Apple Inc.

Concept.io, Inc.

Georgios Sofianatos

Dominic J Hughes

Martin James Murrett

Gurumurthy D Ramkumar

Ravi Chandra Jammalamadaka

Donald Rozinak Beaver

Mark A Gingrich

Amogh Mahapatra

Kang Tu

Justin W Sung

Guillermo Ortiz

Ian J Elseth

Casey Maureen Dougherty

Steve E Marmon

Collin D Ruffenach

Jason A Novak

Gregory C Langmead

Chris Cornelius

Dominic James Doran Hughes

Max Delgadillo

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

In one exemplary aspect, a sorted list of scored media content episodes is received with a computing device of a user. Each respective media content episode is scored by an iterative autotuning prediction algorithm, and wherein each element of the sorted list of scored media content episodes comprises a value that represents a likelihood of a user listening to the respective media content episode and a reference to a location of the respective media content episode. A number of bytes of a download iteration for each media content episode is determined based on value that represents a likelihood of the user listening to the respective media content episode and an index of the respective media content episode in the sorted list. It is detected that a mobile device is in the preferred network. The download iteration is implemented for each media content episode when it is detected that the mobile device is in the preferred network.

Method and system of automatically downloading media content in a preferred network

In one exemplary embodiment, a method of a computerized media-content recommender includes receiving a user-judgment score based on an historical user-listening data with respect to a media content. A first prediction score for a user with respect to the media content is calculated with a media-content recommender. The media-content recommender includes a first set of prediction parameters. A first prediction error including a difference between the user-judgment score and the first prediction score is determined. At least one parameter value of the first set of prediction parameters is modified with a machine-learning optimization technique to generate a second set of prediction parameters. A second prediction score for the user with respect to the media content is calculated with a media-content recommender. A second prediction error including a difference between the user-judgment score and the second prediction score is calculated.

Method and system of iteratively autotuning prediction parameters in a media content recommender

Growing community of inventors

Cupertino, CA, United States of America

Georgios Sofianatos