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 .
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-4, 7, 9-12, 15 and 17-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Mackell et al. (US 2020/0344278 A1)(hereinafter Mackell).
Re claim 1, Mackell discloses a computer-implemented method for automatically producing live video content received from one or more collaborators, the computer-implemented method comprising: receiving, by one or more processors, a plurality of video streams from one or more user devices, wherein the plurality of video streams correspond to one or more views of an event, and wherein the plurality of video streams include a plurality of video frames (see ¶s 21-23 for receiving, by one or more processors, a plurality of video streams from one or more user devices, wherein the plurality of video streams correspond to one or more views of an event, and wherein the plurality of video streams include a plurality of video frames (i.e. in particular, the meeting room 20 includes one or more cameras (represented by camera 22 in FIG. 2) that can be of any suitable types (e.g., high definition or HD digital cameras providing a suitable resolution, such as 720p, 1080i, or 1080p), capture video images or video frames at any one or more suitable frame rates (e.g., 15 frames per second or fps, 24 fps, 30 fps, 60 fps, etc.) and are suitably oriented and/or movable (e.g., automatically or manually movable) to capture video images at one or more locations within the room in which participants are located, where the one or more cameras capture video images within the areas of the room for processing by a meeting software application module 200 of one or more computing devices associated with the meeting room 20 as described in fig. 2 paragraph 20, furthermore, the computing device(s) of a meeting room 20 can include a meeting application software module (or meeting app module) 200 that includes one or more software applications that perform functions associated with an online meeting, including communications with other endpoints engaged in the meeting over the network(s) 10 (including sending and receiving of audio content, video content and/or other content associated with the online meeting as described in fig. 2 paragraph 24))); detecting, by the one or more processors, scene data for each of the plurality of video streams (see ¶s 24, 26-27 for detecting, by the one or more processors, scene data for each of the plurality of video streams (i.e. the meeting application module 200 includes one or more software applications that facilitate face detection of one or more participants in the meeting room 20, room coordinate locations of one or more speaking participants in the meeting room 20, and identification of one or more participants in the room and their room coordinate locations based upon the face detection analysis as well as the identified locations of speaking participants in the meeting room 20, this combination of detection features facilitates providing an automatic identifier (e.g., a label including participant name) associated with identified participants in the meeting room and/or identified speaking participants in the meeting room during the online meeting as described in fig. 2 paragraph 25, furthermore, a scene for the captured video content refers to an established number of ROIs (e.g., identified participants) within one or more video frames, scene change can occur, e.g., when a number and/or location of one or more ROIs within a scene has changed, for example, a scene change can occur when a new participant enters the meeting room and is captured and identified in the video content, or when an identified participant leaves the room or is no longer captured in the video content, or when an identified speaking participant is moving during speaking (thus causing a movement in ROI location within captured video frames) which in turn can cause a change in the viewpoint image within the room to maintain the identified speaking participant within captured video content (e.g., a camera capturing video content within the meeting room is automatically moved to follow the speaking participant and/or video content being captured is changed from one camera to another camera within the meeting room) as described in fig. 2 paragraph 30). Also, see fig. 4 paragraphs 28-29, 37, 40); generating, by the one or more processors, categorization data for each of the plurality of video frames based on the scene data (see ¶s 21-24 for generating, by the one or more processors, categorization data for each of the plurality of video frames based on the scene data (i.e. in particular, the meeting room 20 includes one or more cameras (represented by camera 22 in FIG. 2) that can be of any suitable types (e.g., high definition or HD digital cameras providing a suitable resolution, such as 720p, 1080i, or 1080p), capture video images or video frames at any one or more suitable frame rates (e.g., 15 frames per second or fps, 24 fps, 30 fps, 60 fps, etc.) and are suitably oriented and/or movable (e.g., automatically or manually movable) to capture video images at one or more locations within the room in which participants are located, where the one or more cameras capture video images within the areas of the room for processing by a meeting software application module 200 of one or more computing devices associated with the meeting room 20 as described in fig. 2 paragraph 20, furthermore, the meeting application module 200 includes one or more software applications that facilitate face detection of one or more participants in the meeting room 20, room coordinate locations of one or more speaking participants in the meeting room 20, and identification of one or more participants in the room and their room coordinate locations based upon the face detection analysis as well as the identified locations of speaking participants in the meeting room 20, this combination of detection features facilitates providing an automatic identifier (e.g., a label including participant name) associated with identified participants in the meeting room and/or identified speaking participants in the meeting room during the online meeting as described in fig. 2 paragraph 25, moreover, a scene for the captured video content refers to an established number of ROIs (e.g., identified participants) within one or more video frames, scene change can occur, e.g., when a number and/or location of one or more ROIs within a scene has changed, for example, a scene change can occur when a new participant enters the meeting room and is captured and identified in the video content, or when an identified participant leaves the room or is no longer captured in the video content, or when an identified speaking participant is moving during speaking (thus causing a movement in ROI location within captured video frames) which in turn can cause a change in the viewpoint image within the room to maintain the identified speaking participant within captured video content (e.g., a camera capturing video content within the meeting room is automatically moved to follow the speaking participant and/or video content being captured is changed from one camera to another camera within the meeting room) as described in fig. 2 paragraph 30)). Also, see figs. 3-4 paragraphs 26, 32, 35-37, 40-41); selecting, by the one or more processors, a plurality of scenes from the plurality of video streams based on the categorization data (see ¶s 21-24 for selecting, by the one or more processors, a plurality of scenes from the plurality of video streams based on the categorization data (i.e. in particular, the meeting room 20 includes one or more cameras (represented by camera 22 in FIG. 2) that can be of any suitable types (e.g., high definition or HD digital cameras providing a suitable resolution, such as 720p, 1080i, or 1080p), capture video images or video frames at any one or more suitable frame rates (e.g., 15 frames per second or fps, 24 fps, 30 fps, 60 fps, etc.) and are suitably oriented and/or movable (e.g., automatically or manually movable) to capture video images at one or more locations within the room in which participants are located, where the one or more cameras capture video images within the areas of the room for processing by a meeting software application module 200 of one or more computing devices associated with the meeting room 20 as described in fig. 2 paragraph 20, furthermore, the meeting application module 200 includes one or more software applications that facilitate face detection of one or more participants in the meeting room 20, room coordinate locations of one or more speaking participants in the meeting room 20, and identification of one or more participants in the room and their room coordinate locations based upon the face detection analysis as well as the identified locations of speaking participants in the meeting room 20, this combination of detection features facilitates providing an automatic identifier (e.g., a label including participant name) associated with identified participants in the meeting room and/or identified speaking participants in the meeting room during the online meeting as described in fig. 2 paragraph 25, moreover, a scene for the captured video content refers to an established number of ROIs (e.g., identified participants) within one or more video frames, scene change can occur, e.g., when a number and/or location of one or more ROIs within a scene has changed, for example, a scene change can occur when a new participant enters the meeting room and is captured and identified in the video content, or when an identified participant leaves the room or is no longer captured in the video content, or when an identified speaking participant is moving during speaking (thus causing a movement in ROI location within captured video frames) which in turn can cause a change in the viewpoint image within the room to maintain the identified speaking participant within captured video content (e.g., a camera capturing video content within the meeting room is automatically moved to follow the speaking participant and/or video content being captured is changed from one camera to another camera within the meeting room) as described in fig. 2 paragraph 30)). Also, see figs. 3-4 paragraphs 26, 32, 35-37, 40-41); generating, by the one or more processors, a video sequence from the plurality of scenes based on the categorization data (see ¶s 24, 37 for generating, by the one or more processors, a video sequence from the plurality of scenes based on the categorization data (i.e. the meeting application module 200 includes one or more software applications that facilitate face detection of one or more participants in the meeting room 20, room coordinate locations of one or more speaking participants in the meeting room 20, and identification of one or more participants in the room and their room coordinate locations based upon the face detection analysis as well as the identified locations of speaking participants in the meeting room 20, this combination of detection features facilitates providing an automatic identifier (e.g., a label including participant name) associated with identified participants in the meeting room and/or identified speaking participants in the meeting room during the online meeting as described in fig. 2 paragraph 25, furthermore, a scene for the captured video content refers to an established number of ROIs (e.g., identified participants) within one or more video frames, scene change can occur, e.g., when a number and/or location of one or more ROIs within a scene has changed, for example, a scene change can occur when a new participant enters the meeting room and is captured and identified in the video content, or when an identified participant leaves the room or is no longer captured in the video content, or when an identified speaking participant is moving during speaking (thus causing a movement in ROI location within captured video frames) which in turn can cause a change in the viewpoint image within the room to maintain the identified speaking participant within captured video content (e.g., a camera capturing video content within the meeting room is automatically moved to follow the speaking participant and/or video content being captured is changed from one camera to another camera within the meeting room) as described in fig. 2 paragraph 30, moreover, generating and transmission of video and/or audio content from a meeting room 20 to an endpoint 30 (or any other endpoint) engaged in an online meeting is described with regard to FIGS. 1-3 as well as the flowcharts of FIGS. 4-6 paragraph 38). Also, see figs. 3-4 paragraphs 26, 32, 35-37, 40-41); and sending, by the one or more processors, the video sequence to at least one client device (see ¶s 24, 37 for sending, by the one or more processors, the video sequence to at least one client device (i.e. generating and transmission of video and/or audio content from a meeting room 20 to an endpoint 30 (or any other endpoint) engaged in an online meeting is described with regard to FIGS. 1-3 as well as the flowcharts of FIGS. 4-6 paragraph 38). Also, see fig. 4 paragraphs 40-41, 45-47, 53)
Re claim 2, Mackell as discussed in claim 1 above discloses all the claim limitations with additional claimed feature wherein the scene data includes one or more scenes detected in the plurality of video streams (see fig. 4 ¶s 20, 29 for the scene data includes one or more scenes detected in the plurality of video streams (i.e. a scene for the captured video content refers to an established number of ROIs (e.g., identified participants) within one or more video frames, scene change can occur, e.g., when a number and/or location of one or more ROIs within a scene has changed, for example, a scene change can occur when a new participant enters the meeting room and is captured and identified in the video content, or when an identified participant leaves the room or is no longer captured in the video content, or when an identified speaking participant is moving during speaking (thus causing a movement in ROI location within captured video frames) which in turn can cause a change in the viewpoint image within the room to maintain the identified speaking participant within captured video content (e.g., a camera capturing video content within the meeting room is automatically moved to follow the speaking participant and/or video content being captured is changed from one camera to another camera within the meeting room) as described in fig. 2 paragraph 30))
Re claim 3, Mackell as discussed in claim 1 above discloses all the claim limitations with additional claimed feature wherein the categorization data includes tagging data, and wherein the tagging data corresponds to at least one feature of the scene data (see ¶ 26 for the categorization data includes tagging data, and wherein the tagging data corresponds to at least one feature of the scene data (i.e. the meeting application module 200 includes one or more software applications that facilitate face detection of one or more participants in the meeting room 20, room coordinate locations of one or more speaking participants in the meeting room 20, and identification of one or more participants in the room and their room coordinate locations based upon the face detection analysis as well as the identified locations of speaking participants in the meeting room 20, this combination of detection features facilitates providing an automatic identifier (e.g., a label including participant name) associated with identified participants in the meeting room and/or identified speaking participants in the meeting room during the online meeting as described in fig. 2 paragraph 25, furthermore, a scene for the captured video content refers to an established number of ROIs (e.g., identified participants) within one or more video frames, scene change can occur, e.g., when a number and/or location of one or more ROIs within a scene has changed, for example, a scene change can occur when a new participant enters the meeting room and is captured and identified in the video content, or when an identified participant leaves the room or is no longer captured in the video content, or when an identified speaking participant is moving during speaking (thus causing a movement in ROI location within captured video frames) which in turn can cause a change in the viewpoint image within the room to maintain the identified speaking participant within captured video content (e.g., a camera capturing video content within the meeting room is automatically moved to follow the speaking participant and/or video content being captured is changed from one camera to another camera within the meeting room) as described in fig. 2 paragraph 30)). Also, see figs. 3-4 paragraphs 32, 35-37, 40-41)
Re claim 4, Mackell as discussed in claim 3 above discloses all the claim limitations with additional claimed feature further comprising: augmenting, by the one or more processors, the video sequence with augmentation data based on data associated with the categorization data or the tagging data (see ¶s 24, 37 for the video sequence with augmentation data based on data associated with the categorization data or the tagging data (i.e. to enhance the experience at the recipient end of the video content (i.e., a remote participant), it is desirable to enable automatic and correct identification of participants in a meeting room, in which identifiers associated with participants (e.g., name labels) are synchronous with the participants in consecutive video frames, even during the occurrence of movements of participants, scene changes and/or crop changes in the video content as described in paragraph 2, furthermore, the meeting application module 200 includes one or more software applications that facilitate face detection of one or more participants in the meeting room 20, room coordinate locations of one or more speaking participants in the meeting room 20, and identification of one or more participants in the room and their room coordinate locations based upon the face detection analysis as well as the identified locations of speaking participants in the meeting room 20, this combination of detection features facilitates providing an automatic identifier (e.g., a label including participant name) associated with identified participants in the meeting room and/or identified speaking participants in the meeting room during the online meeting as described in fig. 2 paragraph 25, moreover, a scene for the captured video content refers to an established number of ROIs (e.g., identified participants) within one or more video frames, scene change can occur, e.g., when a number and/or location of one or more ROIs within a scene has changed, for example, a scene change can occur when a new participant enters the meeting room and is captured and identified in the video content, or when an identified participant leaves the room or is no longer captured in the video content, or when an identified speaking participant is moving during speaking (thus causing a movement in ROI location within captured video frames) which in turn can cause a change in the viewpoint image within the room to maintain the identified speaking participant within captured video content (e.g., a camera capturing video content within the meeting room is automatically moved to follow the speaking participant and/or video content being captured is changed from one camera to another camera within the meeting room) as described in fig. 2 paragraph 30, additionally, generating and transmission of video and/or audio content from a meeting room 20 to an endpoint 30 (or any other endpoint) engaged in an online meeting is described with regard to FIGS. 1-3 as well as the flowcharts of FIGS. 4-6 paragraph 38). Also, see figs. 3-4 paragraphs 26, 32, 35-37, 40-41)
Re claim 7, Mackell as discussed in claim 1 above discloses all the claim limitations with additional claimed feature further comprising: analyzing, by the one or more processors, the plurality of video streams using one or more object recognition algorithms or one or more scene detection algorithms configured to detect the scene data for each of the plurality of video streams (see ¶s 24, 26-27 for analyzing, by the one or more processors, the plurality of video streams using one or more object recognition algorithms or one or more scene detection algorithms configured to detect the scene data for each of the plurality of video streams (i.e. the meeting application module 200 includes one or more software applications that facilitate face detection of one or more participants in the meeting room 20, room coordinate locations of one or more speaking participants in the meeting room 20, and identification of one or more participants in the room and their room coordinate locations based upon the face detection analysis as well as the identified locations of speaking participants in the meeting room 20, this combination of detection features facilitates providing an automatic identifier (e.g., a label including participant name) associated with identified participants in the meeting room and/or identified speaking participants in the meeting room during the online meeting as described in fig. 2 paragraph 25, furthermore, a scene for the captured video content refers to an established number of ROIs (e.g., identified participants) within one or more video frames, scene change can occur, e.g., when a number and/or location of one or more ROIs within a scene has changed, for example, a scene change can occur when a new participant enters the meeting room and is captured and identified in the video content, or when an identified participant leaves the room or is no longer captured in the video content, or when an identified speaking participant is moving during speaking (thus causing a movement in ROI location within captured video frames) which in turn can cause a change in the viewpoint image within the room to maintain the identified speaking participant within captured video content (e.g., a camera capturing video content within the meeting room is automatically moved to follow the speaking participant and/or video content being captured is changed from one camera to another camera within the meeting room) as described in fig. 2 paragraph 30). Also, see fig. 4 paragraphs 28-29, 37, 40)
Re claim 9, Mackell discloses a non-transitory machine-readable storage medium for automatically producing live video content received from one or more collaborators that provides instructions that, if executed by a processor, will cause the processor to perform operations comprising: receiving, by one or more processors, a plurality of video streams from one or more user devices, wherein the plurality of video streams correspond to one or more views of an event, and wherein the plurality of video streams include a plurality of video frames (see ¶s 21-23 for receiving, by one or more processors, a plurality of video streams from one or more user devices, wherein the plurality of video streams correspond to one or more views of an event, and wherein the plurality of video streams include a plurality of video frames (i.e. in particular, the meeting room 20 includes one or more cameras (represented by camera 22 in FIG. 2) that can be of any suitable types (e.g., high definition or HD digital cameras providing a suitable resolution, such as 720p, 1080i, or 1080p), capture video images or video frames at any one or more suitable frame rates (e.g., 15 frames per second or fps, 24 fps, 30 fps, 60 fps, etc.) and are suitably oriented and/or movable (e.g., automatically or manually movable) to capture video images at one or more locations within the room in which participants are located, where the one or more cameras capture video images within the areas of the room for processing by a meeting software application module 200 of one or more computing devices associated with the meeting room 20 as described in fig. 2 paragraph 20, furthermore, the computing device(s) of a meeting room 20 can include a meeting application software module (or meeting app module) 200 that includes one or more software applications that perform functions associated with an online meeting, including communications with other endpoints engaged in the meeting over the network(s) 10 (including sending and receiving of audio content, video content and/or other content associated with the online meeting as described in fig. 2 paragraph 24))); detecting, by the one or more processors, scene data for each of the plurality of video streams (see ¶s 24, 26-27 for detecting, by the one or more processors, scene data for each of the plurality of video streams (i.e. the meeting application module 200 includes one or more software applications that facilitate face detection of one or more participants in the meeting room 20, room coordinate locations of one or more speaking participants in the meeting room 20, and identification of one or more participants in the room and their room coordinate locations based upon the face detection analysis as well as the identified locations of speaking participants in the meeting room 20, this combination of detection features facilitates providing an automatic identifier (e.g., a label including participant name) associated with identified participants in the meeting room and/or identified speaking participants in the meeting room during the online meeting as described in fig. 2 paragraph 25, furthermore, a scene for the captured video content refers to an established number of ROIs (e.g., identified participants) within one or more video frames, scene change can occur, e.g., when a number and/or location of one or more ROIs within a scene has changed, for example, a scene change can occur when a new participant enters the meeting room and is captured and identified in the video content, or when an identified participant leaves the room or is no longer captured in the video content, or when an identified speaking participant is moving during speaking (thus causing a movement in ROI location within captured video frames) which in turn can cause a change in the viewpoint image within the room to maintain the identified speaking participant within captured video content (e.g., a camera capturing video content within the meeting room is automatically moved to follow the speaking participant and/or video content being captured is changed from one camera to another camera within the meeting room) as described in fig. 2 paragraph 30). Also, see fig. 4 paragraphs 28-29, 37, 40); generating, by the one or more processors, categorization data for each of the plurality of video frames based on the scene data (see ¶s 21-24 for generating, by the one or more processors, categorization data for each of the plurality of video frames based on the scene data (i.e. in particular, the meeting room 20 includes one or more cameras (represented by camera 22 in FIG. 2) that can be of any suitable types (e.g., high definition or HD digital cameras providing a suitable resolution, such as 720p, 1080i, or 1080p), capture video images or video frames at any one or more suitable frame rates (e.g., 15 frames per second or fps, 24 fps, 30 fps, 60 fps, etc.) and are suitably oriented and/or movable (e.g., automatically or manually movable) to capture video images at one or more locations within the room in which participants are located, where the one or more cameras capture video images within the areas of the room for processing by a meeting software application module 200 of one or more computing devices associated with the meeting room 20 as described in fig. 2 paragraph 20, furthermore, the meeting application module 200 includes one or more software applications that facilitate face detection of one or more participants in the meeting room 20, room coordinate locations of one or more speaking participants in the meeting room 20, and identification of one or more participants in the room and their room coordinate locations based upon the face detection analysis as well as the identified locations of speaking participants in the meeting room 20, this combination of detection features facilitates providing an automatic identifier (e.g., a label including participant name) associated with identified participants in the meeting room and/or identified speaking participants in the meeting room during the online meeting as described in fig. 2 paragraph 25, moreover, a scene for the captured video content refers to an established number of ROIs (e.g., identified participants) within one or more video frames, scene change can occur, e.g., when a number and/or location of one or more ROIs within a scene has changed, for example, a scene change can occur when a new participant enters the meeting room and is captured and identified in the video content, or when an identified participant leaves the room or is no longer captured in the video content, or when an identified speaking participant is moving during speaking (thus causing a movement in ROI location within captured video frames) which in turn can cause a change in the viewpoint image within the room to maintain the identified speaking participant within captured video content (e.g., a camera capturing video content within the meeting room is automatically moved to follow the speaking participant and/or video content being captured is changed from one camera to another camera within the meeting room) as described in fig. 2 paragraph 30)). Also, see figs. 3-4 paragraphs 26, 32, 35-37, 40-41); selecting, by the one or more processors, a plurality of scenes from the plurality of video streams based on the categorization data (see ¶s 21-24 for selecting, by the one or more processors, a plurality of scenes from the plurality of video streams based on the categorization data (i.e. in particular, the meeting room 20 includes one or more cameras (represented by camera 22 in FIG. 2) that can be of any suitable types (e.g., high definition or HD digital cameras providing a suitable resolution, such as 720p, 1080i, or 1080p), capture video images or video frames at any one or more suitable frame rates (e.g., 15 frames per second or fps, 24 fps, 30 fps, 60 fps, etc.) and are suitably oriented and/or movable (e.g., automatically or manually movable) to capture video images at one or more locations within the room in which participants are located, where the one or more cameras capture video images within the areas of the room for processing by a meeting software application module 200 of one or more computing devices associated with the meeting room 20 as described in fig. 2 paragraph 20, furthermore, the meeting application module 200 includes one or more software applications that facilitate face detection of one or more participants in the meeting room 20, room coordinate locations of one or more speaking participants in the meeting room 20, and identification of one or more participants in the room and their room coordinate locations based upon the face detection analysis as well as the identified locations of speaking participants in the meeting room 20, this combination of detection features facilitates providing an automatic identifier (e.g., a label including participant name) associated with identified participants in the meeting room and/or identified speaking participants in the meeting room during the online meeting as described in fig. 2 paragraph 25, moreover, a scene for the captured video content refers to an established number of ROIs (e.g., identified participants) within one or more video frames, scene change can occur, e.g., when a number and/or location of one or more ROIs within a scene has changed, for example, a scene change can occur when a new participant enters the meeting room and is captured and identified in the video content, or when an identified participant leaves the room or is no longer captured in the video content, or when an identified speaking participant is moving during speaking (thus causing a movement in ROI location within captured video frames) which in turn can cause a change in the viewpoint image within the room to maintain the identified speaking participant within captured video content (e.g., a camera capturing video content within the meeting room is automatically moved to follow the speaking participant and/or video content being captured is changed from one camera to another camera within the meeting room) as described in fig. 2 paragraph 30)). Also, see figs. 3-4 paragraphs 26, 32, 35-37, 40-41); generating, by the one or more processors, a video sequence from the plurality of scenes based on the categorization data (see ¶s 24, 37 for generating, by the one or more processors, a video sequence from the plurality of scenes based on the categorization data (i.e. the meeting application module 200 includes one or more software applications that facilitate face detection of one or more participants in the meeting room 20, room coordinate locations of one or more speaking participants in the meeting room 20, and identification of one or more participants in the room and their room coordinate locations based upon the face detection analysis as well as the identified locations of speaking participants in the meeting room 20, this combination of detection features facilitates providing an automatic identifier (e.g., a label including participant name) associated with identified participants in the meeting room and/or identified speaking participants in the meeting room during the online meeting as described in fig. 2 paragraph 25, furthermore, a scene for the captured video content refers to an established number of ROIs (e.g., identified participants) within one or more video frames, scene change can occur, e.g., when a number and/or location of one or more ROIs within a scene has changed, for example, a scene change can occur when a new participant enters the meeting room and is captured and identified in the video content, or when an identified participant leaves the room or is no longer captured in the video content, or when an identified speaking participant is moving during speaking (thus causing a movement in ROI location within captured video frames) which in turn can cause a change in the viewpoint image within the room to maintain the identified speaking participant within captured video content (e.g., a camera capturing video content within the meeting room is automatically moved to follow the speaking participant and/or video content being captured is changed from one camera to another camera within the meeting room) as described in fig. 2 paragraph 30, moreover, generating and transmission of video and/or audio content from a meeting room 20 to an endpoint 30 (or any other endpoint) engaged in an online meeting is described with regard to FIGS. 1-3 as well as the flowcharts of FIGS. 4-6 paragraph 38). Also, see figs. 3-4 paragraphs 26, 32, 35-37, 40-41); and sending, by the one or more processors, the video sequence to at least one client device (see ¶s 24, 37 for sending, by the one or more processors, the video sequence to at least one client device (i.e. generating and transmission of video and/or audio content from a meeting room 20 to an endpoint 30 (or any other endpoint) engaged in an online meeting is described with regard to FIGS. 1-3 as well as the flowcharts of FIGS. 4-6 paragraph 38). Also, see fig. 4 paragraphs 40-41, 45-47, 53)
Re claim 10, Mackell as discussed in claim 2 above discloses all the claimed limitations of claim 10.
Re claim 11, Mackell as discussed in claim 3 above discloses all the claimed limitations of claim 11.
Re claim 12, Mackell as discussed in claim 4 above discloses all the claimed limitations of claim 12.
Re claim 15, Mackell as discussed in claim 7 above discloses all the claimed limitations of claim 15.
Re claim 17, Mackell discloses a computer system for automatically producing live video content received from one or more collaborators, comprising: a memory having processor-readable instructions stored therein (i.e. computing device 800 also includes a main memory 804, such as a random access memory (RAM) or other dynamic storage device (e.g., dynamic RAM (DRAM), static RAM (SRAM), and synchronous DRAM (SD RAM)), coupled to the bus 802 for storing information and instructions to be executed by processor 803 as described in fig. 8 paragraph 61); and one or more processors configured to access the memory and execute the processor-readable instructions, which when executed by the one or more processors configures the one or more processors to perform a plurality of functions, including functions for (i.e. computing device 800 includes a bus 802 or other communication mechanism for communicating information, and a processor 803 coupled with the bus 802 for processing the information, while the figure shows a single block for a processor, it should be understood that any number of processors 803 can be provided representing a plurality of processing cores, each of which can perform separate processing, the computing device 800 also includes a main memory 804, such as a random access memory (RAM) or other dynamic storage device (e.g., dynamic RAM (DRAM), static RAM (SRAM), and synchronous DRAM (SD RAM)), coupled to the bus 802 for storing information and instructions to be executed by processor 803 as described in fig. 8 paragraph 61): receiving, by one or more processors, a plurality of video streams from one or more user devices, wherein the plurality of video streams correspond to one or more views of an event, and wherein the plurality of video streams include a plurality of video frames (see ¶s 21-23 for receiving, by one or more processors, a plurality of video streams from one or more user devices, wherein the plurality of video streams correspond to one or more views of an event, and wherein the plurality of video streams include a plurality of video frames (i.e. in particular, the meeting room 20 includes one or more cameras (represented by camera 22 in FIG. 2) that can be of any suitable types (e.g., high definition or HD digital cameras providing a suitable resolution, such as 720p, 1080i, or 1080p), capture video images or video frames at any one or more suitable frame rates (e.g., 15 frames per second or fps, 24 fps, 30 fps, 60 fps, etc.) and are suitably oriented and/or movable (e.g., automatically or manually movable) to capture video images at one or more locations within the room in which participants are located, where the one or more cameras capture video images within the areas of the room for processing by a meeting software application module 200 of one or more computing devices associated with the meeting room 20 as described in fig. 2 paragraph 20, furthermore, the computing device(s) of a meeting room 20 can include a meeting application software module (or meeting app module) 200 that includes one or more software applications that perform functions associated with an online meeting, including communications with other endpoints engaged in the meeting over the network(s) 10 (including sending and receiving of audio content, video content and/or other content associated with the online meeting as described in fig. 2 paragraph 24))); detecting, by the one or more processors, scene data for each of the plurality of video streams (see ¶s 24, 26-27 for detecting, by the one or more processors, scene data for each of the plurality of video streams (i.e. the meeting application module 200 includes one or more software applications that facilitate face detection of one or more participants in the meeting room 20, room coordinate locations of one or more speaking participants in the meeting room 20, and identification of one or more participants in the room and their room coordinate locations based upon the face detection analysis as well as the identified locations of speaking participants in the meeting room 20, this combination of detection features facilitates providing an automatic identifier (e.g., a label including participant name) associated with identified participants in the meeting room and/or identified speaking participants in the meeting room during the online meeting as described in fig. 2 paragraph 25, furthermore, a scene for the captured video content refers to an established number of ROIs (e.g., identified participants) within one or more video frames, scene change can occur, e.g., when a number and/or location of one or more ROIs within a scene has changed, for example, a scene change can occur when a new participant enters the meeting room and is captured and identified in the video content, or when an identified participant leaves the room or is no longer captured in the video content, or when an identified speaking participant is moving during speaking (thus causing a movement in ROI location within captured video frames) which in turn can cause a change in the viewpoint image within the room to maintain the identified speaking participant within captured video content (e.g., a camera capturing video content within the meeting room is automatically moved to follow the speaking participant and/or video content being captured is changed from one camera to another camera within the meeting room) as described in fig. 2 paragraph 30). Also, see fig. 4 paragraphs 28-29, 37, 40); generating, by the one or more processors, categorization data for each of the plurality of video frames based on the scene data (see ¶s 21-24 for generating, by the one or more processors, categorization data for each of the plurality of video frames based on the scene data (i.e. in particular, the meeting room 20 includes one or more cameras (represented by camera 22 in FIG. 2) that can be of any suitable types (e.g., high definition or HD digital cameras providing a suitable resolution, such as 720p, 1080i, or 1080p), capture video images or video frames at any one or more suitable frame rates (e.g., 15 frames per second or fps, 24 fps, 30 fps, 60 fps, etc.) and are suitably oriented and/or movable (e.g., automatically or manually movable) to capture video images at one or more locations within the room in which participants are located, where the one or more cameras capture video images within the areas of the room for processing by a meeting software application module 200 of one or more computing devices associated with the meeting room 20 as described in fig. 2 paragraph 20, furthermore, the meeting application module 200 includes one or more software applications that facilitate face detection of one or more participants in the meeting room 20, room coordinate locations of one or more speaking participants in the meeting room 20, and identification of one or more participants in the room and their room coordinate locations based upon the face detection analysis as well as the identified locations of speaking participants in the meeting room 20, this combination of detection features facilitates providing an automatic identifier (e.g., a label including participant name) associated with identified participants in the meeting room and/or identified speaking participants in the meeting room during the online meeting as described in fig. 2 paragraph 25, moreover, a scene for the captured video content refers to an established number of ROIs (e.g., identified participants) within one or more video frames, scene change can occur, e.g., when a number and/or location of one or more ROIs within a scene has changed, for example, a scene change can occur when a new participant enters the meeting room and is captured and identified in the video content, or when an identified participant leaves the room or is no longer captured in the video content, or when an identified speaking participant is moving during speaking (thus causing a movement in ROI location within captured video frames) which in turn can cause a change in the viewpoint image within the room to maintain the identified speaking participant within captured video content (e.g., a camera capturing video content within the meeting room is automatically moved to follow the speaking participant and/or video content being captured is changed from one camera to another camera within the meeting room) as described in fig. 2 paragraph 30)). Also, see figs. 3-4 paragraphs 26, 32, 35-37, 40-41); selecting, by the one or more processors, a plurality of scenes from the plurality of video streams based on the categorization data (see ¶s 21-24 for selecting, by the one or more processors, a plurality of scenes from the plurality of video streams based on the categorization data (i.e. in particular, the meeting room 20 includes one or more cameras (represented by camera 22 in FIG. 2) that can be of any suitable types (e.g., high definition or HD digital cameras providing a suitable resolution, such as 720p, 1080i, or 1080p), capture video images or video frames at any one or more suitable frame rates (e.g., 15 frames per second or fps, 24 fps, 30 fps, 60 fps, etc.) and are suitably oriented and/or movable (e.g., automatically or manually movable) to capture video images at one or more locations within the room in which participants are located, where the one or more cameras capture video images within the areas of the room for processing by a meeting software application module 200 of one or more computing devices associated with the meeting room 20 as described in fig. 2 paragraph 20, furthermore, the meeting application module 200 includes one or more software applications that facilitate face detection of one or more participants in the meeting room 20, room coordinate locations of one or more speaking participants in the meeting room 20, and identification of one or more participants in the room and their room coordinate locations based upon the face detection analysis as well as the identified locations of speaking participants in the meeting room 20, this combination of detection features facilitates providing an automatic identifier (e.g., a label including participant name) associated with identified participants in the meeting room and/or identified speaking participants in the meeting room during the online meeting as described in fig. 2 paragraph 25, moreover, a scene for the captured video content refers to an established number of ROIs (e.g., identified participants) within one or more video frames, scene change can occur, e.g., when a number and/or location of one or more ROIs within a scene has changed, for example, a scene change can occur when a new participant enters the meeting room and is captured and identified in the video content, or when an identified participant leaves the room or is no longer captured in the video content, or when an identified speaking participant is moving during speaking (thus causing a movement in ROI location within captured video frames) which in turn can cause a change in the viewpoint image within the room to maintain the identified speaking participant within captured video content (e.g., a camera capturing video content within the meeting room is automatically moved to follow the speaking participant and/or video content being captured is changed from one camera to another camera within the meeting room) as described in fig. 2 paragraph 30)). Also, see figs. 3-4 paragraphs 26, 32, 35-37, 40-41); generating, by the one or more processors, a video sequence from the plurality of scenes based on the categorization data (see ¶s 24, 37 for generating, by the one or more processors, a video sequence from the plurality of scenes based on the categorization data (i.e. the meeting application module 200 includes one or more software applications that facilitate face detection of one or more participants in the meeting room 20, room coordinate locations of one or more speaking participants in the meeting room 20, and identification of one or more participants in the room and their room coordinate locations based upon the face detection analysis as well as the identified locations of speaking participants in the meeting room 20, this combination of detection features facilitates providing an automatic identifier (e.g., a label including participant name) associated with identified participants in the meeting room and/or identified speaking participants in the meeting room during the online meeting as described in fig. 2 paragraph 25, furthermore, a scene for the captured video content refers to an established number of ROIs (e.g., identified participants) within one or more video frames, scene change can occur, e.g., when a number and/or location of one or more ROIs within a scene has changed, for example, a scene change can occur when a new participant enters the meeting room and is captured and identified in the video content, or when an identified participant leaves the room or is no longer captured in the video content, or when an identified speaking participant is moving during speaking (thus causing a movement in ROI location within captured video frames) which in turn can cause a change in the viewpoint image within the room to maintain the identified speaking participant within captured video content (e.g., a camera capturing video content within the meeting room is automatically moved to follow the speaking participant and/or video content being captured is changed from one camera to another camera within the meeting room) as described in fig. 2 paragraph 30, moreover, generating and transmission of video and/or audio content from a meeting room 20 to an endpoint 30 (or any other endpoint) engaged in an online meeting is described with regard to FIGS. 1-3 as well as the flowcharts of FIGS. 4-6 paragraph 38). Also, see figs. 3-4 paragraphs 26, 32, 35-37, 40-41); and sending, by the one or more processors, the video sequence to at least one client device (see ¶s 24, 37 for sending, by the one or more processors, the video sequence to at least one client device (i.e. generating and transmission of video and/or audio content from a meeting room 20 to an endpoint 30 (or any other endpoint) engaged in an online meeting is described with regard to FIGS. 1-3 as well as the flowcharts of FIGS. 4-6 paragraph 38). Also, see fig. 4 paragraphs 40-41, 45-47, 53)
Re claim 18, Mackell as discussed in claim 2 above discloses all the claimed limitations of claim 18.
Re claim 19, Mackell as discussed in claim 3 above discloses all the claimed limitations of claim 19.
Re claim 20, Mackell as discussed in claim 4 above discloses all the claimed limitations of claim 20.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 5, 6, 13 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Mackell et al. (US 2020/0344278 A1)(hereinafter Mackell) as applied to claims 1-4, 7, 9-12, 15 and 17-20 above, and further in view of Wire et al. (US 2016/0023116 A1)(hereinafter Wire).
Re claim 5, Mackell as discussed in claim 4 above discloses all the claimed limitations but fails to explicitly teach further comprising: obscuring, by the one or more processors, at least one video frame of the plurality of video frames based on the tagging data or the augmentation data. However, the reference of Wire explicitly teaches further comprising: obscuring, by the one or more processors, at least one video frame of the plurality of video frames based on the tagging data or the augmentation data (see fig. 6 ¶s 55-60 for obscuring, by the one or more processors (i.e. processors 112, 136 as shown in fig. 1), at least one video frame of the plurality of video frames based on the tagging data or the augmentation data (i.e. while FIG. 7 shows the results of blocking video content in the context of a reaction game, similar techniques for automatically disabling or obscuring video feeds based on recognizing flagged objects may adapted for numerous other applications, for example, a frequent user of a streaming video service may create a list of preferences about objects they would not like to see within incoming streams, the service may use a flagged object database and video recognition technology to obscure portions of incoming video streams having those objects, the objects may be selectively blurred or a video stream may be disabled altogether, similarly, certain brand logos and written text may also be selectively blocked within video streams (e.g., to avoid copyright or trademark infringement) as described in fig. 7 paragraph 66, furthermore, for example, in some scenarios, the safety protocol may dictate censoring (e.g., blurring or overlaying with censoring graphics) only portions of image frames within a stream, this may be useful when the flagged object is incidentally in the background of one or more image frames and a receiving party indicates that they do not wish to see such content (e.g., a person who has a phobia of a typically-mundane object or who strongly dislikes a certain brand) as described in fig. 10 paragraph 87). also, see paragraphs 72, 82-83, 85)
Therefore, taking the combined teachings of Mackell and Wire as a whole, it would have been obvious before the effective filing date of the claimed invention to incorporate this feature (obscuring) into the system of Mackell as taught by Wire.
One will be motivated to incorporate the above feature into the system of Mackell as taught by Wire for the benefit of blocking video content in the context of a reaction game, similar techniques for automatically disabling or obscuring video feeds based on recognizing flagged objects may be adapted for numerous other applications, for example, a frequent user of a streaming video service may create a list of preferences about objects they would not like to see within incoming streams, wherein the service may use a flagged object database and video recognition technology to obscure portions of incoming video streams having those objects, wherein the objects may be selectively blurred or a video stream may be disabled altogether, wherein such features may be immensely useful to individuals having phobias towards particular animals or other objects, similarly, certain brand logos and written text may also be selectively blocked within video streams (e.g., to avoid copyright or trademark infringement) in order to improve efficiency when obscuring video feeds based on recognizing flagged objects which is immensely useful to individuals having phobias towards particular animals or other objects (see fig. 7 ¶ 66)
Re claim 6, Mackell as discussed in claim 4 above discloses all the claimed limitations but fails to explicitly teach wherein the augmentation data includes at least one of: supporting visualization, a contradictory visualization, and source data. However, the reference of Wire explicitly teaches wherein the augmentation data includes at least one of: supporting visualization, a contradictory visualization, and source data (see fig. 1 ¶ 41 for the augmentation data includes at least one of: supporting visualization, a contradictory visualization, and source data (i.e. During a game session, participants may attempt to incite one another into exhibiting an emotional response by using features built into the game, for example, participants may select visual overlays (e.g., digital stickers or animations), audio clips, or other features from a selectable feature window 630 that may be presented to their opponents as described in fig. 6 paragraph 57))
Therefore, taking the combined teachings of Mackell and Wire as a whole, it would have been obvious before the effective filing date of the claimed invention to incorporate this feature (visualization) into the system of Mackell as taught by Wire.
One will be motivated to incorporate the above feature into the system of Mackell as taught by Wire for the benefit of having a communications server that may be used to enable the communication and gameplay between the participants, wherein during a game session, the participants may receive video streams or image frames of one another as well as corresponding audio streams through the communications server, wherein the participants may further be permitted to select one or more features (e.g., visual overlays or audio clips) to be presented to the other users through their devices to provoke a response such that their opponents trigger a loss criterion in order to have a user friendly interaction (see ¶ 8)
Re claim 13, the combination of Mackell and Wire as discussed in claim 5 above discloses all the claimed limitations of claim 13.
Re claim 14, the combination of Mackell and Wire as discussed in claim 6 above discloses all the claimed limitations of claim 14.
Claims 8 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Mackell et al. (US 2020/0344278 A1)(hereinafter Mackell) as applied to claims 1-4, 7, 9-12, 15 and 17-20 above, and further in view of MAKINEN et al. (US 2021/0160549 A1)(hereinafter MAKINEN).
Re claim 8, Mackell as discussed in claim 4 above discloses all the claimed limitations but fails to explicitly teach wherein analyzing further comprises: analyzing, by the one or more processors via a machine-learning model, audio data of the plurality of video streams containing speech data; and generating, by the one or more processors, the categorization data based on the speech data. However, the reference of MAKINEN explicitly teaches wherein analyzing further comprises: analyzing, by the one or more processors via a machine-learning model, audio data of the plurality of video streams containing speech data (see ¶ 92 for analyzing, by the one or more processors via a machine-learning model, audio data of the plurality of video streams containing speech data (i.e. in block 920, the processor may analyze the images in the real-time video stream, the processor may use deep learning, which is part of a broader family of machine learning methods based on artificial neural networks, this deep learning may be supervised, semi-supervised, or unsupervised, the deep learning may be applied to frameworks for participants (i.e., players), officials, and primary event objects (e.g., the game ball or puck) as described in fig. 9 paragraph 93, furthermore, a typical mobile device 1200 also includes a sound encoding/decoding encoder module 1210, which digitizes sound received from a microphone into data packets suitable for wireless transmission and decodes received sound data packets to generate analog signals that are provided to the speaker to generate sound as described in fig. 12 paragraph 135). Also, see paragraphs 94-96); and generating, by the one or more processors, the categorization data based on the speech data (see ¶ 92 for generating, by the one or more processors, the categorization data based on the speech data (i.e. in particular, six of the 10 occupants have been identified and areas within the image associated with each occupant tagged, this includes a first referee area 651, a second referee 652, only two player areas 652, 655 from the goalie's team, and three player areas from the opposing team 653, 654, 656 as described fig. 6 paragraph 86, furthermore, in block 920, the processor may analyze the images in the real-time video stream, the processor may use deep learning, which is part of a broader family of machine learning methods based on artificial neural networks, this deep learning may be supervised, semi-supervised, or unsupervised, the deep learning may be applied to frameworks for participants (i.e., players), officials, and primary event objects (e.g., the game ball or puck) as described in fig. 9 paragraph 93, moreover, a typical mobile device 1200 also includes a sound encoding/decoding encoder module 1210, which digitizes sound received from a microphone into data packets suitable for wireless transmission and decodes received sound data packets to generate analog signals that are provided to the speaker to generate sound as described in fig. 12 paragraph 135). Also, see paragraphs 94-96)
Therefore, taking the combined teachings of Mackell and MAKINEN as a whole, it would have been obvious before the effective filing date of the claimed invention to incorporate this feature (analyzing) into the system of Mackell as taught by MAKINEN.
One will be motivated to incorporate the above feature into the system of Mackell as taught by MAKINEN for the benefit of analyzing images in a real-time video stream, wherein the processor may use deep learning, which is part of a broader family of machine learning methods based on artificial neural networks, wherein this deep learning may be supervised, semi-supervised, or unsupervised, wherein the deep learning may be applied to frameworks for participants (i.e., players), officials, and primary event objects (e.g., the game ball or puck), wherein objects such as event participants (i.e., players), officials, and other elements within the analyzed video images may be identified in order to determine whether any particular volumetric spaces qualify as containing content of interest, wherein in particular, the processor may use a threshold number (e.g., two players and one referee) of objects (i.e., occupants) to determine whether a given volumetric space qualifies as containing content of interest in order to ease the processing time when analyzing the images in the real-time video stream (see fig. 9 ¶s 93-94)
Re claim 16, the combination of Mackell and MAKINEN as discussed in claim 8 above discloses all the claimed limitations of claim 16.
Conclusion
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5/21/2026
/JOSE M. MESA/
Examiner
Art Unit 2484
/THAI Q TRAN/ Supervisory Patent Examiner, Art Unit 2484
Prosecution Insights