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 §103
2. 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 1-9, and 14-20 are rejected under 35 U.S.C. 103 as being unpatentable over Olivieri et al (Pub. No. US 2022/201248 A1; hereinafter Olivieri) in view of Carter et al (Patent No.: US 9,781,174 A1; hereinafter Carter)
Consider claims 1, and 14, Olivieri clearly shows and discloses a system, and a method for monitoring quality of a conferencing session between a plurality of participant devices (abstract, fig. 1-3, and paragraph 0003), the method comprising: monitoring one or more data streams of the conferencing session, wherein the conferencing session comprises one or more components of an audio component, a video component, or a shared content component (the audio difficulties monitoring engine of the audio or video conferencing system server complex (106) determines a number of audio difficulties data communications (205) that are received from the various participants) (paragraphs: 0052 and 0070 and fig. 2 and fig. 3 ); determining presenter contextual information for media transmitted over the one or more data streams by a presenter device of the plurality of participant devices (sufficient number of audio difficulties data communications must be received from a sufficient number of conferencing system terminal devices before suggesting to the presenter that the problem may be due to their conferencing system terminal device 104 or its connection to the network 105) (paragraphs: 0033, 0062-0063 and 0071; fig. 2 and fig. 3); identifying a mismatch between the presenter contextual information and a first participant contextual information for a first participant device of the plurality of participant devices (determines whether the plurality of audio difficulties data communications exceed a predefined audio difficulty communication threshold) (paragraphs: 0033, 0059, 0071-0073); devices, wherein identifying the mismatch comprises using a machine learning model to identify a semantic inconsistency based on the one or more components of the conferencing session (a user may employ the user interface devices (1003) of the server complex (106) to enter various parameters, constructs, rules, and/or paradigms that instruct or otherwise guide the process engines in detecting contextual information. the process engines can comprise an artificial neural network or other similar technology in one or more embodiments) (paragraphs: 0052 and 0070, and 1032- 0133); and providing a mismatch notification to the presenter device for an identified mismatch (communication device of the conferencing system terminal device 101 to transmit an audio difficulties data communication 205 across the network 105. The one or more processors of the conferencing system terminal device 101 may cause the communication device of the conferencing system terminal device 101 to transmit the audio difficulties data communication 205 across the network 105 to the other conferencing system terminal devices engaged in the videoconference) (paragraphs: 0033,0052 0063, 0073, audio difficulties response data communication 210); however, Olivieri does not disclose another example for monitoring one or more data streams of the conferencing session.
In the same field of endeavor, Carter clearly specifically discloses wherein identifying the mismatch comprises using a machine learning model to identify a semantic inconsistency at least in part by determining that a semantic relevance between the presenter contextual information and the first participant contextual information is below a relevance threshold (comparing an audio stream within the received communications stream with a video stream within the received communications stream; and (b) determining at least a subset of the one or more quality factors based on the comparing; User-A's client device determines whether the video quality is poor by comparing it to the video quality for other participants. the output meter 412 includes qualitative threshold indicators 413 and 415. In some implementations, the region below the qualitative threshold indicator 413 is labeled as “poor” quality. In some implementations, the region between the qualitative threshold indicators 413 and 415 is labeled “moderate” quality. FIG. 4H further shows warning 416 including a text communications affordance 418 for enabling User-A to communicate with Users B and C via text) (abstract, col. 12, lines 5-25, col. 13, line 8-24 and fig. 4E, col. 11, lines 53-66; col. 12, line 47-65 and fig. 4G, label 414; fig. 8B, labels: 814-824)
Therefore, it would have been obvious to a person of ordinary skill in the art at the time the invention was made to incorporate the teaching of Carter into teaching of Olivieri for the purpose of providing more examples for monitoring one or more data streams of the conferencing session.
Consider claims 2, and 15, Olivieri and Carter clearly show the system, and the method, wherein determining the presenter contextual information comprises one or more of: determining a presenter audio status for an audio component of the conferencing session provided by the presenter device; determining a presenter video status for a video component of the conferencing session provided by the presenter device; or determining a presenter shared content status for a shared content component of the conferencing session provided by the presenter device (Olivieri: paragraphs: 0033, 0062-0063 and 0071; fig. 2 and fig. 3).
Consider claims 3, and 16, Olivieri and Carter clearly show the system, and the method, the method further comprising receiving the first participant contextual information from the first participant device, wherein the first participant contextual information includes one or more of a participant audio status of the audio component, a participant video status of the video component, or a participant shared content status of the shared content component (Olivieri: paragraphs: 0033,0052 0063, 0073, audio difficulties response data communication 210).
Consider claims 4, and 17, Olivieri and Carter clearly show the system, and the method, wherein identifying the mismatch comprises generating the mismatch notification when: the presenter audio status indicates a presence of the audio component and the participant audio status indicates an absence of the audio component, the presenter video status indicates a presence of the video component and the participant video status indicates an absence of the video component, or the presenter shared content status indicates a presence of the shared content component and the participant shared content status indicates an absence of the shared content component (Olivieri: paragraphs: 0033, 0059, 0071-0073).
Consider claims 5, and 18, Olivieri and Carter clearly show the system, and the method, wherein the conferencing session has multiple participants and at least one presenter sharing data including: a first audio component and the participant audio status indicates an absence of the first audio component, a first video component and the participant video status indicates an absence of the first video component, or a first shared content component and the participant shared content status indicates an absence of the first shared content component (Olivieri: fig. 4 and fig. 6 - fig.8).
Consider claims 6, and 19, Olivieri and Carter clearly show the system, and the method, wherein the shared content component comprises one or more of a screen sharing session, app sharing session, collaborative tool sharing session, or document sharing session (Carter: fig. 4F and col. 12, lines 30-46).
Consider claim 7, Olivieri and Carter clearly show the method, wherein identifying the mismatch comprises generating the mismatch notification when a machine learning model flags an inconsistency between: the first audio component and the first video component, the first audio component and the first shared content component, or the first video component and the first shared content component (Olivieri: paragraphs: 0132 -0133).
Consider claim 8, Olivieri and Carter clearly show the method, wherein the first shared content comprises a document and identifying the mismatch comprises: labeling pages of the document with keywords based on content within the document; and providing the labeled pages and the first audio component to the machine learning model (Olivieri: paragraphs: fig. 11, label: 207 and 1112; Carter: col. 11, lines 53-66; col. 12, line 47-65 and fig. 4G, label 414; col. 20, lines 61- col. 21, lines 1-7).
Consider claim 9, Olivieri and Carter clearly show the method, the method further comprising: generating the mismatch notification to identify one or more remediation options for the identified mismatch (Olivieri: fig. 4; and Carter: fig. 4E).
Consider claim 20, Olivieri and Carter clearly show the system further comprising the presenter device and the first participant device; wherein the presenter device comprises the first context processor and the first participant device comprises a second context processor (Olivieri: fig. 4 and fig. 11).
Response to Arguments
The present Office Action is in response to Applicant’s amendment filed on April 22, 2026. Applicant amended claims 1, and 14. Claims 1-9, and 14-20 are now pending in the present application.
Applicant argues on the Applicant’s Response that Redmon and Azim failed to teach the limitation “wherein identifying the mismatch comprises using a machine learning model to identify a semantic inconsistency at least in part by determining that a semantic relevance between the presenter contextual information and the first participant contextual information is below a relevance threshold." Also Applicant argues claim 8.
The Examiner respectfully disagrees with Applicants’ arguments regarding claims 1, and 14. Carter shows comparing an audio stream within the received communications stream with a video stream within the received communications stream; and (b) determining at least a subset of the one or more quality factors based on the comparing; User-A's client device determines whether the video quality is poor by comparing it to the video quality for other participants. In some implementations, User-A's client device determines whether the video quality is poor by comparing it one or more user preferences and/or settings (col. 12, lines 1-30 ; and fig. 4E); User-A's client device determines whether the video quality is poor by comparing it to the video quality for other participants. In some implementations, User-A's client device determines whether the video quality is poor by comparing it one or more user preferences and/or settings. (col. 13, lines 8-25; and fig. 4H). the first client device compares (822) an audio stream within the received communications stream with a video stream within the received communications stream; and determines (824) at least a subset of the one or more quality factors based on the comparison. For example, comparing the audio and video streams optionally includes comparing pixel changes within the video with the audio, performing OCR on the video and comparing with audio, and the like. In some implementations, comparing the audio and video streams includes comparing factors corresponding to the audio quality with factors corresponding to the video quality. For example, the first client device compares the audio stream with the video stream by communications processing module 220 in FIG. 2 (col. 20, lines 61- col. 21, lines 1-7). Moreover, Carter teaches the output meter 412 includes qualitative threshold indicators 413 and 415. In some implementations, the region below the qualitative threshold indicator 413 is labeled as “poor” quality. In some implementations, the region between the qualitative threshold indicators 413 and 415 is labeled “moderate” quality. In some implementations, the region above qualitative threshold indicator 415 is labeled “good” quality. Thus, in accordance with some implementations, FIG. 4G shows the current output quality indicator 414 in the “good” region. In some implementations, current quality indicator 414 indicates that there is a problem with User-A's outgoing content and/or video, as received by another participant (e.g., User-B and/or User-C) (col. 11, lines 53-66; col. 12, line 47-65 and fig. 4G, label 414). Moreover, Carter teaches claim 8, Carter shoes comparing the audio and video streams includes comparing factors corresponding to the audio quality with factors corresponding to the video quality. For example, the first client device compares the audio stream with the video stream by communications processing module 220 in FIG. 2 (col. 20, lines 61- col. 21, lines 1-7). As a result, Olivieri and Carter teach all the limitation of claims 1 and 14.
Conclusion
THIS ACTION IS MADE FINAL. 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 extension fee 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 Amal Zenati whose telephone number is 571- 270- 1947. The examiner can normally be reached on 8:00 -5:00 M-F.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ahmad Matar can be reached on 571- 272- 7488. The fax phone number for the organization where this application or proceeding is assigned is 571- 273-8300.
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/AMAL S ZENATI/Primary Examiner, Art Unit 2693
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