sakurabu, [0138-139] the image (first image) is divided into a plurality of cells and an anchor box is used to generate a specific region (specific region of the first image) which generates the virtual frames (at least a second image), denoted as 112(a-c) in figure 8, where a virtual frame (second image), denoted as 112(a) in figure 8, is generated including multiple virtual cells (114, in figure 8) of the first image, the multiple virtual cells being the partial images of the first image);
generate a third image corresponding to the second image by inputting the second image into a machine learning model (Sakurabu, [0146] the virtual frames/second virtual frame (second image) are analyzed by a machine learning model to generate a bounding box around the subject of the image based on the virtual frame, this becomes an area frame (third image) as shown in figure 9);
and generate a fourth image based on the first image and the third image (Sakurabu, [0146] the area frame (third image), denoted as 134 in figure 9, and the captured image (first image), denoted as 108 in figure 9, are used to generate a subject image/person image (fourth image)), wherein the second image includes at least a part of the specific region (Sakurabu, [0138-139] the image (first image) is divided into a plurality of cells and an anchor box is used to generate a specific region (specific region of the first image) which generates the virtual frames (at least a second image), denoted as 112(a-c) in figure 8, where a virtual frame (second image), denoted as 112(a) in figure 8, is generated including multiple virtual cells (114, in figure 8) of the first image, the multiple virtual cells being the partial images of the first image, where the specific region is at least partially included in the virtual frames as shown in figure 8).
Regarding claim 12 Sakurabu discloses; An image processing apparatus comprising: a memory storing instructions (Sakurabu, [0165] the CPU executes instructions stored on a memory);
and at least one processor that executes the instructions to (Sakurabu, [0268] a processor implements the functions of the entire system):
determine a specific region in a first image (Sakurabu, [0137] the captured image (first image) is divided into a plurality of regions and this division is used to generate multiple virtual frames including specific regions in the first image as shown in figure 8, [0138-139] the image is divided into a plurality of cells and an anchor box is used to generate a specific region which generates the virtual frames);
acquire a second image corresponding to the specific region (Sakurabu, [0138-139] the image (first image) is divided into a plurality of cells and an anchor box is used to generate a specific region (specific region of the first image) which generates the virtual frames (at least a second image), denoted as 112(a-c) in figure 8, where a virtual frame (second image), denoted as 112(a) in figure 8, is generated including multiple virtual cells (114, in figure 8) of the first image, the multiple virtual cells being the partial images of the first image, where the specific region is at least partially included in the virtual frames as shown in figure 8);
generate a third image corresponding to the second image by inputting the second image into a machine learning model (Sakurabu, [0146] the virtual frames/second virtual frame (second image) are analyzed by a machine learning model to generate a bounding box around the subject of the image based on the virtual frame, this becomes an area frame (third image) as shown in figure 9);
generate a fourth image based on the first image and the third image (Sakurabu, [0146] the area frame (third image), denoted as 134 in figure 9, and the captured image (first image), denoted as 108 in figure 9, are used to generate a subject image/person image (fourth image)),
wherein the specific region is determined based on at least one of information regarding a distance corresponding to the first image(Sakurabu, [0097]-[0098] the system uses two modes, manual focus (MF) and auto-focus (AF) to focus on the subject in the distance, this may include using a focus position calculation to focus the image on a subject based on the subject distance, which in [0146] is used to determine the focus area (specific region) in the captured image (first image)), semantic area division information (Sakurabu, [0140] the anchor box/focus area in the image (specific region) is set and scored based on a probability that a person is included in the box/region, which is analogous to the semantic division information applicant defined in paragraph [0065] of the applicant’s specification), cropping information (Sakurabu, [0133] and [0142] the bounding box/anchor box (specific region) information is also determined in part by position information specifying the width and height of the region (cropping information)), image circle information (Sakurabu, [0077] the image sensor is configured to capture an image within an imaging range including a subject, therefore this is analogous to what applicant defines as the image circle information in paragraph [0067] of the applicant’s specification, where the applicant notes that the image circle information is information about the area of the image which is picked up by the optical system/camera lens because the system is set up to capture images within the range that the system can pick up), and an optical performance (Sakurabu, [0191] the focus area (specific region) is in part determined by a calculated focusing position with respect to the distance and depth information to calculated focus needed in the image, which satisfies the definition of optical performance set in paragraph [0068] of the applicant’s specification notes that this has to do with the area being set based on the requirement of the area for sharpening, or blur correction).
Regarding claim 13 Sakurabu discloses; An image pickup apparatus comprising: the image processing apparatus according to claim 11 (Sakurabu, [0077] the system has an image apparatus which multiple sensors which convert incident subject light into a subject image); and an image sensor (Sakurabu, [0077], the system has an image sensor).
Regarding claim 14 Sakurabu discloses; An image pickup apparatus comprising: the image processing apparatus according to claim 12 (Sakurabu, [0077] the system has an image apparatus which multiple sensors which convert incident subject light into a subject image); and an image sensor (Sakurabu, [0077], the system has an image sensor).
Regarding claim 15 Sakurabu discloses; A non-transitory computer-readable storage medium storing a computer program that causes a computer to execute the image processing method according to claim 1 (Sakurabu, [0268] a processor implements the functions of the entire system).
Regarding claim 16 discloses; A non-transitory computer-readable storage medium storing a computer program that causes a computer to execute the image processing method according to claim 2 (Sakurabu, [0268] a processor implements the functions of the entire system).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
3. Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Sakurabu (US 20230326170 A1) in view of Inoue (US 20150281560 A1).
Regarding claim 9 Sakurabu fails to disclose; The image processing method according to claim 1, wherein the fourth image is an image obtained by applying at least one of sharpening, resolution enhancement, and defocus blur conversion to the first image.
However, in the same field of endeavor, Inoue teaches; wherein the fourth image is an image obtained by applying at least one of sharpening (Inoue, [0137]-[0138] the parallax difference may be decrease bringing the misalignment of the images to 0, which both focuses and sharpens the image), resolution enhancement (Inoue, [0118] the system uses an image pickup device to generate multiple images of higher resolutions by changing the exposure conditions of individual pixels), and defocus blur conversion to the first image (Inoue, [0095]-[0096] the portions of the subject image can have their focus adjusted by changing the displacement phase, which adjusts which portions of the image are in focus using the pixel value phase displacement, where in [0111] the pixel phase displacement is the defocus amount between two images, therefore adjusting this would be analogous to de-focus blur conversion as taught in applicant’s specification paragraphs [0062]-[0063]).
The combination of Sakurabu and Inoue would be obvious to one of ordinary skill in the art prior to the effective filing date of the presently claimed invention. The motivation for the combination lies in that the image enhancement techniques of Inoue would allow the system of Sakurabu to correct focus misalignment of parallax images more quickly and effectively. (Inoue, [0008]- [0010], and [0022]- [0026])
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. For a listing of analogous prior art as determined by the examiner please see the attached PTO-892 Notice of References Cited form.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JORDAN M ELLIOTT whose telephone number is (703)756-5463. The examiner can normally be reached M-F 8AM-5PM ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Emily Terrell can be reached at (571) 270-3717. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/J.M.E./Examiner, Art Unit 2666
/EMILY C TERRELL/Supervisory Patent Examiner, Art Unit 2666
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