System

A system comprising:

• a communication interface configured to communicate with a client terminal via a packet-switched network;
• a processor;
• a random access memory;
• a memory storing a data generation model obtained by machine learning and an emotion identification model obtained by machine learning;
• a database;
• and
• circuitry configured to:
• receive, from the client terminal via the communication interface, sensor data representing a physiological state of a user, the sensor data comprising at least one of heart rate data, blood pressure data, body weight data, or sleep pattern data;
• preprocess the received sensor data by applying at least one of noise removal, normalization, or missing value imputation;
• analyze the preprocessed sensor data by inputting the preprocessed sensor data into the data generation model to generate plan data based on the physiological state, a goal, a preference, and a constraint of the user, the plan data comprising item identifiers, quantities, and a usage schedule optimized to minimize surplus items;
• generate list data by extracting the item identifiers and the quantities from the plan data, integrating duplicate items, and adjusting the list data based on an inventory status stored in the database;
• estimate an emotion of the user by inputting multimodal input data into the emotion identification model;
• generate recommendation data based on the list data and context data associated with the user by applying a multivariate scoring algorithm, and adjust a manner of expressing the recommendation data based on the estimated emotion;
• and
• transmit the recommendation data to the client terminal via the communication interface and the packet-switched network.

A method performed by circuitry of a system, the method comprising:

• receiving, via a communication interface coupled to a packet-switched network, sensor data transmitted from a client terminal, the sensor data representing a physiological state of a user captured by a sensor of the client terminal;
• analyzing the received sensor data by inputting the sensor data into a data generation model obtained by machine learning to generate plan data based on the physiological state, a goal, a preference, and a constraint of the user;
• generating list data by extracting item identifiers and quantities from the plan data;
• and
• generating recommendation data based on the list data and context data associated with the user, and transmitting the recommendation data to the client terminal via the communication interface and the packet-switched network.