Abstract: Methods for aligning an extended reality (XR) environment with a physical environment such that a physical position and movement of a physical entity within the physical environment is identically aligned and identically mirrored by a corresponding XR position and corresponding movement of an XR entity within the XR environment. The alignment process assigns a position to the XR environment with respect to the physical environment such that certain elements of one environment are defined as being co-planar with certain surfaces of the other environment. Next, two planes or two lines in one environment intersect to define a first intersection. A second intersection in the other environment is located at the intersection of the first environment to align the two environments.

Abstract: A method for aligning extended reality (XR) content with a physical environment by, first, identifying a first elongate intersection between two physical surfaces. Then, an intersection point of a XR model is located along the first elongate intersection.

Abstract: A method for aligning extended reality (XR) content with a physical environment by, first, identifying a first elongate intersection between two physical surfaces. Then, an intersection point of a XR model is located along the first elongate intersection.

Abstract: Methods for providing a machine learning (ML) final inference to a user, wherein an ML model and a computer-based content generation system (CGS) receives possible inputs and generates possible inferences, which are stored in association with the possible inputs to a memory so that they may be recalled based on the possible inputs. After receiving an actual input and an acceptability criterion, the CGS identifies a possible input that acceptably matches the actual input by satisfying the acceptability criterion. If a match is identified, the CGS substitutes the matching possible input in place of the actual input and outputs the possible inference corresponding to the matching possible input as the final inference to a user or to a second ML model. When a match is identified, inference is never performed on the actual input and the possible inferences are generated prior to receipt of the actual input.

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.

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.

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.