	US 11,721,230 B2
	Personalized learning system and method for the automated generation of structured learning assets based on user data
Andrew Smith Lewis, San Diego, CA (US); Paul Mumma, San Diego, CA (US); Alex Volkovitsky, San Diego, CA (US); Iain Harlow, San Diego, CA (US); and Kyle Stewart, San Diego, CA (US)
Assigned to CEREGO JAPAN KABUSHIKI KAISHA, Tokyo (JP)
Filed by Cerego Japan Kabushiki Kaisha, Tokyo (JP)
Filed on Apr. 20, 2022, as Appl. No. 17/724,786.
Application 17/724,786 is a continuation of application No. 17/533,324, filed on Nov. 23, 2021, granted, now 11,348,476.
Application 17/533,324 is a continuation of application No. 17/350,121, filed on Jun. 17, 2021, granted, now 11,217,110, issued on Jan. 4, 2022.
Application 17/350,121 is a continuation of application No. 17/095,035, filed on Nov. 11, 2020, granted, now 11,081,018, issued on Aug. 3, 2021.
Application 17/095,035 is a continuation of application No. 15/836,631, filed on Dec. 8, 2017, granted, now 10,861,344, issued on Dec. 8, 2020.
Claims priority of provisional application 62/452,634, filed on Jan. 31, 2017.
Prior Publication US 2022/0246052 A1, Aug. 4, 2022
This patent is subject to a terminal disclaimer.
Int. Cl. G09B 7/04 (2006.01); G09B 5/06 (2006.01); G09B 7/07 (2006.01); G09B 7/08 (2006.01); G09B 5/12 (2006.01); G06N 20/00 (2019.01); G06N 5/04 (2023.01); G09B 19/00 (2006.01); G06N 7/01 (2023.01)

CPC G09B 7/04 (2013.01) [G06N 5/04 (2013.01); G06N 20/00 (2019.01); G09B 5/065 (2013.01); G09B 5/125 (2013.01); G09B 7/07 (2013.01); G09B 7/08 (2013.01); G06N 7/01 (2023.01); G09B 19/00 (2013.01)]

20 Claims

1. A learning system comprising:

a non-transitory memory; and

one or more hardware processors configured or programmed to read instructions from the non-transitory memory to cause the learning system to perform operations including:

generating a semantic model based on a source material using a machine learning algorithm; and

selecting one or more candidate knowledge items from the source material using the semantic model.