18244625. TIME MARKING CHAPTERS IN MEDIA ITEMS AT A PLATFORM USING MACHINE-LEARNING simplified abstract (GOOGLE LLC)

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TIME MARKING CHAPTERS IN MEDIA ITEMS AT A PLATFORM USING MACHINE-LEARNING

Organization Name

GOOGLE LLC

Inventor(s)

Chenjie Gu of Mountan View CA (US)

Wei-Hong Chuang of Mountain View CA (US)

Min-Hsuan Tsai of San Jose CA (US)

Jianfeng Yang of Mountain View CA (US)

Ji Zhang of Mountain View CA (US)

Honglu Zhou of Highland Park NJ (US)

Hassan Akbari of New York NY (US)

TIME MARKING CHAPTERS IN MEDIA ITEMS AT A PLATFORM USING MACHINE-LEARNING - A simplified explanation of the abstract

This abstract first appeared for US patent application 18244625 titled 'TIME MARKING CHAPTERS IN MEDIA ITEMS AT A PLATFORM USING MACHINE-LEARNING

Simplified Explanation

Methods and systems are disclosed for using machine learning to mark the time of media items on a platform. The system takes an identified media item as input and uses a machine learning model to generate one or more outputs. These outputs include time marks that identify each content segment of the media item. Each content segment is associated with a segment start indicator on a timeline of the media item. The system then determines the resulting duration of the combination of content segments for which time marks were obtained. If the resulting duration is less than the duration of the media item, the system provides further inputs to the machine learning model.

  • The system uses machine learning to mark the time of content segments in media items.
  • It takes an identified media item as input and generates time marks for each content segment using a machine learning model.
  • The system determines the resulting duration of the content segments and provides further inputs to the machine learning model if the resulting duration is less than the duration of the media item.

Potential Applications

  • This technology can be used in video editing software to automatically mark the time of different scenes or segments in a video.
  • It can be applied in content management systems to categorize and organize media items based on their content segments.
  • This technology can also be used in recommendation systems to analyze the duration and content segments of media items and provide personalized recommendations to users.

Problems Solved

  • Manual marking of content segments in media items can be time-consuming and prone to errors. This technology automates the process using machine learning, saving time and improving accuracy.
  • It solves the problem of identifying and categorizing different content segments within a media item, which can be challenging without automated tools.
  • This technology addresses the issue of managing and organizing large amounts of media items by providing a systematic way to mark and analyze their content segments.

Benefits

  • The use of machine learning reduces the need for manual effort in marking content segments, making the process more efficient.
  • Automated time marking of content segments allows for faster editing and organization of media items.
  • This technology enables more accurate analysis and categorization of media items based on their content segments, leading to improved recommendation systems and content management.


Original Abstract Submitted

Methods and systems for time marking of media items at a platform using machine-learning are provided herein. An indication of a identified media item is provided as input to a machine-learning model and one or more outputs of the machine-learning model is obtained. The one or more obtained outputs comprise time marks identifying each of the plurality of content segments of the media item. Each of the plurality of content segments is associated with a segment start indicator for a timeline of the media item. A resulting duration is determined of a combination of the plurality of content segments for which the time marks were obtained from the one or more of outputs of the machine-learning model. Responsive to determining that the resulting duration is less than the duration of the media item, one or more further inputs is provided to the machine learning model.

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