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 Arguments
Claims 1 and 8 have been amended.
Claims 14-20 are canceled.
Claims 21-27 are newly added.
Claims 1-13 and 21-27 are presently pending.
Applicant's arguments filed 02 February 2026 have been fully considered but they are not persuasive.
Regarding Applicant’s argument that Hou and Bhide fail to disclose ‘track a current timestamp associated with encoding a portion of the multimedia content being streamed to the user device’ (see Remarks, pg. 9), the Examiner disagrees. Applicant's arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references. The language of the amended limitation “track a current timestamp associated with encoding a portion of the multimedia content being streamed to the user device’ does not specifically define the relationship of the claimed ‘current timestamp’ and the ‘encoding a portion of the multimedia content’, merely requiring that there be some timestamp that is related in some general manner to the encoded media. Hou [0034], [0039-40] and [0043-45] disclose that the system may stream media content to a user, wherein that user may input comments which will be associated with a timestamp within the content at the timepoint at which the comment was input. It would follow that some current timestamp is clearly associated with the streamed content and tracked. Furthermore, it’d be inherent and/or inferably understood by those of ordinary skill in the art that streaming multimedia content would be encoded at some point for transmission across the delivery network. As such, given the broad language of the claimed limitation, Hou still discloses to ‘track a current timestamp associated with encoding a portion of the multimedia content being streamed to the user device’.
Even arguendo that the claimed language necessarily required the narrower description of [0020] of the Instant Specification as discussed in the Applicant-Initiated Interview of 29 January 2026, at least Fahnestock et al. (US 2019/0373309 A1) teaches where media providers may watermark and encode timestamps into distributed content for purposes of monitoring what media is distributed to and received by end users.
It is noted that the Examiner took Official Notice for several limitations including: receiving a request for multimedia content and streaming the requested content to a user device; left or right-clicking interactions; and emailing of a ticket/report as being widely understood to be known in the art. As Applicant has failed to traverse the Official Notices in a timely manner, the Official Notice is taken to be admitted prior art, and the Official Notice is made final. See MPEP 2144.03(C).
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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-4, 6-13, and 21-27 are rejected under 35 U.S.C. 103 as being unpatentable over Hou (US 2014/0226953 A1) (of record, hereinafter Hou), in view of Bhide et al. (US 2020/0302006 A1) (of record, hereinafter Bhide).
Regarding Claim 1, Hou discloses system for gathering and assessing feedback on multimedia content, [Figs. 1-4] the system comprising:
one or more memories; [Fig. 4: 0028-29, 0041, 0057: system may be embodied as instructions stored on some storage and executed by a processor] and
one or more processors, communicatively coupled to the one or more memories, [Fig. 4: 0028-29, 0041, 0057: system may be embodied as instructions stored on some storage and executed by a processor] configured to:
stream the multimedia content to a user device, [Figs. 1-4; 0033: playback-management apparatus 114 may stream content over a network to one or more user devices] track a current timestamp associated with encoding a portion of the multimedia content being streamed to the user device; [0034, 0039-40, 0043-45: during playback of content progress of the user may be maintained, where user may initiate input of comments that will be associated with a timestamp of the content at which the comment was entered, where it would be implicitly understood that streamed media content would be encoded in some manner, and that such an association of input + content would inferably require tracking the timestamps of each encoded segment being presented by the user device (i.e., current timestamp) when the input is provided]
receive, from the user device, an indication of an interaction, wherein the interaction is associated with the current timestamp and a portion of a pixel space of the multimedia content; [Fig. 2B; 0034-36, 0044-45, 0049-54: during playback of media content, user may initiate input to provide overly 28 to enable entering of a comment which may include text and/or graphical inputs, wherein inputs may have associated time-stamp of the media content, and wherein overlay may also be associated with a specific point and/or region within a current frame of the media content]
receive, from the user device, text provided by a user of the user device; [Fig. 2B; 0032-36, 0044-45, 0049-54: during playback of media content, user may initiate input to provide overly 28 to enable entering of a comment which may include text and/or graphical inputs, wherein inputs may have associated time-stamp of the media content, and wherein overlay may also be associated with a specific point and/or region within a current frame of the media content]
transmit the text and the proposed change. [Figs. 1-3; 0032-34, 0045: user inputs as well as input from other users may be provided and displayed during subsequent playback of content so that the user and/or other users may utilize feedback/comments to edit and/or otherwise modify the video product]
Hou fails to explicitly disclose to provide the text to a machine learning model to receive an indication of a proposed change to the multimedia content; and transmit, to a ticket system, a command to open a ticket including the text and the proposed change.
Bhide, in analogous art, teaches disclose to provide the text to a machine learning model to receive an indication of a proposed change to the multimedia content; [Figs. 1-2, 4; 0012-13, 0022-24, 0038-39: comments from a plurality of users associated with some content (such as the feedback for video product of Hou above) may be clustered into topics and/or sentiments through various neural networks/learning models, etc.; 0038-39: a report may be generated for an authorized user (administrator) indicating overview/summary of grouped comments as well recommendations of changes in accordance with analyzed comments] and
transmit, to a ticket system, a command to open a ticket including the text and the proposed change. [Figs. 1-3; 0038-39: a report may be generated for an authorized user (administrator) indicating overview/summary of grouped comments as well recommendations of changes in accordance with analyzed comments (wherein the report effectively comprises a ticket including proposed changes)]
It would have been obvious to one of ordinary skill in the art prior to the filing date of the invention to modify the method of Hou with the teachings of Bhide to provide the text and additional feedback to a learning model to cluster and transmit and indication of the text and additional feedback in order to automatically parse through, group, and summarize comments that may be of interest to the publishers/creator of a content that such as additional information, interesting opinions, or helpful corrections. [Bhide – 0002, 0011-13]
Regarding Claim 2, Hou and Bhide disclose all of the limitations of Claim 1, which are analyzed as previously discussed with respect to that claim.
Furthermore, Hou discloses wherein the one or more processors are configured to: pause streaming of the multimedia content in response to the indication of the interaction; [Hou – 0033-36: during playback of media content, user may initiate input to provide overly 28 to enable entering of a comment which may include text and/or graphical inputs; where such interaction will pause playback of the content] and
resume streaming of the multimedia content in response to the text. [Hou – 0037: playback may resume after input is submitted]
Regarding Claim 3, Hou and Bhide disclose all of the limitations of Claim 1, which are analyzed as previously discussed with respect to that claim.
Furthermore, Hou discloses wherein the one or more processors are configured to: continue streaming the multimedia content after receiving the indication of the interaction. [Hou – 0037: playback may resume after input is submitted]
Regarding Claim 4, Hou and Bhide disclose all of the limitations of Claim 1, which are analyzed as previously discussed with respect to that claim.
Furthermore, Hou discloses wherein the system may store content and subsequently retrieve and stream said content to user devices. [Hou – Fig. 1; 0033-34]
Hou and Bhide fail to explicitly disclose wherein the one or more processors are configured to: receive, from the user device, a request for the multimedia content, wherein the multimedia content is streamed to the user device in response to the request.
However, the Examiner takes Official Notice that it would be implicitly obvious to one of ordinary skill in the art that retrieval and streaming of content to a user device may be done from a specific input request for particular content from such a streaming media system so that a user of the device may receive and view specific content of interest.
Regarding Claim 6, Hou and Bhide disclose all of the limitations of Claim 1, which are analyzed as previously discussed with respect to that claim.
Furthermore, Hou discloses wherein the interaction may be provided through input devices such as interacting with a mouse and/or touchpad of a laptop computer, and by dragging and or selecting a portion of the pixel space. [Hou – Fig. 1; 0034-36]
Hou and Bhide fail to explicitly disclose wherein the interaction comprises a left-click or a right-click on the portion of the pixel space.
However, the Examiner takes Official Notice that left-clicking or right-clicking are well-known and conventional mouse input interactions that could be utilized for interacting with any user interface, such as the one of Hou and Bhide above.
Regarding Claim 7, Hou and Bhide disclose all of the limitations of Claim 1, which are analyzed as previously discussed with respect to that claim.
Furthermore, Hou discloses wherein the interaction comprises a drag-and-drop onto the portion of the pixel space. [Hou – Fig. 3; 0034-36: user may drag overlay bubble to a different part or region of the frame]
Regarding Claim 8, Hou discloses a method of gathering and assessing feedback on multimedia content, comprising:
streaming, from a multimedia host and to a user device, the multimedia content; [Figs. 1-4; 0033: playback-management apparatus 114 may stream content over a network to one or more user devices]
tracking, from a multimedia host and to a user device, a current timestamp associated with encoding a portion of the multimedia content being streamed to the user device; [0034, 0039-40, 0043-45: during playback of content progress of the user may be maintained, where user may initiate input of comments that will be associated with a timestamp of the content at which the comment was entered, where it would be implicitly understood that streamed media content would be encoded in some manner, and that such an association of input + content would inferably require tracking the timestamps of each encoded segment being presented by the user device (i.e., current timestamp) when the input is provided]
receiving, at the multimedia host and from the user device, an indication of an interaction, wherein the interaction is associated with the current timestamp and a portion of a pixel space of the multimedia content; [Fig. 2B; 0034-36, 0044-45, 0049-54: during playback of media content, user may initiate input to provide overly 28 to enable entering of a comment which may include text and/or graphical inputs, wherein inputs may have associated time-stamp of the media content, and wherein overlay may also be associated with a specific point and/or region within a current frame of the media content]
receiving, at the multimedia host and from the user device, text provided by a user of the user device; [Fig. 2B; 0032-36, 0044-45, 0049-54: during playback of media content, user may initiate input to provide overly 28 to enable entering of a comment which may include text and/or graphical inputs, wherein inputs may have associated time-stamp of the media content, and wherein overlay may also be associated with a specific point and/or region within a current frame of the media content]
transmitting, from the multimedia host and to a device, an indication of the text and the additional feedback. [Figs. 1-3; 0032-34, 0045: user inputs as well as input from other users may be provided and displayed during subsequent playback of content so that the user and/or other users may utilize feedback/comments to edit and/or otherwise modify the video product]
Hou fails to explicitly disclose providing, by the multimedia host, the text to a machine learning model to cluster the text with additional feedback provided by one or more additional users; and transmitting, from the multimedia host and to an administrator device, an indication of the text and the additional feedback. (Emphasis on particular elements of the limitation not explicitly disclosed by Hou).
Bhide, in analogous art, teaches disclose providing, by the multimedia host, the text to a machine learning model to cluster the text with additional feedback provided by one or more additional users; [Figs. 1-2, 4; 0012-13, 0022-24, 0038-39: comments from a plurality of users associated with some content (such as the feedback for video product of Hou above) may be clustered into topics and/or sentiments through various neural networks/learning models, etc.] and
transmitting, from the multimedia host and to an administrator device, an indication of the text and the additional feedback. [Figs. 1-3; 0038-39: a report may be generated for an authorized user (administrator) indicating overview/summary of grouped comments as well recommendations of changes in accordance with analyzed comments]
It would have been obvious to one of ordinary skill in the art prior to the filing date of the invention to modify the method of Hou with the teachings of Bhide to provide the text and additional feedback to a learning model to cluster and transmit and indication of the text and additional feedback in order to automatically parse through, group, and summarize comments that may be of interest to the publishers/creator of a content that such as additional information, interesting opinions, or helpful corrections. [Bhide – 0002, 0011-13]
Regarding Claim 9, Hou and Bhide disclose all of the limitations of Claim 8, which are analyzed as previously discussed with respect to that claim.
Furthermore, Bhide discloses wherein the machine learning model clusters the text based on sentiment. [Bhide – Figs. 1-2, 4; 0015, 0022-24, 0038-39: comments from a plurality of users associated with some content (such as the feedback for video product of Hou above) may be clustered into topics and/or sentiments through various neural networks/learning models, etc.]
Regarding Claim 10, Hou and Bhide disclose all of the limitations of Claim 8, which are analyzed as previously discussed with respect to that claim.
Furthermore, Bhide discloses wherein the machine learning model clusters the text based on content. [Bhide – Figs. 1-2, 4; 0015, 0022-24, 0038-39: comments from a plurality of users associated with some content (such as the feedback for video product of Hou above) may be clustered into topics and/or sentiments, or types of comments (e.g., questions, corrections, opinions, etc.)]
Regarding Claim 11, Hou and Bhide disclose all of the limitations of Claim 8, which are analyzed as previously discussed with respect to that claim.
Furthermore, Hou and Bhide disclose providing the text to an additional machine learning model to receive an indication of a proposed change to the multimedia content, wherein the indication of the text and the additional feedback further indicates the proposed change. [Hou – Figs. 1, 4; 0061: where one or more components may be located and distributed over a network, such as a cloud computing system; Bhide – Figs. 1-4; 0022-23: where comment elements may be determined through use of various natural language processing/machine learning algorithms, etc.; 0038-39: a report may be generated for an authorized user (administrator) indicating overview/summary of grouped comments as well recommendations of changes in accordance with analyzed comments; 0045, 0051: wherein the system may be implemented in a cloud computing environment; (where it would be implicitly understood that in a cloud computing system, various elements may be split across a plurality of devices/nodes – see also MPEP 2144.04(V)-(VI)).]
Regarding Claim 12, Hou and Bhide disclose all of the limitations of Claim 8, which are analyzed as previously discussed with respect to that claim.
Furthermore, Bhide discloses wherein providing the text to the machine learning model comprises: transmitting, to a machine learning host associated with the machine learning model, the text; and receiving, from the machine learning host, an indication of the additional feedback. [Bhide – Figs. 1-4; 0022-23: where comment elements may be determined through use of various natural language processing/machine learning algorithms, etc.; 0038-39: a report may be generated for an authorized user (administrator) indicating overview/summary of grouped comments as well recommendations of changes in accordance with analyzed comments;]
Regarding Claim 13, Hou and Bhide disclose all of the limitations of Claim 8, which are analyzed as previously discussed with respect to that claim.
Furthermore, Bhide discloses wherein the indication of the text and the additional feedback is included in a report. [Bhide – 0038-39]
Hou and Bhide fail to explicitly specify wherein the indication of the text and the additional feedback is included in an email message.
However, the Examiner takes Official Notice that providing reports to some user (such as the report of Bhide to the authorized user) via email would be readily obvious to one of ordinary skill in the art as email communication of information has been widely known and utilized as a means of digitally transmitting information to others.
Regarding Claim 21, Claim 21 recites a non-transitory CRM comprising instructions that perform the functions of the system of Claim 1. As such, Claim 21 is analyzed and rejected similarly as that claim.
Regarding Claim 22, Hou and Bhide disclose all of the limitations of Claim 21, which are analyzed as previously discussed with respect to that claim.
Furthermore, Claim 22 recites nearly identical limitations as Claim 9 and are rejected similarly as that claim.
Regarding Claim 23, Hou and Bhide disclose all of the limitations of Claim 21, which are analyzed as previously discussed with respect to that claim.
Furthermore, Claim 23 recites nearly identical limitations as Claim 11 and are rejected similarly as that claim.
Regarding Claim 24, Hou and Bhide disclose all of the limitations of Claim 21, which are analyzed as previously discussed with respect to that claim.
Furthermore, Claim 24 recites nearly identical limitations as Claim 12 and are rejected similarly as that claim.
Regarding Claim 25, Hou and Bhide disclose all of the limitations of Claim 21, which are analyzed as previously discussed with respect to that claim.
Furthermore, Bhide discloses wherein the ticket tags, in the command, a corresponding administrator associated with the multimedia content. [Bhide – Figs. 1-3; 0021: wherein some users may be authorized to edit the content; 0038-39: a report may be automatically generated for an authorized user (administrator) indicating overview/summary of grouped comments as well recommendations of changes in accordance with analyzed comments (where it would be inferably understood that the system would need some way of ‘tagging’ or associating the report with some authorized user – i.e., administrator – such that the report for a given content is provided to the appropriate user. See MPEP 2144.01)]
Regarding Claim 26, Hou and Bhide disclose all of the limitations of Claim 21, which are analyzed as previously discussed with respect to that claim.
Hou and Bhide fail to explicitly disclose wherein the machine learning model includes a regression algorithm.
However, the Examiner takes Official Notice that it is widely known and understood by those of ordinary skill in the art that learning models, such as those of Hou and Bhide above, may include regression algorithms.
Regarding Claim 27, Hou and Bhide disclose all of the limitations of Claim 21, which are analyzed as previously discussed with respect to that claim.
Furthermore, Hou and Bhide disclose wherein the proposed change includes one or more of: a visual change to a particular frame, a textual change to a particular frame, audio change to a particular frame, a visual change to a set of frames, a textual change to a set of frames, or audio change to a set of frames. [Hou – 0004-6, 0032: 0045: examples for feedback to update/edit the audio/video product, with examples including commenting that music too loud; Bhide – 0039: changes may include corrections, adding/deleting text, answer questions/etc.]
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hou and Bhide as applied to claim 1 above, and further in view of Gappa et al. (US 2008/0060084 A1) (of record, hereinafter Gappa).
Regarding Claim 5, Hou and Bhide disclose all of the limitations of Claim 1, which are analyzed as previously discussed with respect to that claim.
Hou and Bhide fail to explicitly disclose wherein the multimedia content comprises a current training, and wherein the one or more processors are configured to: detect an end of a previous training, wherein the multimedia content is streamed to the user device in response to the end of the previous training.
Gappa, in analogous art teaches wherein the multimedia content comprises a current training, and wherein the one or more processors are configured to: detect an end of a previous training, wherein the multimedia content is streamed to the user device in response to the end of the previous training. [0008, 0014, 0024: content that may be stored in some central server (such as the repository of Hou and Bhide above), may comprise instructional content (i.e., some sort of training) that may be presented in a playlist as a sequence of content in succession (i.e., it would be implicitly understood that playlists may stream a new instructional content following conclusion of a previous content. See also MPEP 2144.01)]
It would have been obvious to one of ordinary skill in the art prior to the filing date of the invention to modify the method of Hou and Bhide with the teachings of Gappa to specify training content and streaming new content in response to an end of a previous training as it is understood that content, such as instructional/training content, may be provisioned in a sequence based on a playlist. [Gappa – ABST; 0008, 0014, 0024]
Conclusion
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 WILLIAM J KIM whose telephone number is (571)272-2767. The examiner can normally be reached 9:30am - 5:30pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Hadi Armouche can be reached at (571) 270-3618. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/WILLIAM J KIM/Primary Examiner, Art Unit 2409