DETAILED ACTION
Claims 1-13 and 15-22 are pending in this application and have been examined under the priority date 07/30/2021 in accordance with the applicant’s claim for foreign priority. Claims 1, 13 and 15 have been amended, claim 14 has been canceled and claims 21-22 have been newly added.
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 04/17/2024 and 04/09/2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Response to Arguments
Claim Interpretation
Applicant’s arguments (see Remarks filed 05/18/2026) have been fully considered by the examiner and are persuasive in view of the amendments to claim 13. Accordingly, the claim interpretations under 35 U.S.C. 112(f) have been withdrawn.
35 U.S.C. 101
Applicant’s arguments (see Remarks filed 05/18/2026) regarding the rejections under 35 U.S.C. 101 have been fully considered by the examiner.
Regarding the arguments against the rejections pertaining to claims 1-20 for being drawn to an abstract idea, the examiner does not find these arguments persuasive. Applicant argues that the newly added limitations to claims 1 and 13 translate the claim into practical application because training data is created from a large set of image data and therefore could not be as a mental process, abstract idea, or step of mere data gathering. The examiner respectfully disagrees, that the amended limitations meaningfully translate the claim into practical application. Taking claim 1 as example, claim 1 recites the following limitations which may be classified as an abstract idea, step of mere data gathering or a mental process as noted and explained by the examiner below;
“A data creation apparatus (Additional element, highly general) that creates training data for performing machine learning from a plurality of pieces of image data in which accessory information is recorded, the data creation apparatus comprising: (Step of mere data gathering in which a human could reasonably collect a set of images, regardless of amount, and as a mental process or abstract idea determine the accessory information by looking at the image)
a processor, wherein the processor is configured to execute (Additional element, highly general):
setting processing of receiving and setting a first condition for selecting first selection image data based on the accessory information from the plurality of pieces of image data; (abstract idea or mental process in which a human could determine or be sent a condition or conditions used to select images by looking at the image)
selection processing of selecting the first selection image data in which the accessory information conforming to the first condition is recorded from the plurality of pieces of image data; (Mental process in which the image can be selected and the data can be manually recorded by a human)
suggestion processing of automatically setting a second condition for selecting second selection image data based on the accessory information from non-selection image data that does not conform to the first condition among the plurality of pieces of image data and suggesting the second condition to the user; (Abstract idea or Mental process in which the image can be selected and the data can be manually recorded by a human)
and creation processing of creating the training data based on the first selection image data in a case where the user has not employed the second condition (Step of mere data gathering in which a human could select a set of images to train a model and determine by looking at the images which conditions apply)
and creating the training data based on the first selection image data and on the second selection image data in a case where the user has employed the second condition. (Step of mere data gathering in which a human could select a set of images to train a model and determine by looking at the images which conditions apply)
and wherein the plurality of pieces of image data, the training data, the first selection image data, the non-selection image data and the second selection image data are digital data. (Step of mere data gathering in which the data may all be stored digitally on a computer or other digital medium)”
The above limitations are steps which could practically be performed as a mental process or step of mere data gathering performed by a human under step 2A prong 1 (MPEP 2106). Under step 2A prong 2, the claim recites the additional elements of “A data creation apparatus” and “a processor”, which are recited with a high level of generality and fail to translate the steps into practical application or amount to significantly more. Under step 2B, the claim does not recite any limitations which translate the claim into practical application or amount to significantly more. Therefore, for at least these reasons the examiner maintains the rejections made to claims 1-20 under 35 U.S.C. 101.
Regarding the arguments against the rejections under 35 U.S.C. 101 to claims 14 and 15 as being drawn to a program per se and to computer readable media, the examiner finds these arguments persuasive in view of the amendments. Accordingly, the rejections made to claims 14 and 15 as being drawn to a program per se, and to computer readable media have been withdrawn by the examiner.
35 U.S.C. 102
Applicant’s arguments (see Remarks filed 05/18/2026) regarding the rejections made under 35 U.S.C. 102 have been fully considered by the examiner and are not persuasive. Applicant argues (see page 8-9 of Remarks filed 05/18/2026) that Haneda fails to teach “suggestion processing of automatically setting a second condition for selecting second selection image data based on the accessory information from non-selection image data that does not conform to the first condition among the plurality of pieces of image data and suggesting the second condition to a user”. The examiner respectfully disagrees that Haneda fails to teach this limitation. The broadest reasonable interpretation of the aforementioned limitation is that a second condition is used to select a second set of images based on information collected from a different set of image data (non-selection image data) which does not meet a first condition, and then suggesting a condition to the user based on this. Haneda [0142]-[0144] teaches that condition matching for multiple conditions may be performed, indicating at least two different conditions, and this matching is used to select image data. Further, in [0144] Haneda teaches that two images with different capturing conditions are compared, and the conditions of image that is not conforming is used to suggest “advice” to the user for a second set of capture conditions. This would be analogous to provided suggested second conditions to a user when the conditions of a set of images non-conforming to a first condition have been assessed and basing this suggested condition off the non-selected data. Therefore, for at least the reasons listed above, the rejections made under 35 U.S.C. 102 over Haneda are respectfully maintained by the examiner.
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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 1-13 and 15-22 are rejected under 35 U.S.C. 101 because they are drawn to an abstract idea or mental process, without components which meaningfully translate the claims into practical application or significantly more.
Regarding claims 1 and 13, the following claim limitations are directed to one of an abstract idea, mental process or steps of mere data gathering without significantly more;
“A data creation apparatus that creates training data for performing machine learning from a plurality of pieces of image data in which accessory information is recorded, the data creation apparatus comprising: (Step of mere data gathering in which a human could reasonably collect a set of images, and as a mental process determine the accessory information by looking at the image)
a processor, wherein the processor is configured to execute:
setting processing of receiving and setting a first condition for selecting first selection image data based on the accessory information from the plurality of pieces of image data; (abstract idea or mental process in which a human could determine or be sent a condition or conditions used to select images by looking at the image)
selection processing of selecting the first selection image data in which the accessory information conforming to the first condition is recorded from the plurality of pieces of image data; (Mental process in which the image can be selected and the data can be manually recorded by a human)
suggestion processing of automatically setting a second condition for selecting second selection image data based on the accessory information from non-selection image data that does not conform to the first condition among the plurality of pieces of image data and suggesting the second condition to the user; (Abstract idea or Mental process in which the image can be selected and the data can be manually recorded by a human)
and creation processing of creating the training data based on the first selection image data in a case where the user has not employed the second condition (Step of mere data gathering in which a human could select a set of images to train a model and determine by looking at the images which conditions apply)
and creating the training data based on the first selection image data and on the second selection image data in a case where the user has employed the second condition. (Step of mere data gathering in which a human could select a set of images to train a model and determine by looking at the images which conditions apply)
and wherein the plurality of pieces of image data, the training data, the first selection image data, the non-selection image data and the second selection image data are digital data. (Step of mere data gathering)”
The above limitations are steps which could practically be performed as a mental process or step of mere data gathering performed by a human under step 2A prong 1 (MPEP 2106). Under step 2A prong 2, the claim recites the additional elements of “A data creation apparatus” and “a processor”, which fail to translate the steps into practical application or amount to significantly more.
Dependent claims 2-12, and 14-20 follow the same logic and do not limitations that that further translate the claims into practical application or amount to significantly more.
Regarding claim 2, claim 2 recites the limitations; “wherein the processor is configured to, in a case where the user has employed the second condition, execute second selection processing of selecting the second selection image data in which the accessory information conforming to the second condition is recorded from the non-selection image data.”
The above limitations are recited with a high level of generality and are drawn to a mental process or steps of mere data gathering without significantly more. The claim recites the additional element of “a processor”, which fails to translate the steps into practical application or amount to significantly more.
Regarding claims 3 and 16, claims 3 and 16 recite the limitations; “wherein the processor is configured to execute the machine learning based on an employment result of whether or not the user has employed the second condition, and in the suggestion processing, the second condition is suggested based on the machine learning of the employment result. (Step of mere data gathering where a human can look at an image, see if the condition is met, and run a program)”
The above limitations are recited with a high level of generality and are drawn to a mental process or steps of mere data gathering without significantly more. The claim recites the additional element of “a processor”, which fails to translate the steps into practical application or amount to significantly more.
Regarding claim 4 and 17, claims 4 and 17 recite the limitations; “wherein the processor is configured to execute notification processing of providing notification of information related to the second condition. (step of mere data gathering where information is gathered and sent via message about a determined condition)”
The above limitations are recited with a high level of generality and are drawn to a mental process or steps of mere data gathering without significantly more. The claim recites the additional element of “a processor”, which fails to translate the steps into practical application or amount to significantly more.
Regarding claim 5 and 18, claims 5 and 18 recite the limitations; “wherein the first condition and the second condition include an item related to the accessory information and content related to the item. (Mental process of looking at an image and determining conditions related to observed information)”
The above limitations are recited with a high level of generality and are drawn to a mental process or steps of mere data gathering without significantly more.
Regarding claim 6, claim 6 recites the limitations; “wherein the first condition and the second condition have the same item and different content.”
The above limitations are recited with a high level of generality and are drawn to a mental process or steps of mere data gathering without significantly more.
Regarding claim 7, claim 7 recites the limitations; “wherein the item is availability information related to use of image data as the training data.”
The above limitations are recited with a high level of generality and are drawn to a mental process or steps of mere data gathering without significantly more.
Regarding claim 8, claim 8 recites the limitations; “wherein the availability information includes at least one of user information related to use of the image data, restriction information related to restriction of an aim of use of the image data, or copyright holder information of the image data.”
The above limitations are recited with a high level of generality and are drawn to a mental process or steps of mere data gathering without significantly more.
Regarding claim 9, claim 9 recites the limitations; “wherein the content of the first condition is content of selecting image data based on the availability information, and the content of the second condition is content of selecting image data in which the availability information is not recorded or image data in which the availability information indicating that there is no restriction on use of the image data is recorded.”
The above limitations are recited with a high level of generality and are drawn to a mental process or steps of mere data gathering without significantly more.
Regarding claim 10, claim 10 recites the limitations; “wherein the item is an item related to a type of a subject captured in an image based on image data.”
The above limitations are recited with a high level of generality and are drawn to a mental process or steps of mere data gathering without significantly more.
Regarding claims 11 and 19, claims 11 and 19 recite the limitations; “wherein the first condition is a condition related to a subject captured in an image based on image data, (Mental process of determining a condition based on looking at an image and seeing what the image is of)
and the suggestion processing is processing of suggesting the second condition based on a feature of the subject of the first condition. (Mental process of determining a condition based on looking at an image and seeing what the image is of)”
The above limitations are recited with a high level of generality and are drawn to a mental process or steps of mere data gathering without significantly more.
Regarding claim 12 and claim 20, claims 12 and 20 recite the limitations; “wherein the suggestion processing is processing of suggesting the second condition of a higher-level concept obtained by making the first condition more abstract. (Mental process in which a human could generate a suggest based on a first condition)”
The above limitations are recited with a high level of generality and are drawn to a mental process or steps of mere data gathering without significantly more.
Regarding claim 15, claim 15 recites the limitations; “A non-transitory computer readable recording medium on which a program causing a computer to execute each processing of the data creation apparatus according to claim 1 is recorded. (Mere data gathering in which a human can manually execute a program on a computer)”
The above limitations are recited with a high level of generality and are drawn to a mental process or steps of mere data gathering without significantly more. The claim recites the additional elements of “computer readable recording medium”, “ a computer” and “a program”, which fail to translate the steps into practical application or amount to significantly more.
Regarding claims 21 and 22, the claims recite the limitations; “The data creation apparatus according to wherein the processor is configured to not select the second selection image data in which the accessory information conforming to the second condition is recorded from the non-selection image data and to create the training data based on the first selection image data in a case where the user has not employed the second condition (Abstract idea which could be achieved by a human assessing the conditions and determining which data to use),
and wherein the processor is configured to select the second selection image data in which the accessory information conforming to the second condition is recorded from the non-selection image data and to create the training data based on the first selection image data and on the second selection image data in a case where the user has employed the second condition (Abstract idea which could be achieved by a human assessing the conditions and determining which data to use).
The above limitations are recited with a high level of generality and are drawn to a mental process or steps of mere data gathering without significantly more. The claims recite the additional element of a processor, which fails to translate the steps into practical application or amount to significantly more.
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 1-13 and 15-22 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Haneda (US 20190197359 A1).
Regarding claim 1 Haneda discloses; A data creation apparatus that creates training data for performing machine learning from a plurality of pieces of image data in which accessory information is recorded (Haneda, abstract, the system is a learning device that takes in photographs and generates training data to train a model), the data creation apparatus comprising:
a processor, wherein the processor is configured to execute (Haneda, [0046] the system has a processor which receives and processes the images, as well as executes machine learning and training functions):
setting processing of receiving and setting a first condition for selecting first selection image data based on the accessory information from the plurality of pieces of image data (Haneda, [0125] the system has a setting condition section where information such as shooting information, focal length, exposure, and conditions of the accessory which is analogous to the accessory information described in figure 6 of the applicant’s specification, where the accessory information is defined as information about imaging condition, subject info/object info, image quality, availability, history and purpose, further, [0129] the system acquires accessory information from the camera which determines and sets the conditions);
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(Haneda, [0125])
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selection processing of selecting the first selection image data in which the accessory information conforming to the first condition is recorded from the plurality of pieces of image data (Haneda, [0030] the interference engine matches an image’s setting information with a request from a user (first condition), where the setting information contains information about the image analogous to the accessory information);
suggestion processing of automatically setting a second condition for selecting second selection image data based on the accessory information from non-selection image data that does not conform to the first condition among the plurality of pieces of image data and suggesting the second condition to a user (Haneda, [0142]-[0144] if the condition is not a match for certain images, a set of conditions are generated and presented as advice (second condition is suggested) for image shooting/capture conditions which would result in the image matching the user then request (first condition));
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(Haneda, [0142]-[0143])
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and creation processing of creating the training data based on the first selection image data in a case where the user has not employed the second condition (Haneda, [0048] in a situation where the image data has been determined as “like” or “correct (meeting the first request or condition) that data is used as training, if the first condition is satisfied the user will not have employed the second condition)
and creating the training data based on the first selection image data and on the second selection image data in a case where the user has employed the second condition (Haneda, [0048] images not evaluated as being correct may also be input as training data (first and second conditions) in and event where the second set of images is similar to the first set, but not an exact match (the second condition is employed as described in [0142]-[0144] above)),
wherein the plurality of pieces of image data, the training data, the first selection image data, the non-selection image data and the second selection image data are digital data (Haneda,[0115] the system has a communication section where the images are saved and uploaded/transmitted, [0117] the images are stored on a computing device and divided into multiple datasets, [0097] training data images are transmitted to different layers of the machine learning model, indicating that all the images and the training data are digital data).
Regarding claim 2 Haneda discloses; The data creation apparatus according to claim 1, wherein the processor is configured to,
in a case where the user has employed the second condition, execute second selection processing of selecting the second selection image data in which the accessory information conforming to the second condition is recorded from the non-selection image data (Haneda, [0144] if the condition is not a match for certain images, a set of conditions are generated and presented as advice (second condition is suggested) for image shooting/capture conditions which would result in the image matching the user theme request (first condition), additionally, in a situation where the images the user has selected are not likely to be liked by the target audience (first condition not met) a set of images which would likely meet the criteria of being liked will be selected based on the inference information (second condition is used to select images which conform to the condition)).
Regarding claim 3 Haneda discloses; The data creation apparatus according to claim 1, wherein the processor is configured to execute the machine learning based on an employment result of whether or not the user has employed the second condition, and in the suggestion processing, the second condition is suggested based on the machine learning of the employment result (Haneda, [0144] if the condition is not a match for certain images, a set of conditions are generated and presented as advice (second condition is suggested) for image shooting/capture conditions which would result in the image matching the user theme request (first condition), [0146] and [0147] after the camera shooting/image capture advice is suggested (second condition for image capture), it can be determined whether or not the advice has been adopted (second suggestion has been employed) and if the image may be captured, further, as described in [0147] the advice suggested (second condition suggesting) that is not taken into account (employment of the second suggestion) may be taken into consideration by the machine learning system as advice that is of no use to the user, and may not be suggested in the future).
Regarding claim 4 Haneda discloses; The data creation apparatus according to claim 1, wherein the processor is configured to execute notification processing of providing notification of information related to the second condition (Haneda, [0144] the user is displayed with the suggestion/ advice information (second condition suggestion)).
Regarding claim 5 Haneda discloses; The data creation apparatus according to claim 1, wherein the first condition and the second condition include an item related to the accessory information and content related to the item (Haneda, [0125] the system has a setting condition section where information such as shooting information, focal length, exposure, and conditions of the accessory which is analogous to the accessory information described in figure 6 of the applicant’s specification, where the accessory information is defined as information about imaging condition, subject info/object info, image quality, availability, history and purpose, and [0144] if the condition is not a match for certain images, a set of conditions are generated and presented as advice (second condition is suggested) for image shooting/capture conditions which would result in the image matching the user theme request (first condition)).
Regarding claim 6 Haneda discloses; The data creation apparatus according to claim 5, wherein the first condition and the second condition have the same item and different content (Haneda, [0144] the user may be trying to capture optimal images of an item, if the images captured will not receive the determination of “good” or “liked” by the target audience (first condition) the system generates advice for the user (second condition) to capture the same item in a way which likely be liked by the target audience, both conditions pertain to better tailoring the image to a preferred theme (item)).
Regarding claim 7 Haneda discloses; The data creation apparatus according to claim 6, wherein the item is availability information related to use of image data as the training data (Haneda, [0144] the user may be trying to capture optimal images of an item, if the images captured will not receive the determination of “good” or “liked” by the target audience (first condition) the system generates advice for the user (second condition) to capture the same item in a way which likely be liked by the target audience, both conditions pertain to better tailoring the image to a preferred theme (item), where per [0021] the theme is the object being captured, and the target audience, and requirements for the photograph, [0024] the theme (item/availability information) is used to help train the inference model to infer types of images that fit that theme (Use of the image data in training)).
Regarding claim 8 Haneda discloses; The data creation apparatus according to claim 7, wherein the availability information includes at least one of user information related to use of the image data, restriction information related to restriction of an aim of use of the image data, or copyright holder information of the image data (Haneda, [0144] the user may be trying to capture optimal images of an item, if the images captured will not receive the determination of “good” or “liked” by the target audience (first condition) the system generates advice for the user (second condition) to capture the same item in a way which likely be liked by the target audience, both conditions pertain to better tailoring the image to a preferred theme (item), where per [0021] the theme is the object being captured, and the target audience, and requirements for the photograph, [0024] the theme (item/availability information) is used to help train the inference model to infer types of images that fit that theme (Use of the image data in training), where the disclosure of the theme to the user constitutes user information related to use of the image data, further [0022] notes that the user is present with a menu of the demographic information for the photo and theme, which further provides information to the user on the use of the image data and restriction of use because the user’s data is being restricted to a certain demographic target audience).
Regarding claim 9 Haneda discloses; The data creation apparatus according to claim 7, wherein the content of the first condition is content of selecting image data based on the availability information (Haneda, [0030] the interference engine matches an image’s setting information with a request from a user (first condition), where the setting information contains information about the image analogous to the accessory information, [0026] where the setting condition may include a theme, where the theme (item/availability information) is used to help train the inference model to infer types of images that fit that theme),
and the content of the second condition is content of selecting image data in which the availability information is not recorded or image data in which the availability information indicating that there is no restriction on use of the image data is recorded (Haneda, [0049] scene/theme (item) determination may be omitted depending the user’s preference (second condition), where the scene/theme is the limiting item/availability information which is being omitted, indicating no restriction on the data).
Regarding claim 10 Haneda discloses; The data creation apparatus according to claim 6, wherein the item is an item related to a type of a subject captured in an image based on image data (Haneda, [0037] the scene/theme (item) is the subject of the image, for example the them may be “Paris” where the image is of people and scenery falling into that category).
Regarding claim 11 Haneda discloses; The data creation apparatus according to claim 1, wherein the first condition is a condition related to a subject captured in an image based on image data (Haneda, [0030] the interference engine matches an image’s setting information with a request from a user (first condition), where the setting information contains information about the image analogous to the accessory information, [0026] where the setting condition may include a theme, where the theme (item/availability information/subject captured) is used to help train the inference model to infer types of images that fit that theme),
and the suggestion processing is processing of suggesting the second condition based on a feature of the subject of the first condition (Haneda, [0144] the user may be trying to capture optimal images of an item, if the images captured will not receive the determination of “good” or “liked” by the target audience (first condition) the system generates advice for the user (second condition) to capture the same item in a way which likely be liked by the target audience, both conditions pertain to better tailoring the image to a preferred theme (item), [0170] the advice generated may be based on a feature of the item).
Regarding claim 12 Haneda discloses; The data creation apparatus according to claim 1, wherein the suggestion processing is processing of suggesting the second condition of a higher-level concept obtained by making the first condition more abstract (Haneda, [0056] the advice/guidance information (second condition/suggestion) is advice for capturing images that will be evaluated highly based upon the theme/user request (first condition), this can be general advice for the adjustment of image capturing conditions, or advice on whether it is even possible for the image to receive good evaluations for fitting the theme/request specifications).
Regarding claim 13 Haneda discloses; A data creation method of creating training data for performing machine learning from a plurality of pieces of image data in which accessory information is recorded, the data creation method comprising (Haneda, abstract, the system is a learning device that takes in photographs and generates training data to train a model):
receiving and setting a first condition for selecting first selection image data based on the accessory information from the plurality of pieces of image data (Haneda, [0125] the system has a setting condition section where information such as shooting information, focal length, exposure, and conditions of the accessory which is analogous to the accessory information described in figure 6 of the applicant’s specification, where the accessory information is defined as information about imaging condition, subject info/object info, image quality, availability, history and purpose, further, [0129] the system acquires accessory information from the camera which determines and sets the conditions);
(Haneda, [0030] the interference engine matches an image’s setting information with a request from a user (first condition), where the setting information contains information about the image analogous to the accessory information);
automatically setting a second condition for selecting second selection image data based on the accessory information from non-selection image data that does not conform to the first condition among the plurality of pieces of image data and suggesting the second condition to a user (Haneda, [0142]-[0144] if the condition is not a match for certain images, a set of conditions are generated and presented as advice (second condition is suggested) for image shooting/capture conditions which would result in the image matching the user then request (first condition));
and the user has not employed the second condition (Haneda, [0048] in a situation where the image data has been determined as “like” or “correct (meeting the first request or condition) that data is used as training, if the first condition is satisfied the user will not have employed the second condition)
and creating the training data based on the first selection image data and on the second selection image data in a case where the user has employed the second condition (Haneda, [0048] images not evaluated as being correct may also be input as training data (first and second conditions) in and event where the second set of images is similar to the first set, but not an exact match (the second condition is employed as described in [0142]-[0144] above)),
wherein the plurality of pieces of image data, the training data, the first selection image data, the non-selection image data and the second selection image data are digital data (Haneda,[0115] the system has a communication section where the images are saved and uploaded/transmitted, [0117] the images are stored on a computing device and divided into multiple datasets, [0097] training data images are transmitted to different layers of the machine learning model, indicating that all the images and the training data are digital data).
Regarding claim 15 Haneda discloses; A non-transitory computer readable recording medium on which a program causing a computer to execute each processing of the data creation apparatus according to claim 1 is recorded (Haneda, [0196] the system has a non-transitory computer readable medium causing a computer to execute the method).
Regarding claim 16 Haneda discloses; The data creation apparatus according to claim 2, wherein the processor is configured to execute the machine learning based on an employment result of whether or not the user has employed the second condition, and in the suggestion processing, the second condition is suggested based on the machine learning of the employment result (Haneda, [0144] if the condition is not a match for certain images, a set of conditions are generated and presented as advice (second condition is suggested) for image shooting/capture conditions which would result in the image matching the user theme request (first condition), [0146] and [0147] after the camera shooting/image capture advice is suggested (second condition for image capture), it can be determined whether or not the advice has been adopted (second suggestion has been employed) and if the image may be captured, further, as described in [0147] the advice suggested (second condition suggesting) that is not taken into account (employment of the second suggestion) may be taken into consideration by the machine learning system as advice that is of no use to the user, and may not be suggested in the future).
Regarding claim 17 Haneda discloses; The data creation apparatus according to claim 2, wherein the processor is configured to execute notification processing of providing notification of information related to the second condition (Haneda, [0144] the user is displayed with the suggestion/ advice information (second condition suggestion)).
Regarding claim 18 Haneda discloses; The data creation apparatus according to claim 2, wherein the first condition and the second condition include an item related to the accessory information and content related to the item (Haneda, [0125] the system has a setting condition section where information such as shooting information, focal length, exposure, and conditions of the accessory which is analogous to the accessory information described in figure 6 of the applicant’s specification, where the accessory information is defined as information about imaging condition, subject info/object info, image quality, availability, history and purpose, and [0144] if the condition is not a match for certain images, a set of conditions are generated and presented as advice (second condition is suggested) for image shooting/capture conditions which would result in the image matching the user theme request (first condition)).
Regarding claim 19 Haneda discloses; The data creation apparatus according to claim 2, wherein the first condition is a condition related to a subject captured in an image based on image data (Haneda, [0030] the interference engine matches an image’s setting information with a request from a user (first condition), where the setting information contains information about the image analogous to the accessory information, [0026] where the setting condition may include a theme, where the theme (item/availability information/subject captured) is used to help train the inference model to infer types of images that fit that theme),
and the suggestion processing is processing of suggesting the second condition based on a feature of the subject of the first condition (Haneda, [0144] the user may be trying to capture optimal images of an item, if the images captured will not receive the determination of “good” or “liked” by the target audience (first condition) the system generates advice for the user (second condition) to capture the same item in a way which likely be liked by the target audience, both conditions pertain to better tailoring the image to a preferred theme (item), [0170] the advice generated may be based on a feature of the item).
Regarding claim 20 Haneda discloses; The data creation apparatus according to claim 2, wherein the suggestion processing is processing of suggesting the second condition of a higher-level concept obtained by making the first condition more abstract (Haneda, [0056] the advice/guidance information (second condition/suggestion) is advice for capturing images that will be evaluated highly based upon the theme/user request (first condition), this can be general advice for the adjustment of image capturing conditions, or advice on whether it is even possible for the image to receive good evaluations for fitting the theme/request specifications).
Regarding claim 21, Haneda discloses; The data creation apparatus according to wherein the processor is configured to not select the second selection image data in which the accessory information conforming to the second condition is recorded from the non-selection image data (Haneda, [0144] if the camera conditions are not a match for certain images, a new set of camera conditions are generated and presented to the user as advice (second condition), [0052] the sets of images are selectable based on whether they match a specific profile, indicating that sets of images may not be selected if they meet a certain condition)
and to create the training data based on the first selection image data in a case where the user has not employed the second condition (Haneda, [0052] the training data is selectable based on cases, and a first training data is selected),
and wherein the processor is configured to select the second selection image data in which the accessory information conforming to the second condition is recorded from the non-selection image data (Haneda, [0144] if the camera conditions are not a match for certain images, a new set of camera conditions are generated and presented to the user as advice (second condition), [0052] the sets of images are selectable based on whether they match a specific profile)
and to create the training data based on the first selection image data and on the second selection image data in a case where the user has employed the second condition (Haneda, [0051] first and second training data both may be used to train the model).
Regarding claim 22, Haneda discloses; The data creation method according to wherein the method does not select the second selection image data in which the accessory information conforming to the second condition is recorded from the non-selection image data (Haneda, [0144] if the camera conditions are not a match for certain images, a new set of camera conditions are generated and presented to the user as advice (second condition), [0052] the sets of images are selectable based on whether they match a specific profile, indicating that sets of images may not be selected if they meet a certain condition)
and creates the training data based on the first selection image data in a case where the user has not employed the second condition (Haneda, [0052] the training data is selectable based on cases, and a first training data is selected),
and wherein the method selects the second selection image data in which the accessory information conforming to the second condition is recorded from the non-selection image data (Haneda, [0144] if the camera conditions are not a match for certain images, a new set of camera conditions are generated and presented to the user as advice (second condition), [0052] the sets of images are selectable based on whether they match a specific profile)
and creates the training data based on the first selection image data and on the second selection image data in a case where the user has employed the second condition (Haneda, [0051] first and second training data both may be used to train the model).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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 of Cited form.
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/J.M.E./Examiner, Art Unit 2666
/EMILY C TERRELL/Supervisory Patent Examiner, Art Unit 2666
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