Apple Inc.

Kang Tu

Martin James Murrett

Ravi Chandra Jammalamadaka

Donald Rozinak Beaver

Dominic J Hughes

Mark A Gingrich

Georgios Sofianatos

Amogh Mahapatra

Justin W Sung

Guillermo Ortiz

Ian J Elseth

Casey Maureen Dougherty

Steve E Marmon

Collin D Ruffenach

Jason A Novak

Gregory C Langmead

Pushkaraj Bhirud

Donald R Beaver

Aspects of the present disclosure involve a mobile or computer reader application that obtains articles or other computer files from a central database and displays the articles to a user of the device. The reader application may be customizable around one or more characteristics of the user of the device. In one embodiment, the type and number of articles provided to the device and displayed in the reader application may be based on the determination of a category or type of usage of the application is performed by the user. Further, the determination of the use of the reader application on the device is performed by and contained within the device such that usage information is not shared with overall article providing system. In another embodiment, the article providing system and/or device may determine recommendations to provide to a user of the reading application. These recommendations may be based on one or more selected interests or topics of the user of the reading application.

Reader application with a personalized feed and method of providing recommendations while maintaining user privacy

Aspects of the present disclosure involve a mobile or computer reader application that obtains articles or other computer files from a central database and displays the articles to a user of the device. In addition to providing the articles to the reader application, an article providing system may also determine the quality or popularity of particular articles and provide the most popular articles to users of the system. In one embodiment, the system may receive one or more anonymous interaction metrics from one or more devices connected to the system. The anonymous interaction metrics may be associated with a particular article and provide some indication of a user's engagement with the article. The system utilizes these interaction metrics or measurements to set or adjust a score or ranking associated with the particular article. The score may then be utilized by the system to rank the article in relation to other articles available through the system to provide the most popular or highest ranked articles to users of the system.

Reader application system utilizing article scoring and clustering

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Cupertino, CA, United States of America

Kang Tu