DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
The reply filed on 17 April 2026 has been entered. Applicant’s arguments with respect to claims 1-19 and 21-24 have been considered but are moot in view of new ground(s) of rejection caused by the amendments. An applicant interview is recommended in this case.
Claims 1-19 and 21-24 are pending in this application and have been considered below. Claim 20 is canceled by the applicant.
Priority
Receipt is acknowledged that application is a National Stage application of PCT JP2022 023213. Priority to JP 2021-125785 with a priority date of 30 July 2021 is acknowledged under 35 USC 119(e) and 37 CFR 1.78.
Information Disclosure Statement
The IDSs dated 9 April 2026 and 4 May 2026 have been considered and placed in the application file. The IDS dated 9 April 2024 that has been previously considered remains placed in the application file.
Specification - Title
The title has been amended. The objection to the title is withdrawn.
1st Claim Interpretation
Under MPEP 2143.03, "All words in a claim must be considered in judging the patentability of that claim against the prior art." In re Wilson, 424 F.2d 1382, 1385, 165 USPQ 494, 496 (CCPA 1970). As a general matter, the grammar and ordinary meaning of terms as understood by one having ordinary skill in the art used in a claim will dictate whether, and to what extent, the language limits the claim scope. Language that suggests or makes a feature or step optional but does not require that feature or step does not limit the scope of a claim under the broadest reasonable claim interpretation. In addition, when a claim requires selection of an element from a list of alternatives, the prior art teaches the element if one of the alternatives is taught by the prior art. See, e.g., Fresenius USA, Inc. v. Baxter Int’l, Inc., 582 F.3d 1288, 1298, 92 USPQ2d 1163, 1171 (Fed. Cir. 2009).
Claims 6-10 recite “or.” Since “or” is disjunctive, any one of the elements found in the prior art is sufficient to reject the claim. While citations have been provided for completeness and rapid prosecution, only one element is required. Because, on balance, it appears the disjunctive interpretation enjoys the most specification support and for that reason the disjunctive interpretation (one of A, B OR C) is being adopted for the purposes of this Office Action. Applicant’s comments and/or amendments relating to this issue are invited to clarify the claim language and the prosecution history.
2nd Claim Interpretation
Claim 16 has been amended. The interpretation under 35 USC 112(f) is no longer appropriate. The interpretation of claim 16 under 35 USC 112 (f) is withdrawn.
3rd Claim Interpretation
Claim 14 has been amended. Claim 14 still recites “the image quality information do not satisfy the setting condition.” The phrase “do not” is considered to be a negative limitation because the word “not” is exclusionary in nature. According to MPEP § 2173.05(i) “Any negative limitation or exclusionary proviso must have basis in the original disclosure.” The specification defines this phrase in paragraph [0141]. As showing a negative is not reasonable, any prior art reference that does not explicitly show the recited limitation suffices to reject the limitation.
Claim Rejections - 35 USC § 112
Claims 12 and 14 have been amended. The rejection of claims 12 and 14 under 35 USC 112 is withdrawn.
1st Claim Rejections - 35 USC § 101
Claims 1 and 19 have been amended. The first rejection of claims 1-15, 18 and 19-20 as not being statutory subject matter is withdrawn.
2nd Claim Rejections - 35 USC § 101
Claims 1-5, 9-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The limitations, under their broadest reasonable interpretation, cover mental process using images/ drawings (concept performed in a human mind, including as observation, evaluation, judgment, opinion, prediction, etc.).
This judicial exception is not integrated into a practical application because the steps do not add meaningful limitations to be considered specifically applied to a particular technological problem to be solved. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the steps of the claimed invention can be done mentally and no additional features in the claims would preclude them from being performed as such.
According to the USPTO guidelines, a claim is directed to non-statutory subject matter if:
STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), or
STEP 2: the claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis:
STEP 2A (PRONG 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon?
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application?
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
Using the two-step inquiry, it is clear that claims 1, 16, 17 and 19 are directed to an abstract idea as shown below:
STEP 1: Do the claims fall within one of the statutory categories?
Yes. Claim 17 is directed to a method, i.e., process, and claims 1,16 and 19 are directed to an apparatus i.e., a machine.
STEP 2A (PRONG 1): Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?
YES, the claims are directed toward a mental process (i.e., abstract idea).
With regard to STEP 2A (PRONG 1), the guidelines provide three groupings of subject matter that are considered abstract ideas:
Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations;
Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions); and
Mental processes – concepts that are practicably performed in the human mind (including an observation, evaluation, judgment, opinion).
The apparatus in claim 1, for example, comprises a mental process that can be practicably performed in the human mind therefore, an abstract idea.
Claim 1 recites:
setting condition related to identification information and to image quality information with respect to a plurality of pieces of image data…
creating the training data based on selection image data in which the identification information and the image quality information satisfying the setting condition are recorded
These limitations, as drafted, under their broadest reasonable interpretation, cover performance of the limitations in the mind or by a human. The Examiner notes that under MPEP 2106.04(a)(2)(III), the courts consider a mental process (thinking) that “can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’" 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 ("‘[M]ental processes and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’" (quoting Benson, 409 U.S. at 67, 175 USPQ at 675)); Parker v. Flook, 437 U.S. 584, 589, 198 USPQ 193, 197 (1978) (same).
As such, a person could present image(s)/ drawing(s) and record quality information about the images on a sheet of paper. The mere nominal recitation that the various steps are being executed by a processor (e.g., processing unit) does not take the limitations out of the mental process grouping. Thus, the claims recite a mental process.
If a claim limitation, under its broadest reasonable interpretation, covers performance of a mental step which could be performed with a simple tool such as a pen and paper, then it falls within the “mental steps” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application?
NO, the claims do not recite additional elements that integrate the judicial exception into a practical application.
With regard to STEP 2A (prong 2), whether the claim recites additional elements that integrate the judicial exception into a practical application, the guidelines provide the following exemplary considerations that are indicative that an additional element (or combination of elements) may have integrated the judicial exception into a practical application:
an additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to affect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
While the guidelines further state that the exemplary considerations are not an exhaustive list and that there may be other examples of integrating the exception into a practical application, the guidelines also list examples in which a judicial exception has not been integrated into a practical application:
an additional element merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea;
an additional element adds insignificant extra-solution activity to the judicial exception; and
an additional element does no more than generally link the use of a judicial exception to a particular technological environment or field of use.
Thus, Claims 1-5, 9-19 do not recite any of the exemplary considerations that are indicative of an abstract idea having been integrated into a practical application.
Thus, since Claims 1, 16, 17 and 19 are/is: (a) directed toward an abstract idea, (b) do not recite additional elements that integrate the judicial exception into a practical application, and (c) do not recite additional elements that amount to significantly more than the judicial exception, Claims 1, 16, 17 and 19 are not eligible subject matter under 35 U.S.C 101.
Similar analysis is made for the dependent claims 2-5, 9-15 and 18 and the dependent claims are similarly identified as: being directed towards an abstract idea, not reciting additional elements that integrate the judicial exception into a practical application, and not reciting additional elements that amount to significantly more than the judicial exception.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-19 and 21-24 (all claims) are rejected under 35 U.S.C. 103 as obvious over US Patent Publication 2022/0114207 A1, (Kawabata et al.) in view of US Patent Publication 2022/0067081 A1, (Saito et al.). The references are listed in a PTO-892 from the Office Action in which they are first used.
Claim 1
Regarding Claim 1, Kawabata et al. teach a data creation apparatus that creates training data used in machine learning from image data ("level of focus and the corresponding image will be used as training datasets to generate the model used by the image recognition engine discussed above," paragraph [0125]) in which accessory information is recorded ("An image processing apparatus and method is provided which receives image data and tags the image data with a first type of tag indicative of elements in the image," paragraph [0004]) in an image in which a plurality of subjects are captured ("the tag may list a calculated probability that an image, a subject within an image, or a part of the subject within an image may be in focus," paragraph [0125] where a subject within an image teaches a plurality of subjects), the data creation apparatus comprising a processor ("an example of the dedicated hardware, there are an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), and a digital signal processor (DSP), and the like," paragraph [0047]), wherein the processor is configured to execute:
setting processing of setting any setting condition related to identification information and to image quality information with respect to a plurality of pieces of image data in which the accessory information including a plurality of pieces of the identification information assigned in association with the plurality of subjects and a plurality of pieces of the image quality information assigned in association with the plurality of subjects is recorded ("identify objects on the image data, but also evaluate quantitative features related to image quality such as sharpness, focus, tilt, noise, exposure, dynamic range, aberration, diffraction distortion and vignetting," paragraph [0034]); and
creation processing of creating the training data based on selection image data in which both ("for sample image A, a tag may be created with "chromatic aberration level-0.24, diffraction level=0.46, barrel distortion level 1 .4, circular lens flare=0.2, vignetting=0.78". In other words, the training data for training the AI Engine are plurality of set of image itself and numerical values or words of each tag for the image," paragraph [0135] where barrel distortion level is an image quality information).
Kawabata et al. is not relied upon to explicitly teach all of identification information.
However, Saito et al. teach the identification information ("As mentioned earlier, the positive images and negative images identified by the identification unit 13 are sent to the training data output unit 14, together with the positional information of the object regions of the positive images. At step S40 in FIG. 2, the training data acquisition apparatus 100 generates positive data at the training data output unit 14, based on the identified positive images, the positional information of the object regions of the positive images, and the correct label, which is based on the query text," paragraph [0083]).
Therefore, taking the teachings of Kawabata et al. and Saito et al. as a whole, it would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify “Image Searching and Evaluation using Tags” as taught by Kawabata et al. to use “Training Data Acquisition Apparatus” as taught by Saito et al., showing that Kawabata et al. and Saito et al. are analogous art because both are image searching for training data acquisition. The suggestion/motivation for combination is that, “To reduce the cost, the acquisition scheme may be designed such that manual teaching can be performed with regard to only part of the data to obtain a model as a result of the training, and ultimate training data can be acquired through inference using this model” as noted by the Saito et al. disclosure in paragraph [0004], which also motivates combination because the combination would predictably have a lower cost for acquiring training data as there is a reasonable expectation that training data needs are always increasing and growing more specialized; and/or because doing so merely combines prior art elements according to known methods to yield predictable results.
Claim 2
Regarding claim 2, Kawabata et al. teach the data creation apparatus according to claim 1,
wherein the image quality information is information related to any of resolution of the subject in the image indicated by the image data, brightness of the subject, and noise occurring at a position of the subject ("identify objects on the image data, but also evaluate quantitative features related to image quality such as sharpness, focus, tilt, noise, exposure, dynamic range, aberration, diffraction distortion and vignetting," paragraph [0034] where exposure is brightness, see "Exposure denotes the image's overall brightness." paragraph [0128]).
Claim 3
Regarding claim 3, Kawabata et al. teach the data creation apparatus according to claim 2,
wherein the image quality information is resolution information related to the resolution ("identify objects on the image data, but also evaluate quantitative features related to image quality such as sharpness, focus, tilt, noise, exposure, dynamic range, aberration, diffraction distortion and vignetting," paragraph [0034] where sharpness teaches resolution), and
the resolution information is information determined in accordance with blurriness and shake levels of the subject in the image indicated by the image data ("identify objects on the image data, but also evaluate quantitative features related to image quality such as sharpness, focus, tilt, noise, exposure, dynamic range, aberration, diffraction distortion and vignetting," paragraph [0034] where blurriness is focus).
Claim 4
Regarding claim 4, Kawabata et al. teach the data creation apparatus according to claim 2,
wherein the image quality information is resolution information related to the resolution ("identify objects on the image data, but also evaluate quantitative features related to image quality such as sharpness, focus, tilt, noise, exposure, dynamic range, aberration, diffraction distortion and vignetting," paragraph [0034] where sharpness teaches resolution), and
the resolution information is resolution level information related to a resolution level of the subject in the image indicated by the image data ("the CPU 119 analyzes the image based on image data such as focus data of each pixel, distance data of each pixel," paragraph [0064] where distance data of each pixel is resolution).
Claim 5
Regarding claim 5, Kawabata et al. teach the data creation apparatus according to claim 4,
wherein the setting condition is a condition including an upper limit and a lower limit of the resolution level of the subject ("In this case, the user 101 designates the threshold as 0.40, then the CPU 119 converts only contents which has numerical value higher than the threshold 0.40," paragraph [0084] where threshold teaches an upper and lower limit, and "the CPU 119 analyzes the image based on image data such as focus data of each pixel, distance data of each pixel," paragraph [0064] where distance data of each pixel is resolution).
Claim 6
Regarding claim 6, Kawabata et al. teach the data creation apparatus according to claim 2,
wherein the image quality information is information related to the brightness of the subject or information related to the noise occurring at the position of the subject ("identify objects on the image data, but also evaluate quantitative features related to image quality such as sharpness, focus, tilt, noise, exposure, dynamic range, aberration, diffraction distortion and vignetting," paragraph [0034] where exposure is brightness, see "Exposure denotes the image's overall brightness." paragraph [0128]),
the information related to the brightness is a brightness value corresponding to the subject ("identify objects on the image data, but also evaluate quantitative features related to image quality such as sharpness, focus, tilt, noise, exposure, dynamic range, aberration, diffraction distortion and vignetting," paragraph [0034]), and
the information related to the noise is an S/N value corresponding to the subject ("For example, the user 101, via the display, can define the correction strength of noise and direction as "strong" and "down" resulting in a strong noise reduction correction being performed. The selection of the strength is not only limited to the selection of three levels, but a selection of number defining the strength may also be input by the user," paragraph [0106] where a number defining the strength of noise is a signal to noise (S/N) value).
Claim 7
Regarding claim 7, Kawabata et al. teach the data creation apparatus according to claim 6,
wherein the setting condition is a condition including an upper limit and a lower limit of the brightness value or an upper limit and a lower limit of the S/N value corresponding to the subject ("In this case, the user 101 designates the threshold as 0.40, then the CPU 119 converts only contents which has numerical value higher than the threshold 0.40," paragraph [0084] where threshold teaches an upper and lower limit, and "identify objects on the image data, but also evaluate quantitative features related to image quality such as sharpness, focus, tilt, noise, exposure, dynamic range, aberration, diffraction distortion and vignetting," paragraph [0034] where exposure is brightness, see "Exposure denotes the image's overall brightness." paragraph [0128]).
Claim 8
Regarding claim 8, Kawabata et al. teach the data creation apparatus according to claim 1,
wherein the accessory information further includes a plurality of pieces of positional information assigned in association with the plurality of subjects ("To identify a composition, estimates can be made using a method such as semantic segmentation which separates an object into regions, and calculating the position and center point of each object," paragraph [0139]), and
the positional information is information indicating a position of the subject in the image indicated by the image data ("To identify a composition, estimates can be made using a method such as semantic segmentation which separates an object into regions, and calculating the position and center point of each object," paragraph [0139]).
Claim 9
Regarding claim 9, Kawabata et al. teach the data creation apparatus according to claim 1, wherein the processor is configured to further execute:
display processing of displaying an image indicated by the selection image data or a sample image having image quality satisfying the setting condition before executing the creation processing ("FIG. 17A-17C illustrate other embodiments for displaying the suggested tag list in region 1608," paragraph [0165]).
Claim 10
Regarding claim 10, Kawabata et al. teach the data creation apparatus according to claim 9,
wherein two or more pieces of the selection image data are selected from the plurality of pieces of image data ("In S1509, the CPU 119 aggregates all tags determined in S1505 and S1508 and determines tags to display as a final results with confidence values. In one exemplary output, the aggregation can take the output individually from S1503-S1505 which indicates that suggested tags for the input image is "A, B, C, D, E" and S1506-S1508 which indicates that suggested tags for the input image is "F, A, G, E, I". The aggregation may then compare common tag values and aggregate the suggested tags as "A, E" because both of these appeared in the individual outputs discussed above," paragraph [0159] and "Selection of at least one image causes the selected image to be displayed in region 1606," paragraph [0164]), and
in the display processing, an image of a part of the selection image data among the two or more pieces of the selection image data is displayed ("FIG. 17A-17C illustrate other embodiments for displaying the suggested tag list in region 1608," paragraph [0165]).
Claim 11
Regarding claim 11, Kawabata et al. teach the data creation apparatus according to claim 10,
wherein in the display processing, images of the selected pieces of the selection image data are displayed based on a priority level set for each selection image data ("From there, a list of suggested tags to be associated with this image along with the confidence score that the suggested tag is correct is displayed," paragraph [0164] where a confidence score is a priority level).
Claim 12
Regarding claim 12, Kawabata et al. teach the data creation apparatus according to claim 1, wherein the processor is configured to further execute:
determination processing of determining a purpose of the machine learning in accordance with designation information input by a user ("For example, the user 101, via the display, can define the correction strength of noise and direction as "strong" and "down" resulting in a strong noise reduction correction being performed. The selection of the strength is not only limited to the selection of three levels, but a selection of number defining the strength may also be input by the user," paragraph [0106] where the purpose is noise reduction), and
in the setting processing, the setting condition corresponding to the purpose is set ("Then, the correction is performed from the Tn=l," paragraph [0107]).
Claim 13
Regarding claim 13, Kawabata et al. teach the data creation apparatus according to claim 1, wherein the processor is configured to further execute:
determination processing of determining a purpose of the machine learning in accordance with designation information input by a user ("For example, the user 101, via the display, can define the correction strength of noise and direction as "strong" and "down" resulting in a strong noise reduction correction being performed. The selection of the strength is not only limited to the selection of three levels, but a selection of number defining the strength may also be input by the user," paragraph [0106] where the purpose is noise reduction), and
in the setting processing, the setting condition corresponding to the purpose is suggested to the user before setting the setting condition ("From there, a list of suggested tags to be associated with this image along with the confidence score that the suggested tag is correct is displayed," paragraph [0164]).
Claim 14
Regarding claim 14, Kawabata et al. teach the data creation apparatus according to claim 1, wherein the processor is configured to further execute:
suggestion processing of suggesting an additional condition different from the setting condition to a user ("From there, a list of suggested tags to be associated with this image along with the confidence score that the suggested tag is correct is displayed," paragraph [0164]),
the additional condition is a condition set with respect to the accessory information ("From there, a list of suggested tags to be associated with this image along with the confidence score that the suggested tag is correct is displayed," paragraph [0164]),
additional image data is selected under the additional condition from non-selection image data of which the identification information and the image quality information do not satisfy the setting condition ("To associate the tags with the image in region 1606, user may select tagging icon 1610 which causes association processing to associate the tag with the image," paragraph [0164]), and
the creation processing after selecting the additional image data, the training data is created based on the selection image data and on the additional image data ("level of focus and the corresponding image will be used as training datasets to generate the model used by the image recognition engine discussed above," paragraph [0125]).
Claim 15
Regarding claim 15, Kawabata et al. teach a storage device that stores the plurality of pieces of image data to be used for creating the training data via the data creation apparatus according to claim 1 ("The recording medium 104 may be a memory card for storing captured image data and may be considered storage device," paragraph [0035]).
Claim 16
Regarding claim 16, Kawabata et al. teach a data processing system comprising:
a data creation apparatus that is composed of a computer which has a first processor ("an example of the dedicated hardware, there are an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), and a digital signal processor (DSP), and the like," paragraph [0047]); and
a learning apparatus that is composed of a computer which has a second processor ("an example of the dedicated hardware, there are an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), and a digital signal processor (DSP), and the like," paragraph [0047]):
wherein the first processor is configured to create training data from image data ("level of focus and the corresponding image will be used as training datasets to generate the model used by the image recognition engine discussed above," paragraph [0125]) in which accessory information is recorded ("An image processing apparatus and method is provided which receives image data and tags the image data with a first type of tag indicative of elements in the image," paragraph [0004]) in an image in which a plurality of subjects are captured ("the tag may list a calculated probability that an image, a subject within an image, or a part of the subject within an image may be in focus," paragraph [0125] where a subject within an image teaches a plurality of subjects),
The second processor is configured to perform machine learning using the training data ("the cloud storage server 103 may include one or more dedicated hardware or a graphics processing unit (GPU), which is different from the CPU 119, and the GPU or the dedicated hardware may perform a part of the processes by the CPU 119," paragraph [0047]), and the first processor is further configured to execute:
setting processing of setting any setting condition related to identification information and to image quality information with respect to a plurality of pieces of image data in which the accessory information including a plurality of pieces of the identification information assigned in association with the plurality of subjects and a plurality of pieces of the image quality information assigned in association with the plurality of subjects is recorded ("identify objects on the image data, but also evaluate quantitative features related to image quality such as sharpness, focus, tilt, noise, exposure, dynamic range, aberration, diffraction distortion and vignetting," paragraph [0034]); and
creation processing of creating the training data based on selection image data in which both ("for sample image A, a tag may be created with "chromatic aberration level-0.24, diffraction level=0.46, barrel distortion level 1 .4, circular lens flare=0.2, vignetting=0.78". In other words, the training data for training the AI Engine are plurality of set of image itself and numerical values or words of each tag for the image," paragraph [0135] where barrel distortion level is an image quality information).
Kawabata et al. is not relied upon to explicitly teach all of the identification information.
However, Saito et al. teach the identification information ("As mentioned earlier, the positive images and negative images identified by the identification unit 13 are sent to the training data output unit 14, together with the positional information of the object regions of the positive images. At step S40 in FIG. 2, the training data acquisition apparatus 100 generates positive data at the training data output unit 14, based on the identified positive images, the positional information of the object regions of the positive images, and the correct label, which is based on the query text," paragraph [0083])
Kawabata et al. and Saito et al. are combined as per claim 1.
Claim 17
Regarding claim 17, Kawabata et al. teach a data creation method of creating training data used in machine learning from image data ("level of focus and the corresponding image will be used as training datasets to generate the model used by the image recognition engine discussed above," paragraph [0125]) in which accessory information is recorded in an image ("An image processing apparatus and method is provided which receives image data and tags the image data with a first type of tag indicative of elements in the image," paragraph [0004]) in which a plurality of subjects are captured ("the tag may list a calculated probability that an image, a subject within an image, or a part of the subject within an image may be in focus," paragraph [0125] where a subject within an image teaches a plurality of subjects), the data creation method comprising:
a setting step of setting any setting condition related to identification information and to image quality information with respect to a plurality of pieces of image data in which the accessory information including a plurality of pieces of the identification information assigned in association with the plurality of subjects and a plurality of pieces of the image quality information assigned in association with the plurality of subjects is recorded ("identify objects on the image data, but also evaluate quantitative features related to image quality such as sharpness, focus, tilt, noise, exposure, dynamic range, aberration, diffraction distortion and vignetting," paragraph [0034]); and
a creation step of creating the training data based on selection image data in which both ("for sample image A, a tag may be created with "chromatic aberration level-0.24, diffraction level=0.46, barrel distortion level 1 .4, circular lens flare=0.2, vignetting=0.78". In other words, the training data for training the AI Engine are plurality of set of image itself and numerical values or words of each tag for the image," paragraph [0135] where barrel distortion level is an image quality information).
Kawabata et al. is not relied upon to explicitly teach all of the identification information.
However, Saito et al. teach the identification information ("As mentioned earlier, the positive images and negative images identified by the identification unit 13 are sent to the training data output unit 14, together with the positional information of the object regions of the positive images. At step S40 in FIG. 2, the training data acquisition apparatus 100 generates positive data at the training data output unit 14, based on the identified positive images, the positional information of the object regions of the positive images, and the correct label, which is based on the query text," paragraph [0083])
Kawabata et al. and Saito et al. are combined as per claim 1.
Claim 18
Regarding claim 18, Kawabata et al. teach a non-transitory computer-readable recording medium storing a program causing a computer to function as the data creation apparatus according to claim 1, the program causing the computer to execute each of the setting processing and the creation processing ("FIG. 1A shows a schematic diagram of a system where a user 101 edits, stores and/or organizes image data through an application executing on a local PC 102," paragraph [0034]).
Claim 19
Regarding claim 19, Kawabata et al. teach an imaging apparatus ("level of focus and the corresponding image will be used as training datasets to generate the model used by the image recognition engine discussed above," paragraph [0125]) that comprises a processor ("an example of the dedicated hardware, there are an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), and a digital signal processor (DSP), and the like," paragraph [0047]),wherein the processor is configured to execute:
imaging processing of capturing an image in which a plurality of subjects are captured ("the tag may list a calculated probability that an image, a subject within an image, or a part of the subject within an image may be in focus," paragraph [0125] where a subject within an image teaches a plurality of subjects); and
generation processing of generating image data by recording accessory information in the image ("An image processing apparatus and method is provided which receives image data and tags the image data with a first type of tag indicative of elements in the image," paragraph [0004]), and
the accessory information includes a plurality of pieces of identification information assigned in association with the plurality of subjects and a plurality of pieces of image quality information assigned in association with the plurality of subjects ("identify objects on the image data, but also evaluate quantitative features related to image quality such as sharpness, focus, tilt, noise, exposure, dynamic range, aberration, diffraction distortion and vignetting," paragraph [0034]),
the accessory information is information for selecting selection image data to be used for creating training data for machine learning ("identify objects on the image data, but also evaluate quantitative features related to image quality such as sharpness, focus, tilt, noise, exposure, dynamic range, aberration, diffraction distortion and vignetting," paragraph [0034]), and
the selection image data is the image data in which both recorded ("for sample image A, a tag may be created with "chromatic aberration level-0.24, diffraction level=0.46, barrel distortion level 1 .4, circular lens flare=0.2, vignetting=0.78". In other words, the training data for training the AI Engine are plurality of set of image itself and numerical values or words of each tag for the image," paragraph [0135] where barrel distortion level is an image quality information).
Kawabata et al. is not relied upon to explicitly teach all of the identification information.
However, Saito et al. teach the identification information ("As mentioned earlier, the positive images and negative images identified by the identification unit 13 are sent to the training data output unit 14, together with the positional information of the object regions of the positive images. At step S40 in FIG. 2, the training data acquisition apparatus 100 generates positive data at the training data output unit 14, based on the identified positive images, the positional information of the object regions of the positive images, and the correct label, which is based on the query text," paragraph [0083])
Kawabata et al. and Saito et al. are combined as per claim 1.
Claim 21
Regarding claim 21, Kawabata et al. teach the data creation apparatus according to claim 1,
wherein the accessory information includes the plurality of pieces of the identification information, the plurality of pieces of the image quality information ("identify objects on the image data, but also evaluate quantitative features related to image quality such as sharpness, focus, tilt, noise, exposure, dynamic range, aberration, diffraction distortion and vignetting," paragraph [0034]), and a plurality of pieces of positional information assigned in association with the plurality of subjects ("To identify a composition, estimates can be made using a method such as semantic segmentation which separates an object into regions, and calculating the position and center point of each object," paragraph [0139]),
the positional information is information indicating a position of the subject in the image indicated by the image data ("The composition means position, configuration and/or angle of the objects in an image. For example, a rule of thirds is one of the composition type, and that means that the image is divided evenly into thirds, both horizontally and vertically, and the subject of the image is placed at the intersection of those dividing lines, or along one of the lines itself," paragraph [0138]),
the image quality information and the positional information ("The composition means position, configuration and/or angle of the objects in an image. For example, a rule of thirds is one of the composition type, and that means that the image is divided evenly into thirds, both horizontally and vertically, and the subject of the image is placed at the intersection of those dividing lines, or along one of the lines itself," paragraph [0143]), and
the selection image data is the image data in which the identification information, the image quality information and the positional information are recorded, the identification information, the image quality information and the positional information satisfying the setting condition ("Generally, the visual search and tagging operations include an input phase, an indexing phase, a search phase and an aggregation phase whereby results of the search phased are processed in order to determine the tags suggested for the image along with a confidence value associated with each of the suggested tags," paragraph [0153]).
Kawabata et al. is not relied upon to explicitly teach all of the setting condition is a condition related to the identification information.
However, Saito et al. teach the setting condition is a condition related to the identification information ("the training data preferably includes images of various illumination conditions and shooting angles. Thus, by setting suitable threshold values, the training data acquisition apparatus 100 can control the trade-off between increased variety in the extracted data and erroneous inclusion of data that is not a detection target object," paragraph [0088]).
Kawabata et al. and Saito et al. are combined as per claim 1.
Claim 22
Regarding claim 22, Kawabata et al. teach the data processing system according to claim 16,
wherein the accessory information includes the plurality of pieces of the identification information, the plurality of pieces of the image quality information ("identify objects on the image data, but also evaluate quantitative features related to image quality such as sharpness, focus, tilt, noise, exposure, dynamic range, aberration, diffraction distortion and vignetting," paragraph [0034]), and a plurality of pieces of positional information assigned in association with the plurality of subjects ("To identify a composition, estimates can be made using a method such as semantic segmentation which separates an object into regions, and calculating the position and center point of each object," paragraph [0139]),
the positional information is information indicating a position of the subject in the image indicated by the image data ("The composition means position, configuration and/or angle of the objects in an image. For example, a rule of thirds is one of the composition type, and that means that the image is divided evenly into thirds, both horizontally and vertically, and the subject of the image is placed at the intersection of those dividing lines, or along one of the lines itself," paragraph [0138]),
the image quality information and the positional information ("The composition means position, configuration and/or angle of the objects in an image. For example, a rule of thirds is one of the composition type, and that means that the image is divided evenly into thirds, both horizontally and vertically, and the subject of the image is placed at the intersection of those dividing lines, or along one of the lines itself," paragraph [0143]), and
the selection image data is the image data in which the identification information, the image quality information and the positional information are recorded, the identification information, the image quality information and the positional information satisfying the setting condition("Generally, the visual search and tagging operations include an input phase, an indexing phase, a search phase and an aggregation phase whereby results of the search phased are processed in order to determine the tags suggested for the image along with a confidence value associated with each of the suggested tags," paragraph [0153]).
Kawabata et al. is not relied upon to explicitly teach all of the setting condition is a condition related to the identification information.
However, Saito et al. teach the setting condition is a condition related to the identification information ("the training data preferably includes images of various illumination conditions and shooting angles. Thus, by setting suitable threshold values, the training data acquisition apparatus 100 can control the trade-off between increased variety in the extracted data and erroneous inclusion of data that is not a detection target object," paragraph [0088]).
Kawabata et al. and Saito et al. are combined as per claim 1.
Claim 23
Regarding claim 23, Kawabata et al. teach the data creation method according to claim 17,
wherein the accessory information includes the plurality of pieces of the identification information, the plurality of pieces of the image quality information ("identify objects on the image data, but also evaluate quantitative features related to image quality such as sharpness, focus, tilt, noise, exposure, dynamic range, aberration, diffraction distortion and vignetting," paragraph [0034]), and a plurality of pieces of positional information assigned in association with the plurality of subjects ("To identify a composition, estimates can be made using a method such as semantic segmentation which separates an object into regions, and calculating the position and center point of each object," paragraph [0139]),
the positional information is information indicating a position of the subject in the image indicated by the image data ("The composition means position, configuration and/or angle of the objects in an image. For example, a rule of thirds is one of the composition type, and that means that the image is divided evenly into thirds, both horizontally and vertically, and the subject of the image is placed at the intersection of those dividing lines, or along one of the lines itself," paragraph [0138]),
the image quality information and the positional information ("The composition means position, configuration and/or angle of the objects in an image. For example, a rule of thirds is one of the composition type, and that means that the image is divided evenly into thirds, both horizontally and vertically, and the subject of the image is placed at the intersection of those dividing lines, or along one of the lines itself," paragraph [0143]), and
the selection image data is the image data in which the identification information, the image quality information and the positional information are recorded, the identification information, the image quality information and the positional information satisfying the setting condition("Generally, the visual search and tagging operations include an input phase, an indexing phase, a search phase and an aggregation phase whereby results of the search phased are processed in order to determine the tags suggested for the image along with a confidence value associated with each of the suggested tags," paragraph [0153]).
Kawabata et al. is not relied upon to explicitly teach all of the setting condition is a condition related to the identification information
However, Saito et al. teach the setting condition is a condition related to the identification information ("the training data preferably includes images of various illumination conditions and shooting angles. Thus, by setting suitable threshold values, the training data acquisition apparatus 100 can control the trade-off between increased variety in the extracted data and erroneous inclusion of data that is not a detection target object," paragraph [0088]).
Kawabata et al. and Saito et al. are combined as per claim 1.
Claim 24
Regarding claim 24, Kawabata et al. teach the imaging apparatus according to claim 19,
wherein the accessory information includes the plurality of pieces of the identification information, the plurality of pieces of the image quality information ("identify objects on the image data, but also evaluate quantitative features related to image quality such as sharpness, focus, tilt, noise, exposure, dynamic range, aberration, diffraction distortion and vignetting," paragraph [0034]), and a plurality of pieces of positional information assigned in association with the plurality of subjects ("To identify a composition, estimates can be made using a method such as semantic segmentation which separates an object into regions, and calculating the position and center point of each object," paragraph [0139]),
the positional information is information indicating a position of the subject in the image indicated by the image data ("The composition means position, configuration and/or angle of the objects in an image. For example, a rule of thirds is one of the composition type, and that means that the image is divided evenly into thirds, both horizontally and vertically, and the subject of the image is placed at the intersection of those dividing lines, or along one of the lines itself," paragraph [0138]),
the image quality information and the positional information("The composition means position, configuration and/or angle of the objects in an image. For example, a rule of thirds is one of the composition type, and that means that the image is divided evenly into thirds, both horizontally and vertically, and the subject of the image is placed at the intersection of those dividing lines, or along one of the lines itself," paragraph [0143]), and
the selection image data is the image data in which the identification information, the image quality information and the positional information are recorded, the identification information, the image quality information and the positional information satisfying the setting condition("Generally, the visual search and tagging operations include an input phase, an indexing phase, a search phase and an aggregation phase whereby results of the search phased are processed in order to determine the tags suggested for the image along with a confidence value associated with each of the suggested tags," paragraph [0153]).
Kawabata et al. is not relied upon to explicitly teach all of the setting condition is a condition related to the identification information.
However, Saito et al. teach the setting condition is a condition related to the identification information ("the training data preferably includes images of various illumination conditions and shooting angles. Thus, by setting suitable threshold values, the training data acquisition apparatus 100 can control the trade-off between increased variety in the extracted data and erroneous inclusion of data that is not a detection target object," paragraph [0088]).
Kawabata et al. and Saito et al. are combined as per claim 1.
Reference Cited
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
US Patent Publication 2021 0256307 A1 to Papli discloses utilize video data to provide the necessary number of images and view angles needed to train a machine learning product detection/recognition system to recognize a specific product within later provided images. In various embodiments, a user may provide video data and the video data may be transformed in a manner that may aid in training of the machine learning system.
Non Patent Publication “An easy-to-use image labeling platform for automatic magnetic resonance image quality assessment” to Kustner et al. discloses model observers (MO) which mimic the human visual system can help to support the Human Observers (HO) during this reading process or can provide feedback to the magnetic resonance (MR) scanner and/or HO about the derived image quality. For this purpose MOs are trained on HO-derived image labels with respect to a certain diagnostic task. We propose a non-reference image quality assessment system based on a machine-learning approach with a deep neural network and active learning to keep the amount of needed labeled training data small. A labeling platform is developed as a web application with accounted data security and confidentiality to facilitate the HO labeling procedure..
Conclusion
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/H.E.W/Examiner, Art Unit 2664
Date: 29 June 2026
/JENNIFER MEHMOOD/Supervisory Patent Examiner, Art Unit 2664
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