Patent Application
20200381114
The present invention relates to an assistance system, an assistance method, and an assistance program, which assist a medical examination performed by a health care worker.
In recent years, Japan has entered a super-aging society, and there is growing concern about insufficient health care workers and poor medical care quality. Therefore, in order to achieve an efficient medical care and to improve medical care quality, regional medical cooperation has been promoted in treating a patient in cooperation with a plurality of medical institutions.
For example, PTL 1 below discloses a regional medical cooperation system that assists patient introduction between the medical institutions.
PTL 1: Pamphlet of International Publication No. 2014/097466
One of major problems in an aging society is rising medical expenses as social security expenses. There are various reasons for the rising medical expenses, such as expensive therapeutic drug launching and an advanced medical care. However, in particular, excessive medical examinations requested by elderly persons are regarded as one of the problems.
For example, the elderly persons are difficult to determine their own health conditions in a case where they are ill. Accordingly, the elderly persons actively visit medical institutions such as hospitals. In addition, some of the elderly persons, even though they are aware that a medical examination at the medical institution is actually unnecessary, in order to ease psychological loneliness, often visit the medical institution serving as a community where many elderly persons stay. As a result, there are problems such as increasing medical expenses, increasing workloads of health care worker such as doctors, and excessive medicine prescriptions.
On the other hand, while the health care worker recognizes that the elderly person does not need a prescription, there is a possibility that the health care worker may prescribe the medicines such as drugs for the elderly person who visits the hospital.
As described above, a healthy elderly person receives the medical examinations such as “first-of-all medical treatment”, “self-comfort medical treatment”, and “unnecessary medical treatment”. Accordingly, these behaviors lead to the “excessive medicine prescriptions”. As a result, there is a problem of the rising medical expenses.
The present invention is made in view of the above-described circumstances, and an object thereof is to provide an assistance system, an assistance method, and an assistance program, which contribute to reduced medical expenses.
According to the present invention, in order to achieve the above-described object, there is provided an assistance system for assisting a medical examination performed by a health care worker. The assistance system includes a data acquisition unit that acquires medical examination scheduled person data relating to a medical examination scheduled person having a scheduled medical examination in a medical institution, visit data relating to a visit history of the medical examination scheduled person who visits the medical institution, and medical examination data relating to a medical examination content of the medical examination scheduled person who received a medical examination in the medical institution in the past, a learning unit that performs machine learning by using the medical examination scheduled person data, the visit data, and the medical examination data, and a presentation unit that presents whether or not the medical examination is required for the medical examination scheduled person, based on a result of the machine learning.
According to the present invention, in order to achieve the above-described object, there is provided an assistance method for assisting a medical examination performed by a health care worker. The assistance method includes a data acquisition step of acquiring medical examination scheduled person data relating to a medical examination scheduled person having a scheduled medical examination in a medical institution, visit data relating to a visit history of the medical examination scheduled person who visits the medical institution, and medical examination data relating to a medical examination content of the medical examination scheduled person who received a medical examination in the medical institution in the past, a learning step of performing machine learning by using the medical examination scheduled person data, the visit data, and the medical examination data, and a presentation step of presenting whether or not the medical examination is required for the medical examination scheduled person, based on a result of the machine learning.
According to the present invention, in order to achieve the above-described object, there is provided an assistance program that causes a computer to execute a process for assisting a medical examination performed by a health care worker. The process includes a data acquisition step of acquiring medical examination scheduled person data relating to a medical examination scheduled person having a scheduled medical examination in a medical institution, visit data relating to a visit history of the medical examination scheduled person who visits the medical institution, and medical examination data relating to a medical examination content of the medical examination scheduled person who received a medical examination in the medical institution in the past, a learning step of performing machine learning by using the medical examination scheduled person data, the visit data, and the medical examination data, and a presentation step of presenting whether or not the medical examination is required for the medical examination scheduled person, based on a result of the machine learning.
According to the present invention, whether or not a medical examination performed by a health care worker is required for a medical examination scheduled person is presented, based on a result of machine learning. The health care worker can refer to a presented content to avoid the medical examination for the medical examination scheduled person who does not need the medical examination. As a result, it is possible to prevent increasing workloads of the health care worker and to prevent medicines from being excessively prescribed for elderly persons who visit a medical institution. Therefore, medical expenses can be effectively reduced.
Hereinafter, an embodiment according to the present invention will be described with reference to the accompanying drawings. In the description of the drawings, the same reference numerals will be assigned to the same elements, and repeated description will be omitted. In addition, dimensional ratios in the drawings are exaggerated for convenience of the description, and may be different from actual ratios in some cases.
As illustrated in
As illustrated in
In the present embodiment, the assistance system 100 is configured to include an interactive device capable of communicating with a person through a dialog. As the interactive device, for example, a robot equipped with an AI and having an interactive function can be used. For example, the interactive device can be equipped with a display capable of displaying a still image or a moving image, a speaker capable of outputting sound or music, and a camera function capable of capturing a still image or a moving image. Although not particularly limited, an exterior design of the interactive robot can include, for example, a humanoid type and an animal type.
Hereinafter, the assistance system 100 will be described in detail.
The hardware configuration of the assistance system 100 will be described.
Although not particularly limited, the assistance system 100 can be configured to include, for example, a mainframe or a computer cluster. As illustrated in
The CPU 110 controls each unit, and performs various arithmetic processes in accordance with various programs stored in the storage unit 120.
The storage unit 120 is configured to include a read only memory (ROM) for storing various programs or various data items, a random access memory (RAM) for temporarily storing programs or data as a work region, and a hard disk for storing various programs including an operating system or various data items.
The input-output I/F 130 is an interface for connecting input devices such as a keyboard, a mouse, a scanner, and a microphone and output devices such as a display, a speaker, and a printer.
The communication unit 140 is an interface for communicating with the medical institution terminal 200 and the medical examinee terminal 300.
Next, a main function of the assistance system 100 will be described.
The storage unit 120 stores various data such as medical examination scheduled person data D1, visit data D2, medical examination data D3, and other data D4. In addition, the storage unit 120 stores an assistance program for providing an assistance method according to the present embodiment.
As illustrated in
The data acquisition unit 111 will be described.
The data acquisition unit 111 acquires the medical examination scheduled person data D1, the visit data D2, the medical examination data D3, and other data D4.
As illustrated in
For example, the medical examination scheduled person data D1 can include data relating to genetic information of the medical examination scheduled person. The genetic information may include not only genetic information on the medical examination scheduled person but also genetic information of relatives. For example, the genetic information can be configured to include a DNA test result. For example, when a disease of the medical examination scheduled person is determined, the genetic information can be used to determine whether the disease is strongly affected by genetic factors.
The medical examination scheduled person data D1, the visit data D2, and the medical examination data D3 are stored in the storage unit 120 in a state of being associated with each medical examination scheduled person. In addition, each of the data D1, D2, and D3 can be stored and managed using a known electronic medical record, for example.
As illustrated in
For example, the data acquisition unit 111 acquires the medical examination scheduled person data D1, the visit data D2, and the medical examination data D3 from the medical institution terminal 200 of each medical institution and the medical examinee terminal 300 of each medical examination scheduled person.
The other data D4 which is an acquisition target of the data acquisition unit 111 can include the regional data D41 illustrated in
As illustrated in
As illustrated in
For example, the data acquisition unit 111 can acquire the regional data D41 and the weather data D42 from the Internet.
As illustrated in
In addition, for example, the medical institution data D43 can include data relating to a congestion status of the medical institution. For example, the data relating to the congestion status includes a congestion status (outpatient congestion status or hospital admission congestion status) of the medical institution located within a prescribed range from a home of the medical examination scheduled person. For example, when the medical examination scheduled person visits a prescribed medical institution, the assistance system 100 can provide information (timetables or transit guidance) on the most suitable transportation system for the medical examination scheduled person, based on data relating to the traffic information or data relating to the congestion status, can recommend a doctor having excellent therapeutic outcomes for a specific disease, or can present the medical institution for which the doctor works. In addition, the assistance system 100 may automatically present the medical institution with the transportation system, and may automatically reserve the medical examination in accordance with an arrival time at the medical institution.
In addition, for example, the other data D4 can include reuse data relating to the medical devices and the medicines. For example, the reuse data includes information relating to whether the medical devices can be reused by performing cleaning or sterilization. For example, as the above-described medical devices, single-use medical devices may be used, but medical devices (some configuration components of the medical devices) other than the single-use medical devices also may be used. In addition, for example, the reuse data can include information relating to surplus medicines. The information relating to the surplus medicines includes information relating to whether a drug (for example, a liquid drug) stored in a predetermined amount in a container such as a bottle can be used for the plurality of medical examination scheduled persons. For example, the drug can be treated as a reusable drug in a case where the drug stored in a specific container can be administered to the medical examination scheduled person and the drug stored in a similar container can be administered to another medical examination scheduled person.
For example, the reuse data can be acquired on a real-time basis from a hospital information system of the medical institution that owns the medical devices and the medicine which are reuse targets.
For example, the data acquisition unit 111 can acquire medical data as other information useful for assisting the health care worker. For example, the medical data includes data relating to medical knowledge, which includes disease data relating to diseases (disease name, symptoms, and whether receiving the treatment is required), treatment data relating to treatment (treatment method, time required for the treatment, required facilities and drugs, and wholesale prices thereof), and data relating to a medical insurance system. For example, the data acquisition unit 111 can acquire the medical data from the Internet, or can acquire the medical data from electronic data of medical specialty books captured by a scanner or the like.
Next, the learning unit 112 will be described.
The learning unit 112 performs machine learning by using the medical examination scheduled person data D1, the visit data D2, the medical examination data D3, and other data D4. In the description herein, the “machine learning” means analyzing input data by using an algorithm, extracting useful rules and criteria from an analysis result thereof, and developing the algorithm.
The assistance system 100 according to the present embodiment presents whether or not the medical examination performed by the health care worker is required, and also presents prescription conditions of the medicines. Based on each data described above, the assistance system 100 performs the machine learning so that the presentation contents do not become invalid. The learning unit 112 performs the machine learning. In this manner, the assistance system 100 predicts current and future dynamic states of the medical examination scheduled person from past dynamic states of the medical examination scheduled person (frequency of visits to the medical institution, contents of the medical examination, results of the medical examination, prescriptions of the medicines, and usages of the medicines). The assistance system 100 proposes suitable countermeasures to the health care worker, based on a prediction result. For example, the learning unit 112 can learn the prescription condition of preferable medicines, based on the medical institution prescription data D31 and/or the pharmacy prescription data D32 acquired from a plurality of persons.
Specifically, in a case where the medical examination scheduled person who visits the medical institution or the medical examination scheduled person before visiting the medical institution requests for the medical examination, the presentation unit 113 presents whether or not the medical examination is required to the health care worker, based on a result of the machine learning of the learning unit 112. In addition, the presentation unit 113 also presents the prescription conditions of the medicines prescribed by the health care worker for the medical examination scheduled person. Here, for example, the prescription condition includes determining whether or not the prescription of the medicine is required, and specifying the type, the prescription dose, the usage, the dosage form of the drugs. In addition, as an example of the presentation performed by the presentation unit 113, for example, based on the medical institution prescription data D31 and/or the pharmacy prescription data D32 acquired from a plurality of persons, the presentation unit 113 may present sharing the surplus medicine within one household (for example, a married couple or parent and child). The presentation unit 113 may present using the medicine of someone who no longer needs to take the medicine for some reason for another person in a predetermined population. Alternatively, the presentation unit 113 may present the persons having the prescription of the same medicine to jointly purchase the medicine so as to reduce the purchasing costs.
When presenting whether or not the medical examination performed by the health care worker is required and the prescription conditions of the medicines, the presentation unit 113 presents the presentation contents and the presentation basis that leads to the presentation. For example, in the present embodiment, as will be described later, in a case where it is determined that the medical examination performed by the health care worker is not required, the basis is presented, based on each data. In a case where a plurality of bases are presented, the plurality of bases can be presented. The health care worker can satisfactorily adopt the respective presentation contents by being presented whether or not the medical examination performed by the health care worker is required and the prescription conditions of the medicines together with the basis. As a method of presenting the basis, for example, a relationship between data items may be displayed using a graph or a table, or an event serving as a factor of the basis may be specifically displayed together with a numerical value such as a contribution ratio.
In the present embodiment, the presentation unit 113 performs the presentation in a case where the health care worker or the medical examination scheduled person requests for the presentation. However, timing for the presentation by the presentation unit 113 is not particularly limited. For example, the presentation unit 113 may automatically acquire the data on an irregular or regular basis. Even if the health care worker or the medical examination scheduled person does not request for the presentation, in a case where it is predicted that the medical examination scheduled person visits the medical institution, the presentation unit 113 may automatically present a suitable countermeasure policy for the medical examination scheduled person to the medical institution, or the health care worker. In addition, for example, the presentation unit 113 may acquire the data relating to the dynamic state of the medical examination scheduled person on an irregular or regular basis, and may present future predictions of the treatment policy to the medical examination scheduled person who is predicted to visit the medical institution.
Referring to
The algorithm of the machine learning is generally classified into supervised learning, unsupervised learning, and reinforcement learning. In the algorithm of the supervised learning, a set of input data and result data is provided for the learning unit 112 to perform the machine learning. In the algorithm of the unsupervised learning, only the input data is provided in a large amount for the learning unit 112 to perform the machine learning. In the algorithm of the reinforcement learning, an environment is changed, based on a solution output by the algorithm, and a correction is added, based on a reward indicating how correct the output solution is. The algorithm of the machine learning of the learning unit 112 may be any one of the supervised learning, the unsupervised learning, and the reinforcement learning. In the present embodiment, a case where the learning unit 112 performs the machine learning by using the algorithm of the supervised learning will be described as an example.
First, the data acquisition step (S1) will be described.
In the data acquisition step (S1), the data acquisition unit 111 acquires the medical examination scheduled person data D1, the visit data D2, the medical examination data D3, and other data D4, and stores the data in the storage unit 120. The timing for the data acquisition unit 111 to acquire the medical examination scheduled person data D1, the visit data D2, the medical examination data D3, and other data D4 is not particularly limited. For example, the data may be acquired every predetermined time, or may be acquired at the timing when the data is changed. The data acquisition unit 111 acquires the medical examination scheduled person data D1, the visit data D2, the medical examination data D3, and other data D4 over a predetermined period, and stores the data in the storage unit 120. Therefore, a large amount of the input data and the solution data for performing the supervised learning are stored in the storage unit 120.
For example, in the present embodiment, when the medical examination scheduled person visits the medical institution, the medical institution acquires and confirms each data of the medical examination scheduled person (the medical examination scheduled person data D1, the visit data D2, and the medical examination data D3) inside or outside a predetermined region, based on a medical examination voucher, a health insurance card, shared data of the regional medical care using electronic medical records, and an individual number. In addition, at this time, countermeasures to the medical examination scheduled person are dealt with by one or more interactive devices, and hearing of a testimony relating to the medical examination is received from the medical examination scheduled person. A result of the hearing is used together with each data in the learning step (to be described later).
A method of acquiring information from the medical examination scheduled person is not limited to a method of acquiring linguistic information through the hearing as described above. For example, the assistance system 100 may acquire biological information. For example, the method of acquiring the biological information includes a method of acquiring a body temperature or oxygen saturation by using infrared rays, and a method of acquiring a degree of progression of arteriosclerosis by measuring pulse waves of peripheral blood vessels. In addition, the assistance system 100 may acquire information relating to a reaction of the medical examination scheduled person during the hearing (the degree of facial redness tide or motor function) via the interactive device. In addition, the assistance system 100 can be provided with the algorithm that determines behavior authenticity of the medical examination scheduled person, based on the information obtained using the hearing and the above-described respective methods, and that confirms validity of each of the information obtained from the medical examination scheduled person.
The information can be acquired from the medical examination scheduled person only by the interactive device included in the assistance system 100. However, for example, the information may be acquired by a person (health care worker), or may be acquired by both the interactive device and the person. For example, regarding an item of which information processing is not smoothly performed using the interactive device alone, the person communicates with the medical examination scheduled person through the interactive device, and inputs the acquired information. In this manner, the information can be more accurately and smoothly acquired from the medical examination scheduled person.
Next, the learning step (S2) will be described.
In the learning step (S2), the learning unit 112 applies the algorithm of the supervised learning to a large data set stored in the storage unit 120. The algorithm of the supervised learning is not particularly limited. However, for example, known algorithms such as a least squares method, a linear regression, an autoregression, and a neural network can be used.
Based on the acquired data, the learning unit 112 predicts current and future dynamic states relating to the visit of the medical examination scheduled person to the medical institution. In addition, referring to the above-described hearing result and predicted result, presentation of whether or not the medical examination by the health care worker is required, and that of the prescription dynamic states of the medicines are performed.
In addition, with regard to the medical device used for the surgery or the treatment, the learning unit 112 can perform the machine learning on information useful for determining the reuse of the medical device, based on the information regarding whether or not the medical device can be reused, which method (cleaning or sterilization method) enables the medical device to be reused in a case where the medical device can be reused, and which configuration member of the medical device can be reused. In addition, with regard to the medicine used for the surgery or the medical treatment, the learning unit 113 can perform the machine learning on the information useful for determining the reuse of the medicine, based on the information regarding whether or not the medicine can be reused and which method (storage method of the medicine or method of providing the medicine for the medical examination scheduled person) enables the medicine to be reused in a case where the medicine can be reused. The presentation unit 113 can provide the medical institution with the information relating to the reuse of the medical device or the medicine by presenting a learning result of the above-described machine learning. The medical institution can effectively reduce medical expenses in such a way that the learning result relating to the reuse is acquired from or shared with one specific medical institution or a plurality of the medical institutions.
Next, the presentation step (S3) will be described.
For example, as illustrated in
An example of the presentation content and the presentation basis will be described with reference to
For example, in a case where it is determined as the presentation content that the medical examination performed by the doctor is not required, a main reason leading to the determination result is displayed as the presentation basis. In addition, with regard to whether or not the medical examination is required and the prescription conditions of the medicines, the determination result is displayed as the presentation content.
As illustrated in
As illustrated in
In addition, as illustrated in
For example, in a case where the presentation unit 113 presents that the medical examination performed by the health care worker is not required, the presentation unit 113 may present a method other than the conversation using the interactive device, as another medical examination practice that replaces the medical examination by the health care worker. For example, the presentation unit 113 can present a conversation with a volunteer staff, a conversation with another medical examination scheduled person, or a friendship with an animal.
In a case where a countermeasure to a specific medical examination scheduled person is presented, the assistance system 100 may cause the data acquisition unit 111 to acquire again the data such as the medical examination scheduled person data D1, the visit data D2, and the medical examination data D3. Then, the learning unit 112 may perform the machine learning again by using newly acquired data, and may update a learning model. Based on the updated learning model, for example, the assistance system 100 can predict the future dynamic states of the same medical examination scheduled person or a different medical examination scheduled person, can accumulate the result as new data, and can use the result for the next proposal.
As described above, the assistance system 100 according to the present embodiment includes the data acquisition unit 111 that acquires the medical examination scheduled person data D1 relating to the medical examination scheduled person having the scheduled medical examination in the medical institution, the visit data D2 relating to the visit history of the medical examination scheduled person who visits the medical institution, and the medical examination data D3 relating to the medical examination content in which the medical examination scheduled person received the medical examination in the medical institution in the past, the learning unit 112 that performs the machine learning by using the medical examination scheduled person data D1, the visit data D2, and the medical examination data D3, and the presentation unit 113 that presents whether or not the medical examination is required for the medical examination scheduled person, based on the result of the machine learning.
As described above, based on the result of the machine learning, the assistance system 100 presents whether or not the medical examination performed by the health care worker is required for the medical examination scheduled person. The health care worker can refer to the presented content to avoid the medical examination for the medical examination scheduled person who does not need the medical examination. As a result, it is possible to prevent increasing workloads of the health care worker and to prevent the medicines from being excessively prescribed for elderly persons who visit the medical institution. Therefore, medical expenses can be effectively reduced.
In addition, in a case where the presentation unit 113 presents that the medical examination is not required, the presentation unit 113 presents another medical examination practice that replaces the medical examination of the health care worker. Therefore, the medical examination scheduled person can be highly satisfied with visiting the medical institution, even in a case where the medical examination is not performed by the health care worker.
In addition, the presentation unit 113 presents the communication with the medical examination scheduled person by using the interactive device, as another medical examination practice. Therefore, the medical examination scheduled person can be further satisfied while the increase in the workloads of the health care worker is suppressed.
In addition, the medical examination data D3 includes prescription data D31 and D32 relating to the medicine prescribed for the medical examination scheduled person. The learning unit 112 learns the recommended prescription conditions of the medicines, based on the medical examination scheduled person data D1, the visit data D2, the medical examination data D3, and the prescription data D31 and D32. Then, the presentation unit 113 presents the prescription conditions of the medicines, based on the result of the machine learning. Therefore, the assistance system 100 can more properly determine whether or not the medicine needs to be prescribed. In a case where the medicine is prescribed, the assistance system 100 can provide a proper prescription dose and a proper type of the medicine.
In addition, the presentation unit 113 presents the presentation basis together with the presentation content. Therefore, the health care worker or the medical examination scheduled person can satisfactorily adopt the presented content.
In addition, the assistance method according to the present embodiment includes the data acquisition step (S1) of acquiring the medical examination scheduled person data D1 relating to the medical examination scheduled person having the scheduled medical examination in the medical institution, the visit data D2 relating to the visit history of the medical examination scheduled person who visits the medical institution, and the medical examination data D3 relating to the medical examination content in which the medical examination scheduled person received the medical examination in the medical institution in the past, the learning step (S2) of performing the machine learning by using the medical examination scheduled person data D1, the visit data D2, and the medical examination data D3, and the presentation step (S3) of presenting whether or not the medical examination is required for the medical examination scheduled person, based on the result of the machine learning. Therefore, the health care worker can refer to the presented content to avoid the medical examination for the medical examination scheduled person who does not need the medical examination. As a result, it is possible to prevent increasing workloads of the health care worker and to prevent the medicines from being excessively prescribed for elderly persons who visit the medical institution. Therefore, medical expenses can be effectively reduced.
In addition, the assistance program according to the present embodiment causes a computer to execute a process including the data acquisition step (S1) of acquiring the medical examination scheduled person data D1 relating to the medical examination scheduled person having the scheduled medical examination in the medical institution, the visit data D2 relating to the visit history of the medical examination scheduled person who visits the medical institution, and the medical examination data D3 relating to the medical examination content in which the medical examination scheduled person received the medical examination in the medical institution in the past, the learning step (S2) of performing the machine learning by using the medical examination scheduled person data D1, the visit data D2, and the medical examination data D3, and the presentation step (S3) of presenting whether or not the medical examination is required for the medical examination scheduled person, based on the result of the machine learning. Therefore, the health care worker can refer to the presented content to avoid the medical examination for the medical examination scheduled person who does not need the medical examination. As a result, it is possible to prevent increasing workloads of the health care worker and to prevent the medicines from being excessively prescribed for elderly persons who visit the medical institution. Therefore, medical expenses can be effectively reduced.
Hitherto, the assistance system, the assistance method, and the assistance program according to the present invention have been described with reference to the embodiment. However, the present invention is not limited only to each configuration described herein, and can be appropriately modified based on the description in the appended claims.
For example, the assistance system, the assistance method, and the assistance program according to the above-described embodiment may share the acquired data and presentation content with a plurality of medical institutions, or may be used only for a single medical institution.
In addition, the data used for the machine learning by the assistance system according to the present invention is not particularly limited as long as at least the medical examination scheduled person data, the visit data, and the medical examination data are used. In addition, the presentation content is sufficient when including at least whether or not the medical examination is required for the medical examination scheduled person.
In addition, in a case where the medical examination data includes the prescription data, the prescription data is sufficient when including at least one of the medical institution prescription data and the pharmacy prescription data.
In addition, in the assistance system according to the above-described embodiment, the learning unit performs the machine learning by using the algorithm of the supervised learning. However, the algorithm used for the machine learning by the learning unit may be the algorithm of the unsupervised learning, or may be the algorithm of the reinforcement learning. In addition, the learning unit may perform the machine learning by using a plurality of types of the algorithms.
In addition, the means and the method for performing various processes in the assistance system according to the above-described embodiment may be realized by a dedicated hardware circuit or a programmed computer. In addition, for example, the assistance program may be provided by a computer-readable recording medium such as a compact disc read only memory (CD-ROM), or may be provided online via a network such as the Internet. In this case, the program recorded in the computer-readable recording medium is usually transferred to and stored in the storage unit such as a hard disk. In addition, the assistance program may be provided as single application software.
This application is based on Japanese Patent Application No. 2017-230847 filed on Nov. 30, 2017, the disclosure of which is incorporated by reference in its entirety.
| Number | Date | Country | Kind |
|---|---|---|---|
| 2017-230847 | Nov 2017 | JP | national |
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/JP2018/028728 | 7/31/2018 | WO | 00 |
