DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 11, 2024, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
1. This action is responsive to an amendment filed on 06/10/2026. Claims 1-6,8-13, 15-20 are pending.
Response to Arguments
2. Applicants arguments filed in the 06/10/2026 remarks have been fully considered but are moot in view of new ground(s) of rejection which is deemed appropriate to address all of the needs at this time.
Claim Rejections - 35 USC § 103
3. 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 (i.e., changing from AIA to pre-AIA ) 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
4. Claim(s) 1, 8, 15 are rejected under 35 U.S.C. 103 as being unpatentable over Kelly (US 2022/0327961) in view of Holzer et al. (US 2021/0225065).
Regarding claim 1, Kelly discloses a computer-implemented method comprising: extracting one or more video frames from video streams of a set of communication sessions (see ¶ 0019. “a sequence of these per-frame features”), wherein the video frames include a representation of an object (Fig. 5, 53), and wherein the object is associated with an issue for which the communication session is established (see fig. 5, ¶ 0019. sign language information.); defining a training dataset from the one or more video frames and features extracted from the set of communication sessions (Fig. 2-3, ¶ 0028-0033); training a neural network using the training dataset, the neural network being configured to generate predictions of actions associated with the object (see Fig. 4, 407-408, ¶ 0028-0033); extracting a video frame from a new video stream of a new communication session, wherein the video frame includes a representation of a particular object, and wherein the particular object is associated with a particular issue that impacts an operability of the particular object (see ¶ 0019, 0028. “In real time, or after the signing is completed, the sign language information is sent to 12, which extracts out features (e.g. body pose key points, hand key points, hand pose, thresholded image, etc. . . . System performs pose detection via Convolutional Pose Machines in and hand localization via a RCNN. CNN additionally locates non-signing regions by outputting a special flag value (i.e. 0=intrasign region, 1=intersign region, 2=nonsigning region). The comparator in 211 then first determines if the entire signing region of the feature vector is contained within the list of pre-recorded sentences in the sentence base 214 (a database of sentences) via K Nearest-Neighbors (KNN) with a Dynamic Time Warping (DTVV) distance metric. If the feature vector does not correspond to a sentence, the comparator 211 then goes through each signs' corresponding region in the feature queue and determines if that sign was fingerspelled (done through a binary classifier). If the system determines or not the signing object is interpreted or not.); executing the neural network using the video frame from the new video stream, wherein the neural network generates a predicted action associated with the particular object (see ¶ 0019, 0028-0033. The features produced by 12 are then transmitted to component 13 which extracts sign language information (e.g. detecting if an individual is signing, transcribing that signing into gloss, or translating that signing into a target language) from a sequence of these per-frame features.”); and facilitating a transmission of a communication to a device of the new communication session, the communication including a representation of the predicted action (“Finally, the output is displayed on 14.” [0019]; Fig. 1, 14).
Kelly does not teach wherein the predicted action includes a maintenance action or a repair action configured to restore operability of the particular object; wherein when the predicted action is performed, the predicted action resolves the particular issue.
Holzer teaches wherein the predicted action includes a maintenance action or a repair action configured to restore operability of the particular object; wherein when the predicted action is performed, the predicted action resolves the particular issue (see fig. 2-3, ¶ 0053, 0058, 0060-0069, 0104, 0205, 0264-0265. The system uses a neural network that is trained to predict coordinates of the a visible object, once the locations are mapped, the points may be lifted to 3D based on a predefined correspondence between the top-down view and a 3D mesh. Then, the transformation between the image points and the 3D mesh may be used to obtain the 3D orientation. Thus determining a representation of an object from a multi-view representation of the object, the multi-view representation including a plurality of images of the object, each of the images being captured from a respective viewpoint, the multi-view representation being navigable in one or more dimensions and generating an action shot video of the scene, the action shot video including a rendering of the object determined based on the representation and the camera pose. Therefore the predicted system provides a maintenance of the image in order to keep the image in form for each frame where the object will be fluid and not distorted. This would be considered maintenances to restore object to proper image view, and this would resolve any discrepancy of the object.)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Kelly to incorporate the system to correct image issues in a conferencing session. The modification provides for keeping an object fluid and not distorted.
Regarding claim 8, Kelly discloses a system comprising: one or more processors; and a non-transitory machine-readable storage medium storing instructions that when executed by the one or more processors, cause the one or more processors to perform operations including: extracting one or more video frames from video streams of a set of communication sessions (see ¶ 0019. “a sequence of these per-frame features”), wherein the video frames include a representation of an object (see fig. 5, 53), and wherein the object is associated with an issue for which the communication session is established (see ¶ 0019, 0028-0033. “sign language information”); defining a training dataset from the one or more video frames and features extracted from the set of communication sessions (Fig. 2-3, ¶ 0028-0033); training a neural network using the training dataset, the neural network being configured to generate predictions of actions associated with the object (Fig. 4, 407-408, ¶ 0028-0033); extracting a video frame from a new video stream of a new communication session, wherein the video frame includes a representation of a particular object, and wherein the particular object is associated with a particular issue that impacts an operability of the particular object (see ¶ 0019, 0028-0033. “In real time, or after the signing is completed, the sign language information is sent to 12, which extracts out features (e.g. body pose key points, hand key points, hand pose, thresholded image, etc. . . . System performs pose detection via Convolutional Pose Machines in and hand localization via a RCNN. CNN additionally locates non-signing regions by outputting a special flag value (i.e. 0=intrasign region, 1=intersign region, 2=nonsigning region). The comparator in 211 then first determines if the entire signing region of the feature vector is contained within the list of pre-recorded sentences in the sentence base 214 (a database of sentences) via K Nearest-Neighbors (KNN) with a Dynamic Time Warping (DTVV) distance metric. If the feature vector does not correspond to a sentence, the comparator 211 then goes through each signs' corresponding region in the feature queue and determines if that sign was fingerspelled (done through a binary classifier). If the system determines or not the signing object is interpreted or not.); executing the neural network using the video frame from the new video stream, wherein the neural network generates a predicted action associated with the particular object (“The features produced by 12 are then transmitted to component 13 which extracts sign language information (e.g. detecting if an individual is signing, transcribing that signing into gloss, or translating that signing into a target language) from a sequence of these per-frame features.” Kelly [0019]); and facilitating a transmission of a communication to a device of the new communication session, the communication including a representation of the predicted action (“Finally, the output is displayed on 14.” Kelly [0019]; Fig. 1, 14; Kelly).
Kelly does not teach wherein the predicted action includes a maintenance action or a repair action configured to restore operability of the particular object; wherein when the predicted action is performed, the predicted action resolves the particular issue.
Holzer teaches wherein the predicted action includes a maintenance action or a repair action configured to restore operability of the particular object; wherein when the predicted action is performed, the predicted action resolves the particular issue (see fig. 2-3, ¶ 0053, 0058, 0060-0069, 0104, 0205, 0264-0265. The system uses a neural network that is trained to predict coordinates of the a visible object, once the locations are mapped, the points may be lifted to 3D based on a predefined correspondence between the top-down view and a 3D mesh. Then, the transformation between the image points and the 3D mesh may be used to obtain the 3D orientation. Thus determining a representation of an object from a multi-view representation of the object, the multi-view representation including a plurality of images of the object, each of the images being captured from a respective viewpoint, the multi-view representation being navigable in one or more dimensions and generating an action shot video of the scene, the action shot video including a rendering of the object determined based on the representation and the camera pose. Therefore the predicted system provides a maintenance of the image in order to keep the image in form for each frame where the object will be fluid and not distorted. This would be considered maintenances to restore object to proper image view, and this would resolve any discrepancy of the object.)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Kelly to incorporate the system to correct image issues in a conferencing session. The modification provides for keeping an object fluid and not distorted.
Regarding claim 15, Kelly discloses extracting one or more video frames from video streams of a set of communication sessions (“a sequence of these per-frame features” Kelly [0019]), wherein the video frames include a representation of an object (Fig. 5, 53; Kelly), and wherein the object is associated with an issue for which the communication session is established (“sign language information, ¶ 0019, 0028-0033); defining a training dataset from the one or more video frames and features extracted from the set of communication sessions (Fig. 3; ¶ 0028-0033); training a neural network using the training dataset, the neural network being configured to generate predictions of actions associated with the object (Fig. 4, 407-408; ¶ 0028-0033); extracting a video frame from a new video stream of a new communication session, wherein the video frame includes a representation of a particular object, and wherein the particular object is associated with a particular issue that impacts an operability of the particular object (see ¶ 0019, 0028-0033. “In real time, or after the signing is completed, the sign language information is sent to 12, which extracts out features (e.g. body pose key points, hand key points, hand pose, thresholded image, etc. . . . System performs pose detection via Convolutional Pose Machines in and hand localization via a RCNN. CNN additionally locates non-signing regions by outputting a special flag value (i.e. 0=intrasign region, 1=intersign region, 2=nonsigning region). The comparator in 211 then first determines if the entire signing region of the feature vector is contained within the list of pre-recorded sentences in the sentence base 214 (a database of sentences) via K Nearest-Neighbors (KNN) with a Dynamic Time Warping (DTVV) distance metric. If the feature vector does not correspond to a sentence, the comparator 211 then goes through each signs' corresponding region in the feature queue and determines if that sign was fingerspelled (done through a binary classifier). If the system determines or not the signing object is interpreted or not.); executing the neural network using the video frame from the new video stream, wherein the neural network generates a predicted action associated with the particular object (“The features produced by 12 are then transmitted to component 13 which extracts sign language information (e.g. detecting if an individual is signing, transcribing that signing into gloss, or translating that signing into a target language) from a sequence of these per-frame features.” Kelly [0019]); and facilitating a transmission of a communication to a device of the new communication session, the communication including a representation of the predicted action (“Finally, the output is displayed on 14.” Kelly [0019]; Fig. 1, 14; Kelly).
Kelly does not teach wherein the predicted action includes a maintenance action or a repair action configured to restore operability of the particular object; wherein when the predicted action is performed, the predicted action resolves the particular issue.
Holzer teaches wherein the predicted action includes a maintenance action or a repair action configured to restore operability of the particular object; wherein when the predicted action is performed, the predicted action resolves the particular issue (see fig. 2-3, ¶ 0053, 0058, 0060-0069, 0104, 0205, 0264-0265. The system uses a neural network that is trained to predict coordinates of the a visible object, once the locations are mapped, the points may be lifted to 3D based on a predefined correspondence between the top-down view and a 3D mesh. Then, the transformation between the image points and the 3D mesh may be used to obtain the 3D orientation. Thus determining a representation of an object from a multi-view representation of the object, the multi-view representation including a plurality of images of the object, each of the images being captured from a respective viewpoint, the multi-view representation being navigable in one or more dimensions and generating an action shot video of the scene, the action shot video including a rendering of the object determined based on the representation and the camera pose. Therefore the predicted system provides a maintenance of the image in order to keep the image in form for each frame where the object will be fluid and not distorted. This would be considered maintenances to restore object to proper image view, and this would resolve any discrepancy of the object.)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Kelly to incorporate the system to correct image issues in a conferencing session. The modification provides for keeping an object fluid and not distorted.
5. Claim(s) 2, 4, 5, 6, 9, 11, 12, 13, 16, 17, 18, 19, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Kelly (US 2022/0327961) in view of Holzer et al. (US 2021/0225065).
Regarding claim 2, Kelly discloses the computer-implemented method of claim 1, wherein the new communication session is between a user device (Fig. 1, 11; Kelly) and a terminal device (Fig. 1, 14; Kelly).
Regarding claim 4, Kelly discloses the computer-implemented method of claim 1, wherein the neural network is an ensemble network comprising two or more neural networks configured to generate outputs of different types (“In the processing module 202, the feature train is split into each individual sign via the sign-splitting component 209 via a 1D Convolutional Neural Network which highlights the sign transition periods.” Kelly [0028]).
Regarding claim 5, Kelly discloses the computer-implemented method of claim 1, wherein the neural network is configured to generate a boundary box over the object (“These results are combined to find the bounding box of both the dominant and non-dominant hand by iterating through all bounding boxes found from 205 and finding the one closest to each wrist joint produced by 206.” Kelly [0023]).
Regarding claim 6, Kelly discloses the computer-implemented method of claim 1, wherein the neural network is configured to generate a predicted identification of the object (Fig. 4, 407-408; Kelly).
Regarding claim 9, Kelly discloses the system of claim 8, wherein the new communication session is between a user device (Fig. 1, 11; Kelly) and a terminal device (Fig. 1, 14; Kelly).
Regarding claim 11, Kelly discloses the system of claim 8, wherein the neural network is an ensemble network comprising two or more neural networks configured to generate outputs of different types (“In the processing module 202, the feature train is split into each individual sign via the sign-splitting component 209 via a 1D Convolutional Neural Network which highlights the sign transition periods.” Kelly [0028]).
Regarding claim 12, Kelly discloses the system of claim 8, wherein the neural network is configured to generate a boundary box over the object (“These results are combined to find the bounding box of both the dominant and non-dominant hand by iterating through all bounding boxes found from 205 and finding the one closest to each wrist joint produced by 206.” Kelly [0023]).
Regarding claim 13, Kelly discloses the system of claim 8, wherein the neural network is configured to generate a predicted identification of the object (Fig. 4, 407-408; Kelly).
Regarding claim 16, Kelly, in view of Ramakrishnan, discloses the non-transitory machine-readable storage medium of claim 15, wherein the new communication session is between a user device (Fig. 1, 11; Kelly) and a terminal device (Fig. 1, 14; Kelly).
Regarding claim 17, Kelly, in view of Ramakrishnan, discloses the non-transitory machine-readable storage medium of claim 15, wherein the particular issue is associated with a hardware or software fault in a device operated by a user (Fig. 3, 312; Ramakrishnan).
Regarding claim 18, Kelly, in view of Ramakrishnan, discloses the non-transitory machine-readable storage medium of claim 15, wherein the neural network is an ensemble network comprising two or more neural networks configured to generate outputs of different types (“In the processing module 202, the feature train is split into each individual sign via the sign-splitting component 209 via a 1D Convolutional Neural Network which highlights the sign transition periods.” Kelly [0028]).
Regarding claim 19, Kelly, in view of Ramakrishnan, discloses the non-transitory machine-readable storage medium of claim 15, wherein the neural network is configured to generate a boundary box over the object (“These results are combined to find the bounding box of both the dominant and non-dominant hand by iterating through all bounding boxes found from 205 and finding the one closest to each wrist joint produced by 206.” Kelly [0023]).
Regarding claim 20, Kelly, in view of Ramakrishnan, discloses the non-transitory machine-readable storage medium of claim 15, wherein the neural network is configured to generate a boundary box over the object (“These results are combined to find the bounding box of both the dominant and non-dominant hand by iterating through all bounding boxes found from 205 and finding the one closest to each wrist joint produced by 206.” Kelly [0023]).
6. Claim(s) 3, 10 are rejected under 35 U.S.C. 103 as being unpatentable over Kelly (US 2022/0327961) in view of Holzer et al. (US 2021/0225065) in further view of Ramakrishnan et. al (US 20240146871).
Regarding claim 3, Kelly discloses the computer-implemented method of claim 1, wherein the particular issue is associated with a hardware or software fault in a device operated by a user.
Ramakrishnan teaches wherein the particular issue is associated with a hardware or software fault in a device operated by a user (see fig. 3, 312, ¶ 0083. The event viewer may track failed attempts at firmware or software updates in an embodiment at block 312. For example, the data collector 261 may also gather information from an event viewer 265 (e.g., Microsoft® Event Viewer) tracking computing events relating to software, firmware, and hardware in real-time).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Kelly and Holzer to incorporate track failed attempts at firmware or software updates. The modification provides tracking issues with the software or hardware in a device in a video conference environment.
Regarding claim 10, Kelly discloses the system of claim 8, wherein the particular issue is associated with a hardware or software fault in a device operated by a user.
Ramakrishnan teaches wherein the particular issue is associated with a hardware or software fault in a device operated by a user (see fig. 3, 312, ¶ 0083. The event viewer may track failed attempts at firmware or software updates in an embodiment at block 312. For example, the data collector 261 may also gather information from an event viewer 265 (e.g., Microsoft® Event Viewer) tracking computing events relating to software, firmware, and hardware in real-time).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Kelly and Holzer to incorporate track failed attempts at firmware or software updates. The modification provides tracking issues with the software or hardware in a device in a video conference environment.
Conclusion
7. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ASSAD MOHAMMED whose telephone number is (571)270-7253. The examiner can normally be reached 9:00AM-5:00PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Duc Nguyen can be reached at 571-272-7503. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ASSAD MOHAMMED/Examiner, Art Unit 2691
/DUC NGUYEN/Supervisory Patent Examiner, Art Unit 2691
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