Apple Inc.

Concept.io, Inc.

Dominic J Hughes

Steve E Marmon

Mark A Gingrich

Martin James Murrett

Ravi Chandra Jammalamadaka

Donald Rozinak Beaver

Collin D Ruffenach

Gregory C Langmead

Georgios Sofianatos

Amogh Mahapatra

Pushkaraj Bhirud

Gurumurthy D Ramkumar

Ian J Elseth

Chi Wai Lau

Justin W Sung

Kang Tu

Guillermo Ortiz

Casey Maureen Dougherty

Jason A Novak

Kamal Mahmood Ali

Shizhu Liu

Daniel Ribeiro Silva

Anil A Sewani

Imran A Chaudhri

Anton M Davydov

Charles J Migos

Vincent M Lane

Alden Maxwell Haley

Michel Elings

Alexander C MacLean

Jip Van Der Velde

Genevieve Garand

Aimee Machado

Jonathan M Penn

Donald R Beaver

Some embodiments provide a GUI for a document reader application that displays a selectable representation of content that, when selected, cause the content to be displayed in the GUI. GUI controls may be exposed in response to a user input slide operation on the selectable representation of content.

Arrangements of documents in a document feed

In some implementations, a computing device can present a multi-language grouping of topics. For example, the computing device can determine a primary and secondary language for a user of the computing device. The computing device can request configuration that includes a tag language mapping that can be used to translate topic tags corresponding to the secondary language to topic tags corresponding to the primary language. When the computing device receives tagged content items associated with the secondary language, the computing device can translate the secondary language tags corresponding to the tagged content items into semantically equivalent topic tags in the primary language. The computing device can then group primary language content items and secondary language content items into multi-language groupings based on the topics corresponding to the translated content item tags. The computing device can then present the multi-language topic groupings of content items.

Multi-language grouping of content items based on semantically equivalent topics

In some implementations, a computing system can determine how important an investment identifier is to a content item that mentions the investment identifier. For example, a content item may describe a story, event, etc., related to an investment identifier. The content item may mention the investment identifier by mentioning the investment identifier, proxies for the investment identifier, or other equivalents associated with the investment identifier. The computing system can determine locations in the content item where the investment identifier is mentioned and/or how frequently the investment identifier is mentioned. Based on the locations and/or frequency of mentions, the computing system can determine an importance score that represents how important the investment identifier is to the story described by the content item. The importance score can be stored in metadata for the content item and used when determining which content items to present to a user.

Determining importance of investment identifier to content of content item

Some embodiments provide a GUI for a document reader application that displays an aggregated feed with sections for different groups of personalized documents. Some embodiments provide a method for arranging documents within the different sections and for ordering the different sections within the aggregated feed. In some embodiments, the groups are dynamically generated at a device based on content (e.g., tags) of the documents.

Some embodiments of the invention provide a method that detects document attribute preferences for viewing documents (e.g., articles) on a device. The method displays several documents on a display screen of the device, with each document having one or more associated attributes. In some embodiments, different documents can be associated with the same attribute or the same number of attributes, or can be associated with different attributes or a different number of attributes. In some embodiments, the method detects viewer interactions with one or more displayed documents, and based on these interactions, computes attribute scores for several attributes associated with a number of the documents. The computed attribute scores identify a preference ranking for attributes associated with the documents. Some embodiments use the computed attribute scores to select subsequent documents for display on the device and/or to arrange the display of subsequent documents on the device.

Document selection and display based on detected viewer preferences

Some embodiments provide a document viewing application with first and second document channel browsing modes. The first mode displays selectable items corresponding to document channels. Selection of an item causes the application to add the corresponding document channel to a set of selected document channels from which to retrieve documents for presentation within the application and dynamically add items corresponding to document channels related to the selected document channel to the selectable items for display in the first mode. The second mode displays a first set of selectable items corresponding to sources of documents and a second set of selectable items corresponding to document topics. Selection of an item in the second browsing mode causes the application to add the corresponding document channel to the set of selected document channels and modify the selectable items presented in the second mode a next time the second mode is entered.

Document channel selection for document viewing application

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

Aspects of the present disclosure may involve a computer implemented method of disambiguating a string from an article involving an electronic device including one or more hardware processing units, and accessing a disambiguation database comprising a plurality of string-entity combinations and associated metrics for each of the plurality of string-entity combinations. The associated metrics may include a metric associated with an arrival probability of linking at a web page for a particular entity after a specified number of links from a starting page. The method may involve generating a clique graph for each candidate entity of the article, and generating a probability that a particular candidate entity matches a particular string associated with the particular candidate entity as a function of score attributes generated from the clique graph and the arrival probability of linking at the page for the particular entity after the specified number of links.

Apparatus, system and method for string disambiguation and entity ranking

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

Dominic J Hughes