WO2023026439A1 - Système de gestion de la santé d'animaux - Google Patents

Système de gestion de la santé d'animaux Download PDF

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Publication number
WO2023026439A1
WO2023026439A1 PCT/JP2021/031395 JP2021031395W WO2023026439A1 WO 2023026439 A1 WO2023026439 A1 WO 2023026439A1 JP 2021031395 W JP2021031395 W JP 2021031395W WO 2023026439 A1 WO2023026439 A1 WO 2023026439A1
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WO
WIPO (PCT)
Prior art keywords
animal
learning model
answer
management system
question
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Application number
PCT/JP2021/031395
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English (en)
Japanese (ja)
Inventor
純 岡崎
Original Assignee
株式会社Peco
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 株式会社Peco filed Critical 株式会社Peco
Priority to JP2022504232A priority Critical patent/JP7049030B1/ja
Priority to PCT/JP2021/031395 priority patent/WO2023026439A1/fr
Priority to JP2022042617A priority patent/JP2023033080A/ja
Publication of WO2023026439A1 publication Critical patent/WO2023026439A1/fr

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    • A—HUMAN NECESSITIES
    • A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
    • A01K—ANIMAL HUSBANDRY; AVICULTURE; APICULTURE; PISCICULTURE; FISHING; REARING OR BREEDING ANIMALS, NOT OTHERWISE PROVIDED FOR; NEW BREEDS OF ANIMALS
    • A01K29/00—Other apparatus for animal husbandry
    • A—HUMAN NECESSITIES
    • A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
    • A01K—ANIMAL HUSBANDRY; AVICULTURE; APICULTURE; PISCICULTURE; FISHING; REARING OR BREEDING ANIMALS, NOT OTHERWISE PROVIDED FOR; NEW BREEDS OF ANIMALS
    • A01K67/00—Rearing or breeding animals, not otherwise provided for; New or modified breeds of animals
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A40/00—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production
    • Y02A40/70—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production in livestock or poultry

Definitions

  • the present invention relates to an animal health management system.
  • the present invention was made in view of such a background, and aims to provide a technology that allows breeders to quickly ascertain the health condition of animals.
  • the main invention of the present invention for solving the above problems is a system for managing the health of animals, wherein the answer to a first question related to the health condition of the animal is used as input data, and a veterinary medical worker designates A learning model storage unit that stores a learning model created by machine learning using a second question to be asked after the first question as teacher data, and an inquiry transmission unit that transmits the first question to the owner terminal , an answer receiving unit that receives the answer to the first question from the owner terminal, and an inference unit that gives the answer to the learning model to ask for the second question.
  • the breeder can grasp the health condition of the animal at an early stage.
  • FIG. 3 is a diagram showing an example hardware configuration of a management server 20;
  • FIG. 3 is a diagram showing an example of software configuration of a management server 20;
  • FIG. It is a figure explaining operation
  • a system for managing animal health comprising: It was created by machine learning using the answers to the first question related to the health condition of the animal as input data and the second question to be asked after the first question specified by the veterinarian as teacher data.
  • An animal health management system comprising: [Item 2] The animal health management system according to item 1, The learning model storage unit stores the learning model for each animal species, the reasoning unit providing the answer to the learning model corresponding to the animal species of the animal; An animal health management system characterized by: [Item 3] The animal health management system according to item 2, The learning model storage unit stores the learning model in association with at least one of animal species, breed, body weight, body weight rank, age and generation, the reasoning unit providing the answer to the learning model corresponding to the animal species of the animal and at least one of the breed, weight, weight rank, age, and age group of the animal; An animal health management system characterized by: [Item 4] The animal health management system according to item 1, The learning model is learned using the answers and animal species as the input data, the reasoning
  • a periodic medical examination system according to an embodiment of the present invention will be described below.
  • the regular check-up system of this embodiment is intended to support regular check-ups of animals.
  • the veterinary institution side periodically asks the owner of the animal such as a pet to determine the health condition of the animal, especially the necessity of visiting the hospital (triage). It is.
  • AI can be used to automatically determine the content of the medical interview.
  • the AI can be, for example, a classifier that determines which of the pre-prepared questions to ask next.
  • the AI can use a learning model trained on questions manually determined by a veterinarian, such as a veterinarian.
  • a learning model is created for each animal species, but it is also possible to provide animal species as input data for AI learning.
  • an owner can give an image of a specific part of an animal as input data.
  • FIG. 1 is a diagram showing an example of the overall configuration of the regular medical examination system of this embodiment.
  • the regular check-up system of this embodiment includes a management server 20 .
  • the management server 20 is communicably connected to each of the owner terminal 10 and the medical institution terminal 30 via the communication network 40 .
  • the communication network 40 is, for example, the Internet, and is constructed by a public telephone line network, a mobile telephone line network, a wireless communication path, Ethernet (registered trademark), or the like.
  • the owner terminal 10 is a computer operated by an animal caregiver (owner or person taking care of the animal).
  • Owner terminal 10 is, for example, a smart phone, a tablet computer, a personal computer, or the like. It is assumed that the owner terminal 10 has a camera (not shown) and is capable of taking pictures.
  • the medical institution terminal 30 is a computer operated by veterinary medical personnel (such as nurses and doctors at veterinary hospitals) at veterinary medical institutions.
  • Medical institution terminal 30 is, for example, a tablet computer.
  • the medical institution terminal 30 can be, for example, any computer such as a smart phone or a personal computer.
  • the management server 20 is a computer that conducts regular medical interviews and determines whether animal patients need to visit the hospital.
  • the management server 20 may be a general-purpose computer such as a workstation or personal computer, or a virtual computer logically implemented by cloud computing.
  • FIG. 2 is a diagram showing a hardware configuration example of the management server 20. As shown in FIG. Note that the illustrated configuration is an example, and other configurations may be employed.
  • the management server 2 includes a CPU 201 , a memory 202 , a storage device 203 , a communication interface 204 , an input device 205 and an output device 206 .
  • the storage device 203 is, for example, a hard disk drive, solid state drive, flash memory, etc., which stores various data and programs.
  • the communication interface 204 is an interface for connecting to the communication network 3, and includes, for example, an adapter for connecting to Ethernet (registered trademark), a modem for connecting to a public telephone network, and a wireless communication device for performing wireless communication.
  • the input device 205 is, for example, a keyboard, mouse, touch panel, button, microphone, etc. for inputting data.
  • the output device 206 is, for example, a display, printer, speaker, or the like that outputs data.
  • Each functional unit of the management server 20, which will be described later, is implemented by the CPU 201 reading a program stored in the storage device 203 into the memory 202 and executing it. is implemented as part of the storage provided by
  • FIG. 3 is a diagram showing a software configuration example of the management server 20.
  • the management server 20 includes an inquiry transmission unit 211, an answer reception unit 212, a hospital visit necessity transmission unit 214, a hospital visit necessity acquisition unit 215, a learning processing unit 216, an owner information storage unit 231, an animal information storage unit 232, and a question storage unit 233. , an inquiry storage unit 234 , an answer history storage unit 235 , a triage history storage unit 236 , and a learning model storage unit 237 .
  • the owner information storage unit 231 stores information (hereinafter referred to as owner information) regarding the owner of an animal (patient animal) undergoing veterinary medical care.
  • the owner information includes an owner ID that identifies the owner, name, address, and contact information. Information about the owner other than name, address and contact information may be included.
  • the animal information storage unit 232 stores information about patient animals (hereinafter referred to as animal information).
  • the animal information includes the name, species, and breed of the patient animal in association with an owner ID indicating the owner of the patient animal and an animal ID identifying the patient animal.
  • Animal information can also include age, weight and preventive history. The age may be calculated by including the date of birth instead of the age.
  • body weight and preventive history (whether or not vaccination such as vaccines and medicines have been taken within a predetermined period) can be obtained, for example, from the medical record information related to medical care and examination of the patient animal. can.
  • the management server 20 can periodically access a chart server that manages chart information, obtain body weight and preventive history, and update animal information.
  • the animal information can include various attributes of the patient animal.
  • the question storage unit 233 stores information related to questions about the animal's health condition (hereinafter referred to as question information).
  • the question information can include the content of the question in association with the question ID specifying the question and the animal species to which the question corresponds.
  • the content of the questions includes questions to be answered in free text, questions to be answered by selecting one or more from two or three or more choices, and answers with values (real numbers or integers) within a predetermined range. It can include questions such as scheduling questions.
  • the questions can be text data, for example, and can include images and sounds.
  • the question includes a question to be answered with an image of a specific site.
  • the medical inquiry storage unit 234 stores the transmitted information regarding one medical inquiry (hereinafter referred to as medical inquiry information).
  • the medical inquiry information includes, in association with the medical inquiry ID identifying the medical inquiry, the date and time when the medical inquiry was sent, the animal ID indicating the animal to be medically examined, and the question ID indicating the question to be included in the medical inquiry.
  • a plurality of question IDs may be set for one inquiry ID.
  • the answer history storage unit 235 stores the history of answers from the owner to questions related to medical interviews.
  • the answer history stored by the answer history storage unit 235 includes a question ID indicating a question and an answer to the question in association with an interview ID, an animal ID, and the date and time when the interview was conducted (or the answer was received). be It should be noted that sets of question IDs and answers corresponding to the number of questions can be registered for one inquiry ID.
  • the triage history storage unit 236 stores a history (hereinafter referred to as triage history).
  • the diagnosis history includes an interview ID, necessity of visiting the hospital, actions, and comments. Whether a visit to the hospital is necessary or not is either necessary or unnecessary.
  • the action is intervention information for the animal, and is information indicating actions that the animal should take, such as "Let's take a walk" and "Let's feed”. Action options can be preconfigured. The action may include that a hospital visit is required.
  • the comment is a comment on the interview result by the veterinarian.
  • a comment can be text data, for example. Comments may include possible illnesses.
  • the learning model storage unit 237 stores a learning model for determining the next interview content (questions to be included in the interview) based on the interview results.
  • the learning model storage unit 237 can store learning models in association with animal species.
  • the animal species may be given as input data (feature amount) of the learning model.
  • the learning model storage unit 237 can also store learning models in association with animal species and breeds. For example, even for the same dog, the question to be asked may differ between a large dog and a small dog. Therefore, learning models can be stored in association with weight ranks (ranges determined by maximum and minimum) in addition to or in place of animal species and breeds. In addition to or in place of at least one of animal species and breeds and body weight, learning models can also be stored in association with ages (age ranges).
  • the animal species and breed may be given as input data (feature amounts) of the learning model.
  • at least one of weight, weight rank, age, and generation may be included as a feature value to be given to the learning model.
  • past interview results can be included as a feature quantity to be given to the learning model.
  • a time lag time information indicating how long ago the interview was or how many days ago from the present time
  • Questions to ask next determined by veterinarians can be employed as training data provided to train the learning model. That is, it is possible to use machine learning to learn the content of the next question to be asked according to the answers to the past questions, which were determined by the veterinary medical staff, and use it as a learning model.
  • the learning model is created and updated by the learning processing unit 216, which will be described later.
  • the inquiry transmission unit 211 periodically transmits an inquiry to the owner terminal 10.
  • the inquiry sending unit 211 can create one inquiry by selecting one or a plurality of questions stored in the question storage unit 233 corresponding to the animal species of the patient animal.
  • the medical inquiry sending unit 211 can, for example, randomly select a predetermined number of questions that satisfy the presentation conditions from among the questions corresponding to the animal species of the patient animal.
  • the medical inquiry sending unit 211 may select a question according to a predetermined rule set in advance, for example.
  • the answer receiving unit 212 receives answers to each medical interview question from the owner terminal 10 .
  • the answer receiving unit 212 can accept uploading of an image when the question includes a question to be answered with an image of a specific part.
  • the reply receiving section 212 can register the received reply in the reply history storage section 235 .
  • the hospital visit necessity acquisition unit 215 can send a message to the medical institution terminal 30 requesting a determination of the necessity of a hospital visit.
  • the hospital visit necessity determination unit 213 can include the result of medical examination (answers to questions, that is, selection results of options, images, etc.) in a message and transmit it to the medical institution terminal 30 .
  • the medical institution terminal 30 can output the medical interview result and accept input from medical personnel such as a veterinarian as to whether or not a visit to the hospital is necessary. In the medical institution terminal 30, it is also possible to input intervention information (actions) and comments for the animal according to the interview results.
  • the hospital visit necessity acquisition unit 215 can create a triage history including the hospital visit necessity acquired from the medical institution terminal 30 and an interview ID indicating an interview corresponding to the interview result, and register the triage history in the triage history storage unit 236. can. Further, when actions and comments are transmitted from the medical institution terminal 30, the actions and comments can be included in the triage history.
  • the hospital visit necessity transmission unit 214 transmits to the owner terminal 10 the necessity of a hospital visit determined based on the results of the interview.
  • the learning processing unit 216 creates and updates a learning model for determining the inquiry content. For example, if the interview storage unit 234 stores an interview prepared by a veterinarian, the learning processing unit 216 reads out the interview content (question information) corresponding to each animal species. As input data, machine learning is performed using the question ID corresponding to the inquiry sent next to the inquiry (the oldest date and time among the inquiry information with the same animal ID and later date and time) as teacher data. A learning model created or updated through learning can be registered in the learning model storage unit 237 . Since a medical interview can include a plurality of questions, learning can be performed using each of the questions included in the following medical interview after a certain medical interview as teacher data.
  • the learning processing unit 216 can update the learning model periodically or at timing determined by a predetermined rule. In this case, the learning processing unit 216 can transmit the content of the interview and its answer to the medical institution terminal 30, receive input of the content of the next question (question ID) to be asked from the veterinary medical staff, and input the answer. Machine learning can be performed using the question ID input by the veterinarian as teacher data to update the learning model.
  • FIG. 4 is a diagram for explaining the operation of the periodic medical examination system of this embodiment.
  • the management server 20 creates an inquiry (S301).
  • the medical interview can be prepared by reading out a predetermined number of questions that correspond to the animal species of the patient animal and satisfy the provision conditions among the questions stored in the question storage unit 233 .
  • the management server 20 transmits the created medical interview to the owner terminal 10 (S302).
  • An interview is displayed on the owner terminal 10.
  • the medical interview may be conducted, for example, in a chat format, or may be conducted using a form on a web page.
  • a camera included in the owner terminal 10 can be activated to obtain an image captured by the camera as an answer.
  • the owner terminal 10 transmits the inputted answer to the management server 20 .
  • the management server 20 can receive a response from the owner terminal 10 (S303) and register the received response in the response history storage unit 235 (S304).
  • the management server 20 transmits a message with an answer set to the medical institution terminal 30, and inquires whether or not it is necessary to visit the hospital (S305).
  • the medical institution terminal 30 receives an input of whether or not to visit the hospital from the medical personnel such as a veterinarian, and transmits the input to the management server 20.
  • the management server 20 receives the necessity of visiting the hospital from the medical institution terminal 30, and determines whether or not to visit the hospital.
  • the information is registered in the triage history storage unit 236 (S306), and the necessity of visiting the hospital is transmitted to the owner terminal 10 (S307).
  • the management server 20 periodically repeats the processing from S301 until the period for sending periodic medical interviews ends (S308: NO). Before proceeding to S301, the management server 20 can give the answer to the medical inquiry to the learning model corresponding to the animal species, determine the question to be sent for the next medical inquiry, and register it in the medical inquiry storage unit 234 (S309).
  • the medical inquiry information may not set the date and time, or may set the date and time when the next medical inquiry is scheduled to be sent.
  • the veterinary institution can proactively send medical interviews to the breeders of patient animals periodically to confirm the health condition of the patient animals.
  • AI learning model
  • AI can be used to automatically determine the content of the next inquiry based on the result of the inquiry.
  • a learning model is created for each animal species, but one or more learning models may be used as a whole, and the animal species may be given as a feature amount.
  • the learning model can include an image captured by the owner terminal 10 in the feature amount of the learning model.
  • the learning model can include an image captured by the owner terminal 10 in the feature amount of the learning model.
  • test results at veterinary medical institutions may be given as input data (feature values) for the learning model.
  • the medical chart information is managed by the management server 20, or the medical chart server that manages the medical chart information is accessed to obtain the test results included in the medical chart information. can.
  • the time lag can be used as input data together with the inspection results.
  • the learning model is a classifier that determines the question to be sent next from among the given questions. or a transformer.
  • a classifier for determining the necessity of visiting the hospital can be stored in the learning model storage unit for each animal species.
  • a classifier using animal species as input data (feature amount) may be stored.
  • the input data (feature amount) of the hospital visit necessity classifier can be an answer to an interview.

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  • Life Sciences & Earth Sciences (AREA)
  • Environmental Sciences (AREA)
  • Animal Husbandry (AREA)
  • Biodiversity & Conservation Biology (AREA)
  • Animal Behavior & Ethology (AREA)
  • Zoology (AREA)
  • Medical Treatment And Welfare Office Work (AREA)

Abstract

Le problème décrit par la présente invention est de permettre à un propriétaire de reconnaître précocement l'état de santé d'un animal. La solution selon l'invention porte sur un système de gestion de la santé animale caractérisé en ce qu'il comprend : une unité de stockage de modèle d'apprentissage stockant un modèle d'apprentissage créé par apprentissage automatique à l'aide d'une réponse à une première question associée à l'état de santé d'un animal en tant que données d'entrée et une seconde question à demander après la première question spécifiée par un agent vétérinaire en tant que données d'enseignant ; une unité de soumission de questionnaire soumettant la première question à un terminal de propriétaire ; une unité de réception de réponse recevant une réponse à la première question provenant du terminal de propriétaire ; et une unité d'inférence fournissant la réponse au modèle d'apprentissage pour obtenir la seconde question.
PCT/JP2021/031395 2021-08-26 2021-08-26 Système de gestion de la santé d'animaux WO2023026439A1 (fr)

Priority Applications (3)

Application Number Priority Date Filing Date Title
JP2022504232A JP7049030B1 (ja) 2021-08-26 2021-08-26 動物健康管理システム
PCT/JP2021/031395 WO2023026439A1 (fr) 2021-08-26 2021-08-26 Système de gestion de la santé d'animaux
JP2022042617A JP2023033080A (ja) 2021-08-26 2022-03-17 動物健康管理システム

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PCT/JP2021/031395 WO2023026439A1 (fr) 2021-08-26 2021-08-26 Système de gestion de la santé d'animaux

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JP7220822B1 (ja) 2022-03-23 2023-02-10 大塚製薬株式会社 プログラム、情報処理装置、及び情報処理方法
JP7152822B1 (ja) 2022-04-26 2022-10-13 株式会社Emyu ペット診療相談支援システム、ペット診療相談支援方法およびプログラム

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2002132918A (ja) * 2000-10-26 2002-05-10 Matsushita Electric Ind Co Ltd ペット情報管理システム
JP2003242248A (ja) * 2002-02-18 2003-08-29 Ip Corporation:Kk 問診判定方法及び問診判定システム
JP2019079503A (ja) * 2017-10-26 2019-05-23 株式会社メディアコンテンツファクトリー 問診システム及びそのプログラム

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2002132918A (ja) * 2000-10-26 2002-05-10 Matsushita Electric Ind Co Ltd ペット情報管理システム
JP2003242248A (ja) * 2002-02-18 2003-08-29 Ip Corporation:Kk 問診判定方法及び問診判定システム
JP2019079503A (ja) * 2017-10-26 2019-05-23 株式会社メディアコンテンツファクトリー 問診システム及びそのプログラム

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
ARITA RYUTARO, TETSUHIRO YOSHINO, YUKO HORIBA, HIROAKI HIKIAMI, YUTAKA SHIMADA, TAKAO NAMIKI, EIICHI TAHARA, KIYOSHI MINAMIZAWA, : "Problem Extraction of Browser-Based Questionnaire System and its Solution for a Patient-Centered System", KAMPO MED., vol. 69, no. 1, 1 January 2018 (2018-01-01), pages 82 - 90, XP093039893, DOI: 10.3937/kampomed.69.82 *

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JP2023033080A (ja) 2023-03-09
JPWO2023026439A1 (fr) 2023-03-02

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