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 .
Response to Amendment
The Amendment filed March 16th 2026 has been entered. Claims 1-10 and 12-21 are pending in the application. Claim 11 was previously cancelled. Applicant’s amendments to the Claims 1 and 16 have overcome the §103 rejections previously set forth in the Final Office Action mailed January 23rd, 2026. Newly found reference Li (US 20150213604 A1) was used for the newly amended claim limitations.
Response to Arguments
Applicant’s arguments with respect to claims 1, 3, and 16 regarding the newly amended limitations have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 103
The 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 1, 2, 4, 5, 6, 7, 8, 9, 12, 16, 17, 18, 19, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Chand (US 20190130629 A1) in view of Li (US 20150213604 A1) and Astarabadi (US 20200358983 A1).
Regarding claim 1:
Chand teaches:
A method, comprising:
accessing, via a first computing device associated with a first user (Chand: At operation 602, the communication module 302 detects an initiation of a communication session at a first client device [0049]), a collaborative program configured to support multiple participants (Chand: The messaging system 100 includes multiple client devices 102, each of which hosts a number of applications including a messaging client application 104, [0023]);
obtaining, by the first computing device, a set of mesh data corresponding to a virtual representation of a face of a participant in the collaborative program (Chand: The front facing camera of the first mobile device captures image data (e.g., pictures, video data) in real time, and generates a mesh representation of a face of the user based on the image data [0017]);
generating, by one or more processors of the first computing device, a hull of a user mask, the hull delineating a perimeter of the user mask in accordance with the set of mesh data (Chand: mesh representation 404 [0045], see Note 1A);
generating by the one or more processors, a set of facial features (Chand: the image module 306 may select a set of avatar attributes from among a database of avatar attributes, based on the image data extracted from the image 402 [0045]) in accordance with the set of mesh data (see Note 1A);
incorporating the user mask into a graphical interface of the collaborative program (Chand: The animation module 308 generates the 3D avatar 406 based on the mesh representation 404 [0045]; Chand: The display module 304 may cause display of the 3D avatar 406 at the mobile devices engaged in the communication session. [0045]);
Note 1A: In Figure 4 below, the hull and facial features have been identified by the Examiner as parts of the mesh representation. Therefore, the Examiner interprets Chand to teach generating the user mask that comprises a hull and a set of facial features.
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Figure 4 of Chand, edited to highlight the hull and facial features of the mesh representation 404.
Chand fails to explicitly teach:
generating, by one or more processors of the first computing device, a hull of a user mask, the hull delineating a perimeter of the user mask in accordance with the set of mesh data, wherein the hull is a structural boundary object distinct from the mesh data;
generating by the one or more processors, a set of facial features in accordance with the set of mesh data, wherein the facial features are generated separately from the hull;
assembling, by the one or more processors, the user mask by combining the hull and the set of facial features;
adjusting at least one of a resolution or a detail of the user mask based on available bandwidth for a communication connection associated with the collaborative program.
Li teaches:
A method, comprising:
accessing, via a first computing device associated with a first user, a collaborative program configured to support multiple participants (Li: the disclosed techniques […] can be used, for example, in video-based collaborative contexts, such as peer-to-peer or multi-point video conferencing [0042]);
obtaining, by the first computing device, a set of mesh data corresponding to a virtual representation of a face of a participant in the collaborative program (Li: An example initial three-dimensional (3-D) morphable face model is shown in FIG. 3C [0061]);
generating, by one or more processors of the first computing device (Li: Device 102 further may include one or more processors 218 configured to perform operations associated with device 102 and one or more of the modules included therein [0045]), a hull of a user mask, the hull delineating a perimeter of the user mask in accordance with the set of mesh data, wherein the hull (LI: edges 406 [0056]; see Note 1B) is a structural boundary object distinct from the mesh data (Li: An example initial three-dimensional (3-D) morphable face model is shown in FIG. 3C [0061]; see Note 1B);
generating by the one or more processors, a set of facial features (Li: facial expression detection module 312 may determine the size and/or position of various facial features (e.g., forehead, chin, eyes, nose, mouth, cheeks, facial contour, etc.) [0054]) in accordance with the set of mesh data (Li: the retrieved expression sequence representative of the detected facial expression changes can be copied to the avatar to drive the avatar in performing the same facial expression changes [0082]; see Note 1C), wherein the facial features are generated separately from the hull (see Note 1D);
assembling, by the one or more processors, the user mask by combining the hull and the set of facial features (Li: [The detected key points 404 and edges 406] position (three-dimensional coordinates) can be used to measure error in fitting the three-dimensional (3-D) morphable face model to the two-dimensional (2-D) input image. [0064]; see Note 1E);
incorporating the user mask into a graphical interface of the collaborative program (Li: The avatar control module 210 may cause a display module 212 to display an avatar on a display of device 102 or otherwise operatively coupled with device 102 [0043]); and
Note 1B: Li teaches: “the facial parameters may include one or more key points 404 and associated edges 406 connecting one or more key points 404” [0056] and that “the key points and edges are generated from an image of a user's face, as opposed to having predefined key points” [0067]. The edges 406 (shown in Fig. 3B of Li) are distinct from the avatar mesh (shown in Fig. 3C of Li). The Examiner recognizes that the edges 406 may be difficult to see in the drawings of Li and therefore a higher resolution version of the Figure is reproduced below.
Note 1C: The Examiner understands Li to teach generating the set of facial features in accordance with the mesh data, because Li teaches: “Avatar control module 210 may include […] graphics processing code (or instruction sets) that are […] operable to generate parameters for animating the avatar selected by avatar selection module 208 based on the face/head position and/or facial characteristics 206 detected by face detection module 204.” [0042]. In other words, Li teaches parameters generated based on both the facial features and the avatar mesh.
Li further teaches: “human avatars may require parameter settings (e.g., different avatar features may be altered) to demonstrate emotions like happiness, sadness, anger, surprise, etc.” [0041]. The specification of the present application teaches that: “Another approach altogether would be to use the facial features to drive a 3D model puppet. In this case, some 3D points on a puppet model would be connected to the face mesh features, and the motion of the model would be driven by the motion of the mesh.” [0037]. Because Li discusses animation of the face mesh via the parameters, the Examiner understands the parameters of Li to also be analogous to facial features.
Note 1D: Li teaches that: “facial parameter module 308 may include custom, proprietary, known, and/or after-developed facial parameter code (or instruction sets) that are generally well-defined and operable to generate the key points 404 and connecting edges 406” [0057].
Li separately teaches that: “facial expression detection module 312 may determine the size and/or position of various facial features (e.g., forehead, chin, eyes, nose, mouth, cheeks, facial contour, etc.)” [0054]. That is, the edges 406 and the facial features are not generated together, and are generated by different modules. Therefore, the Examiner understands Li to teach the facial features as being generated separately from the hull.
Note 1E: In paragraph [0065], Li teaches that “[The detected key points 404 and edges 406] position (three-dimensional coordinates) can be used to measure error in fitting the three-dimensional (3-D) morphable face model to the two-dimensional (2-D) input image.” [0064] and that “Errors are calculated as differences between the projected key points and the detected key points 404. The calculation is then performed iteratively […] to produce a new three-dimensional (3-D) model.” [0065]. In other words, utilizing the hull (edges 406, alongside key points 404), a user mask (new 3D avatar) is generated.
The avatar is further augmented with the facial features, as taught in paragraph [0082]: “Based on the detected facial features and movements thereof, an expression sequence that resembles […] the user's detected facial expression can be retrieved, […] In turn, the retrieved expression sequence representative of the detected facial expression changes can be copied to the avatar to drive the avatar in performing the same facial expression changes …”
Therefore, the Examiner interprets Li to teach “assembling, by the one or more processors, the user mask by combining the hull and the set of facial features”.
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Higher resolution version of Figure 3B of Li.
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Li with Chand. Generating the hull separately from the mesh data, as in Li, would benefit the Chand teachings because “the disclosed techniques may help to reduce communications bandwidth use, preserve the individual's anonymity, and/or provide enhanced entertainment value (e.g., amusement) for the individual, for example.” (Li, [0014])
Chand in view of Li still fails to teach:
adjusting at least one of a resolution or a detail of the user mask based on available bandwidth for a communication connection associated with the collaborative program.
Astarabadi teaches:
adjusting at least one of a resolution or a detail of the user mask (Astarabadi: suppress extraction of texture information from video frames at the first device and incorporation of texture data in synthetic video frames at the second device, which may reduce time to generate this synthetic video feed and preserve resolution of the synthetic video feed—while also decreasing realism of the synthetic video feed—at the second device [0061]) based on available bandwidth for a communication connection associated with the collaborative program (Astarabadi: if processing limitations (e.g., for facial landmark extraction, modeling, realism overlay generation) or rendering limitations (e.g., rendering the first user's face with the realism overlay over a synthetic background) at either the first or second device result in a delay—between capture of a video frame at the first device and rendering of a corresponding photorealistic synthetic frame at the second device—in excess of a threshold duration of time (e.g., 200 milliseconds) [0061]; see Note 1F).
Note 1F: The delay described by Astarabadi in [0061] as cited above is measured between capture at the first device and display on the second device, which will necessarily differ based on the bandwidth of the connection between the two devices. Because Astarabadi may “suppress extraction of texture information” based on the delay, which impacts the level of detail or “realism” of the synthetic feed (said feed comprises a 3D face mesh: “projecting the deformed 3D face mesh onto an image plane to generate a first synthetic video frame depicting the first user” [0009]), Astarabadi teaches changing at least one of a resolution or a detail of the user mask based on available bandwidth for a communication connection associated with the collaborative program.
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Astarabadi with Chand in view of Li. Changing at least one of a resolution or a detail of a model based on available bandwidth for a communication connection associated with the collaborative program, as in Astarabadi, would benefit the Chand in view of Li teachings by increasing the performance of the messaging client application by ensuring that lower bandwidth connections can support conferences with 3D avatars: “Therefore, the first method S100 can enable a high-quality video call with significantly less upload bandwidth to transmit a representation of a first video from the first device to a computer network and significantly less download bandwidth required to download this representation of the first video to the second device, and vice versa,” Astarabadi, [0011].
Regarding claim 2:
Chand in view of Li and Astarabadi teaches:
The method of claim 1 (as shown above), wherein:
the participant is a second user associated with a second computing device (Chand: A user of the second client device [0022]); and
obtaining the set of mesh data comprises receiving the set of mesh data corresponding to the virtual representation of the face of the second user from the second computing device (Chand: the communication interface at the first client device includes a presentation of a 3D avatar associated with the user of the second client device [0022]; see Note 2A).
Note 2A: Chand teaches: “The front facing camera of the first mobile device captures image data (e.g., pictures, video data) in real time, and generates a mesh representation of a face of the user based on the image data. The chat presence system may thereby animate the 3D avatar associated with the user of the first client device within the communication interface, based on the mesh representation.” [0017]. That is, the camera of a first device is used to obtain a mesh representation corresponding to a 3D avatar. Because Chand teaches that a 3D avatar associated with the user of the second client device may be received and displayed in [0022] as cited above, Chand teaches that obtaining the set of mesh data comprises receiving the set of mesh data corresponding to the virtual representation of the face of the second user from the second computing device
Regarding claim 4:
Chand in view of Li and Astarabadi teaches:
The method of claim 1 (as shown above), wherein the method further comprises updating the user mask (Chand: In some example embodiments, the animating the 3D avatar may include causing the 3D avatar to mimic facial movements of a user, based on the mesh representation of the face of the user [0019]; see Note 4A) based on a newer set of mesh data (Chand: The image 402 may comprise a photograph, or in some embodiments may include real-time video [0043]; see Note 4A).
Note 4A: Animating the 3D avatar, as Chand teaches in [0019] cited above, requires updating the 3D avatar. Furthermore, because Chand teaches that the mesh representation is generated from image 402: “The animation module 308 generates the mesh representation 404 based on the image 402,” [0044], when the image is real-time video, the mesh representation must be updated based on newer video content that may differ from older video content.
Regarding claim 5:
Chand in view of Li and Astarabadi teaches:
The method of claim 1 (as shown above), wherein the method further comprises performing pre-processing on the set of obtained mesh data based on a user interaction with the collaborative program (Chand: The chat presence system animates the 3D avatar associated with the user, for example by rotating an orientation of the 3D avatar, or moving a pair of eyes of the 3D avatar, based on the point of gaze information from the mesh representation [0018]; see Note 5A).
Note 5A: Chand teaches that a chat presence system may perform processing based on a change in reflections of the user’s eyes: “the chat presence system may perform eye tracking techniques, such as optical tracking, by […] analyzing the reflected light to extract and determine point of gaze information based on the change in reflections,” [0018]. The processing is performed based on the obtained mesh data, as Chand teaches: “the mesh representation of the face of the user includes an indication of a point of gaze of the user,” [0018]. Furthermore, the processing is performed prior to animating the 3D avatar with said gaze information: “The chat presence system animates the 3D avatar associated with the user, for example by rotating an orientation of the 3D avatar, or moving a pair of eyes of the 3D avatar, based on the point of gaze information from the mesh representation,” [0018]. Therefore, under broadest reasonable interpretation, said processing may be considered “pre-processing”.
Regarding claim 6:
Chand in view of Li and Astarabadi teaches:
The method of claim 5 (as shown above), wherein the pre-processing includes rotating point coordinates of the set of mesh data (Chand: moving a pair of eyes of the 3D avatar, [0018]).
Note 6A: Chand teaches that the 3D avatar is animated based on the mesh representation: “the animating the 3D avatar may include causing the 3D avatar to mimic facial movements of a user, based on the mesh representation of the face of the user,” [0019]. Chand further teaches that the mesh representation comprises a point of gaze of the avatar: “the mesh representation of the face of the user includes an indication of a point of gaze of the user, wherein the point of gaze represents a direction in which the user is looking,” [0018]. Moving a pair of eyes is analogous to rotating them. Therefore, because the eyes are a facial feature represented by the mesh representation as shown in Figure 4 of Chand (i.e., the eyes will be represented by point coordinates when they are part of a mesh), when Chand recites “moving a pair of eyes of the 3D avatar,” Chand describes rotating point coordinates of the set of mesh data.
Regarding claim 7:
Chand in view of Li and Astarabadi teaches:
The method of claim 5 (as shown above), wherein rotating the point coordinates (Chand: rotating an orientation of the 3D avatar, [0018]; see Note 6A) causes the user mask to change orientation to indicate where the participant's focus is (Chand: the mesh representation of the face of the user includes an indication of a point of gaze of the user, wherein the point of gaze represents a direction in which the user is looking [0018]).
Regarding claim 8:
Chand in view of Li and Astarabadi teaches:
The method of claim 1 (as shown above), wherein the user mask illustrates at least one of a facial expression or positioning of the participant's head (Chand: as a user moves their head, and changes facial expressions, the chat presence system generates the mesh representation of the user's face in real time, and animates the 3D avatar associated with the user based on the mesh representation [0019]).
Regarding claim 9:
Chand in view of Astarabadi teaches:
The method of claim 1 (as shown above), wherein the participant is the first user, and the method further comprises generating the set of mesh data from a frame captured by a camera associated with the first computing device (Chand: The front facing camera of the first mobile device captures image data (e.g., pictures, video data) in real time, and generates a mesh representation of a face of the user based on the image data [0017]).
Regarding claim 12:
Chand in view of Li and Astarabadi teaches:
The method of claim 1 (as shown above),
Chand in view of Li fails to teach:
further comprising changing at least one of a resolution or a detail of the user mask based on computer processing usage associated with the one or more processors of the first computing device.
Astarabadi teaches:
further comprising changing at least one of a resolution or a detail of the user mask (Astarabadi: suppress extraction of texture information from video frames at the first device and incorporation of texture data in synthetic video frames at the second device, which may reduce time to generate this synthetic video feed and preserve resolution of the synthetic video feed—while also decreasing realism of the synthetic video feed—at the second device [0061]) based on computer processing usage (Astarabadi: processing limitations [0061]) associated with the one or more processors of the first computing device (Astarabadi: if processing limitations (e.g., for facial landmark extraction, modeling, realism overlay generation) or rendering limitations (e.g., rendering the first user's face with the realism overlay over a synthetic background) at either the first or second device result in a delay—between capture of a video frame at the first device and rendering of a corresponding photorealistic synthetic frame at the second device—in excess of a threshold duration of time (e.g., 200 milliseconds), [0061]; see Note 12A).
Note 12A: Astarabadi teaches that a delay based on “processing limitations […] at the first device” [0061] may be detected. A delay caused processing limitations is analogous to a delay caused by computer processor usage. Therefore, because Astarabadi may “suppress extraction of texture information” based on the delay, which impacts the level of detail or “realism” of the synthetic feed (said feed comprises a 3D face mesh: “projecting the deformed 3D face mesh onto an image plane to generate a first synthetic video frame depicting the first user” [0009]), Astarabadi teaches changing at least one of a resolution or a detail of the user mask based on computer processing usage associated with the one or more processors of the first computing device.
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Astarabadi with Chand in view of Li. Changing at least one of a resolution or a detail of a model based on computer processing usage associated with the one or more processors of the first computing device, as in Astarabadi, would benefit the Chand in view of Li teachings by increasing the performance of the messaging client application by ensuring that lower bandwidth connections can support conferences with 3D avatars: “Therefore, the first method S100 can enable a high-quality video call with significantly less upload bandwidth to transmit a representation of a first video from the first device to a computer network and significantly less download bandwidth required to download this representation of the first video to the second device, and vice versa,” Astarabadi, [0011].
Regarding claim 16:
Claim 16 is substantially similar to Claim 1, and is therefore rejected for similar reasons. Claim 16 contains the following notable differences:
Claim 16 claims a computing device instead of a method. Chand teaches a computing device (Chand: machine 1000 [0064]) comprising: memory configured to store data associated with a collaborative program; and one or more processors operatively coupled to the memory (Chand: The software architecture 906 may execute on hardware such as machine 1000 of FIG. 10 that includes, among other things, processors 1004, memory 1014 [0064]).
Regarding claim 17:
Claim 17 is substantially similar to Claim 4, and is therefore rejected for similar reasons. Claim 17 contains the following notable differences:
Claim 17 claims a computing device instead of a method. In the rejection of Claim 16 it was shown that Chand teaches a computing device.
Regarding claim 18:
Claim 18 is substantially similar to Claim 5, and is therefore rejected for similar reasons. Claim 18 contains the following notable differences:
Claim 18 claims a computing device instead of a method. In the rejection of Claim 16 it was shown that Chand teaches a computing device.
Regarding claim 19:
Claim 19 is substantially similar to Claim 8, and is therefore rejected for similar reasons. Claim 19 contains the following notable differences:
Claim 19 claims a computing device instead of a method. In the rejection of Claim 16 it was shown that Chand teaches a computing device.
Regarding claim 20:
Chand in view of Li and Astarabadi teaches:
The computing device of claim 16 (as shown above), wherein: the computing device further includes at least one camera; the participant is a user of the computing device; and the one or more processors are further configured to generate the set of mesh data from a frame captured by the one or more cameras associated (Chand: The front facing camera of the first mobile device captures image data (e.g., pictures, video data) in real time, and generates a mesh representation of a face of the user based on the image data [0017]).
Claim 3 is rejected under 35 U.S.C 103 as being unpatentable over Chand (US 20190130629 A1) in view of Li (US 20150213604 A1), Astarabadi (US 20200358983 A1) and Imoto (US 20180182145 A1).
Chand in view of Li and Astarabadi teaches:
The method of claim 1 (as shown above),
Chand in view of Li and Astarabadi fails to teach:
wherein generating the hull and generating the set of facial features are performed in parallel.
Imoto teaches:
wherein generating the hull and generating the set of facial features are performed in parallel (Imoto: a process in which the steps are not necessarily processed in a time-series manner but are executed in parallel or individually is included, [0257]; see Note 3A).
Note 3A: Figure 2 of Imoto showcases a flowchart where “the control unit 108 detects the feature points as shown in the center left diagrams in FIG. 2 in images,” [0047] and then “the control unit 108 generates face models (hereinafter, also referred to as face masks) on the basis of the detected feature points,” [0048]. Imoto further teaches: “not only a process in which steps shown in the flowcharts of the above embodiments are performed in a time-series manner in accordance with a described sequence but also a process in which the steps are not necessarily processed in a time-series manner but are executed in parallel or individually is included,” [0257]. Therefore, it would be obvious to one of ordinary skill in the art to generate the hull and set of facial features in parallel.
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Imoto with Chand in view of Li and Astarabadi. Having the generating of the hull and the generating of the set of facial features be performed in parallel, as in Imoto, would benefit the Chand in view of Li and Astarabadi teachings by increasing the speed of processing of the image by performing both generating steps at the same time.
Claims 10 and 21 are rejected under 35 U.S.C 103 as being unpatentable over Chand (US 20190130629 A1) in view of Li (US 20150213604 A1) and Astarabadi (US 20200358983 A1) and Kinnebrew (US 20140071163 A1).
Regarding claim 10:
Chand in view of Li and Astarabadi teaches:
The method of claim 1 (as shown above),
Chand in view of Li and Astarabadi fails to teach:
further comprising changing at least one of a resolution or a detail of the user mask based on a detected motion of the participant.
Kinnebrew teaches:
further comprising changing at least one of a resolution or a detail of a model based on a detected motion of the participant (Kinnebrew: Based on detecting the user's movement, the holographic object presentation program 14 may increase the default information detail level of the globe 234 from the low detail information level to the medium detail information level corresponding to a globe 234' shown in FIG. 9, [0058]; see Note 10A).
Note 10A: As cited in [0058] above, Kinnebrew teaches that based on a detected motion of the user, a level of detail (LOD) of a model in the scene may be changed. Chand teaches a 3D avatar represented by a model that may move: “the animating the 3D avatar may include causing the 3D avatar to mimic facial movements of a user,” [0019]. Therefore, when the teachings of Kinnebrew are combined with Chand in view of Li and Astarabadi, it would be obvious to one of ordinary skill in the art to change at least a detail of the user mask based on a detected motion of the participant.
It is noted that level of detail or “LOD” is commonly used in the art as a synonym for mesh resolution. Therefore, it would also be obvious to one of ordinary skill in the art to change at least a resolution of the user mask based on a detected motion of the participant.
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Kinnebrew with Chand in view of Li and Astarabadi. Changing at least one of a resolution or a detail of a model based on a detected motion of the participant, as in Kinnebrew, would benefit the Chand in view of Li and Astarabadi teachings by increasing the performance of the messaging client application by ensuring high-detail models or meshes are reduced to a lower level of detail when a user controlling the model moves such that specific details are no longer visible.
Regarding claim 21:
Chand in view of Li and Astarabadi teaches:
The computing device of claim 16 (as shown above),
Chand in view of Li and Astarabadi fails to teach:
wherein the one or more processors are further configured to: change at least one of a resolution or a detail of the user mask based on a detected motion of the participant; or change at least one of the resolution or the detail of the user mask based on computer processing usage associated with the one or more processors.
Kinnebrew teaches:
wherein the one or more processors are further configured to: change at least one of a resolution or a detail of the user mask based on a detected motion of the participant (Kinnebrew: Based on detecting the user's movement, the holographic object presentation program 14 may increase the default information detail level of the globe 234 from the low detail information level to the medium detail information level corresponding to a globe 234' shown in FIG. 9, [0058]; see Note 10A);
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Kinnebrew with Chand in view of Li and Astarabadi. Changing at least one of a resolution or a detail of a model based on a detected motion of the participant, as in Kinnebrew, would benefit the Chand in view of Li and Astarabadi teachings by increasing the performance of the messaging client application by ensuring high-detail models or meshes are reduced to a lower level of detail when a user controlling the model moves such that specific details are no longer visible.
Claims 13 and 14 are rejected under 35 U.S.C 103 as being unpatentable over Chand (US 20190130629 A1) in view of Li (US 20150213604 A1) and Astarabadi (US 20200358983 A1) and Chang (US 20170149918 A1).
Regarding claim 13:
Chand in view of Li and Astarabadi teaches:
The method of claim 1 (as shown above),
Chand in view of Li and Astarabadi fails to explicitly teach:
wherein upon determining that there is connectivity issue associated with the collaborative program, the method further includes locally updating the user mask without using a newer set of mesh data.
Chang teaches:
wherein upon determining that there is connectivity issue associated with the broadcast server, the method further includes locally updating the data without using a newer set of data (Chang: in instances in which the broadcast server dies or goes offline (e.g., due to […] network connectivity issues, etc.) and the cache server has not received a message or a data update from the broadcast server within a predetermined broadcast time period, the cache server is configured to switch from the broadcast mode to the timer mode […] During operation in the timer mode, the cache server updates data stored in its cache without using the broadcast server since the broadcast server is incapable of broadcasting,” [0022] ).
Note 13A: Chang teaches a cache server that receives data from a broadcast server: “the broadcast server is configured to continually send messages and/or data updates to a cache server that is registered to receive (e.g., “listen” for) the broadcasts,” [0022]. Chang further teaches: “in instances in which the broadcast server dies or goes offline (e.g., due to a power failure, a software crash, a hardware crash, network connectivity issues, etc.) and the cache server has not received a message or a data update from the broadcast server within a predetermined broadcast time period, the cache server is configured to switch from the broadcast mode to the timer mode based on a determination that the broadcast server is likely dead or offline. During operation in the timer mode, the cache server updates data stored in its cache without using the broadcast server since the broadcast server is incapable of broadcasting,” [0022]. That is, when the connection to the broadcast server is lost for a threshold of time, the cache server continues updating locally without using new data from the broadcast server.
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Chang with Chand in view of Li and Astarabadi. Determining that there is connectivity issue associated with the collaborative program, the method further includes locally updating the user mask without using a newer set of mesh data, as in Chang, would benefit the Chand in view of Li and Astarabadi teachings by ensuring that even when connection to the second device is lost, the user experience may be maintained until the connection can be re-established.
Regarding claim 14:
Chand in view of Li and Astarabadi in view of Chang teaches:
The method of claim 13 (as shown above), wherein the connectivity issue is a loss of connection that exceeds a threshold amount of time (Chang: in instances in which the broadcast server dies or goes offline (e.g., due to a power failure, a software crash, a hardware crash, network connectivity issues, etc.) and the cache server has not received a message or a data update from the broadcast server within a predetermined broadcast time period [0022]).
Claim 15 is rejected under 35 U.S.C 103 as being unpatentable over Chand (US 20190130629 A1) in view of Li (US 20150213604 A1) and Astarabadi (US 20200358983 A1) and Park (NPL: A New Concave Hull Algorithm and Concaveness Measure for n-dimensional Datasets).
Chand in view of Li and Astarabadi teaches:
The method of claim 1 (as shown above),
Chand in view of Li and Astarabadi fails to explicitly teach:
wherein generating the hull includes performing a hull concavity operation to delineate the perimeter of the user mask.
Park teaches:
wherein generating the hull includes performing a hull concavity operation (Park: Concave hull algorithm for 3-dimensional dataset, Pg. 5-6, Algorithm 2) to delineate the perimeter of the user mask (see Note 15A).
Note 15A: Park showcases in Figures 1 and 2 concave hull that delineate the perimeter of a set of points. Chand teaches that a mesh representation 404 is a polygon mesh (“The mesh representation 404 therefore provides a polygon mesh representation of the face,” [0044]) that comprises a plurality of points. Similarly, Li teaches that “associated edges 406 connecting one or more key points 404 to one another. The key points 404 and associated edges 406 form an overall facial pattern of a user based on the identified facial landmarks.” Because both Chand and Li teach generating edges for a set of points representing a face, it would be obvious to one of ordinary skill in the art to utilize the method taught by Park to generate a concave hull.
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Example hulls as shown in Park. The concave hulls delineate a perimeter around the set of points.
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Park with Chand in view of Li and Astarabadi. Changing at least one of a resolution or a detail of a model based on available bandwidth for a communication connection associated with the collaborative program, as in Park, would benefit the Chand in view of Li and Astarabadi teachings by increasing accuracy of the geometry without sacrificing performance: “The concave hull approach is a more advanced approach used to capture the exact shape of the surface of a dataset,” (Park, Pg. 1, Section 1: Introduction, par. 2); “If the convex hull is substituted with a concave hull in those tasks, increased performance or accuracy can be expected,” (Park, Pg. 12, Section 5: Conclusion, par 1).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Rivard (US 20190222807 A1) teaches methods pertaining to how a real-time face model may be automatically transmitted based on network latency threshold or a dropped packet threshold condition.
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