DYNAMIC PRESENTATION OF CONTENT SUGGESTIONS FOR ANNOTATION

Organization Name

apple inc.

Inventor(s)

Adeeti V. Ullal of Los Altos CA (US)

Allison L. Gilmore of Redwood City CA (US)

Pejman Lotfali Kazemi of San Francisco CA (US)

Yann J. Renard of San Diego CA (US)

Hyo Jeong Shin of San Jose CA (US)

Guanling Feng of Santa Clara CA (US)

Alexander G. Bruno of Cupertino CA (US)

Jaehyun Bae of San Carlos CA (US)

Ayse S. Cakmak of Santa Clara CA (US)

DYNAMIC PRESENTATION OF CONTENT SUGGESTIONS FOR ANNOTATION - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240378371 titled 'DYNAMIC PRESENTATION OF CONTENT SUGGESTIONS FOR ANNOTATION

The technology described in the abstract provides a framework for generating personalized and relevant content suggestions for a user of an electronic device.

The system collects data from various sources such as location, motion sensors, routine places, contacts, calls, and proximity to people and devices.
The collected data is analyzed using inference technology to identify patterns and anomalies.
The system can detect routine activities and identify anomalies based on the duration and frequency of activities, social interactions, and changes in user behavior.
Modules are included for grouping related activities and summarizing individual events and coarse-grained activities.
A ranking algorithm generates recommendations based on factors like recency, distinctiveness, media richness, and user engagement.
The system adjusts recommendation ranking based on prior analytics and user preferences.

Potential Applications: This technology can be applied in personalized content recommendations for various electronic devices, including smartphones, tablets, and smartwatches. It can also be used in targeted advertising, social media platforms, and e-commerce websites.

Problems Solved: This technology addresses the challenge of providing users with relevant and personalized content suggestions based on their behavior and preferences. It also helps in improving user engagement and satisfaction with electronic devices.

Benefits: The technology enhances user experience by offering personalized content suggestions, increasing user engagement, and improving the overall usability of electronic devices. It also helps in optimizing advertising strategies and increasing user interaction with digital content.

Commercial Applications: Title: Personalized Content Recommendation System This technology can be commercially used in digital marketing, e-commerce platforms, social media networks, and mobile applications to enhance user experience, increase user engagement, and drive revenue through targeted advertising.

Questions about the technology: 1. How does the system analyze data from various sources to generate personalized content suggestions? The system uses inference technology to identify patterns and anomalies in the collected data, allowing it to detect routine activities and anomalies based on user behavior.

2. What factors does the ranking algorithm consider when generating recommendations? The ranking algorithm takes into account factors such as recency, distinctiveness, media richness, and user engagement to generate personalized content suggestions for the user.

Original Abstract Submitted

the subject technology provides a framework for generating personalized and relevant content suggestions for a user of an electronic device. the system collects data from various sources, including location, motion sensors, routine places, contacts, calls, and proximity to people and devices. the collected data is analyzed using inference technology to identify patterns and anomalies. the system can detect routine activities and identify anomalies based on the duration and frequency of activities, social interactions, and changes in user behavior. the system includes modules for grouping related activities and summarizing individual events and coarse-grained activities. the system also includes a ranking algorithm that generates recommendations based on various factors such as recency, distinctiveness, media richness, and user engagement. the system further includes a method of adjusting recommendation ranking based on prior analytics and user preferences.