Information processing device, information processing method, and program

Abstract

To provide an information processing system, an information processing device, an information processing method, and a program that can more efficiently and highly accurately determine a document that contains personal information.SOLUTION: A method for determining whether processing-target text data contains personal information using a learned model that has been learned with data in which text data is associated with information indicating that personal information is contained in the text data, includes the steps of: acquiring the text data; and determining whether the acquired text data contains personal information using a learned model that has been learned with data in which text data is associated with information indicating that personal information is contained in the text data.SELECTED DRAWING: None

Description

[Technical field]

[0001] The present invention relates to an information processing device, an information processing method, and a program. [Background technology]

[0002] As the scope of application of the Personal Information Protection Act has been expanded following amendments, the risk of information leaks for businesses has increased, and measures to prevent personal information leaks have become increasingly important.

[0003] When handling personal information, it is necessary to take necessary measures to safely manage the personal data; however, because electronic personal data is easily copied, it is easy to fall into a difficult-to-manage state, such as when copied files are left behind when working on an individual's device, after the work is completed.

[0004] Furthermore, when sending data to an external party via e-mail or the like, it is necessary to check not only for erroneous sending but also for files that include personal information being attached by mistake.

[0005] However, for an organization, checking all of the files stored on devices managed by each individual or opening and checking files attached to e-mails each time would require huge costs.

[0006] In order to properly manage personal information within an organization while reducing enormous operational costs, there is a demand for technology that can identify files that contain personal information with a high degree of accuracy.

[0007] Patent document 1 describes a technology that detects personal information forming information that may form personal information, identifies the personal information from the positional relationship of the detected personal information forming information, measures the number of characters and number of items of the personal information, and displays the measured value as an index value indicating the degree to which personal information is included. [Prior art documents] [Patent documents]

[0008] [Patent Document 1] JP 2008-117287 A DISCLOSURE OF THEINVENTION [Problem to be solved by the invention]

[0009] However, in Patent Document 1, the formula for calculating the index value indicating the degree to which personal information is included needs to be manually adjusted, and it takes time and effort to calculate an appropriate index value.

[0010] Therefore, an object of the present invention is to provide a mechanism for determining whether a document contains personal information more efficiently and with higher accuracy. [Means for solving the problem]

[0011] The information processing system of the present invention is characterized by comprising an acquisition means for acquiring text data, and a determination means for determining whether the text data acquired by the acquisition means contains personal information using a trained model trained from data that associates the text data with information that the text data contains personal information. Effect of the Invention

[0012] According to the present invention, it is possible to more efficiently and accurately determine whether a document contains personal information. [Brief description of the drawings]

[0013] [Figure 1] 1 is a diagram showing an example of a system configuration of a Web mail service using a personal information inspection device in an embodiment of the present invention. [Diagram 2] 2 is a block diagram showing an example of a hardware configuration of a personal information inspection device, a Web mail service, an administrator terminal, and a client terminal in an embodiment of the present invention. FIG. [Diagram 3] 1 is a diagram illustrating an example of a functional configuration of a personal information inspection device and a Web mail service in an embodiment of the present invention. [Figure 4] 11 is a flowchart illustrating an example of a process for learning personal information in an embodiment of the present invention. [Diagram 5] 2 is a diagram showing an example of a configuration of a judgment knowledge storage area in the embodiment of the present invention. FIG. [Figure 6] 1 is a flowchart showing an example of a process for generating a feature vector for a document in an embodiment of the present invention. [Figure 7] FIG. 4 is a diagram showing an example of learning data in the embodiment of the present invention. [Figure 8] 11 is a diagram showing an example of personal information forming information acquired from a document in the embodiment of the present invention. FIG. [Figure 9] FIG. 4 is a diagram showing an example of a feature vector in the embodiment of the present invention. [Figure 10] FIG. 4 is a diagram showing an example of a feature amount storage table in the embodiment of the present invention. [Figure 11] FIG. 2 is a diagram showing an example of a machine learning model acquired as a result of learning in an embodiment of the present invention. [Figure 12] 11 is a flowchart showing an example of a personal information index calculation process in the embodiment of the present invention. [Figure 13] FIG. 4 is a diagram showing an example of a personal information index calculation formula in the embodiment of the present invention. [Figure 14] FIG. 11 is a diagram showing an example of a process of calculating a predicted value in the embodiment of the present invention. [Figure 15] 11 is a flowchart showing an example of an e-mail inspection process in a Web mail service in an embodiment of the present invention. [Figure 16] FIG. 2 is a diagram showing an example of a mail editing screen in a Web mail service in an embodiment of the present invention. [Figure 17] FIG. 11 is a diagram showing an example of a transmission confirmation screen in a Web mail service in the embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0014] Hereinafter, an embodiment of the present invention will be described in detail with reference to the drawings.

[0015] FIG. 1 is a diagram showing an example of a system configuration of a Web mail service using a personal information inspection device according to an embodiment of the present invention.

[0016] The personal information inspection device 100, the Web mail service 110, and the administrator terminal 120 are configured to be connected via a local area network 130. The Web mail service 110 is also configured to be connectable to a client 150 via an external network 140.

[0017] The Web mail service 110 provides the client terminal 150 with the functions of a typical email client, ie, the function of editing email and the function of sending and receiving email.

[0018] Furthermore, when a user sends an email or explicitly instructs inspection of personal information on the client terminal 150, the Web mail service 110 requests the personal information inspection device 100 to execute a process for determining whether or not personal information exists.

[0019] If the personal information inspection device 100 determines that the email to be sent may contain personal information, the Webmail service 110 displays a message to the user prompting the user to confirm whether or not personal information is present and to decide whether or not to send the email, and controls the sending based on the user's decision on whether or not to send the email.

[0020] In the embodiment of the present invention, the personal information inspection device 100 may be configured as a service provided to the client terminal 150 via the external network 140, or may be configured as a part of the functions of the Web mail service 110.

[0021] It may also be configured as a function of a mail client program that runs on the client terminal 150 .

[0022] FIG. 2 is a block diagram showing an example of the hardware configuration of the personal information inspection device 100, the Web mail service 110, the administrator terminal 120, and the client terminal 150 in the embodiment of the present invention.

[0023] As shown in FIG. 2, the information processing device has a CPU (Central Processing Unit) 201, a ROM (Read Only Memory) 202, a RAM (Random Access Memory) 203, a storage device 204, an input controller 205, an audio controller 206, a video controller 207, a memory controller 208, and a communication I / F controller 209 connected via a system bus 200.

[0024] The CPU 201 comprehensively controls each device and controller connected to the system bus 200 .

[0025] ROM 202 or external memory 213 holds the BIOS (Basic Input / Output System) and OS (Operating System), which are control programs executed by CPU 201, computer-readable and executable programs for realizing this information processing method, and various necessary data (including data tables).

[0026] The RAM 203 functions as a main memory, a work area, etc., for the CPU 201. The CPU 201 loads programs and the like required for executing processes from the ROM 202 or the external memory 213 into the RAM 203, and executes the loaded programs to realize various operations.

[0027] The input controller 205 controls input from an input device such as a keyboard 210 or a pointing device such as a mouse (not shown). If the input device is a touch panel, the user can give various instructions by pressing (touching with a finger or the like) icons, cursors, or buttons displayed on the touch panel.

[0028] The touch panel may be a touch panel capable of detecting positions touched by multiple fingers, such as a multi-touch screen.

[0029] The video controller 207 controls display on an external output device such as a display 212. The display also includes the display of a notebook computer integrated with the main body. Note that the external output device is not limited to a display, and may be, for example, a projector. In addition, for the device capable of receiving the above-mentioned touch operation, an input device is also provided.

[0030] The video controller 207 is capable of controlling a video memory (VRAM) for display control, and can use part of the RAM 203 as the video memory area, or can provide a separate dedicated video memory.

[0031] The memory controller 208 controls access to the external memory 213. As the external memory, an external storage device (hard disk) for storing a boot program, various applications, font data, user files, edit files, and various data, a flexible disk (FD), or a compact flash (registered trademark) memory connected to a PCMCIA card slot via an adapter, etc., can be used.

[0032] The communication I / F controller 209 connects and communicates with external devices via a network, and executes communication control processing on the network. For example, communication using TCP / IP, telephone lines such as ISDN, and communication using 4G and 5G lines for mobile phones are possible.

[0033] The CPU 201 enables display on the display 212 by, for example, executing a process of expanding (rasterizing) an outline font in a display information area in the RAM 203. The CPU 201 also enables a user to give instructions using a mouse cursor (not shown) or the like on the display 212.

[0034] FIG. 3 is a diagram illustrating an example of a functional configuration of the personal information inspection device and the Web mail service.

[0035] The mail editing processor 311 has a function of creating and editing e-mail based on operations and instructions from the user received via a screen displayed on the Web browser 320 of the client terminal 150 .

[0036] When the transmission control processing unit 312 receives an instruction to send an e-mail from a user, it extracts text (such as the body and attached files) from the e-mail and controls the calculation process of the personal information index by each processing unit of the personal information inspection device 100. Also, based on the result of the calculation process of the personal information index, it has a function of displaying a confirmation dialog shown in Fig. 17 and receiving a permission or denial of transmission from the user to control the transmission process of the e-mail.

[0037] The transmission processing unit 313 has a function of sending out, to the external network 140, the e-mail that has been decided to be transmitted by the transmission control processing unit 312.

[0038] The personal information learning processing unit 301 has a function of executing personal information learning processing shown in the flowchart of FIG.

[0039] The feature vector generation processing unit 302 has a function of generating a feature vector for the document to be processed.

[0040] The judgment knowledge storage area 303 is an area for storing pairs of the feature vectors generated by the feature vector generation processing unit 302 and labels indicating personal information or non-personal information that are assigned to each document in the learning data.

[0041] The personal information inspection processing unit 304 has a function of executing a process shown in FIG. 12 for calculating the possibility that the text content obtained from the transmission control processing unit 312 is personal information.

[0042] (Personal information learning processing) Next, the personal information learning process executed by the personal information learning processing unit 301 in the embodiment of the present invention will be described with reference to the flowchart of FIG. FIG. 4 is a flowchart showing a process in which the CPU 201 of the personal information inspection device 100 reads and executes a specified control program, and which learns the characteristics of personal information based on documents uploaded from the administrator terminal 120 that have been pre-classified into personal information and non-personal information.

[0043] In step S401, the personal information learning processing unit 301 acquires a document uploaded from the Web browser 330 in the administrator terminal 130 as learning data.

[0044] In step S402, the personal information learning processing unit 301 starts a repeat process up to step S405 for the documents in the learning data acquired in step S401.

[0045] In step S403, the personal information learning processing unit 301 generates a feature vector for the document to be processed using the feature vector generation processing unit 302. The method of generating the feature vector will be described later.

[0046] In step S404, personal information learning processing unit 301 stores a pair of the feature vector generated in step S403 and a label indicating personal information / non-personal information assigned to each document in the learning data in feature vector storage table 501 in judgment knowledge storage area 303 shown in FIG. 5.

[0047] In step S405, if there is still a document to be processed, the personal information learning processing unit 301 repeats the process from step S402. If there is no document to be processed, the process proceeds to step S406.

[0048] In step S406, personal information learning processing unit 301 performs learning using machine learning on the feature vectors and labels (personal information / non-personal information) in feature vector storage table 501, and acquires a model (trained model) as a result of the learning.

[0049] In step S 407 , personal information learning processing unit 301 stores the model acquired in step S 406 in model storage table 502 in judgment knowledge storage area 303 .

[0050] In the embodiment of the present invention, the personal information learning process is configured to be executed on the same hardware as the personal information inspection process, but it may be configured to be executed on different hardware. Although the learning data is configured to be uploaded, it may be configured to be managed on the hardware that performs the personal information learning process.

[0051] (Feature vector generation process) Next, a feature vector generation process executed by the feature vector generation processing unit 302 in the embodiment of the present invention will be described with reference to the flowchart in FIG.

[0052] In step S601, the feature vector generation processing unit 302 uses morphological analysis and pattern matching to extract personal information forming information, along with the position of appearance and the number of characters, from the text of the document received as input, based on words and patterns previously defined as personal information items or sensitive information items.

[0053] In step S602, the feature vector generation processing unit 302 obtains statistical values ​​based on the type and the number of characters from the document and the extracted personal information forming information.

[0054] In step S603, the feature vector generation processing unit 302 organizes the statistical values ​​calculated in step S602 into a feature vector.

[0055] (Specific example of personal information learning processing) Next, as a specific example of the personal information learning process, a case where the process shown in FIG. 4 is performed on learning data 700 shown in FIG. 7 will be described.

[0056] In step S401, the personal information learning processing unit 301 acquires the document 700 uploaded from the Web browser 330 in the administrator terminal 130 as learning data.

[0057] In step S402, the personal information learning processing unit 301 starts a repeat process up to step S405 for the document 701 in the learning data 700 acquired in step S401.

[0058] In step S403, the personal information learning processing unit 301 generates a feature vector for the document 701 to be processed, using the feature vector generation processing unit 302.

[0059] In step S601, the feature vector generation processing unit 302 extracts personal information formation information 801 shown in Figure 8 from the text of the document 701 received as input, using morphological analysis and pattern matching based on words and patterns previously defined as personal information items and sensitive information items, along with their occurrence positions and number of characters.

[0060] In step S602, the feature vector generation processing unit 302 obtains statistical values ​​based on the type and number of characters shown in FIG. 9 from the document and the extracted personal information forming information 801.

[0061] In step S603, the feature vector generation processing unit 302 organizes the statistical values ​​calculated in step S602 into a feature vector 901. In the embodiment of the present invention, processes such as normalization are omitted for ease of explanation, but normalization may be performed.

[0062] In step S404, personal information learning processing unit 301 stores, in feature vector storage table 501 shown in FIG. 10, a set 1001 of feature vector 901 generated in step S403 and label TRUE indicating personal information / non-personal information assigned to each document in the learning data.

[0063] In step S405, since there is still a document 702 to be processed, the personal information learning processing unit 301 repeats the process from step S402.

[0064] Similar steps are then performed for all documents in the training data 700.

[0065] In step S406, the personal information learning processing unit 301 performs learning on the feature vector storage table 501 by machine learning to obtain a model.

[0066] In the embodiment of the present invention, a linear SVM (Support Vector Machine) is used.

[0067] In step S407, personal information learning processing unit 301 stores the model shown in Fig. 11 acquired as a learning result in model storage table 502 in judgment knowledge storage area 303. The acquired model determines that the information is personal information if the predicted value (calculation result) is a positive value.

[0068] In the embodiment of the present invention, for ease of explanation, a linear SVM is used as the machine learning method. However, any method may be used as long as it can classify results into two values, true or false, and the degree of the classification can be expressed as a numerical value.

[0069] (Personal information inspection processing) Next, the personal information inspection process executed by the personal information inspection processing unit 304 in the embodiment of the present invention will be described with reference to the flowchart of FIG.

[0070] The flowchart in FIG. 12 is a process in which CPU 201 of personal information inspection device 100 reads out and executes a predetermined control program, and is a flowchart showing a process for calculating, for an input text, a numerical value indicating the degree to which the content of the text is likely to be personal information.

[0071] In step S1201, the personal information inspection processing unit 304 acquires the text of the document to be processed.

[0072] In step S1202, the personal information inspection processing unit 304 acquires a feature vector for the input document using the flowchart of FIG. 6 for the text acquired in step S1201.

[0073] In step S1203, the personal information inspection processing unit 304 calculates a predicted value for the feature vector acquired in step S1202, using the model stored in the model storage table 502 in the judgment knowledge storage area 303.

[0074] In step S1204, personal information inspection processing unit 304 converts the predicted value calculated in step S1203 into a value that is easy to intuitively grasp, and outputs it as a personal information index. In the embodiment of the present invention, the personal information index calculation formula shown in FIG. 13 is used, but other calculation formulas may be used, or the predicted value may be used as is.

[0075] (Specific examples of personal information inspection processing) Next, as a specific example of personal information inspection processing, a case where the processing shown in FIG. 12 is performed on an inspection target document 1401 shown in FIG. 14 will be described.

[0076] In step S1201, the personal information inspection processing unit 304 acquires text 1402 from a document 1401 to be processed.

[0077] In step S1202, the personal information inspection processing unit 304 acquires a feature vector 1403 for the input document using the flowchart of FIG. 6 for the text acquired in step S1201.

[0078] In step S1203, the personal information inspection processing unit 304 calculates 1460.960 as a predicted value for the feature vector 1403 acquired in step S1202, using the model stored in the model storage table 502 in the judgment knowledge storage area 303 (1404).

[0079] In step S1204, the personal information inspection processing unit 304 calculates the personal information index for the predicted value 1460.960 calculated in step S1203, using the personal information index calculation formula shown in Fig. 13. Here, it is assumed that Rmin = -1700 and Rmax = 2000. As a result, the personal information index is 50 + 50 × 1460.960 ÷ max (2000, - (-1700)) = 86.524.

[0080] In this way, by simply preparing a set of documents that have been determined to contain personal information, it is possible to calculate a personal information index, which is the degree of certainty that the documents are personal information, without the need for manual adjustment. Furthermore, since the personal information index is calculated based on statistical information, even if there is some error in the extraction of the personal information forming information, it is possible to indicate the degree of certainty that the information is personal information with high accuracy. By using the calculated personal information index, processing according to the degree of accuracy can be realized, enabling efficient management of personal information.

[0081] Next, with reference to the flowchart shown in FIG. 15, a specific application example of the present invention will be described in which the sending of personal information is confirmed when sending an e-mail and feedback is obtained from the user. In this embodiment, sending e-mail will be described as an application example, however, there are other application examples such as determining whether a document contains personal information when printing a document, or when uploading a document to a web service.

[0082] FIG. 15 is a flow chart showing the processing in which the CPU 201 of the Webmail service 110 reads out and executes a specified control program, and which is performed when a send instruction is given by pressing the Send button 1602 on an email editing screen 1601 such as that shown in FIG. 16 displayed on the Web browser 320 of the client terminal 150.

[0083] In step S1501, the transmission control processor 312 receives an instruction to transmit an e-mail.

[0084] In step S1502, the transmission control processor 312 starts repeating the process up to step S1509 for the file attached to the e-mail instructed to be sent. Note that not only the attached file but also the body of the e-mail may be the target.

[0085] In step S1503, the transmission control processor 312 extracts text from the attached file to be processed. If the attached file is an image, the text is obtained by performing OCR processing on the image.

[0086] In step S1504, transmission control processing unit 312 calculates a personal information index for the text extracted in step S1503 using personal information inspection device 100.

[0087] In step S1505, if the personal information index calculated in step S1504 is equal to or greater than the specified value, the transmission control processing unit 312 moves the process to step S1506. If it is less than the specified value, the process moves to step S1509.

[0088] In step S1506, the transmission control processing unit 312 displays the confirmation dialog shown in FIG. 17, and obtains the user's decision as to whether or not to transmit the information and whether or not the information is personal information.

[0089] In step S1507, the transmission control processing unit 312 sends the user's judgment as to whether or not the information obtained in step S1505 is personal information to the personal information inspection device 100, and instructs the personal information inspection device 100 to store the judgment in the feature vector storage table 501 together with the feature vector being processed.

[0090] In step S1508, if the transmission permission acquired in step S1505 is "transmit", the transmission control processing unit 312 moves the process to step S1509. If the transmission permission is "cancel", the process ends.

[0091] In step S1509, if there is still an attached file to be processed, the transmission control processing section 312 repeats the process from step S1502. If there is no attached file to be processed, the process proceeds to step S1510.

[0092] In step S1510, the transmission control processing unit 312 sends the e-mail to the transmission processing unit 313, which then sends out the e-mail.

[0093] In this way, by collecting the user's judgment of whether or not something is personal information, the personal information inspection device 100 can periodically perform a learning process, making it possible to calculate a personal information index that matches the user's judgment without the need for manual adjustment.

[0094] In the embodiment of the present invention, learning is performed from all the stored feature vectors, but when the feature vectors are stored, information identifying the department to which the user belongs or the user may be added, and learning may be performed for each department or user. Then, for documents related to the user or the department to which the user belongs, a model generated by this learning may be used to determine whether or not personal information is included.

[0095] The present invention can be embodied, for example, as a system, an apparatus, a method, a program, or a recording medium, etc. Specifically, the present invention may be applied to a system composed of a plurality of devices, or may be applied to an apparatus composed of a single device.

[0096] The program of the present invention is a program that enables a computer to execute the processing methods of the flowcharts shown in Figures 4 and 12, and the storage medium of the present invention stores a program that enables a computer to execute the processing methods of Figures 4 and 12. The program of the present invention may be a program for each processing method of each device in Figures 4 and 12.

[0097] As described above, it goes without saying that the object of the present invention can be achieved by supplying a recording medium on which a program that realizes the functions of the above-mentioned embodiments is recorded to a system or device, and having the computer (or CPU or MPU) of that system or device read and execute the program stored on the recording medium.

[0098] In this case, the program itself read out from the recording medium will realize the novel functions of the present invention, and the recording medium on which the program is recorded will constitute the present invention.

[0099] Examples of recording media for supplying the program include flexible disks, hard disks, optical disks, magneto-optical disks, CD-ROMs, CD-Rs, DVD-ROMs, magnetic tapes, non-volatile memory cards, ROMs, EEPROMs, silicon disks, etc.

[0100] Furthermore, it goes without saying that the functions of the above-mentioned embodiments are not only realized by the computer executing a program it has read, but also includes cases where an OS (operating system) running on the computer performs some or all of the actual processing based on the instructions of the program, and the functions of the above-mentioned embodiments are realized through that processing.

[0101] Furthermore, it goes without saying that this also includes cases where a program read from a recording medium is written into a memory provided on a function expansion board inserted into a computer or a function expansion unit connected to a computer, and then a CPU or the like provided on the function expansion board or function expansion unit performs some or all of the actual processing based on the instructions of the program code, thereby realizing the functions of the above-mentioned embodiments.

[0102] The present invention may be applied to a system consisting of multiple devices, or to an apparatus consisting of a single device. Needless to say, the present invention can also be applied to a case where the object is achieved by supplying a program to a system or apparatus. In this case, the effect of the present invention can be enjoyed by the system or apparatus by reading a recording medium storing a program for achieving the present invention into the system or apparatus.

[0103] Furthermore, by downloading and reading a program for achieving the present invention from a server, database, etc. on a network using a communication program, the system or device can enjoy the effects of the present invention. Note that the present invention also includes configurations that combine the above-mentioned embodiments and their modified examples. [Explanation of symbols]

[0104] 100 Personal information inspection device 110 Webmail Services 120 Administrator terminal 130 LAN 140 External Network 150 Client terminal 150

Claims

1. An acquisition means for acquiring text data for which a transmission instruction has been accepted; An output means for outputting information related to a result of an inference process using a trained model for the acquired text data; a selection receiving means for receiving a selection from a user as to whether personal information is included in the text data for which the transmission instruction has been received; A control means for controlling learning of the trained model using the selection accepted by the selection accepting means and the text data; An information processing system comprising:

2. The information processing system of claim 1, characterized in that the information relating to the result of the inference processing is information regarding the possibility that the text data contains personal information.

3. An information processing system as described in claim 1 or 2, characterized in that the information relating to the result of the inference processing includes information indicating text data that has been determined to possibly contain personal information.

4. An information processing system described in any one of claims 1 to 3, characterized in that the information relating to the result of the inference processing is a result based on a personal information index relating to the text data.

5. An information processing system as described in any one of claims 1 to 4, characterized in that the output means outputs information related to the result of the inference processing when a personal information index related to the text data satisfies a predetermined condition.

6. The information processing system according to claim 5, characterized in that the case where the specified condition is satisfied is a case where the personal information index output by the trained model satisfies a predetermined value.

7. An extraction means for extracting personal information forming information from the text data; The information processing system according to any one of claims 1 to 6, further comprising a generation means for generating a trained model using data that associates the personal information formation information extracted by the extraction means with information on whether or not the personal information set in the text data from which the personal information formation information was extracted is included.

8. The information processing system according to claim 7, characterized in that the generation means generates a trained model using data correlating the number of extractions of each type of personal information formation information extracted by the extraction means with information on whether or not the personal information set in the text data from which the personal information formation information was extracted is included.

9. A selection of whether or not personal information is included, which is accepted by the selection accepting means, is associated with information relating to the user who made the selection or the user's affiliation, and is stored; The information processing system according to claim 7 or 8, characterized in that the generation means generates a trained model for each user or each affiliation of the user who made the selection.

10. An information processing system as described in any one of claims 1 to 9, characterized in that the text data includes at least one of the text contained in the body of an email and a file attached to the email.

11. An information processing system as described in any one of claims 1 to 10, characterized in that the output means outputs information relating to the results of inference processing using a trained model for the text data for which the transmission instruction was accepted in response to accepting the transmission instruction.

12. An information processing system as described in any one of claims 1 to 11, characterized in that the selection receiving means receives a selection of whether or not personal information is included in the text data for which the transmission instruction has been received in response to receiving the transmission instruction.

13. A program for causing a computer to function as each of the means described in any one of claims 1 to 12.

14. An acquisition step in which an acquisition means of the information processing system acquires text data for which a transmission instruction has been accepted; An output step in which an output means of the information processing system outputs information related to a result of an inference process using a trained model for the acquired text data; a selection receiving step in which a selection receiving means of the information processing system receives a selection from a user as to whether personal information is included in the text data for which the transmission instruction has been received; A control step in which a control means of the information processing system controls the learning of the trained model using the selection accepted by the selection accepting step and the text data; An information processing method comprising:

15. An acquisition means for acquiring text data for which a transmission instruction has been accepted; An output means for outputting information related to a result of an inference process using a trained model for the acquired text data; a selection receiving means for receiving a selection from a user as to whether personal information is included in the text data for which the transmission instruction has been received; A control means for controlling learning of the trained model using the selection accepted by the selection accepting means and the text data; An information processing device comprising:

Images