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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 26 May 2026 has been entered.
Examiner’s Comments
The Examiner contacted Applicant’s representative on at least 29 July 2026 and 04 August 2026 proposing an amendment to Claim 8 to align with the subject matter claimed in Independent claims 1 and 21 to place application in condition for Allowance. However, Applicant did not assent to the proposed changes as of the mailing of this action.
Response to Arguments
Claims 1, 8, and 21 have been amended.
Claims 1-13 and 21-27 are presently pending.
Applicant’s arguments, see Remarks, filed 26 May 2026, with respect to the Rejections of Claims 1-7 and 21-27 have been fully considered and are persuasive. The rejections of Claims 1-7 and 21-27 has been withdrawn.
Applicant's arguments filed 26 May 2026 with respect to the Rejections of Claims 8-13 have been fully considered but they are not persuasive.
Regarding Applicant’s arguments that Hou and Bhide fail to teach ‘wherein the indication of the text and additional feedback is a proposed change to a particular frame of the multimedia content’ (see Remarks, pgs. 11-12), the Examiner disagrees. Examiner draws attention to Hou [0045] which notes that feedback for proposed changes may be made with respect to specific timestamps within a video. Hou [0036], [0049], and [0054] further specify that feedback may be made with respect to specific frames within the underlying content. Bhide further teaches employing a machine learning system to cluster and summarize proposed changes (such as the frame-specific changes of Hou) as a report to some authorized user. As such, the combined teachings of Hou and Bhide disclose and teach all of the limitations of the claimed invention.
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 8-13 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 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 the multimedia host, 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]
wherein the indication of the text and the additional feedback is a proposed change to a particular frame of the multimedia content. [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, wherein the timestamps may be associated with a specific timestamp; 0036, 0049, 0054: text-based comment may be to a specific frame and/or region of a frame]
Hou fails to explicitly disclose providing, by the multimedia host, the text to a machine learning model to cluster the text with additional feedback related to the portion of the multimedia content 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, wherein the indication of the text and the additional feedback is a proposed change to a particular frame of the multimedia content based on the machine learning model. (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 related to the portion of the multimedia content 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 specific frames of the 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]
wherein the indication of the text and the additional feedback is a proposed change to a particular frame of the multimedia content based on the machine learning model. [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 specific frames of the video product of Hou above) may be clustered into topics and/or sentiments through various neural networks/learning models, etc. and provided as a report to the authorized user]
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.
Allowable Subject Matter
Claims 1-7 and 21-27 are allowed.
Conclusion
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.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
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.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/WILLIAM J KIM/Primary Examiner, Art Unit 2409
Prosecution Insights