The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Jun. 11, 2024
Filed:
Nov. 08, 2019
Nippon Telegraph and Telephone Corporation, Tokyo, JP;
Sunyong Kim, Musashino, JP;
Ippei Shake, Musashino, JP;
Kazuaki Obana, Musashino, JP;
Atsuhiko Maeda, Musashino, JP;
Michiharu Takemoto, Musashino, JP;
Yukio Kikuya, Musashino, JP;
Hiroshi Sato, Musashino, JP;
Tetsuo Kawano, Musashino, JP;
Kenichi Fukuda, Tokyo, JP;
NIPPON TELEGRAPH AND TELEPHONE CORPORATION, Tokyo, JP;
Abstract
Provided is a technique for efficiently predicting the number (quantity) of occurrences of emergency medical service requests in a target area. An emergency medical service demand prediction device according to an embodiment obtains actual history data including information about dates and times of occurrences of emergency medical service requests, information about positions of the occurrences of the emergency medical service requests, and information about illnesses and injuries that caused the emergency medical service requests; the device generates a first learning model that receives an input of first learning-purpose data generated on a basis of learning-purpose actual history data and outputs illness/injury groups; the device generates a second learning model that receives an input of second learning-purpose data generated on a basis of the learning-purpose actual history data and the illness/injury groups output from the first learning model and outputs a value indicating a quantity of occurrences of emergency medical service requests for each unit area; and the device predicts a quantity of occurrences of emergency medical service requests in each unit area, by inputting, to the second learning model having been trained, prediction-purpose data generated on a basis of prediction-purpose actual history data and the illness/injury groups output from the first learning model having been trained.