The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jun. 13, 2017

Filed:

Jun. 12, 2014
Applicant:

Audible, Inc., Newark, NJ (US);

Inventors:

Dikla Treves, New York, NY (US);

Francis Andrew Shanahan, Watchung, NJ (US);

Lisa Marie Stringer, Jersey City, NJ (US);

Guy Ashley Story, Jr., New York, NY (US);

Aman Sagar, Hoboken, NJ (US);

Puneet Girdhar, Scotch Plains, NJ (US);

Jodi Rothstein, New York, NY (US);

Assignee:

Audible, Inc., Newark, NJ (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 15/16 (2006.01); H04L 29/08 (2006.01);
U.S. Cl.
CPC ...
H04L 67/22 (2013.01);
Abstract

Systems and methods are disclosed enabling recommendations of content items based on a difficulty of the content item as well as a skill level of the user. Skill-based content recommendations may be utilized, for example, in recommending content to language learners. Skill-based recommendations may be based on a variety of difficulty metrics of the content item, such as vocabulary and complexity of the language (e.g., words per paragraph, syllables per word, etc.), as well as a variety of skill metrics of the user (e.g., as explicitly provided by the user or implicitly determined based on a user's interaction with content items). Advantageously, such metrics can enable generation of recommendations based on a multi-dimensional difficulty assessment. Further, difficulty metrics, skill metrics, or the relationship between such metrics may be dynamically updated over time based on continued feedback from users, such that recommendations are dynamically improved.


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