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 .
Amendment
Applicant submitted amendments on 05/04/2026. The Examiner acknowledges the amendment and has reviewed the claims accordingly.
Priority
Receipt is acknowledged that application is a PCT/JP2022/023225. Priority to Japan JP2021-125772 with a priority date of 07/30/2021 is acknowledged under 35 USC 119(e) and 37 CFR 1.78. Copies of certified papers required by 37 CFR 1.55 have been retrieved.
Information Disclosure Statement
The IDS dated 04/15/2024, 04/16/2026, and 05/04/2026 have been considered and placed in the application file.
Applicant Arguments:
In regards to the argument on Argument 1, Applicant/s state/s “Applicant traverses the provisional rejection for the reasons advanced in detail below. Applicant submits herewith a Terminal Disclaimer over Application No. 18/420,705. Accordingly, Applicant respectfully requests reconsideration and withdrawal of this provisional rejection.” (See Remarks Pg 6, paragraph 2-3). Therefore the provisional double patenting rejection should be withdrawn.
In regards to the argument on Argument 2, Applicant/s state/s “Amended claim 16 recites "a non-transitory computer-readable recording medium ... " In view of the above, Applicant submits that the claims, as presented herein, fully satisfy the requirements of 35 U.S.C. § 101.” (See Remarks Pg 6, paragraph 5). Therefore U.S.C 101 rejection on Claim 16 should be withdrawn.
In regards to the argument on Argument 3, Applicant/s state/s “Applicant contends that the request for relearning of Haneda that allegedly corresponds to the claimed "additional condition" and the time stamps of Haneda that allegedly correspond to the claimed "additional image data" are completely different from the claimed features. Even if the time stamps of Haneda could somehow be considered to correspond to the claimed "additional image data," Applicant contends that Haneda does not disclose how to create training data from time stamps and that a person skilled in the art would not easily arrive at the claimed features based on the teachings of Haneda.” (See Remarks Pg 9, paragraph 1). Therefore U.S.C 103 rejection on Claim 1, 14, 15, and 12 should be withdrawn.
In regards to the argument on Argument 4, Applicant/s state/s “the cited references, taken alone or in combination, fail to disclose, suggest, or otherwise render obvious the features recited in the independent claims. Thus, Applicant submits that the independent claims are allowable. Applicant further submits that the dependent claims are allowable at least by virtue of their dependence on the independent claims, and for the additional features recited.” (See Remarks Pg 9, paragraph 3). Therefore U.S.C 103 rejection on Claims 1-16 should be withdrawn.
Examiner’s Responses:
In response to Argument 1, Applicant’s arguments, see Remarks, filed 05/04/2026, with respect to the provisional double patenting rejection have been considered and are persuasive. Therefore, the rejection has been withdrawn due to the filing of the terminal disclaimer by the applicant.
In response to Argument 2, Applicant’s arguments, see Remarks, filed 05/04/2026, with respect to the U.S.C 101 rejection of Claim 16 have been considered and are persuasive. Therefore, the U.S.C 101 rejection has been withdrawn due to amendments.
In response to Argument 3, Applicant’s arguments, see Remarks, filed 05/04/2026, with respect to the U.S.C 103 rejections of Claim 1, 12, 14, and 15 have been considered but are moot in view of new ground(s) of rejection caused by the amendments. A new ground(s) of rejection is made for claims 1- 4, 6, and 8-16 under 35 U.S.C. 103 in view of Haneda et al. (US Patent Publication 20200242154 A1 hereafter referred to as Haneda) in view of Winn et al (WO Patent Publication WO 2019126723 A1 hereafter referred to as Winn).
The Examiner finds that Haneda teaches on the amendment claim language “with respect to the accessory Information” and “the additional image data being data” in the amended independent claims 1, 14, and 15.
Specifically, Haneda teaches accessory information about the images in ¶0085 and Fig 3A 100, the examiner is interpreting that accessory information can be interpreted as metadata as there is no special definition of accessory information present in the claims. Haneda teaches additional image data about the images in ¶0006 and Fig 14, the examiner is interpreting that additional image data can be interpreted as metadata as there is no special definition of additional image data present in the claims. Applicant argues that “"additional condition" and the time stamps of Haneda that allegedly correspond to the claimed "additional image data" are completely different from the claimed features. Even if the time stamps of Haneda could somehow be considered to correspond to the claimed "additional image data," Applicant contends that Haneda does not disclose how to create training data from time stamps and that a person skilled in the art would not easily arrive at the claimed features based on the teachings of Haneda”. However, we determine claim scope not solely on the basis of claim language, but also on giving claims their broadest reasonable construction in light of the specification as it would be interpreted by one of ordinary skill in the art. In re Am. Acad. of Sci. Tech. Ctr., 367 F.3d 1359, 1364 (Fed. Cir. 2004). See also Superguide Corp. v. DirecTV Enterprises, Inc., 358 F.3d 870, 875 (Fed. Cir. 2004) (“Though understanding the claim language may be aided by explanations contained in the written description, it is important not to import into a claim limitations that are not part of the claim.”). The Examiner interprets that under broadest reasonable interpretation “additional image data” has no special definition in the claims, and therefore can be interpreted as any data about the image, therefore this can be interpreted as a time stamp, or really any other characteristic of the image that is recorded, and based on the cited references this can be time stamps. Therefore, the Examiner interprets that Haneda teaches the main concept of using metadata to select images, the additional details of the functions of the main concepts as stated above by the applicant in the amendments is taught by Winn in the details of the rejection below. The Examiner will maintain prior art Cao and details of the rejection are below.
In response to Argument 4, Applicant’s arguments, see Remarks, filed 05/04/2026, with respect to the U.S.C 103 rejections of Claims 1, 12, 14, and 15 and their dependent claims have been considered but are moot in view of new ground(s) of rejection caused by the amendments. A new ground(s) of rejection is made for claims 5-7 under 35 U.S.C. 103 in view of Haneda et al. (US Patent Publication 20200242154 A1 hereafter referred to as Haneda) in view of Winn et al (WO Patent Publication WO 2019126723 A1 hereafter referred to as Winn) in further view of Karia et al (US Patent Publication 2020/0364358 A1 hereafter referred to as Karia).
Claim Interpretation
The claims in this application are given their broadest reasonable interpretation using the
plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification.
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).
Claim 1 recite “or ” then listing “first information related to the machine learning or to second information related to a creator of the image data, a creator of the accessory information, or a right holder of the image data”. Since “and/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.
Claim 1 recite “or ” then listing “the first information or the second information satisfying the setting condition is recorded;”. Since “and/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.
Claim 10 recite “or ” then listing “first setting condition related to the first information or to the second information and any second setting condition”. Since “and/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.
Claim 11 recite “or ” then listing “image processing performed with respect to the image by the apparatus, or an imaging environment of the image”. Since “and/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.
Claim 14 recite “or ” then listing “first information related to the machine learning or to second information related to a creator of the image data, a creator of the accessory information, or a right holder of the image data”. Since “and/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.
Claim 15 recite “or ” then listing “first information related to the machine learning or to second information related to a creator of the image data, a creator of the accessory information, or a right holder of the image data”. Since “and/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.
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.
Claims 1- 4, 6, and 8-16 are rejected under 35 U.S.C. 103 as unpatentable over Haneda et al. (US Patent Publication 20200242154 A1 hereafter referred to as Haneda) in view of Winn et al (WO Patent Publication WO 2019126723 A1 hereafter referred to as Winn).
Regarding Claim 1, Haneda teaches a data creation apparatus (Haneda ¶0005, disclose an image file generation device) that creates training data (Haneda ¶0004 and Fig 13 discloses creating training data from image data) used in machine learning from image data (Haneda ¶0007, Fig 3A and 3B discloses the training data being associated with image data to create an interference model) in which accessory information is recorded (Haneda ¶0006 and Fig 14 discloses metadata about the image Fig 12 105 discloses a storage section where metadata is saved), the data creation apparatus (Haneda ¶0005, disclose an image file generation device) being configured to execute:
of setting any setting condition (Haneda ¶0045, ¶0049 and Fig 1B 101b discloses a setting control section) related to first information related to the machine learning (Haneda ¶0049 discloses the setting control section setting interference by the interference engine) or to second information related to a creator of the image data (Haneda ¶0090 discloses administration information related to the creation date of the inference model including the training data), a creator of the accessory information (Haneda ¶0085 and Fig 3A 100 and discloses information about the camera creating metadata based on the captured images), or a right holder of the image data (Haneda ¶0098 discloses copyright and user rights in association with the training data) with respect to a plurality of pieces of the image data (Haneda Fig 12 105a discloses image data made up of multiple test data candidates and image files with metadata) in which the accessory information (Haneda ¶0006 and Fig 14 discloses metadata about the image Fig 12 105 discloses a storage section where metadata is saved) including the first information (Haneda ¶0049 discloses the setting control section setting interference by the interference engine) or the second information is recorded (Haneda ¶0090 discloses administration information related to the creation date of the inference model including the training data);
with respect to the accessory Information (Haneda ¶0085 and Fig 3A 100 and discloses information about the camera creating metadata based on the captured images, the examiner is interpreting that the metadata ),
the additional image data being data (Haneda ¶0006 and Fig 14 discloses metadata about the image)
creation processing of creating the training data (Haneda Fig 2 302 and ¶0068- ¶0070 discloses a creation section where the training data is created) based on the selection image data (Haneda ¶0093, ¶0167, ¶0206, Fig 4 and Fig 19B discloses selection of images and metadata selection) and on additional image data (Haneda ¶0006 and Fig 14 discloses metadata about the image).
Haneda does not explicitly teach first setting processing, second setting processing of setting an additional condition the additional condition being different from the setting condition;
first selecting processing of selecting, from the plurality of pieces of the image data selection image data in which the first information or the second information satisfying the setting condition is recorded;
second selecting processing of selecting additional image data from non-selection image data of which the first information or the second information does not satisfy the setting condition in which the accessory information satisfying the additional condition is recorded.
Winn is in the same field of data permission and image selection in image analysis. Further, Winn teaches first setting processing (Winn ¶004 discloses a first user input indicative of electing an image in a library), second setting processing of setting an additional condition (Winn Fig 2 202, ¶0048 discloses a second processing condition of having user consent to use user data, Winn ¶0013 also discloses the condition could be to delete or archive the images presented, which the examiner is interpreting as an additional condition as it is different from the first condition) the additional condition being different from the setting condition (Winn Fig 2 202, ¶0048 discloses a second processing condition of having user consent to use user data, which is different from the first condition of selecting an image or searching for an image with text as described in ¶0026, Winn ¶0013 also discloses the condition could be to delete or archive the images presented, which the examiner is interpreting as an additional condition as it is different from the first condition);
first selecting processing of selecting, from the plurality of pieces of the image data (Winn ¶0052 discloses the user may select one or more of the plurality of images by selecting a corresponding image element displayed on a screen (e.g., an image thumbnail or other image representation, or a display of the entire image), selection image data in which the first information or the second information satisfying the setting condition is recorded (Winn ¶0026 discloses that the search for images based on user selections is recorded in the storage as additional images from the user's image library that have matching characteristics as the selected images are automatically identified and presented for the user to select, based on satisfying the users query);
second selecting processing of selecting additional image data (Winn ¶0051 discloses images being grouped by data such as time location of facial recognition/tagging) from non-selection image Data (Winn ¶0063 discloses identifying images that were not selected that have similar characteristics as the images that were selected by the user) of which the first information or the second information does not satisfy the setting condition (Winn Fig 2, 202, 206 discloses that if the user does not consent then user data is not used and process is changed), in which the accessory information satisfying the additional condition is recorded (Winn Fig 2 202, ¶0048 discloses a second processing condition of having user consent to use user data and recording whether the user has consenting and how the process changed if the user has not consented).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Haneda by adding a second processing setting of additional image data in the form of image data information as taught by Winn, to make an invention that can identify the user permissions and if the user permissions will affect the selection process based on the satisfied image selection process; thus one of ordinary skilled in the art would be motivated to combine the references since there is a need to reduce or eliminate the need to manually provide text or other complex input to specify search queries to determine images to select. Furthermore, the suggested image selections reduce the time and processing for the display of images and reduce the number of manually-specified searches received to find images. (Winn ¶0027).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Regarding Claim 2, Haneda in view of Winn teaches the data creation apparatus (Haneda ¶0005, disclose an image file generation device) according to claim 1, which is configured to further execute:
acquisition processing of acquiring (Haneda Fig 15 S21 discloses the acquisition of the first training data and ¶0179 discloses acquiring the image with a camera) the plurality of pieces of image data (Haneda Fig 12 105a discloses image data made up of multiple test data candidates and image files with metadata) in which the accessory information (Haneda ¶0006 and Fig 14 discloses metadata about the image Fig 12 105 discloses a storage section where metadata is saved) including the first information (Haneda ¶0049 discloses the setting control section setting interference by the interference engine) is recorded (Haneda ¶0006 and Fig 14 discloses metadata about the image Fig 12 105 discloses a storage section where metadata is saved),
wherein the first information (Haneda ¶0049 discloses the setting control section setting interference by the interference engine) is permission information related to permission to use (Haneda ¶0197, ¶0215, and ¶0226 discloses rights to images and if the images are used in the training data and if the data used is a security concern) and the image data in creating the training data (Haneda ¶0004 and Fig 13 discloses creating training data from image data) in the machine learning (Haneda ¶0007, Fig 3A and 3B discloses the training data being associated with image data to create an interference model). See Claim 1 for rationale, its parent claim.
Regarding Claim 3, Haneda in view of Winn teaches the data creation apparatus (Haneda ¶0005, disclose an image file generation device) according to claim 2,
wherein the permission information (Haneda ¶0197, ¶0215, and ¶0226 discloses rights to images and if the images are used in the training data and if the data used is a security concern) includes information related to a person (Haneda ¶0098 discloses the copyright and portrait rights may be related to the users own personal needs) related to the permission for the image data (Haneda ¶0197, ¶0215, and ¶0226 discloses rights to images and if the images are used in the training data and if the data used is a security concern). See Claim 1 for rationale, its parent claim.
Regarding Claim 4, Haneda in view of Winn teaches the data creation apparatus (Haneda ¶0005, disclose an image file generation device) according to claim 1, which is configured to further execute:
acquisition processing of acquiring (Haneda Fig 15 S21 discloses the acquisition of the first training data and ¶0179 discloses acquiring the image with a camera) the plurality of pieces of image data (Haneda Fig 12 105a discloses image data made up of multiple test data candidates and image files with metadata) in which the accessory information (Haneda ¶0006 and Fig 14 discloses metadata about the image Fig 12 105 discloses a storage section where metadata is saved) including the first information (Haneda ¶0049 discloses the setting control section setting interference by the interference engine) is recorded (Haneda ¶0006 and Fig 14 discloses metadata about the image Fig 12 105 discloses a storage section where metadata is saved),
wherein the first information (Haneda ¶0049 discloses the setting control section setting interference by the interference engine) is history information (Haneda Fig 21 Item 14 and Fig 22 S207 discloses history information) related to a history of use (Haneda ¶0317 discloses history information based off use in a previous model) as the training data (Haneda ¶0004 and Fig 13 discloses creating training data from image data) in machine learning (Haneda ¶0007, Fig 3A and 3B discloses the training data being associated with image data to create an interference model) in the past. See Claim 1 for rationale, its parent claim.
Regarding Claim 6, Haneda in view of Winn teaches the data creation apparatus (Haneda ¶0005, disclose an image file generation device) according to claim 1, which is configured to further execute:
acquisition processing of acquiring (Haneda Fig 15 S21 discloses the acquisition of the first training data and ¶0179 discloses acquiring the image with a camera) the plurality of pieces of image data (Haneda Fig 12 105a discloses image data made up of multiple test data candidates and image files with metadata) in which the accessory information (Haneda ¶0006 and Fig 14 discloses metadata about the image Fig 12 105 discloses a storage section where metadata is saved) including the first information (Haneda ¶0049 discloses the setting control section setting interference by the interference engine) is recorded (Haneda ¶0006 and Fig 14 discloses metadata about the image Fig 12 105 discloses a storage section where metadata is saved),
wherein the first information is purpose information (Haneda ¶0183 and ¶0188 discloses purpose information) related to a purpose of the machine learning (Haneda ¶0188 discloses the annotations including the purpose of the training data in the image). See Claim 1 for rationale, its parent claim.
Regarding Claim 8, Haneda in view of Winn teaches the data creation apparatus (Haneda ¶0005, disclose an image file generation device) according to claim 1, which is configured to further execute:
acquisition processing of acquiring (Haneda Fig 15 S21 discloses the acquisition of the first training data and ¶0179 discloses acquiring the image with a camera) the plurality of pieces of image data (Haneda Fig 12 105a discloses image data made up of multiple test data candidates and image files with metadata) in which the accessory information (Haneda ¶0006 and Fig 14 discloses metadata about the image Fig 12 105 discloses a storage section where metadata is saved) further including learning information (Haneda ¶0280 discloses learning the event that occurs in the image and ¶0009 and Fig 11 discloses a learning section) is recorded (Haneda ¶0006 and Fig 14 discloses metadata about the image Fig 12 105 discloses a storage section where metadata is saved),
wherein the learning information (Haneda ¶0280 discloses learning the event that occurs in the image and ¶0009 and Fig 11 discloses a learning section) is information related to a subject in an image recorded (Haneda ¶0006 and Fig 14 discloses metadata about the image Fig 12 105 discloses a storage section where metadata is saved) in the image data (Haneda ¶0276 and ¶0280 discloses using the annotations for learning the object and the character tics of the object in the image). See Claim 1 for rationale, its parent claim.
Regarding Claim 9, Haneda in view of Winn teaches the data creation apparatus (Haneda ¶0005, disclose an image file generation device) according to claim 8,
wherein in the acquisition processing, (Haneda Fig 15 S21 discloses the acquisition of the first training data and ¶0179 discloses acquiring the image with a camera) the plurality of pieces of image data (Haneda Fig 12 105a discloses image data made up of multiple test data candidates and image files with metadata) in which the accessory information (Haneda ¶0006 and Fig 14 discloses metadata about the image Fig 12 105 discloses a storage section where metadata is saved) including the second information is recorded are acquired (Haneda ¶0090 discloses administration information related to the creation date of the inference model including the training data), and
the second information is creator information (Haneda ¶0090 discloses administration information related to the creation date of the inference model including the training data) related to a creator (Haneda ¶0090 discloses administration information related to the creation date of the inference model including the training data) of the learning information (Haneda ¶0280 discloses learning the event that occurs in the image and ¶0009 and Fig 11 discloses a learning section). See Claim 1 for rationale, its parent claim.
Regarding Claim 10, Haneda in view of Winn teaches the data creation apparatus (Haneda ¶0005, disclose an image file generation device) according to claim 1, which is configured to further execute:
acquisition processing of acquiring (Haneda Fig 15 S21 discloses the acquisition of the first training data and ¶0179 discloses acquiring the image with a camera) the plurality of pieces of image data (Haneda Fig 12 105a discloses image data made up of multiple test data candidates and image files with metadata) in which the accessory information (Haneda ¶0006 and Fig 14 discloses metadata about the image Fig 12 105 discloses a storage section where metadata is saved) including imaging condition information (Haneda ¶0172 discloses the conditions of a photographed object) related to an imaging condition of an image is recorded (Haneda ¶0172 discloses the conditions being the conditions under which the image was taken),
wherein in the first (Winn ¶004 discloses a first user input indicative of electing an image in a library) setting processing (Haneda ¶0045, ¶0049 and Fig 1B 101b discloses a setting control section), each of any first setting condition (Haneda ¶0089 discloses the multiple setting conditions of the camera that captures the images) related to the first information (Haneda ¶0049 discloses the setting control section setting interference by the interference engine) or to the second information and any second setting condition (Haneda ¶0089 discloses the multiple setting conditions of the camera that captures the images including the setting relating to the shooting mode which is related to the image conditions) related to the imaging condition information is set (Haneda ¶0172 discloses the conditions of a photographed object), and
in the creation processing, the training data is created (Haneda Fig 2 302 and ¶0068- ¶0070 discloses a creation section where the training data is created) based on the selection image data (Haneda ¶0093, ¶0167, ¶0206, Fig 4 and Fig 19B discloses selection of images and metadata selection) in which the first information (Haneda ¶0049 discloses the setting control section setting interference by the interference engine) or the second information (Haneda ¶0090 discloses administration information related to the creation date of the inference model including the training data) satisfying the first setting condition (Haneda ¶0089 discloses the multiple setting conditions of the camera that captures the images) and the imaging condition information (Haneda ¶0172 discloses the conditions being the conditions under which the image was taken) satisfying the second setting condition are recorded (Haneda ¶0089 discloses the multiple setting conditions of the camera that captures the images including the setting relating to the shooting mode which is related to the image conditions). See Claim 1 for rationale, its parent claim.
Regarding Claim 11, Haneda in view of Winn teaches the data creation apparatus (Haneda ¶0005, disclose an image file generation device) according to claim 10,
wherein the imaging condition information (Haneda ¶0172 discloses the conditions being the conditions under which the image was taken) is information related to at least one of an apparatus that has captured the image (Haneda ¶0049 discloses a camera capturing the image of the object and focusing on certain objects within the frame including conditions of the image), image processing performed with respect to the image by the apparatus (Haneda ¶0046 and ¶0051 discloses performing image processing on the image), or an imaging environment of the image (Haneda ¶0092, ¶0138, ¶0157, ¶0259 discloses characteristics included in the image environment such as darkness). See Claim 1 for rationale, its parent claim.
Regarding Claim 12, Haneda in view of Winn teaches the data creation apparatus (Haneda ¶0005, disclose an image file generation device) according to claim 1, which is configured to further execute
suggestion processing of suggesting (Winn ¶0013 discloses a suggested action element being displayed on the user interface) the additional condition to a user (Winn Fig 2 202, ¶0048 discloses a second processing condition of having user consent to use user data, Winn ¶0013 also discloses the condition could be to delete or archive the images presented, which the examiner is interpreting as an additional condition as it is different from the first condition). See Claim 1 for rationale, its parent claim.
Regarding Claim 13, Haneda in view of Winn teaches a storage device (Haneda ¶0041, ¶0054 discloses a storage section) that stores a plurality of pieces of image data (Haneda Fig 12 105a discloses image data made up of multiple test data candidates and image files with metadata) to be used for creating the training data (Haneda ¶0007, Fig 3A and 3B discloses the training data being associated with image data to create an interference model) via the data creation apparatus (Haneda ¶0005, disclose an image file generation device) according to claim 1. See Claim 1 for rationale, its parent claim.
Regarding Claim 14, Haneda teaches a data processing system (Haneda Fig 4 and ¶0012 discloses the data processing system of the apparatus) comprising:
a data creation apparatus (Haneda ¶0005, disclose an image file generation device) that creates training data (Haneda ¶0004 and Fig 13 discloses creating training data from image data) from image data (Haneda ¶0007, Fig 3A and 3B discloses the training data being associated with image data to create an interference model) in which accessory information is recorded (Haneda ¶0006 and Fig 14 discloses metadata about the image Fig 12 105 discloses a storage section where metadata is saved); and
a learning apparatus (Haneda ¶0017, ¶0044, and Fig 9 disclose a learning device) that performs machine learning using the training data (Haneda ¶0007, Fig 3A and 3B discloses the training data being associated with image data to create an interference model), the data processing system (Haneda Fig 4 and ¶0012 discloses the data processing system of the apparatus) being configured to execute:
of setting any setting condition (Haneda ¶0045, ¶0049 and Fig 1B 101b discloses a setting control section) related to first information related to the machine learning (Haneda ¶0049 discloses the setting control section setting interference by the interference engine) or to second information related to a creator of the image data (Haneda ¶0090 discloses administration information related to the creation date of the inference model including the training data), a creator of the accessory information (Haneda ¶0085 and Fig 3A 100 and discloses information about the camera creating metadata based on the captured images), or a right holder of the image data (Haneda ¶0098 discloses copyright and user rights in association with the training data) with respect to a plurality of pieces of the image data (Haneda Fig 12 105a discloses image data made up of multiple test data candidates and image files with metadata) in which the accessory information (Haneda ¶0006 and Fig 14 discloses metadata about the image Fig 12 105 discloses a storage section where metadata is saved) including the first information (Haneda ¶0049 discloses the setting control section setting interference by the interference engine) or the second information is recorded (Haneda ¶0090 discloses administration information related to the creation date of the inference model including the training data);
with respect to the accessory Information (Haneda ¶0085 and Fig 3A 100 and discloses information about the camera creating metadata based on the captured images, the examiner is interpreting that the metadata ),
the additional image data being data (Haneda ¶0006 and Fig 14 discloses metadata about the image)
creation processing of creating the training data (Haneda Fig 2 302 and ¶0068- ¶0070 discloses a creation section where the training data is created) based on the selection image data (Haneda ¶0093, ¶0167, ¶0206, Fig 4 and Fig 19B discloses selection of images and metadata selection) ) and on additional image data (Haneda ¶0006 and Fig 14 discloses metadata about the image).
Haneda does not explicitly teach first setting processing, second setting processing of setting an additional condition, the additional condition being different from the setting condition;
first selecting processing of selecting, from the plurality of pieces of the image data, selection image data in which the first information or the second information satisfying the setting condition is recorded;
second selecting processing of selecting additional image data from non-selection image data of which the first information or the second information does not satisfy the setting condition in which the accessory information satisfying the additional condition is recorded.
Winn is in the same field of data permission and image selection in image analysis. Further, Winn teaches first setting processing (Winn ¶004 discloses a first user input indicative of electing an image in a library) second setting processing of setting an additional condition (Winn Fig 2 202, ¶0048 discloses a second processing condition of having user consent to use user data, Winn ¶0013 also discloses the condition could be to delete or archive the images presented, which the examiner is interpreting as an additional condition as it is different from the first condition) the additional condition being different from the setting condition (Winn Fig 2 202, ¶0048 discloses a second processing condition of having user consent to use user data, which is different from the first condition of selecting an image or searching for an image with text as described in ¶0026, Winn ¶0013 also discloses the condition could be to delete or archive the images presented, which the examiner is interpreting as an additional condition as it is different from the first condition);
first selecting processing of selecting, from the plurality of pieces of the image data (Winn ¶0052 discloses the user may select one or more of the plurality of images by selecting a corresponding image element displayed on a screen (e.g., an image thumbnail or other image representation, or a display of the entire image), selection image data in which the first information or the second information satisfying the setting condition is recorded (Winn ¶0026 discloses that the search for images based on user selections is recorded in the storage as additional images from the user's image library that have matching characteristics as the selected images are automatically identified and presented for the user to select, based on satisfying the users query);
second selecting processing of selecting additional image data (Winn ¶0051 discloses images being grouped by data such as time location of facial recognition/tagging) from non-selection image Data (Winn ¶0063 discloses identifying images that were not selected that have similar characteristics as the images that were selected by the user) of which the first information or the second information does not satisfy the setting condition (Winn Fig 2, 202, 206 discloses that if the user does not consent then user data is not used and process is changed), in which the accessory information satisfying the additional condition is recorded(Winn Fig 2 202, ¶0048 discloses a second processing condition of having user consent to use user data and recording whether the user has consenting and how the process changed if the user has not consented).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Haneda by adding a second processing setting of additional image data in the form of additional image information as taught by Winn, to make an invention that can identify the user permissions and if the user permissions will affect the selection process based on the satisfied image selection process; thus one of ordinary skilled in the art would be motivated to combine the references since there is a need to reduce or eliminate the need to manually provide text or other complex input to specify search queries to determine images to select. Furthermore, the suggested image selections reduce the time and processing for the display of images and reduce the number of manually-specified searches received to find images. (Winn ¶0027).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Regarding Claim 15, Haneda teaches a data creation method (Haneda ¶0002, ¶0005, ¶0007 discloses an image file generating method) of creating training data (Haneda ¶0004 and Fig 13 discloses creating training data from image data) used in machine learning from image data (Haneda ¶0007, Fig 3A and 3B discloses the training data being associated with image data to create an interference model) in which accessory information is recorded (Haneda ¶0006 and Fig 14 discloses metadata about the image Fig 12 105 discloses a storage section where metadata is saved), the data creation method (Haneda ¶0002, ¶0005, ¶0007 discloses an image file generating method) comprising:
of setting any setting condition (Haneda ¶0045, ¶0049 and Fig 1B 101b discloses a setting control section) related to first information related to the machine learning (Haneda ¶0049 discloses the setting control section setting interference by the interference engine) or to second information related to a creator of the image data (Haneda ¶0090 discloses administration information related to the creation date of the inference model including the training data), a creator of the accessory information (Haneda ¶0085 and Fig 3A 100 and discloses information about the camera creating metadata based on the captured images), or a right holder of the image data (Haneda ¶0098 discloses copyright and user rights in association with the training data) with respect to a plurality of pieces of the image data (Haneda Fig 12 105a discloses image data made up of multiple test data candidates and image files with metadata) in which the accessory information (Haneda ¶0006 and Fig 14 discloses metadata about the image Fig 12 105 discloses a storage section where metadata is saved) including the first information (Haneda ¶0049 discloses the setting control section setting interference by the interference engine) or the second information is recorded (Haneda ¶0090 discloses administration information related to the creation date of the inference model including the training data);
with respect to the accessory Information (Haneda ¶0085 and Fig 3A 100 and discloses information about the camera creating metadata based on the captured images, the examiner is interpreting that the metadata),
the additional image data being data (Haneda ¶0006 and Fig 14 discloses metadata about the image)
a creation step of creating the training data (Haneda Fig 2 302 and ¶0068- ¶0070 discloses a creation section where the training data is created) based on the selection image data (Haneda ¶0093, ¶0167, ¶0206, Fig 4 and Fig 19B discloses selection of images and metadata selection) and on additional image data (Haneda ¶0006 and Fig 14 discloses metadata about the image).
Haneda does not explicitly teach a first setting step, second setting processing of setting an additional condition the additional condition being different from the setting condition;
first selecting processing of selecting, from the plurality of pieces of the image data, selection image data in which the first information or the second information satisfying the setting condition is recorded,
second selecting processing of selecting additional image data from non-selection image data of which the first information or the second information does not satisfy the setting, in which the accessory information satisfying the additional condition is recorded.
Winn is in the same field of data permission and image selection in image analysis. Further, Winn teaches a first setting step (Winn ¶004 discloses a first user input indicative of electing an image in a library), second setting processing of setting an additional condition (Winn Fig 2 202, ¶0048 discloses a second processing condition of having user consent to use user data, Winn ¶0013 also discloses the condition could be to delete or archive the images presented, which the examiner is interpreting as an additional condition as it is different from the first condition)) the additional condition being different from the setting condition (Winn Fig 2 202, ¶0048 discloses a second processing condition of having user consent to use user data, which is different from the first condition of selecting an image or searching for an image with text as described in ¶0026, Winn ¶0013 also discloses the condition could be to delete or archive the images presented, which the examiner is interpreting as an additional condition as it is different from the first condition);
first selecting processing of selecting, from the plurality of pieces of the image data (Winn ¶0052 discloses the user may select one or more of the plurality of images by selecting a corresponding image element displayed on a screen (e.g., an image thumbnail or other image representation, or a display of the entire image), selection image data in which the first information or the second information satisfying the setting condition is recorded (Winn ¶0026 discloses that the search for images based on user selections is recorded in the storage as additional images from the user's image library that have matching characteristics as the selected images are automatically identified and presented for the user to select, based on satisfying the users query);
second selecting processing of selecting additional image data (Winn ¶0051 discloses images being grouped by data such as time location of facial recognition/tagging) from non-selection image Data (Winn ¶0063 discloses identifying images that were not selected that have similar characteristics as the images that were selected by the user) of which the first information or the second information does not satisfy the setting condition (Winn Fig 2, 202, 206 discloses that if the user does not consent then user data is not used and process is changed), in which the accessory information satisfying the additional condition is recorded(Winn Fig 2 202, ¶0048 discloses a second processing condition of having user consent to use user data and recording whether the user has consenting and how the process changed if the user has not consented).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Haneda by adding a second processing setting of additional image data in the form of additional data information as taught by Winn, to make an invention that can identify the user permissions and if the user permissions will affect the selection process based on the satisfied image selection process; thus one of ordinary skilled in the art would be motivated to combine the references since there is a need to reduce or eliminate the need to manually provide text or other complex input to specify search queries to determine images to select. Furthermore, the suggested image selections reduce the time and processing for the display of images and reduce the number of manually-specified searches received to find images. (Winn ¶0027).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Regarding Claim 16, Haneda in view of Winn teaches a non-transitory computer readable recording medium (Winn ¶0013, ¶0145 discloses a non-transitory computer readable medium) storing program (Haneda ¶0042-¶0045 discloses a program) a causing a computer to function (Haneda ¶0330-¶0331, ¶discloses hardware that generally make up computers executing computer programs) as the data creation apparatus (Haneda ¶0005, disclose an image file generation device) according to claim 1, the program causing the computer to execute (Haneda ¶0330-¶0331, ¶discloses hardware that generally make up computers executing computer programs) each of the setting processing (Haneda ¶0045, ¶0049 and Fig 1B 101b discloses a setting control section) and the creation processing (Haneda Fig 2 302 and ¶0068- ¶0070 discloses a creation section where the training data is created). See Claim 1 for rationale, its parent claim.
Claims 5 and 7 are rejected under 35 U.S.C. 103 as unpatentable over Haneda in view of Winn in further view of Karia et al (US Patent Publication 2020/0364358 A1hereafter referred to as Karia).
Regarding Claim 5, Haneda in view of Winn teaches the data creation apparatus (Haneda ¶0005, disclose an image file generation device) according to claim 4,
wherein the history information (Haneda Fig 21 Item 14 and Fig 22 S207 discloses history information) the training data (Haneda ¶0004 and Fig 13 discloses creating training data from image data)
in the machine learning (Haneda ¶0007, Fig 3A and 3B discloses the training data being associated with image data to create an interference model) using the training data (Haneda ¶0004 and Fig 13 discloses creating training data from image data) created based on the image data (Haneda ¶0007, Fig 3A and 3B discloses the training data being associated with image data to create an interference model).
Haneda in view of Winn does not explicitly disclose information related to whether or not is used as correct answer data.
Karia is in the same field of data permission in image analysis. Further, Karia teaches information related to whether or not (Karia ¶0079-¶0081 discloses using proof of work for the training of algorithm including correct answers) is used as correct answer data (Karia ¶0079-¶0081 discloses using proof of work for the training of algorithm including correct answers).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Haneda in view of Winn by incorporating the owner information and additional of correct answer training for the model as taught by Karia, to make an invention that can identify the owner information and its role in the use in training a model; thus one of ordinary skilled in the art would be motivated to combine the references since there is a need protect users since digitization has revolutionized and increased the amount of data generated about an individual, its holistic management on a secured platform is still a gap to fill. (Kiara ¶0038).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Regarding Claim 7, Haneda in view of Winn teaches the data creation apparatus (Haneda ¶0005, disclose an image file generation device) according to claim 1, which is configured to further execute:
acquisition processing of acquiring (Haneda Fig 15 S21 discloses the acquisition of the first training data and ¶0179 discloses acquiring the image with a camera) the plurality of pieces of image data (Haneda Fig 12 105a discloses image data made up of multiple test data candidates and image files with metadata) in which the accessory information (Haneda ¶0006 and Fig 14 discloses metadata about the image Fig 12 105 discloses a storage section where metadata is saved) including the first information (Haneda ¶0049 discloses the setting control section setting interference by the interference engine) is recorded (Haneda ¶0006 and Fig 14 discloses metadata about the image Fig 12 105 discloses a storage section where metadata is saved), related to a copyright owner of the image data (Haneda ¶0098 discloses the copyright and portrait rights may be related to the users own personal needs).
Haneda in view of Winn does not explicitly disclose wherein the second information is owner information.
Karia is in the same field of data permission in image analysis. Further, Karia teaches wherein the second information is owner information (Karia ¶0055 discloses ownership and access information about assets and user data).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Haneda in view of Winn by incorporating the owner information and additional of correct answer training for the model as taught by Karia, to make an invention that can identify the owner information and its role in the use in training a model; thus one of ordinary skilled in the art would be motivated to combine the references since there is a need protect users since digitization has revolutionized and increased the amount of data generated about an individual, its holistic management on a secured platform is still a gap to fill. (Kiara ¶0038).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RACHEL ROBERTS whose telephone number is (571)272-6413. The examiner can normally be reached Monday- Friday 7:30am- 5:00pm.
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/RACHEL L ROBERTS/Examiner, Art Unit 2674
/ONEAL R MISTRY/Supervisory Patent Examiner, Art Unit 2674
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