System

A system comprising:

• circuitry configured to:
• receive, via a communication interface coupled to a packet-switched network, character information and a nuance category from a terminal device, validate and normalize the character information;
• apply linguistic analysis including morphological analysis, part-of-speech tagging, dependency parsing, and named-entity extraction to generate analysis information, and calculate characteristic values representing at least honorific expression presence, request sentence classification, and politeness degree;
• execute emotion analysis on the character information to obtain an emotion category and an emotion intensity, and select a nuance pattern including a representation style, a writing style, and a length constraint based on the emotion category and the nuance category;
• construct a prompt sentence based on the analysis information, the nuance pattern, and the character information, input the prompt sentence to a generative neural network model, and obtain transformed character information from the generative neural network model;
• apply postprocessing to the transformed character information based on at least the length constraint and a prohibited-expression constraint to generate display character information;
• transmit the display character information to the terminal device via the communication interface;
• and
• store past character information and corresponding nuance categories in a storage device, perform machine learning processing on the stored information to extract and update a plurality of nuance patterns, and provide updated nuance pattern information to the terminal device as selectable options.

A method comprising:

• receiving, by circuitry via a communication interface coupled to a packet-switched network, character information and a nuance category from a terminal device, validating the character information by determining at least a string length and a character type, and normalizing the character information;
• applying linguistic analysis to the normalized character information including at least morphological analysis, part-of-speech tagging, dependency parsing, and named-entity extraction, and calculating characteristic values representing expression attributes of the character information including at least presence or absence of honorific expressions, whether the character information is a request sentence, and a degree of politeness, to generate analysis information;
• executing emotion analysis on the character information to obtain an emotion category and an emotion intensity, and selecting or adjusting a nuance pattern based on the emotion category and the nuance category;
• constructing a prompt sentence based on the analysis information, the nuance pattern, and the character information, and inputting the prompt sentence to a generative neural network model to obtain transformed character information;
• applying postprocessing to the transformed character information based on at least one of the length constraint, a vocabulary constraint, and a prohibited-expression constraint to generate display character information;
• and
• transmitting the display character information to the terminal device via the communication interface, and storing past character information and corresponding nuance categories in a storage device and performing machine learning processing on the stored information to extract and update a plurality of nuance patterns.