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 .
Response to Amendment
Applicant’s submission filed 2/3/2026 has been entered. The claims 8-10 and 18-19 have been cancelled. The claims 1, 6, 11, 16 and 20 have been amended. The claims 1-7, 11-17 and 20 are pending in the current application.
Response to Arguments
Applicant's arguments filed 2/3/2026 have been fully considered but they are not persuasive.
Evidences Relied Upon:
Interactive overlay and chat window of the three Tu-provisional applications:
Tu-provisional ‘207 teaches at Paragraph 0025 that the host can also respond to questions and comments made by users through the interactive overlay.
Tu-provisional ‘733 teaches at Paragraph 0035 that the operator can see and respond to the comments and questions 330 made by viewers in a chat as part of an interactive overlay associated with the short-form video.
Tu-provisional teaches at FIG. 4 and Paragraph 0044 that the livestream videos are used to interact with users as the host presents products and as the host interacts with and presents the products for sale, a product card can be included within a livestream shopping window. The ecommerce environment can further include a chat window 490. The chat window can include comments and questions from viewers. The mobile device user can interact with the host user.
In addition to the interactive overlays and/or chat windows communicated between the host user and customer user, customer’s interaction with a product card results in the host generation of a virtual purchase cart showing the natural language description (such as the product name, product size and produce price) of the product. It is noted that presentation of the metadata is different in concept from the metadata (data) of the product. Tu-provisional ‘733 teaches at Paragraph 0023 and Paragraph 0048 a virtual purchase cart (which shows the product size, the product pricing, the product name in the natural language description in response to the user interaction with the product card). The virtual purchase cart includes the natural language description of the product. This feature can be found in detail in US-provisional 63/437,397. The details of the sequence of the interactive product card communicated between the host user and the customer user with the virtual purchase cart showing the natural language description of the product item in the Tu-provisional application 63/437, 397 at FIGS. 4-8 (the application is not enumerated in the rejection).
In Remarks, applicant argued that metadata of Tu-provisional ‘733 is not equivalent to the claimed “embeddings associated with existing digital content or natural language description of the existing digital content”.
In response, Applicant failed to properly map the elements in the three Tu-provisional applications to the claimed natural language description of the existing digital content. The responses in the interactive overlay or chat window by the host user to the customer user constitutes the natural language description of the existing digital content. Moreover, the audio associated with the short-form video which is generated in response to the questions and comments made by the customer user. Applicant argued that the audio is inherently part of the existing digital content. However, the audio is generated by the synthetic user (host user) by way of the artificial intelligence (machine learning model) in response to the questions and comments made by the customer user which is the new digital content and is not part of the existing/old digital content.
In Remarks, applicant argued that Tu-provisional ‘733 does not teach or suggests processing “the prompt, the determined characteristics, and the generated features through the generative model to generate the video component using a subset of the set of assets.
The examiner cannot concur. Tu-provisional applications have taught processing the prompt (customer user questions/comments), the determined characteristics (the metadata) and the generated features (host responses to customer user questions/comments) to generate the new short-form video using a subset of assets in the library.
Tu-provisional ‘207 teaches at Paragraph 0025 that the host can also respond to questions and comments made by users through the interactive overlay.
It would have been obvious to one of the ordinary skill in the art before the filing date of the instant application to have combined Tu-provisional ‘207’s interactive overlay with Tu-provisional ‘733’s interactive overlay to have allowed the host respond to questions and comments through the interactive overlay using natural language description of the product such as product model inquiry, pricing inquiry. The interactive overlay includes a chat window for interaction between the host and the customer. One of the ordinary skill in the art would have provided text response to comments and questions from the customer.
Tu-provisional ‘178’s teaches at FIG. 4 and Paragraph 0044 which clearly shows a chat window with text interactions between the customer user and host user (synthetic human). This can enable additional engagement with the ecommerce short-form livestream video and the host user. The customer user can interact with the host user and the product card in order to learn more about the product. A text response from the host user (synthetic human) to the customer user constitutes the natural language description of the existing product. The text interactions/responses in the chat window to customer user’s questions constitute the claimed natural language description of the existing product (e.g., shoe) including the prices in US dollars from the host user. The chat window allows for the interaction between the customer user and the host user appears within a video chat.
Tu-provisional ‘733 teaches at Paragraph 0035 that the operator can see and respond to the comments and questions 330 made by viewers in a chat as part of an interactive overlay associated with the short-form video.
However, claim limitation is subject to broadest reasonable interpretation consistent with applicant’s specification. The claim 1 alternatively requires natural language description of the existing digital content. Tu-provisional ‘733’s product description including product name, category, sizes, price meets the claimed natural language description of the existing product and at Paragraph 0028 generating text comments and questions and responses to questions by the machine learning model and at Paragraph 0037 that the inserting of the synthesized video segment 370 forms a response to comments 330 including “Great, but can he play baseball” and the video host operator’s audio (or text) response is “Yes, I can”.
Tu-provisional ‘733 teaches at Paragraph 0033 that an interactive overlay can be rendered with the short-form video, allowing questions, comments to be added by viewers and at Paragraph 0035 that the operator 320 can see comments and questions 330 made by viewers in a chart as part of an interactive overlay and can use the video segments 350 to respond to the comments.
Applicant individually attacked Tu-provisional ‘733 while the examiner relied upon the features of the various Tu provisional applications.
Moreover, Tu-provisional ‘733 teaches at Paragraph 0019 that the machine-learning model generates questions and responses to the user with natural language descriptions “Shop for Gear” linked to a short-form video to highlights products and at Paragraph 0020 that the customizing of the short-form video includes adding an interactive overlay to the synthesized short-form video.
Tu-provisional ‘733 teaches an interactive overlay including the host user’s response to the customer user’s request through audio or text dialog. Tu-provisional ‘733 teaches at Paragraph 0035 that the operator can see and respond to the comments and questions 330 made by viewers in a chat as part of an interactive overlay associated with the short-form video and at Paragraph 0037 that the operator 320 dynamically inserts a synthesized video segment 370 into the short-form video 310 which forms a response to comments 330 made by viewers 312. The synthesized video segment 370 includes natural language description (audio or text description) of the product item.
Tu-provisional applications disclose not only audio dialogs between the host user and the customer user, but also text dialogs in the chat window between the host user and the customer user as specifically disclosed in Tu-provisional ‘178. The chat window allows for the interaction between the customer user and the host user appears within a video chat.
For example, Tu-provisional ‘178’s teaches at FIG. 4 and Paragraph 0044 which clearly shows a chat window with text interactions between the customer user and host user (synthetic human). This can enable additional engagement with the ecommerce short-form livestream video and the host user. The customer user can interact with the host user and the product card in order to learn more about the product. A text response from the host user (synthetic human) to the customer user constitutes the natural language description of the existing product. The text interactions/responses in the chat window to customer user’s questions constitute the claimed natural language description of the existing product (e.g., shoe) including the prices in US dollars from the host user. The chat window allows for the interaction between the customer user and the host user appears within a video chat.
Additionally, Tu-provisional ‘178 teaches at Paragraph 0001 that the product representation can include an image or video of the product, on-screen text and/or images, spoken words, metadata embedded within the user data of video stream and product details can be selected through a user action and additional product information can be displayed.
Tu-provisional ‘178 teaches at Paragraph 0046 that ecommerce linkage includes a price, a shipping price, and a shipping method, and details about the products such as color, size, quantities and at Paragraph 0063 that the ecommerce environment allows the host website to provide all the detailed product information such as color, sizes, quantities, pricing, shipping methods and pricing.
However, Tu-provisional ‘733 teaches an updated interactive overlay (new interactive overlay) including associations to one or more products and the associations include metadata on each product including product name, category, sizes, price, color and so on and the associations can be made by a machine learning model.
Tu_provisional ‘733 teaches at Paragraph 0014 that an interactive overlay can be added so that viewers can respond to the short-form videos as the videos play and viewers can ask questions….the machine learning model can prompt viewers regarding related short-form videos and the machine learning model can also generate synthetic short-form videos when they are needed to respond to questions or comments not addressed by videos in the library and at Paragraph 0016 that the machine learning model can analyze the contents of the short-form videos in the library and match product names, categories and so on to products supplied by an ecommerce website.
Tu_provisional ‘733 teaches at Paragraph 0014 the viewers ask questions, make comments and respond to prompt generated by the machine learning model.
Tu-provisional ‘207 at Paragraph 0024 that a lead generation prompt includes a small set of questions for a user to allow a sales representative to follow up with additional information regarding a product and user responses to questions or comments appearing in the interactive overlay can be recorded and choices of additional short-form videos can be presented and selected; options to purchase products portrayed in the video can be chosen. The interactions can be forwarded to the machine learning model in order to learn and update associations to one or more products made by users and at Paragraph 0025 that the host can also respond to questions and comments made by users through the interactive overlay the immediate livestream includes highlighting the one or more products for sale to the user.
Tu-provisional ‘733 teaches at Paragraph 0051 that a list of products for sale can be stored and associated with short-form videos stored in the library. The associations can be based on metadata on each product including product name, category, sizes, price, color and so on and the associations can be made by a machine learning model and at Paragraph 0054 that the short-form videos can be customized to highlight products that have been confirmed to be in stock.
Applicant also argued with the claim limitation of embedding. However, generating a text embedding within GAN based on the text in the chat window is old and well known as GAN in the text-to-image generation has a text encoder to generate the encoded/embedded text such that the short-form video generated by the GAN is based on the text encoding/embedding as the text encoder is included in GAN. A text encoder is inherently included in GAN (see Zhang et al. US-PGPUB No. 2023/0081171----it is not cited as a reference, as is merely to show that text encoder is inherently included in GAN to encode the text prompt or text description of the product to generate an image based on the text prompt).
Interactive overlay and chat window of the three Tu-provisional applications:
Tu-provisional ‘207 teaches at Paragraph 0025 that the host can also respond to questions and comments made by users through the interactive overlay.
Tu-provisional ‘733 teaches at Paragraph 0035 that the operator can see and respond to the comments and questions 330 made by viewers in a chat as part of an interactive overlay associated with the short-form video.
Tu-provisional teaches at FIG. 4 and Paragraph 0044 that the livestream videos are used to interact with users as the host presents products and as the host interacts with and presents the products for sale, a product card can be included within a livestream shopping window. The ecommerce environment can further include a chat window 490. The chat window can include comments and questions from viewers. The mobile device user can interact with the host user.
The interactive overlays and/or chat windows are generated by the GAN including a text/audio encoder that generates text embedding from the text response or audio response of the host (GAN) to the customer user’s comments and questions.
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 1-7, 11-17 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Tu et al. US-PGPUB No. 2025/0252446 (hereinafter Tu-provisional ‘733 based on the provisional application 63/458,733’s filing date); in view of
Tu et al. US-PGPUB No. 2025/0252446 (hereinafter Tu-provisional ‘178 based on the provisional application 63/458,178’s filing date).
Tu et al. US-PGPUB No. 2025/0252446 (hereinafter Tu-provisional ‘207 based on the provisional application 63/464,207’s filing date).
Re Claim 1:
Tu-provisional ‘733/Tu-provisional ‘207/Tu-provisional ‘178 teaches a method for generating a video component, the method comprising:
receiving, by one or more processors, existing digital content associated with an entity or product, wherein the existing digital content comprises a set of assets (
Tu_provisional ‘733 teaches at Paragraph 0014 that the machine learning model can also generate synthetic short-form videos when they are needed to respond to questions or comments not addressed by videos in the library and at Paragraph 0019 that the short-form video to be shown can be customized based on one or more products for sale and customization includes adding an interactive overlay to one or more of the plurality of short-form videos. As the machine learning model takes in viewer responses to the short-form video, questions can be put to the user that can lead to additional short-form videos and at Paragraph 0023 that a product card is a graphical element such as thumbnail video, symbol that is displayed in front of the video and at Paragraph 0035 that the comment can be recorded 330 and accessed by the short-form video operator 320 and the short-form video operator can access a library of related video segments 350 and select a video segment that includes an individual playing baseball.
Tu_provisional ‘733 teaches at Paragraph 0014 that an interactive overlay can be added so that viewers can respond to the short-form videos as the videos play and viewers can ask questions….the machine learning model can prompt viewers regarding related short-form videos and the machine learning model can also generate synthetic short-form videos when they are needed to respond to questions or comments not addressed by videos in the library and at Paragraph 0016 that the machine learning model can analyze the contents of the short-form videos in the library and match product names, categories and so on to products supplied by an ecommerce website.
Tu_provisional ‘207 teaches at Paragraph 0019 that additional information, including header information, color schemes, company logos can also be placed into the container unit in specific locations
Tu_provisional ‘207 teaches at Paragraph 0018 that the website offers products for sale, a library of short-form vidoes related to the products. As customers peruse the ecommerce website, their actions are tracked and categorized by an AI machine learning model and at Paragraph 0041 that once recorded, the livestream event can be replayed and expanded upon as viewers comment and interact with the replay of the livestream event in real time and at Paragraph 0042 that one or more livestreams 350 can be created by the creating component 340.
Tu_provisional ‘207 teaches at Paragraph 0019 that additional information, including header information, color schemes, company logos can also be placed into the container unit in specific locations and at Paragraph 0034 that the interactive overlay includes an ability for the user to update quantity, price, size, color or other variable aspects of a product);
receiving, by one or more processors, a prompt for a generative model to generate the video component from the prompt (
Tu-provisional ‘733 teaches at Paragraph 0035 that the operator 320 can be an artificial intelligence (AI) machine learning model 340 with access to a library of related short-form video segments 350. “Great, but can he play baseball?” can be made by a viewer 312 as the short-form video 310 is rendered to the viewers 312….the comment can be recorded 330 and accessed by the short-form video operator 320 and the short-form video operator can access a library of related video segments 350 and select a video segment that includes an individual playing baseball.
Tu_provisional ‘733 teaches at Paragraph 0014 that an interactive overlay can be added so that viewers can respond to the short-form videos as the videos play and viewers can ask questions….the machine learning model can prompt viewers regarding related short-form videos and the machine learning model can also generate synthetic short-form videos when they are needed to respond to questions or comments not addressed by videos in the library and at Paragraph 0016 that the machine learning model can analyze the contents of the short-form videos in the library and match product names, categories and so on to products supplied by an ecommerce website.
Tu_provisional ‘207 teaches at Paragraph 0024 that a lead generation prompt includes a small set of questions for a user to allow a sales representative to follow up with additional information regarding a product and user responses to questions or comments appearing in the interactive overlay can be recorded and choices of additional short-form videos can be presented and selected; options to purchase products portrayed in the video can be chosen. The interactions can be forwarded to the machine learning model in order to learn and update associations to one or more products made by users and at Paragraph 0025 the immediate livestream includes highlighting the one or more products for sale to the user.
Tu-provisional ‘207 teaches at Paragraph 0025 that the host can also respond to questions and comments made by users through the interactive overlay.
It would have been obvious to one of the ordinary skill in the art before the filing date of the instant application to have combined Tu-provisional ‘207’s interactive overlay with Tu-provisional ‘733’s interactive overlay to have allowed the host respond to questions and comments through the interactive overlay using natural language description of the product such as product model inquiry, pricing inquiry. The interactive overlay includes a chat window for interaction between the host and the customer. One of the ordinary skill in the art would have provided text response to comments and questions from the customer.
Tu-provisional ‘733 teaches at Paragraph 0051 that a list of products for sale can be stored and associated with short-form videos stored in the library. The associations can be based on metadata on each product including product name, category, sizes, price, color and so on and the associations can be made by a machine learning model and at Paragraph 0054 that the short-form videos can be customized to highlight products that have been confirmed to be in stock.
Tu-provisional ‘207 teaches at Paragraph 0029 that the sales associate can display the list of products associated with the group of users identified by the machine learning model and can adjust the list based on similar products in the same category. The machine learning model can record the additional items so that the related product are made available to sales associates in additional immediate livestreams generated by the website….The initiating renders the livestream session, including the ecommerce environment and the interactive overlay, so that users can purchase products and interact with the host and other users as the livestream plays);
determining, by the one or more processors, characteristics associated with the entity or product (
Tu-provisional ‘733 teaches at Paragraph 0051 that the customizing is based on one or more products for sale on a website and the customizing includes adding an interactive overlay to one or more of the plurality of short-form videos that were selected from the library. A list of products for sale can be stored and associated with short-form videos stored in the library. The associations can be based on metadata on each product including product name, category, sizes, price, color and so on and the associations can be made by a machine learning model and at Paragraph 0054 that the short-form videos can be customized to highlight products that have been confirmed to be in stock.
Tu-provisional ‘733 teaches at Paragraph 0035 that the operator 320 can be an artificial intelligence (AI) machine learning model 340 with access to a library of related short-form video segments 350. “Great, but can he play baseball?” can be made by a viewer 312 as the short-form video 310 is rendered to the viewers 312….the comment can be recorded 330 and accessed by the short-form video operator 320 and the short-form video operator can access a library of related video segments 350 and select a video segment that includes an individual playing baseball and at Paragraph 0037 the operator 320 using an AI machine learning model 340 dynamically inserts a synthesized video segment 370 into the short-form video 310);
Generating, by the one or more processors, based on the existing digital content, features associated with the existing digital content, wherein the features comprise at least one of embeddings associated with the existing digital content or natural language description of the existing digital content (
Tu-provisional ‘207 teaches at Paragraph 0025 that the host can also respond to questions and comments made by users through the interactive overlay.
Tu-provisional ‘733 teaches at Paragraph 0035 that the operator can see and respond to the comments and questions 330 made by viewers in a chat as part of an interactive overlay associated with the short-form video.
Tu-provisional teaches at FIG. 4 and Paragraph 0044 that the livestream videos are used to interact with users as the host presents products and as the host interacts with and presents the products for sale, a product card can be included within a livestream shopping window. The ecommerce environment can further include a chat window 490. The chat window can include comments and questions from viewers. The mobile device user can interact with the host user.
In addition to the interactive overlays and/or chat windows communicated between the host user and customer user, customer’s interaction with a product card results in the host generation of a virtual purchase cart showing the natural language description (such as the product name, product size and produce price) of the product. It is noted that presentation of the metadata is different in concept from the metadata (data) of the product. Tu-provisional ‘733 teaches at Paragraph 0023 and Paragraph 0048 a virtual purchase cart (which shows the product size, the product pricing, the product name in the natural language description in response to the user interaction with the product card). The virtual purchase cart includes the natural language description of the product.
Tu-provisional ‘178’s teaches at FIG. 4 and Paragraph 0044 which clearly shows a chat window with text interactions between the customer user and host user (synthetic human). This can enable additional engagement with the ecommerce short-form livestream video and the host user. The customer user can interact with the host user and the product card in order to learn more about the product. A text response from the host user (synthetic human) to the customer user constitutes the natural language description of the existing product. The text interactions/responses in the chat window to customer user’s questions constitute the claimed natural language description of the existing product (e.g., shoe) including the prices in US dollars from the host user. The chat window allows for the interaction between the customer user and the host user appears within a video chat.
Tu-provisional ‘733 teaches at Paragraph 0035 that the operator can see and respond to the comments and questions 330 made by viewers in a chat as part of an interactive overlay associated with the short-form video.
Tu-provisional ‘733 teaches at Paragraph 0051-0052 that the rendering of the short-form videos can be based on question or comment made by a user and the rendering includes an interactive overlay which allows the user to respond to the short-form video as it plays. Tu-provisional ‘733 teaches at Paragraph [0051] The system 600 includes a customizing component 630. The customizing component 630 can include functions and instructions for customizing the graph structure in a back-end environment, wherein the customizing is based on one or more products for sale on a website. In embodiments, the customizing includes adding an interactive overlay to one or more of the plurality of short-form videos that were selected from the library. A list of products and services for sale on a website can be stored and associated with short-form videos stored in the library of short-form videos. The associations can be based on metadata on each product or service, including product name, category, sizes, price, color, and so on. In some embodiments, the associations can be made by a machine learning model. The machine learning model can analyze the contents of the short-form videos in the library and match product names, categories, and so on to products and services supplied by an ecommerce website. As matches are made between products offered by the ecommerce website and short-form videos in the library, the graph structure can be customized to display the matched short-form videos and a first-pass arrangement of the short-form videos based on associations between the matched short-form videos.
Tu-provisional ‘733 teaches at Paragraph 0033 that the host individual performing in the video segments can be a synthetic presenter generated as part of a synthetic short-form video created by an AI machine learning model. Tu-provisional ‘733 teaches at FIG. 4 and Paragraph 0044 the machine learning engine 440 can be used to generate a short-form video 470 and play it for a viewer as part of a livestream event. an e-commerce environment 480 can be rendered to include a virtual purchase cart and on-screen product cards 490 as part of the short-form video 470.
Tu-provisional ‘733 teaches at Paragraph 0035 that the operator 320 can be an artificial intelligence (AI) machine learning model 340 with access to a library of related short-form video segments 350. “Great, but can he play baseball?” can be made by a viewer 312 as the short-form video 310 is rendered to the viewers 312….the comment can be recorded 330 and accessed by the short-form video operator 320 and the short-form video operator can access a library of related video segments 350 and select a video segment that includes an individual playing baseball and at Paragraph 0037 the operator 320 using an AI machine learning model 340 dynamically inserts a synthesized video segment 370 into the short-form video 310..
Tu_provisional ‘733 teaches at Paragraph 0014 that the machine learning model can also generate synthetic short-form videos when they are needed to respond to questions or comments not addressed by videos in the library and at Paragraph 0019 that the short-form video to be shown can be customized based on one or more products for sale and customization includes adding an interactive overlay to one or more of the plurality of short-form videos. As the machine learning model takes in viewer responses to the short-form video, questions can be put to the user that can lead to additional short-form videos and at Paragraph 0023 that a product card is a graphical element such as thumbnail video, symbol that is displayed in front of the video and at Paragraph 0035 that the comment can be recorded 330 and accessed by the short-form video operator 320 and the short-form video operator can access a library of related video segments 350 and select a video segment that includes an individual playing baseball.
Tu_provisional ‘733 teaches at Paragraph 0014 that an interactive overlay can be added so that viewers can respond to the short-form videos as the videos play and viewers can ask questions….the machine learning model can prompt viewers regarding related short-form videos and the machine learning model can also generate synthetic short-form videos when they are needed to respond to questions or comments not addressed by videos in the library and at Paragraph 0016 that the machine learning model can analyze the contents of the short-form videos in the library and match product names, categories and so on to products supplied by an ecommerce website); and
processing, by the one or more processors, the prompt, the determined characteristics and the generated features through the generative model to generate the video component using a subset of the set of assets (
Tu-provisional applications have taught processing the prompt (customer user questions/comments), the determined characteristics (the metadata) and the generated features (host responses to customer user questions/comments) to generate the new short-form video using a subset of assets in the library.
Tu-provisional ‘733 teaches at Paragraph 0035 that the operator 320 can be an artificial intelligence (AI) machine learning model 340 with access to a library of related short-form video segments 350. “Great, but can he play baseball?” can be made by a viewer 312 as the short-form video 310 is rendered to the viewers 312….the comment can be recorded 330 and accessed by the short-form video operator 320 and the short-form video operator can access a library of related video segments 350 and select a video segment that includes an individual playing baseball.
Tu-provisional ‘733 teaches at Paragraph 0051 that the customizing is based on one or more products for sale on a website and the customizing includes adding an interactive overlay to one or more of the plurality of short-form videos that were selected from the library. A list of products for sale can be stored and associated with short-form videos stored in the library. The associations can be based on metadata on each product including product name, category, sizes, price, color and so on and the associations can be made by a machine learning model. The machine-learning model can analyze the contents of the short-form videos in the library and match product names, categories and so on to products supplied by an ecommerce website. Short-form video highlighting a sup pot can be linked to another short-form video demonstrating a preparation of a soup recipe and at Paragraph 0052 one or more users can be presented with one or more short-form videos related to a product for sale and at Paragraph 0054 that the short-form videos can be customized to highlight products that have been confirmed to be in stock).
Tu_provisional ‘207 teaches at Paragraph 0019 that additional information, including header information, color schemes, company logos can also be placed into the container unit in specific locations.
It would have been obvious to one of the ordinary skill in the art before the filing date of the instant application to have incorporated Tu-Provisional ‘207’ teaching of placing additional information including color schemes, company logos related to the product to have displayed the product of Tu-provisional ‘733 with company logos. One of the ordinary skill in the art would have been motivated to have provided additional information for the products.
Tu-Provisional ‘178 teaches at FIG. 4 and Paragraph 0042-0044 that the catalog of products 452 offered for sale can be updated 450 by the host user ID to accommodate user requests and changes can be made to a short-form video livestream 440 to demonstrate different products and the short-form videos can be updated and expanded….Adjustments to the product offerings, pricing, listing order, and so on can be made dynamically as sales performance and engagement statistics are collected and analyzed. The ecommerce environment (e.g., item 480 of FIG. 4) includes product prices, shipping price, and shipping methods and the e-commerce environment allows to provide all the detailed product information such as color, sizes, quantities and so on; pricing, shipping methods and pricing and payment processing infrastructure generated by a user…this allows the micro-website resources to be dedicated to the presentation, highlighting and sale of items contained in the pre-approved catalogs of products through the creation and augmenting of short-form livestream video.
Tu-provisional ‘733 teaches at Paragraph 0033 that the host individual performing in the video segments can be a synthetic presenter generated as part of a synthetic short-form video created by an AI machine learning model. Tu-provisional ‘733 teaches at FIG. 4 and Paragraph 0044 the machine learning engine 440 can be used to generate a short-form video 470 and play it for a viewer as part of a livestream event. an e-commerce environment 480 can be rendered to include a virtual purchase cart and on-screen product cards 490 as part of the short-form video 470.
It would have been obvious to one of the ordinary skill in the art before the filing date of the instant application to have added the set of assets including the product prices or product colors/sizes and text description about a product to the product picture in the e-commerce environment overlaid with the live-stream video according to Tu-provisional ‘178 to have displayed additional assets of a product within the e-commerce environment as part of a livestream generated by the machine learning engine of Tu-provisional ‘733. One of the ordinary skill in the art would have been motivated to have provided additional assets of the product.
Re Claim 2:
The claim 2 encompasses the same scope of invention as that of the claim 1 except additional claim limitation that the set of assets includes one or more text, logos, images, audio, or videos.
Tu_provisional ‘207 and Tu-provisional ‘733 further teach the claim limitation that the set of assets includes one or more text, logos, images, audio, or videos
(Tu-provisional teaches at Paragraph 0051 that the customizing is based on one or more products for sale on a website and the customizing includes adding an interactive overlay to one or more of the plurality of short-form videos that were selected from the library. A list of products for sale can be stored and associated with short-form videos stored in the library. The associations can be based on metadata on each product including product name, category, sizes, price, color and so on and the associations can be made by a machine learning model and at Paragraph 0054 that the short-form videos can be customized to highlight products that have been confirmed to be in stock.
Tu_provisional ‘207 teaches at Paragraph 0019 that additional information, including header information, color schemes, company logos can also be placed into the container unit in specific locations.
Tu_provisional ‘207 teaches at Paragraph 0019 that additional information, including header information, color schemes, company logos can also be placed into the container unit in specific locations and at Paragraph 0034 that the interactive overlay includes an ability for the user to update quantity, price, size, color or other variable aspects of a product).
It would have been obvious to one of the ordinary skill in the art before the filing date of the instant application to have incorporated Tu-Provisional ‘207’ teaching of placing additional information including color schemes, company logos related to the product to have displayed the product of Tu-provisional ‘733 with company logos. One of the ordinary skill in the art would have been motivated to have provided additional information for the products.
Tu_provisional ‘178 further teach the claim limitation that the set of assets includes one or more text, logos, images, audio, or videos (Tu-Provisional ‘178 teaches at FIG. 4 and Paragraph 0042-0044 that the catalog of products 452 offered for sale can be updated 450 by the host user ID to accommodate user requests and changes can be made to a short-form video livestream 440 to demonstrate different products and the short-form videos can be updated and expanded….Adjustments to the product offerings, pricing, listing order, and so on can be made dynamically as sales performance and engagement statistics are collected and analyzed. the ecommerce environment (e.g., 480 of FIG. 4) includes product prices, shipping price, and shipping methods and the e-commerce environment allows to provide all the detailed product information such as color, sizes, quantities and so on; pricing, shipping methods and pricing and payment processing infrastructure generated by a user…this allows the micro-website resources to be dedicated to the presentation, highlighting and sale of items contained in the pre-approved catalogs of products through the creation and augmenting of short-form livestream video).
Tu-provisional ‘733 teaches at Paragraph 0033 that the host individual performing in the video segments can be a synthetic presenter generated as part of a synthetic short-form video created by an AI machine learning model. Tu-provisional ‘733 teaches at FIG. 4 and Paragraph 0044 the machine learning engine 440 can be used to generate a short-form video 470 and play it for a viewer as part of a livestream event. an e-commerce environment 480 can be rendered to include a virtual purchase cart and on-screen product cards 490 as part of the short-form video 470.
It would have been obvious to one of the ordinary skill in the art before the filing date of the instant application to have added the set of assets including the product prices or product colors/sizes and text description about a product to the product picture in the e-commerce environment overlaid with the live-stream video according to Tu-provisional ‘178 to have displayed additional assets of a product within the e-commerce environment as part of a livestream overlaid with e-commerce environment generated by the machine learning engine of Tu-provisional ‘733. One of the ordinary skill in the art would have been motivated to have provided additional assets of the product.
Re Claim 3:
The claim 3 encompasses the same scope of invention as that of the claim 1 except additional claim limitation that the determined characteristics include one or more of a color or a font associated with the entity or product.
Tu_provisional ‘207 and Tu-provisional ‘733 further teach the claim limitation that the determined characteristics include one or more of a color or a font associated with the entity or product (Tu-provisional ‘733 teaches at Paragraph 0051 that the customizing is based on one or more products for sale on a website and the customizing includes adding an interactive overlay to one or more of the plurality of short-form videos that were selected from the library. A list of products for sale can be stored and associated with short-form videos stored in the library. The associations can be based on metadata on each product including product name, category, sizes, price, color and so on and the associations can be made by a machine learning model and at Paragraph 0054 that the short-form videos can be customized to highlight products that have been confirmed to be in stock.
Tu-provisional ‘733 teaches at Paragraph 0051 that the customizing is based on one or more products for sale on a website and the customizing includes adding an interactive overlay to one or more of the plurality of short-form videos that were selected from the library. A list of products for sale can be stored and associated with short-form videos stored in the library. The associations can be based on metadata on each product including product name, category, sizes, price, color and so on and the associations can be made by a machine learning model and at Paragraph 0054 that the short-form videos can be customized to highlight products that have been confirmed to be in stock.
Tu_provisional ‘207 teaches at Paragraph 0019 that additional information, including header information, color schemes, company logos can also be placed into the container unit in specific locations and at Paragraph 0034 that the interactive overlay includes an ability for the user to update quantity, price, size, color or other variable aspects of a product).
It would have been obvious to one of the ordinary skill in the art before the filing date of the instant application to have incorporated Tu-Provisional ‘207’ teaching of placing additional information including color schemes, company logos related to the product to have displayed the product of Tu-provisional ‘733 with company logos. One of the ordinary skill in the art would have been motivated to have provided additional information for the products.
Tu_provisional ‘178 further teach the claim limitation that the determined characteristics include one or more of a color or a font associated with the entity or product (Tu-Provisional ‘178 teaches at FIG. 4 and Paragraph 0042-0044 that the catalog of products 452 offered for sale can be updated 450 by the host user ID to accommodate user requests and changes can be made to a short-form video livestream 440 to demonstrate different products and the short-form videos can be updated and expanded….Adjustments to the product offerings, pricing, listing order, and so on can be made dynamically as sales performance and engagement statistics are collected and analyzed. the ecommerce environment includes product prices, shipping price, and shipping methods and the e-commerce environment allows to provide all the detailed product information such as color, sizes, quantities and so on; pricing, shipping methods and pricing and payment processing infrastructure generated by a user…this allows the micro-website resources to be dedicated to the presentation, highlighting and sale of items contained in the pre-approved catalogs of products through the creation and augmenting of short-form livestream video).
Tu-provisional ‘733 teaches at Paragraph 0033 that the host individual performing in the video segments can be a synthetic presenter generated as part of a synthetic short-form video created by an AI machine learning model. Tu-provisional ‘733 teaches at FIG. 4 and Paragraph 0044 the machine learning engine 440 can be used to generate a short-form video 470 and play it for a viewer as part of a livestream event. an e-commerce environment 480 can be rendered to include a virtual purchase cart and on-screen product cards 490 as part of the short-form video 470.
It would have been obvious to one of the ordinary skill in the art before the filing date of the instant application to have added the set of assets including the product prices or product colors/sizes and text description about a product to the product picture in the e-commerce environment overlaid with the live-stream video according to Tu-provisional ‘178 to have displayed additional assets of a product within the e-commerce environment as part of a livestream overlaid with e-commerce environment generated by the machine learning engine of Tu-provisional ‘733. One of the ordinary skill in the art would have been motivated to have provided additional assets of the product.
Re Claim 4:
The claim 4 encompasses the same scope of invention as that of the claim 1 except additional claim limitation that the subset of assets is determined by a representation engine.
Tu-provisional ‘733 at least suggests the claim limitation that the subset of assets is determined by a representation engine the subset of assets is determined by a representation engine (
Tu-provisional ‘733 teaches at Paragraph 0033 that the host individual performing in the video segments can be a synthetic presenter generated as part of a synthetic short-form video created by an AI machine learning model. Tu-provisional ‘733 teaches at FIG. 4 and Paragraph 0044 the machine learning engine 440 can be used to generate a short-form video 470 and play it for a viewer as part of a livestream event. an e-commerce environment 480 can be rendered to include a virtual purchase cart and on-screen product cards 490 as part of the short-form video 470.
Tu-provisional ‘733 teaches at Paragraph 0035 that the operator 320 can be an artificial intelligence (AI) machine learning model 340 with access to a library of related short-form video segments 350. “Great, but can he play baseball?” can be made by a viewer 312 as the short-form video 310 is rendered to the viewers 312….the comment can be recorded 330 and accessed by the short-form video operator 320 and the short-form video operator can access a library of related video segments 350 and select a video segment that includes an individual playing baseball and at Paragraph 0037 the operator 320 using an AI machine learning model 340 dynamically inserts a synthesized video segment 370 into the short-form video 310..
Tu_provisional ‘733 teaches at Paragraph 0014 that the machine learning model can also generate synthetic short-form videos when they are needed to respond to questions or comments not addressed by videos in the library and at Paragraph 0019 that the short-form video to be shown can be customized based on one or more products for sale and customization includes adding an interactive overlay to one or more of the plurality of short-form videos. As the machine learning model takes in viewer responses to the short-form video, questions can be put to the user that can lead to additional short-form videos and at Paragraph 0023 that a product card is a graphical element such as thumbnail video, symbol that is displayed in front of the video and at Paragraph 0035 that the comment can be recorded 330 and accessed by the short-form video operator 320 and the short-form video operator can access a library of related video segments 350 and select a video segment that includes an individual playing baseball.
Tu_provisional ‘733 teaches at Paragraph 0014 that an interactive overlay can be added so that viewers can respond to the short-form videos as the videos play and viewers can ask questions….the machine learning model can prompt viewers regarding related short-form videos and the machine learning model can also generate synthetic short-form videos when they are needed to respond to questions or comments not addressed by videos in the library and at Paragraph 0016 that the machine learning model can analyze the contents of the short-form videos in the library and match product names, categories and so on to products supplied by an ecommerce website).
Tu_provisional ‘178 further teach the claim limitation that the subset of assets is determined by a representation engine (Tu-Provisional ‘178 teaches at FIG. 4 and Paragraph 0042-0044 that the catalog of products 452 offered for sale can be updated 450 by the host user ID to accommodate user requests and changes can be made to a short-form video livestream 440 to demonstrate different products and the short-form videos can be updated and expanded….Adjustments to the product offerings, pricing, listing order, and so on can be made dynamically as sales performance and engagement statistics are collected and analyzed. the ecommerce environment includes product prices, shipping price, and shipping methods and the e-commerce environment allows to provide all the detailed product information such as color, sizes, quantities and so on; pricing, shipping methods and pricing and payment processing infrastructure generated by a user…this allows the micro-website resources to be dedicated to the presentation, highlighting and sale of items contained in the pre-approved catalogs of products through the creation and augmenting of short-form livestream video).
Tu-provisional ‘733 teaches at Paragraph 0033 that the host individual performing in the video segments can be a synthetic presenter generated as part of a synthetic short-form video created by an AI machine learning model. Tu-provisional ‘733 teaches at FIG. 4 and Paragraph 0044 the machine learning engine 440 can be used to generate a short-form video 470 and play it for a viewer as part of a livestream event. an e-commerce environment 480 can be rendered to include a virtual purchase cart and on-screen product cards 490 as part of the short-form video 470.
It would have been obvious to one of the ordinary skill in the art before the filing date of the instant application to have added the set of assets including the product prices or product colors/sizes and text description about a product to the product picture in the e-commerce environment overlaid with the live-stream video according to Tu-provisional ‘178 to have displayed additional assets of a product within the e-commerce environment as part of a livestream overlaid with e-commerce environment generated by the machine learning engine of Tu-provisional ‘733. One of the ordinary skill in the art would have been motivated to have provided additional assets of the product.
Re Claim 5:
The claim 5 encompasses the same scope of invention as that of the claim 1 except additional claim limitation that the generative model comprises a characteristic engine configured to determine the characteristics associated with the entity or product.
Tu-provisional ‘733 at least suggests the claim limitation that the generative model comprises a characteristic engine configured to determine the characteristics associated with the entity or product (
Tu-provisional ‘733 teaches at Paragraph 0051 that the customizing is based on one or more products for sale on a website and the customizing includes adding an interactive overlay to one or more of the plurality of short-form videos that were selected from the library. A list of products for sale can be stored and associated with short-form videos stored in the library. The associations can be based on metadata on each product including product name, category, sizes, price, color and so on and the associations can be made by a machine learning model and at Paragraph 0054 that the short-form videos can be customized to highlight products that have been confirmed to be in stock.
Tu-provisional ‘733 teaches at Paragraph 0033 that the host individual performing in the video segments can be a synthetic presenter generated as part of a synthetic short-form video created by an AI machine learning model. Tu-provisional ‘733 teaches at FIG. 4 and Paragraph 0044 the machine learning engine 440 can be used to generate a short-form video 470 and play it for a viewer as part of a livestream event. an e-commerce environment 480 can be rendered to include a virtual purchase cart and on-screen product cards 490 as part of the short-form video 470.
Tu-provisional ‘733 teaches at Paragraph 0035 that the operator 320 can be an artificial intelligence (AI) machine learning model 340 with access to a library of related short-form video segments 350. “Great, but can he play baseball?” can be made by a viewer 312 as the short-form video 310 is rendered to the viewers 312….the comment can be recorded 330 and accessed by the short-form video operator 320 and the short-form video operator can access a library of related video segments 350 and select a video segment that includes an individual playing baseball and at Paragraph 0037 the operator 320 using an AI machine learning model 340 dynamically inserts a synthesized video segment 370 into the short-form video 310..
Tu_provisional ‘733 teaches at Paragraph 0014 that the machine learning model can also generate synthetic short-form videos when they are needed to respond to questions or comments not addressed by videos in the library and at Paragraph 0019 that the short-form video to be shown can be customized based on one or more products for sale and customization includes adding an interactive overlay to one or more of the plurality of short-form videos. As the machine learning model takes in viewer responses to the short-form video, questions can be put to the user that can lead to additional short-form videos and at Paragraph 0023 that a product card is a graphical element such as thumbnail video, symbol that is displayed in front of the video and at Paragraph 0035 that the comment can be recorded 330 and accessed by the short-form video operator 320 and the short-form video operator can access a library of related video segments 350 and select a video segment that includes an individual playing baseball.
Tu_provisional ‘733 teaches at Paragraph 0014 that an interactive overlay can be added so that viewers can respond to the short-form videos as the videos play and viewers can ask questions….the machine learning model can prompt viewers regarding related short-form videos and the machine learning model can also generate synthetic short-form videos when they are needed to respond to questions or comments not addressed by videos in the library and at Paragraph 0016 that the machine learning model can analyze the contents of the short-form videos in the library and match product names, categories and so on to products supplied by an ecommerce website).
Tu_provisional ‘178 further teach the claim limitation that the subset of assets is determined by a representation engine (
Tu-provisional ‘178 teaches at Paragraph 0001 that the product representation can include an image or video of the product, on-screen text and/or images, spleen words or other verbal utterances, metadata embedded within the user data of a video stream. A product card can be created and displayed based on the product representation. Additional information about the product can be displayed.
Tu-Provisional ‘178 teaches at FIG. 4 and Paragraph 0042-0044 that the catalog of products 452 offered for sale can be updated 450 by the host user ID to accommodate user requests and changes can be made to a short-form video livestream 440 to demonstrate different products and the short-form videos can be updated and expanded….Adjustments to the product offerings, pricing, listing order, and so on can be made dynamically as sales performance and engagement statistics are collected and analyzed. the ecommerce environment (e.g., 480) includes product prices, shipping price, and shipping methods and the e-commerce environment allows to provide all the detailed product information such as color, sizes, quantities and so on; pricing, shipping methods and pricing and payment processing infrastructure generated by a user…this allows the micro-website resources to be dedicated to the presentation, highlighting and sale of items contained in the pre-approved catalogs of products through the creation and augmenting of short-form livestream video).
Tu-provisional ‘733 teaches at Paragraph 0033 that the host individual performing in the video segments can be a synthetic presenter generated as part of a synthetic short-form video created by an AI machine learning model. Tu-provisional ‘733 teaches at FIG. 4 and Paragraph 0044 the machine learning engine 440 can be used to generate a short-form video 470 and play it for a viewer as part of a livestream event. an e-commerce environment 480 can be rendered to include a virtual purchase cart and on-screen product cards 490 as part of the short-form video 470.
It would have been obvious to one of the ordinary skill in the art before the filing date of the instant application to have added the set of assets including the product prices or product colors/sizes and text description about a product to the product picture in the e-commerce environment overlaid with the live-stream video according to Tu-provisional ‘178 to have displayed additional assets of a product within the e-commerce environment as part of a livestream generated by the machine learning engine of Tu-provisional ‘733. One of the ordinary skill in the art would have been motivated to have provided additional assets of the product.
Re Claim 6:
The claim 6 encompasses the same scope of invention as that of the claim 1 except additional claim limitation that determining a personality associated with the entity or product,
wherein: the personality associated with the entity or product is based on the embeddings associated with the existing digital content, and generating the video component includes using the determined personality.
Tu-provisional applications teach the claim limitation that determining a personality associated with the entity or product,
wherein: the personality associated with the entity or product is based on the embeddings associated with the existing digital content, and generating the video component includes using the determined personality (
Interactive overlay and chat window of the three Tu-provisional applications:
Tu-provisional ‘207 teaches at Paragraph 0025 that the host can also respond to questions and comments made by users through the interactive overlay.
Tu-provisional ‘733 teaches at Paragraph 0035 that the operator can see and respond to the comments and questions 330 made by viewers in a chat as part of an interactive overlay associated with the short-form video.
Tu-provisional teaches at FIG. 4 and Paragraph 0044 that the livestream videos are used to interact with users as the host presents products and as the host interacts with and presents the products for sale, a product card can be included within a livestream shopping window. The ecommerce environment can further include a chat window 490. The chat window can include comments and questions from viewers. The mobile device user can interact with the host user.
The interactive overlays and/or chat windows are generated by the GAN including a text/audio encoder that generates text embedding from the text response or audio response of the host (GAN) to the customer user’s comments and questions.
Tu-provisional ‘733 teaches at Paragraph 0051-0052 that the rendering of the short-form videos can be based on question or comment made by a user and the rendering includes an interactive overlay which allows the user to respond to the short-form video as it plays. Tu-provisional ‘733 teaches at Paragraph [0051] The system 600 includes a customizing component 630. The customizing component 630 can include functions and instructions for customizing the graph structure in a back-end environment, wherein the customizing is based on one or more products for sale on a website. In embodiments, the customizing includes adding an interactive overlay to one or more of the plurality of short-form videos that were selected from the library. A list of products and services for sale on a website can be stored and associated with short-form videos stored in the library of short-form videos. The associations can be based on metadata on each product or service, including product name, category, sizes, price, color, and so on. In some embodiments, the associations can be made by a machine learning model. The machine learning model can analyze the contents of the short-form videos in the library and match product names, categories, and so on to products and services supplied by an ecommerce website. As matches are made between products offered by the ecommerce website and short-form videos in the library, the graph structure can be customized to display the matched short-form videos and a first-pass arrangement of the short-form videos based on associations between the matched short-form videos.
Tu-provisional ‘733 teaches at Paragraph 0033 that the host individual performing in the video segments can be a synthetic presenter generated as part of a synthetic short-form video created by an AI machine learning model. Tu-provisional ‘733 teaches at FIG. 4 and Paragraph 0044 the machine learning engine 440 can be used to generate a short-form video 470 and play it for a viewer as part of a livestream event. an e-commerce environment 480 can be rendered to include a virtual purchase cart and on-screen product cards 490 as part of the short-form video 470.
Tu-provisional ‘733 teaches at Paragraph 0035 that the operator 320 can be an artificial intelligence (AI) machine learning model 340 with access to a library of related short-form video segments 350. “Great, but can he play baseball?” can be made by a viewer 312 as the short-form video 310 is rendered to the viewers 312….the comment can be recorded 330 and accessed by the short-form video operator 320 and the short-form video operator can access a library of related video segments 350 and select a video segment that includes an individual playing baseball and at Paragraph 0037 the operator 320 using an AI machine learning model 340 dynamically inserts a synthesized video segment 370 into the short-form video 310..
Tu_provisional ‘733 teaches at Paragraph 0014 that the machine learning model can also generate synthetic short-form videos when they are needed to respond to questions or comments not addressed by videos in the library and at Paragraph 0019 that the short-form video to be shown can be customized based on one or more products for sale and customization includes adding an interactive overlay to one or more of the plurality of short-form videos. As the machine learning model takes in viewer responses to the short-form video, questions can be put to the user that can lead to additional short-form videos and at Paragraph 0023 that a product card is a graphical element such as thumbnail video, symbol that is displayed in front of the video and at Paragraph 0035 that the comment can be recorded 330 and accessed by the short-form video operator 320 and the short-form video operator can access a library of related video segments 350 and select a video segment that includes an individual playing baseball.
Tu_provisional ‘733 teaches at Paragraph 0014 that an interactive overlay can be added so that viewers can respond to the short-form videos as the videos play and viewers can ask questions….the machine learning model can prompt viewers regarding related short-form videos and the machine learning model can also generate synthetic short-form videos when they are needed to respond to questions or comments not addressed by videos in the library and at Paragraph 0016 that the machine learning model can analyze the contents of the short-form videos in the library and match product names, categories and so on to products supplied by an ecommerce website).
Re Claim 7:
The claim 7 encompasses the same scope of invention as that of the claim 6 except additional claim limitation that the generative model comprises a personality engine configured to determine the personality of the entity or product.
Tu-provisional ‘733 teaches the claim limitation that the generative model comprises a personality engine configured to determine the personality of the entity or product (
Tu-provisional ‘733 teaches at Paragraph 0051-0052 that the rendering of the short-form videos can be based on question or comment made by a user and the rendering includes an interactive overlay which allows the user to respond to the short-form video as it plays. Tu-provisional ‘733 teaches at Paragraph [0051] The system 600 includes a customizing component 630. The customizing component 630 can include functions and instructions for customizing the graph structure in a back-end environment, wherein the customizing is based on one or more products for sale on a website. In embodiments, the customizing includes adding an interactive overlay to one or more of the plurality of short-form videos that were selected from the library. A list of products and services for sale on a website can be stored and associated with short-form videos stored in the library of short-form videos. The associations can be based on metadata on each product or service, including product name, category, sizes, price, color, and so on. In some embodiments, the associations can be made by a machine learning model. The machine learning model can analyze the contents of the short-form videos in the library and match product names, categories, and so on to products and services supplied by an ecommerce website. As matches are made between products offered by the ecommerce website and short-form videos in the library, the graph structure can be customized to display the matched short-form videos and a first-pass arrangement of the short-form videos based on associations between the matched short-form videos.
Tu-provisional ‘733 teaches at Paragraph 0033 that the host individual performing in the video segments can be a synthetic presenter generated as part of a synthetic short-form video created by an AI machine learning model. Tu-provisional ‘733 teaches at FIG. 4 and Paragraph 0044 the machine learning engine 440 can be used to generate a short-form video 470 and play it for a viewer as part of a livestream event. an e-commerce environment 480 can be rendered to include a virtual purchase cart and on-screen product cards 490 as part of the short-form video 470.
Tu-provisional ‘733 teaches at Paragraph 0035 that the operator 320 can be an artificial intelligence (AI) machine learning model 340 with access to a library of related short-form video segments 350. “Great, but can he play baseball?” can be made by a viewer 312 as the short-form video 310 is rendered to the viewers 312….the comment can be recorded 330 and accessed by the short-form video operator 320 and the short-form video operator can access a library of related video segments 350 and select a video segment that includes an individual playing baseball and at Paragraph 0037 the operator 320 using an AI machine learning model 340 dynamically inserts a synthesized video segment 370 into the short-form video 310..
Tu_provisional ‘733 teaches at Paragraph 0014 that the machine learning model can also generate synthetic short-form videos when they are needed to respond to questions or comments not addressed by videos in the library and at Paragraph 0019 that the short-form video to be shown can be customized based on one or more products for sale and customization includes adding an interactive overlay to one or more of the plurality of short-form videos. As the machine learning model takes in viewer responses to the short-form video, questions can be put to the user that can lead to additional short-form videos and at Paragraph 0023 that a product card is a graphical element such as thumbnail video, symbol that is displayed in front of the video and at Paragraph 0035 that the comment can be recorded 330 and accessed by the short-form video operator 320 and the short-form video operator can access a library of related video segments 350 and select a video segment that includes an individual playing baseball.
Tu_provisional ‘733 teaches at Paragraph 0014 that an interactive overlay can be added so that viewers can respond to the short-form videos as the videos play and viewers can ask questions….the machine learning model can prompt viewers regarding related short-form videos and the machine learning model can also generate synthetic short-form videos when they are needed to respond to questions or comments not addressed by videos in the library and at Paragraph 0016 that the machine learning model can analyze the contents of the short-form videos in the library and match product names, categories and so on to products supplied by an ecommerce website).
Re Claim 11:
The claim 11 is in parallel with the claim 1 in the form of an apparatus. The claim 11 is subject to the same rationale of rejection as the claim 1.
Tu-provisional ‘733 further teaches the claim limitation of a system for generating a video component, the system comprising:
one or more processors, the one or more processors configured to [perform the method steps of the claim 1] (Tu-provisional ‘733 teaches at Paragraph 0062 that a programmable apparatus which executes any of the above-mentioned computer program products or computer-implemented methods may include one or more microprocessors, microcontrollers, embedded microcontrollers, programmable digital signal processors, programmable devices, programmable gate arrays, programmable array logic, memory devices, application specific integrated circuits, or the like. Each may be suitably employed or configured to process computer program instructions, execute computer logic, store computer data, and so on.
Tu-provisional ‘733 teaches at Paragraph [0063] It will be understood that a computer may include a computer program product from a computer-readable storage medium and that this medium may be internal or external, removable and replaceable, or fixed. In addition, a computer may include a Basic Input/Output System (BIOS), firmware, an operating system, a database, or the like that may include, interface with, or support the software and hardware described herein).
Re Claim 12:
The claim 12 encompasses the same scope of invention as that of the claim 11 except additional claim limitation that the set of assets includes one or more text, logos, images, audio, or videos.
The claim 12 is in parallel with the claim 2 in the form of an apparatus. The claim 12 is subject to the same rationale of rejection as the claim 2.
Re Claim 13:
The claim 13 encompasses the same scope of invention as that of the claim 11 except additional claim limitation that the determined characteristics include one or more of a color or a font associated with the entity or product.
The claim 13 is in parallel with the claim 3 in the form of an apparatus. The claim 13 is subject to the same rationale of rejection as the claim 3.
Re Claim 14:
The claim 14 encompasses the same scope of invention as that of the claim 11 except additional claim limitation that the subset of assets is determined by a representation engine.
The claim 14 is in parallel with the claim 4 in the form of an apparatus. The claim 14 is subject to the same rationale of rejection as the claim 4.
Re Claim 15:
The claim 15 encompasses the same scope of invention as that of the claim 11 except additional claim limitation that the generative model comprises a characteristic engine configured to determine the characteristics associated with the entity or product.
The claim 15 is in parallel with the claim 5 in the form of an apparatus. The claim 15 is subject to the same rationale of rejection as the claim 5.
Re Claim 16:
The claim 16 encompasses the same scope of invention as that of the claim 11 except additional claim limitation that the one or more processors are further configured to:
determine a personality associated with the entity or product, wherein: the personality associated with the entity or product is based on the embeddings associated with the existing digital content, and generating the video component includes using the determined personality.
The claim 16 is in parallel with the claim 6 in the form of an apparatus. The claim 16 is subject to the same rationale of rejection as the claim 6.
Re Claim 17:
The claim 17 encompasses the same scope of inventio as that of the claim 16 except additional claim limitation that the generative model comprises a personality engine configured to determine the personality of the entity or product.
The claim 17 is in parallel with the claim 7 in the form of an apparatus. The claim 17 is subject to the same rationale of rejection as the claim 7.
Re Claim 20:
The claim 20 is in parallel with the claim 1 in the form of a computer program product. The claim 20 is subject to the same rationale of rejection as the claim 1.
Moreover, Tu-provisional ‘733 further teaches the claim limitation of one or more non-transitory computer-readable storage media encoding instructions that, when executed by one or more processors, cause the one or more processors to perform operations [of the claim 1] (Tu-provisional ‘733 teaches at Paragraph 0062 that a programmable apparatus which executes any of the above-mentioned computer program products or computer-implemented methods may include one or more microprocessors, microcontrollers, embedded microcontrollers, programmable digital signal processors, programmable devices, programmable gate arrays, programmable array logic, memory devices, application specific integrated circuits, or the like. Each may be suitably employed or configured to process computer program instructions, execute computer logic, store computer data, and so on.
Tu-provisional ‘733 teaches at Paragraph [0063] It will be understood that a computer may include a computer program product from a computer-readable storage medium and that this medium may be internal or external, removable and replaceable, or fixed. In addition, a computer may include a Basic Input/Output System (BIOS), firmware, an operating system, a database, or the like that may include, interface with, or support the software and hardware described herein).
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 JIN CHENG WANG whose telephone number is (571)272-7665. The examiner can normally be reached Mon-Fri 8:00-5:00.
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/JIN CHENG WANG/Primary Examiner, Art Unit 2617