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.

Abstract: Techniques for video generation based on machine learned video template usage are disclosed. A plurality of videos is obtained, and video scene analysis on each video is performed. Video cuts for each video are detected, and objects within each video are identified. The identifying includes detecting a person, face, building, or vehicle. Metadata is categorized for each of the videos based on the scene analysis, the video cuts, and the objects within the videos. Template information is stored, including the categorized metadata, on each of the videos. Each video is stored as a template video along with the template information. The template information on a subset of videos is ranked. A basis video is selected based on the template information. A further video is generated based on the basis video. The further video is stored as a further template video along with the template videos.

Abstract: Disclosed embodiments provide techniques for dynamic synthetic video chat agent replacement. A human host receives a request for a video chat initiated by a user. An image for a synthetic host including a representation of an individual is retrieved. The image of the synthetic host is selected based on information about the user. Aspects of the individual included in the image are extracted using one or more processors. A video performance by the human host responding to the statement or query by the user is captured. A synthetic host performance is created in which the video performance of the human host is dynamically replaced by the individual that was extracted so that the synthetic host performance responds to the user statement or query. The synthetic host performance is rendered to the user and supplemented with additional synthetic host performances as the video chat continues.

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: Disclosed embodiments provide techniques for augmented performance replacement in a short-form video. A short-form video is accessed, including a performance by a first individual. Using one or more processors, the performance of the first individual is isolated. Specific elements of the performance including gestures, clothing, expressions, and accessories are included in the isolation process. An image of a second individual is retrieved and information on the second individual is extracted from the image. A second short-form video is created by replacing the performance of the first individual with the second individual. The second short-form video is augmented based on viewer interaction. The augmenting of the second short-form video occurs dynamically. The augmenting includes additional audio content based on comments, responses to live polls or surveys, or questions and answers from viewers. The augmenting includes switching audio content in the second short-form video with additional audio content.

Abstract: A method for enabling a user to select and interact with text, lines, or paragraphs of a document, in the case where the document is available as a PDF or image. Embodiments enable the representation of a document to go beyond simple extraction of text, including organizing the text into logical groups of benefit to a user, such as paragraph, header, footer, or table (as examples), and labelling them as such. This facilitates subsequent processing, including application of machine learning (ML) algorithms to leverage this explicit information.

Abstract: Disclosed techniques enable cluster-based dynamic content with multi-dimensional vectors for video content analysis. User-specific data vectors on a plurality of users are accessed, which include shopping history and video consumption behavior. A plurality of clusters, based on the user-specific data vectors, is developed. A user, from the plurality of users, is associated with one or more clusters from the plurality of clusters. The user is identified as viewing media content. A container unit is inserted into the media content that is being viewed and is populated with at least one short-form video from a library of short-form videos. The populating is based on the identifying. An ecommerce purchase of a product for sale to the user is enabled. The product for sale is relevant to the one or more clusters and the at least one short-form video. The ecommerce purchase is accomplished within a short-form video window.

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: Disclosed embodiments provide techniques for dynamic population of contextually relevant videos in an ecommerce environment. A library of short-form videos is accessed from a server, and one or more short-form videos within the library is associated with a product for sale. In cases where a user searches for a product for sale on a website, a short-form video request is received. One or more short-form videos are selected from the library based on the product associations. In some instances, the video selection is optimized based on metadata, a bid from an advertiser, or machine learning. A container unit is inserted into the website and is dynamically populated with the short-form videos. A microsite is generated, enabling an ecommerce purchase. The microsite can use a product card or display a virtual purchase cart while the video plays.

Abstract: Disclosed herein are humanized antibodies, antigen-binding fragments thereof, and antibody conjugates, that are capable of specifically binding to certain biantennary Lewis antigens, which antigens are expressed in a variety of cancers. The presently disclosed antibodies are useful to target antigen-expressing cells for treatment or detection of disease, including various cancers. Also provided are polynucleotides, vectors, and host cells for producing the disclosed antibodies and antigen-binding fragments thereof. Pharmaceutical compositions, methods of treatment and detection, and uses of the antibodies, antigen-binding fragments, antibody conjugates, and compositions are also provided.

Abstract: Disclosed embodiments provide techniques for automated video processing. Short-form videos are analyzed and video segments are created from the short-form videos. A related short-form video can be used to create a new video segment, promoting a short-form video. The new video segment is populated on the website by a floating video player or banner ad providing a link to the related short-form video. Video segments can promote and/or demonstrate a product and/or service. The video segments show and describe various aspects of a product. The video segments are disseminated to one or more websites. The video segments can be shown as video thumbnails, banner advertisements, or rich media advertisements. The video segments can be disseminated based on context, and different segments can be provided to different websites. The different video segments can be disseminated based on demographic information, geographic region, and/or other criteria.

Abstract: Techniques for video manipulation based on machine learned video curating are disclosed. Web page content is loaded, where the content includes a frame for short-form videos. The content of the web page is analyzed for textual information. A short-form video server is accessed. Short-form videos are selected from the short-form video server, where the selecting includes automatically curating the short-form videos. Adaptive learning is used for the selecting, based on a user's web page behavior. The adaptive learning includes collecting the user's web page behavior before the selecting. Automatic curating includes selecting, by a neural network, a subset of short-form videos appropriate for the web page. The web page frame is populated with the short-form videos obtained from the video server. Representations of the short-form videos are displayed within the frame on the web page. The short-form videos are auto played within the frame.

Abstract: Techniques for video manipulation using a product card within a short-form video environment are disclosed. Display is accomplished using a video stream interface that can be executed on an electronic device associated with a user. The video stream, such as a short-form video or livestream video, can include one or more products. The display shows product cards associated with the products within the video. The product cards originate from a repository, a third-party website, or some other source. The product information includes text, video, and/or audio content. The user is provided an option to purchase one or more of the products presented within the video by selecting the product card. The viewer selects product cards displayed in front of the video to learn more about the products and/or purchase the products.

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 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.