JP2021026384A - Personal property insurance proposal program and personal property insurance condition inclusion possibility determination program - Google Patents

Abstract

To provide a personal property insurance proposal program and a personal property insurance condition inclusion possibility determination program that can easily determine an insurance condition inclusion possibility for a target.SOLUTION: An insurance condition inclusion possibility determination program that determines insurance condition inclusion possibility for a target causes a computer to perform: an information acquisition step of acquiring an insurance condition of the target and state information indicating a state of the target; an association degree acquisition step of acquiring in advance three or more levels of degree of association between a combination of a past insurance condition and past state information and insurance condition inclusion possibility for the combination; and a determination step of referring to the degree of association acquired in the association degree acquisition step and determining the insurance condition inclusion possibility based on the insurance condition and the state information acquired via the information acquisition step.SELECTED DRAWING: Figure 3

Description

The present invention relates to a movable property insurance proposal program that proposes movable property insurance conditions to a user, and a movable property insurance condition inclusion possibility determination program that determines the inclusiveness of movable property insurance conditions to a user.

Movable property insurance, which provides compensation in the event that a camera, piano, or other movable property breaks down or is stolen, has been widely used.

In recent years, tailor-made insurance tailored to each individual has become widespread. When this custom-made insurance condition is set for each individual, the work of searching for the optimum condition based on the transportation condition and usage condition of movable property is performed, but the burden of the work is excessive. Becomes

For this reason, it will be 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.

In addition to this, the burden of labor to determine whether or not the user is included in the movable property insurance conditions including disclaimers and special provisions is extremely heavy, and artificial intelligence is used for these tasks. The current situation is that the technology to assist is not yet proposed.

JP-A-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 propose the optimum custom-made movable property insurance conditions for each individual. The purpose is to provide a possible movable property insurance proposal program and a movable property insurance condition inclusion possibility judgment program that can automatically judge the inclusion of movable property insurance conditions to the user.

The movable property insurance condition proposal program to which the present invention is applied is an information acquisition step for acquiring guarantee information regarding a movable property guarantee in the movable property insurance condition proposal program that proposes movable property insurance conditions to a user, and a previously acquired reference. Refer to the degree of association acquisition step for acquiring the degree of association between the warranty card information and the movable property insurance condition in advance, and the degree of association acquired in the above-mentioned degree of association acquisition step, and obtain the above information through the above-mentioned information acquisition step. It is characterized by having a computer perform a search step to search for movable property insurance conditions to be proposed based on the guarantee information.

The movable property insurance condition proposal program to which the present invention is applied is an information acquisition step for acquiring movable property attribute information and a previously acquired reference attribute in the movable property insurance condition proposal program that proposes movable property insurance conditions to users. The above attribute information acquired through the above information acquisition step by referring to the association degree acquisition step for acquiring the information and the degree of association of three or more stages of the movable property insurance condition in advance and the association degree acquired in the above association degree acquisition step. Based on the above, it is characterized by having a computer execute a search step for searching for movable property insurance conditions to be proposed.

The movable property insurance condition inclusion possibility judgment program to which the present invention is applied provides the user with status information indicating the state of movable property in the movable property insurance condition inclusion possibility judgment program for judging the inclusion possibility of the movable property insurance condition. Three or more stages of association between the information acquisition step to acquire the movable property insurance condition of the movable property, the combination of the previously acquired reference status information and the reference movable property insurance condition, and the possibility of including the movable property insurance condition for the combination. It is possible to include the movable property insurance condition based on the state information acquired through the information acquisition step and the movable property insurance condition by referring to the association degree acquisition step for acquiring the degree in advance and the association degree acquired in the related degree acquisition step. It is characterized in that a computer is made to perform a judgment step for judging sex.

The movable property insurance condition inclusion possibility judgment program to which the present invention is applied is the movable property insurance condition inclusion possibility judgment program for determining the inclusion possibility of the movable property insurance condition for the user, and the attribute information of the movable property and the movable property of the movable property. Three or more levels of association between the information acquisition step for acquiring movable property insurance conditions, the combination of previously acquired reference attribute information and reference movable property insurance conditions, and the possibility of inclusion of movable property insurance conditions for the combination in advance. With reference to the degree of association acquisition step to be acquired and the degree of association acquired in the above-mentioned degree of association acquisition step, the possibility of including the movable property insurance condition is determined based on the attribute information acquired through the above-mentioned information acquisition step and the movable property insurance condition. It is characterized by having a computer execute a decision step to be performed.

The movable property insurance condition inclusion possibility judgment program to which the present invention is applied is, in the movable property insurance condition inclusion possibility judgment program for judging the inclusion possibility of the movable property insurance condition, the guarantee information regarding the movable property guarantee and the said Three or more levels of association between the information acquisition step for acquiring movable property insurance conditions, the combination of previously acquired reference guarantee information and reference movable property insurance conditions, and the inclusion of movable property insurance conditions for that combination. The possibility of including the movable property insurance condition based on the guarantee card information and the movable property insurance condition acquired through the above information acquisition step by referring to the association degree acquisition step acquired in advance and the association degree acquired in the above-mentioned association degree acquisition step. It is characterized by having a computer execute a judgment step to judge.

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

It is a block diagram which shows the whole structure of the movable property insurance proposal system 1 in which the movable property 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 which shows the example of the degree of association which outputs the movable property insurance condition as an output solution with guarantee information as an input parameter. It is a figure which shows another example of the degree of association which outputs the movable property insurance condition as an output solution, which is the guarantee card information and attribute information as an input parameter. It is a figure which shows another example of the degree of association which makes the attribute information as an input parameter, and outputs the movable property insurance condition as an output solution. It is a figure which shows the example of the degree of association which outputs the state information and movable property insurance conditions as input parameters, and outputs the inclusiveness of insurance conditions as an output solution. It is a figure which shows the example of the degree of association which outputs state information, attribute information, movable property insurance condition as an input parameter, and outputs the inclusion possibility of the insurance condition as an output solution. It is a figure which shows the example of the degree of association which outputs state information, attribute information, guarantee information, movable property insurance condition as an input parameter, and outputs the inclusion possibility of an insurance condition as an output solution. It is a figure which shows the example of the degree of association which outputs the attribute information as an input parameter, the movable property insurance condition, and the inclusion possibility of an insurance condition as an output solution. It is a figure which shows the example of the degree of association which outputs the guarantee information and the movable property insurance condition as an input parameter, and outputs the inclusiveness of an insurance condition as an output solution.

Movables insurance proposal program Hereinafter, the movables 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 movable property insurance proposal system 1 to which a movable property insurance proposal program to which the present invention is applied is implemented. The movable property 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 using 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 guarantee information indicating the previously acquired movable property guarantee. The reference warranty information includes all information regarding the movable property warranty, and may be an image of the warranty or text information of the warranty. Movables include, for example, all movables such as vehicles such as automobiles, furniture, home appliances, musical instruments, and goods.

Reference attribute information is also stored in the database 3. This reference attribute information is related to the attribute of movable property, and includes all information related to the movable property such as purchase time, place of purchase, product version, instruction manual, and the like.

Information on movable property insurance conditions is also accumulated in the database 3. Movable property insurance is also called movable property comprehensive insurance. Movable property insurance conditions indicate all the conditions for getting movable property insurance and all the conditions for applying movable property insurance. Movable property insurance may also include one or both of the insurance disclaimer and the insurance covenant.

The database 3 stores reference status information indicating the status of past movables. State information includes any information that indicates the state of movables. The reference status information is the status information of the movable property previously acquired. The state information indicates the state of movable property, and includes, for example, failure information indicating a failure state, theft information indicating whether or not the movable property has been stolen, and the like. State information is obtained by collecting state information about various objects. The failure information includes, for example, failure information such as total loss, half loss, and partial damage of movable property.

The state information may include the state of movable property as image information, or may include the state of movable property as text information. As the state information, for example, the failure information may be an image. The image information may be an image captured by a smartphone, a camera, or the like, or may be a satellite image captured by an artificial satellite. For example, the state information may include the damage status of the automobile, the damage status of the home appliance, and the like as image information. For example, the state information may include text information indicating that the musical instrument as movable property has been stolen.

Database 3 also contains information on the inclusion of movable property insurance conditions. This inclusion possibility data is information on whether or not it is determined that a certain movable property insurance condition is satisfied based on past cases of movable property failure or the like. These data on the possibility of inclusion are data on cases where it is judged that the movable property insurance conditions are met or not, by comparing the past cases of movable property failures with the movable property insurance conditions. Is collected and accumulated.

That is, in the database 3, in addition to such reference guarantee card information, any one or more of the reference attribute information and each data of the movable property insurance conditions are stored in association with each other. Further, in the database 3, reference state information and data on the possibility of inclusion of the movable property insurance condition are stored in association with each other with respect to the movable property insurance condition of the movable property. Further, in the database 3, reference attribute information and data on the possibility of including the movable property insurance condition are stored in association with each other with respect to the movable property insurance condition of the movable property. Further, in the database 3, the reference guarantee information and the data on the inclusiveness of the movable property insurance condition are stored in association with each other with respect to the movable property insurance condition of the movable property. Further, in this database 3, reference state information, reference attribute information, and data on the possibility of inclusion of the movable property insurance condition are stored in association with each other with respect to the movable property insurance condition of the movable property. Further, in this database 3, each data of the possibility of including the movable property insurance condition is stored for any one or more of the reference state information, the reference guarantee card information, and the reference attribute information, and the movable property insurance condition of the movable property. It is remembered as being linked to each other.

The search device 2 is composed of electronic devices such as a personal computer (PC), but is embodied in all other electronic devices such as mobile phones, smartphones, tablet terminals, and wearable terminals in addition to PCs. 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 by the control unit 24 and executed.

The operation in the movable property insurance proposal system 1 having the above-described configuration will be described.

In the movable property insurance proposal system 1, for example, as shown in FIG. 3, it is premised that three or more levels of association between the reference guarantee information and the movable property insurance conditions are set and acquired in advance. The reference warranty information includes image information and text information of the previous movable property warranty.

Movable property insurance conditions are a concept that includes all the conditions for getting insurance, the conditions under which insurance is applied, and the conditions for which compensation, etc. are paid, and includes one or both of the disclaimer of insurance and the special provisions of insurance. It may be.

That is, through this reference guarantee information and the data set of movable property insurance conditions, it is possible to know what kind of movable property insurance conditions are applied according to the contents of the guarantee in the reference guarantee information. Regarding the movable property insurance conditions, the custom-made movable property insurance conditions set for each movable property may be read from a database stored in an insurance company or the like and used. In other words, by collecting reference guarantee information according to the contents of the past guarantee for each movable property and the data set of the movable property insurance conditions set for the movable property, any movable property insurance with the contents of any guarantee in the past It becomes possible to know whether the condition has been set.

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

The reference guarantee information is associated with each other through three or more levels of association with the movable property insurance conditions as this output solution. Guarantee information for reference is arranged on the left side through this degree of association, and each movable property insurance condition is arranged on the right side through this degree of association. The degree of association indicates which movable property insurance condition is highly relevant to the reference guarantee information arranged on the left side. In other words, this degree of association is an index showing what kind of movable property insurance condition each reference guarantee information is likely to be associated with, and the most reliable movable property insurance condition is selected from the reference guarantee information. It shows the accuracy above. In the example of FIG. 3, w13 to w19 are shown as the degree of association. These w13 to w19 are shown in 10 stages as shown in Table 1 below, and the closer to 10 points, the higher the degree of relevance to the movable property insurance conditions, and conversely, the closer to 1 point, the more movable property. It shows that the insurance conditions are not related to each other.

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 guarantee information and the movable property insurance condition 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 warranty information P01 includes a warranty against a natural failure that the zoom lens of the camera does not come out, and the image of the warranty of this camera is included in this. At this time, it is investigated what kind of custom-made movable property insurance conditions in the past were applied to the movable property having such reference guarantee information P01.

This analysis and analysis may be performed by artificial intelligence. In such a case, for example, in the case of reference guarantee information P01, analysis is performed from the past data of the database stored in the insurance company. If there are many applicable movable property insurance conditions Q1, the degree of association that leads to this movable property insurance condition Q1 is set higher, there are many cases of movable property insurance condition Q2, and if there are few movable property insurance conditions Q1, movable property insurance The degree of association that leads to condition Q2 is set high, and the degree of association that leads to condition Q1 of movable property insurance is set 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 movable property insurance conditions for movables from now on, the optimum movable property insurance conditions will be searched using the above-mentioned learned data. In such a case, the movable property performance information of the movable property for which the movable property insurance condition is to be newly set is newly acquired.

The newly acquired warranty card information is input by the information acquisition unit 9 described above.

Based on the guarantee information newly obtained in this way, it is estimated what movable property 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 guarantee information is the same as or similar to P02, the movable property insurance condition Q2 is associated with w15 and the movable property insurance condition Q3 is associated with the association degree w16 via the degree of association. In such a case, the movable property insurance condition Q2, which has 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 movable property 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 as long as it is based on the degree of association, it may be selected in any other priority.

In this way, the newly acquired warranty card information and the reference warranty card information are consistent with each other in terms of information type. Therefore, when the new warranty card information is acquired, the same or similar reference is made. It is possible to estimate the optimal movable property insurance conditions by immediately referring to the guarantee card information.

By the way, when acquiring the image information and text information of the previous movable property guarantee as the guarantee information, the reference guarantee information is learned in advance between the image information of the guarantee and the text information of the guarantee corresponding to these. Need to keep. Then, the optimum movable property insurance conditions are searched for through the reference guarantee information according to the actually acquired guarantee information.

At this time, the above-mentioned degree of association may be configured by combining the image information and the text information of the guarantee card. Then, the solution may be searched by learning the movable property insurance condition as the search solution for the combination.

Then, among the reference guarantee information, the combination of the acquired image information and the reference information according to the text information and the degree of association with the movable property insurance condition at three or more levels are acquired in advance. Next, using this, the movable property insurance conditions to be proposed based on the degree of association with the movable property insurance conditions for the combination of the newly acquired image information and the reference information according to the text information as described above. To explore.

In the example of FIG. 4, it is premised that the combination of the reference guarantee card 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 guarantee card 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 guarantee information as such input data. Each intermediate node is further linked to the output. In this output, the movable property insurance conditions as the output solution are displayed.

Each combination (intermediate node) of the reference guarantee information and the reference attribute information is associated with each other through three or more levels of association with the movable property insurance conditions as this output solution. The reference guarantee information and the reference attribute information are arranged on the left side through this degree of association, and the movable property insurance conditions are arranged on the right side through this degree of association. The degree of association indicates the degree of relevance to the movable property insurance conditions with respect to the reference guarantee card information and the reference attribute information arranged on the left side. In other words, this degree of association is an index showing what kind of movable property insurance conditions each reference guarantee information and reference attribute information is likely to be associated with, and from the reference guarantee information and reference attribute information. It shows the accuracy in selecting the most probable movable property insurance conditions. 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 search device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, the search device 2 accumulates past data on the reference guarantee information, the reference attribute information, and the movable property 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 movable property guarantee card set in the past is the reference guarantee card information P11. At this time, when the attribute information of the movable property is "model number XX, purchased two years ago", the movable property 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 warranty information P11 and the reference attribute information P16 “model number XX, purchased two years ago”, the movable property insurance conditions are analyzed from the past data. If the movable property insurance condition has many cases of movable property insurance condition Q3, the degree of association leading to this movable property insurance condition Q3 is set higher, and there are many cases of movable property insurance condition Q4 and few cases of movable property insurance condition Q3. The degree of association that leads to the movable property insurance condition Q4 is set high, and the degree of association that leads to the movable property insurance condition Q3 is set low. For example, in the example of the intermediate node 61a, the output of the movable property insurance condition Q1 and the movable property insurance condition Q2 is linked, but from the previous case, the degree of association of w13 connected to the movable property insurance condition Q1 is set to 7 points, and the movable property insurance condition Q2. The degree of association of w14 that leads to 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 guarantee information P11, and the degree of association of the movable property insurance condition Q3 is w15 and the movable property insurance condition Q5. The degree of association is w16. The node 61c is a node in which the reference attribute information P15 and P17 are combined with the reference guarantee information P12, and the degree of association of the movable property insurance condition Q2 is w17 and the degree of association of the movable property 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 movable property insurance conditions to be newly searched from now on, the movable property insurance conditions will be estimated using the above-mentioned learned data. In such a case, a new guarantee for movable property for which the movable property insurance condition is to be set is newly acquired, and the attribute information of the movable property is acquired.

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

Based on the newly acquired guarantee card information and attribute information in this way, we will try to search for the optimum movable property 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 warranty 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 node In 61d, the movable property insurance condition Q3 is associated with w19, and the movable property insurance condition Q4 is associated with the degree of association w20. In such a case, the movable property insurance condition Q3, which has 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 movable property 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 as long as it is based on the degree of association, it may be selected in any other priority.

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, when one or more of the purchase time, the purchase place, the product version, and the instruction manual are acquired as the attribute information, for example, the purchase time, the purchase place, the product version, as the reference attribute information, It is necessary to learn the instruction manual in advance with the movable property insurance conditions together with the reference warranty information. Then, the movable property insurance conditions are searched for based on the reference attribute information according to the actually acquired purchase time, purchase place, product version, and instruction manual.

At this time, the above-mentioned degree of association may be configured by any combination of two or more of the purchase time, the place of purchase, the product version, and the instruction manual. Then, the solution may be searched by learning the movable property insurance condition as the search solution for the combination.

In the animal insurance proposal system 1, when the reference attribute information is learned from the animal insurance condition, the reference attribute information alone is used as shown in FIG. 5 without including the reference guarantee information. It may be made to train with the data set associated with. That is, it is premised that the degree of association between the reference attribute information and the animal insurance conditions is set and acquired in advance. That is, through this reference attribute information and the data set of movable property insurance conditions, it is possible to know what kind of movable property insurance conditions are applied according to the reference attribute information. Regarding the formation of the degree of association and the solution search shown in FIG. 5, the explanation below will be omitted by quoting the explanation of FIG.

Movable property condition conditional feasibility determination program Next, the movable property insurance condition conditional feasibility determination program to which the present invention is applied will be described. Similarly, the movable property 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, which are necessary for executing the movable property insurance condition conditional feasibility determination program, omit the explanation below by quoting the explanations of FIGS. 1 and 2 described above. To do.

The operation of the movable property insurance condition conditional feasibility judgment program having the above-described configuration will be described.

The movable property insurance condition conditional feasibility determination program is premised on the formation of a combination of the reference state information and the reference movable property insurance condition, as shown in FIG. 6, for example. The reference movable property insurance conditions indicate all the conditions for obtaining movable property insurance and all the conditions for applying movable property insurance. Movable property insurance may also include one or both of the insurance disclaimer and the insurance covenant. In fact, the reference state information and the reference movable property insurance condition are composed of a data set of the movable property state and the movable property insurance condition set for the movable property when focusing on a certain movable property.

As the input data, the reference status information and the reference movable property insurance conditions are arranged in this way. The intermediate node shown in FIG. 6 is a combination of the reference state information and the reference movable property insurance conditions as such input data. Each intermediate node is further linked to the output. In this output, the likelihood of including movable property insurance conditions as an output solution is shown as a percentage.

Each combination (intermediate node) of the reference state information regarding movable property and the reference movable property insurance condition is related to each other through three or more levels of linkage to the inclusiveness of the movable property insurance condition as this output solution. There is. In the example of FIG. 6, 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 movable property 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 movable property 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, in determining the possibility of inclusion of the actual movable property insurance condition, the judgment device 2 actually displays the state of each movable property, the data of the movable property insurance condition subscribed to the movable property, and each case in the insurance company. Data on whether or not it has been applied as included in the movable property insurance conditions is accumulated, and by analyzing and analyzing these data, the degree of association shown in FIG. 6 is created. This analysis and analysis may be performed by artificial intelligence.

Further, the degree of association shown in FIG. 6 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. 6, the node 61a is a node of the combination of the reference movable property insurance condition P20 with respect to the reference state information P31, and the inclusion possibility “30%” of the movable property insurance condition is the degree of association. w13, "60%" is the degree of association w14. The node 61b is a node that is a combination of the reference movable property insurance condition P18 with respect to the reference state information P31, and the possibility of inclusion of the movable property insurance condition “85%” is the degree of association w15 and “70%” is the degree of association w16. It is said that.

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 in order for the insurance to be applied to movables that will actually make a new decision. In such a case, we will obtain status information regarding movables that are to be newly insured. Similarly, the movable property insurance conditions related to the movable property insurance that the movable property is going to receive are also acquired. The state information and movable property 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 electronically in which the insurance conditions are described. The data may be accessed via the Internet or the like to extract text data. Further, if the conditions related to 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 acquired status information is the same or similar to P31 and the similarly acquired movable property insurance conditions are the same or similar to P20, it corresponds to node 61a and the association attached to that node 61a. Output the possibility of applying insurance conditions according to the degree. 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 user or the insurance company can understand the possibility of conditional inclusion of the insurance to be enrolled in the movable property from the state of the movable property.

It is not essential to select the one with the higher degree of association as the optimum solution, and the one with the lower degree of association is selected as the optimum solution, although the degree of association is low but the association itself is recognized. May be. 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. 7, in addition to the above-mentioned reference state information and the reference movable property insurance condition, the combination of the reference attribute information and the possibility of including the movable property insurance condition for the combination have three or more levels of association. An example of the setting is shown.

In such a case, as shown in FIG. 7, the degree of association is such that the set of combinations of the reference state information, the reference attribute information, and the reference movable property insurance condition is set as the nodes 61a to 61e of the intermediate node as described above. It will be expressed.

For example, in FIG. 7, the reference state information P32 is associated with the reference degree w3, the reference attribute information P15 is associated with the association degree w7, and the reference movable property insurance condition P21 is associated with the association degree w11. Similarly, in the node 61e, the reference state information P33 is associated with the association degree w5, the reference attribute information P15 is associated with the association degree w8, and the reference movable property 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 state information, the attribute information, and the movable property insurance condition.

FIG. 8 shows a combination of the above-mentioned reference state information, the reference movable property insurance condition, the reference attribute information, and the reference guarantee information, and the possibility of including the movable property insurance condition for the combination. An example is shown in which three or more levels of association are set.

In such a case, as shown in FIG. 8, the degree of association is such that a set of combinations of reference state information, reference attribute information, reference guarantee information, and reference animal insurance conditions is an intermediate node as described above. It will be expressed as nodes 61a to 61e of.

For example, in FIG. 8, in the node 61c, the reference state information P32 has the association degree w3, the reference attribute information P15 has the association degree w7, the reference guarantee information P11 has the association degree w1, and the reference movable property insurance. The condition P21 is associated with the degree of association w11. Similarly, in the node 61e, the reference state information P33 has an association degree w5, the reference attribute information P15 has an association degree w8, the reference guarantee information P11 has an association degree w2, and the reference movable property insurance condition P20 is associated. It is related at degree w10.

Similarly, when such a degree of association is set, the search solution is determined based on the newly acquired state information, the attribute information, the guarantee card information, and the movable property insurance condition.

In determining the search solution, the degree of association shown in FIG. 8 acquired in advance is referred to. For example, the acquired state information is the same as or similar to the reference state information P32, the acquired attribute information corresponds to the reference attribute information P15, the acquired guarantee information corresponds to the reference guarantee information P11, and is acquired. When the movable property insurance condition corresponds to the reference movable property insurance condition P21, the combination is associated with the node 61c, and this node 61c has a degree of association w17 with a possibility of including the movable property insurance condition "60%". In addition, the possibility of inclusion of the condition is "45%", and 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.

As described above, in the movable property insurance condition inclusion possibility judgment program, the combination of the reference state information, the reference attribute information, any one or more of the reference attribute information, and the reference movable property insurance condition, and the combination thereof. Three or more levels of association with the inclusiveness of movable property insurance conditions may be set. Similarly, when such a degree of association is set, the search solution is determined based on any one or more of the newly acquired state information, the attribute information, the guarantee card information, and the movable property insurance condition.

In the animal insurance condition inclusion possibility judgment program, when the reference attribute information is learned from the reference animal insurance condition, the reference attribute information is not included and the reference attribute information is not included, as shown in FIG. Information may be trained in a dataset associated with reference animal insurance conditions. That is, it is premised that the combination of the reference attribute information, the reference movable property insurance condition, and the inclusion possibility of the movable property insurance condition are set and acquired in advance at three or more levels. That is, through the combination of this reference attribute information and the reference movable property insurance condition and the data set of the inclusiveness of the reference movable property insurance condition, the movable property is according to the combination of the reference attribute information and the reference movable property insurance condition. You can see how the coverage of insurance conditions was judged and applied. Regarding the formation of the degree of association and the solution search shown in FIG. 9, the explanation below will be omitted by quoting the explanation of FIG.

In addition, in the animal insurance condition inclusion possibility judgment program, when learning the reference guarantee card information from the reference animal insurance condition, the reference guarantee card is not included as shown in FIG. Information may be trained in a dataset associated with reference animal insurance conditions. That is, it is premised that the combination of the reference guarantee information, the reference movable property insurance condition, and the inclusion possibility of the movable property insurance condition are set and acquired in advance at three or more levels. That is, through the combination of this reference guarantee information and the reference movable property insurance condition and the data set of the inclusiveness of the reference movable property insurance condition, the movable property depends on the combination of the reference guarantee document information and the reference movable property insurance condition. You can see how the coverage of insurance conditions was judged and applied. Regarding the formation of the degree of association and the solution search shown in FIG. 10, the explanation below will be omitted by quoting the explanation of FIG.

Further, as the reference movable property insurance condition, a combination with the exemption judgment standard based on the disclaimer of insurance may be formed. For example, the disclaimer is "no damage for one year". In the presence of such disclaimers, each combination of at least one of the reference attribute information, reference status information and reference warranty information and the disclaimer criteria based on the insurance disclaimer as a reference animal insurance condition. (Intermediate nodes) may form a trained model that correlates with each other through three or more levels of association with the possibility of insurance coverage as this output solution.

After creating such a learned model, we will determine the possibility of applying the disclaimer regarding the insurance to movables that are actually going to receive new insurance. In such a case, at least one of the attribute information, the state information and the guarantee information regarding the movable property to be newly insured will be acquired. Similarly, the disclaimer regarding the insurance that the movable property intends to receive is also obtained. To obtain the disclaimer regarding insurance that the movable property intends to receive, for example, a smartphone or the like may be used to image a sentence in which the disclaimer is described, recognize the text, and extract text data. The electronic data in which the disclaimer is described may be accessed via the Internet or the like to extract text data. Further, if the disclaimer regarding the insurance to be received by the movable property is input in advance on the system side, it may be used as it is.

The newly acquired disclaimer is compared with the disclaimer classified as the reference movable property insurance condition, and the judgment of the possibility of inclusion is requested through the reference movable property insurance condition corresponding to the acquired disclaimer. ..

Further, as the reference movable property insurance condition, a combination with a special provision of insurance may be formed. In the presence of such special provisions, each combination (intermediate node) of at least one of the reference attribute information, the reference status information, and the reference guarantee information and the special provision of insurance as a reference animal insurance condition , As this output solution, a trained model may be formed in which the possibility of insurance coverage is linked to each other through three or more levels of linkage.

From now on, we will judge the possibility of applying the special provisions regarding the insurance to movables that are going to receive new insurance. In such a case, at least one of the reference attribute information, the reference status information, and the reference guarantee information regarding the movable property to be newly insured will be acquired. Similarly, the special provisions regarding the insurance that the movable property intends to receive are also acquired. The acquisition of the insurance covenant that the movable property intends to receive may be the same as the method of obtaining the disclaimer.

The newly acquired special provisions are compared with the special provisions classified as reference movable property insurance conditions, and the judgment of the possibility of inclusion is requested through the reference movable property insurance conditions corresponding to the acquired special provisions. ..

At this time, by incorporating both the disclaimer and the special provision in the reference movable property insurance conditions, the judgment of inclusion may be made at the same time. That is, by utilizing the three or more levels of association between the above combination having movable property insurance conditions including one or both of the insurance disclaimer or the insurance covenant and the possibility of including the movable property insurance conditions for the combination, a new degree of association is used. The feasibility will be judged from the movable property insurance conditions including one or both of the insurance disclaimer or the insurance special provisions acquired in.

According to the present invention having the above-described configuration, anyone can easily propose the optimum movable property insurance conditions or judge the possibility of including the movable property insurance conditions without any special skill or experience. Further, according to the present invention, it is possible to determine the search solution with higher accuracy than that performed by 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 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 movable property insurance conditions based on the degree of association expressed by such three or more levels of numerical values, or by judging the inclusiveness of the movable property insurance conditions, as a candidate for the possibility of a search solution. Under multiple possible situations, it is possible to search and display in descending order of the degree of association. 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%. Remind 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 should be emphasized 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. In addition, when regional characteristic information is acquired, and in addition to this, property information, external environmental information, and knowledge, information, and data regarding the type of industry and price to be introduced are acquired, the degree of association is increased or decreased accordingly. Let me.

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 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 Movable property insurance proposal system 2 Search device 21 Internal bus 23 Display unit 24 Control unit 25 Operation unit 26 Communication unit 27 Estimate unit 28 Storage unit 61 node

Claims (7)

In the movable property insurance condition proposal program that proposes movable property insurance conditions to users
Information acquisition step to acquire guarantee information about movable property guarantee,
The linking degree acquisition step to acquire the linkage degree of 3 or more levels in advance between the previously acquired reference guarantee information and the movable property insurance conditions, and
The feature is that the computer is made to execute a search step for searching for movable property insurance conditions to be proposed based on the guarantee card information acquired through the information acquisition step with reference to the association degree acquired in the association degree acquisition step. Movable property insurance condition proposal program. In the movable property insurance condition proposal program that proposes movable property insurance conditions to users
Information acquisition step to acquire attribute information of movable property,
A degree of association acquisition step for acquiring in advance three or more levels of association between the previously acquired reference attribute information and movable property insurance conditions, and
It is characterized by having a computer execute a search step for searching for movable property insurance conditions to be proposed based on the attribute information acquired through the information acquisition step with reference to the association degree acquired in the association degree acquisition step. Movable property insurance condition proposal program. The above search step is characterized by searching for movable property insurance conditions to be proposed by referring to three or more levels of association with movable property insurance conditions including one or both of the insurance disclaimer or the insurance covenant. The movable property insurance condition proposal program according to claim 1 or 2. In the movable property insurance condition conditional feasibility judgment program that judges the feasibility of movable property insurance condition for the user
Status information indicating the state of movable property, information acquisition step to acquire movable property insurance conditions of the movable property, and
A degree of association acquisition step for acquiring in advance three or more levels of association between the previously acquired reference state information and the reference movable property insurance condition and the possibility of including the movable property insurance condition for the combination.
With reference to the degree of association acquired in the above step of acquiring the degree of association, the computer executes a judgment step of determining the possibility of including the movable property insurance condition based on the state information acquired through the above information acquisition step and the movable property insurance condition. Movable property insurance condition inclusion possibility judgment program characterized by letting. In the movable property insurance condition conditional feasibility judgment program that judges the feasibility of movable property insurance condition for the user
Movable property attribute information, information acquisition step to acquire movable property insurance conditions of the movable property,
A degree of association acquisition step for acquiring in advance three or more levels of association between the previously acquired reference attribute information and the reference movable property insurance condition and the possibility of including the movable property insurance condition for the combination.
With reference to the degree of association acquired in the above step of acquiring the degree of association, the computer executes a judgment step of determining the possibility of including the movable property insurance condition based on the attribute information acquired through the above step of acquiring information and the movable property insurance condition. Movable property insurance condition inclusion possibility judgment program characterized by letting. In the movable property insurance condition conditional feasibility judgment program that judges the feasibility of movable property insurance condition for the user
Guarantee information about movable property guarantee, information acquisition step to acquire movable property insurance conditions of the movable property,
A degree of association acquisition step for acquiring in advance three or more levels of association between the previously acquired reference guarantee information and the reference movable property insurance condition and the possibility of inclusion of the movable property insurance condition for the combination.
With reference to the degree of association acquired in the above step of acquiring the degree of association, the computer executes a judgment step for determining the feasibility of including the movable property insurance condition based on the guarantee card information acquired through the above information acquisition step and the movable property insurance condition. Movable property insurance condition inclusion possibility judgment program characterized by letting. In the above information acquisition step, the movable property insurance conditions including one or both of the insurance disclaimer and the insurance covenant are acquired.
In the above judgment step, the degree of association between the combination of movable property insurance conditions including one or both of the insurance disclaimer or the insurance covenant and the inclusion of movable property insurance conditions for the combination is referred to. It is characterized by determining the possibility of inclusion of the movable property insurance condition based on the guarantee card information acquired through the above information acquisition step and the movable property insurance condition including one or both of the insurance disclaimer or the insurance special clause. The movable property insurance condition inclusion possibility judgment program according to any one of claims 4 to 6.

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