18669259. ACTION SUGGESTIONS FOR USER-SELECTED CONTENT simplified abstract (Google LLC)
Contents
ACTION SUGGESTIONS FOR USER-SELECTED CONTENT
Organization Name
Inventor(s)
Matthew Sharifi of Kilchberg (CH)
Daniel Ramage of Seattle WA (US)
David Petrou of Brooklyn NY (US)
ACTION SUGGESTIONS FOR USER-SELECTED CONTENT - A simplified explanation of the abstract
This abstract first appeared for US patent application 18669259 titled 'ACTION SUGGESTIONS FOR USER-SELECTED CONTENT
The abstract describes a system and method for suggesting actions based on selected text displayed on a mobile device. The method involves converting a selection into a query, providing the query to a model that predicts actions associated with mobile applications, receiving predicted actions, and displaying them on the device.
- Converting selected text into a query
- Predicting actions based on the query
- Associating actions with mobile applications
- Displaying predicted actions on the mobile device
- Generating positive training examples for the model from search records
Potential Applications: - Enhancing user experience on mobile devices - Improving efficiency in accessing mobile applications - Personalizing suggestions based on user behavior
Problems Solved: - Streamlining the process of accessing relevant mobile applications - Providing tailored suggestions to users based on their interactions
Benefits: - Increased user engagement with mobile applications - Time-saving for users in finding relevant actions - Enhanced user satisfaction with personalized suggestions
Commercial Applications: Title: Mobile Action Suggestion System for Enhanced User Experience This technology can be utilized in mobile devices, smart assistants, and other digital platforms to provide personalized action suggestions to users, improving overall user experience and engagement. The market implications include increased user retention, higher app usage, and potential partnerships with mobile application developers.
Prior Art: Further research can be conducted in the field of mobile device interaction, natural language processing, and personalized recommendation systems to explore prior art related to this technology.
Frequently Updated Research: Stay updated on advancements in mobile device interaction, machine learning models for action prediction, and user behavior analysis to enhance the capabilities of this technology.
Questions about Mobile Action Suggestion System: 1. How does this technology improve user engagement with mobile applications? - This technology enhances user engagement by providing personalized action suggestions based on selected text, streamlining the process of accessing relevant mobile applications. 2. What are the potential commercial applications of this system? - The system can be applied in various digital platforms to enhance user experience, improve user retention, and increase app usage.
Original Abstract Submitted
Systems and methods are provided for suggesting actions for selected text based on content displayed on a mobile device. An example method can include converting a selection made via a display device into a query, providing the query to an action suggestion model that is trained to predict an action given a query, each action being associated with a mobile application, receiving one or more predicted actions, and initiating display of the one or more predicted actions on the display device. Another example method can include identifying, from search records, queries where a website is highly ranked, the website being one of a plurality of websites in a mapping of websites to mobile applications. The method can also include generating positive training examples for an action suggestion model from the identified queries, and training the action suggestion model using the positive training examples.
