A computer that selectively provides a recommendation is described. During operation, the computer may obtain game information associated with one or more video games played by an individual, where the game information specifies decisions and behaviors in the one or more video games while the individual played the one or more video games. Then, the computer may compute, for the individual, scores for a set of predefined attributes associated with occupations based at least in part on the decisions and/or the behaviors. Next, the computer may selectively provide the recommendation based at least in part on the computed scores, where the recommendation is associated with: an academic area of study for the individual, an employment opportunity for the individual, and/or an occupation for the individual.

A computer that selectively provides a recommendation is described. During operation, the computer may obtain game information associated with one or more video games played by an individual, where the game information specifies decisions and behaviors in the one or more video games while the individual played the one or more video games. Then, the computer may compute, for the individual, scores for a set of predefined attributes associated with occupations based at least in part on the decisions and/or the behaviors. Next, the computer may selectively provide the recommendation based at least in part on the computed scores, where the recommendation is associated with: an academic area of study for the individual, an employment opportunity for the individual, and/or an occupation for the individual.

A scoring system is disclosed for a shuffleboard table 2 having a sliding surface 4 on which a puck 6 can be thrown. A camera 26 is arranged to point in a direction 28 that is angled relative to the normal 30 of the sliding surface 4. A puck 6 can be thrown and a computer 32 can detect a moving object within a throwing zone 16. The computer 32 analyses whether the detected moving object corresponds to a valid puck. When all pucks are stationary on the sliding surface 4 the computer 32 can calculate a score.

This invention allows the players of multiplayer games like poker, chess, backgammon, go, board games, and video games to play the game online while commenting on the game and explaining their strategies to an online audience without sharing that information with their opponents.

Examples herein include systems for detecting cheating and changes in mental performance in card games, including bridge. The system can acquire board data for multiple events, the events including bridge games. For each event, determining performance values for a player based on the board and play data, wherein the performance values may additionally be based in part on timing data. The system can detect a deviation of by comparing the performance values against a threshold, wherein the threshold is based on past performance of known cheating players or the individual player. Then the system can alert a user when the likelihood of cheating or other changes exceeds a threshold.

Examples herein include systems for detecting cheating and changes in mental performance in card games, including bridge. The system can acquire board data for multiple events, the events including bridge games. For each event, determining performance values for a player based on the board and play data, wherein the performance values may additionally be based in part on timing data. The system can detect a deviation of by comparing the performance values against a threshold, wherein the threshold is based on past performance of known cheating players or the individual player. Then the system can alert a user when the likelihood of cheating or other changes exceeds a threshold.

An electronic device and a method for detecting abnormal device operation are provided. The method includes: obtaining multiple action events of a movable input device, and each action event including a relative coordinate and a time stamp of the movable input device; generating multiple absolute coordinates based on the relative coordinate of each action event; estimating multiple speed vectors based on the absolute coordinates and the time stamp of each action event; estimating multiple acceleration vectors based on the speed vectors and the time stamp of each action event; and estimating a probability of abnormal operation based on the speed vectors and the acceleration vectors.

An electronic device and a method for detecting abnormal device operation are provided. The method includes: obtaining multiple action events of a movable input device, and each action event including a relative coordinate and a time stamp of the movable input device; generating multiple absolute coordinates based on the relative coordinate of each action event; estimating multiple speed vectors based on the absolute coordinates and the time stamp of each action event; estimating multiple acceleration vectors based on the speed vectors and the time stamp of each action event; and estimating a probability of abnormal operation based on the speed vectors and the acceleration vectors.

Various aspects of the subject technology relate to systems, methods, and machine-readable media for automated detection of emergent behaviors in interactive agents of an interactive environment. The disclosed system represents an artificial intelligence based entity that utilizes a trained machine-learning-based clustering algorithm to group users together based on similarities in behavior. The clusters are processed based on a determination of the type of activity of the clustered users. In order to identify new categories of behavior and to label those new categories, a separate cluster analysis is performed using interaction data obtained at a subsequent time. The additional cluster analysis determines any change in behavior between the clustered sets and/or change in the number of users in a cluster. The identification of emergent user behavior enables the subject system to treat users differently based on their in-game behavior and to adapt in near real-time to changes in their behavior.

Various aspects of the subject technology relate to systems, methods, and machine-readable media for automated detection of emergent behaviors in interactive agents of an interactive environment. The disclosed system represents an artificial intelligence based entity that utilizes a trained machine-learning-based clustering algorithm to group users together based on similarities in behavior. The clusters are processed based on a determination of the type of activity of the clustered users. In order to identify new categories of behavior and to label those new categories, a separate cluster analysis is performed using interaction data obtained at a subsequent time. The additional cluster analysis determines any change in behavior between the clustered sets and/or change in the number of users in a cluster. The identification of emergent user behavior enables the subject system to treat users differently based on their in-game behavior and to adapt in near real-time to changes in their behavior.