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 . This Office Action is responsive to the communications filed on 28 January 2026. Claims 1-23 are pending.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 28 January 2026 has been entered.
Claim Rejections - 35 USC § 103
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 1-3, 7-8, 11-13 and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Ghandhi et al. (Hereinafter, Ghandhi, US 11,245,871 B1) in view of Hu et al.( Hereinafter, Hu, ThingShare: Ad-Hoc Digital Copies of Physical Objects for Sharing Things in Video Meetings. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI '23), April 23–28, 2023, Hamburg, Germany. ACM, New York, NY, USA 22 Pages).
Per claim 1,Gandhi discloses a method (e.g., Fig. 3; column 6, lines 20-32) comprising:
identifying a first event associated with a first client device of a plurality of client devices of a plurality of participants of a video conference (e.g., client 201 as shown in Fig. 2; column 5, lines 28-48) (e.g., step 301 as shown in Fig. 3; column 6, lines 33-36,” At 301, users join a video meeting. For example, users join a video conference meeting using a cloud hosted service. The cloud hosted service can allow a user to start and invite other users into the meeting… “), the first event indicating a request to activate a virtual whiteboard for presentation in a user interface (UI) comprising a plurality of regions that display a plurality of visual items each corresponding to one of a plurality of video streams from the plurality of client devices(e.g., step 303 as shown in Fig. 3; column 6, lines 47-50, “At 303, a virtual shared whiteboard is created. For example, a first user configures and creates a virtual shared whiteboard. The user can invite and/or grant other users access to the whiteboard ... “);
responsive to identifying the first event, providing, for presentation within instances of the UI at the plurality of client devices, a virtual whiteboard UI element for real-time display of content among the plurality of participants of the video conference(e.g., whiteboards 203 and 213 as shown in Fig. 2; column 5, lines 28--48); but does not expressly disclose:
identifying a second event associated with the first client device the second event comprising one or more first video gestures that identify a selection of first content, among a plurality of content types, for insertion within the virtual whiteboard UI element;
performing a first computer vision operation, implementing a machine learning model, to detect one or more second video gestures associated with a first user of the first client device, the one or more second video gestures indicating second content for inclusion within the first content for presentation within the virtual whiteboard UI element; and
providing, for presentation at the plurality of client devices, the second content within the first content within respective instances of the virtual whiteboard UI element.
Hu discloses:
identifying a second event associated with the first client device Examiner’s Note: Hu discloses identifying a “Person View’ event in the first client device for sharing digital copies of objects. ); the second event comprising one or more first video gestures that identify a selection of first content, among a plurality of content types, for insertion within the virtual whiteboard UI element (Fig. 1; 1 Introduction, “ Informed by the findings from the formative study, we developed ThingShare, a videoconferencing system that supports users to fluidly share physical objects with a remote partner by creating digital copies. ThingShare provides user interface controls and interactions for users to easily create, manipulate, and reference digital copies for effective discussions around physical objects ... “; 4.2 Supporting Person-Centric Sharing of Physical Objects; Examiner’s Note: As shown in Fig. 1, a first client user can gesture with an object. The object is segmented using computer vision and displayed for videoconferencing users. ) ;
performing a first computer vision operation, implementing a machine learning model, to detect one or more second video gestures associated with a first user of the first client device, the one or more second video gestures indicating second content for inclusion within the first content for presentation within the virtual whiteboard UI element(4.5 Applying Instance Segmentation in Video-Conferencing Interfaces; Examiner’s Note: Hu discloses using Yolact to segment objects from a video image. ); and
providing, for presentation at the plurality of client devices, the second content within the first content within respective instances of the virtual whiteboard UI element (Abstract; Fig. 1; 3.2 Findings; page 6; 3.4 ThingShare Design Goals, page 7, “ G1: Provide in-context and detailed views of sharing the physical objects. “ ; Examiner’s Note: Hu discloses that ThingShare provides ad-hoc sharing and showing behavior of “holding a physical object up to the camera” as described in Section 3.3. ) .
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to use Hu’s ThingShare in the video conference virtual whiteboard of Gandhi for making initiating object centric conversations more efficient and providing a more stable and comprehensive view of shared objects as suggested by Hu(See Abstract).
Per claim 2, Gandhi and Hu disclose the method of claim 1, wherein identifying the first event associated with the first client device of the plurality of client devices of the plurality of participants of the video conference, comprises:
receiving, from the first client device, a first video segment of a first video stream of the plurality of video streams(Gandhi, e.g., Step 301 as shown in Fig. 3; Abstract, “ A video of a user is received … “; column 2, lines 21-52; column 3, lines 10-13; column 6, lines 33-45); and
performing a second computer vision operation on the first video segment to detect one or more second video that qualify as a predetermined video gesture indicative of the request to activate the virtual whiteboard for presentation in the UI(e.g., Step 705 as shown in Fig. 7; column 12, lines 59-67 to column 13, lines 1-24; Examiner’s Note: Ghandi discloses receiving a drawing gesture to present a drawing in the whiteboard.).
Per claim 3, Gandhi and Hu disclose the method of claim 2, wherein identifying the second event associated with the first client device, the second event comprising the one or more first video gestures that identify the selection of the first content, among the plurality of content types, for insertion within the virtual whiteboard UI element (Hu, Fig. 1; 1 Introduction, “ Informed by the findings from the formative study, we developed ThingShare, a videoconferencing system that supports users to fluidly share physical objects with a remote partner by creating digital copies. ThingShare provides user interface controls and interactions for users to easily create, manipulate, and reference digital copies for effective discussions around physical objects ... “; 4.2 Supporting Person-Centric Sharing of Physical Objects; Examiner’s Note: As shown in Fig. 1, a first client user can gesture with an object. The object is segmented using computer vision and displayed for videoconferencing users. ), comprises:
receiving, from the first client device, a second video segment of a second video stream of the plurality of video streams(Gandhi, e.g., Step 401 as shown in Fig. 4; column 8, lines 48-51);
performing, on the third video segment, a second computer vision operation, implementing the machine learning model, that detects one or more video third gestures associated with the first user of the first client device(Hu, 4.3 Supporting Task-Centric Sharing of Physical Objects, page 10; 4.5 Applying Instance Segmentation in Video-Conferencing Interfaces; Examiner’s Note: Hu discloses using Yolact to segment objects from a video image. Hu discloses clicking on objects in a personal view of a first client and dragging them into a task view ); and
determining the first content based on the one or morefirst video gestures associated with the first user(Hu, 4.2 Supporting Person-Centric Sharing of Physical Objects, page 9; 4.3 Supporting Task-Centric Sharing of Physical Objects, page 10; 5.4 Task, page 12; Examiner’s Note: As described in section 4.2.1 of Hu, a first user can show/hide object, freeze, snapshots, short video, focus share, and delete objects.).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to use Hu’s ThingShare in the video conference virtual whiteboard of Gandhi for making initiating object centric conversations more efficient and providing a more stable and comprehensive view of shared objects as suggested by Hu(See Abstract).
Per claim 7, Gandhi and Hu disclose the method of claim 1, wherein identifying the first event associated with the first client device of the plurality of client devices of the plurality of participants of the video conference, comprises: receiving an indication of a user selection of a UI element of the UI that indicates the request to activate the virtual whiteboard for presentation in the UI (Gandhi, e.g., step 705 as shown in Fig. 7; column 12, lines 59-62).
Per claim 8, Gandhi and Hu disclose the method of claim 1, further comprising:
identifying a third event associated with a second client device of the plurality of client devices, the third event indicating third content for presentation within the virtual whiteboard UI element (Gandhi, e.g., step 305 as shown in Fig. 3; column 7, lines 27-54); and
providing, for presentation at the plurality of client devices, the third content within respective instances of the virtual whiteboard UI element (Gandhi, e.g., step 307 as shown in Fig. 3; column 7, lines 55-65).
Per claim 11, Gandhi discloses a system (e.g., Fig. 2) comprising:
a memory (column 1, lines 54-67 to column 2, lines 1-4); and
a processing device, coupled to the memory(column 1, lines 54-67 to column 2, lines 1-4), to perform operations comprising:
identifying a first event associated with a first client device of a plurality of client devices of a plurality of participants of a video conference (e.g., client 201 as shown in Fig. 2; column 5, lines 28-48) (e.g., step 301 as shown in Fig. 3; column 6, lines 33-36,” At 301, users join a video meeting. For example, users join a video conference meeting using a cloud hosted service. The cloud hosted service can allow a user to start and invite other users into the meeting… “), the first event indicating a request to activate a virtual whiteboard for presentation in a user interface (UI) comprising a plurality of regions that display a plurality of visual items each corresponding to one of a plurality of video streams from the plurality of client devices(e.g., step 303 as shown in Fig. 3; column 6, lines 47-50, “At 303, a virtual shared whiteboard is created. For example, a first user configures and creates a virtual shared whiteboard. The user can invite and/or grant other users access to the whiteboard ... “);
responsive to identifying the first event, providing, for presentation within instances of the UI at the plurality of client devices, a virtual whiteboard UI element for real-time display of content among the plurality of participants of the video conference(e.g., whiteboards 203 and 213 as shown in Fig. 2; column 5, lines 28--48); but does not expressly disclose:
identifying a second event associated with the first client device the second event comprising one or more first video gestures that identify a selection of first content, among a plurality of content types, for insertion within the virtual whiteboard UI element;
performing a first computer vision operation, implementing a machine learning model, to detect one or more second video gestures associated with a first user of the first client device, the one or more second video gestures indicating second content for inclusion within the first content for presentation within the virtual whiteboard UI element; and
providing, for presentation at the plurality of client devices, the second content within the first content within respective instances of the virtual whiteboard UI element.
Hu discloses:
identifying a second event associated with the first client device Examiner’s Note: Hu discloses identifying a “Person View’ event in the first client device for sharing digital copies of objects. ); the second event comprising one or more first video gestures that identify a selection of first content, among a plurality of content types, for insertion within the virtual whiteboard UI element (Fig. 1; 1 Introduction, “ Informed by the findings from the formative study, we developed ThingShare, a videoconferencing system that supports users to fluidly share physical objects with a remote partner by creating digital copies. ThingShare provides user interface controls and interactions for users to easily create, manipulate, and reference digital copies for effective discussions around physical objects ... “; 4.2 Supporting Person-Centric Sharing of Physical Objects; Examiner’s Note: As shown in Fig. 1, a first client user can gesture with an object. The object is segmented using computer vision and displayed for videoconferencing users. ) ;
performing a first computer vision operation, implementing a machine learning model, to detect one or more second video gestures associated with a first user of the first client device, the one or more second video gestures indicating second content for inclusion within the first content for presentation within the virtual whiteboard UI element(4.5 Applying Instance Segmentation in Video-Conferencing Interfaces; Examiner’s Note: Hu discloses using Yolact to segment objects from a video image. ); and
providing, for presentation at the plurality of client devices, the second content within the first content within respective instances of the virtual whiteboard UI element (Abstract; Fig. 1; 3.2 Findings; page 6; 3.4 ThingShare Design Goals, page 7, “ G1: Provide in-context and detailed views of sharing the physical objects. “ ; Examiner’s Note: Hu discloses that ThingShare provides ad-hoc sharing and showing behavior of “holding a physical object up to the camera” as described in Section 3.3. ) .
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to use Hu’s ThingShare in the video conference virtual whiteboard of Gandhi for making initiating object centric conversations more efficient and providing a more stable and comprehensive view of shared objects as suggested by Hu(See Abstract).
Per claim 12, Gandhi and Hu disclose the system of claim 11, wherein identifying the first event associated with the first client device of the plurality of client devices of the plurality of participants of the video conference, comprises:
receiving, from the first client device, a first video segment of a first video stream of the plurality of video streams(Gandhi, e.g., Step 301 as shown in Fig. 3; Abstract, “ A video of a user is received … “; column 2, lines 21-52; column 3, lines 10-13; column 6, lines 33-45); and
performing a second computer vision operation on the first video segment to detect one or more second video that qualify as a predetermined video gesture indicative of the request to activate the virtual whiteboard for presentation in the UI(Gandhi, e.g., Step 705 as shown in Fig. 7; column 12, lines 59-67 to column 13, lines 1-24; Examiner’s Note: Ghandi discloses receiving a drawing gesture to present a drawing in the whiteboard.).
Per claim 13, Gandhi and Hu disclose the system of claim 12, wherein identifying the second event associated with the first client device, the second event comprising the one or more first video gestures that identify the selection of the first content, among the plurality of content types, for insertion within the virtual whiteboard UI element (Hu, Fig. 1; 1 Introduction, “ Informed by the findings from the formative study, we developed ThingShare, a videoconferencing system that supports users to fluidly share physical objects with a remote partner by creating digital copies. ThingShare provides user interface controls and interactions for users to easily create, manipulate, and reference digital copies for effective discussions around physical objects ... “; 4.2 Supporting Person-Centric Sharing of Physical Objects; Examiner’s Note: As shown in Fig. 1, a first client user can gesture with an object. The object is segmented using computer vision and displayed for videoconferencing users. ), comprises:
receiving, from the first client device, a second video segment of a second video stream of the plurality of video streams(Gandhi, e.g., Step 401 as shown in Fig. 4; column 8, lines 48-51);
performing, on the third video segment, a second computer vision operation, implementing the machine learning model, that detects one or more video third gestures associated with the first user of the first client device(Hu, 4.3 Supporting Task-Centric Sharing of Physical Objects, page 10; 4.5 Applying Instance Segmentation in Video-Conferencing Interfaces; Examiner’s Note: Hu discloses using Yolact to segment objects from a video image. Hu discloses clicking on objects in a personal view of a first client and dragging them into a task view ); and
determining the first content based on the one or morefirst video gestures associated with the first user(Hu, 4.2 Supporting Person-Centric Sharing of Physical Objects, page 9; 4.3 Supporting Task-Centric Sharing of Physical Objects, page 10; 5.4 Task, page 12; Examiner’s Note: As described in section 4.2.1 of Hu, a first user can show/hide object, freeze, snapshots, short video, focus share, and delete objects.).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to use Hu’s ThingShare in the video conference virtual whiteboard of Gandhi for making initiating object centric conversations more efficient and providing a more stable and comprehensive view of shared objects as suggested by Hu(See Abstract).
Per claim 17, Gandhi and Hu disclose the system of claim 11, wherein identifying the first event associated with the first client device of the plurality of client devices of the plurality of participants of the video conference, comprises: receiving an indication of a user selection of a UI element of the UI that indicates the request to activate the virtual whiteboard for presentation in the UI (Ghandhi, e.g., step 705 as shown in Fig. 7; column 12, lines 59-62).
Per claim 18, Gandhi and Hu disclose the system of claim 11, the operations further comprising:
identifying a third event associated with a second client device of the plurality of client devices, the third event indicating third content for presentation within the virtual whiteboard UI element (Ghandhi, e.g., step 305 as shown in Fig. 3; column 7, lines 27-54); and
providing, for presentation at the plurality of client devices, the third content within respective instances of the virtual whiteboard UI element (Ghandhi, e.g., step 307 as shown in Fig. 3; column 7, lines 55-65).
Per claim 19, Gandhi discloses a non-transitory computer-readable medium that, responsive to an execution of instruction by a processing device (column 1, lines 54-67 to column 2, lines 1-4), cause the processing device to perform operations comprising:
identifying a first event associated with a first client device of a plurality of client devices of a plurality of participants of a video conference (e.g., client 201 as shown in Fig. 2; column 5, lines 28-48) (e.g., step 301 as shown in Fig. 3; column 6, lines 33-36,” At 301, users join a video meeting. For example, users join a video conference meeting using a cloud hosted service. The cloud hosted service can allow a user to start and invite other users into the meeting… “), the first event indicating a request to activate a virtual whiteboard for presentation in a user interface (UI) comprising a plurality of regions that display a plurality of visual items each corresponding to one of a plurality of video streams from the plurality of client devices(e.g., step 303 as shown in Fig. 3; column 6, lines 47-50, “At 303, a virtual shared whiteboard is created. For example, a first user configures and creates a virtual shared whiteboard. The user can invite and/or grant other users access to the whiteboard ... “);
responsive to identifying the first event, providing, for presentation within instances of the UI at the plurality of client devices, a virtual whiteboard UI element for real-time display of content among the plurality of participants of the video conference(e.g., whiteboards 203 and 213 as shown in Fig. 2; column 5, lines 28--48); but does not expressly disclose:
identifying a second event associated with the first client device the second event comprising one or more first video gestures that identify a selection of first content, among a plurality of content types, for insertion within the virtual whiteboard UI element;
performing a first computer vision operation, implementing a machine learning model, to detect one or more second video gestures associated with a first user of the first client device, the one or more second video gestures indicating second content for inclusion within the first content for presentation within the virtual whiteboard UI element; and
providing, for presentation at the plurality of client devices, the second content within the first content within respective instances of the virtual whiteboard UI element.
Hu discloses:
identifying a second event associated with the first client device Examiner’s Note: Hu discloses identifying a “Person View’ event in the first client device for sharing digital copies of objects. ); the second event comprising one or more first video gestures that identify a selection of first content, among a plurality of content types, for insertion within the virtual whiteboard UI element (Fig. 1; 1 Introduction, “ Informed by the findings from the formative study, we developed ThingShare, a videoconferencing system that supports users to fluidly share physical objects with a remote partner by creating digital copies. ThingShare provides user interface controls and interactions for users to easily create, manipulate, and reference digital copies for effective discussions around physical objects ... “; 4.2 Supporting Person-Centric Sharing of Physical Objects; Examiner’s Note: As shown in Fig. 1, a first client user can gesture with an object. The object is segmented using computer vision and displayed for videoconferencing users. ) ;
performing a first computer vision operation, implementing a machine learning model, to detect one or more second video gestures associated with a first user of the first client device, the one or more second video gestures indicating second content for inclusion within the first content for presentation within the virtual whiteboard UI element(4.5 Applying Instance Segmentation in Video-Conferencing Interfaces; Examiner’s Note: Hu discloses using Yolact to segment objects from a video image. ); and
providing, for presentation at the plurality of client devices, the second content within the first content within respective instances of the virtual whiteboard UI element (Abstract; Fig. 1; 3.2 Findings; page 6; 3.4 ThingShare Design Goals, page 7, “ G1: Provide in-context and detailed views of sharing the physical objects. “ ; Examiner’s Note: Hu discloses that ThingShare provides ad-hoc sharing and showing behavior of “holding a physical object up to the camera” as described in Section 3.3. ) .
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to use Hu’s ThingShare in the video conference virtual whiteboard of Gandhi for making initiating object centric conversations more efficient and providing a more stable and comprehensive view of shared objects as suggested by Hu(See Abstract).
Per claim 20, Gandhi and Hu disclose the non-transitorily computer-readable medium of claim 19, wherein identifying the first event associated with the first client device of the plurality of client devices of the plurality of participants of the video conference, comprises:
receiving, from the first client device, a first video segment of a first video stream of the plurality of video streams(Gandhi, e.g., Step 301 as shown in Fig. 3; Abstract, “ A video of a user is received … “; column 2, lines 21-52; column 3, lines 10-13; column 6, lines 33-45); and
performing a second computer vision operation on the first video segment to detect one or more second video that qualify as a predetermined video gesture indicative of the request to activate the virtual whiteboard for presentation in the UI(e.g., Step 705 as shown in Fig. 7; column 12, lines 59-67 to column 13, lines 1-24; Examiner’s Note: Ghandi discloses receiving a drawing gesture to present a drawing in the whiteboard.).
Claims 4, 14, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Ghandhi et al. (Hereinafter, Ghandhi, US 11,245,871 B1) in view of Hu et al. (Hereinafter, Hu, ThingShare: Ad-Hoc Digital Copies of Physical Objects for Sharing Things in Video Meetings. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI '23), April 23–28, 2023, Hamburg, Germany. ACM, New York, NY, USA 22 Pages), and further in view of Hebbalahuppe et al. (Hereinafter, Hebbalahuppe, US 2019/0107894 A1).
Per claim 4, Gandhi and Hu disclose the method of claim [[3]] 1, but do not expressly disclose wherein performing the first computer vision operation, implementing the machine learning model, second video gestures
sampling ,from a plurality of frames a subset of frames of the first video segment to reduce computer resources used to identify the first event, the subset of frames including fewer frames than the plurality of frames of the first video segment; and
performing the first computer vision operation on the subset of frames of the first video segment; wherein an indication of the subset of frames is provided as input to the machine learning model.
Hebbalahuppe discloses wherein performing the first computer vision operation, implementing the machine learning model, second video gestures
sampling ,from a plurality of frames a subset of frames of the first video segment to reduce computer resources used to identify the first event, the subset of frames including fewer frames than the plurality of frames of the first video segment (e.g.,, step 410 as shown in Fig. 4; paragraph [0037], “… The frames of the media stream captured in FPV are streamed for processing to the gesture recognition system (for example, the system 302 of FIG. 3), at 410. In an implementation, the frames obtained from the device 408 are first down-scaled, for example to a resolution of, for example, 320×240, to achieve real-time performance by reducing the computational time without compromising on quality …. “); and
performing the first computer vision operation on the subset of frames of the first video segment (e.g., step 412 as shown in Fig. 4; [paragraph 0038], “At 412, the gesture recognition system receives a plurality of frames of the media stream. The frames are the RGB frames acquired from the device 408. The RGB frames include RGB image data associated with the plurality of frames of the scene. Herein, the RGB image data refers to data corresponding Red, Green and Blue colors associated with the frames.” ); wherein an indication of the subset of frames is provided as input to the machine learning model (e.g., step 414 as shown in Fig. 4; paragraph [0039], “At 414, a temporal information associated with the dynamic hand gesture is estimated from the RGB image data by using a deep learning model. In an embodiment, the gesture recognition system estimated the temporal information associated with the dynamic hand gesture ...” ).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to use Hebbalahuppe’s system and method in the video conference virtual whiteboard of Gandhi for reducing the complexity and cost of performing hand-gesture recognition as suggested by Hebbalahuppe (See paragraph [0003]).
Per claim 14, Gandhi and Hu disclose the system of claim [[13]] 11, but do not expressly disclose wherein performing the first computer vision operation, implementing the machine learning model, second video gestures
sampling ,from a plurality of frames a subset of frames of the first video segment to reduce computer resources used to identify the first event, the subset of frames including fewer frames than the plurality of frames of the first video segment; and
performing the first computer vision operation on the subset of frames of the first video segment; wherein an indication of the subset of frames is provided as input to the machine learning model.
Hebbalahuppe discloses wherein performing the first computer vision operation, implementing the machine learning model, second video gestures
sampling ,from a plurality of frames a subset of frames of the first video segment to reduce computer resources used to identify the first event, the subset of frames including fewer frames than the plurality of frames of the first video segment (e.g.,, step 410 as shown in Fig. 4; paragraph [0037], “… The frames of the media stream captured in FPV are streamed for processing to the gesture recognition system (for example, the system 302 of FIG. 3), at 410. In an implementation, the frames obtained from the device 408 are first down-scaled, for example to a resolution of, for example, 320×240, to achieve real-time performance by reducing the computational time without compromising on quality …. “); and
performing the first computer vision operation on the subset of frames of the first video segment (e.g., step 412 as shown in Fig. 4; [paragraph 0038], “At 412, the gesture recognition system receives a plurality of frames of the media stream. The frames are the RGB frames acquired from the device 408. The RGB frames include RGB image data associated with the plurality of frames of the scene. Herein, the RGB image data refers to data corresponding Red, Green and Blue colors associated with the frames.” ); wherein an indication of the subset of frames is provided as input to the machine learning model (e.g., step 414 as shown in Fig. 4; paragraph [0039], “At 414, a temporal information associated with the dynamic hand gesture is estimated from the RGB image data by using a deep learning model. In an embodiment, the gesture recognition system estimated the temporal information associated with the dynamic hand gesture ...” ).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to use Hebbalahuppe’s system and method in the video conference virtual whiteboard of Gandhi for reducing the complexity and cost of performing hand-gesture recognition as suggested by Hebbalahuppe (See paragraph [0003]).
Per claim 21, Gandhi and Hu disclose the method of claim 4, but do not expressly disclose wherein the subset of frames comprises lower resolution frames than received from the first client device.
Hebbalahuppe discloses wherein the subset of frames comprises lower resolution frames than received from the first client device (e.g.,, step 410 as shown in Fig. 4; paragraph [0037], “… The frames of the media stream captured in FPV are streamed for processing to the gesture recognition system (for example, the system 302 of FIG. 3), at 410. In an implementation, the frames obtained from the device 408 are first down-scaled, for example to a resolution of, for example, 320×240, to achieve real-time performance by reducing the computational time without compromising on quality …. “).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to use Hebbalahuppe’s system and method in the video conference virtual whiteboard of Gandhi for reducing the complexity and cost of performing hand-gesture recognition as suggested by Hebbalahuppe (See paragraph [0003]).
Claims 5 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Ghandhi et al. (Hereinafter, Ghandhi, US 11,245,871 B1) in view of Hu et al.( Hereinafter, Hu, ThingShare: Ad-Hoc Digital Copies of Physical Objects for Sharing Things in Video Meetings. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI '23), April 23–28, 2023, Hamburg, Germany. ACM, New York, NY, USA 22 Pages), and further in view of Insley (US 2023/0326144 A1).
Per claim 5, Ghandi and Hu disclose the method of claim 3, but do not expressly disclose wherein performing, on the third video segment, the second computer vision operation, implementing the machine learning model, that detects the one or morefirst video gestures associated with the first user of the first client device, comprises: performing a ray casting operation that projects a line from an object associated with the first user to a location on a virtual plane and detects changes in the location on the virtual plane based on movement of the object.
Insley discloses wherein performing, on the third video segment, the second computer vision operation, implementing the machine learning model, that detects the one or morefirst video gestures associated with the first user of the first client device, comprises: performing a ray casting operation that projects a line from an object associated with the first user to a location on a virtual plane and detects changes in the location on the virtual plane based on movement of the object (paragraph [0015], “… In the far-field region, the user can interact with the displayed object using ray interactions. For example, a ray can extend from a user's body (e.g., from the user's hand or wrist) to target objects in the far-field region ... “; paragraphs [0021-0022]) and detects changes in the location on the virtual plane based on movement of the object (paragraphs [0017-0019]; paragraphs [0058]; paragraph [0063]; paragraph [0067]).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to use the artificial reality device of Insley in the video conference virtual whiteboard of Ghandi and Hu for improving user interactions as suggested by Insley (See paragraph [0002]).
Per claim 15, Gandhi and Hu disclose the system of claim 13, but do not expressly disclose wherein performing, on the third video segment, the second computer vision operation, implementing the machine learning model, that detects the one or morefirst video gestures associated with the first user of the first client device, comprises: performing a ray casting operation that projects a line from an object associated with the first user to a location on a virtual plane and detects changes in the location on the virtual plane based on movement of the object.
Insley discloses wherein performing, on the third video segment, the second computer vision operation, implementing the machine learning model, that detects the one or morefirst video gestures associated with the first user of the first client device, comprises: performing a ray casting operation that projects a line from an object associated with the first user to a location on a virtual plane and detects changes in the location on the virtual plane based on movement of the object (paragraph [0015], “… In the far-field region, the user can interact with the displayed object using ray interactions. For example, a ray can extend from a user's body (e.g., from the user's hand or wrist) to target objects in the far-field region ... “; paragraphs [0021-0022]) and detects changes in the location on the virtual plane based on movement of the object (paragraphs [0017-0019]; paragraphs [0058]; paragraph [0063]; paragraph [0067]).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to use the artificial reality device of Insley in the video conference virtual whiteboard of Ghandi and Hu for improving user interactions as suggested by Insley (See paragraph [0002]).
Claims 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Gandhi et al. (Hereinafter, Gandhi, US 11,245,871 B1) in view of Hu et al.( Hereinafter, Hu, ThingShare: Ad-Hoc Digital Copies of Physical Objects for Sharing Things in Video Meetings. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI '23), April 23–28, 2023, Hamburg, Germany. ACM, New York, NY, USA 22 Pages), and further in view of Tan et al. (Hereinafter, Tan, US 2013/0290874 A1, See IDS dated 10/08/2024).
Per claim 6, Gandhi and Hu disclose the method of claim 1, but do not expressly disclose wherein the virtual whiteboard UI element is presented as a background layer of a first visual item of the plurality of visual items, the first visual item representing a first video stream from the first client device.
Tan discloses wherein the virtual whiteboard UI element (e.g., collaboration content 410 as shown in Figs 4A-4C ) is presented as a background layer of a first visual item of the plurality of visual items(e.g., video component 412 as shown in Figs 4A-4C), the first visual item representing a first video stream from the first client device(paragraphs [0052-0054] disclose overlaying items over a whiteboard.).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to use the display characteristic adjustments of Tan in the video conference virtual whiteboard of Gandhi and Hu for sharing information more easily in multi-party videoconferences as suggested by Tan(See paragraph [0001]).
Per claim 16, Gandhi and Hu disclose the system of claim 11, but do not expressly disclose wherein the virtual whiteboard UI element is presented as a background layer of a first visual item of the plurality of visual items, the first visual item representing a first video stream from the first client device.
Tan discloses wherein the virtual whiteboard UI element (e.g., collaboration content 410 as shown in Figs 4A-4C ) is presented as a background layer of a first visual item of the plurality of visual items(e.g., video component 412 as shown in Figs 4A-4C), the first visual item representing a first video stream from the first client device(paragraphs [0052-0054] disclose overlaying items over a whiteboard.).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to use the display characteristic adjustments of Tan in the video conference virtual whiteboard of Gandhi and Hu for sharing information more easily in multi-party videoconferences as suggested by Tan(See paragraph [0001]).
Claims 9-10 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Ghandhi et al. (Hereinafter, Gandhi, US 11,245,871 B1) in view of Hu et al. (Hereinafter, Hu, ThingShare: Ad-Hoc Digital Copies of Physical Objects for Sharing Things in Video Meetings. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI '23), April 23–28, 2023, Hamburg, Germany. ACM, New York, NY, USA 22 Pages), and further in view of Mauchly (US 2015/0172562 A1).
Per claim 9, Gandhi and Hu disclose the method of claim 8, but do not expressly disclose the method as further comprising:
filtering the first content, the second content, and the
providing, for presentation at the plurality of client devices, the fourth
Mauchly discloses:
filtering the first content, the second content, and the
providing, for presentation at the plurality of client devices, the fourth
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to use the adaptive switching of Mauchly in the video conference virtual whiteboard of Gandhi and Hu for electronically correcting a warped electronic image as suggested by Mauchly(See paragraph [0004]).
Per claim 10, Gandhi and Hu disclose the method of claim 1, but do not expressly disclose the method as further comprising:
converting the first content and the second content of the virtual whiteboard UI element into content for a document application; and
providing access to a file of the document application, the file comprising the content converted from a first format of the first content and the second content of the virtual whiteboard UI element to a second format of the file.
Mauchly discloses:
converting the first content and the second content of the virtual whiteboard UI element into content for a document application (e.g., step 110 as shown in Fig. 4; paragraph [0030]); and
providing access to a file of the document application, the file comprising the content converted from a first format of the first content and the second content of the virtual whiteboard UI element to a second format of the file (e.g., step 125 as shown in Fig. 4; paragraph [0035]).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to use the adaptive switching of Mauchly in the video conference virtual whiteboard of Gandhi and Hu for electronically correcting a warped electronic image as suggested by Mauchly(See paragraph [0004]).
Per claim 23, Gandhi, Hu, and Mauchly disclose the method of claim 9, wherein the criterion comprises at least one of: participant contribution, a location in the virtual whiteboard UI element, a time range, or a specified virtual whiteboard UI element (Hu, Fig. 4; 4.1.3 Video Layouts, page 8-9; Examiner’s Note: He discloses dragging and dropping an object from ‘Person View’ to an area of the whiteboard to provide a new content to the whiteboard. ).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to use Hu’s ThingShare in the video conference virtual whiteboard of Gandhi for making initiating object centric conversations more efficient and providing a more stable and comprehensive view of shared objects as suggested by Hu(See Abstract).
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Ghandhi et al. (Hereinafter, Gandhi, US 11,245,871 B1) in view of Hu et al. (Hereinafter, Hu, ThingShare: Ad-Hoc Digital Copies of Physical Objects for Sharing Things in Video Meetings. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI '23), April 23–28, 2023, Hamburg, Germany. ACM, New York, NY, USA 22 Pages), and further in view of Pham et al. (Hereinafter, Pham, US 2021/0081475 A1).
Per claim 22, Gandhi and Hu disclose the method of claim 10, but do not expressly disclose wherein converting the first content of the virtual whiteboard UI element into content for the document application comprises:
providing the first content as input to a trained generative machine learning model; and
generating, by the trained generative machine learning model, a summary of the first content for the document application.
Pham discloses wherein converting the first content of the virtual whiteboard UI element into content for the document application comprises:
providing the first content as input to a trained generative machine learning model (paragraph [0083]); and
generating, by the trained generative machine learning model, a summary of the first content for the document application (paragraph [0042]; paragraph [0052]; paragraph [0092]).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to use the content integration of Pham in the video conference virtual whiteboard of Gandhi and Hu for editing a document, personalizing it based on each user's specific preferences and history as suggested by Pham(See paragraph [0002]).
Response to Arguments
Summary of Interview
Examiner acknowledges applicant’s remarks regarding the interview on January 20, 2026.
Applicant's arguments filed 28 January 2026 have been fully considered but they are not persuasive.
Applicant argues, with respect to claims 1,11 and 19, that Gandhi and Robinson do not teach or suggest at least "identifying a second event associated
with the first client device, the second event comprising one or more first video gestures
that identify a selection of first content, among a plurality of content types, for insertion
within the virtual whiteboard UI element" and "performing a first computer vision
operation, implementing a machine learning model, to detect one or more second video
gestures associated with a first user of the first client device, the one or more second video gestures indicating second content for inclusion within the first content for presentation within the virtual whiteboard UI element," as recited in claim 1.
Examiner disagrees since Gandhi and Robinson were not relied to teach or suggest at least "identifying a second event associated with the first client device, the second event comprising one or more first video gestures that identify a selection of first content, among a plurality of content types, for insertion within the virtual whiteboard UI element" and "performing a first computer vision operation, implementing a machine learning model, to detect one or more second video gestures associated with a first user of the first client device, the one or more second video gestures indicating second content for inclusion within the first content for presentation within the virtual whiteboard UI element," as recited in claim 1. Similar language is also included in independent claims 11 and 19. Thus, claims 1,11 19 and corresponding dependent claims are not patentable.
In view of the remarks above, Examiner maintains the rejection of claims 1-4,7-8, 11-14 and 17-20.
Conclusion
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DARRIN HOPE
Examiner
Art Unit 2178
/STEPHEN S HONG/Supervisory Patent Examiner, Art Unit 2178