JP2021039404A - Lifestyle disease insurance proposing program and insurance condition inclusion possibility determination program - Google Patents

Abstract

To automatically propose the custom-made lifestyle disease insurance condition best for each person.SOLUTION: The lifestyle disease insurance proposing program for proposing an insurance condition according to the risks of a lifestyle disease causes a computer to execute: an information acquisition step of acquiring health state information showing the health state of a customer; and a search step of searching for an insurance condition to propose, on the basis of the health state information acquired in the information acquisition step, with reference to three or more steps of the relevance between health state information for reference acquired earlier and the insurance condition.SELECTED DRAWING: Figure 3

Description

The present invention is a lifestyle-related disease insurance proposal program that proposes insurance conditions for lifestyle-related disease insurance to customers, and an insurance condition inclusion possibility determination that determines the possibility of including insurance conditions for lifestyle-related disease insurance for customers. Regarding the program.

Insurance for lifestyle-related diseases such as cancer, myocardial infarction, cerebral infarction, hypertension, and diabetes is widespread. Especially in recent years, custom-made lifestyle-related disease insurance tailored to each individual is becoming widespread. When the conditions of this custom-made lifestyle-related disease insurance are set for each individual, the work of searching for the optimum conditions based on various situations of the customer is performed, but the burden of the work is excessive. Become a thing

For this reason, it is necessary to consider assisting these tasks by utilizing artificial intelligence, and although technologies that utilize artificial intelligence have been proposed for considering insurance conditions (for example). , Patent Documents 1 and 2), none of which is intended to be a custom-made insurance tailored to each individual.

Japanese Unexamined Patent Publication No. 2017-37489 JP-A-2017-167944

Therefore, the present invention has been devised in view of the above-mentioned problems, and the purpose of the present invention is to automatically set the optimal custom-made lifestyle-related disease insurance conditions for each individual. The purpose is to provide a lifestyle-related disease insurance proposal program that can be proposed, and an insurance condition inclusion possibility judgment program that judges the inclusion possibility of insurance conditions of lifestyle-related disease insurance.

The lifestyle-related disease insurance proposal program according to the present invention is a lifestyle-related disease insurance proposal program that proposes insurance conditions according to the risk of lifestyle-related diseases. Search for insurance conditions to be proposed based on the above-mentioned health condition information acquired through the above-mentioned information acquisition step by referring to the degree of association between the previously acquired reference health condition information and the insurance conditions at three or more levels. It is characterized by having a computer perform steps.

The lifestyle-related disease insurance proposal program according to the present invention is an information acquisition step for acquiring lifestyle-related information indicating a customer's lifestyle in a lifestyle-related disease insurance proposal program that proposes insurance conditions according to the risk of lifestyle-related diseases. Search for insurance conditions to be proposed based on the lifestyle-related information acquired through the above information acquisition step by referring to the degree of association between the previously acquired reference lifestyle-related information and the insurance conditions at three or more levels. It is characterized by having a computer perform steps.

The insurance condition inclusiveness determination program according to the present invention is an insurance condition inclusiveness determination program for determining the inclusiveness of the insurance conditions of lifestyle disease insurance for the customer, and the health condition information indicating the health condition of the customer. And the information acquisition step to acquire the insurance conditions of the lifestyle illness insurance that the customer has subscribed to, the combination of the previously acquired reference health condition information and the reference insurance conditions, and the insurance conditions for the combination. Based on the health condition information acquired through the above information acquisition step and the insurance conditions, the judgment step to judge the inclusion of the insurance conditions is performed by the computer. It is characterized by having it executed.

The insurance condition inclusiveness determination program according to the present invention is an insurance condition inclusiveness determination program for determining the inclusiveness of the insurance conditions of lifestyle-related disease insurance for the customer, and lifestyle information indicating the lifestyle of the customer. And the information acquisition step to acquire the insurance conditions of the lifestyle-related disease insurance that the customer has subscribed to, the combination having the previously acquired reference lifestyle information and the reference insurance conditions, and the insurance conditions for the combination. Based on the lifestyle-related lifestyle and insurance conditions acquired through the above information acquisition step, using the degree of association with the inclusiveness of the above three or more levels, the computer is provided with a judgment step to determine the inclusiveness of the insurance conditions. It is characterized by being executed.

According to the present invention having the above-described configuration, it is possible to automatically propose the optimum custom-made lifestyle-related disease insurance conditions for each individual. In addition, the inclusion of insurance conditions can be automatically determined.

It is a block diagram which shows the whole structure of the lifestyle-related disease insurance proposal system 1 in which the lifestyle-related disease insurance proposal program to which this invention is applied is implemented. It is a figure which shows the specific configuration example of a search device. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention.

Insurance Proposal Program Hereinafter, the insurance proposal program to which the present invention is applied will be described in detail with reference to the drawings.

FIG. 1 is a block diagram showing an overall configuration of a lifestyle-related disease insurance proposal system 1 in which a lifestyle-related disease insurance proposal program to which the present invention is applied is implemented. The lifestyle-related disease insurance proposal system 1 includes an information acquisition unit 9, a search device 2 connected to the information acquisition unit 9, and a database 3 connected to the search device 2.

The information acquisition unit 9 is a device for a person who uses this system to input various commands and information, and specifically, is composed of a keyboard, buttons, a touch panel, a mouse, a switch, and the like. The information acquisition unit 9 is not limited to a device for inputting text information, and may be configured by a device such as a microphone that can detect voice and convert it into text information. Further, the information acquisition unit 9 may be configured as an image pickup device capable of capturing an image of a camera or the like. The information acquisition unit 9 may be configured by a scanner having a function of recognizing a character string from a paper-based document. Further, the information acquisition unit 9 may be integrated with the search device 2 described later. The information acquisition unit 9 outputs the detected information to the search device 2. Further, the information acquisition unit 9 may be configured by means for specifying the position information by scanning the map information. The information acquisition unit 9 also includes a temperature sensor, a humidity sensor, a flow rate sensor, and other sensors capable of identifying substances and physical properties. The information acquisition unit 9 may be configured by means for automatically fetching character strings and data posted on a site on the Internet.

The database 3 stores various information necessary for performing a solution search. The database 3 stores reference health condition information indicating the health condition of the customer. This reference health status information consists of previously acquired data indicating the health status of the customer, such as body temperature, pulse, blood test results, electrocardiogram, X-ray test, blood pressure, endoscopy, and diagnosis results by a doctor. Etc. are included.

In addition, the database 3 stores reference lifestyle information indicating the lifestyle of the customer. This reference lifestyle information includes, for example, wake-up time, bedtime, sleep time, working hours, overtime hours, frequency and amount of drinking, meal time and amount, exercise frequency and amount of exercise, and nutrients of meals to be ingested in a day. , Commuting time, etc. are included.

Further, the database 3 stores reference attribute information related to customer attribute information. The reference attribute information includes, for example, the age, gender, occupation, place of residence, and family status of the customer, as well as past medical history, family medical history, allergies to the customer or family, and surgical history.

Database 3 also contains information on the inclusiveness of insurance conditions. This inclusion possibility data is information on whether or not it is determined that a certain insurance condition is satisfied based on past cases such as the customer's health condition and lifestyle. These data on the possibility of inclusion are obtained by comparing past cases such as the health condition and lifestyle of the customer with the insurance conditions, and when it is judged that the insurance conditions are satisfied or when it is not judged so. This is the data collected and accumulated.

That is, in the database 3, in addition to such reference health condition information and reference lifestyle information, any one or more of reference attribute information and each data of insurance conditions are stored in association with each other. .. Further, in the database 3, in addition to the reference health condition information and the reference lifestyle-related information, each data of the possibility of including the insurance condition is stored in the insurance condition of the customer for any one or more of the reference attribute information. It is associated and remembered.

The search device 2 is composed of, for example, an electronic device such as a personal computer (PC), but is embodied in any other electronic device such as a mobile phone, a smartphone, a tablet terminal, a wearable terminal, etc., in addition to the PC. It may be converted. The user can obtain a search solution by the search device 2.

FIG. 2 shows a specific configuration example of the search device 2. The search device 2 performs wired communication or wireless communication with a control unit 24 for controlling the entire search device 2 and an operation unit 25 for inputting various control commands via operation buttons, a keyboard, or the like. A communication unit 26 for the purpose, an estimation unit 27 for making various judgments, and a storage unit 28 for storing a program for performing a search to be executed represented by a hard disk or the like are connected to the internal bus 21, respectively. .. Further, a display unit 23 as a monitor that actually displays information is connected to the internal bus 21.

The control unit 24 is a so-called central control unit for controlling each component mounted in the search device 2 by transmitting a control signal via the internal bus 21. Further, the control unit 24 transmits various control commands via the internal bus 21 in response to the operation via the operation unit 25.

The operation unit 25 is embodied by a keyboard or a touch panel, and an execution command for executing a program is input from the user. When the execution command is input by the user, the operation unit 25 notifies the control unit 24 of the execution command. Upon receiving this notification, the control unit 24, including the estimation unit 27, executes a desired processing operation in cooperation with each component. The operation unit 25 may be embodied as the information acquisition unit 9 described above.

The estimation unit 27 estimates the search solution. The estimation unit 27 reads out various information stored in the storage unit 28 and various information stored in the database 3 as necessary information when executing the estimation operation. The estimation unit 27 may be controlled by artificial intelligence. This artificial intelligence may be based on any well-known artificial intelligence technique.

The display unit 23 is composed of a graphic controller that creates a display image based on the control by the control unit 24. The display unit 23 is realized by, for example, a liquid crystal display (LCD) or the like.

When the storage unit 28 is composed of a hard disk, predetermined information is written to each address based on the control by the control unit 24, and is read out as needed. Further, the storage unit 28 stores a program for executing the present invention. This program is read and executed by the control unit 24.

The operation in the lifestyle-related disease insurance proposal system 1 having the above-described configuration will be described.

In the lifestyle-related disease insurance proposal system 1, for example, as shown in FIG. 3, it is premised that three or more levels of association between reference health condition information and insurance conditions are set and acquired in advance. Reference health status information consists of previously acquired data indicating the health status of the customer, such as body temperature, pulse, blood test results, electrocardiogram, X-ray test, blood pressure, endoscopy, and diagnosis results by a doctor. Is included.

Insurance conditions are a concept that includes all the conditions for getting insurance, the conditions for which insurance is applied, and the conditions for which compensation is paid. For example, if you have cancer, your doctor's medical certificate clearly states that you have cancer, and you must be hospitalized for 3 months or more.

In other words, through this reference health condition information and the data set of insurance conditions, it is possible to know what insurance conditions have been applied according to the various health conditions that have occurred in the reference health condition information. As for the insurance conditions, the custom-made lifestyle-related disease insurance conditions set for each customer may be read out from a database stored in an insurance company or the like and used. In other words, by collecting reference health condition information according to the past health condition of each customer and a data set of insurance conditions set for the customer, any insurance condition can be set for the customer in any health condition in the past. It becomes possible to know whether it was done.

In the example of FIG. 3, it is assumed that the input data is, for example, reference health condition information P01 to P03. The reference health condition information as such input data is linked to the output. In this output, insurance conditions Q1 to Q4 as output solutions are displayed.

The reference health condition information is associated with each other through three or more levels of association with the insurance conditions as the output solution. Reference health information is arranged on the left side through this degree of association, and each insurance condition is arranged on the right side through this degree of association. The degree of association indicates which insurance condition is highly relevant to the reference health condition information arranged on the left side. In other words, this degree of association is an indicator of what insurance conditions each reference health condition information is likely to be associated with, and the most probable insurance condition is selected from the reference health condition information. It shows the accuracy above. In the example of FIG. 3, w13 to w19 are shown as the degree of association. As shown in Table 1 below, w13 to w19 are shown in 10 stages. The closer to 10 points, the higher the degree of relevance to the insurance conditions, and conversely, the closer to 1 point, the more the insurance conditions. It shows that the degree of relevance to each other is low.

The search device 2 acquires in advance the degree of association w13 to w19 of three or more stages shown in FIG. That is, the search device 2 accumulates past data as to which of the reference health condition information and the insurance conditions in that case is adopted in determining the actual search solution, and analyzes and analyzes these. By doing so, the degree of association shown in FIG. 3 is created.

For example, it is assumed that the reference health condition information P01 is blood pressure ●●, blood glucose level ●●, and electrocardiogram is ●● points. At this time, it is investigated what kind of custom-made insurance conditions in the past were applied to the customer having such reference health condition information P01.

This analysis and analysis may be performed by artificial intelligence. In such a case, for example, in the case of reference health condition information P01, the past data of the database stored in the insurance company is analyzed. If there are many applied insurance conditions Q1, the degree of association that leads to this insurance condition Q1 is set higher, and if there are many cases of insurance condition Q2 and there are few insurance conditions Q1, the association that leads to insurance condition Q2 is set. Increase the degree and set the degree of association that leads to insurance condition Q1 low.

Further, the degree of association shown in FIG. 3 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, when actually setting insurance conditions for lifestyle-related diseases to customers from now on, the above-mentioned learned data will be used to search for the optimum insurance conditions. In such a case, new health condition information indicating the health condition of the customer who intends to newly set insurance conditions for lifestyle-related diseases is acquired.

The newly acquired health condition information is input by the above-mentioned information acquisition unit 9.

Based on the newly acquired health condition information in this way, it is estimated what kind of insurance conditions should actually be applied. In such a case, the degree of association shown in FIG. 3 (Table 1) acquired in advance is referred to. For example, when the newly acquired health condition information is the same as or similar to P02, the insurance condition Q2 is associated with the association degree w15 and the insurance condition Q3 is associated with the association degree w16 through the association degree. In such a case, the insurance condition Q2 with a higher degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the insurance condition Q3, which has the lowest degree of association but the association itself is recognized, may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and any other output solution may be selected in any other priority as long as it is based on the degree of association.

In this way, the newly acquired health condition information and the reference health condition information are consistent with each other in terms of the type of information. Therefore, when the new health condition information is acquired, it is the same or the same. It is possible to immediately refer to similar reference health condition information and estimate the optimal insurance conditions.

By the way, when one or more of body temperature, pulse, blood test result, electrocardiogram, X-ray test, blood pressure, endoscopy, diagnosis result by doctor, etc. are acquired as health condition information, it is used as reference health condition information. , It is necessary to learn in advance from the body temperature, pulse, blood test result, electrocardiogram, X-ray test, blood pressure, endoscopy, diagnosis result by a doctor, etc. according to these. Then, it is optimal through the health condition information actually acquired according to the body temperature, pulse, blood test result, electrocardiogram, X-ray examination, blood pressure, endoscopy, diagnosis result by a doctor, etc. You will be searching for lifestyle-related disease insurance conditions.

At this time, the above-mentioned degree of association may be composed of any two or more combinations of body temperature, pulse, blood test result, electrocardiogram, X-ray test, blood pressure, endoscopy, diagnosis result by a doctor, and the like. Then, the solution may be searched by learning the insurance conditions as the search solution for the combination.

That is, as health condition information, any two or more of the customer's body temperature, pulse, blood test result, electrocardiogram, X-ray test, blood pressure, endoscopy, diagnosis result by a doctor, etc. are acquired. Then, among the reference health condition information, the combination of the reference health condition information according to the two or more acquired information and the degree of association with the insurance condition at three or more levels are acquired in advance. Next, using this, lifestyle-related diseases that should be proposed based on the degree of association with insurance conditions for the combination of reference health condition information according to the newly acquired two or more information as described above. Explore insurance terms.

In addition, it is possible to predict the risk of lifestyle-related diseases to some extent from these health condition information, attribute information, and lifestyle-related information. Then, it becomes possible to set insurance conditions according to the risk of this lifestyle-related disease.

In the example of FIG. 4, it is premised that a combination of the reference health condition information and the reference attribute information is formed.

In the example of FIG. 4, it is assumed that the input data is, for example, reference health state information P11 to P13 and reference attribute information P14 to 17. The intermediate node shown in FIG. 4 is a combination of reference attribute information and reference health condition information as such input data. Each intermediate node is further linked to the output. In this output, the insurance conditions for lifestyle-related diseases are displayed as the output solution.

Each combination (intermediate node) of the reference health condition information and the reference attribute information is associated with each other through three or more levels of association with the insurance conditions as the output solution. Reference health status information and reference attribute information are arranged on the left side via this degree of association, and insurance conditions are arranged on the right side via this degree of association. The degree of association indicates the degree of relevance to which insurance condition is high with respect to the reference health condition information and the reference attribute information arranged on the left side. In other words, this degree of association is an index indicating what kind of insurance conditions each reference health condition information and reference attribute information is likely to be associated with, and is a reference health condition information and reference attribute information. It shows the accuracy in selecting the most probable insurance conditions from. In the example of FIG. 4, w13 to w22 are shown as the degree of association. As shown in Table 1, these w13 to w22 are shown in 10 stages, and the closer to 10 points, the higher the degree of relevance of each combination as an intermediate node to the price as an output, and conversely. The closer it is to one point, the less relevant each combination as an intermediate node is to the price as an output.

The estimation device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, the estimation device 2 accumulates past data on the reference health condition information, the reference attribute information, and the insurance conditions in that case in determining the actual search solution, and these By analyzing and analyzing, the degree of association shown in FIG. 4 is created.

For example, it is assumed that the customer's health condition set in the past is the reference health condition information P11. At this time, when the attribute information of the customer is "male, 40s, diabetic, grandfather died of cancer", the insurance conditions are investigated in the previous data.

This analysis and analysis may be performed by artificial intelligence. In such a case, for example, in the case of reference health condition information P11 and reference attribute information P16 "male, 40s, diabetic, grandfather died of cancer", the insurance conditions are past data. Analyze from. If there are many cases of insurance condition Q3, the degree of association that leads to this insurance condition Q3 is set higher, and if there are many cases of insurance condition Q4 and there are few cases of insurance condition Q3, the insurance condition Set the degree of association that leads to Q4 high and the degree of association that leads to insurance condition Q3 low. For example, in the example of the intermediate node 61a, the output of insurance condition Q1 and insurance condition Q2 is linked, but from the previous case, the degree of association of w13 connected to insurance condition Q1 is set to 7 points, and w14 connected to insurance condition Q2. The degree of association is set to 2 points.

Further, the degree of association shown in FIG. 4 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

In the example of the degree of association shown in FIG. 4, the node 61b is a node in which the reference attribute information P14 is combined with the reference health condition information P11, the degree of association of the insurance condition Q3 is w15, and the association of the insurance condition Q5. The degree is w16. The node 61c is a node in which the reference attribute information P15 and P17 are combined with the reference health condition information P12, and the degree of association of the insurance condition Q2 is w17 and the degree of association of the insurance condition Q4 is w18.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, when actually estimating the insurance conditions to be newly searched from now on, the insurance conditions will be estimated using the above-mentioned learned data. In such a case, the health condition information of the customer who intends to newly set the insurance conditions is newly acquired, and the attribute information of the customer is acquired.

The newly acquired health condition information and attribute information may be acquired via a user interface such as a keyboard.

Based on the newly acquired health condition information and attribute information in this way, we will try to search for the optimal lifestyle-related disease insurance conditions. In such a case, the degree of association shown in FIG. 4 (Table 1) acquired in advance is referred to. For example, when the newly acquired health condition information is the same as or similar to P12 and the attribute information is P17, the node 61d is associated through the degree of association, and this The node 61d is associated with the insurance condition Q3 by w19 and the insurance condition Q4 by the degree of association w20. In such a case, the insurance condition Q3 with a higher degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the insurance condition Q4, which has the lowest degree of association but the association itself is recognized, may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and any other output solution may be selected in any other priority as long as it is based on the degree of association.

In addition, Table 2 below shows examples of the degrees of association w1 to w12 extending from the input.

The intermediate node 61 may be selected based on the degree of association w1 to w12 extending from this input. That is, the larger the degree of association w1 to w12, the heavier the weighting in the selection of the intermediate node 61 may be. However, the association degrees w1 to w12 may all have the same value, and the weightings in the selection of the intermediate node 61 may all be the same.

By the way, as attribute information, for example, in addition to the customer's age, gender, occupation, place of residence, family status, any one or more of past medical history, family medical history, allergies to the customer or family, surgical history, etc. When acquiring, as reference attribute information, in addition to the customer's age, gender, occupation, place of residence, family status, past medical history, family medical history, allergies to the customer or family, etc. It is necessary to learn in advance the surgical history, etc. together with the reference health condition information and the insurance conditions. Then, in addition to the customer's age, gender, occupation, place of residence, and family status actually acquired, reference attribute information according to past medical history, family medical history, allergies to the customer or family, surgical history, etc. You will be exploring the insurance terms through.

At this time, in addition to the above-mentioned degree of association with the customer's age, gender, occupation, place of residence, and family status, any one or more of past medical history, family medical history, allergies to the customer or family, surgical history, etc. It may be configured by the combination of. Then, the solution may be searched by learning the insurance conditions as the search solution for the combination.

At this time, as reference attribute information, any one or more of the customer's previous medical history and the medical history of the customer's family may be acquired. Three or more levels of association between a combination that has reference attribute information that includes any one or more of the previous medical history of another customer acquired earlier and the medical history of the family of the other customer, and the insurance conditions for that combination. Use it and search for solutions to insurance conditions in the same way as above.

In the example of FIG. 5, it is premised that a combination of reference health condition information and reference lifestyle information is formed.

In the example of FIG. 5, it is assumed that the input data is, for example, reference health condition information P11 to P13 and reference lifestyle information P41 to 44. The intermediate node shown in FIG. 5 is a combination of reference health condition information and reference lifestyle information as such input data. Each intermediate node is further linked to the output. In this output, the insurance conditions for lifestyle-related diseases are displayed as the output solution.

Each combination (intermediate node) of the reference health condition information and the reference lifestyle-related information is associated with each other through three or more levels of association with the insurance conditions as the output solution. Reference health status information and reference lifestyle information are arranged on the left side via this degree of association, and insurance conditions are arranged on the right side via this degree of association. The degree of association indicates the degree of relevance to which insurance condition is higher for the reference health condition information and the reference lifestyle information arranged on the left side. In other words, this degree of association is an index showing what kind of insurance conditions each reference health condition information and reference lifestyle information are likely to be associated with, and is a reference health condition information and reference life. It shows the accuracy in selecting the most probable insurance conditions from habitual information.

The estimation device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, the estimation device 2 accumulates past data on the reference health condition information, the reference lifestyle information, and the insurance conditions in that case in determining the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 5 is created.

For example, it is assumed that the customer's health condition set in the past is the reference health condition information P11. At this time, when the customer's lifestyle information is "sleeping time ●●, overtime hours ●●, nutrients to be ingested ●●, amount of exercise per day ●●", the insurance conditions are investigated in the previous data. ..

This analysis and analysis may be performed by artificial intelligence. In such a case, for example, when the reference health condition information P11 and the reference lifestyle information P43 “sleep time ●●, overtime work ●●, nutrients to be ingested ●●, daily exercise amount ●●”, The insurance conditions are analyzed from past data. If there are many cases of insurance condition Q3, the degree of association that leads to this insurance condition Q3 is set higher, and if there are many cases of insurance condition Q4 and there are few cases of insurance condition Q3, the insurance condition Set the degree of association that leads to Q4 high and the degree of association that leads to insurance condition Q3 low. For example, in the example of the intermediate node 61a, the output of insurance condition Q1 and insurance condition Q2 is linked, but from the previous case, the degree of association of w13 connected to insurance condition Q1 is set to 7 points, and w14 connected to insurance condition Q2. The degree of association is set to 2 points.

Further, the degree of association shown in FIG. 5 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

In the example of the degree of association shown in FIG. 5, the node 61b is a node that is a combination of the reference health condition information P11 and the reference lifestyle-related information P41, and the degree of association of the insurance condition Q3 is w15 and the insurance condition Q5. The degree of association is w16. The node 61c is a node that is a combination of the reference lifestyle-related information P42 and P44 with respect to the reference health condition information P12, and the degree of association of the insurance condition Q2 is w17 and the degree of association of the insurance condition Q4 is w18. ..

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, when actually estimating the insurance conditions to be newly searched from now on, the insurance conditions will be estimated using the above-mentioned learned data. In such a case, the health condition information of the customer who intends to newly set the insurance conditions is newly acquired, and the lifestyle information of the customer is acquired.

The newly acquired health condition information and lifestyle information may be acquired via a user interface such as a keyboard.

Based on the newly acquired health condition information and lifestyle-related information in this way, we will try to search for the optimum insurance conditions. In such a case, the degree of association shown in FIG. 5 (Table 1) acquired in advance is referred to. For example, when the newly acquired health condition information is the same as or similar to P12 and the lifestyle information is P44, the node 61d is associated through the degree of association. The node 61d is associated with the insurance condition Q3 by w19 and the insurance condition Q4 by the degree of association w20. In such a case, the insurance condition Q3 with a higher degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the insurance condition Q4, which has the lowest degree of association but the association itself is recognized, may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and any other output solution may be selected in any other priority as long as it is based on the degree of association.

By the way, as lifestyle information attribute information, for example, the customer's wake-up time, bedtime, sleep time, working time, overtime hours, frequency and amount of drinking, meal time and amount, exercise frequency and exercise amount, meals to be consumed in a day It is necessary to learn in advance the nutrients, commuting time, etc., together with the reference health condition information, with the insurance conditions. Then, the customer's actually acquired wake-up time, bedtime, sleeping time, working time, overtime hours, frequency and amount of drinking, meal time and amount, exercise frequency and amount of exercise, dietary nutrients to be consumed in a day, commuting The insurance conditions will be searched through the reference lifestyle information according to the time.

At this time, the above-mentioned degree of association is used for the customer's wake-up time, bedtime, sleeping time, working hours, overtime hours, frequency and amount of drinking, meal time and amount, exercise frequency and amount of exercise, and nutrients of the meal taken in a day. , Commuting time, etc. may be combined with any one or more. Then, the solution may be searched by learning the insurance conditions as the search solution for the combination.

In the lifestyle-related disease insurance proposal system 1, when learning from the insurance conditions, as shown in FIG. 6, even if the reference lifestyle information alone is trained with the data set linked to the insurance conditions. Good. That is, it is premised that three or more levels of association between the reference lifestyle-related information and the insurance conditions are set and acquired in advance. That is, through this reference lifestyle information and the data set of lifestyle-related disease insurance conditions, it is possible to know what kind of insurance conditions have been applied according to the reference lifestyle information. Regarding the formation of the degree of association and the solution search shown in FIG. 6, the explanation below will be omitted by quoting the explanation of FIG.

FIG. 7 shows an example in which a combination of reference lifestyle information and reference attribute information is formed. In the example of FIG. 7, it is assumed that the input data is, for example, reference lifestyle information P41 to P43 and reference attribute information P14 to 17. The intermediate node shown in FIG. 7 is a combination of reference attribute information and reference lifestyle information as such input data. Each intermediate node is further linked to the output. In this output, the insurance conditions for lifestyle-related diseases are displayed as the output solution.

Each combination (intermediate node) of the reference lifestyle information and the reference attribute information is associated with each other through three or more levels of association with the insurance conditions as the output solution. Let us learn such a degree of association in advance. Through the above information acquisition step, using the combination of the previously acquired reference attribute information regarding the customer's attribute, the above reference lifestyle information, and the insurance conditions for the combination at least three levels. It is possible to search for the insurance conditions based on the acquired attribute information and lifestyle information.

Further, in the present embodiment, as shown in FIG. 8, each combination (intermediate node) of the reference lifestyle information, the reference attribute information, and the reference health condition information is used as the insurance condition as the output solution. On the other hand, they may be learned to be related to each other through three or more levels of connection. It is possible to search for the insurance condition in the same way based on the attribute information, lifestyle information and health condition information acquired through the information acquisition step by using the degree of association with the insurance condition for the combination in three or more stages. It becomes.

Insurance Condition Inclusion Possibility Judgment Program Next, the insurance condition inclusion feasibility judgment program to which the present invention is applied will be described. Similarly, the insurance condition inclusion feasibility determination program uses the components described in the block diagram shown in FIG. The information acquisition unit 9, the search device 2, and the database 3 required for executing the insurance condition inclusion feasibility determination program omit the following description by quoting the above-mentioned explanations of FIGS. 1 and 2. ..

The operation of the insurance condition inclusion feasibility determination program having the above-described configuration will be described.

The insurance condition inclusion feasibility determination program is premised on the formation of a combination of reference health condition information and reference insurance conditions, as shown in FIG. 9, for example. The reference insurance conditions indicate all the conditions for getting lifestyle-related disease insurance and all the conditions for applying lifestyle-related disease insurance. In fact, the reference health condition information and the reference insurance condition are composed of a data set of the health condition of the customer and the insurance condition set for the customer when the customer is focused on.

As the input data, the reference health condition information and the reference insurance conditions are arranged side by side. The intermediate node shown in FIG. 9 is a combination of reference health condition information and reference insurance conditions as such input data. Each intermediate node is further linked to the output. In this output, the likelihood of including insurance conditions as an output solution is shown as a percentage.

Each combination (intermediate node) of the reference health condition information about the customer and the reference insurance condition is associated with each other through three or more levels of association with the inclusiveness as this output solution. In the example of FIG. 9, the score of the degree of association is shown in three or more stages, and the higher the score, the higher the degree of relevance of each combination as an intermediate node to the inclusiveness of the insurance condition as an output. On the contrary, the closer to the score, the lower the degree of relevance of each combination as an intermediate node to the inclusiveness of the insurance condition as an output.

The determination device 2 acquires in advance the degree of association of three or more levels shown in FIG. That is, the judgment device 2 determines the possibility of inclusion of the actual insurance conditions, and includes data indicating the health condition of each customer, data on the insurance conditions for lifestyle-related diseases to which the customer is subscribed, and an insurance company. In the above, data on whether or not each case was actually applied as being included in the insurance conditions for lifestyle-related diseases is accumulated, and by analyzing and analyzing these data, the degree of association shown in FIG. 9 is created. This analysis and analysis may be performed by artificial intelligence.

Further, the degree of association shown in FIG. 9 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the inclusiveness of the node of this neural network as an output corresponds to the above-mentioned degree of association.

In the example of the degree of association shown in FIG. 9, the node 61a is a node in which the reference insurance condition P20 is combined with the reference health condition information P11, and the coverage possibility “30%” of the insurance condition is the association degree w13. , "60%" is the degree of association w14. The node 61b is a node of the combination of the reference insurance condition P18 with respect to the reference health condition information P11, and the coverage possibility “85%” of the insurance condition is the association degree w15 and “70%” is the association degree w16. Has been done.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, we will determine the possibility that the necessary insurance conditions will be met when the insurance is applied to the customer who will actually make a new decision. In such a case, we will acquire health condition information about the customer who is going to receive new insurance. Similarly, the insurance conditions related to the lifestyle-related disease insurance that the customer intends to receive are also acquired. The insurance conditions may be acquired by, for example, capturing the text of the insurance conditions with a smartphone or the like, recognizing the text, and extracting the text data, or using the Internet or the like in the electronic data in which the insurance conditions are described. It may be accessed via and the text data may be extracted. Further, if the conditions regarding the insurance to be received by the user are input in advance on the system side, they may be used as they are.

If the health condition information obtained from the customer is the same or similar to P11 and the insurance conditions obtained in the same way are the same or similar to P20, it corresponds to node 61a and is attached to the node 61a. Output the possibility of applying insurance conditions according to the degree of association. That is, the possibility of application is advised by the weight of W13 that the possibility of application is 30%, and the possibility of application is advised by the weight of w14. As a result, the customer or the insurance company can understand from the health condition of the customer the possibility of including the conditions of the lifestyle-related disease insurance that the customer subscribes to.

It is not essential to select the one with a higher degree of association as the optimum solution, and the one with a lower degree of association is selected as the optimum solution, although the degree of association is low but the association itself is recognized. You may. Of course, other than this, an output solution to which the arrows are not connected may be selected. That is, the selection of the optimum solution is not limited to the case where the optimum solution is selected in order from the one with the highest degree of association, and may be selected in order from the one with the lowest degree of association depending on the case. It may be selected in any priority. In this way, after the input data is assigned to any of the reference input parameters, the inclusion possibility as an output solution is selected based on the degree of association set in the reference input parameter.

In FIG. 10, in addition to the above-mentioned reference health condition information and reference insurance conditions, a combination of reference attribute information and the possibility of including lifestyle-related disease insurance conditions for the combination are associated with three or more stages. An example in which the degree is set is shown.

In such a case, as shown in FIG. 10, the degree of association is such that the set of combinations of the reference health condition information, the reference attribute information, and the reference attribute information is set as the nodes 61a to 61e of the intermediate node as described above. It will be expressed.

For example, in FIG. 10, in the node 61c, the reference health state information P12 is associated with the association degree w3, the reference attribute information P15 is associated with the association degree w7, and the reference insurance condition P21 is associated with the association degree w11. Similarly, in the node 61e, the reference health state information P13 is associated with the association degree w5, the reference attribute information P15 is associated with the association degree w8, and the reference insurance condition P20 is associated with the association degree w10.

Similarly, when such a degree of association is set, the search solution is determined based on the newly acquired health condition information, the attribute information, and the insurance conditions.

In determining this search solution, the degree of association shown in FIG. 10 acquired in advance is referred to. For example, when the acquired health condition information is the same as or similar to the reference health condition information P12, the acquired attribute information corresponds to the reference attribute information P15, and the acquired insurance condition corresponds to the reference insurance condition P21. , The combination is associated with node 61c, and this node 61c has a degree of association w17 with a possibility of inclusion of conditions for lifestyle-related disease insurance of "60%" and a possibility of inclusion of conditions of "45%". Is the degree of association w18. As a result of such a degree of association, a search solution is actually obtained based on w17 and w18.

In the example of FIG. 11, it is premised that a combination of the reference lifestyle information and the reference insurance condition is formed. As input data, such reference lifestyle-related information and reference insurance conditions are lined up. The intermediate node shown in FIG. 11 is a combination of reference insurance conditions with reference lifestyle information as such input data. Each intermediate node is further linked to the output. In this output, the likelihood of including insurance conditions as an output solution is shown as a percentage.

Each combination (intermediate node) of the reference lifestyle-related information about the customer and the reference insurance condition is linked to each other through three or more levels of linkage to the inclusiveness as this output solution.

The determination device 2 acquires in advance the degree of association of three or more levels shown in FIG. That is, the judgment device 2 determines the possibility of inclusion of the actual insurance conditions, the data showing the lifestyle of each customer, the data of the insurance conditions of the lifestyle-related disease to which the customer is subscribed, and the insurance company. In the above, data on whether or not each case was actually applied as being included in the insurance conditions for lifestyle-related diseases is accumulated, and by analyzing and analyzing these data, the degree of association shown in FIG. 11 is created. This analysis and analysis may be performed by artificial intelligence.

Further, the degree of association shown in FIG. 11 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the inclusiveness of the node of this neural network as an output corresponds to the above-mentioned degree of association.

In the example of the degree of association shown in FIG. 11, the node 61a is a node in which the reference insurance condition P20 is combined with the reference lifestyle information P41, and the coverage possibility “30%” of the insurance condition is the association degree w13. , "60%" is the degree of association w14. The node 61b is a node that is a combination of the reference insurance condition P18 with respect to the reference lifestyle information P41, and the possibility of inclusion of the insurance condition "85%" is the degree of association w15 and "70%" is the degree of association w16. Has been done.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, we will determine the possibility that the necessary insurance conditions will be met when the insurance is applied to the customer who will actually make a new decision. In such a case, we will acquire lifestyle-related information about customers who are going to receive new insurance. Similarly, the insurance conditions related to the lifestyle-related disease insurance that the customer intends to receive are also acquired.

If the lifestyle information acquired from the customer is the same or similar to P41 and the insurance conditions acquired similarly are the same or similar to P20, it corresponds to node 61a and is attached to the node 61a. Output the possibility of applying insurance conditions according to the degree of association. That is, the possibility of application is advised by the weight of W13 that the possibility of application is 30%, and the possibility of application is advised by the weight of w14. As a result, the customer or the insurance company can understand from the health condition of the customer the possibility of including the conditions of the lifestyle-related disease insurance that the customer subscribes to.

At this time, as shown in FIG. 10 described above, in addition to the reference lifestyle information and the reference insurance condition, the combination of the reference attribute information and the possibility of including the lifestyle-related disease insurance condition for the combination. It is also possible to set the degree of association of 3 or more levels for learning. When input of lifestyle-related information, insurance conditions, and attribute information is accepted, it is possible to search for the possibility of inclusion by referring to the degree of association.

According to the present invention having the above-described configuration, anyone can easily propose the optimum insurance conditions or judge the possibility of including the insurance conditions without any special skill or experience. Further, according to the present invention, it is possible to make a judgment of this search solution with higher accuracy than a human being. Further, by configuring the above-mentioned degree of association with artificial intelligence (neural network or the like), it is possible to further improve the discrimination accuracy by learning this.

Further, according to the present invention, it is characterized in that an optimum solution search is performed through the degree of association set in three or more stages. The degree of association can be described by, for example, a numerical value from 0 to 100% in addition to the above-mentioned 5 stages, but the degree of association is not limited to this, and any stage can be described as long as it can be described by a numerical value of 3 or more stages. It may be configured.

By proposing the most probable insurance conditions for lifestyle-related diseases based on the degree of association expressed by the numerical values of three or more levels, or by judging the inclusiveness of the insurance conditions, the possibility of exploratory solutions It is also possible to search and display in descending order of the degree of association under the situation where a plurality of candidates are considered. If the users can be displayed in descending order of the degree of association in this way, it is possible to preferentially display more probable search solutions.

In addition to this, according to the present invention, it is possible to judge without overlooking the discrimination result of the output having an extremely low degree of association such as 1%. It warns the user that even a judgment result with an extremely low degree of association is connected as a slight sign, and may be useful as the judgment result once every tens or hundreds of times. be able to.

Further, according to the present invention, there is an advantage that the search policy can be determined by the method of setting the threshold value by performing the search based on the degree of association of three or more stages. If the threshold value is lowered, even if the above-mentioned degree of association is 1%, it can be picked up without omission, but it is unlikely that a more appropriate discrimination result can be detected favorably, and a lot of noise may be picked up. is there. On the other hand, if the threshold value is raised, there is a high possibility that the optimum search solution can be detected with high probability, but the degree of association is usually low and it is passed through, but it is suitable to appear once in tens or hundreds of times. Sometimes the solution is overlooked. It is possible to decide which one to prioritize based on the ideas of the user side and the system side, but it is possible to increase the degree of freedom in selecting the points to be emphasized.

Further, in the present invention, the above-mentioned degree of association may be updated. This update may reflect information provided via a public communication network such as the Internet. Further, when knowledge, information, and data regarding each input parameter and output parameter of the present invention are acquired, the degree of association is increased or decreased according to these.

In other words, this update corresponds to learning in the sense of artificial intelligence. It can be said that it is a learning act because it acquires new data and reflects it in the learned data.

In addition, this update of the degree of association is not based on the information that can be obtained from the public communication network, but is also updated by the system side or the user side based on the contents of research data, treatises, conference presentations, newspaper articles, books, etc. by experts. It may be updated artificially or automatically. Artificial intelligence may be utilized in these update processes.

Further, the process of first creating the trained model and the above-mentioned update may use not only supervised learning but also unsupervised learning, deep learning, reinforcement learning, and the like. In the case of unsupervised learning, instead of reading and learning the data set of input data and output data, information corresponding to the input data is read and trained, and the degree of association related to the output data is self-formed from there. You may let it.

Further, the degree of association of each combination described above is the degree of association of a combination having one factor and another factor, and it goes without saying that other elements other than these may be associated with the degree of association.

1 Lifestyle-related disease insurance proposal program 2 Estimator 21 Internal bus 23 Display unit 24 Control unit 25 Operation unit 26 Communication unit 27 Estimate unit 28 Storage unit 61 node

Claims (10)

In the lifestyle-related disease insurance proposal program that proposes insurance conditions according to the risk of lifestyle-related diseases
Information acquisition step to acquire health condition information showing the health condition of the customer,
Search for insurance conditions to be proposed based on the above-mentioned health condition information acquired through the above-mentioned information acquisition step by referring to the degree of association between the previously acquired reference health condition information and the insurance conditions at three or more levels. A lifestyle-related disease insurance proposal program characterized by having a computer perform steps. In the above information acquisition step, lifestyle information indicating the customer's lifestyle is further acquired, and the lifestyle information is acquired.
In the above search step, three or more levels of association between the previously acquired reference habit information regarding the lifestyle-related information of other customers, the combination having the above reference health condition information, and the insurance conditions for the combination are used. The lifestyle-related disease insurance proposal program according to claim 1, wherein the insurance conditions to be proposed are searched based on the health condition information acquired through the above information acquisition step and the lifestyle-related information. In the above information acquisition step, further customer attribute information is acquired.
In the above search step, the above-mentioned information is used by using three or more levels of association between the previously acquired reference attribute information of another customer, the combination having the above-mentioned reference health condition information, and the insurance conditions for the combination. The lifestyle-related disease insurance proposal program according to claim 1, wherein the insurance conditions to be proposed are searched based on the health condition information acquired through the acquisition step and the attribute information. In the lifestyle-related disease insurance proposal program that proposes insurance conditions according to the risk of lifestyle-related diseases
Information acquisition steps to acquire lifestyle information that indicates the customer's lifestyle,
Search for insurance conditions to be proposed based on the lifestyle-related information acquired through the above information acquisition step by referring to the degree of association between the previously acquired reference lifestyle-related information and the insurance conditions at three or more levels. A lifestyle-related disease insurance proposal program characterized by having a computer perform steps. In the above information acquisition step, further customer attribute information is acquired.
In the above search step, the above information is used by using three or more levels of association between the previously acquired reference attribute information of another customer, the combination having the above reference lifestyle-related information, and the insurance conditions for the combination. The lifestyle-related disease insurance proposal program according to claim 4, wherein the insurance conditions to be proposed are searched based on the lifestyle-related information acquired through the acquisition step and the attribute information. As the above reference attribute information, one or more of the customer's previous medical history and the medical history of the customer's family are acquired.
In the above search step, a combination having a previously acquired medical history of another customer and reference attribute information including any one or more of the medical history of the family of the other customer and insurance conditions for the combination are used. The lifestyle-related disease insurance proposal program according to claim 3 or 5, which is characterized by utilizing a degree of association of one level or higher. In the insurance condition inclusion feasibility judgment program that judges the inclusiveness of the insurance conditions of lifestyle-related disease insurance for customers
Health condition information showing the health condition of the customer, information acquisition step to acquire the insurance conditions of the lifestyle-related disease insurance that the customer has,
Obtained through the above information acquisition step using the three or more levels of association between the previously acquired reference health condition information, the combination having the reference insurance conditions, and the inclusiveness of the insurance conditions for the combination. An insurance condition inclusion possibility judgment program characterized in that a computer executes a judgment step for judging the inclusiveness of the insurance condition based on the health condition information and the insurance condition. In the above information acquisition step, further customer attribute information is acquired.
In the above judgment step, the combination having the reference attribute information regarding the previously acquired customer attribute, the reference health condition information, and the reference insurance condition, and the possibility of including the insurance condition for the combination are 3 The fourth aspect of claim 7, wherein the possibility of inclusion of the insurance condition is determined based on the attribute information, the health condition information, and the insurance condition acquired through the above information acquisition step by using the degree of association of the stage or higher. Insurance condition inclusion possibility judgment program. In the insurance condition inclusion feasibility judgment program that judges the inclusiveness of the insurance conditions of lifestyle-related disease insurance for customers
Lifestyle information that shows the lifestyle of the customer, information acquisition steps to acquire the insurance conditions of the lifestyle-related disease insurance that the customer has subscribed to, and
Obtained through the above information acquisition step using the three or more levels of association between the previously acquired reference lifestyle-related information, the combination having the reference insurance conditions, and the inclusiveness of the insurance conditions for the combination. An insurance condition inclusion possibility judgment program characterized by having a computer execute a judgment step for judging the inclusiveness of the insurance condition based on the lifestyle-related lifestyle and the insurance condition. In the above information acquisition step, further customer attribute information is acquired.
In the above-mentioned determination step, the combination having the reference attribute information regarding the previously acquired customer attribute, the above-mentioned reference lifestyle-related information, and the above-mentioned reference insurance condition, and the possibility of including the insurance condition for the combination are 3 The ninth aspect of claim 9, which is characterized in that the possibility of including the insurance conditions is determined based on the attribute information, lifestyle-related information, and insurance conditions acquired through the above information acquisition step by using the degree of association of one level or higher. Insurance condition inclusion possibility judgment program.

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