Abstract: A video is rendered. The video features a host and is viewed by one or more viewers. A video segment is accessed. The video segment is related to the video and is accessed by an operator. The video segment includes a performance by an individual. A synthesized video segment is created from the video segment that was accessed. The synthesized video segment includes the performance as accomplished by the host. At least one insertion point within the video is determined for the synthesized video segment. The synthesized video segment is inserted by the operator into the video at the at least one insertion point. The inserting is accomplished dynamically and appears seamless to a viewer. A remainder of the video is rendered after the at least one insertion point. The determining at least one insertion point includes a response to an interaction by the viewers of the video.

Abstract: Techniques for display using frame and child frame technology for video and webpage rendering are disclosed. A video stream is received. The video stream is displayed as a video in a window on a device display along with a link. The link includes a webpage uniform resource locator (URL). A selection of the link made by a user is received. The selection is made in response to a call to action. The video is rendered along with results of the link that was selected. Rendering of the results of the link includes a webpage associated with the webpage URL. The user can interact with the webpage while the video is being rendered. The video is reduced to render the video as a reduced video on a portion of the device display. The rendering the reduced video and webpage is accomplished without a popup window.

Abstract: Information on one or more products is obtained from a third-party information source. The one or more products can comprise a product, a service, a coupon, or a membership. A video about the one or more products is rendered by a video user interface to a user. The video user interface and the third-party information source are interfaced between each other based on a user interaction with the video user interface. Product information is fetched from a server based on the interfacing between the video user interface and the third-party information source. The video is augmented with further information based on the fetching.

Abstract: Techniques for manipulating an expandable video loop with replacement audio are provided. A video that includes a performance by an individual is accessed. One or more processors are used to identify a minimum desired loop within the performance by the individual. Video information is extracted from the video loop and analyzed with machine learning. An audio file is obtained based on a script written by a user. The script is combined with a synthesized voice which is created from the individual video performance to obtain the audio file. The extracted video information is used to manipulate the lips of the individual based on the audio file. A second video loop is created, combining the individual from the first video loop with the video of the manipulated lips of the individual and the synthesized audio file. The second video loop is appended to the end of the first video loop.

Abstract: Techniques for inventory and product control are disclosed. An instant messaging (IM) system that recognizes an ecommerce business entity as a valid IM system identity is accessed. The IM system interfaces between one or more customers and the ecommerce business entity. The ecommerce business entity identity is bound to one or more back-office management systems. One or more products for sale are detected in an IM thread between the ecommerce entity and one or more customers. A product description page (PDP) based on the one or more detected products is created. A link to the PDP is provided to the ecommerce business entity. The ecommerce business entity sends the PDP link to the one or more customers. The IM system collects responses from the customers and manages products of the ecommerce business using the back-office management systems, including inventory, ordering, shipping, and transaction processing.

Abstract: Disclosed embodiments provide techniques for livestream with synthetic scene insertion. A prerecorded livestream featuring a host is rendered to one or more viewers. An operator accesses a video segment related to the prerecorded livestream. The related video segment includes a performance by an individual. The operator retrieves an image of the host of the prerecorded livestream and creates from the related video segment a synthesized video segment that includes the performance of the individual accomplished by the host. One or more insertion points are determined within the prerecorded livestream for the insertion of the synthesized video segment. The operator inserts the synthesized video segment into the prerecorded livestream at the one or more determined insertion points. The insertion is accomplished dynamically and appears seamless to the viewer. The remainder of the prerecorded video is rendered to the one or more viewers after the insertion point.

Abstract: Techniques for a multi-hosted livestream in an open web ecommerce environment are disclosed. A livestream event that includes a first host and a plurality of viewers is initiated on a website. A product for sale is included in the livestream event that is displayed in a livestream window. A second host is added to the livestream event. The first host and second host appear simultaneously in the livestream event. A product card representing the at least one product for sale is pinned in the livestream window by one of the plurality of hosts. Purchase details of the at least one product for sale are revealed, based on a first action with the product card by a viewer. The revealing is rendered to the viewer. An ecommerce purchase of the product by the viewer is enabled. The ecommerce purchase is accomplished within the livestream window.

Abstract: Techniques for video processing using artificial intelligence are disclosed. An embedded interface included on a website and/or mobile application is accessed. The embedded interface includes one or more products for sale. A user requests an interaction based on a product for sale. A first video segment including a synthetic human is displayed to the user. The embedded interface collects user input in response to the video segment. One or more classifiers, which can comprise one or more lightweight LLMs, classify the user input, identifying a type of conversation. The user input is routed by a controller to a module which provides instructions to a final LLM. The final LLM creates a response to the interaction with the user, based on the instructions. The response is used to generate a second video segment that is displayed to the user.

Abstract: Techniques for display using a video stream interface based on third-party webpage information are disclosed. Information on one or more products is obtained from a website by a third party. The one or more products comprise a product, a service, a coupon, or a membership. A video about the one or more products is rendered by a video user interface to a user. The video user interface comprises a video player in combination with a user interface. The video user interface and the third party are interfaced between each other based on a user interaction with the video user interface. Product information is fetched from a server based on the interfacing between the video user interface and the third party. Further product information comprises a reaction, a chat, a transaction, a promotion, and an interaction. The video is augmented with further information based on the fetching.

Abstract: Techniques for video usage within a frame widget retail environment are disclosed. A library of short-form videos is accessed. A website is evaluated for products represented within an online retail environment. One or more products are identified from the webpage that was evaluated. At least one short-form video is chosen from the library to be included in the webpage. The choosing of the at least one video is based on the identified product. A frame widget is inserted into the webpage. The placement of the frame widget is determined by the evaluation of the webpage. The frame widget is populated with at least one video that was chosen along with other videos. The video that was chosen is rendered in the frame widget. The rendering is accomplished by a user action.

Abstract: Techniques for managing artificial intelligence interactions are disclosed. A large language model (LLM) is trained, including information related to products for sale. The product information resides in a product knowledge base. Users interacting with a synthetic human generate input related to the products for sale through an embedded interface included in a website or application. The user input is captured and the LLM creates responses based on information in the product knowledge base. The responses are used to produce video segments that are presented to the user. The user creates additional input based on the video responses from the LLM. The additional input is evaluated and used to trigger self-improving steps to improve the content of the product knowledge base and the responses generated for the user. The self improving includes quality metrics; self-learning instructions; information gap identification; and information collection from third-party websites, product experts, and sellers.

Abstract: Techniques for video streaming are disclosed. Audio input related to products for sale is received from a user within an embedded interface. The embedded interface includes an artificial intelligence virtual assistant represented by a synthetic human host. The audio input is captured by a natural language processing engine, creating a data segment which is sent to a large language model (LLM). The data segment can be divided into one or more subsegments. Each subsegment can be processed independently by the LLM to generate a response based on product information stored in the LLM database. The LLM responses are converted by a text-to-speech (TTS) converter into an audio response. The audio responses are used to produce a video segment including the synthetic host performing the audio stream. The capturing and processing of user audio continues to allow a human-like dialogue with the synthetic human host.

Abstract: Techniques for video streaming are disclosed. A plurality of audio files is accessed. The audio files comprise answers from a large language model to a user requesting information on products for sale. Each audio file is used to produce a video segment, featuring an animated artificial intelligence virtual assistant performing the audio file. The animating uses a Mel spectrogram analysis of the audio file. The audio analysis allows each word, sound, or phoneme of the audio file to be aligned with the matching video frame of the virtual assistant speaking each word. As the first video segment is streamed to the user, additional audio files are used to produce more video segments. The subsequent video segments are buffered based on a probabilistic model as the previous segments are streamed, so that as each video segment is completed, the next segment begins.

Abstract: A computer-implemented method for video sharing is disclosed. A website which hosts multiple users and includes numerous products for sale is accessed. User information is collected and used to match a user with a sales associate. A communications agent is installed on the website which enables an interaction between a website user and a sales associate. The sales associate can be a human or an artificial intelligence agent. The user information is used to predict purchasing intentions of the user. The sales associate can obtain a uniform resource locator (URL) within the website domain based on the purchasing intentions of the user and can direct the user's web browser to the URL, with the user's approval. The directing to the URL is accomplished without taking control of the user's device. The directing is based on the communications agent installed on the website.

Abstract: Video processing using artificial intelligence is disclosed. An embedded interface including products for sale is accessed. A user requests an interaction based on one of the products. The embedded interface initiates a video segment including a synthetic human in response to the user request. The user submits a question or comment. The interface collects the user input and converts it into a dataset readable by a large language model (LLM). The LLM generates a response to the user request. The response is used to generate an audio stream. The audio stream includes simulated human speech errors and pauses. The audio stream is segmented and a video clip is synthesized for each audio segment. The video clips are assembled into a new video segment which is presented to the user. Additional user interactions are collected and new video segments are generated in response.

Abstract: Techniques for manipulating video livestream background images are disclosed. A short-form video, such as a livestream video or livestream replay video, can be analyzed for context. Computer-implemented techniques may be used for performing entity detection, and can also detect a change in subject based on speech and/or actions of a host individual. The subject can include a particular product. The detecting a change in subject can include detecting a foreground object and identifying the foreground object as a product. The identification of the foreground object as a product can include performing optical character recognition on text imprinted on a foreground object. The identification of the foreground object as a product can include image recognition techniques. The identification of the foreground object as a product can include scanning of an optical code such as a barcode that is imprinted on the product.

Abstract: Techniques for manipulating an expandable video loop with replacement audio are provided. A video that includes a performance by an individual is accessed. One or more processors are used to identify a minimum desired loop within the performance by the individual. Video information is extracted from the video loop and analyzed with machine learning. An audio file is obtained based on a script written by a user. The script is combined with a synthesized voice which is created from the individual video performance to obtain the audio file. The extracted video information is used to manipulate the lips of the individual based on the audio file. A second video loop is created, combining the individual from the first video loop with the video of the manipulated lips of the individual and the synthesized audio file. The second video loop is appended to the end of the first video loop.

Abstract: A computer-implemented method for video analysis is disclosed. A livestream, including a livestream chat, is accessed. The livestream includes products for sale. The livestream includes timestamps based on when each product is highlighted. The timestamps allow users to jump to livestream locations based on the highlighted products. The livestream is monitored by a large language model. The large language model (LLM) detects a question from users in the livestream, and the LLM determines answers to the question. An engagement metric is calculated for each answer. The engagement metric is predictive of future engagement in the livestream by users if the answer is posted. A human assistant in the livestream reviews the LLM answer with the highest engagement score and chooses to edit the answer, to post the answer, or not to post the answer to the livestream.

Abstract: Disclosed embodiments provide techniques for video chat initiation based on machine learning. A website that includes products for sale is accessed. The website is viewed by multiple users. User information is collected and analyzed based on machine learning. The machine learning model is used to predict a purchase intention for a user regarding one or more products for sale. The prediction is based on the machine learning analysis. Information about one or more sales associates is gathered and analyzed based on machine learning. The analysis of sales associate information is used to match a user with a purchase intention with a sales associate. The sales associate can initiate a chat interaction with the user through an overlay on the website. The chat interaction can provide additional user information for the machine learning model. During the chat interaction, the user can purchase one or more products.