17835550. GENERATING TITLES FOR CONTENT SEGMENTS OF MEDIA ITEMS USING MACHINE-LEARNING simplified abstract (Google LLC)

GENERATING TITLES FOR CONTENT SEGMENTS OF MEDIA ITEMS 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)

Keren Gu-lemberg of San Francisco CA (US)

Flora Xue of Mountain View CA (US)

Shubham Agrawal of Mountainview CA (US)

Yuzhu Dong of Mountainview CA (US)

Ji Zhang of Mountainview CA (US)

Mahdis Mahdieh of Los Altos CA (US)

Gagan Bansal of Sunnyvale CA (US)

Kai Chen of Brisbane CA (US)

GENERATING TITLES FOR CONTENT SEGMENTS OF MEDIA ITEMS USING MACHINE-LEARNING - A simplified explanation of the abstract

This abstract first appeared for US patent application 17835550 titled 'GENERATING TITLES FOR CONTENT SEGMENTS OF MEDIA ITEMS USING MACHINE-LEARNING

Simplified Explanation

Methods and systems for predicting titles for content segments of media items using machine learning are described in this patent application. The invention involves providing a media item with multiple content segments to users. The first content segment and the title of the second content segment are inputted into a machine learning model. This model is trained to predict a title for the first content segment that is consistent with the title of the second content segment. The output of the machine learning model provides the predicted title for the first content segment. The indication of each content segment and its respective title is then presented to users.

• The invention uses machine learning to predict titles for content segments of media items.
• It takes into account the title of the preceding content segment to ensure consistency.
• The predicted titles are provided to users of a platform.
• The method can be applied to various types of media items, such as videos, articles, or podcasts.

Potential Applications

This technology has potential applications in various industries and platforms, including:

• Media streaming platforms: Predicting titles for content segments can enhance the user experience by providing more informative and engaging titles for each segment.
• News websites: Automatically generating titles for different sections of an article can improve readability and navigation.
• Podcast platforms: Predicting titles for different segments of a podcast episode can help users quickly identify and navigate to specific topics of interest.

Problems Solved

The technology addresses the following problems:

• Inconsistency in titles: By using machine learning to predict titles, the invention ensures that the titles of content segments are consistent with each other, providing a cohesive and coherent user experience.
• Manual effort: Instead of manually assigning titles to each content segment, the invention automates the process, saving time and effort for content creators and platform administrators.
• User engagement: By providing more informative and engaging titles for content segments, the technology can increase user engagement and improve the overall user experience.

Benefits

The benefits of this technology include:

• Improved user experience: Consistent and informative titles for content segments make it easier for users to navigate and find relevant information within media items.
• Time and cost savings: Automating the title prediction process reduces the need for manual effort, saving time and resources for content creators and platform administrators.
• Increased user engagement: Engaging titles for content segments can attract and retain users, leading to higher user engagement and potentially increased platform usage.

Original Abstract Submitted

Methods and systems for predicting titles for contents segments of media items at a platform using machine-learning are provided herein. A media item is provided to users of a platform, the media item having a plurality of content segments comprising a first content segment and a second content segment preceding the first content segment in the media item. The first content segment and a title of the second content segment are provided as input to a machine-learning model trained to predict a title for the first content segment that is consistent with the title of the second content segment. One or more outputs of the machine-learning model are obtained which indicate the title for the first content segment. An indication of each content segment and a respective title of each content segment are provided for presentation to at least one user of the one or more users.