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:
Sep. 27, 2022

Filed:

Jan. 31, 2020
Applicant:

Disney Enterprises, Inc., Burbank, CA (US);

Inventors:

Michael P. Goslin, Sherman Oaks, CA (US);

Erika Varis Doggett, Los Angeles, CA (US);

Anthony M. Accardo, Los Angeles, CA (US);

Noel Brandon Vasquez, New York, NY (US);

Assignee:

Disney Enterprises, Inc., Burbank, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 16/2455 (2019.01); H04N 21/466 (2011.01); G06F 16/23 (2019.01); G06Q 10/08 (2012.01); H04N 21/436 (2011.01); H04N 21/472 (2011.01); H04N 21/41 (2011.01); G09B 19/00 (2006.01); G06F 9/451 (2018.01);
U.S. Cl.
CPC ...
H04N 21/4668 (2013.01); G06F 9/453 (2018.02); G06F 16/2379 (2019.01); G06F 16/2455 (2019.01); G06Q 10/087 (2013.01); G09B 19/003 (2013.01); H04N 21/4131 (2013.01); H04N 21/43615 (2013.01); H04N 21/47217 (2013.01); G09B 19/0092 (2013.01);
Abstract

A recommendation engine generates contextually relevant media programs tailored to the specific context of the user. The recommendation engine interfaces with one or more Internet-of-Things (IoT) appliances to determine one or more food items stored within the IoT appliance(s) and/or one or more cooking capabilities associated with the IoT appliance(s). The recommendation engine also gathers profile data that reflects a set of preferences associated with the user. Based on the gathered data, the recommendation engine queries a database of cooking program data associated with various cooking programs. The recommendation engine obtains cooking program data that is contextually relevant to the user and then generates a contextual cooking program that describes how to perform a given recipe. During playback of the contextual cooking program, the recommendation engine can modify the contextual cooking program based on the progress of the user in following the recipe.


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