DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Double Patenting
2. The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
3. Claims 1-2 and 4-5 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 and 4-5 of U.S. Patent No. 11,763,481 (patent 481). Although the claims at issue are not identical, they are not patentably distinct from each other because they are similar to each other.
4 The following table show correspondence between claims of present application with claims of patent 481.
Claims of present application
1
2
4
5
Claims of patent 481
1 and 4
1 and 4
5
5
5. The following table shows the correspondence between the limitations of claim 1 of present application and claims 1 and 4 of patent 481.
Claim 1 of present application
Claims 1 and 4 of patent 481
1. A method comprising:
receiving, by one or more processors, a video that depicts an object;
identifying a pose of the object depicted in the video;
1. A method comprising:
receiving, by one or more processors, a video that depicts an object;
identifying a pose of the object depicted in the video;
determining, based on the pose of the object, that the video comprises a mirror reflection of the object;
in response to determining that the video comprises the mirror reflection of the object,
computing a 3D position for placement of a 3D virtual object relative to a 3D reference point of the object;
causing to be displayed the 3D virtual object within the video at the 3D position; and
updating the 3D position of the 3D virtual object in the video as the 3D reference point changes based on 3D movement of the object.
and
applying a neural network to the video that depicts the object to determine that the video comprises a mirror reflection of the object, the neural network trained to establish a relationship between a plurality of training videos depicting objects and a ground-truth mirror reflection classification.
4. The method of claim 1, further comprising:
applying a neural network to the video that depicts the object to determine that the video comprises the mirror reflection, the neural network being trained to establish a relationship between a plurality of training videos depicting objects and a ground-truth mirror reflection classification.
6. Claims 1 and 4-5 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 and 4-5 of U.S. Patent No. 12,217,453 (patent 453). Although the claims at issue are not identical, they are not patentably distinct from each other because they are similar to each other.
7 The following table show correspondence between claims of present application with claims of patent 481.
Claims of present application
1
4
5
Claims of patent 453
1 and 4
5
5
8. The following table shows the correspondence between the limitations of claim 1 of present application and claims 1 and 4 of patent 453.
Claim 1 of present application
Claims 1 and 4 of patent 453
1. A method comprising:
receiving, by one or more processors, a video that depicts an object;
identifying a pose of the object depicted in the video;
1. A method comprising:
receiving, by one or more processors, a video that depicts a person;
determining, based on a pose of the person, that the video depicts a mirror reflection of the person and that the person is capturing the video while standing in front of a mirror; and
in response to determining that the person is capturing the video while standing in front of the mirror, causing display of a three-dimensional (3D) virtual object in the video.
and
applying a neural network to the video that depicts the object to determine that the video comprises a mirror reflection of the object, the neural network trained to establish a relationship between a plurality of training videos depicting objects and a ground-truth mirror reflection classification.
4. The method of claim 1, further comprising:
applying a neural network to the video that depicts the person to determine that the video depicts the mirror reflection, the neural network being trained to establish a relationship between a plurality of training videos depicting objects and a ground-truth mirror reflection classification.
Claim Rejections - 35 USC § 103
9. 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.
10. 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.
11. Claims 1-2, 4, 6, 14-16, 18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Temple et al. (US Patent Application Publication No. 2024/0212272 A1) in view of Wu et al. (US Patent Application Publication No. 2022/0318954 A1) and further in view of Harrises et al. (US Patent Application Publication No. 2019/0371028 A1).
12. Regarding Claim 1, Temple discloses A method comprising: (Abstract reciting “Various implementations disclosed herein include devices, systems, and methods that present virtual content based on detecting a reflection and determining the context associated with a use of the electronic device in the physical environment. …”)
receiving, by one or more processors, (paragraph [0036] reciting “In some implementations, the device 10 has a graphical user interface (GUI), one or more processors, memory and one or more modules, programs or sets of instructions stored in the memory for performing multiple functions. …”) a video (paragraph [0034] reciting “In some implementations, the device 10 includes sensors 60, 65 for acquiring image data of the physical environment. The image data can include light intensity image data and/or depth data. For example, sensor 60 may be a video camera for capturing RGB data, and sensor 65 may be a depth sensor (e.g., a structured light, a time-of-flight, or the like) for capturing depth data.”) that depicts an object; (see FIG. 2 wherein 200 shows view of person 225, who is capturing the mirror reflected image. Person 225 corresponds to the object.)
and applying a neural network to the video that depicts the object to determine that the video comprises a mirror reflection of the object, (paragraph [0061] reciting “In some implementations, detecting the reflection, such as a mirror, is based on an object detection technique using machine learning (e.g., a neural network, decision tree, support vector machine, Bayesian network, or the like).“) While not explicitly disclosed by Temple, Wu discloses the neural network trained to establish a relationship between a plurality of training videos depicting objects and a ground-truth mirror reflection classification. (paragraph [0021] reciting “In some implementations, the instance segmentation 402 includes a two-part image recognition. In a first part, the instance segmentation 402 classifies the image as either having or not having particular types of reflective objects, examples of which include glasses or mirrors. In some examples, this part is implemented as a neural network classifier trained with images containing or not containing such objects and labeled as such. … Again, in some examples, this part is implemented as a neural network classifier trained with images containing or not containing reflections and labeled as such. In the event that the image does not contain a reflection, the technique
400 does not further process the image (does not perform operations 404, 406, or 408).)” The labeling as such corresponds to ground truth mirror reflection classification and the training of the neural network involves obtaining images (a video) with either mirror reflect or without mirror reflection.)
It would have been obvious to a person of ordinary skills in the art before the effective filing date of the claimed invention to modify Temple with Wu so that the neural network in Temple has been trained to identify mirror reflection objects prior. This is an obviously beneficial modification since it allows the neural network in Temple to be trained to detect reflections.
While the combination of Temple and Wu does not explicitly disclose, Harrises discloses identifying a pose of the object depicted in the video; (paragraph [0382] reciting “In some embodiments, the remote data repository 74 may be configured to store a world map of a user's environment, a user's avatar, or a user's face model. The local processing module 70 and/or the remote processing module 72 may identify a mirror in a user's environment and detect a user's pose (such as, e.g., a head pose or a body gesture). The processing modules 70 and 72 may communicate with the remote data repository 74 to create an animation of the user's avatar or to synthesize a user's image based on the information of the user stored in the remote data repository and the detected pose. The processing modules 70 and 72 may further communicate with the display 62 and present the visualizations of another user or the same user in the user's environment (such as overlaying the user's image over a mirror in the user's environment).” The pose of the user in the mirror detected in Temple is determined by processing modules 70/72 in Harrises.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Temple and Wu with Harrises so that a user’s body pose in the mirror can be detected. This is a beneficial modification as it allows Temple to overlay the image with respect to the pose. Temple already discloses overlaying virtual data next to the user’s reflection and Harrises allows this image to align with the pose because Harrises discloses rendering avatars that are aligned with user’s pose. This results in more realistic and more creative manners of overlaying virtual images next to the reflected user’s body in Temple.
13. Regarding Claim 2, Temple further discloses The method of claim 1, further comprising: computing a 3D position for placement of a 3D virtual object relative to a 3D reference point of the object; and updating the 3D position of the 3D virtual object in the video based on 3D movement of the object using the 3D reference point. (see FIG. 3; paragraph [0013] reciting “In some aspects, the context comprises movements of a user of the electronic device with respect to the reflected image, and presenting the virtual content is based on the movements of the user. In some aspects, the context comprises a user interaction with the reflected image, and presenting the virtual content is based on the user interaction with the reflected image.”;
paragraph [0015] reciting “In some aspects, a depth position of the 3D location of the virtual content is the same as a depth of a reflected object detected in the reflected image.”
Thus a virtual content can be rendered based on the depth (3D location) of the object and on the movement of that object.)
14. Regarding Claim 4, Wu further discloses The method of claim 1, further comprising: obtaining a set of training data comprising the plurality of training videos; and applying the neural network to a first training video of the plurality of training videos to estimate a classification that the first training video comprises a mirror reflection of a given object. (paragraph [0021] reciting “In some implementations, the instance segmentation 402 includes a two-part image recognition. In a first part, the instance segmentation 402 classifies the image as either having or not having particular types of reflective objects, examples of which include glasses or mirrors. In some examples, this part is implemented as a neural network classifier trained with images containing or not containing such objects and labeled as such. In the event that instance segmentation 402 determines that one of such objects is included in the region of interest, the instance segmentation 402 proceeds to the second part. In the event that the instance segmentation 402 determines that no such object is included within the region of interest, the instance segmentation 402 does not proceed to the second part and does not further process the input image (i.e., does not continue to operations 404, 406, or 408). In a second part, the instance segmentation 402 classifies the image as either including or not including a reflection. Again, in some examples, this part is implemented as a neural network classifier trained with images containing or not containing reflections and labeled as such. In the event that the image does not contain a reflection, the technique 400 does not further process the image (does not perform operations 404, 406, or 408).”
A video or a plurality of videos are just collections of images. The neural network classifier is trained on these images (or videos) and are labeled as such.)
15. Regarding Claim 6, Temple further discloses The method of claim 1, further comprising: determining that a rear-facing camera of a device is being used to capture the video; (see FIG. 1 wherein cameras 60 and 65 are rear facing cameras used to capture video.) and determining that the video comprises the mirror reflection of the object based on determining that the rear-facing camera is being used to capture the video. (see FIG. 3 wherein the virtual object is placed on the reflected person 325. Therefore, the determination of the reflected person 325 is based on rear camera being used because it’s using a live camera view 300 from rear captured cameras.)
16. Regarding Claim 14, Harrises further discloses The method of claim 1, comprises at least one of retouching or brightening one or more portions of the object depicted in the video or removing a background depicted in the video. (paragraph [0482] reciting “… For example, the display system may be configured to change the perceived lighting of the ambient environment. Lowlight conditions may be achieved by blocking light from reaching the user, while bright conditions may be achieved by augmenting the light reaching the user. Such changes in apparent lighting conditions may be used to, e.g., determine how an evening gown looks in low light or to determine how a bathing suit looks in bright light, respectively. In addition, the display system may be configured to change the appearance of the ambient environment itself. …” It would have been obvious to allow adjustment to ambient light so that the displayed virtual objects and captured reflect user can be brighter for view.)
17. Regarding Claim 15, Temple further discloses The method of claim 1, wherein a 3D virtual object is presented together with the object depicted in the video. (see FIG. 3 wherein virtual 3D object 322 is rendered atop the user reflection 325.)
18. Regarding Claim 16, Temple further discloses The method of claim 1, further comprising: determining that the object has moved from a first 3D position to a second 3D position between a first frame and a second frame of the video; computing a distance and trajectory of movement of the object from the first 3D position to the second 3D position; and moving a 3D virtual object from a third 3D position to a fourth 3D position based on the distance and trajectory of the movement of the object from the first 3D position to the second 3D position. (paragraph [0013] reciting “In some aspects, the context comprises movements of a user of the electronic device with respect to the reflected image, and presenting the virtual content is based on the movements of the user. In some aspects, the context comprises a user interaction with the reflected image, and presenting the virtual content is based on the user interaction with the reflected image.”)
19. Regarding Claim 18, Temple discloses A system comprising: (Abstract reciting “Various implementations disclosed herein include devices, systems, and methods that present virtual content based on detecting a reflection and determining the context associated with a use of the electronic device in the physical environment. …”)
one or more processors configured to perform operations comprising: (paragraph [0036] reciting “In some implementations, the device 10 has a graphical user interface (GUI), one or more processors, memory and one or more modules, programs or sets of instructions stored in the memory for performing multiple functions. …”)
receiving a video (paragraph [0034] reciting “In some implementations, the device 10 includes sensors 60, 65 for acquiring image data of the physical environment. The image data can include light intensity image data and/or depth data. For example, sensor 60 may be a video camera for capturing RGB data, and sensor 65 may be a depth sensor (e.g., a structured light, a time-of-flight, or the like) for capturing depth data.”) that depicts an object; (see FIG. 2 wherein 200 shows view of person 225, who is capturing the mirror reflected image. Person 225 corresponds to the object.)
and applying a neural network to the video that depicts the object to determine that the video comprises a mirror reflection of the object, (paragraph [0061] reciting “In some implementations, detecting the reflection, such as a mirror, is based on an object detection technique using machine learning (e.g., a neural network, decision tree, support vector machine, Bayesian network, or the like).“)
While not explicitly disclosed by Temple, Wu discloses the neural network trained to establish a relationship between a plurality of training videos depicting objects and a ground-truth mirror reflection classification. (paragraph [0021] reciting “In some implementations, the instance segmentation 402 includes a two-part image recognition. In a first part, the instance segmentation 402 classifies the image as either having or not having particular types of reflective objects, examples of which include glasses or mirrors. In some examples, this part is implemented as a neural network classifier trained with images containing or not containing such objects and labeled as such. … Again, in some examples, this part is implemented as a neural network classifier trained with images containing or not containing reflections and labeled as such. In the event that the image does not contain a reflection, the
technique 400 does not further process the image (does not perform operations
404, 406, or 408).)” The labeling as such corresponds to ground truth mirror reflection classification and the training of the neural network involves obtaining images (a video) with either mirror reflect or without mirror reflection.)
It would have been obvious to a person of ordinary skills in the art before the effective filing date of the claimed invention to modify Temple with Wu so that the neural network in Temple has been trained to identify mirror reflection objects prior. This is an obviously beneficial modification since it allows the neural network in Temple to be trained to detect reflections.
While the combination of Temple and Wu does not explicitly disclose, Harrises discloses identifying a pose of the object depicted in the video; (paragraph [0382] reciting “In some embodiments, the remote data repository 74 may be configured to store a world map of a user's environment, a user's avatar, or a user's face model. The local processing module 70 and/or the remote processing module 72 may identify a mirror in a user's environment and detect a user's pose (such as, e.g., a head pose or a body gesture). The processing modules 70 and 72 may communicate with the remote data repository 74 to create an animation of the user's avatar or to synthesize a user's image based on the information of the user stored in the remote data repository and the detected pose. The processing modules 70 and 72 may further communicate with the display 62 and present the visualizations of another user or the same user in the user's environment (such as overlaying the user's image over a mirror in the user's environment).” The pose of the user in the mirror detected in Temple is determined by processing modules 70/72 in Harrises.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Temple and Wu with Harrises so that a user’s body pose in the mirror can be detected. This is a beneficial modification as it allows Temple to overlay the image with respect to the pose. Temple already discloses overlaying virtual data next to the user’s reflection and Harrises allows this image to align with the pose because Harrises discloses rendering avatars that are aligned with user’s pose. This results in more realistic and more creative manners of overlaying virtual images next to the reflected user’s body in Temple.20. Regarding Claim 20, Temple discloses A non-transitory machine-readable storage medium (paragraph [0018] reciting “In accordance with some implementations, a non-transitory computer readable storage medium has stored therein instructions that are computer-executable to perform or cause performance of any of the methods described herein. In accordance with some implementations, a device includes one or more processors, a non-transitory memory, and one or more programs; the one or more programs are stored in the non-transitory memory and configured to be executed by the one or more processors and the one or more programs include instructions for performing or causing performance of any of the methods described herein.”) including an augmented reality system (paragraph [0065] reciting “… Additionally, the XR environment may be presented to the user where virtual reality images maybe overlaid onto the live view (e.g., augmented reality (AR)) of the physical environment. In some implementations, tracking the gaze of the user relative to the display includes tracking a pixel the user's gaze is currently focused upon.”) that includes instructions that, when executed by one or more processors of a machine, (paragraph [0036] reciting “In some implementations, the device 10 has a graphical user interface (GUI), one or more processors, memory and one or more modules, programs or sets of instructions stored in the memory for performing multiple functions. …”) cause the machine to perform operations comprising:
receiving a video (paragraph [0034] reciting “In some implementations, the device 10 includes sensors 60, 65 for acquiring image data of the physical environment. The image data can include light intensity image data and/or depth data. For example, sensor 60 may be a video camera for capturing RGB data, and sensor 65 may be a depth sensor (e.g., a structured light, a time-of-flight, or the like) for capturing depth data.”) that depicts an object; (see FIG. 2 wherein 200 shows view of person 225, who is capturing the mirror reflected image. Person 225 corresponds to the object.)
and applying a neural network to the video that depicts the object to determine that the video comprises a mirror reflection of the object, (paragraph [0061] reciting “In some implementations, detecting the reflection, such as a mirror, is based on an object detection technique using machine learning (e.g., a neural network, decision tree, support vector machine, Bayesian network, or the like).“) While not explicitly disclosed by Temple, Wu discloses the neural network trained to establish a relationship between a plurality of training videos depicting objects and a ground-truth mirror reflection classification. (paragraph [0021] reciting “In some implementations, the instance segmentation 402 includes a two-part image recognition. In a first part, the instance segmentation 402 classifies the image as either having or not having particular types of reflective objects, examples of which include glasses or mirrors. In some examples, this part is implemented as a neural network classifier trained with images containing or not containing such objects and labeled as such. … Again, in some examples, this part is implemented as a neural network classifier trained with images containing or not containing reflections and labeled as such. In the event that the image does not contain a reflection, the technique
400 does not further process the image (does not perform operations 404, 406, or 408).)” The labeling as such corresponds to ground truth mirror reflection classification and the training of the neural network involves obtaining images (a video) with either mirror reflect or without mirror reflection.)
It would have been obvious to a person of ordinary skills in the art before the effective filing date of the claimed invention to modify Temple with Wu so that the neural network in Temple has been trained to identify mirror reflection objects prior. This is an obviously beneficial modification since it allows the neural network in Temple to be trained to detect reflections.
While the combination of Temple and Wu does not explicitly disclose, Harrises discloses identifying a pose of the object depicted in the video; (paragraph [0382] reciting “In some embodiments, the remote data repository 74 may be configured to store a world map of a user's environment, a user's avatar, or a user's face model. The local processing module 70 and/or the remote processing module 72 may identify a mirror in a user's environment and detect a user's pose (such as, e.g., a head pose or a body gesture). The processing modules 70 and 72 may communicate with the remote data repository 74 to create an animation of the user's avatar or to synthesize a user's image based on the information of the user stored in the remote data repository and the detected pose. The processing modules 70 and 72 may further communicate with the display 62 and present the visualizations of another user or the same user in the user's environment (such as overlaying the user's image over a mirror in the user's environment).” The pose of the user in the mirror detected in Temple is determined by processing modules 70/72 in Harrises.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Temple and Wu with Harrises so that a user’s body pose in the mirror can be detected. This is a beneficial modification as it allows Temple to overlay the image with respect to the pose. Temple already discloses overlaying virtual data next to the user’s reflection and Harrises allows this image to align with the pose because Harrises discloses rendering avatars that are aligned with user’s pose. This results in more realistic and more creative manners of overlaying virtual images next to the reflected user’s body in Temple.
Allowable Subject Matter
21. Claims 3, 5, 7-13, 17 and 19 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
22. The following is a statement of reasons for the indication of allowable subject matter: Claim 3 recites the limitation obtaining a target pose representing a target user posing in front of a mirror; comparing the pose of the object depicted in the video with the target pose; and in response to determining that the pose of the object corresponds to the target pose, determining that the object is capturing the video while standing in front of the mirror which neither disclosed nor suggested by the cited references, either singly or in combination. The closest art on record is Temple but Temple fails to disclose this limitation. Temple does disclose using neural network to recognize the object in the image is a reflection from a mirror but fails to state that this determination is based on pose. The other cited references Wu and Harrises also fail to remedy this lack of teaching and suggestion.
23. Claim 5 recites the limitation comparing the estimated classification with the ground-truth mirror reflection classification associated with the first training video; and updating one or more parameters of the neural network based on a result of comparing the estimated classification with the ground-truth mirror reflection classification which is neither disclosed nor suggested by the cited references, either singly or in combination. The closest art on record is Wu but Wu fails to disclose this limitation. Wu merely discloses training neural network classifier with image either showing or not showing mirror objects which are labelled. Nothing in Wu suggests or discloses this limitation. Temple and Harrises also fails to disclose this limitation.
24. Claim 7 recites the limitation identifying a hand of the object depicted in the video; and determining a position of the hand of the object depicted in the video to determine that the video depicts the mirror reflection of the object which is neither disclosed nor suggested by the cited references, either singly or in combination. The closest art on record is Temple which fails to disclose this limitation. Temple merely discloses detecting entire body and is not based on detecting the hand in particular to make a mirror reflection determination.
25. Claims 8-11 depend from claim 7.
26. Claim 12 recites the limitation causing display of a 3D virtual object in the video including enlarging a size of a depiction of mirror to remove the depiction of a frame of the mirror from the video, the enlarging comprising: blending pixels inside the mirror that are within a threshold distance of the frame of the mirror with pixels of the frame of the mirror; and blending pixels outside of the mirror that are within the threshold distance of the frame of the mirror with the pixels inside of the mirror which is neither disclosed nor suggested by the cited references, either singly or in combination. The closest art on record is Temple which fails to disclose this limitation.
27. Claim 13 depends from claim 12.
28. Claim 17 recites the limitation wherein positioning of a set of skeletal joints of the object in the video is tracked using images captured by an RGB camera of a device without using a depth sensor which is neither disclosed nor suggested by the cited references, either singly or in combination. The closest art on record is Temple which fails to disclose this limitation.
29. Claim 19 recites the limitation wherein positioning of a set of skeletal joints of the object in the video is tracked using images captured by an RGB camera of a device without using a depth sensor which is neither disclosed nor suggested by the cited references, either singly or in combination. The closest art on record is Temple which fails to disclose this limitation.
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Any inquiry concerning this communication or earlier communications from the examiner should be directed to FRANK S CHEN whose telephone number is (571)270-7993. The examiner can normally be reached Mon - Fri 8-11:30 and 1:30-6.
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/FRANK S CHEN/Primary Examiner, Art Unit 2611
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