DETAILED ACTION
This action is responsive to amendment filed on February 27th, 2026.
Claims 1, 2, and 4~15 are examined.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant’s arguments with respect to claims 1, 2, and 4~15 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1, 3, and 8~13 are rejected under 35 U.S.C. 103 as being unpatentable over Leung et al. hereinafter Leung (U.S 2020/0051334) in view of Faulkner (U.S 2020/0184653).
Regarding Claim 1,
Leung taught an image file comprising:
image data including a subject image and an artificial image [¶22, AR object 220 embedded into the image 100 to generate the image 200];
determining, by the one or more processors, whether the identified data of the plurality of electronic activities satisfies a communication policy [¶55, execute the recommendations generated by one or more of the analysis and recommendation modules 230, 232, and 234] and
supplementary data including related data of the artificial image [¶23, metadata of the image 200 embedded into the image 200 includes contextual environment data of the image 200 (for example, generated by modifying the contextual environment data of the image 100 to reflect the compositing of the AR object 220)].
Leung did not specifically teach wherein the related data includes position data regarding a position of the artificial image.
Faulkner taught wherein the related data includes position data regarding a position of the artificial image [¶50, the model data 109 can include positioning data indicating a location of the virtual object 111. The positioning data can be in any suitable format, which may include the use of Global Positioning Data (GPS) or other coordinate-based data formats to indicate a three-dimensional location of the virtual object. The updates may be sent to the first computing device 101A or other computing devices periodically. The updates may also be sent in response to a particular action, e.g., that the virtual object 111 has been moved or modified; ¶25; metadata 105 comprising one or more images 108 of the objects, and/or model data 109].
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention was made, to combine, Faulker’s teaching of limitations with the teachings of Leung, because the combination would provide enhanced techniques for tracking the movement of real-world objects for improved positioning of virtual objects shared within a collaborative environment [¶3].
Regarding Claim 2,
Leung taught wherein the related data includes type data regarding a type of the artificial image [¶36, AR object may be an entire file that describes the properties and characteristics of the AR object].
Regarding Claim 8,
Leung taught wherein the related data includes processed data regarding processing information of a processed image or a rights relationship of the processed image in a case in which the artificial image is changed into the processed image by a generation apparatus that generates the image file [¶30, Fig. 5].
Regarding Claim 9,
Leung taught wherein the related data includes creation data regarding a creation method or a creation entity of the artificial image [¶36, AR object may be an entire file that describes the properties and characteristics of the AR object].
Regarding Claim 10,
Leung taught further comprising: reference image data including the subject image without including the artificial image [¶18, Fig. 1, image 100 captured by a camera of a device].
Regarding Claim 11,
Leung taught wherein the supplementary data includes viewing location data regarding a viewing location of reference image data including the subject image without including the artificial image [¶19, embed the generated metadata into the image 100].
Regarding Claims 12~13, the claims are similar in scope to claim 1 and therefore, rejected under the same rationale.
Claims 4 and 5 are rejected under 35 U.S.C. 103 as being unpatentable over Leung and Faulkner in view of Morrison et al. hereinafter Morrison (U.S 2012/0191709).
Regarding Claim 4,
Leung-Morrison taught wherein the related data includes rights-relationship data regarding a right of the artificial image [¶68, examples of photo metadata include…administrative metadata (such as licensing or rights usage terms, specific restrictions on using an image, model releases, provenance information, such as the identity of the creator, and contact information for the rights holder or licensor)].
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention was made, to combine, Morrison’s teaching of limitations with the teachings of Leung and Faulkner, because the combination would prevent images to be used without approval from the owner thus improving user privacy.
Regarding Claim 5,
Leung-Morrison taught wherein the related data includes permission/non-permission data regarding machine learning using the artificial image [¶68, examples of photo metadata include…administrative metadata (such as licensing or rights usage terms, specific restrictions on using an image, model releases, provenance information, such as the identity of the creator, and contact information for the rights holder or licensor)]. The rationale to combine as discussed in claim 4, applies here as well.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Leung and Faulkner in view of Morita et al. hereinafter Morita (U.S 2019/0251471).
Regarding Claim 6,
Leung-Faulkner-Morita taught wherein the related data includes accuracy data regarding an accuracy of the artificial image [¶44, the accuracy evaluation section 408 receives a correct value of a classification result of the query image and the image classification result of classification performed by the image classification section 405 from the machine learning control section 410, and calculates image classification accuracy].
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention was made, to combine, Morita’s teaching of limitations with the teachings of Leung and Faulkner, because the combination provides a machine learning device that can reliably and promptly improve image classification accuracy [Morita: ¶21].
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Leung and Faulkner in view of Wang et al. hereinafter Wang (U.S 2020/0311976).
Regarding Claim 7,
Leung-Faulkner-Wang taught wherein the related data includes percentage data regarding a percentage of the artificial image in an image indicated by the image data [¶35, the virtual information quantity 203 may record a percentage of virtual content 110 in the image].
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention was made, to combine, Wang’s teaching of limitations with the teachings of Leung and Faulkner, because the combination enables virtual content information to be efficiently and rapidly searched, allowing the desired image to be retrieved [Wang: ¶65].
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. hereinafter Wang (U.S 2020/0311976) in view of Faulkner (2020/0184653).
Regarding Claim 14,
Wang taught a method comprising:
an acquisition step of acquiring a plurality of image files [¶40, device data 210 includes a plurality of images 211]; and
a search step of performing a search on the plurality of image files, wherein the plurality of image files include an image file including image data including a subject image and an artificial image [¶55, search the virtual content information 200 for an image 211 with specified virtual content 110], and
supplementary data including related data of the artificial image [¶33, Fig. 3A, the virtual content information 200 includes a virtual object number 201, a virtual information quantity 203, virtual object contours 205, a virtual object source 207, and a virtual object timestamp 209].
Wang did not specifically teach wherein the related data includes position data regarding a position of the artificial image.
Faulkner taught wherein the related data includes position data regarding a position of the artificial image [¶50, the model data 109 can include positioning data indicating a location of the virtual object 111. The positioning data can be in any suitable format, which may include the use of Global Positioning Data (GPS) or other coordinate-based data formats to indicate a three-dimensional location of the virtual object. The updates may be sent to the first computing device 101A or other computing devices periodically. The updates may also be sent in response to a particular action, e.g., that the virtual object 111 has been moved or modified; ¶25; metadata 105 comprising one or more images 108 of the objects, and/or model data 109].
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention was made, to combine, Faulker’s teaching of limitations with the teachings of Wang, because the combination would provide enhanced techniques for tracking the movement of real-world objects for improved positioning of virtual objects shared within a collaborative environment [¶3].
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Wang and Faulkner in view of Morita et al. hereinafter Morita (U.S 2019/0251471).
Regarding Claim 15,
Wang-Faulkner-Morita taught further comprising: a generation step of generating training data for machine learning based on the image file searched in the search step using the supplementary data from the plurality of image files [¶46, the machine learning control section 410 performs machine learning using the images and metadata received from the image database 422 and a similar image search result received from the image search section 407 in accordance with the machine learning condition received from the learning condition input section 409, and controls the accuracy evaluation section 408 to calculate image classification accuracy in a case of using a machine learning parameter obtained by this machine learning].
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention was made, to combine, Morita’s teaching of limitations with the teachings of Wang and Faulkner, because the combination provides a machine learning device that can reliably and promptly improve image classification accuracy [Morita: ¶21].
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 HEE SOO KIM whose telephone number is (571)270-3229. The examiner can normally be reached M-F 9AM-5PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Nicholas Taylor can be reached on (571) 272-3889. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/HEE SOO KIM/Primary Examiner, Art Unit 2443