System

A system comprising:

• circuitry configured to:
• receive, via a communication interface coupled to a packet-switched network, input sequence data from a terminal device, and execute language analysis processing including tokenization, morphological analysis, syntactic parsing, and dependency analysis on the input sequence data to generate language feature data including part-of-speech information, syntactic information, word frequency information, context information, and dependency information;
• convert the language feature data and a prompt sentence including an instruction sentence into numerical vector data by combining token embeddings with feature encodings derived from the language feature data, the numerical vector data being inputtable to a transformer-based generative neural network model comprising a plurality of self-attention layers and feed-forward layers;
• generate, by using the transformer-based generative neural network model, classification result data including a multi-class classification label, a probability value for each class, and importance scores for individual token-level units computed from attention weights, the classification result data indicating presence or absence and a type of a predetermined condition in the input sequence data;
• specify, based on the importance scores, at least one portion of the input sequence data that contributes to the classification, generate a content generation prompt sentence including the at least one portion and the classification result data, and input the content generation prompt sentence into an image generation neural network model to generate output media data visually indicating content and position of the predetermined condition;
• and
• transmit the classification result data and the output media data to the terminal device via the communication interface.

A method performed by circuitry of a system coupled to a packet-switched network via a communication interface, the method comprising:

• receiving, via the communication interface, input sequence data from a terminal device;
• executing language analysis processing on the input sequence data to segment the input sequence data into token-level units and to generate language feature data including at least syntactic information and dependency information;
• converting the language feature data and a prompt sentence into numerical vector data inputtable to a generative neural network model operating on a deep learning framework;
• generating, by using the generative neural network model, classification result data indicating presence or absence of a predetermined condition in the input sequence data, together with confidence information;
• specifying, based on the classification result data, at least one portion of the input sequence data that contributes to the classification and generating a content generation prompt sentence for expressing the classification result data as output media data;
• and
• transmitting the classification result data and the output media data to the terminal device via the communication interface.