Agreement information confirming device and agreement information confirming method

Abstract

To confirm agreement information with details necessary for a user without taking refurbishment manhours for a conventional service that manages agreement information when the user confirms the agreement information.SOLUTION: An agreement information confirming device that confirms agreement information on the utilization of personal information of a user includes: an agreement information obtaining unit that obtains, for each service for the user, agreement information and information relating to one or more user services that utilize the agreement information; an agreement information collecting unit that collects and manages the obtained agreement information for each service for the user; and an agreement information displaying unit that displays the collected agreement information. When collecting the agreement information for each service for the user and when the agreement information involves missing information, the agreement information collecting unit complements the missing information using the information relating to the service for the user and relevant information to be used to interpret the agreement information.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a consent information confirmation device and a consent information confirmation method for confirming consent information related to personal information. [Background technology]

[0002] There are cases where services that utilize personal information require the consent of users. Because these services are provided by various businesses, the personal information of users and their consent information are managed in a decentralized manner. Therefore, in order for users to understand their own consent information, they had to access each piece of distributed consent information individually.

[0003] To address this issue, Patent Document 1 discloses a technology that aggregates consent information and makes it possible to present it to users. By using this technology, users can confirm aggregated consent information regarding distributed consent information. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2014-228961 Summary of the Invention [Problem to be solved by the invention]

[0005] The technology disclosed in Patent Document 1 requires that the service providing device and information management device have the assumed specifications, and existing services must be modified. Furthermore, the consent information handled by existing services is in a format designed individually for each service, so when it comes to aggregating information, there is insufficient information for each service, making simple aggregation difficult. Furthermore, the terms and conditions on which users' consent is based are written differently for each service, so even if the missing information is supplemented from the terms and conditions, it may not be possible to obtain the level of detailed information desired by the user.

[0006] Therefore, an object of the present invention is to provide a technology for confirming consent information with the level of detail required by the user without requiring the man-hours required for modifying existing services that manage consent information. [Means for solving the problem]

[0007] In order to solve the above problems, one representative consent information confirmation device of the present invention is a consent information confirmation device that confirms consent information regarding the use of a user's personal information, and is equipped with a consent information acquisition unit that acquires consent information and information related to one or more user-oriented services that utilize the consent information for each user-oriented service, a consent information aggregation unit that aggregates and manages the acquired consent information for each user-oriented service, and a consent information display unit that displays the aggregated consent information.When aggregating the consent information for each user-oriented service, if there is missing information in the consent information, the consent information aggregation unit supplements the missing information using information related to the user-oriented service and related information used to interpret the consent information. [Effects of the Invention]

[0008] According to the present invention, consent information can be effectively grasped and confirmed without requiring man-hours to modify an existing service that manages consent information. Problems, configurations, and effects other than those described above will become apparent from the following description of the preferred embodiments. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 is a diagram showing an example of the configuration blocks of a consent information confirmation device according to the present invention and its surroundings. [Figure 2] FIG. 2 is a block diagram illustrating an example of a hardware configuration of the consent information verification device. [Figure 3] FIG. 3 is a flowchart illustrating an example of a processing mode performed by the consent information verification device according to the first embodiment. [Figure 4] FIG. 4 is a diagram illustrating an example of the consent information. [Figure 5] FIG. 5 is a diagram showing one aspect of the process of supplementing the missing information in step S105. [Figure 6] FIG. 6 is a diagram illustrating an example of the consent information collection management table. [Figure 7] FIG. 7 is a diagram showing a specific example of a display screen of the consent information display unit. [Figure 8] FIG. 8 is a flowchart illustrating an example of a processing mode performed by the consent information verification device according to the second embodiment. [Figure 9] FIG. 9 is a diagram showing a specific example of the display screen of the consent information display unit in step S201. [Figure 10] FIG. 10 is a diagram showing one aspect of the process of supplementing the missing information in step S203. [Figure 11] FIG. 11 is a diagram showing a specific example of the display screen of the consent information display unit in step S205. [Figure 12] FIG. 12 is a diagram showing a specific example of a display screen of the consent information display unit for a business operator. DETAILED DESCRIPTION OF THE INVENTION

[0010] Hereinafter, as modes for carrying out the present invention, Examples 1 to 3 will be described with reference to the drawings. Note that the present invention is not limited to these Examples. In addition, in the description of the drawings, the same parts are denoted by the same reference numerals.

[0011] The device configuration according to the present invention shown in FIGS. 1 and 2 is common to the first to third embodiments. 1 is a diagram showing an example of the configuration blocks of a consent information confirmation device 100 according to the present invention and its peripheral aspects. The consent information confirmation device 100 is configured to include at least a consent information acquisition unit 110, a consent information aggregation unit 120, related data 130, a consent information aggregation management table 140, and a consent information display unit 150.

[0012] The consent information acquisition unit 110 acquires the consent information 163 and information about the service providing device 160 from the service providing device 160 that holds the consent information 163 of the user 101. Here, the consent information 163 is information for managing the expression of intent that the user 101 of the consent information confirmation device 100 makes to the service providing device 160.

[0013] In summary, the service providing device 160 presents the user 101 with terms and conditions (hereinafter abbreviated as "terms and conditions") 162 and the like that form the basis for the user's consent, and after obtaining consent regarding the use of data 165 related to the user 101, the service providing device 160 refers to consent information 163 that reflects the consent, and provides a service to the user 101 and the like by utilizing the target data 165. The format of the consent information 163 differs depending on the service providing device 160. A specific example of the consent information 163 will be described later with reference to FIG. 4.

[0014] Here, the information related to the service providing device 160 is used to interpret the data 165 handled by the service providing device 160. For example, it includes information related to details of the service provided by the service providing device 160, information related to the data 165 handled by the service providing device 160, etc. Although details will be described later, the information related to the service providing device 160 includes various information to be managed and displayed by the service providing device 160, the user interface (hereinafter abbreviated as "UI") of the service providing device 160, the processing of the service providing device 160, and the entity or reference thereof.

[0015] The consent information aggregating unit 120 interprets the consent information 163 from the service providing device 160, acquired by the consent information acquisition unit 110, based on related data 130 used for interpreting the consent information 163, previously stored in the consent information verification device 100, converts the information into a common format, and adds the consent information 163 converted into the common format to the consent information aggregation management table 140. This processing will be described later using an example of a specific flowchart shown in FIG. 3. Here, the related data 130 refers to background information related to the consent information 163. For example, this includes types of personal information under Japanese law and guidelines on how to handle such information. The consent information aggregation management table 140 is created, added to, updated, and managed by the consent information aggregating unit 120. A specific example of the consent information aggregation management table 140 will be described later using FIG. 6.

[0016] The consent information display unit 150 acquires the consent information 163 existing in the consent information aggregation management table 140, and displays it to the user 101 and, if necessary, the business operator 102. A specific example of a UI using the consent information display unit 150 will be described later with reference to FIG. 7 etc.

[0017] 2 is a block diagram showing an example of the hardware configuration of the consent information verification device 100. The consent information verification device 100 includes hardware such as a calculation unit 210, a storage unit 220, an input / output unit 230, and a communication unit 240.

[0018] Among these, the calculation unit 210 includes a consent information acquisition unit 110, a consent information aggregation unit 120, and a consent information display unit 150. The calculation unit 210 and each of the functional units included in the calculation unit 210 can be configured by hardware such as a circuit device that implements these functions, or can also be configured by a calculation device such as a CPU (Central Processing Unit) executing software that implements these functions.

[0019] The storage unit 220 can be configured by a storage device that stores data used by the calculation unit 210 (for example, the consent information collection management table 140 shown in FIG. 1).

[0020] The input / output unit 230 is used to display the processing results of the calculation unit 210 and the user interface of the consent information display unit 150 (described later) on the screen.

[0021] The communication unit 240 is, for example, a communication chip or a NIC (Network Interface Card), and is used for communication with the service providing device 160, the user 101, the business operator 102, and the like.

[0022] When each unit of the consent information verification device 100 receives data, the data is received via the input / output unit 230 or the communication unit 240 . [Example]

[0023] As a first embodiment of the present invention, a processing mode from obtaining consent information to updating the consent information collection management table will be described. 3 is a flowchart illustrating an example of a processing mode performed by the consent information verification device 100 according to the embodiment 1. The processing flow according to this flowchart is performed for each service for a user. The following describes the processing content of each step shown in Fig. 3. The processing entity of each step is the calculation unit 210, which includes the consent information acquisition unit 110, the consent information collection unit 120, and the consent information display unit 150.

[0024] (1) Step S101 The consent information acquisition unit 110 acquires consent information 163 from the service providing device 160 . Fig. 4 is a diagram showing an example of the consent information 163. In Fig. 4, the consent information 163 is written in the json (javascript object notation) format. The consent information 163 includes, for example, the following parts A to D.

[0025] A. Section 301 of the Regulations This is the section that indicates what terms and conditions you are agreeing to. For example, the terms 162 that the service providing device 160 displays to the user 101 include a privacy policy and terms of use. In FIG. 4, "'kiyaku':'privacy policy'" written in the terms and conditions section 301 means that the terms and conditions section 301 of the consent information 163 is consent regarding the "privacy policy."

[0026] A. Data section 302 This is the section that indicates what data consent is being given. For example, the data 165 handled by the service providing device 160 includes names, addresses, and telephone numbers. In FIG. 4, "data:address" written in the data portion 302 means that "address" is used as the data portion 302 of the consent information 163.

[0027] C Processing part 303 This is the part that indicates what kind of processing is permitted. For example, the processing performed by utilizing the data 165 handled by the service providing device 160 includes recommendations and product delivery based on geographic information. In FIG. 4, the "process":"recommend event based on location" in the processing section 303 means that the processing section 303 of the consent information 163 uses the "address" to "recommend event."

[0028] D. Consent Section 304 This is the section that indicates whether consent exists or not. For example, the results of the user 101's expression of intent to the service providing device 160 include consent, non-consent, and non-consent. In FIG. 4, "consentFlag":"OK" written in consent section 304 of consent information 163 means that user 101 "agrees."

[0029] As a specific method for acquiring the consent information 163, if the service providing device 160 makes an API public, the consent information 163 can be acquired via the API. At that time, the authentication information of the user 101 may be obtained by setting an access key for the API acquired by the user 101 in the consent information confirmation device 100 and utilizing that authentication information. Alternatively, the consent information 163 may be directly input by the user 101 or the business operator 102 of the service providing device 160.

[0030] The rules portion 301 also includes the rules 162. For example, even if the agreement information 163 held by the service providing device 160 does not include the rules 162, the rules 162 can be referenced within the service providing device 160 from the rule name. Therefore, even if the agreement information 163 acquired via the API does not include the agreement 162, the agreement 162 is acquired as a separate API. Furthermore, the agreement 162 displayed on the screen is acquired by a technique called web scraping, which extracts information from content displayed on a website. In this way, the agreement 162 can be acquired. In this way, the terms 162 are included as part of the consent information 163 .

[0031] (2) Step S102 The consent information aggregating unit 120 checks what information is included in the consent information 163 acquired by the consent information acquiring unit 110 in step S101. Specifically, it checks whether the content of the consent information 163 includes the data part 302, the processing part 303, and the consent part 304.

[0032] In addition, when each content is included, the details of the content are also confirmed. For example, with regard to the data portion 302, it is confirmed whether the address is one that lists only the prefectural capital or one that includes the street number. This is because the handling that the user 101 is willing to accept varies depending on the subject of the data 165. Similarly, with regard to the processing portion 303, it is confirmed whether the data will be processed after being anonymized or whether it will be processed as raw data.

[0033] Regarding the terms and conditions section 301 of the consent information 163, when the user 101 agrees to the terms and conditions 162, values ​​corresponding to the data section 302, processing section 303 and consent section 304 are stored, so this is not a required confirmation item.

[0034] As a specific confirmation method, the data items of the agreement information 163 are checked to determine whether the data part 302, the processing part 303, and the agreement part 304 contain the corresponding words. For example, for the data portion 302, words such as "data" and "thing" are relevant. Alternatively, it may be determined whether a word is close to the data portion using similarity using Word2vec or the like, which converts words into vectors and makes it possible to calculate word similarity. Furthermore, if there is an explanation (hereinafter referred to as "metadata") about the data item of the consent information 163, it may be possible to use morphological analysis, which breaks down sentences into words, to calculate similarity from this metadata. In addition, it may be determined using a machine learning model that has been trained in advance. Alternatively, it may be possible to have the user 101 or the operator 102 of the service providing device 160 input the information directly.

[0035] Then, the details of the data are checked for the contents corresponding to each of the data section 302, the processing section 303, and the consent section 304. For example, for the data portion 302, the details are checked, such as whether it is written as "XX prefecture" or "XX prefecture, XX city, XX address." Also, for the processing portion 303, the details are checked, such as whether it is written as "recommended after anonymization" or "recommended as raw data." Specifically, morphological analysis or the like is used on the data to determine whether it contains words such as an address or anonymization. Furthermore, the determination may be made using a pre-trained machine learning model. Alternatively, the user 101 or the operator of the service providing device 160 may input the information directly.

[0036] The above data items and details of the consent information 163 to be confirmed are merely examples, and other information may be used. For example, it may be confirmed whether or not the data 165 includes a condition for utilizing the data 165. It may also be confirmed as to where the data 165 may be provided. In particular, when the service providing device 160 has a function for sharing the data 165 with other service providing devices 160, it is necessary for the user 101 to know to what destination the data 165 may be provided, and therefore the sharing function is effective. In these cases, confirmation is made regarding data items defined in a common format.

[0037] (3) Step S103 The consent information aggregating unit 120 checks whether or not any information is missing based on the check result in step S102. For example, it may not include all of the data portion 302, the processing portion 303, and the consent portion 304. Also, the written content may not be detailed enough to include information that can be used to determine whether the address includes only the prefecture or the house number. In this way, based on the confirmation result of step S102, it is confirmed whether there is any missing information regarding the consent information 163.

[0038] (4) Step S104 If it is determined in step S103 that the information is insufficient, the consent information acquisition unit 110 acquires information about the service providing device 160 from the service providing device 160. Specifically, by utilizing web scraping, which automatically extracts specific information from a website, information on the data 165 to be handled is obtained from a form screen where the user 101 inputs data 165 into the service providing device 160.

[0039] Also, information about the data 160 to be handled and processing may be obtained from the specifications of the API provided by the service providing device 160. Furthermore, information about metadata of the consent information may be obtained from the specifications of the service providing device 160. Alternatively, the user 101 or the operator of the service providing device 160 may input the information directly.

[0040] (5) Step S105 The consent information aggregation unit 120 supplements the missing information confirmed in step S103 with respect to the consent information 163 acquired in step S101, using the information about the service providing device 160 acquired in step S104 and the related data 130 used to interpret the consent information 163 previously stored in the consent information confirmation device 100.

[0041] As a specific supplementing method, if the missing information confirmed in step S103 is a data item, an executable statement is executed for a trained machine learning model including the information about the service providing device 160 acquired in step S104 and the related data 130 used to interpret the consent information 163 previously stored in the consent information confirmation device 100, and the data corresponding to the relevant data item is supplemented based on the results. Here, the trained machine learning model is assumed to be a generic machine learning model including a generative AI, and as a constituent requirement, it is provided in the consent information confirmation device 100, and in accordance with FIG. 2, it is provided in the calculation unit 210.

[0042] FIG. 5 is a diagram showing one aspect of the process of supplementing the missing information in step S105. As shown in Figure 5, for example, if the consent information 163 does not contain any description of its data portion 302 and processing portion 303, and only contains information about which terms 162 are agreed to ("kiyaku":"Privacy Policy" and ""consentFlag":"OK"), the trained machine learning model 125 is given the task of supplementing the data portion 302 and processing portion 303 of the consent information 163 using the terms information and information about the service providing device 160.

[0043] By supplementing the results inferred by the trained machine learning model 125, the missing data portion 302 and processing portion 303 are supplemented ("data":"address" and "process":"event planning"), and supplemented consent information 163 is obtained.

[0044] Furthermore, in order to improve the accuracy of the inference result, it may be possible to provide a process for checking whether the inference result is as expected, and for retrying if the result is not as expected. For example, it is checked whether the format of the result of the trained machine learning model 125 is the same JSON format as the consent information 163, whether the supplementary result is blank, whether the supplementary result uses words contained in the agreement information or information related to the service providing device 160, etc.

[0045] The above supplementary method is an example, and other methods may be used. For example, if the data portion 302 is insufficient, morphological analysis, which breaks down sentences into words, may be applied to the text of the agreement, and then the words may be converted into vectors. Word2vec, which enables word similarity to be calculated, may be used to extract words that are close to the data portion 302 based on similarity. Furthermore, web scraping, which automatically extracts specific information from a website, may be used to obtain information about the data 165 to be handled from a form screen where the user 101 inputs data into the service providing device 160.

[0046] (6) Step S106 Based on the consent information 163 in which the missing information has been supplemented in step S105, or on the consent information 163 for which it has been determined in step S103 that there is no missing information, the consent information aggregating unit 120 updates and stores the data items of the consent information 163 corresponding to the data items managed in the consent information aggregation management table 140. This makes it possible to aggregate information related to necessary data items of the consent information 163 that was dispersed.

[0047] 6 is a diagram showing an example of the consent information collection management table 140. The consent information collection management table 140 updated in step S106 will be described with reference to this FIG. The consent information aggregation management table 140 has data items of management target 501, data 502, process 503, and consent 504. Each data item will be explained below.

[0048] Managed object 501: Information on the service providing device 160. Specifically, the managed object 501 includes the name of the service providing device 160, the URL of the service providing device 160, and the like.

[0049] Data 502: Information relating to the data 165 handled by the service providing device 160. Specifically, it includes values ​​corresponding to the data items of the data portion 302 in the consent information 163.

[0050] Process 503: Information about a process performed by utilizing the data 165 handled by the service providing device 160. Specifically, it includes a value corresponding to the data item of the process part 303 in the consent information 163.

[0051] Consent 404: Information relating to the result of the user 101 expressing his / her intention to the service providing device 160. Specifically, it includes a value corresponding to the data item of the consent part 304 in the consent information 163.

[0052] Furthermore, the consent information aggregation management table 140 does not need to include all of the above information, and information in a different format may be stored for each item. For example, a data item may be provided that includes information regarding the conditions under which the service providing device 160 utilizes the data 165. In this case, when checking in step S102 or making the judgment in step S103, it is determined whether any additional data items are missing, and if so, the missing data items are supplemented, thereby enabling addition to the consent information aggregation management table 140.

[0053] As described above, when there is a deficiency in the consent information 163 of the service providing device 160, the consent information aggregating unit 120 supplements the missing part of the consent information 163 of the service providing device 160 using information about the service providing device 160 and related data 130 used to interpret the consent information 163 previously stored in the consent information confirmation device 100, and stores the supplemented information in the consent information aggregation management table 140. However, the method is not limited to the above method.

[0054] For example, the missing information may be supplemented with only information about the service providing device 160. Also, if the agreement information included in the consent information 163 is detailed, the missing information can be supplemented by simply reading the agreement information. Furthermore, information about the service providing device 160 may be acquired in advance. By acquiring information about the service providing device 160 in advance, the time required for the user 101 to check the supplemented consent information 163 can be reduced.

[0055] Next, an operation mode in which the consent information display unit 150 displays the supplemented consent information 163 to the user 101 will be described. Fig. 7 is a diagram showing a specific example of a display screen of the consent information display unit 150. The consent information display unit 150 displays the consent information confirmation GUI 600 shown in Fig. 7. The user 101 confirms his / her own consent information 163 on the consent information confirmation GUI 600.

[0056] 7 shows an example in which the service providing device 160 displays "data analysis service," "data acquisition service," and "moving service" for the items of the management target 601. Each of these services corresponds to the management target 501 in the consent information aggregation management table 140.

[0057] 7 shows an example in which "Address (prefecture only)," "Address (with street number)," and "Name" are displayed for the items of data 602 as the data portion 302 of the consent information 163. Each of these data corresponds to data 502 in the consent information aggregation management table 140.

[0058] 7 shows an example in which "use raw data" or "use after anonymization" is displayed for the item of process 603 as the process part 303 of the consent information 163. Each of these data corresponds to process 503 of the consent information aggregation management table 140.

[0059] 7 shows an example in which "◯" or " " is displayed for the consent 604 item as the consent portion 304 of the consent information 163. These "◯" and " " correspond to consent 504 in the consent information aggregation management table 140.

[0060] When the consent information aggregating unit 120 reflects the new consent information 163 in the consent information aggregation management table 140, the displayed results change.

[0061] 7, an item "inconsistency determination result" is displayed as a result of determining whether or not there is any inconsistency in the integrated consent information 163 in the consent information aggregation management table 140. For example, in the "data analysis service," the consent regarding "address" of the data 165 of the service providing device 160 is "○," whereas in the "moving service," the consent regarding "address" of the data 165 of the service providing device 160 is " ", so the consent statuses are different for the same data 165. The "inconsistency determination result" is that there is a contradiction in the consent status of the user 101.

[0062] As a specific determination method, an execution statement for a pre-trained machine learning model is executed against the consent information aggregation management table 140, and a contradiction is determined based on the execution result. As another determination method, since the consent information aggregation unit 120 can compare the same data items in the consent information aggregation management table 140 for each piece of consent information 163 managed by the service providing device 160, the same values ​​may be extracted from the data 502 or process 503 of the data item in the consent information aggregation management table 140, and it may be determined whether the same value is stored in the consent 504 of the data item.

[0063] 7 is an example, and other information may be displayed without being limited to this. Furthermore, the displayed information may be checked not by the user 101 but by the business operator 102 that operates the service providing device 160. In this case, the business operator 102 will be able to check the consent status of the user 101.

[0064] As described above, the operation mode of the consent information confirmation device 100 according to the first embodiment makes it possible to supplement the missing consent information 163 by using information about the service providing device 160 and the related data 130 used to interpret the consent information 163 that is previously stored in the consent information confirmation device 100. Then, the consent information aggregation management table 140 is updated, and the user 101 can grasp the consent information 163 with the missing information supplemented.

[0065] Therefore, when the user 101 confirms the consent information 163, the consent information confirmation device 100 can quickly and accurately grasp what kind of consent has been given without modifying the service providing device 160. [Example]

[0066] In the second embodiment of the present invention, when the user 101 gives consent to the service providing device 160, the user 101 confirms what kind of consent information 163 will be stored in the service providing device 160 by giving consent. This allows the user 101 to easily confirm the consent information 163 from the contents of the agreement text before giving consent.

[0067] Fig. 8 is a flowchart illustrating an example of a processing mode performed by the consent information confirmation device 100 according to the second embodiment. The processing content of each step shown in Fig. 8 will be described below. The processing entity of each step is the calculation unit 210, which includes the consent information acquisition unit 110, the consent information aggregation unit 120, and the consent information display unit 150.

[0068] (7) Step S201 The consent information acquisition unit 110 acquires the agreement information from the service providing device 160 . As a specific acquisition method, for example, the terms and conditions 162 are acquired via the API of the service providing device 160. Alternatively, the terms and conditions 162 displayed on the screen may be acquired by a technique called web scraping, which extracts information from content displayed on a website. Alternatively, the terms and conditions may be directly input by the user 101 or the business operator 102 of the service providing device 160.

[0069] 9 is a diagram showing a specific example of a display screen of the consent information display unit 150 in step S201. The consent information display unit 150 displays a consent information confirmation GUI 700 shown in FIG. In step S201, the user 101 determines whether to input the agreement information on the agreement information confirmation GUI 700 by "automatically inputting from a URL" or "directly inputting."

[0070] If "Automatically input from URL" is selected, by inputting the URL of the service providing device 160, the consent information confirmation device 100 will obtain the terms 162 via web scraping or API and automatically input them. If "direct input" is selected, the user 101 directly inputs the terms and conditions 162 displayed by the service providing device 160.

[0071] After entering the information, click "Confirm" to proceed to the next step. The display information shown in FIG. 9 is an example, and is not limited to this, and other information may be used.

[0072] (8) Step S202 This step S202 is the same as step S104 in the first embodiment shown in Fig. 3. In step S202, since the state is before consent, the consent information 163 does not exist in the service providing device 160. Therefore, it is necessary to supplement the data items for which the required information is missing in the consent information aggregation management table 140, and the consent information acquisition unit 110 acquires information related to the service providing device 160 from the service providing device 160.

[0073] (9) Step S203 The consent information aggregation unit 120 supplements missing information regarding the terms and conditions information acquired in step S201 using at least the information regarding the service providing device 160 acquired in step S202 and the related data 130 used to interpret the consent information 163 previously stored in the consent information confirmation device 100.

[0074] 8, since the state is before consent, consent information 163 does not exist in the service providing device 160. Therefore, data items of consent information 163 that lack necessary information are supplemented in the consent information aggregation management table 140. The specific supplementing method is the same as step S105 of the first embodiment shown in FIG.

[0075] 10 is a diagram showing one mode of the process of supplementing the missing information in step S203. For example, as shown in FIG. 10, the trained machine learning model 125 is given a task of supplementing the data portion 302, the processing portion 303, and the consent portion 304 of the consent information 163 using the agreement information and information related to the service providing device 160 as input. By supplementing the results of the inference made by this trained machine learning model 125, the missing data portion 302 ("data:address"), the processing portion 303 ("process:data analysis"), and the consent portion 304 ("consentFlag:consentFlag").

[0076] (10) Step S204 This step S204 is processing for updating the consent information aggregation management table, and is the same as step S106 in the first embodiment shown in Fig. 3. A detailed description will be omitted.

[0077] (11) Step S205 The consent information display unit 150 displays the integrated consent information 163 to the user 101. 11 is a diagram showing a specific example of the display screen of the consent information display unit 150 in step S205. The consent information display unit 150 displays a consent information confirmation GUI 900 shown in FIG.

[0078] 11 shows an example of displaying consent information in which missing information has been supplemented in step S203 under the heading "Expected consent information." The user 101 checks what consent information 163 will be stored if he or she agrees to the terms 162 on the consent information confirmation GUI 900. The information displayed in FIG. 11 is an example, and is not limited to this, and other information may also be used.

[0079] As described above, in the second embodiment of the present invention, when the user 101 gives consent to the service providing device 160, the user 101 confirms what kind of consent information 163 will be stored in the service providing device 160 as a result of the consent. This allows the user 101 to easily confirm the content of the agreement 162 and the consent information before giving consent. [Example]

[0080] In a third embodiment of the present invention, the business operator 102 that operates the service providing device 160 checks the consent information 163 of the user 101 and confirms the acceptable range of consent of the user 101. This enables the business operator 102 to update the rules 162 of the service providing device 160 and expand the range of services provided by the service providing device 160. In Fig. 1, the processing mode by the business operator 102 in the third embodiment is indicated by a dashed line.

[0081] The following describes an operation mode for displaying consent information 163 of user 101 to business operator 102. 12 is a diagram showing a specific example of a display screen of the consent information display unit 150 for the business operator 102. The consent information display unit 150 displays a consent information confirmation GUI 1000 shown in FIG.

[0082] FIG. 12 shows an example in which consent information 163 relating to a specific user 101 is displayed under the item "User A's consent information," and a proposed update to the terms 162 is displayed under the item "Proposed update to terms."

[0083] For example, a data analysis service provider 102 checks the consent information 163 of a user 101 who is using the "data analysis service," and also checks the consent status obtained from the user 101 regarding consent for name and address for "other services." This allows the provider 102 to understand that if the use of name is added to the terms and conditions 162 of the "data analysis service," the user 101 is likely to agree because there is a track record of consent for name for "other services."

[0084] In this way, by checking the consent status for "other services," it is possible to efficiently create rules 162 that expand the range of data that can be utilized by service providing device 160. Here, the displayed information shown in Fig. 12 is an example, and is not limited to this, and other information may also be used.

[0085] As a specific method for creating a proposed update to the terms and conditions, for example, a task of creating a proposed update to the terms and conditions 162 using the consent information 163 in the consent information aggregation management table 140 is given to a trained machine learning model. By supplementing the inference results, the proposed update to the terms and conditions 162 is displayed to the business operator 102. The specific method for creating a proposed update to the terms and conditions shown here is an example, and is not limited to this, and other methods may also be used.

[0086] As described above, in the third embodiment of the present invention, the business operator 102 operating the service providing device 160 checks the consent information 163 of the user 101 and checks the acceptable range of consent of the user 101. This enables the business operator 102 to update the rules 162 of the service providing device 160 and expand the range of services provided by the service providing device 160.

[0087] Although Examples 1 to 3 have been described above as embodiments of the present invention, the present invention is not limited to the above-described Examples, and various modifications are possible within the scope of the gist of the present invention. [Explanation of symbols]

[0088] 100: Consent information confirmation device, 101: User, 102: Business operator, 110: consent information acquisition unit, 120: consent information aggregation unit, 125: Trained machine learning model, 130: Related data, 140: consent information aggregation management table, 150: consent information display unit, 160: Service providing device, 161: Consent acquisition unit, 162: Terms, 163: Consent information, 164: Service providing unit, 165: Data, 210: Calculation unit, 220: Storage unit, 230: Input / output unit, 240: Communication unit

Claims

1. A consent information confirmation device that confirms consent information regarding the use of a user's personal information, a consent information acquisition unit that acquires, for each service for the user, information related to the consent information and one or more services for the user that utilize the consent information; a consent information aggregation unit that aggregates and manages the acquired consent information for each service for the user; a consent information display unit that displays the aggregated consent information; Equipped with When aggregating the consent information for each service for the user, if there is missing information in the consent information, the consent information aggregation unit supplements the missing information using information related to the service for the user and related information used to interpret the consent information. A consent information confirmation device characterized by:

2. 2. The consent information confirmation device according to claim 1, Equipped with pre-trained machine learning models, The consent information aggregation unit inputs the consent information into the trained machine learning model that uses information related to the service for the user and related information used to interpret the consent information, and supplements the missing information with an inference result of the trained machine learning model. A consent information confirmation device characterized by:

3. 3. The consent information confirmation device according to claim 2, The trained machine learning model determines whether or not there is a contradiction in the consent information aggregated by the consent information aggregation unit; The consent information display unit displays the result of the determination. A consent information confirmation device characterized by:

4. 2. The consent information confirmation device according to claim 1, Equipped with pre-trained machine learning models, the consent information aggregation unit inputs information about terms and conditions related to the service for the user into the trained machine learning model, which uses at least information related to the service for the user and related information used to interpret the consent information, and supplements the consent information that is expected if the user agrees to the terms and conditions based on an inference result of the trained machine learning model; The consent information display unit displays the supplemented consent information. A consent information confirmation device characterized by:

5. 2. The consent information confirmation device according to claim 1, Equipped with pre-trained machine learning models, The trained machine learning model checks the tolerance range for the consent information aggregated by the consent information aggregation unit, and creates a proposed update to the terms and conditions related to the service for the user; the consent information display unit displays the update proposal. A consent information confirmation device characterized by:

6. A method for confirming consent information regarding the use of a user's personal information, comprising: Acquire, for each service for the user, information related to the consent information and one or more services for the user that utilize the consent information; When aggregating the consent information for each service for the user obtained, if there is missing information in the consent information, the missing information is supplemented using information related to the service for the user and related information used to interpret the consent information. A method for confirming consent information.

7. 7. The consent information confirmation method according to claim 6, The consent information is input into a trained machine learning model that uses information about the service for the user and related information used to interpret the consent information, and the missing information is supplemented by the results of inferences made by the trained machine learning model. A method for confirming consent information.

8. The consent information confirmation method according to claim 7, determining whether or not there is a contradiction in the aggregated consent information using the trained machine learning model; Display the result of the judgment A method for confirming consent information.

9. 7. The consent information confirmation method according to claim 6, inputting information about the terms and conditions related to the service for the user into a trained machine learning model that uses at least information related to the service for the user and related information used to interpret the consent information, and supplementing the consent information that would be expected if the user agreed to the terms and conditions based on the inference results of the trained machine learning model; Display the supplemented consent information A method for confirming consent information.

10. 7. The consent information confirmation method according to claim 6, Using a trained machine learning model, the aggregated consent information is checked for tolerances regarding the consent information, and a proposal to update the terms and conditions related to the service for the user is created; Display the proposed update you created A method for confirming consent information.

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