System

A system comprising:

• circuitry configured to:
• receive, via a communication interface coupled to a packet-switched network, audio-derived text data and user identification data from a terminal device, execute affective state analysis using a natural language processing model to generate affective-annotated text data comprising affective labels and affective scores, and store the affective-annotated text data in a storage device;
• generate feature information representing behavioral tendency patterns from the stored affective-annotated text data, construct a classification model that estimates a deviation degree based on the feature information, and execute behavioral pattern detection using the classification model;
• compute statistical information comprising at least one of trend metrics and deviation metrics over a time window, generate a prompt sentence embedding the behavioral pattern detection results, the statistical information, and a threshold configuration parameter, and execute inference processing using a generative neural network model to obtain response information;
• and
• update at least one of the threshold configuration parameter of the classification model, the affective state analysis processing parameters, and the behavioral pattern detection configuration based on the response information, and transmit a notification message to a designated terminal device via the communication interface when a behavioral pattern satisfies a notification condition.

A method comprising:

• receiving, via a communication interface coupled to a packet-switched network, audio-derived text data and associated user identification data and time information from a terminal device;
• executing affective state analysis processing on the text data using a natural language processing model to generate affective-annotated text data comprising at least one affective label and an affective score, and storing the affective-annotated text data in a storage device in association with the user identification data and the time information;
• generating, from a set of stored affective-annotated text data, feature information representing behavioral tendency patterns, constructing a classification model that estimates a presence or degree of deviation in the behavioral tendency patterns based on the feature information, and executing behavioral pattern detection for each user using the classification model;
• computing statistical information over time for the affective labels, affective scores, phrase occurrences, and classification model outputs, generating a prompt sentence based on the behavioral pattern detection results and the statistical information, executing inference processing using a generative neural network model with the prompt sentence as input, and receiving response information from the generative neural network model;
• and
• updating at least one of parameters, processing content, and configuration of the affective state analysis processing and the behavioral pattern detection processing based on the response information.