A battery life learning device, comprising: a learning section configured to obtain, using a neural network, a learned prediction model for predicting a remaining life of a vehicle battery from time-series data of a deterioration characteristic of the vehicle battery, wherein the deterioration characteristic of the vehicle battery is an internal resistance or a full charge capacity of the vehicle battery, the learned prediction model is obtained by learning a prediction model from the time-series data of the deterioration characteristic of the vehicle battery based on learning data, the learning data including time-series data of the deterioration characteristic at a predetermined time point in the past of a vehicle battery for learning that has reached an end of life and the remaining life at the predetermined time point of the vehicle battery for learning, the learning data comprising time-series data of the deterioration characteristic acquired for an entire life period from when a new vehicle battery reaches the end of its life; the battery life learning device characterized by further comprising: a generation section configured to generate a plurality of partial time-series data as the learning data, the plurality of partial time-series data being generated by: acquiring the time-series data of the deterioration characteristic of the vehicle battery for learning, dividing the acquired time-series data into partial time-series data of the deterioration characteristics for each predetermined period, and assigning the remaining life of the vehicle battery for learning as a label for each divided partial time-series data. a learning section configured to obtain, using a neural network, a learned prediction model for predicting a remaining life of a vehicle battery from time-series data of a deterioration characteristic of the vehicle battery, wherein the deterioration characteristic of the vehicle battery is an internal resistance or a full charge capacity of the vehicle battery, the learned prediction model is obtained by learning a prediction model from the time-series data of the deterioration characteristic of the vehicle battery based on learning data, the learning data including time-series data of the deterioration characteristic at a predetermined time point in the past of a vehicle battery for learning that has reached an end of life and the remaining life at the predetermined time point of the vehicle battery for learning, the learning data comprising time-series data of the deterioration characteristic acquired for an entire life period from when a new vehicle battery reaches the end of its life; the deterioration characteristic of the vehicle battery is an internal resistance or a full charge capacity of the vehicle battery, the learned prediction model is obtained by learning a prediction model from the time-series data of the deterioration characteristic of the vehicle battery based on learning data, the learning data including time-series data of the deterioration characteristic at a predetermined time point in the past of a vehicle battery for learning that has reached an end of life and the remaining life at the predetermined time point of the vehicle battery for learning, the learning data comprising time-series data of the deterioration characteristic acquired for an entire life period from when a new vehicle battery reaches the end of its life; the battery life learning device characterized by further comprising: a generation section configured to generate a plurality of partial time-series data as the learning data, the plurality of partial time-series data being generated by: acquiring the time-series data of the deterioration characteristic of the vehicle battery for learning, dividing the acquired time-series data into partial time-series data of the deterioration characteristics for each predetermined period, and assigning the remaining life of the vehicle battery for learning as a label for each divided partial time-series data. acquiring the time-series data of the deterioration characteristic of the vehicle battery for learning, dividing the acquired time-series data into partial time-series data of the deterioration characteristics for each predetermined period, and assigning the remaining life of the vehicle battery for learning as a label for each divided partial time-series data.