DETAILED ACTION
Claims 1-20 are pending.
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 Objections
Claims 1-2 are objected to because of the following informalities:
There are two Claim 1s, and a missing Claim 8. Examiner is treating the second Claim 1 as Claim 8 until a correction is made.
Appropriate correction is required.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-3, 5-12, 14-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Ergen (US 2023/0064341 A1).
With regards to Claim 1, Ergen teaches a computer-implemented method, comprising: receiving user data associated with a user profile, wherein the user profile is associated with a user device (i.e., where user viewing history 402 and other user profile data 404 are received by the interruption detection system via a device, Paragraph 35; Figure 4); generating an ordered list of communication channels based on the user profile (i.e., After the features are extracted at operation 408, domains classified at operation 410, and preferences determined at operation 412, the system may determine which candidate multimedia items should be displayed at operation 414. Continuing from the earlier “Halloween” example, a user's viewing history may demonstrate that the user has been frequently watching Halloween-themed multimedia. The possible domain classification for a Halloween-themed movie may be “holiday.” As such, other multimedia items that are classified as “holiday” domains may be more relevant to the user, Paragraph 42; Paragraphs 43-44; Figure 4); receiving a timer value including a duration of time (i.e., At block 314, process 300 determines commercial break is about to end. For example, process 300 can detect the commercial break has a threshold of time (e.g., any threshold of time, such as seconds, minutes, etc.) remaining before completing, Paragraph 34; Figure 3; Paragraph 44);facilitating a connection with a first communication channel of the ordered list of communication channels (i.e., At block 308, when the commercial begins, process 300 switches to identified alternate channel of the media-delivery platform or to the alternate media-delivery platform and plays the same media content. Process 300 can align the media content, on the alternate channel or alternate media-delivery platform, with a start of the commercial break. In some cases, process 300 can align the media content on the alternate source with the media content on the original source, so the user has a continuous viewing experience. For example, when the commercial break starts, the user continues at the same location in the movie or TV show from the alternate source, as the location the movie or TV show was at when the commercial began, Paragraph 32; Figure 4); outputting media content associated with the first communication channel (i.e., At block 308, when the commercial begins, process 300 switches to identified alternate channel of the media-delivery platform or to the alternate media-delivery platform and plays the same media content. Process 300 can align the media content, on the alternate channel or alternate media-delivery platform, with a start of the commercial break. In some cases, process 300 can align the media content on the alternate source with the media content on the original source, so the user has a continuous viewing experience. For example, when the commercial break starts, the user continues at the same location in the movie or TV show from the alternate source, as the location the movie or TV show was at when the commercial began, Paragraph 32; Figure 4); receiving a characteristic associated with the first communication channel (i.e., The displayed content is then subsequently stored in Historical ML suggestions database 420 for future reference (e.g., in determining the candidate multimedia items for viewing at step 414), Paragraph 45); generating a modified ordered list of communication channels based on the characteristic (i.e., The displayed content is then subsequently stored in Historical ML suggestions database 420 for future reference (e.g., in determining the candidate multimedia items for viewing at step 414), Paragraph 45; Figure 4; Step 414 is done again); and facilitating a connection with a second communication channel of the modified ordered list of communication channels, wherein the second communication channel is different from the first communication channel (i.e., Figure 4; Steps 414-420 can be done multiple times, each time a new modified list can be ordered and delivered).
With regards to Claim 8, Ergen teaches receiving user input from the user device, wherein the user input causes the output of the media content associated with the second communication channel (i.e., notification may be provided to the user, requesting feedback of whether the user enjoyed the selection of the alternative multimedia content during the commercial break, Paragraph 45)
With regards to Claim 2, Ergen teaches wherein the ordered list is generated by a machine-learning model (i.e., The received data may be converted into particular representations that may be understood and processed by a machine utilizing machine-learning algorithms (e.g., ML Engine 406) to intelligently disassemble the user viewing history and other user profile data and identify media content for the user, Paragraph 35).
With regards to Claim 3, Ergen teaches wherein the machine-learning model generates the ordered list further based on a communication channel history associated with the user device (i.e., Additionally, at operation 414, the multimedia items that are ultimately decided to be displayed may be determined using historical selections from the Historical ML suggestions database 420. For instance, certain content that was viewed by the user may be notated in database 420, and content suggestions that were ignored (not viewed) by the user may be notated in database 420, as well. The multimedia items that were viewed and enjoyed by the user may further help determine the candidate multimedia items to be select at step 414, Paragraph 43)
With regards to Claim 5, Ergen teaches wherein the user profile comprises at least one of demographic data, communication channel history, or user preferences (i.e., The user viewing history 402 and other user profile data 404 may be transmitted to Machine-Learning (ML) Engine 406, where the data may be used to train at least one ML model and/or compared against an already-trained ML model or models. Other user profile data 404 may comprise data from a user's social media account, user responses to a profile survey, and/or data from other sources…, Paragraph 36)
With regards to Claim 6, Ergen teaches wherein the first communication channel is associated with media content that is of a same type as media content associated with the second communication channel (i.e., In other examples, a user may indicate that he/she has a favorite sports team. The ML Engine 406 may determine which team that is based on user viewing history 402 and other user profiled data 404. As a result, the interruption detection system may select to display particular games in which the team is playing, but also television shows/documentaries that may be about that particular sports team (e.g., a highlight reel of the sports team on ESPN). It should be appreciated that multiple preferences may be predicted at operation 412. In other examples, the interruption detection system can indicate the type of the content the user is currently viewing. The ML Engine 406 may determine based on user viewing history 402 and other user profiled data 404, media content that is similar to the media content the user is watching. For example, if the user is viewing a football game, the ML Engine 406 may select other football games to display. As a result, the interruption detection system may select to display particular types of sports games, Paragraph 41)
With regards to Claim 7, Ergen teaches wherein an identification of the first communication channel is stored within the user profile (i.e., Once the alternative multimedia item(s) is selected, by the multimedia content delivery manager 416, to be displayed during the interruption, the alternative multimedia item(s) is displayed for the user to view at step 418. The displayed content is then subsequently stored in Historical ML suggestions database 420 for future reference (e.g., in determining the candidate multimedia items for viewing at step 414). Once a multimedia item is selected and/or displayed, a notification may be provided to the user, requesting feedback of whether the user enjoyed the selection of the alternative multimedia content during the commercial break, Paragraph 45; Paragraph 15, viewing history is monitored)
The limitations of Claim 9 are rejected in the analysis of Claim 1 above, and the claim is rejected on that basis.
The limitations of Claim 10 are rejected in the analysis of Claim 8 above, and the claim is rejected on that basis.
The limitations of Claim 11 are rejected in the analysis of Claim 2 above, and the claim is rejected on that basis.
The limitations of Claim 12 are rejected in the analysis of Claim 3 above, and the claim is rejected on that basis.
The limitations of Claim 14 are rejected in the analysis of Claim 5 above, and the claim is rejected on that basis.
The limitations of Claim 15 are rejected in the analysis of Claim 6 above, and the claim is rejected on that basis.
The limitations of Claim 16 are rejected in the analysis of Claim 7 above, and the claim is rejected on that basis.
The limitations of Claim 17 are rejected in the analysis of Claim 1 above, and the claim is rejected on that basis.
The limitations of Claim 18 are rejected in the analysis of Claim 8 above, and the claim is rejected on that basis.
The limitations of Claim 19 are rejected in the analysis of Claim 2 above, and the claim is rejected on that basis.
The limitations of Claim 20 are rejected in the analysis of Claim 3 above, and the claim is rejected on that basis.
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 4 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Ergen (US 2023/0064341 A1) in view of Mishra (US 2022/0376994 A1).
With regards to Claim 4, Ergen teaches the above disclosed subject matter. However, Ergen does not explicitly disclose wherein the machine-learning model was trained using transfer learning. Mishra does teach wherein the machine-learning model was trained using transfer learning (i.e., or example, if the stability of the video stream is predicted to have high latency or interruption, then the resources devoted to that video stream can be adjusted to maximize their use either by reassignment or by increasing resources to attempt to improve stability by the RCA service 225, Paragraph 34; The embodiments extract knowledge from the collection of issues (sometimes referred to as tickets) in the support management system, apply deep learning and similar machine learning techniques to find the best possible solution and to apply the solution to fix issues with the content delivery infrastructure…, Paragraph 45; The transfer learning component 303 configures and updates the pre-trained language transformer models to adapt the models to be utilized with the specific set of available inputs for a target content delivery infrastructure…, Paragraph 53) in order to provide improved interruption prediction (Paragraph 4). Therefore, based on Ergen in view of Mishra, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to utilize the teachings of Mishra with the system of Ergen in order to to provide improved interruption prediction.
The limitations of Claim 13 are rejected in the analysis of Claim 4 above, and the claim is rejected on that basis.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SURAJ M JOSHI whose telephone number is (571)270-7209. The examiner can normally be reached Monday - Friday 8-6 ET.
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/SURAJ M JOSHI/Primary Examiner, Art Unit 2447 September 5, 2026
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