System

A system comprising:

• circuitry configured to:
• receive, via a communication interface coupled to a packet-switched network, identification data and analysis conditions from a terminal device;
• automatically collect public information via an information communication network, apply natural language processing to remove noise and extract useful information, and convert the extracted useful information into structured data;
• classify the public information to generate input features, and apply a trained prediction model to the structured data and the input features to compute candidate action items and corresponding occurrence probabilities as prediction information;
• construct a dynamically updated prompt sentence encoding the prediction information, the input features, and the analysis conditions, and execute a generative neural network model using the prompt sentence to generate explanation text;
• generate visualization data from the prediction information and the explanation text, and transmit display screen data encoding the visualization data to the terminal device via the communication interface;
• and
• acquire user evaluation data or correction instruction data from the terminal device, and update parameters of the prediction model and the prompt sentence generation algorithm based on the user evaluation data or correction instruction data.

A method comprising:

• receiving, via a communication interface coupled to a packet-switched network, identification data and analysis conditions from a terminal device, and automatically collecting public information via an information communication network based on the analysis conditions;
• performing text analysis processing and natural language processing on the collected public information to remove noise and extract useful information corresponding to predetermined temporal and topical conditions, and converting the extracted useful information into structured data suitable for machine learning processing;
• classifying the public information based on the analysis conditions and generating input features from classification results;
• calculating, using a trained prediction model and based on the structured data and the input features, a plurality of candidate action items and an occurrence probability corresponding to each candidate action item, to generate prediction information;
• generating, based on the prediction information, the classification results, and the analysis conditions, a dynamically updated prompt sentence for input to a generative neural network model;
• transmitting the prompt sentence to the generative neural network model, acquiring explanation text or strategy proposal text from the generative neural network model, and generating visualization data based on the prediction information and the explanation text;
• and
• acquiring user evaluation data or correction instruction data from the terminal device, storing the acquired data as learning data, and updating parameters of the prediction model and a prompt sentence generation algorithm based on the learning data.