Information processing system, terminal device, information processing method, and program

Abstract

An information processing system 100 comprises a database 10 of distributed type. The database 10 of distributed type stores verification information about information pertaining to a machine learning model. The verification information is transmitted from a first terminal device 30. The verification information is transmitted from the distributed database 10 to a second terminal device 40 in response to a request from the second terminal device 40.

Description

Information processing system, terminal device, information processing method, and computer-readable recording medium

[0001] The present disclosure relates to terminal devices for executing distributed processing, information processing systems and information processing methods using these devices, and further to computer-readable recording media on which programs for realizing these devices are recorded.

[0002] In recent years, medical devices utilizing artificial intelligence have been developed in the medical field. Examples of medical devices utilizing artificial intelligence include diagnostic imaging devices. Diagnostic imaging devices are combined with endoscopes, X-ray imaging devices, etc., and detect lesions by applying captured images to a machine learning model (see, for example, Patent Literatures 1 and 2). The machine learning model is constructed, for example, by using image data and teacher data as training data.

[0003] Before being sold, medical devices must be verified, approved, and licensed by a public institution. One such institution (verifier) ​​is the Pharmaceuticals and Medical Devices Agency (PMDA).

[0004] In the case of medical devices that use artificial intelligence, verifiers may request the manufacturer of the medical device to provide the data used to build the machine learning model during the verification process. Disclosure of such data would be disadvantageous to the medical device manufacturer in terms of the risk of know-how being leaked. On the other hand, if verifiers approve such data without requiring the manufacturer to disclose it, it is desirable to prevent the manufacturer from engaging in fraudulent acts such as later tampering with the data.

[0005] JP 2009-078161 A JP 2020-121049 A

[0006] For this reason, a system is needed that allows data to be exchanged between medical device manufacturers and verifiers without disclosing the data used for approval and while preventing its tampering, but such a system does not currently exist.

[0007] One example of the purpose of the present disclosure is to provide a mechanism for preventing tampering with data used to approve medical devices without disclosing the data.

[0008] In order to achieve the above object, an information processing system according to one aspect of the present disclosure comprises a distributed database, wherein the distributed database stores verification information relating to a machine learning model, and the verification information is transmitted from a first terminal device and, in response to a request from a second terminal device, is transmitted from the distributed database to the second terminal device.

[0009] In order to achieve the above object, a first terminal device according to one aspect of the present disclosure is characterized by comprising: a verification information generating means for generating verification information from information relating to a machine learning model; and a verification information transmitting means for transmitting the calculated verification information to a distributed database for storage.

[0010] In order to achieve the above object, a second terminal device according to one aspect of the present disclosure is characterized by comprising: a verification information acquisition means for acquiring verification information of information relating to a machine learning model stored in a distributed database; a verification means for determining whether verification information acquired from a source other than the distributed database is identical to the verification information of the information relating to the machine learning model acquired by the verification information acquisition means, thereby verifying the machine learning model; and a verification result transmission means for transmitting the results of the verification by the verification unit to the distributed database for storage.

[0011] In addition, to achieve the above-mentioned object, an information processing method according to one aspect of the present disclosure is characterized in that a first terminal device generates verification information from information relating to a machine learning model and transmits the calculated verification information to a distributed database; the distributed database stores the transmitted verification information and transmits the verification information to a second terminal device in response to a request from the second terminal device; and the second terminal device acquires the verification information stored in the distributed database and verifies the machine learning model by determining whether the verification information obtained from a source other than the distributed database is identical to the verification information stored in the distributed database.

[0012] To achieve the above object, a first computer-readable recording medium according to one aspect of the present disclosure is characterized in that it stores a program including instructions to cause a computer to generate verification information from information relating to a machine learning model, and to transmit the calculated verification information to a distributed database, thereby storing the verification information in the distributed database.

[0013] Furthermore, in order to achieve the above object, a second computer-readable recording medium according to one aspect of the present disclosure is characterized in that it records a program including instructions to cause a computer to: acquire verification information for information relating to a machine learning model stored in a distributed database; verify the machine learning model by determining whether verification information obtained from a source other than the distributed database is identical to verification information for information relating to the machine learning model stored in the distributed database; and send the results of the verification to the distributed database, thereby storing the verification information in the distributed database.

[0014] As described above, according to the present disclosure, it is possible to prevent tampering with data used to approve medical devices without disclosing the data.

[0015] Fig. 1 is a configuration diagram showing the schematic configuration of an information processing system, a first terminal device, and a second terminal device in an embodiment. Fig. 2 is a diagram showing an example of information stored in a distributed database and terminal devices in an embodiment. Fig. 3 is a flow diagram showing the operation of a first terminal device in an embodiment. Fig. 4 is a flow diagram showing the operation of a second terminal device in an embodiment. Fig. 5 is a block diagram showing an example of a computer that realizes the terminal devices in an embodiment.

[0016] (Embodiments) Hereinafter, an information processing system, a terminal device, an information processing method, and a program according to an embodiment will be described with reference to FIGS.

[0017] [Device Configuration] First, the schematic configuration of an information processing system and a terminal device in an embodiment will be described with reference to Fig. 1. Fig. 1 is a diagram showing the schematic configuration of an information processing system, a first terminal device, and a second terminal device in an embodiment.

[0018] 1, an information processing system 100 according to an embodiment includes a distributed database 10. The distributed database 10 stores verification information for information relating to machine learning models. This verification information is transmitted from a first terminal device 30. Furthermore, this verification information is transmitted from the distributed database 10 to a second terminal device 40 in response to a request from the second terminal device 40.

[0019] 1, a first terminal device 30 and a second terminal device 40 are connected to a distributed database 10 via a network 20 such as the Internet. The first terminal device 30 includes a verification information generation unit 31 and a verification information transmission unit 32. The verification information generation unit 31 generates verification information from information related to the machine learning model. The verification information transmission unit 32 functions as a verification information generation means.

[0020] The verification information transmitting unit 32 transmits the calculated verification information to the distributed database 10 for storage therein. The verification information transmitting unit 32 functions as a verification information transmitting means.

[0021] The second terminal device 40 includes a verification information acquisition unit 41, a verification unit 42, and a verification result transmission unit 43. The verification information acquisition unit 41 acquires verification information about the machine learning model stored in the distributed database 10. The verification information acquisition unit 41 functions as a verification information acquisition means.

[0022] The verification unit 42 verifies the machine learning model by determining whether the verification information obtained from a source other than the distributed database is identical to the verification information regarding the machine learning model obtained by the verification information acquisition unit 41. The verification unit 42 functions as a verification means.

[0023] The verification result transmission unit 43 transmits the result of the verification by the verification unit 42 to the distributed database 10, where it is stored. The verification result transmission unit 43 functions as a verification result transmission means.

[0024] In this way, the first terminal device transmits only the verification information of the data used to approve the medical device, i.e., the information related to machine learning, to the distributed database 10. Then, the second terminal device acquires the verification information stored in the distributed database 10 and uses the acquired verification information to verify the identity of the information related to machine learning.

[0025] In other words, the information processing system 100 in the embodiment can provide a mechanism that allows data to be exchanged between medical device manufacturers and verifiers without disclosing the data used to approve medical devices and preventing tampering.

[0026] Next, the configurations and functions of the information processing system 100, the first terminal device 30, and the second terminal device 40 in the embodiment will be described in detail.

[0027] First, the information processing system 100 is a data management system for managing data, particularly highly confidential data, and as described above, includes a distributed database 10. In the embodiment, the information processing system 100 is used to manage data necessary for approval of medical devices. However, the use of the information processing system 100 is not particularly limited.

[0028] In addition, in the embodiment, the distributed database 10 is constructed by a plurality of nodes connected via a network. For example, the distributed database 10 is constructed by a plurality of nodes 11 using distributed ledger technology. In this case, each node 11 performs peer-to-peer data communication with other nodes 11 to form a distributed ledger. Some distributed ledgers have a function to prevent data tampering by having multiple nodes hold data and mutually verifying the data held by each node. Furthermore, an example of such a distributed ledger is a system using a blockchain.

[0029] Each node 11 is actually connected to the network 20 so as to be able to communicate data with the other nodes 11, and performs peer-to-peer data communication with the other nodes 11 via the network 20. Specific examples of the nodes 11 include general-purpose personal computers (PCs), server devices, smartphones, and tablet terminal devices.

[0030] In the embodiment, the user of the first terminal device 30 is the creator of the machine learning model, i.e., the manufacturer of the medical device equipped with the machine learning model, and the user of the second terminal device 40 is the person who verifies the medical device, such as the person in charge of the PMDA or the like.

[0031] Furthermore, in this embodiment, in addition to the first terminal device 30 and the second terminal device 40, a third terminal device 50 is also connected to the distributed database 10 via the network 20. The user of the third terminal device 50 is an authorizer who authorizes medical devices using the results of the verification performed by the second terminal device 40. An example of an authorizer is an official at an administrative agency who authorizes medical devices.

[0032] Here, transmission and reception of information in the information processing system 100 will be specifically described with reference to Fig. 2. Fig. 2 is a diagram showing an example of information stored in a distributed database and terminal devices in the embodiment.

[0033] In the example shown in Figure 2, the information about the machine learning model includes document data related to the machine learning model, test data input into the machine learning model, training data used to build the machine learning model, a learning program used to build the machine learning model, and environmental information.

[0034] The document data related to the machine learning model is information about the medical device that uses the machine learning model, such as data describing a clinical trial plan, etc. The environmental information is information that specifies the environment during machine learning, and includes, for example, hardware conditions, the operating system version, etc.

[0035] As shown in FIG. 2 , the manufacturer of the medical device prepares document data, test data, training data, a learning program, and environmental information related to the machine learning model on the first terminal device 30 .

[0036] In the first terminal device 30, the verification information generation unit 31 generates verification information from each of document data, test data, training data, a learning program, and environmental information related to a machine learning model.

[0037] Verification information is generated from data to be verified and is used to verify or prove the identity of the original data. In the embodiment, identical verification information is generated from identical data. Furthermore, if the data is different, the generated verification information will be different with a practically sufficient probability. Therefore, it is difficult for a third party to infer the original data from the verification information, and it is also difficult for a third party to find data for generating identical verification information. A specific example of such verification information is a hash value.

[0038] An example of a method for verifying or proving the identity of information about a machine learning model using verification information is as follows: First, a specific terminal device generates verification information about the information about the machine learning model in advance and saves the generated verification information. Next, another terminal device generates verification information about the information about the machine learning model at a later time and determines whether it is identical to the previously saved verification information. If they are identical, it can be determined that the information about the machine learning model at the time the verification information was saved is identical to the information about the machine learning model at the time of the determination. This makes it possible to verify or prove that the information about the machine learning model has not been tampered with since the verification information was saved.

[0039] Specifically, the verification information generation unit 31 calculates hash values ​​from information related to the machine learning model, i.e., document data, test data, training data, learning program, and environmental information related to the machine learning model. Hereinafter, the verification information will be referred to as a "hash value." The verification information transmission unit 32 then transmits each hash value, which constitutes the verification information, to the distributed database 10.

[0040] As a result, the distributed database 10 stores each hash value transmitted from the first terminal device 30. In Fig. 2, information written in white on a black background indicates a hash value calculated from the original information.

[0041] Furthermore, the first terminal device 30 transmits document data, test data, training data, a learning program, and environmental information related to the machine learning model to the second terminal device 40 in response to an instruction from the second terminal device 40. The second terminal device 40 stores the transmitted document data, test data, training data, a learning program, and environmental information related to the machine learning model.

[0042] In the second terminal device 40, the verification information acquisition unit 41 acquires, from the distributed database 10, document data, test data, training data, learning program, and environmental information related to the machine learning model, as well as their respective hash values.

[0043] In the second terminal device 40, the verification unit 42 calculates hash values ​​for each of the document data, test data, training data, learning program, and environmental information related to the machine learning model transmitted from the first terminal device 30. The verification unit 42 then determines whether each calculated hash value is identical to each hash value acquired from the distributed database 10. Thereafter, the verification result transmission unit 43 transmits the determination result for each hash value to the distributed database 10 as a verification result.

[0044] Furthermore, in the embodiment, the distributed database 10 stores the verification results transmitted from the second terminal device 40. That is, the distributed database 10 also stores the results of the verification performed by the second terminal device 40 on the machine learning model.

[0045] The third terminal device 50 stores the manufacturer's confidential information and the verifier's confidential information. When instructed by the authorizer, the third terminal device 50 accesses the distributed database 10 using the manufacturer's confidential information and the verifier's confidential information. If the manufacturer's confidential information and the verifier's confidential information match pre-stored authentication information, the distributed database 10 permits access to the third terminal device 50 and transmits the verification result. This allows the authorizer to determine whether to approve the medical device based on the verification result.

[0046] 2, the first terminal device 30 may transmit hash values ​​of document data, test data, training data, learning programs, and environmental information related to the machine learning model to the second terminal device 40. In this case, the verification unit 42 in the second terminal device determines whether the hash values ​​transmitted from the first terminal device 30 are identical to the hash values ​​acquired from the distributed database 10.

[0047] In addition, in the embodiment, the first terminal device 30 can also transmit the document data itself to the distributed database 10 for document data related to a machine learning model without calculating a hash value.

[0048] [Device Operation] Next, the operation of the information processing system and terminal device in the embodiment will be described using Figures 3 and 4. In the following description, Figures 1 and 2 will be referenced as appropriate. In the embodiment, an information processing method is implemented by operating the information processing system 100, the first terminal device 30, and the second terminal device 40. Therefore, the description of the information processing method in the embodiment will be replaced by the following description of the operation of the information processing system 100, the first terminal device 30, and the second terminal device 40.

[0049] First, the operation of the first terminal device 30 will be described with reference to Fig. 3. Fig. 3 is a flow chart showing the operation of the first terminal device in the embodiment.

[0050] As shown in Figure 3, first, in the first terminal device 30, the verification information generation unit 31 calculates a hash value as verification information from information about the machine learning model, specifically, from each of the document data, test data, training data, learning program, and environmental information (step A1).

[0051] Next, in the first terminal device 30, the verification information transmitting unit 32 transmits each of the hash values ​​calculated in step A1 to the distributed database 10 (step A2).

[0052] After step A2 is executed, the distributed database 10 stores each hash value transmitted from the first terminal device 30 (see FIG. 2).

[0053] Next, the operation of the second terminal device 40 will be described with reference to Fig. 4. Fig. 4 is a flow chart showing the operation of the second terminal device in the embodiment.

[0054] As shown in Figure 4, first, in the second terminal device 40, the verification information acquisition unit 41 acquires document data, test data, training data, learning program, and environmental information related to the machine learning model, as well as their respective hash values, from the distributed database 10 (step B1).

[0055] Next, in the second terminal device 40, the verification unit 42 instructs the first terminal device 30 to send information about the machine learning model, i.e., document data, test data, training data, learning program, and environmental information (step B2).

[0056] Next, the verification unit 42 calculates a hash value from each of the document data, test data, training data, learning program, and environmental information related to the machine learning model that were sent from the first terminal device 30 after execution of step B2 (step B3).

[0057] Next, the verification unit 42 determines whether each hash value calculated in step B3 is the same as each hash value acquired in step B1 (step B4).

[0058] Thereafter, in the second terminal device 40, the verification result transmitting unit 43 transmits the determination result for each hash value in step B4 as the verification result to the distributed database 10, where it is stored (step B5).

[0059] After executing step B5, the authorizer acquires the verification result via the third terminal device 50. Then, the authorizer determines whether to approve the medical device based on the acquired verification result.

[0060] Although the operation of the second terminal device has been described using Figure 4, the processing shown in Figure 4 does not have to be performed during the medical device verification process. For example, if the verifier does not need information about the machine learning model, the verifier may not require the manufacturer to provide the information about the machine learning model. Furthermore, the verifier, who is the user of the second terminal device 40, may instruct the transmission of only part of the information about the machine learning model in step B2. In this way, the information about the machine learning model is not made public if it is not necessary, and is handled in a manner that allows verification of whether it has been tampered with if necessary.

[0061] As described above, according to the embodiment, data can be exchanged between manufacturers and verifiers, and further between verifiers and authorizers, without disclosing information used to approve medical devices, i.e., information related to machine learning models, and while preventing tampering.

[0062] [Program] The first program in the embodiment may be a program that causes a computer to execute steps A1 and A2 shown in Fig. 3. By installing and executing this program on a computer, the first terminal device 30 in the embodiment can be realized. In this case, the processor of the computer functions and performs processing as the verification information generation unit 31 and the verification information transmission unit 32. Examples of the computer include a general-purpose PC, a smartphone, and a tablet terminal device.

[0063] The second program in the embodiment may be any program that causes a computer to execute steps B1 to B5 shown in Fig. 4. By installing and executing this program on a computer, the second terminal device 40 in the embodiment can be realized. In this case, the processor of the computer functions and performs processing as a verification information acquisition unit 41, a verification unit 42, and a verification result transmission unit 43. Examples of the computer include a general-purpose PC, a smartphone, and a tablet terminal device.

[0064] [Physical Configuration] Here, a computer that realizes the first terminal device 30 and the second terminal device 40 by executing a program in the embodiment will be described with reference to Fig. 5. Fig. 5 is a block diagram showing an example of a computer that realizes the terminal devices in the embodiment.

[0065] 5, the computer 110 includes a CPU (Central Processing Unit) 111, a main memory 112, a storage device 113, an input interface 114, a display controller 115, a data reader / writer 116, and a communication interface 117. These components are connected to each other via a bus 121 so as to be able to communicate data with each other.

[0066] Furthermore, the computer 110 may include a GPU (Graphics Processing Unit) or an FPGA (Field-Programmable Gate Array) in addition to or instead of the CPU 111. In this aspect, the GPU or FPGA can execute the programs in the embodiments.

[0067] The CPU 111 loads a program in the embodiment, which is composed of a group of codes and stored in the storage device 113, into the main memory 112 and executes each code in a predetermined order to perform various calculations. The main memory 112 is typically a volatile storage device such as a DRAM (Dynamic Random Access Memory).

[0068] The program in the embodiment is provided in a state stored in a computer-readable recording medium 120. The program in the embodiment may be distributed over the Internet connected via the communication interface 117.

[0069] Specific examples of the storage device 113 include a hard disk drive and a semiconductor storage device such as a flash memory. The input interface 114 mediates data transmission between the CPU 111 and input devices 118 such as a keyboard and a mouse. The display controller 115 is connected to a display device 119 and controls the display on the display device 119.

[0070] The data reader / writer 116 mediates data transmission between the CPU 111 and the recording medium 120, reads programs from the recording medium 120, and writes processing results from the computer 110 to the recording medium 120. The communication interface 117 mediates data transmission between the CPU 111 and other computers.

[0071] Specific examples of the recording medium 120 include general-purpose semiconductor storage devices such as CF (Compact Flash (registered trademark)) and SD (Secure Digital), magnetic recording media such as flexible disks, or optical recording media such as CD-ROMs (Compact Disk Read Only Memory).

[0072] In the embodiments, the first terminal device 30 and the second terminal device 40 may be realized not by a computer with a program installed, but by hardware corresponding to each unit, such as an electronic circuit. Furthermore, the first terminal device 30 and the second terminal device 40 may each be partially realized by a program and the remaining portion realized by hardware. In the embodiments, the computer is not limited to the computer shown in FIG. 5.

[0073] Some or all of the above-described embodiments can be expressed by (Supplementary Note 1) to (Supplementary Note 20) described below, but are not limited to the following descriptions.

[0074] (Supplementary Note 1) An information processing system comprising: a distributed database; the distributed database stores verification information for information relating to a machine learning model; and the verification information is transmitted from a first terminal device and, in response to a request from a second terminal device, is transmitted from the distributed database to the second terminal device.

[0075] (Supplementary Note 2) The information processing system according to Supplementary Note 1, wherein the information relating to the machine learning model includes test data input to the machine learning model, training data used to build the machine learning model, and a learning program used to build the machine learning model.

[0076] (Supplementary Note 3) The information processing system according to Supplementary Note 2, wherein the information about the machine learning model further includes environmental information that identifies an environment during machine learning.

[0077] (Supplementary Note 4) The information processing system according to Supplementary Note 1 or 2, wherein the distributed database further stores results of verification performed on the machine learning model, and the results are transmitted from the second terminal device.

[0078] (Supplementary Note 5) A terminal device comprising: a verification information generation unit that generates verification information from information related to a machine learning model; and a verification information transmission unit that transmits the calculated verification information to a distributed database for storage.

[0079] (Supplementary Note 6) The terminal device according to Supplementary Note 5, wherein the information about the machine learning model includes test data input to the machine learning model, training data used to build the machine learning model, and a learning program used to build the machine learning model.

[0080] (Supplementary Note 7) The terminal device according to Supplementary Note 6, wherein the information about the machine learning model further includes environmental information that identifies an environment during machine learning.

[0081] (Supplementary Note 8) A terminal device comprising: a verification information acquisition unit that acquires verification information of information relating to a machine learning model stored in a distributed database; a verification unit that verifies the machine learning model by determining whether verification information obtained from a source other than the distributed database is identical to the verification information of the information relating to the machine learning model acquired by the verification information acquisition unit; and a verification result transmission unit that transmits the results of the verification by the verification unit to the distributed database for storage.

[0082] (Supplementary Note 9) The terminal device according to Supplementary Note 8, wherein the information about the machine learning model includes test data input to the machine learning model, training data used to build the machine learning model, and a learning program used to build the machine learning model.

[0083] (Supplementary Note 10) The terminal device according to Supplementary Note 9, wherein the information about the machine learning model further includes environmental information that identifies an environment during machine learning.

[0084] (Supplementary Note 11) An information processing method, in which a first terminal device generates verification information from information related to a machine learning model and transmits the calculated verification information to a distributed database, the distributed database stores the transmitted verification information and transmits the verification information to a second terminal device in response to a request from the second terminal device, and the second terminal device acquires the verification information stored in the distributed database and determines whether the verification information obtained from a source other than the distributed database is identical to the verification information stored in the distributed database, thereby verifying the machine learning model.

[0085] (Supplementary Note 12) The information processing method according to Supplementary Note 11, wherein the information relating to the machine learning model includes test data input to the machine learning model, training data used to build the machine learning model, and a learning program used to build the machine learning model.

[0086] (Supplementary Note 13) The information processing method according to Supplementary Note 12, wherein the information about the machine learning model further includes environmental information that specifies an environment during machine learning.

[0087] (Supplementary Note 14) The information processing method according to Supplementary Note 11 or 12, wherein the second terminal device further transmits a result of the verification to the distributed database, and stores the result in the distributed database.

[0088] (Supplementary Note 15) A computer-readable recording medium having a program recorded thereon, the program including instructions to cause a computer to generate validation information from information relating to a machine learning model, and to transmit the calculated validation information to a distributed database, thereby storing the validation information in the distributed database.

[0089] (Supplementary Note 16) The computer-readable recording medium according to Supplementary Note 15, wherein the information about the machine learning model includes test data input to the machine learning model, training data used to build the machine learning model, and a learning program used to build the machine learning model.

[0090] (Supplementary Note 17) The computer-readable storage medium according to Supplementary Note 16, wherein the information about the machine learning model further includes environmental information that specifies an environment during machine learning.

[0091] (Supplementary Note 18) A computer-readable recording medium having a program recorded thereon, the program including instructions for causing a computer to: obtain verification information for information relating to a machine learning model stored in a distributed database; verify the machine learning model by determining whether verification information obtained from a source other than the distributed database is identical to verification information for information relating to the machine learning model stored in the distributed database; and transmit the results of the verification to the distributed database, thereby storing the verification information in the distributed database.

[0092] (Supplementary Note 19) The computer-readable recording medium according to Supplementary Note 18, wherein the information relating to the machine learning model includes test data input to the machine learning model, training data used to build the machine learning model, and a learning program used to build the machine learning model.

[0093] (Supplementary Note 20) The computer-readable storage medium according to Supplementary Note 19, wherein the information about the machine learning model further includes environmental information that identifies an environment during machine learning.

[0094] Although the present invention has been described above with reference to the embodiments, the present invention is not limited to the above-described embodiments. Various modifications that can be understood by those skilled in the art can be made to the configuration and details of the present invention within the scope of the present invention.

[0095] As described above, according to the present disclosure, it is possible to prevent tampering with data used in the approval of medical devices without disclosing the data. The present disclosure is useful in various fields where it is necessary to prevent tampering with data without disclosing the data.

[0096] 10 Distributed database 20 Network 30 First terminal device 31 Verification information generation unit 32 Verification information transmission unit 40 Second terminal device 41 Verification information acquisition unit 42 Verification unit 43 Verification result transmission unit 50 Third terminal device 100 Information processing system 110 Computer 111 CPU 112 Main memory 113 Storage device 114 Input interface 115 Display controller 116 Data reader / writer 117 Communication interface 118 Input device 119 Display device 120 Recording medium 121 Bus

Claims

1. With a distributed database, The distributed database stores verification information regarding the machine learning model; the verification information is transmitted from a first terminal device, and in response to a request from a second terminal device, the verification information is transmitted from the distributed database to the second terminal device; Information processing system.

2. The information about the machine learning model includes test data input to the machine learning model, training data used to build the machine learning model, and a learning program used to build the machine learning model. The information processing system according to claim 1 .

3. The information about the machine learning model further includes environmental information that specifies an environment during machine learning. The information processing system according to claim 2 .

4. The distributed database further stores a result of validation performed on the machine learning model; The result is transmitted from the second terminal device.

3. The information processing system according to claim 1 or 2.

5. A validation information generation unit that generates validation information from information about the machine learning model; a verification information transmitting unit that transmits the calculated verification information to a distributed database and stores the information in the distributed database; A terminal device comprising:

6. The information about the machine learning model includes test data input to the machine learning model, training data used to build the machine learning model, and a learning program used to build the machine learning model. The terminal device according to claim 5.

7. The information about the machine learning model further includes environmental information that specifies an environment during machine learning. The terminal device according to claim 6.

8. A verification information acquisition unit that acquires verification information of information related to the machine learning model stored in the distributed database; a verification unit that verifies the machine learning model by determining whether verification information obtained from a source other than the distributed database and the verification information of the information related to the machine learning model acquired by the verification information acquisition unit are identical; and a verification result transmission unit that transmits a result of the verification by the verification unit to the distributed database and stores the result in the distributed database; A terminal device comprising:

9. The information about the machine learning model includes test data input to the machine learning model, training data used to build the machine learning model, and a learning program used to build the machine learning model. The terminal device according to claim 8.

10. The information about the machine learning model further includes environmental information that specifies an environment during machine learning. The terminal device according to claim 9.

11. A first terminal device generates verification information from information related to the machine learning model and transmits the calculated verification information to a distributed database; the distributed database stores the transmitted verification information, and transmits the verification information to the second terminal device in response to a request from the second terminal device; the second terminal device acquires the verification information stored in the distributed database, and determines whether the verification information acquired from a source other than the distributed database is identical to the verification information stored in the distributed database, thereby verifying the machine learning model. Information processing methods.

12. The information about the machine learning model includes test data input to the machine learning model, training data used to build the machine learning model, and a learning program used to build the machine learning model. The information processing method according to claim 11.

13. The information about the machine learning model further includes environmental information that specifies an environment during machine learning. The information processing method according to claim 12.

14. the second terminal device further transmits a result of the verification to the distributed database, and stores the result in the distributed database.

13. The information processing method according to claim 11 or 12.

15. On the computer, generating validation information from information about the machine learning model; transmitting the calculated verification information to a distributed database, thereby causing the distributed database to store the verification information; program.

16. The information about the machine learning model includes test data input to the machine learning model, training data used to build the machine learning model, and a learning program used to build the machine learning model. The program according to claim 15.

17. The information about the machine learning model further includes environmental information that specifies an environment during machine learning. The program according to claim 16.

18. On the computer, Obtain validation information for the machine learning model stored in a distributed database, determining whether or not verification information obtained from a source other than the distributed database is identical to verification information regarding the machine learning model stored in the distributed database, thereby verifying the machine learning model; transmitting a result of the verification to the distributed database, thereby storing the verification information in the distributed database; program.

19. The information about the machine learning model includes test data input to the machine learning model, training data used to build the machine learning model, and a learning program used to build the machine learning model.

20. The program of claim 18.

20. The information about the machine learning model further includes environmental information that specifies an environment during machine learning.

20. The program of claim 19.