JP2021117598A - System, method and program for supporting transaction of personal information of user - Google Patents

Abstract

To provide a system, etc. capable of activating transaction of personal information.SOLUTION: The present invention provides a system for supporting transaction of personal information of users, the system including: acquisition means for acquiring data and/or information relating to the users; calculation means for calculating scores representing values of the personal information of the users based on the acquired data and/or information relating to the users; and selling means for selling the personal information to customers by using the scores.SELECTED DRAWING: Figure 1B

Description

The present invention relates to a system, a method, and a program for assisting a user in buying and selling personal information.

It is known to use a blockchain to manage personal information (Patent Document 1). By using the blockchain, the managed personal information cannot be tampered with substantially, and the reliability of the managed personal information is guaranteed.

Japanese Patent No. 6504639

The inventor of the present invention considered that the value of personal information as information would be increased by managing personal information so that it could not be tampered with, and found that personal information could be a target for sale.

An object of the present invention is to provide a system or the like capable of activating the buying and selling of personal information.

In one embodiment of the present invention, the system for supporting the buying and selling of personal information of a user includes acquisition means for acquiring data and / or information about the user and the acquired data and / or information about the user. Based on this, a calculation means for calculating a score indicating the value of the personal information of the user and a sales means for selling the personal information to a consumer by using the score are provided.

In one embodiment, the sales means include a price fluctuation means for fluctuating the price of the personal information based on the score, and the sales means transfers the personal information to the consumer at the fluctuated price. To sell to.

In one embodiment, the sales means include a search means for searching for a user who satisfies a predetermined condition including the score, and the sales means sells the personal information of the searched user to the consumer. ..

In one embodiment, the acquisition means further acquires data and / or information about the consumer, and the calculation means trusts the consumer based on the acquired data and / or information about the consumer. The reliability score indicating the degree is further calculated, and the selling means sells the personal information to the consumer by using the score and the reliability score.

In one embodiment, the sales means include matching means for matching the user with the consumer based on the score and the reliability score, and the sales means is matched with the user. The personal information is sold to the consumer.

In one embodiment, the calculation means calculates the score using a model that has learned the relationship between the data and / or information about the user and the score.

In one embodiment, the calculation means has a plurality of the models, and the calculation means selects a model from which the score should be calculated from the plurality of models based on the attributes of the consumer.

In one embodiment, the acquisition means acquires the data from a device associated with the user.

In one embodiment, the acquisition means acquires the information independently disclosed by the user.

In one embodiment, the system further comprises detection means for detecting that the personal information has been changed.

In one embodiment, the system further comprises a notification means for notifying the consumer that the personal information has been changed in response to detecting that the personal information has been changed.

In one embodiment, the user's personal information is used for the sale of insurance products.

In one embodiment of the invention, a method for assisting a user in buying or selling personal information is to acquire data and / or information about the user and based on the acquired data and / or information about the user. It includes calculating a score indicating the value of the personal information of the user and selling the personal information to a consumer by using the score.

In one embodiment of the present invention, a program for supporting the buying and selling of personal information of a user is executed in a system including a processor unit, and when the program is executed, data and / or information about the user is acquired. Based on the acquired data and / or information about the user, a score indicating the value of the personal information of the user is calculated, and the personal information is sold to the consumer by using the score. The processor unit is made to perform the processing including the processing.

According to the present invention, it is possible to provide a system or the like capable of activating the buying and selling of personal information. As a result, the market for buying and selling personal information can be expanded.

A diagram schematically showing an example of a flow of buying and selling personal information in the marketplace 10 for buying and selling personal information of a user. A diagram schematically showing an example of a flow of buying and selling personal information in the marketplace 10 for buying and selling personal information of a user. The figure which shows an example of the structure of the system 100 for supporting the buying and selling of personal information of a user. The figure which shows an example of the specific configuration of the system 100 for supporting the buying and selling of personal information of a user. Conceptual diagram showing the association of information stored in the database unit 200 The figure which shows an example of the structure of the processor part 120 The figure which shows an example of the structure of the neural network model which can be used by the calculation means 122. A flowchart showing an example (process 500) of processing by the system 100 for supporting the buying and selling of personal information of a user. A flowchart showing another example (process 600) of processing by the system 100 for supporting the buying and selling of personal information of a user.

Hereinafter, embodiments of the present invention will be described with reference to the drawings.

In the following, we will explain by taking as an example to support the buying and selling of personal information used in the insurance industry.

1. 1. Service for supporting the sale and purchase of personal information of a user The inventor of the present invention has developed a new service for supporting the sale and purchase of personal information of a user. This service provides a marketplace for buying and selling personal information of users in order to support the buying and selling of personal information of users. A marketplace is a trading market for conducting electronic commerce. In this marketplace, users such as general consumers can sell personal information, and consumers of personal information such as sales staff of insurance companies can purchase personal information.

1A and 1B are diagrams schematically showing an example of a flow of buying and selling personal information in the marketplace 10 for buying and selling personal information of a user.

In this example, at least one user 20 sells the personal information in the marketplace 10, and at least one insurance company salesperson 30 purchases the personal information of the user 20 in the marketplace 10. In the example shown in FIG. 1A, three users 20 and three salespeople 30 are shown, but the number of users 20 and the number of salespeople 30 are not limited thereto.

In step S1, at least one user 20 provides his / her personal information to the marketplace 10 for sale in the marketplace 10. At least one user 20 can provide his / her personal information to the marketplace 10 by using, for example, his / her own information terminal device (for example, a smartphone, a tablet, a smart watch, a smart glass, a personal computer). Personal information includes, but is not limited to, for example, at least one of name, address, telephone number, e-mail address, age, gender, family structure, occupation, annual income, assets, history, and the like.

In step S2, at least one user 20 provides data and / or information about the user 20 to the marketplace 10.

Here, the data about the user means objective information about the user. Data about the user can be obtained, for example, from the user's information terminal device and / or an IoT device capable of communicating with the user's information terminal device. The data related to the user is, for example, the user's behavior history (for example, exercise history, health activity history, purchase history, saving behavior history, meal history, Web page browsing history, conversation history with AI speaker, SNS usage history, etc.), It includes, but is not limited to, health information (eg, height, weight, blood pressure, heart rate, affected illness, etc.), information on DNA, and the like. Data about the user may include personal information about the user.

Here, the information about the user means subjective information mainly composed of the user. Information about the user can be obtained, for example, by the user entering directly into the information terminal device. Alternatively, the information about the user can be indirectly derived from, for example, a question or question to the user. Information about the user includes, but is not limited to, for example, hobbies, preferences, reasons for action (eg, motives for aspirations for current or past occupations, reasons for changing jobs from past occupations, etc.).

At least one user 20 can provide data and / or information about the user 20 to the marketplace 10 via, for example, its own information terminal device. Alternatively, at least one user 20 can provide data and / or information about the user 20 to the marketplace 10 via, for example, an IoT device associated with at least one user 20.

When data and / or information is provided by at least one user 20, Marketplace 10 calculates the score for that user 20 based on the data and / or information provided. This score is a score indicating the value of the personal information of the user, and in this example, it indicates the value of the personal information in the insurance industry. The higher the score, the higher the value of the user's personal information. For example, a user with a high value of personal information in the insurance industry can be said to be a user who is worth selling an insurance product (for example, a user who has a low risk of delinquent insurance premiums, a user who has a low risk of paying insurance claims, etc.). ..

The score can be used, for example, as an index in deciding which personal information should be bought. For example, when the personal information of a plurality of users has the same selling price, the salesperson can determine that the personal information of the user with the higher score should be bought by referring to the score. The score may be used, for example, to determine the selling price of personal information. The score may be used, for example, to match the user 20 with the salesperson 30.

In the marketplace 10, as shown in FIG. 1B, the selling price and the score of the personal information of each user 20 may be presented to at least one salesperson 30.

For example, for the user 20 specified by ID: AAA, the score is 80, the selling price of the name as personal information is 100 yen, the selling price of the address is 500 yen, and the selling price of the telephone number is 500 yen. , It is presented that the selling price of the e-mail address is 300 yen. For example, for the user 20 specified by ID: BBB, the score is 50, the selling price of the name as personal information is 100 yen, the selling price of the address is 500 yen, and the selling price of the telephone number is 500 yen. , It is presented that the selling price of the e-mail address is 300 yen. For example, for the user 20 specified by ID: CCC, the score is 20, the selling price of the name as personal information is 20 yen, the selling price of the address is 200 yen, and the selling price of the telephone number is 200 yen. , It is presented that the selling price of the e-mail address is 100 yen.

At least one salesperson 30 purchases the personal information of at least one user 20 in consideration of the score of each user 20 and the selling price of the personal information.

In step S3, at least one salesperson 30 provides the marketplace 10 with a purchase request for personal information. At least one salesperson 30 may provide a purchase request for personal information to the marketplace 10 using, for example, his / her own information terminal device (for example, a smartphone, tablet, smart watch, smart glass, personal computer). can. The purchase request includes specifying which personal information of which user to purchase.

In step S4, the personal information specified in the purchase request is provided to at least one salesperson 30. At least one salesperson will pay for the personal information provided.

At least one salesperson 30 can use the purchased personal information for the insurance sales business. For example, at least one salesperson 30 can sell an insurance product to a user based on the purchased personal information. At this time, the purchased personal information is guaranteed to have high value in the insurance industry by the score, so that even if the sales effort is put into it, the cost effectiveness is hardly deteriorated. By visualizing the value of personal information by the score in this way, the salesperson 30 can purchase high-value personal information, which leads to an increase in the purchase motivation of the salesperson 30, which in turn leads to the market. It can lead to activation of Place 10.

The above-mentioned marketplace 10 can be realized by a system for supporting the buying and selling of personal information of a user, which will be described later.

2. Configuration of a system for supporting the buying and selling of personal information of a user FIG. 2A shows an example of the configuration of a system 100 for supporting the buying and selling of personal information of a user.

The system 100 is connected to the database unit 200. Further, the system 100 is connected to at least one user terminal device 300 and at least one consumer terminal device 400 via a network 500.

Although FIG. 2A shows three user terminal devices 300 and two consumer terminal devices 400, the number of user terminal devices 300 and consumer terminal devices 400 is not limited to this. Any number of user terminal devices 300 and consumer terminal devices 400 may be connected to the system 100 via the network 500.

The network 500 can be any kind of network. The network 500 may be, for example, the Internet or a LAN. The network 500 may be a wired network or a wireless network.

The system 100 is further connected to the blockchain network 600. The blockchain network 600 is a network constructed by blockchain technology and can be used to manage personal information of users. By using the blockchain technology, falsification of the managed information can be easily detected, so that the personal information managed by the blockchain network 600 cannot be falsified substantially.

Although it has been described that personal information is managed by the blockchain technology, the present invention is not limited to the blockchain technology. The blockchain network 600 can be constructed by using any technology as long as the information can be managed so as to be substantially tamper-proof.

An example of the system 100 is, but is not limited to, a computer (eg, a server device) installed in a service provider that provides a marketplace for buying and selling personal information of users. An example of the user terminal device 300 is a computer (for example, a terminal device) used by a user who is a seller of personal information, but the present invention is not limited to this. An example of the consumer terminal device 400 is a computer (for example, a terminal device) used by a consumer (salesperson of an insurance company) who is a buyer of personal information, but the present invention is not limited to this. Here, the computer (server device or terminal device) can be any type of computer. For example, the terminal device can be any type of terminal device such as a smartphone, tablet, personal computer, smart glasses, smart watch or the like.

The database unit 200 stores at least various information used for calculating a score indicating the value of the user's personal information.

FIG. 2B shows an example of a specific configuration of the system 100 for supporting the buying and selling of personal information of a user.

The system 100 includes an interface unit 110, a processor unit 120, and a memory unit 130.

The interface unit 110 exchanges information with the outside of the system 100. The processor unit 120 of the system 100 can receive information from the outside of the system 100 via the interface unit 110, and can transmit the information to the outside of the system 100. The interface unit 110 can exchange information in any format.

The interface unit 110 includes, for example, an input unit that enables information to be input to the system 100. It does not matter in what manner the input unit enables the information to be input to the system 100. For example, when the input unit is a receiver, the receiver may input information by receiving information from the outside of the system 100 via the network. Alternatively, when the input unit is a data reading device, the information may be input by reading the information from the storage medium connected to the system 100.

The interface unit 110 includes, for example, an output unit that enables information to be output from the system 100. It does not matter in what mode the output unit enables the information to be output from the system 100. For example, when the output unit is a transmitter, the transmitter may output information by transmitting information to the outside of the system 100 via a network. Alternatively, when the output unit is a data writing device, the information may be output by writing the information to the storage medium connected to the system 100.

The system 100 can transmit information to and / or receive information from the database unit 200 via, for example, the interface unit 110. The system 100 can transmit information to and / or receive information from the user terminal device 300 via, for example, the interface unit 110. The system 100 can transmit information to and / or receive information from the consumer terminal device 400 via, for example, the interface unit 110.

The system 100 can receive the personal information of the user, for example, via the interface unit 110. The system 100 can receive data and / or information about the user, for example, via the interface unit 110. The system 100 can receive data and / or information about the consumer, for example, via the interface unit 110.

The system 100 can receive a purchase request for personal information via, for example, the interface unit 110. The system 100 can transmit the purchased personal information via, for example, the interface unit 110. The system 100 can receive the attributes of the consumer, for example, via the interface unit 110.

The system 100 can receive, for example, a condition for searching a user who should purchase personal information via the interface unit 110. The system 100 can transmit the search result of the user who should purchase the personal information, for example, via the interface unit 110. The system 100 can receive, for example, a matching request requesting matching between a user and a consumer via the interface unit 110. The matching request may include specifying the score of the user to be matched or the score of the consumer to be matched. The system 100 can transmit the matching result via, for example, the interface unit 110.

The system 100 can receive, for example, information indicating that the personal information managed in the blockchain network 600 has been changed via the interface unit 110. As a result, the system 100 can detect that the personal information managed in the blockchain network 600 has been changed. The system 100, for example, responds to the detection that the personal information managed in the blockchain network 600 has been changed, and transmits a notification to that effect to the user terminal device 300 via the interface unit 110. Therefore, it is possible to confirm with the user whether the change is due to the intention of the user or due to tampering. For example, in response to detecting that the personal information managed in the blockchain network 600 has been changed, the system 100 transmits a notification to that effect to the consumer terminal device 400 via the interface unit 110. be able to.

The processor unit 120 executes the processing of the system 100 and controls the operation of the entire system 100. The processor unit 120 reads a program stored in the memory unit 130 and executes the program. This allows the system 100 to function as a system that performs the desired steps. The processor unit 120 may be implemented by a single processor or may be implemented by a plurality of processors.

The memory unit 130 stores a program required for executing the processing of the system 100, data required for executing the program, and the like. The memory unit 130 stores a program for causing the processor unit 120 to perform a process for supporting the buying and selling of personal information of a user (for example, a program for realizing the process shown in FIGS. 5 and 6 to be described later). May be good. Here, it does not matter how the program is stored in the memory unit 130. For example, the program may be pre-installed in the memory unit 130. Alternatively, the program may be installed in the memory unit 130 by being downloaded via the network. In this case, the type of network does not matter. The memory unit 130 may be implemented by any storage means.

The database unit 200 may store, for example, data and / or information about the user.

Data about the user is objective information about the user. Data about the user can be obtained, for example, from the device associated with the user. The device associated with the user includes, for example, an information terminal device of the user, an IoT device capable of acquiring data about the user, and the like. The data related to the user is, for example, the user's behavior history (for example, exercise history, health activity history, purchase history, saving behavior history, meal history, Web page browsing history, conversation history with AI speaker, SNS usage history, etc.), It includes, but is not limited to, health information (eg, height, weight, blood pressure, heart rate, affected illness, etc.), information on DNA, evaluation by others, and the like. Data about the user may include personal information about the user.

The information about the user is subjective information mainly composed of the user. The information about the user can be, for example, information independently disclosed by the user, and can be acquired, for example, by the user directly inputting the information to the information terminal device. Alternatively, the information about the user can be indirectly derived from, for example, a question or question to the user. Information about the user includes, but is not limited to, for example, hobbies, preferences, reasons for action (eg, motives for aspirations for current or past occupations, reasons for changing jobs from past occupations, etc.).

The database unit 200 may store, for example, data and / or information about the consumer.

Data about consumers is objective information about consumers. Data about the consumer can be obtained, for example, from the device associated with the consumer. The device associated with the consumer includes, for example, the consumer's information terminal device. Data about the consumer includes, for example, the consumer's background (eg, educational background, qualifications, achievements, experience), ability information (eg, exam scores, etc.), evaluation by others (eg, evaluation by the customer, evaluation by the boss / colleague). ) Etc., but not limited to these.

Information about consumers is subjective information mainly about consumers. The information about the consumer can be, for example, the information independently disclosed by the consumer, and can be acquired, for example, by the consumer directly inputting the information into the information terminal device. Alternatively, the information about the consumer can be indirectly derived from, for example, an interview or question to the consumer. Information about consumers includes, but is not limited to, for example, hobbies, preferences, reasons for action (eg, motives for aspirations for current or past occupations, reasons for changing jobs from past occupations, etc.).

The database unit 200 stores various information used for calculating a score indicating the value of the user's personal information.

For example, in the database unit 200, the concept of parameters used for score calculation and various information can be stored in association with each other. The parameters used to calculate the score include, for example, "personality". Here, the "personality" is information indicating whether or not a person's personality and / or personality is reliable, and includes, for example, an evaluation from another person. The parameters used to calculate the score indicating the value of personal information in the insurance industry can include, for example, "personality", "money", and "health". Here, the "money" is information about the person's money, and includes, for example, information such as annual income, assets, and whether or not the person is saving money. Here, "health" is information on the health of the person, and includes, for example, information on illness, eating habits, activities for health, and the like.

For example, with respect to the parameter "personality", the database unit 200 may store the concept of personality in association with various information. For example, the concept of "altruistic" (or "transpersonal") regarding "personality" includes keywords such as "volunteer," "donation," "concern," "consultation," "frequently participate in volunteers," and so on. Statuses such as "frequently consulted" and "have donated experience" can be stored in association with each other. For example, the concept of "SDGs" (Sustainable Development Goals, or "Sustainable Development Goals") for "personality" includes keywords related to 17 goals and / or 169 targets (eg, "equality", "environment"). Statuses such as "conservation", etc.), "acting related to 17 goals and / or 169 targets", "having ideas related to 17 goals and / or 169 targets", etc. are associated. Can be stored.

For example, regarding the parameter "money", the database unit 200 may store the concept of money and various information in association with each other. For example, the concept of "rich" (or "cash flow rich") related to "money" is associated with keywords such as "Harvard University" and "MBA", and status such as "annual income of 20 million yen or more". Can be done. For example, the concept of "rich" (or "stock rich") related to "money" may be associated with keywords such as "landowner" and "shareholder", and status such as "assets of 100 million yen or more". ..

For example, regarding the parameter "health", the database unit 200 may store the concept of health and various information in association with each other. For example, the concept of "excellent body" (or "physical health") related to "health" is associated with keywords such as "non-smoking" and "normal blood pressure", and status such as "BMI 18-27". obtain. For example, the concept of "mindfulness" (or "mental health") related to "health" includes keywords such as "rewarding" and "stressless", "stress check result is less than a predetermined value", and "coherence". Statuses such as "high (SOC: Sense of coherence)" can be stored in association with each other.

FIG. 2C is a conceptual diagram showing the association of information stored in the database unit 200.

In the example shown in FIG. 2C, keywords such as "Harvard University", "Stanford University", "MBA", "doctor", and "manager" are associated with the concept of "rich" regarding money. ing. Further, another keyword (for example, "business school", "practitioner", "listed company", etc.) is associated with these keywords. Furthermore, yet another keyword may be associated with these keywords.

Such associations can be made using, for example, artificial intelligence (AI) capable of performing semantic searches, i.e., artificial intelligence that has learned the correlation between keywords.

This artificial intelligence learns the correlation between keywords from a large amount of sentences. For example, this artificial intelligence extracts a plurality of keywords in a sentence by parsing the sentence and identifies the relationship between the plurality of keywords in the sentence. For example, this artificial intelligence learns a keyword as having a strong correlation when it is used in combination with another keyword in many sentences. The artificial intelligence that has learned the correlation between keywords in this way can output a keyword that correlates with the input keyword based on the learned correlation.

For example, if you enter the keyword "rich" in artificial intelligence that has learned the correlation between keywords, the keywords that correlate with "rich" are "Harvard University", "Stanford University", "MBA", and "doctor". , "Manager" etc. can be output. For example, if the keywords "rich" and "doctor" are entered in artificial intelligence that has learned the correlation between keywords, the keywords that correlate with "rich" and "doctor" include "practitioner" and "director". Can be output.

In this way, the database unit 200 can store the keywords input to the artificial intelligence that has learned the correlation between the keywords and the output keywords in association with each other.

In the examples shown in FIGS. 2A and 2B, the database unit 200 is provided outside the system 100, but the present invention is not limited thereto. It is also possible to provide at least a part of the database unit 200 inside the system 100. At this time, at least a part of the database unit 200 may be implemented by the same storage means as the storage means on which the memory unit 130 is mounted, or may be implemented by a storage means different from the storage means on which the memory unit 130 is mounted. You may. In any case, at least a part of the database unit 200 is configured as a storage unit for the system 100. The configuration of the database unit 200 is not limited to a specific hardware configuration. For example, the database unit 200 may be composed of a single hardware component or may be composed of a plurality of hardware components. For example, the database unit 200 may be configured as an external hard disk device of the system 100, or may be configured as a storage on the cloud connected via a network.

In the examples shown in FIGS. 2A and 2B, the blockchain network 600 is provided outside the system 100 separately from the network 500, but the present invention is not limited thereto. For example, the blockchain network 600 may exist on the same network as the network 500, or the system 100 including the blockchain network 600 may be constructed.

FIG. 3 shows an example of the configuration of the processor unit 120.

The processor unit 120 includes an acquisition unit 121, a calculation unit 122, and a sales unit 123.

The acquisition means 121 is configured to acquire data and / or information about the user.

The acquisition means 121 may acquire data and / or information about the user stored in the database unit 200, for example, via the interface unit 110. Alternatively, the acquisition means 121 may acquire data and / or information about the user received from the user terminal device 300, for example, via the interface unit 110. The acquired data and / or information about the user is passed to the calculation means 122.

The acquisition means 121 may be further configured to acquire data and / or information about the consumer.

The acquisition means 121 may acquire data and / or information about the consumer stored in the database unit 200, for example, via the interface unit 110. Alternatively, the acquisition means 121 may acquire data and / or information about the user received from the consumer terminal device 400, for example, via the interface unit 110. The acquired data and / or information about the consumer is passed to the calculation means 122.

The calculation means 122 is configured to calculate a score indicating the value of the user's personal information based on data and / or information about the user.

In one embodiment, the calculation means 122 can calculate the score, for example, according to the degree to which the data and / or information about the user correlates with the parameters used to calculate the score. The calculation means 122 refers to, for example, the database unit 200 that stores various information that correlates with the concept of the parameters used in the calculation of the score, and the data and / or information about the user is related to the parameters used in the calculation of the score. Determine how much it correlates with the concept. Data and / or information about the user may include, for example, information about "personality," "money," and "health."

The calculation means 122 can, for example, determine the degree to which data and / or information about the user correlates with the concept of "personality" and can calculate the score according to the determined degree of correlation. For example, if the data and / or information about the user correlates significantly with the concept of "personality" that is positive for the value of personal information, the calculation means 122 has a high score indicating the value of personal information. can do. For example, the more strongly the data and / or information about the user correlates "altruistically" (or "transpersonally"), the higher the score can be. This is because it is considered that a person who can move for another person has a higher humanity and a lower risk of unpaid insurance premiums, and the value of personal information can be highly evaluated accordingly. For example, the stronger the data and / or information about the user correlates with the "SDGs" (or "Sustainable Development Goals"), the higher the score can be. This is because people who are thinking about making a better world are more human and have a lower risk of unpaid insurance premiums, and the value of personal information can be highly evaluated accordingly. Alternatively, for example, if the data and / or information about the user correlates significantly with the concept of "personality" that is negative to the value of personal information, the calculation means 122 has a score indicating the value of personal information. Can be lowered.

The calculation means 122 can determine, for example, the degree to which data and / or information about the user correlates with the concept of "money" and can calculate the score according to the determined degree of correlation. For example, if the data and / or information about the user correlates significantly with the concept of "money" that is positive for the value of personal information, the calculation means 122 gives a high score indicating the value of personal information. can do. For example, the more strongly the data and / or information about the user correlates with "cash flow rich", the higher the score can be. This is because it is considered that the person who has a lot of cash has a lower risk of unpaid insurance premiums, and the value of personal information can be highly evaluated accordingly. For example, the more strongly the data and / or information about the user correlates with "stock rich", the higher the score can be. This is because it is considered that the person who has more assets has a lower risk of unpaid insurance premiums, and the value of personal information can be highly evaluated accordingly. Alternatively, for example, if the data and / or information about the user correlates significantly with the concept of "money" that is negative to the value of personal information, the calculation means 122 has a score indicating the value of personal information. Can be lowered.

The calculation means 122 can determine, for example, the degree to which data and / or information about the user correlates with the concept of "health" and can calculate the score according to the determined degree of correlation. For example, if the data and / or information about the user correlates significantly with the concept of "health" that is positive for the value of personal information, the calculation means 122 gives a high score indicating the value of personal information. can do. For example, the stronger the data and / or information about the user correlates with "good body" (or "physical health"), the higher the score can be. It is considered that the healthier the person is, the lower the risk of paying insurance claims or benefits due to illness or death, and / or the lower the risk of unpaid insurance premiums because he / she can work continuously. This is because the value of personal information can be highly evaluated. For example, the stronger the data and / or information about the user correlates with "mindfulness" (or "mental health"), the higher the score can be. It is considered that the healthier the person is, the lower the risk of paying insurance claims or benefits due to illness or death, and / or the lower the risk of unpaid insurance premiums because he / she can work continuously. This is because the value of personal information can be highly evaluated. Alternatively, for example, if the data and / or information about the user correlates significantly with the concept of "health" that is negative to the value of personal information, the calculation means 122 has a score indicating the value of personal information. Can be lowered.

In one embodiment, the calculation means 122 calculates a score indicating the value of a user's personal information by using, for example, a machine learning model that learns the relationship between data and / or information and a score regarding a plurality of users. Can be done. Data and / or information about the user may include, for example, information about "personality," "money," and "health."

The machine learning model can be constructed using any machine learning model. The machine learning model can be, for example, a neural network model.

FIG. 4 shows an example of the structure of the neural network model that can be used by the calculation means 122.

The neural network model has an input layer, at least one hidden layer, and an output layer. The number of nodes in the input layer of the neural network model corresponds to the number of dimensions of data and / or information about the input user. The hidden layer of the neural network model can contain any number of nodes. The number of nodes in the output layer of the neural network model corresponds to the number of dimensions of the output data. For example, when outputting a score indicating the value of the user's personal information, the number of nodes in the output layer may be 1.

The neural network model can be pre-learned using the data and / or information about the user acquired by the acquisition means 121. The learning process is a process of calculating the weighting coefficient of each node in the hidden layer of the neural network model by using the data and / or information about the user acquired in advance by the acquisition unit 121.

The learning process is, for example, supervised learning. In supervised learning, for example, data and / or information about a user is used as input teacher data, and the user's score is used as output teacher data, and the information of multiple users is used for each node in the hidden layer of the neural network model. By calculating the weighting factor of, it is possible to build a machine learning model capable of correlating data and / or information about the user with the score.

For example, a set of (input teacher data, output teacher data) for supervised learning is (data and / or information about the first user, score of the first user), (data about the second user). And / or information, score of the second user), ... (Data and / or information about the i-th user, score of the third user), ..., And so on. When data and / or information about a user newly acquired from a user is input to the input layer of such a trained neural network model, the score of the user is output to the output layer.

In supervised learning, for example, the degree to which data and / or information about the user correlates with the parameters used to calculate the score can be used as input supervised data.

As the machine learning model, a plurality of machine learning models may be constructed according to the attributes of the consumer. The attribute of the consumer represents the field to which the consumer belongs and corresponds to the field in which personal information is bought and sold. By properly using the machine learning model according to the attributes of the consumer, even the same user can have different scores depending on the field in which the personal information of the user is bought and sold. This is because the value of personal information can change if the field in which personal information is bought and sold changes.

Each of the plurality of machine learning models can be constructed by setting the score of the user used as output teacher data when constructing the machine learning model as the score in each field where personal information is bought and sold.

Multiple machine learning models are used, for example, for buying and selling personal information in the insurance industry, for buying and selling personal information in the banking industry, and for buying and selling personal information in the real estate industry. It may include a model to be used, a model used for buying and selling personal information in the manufacturing industry, and the like.

The calculation means 123 refers to the attributes of the consumer, selects the corresponding machine learning model from the plurality of machine learning models based on the attributes of the consumer, and uses the selected machine learning model to obtain the user's attributes. The score can be calculated.

The calculated score is passed to the selling means 123.

The calculator 122 may be further configured to calculate a confidence score indicating the confidence of the consumer, based on data and / or information about the consumer.

In one embodiment, the calculation means 122 uses, for example, a machine learning model that learns the relationship between data and / or information about a plurality of consumers and a reliability score to indicate the value of the personal information of the consumers. The degree score can be calculated. Data and / or information about the consumer may include, for example, information about "personality", "knowledge / ability", "experience / achievement".

The machine learning model can be constructed using any machine learning model. The machine learning model can be, for example, a neural network model. The neural network model may have a structure similar to that of the neural network model shown in FIG. 4, for example.

The neural network model has an input layer, at least one hidden layer, and an output layer. The number of nodes in the input layer of the neural network model corresponds to the number of dimensions of data and / or information about the input consumer. The hidden layer of the neural network model can contain any number of nodes. The number of nodes in the output layer of the neural network model corresponds to the number of dimensions of the output data. For example, when outputting a reliability score indicating the value of personal information of a consumer, the number of nodes in the output layer may be 1.

The neural network model can be pre-learned using the data and / or information about the consumer acquired by the acquisition means 121. The learning process is a process of calculating the weighting coefficient of each node in the hidden layer of the neural network model by using the data and / or information acquired in advance by the acquisition unit 121.

The learning process is, for example, supervised learning. In supervised learning, for example, data and / or information about a consumer is used as input teacher data, and the consumer confidence score is used as output teacher data, and information of multiple consumers is used to create a neural network model. By calculating the weighting factor of each node in the hidden layer, it is possible to build a machine learning model that can correlate the data and / or information about the consumer with the confidence score.

For example, a set of (input teacher data, output teacher data) for supervised learning is (data and / or information about the first consumer, confidence score of the first consumer), (second Data and / or information about the consumer, second consumer confidence score), ... (Data and / or information about the i-th consumer, i-th consumer confidence score), ...・ It can be the same. When data and / or information about a consumer newly acquired from a consumer is input to the input layer of such a trained neural network model, the reliability score of the consumer is output to the output layer.

The calculated reliability score is passed to the selling means 123.

See FIG. 2B again. The selling means 123 is configured to sell personal information to consumers. For example, the selling means 123 can sell the personal information to the consumer in response to the purchase request of the personal information received through the interface unit 110. For example, the sales means 123 may transfer the purchase request for personal information received via the interface unit 110 to the user, and sell the personal information to the consumer in response to the user accepting the purchase request. can.

The selling means 123 can sell the personal information to the consumer by using the score. For example, the selling means 123 can present the score to the consumer. Since the score indicates the value of personal information, the consumer can refer to the score and purchase high-value personal information. As a result, consumers' willingness to purchase can be enhanced, and the buying and selling of personal information can be activated.

The selling means 123 may keep the price of the personal information to be sold unchanged or may change it. When the price of the personal information to be sold is changed, the selling means 123 may include, for example, a price changing means for changing the price of the personal information. The selling means 123 can sell the personal information to the consumer at a price changed by the price changing means.

The price fluctuation means can fluctuate the price of personal information based on a score indicating the value of the user's personal information. As a result, the price of personal information can be commensurate with its value.

The price fluctuation means can, for example, proportionally fluctuate the price of personal information so that the higher the score, the higher the price. This is because the score indicates the value of personal information, so the high score is proportional to the high price. Alternatively, the price fluctuation means can, for example, change the price of personal information step by step according to the score. For example, the price fluctuation means can change the price so that the personal information of a user having a score higher than a predetermined threshold is higher than the personal information of a user having a score lower than a predetermined threshold.

The sales means 123 may include a search means for searching for a user who satisfies a predetermined condition in order to enable the consumer to search for the desired personal information. The selling means 123 can sell the personal information of the user searched by the searching means to the consumer.

The predetermined conditions may include any conditions, for example, the user's annual income, the user's occupation, the user's family structure, and the like.

The predetermined condition includes, for example, a score indicating the value of the user's personal information. As a result, the consumer can purchase personal information by narrowing down to, for example, users with a score higher than a predetermined threshold, users with a score lower than a predetermined threshold, or users with a score within a predetermined range. become able to. Predetermined conditions include, for example, the price of personal information. As a result, the consumer purchases personal information by narrowing down to, for example, personal information having a price higher than a predetermined threshold, personal information having a price lower than a predetermined threshold, or personal information having a price within a predetermined range. You will be able to.

The selling means 123 may sell the personal information to the consumer by using the calculated score and the calculated reliability score. By using the reliability score of the consumer, the personal information can be sold only to the trusted consumer, so that the user's willingness to sell can be enhanced and the buying and selling of the personal information can be activated.

The selling means 123 may include, for example, a matching means for matching a user and a consumer based on a score indicating the value of personal information and a reliability score. The selling means 123 can sell the personal information of the user to the consumer who is matched with the user by the matching means. This makes it possible to ensure that the personal information of the appropriate user is sold to the appropriate consumer.

The matching means includes, for example, an extracted user and an extracted consumer by extracting a user to be matched from a plurality of users and extracting a consumer to be matched from a plurality of consumers. Can be matched. The user to be extracted may be, for example, a user having a score equal to or higher than a predetermined threshold, a user having a score equal to or lower than a predetermined threshold, or a user having a score within a predetermined range. The consumer to be extracted may be, for example, a consumer having a score equal to or higher than a predetermined threshold, a consumer having a score equal to or lower than a predetermined threshold, or a consumer having a score within a predetermined range. The predetermined threshold and the predetermined range may be variable or fixed. If the predetermined threshold value and the predetermined range are variable, the user or the consumer may specify the predetermined threshold value and the predetermined range, for example, the user terminal device or the consumer terminal device. Can be included in matching requests sent from.

The matching means may, for example, recommend a sales destination of personal information to a specific user by matching a consumer to whom the personal information should be sold. Alternatively, the matching means may recommend the purchase destination of the personal information to the specific consumer, for example, by matching the user to whom the specific consumer should purchase the personal information.

The processor unit 120 may further include a detection means for detecting that the personal information managed in the blockchain network 600 has been changed. The detection means has changed the personal information managed in the blockchain network 600 based on the information received via the interface unit 110 indicating that the personal information managed in the blockchain network 600 has been changed. It can be detected. Since the change of personal information managed in the blockchain network 600 is considered to be the update of personal information, the update of personal information can be reliably detected by the detection means.

The processor unit 120 further notifies the consumer that the personal information has been changed in response to the detection means detecting that the personal information managed in the blockchain network 600 has been changed. Further means may be provided. Since the notification means can notify the consumer of the change of the personal information, that is, the update of the personal information, the consumer is motivated to purchase the updated personal information. As a result, consumers' willingness to purchase can be enhanced, and the buying and selling of personal information can be activated.

In the example shown in FIG. 2B described above, each component of the processor unit 120 is provided in the same processor unit 120, but the present invention is not limited thereto. A configuration in which each component of the processor unit 120 is distributed to a plurality of processor units is also within the scope of the present invention. At this time, the plurality of processor units may be located in the same hardware component, or may be located in separate hardware components in the vicinity or remote.

Each component of the system 100 described above may be composed of a single hardware component or may be composed of a plurality of hardware components. When it is composed of a plurality of hardware parts, the mode in which each hardware part is connected does not matter. Each hardware component may be connected wirelessly or by wire. The system 100 of the present invention is not limited to a specific hardware configuration. It is also within the scope of the present invention that the processor unit 120 is configured by an analog circuit instead of a digital circuit. The configuration of the system 100 of the present invention is not limited to that described above as long as the function can be realized.

3. 3. Processing by the system for supporting the buying and selling of personal information of the user FIG. 5 shows an example (processing 500) of processing by the system 100 for supporting the buying and selling of personal information of the user. The process 500 is performed in the processor unit 120 of the system 100.

In step S501, the acquisition means 121 of the processor unit 120 acquires data and / or information about the user.

The acquisition means 121 may acquire data and / or information about the user stored in the database unit 200, for example, via the interface unit 110. Alternatively, the acquisition means 121 may acquire data and / or information about the user received from the user terminal device 300, for example, via the interface unit 110.

In step S502, the calculation means 122 of the processor unit 120 calculates a score indicating the value of the user's personal information based on the data and / or information about the user acquired in step S501.

The calculation means 122 can calculate the score, for example, according to the degree to which the data and / or information about the user correlates with the parameters used to calculate the score. The calculation means 122 refers to, for example, the database unit 200 that stores various information that correlates with the concept of the parameters used in the calculation of the score, and the data and / or information about the user is related to the parameters used in the calculation of the score. The degree to which it correlates with the concept can be determined. The parameters used to calculate the score include, for example, "personality," "money," and "health." The calculation means 122 can calculate the score, for example, by determining the degree to which data and / or information about the user correlates with the concept of "personality". Alternatively, or in addition to this, the calculation means 122 can calculate the score, for example, by determining the degree to which data and / or information about the user correlates with the concept of "money". Alternatively, or in addition to this, the calculation means 122 can calculate the score, for example, by determining the degree to which data and / or information about the user correlates with the concept of "health".

The calculation means 122 can calculate a score indicating the value of a user's personal information by using, for example, a machine learning model that learns the relationship between data and / or information and a score regarding a plurality of users. The machine learning model can be constructed using any machine learning model, and the machine learning model can be, for example, a neural network model. Data and / or information about the user may include, for example, information about "personality," "money," and "health."

For example, if the data and / or information about the user acquired in step S501 is input to the machine learning model, the user's score may be output.

For example, the calculation means 122 may select a specific machine learning model from a plurality of machine learning models and calculate the score using the selected machine learning model. The calculation means 122 can select a machine learning model based on, for example, the attributes of the consumer. The attribute of the consumer represents the field to which the consumer belongs and corresponds to the field in which personal information is bought and sold. By properly using the machine learning model according to the attributes of the consumer, even the same user can have different scores depending on the field in which the personal information of the user is bought and sold.

In step S503, the sales means 123 of the processor unit 120 sells the personal information to the consumer by using the score calculated in step S502. For example, the selling means 123 can sell the personal information to the consumer in response to the purchase request of the personal information received through the interface unit 110. At this time, since the consumer can know the value of the personal information through the score, he / she can select the high-value personal information and purchase the personal information.

For example, in step S503, the price fluctuation means of the sales means 123 fluctuates the price of the personal information based on the score calculated in step S502, and the sales means 123 gives the personal information to the consumer at the fluctuated price. You may sell it. As a result, the price of personal information can be commensurate with its value, and proper buying and selling of personal information can be promoted.

For example, in step S503, the search means of the sales means 123 searches for a user who satisfies a predetermined condition provided by the consumer, and the sales means 123 sells the personal information of the searched user to the consumer. You may. Certain conditions may include a score indicating the value of the user's personal information. As a result, the consumer can purchase the personal information after searching for the user who has the personal information to be purchased based on the value of the personal information.

Process 500 allows the consumer to purchase the personal information using the score indicating the value of the personal information, which leads to the increase of the consumer's willingness to purchase, which in turn activates the buying and selling of the personal information. Can lead to.

FIG. 6 shows another example (process 600) of processing by the system 100 for supporting the buying and selling of personal information of a user. The process 600 is a process when the reliability score indicating the reliability of the consumer is used in addition to the score indicating the value of the personal information in order to sell the personal information to the consumer. The process 600 is performed in the processor unit 120 of the system 100.

In step S601, the acquisition means 121 of the processor unit 120 acquires data and / or information about the user. The process in step S601 is the same as the process in step S501.

In step S602, the calculation means 122 of the processor unit 120 calculates a score indicating the value of the user's personal information based on the data and / or information about the user acquired in step S601. The process in step S602 is the same as the process in step S502.

In step S603, the acquisition means 121 of the processor unit 120 acquires data and / or information about the consumer.

The acquisition means 121 may acquire data and / or information about the consumer stored in the database unit 200, for example, via the interface unit 110. Alternatively, the acquisition means 121 may acquire data and / or information about the user received from the consumer terminal device 400, for example, via the interface unit 110.

In step S604, the calculation means 122 of the processor unit 120 calculates a score indicating the value of the personal information of the consumer based on the data and / or information about the consumer acquired in step S603.

The calculation means 122 calculates a reliability score indicating the value of a consumer's personal information by using, for example, a machine learning model that learns the relationship between data and / or information about a plurality of consumers and a reliability score. be able to. The machine learning model can be constructed using any machine learning model, and the machine learning model can be, for example, a neural network model. Data and / or information about the consumer may include, for example, information about "personality", "knowledge / ability", "experience / achievement".

For example, if the data and / or information about the consumer acquired in step S603 is input to the machine learning model, the consumer score may be output.

In step S605, the sales means 123 of the processor unit 120 sells the personal information to the consumer by using the score calculated in step S602 and the reliability score calculated in step S604. For example, the selling means 123 can sell the personal information to the consumer in response to the purchase request of the personal information received through the interface unit 110. At this time, since the user can know the reliability of the consumer who purchases the personal information through the reliability score, the user can select the consumer with high reliability and sell the personal information.

For example, in step S605, the price fluctuation means of the sales means 123 fluctuates the price of the personal information based on the score calculated in step S602, and the sales means 123 gives the personal information to the consumer at the fluctuated price. You may sell it. As a result, the price of personal information can be commensurate with its value, and proper buying and selling of personal information can be promoted.

For example, in step S605, the search means of the sales means 123 searches for a user who satisfies a predetermined condition provided by the consumer, and the sales means 123 sells the personal information of the searched user to the consumer. You may. Certain conditions may include a score indicating the value of the user's personal information. As a result, the consumer can purchase the personal information after searching for the user who has the personal information to be purchased based on the value of the personal information.

For example, in step S605, the matching means of the sales means 123 matches the user and the consumer based on the score indicating the value of the personal information and the reliability score, and the sales means 123 is matched with the user. Personal information may be sold to consumers. This makes it possible to ensure that the personal information of the appropriate user is sold to the appropriate consumer.

The matching means includes, for example, an extracted user and an extracted consumer by extracting a user to be matched from a plurality of users and extracting a consumer to be matched from a plurality of consumers. Can be matched.

The matching means may, for example, recommend a sales destination of personal information to a specific user by matching a consumer to whom the personal information should be sold. Alternatively, the matching means may recommend the purchase destination of the personal information to the specific consumer, for example, by matching the user to whom the specific consumer should purchase the personal information.

According to the process 600, the consumer can purchase the personal information by using the score indicating the value of the personal information, and the user sells the personal information by using the reliability score indicating the reliability of the consumer. This can lead not only to increase the purchasing motivation of consumers but also to increase the selling motivation of users, which in turn can lead to activation of buying and selling of personal information.

In the above-described example with reference to FIGS. 5 and 6, it has been described that the processes are performed in a specific order, but the order of each process is not limited to the described one, and any order that is logically possible is not limited. Can be done at. For example, step S603 may be performed before step S601, step S604 may be performed before step S602, or step S601 may be performed after step S604.

In the above-mentioned example, the system 100 for supporting the buying and selling of personal information in the insurance industry has been described, but the field to which the system 100 is applied is not limited to the insurance industry. The system 100 of the present invention can be used in any field in which the sale and purchase of personal information is involved.

The system 100 of the present invention can be used, for example, to support the buying and selling of personal information in the advertising industry. For example, in the advertising industry, the higher the score indicating the value of personal information, the more valuable the user is to hit the advertisement (for example, the user who has a high appeal effect of the advertisement, the user who is the sales target of the product to be advertised, etc.). .. Therefore, by purchasing the personal information of a user who has a high score indicating the value of the personal information and placing an advertisement on the user who has the purchased personal information, it is more effective than placing an advertisement on an unspecified number of people in the dark clouds. Can be obtained.

In the above-described example with reference to FIGS. 5 and 6, it has been explained that the processing of each step shown in FIGS. 5 and 6 is realized by the program stored in the processor unit 120 and the memory unit 130. , The present invention is not limited to this. At least one of the processes of each step shown in FIGS. 5 and 6 may be realized by a hardware configuration such as a control circuit.

In the above-described example, the case where the system 100 is a computer (for example, a server device) installed in a service provider that provides a marketplace for buying and selling personal information of users has been described as an example, but the present invention has been described. Is not limited to this. The system 100 can be any information processing device including a processor unit.

The present invention is not limited to the above-described embodiments. It is understood that the invention should be construed only by the claims. It will be understood by those skilled in the art that from the description of specific preferred embodiments of the present invention, an equivalent range can be implemented based on the description of the present invention and common general technical knowledge.

The present invention is useful as providing a system or the like for supporting the buying and selling of personal information of a user, which can activate the buying and selling of personal information.

10 Marketplace 20 Users 30 Consumers 100 Systems 200 Database Department 300 User Terminal Equipment 400 Consumer Terminal Equipment 500 Network 600 Blockchain Network

Claims (14)

It is a system to support the buying and selling of personal information of users.
An acquisition method for acquiring data and / or information about a user,
A calculation means for calculating a score indicating the value of the personal information of the user based on the acquired data and / or information about the user, and
A system provided with a sales means for selling the personal information to a consumer by using the score. The selling means includes a price changing means for changing the price of the personal information based on the score.
The system according to claim 1, wherein the selling means sells the personal information to the consumer at the variable price. The selling means includes a search means for searching for a user who satisfies a predetermined condition including the score.
The system according to claim 1 or 2, wherein the selling means sells the personal information of the searched user to the consumer. The acquisition means further acquires data and / or information about the consumer.
The calculation means further calculates a reliability score indicating the reliability of the consumer based on the acquired data and / or information about the consumer.
The system according to any one of claims 1 to 3, wherein the selling means sells the personal information to the consumer by using the score and the reliability score. The selling means includes a matching means for matching the user and the consumer based on the score and the reliability score.
The system according to claim 4, wherein the selling means sells the personal information to the consumer matched with the user. The system according to any one of claims 1 to 5, wherein the calculation means calculates the score by using a model that learns the relationship between data and / or information about a user and the score. The calculation means has a plurality of the models.
The system according to claim 6, wherein the calculation means selects a model for which the score should be calculated from the plurality of models based on the attributes of the consumer. The system according to any one of claims 1 to 7, wherein the acquisition means acquires the data from a device associated with the user. The system according to any one of claims 1 to 8, wherein the acquisition means acquires information independently disclosed by the user. The system according to any one of claims 1 to 9, further comprising a detection means for detecting that the personal information has been changed. The system according to claim 10, further comprising a notification means for notifying the consumer that the personal information has been changed in response to detecting that the personal information has been changed. The system according to any one of claims 1 to 11, wherein the user's personal information is used for selling insurance products. It is a method to support the buying and selling of personal information of users.
To get data and / or information about the user,
To calculate a score indicating the value of the personal information of the user based on the acquired data and / or information about the user.
A method including selling the personal information to a consumer by using the score. A program for supporting the buying and selling of personal information of a user, the program is executed in a system including a processor unit, and when the program is executed, the program is executed.
To get data and / or information about the user,
To calculate a score indicating the value of the personal information of the user based on the acquired data and / or information about the user.
A program that causes the processor unit to perform a process including selling the personal information to a consumer by using the score.

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