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 .
Terminal Disclaimer
The terminal disclaimer filed on 7/6/2026 disclaiming the terminal portion of any patent granted on this application which would extend beyond the expiration date of U.S. Patent 11,769,531 and 12,300,274 has been reviewed and is accepted. The terminal disclaimer has been recorded.
Response to Amendment
The amendments, filed 7/6/2026, have been entered and made of record. Claims 1, 18, and 20 have been amended. Claims 1-20 are pending.
Response to Arguments
Applicant’s arguments in the Remarks filed on 7/6/2026 have been considered but are moot in view of the new ground(s) of rejection.
Claim Rejections - 35 USC § 103
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 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.
Matias in view of Meyer
Claims 1, 2, 4-6, 9, 14-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Matias et al.(USPubN 2017/0110151; hereinafter Matias) in view of Meyer et al.(USPubN 2012/0197992; hereinafter Meyer).
As per claim 1, Matias teaches a method for use in connection with a content-presentation device, the method comprising:
obtaining a first segment of video content; outputting for presentation, via the content-presentation device, the obtained first segment(“users may capture videos and/or video clips using one or more image capturing devices” in Para.[0026], “The user interface 200 may be configured to show a generation of a video from a set of video clips. The user interface 200 may include one or more of a first window 202, a second window 204, a set of user interface elements 206, and/or other components. The first window 202 may be configured to display a set of video clips selected for generating a video” in Para.[0117]);
after outputting for presentation the obtained first segment, causing a user to be prompted for user-input data; receiving user-input data, wherein the user-input data is received in response to the prompting, wherein the received user-input data specifies a characteristic of synthetic content to be generated(“The first timeline 208 may be represented by a first start 210, a first end 212, and/or other features. The first video clip may include a first moment of interest represented by a first user interface element 214. The first user interface element 214 may be positioned alone the first timeline 208 corresponding to a point in time of the first moment of interest. The first user interface element 214 may be selectable by a user to change the point in time with which the first moment of interest may be associated. By way of non-limiting example, the first user interface element 214 may be selectable by a user via a drag-and-drop feature, slide feature, and/or by other techniques that may facilitate positioning/repositioning the first user interface element 214 along the first timeline 208” in Para.[0118]);
using at least the received user-input data to synthetically generate a second segment of the video content, wherein the generated second segment is static, non-interactive content; and outputting for presentation, via the content presentation device, the generated second segment(“system 100 may be configured to facilitate an automatic generation of a video from a set of video clips associated with a user based on one or more moments of interest within individual ones of the video clips” in Para.[0020], “The second window 204 of the user interface 200 may include one or more of a third timeline 228 that may represent a video generated based on the set of video clips portrayed in the first window 202, a representation of supplemental audio 240, and/or other features. The third timeline 228 may be represented by a second start 230, a second end 232, and/or other features.” in Para.[0121]).
Matias is silent about wherein the generated second segment consists of video content other than video content extracted from the obtained first segment.
Meyer teaches wherein the generated second segment consists of video content other than video content extracted from the obtained first segment(“emphasis is placed on the customization of the story telling to the behavior and habits and preferences and profile of the user. In other words, the story fits into how the user carries on activities during the day and multi-tasks a number of activities and events. User behaviors include being involved in the story at a number of times during the day, and with each involvement lasting a short amount of time, preferably in the range of 5 to 15 minutes. Thus, the user can proceed at his own pace. The story telling can also be user-driven based on the timing of the user input, and the content and context of the user input to the triggered events. Additionally, some stories may be used by the user to change user behaviors if the user so desires. Accordingly, for example, three or four short videos of between 5 to 15 minutes can be used during the day of a user to present the story. Thus, for example, long form media such as movies can be presented to users who have a short form or short event mindset or behavior pattern. As events are delivered to the user which are customized to the user's personal profile, the user can interact with the story and the user is or appears to be part of the story. Additionally, using the producer dashboard the story line can be amended in real time and adjusted in accordance with the feedback or sentiment of the user, with current events, with current news events, with popular trends, and/or with marketing incentives. Input from real time social media search engine results such as provided by Topsy.RTM., based on the messages or Tweets.RTM. available from Twitter.RTM., can be used to sample trends and preferences in real time and to adjust the story in accordance with events relevant to the user's profile and, for example, the user's current location or permanent address. Thus, in accordance with embodiments of the invention, stories or learning can be customized and personalized experiences for the user and adapt to the user's preferred engagement patterns.” in Para.[0049], “Storyline extensions can be produced for established serial presentations such television series and movie sequel series. For avid fans or to build a fan base, a series can be extended with events triggered using the embodiments described above to allow these series producers to deliver content to fans. As a use case, embodiments of the invention can be built on an existing fan base for a TV series and offer events with content and characters and storylines and events that are extensions of the TV series. Such an extension can continue to build loyalty among fans for the original TV series and for the extension built using the embodiments of the invention” in Para.[0054], The user can generate short videos for storyline extension bases on television series and movie sequel series which doesn’t include original television series and movie sequel.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings Matias with the above teachings of Meyer in order to enhance user involvement for improving experience of media content.
As per claim 2, Matias and Meyer teach all of limitation of claim 1.
Matias teaches wherein the first segment is a live-action video recording(“distribute live-broadcasts” in Para.[0130]).
As per claim 4, Matias and Meyer teach all of limitation of claim 1.
Matias teaches further comprising: detecting an occurrence of a real-time event occurring proximate a time point at which the first segment is output for presentation, and wherein causing the user to be prompted for input-data comprises causing presentation of a set of user-selectable options based on the real-time event, and wherein receiving the user-input data provided in response to the prompting comprises receiving a selection from the presented set of user-selectable options(“The first timeline 208 may be represented by a first start 210, a first end 212, and/or other features. The first video clip may include a first moment of interest represented by a first user interface element 214. The first user interface element 214 may be positioned alone the first timeline 208 corresponding to a point in time of the first moment of interest. The first user interface element 214 may be selectable by a user to change the point in time with which the first moment of interest may be associated. By way of non-limiting example, the first user interface element 214 may be selectable by a user via a drag-and-drop feature, slide feature, and/or by other techniques that may facilitate positioning/repositioning the first user interface element 214 along the first timeline 208.” in Para.[0118]).
As per claim 5, Matias and Meyer teach all of limitation of claim 1.
Matias teaches further comprising: crowdsourcing user input-data provided in response to prompting associated with multiple other instances of the first segment being presented to other users; wherein causing the user to be prompted for input-data comprises causing presentation of a set of user-selectable options based on the crowdsourced user-input data, and wherein receiving the user-input data provided in response to the prompting comprises receiving a selection from the presented set of user-selectable options(“The user component 108 may be configured to determine user preferences based on obtaining entry and/or selection of one or more preferences from the users directly. By way of non-limiting example, a user may be prompted to input, via user interface, one or more of their preferences with respect to one or more attributes of video clips, and/or other preferences. For example, a user may be promoted to input preferences when they register an account, when generating a video, when uploading a video clip, and/or at other times” in Para.[0065], “By way of further non-limiting illustration, user component 108 may analyze a user's system use patterns to determine preferences related to multiple attributes. By way of non-limiting example, a use pattern related to up-voting videos the users viewed may be analyzed. The use patterns may convey that the user up-votes (e.g., “likes”) an amount of video clips (e.g., up to a threshold amount) that commonly share one or more of a feature point detection attribute related to actions depicted within the video clips having a value of “surfing,” a setting information attribute related to camera position having a value of “mounted on surfboard,” a time attribute having a value of “6 PM,” a geolocation attribute having a value of “Lo Jolla, Calif.,” and/or other values of other attributes. The user component 108 may be configured to determine that the user prefers video clips and/or videos that include one or more of actions depicting surfing, that are recorded from the perspective of the surfboard, has a geolocation of La Jolla, Calif., includes a timestamp of 6 PM (and/or “evening” times), and/or other preferences “ in Para.[0072]).
As per claim 6, Matias and Meyer teach all of limitation of claim 1.
Matias teaches further comprising: in connection with causing the user to be prompted for user-input data, causing presentation of historical data indicating (i) a history user input-data received in connection with the video content and (ii) a history of segments synthetically generated in connection with the video content(“By way of further non-limiting illustration, user component 108 may analyze a user's system use patterns to determine preferences related to multiple attributes. By way of non-limiting example, a use pattern related to up-voting videos the users viewed may be analyzed. The use patterns may convey that the user up-votes (e.g., “likes”) an amount of video clips (e.g., up to a threshold amount) that commonly share one or more of a feature point detection attribute related to actions depicted within the video clips having a value of “surfing,” a setting information attribute related to camera position having a value of “mounted on surfboard,” a time attribute having a value of “6 PM,” a geolocation attribute having a value of “Lo Jolla, Calif.,” and/or other values of other attributes. The user component 108 may be configured to determine that the user prefers video clips and/or videos that include one or more of actions depicting surfing, that are recorded from the perspective of the surfboard, has a geolocation of La Jolla, Calif., includes a timestamp of 6 PM (and/or “evening” times), and/or other preferences” in Para.[0072]).
As per claim 9, Matias and Meyer teach all of limitation of claim 1.
Matias teaches further comprising: receiving user-profile data for the user, wherein using at least the received user-input data to synthetically generate the second segment comprises using at least the received user-input data and the received user-profile data to synthetically generate the second segment(“moment of interest component 112 may be configured to obtain selections of sets of video clips automatically based on one or more users preferences. For example, a set of video clips may include a first video clip, a second video clip, and/or other video clips that commonly share values of one or more attributes. A user preference may specify one or more values of one or more attributes. The set of video clips may be selected based the commonly shared values matching values of one or more attributes specified by the user preference” in Para.[0076]).
As per claim 14, Matias and Meyer teach all of limitation of claim 1.
Matias teaches wherein (i) the obtaining the first segment of video content, (ii) the outputting for presentation, via the via the content-presentation device, the obtained first segment, (iii) the causing the user to be prompted for user-input data, (iv) the receiving user-input data provided in response to the prompting, (v) the using at least the received user-input data to synthetically generate the second segment, and (vi) the outputting for presentation, via the content-presentation device, the generated second segment, are all performed by a computing system that (i) is connected to a content-presentation device, and (ii) facilitates the content-presentation device presenting the video content(“FIG. 2 shows an exemplary implementation of a user interface 200 configured to facilitate editing of an automatically generated video. The user interface 200 may be configured to show a generation of a video from a set of video clips. The user interface 200 may include one or more of a first window 202, a second window 204, a set of user interface elements 206, and/or other components. The first window 202 may be configured to display a set of video clips selected for generating a video. The set of video clips may comprise one or more of a first video clip, a second video clip, and/or other video clip. The first video clip may be represented by a first timeline 208 displayed in the first window 202. The second video clip may be represented by a second timeline 222 displayed in the first window 202” in Para.[0117], “The first timeline 208 may be represented by a first start 210, a first end 212, and/or other features. The first video clip may include a first moment of interest represented by a first user interface element 214. The first user interface element 214 may be positioned alone the first timeline 208 corresponding to a point in time of the first moment of interest. The first user interface element 214 may be selectable by a user to change the point in time with which the first moment of interest may be associated. By way of non-limiting example, the first user interface element 214 may be selectable by a user via a drag-and-drop feature, slide feature, and/or by other techniques that may facilitate positioning/repositioning the first user interface element 214 along the first timeline 208“ in Para.[0118], “The first moment of interest may be associated with a first segment 216. The first segment 216 may correspond to one or more of a second user interface element 218 representing a start of the first segment 216, a third user interface element 220 representing an end of the first segment 216, and/or other features. The second and/or third user interface elements 218, 220 may be selectable by a user to change points in time with which the respective start and end of the first segment 216 may be associated. By way of non-limiting example, the second and/or third user interface elements 218, 220 may be selectable by a user via a drag-and-drop feature, slide feature, and/or by other features that may facilitate positioning/repositioning the start and/or end of the first segment 216 to change a temporal span of the first segment 216 within the first video clip “ in Para.[0119],” It is noted that the second video clip represented by the second timeline 222 may include similar features as those presented above in connection with the first video clip (e.g., and first timeline 208). However, to simplify and clarify the present description, only a fourth user interface element 224 representing a second moment of interest and a second segment 226 that corresponds to the second moment of interest are shown” in Para. [0120], “The second window 204 of the user interface 200 may include one or more of a third timeline 228 that may represent a video generated based on the set of video clips portrayed in the first window 202, a representation of supplemental audio 240, and/or other features. The third timeline 228 may be represented by a second start 230, a second end 232, and/or other features. The third timeline 228 may include a first portion 234 of the video with which the first moment of interest may be associated (e.g., illustrated by the first user interface element 214 being positioned on the third timeline 228 within the first portion 234). The third timeline 228 may include a second portion 236 of the video with which the second moment of interest may be associated (e.g., illustrated by the fourth user interface element 224 being positioned on the third timeline 228 within the second portion 236). In some implementations, the first portion 234 may include the first segment 216, a portion of the first segment 216, and/or an expanded version of the first segment 216. In some implementations, the second portion 260 may include the second segment 226, a portion of the second segment 226, and/or an expanded version of the second segment 226 “ in Para.[0121]).
As per claim 15, Matias and Meyer teach all of limitation of claim 14.
Matias teaches wherein the content-presentation device is a television(“The system 100 may comprise one or more of a server 102, one or more computing platforms 122, and/or other components. Individual computing platforms 122 may include one or more of a cellular telephone, a smartphone, a digital camera, a laptop, a tablet computer, a desktop computer, a television set-top box, smart TV, a gaming console, a client computing platform, and/or other platforms” in Para.[0021]).
As per claim 16, Matias and Meyer teach all of limitation of claim 1.
Matias teaches wherein (i) the obtaining the first segment of video content, (ii) the outputting for presentation, via the content-presentation device, the obtained first segment, (iii) the causing the user to be prompted for user-input data, (iv) the receiving user-input data provided in response to the prompting, (v) the using at least the received user-input data to synthetically generate the second segment, and (vi) the outputting for presentation, via the content-presentation device, the generated second segment, are all performed by a content-presentation device(Para.[0117], [0118], [0119], [0120], [0121]).
As per claim 17, Matias and Meyer teach all of limitation of claim 16.
Matias teaches wherein the content-presentation device is a television(Para.[0021]).
As per claim 18, Matias teaches a non-transitory computer-readable medium having stored thereon program instructions that upon execution by a computing system(Para.[0027], [0136]) and the other limitations in the claim 18 has been discussed in the rejection claim 1 and rejected under the same rationale.
As per claim 20, Matias teaches a computing system configured for performing a set of acts comprising (Para.[0027], [0136]) and the other limitations in the claim 20 has been discussed in the rejection claim 1 and rejected under the same rationale.
Matias in view of Meyer and Kalish
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Matias et al.(USPubN 2017/0110151; hereinafter Matias) in view of Meyer et al.(USPubN 2012/0197992; hereinafter Meyer) further in view of Kalish(USPubN 2022/0028425).
As per claim 3, Matias and Meyer teach all of limitation of claim 1.
Matias and Meyer are silent about further comprising: detecting metadata associated with the first segment, wherein the metadata specifies a set of user-selectable options, wherein causing the user to be prompted for input-data comprises causing presentation of the set of user-selectable options, and wherein receiving the user input-data provided in response to the prompting comprises receiving a selection from the presented set of user-selectable options.
Kalish teaches further comprising: detecting metadata associated with the first segment, wherein the metadata specifies a set of user-selectable options, wherein causing the user to be prompted for input-data comprises causing presentation of the set of user-selectable options, and wherein receiving the user input-data provided in response to the prompting comprises receiving a selection from the presented set of user-selectable options(“determine original video, metadata which includes at least partial information to generate the new video file and parameter which effect/customize video content—in association to creating a new basic standard video file, wherein the partial information include at least an ID or link of the basic video original; upon opening the video the by a client player reading the metadata; checking metadata predefined conditions for playing the video as is; playing video as is in case the video initial condition are met; in case initial condition require user intervention prompting user to update the video rabbling user to input change in customization parameters data; in case user selected option of update providing the user option of update/edit the video Enabling user to input change in customization parameters data” in Abs).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings Matias and Meyer with the above teachings of Kalish in order to improve the content item consumption experience for the users from additional interactive content item that match the user-selected options.
Matias in view of Meyer and Ingel
Claims 7, 8 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Matias et al.(USPubN 2017/0110151; hereinafter Matias) in view of Meyer et al.(USPubN 2012/0197992; hereinafter Meyer) further in view of Ingel et al.(USPubN 2020/0213680; hereinafter Ingel).
As per claim 7, Matias and Meyer teach all of limitation of claim 1.
Matias and Meyer are silent about wherein using at least the received user-input data to synthetically generate the second segment comprises: providing at least the received user-input data to a trained model, wherein the trained model is configured to use at least user-input data as runtime input-data to generate video data representing a segment of video content as runtime output-data; and responsive to providing the user-input data to the trained model, receiving from the trained model, corresponding video data representing a generated segment of video content.
Ingel teaches wherein using at least the received user-input data to synthetically generate the second segment comprises: providing at least the received user-input data to a trained model, wherein the trained model is configured to use at least user-input data as runtime input-data to generate video data representing a segment of video content as runtime output-data; and responsive to providing the user-input data to the trained model, receiving from the trained model, corresponding video data representing a generated segment of video content(“step 2910 may generate the manipulated video using step 470. In another example, a machine learning model may be trained using training examples to manipulate aspects of items depicted in videos in response to user inputs, and step 2910 may use the trained machine learning model to manipulate the at least one aspect of the depiction of the at least one item in the video accessed by step 2902 in response to the input received by step 2908. An example of such training example may include a video and a user input together with a desired manipulated video. For example, the machine learning model may be trained to perform any of the video manipulations discussed herein, including (but not limited to) the manipulations illustrated in FIGS. 28A-28F. In an additional example, an artificial neural network may be configured to manipulate aspects of items depicted in videos in response to user inputs, and step 2910 may use the artificial neural network to manipulate the at least one aspect of the depiction of the at least one item in the video accessed by step 2902 in response to the input received by step 2908. In some example, Generative Adversarial Networks (GAN) may be used to train an artificial neural network configured to manipulate aspects of items depicted in videos in response to user inputs, and step 2910 may use the trained artificial neural network to manipulate the at least one aspect of the depiction of the at least one item in the video accessed by step 2902 in response to the input received by step 2908. In some examples, step 2910 may analyze the video accessed by step 2902 to detect at least part of an item (such as a part of the first item and/or a part of the second item), and step 2910 manipulating a first aspect of the detected at least part of the depiction of the first item (for example in response to a first received input). For example, step 2910 may use object detection algorithms to detect the at least part of the item, and may stitch a depiction of the manipulated aspect of the item over the detected depiction of the at least part of the item in the video accessed by step 2902 (for example, using image and/or video stitching algorithms, using image and/or video matting algorithms, and so forth) to manipulate the video” in Para.[0430]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings Matias and Meyer with the above teachings of Ingel in order to improve the accuracy of an input of a candidate generation model.
As per claim 8, Matias, Meyer and Ingel teach all of limitation of claim 7.
Matias and Meyer are silent about wherein the model was trained by providing to the model as training data, multiple training input-data sets, and for each of the training input-data sets, a respective training output-data set; wherein each of the training input-data sets includes respective (i) user-input data and (ii) video data and/or associated metadata; and wherein each of the training output-data sets includes a respective segment of video content.
Ingel teaches wherein the model was trained by providing to the model as training data, multiple training input-data sets, and for each of the training input-data sets, a respective training output-data set; wherein each of the training input-data sets includes respective (i) user-input data and (ii) video data and/or associated metadata; and wherein each of the training output-data sets includes a respective segment of video content (“step 2910 may generate the manipulated video using step 470. In another example, a machine learning model may be trained using training examples to manipulate aspects of items depicted in videos in response to user inputs, and step 2910 may use the trained machine learning model to manipulate the at least one aspect of the depiction of the at least one item in the video accessed by step 2902 in response to the input received by step 2908. An example of such training example may include a video and a user input together with a desired manipulated video. For example, the machine learning model may be trained to perform any of the video manipulations discussed herein, including (but not limited to) the manipulations illustrated in FIGS. 28A-28F. In an additional example, an artificial neural network may be configured to manipulate aspects of items depicted in videos in response to user inputs, and step 2910 may use the artificial neural network to manipulate the at least one aspect of the depiction of the at least one item in the video accessed by step 2902 in response to the input received by step 2908. In some example, Generative Adversarial Networks (GAN) may be used to train an artificial neural network configured to manipulate aspects of items depicted in videos in response to user inputs, and step 2910 may use the trained artificial neural network to manipulate the at least one aspect of the depiction of the at least one item in the video accessed by step 2902 in response to the input received by step 2908. In some examples, step 2910 may analyze the video accessed by step 2902 to detect at least part of an item (such as a part of the first item and/or a part of the second item), and step 2910 manipulating a first aspect of the detected at least part of the depiction of the first item (for example in response to a first received input). For example, step 2910 may use object detection algorithms to detect the at least part of the item, and may stitch a depiction of the manipulated aspect of the item over the detected depiction of the at least part of the item in the video accessed by step 2902 (for example, using image and/or video stitching algorithms, using image and/or video matting algorithms, and so forth) to manipulate the video” in Para.[0430]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings Matias and Meyer with the above teachings of Ingel in order to improve the accuracy of an input of a candidate generation model.
As per claim 19, Matias and Meyer teach all of limitation of claim 18.
Matias and Meyer are silent about wherein using at least the received user-input data to synthetically generate the second segment comprises: providing at least the received user-input data to a trained model, wherein the trained model is configured to use at least user-input data as runtime input-data to generate video data representing a segment of video content as runtime output-data; and responsive to providing the user-input data to the trained model, receiving from the trained model, corresponding video data representing a generated segment of video content.
Ingel teaches wherein using at least the received user-input data to synthetically generate the second segment comprises: providing at least the received user-input data to a trained model, wherein the trained model is configured to use at least user-input data as runtime input-data to generate video data representing a segment of video content as runtime output-data; and responsive to providing the user-input data to the trained model, receiving from the trained model, corresponding video data representing a generated segment of video content (“step 2910 may generate the manipulated video using step 470. In another example, a machine learning model may be trained using training examples to manipulate aspects of items depicted in videos in response to user inputs, and step 2910 may use the trained machine learning model to manipulate the at least one aspect of the depiction of the at least one item in the video accessed by step 2902 in response to the input received by step 2908. An example of such training example may include a video and a user input together with a desired manipulated video. For example, the machine learning model may be trained to perform any of the video manipulations discussed herein, including (but not limited to) the manipulations illustrated in FIGS. 28A-28F. In an additional example, an artificial neural network may be configured to manipulate aspects of items depicted in videos in response to user inputs, and step 2910 may use the artificial neural network to manipulate the at least one aspect of the depiction of the at least one item in the video accessed by step 2902 in response to the input received by step 2908. In some example, Generative Adversarial Networks (GAN) may be used to train an artificial neural network configured to manipulate aspects of items depicted in videos in response to user inputs, and step 2910 may use the trained artificial neural network to manipulate the at least one aspect of the depiction of the at least one item in the video accessed by step 2902 in response to the input received by step 2908. In some examples, step 2910 may analyze the video accessed by step 2902 to detect at least part of an item (such as a part of the first item and/or a part of the second item), and step 2910 manipulating a first aspect of the detected at least part of the depiction of the first item (for example in response to a first received input). For example, step 2910 may use object detection algorithms to detect the at least part of the item, and may stitch a depiction of the manipulated aspect of the item over the detected depiction of the at least part of the item in the video accessed by step 2902 (for example, using image and/or video stitching algorithms, using image and/or video matting algorithms, and so forth) to manipulate the video” in Para.[0430]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings Matias and Meyer with the above teachings of Ingel in order to improve the accuracy of an input of a candidate generation model.
Matias in view of Meyer and Jiang
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Matias et al.(USPubN 2017/0110151; hereinafter Matias) in view of Meyer et al.(USPubN 2012/0197992; hereinafter Meyer) further in view of Jiang et al.(USPubN 2021/0409640; hereinafter Jiang).
As per claim 10, Matias and Meyer teach all of limitation of claim 1.
Matias and Meyer are silent about wherein using at least the received user-input data and the received user-profile data to synthetically generate the second segment comprises: providing the received user input data and the received user-profile data to a trained model, wherein the trained model is configured to use at least user-input data and user-profile data as runtime input-data to generate video data representing a segment of video content as runtime output-data; and responsive to providing the user input-data and the user profile-data to the trained model, receiving from the trained model, corresponding video data representing a generated segment of video content.
Jiang teaches wherein using at least the received user-input data and the received user-profile data to synthetically generate the second segment comprises: providing the received user input data and the received user-profile data to a trained model, wherein the trained model is configured to use at least user-input data and user-profile data as runtime input-data to generate video data representing a segment of video content as runtime output-data; and responsive to providing the user input-data and the user profile-data to the trained model, receiving from the trained model, corresponding video data representing a generated segment of video content (Para.[0025]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings Matias and Meyer with the above teachings of Jiang in order to improve the accuracy of an input of a candidate generation model.
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 SUNGHYOUN PARK whose telephone number is (571)270-1333. The examiner can normally be reached M - Thur 6:00 am - 4 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, THAI Q TRAN can be reached at (571)272-7382. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SUNGHYOUN PARK/Examiner, Art Unit 2484
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