Loop Now Technologies, Inc.

Google Inc.

Charmpi, LLC

Wu-Hsi Li

Jerry Ting Kwan Luk

Edwin Chiu

Jeremiah Kevin Tu

Ziming Zhuang

Stefan Backor

Hong-Ming Tseng

Loren J Rittle

Ramy S Ayoub

Vincent Yang

Yiyang Gu

Brian J Sibilsky

Yufan Jiang

Michael A Shoss

Xiaochen Zhang

Fuhong Liu

Alvin D Yates

Shi Feng

Kylie Marie Wetzel

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.

Livestream with synthetic scene insertion

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.

Video stream interface based on third-party webpage information

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.

Manipulating video livestream background images

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.

Machine learned video template usage

Disclosed embodiments describe text-driven AI-assisted short-form video creation. Text from a website is extracted to generate extracted text. Possible summary sentences are formed from the extracted text. The forming is based on natural language processing. The summary sentences are ranked according to an engagement metric. Summary sentences from the possible summary sentences are picked based on a threshold engagement metric value. A list of video scenes is generated based on the summary sentences. Each video scene is associated with a summary sentence. A media asset from a media asset library is chosen for each video scene within the list of video scenes. The choosing is accomplished by machine learning. The list of video scenes, including the media asset that was chosen for each video scene, is compiled into a short-form video. Media is extracted from the website included into the short-form video. The compiling includes a dynamically generated image.

Text-driven AI-assisted short-form video creation in an ecommerce environment

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.

Machine learned curating of videos for selection and display

Disclosed techniques include video performance rendering modification based on device rotation metric. A media stream is received from a server. The media stream is targeted for display on a handheld media stream playback device. An orientation of the handheld device is determined during playback of the media stream on the handheld device. A change in the orientation of the handheld device is detected. The change occurs during playback of the media stream. Based on the detecting the change of orientation, a rotation angle of the handheld device is calculated. Based on the rotation angle, the playback speed of the media stream on the handheld device is changed. The changing the playback speed changes direction of playback rendering based on the rotation angle of the handheld device. The changing direction provides forward playback by rotating the handheld device in a clockwise direction from an initial orientation.

Video performance rendering modification based on device rotation metric

Display screen or a portion thereof with a graphical user interface

Disclosed techniques include audio and visual stream rendering modification based on device rotation metric. One or more media streams are received from a server. The one or more media streams are targeted for display on a handheld media stream playback device. An orientation of the handheld device is determined during playback of at least one of the one or more media streams on the handheld device. A change in orientation of the handheld device is detected. The change occurs during playback of the at least one of the one or more media streams. Based on detecting the change in orientation, a rotation angle of the handheld device is calculated. Based on the rotation angle, the playback of at least one of the one or more media streams is changed on the handheld device.

Audio and video stream rendering modification based on device rotation metric

Musical attribution is performed in a two-dimensional (2D) digital representation. A piece of music representing a musical score is inputted. An abstracted representation of blanks of the score, called a digital audio canvas, is produced. Interactive, dynamic attribution is performed by a user to bring to life the musical score of abstracted blanks. Instrumentation selection, relative volume, scale selection, and score tempo are all musical attributes that are conveyed to the score of abstracted blanks. The score of the digital audio canvas is played back using the attributed blanks. The playback of the score is enabled by selecting appropriate abstracted blanks. The appropriate abstracted blanks are included among other blanks for increased educational and enjoyment value. The modified score is converted back into the format of the original inputted piece of music.

Musical attribution in a two-dimensional digital representation

A remote control device for a separate electronic device to be controlled has a secondary display screen, at least one user-input mechanism, and at least one microprocessor running a user interface application for use in providing control over operation of the electronic device to be controlled. The user interface application causes a user interface to be displayed on the display screen of the remote control device, and the user interface displayed on the display screen graphically mirrors a corresponding user interface independently run by the electronic device to be controlled. The remote control device also has a transmitter and receiver mounted within the body enabling a two-way wireless communication link to be established with the electronic device to be controlled. Apparatus and a method for controlling operation of a media display system are also described.

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Somerville, MA, United States of America

Wu-Hsi Li