US 12,394,327 B2
Augmented reality and virtual reality systems
Rajesh Kumar Jha, Irvine, CA (US); and Douglas Choi, Irvine, CA (US)
Assigned to SIMINSIGHTS, INC., Lake Forest, CA (US)
Appl. No. 17/441,534
Filed by SimInsights, Inc., Lake Forest, CA (US)
PCT Filed Feb. 27, 2020, PCT No. PCT/US2020/020231
§ 371(c)(1), (2) Date Sep. 21, 2021,
PCT Pub. No. WO2020/176803, PCT Pub. Date Sep. 3, 2020.
Claims priority of provisional application 62/811,316, filed on Feb. 27, 2019.
Prior Publication US 2022/0172633 A1, Jun. 2, 2022
Int. Cl. G09B 5/06 (2006.01); G06F 3/04815 (2022.01); G06T 15/00 (2011.01); G06T 19/00 (2011.01); G09B 19/00 (2006.01)
CPC G09B 5/065 (2013.01) [G06F 3/04815 (2013.01); G06T 15/00 (2013.01); G06T 19/006 (2013.01); G09B 19/003 (2013.01); G06T 2200/24 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented authoring system for virtual immersive learning environment comprising:
a server comprising a processor for automatically generating a scenario data structure;
the server further comprising an authoring software configured to:
generate first data for one or more virtual objects;
generate second data representing digital rendering of a first virtual learning scenario;
generate the scenario data structure associated with the first virtual learning scenario;
store the scenario data structure in a digital data repository;
generate and store data traces resulting from user interactions with the virtual objects and the virtual learning scenario;
analyze the stored data traces to draw conclusions about users and their experiences within the virtual learning scenario using a suitable artificial intelligence (A.I.) method;
based on the analyzed data traces, refine and enhance the first virtual learning scenario to personalize the learning experience for users;
receive a search request for a requested third data, wherein the search request comprises data representing a second virtual learning scenario;
search the digital data repository for the requested third data;
transmit the requested third data;
analyze user interaction data and scenario elements within a scenario data structure, where the analysis includes processing user interaction logs telemetry data, and virtual object interactions to determine contextually relevant associations within a scenario model, using predefined interaction policies and conditions that govern interactions between virtual objects and users, as specified in the scenario model and task instruction data;
retrieve knowledge items from the scenario data structure based on scenario-defined interaction policies and conditions that specify contextual relationships between virtual objects, task instructions, and user interactions, where retrieval operations ensure selection of knowledge items associated with predefined interaction types, structured scenario components, and metadata attributes representing various content types and concepts, as stored within the task instruction model and scenario data structure; and
filter the retrieved knowledge items by applying user-specific data, including interaction data traces, user feedback, and defined learning objectives, such that the resulting knowledge selection aligns with the learning goals and personalization metrics described in the user feedback data, task instruction model (TIM), and scenario data logging.