Abstract: Systems and methods of deep-learning neurocognitive impairment evaluation using extended reality. One system includes an electronic processor configured to generate an extended reality user environment (“XR-UX”) for display via a user device, the XR-UX including a first augmented object configured to change position within the XR-UX and a second augmented object configured to dynamically change position within the XR-UX based on a position of the user device. The electronic processor is also configured to control the first augmented object to traverse a path within the XR-UX. The electronic processor is also configured to receive motion data describing movement of the user device. The electronic processor is also configured to determine a medical classification based on the physical movement of the user device in relation to the path. The electronic processor is also configured to generate and transmit an assessment report for display, the assessment report including the medical classification.

Abstract: Matching users with similar interests is disclosed, including: obtaining, from an external source, items of interest as indicated by a plurality of users; storing information about the plurality of users and their respective items of interest; and identifying, for a first user of the plurality of users, a set of similar users who have overlapping interests as the first user, wherein the overlapping interests are determined based at least in part on items of interest to the first user and items of interest to the plurality of users.

Abstract: Determining preference activities to present to a current user includes: obtaining a plurality of candidate preference activities; determining a recommendation weight for one of the plurality of candidate preference activities with respect to the current user; and presenting at least some of the plurality of candidate preference activities based on recommendation weights corresponding to the plurality of candidate preference activities.

Abstract: Determining preference activities to present to a current user includes: obtaining a plurality of candidate preference activities; determining a recommendation weight for one of the plurality of candidate preference activities with respect to the current user; and presenting at least some of the plurality of candidate preference activities based on recommendation weights corresponding to the plurality of candidate preference activities.

Abstract: Matching users with similar interests is disclosed, including: obtaining, from an external source, items of interest as indicated by a plurality of users; storing information about the plurality of users and their respective items of interest; and identifying, for a first user of the plurality of users, a set of similar users who have overlapping interests as the first user, wherein the overlapping interests are determined based at least in part on items of interest to the first user and items of interest to the plurality of users.