Mixed Reality Content Generation

Abstract

A mixed reality (MR) training system includes an identification algorithm (ML1) that identifies incidents of concern (IOCs) based on an incident report data set and related contextual data. Each IOC occurs in the data set with a frequency at least equal to a pre-determined threshold or the resulting consequence is at least equal to a different pre-determined threshold. The system also includes a prediction algorithm (ML2) configured to identify predicted changes in the frequency or contextual data of incidents, an experience generation algorithm (ML3) configured to generate an MR training experience based on IOCs identified by ML1 and the predictions of ML2. A fourth algorithm (ML4) tailors and optimizes MR generated training experiences based, in part, on (i) changes in the incident report data or contextual data or (ii) performance data or biometric response data received during or after a user's interaction with the MR training experience.

What is claimed is: 
     
         1 .- 20 . (canceled) 
     
     
         21 . A method for generating and displaying mixed reality (MR) content, the method comprising the steps of:
 providing a display;   providing a computer-based MR system that is configured to receive a plurality of prompts, to generate MR content based on one or more of the plurality of prompts received, and to provide the MR content generated to the display;   providing a first data source that is comprised of: at least one incident record that relates to an incident and contextual data associated with the incident;   with the MR system, providing a prompt based on the at least one incident record in the first data source;   with the MR system, in response to receiving the prompt, programmatically generating a first MR content and delivering the first MR content to the display; and   with the display, displaying the first MR content.   
     
     
         22 . The method of  claim 21  wherein the first data source is comprised of: a plurality of unique incident records that each relate to at least one incident and contextual data associated with the plurality of incident records, wherein the prompt provided by the MR system and the first MR content is based on at least one of the plurality of incident records. 
     
     
         23 . The method of  claim 22  further comprising the steps of:
 based on a review of the plurality of incident records, including the at least one incident and contextual data associated with each incident record, using the MR system to predict future incidents that are unique from the incidents contained in the plurality of incident records and that (1) are expected to occur more frequently than similar incidents in the plurality of records or (2) are expected to have increased consequences than similar incidents in the plurality of records; 
 with the MR system, providing a prompt based on at least one of the predicted future incidents; 
 with the MR system, in response to receiving the prompt that is based on the predicted future incidents predicted, programmatically generating the first MR content based on the at least one of the predicted future incidents. 
 
     
     
         24 . The method of  claim 22  further comprising the steps of, with the MR system, providing a prompt based on two or more of the plurality of incident records and, in response to the prompt, programmatically generating the first MR content based on the two or more incident records. 
     
     
         25 . The method of  claim 24  further comprising the step of programmatically generating the first MR content by combining at least portions of the two or more incident records. 
     
     
         26 . The method of  claim 25  wherein the first MR content is generated based on a combination of the contextual data associated with incidents of the two or more incident records, such that at least a portion of the contextual data associated with the first MR content generated by the MR system is identical to the contextual data associated with each of the incidents of the two or more incident records. 
     
     
         27 . The method of  claim 25  wherein the first MR content is generated based on a mathematical analysis of the contextual data associated with incidents of the two or more incident records, such that at least a portion of the contextual data associated with the first MR content generated by the MR system is different from the contextual data associated with the incidents of the two or more incident records. 
     
     
         28 . The method of  claim 24  further comprising the step of, with the MR system:
 associating a novelty score with each of the plurality of incident records that is a measure of the novelty of each incident record compared against each other incident record in the plurality of incident records; 
 associating a novelty score with the first MR content based on the novelty score of the two or more incident records used in generating the first MR content 
 in generating the first MR content, selecting the two or more incident records based, at least in part, on the novelty score. 
 
     
     
         29 . The method of  claim 28  wherein, in generating the first MR content, the MR system optimizes the first MR content by, in selecting the two or more incident records, prioritizing those incident records having greater novelty scores over those incident records having lower novelty scores. 
     
     
         30 . The method of  claim 24  further comprising the step of, with the MR system:
 associating a likelihood of occurrence score with each of the plurality of incident records that is a measure of the likelihood of the incident in each incident record occurring in real life compared against the likelihood of the incident in each other incident record in the plurality of incident records occurring in real life 
 associating a likelihood of occurrence score with the first MR content based on the likelihood of occurrence score of the two or more incident records used in generating the first MR content 
 in generating the first MR content, selecting the two or more incident records based, at least in part, on the likelihood of occurrence score. 
 
     
     
         31 . The method of  claim 30  wherein, in generating the first MR content, the MR system optimizes the first MR content by, in selecting the two or more incident records, prioritizing those incident records having greater likelihood of occurrence scores over those incident records having lower likelihood of occurrence scores. 
     
     
         32 . The method of  claim 30  further comprising the step of, with the MR system:
 associating a novelty score with each of the plurality of incident records that is a measure of the novelty of each incident record compared against each other incident record in the plurality of incident records; 
 associating a novelty score with the first MR content based on the novelty score of the two or more incident records used in generating the first MR content 
 in generating the first MR content, selecting the two or more incident records based, at least in part, on the novelty score and the likelihood of occurrence score. 
 
     
     
         33 . The method of  claim 32  wherein, in generating the first MR content, the MR system optimizes the first MR content by, in selecting the two or more incident records, prioritizing those incident records having greater novelty scores over those incident records having lower novelty scores or prioritizing those incident records having greater likelihood of occurrence scores over those incident records having lower likelihood of occurrence scores. 
     
     
         34 . The method of  claim 21  wherein the first MR content is displayed via the display to a first user, the method further comprising the step of:
 at the MR system, receiving an input from a second user; 
 using the MR system, in response to receiving the input from the second user, providing an updated prompt based on the input from the second user; 
 with the MR system, in response to receiving the updated prompt, programmatically generating a second MR content and delivering the second MR content to the display; and 
 with the display, displaying the second MR content to the first user. 
 
     
     
         35 . The method of  claim 21  further comprising the step of providing a second data source that is comprised of user data related to a user's past interaction with MR content, including at least one of the following: user performance data related to past performance of the user while interacting with MR content or user biometric response data related to past biometric responses of the user while interacting with MR content, wherein the prompt provided by the MR system is based on the first data source, including the at least one incident record, and the second data source, including at least a portion of the user data in order to tailor the MR content to the user data. 
     
     
         36 . The method of  claim 34  wherein the second data source also includes user demographic information related to the user and wherein the prompt provided by the MR system is based at least on the user demographic information in order to tailor the MR content to the user demographic information. 
     
     
         37 . The method of  claim 34  wherein, in providing the prompt, the MR system applies different weights to the first data source and second data source in order to modify a resulting impact that each of the first data source and the second data source has on the prompt. 
     
     
         38 . The method of  claim 34  further comprising the steps of:
 providing a user-specific goal to the MR system, wherein the user-specific goal is a desired outcome of a user's interaction with MR content generated by the MR system; and 
 in response to receiving a first user data associated with the user's interaction with the first MR content, with the MR system, providing an updated prompt; 
 with the MR system, in response to receiving the updated prompt, programmatically generating a second and different MR content that is calculated by the MR system to result in a second user data associated with the user's interaction with the second MR content, where the second user data is nearer the user-specific goal than the first user data; and 
 with the MR system, delivering the second MR content to the display; and 
 with the display, displaying the first MR content. 
 
     
     
         39 . The method of  claim 21  further comprising the steps of:
 with the MR system, filtering the first data source using the MR system to provide a first filtered sub-set of incident records, where the incident associated with each of those incident records occurs at least a pre-determined and tunable minimum number of times in the first data source; and 
 with the MR system, providing a prompt based on the first filtered sub-set of incident records. 
 
     
     
         40 . The method of  claim 21  further comprising the steps of:
 with the MR system, filtering the first data source using the MR system to provide a first filtered sub-set of incident records, where the contextual data associated with the incident of each of those incident records is associated with at least a pre-determined and tunable minimum resulting consequence caused by the incident; and 
 with the MR system, providing a prompt based on the first filtered sub-set of incident records.