Proposed system, proposed method, and computer program

Abstract

To promote utilization of personal data while ensuring protection of individual information, this proposition system comprises a graph generation unit, a reception unit, and a proposition unit. The graph generation unit uses element identification information for identifying personal data elements, which are data elements constituting personal data, and data location information relating to the personal data elements and representing information holders holding the personal data elements to generate a knowledge graph representing a relationship between a plurality of nodes including a plurality of the information holders. The reception unit receives an information request for requesting information of an information holder represented by data location information relating to personal data elements satisfying user needs. The proposition unit uses a model generated by machine learning to output a recommended information holding means that is an information holder satisfying requirements corresponding to the received information request. The model is generated by machine learning using the generated knowledge graph and information holder requirements corresponding to information requests.

Description

Technical Field

[0001] The present invention relates to a technology that utilizes personal data.

Background Art

[0002] With the development of information and communication technologies, a variety of data groups containing a large amount of data are generated daily. Such data groups are referred to as big data. The utilization of big data, such as applying the knowledge obtained by collecting, storing, and analyzing big data to business, is increasing.

[0003] Big data is roughly classified into open data, personal data, and industrial data, focusing on the data that can be generated by three entities: individuals, companies, and countries. Open data is data provided by countries or local governments. Personal data is data related to individuals, including not only personal data that can identify an individual but also data such as personal attribute information, histories of movement, actions, purchases, and information collected from wearable devices. Industrial data is data other than personal data, including data related to people's "knowledge" such as various know-hows and data during device-to-device communication of industrial machines called M2M (Machine to Machine).

[0004] In addition, Patent Document 1 (Japanese Unexamined Patent Application Publication No. 2022-113621) discloses a technology for supporting the adjustment work of transferring patients such as the elderly from a facility where they are hospitalized to another facility such as a nursing facility. In the configuration of this Patent Document 1, information regarding the acceptance or non-acceptance of patients as facility information is registered in the system, and a facility suitable for the patient's conditions is extracted and presented using this facility information.

Prior Art Documents

Patent Documents

[0005]

Patent Document 1

[0006] Data utilization businesses that want to make use of data sometimes look for sources of data they want to use as reference when launching new projects. In addition, the data they want to use as reference may not be personally identifiable data, but may be associated with personal data. In other words, the data they want to use as reference may be personal data. Businesses that hold personal data may not disclose their personal data holding status from the perspective of protecting personal information. For this reason, it can be difficult for data utilization businesses to find businesses that hold data that is both personal data and has a sufficient amount of data for analysis (i.e., businesses that are appropriate to access). Due to such cases, as well as concerns about protecting personal information, the utilization of personal data is not progressing.

[0007] This invention was conceived to solve the above-mentioned problems. In other words, the main objective of this invention is to provide a technology that promotes the utilization of personal data while protecting personal information. [Means for solving the problem]

[0008] To achieve the above objective, the proposed system according to the present invention, in one aspect, A graph generation unit generates a knowledge graph representing the relationships between multiple nodes, including multiple information holders, using element identification information that identifies personal data elements, which are data elements that constitute personal data, and data location information of personal data elements that represents information holders that hold personal data elements. A reception unit that receives information requests for information about an information holder represented by data location information of personal data elements that meet the user's needs, A proposal unit that uses a model generated by machine learning, which combines the generated knowledge graph and the requirements of information holders that respond to information requests, to output information holders that meet the requirements of the received information request as recommended information holders. It is equipped with.

[0009] Furthermore, the proposed method according to the present invention, in one embodiment, By computer, Using element identification information that identifies personal data elements, which are data elements that constitute personal data, and data location information of personal data elements that represents information holders that hold personal data elements, a knowledge graph is generated that represents the relationships between multiple nodes containing multiple information holders. Furthermore, by computer, We accept information requests that request information about information holders represented by data location information of personal data elements that meet user needs. Using a machine learning model that combines the generated knowledge graph with the requirements for information holders that respond to information requests, the system outputs information holders that meet the requirements for the received information request as recommended information holders.

[0010] Furthermore, the program storage medium according to the present invention, in one embodiment, A process that accepts information requests that request information about an information holder represented by data location information of personal data elements that meet the user's needs, This process involves using a machine learning model that generates a knowledge graph representing the relationships between multiple nodes containing multiple information holders, using element identification information to identify personal data elements and data location information of personal data elements representing information holders that hold personal data elements, and the requirements of information holders that respond to information requests, to output information holders that satisfy the requirements corresponding to the received information request as recommended information holders. It stores a computer program that will be executed by a computer. [Effects of the Invention]

[0011] According to the present invention, while protecting personal information, it is possible to promote the utilization of personal data.

Brief Description of Drawings

[0012] [Figure 1] It is a diagram for explaining the configuration of the proposed system according to the first embodiment of the present invention. [Figure 2] It is a diagram for explaining an example of personal data. [Figure 3] It is a diagram for explaining the data location information of personal data elements. [Figure 4] It is a diagram showing an example of a knowledge graph. [Figure 5] It is a flowchart showing an example of the operation of a processing device constituting the proposed system. [Figure 6] It is a diagram for explaining other embodiments. [Figure 7] It is a flowchart showing an example of the operation of a computer in other embodiments.

Modes for Carrying Out the Invention

[0013] Hereinafter, embodiments according to the present invention will be described with reference to the drawings.

[0014] <First Embodiment> FIG. 1 is a diagram for explaining the configuration of a personal data circulation system including the proposed system according to the present invention. The personal data circulation system 100 is a system for supporting the utilization of personal data. Personal data is not only data of personal information such as name, date of birth, and address that can identify an individual (personal data), but also data including various data related to an individual in which a relationship with the individual is found. Specific examples of data in which a relationship with an individual is found include the individual's attribute information (age, occupation, residential area, etc.), history of movement, behavior, and purchase, data from wearable devices, and the like.

[0015] In the example of FIG. 1, the personal data circulation system 100 is connected to a plurality of information holders 50 and system users 80. Here, the information holder 50 is, for example, an operator that holds personal data. Specific examples of the information holder 50 include a hospital that holds patient data, which is personal data, a sports gym or amusement facility that holds membership information, which is personal data. Further, specific examples of the information holder 50 include service providers such as logistics, retail, and information distribution companies that hold customer information, which is personal data. The system user 80 is, for example, an operator or local public entity that uses the personal data circulation system 100.

[0016] In the first embodiment, the personal data circulation system 100 has a function of presenting to the system user 80, as a recommended information holder, an information holder 50 as an access destination that provides information related to personal data desired by the system user 80. Note that a plurality of information holders 50 and a plurality of system users 80 can be respectively connected to the personal data circulation system 100. Here, the same reference numerals are assigned to a plurality of different information holders connected to the personal data circulation system 100, and in the description here, each of the plurality of information holders is not distinguished. Similarly, for a plurality of different system user operators connected to the personal data circulation system 100, the same reference numerals are assigned to those plurality of system user operators, and in the description here, each of the plurality of system user operators is not distinguished.

[0017] In addition, in the personal data circulation system 100, security measures regarding the connection to the information holder 50 and the connection to the system user 80 are taken, but the description thereof is omitted here.

[0018] The personal data distribution system 100 includes the proposed system 1, and further includes a holder attribute information DB (Database) 2, a personal attribute information DB (Database) 3, and a data location information DB (Database) 4, all of which are connected to the proposed system 1. The holder attribute information DB 2 is a database (storage device) that stores attribute information of the information holder 50 (hereinafter also referred to as holder attribute information). Examples of holder attribute information include the type of business of the operator, its location, and the services it provides. Such holder attribute information is stored in the holder attribute information DB 2 in association with holder identification information that identifies the information holder 50.

[0019] The Personal Attribute Information DB3 is a database (storage device) that stores attribute information (hereinafter also referred to as personal attribute information) about individuals whose personal data is held in the information holder 50. Examples of personal attribute information include information about various personal attributes such as age, the name of the city or town where they live, family structure, occupation, hobbies, and preferences. Such personal attribute information is stored in the Personal Attribute Information DB3 in association with system personal identification information that identifies the individual. System personal identification information is, for example, personal identification information unique to this system and consists of information different from personal identification information of other systems, such as My Number, driver's license number, or passport number.

[0020] Personal attribute information is information related to an individual and is often associated with personal information; here, it constitutes one of the data elements (personal data elements) that make up personal data. Since the personal data distribution system 100 handles such personal attribute information (in other words, personal data), it is equipped with a consent management device (computer device) 7 as shown by the dotted line in Figure 1. The consent management device 7 is a device that has the function of obtaining consent from the individual corresponding to the personal data regarding the handling of personal data when the system stores personal data in the personal attribute information DB3, etc. Here, the configuration of the consent management device 7 is not limited as long as it has the function of obtaining consent regarding the handling of personal data, so its explanation is omitted.

[0021] The data location information DB4 is a database (storage device) where data location information is stored. Data location information is information that associates element identification information, which identifies the type (content) of data elements that constitute personal data (hereinafter also referred to as personal data elements), with location information that represents the information holder 50 that holds the personal data elements. For example, suppose that hospital A and clinic B, which are information holders 50, each hold the personal data of patient X. Figure 2 shows a specific example of the personal data of patient X held by hospital A and clinic B in this case. In the example in Figure 2, the personal data of patient X held by hospital A and clinic B includes personal information, personal attribute information, medical consultation history information, and prescription history information. In such a case, for example, personal data elements such as personal information, personal attributes, medical consultation history, and prescription history may be set as personal data elements of the personal data, but here we will show a specific example in which personal data is further subdivided and personal data elements are set as follows. For example, as shown in Figure 3, information representing the details of medical consultations included in the medical consultation history information is set as a personal data element. In this case, the element identification information used to identify personal data elements does not include disease names, but rather information representing the content of the visit, such as consultation, examination, or test results. On the other hand, location information is information representing the information holder 50 that holds such personal data elements, and consists of information such as the name of a medical institution, such as Hospital A or Clinic B, and holder identification information. In such a case, one piece of data location information for a personal data element relating to patient X is, for example, information in which location information represented by information such as the name of a medical institution, such as "Clinic B," is associated with the element identification information "Consultation," which is associated with the consultation date, August 8, 2022.

[0022] Another example of a personal data element is that each attribute included in personal attribute information, such as "in their 40s," "resides in XX city," and "company employee," may be set as a personal data element. In such a case, an example of data location information for a personal data element is information in which location information, represented by the name of the information holder 50 that holds the personal attribute information, is associated with element identification information, which is information about the attribute name representing "in their 40s." It should be noted that personal data elements may be set as appropriate considering the expected uses of the personal data, and are not limited to the examples described above. Furthermore, the element identification information of a personal data element is stored in the personal attribute information DB3 in association with the personal data element.

[0023] The aforementioned databases—Database 2 (holder attribute information), Database 3 (personal attribute information), and Database 4 (data location information)—are separate and independent in this context as a measure against information leakage due to cyberattacks and other factors.

[0024] The proposed system 1, which constitutes the personal data distribution system 100, comprises a computing unit 10 and a processing unit 20, as shown in Figure 1. These computing units 10 and 20 are computer devices, each comprising a processor 11, 21 and a storage device 30, 40. The storage devices 30, 40 are storage media for storing data and computer programs (hereinafter also referred to as programs) 33, 43. There are many types of storage devices, and the types of storage devices 30, 40 provided by the computing unit 10 and processing unit 20 are not limited to one. Computer devices are often equipped with multiple types of storage devices. Here, the types and number of storage devices 30, 40 provided by the computing unit 10 and processing unit 20 are not limited, and their explanation is omitted. Furthermore, when the computing unit 10 or processing unit 20 is equipped with multiple types of storage devices 30, 40, they will be collectively referred to as storage devices 30, 40.

[0025] The processors 11 and 21 in the arithmetic unit 10 and the processing unit 20, respectively, are composed of processors such as a CPU (Central Processing Unit) and a GPU (Graphics Processing Unit). By reading and executing the programs 33 and 43 stored in the memory devices 30 and 40, the processors 11 and 21 can perform various functions based on the programs 33 and 43. Here, the processor 11 of the arithmetic unit 10 includes a graph generation unit 15 and a learning unit 16 as functional units related to the utilization of personal data. The processor 21 of the processing unit 20 includes a reception unit 25 and a suggestion unit 26.

[0026] The graph generation unit 15 in the computing device 10 generates a knowledge graph using AI (Artificial Intelligence) technology, utilizing data stored in the holder attribute information DB2, personal attribute information DB3, and data location information DB4, respectively. The generated knowledge graph includes, as nodes, multiple information holders 50, individuals whose personal data is held in the information holders 50, and element identification information related to personal attribute information, with the relationships between these nodes represented by edges. The graph generation unit 15 uses element identification information of personal data elements, data location information of personal data elements, and attribute information of the information holders 50 to generate the knowledge graph. Figure 4 schematically represents an example of a knowledge graph. In Figure 4, circles represent nodes in the knowledge graph, and lines connecting the nodes represent edges in the knowledge graph. The nodes constituting the knowledge graph are appropriately set by the designer or others, taking into consideration the purpose of use of the generated knowledge graph. Additionally, if necessary, open data obtained from external information sources 90, such as those shown by the dotted line in Figure 1 (for example, data related to the location area of ​​the information holder 50 or data related to the patient's residential area), may also be used to generate the knowledge graph.

[0027] The learning unit 16 generates a suggestion model using AI technology. In this case, the suggestion model is a model that takes an information request as input and outputs an information holder 50 that satisfies the requirements to respond to the information request as a recommended information holder. An information request is a request for information about an information holder represented by data location information of personal data elements that meet the user's needs. In the first embodiment, the information request is a request for the system user (user) 80 to suggest (introduce) an information holder 50 that holds and is appropriate for accessing personal data-related information (in other words, data of personal data elements that meet the user's needs). To give a specific example, suppose the system user 80 is looking for an information holder 50 that will provide information (data) related to "health promotion" in "○○ City" in order to plan a new service with the theme of "health promotion" in "○○ City". An information request that meets such user needs might be, for example, a request to suggest a recommended information holder 50 that will provide information (data) related to "health promotion" in "○○ City". As a first requirement for an information holder 50 that responds to such information requests, for example, an information holder 50 that houses (holds) a personal data element corresponding to the element identification information of a personal data element, "○○ City," and whose data count is above a predetermined threshold is set. Furthermore, as a second requirement for an information holder 50 that responds to the above-mentioned information requests, an information holder 50 that is directly connected to the node "health promotion" in the knowledge graph, or whose node-to-node distance to the node "health promotion" is below a predetermined value is set.

[0028] The proposal model is generated by machine learning using a knowledge graph generated by the graph generation unit 15 and relational data representing the relationship between the information request and the requirements of the information holder 50 that respond to the information request. The method for generating the proposal model is not limited to any AI technology that generates a model using a knowledge graph, but for example, the proposal model is generated by a method called Link Prediction AI (Artificial Intelligence). Link Prediction AI is an explainable AI technology. The proposal model generated by such an explainable AI technology can not only output an information holder 50 that meets the requirements for responding to the information request as a recommended information holder, but can also output information that explains the basis for that recommendation. For example, suppose an information request such as "Please suggest an information holder 50 that provides information (data) related to "health promotion" in "○○ city" is input to the proposal model. In this case, for example, the proposal model will output name information representing the information holders 50, namely "Sports Gym D" and "Clinic B," as recommended information holders. Furthermore, the proposed model outputs information that, as justification for recommending these information holders 50, for example, that "Sports Gym D" and "Clinic B" hold personal data of individuals, including personal data elements related to "health promotion," in a quantity greater than the number of data points effective for analysis.

[0029] As described above, the proposed model generated by the learning unit 16 is stored in the memory device 40 of the processing unit 20.

[0030] The receiving unit 25 in the processing unit 20 receives information requests output from request sources such as system users 80. Regarding the input of information requests to the processing unit 20, there are two possibilities: the information request is input to the processing unit 20 from the request source via an information communication network, or the information request is input to the processing unit 20 by, for example, a manual input by an operator of the processing unit 20 who has received the information request from the request source. The processing unit 20 is configured to allow both of these information request input methods or one of the pre-selected methods.

[0031] When an information request is received by the receiving unit 25, the proposal unit 26 inputs the received information request into the proposal model generated by the learning unit 16. The proposal unit 26 then returns the information of the information holder 50 output from the proposal model to the request sender as suggested information for the recommended information holder. If the proposal model also outputs information explaining the rationale, that explanation is also returned to the request sender. Examples of reply methods include a method in which the response is returned from the processing unit 20 to the computer device of the system user 80 via an information communication network, or a method in which the operator of the processing unit 20 returns the response using a communication method such as email. Here, the method of returning the response generated by the proposal unit 26 to the information request is not limited. Furthermore, the information output from the proposal model may not only be returned to the request sender, but may also be notified to the operator of the processing unit 20, for example, using a display device (not shown) connected to the processing unit 20. Furthermore, the personal data distribution system 100 may also include a function to mediate between the recommended information holder 50 proposed by the proposed system 1 and the system user 80 that initiated the request.

[0032] The proposed system 1 has the configuration described above. Below, an example of the operation of the processing unit 20 in the proposed system 1 will be explained using Figure 5. Figure 5 is a flowchart illustrating an example of the operation of the processing unit 20. In this explanation of operation, it is assumed that the proposed model is pre-generated by the arithmetic unit 10 and stored, for example, in the storage device 40. As mentioned above, the proposed model is a model that takes an information request as input and outputs an information holder 50 that satisfies the requirements to respond to the information request as the recommended information holder 50. Here, the information holder 50 recommended by the proposed model is an information holder 50 that is presumed to provide information related to the personal data requested by the system user 80 when accessed by the system user 80.

[0033] When such a proposal model is stored in the storage device 40, the receiving unit 25 of the processing device 20 receives an information request (step 101 in Figure 5), and the proposal unit 26 inputs the received information request to the proposal model (step 102). Subsequently, when the proposal model outputs information of the recommended information holder 50 corresponding to the information request, the proposal unit 26 outputs the output information of the proposal model as proposal information to the request sender that issued the information request (step 103).

[0034] The proposed system 1 in the first embodiment has the configuration described above. In other words, when processing information requests related to personal data, the proposed system 1 processes not the personal data elements themselves, such as personal information (personal data) contained in the personal data, but rather element identification information that identifies the personal data elements and data location information of the personal data elements. Therefore, the proposed system 1 can provide system users 80 with information that leads to the utilization of personal data while protecting personal information. In other words, the proposed system 1 can contribute to promoting the utilization of personal data while protecting personal information.

[0035] Furthermore, the proposed system 1 proposes information on recommended information holders 50 in response to information requests using a proposal model generated with a knowledge graph. By using a knowledge graph, even if the number of information holders 50 is enormous, the processing unit 20 can predict information holders 50 that are highly related to the matters related to the information request. This also contributes to promoting the utilization of personal data.

[0036] <Second Embodiment> A second embodiment of the present invention will be described below. In the description of the second embodiment, the same reference numerals will be used for parts of the components that have the same names as those used in the first embodiment, and redundant explanations will be omitted.

[0037] In the second embodiment, the proposal system 1, which constitutes the personal data distribution system 100, outputs information from the information holders 50 that cooperate or share functions. That is, in the proposal system 1 of the second embodiment, the information requests it receives (in other words, the anticipated information requests) are different from those of the first embodiment, a knowledge graph is generated to respond to these information requests, and a proposal model is generated using this knowledge graph. In the proposal system 1 of the second embodiment, the configurations (functions) other than those related to information requests are the same as in the first embodiment.

[0038] In other words, in the second embodiment, the information request is a request for a proposal of a combination of information holders 50 that cooperate or share functions in accordance with the user's needs. Here is a specific example of the information request. Let the information holder 50 be a medical institution. The information request is a request for information that meets the user's need to propose a combination of information holders (medical institutions) 50 that are preferable to share functions using information about the patient's visits, which is a personal data element of the patient. Requirements for fulfilling such an information request include, for example, the requirement that the combination of multiple medical institutions visited by one patient is such that the number of patients visited by the medical institutions in that combination is above a threshold. For example, suppose patient "001" in their 80s with diabetes has visited the cardiology department of medical institution AA. Also, suppose patient "001" has visited the nephrology department of medical institution BB. In such a case, medical institutions AA and BB hold personal data elements related to the visit history that constitute patient "001"'s personal data. Referring to the data location information of personal data elements concerning other patients, it is assumed that, similar to the above, the number of patients visiting the cardiology department of medical institution AA and the nephrology department of medical institution BB exceeds a threshold. In such cases, it is preferable for medical institutions AA and BB to share patient medical record information when conducting examinations and treatments, and are therefore recommended as a combination of collaborating information holders 50. In other words, the proposed system 1 outputs the combination of medical institutions AA and BB, which are information holders 50 that meet the requirements for responding to the information request, as information of the recommended information holder 50. In this case, the request source (system user 80) that sends the information request may be a local government or business that proposes collaboration between medical institutions.

[0039] Another example of an information request is a request from a system user 80 to suggest an information holder 50 that is suitable for sharing functions with the system user itself. Specifically, for example, when a patient is discharged from a medical institution, the medical institution may look for another medical institution to refer the patient to that can see the patient after discharge and prescribe medication. In such a case, the system user 80 is both the medical institution where the patient is hospitalized and the information holder 50. One example of a requirement for fulfilling such an information request is that the information holder (medical institution) 50 must have multiple personal attributes, including the residential area of ​​the patient being referred, and have received treatment from a predetermined number of patients with the same personal attributes. Specifically, one of the first requirements for a medical institution that medical institution P refers a discharged patient "005" to is that the medical institution (information holder 50) has holder attribute information corresponding to the residential area of ​​the discharged patient "005". Furthermore, a second requirement is that the medical institution P holds personal data elements of patient visit history, and that the information holder 50 (medical institution) has received many visits from other patients related to personal data elements representing age attributes of the same age group as patient "005". The requirements for fulfilling the information request are those that satisfy both the first and second requirements.

[0040] In order to output information on recommended information holders 50 in response to the information requests described above, in the proposal system 1 of the second embodiment, the learning unit 16 generates a proposal model as follows. That is, in the first embodiment, the information request is for information on information holders 50 recommended as information providers, whereas in the second embodiment, the information request is for information on information holders 50 recommended as information holders that cooperate or share functions. In the second embodiment, a knowledge graph including a plurality of nodes set to respond to such information requests related to cooperation or sharing functions is generated by the graph generation unit 15 of the computing unit 10. The learning unit 16 of the computing unit 10 generates a proposal model using machine learning (e.g., link prediction AI) with the generated knowledge graph and the requirements of information holders that respond to information requests related to cooperation or sharing functions.

[0041] In the processing unit 20, similar to the first embodiment, the receiving unit 25 receives an information request, the proposal unit 26 inputs the information request to the proposal model, and the output of the proposal model is output as information of the recommended information holder 50 (information holder 50 that cooperates or shares functions).

[0042] The proposed system 1 of the second embodiment, similar to the first embodiment, processes information requests involving personal data not using the personal data elements themselves, but using element identification information that identifies the personal data elements and data location information of the personal data elements. As a result, similar to the first embodiment, the proposed system 1 of the second embodiment can contribute to promoting the utilization of personal data while protecting personal information.

[0043] Furthermore, in the second embodiment as well, the proposed system 1 proposes information on recommended information holders 50 in response to information requests using a proposal model generated with a knowledge graph. Therefore, even if the number of information holders 50 is enormous, the processing unit 20 can predict the appropriate information holder 50 in response to information requests related to collaboration or functional division.

[0044] <Other Embodiments> The present invention is not limited to the first or second embodiment and can take various forms. For example, in the first and second embodiments, the proposal model shows an example in which it outputs information about the recommended information holder 50 and an explanation of the basis for recommending the information holder 50. The proposal model may also be generated to output the following information. That is, the proposal model may be generated to output information representing a person profile related to the information request. To give a specific example, suppose there is an information request to propose an information holder 50 that provides information (data) related to "health promotion" in "○○ city". With respect to such an information request, for example, the proposal model may be generated to output information such as "an 80-year-old man who is receiving rehabilitation treatment" or "a 60-year-old woman who swims for weight loss" as a person profile that is highly related to the nodes "○○ city" and "health promotion" in the knowledge graph.

[0045] Figure 6 is a diagram showing another embodiment of the proposed system according to the present invention. This proposed system 70 comprises a reception unit 71, a proposal unit 72, and a graph generation unit 73. The reception unit 71, the proposal unit 72, and the graph generation unit 73 are functional units that are realized, for example, by a computer device executing a computer program. The graph generation unit 73 generates a knowledge graph that represents the relationships between multiple nodes, including multiple information holders. The generation of this knowledge graph uses element identification information that identifies personal data elements, which are data elements that constitute personal data, and data location information of personal data elements that represents information holders that hold personal data elements. The reception unit 71 receives an information request. The information request is a request for information on information holders represented by the data location information of personal data elements that meet the user's needs. The proposal unit 72 uses a model generated by machine learning. This model is generated by machine learning using the knowledge graph generated by the graph generation unit 73 and the requirements of the information holders that meet the information request. Using this model, the proposal unit 72 outputs information holders that meet the requirements corresponding to the received information request as recommended information holders.

[0046] Figure 7 is a flowchart illustrating an example of the operation of the computer included in the proposed system 70. For example, suppose that the model described above is generated by machine learning using a knowledge graph. First, the reception unit 71 receives an information request that requests information about an information holder represented by data location information of personal data elements that meet the user's needs (step 201 in Figure 7). Subsequently, the proposal unit 72 outputs information about an information holder that satisfies the requirements corresponding to the information request as information about a recommended information holder (step 202).

[0047] In other embodiments, the proposed system 70, by having the configuration described above, aims to protect personal information while promoting the utilization of personal data. This is because, in this proposed system 70, instead of using personal data itself, element identification information that identifies personal data elements constituting personal data, and data location information of personal data elements that represents an information holder that holds the personal data elements, are used. This has the effect of making it easier to provide information related to personal data.

[0048] The present invention has been described above using the embodiments described above as exemplary examples. However, the present invention is not limited to the embodiments described above. That is, the present invention can be applied in various forms that can be understood by those skilled in the art within the scope of the present invention. [Explanation of Symbols]

[0049] 1.70 Proposed Systems 15,73 Graph generation unit 25,71 Reception Department 26,72 Proposal Department

Claims

1. A graph generation means generates a knowledge graph representing the relationships between multiple nodes, including multiple information holders, using element identification information that identifies personal data elements, which are data elements that constitute personal data, and data location information of personal data elements that represents information holders that hold personal data elements. A means for receiving information requests that request information about an information holder represented by data location information of personal data elements that meet the user's needs, A proposed means for outputting information holders that satisfy the requirements corresponding to the received information request as recommended information holders, using a model generated by machine learning that utilizes a generated knowledge graph and the requirements of information holders that respond to information requests. A proposed system equipped with the following features.

2. The aforementioned information request is a request to output information about recommended information holders that will cooperate or share functions. The proposed system according to claim 1.

3. The aforementioned information request is a request to output information about recommended data holders that request information related to personal data. The proposed system according to claim 1.

4. The proposed means further comprises a learning means for generating a model used by the aforementioned proposed means. The proposed system according to claim 1.

5. The learning means generates a model using a link prediction technique that utilizes a knowledge graph. The proposed system according to claim 4.

6. By computer, Using element identification information that identifies personal data elements, which are data elements that constitute personal data, and data location information of personal data elements that represents information holders that hold personal data elements, a knowledge graph is generated that represents the relationships between multiple nodes containing multiple information holders. Furthermore, by computer, We accept information requests that request information about information holders represented by data location information of personal data elements that meet user needs. Using a machine learning model that combines the generated knowledge graph with the requirements for information holders that respond to information requests, the system outputs information holders that meet the requirements for the received information request as recommended information holders. Proposal method.

7. A process that accepts information requests that request information about an information holder represented by data location information of personal data elements that meet the user's needs, This process involves using a machine learning model that generates a knowledge graph representing the relationships between multiple nodes containing multiple information holders, using element identification information to identify personal data elements and data location information of personal data elements representing information holders that hold personal data elements, and the requirements of information holders that respond to information requests, to output information holders that satisfy the requirements corresponding to the received information request as recommended information holders. A computer program that causes a computer to execute something.

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