Abstract: A system for interaction pattern recognition receives an input primary interaction and accesses clusters indicating interaction group patterns. Each cluster includes a respective primary interaction and secondary interactions linked to that primary interaction. Each cluster is identified by a respective non-fungible token.

Abstract: A system receives a first image and extracts a first set of facial features from the first image. The system determines that a user exhibited a first emotion in the first image. The system determines that the user selected a first avatar for use within a virtual environment. The system associates the first avatar with the first emotion of the user. The system receives a second image and extracts a second set of facial features from the second image. The system determines that the user exhibited a second emotion in the second image. The system determines that the first set of facial features corresponds to the second set of facial features. In response, the system presents the first avatar to the user. The system determines that the user selects the first avatar. The system implements the first avatar on behalf of the user for use in the virtual environment.

Abstract: A data manipulation detection device that includes an alert engine configured to receive data from a data source, apply a set of rules for a threat model to the data using a first machine learning model, and to obtain an alert vector in response to applying the set of rules to the data. The device further includes an alert feedback engine configured to receive alert feedback that includes text comments for the alert vector. The device further includes a natural language processing (NLP) training engine configured to identify the text comments for the alert status and identify keywords within the text comments associated with a rule parameter value for a rule. The NLP training engine is further configured to determine a new rule parameter value based on the identified keywords and modify a rule parameter value for the rule based on the new rule parameter value.

Abstract: A data manipulation detection device that includes an alert engine configured to receive data from a data source, apply a set of rules for a threat model to the data using a first machine learning model, and to obtain an alert vector in response to applying the set of rules to the data. The device further includes an alert feedback engine configured to receive alert feedback that includes text comments for the alert vector. The device further includes a natural language processing (NLP) training engine configured to identify the text comments for the alert status and identify keywords within the text comments associated with a rule parameter value for a rule. The NLP training engine is further configured to determine a new rule parameter value based on the identified keywords and modify a rule parameter value for the rule based on the new rule parameter value.