system

Abstract

The system according to the embodiment aims to appropriately monitor the usage status of data and provide feedback while ensuring the quality and security of the data. [Solution] A system according to an embodiment includes a reception unit, a security unit, a monitoring unit, and a feedback unit. The reception unit receives data registration. The security unit guarantees the quality and security of the data received by the reception unit. The monitoring unit monitors the usage status of the data guaranteed by the security unit. The feedback unit provides feedback based on the usage status of the data collected by the monitoring unit.

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have had the problem of not being able to adequately monitor data usage and provide feedback while ensuring data quality and security.

[0005] The system according to the embodiment aims to appropriately monitor the usage status of data and provide feedback while ensuring the quality and security of the data. [Means for solving the problem]

[0006] A system according to an embodiment includes a reception unit, a security unit, a monitoring unit, and a feedback unit. The reception unit receives data registration. The security unit ensures the quality and security of the data received by the reception unit. The monitoring unit monitors the usage status of the data guaranteed by the security unit. The feedback unit provides feedback based on the usage status of the data collected by the monitoring unit. [Effects of the Invention]

[0007] The system according to the embodiment can appropriately monitor the usage status of data and provide feedback while ensuring the quality and security of the data. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A system according to an embodiment of the present invention is a system for creating a real data sharing market. This system manages the process by which data providers register their real data, data users search for the data they need through the system, and data users enter into a usage agreement. For example, the system implements security measures such as data encryption and access control to ensure data quality and security. The system also monitors data usage and provides appropriate feedback. For example, the system collects data usage frequency and user ratings and provides feedback to data providers. This allows data providers to understand the value of their data and revise the terms of data provision. Furthermore, data users can select reliable data by referring to other users' ratings. This creates a real data sharing market and promotes effective data utilization. This system allows data providers to effectively utilize their data and earn revenue. Meanwhile, data users can efficiently obtain the data they need and use it for business and research. Furthermore, the system ensures data quality and security, providing a reliable data sharing market.

[0029] A data sharing system according to an embodiment includes a reception unit, a security unit, a monitoring unit, and a feedback unit. The reception unit allows a data provider to register their own data in the system. The data provider can register data, for example, manually or automatically. Data formats include text data, image data, and audio data. The security unit ensures the quality and security of the data received by the reception unit. For example, the security unit encrypts the data. For encryption, algorithms such as the Advanced Encryption Standard (AES) and the Rivest-Shamir-Adleman (RSA) can be used. The security unit also performs access control to the data. For access control, methods such as role-based access control (RBAC) and attribute-based access control (ABAC) can be used. The monitoring unit monitors the usage status of the data guaranteed by the security unit. For example, the monitoring unit collects the frequency of data usage. For measuring the frequency of usage, for example, the number of accesses or usage status by time period can be used. The monitoring unit also collects evaluations from data users. The evaluations can be collected using, for example, questionnaires, feedback forms, evaluation scores, etc. The feedback unit provides feedback based on the data usage status collected by the monitoring unit. For example, the feedback unit provides feedback to the data provider. The content of the feedback includes, for example, the frequency of data usage and user evaluations. The feedback unit also provides feedback to the data user. Methods of providing feedback include, for example, report format and real-time notification. As a result, the data sharing system according to the embodiment can consistently register data, ensure quality and security, monitor usage status, and provide feedback.

[0030] The security unit can encrypt data. For encryption, algorithms such as AES (Advanced Encryption Standard) and RSA (Rivest-Shamir-Adleman) can be used. For example, the security unit encrypts data using AES. The security unit can also encrypt data using RSA. The security unit can also verify data integrity using SHA (Secure Hash Algorithm). This improves data security by encrypting the data.

[0031] The security unit can perform data access control. For access control, methods such as role-based access control (RBAC) and attribute-based access control (ABAC) can be used. For example, the security unit performs data access control using RBAC. The security unit can also perform data access control using ABAC. The security unit can also strengthen data access control using multi-factor authentication (MFA). This data access control prevents unauthorized use of data.

[0032] The monitoring unit can collect the frequency of data use. For example, the number of accesses or the usage status by time period can be used to measure the frequency of use. For example, the monitoring unit collects the number of accesses. The monitoring unit can also collect the usage status by time period. The monitoring unit can also monitor the frequency of data use in real time. In this way, by collecting the frequency of data use, the usage status of the data can be understood.

[0033] The monitoring unit can collect evaluations from data users. For example, a questionnaire, a feedback form, an evaluation score, or the like can be used to collect the evaluations. For example, the monitoring unit collects evaluations from data users using a questionnaire. The monitoring unit can also collect evaluations from data users using a feedback form. The monitoring unit can also collect evaluations from data users using an evaluation score. In this way, by collecting evaluations from data users, the reliability of data can be evaluated.

[0034] The feedback unit can provide feedback to the data provider. The content of the feedback includes, for example, the frequency of data use and user evaluations. For example, the feedback unit provides feedback regarding the frequency of data use. The feedback unit can also provide feedback regarding the evaluations of data users. The feedback unit can also suggest improvements to the data provider based on the data usage status. In this way, by providing feedback to the data provider, the value of the data can be understood.

[0035] The feedback unit can provide feedback to the data user. Methods of providing feedback include, for example, in the form of a report or in real-time notification. For example, the feedback unit can provide feedback to the data user in the form of a report. The feedback unit can also provide feedback to the data user in the form of a real-time notification. The feedback unit can also provide information useful for selecting data based on the data user's evaluation. Thus, providing feedback to the data user can help the data user select data.

[0036] The reception unit can analyze the data provider's past registration history and suggest the optimal registration method. For example, the reception unit can automatically display as candidates the types of data that the data provider has frequently registered in the past. The reception unit can also preferentially suggest registration methods (voice, text, etc.) that the data provider has used in the past. The reception unit can also predict and suggest the registration method to be used during a specific time period based on the data provider's past registration history. This improves registration efficiency by suggesting the optimal registration method based on the data provider's past registration history.

[0037] The reception unit can automatically generate metadata at the time of registration according to the type and content of data. For example, when a data provider registers image data, the reception unit automatically generates metadata such as image resolution and format. Furthermore, when a data provider registers text data, the reception unit can automatically generate metadata such as the language and number of characters of the text. Furthermore, when a data provider registers audio data, the reception unit can automatically generate metadata such as the length and format of the audio. In this way, automatic generation of metadata according to the type and content of data makes data management more efficient.

[0038] The reception unit can provide customized guidelines at the time of registration based on the industry or specialty of the data provider. For example, when a data provider registers data in the medical field, the reception unit can provide guidelines regarding the registration of medical data. Furthermore, when a data provider registers data in the financial field, the reception unit can also provide guidelines regarding the registration of financial data. Furthermore, when a data provider registers data in the education field, the reception unit can also provide guidelines regarding the registration of educational data. In this way, by providing guidelines according to the industry or specialty, the accuracy of data registration is improved.

[0039] The reception unit can prioritize registering highly relevant data by taking into account the geographical location information of the data provider. For example, if the data provider is in a specific area, the reception unit can prioritize registering data related to that area. Furthermore, if the data provider is on the move, the reception unit can also prioritize registering data related to the current location. Furthermore, if the data provider is in a specific location, the reception unit can also prioritize registering data related to that location. In this way, highly relevant data can be efficiently registered by taking into account the geographical location information.

[0040] The reception unit can analyze the social media activity of the data provider and automatically suggest related data. The reception unit can suggest related data based on, for example, information shared by the data provider on social media. The reception unit can also analyze the content of posts on social media by the data provider and suggest related data. The reception unit can also suggest related data by taking into account the activities of the data provider's friends on social media. In this way, related data can be efficiently suggested by analyzing social media activity.

[0041] The reception unit can customize the registration method by reflecting the data provider's past feedback. For example, the reception unit can suggest the optimal registration method based on feedback provided by the data provider in the past. The reception unit can also preferentially suggest a specific registration method based on the data provider's past feedback. The reception unit can also analyze the data provider's past feedback and customize the registration method. In this way, the accuracy of the registration method is improved by reflecting past feedback.

[0042] The security unit can apply different encryption algorithms depending on the type and importance of the data. For example, the security unit applies a powerful encryption algorithm to highly confidential data. The security unit can also apply a standard encryption algorithm to general data. The security unit can also apply a lightweight encryption algorithm to data of low importance. In this way, by applying an encryption algorithm depending on the type and importance of the data, the accuracy of security is improved.

[0043] The security unit can monitor data access history in real time and detect unauthorized access. For example, the security unit can monitor data access history in real time and detect abnormal access patterns. The security unit can also analyze data access history and detect signs of unauthorized access. The security unit can also issue alerts in real time based on data access history. In this way, unauthorized access can be quickly detected by monitoring access history in real time.

[0044] The security unit can automatically update the security policy based on the industry standards of the data provider. For example, the security unit automatically applies a medical data security policy to a data provider in the medical industry. The security unit can also automatically apply a financial data security policy to a data provider in the financial industry. The security unit can also automatically apply an educational data security policy to a data provider in the education industry. In this way, the accuracy of security is improved by automatically updating security policies based on industry standards.

[0045] The security department can apply different security measures to each region by taking into account the geographic distribution of data. For example, the security department can apply measures based on the security regulations of a specific region to data in that region. The security department can also analyze the geographic distribution of data and apply the most appropriate security measures to each region. The security department can also evaluate the security risks of each region and take appropriate measures. In this way, by taking into account the geographic distribution, it is possible to provide the most appropriate security measures for each region.

[0046] The security department can improve the accuracy of security measures by referring to literature related to the data. For example, the security department can refer to the latest security research and update the security measures for the data. The security department can also refer to related security guidelines to strengthen the security measures for the data. The security department can also refer to security best practices to optimize the security measures for the data. In this way, the accuracy of security measures can be improved by referring to related literature.

[0047] The security department can strengthen security measures by taking into account the market value of the data. For example, the security department applies strong security measures to data with high value. The security department can also take additional security measures for data with high market value. The security department can also evaluate the market value of data and provide appropriate security measures. In this way, security measures for important data are strengthened by taking into account the market value.

[0048] The monitoring unit can analyze the frequency and patterns of data use and detect abnormal usage. For example, the monitoring unit monitors the frequency of data use in real time and detects abnormal usage patterns. The monitoring unit can also analyze data usage patterns and detect abnormal usage. The monitoring unit can also predict abnormal usage based on the data usage history. This makes it possible to quickly detect abnormal usage by analyzing usage frequency and patterns.

[0049] The monitoring unit can improve the accuracy of monitoring by taking into account the attribute information of the data user. The monitoring unit improves the accuracy of monitoring, for example, based on the attribute information of the data user. The monitoring unit can also analyze the attribute information of the data user and propose an optimal monitoring method. The monitoring unit can also adjust the monitoring criteria by referring to the attribute information of the data user. In this way, the accuracy of monitoring is improved by taking into account the attribute information.

[0050] The monitoring unit can analyze the data usage history over the long term and predict trends. The monitoring unit, for example, analyzes the data usage history over the long term and predicts usage trends. The monitoring unit can also predict future usage patterns based on the data usage history. The monitoring unit can also predict usage trends by referring to the data usage history. This makes it possible to predict data usage trends through long-term analysis.

[0051] The monitoring unit can monitor the usage status for each region by taking into account the geographical distribution of data. For example, the monitoring unit monitors the data usage status in a specific region in real time. The monitoring unit can also analyze data usage patterns for each region and detect abnormal usage. The monitoring unit can also predict data usage trends for each region and improve the accuracy of monitoring. This allows for efficient monitoring of the usage status for each region by taking into account the geographical distribution.

[0052] The monitoring unit can analyze the usage status by referring to market data related to the data. The monitoring unit, for example, analyzes the usage status by referring to market data related to the data. The monitoring unit can also predict data usage trends based on the market data. The monitoring unit can also analyze data usage patterns by referring to market data. As a result, by referring to the related market data, the accuracy of the analysis of the usage status is improved.

[0053] The monitoring unit can adjust the monitoring standards taking into account the technical maturity of the data. For example, the monitoring unit evaluates the technical maturity of the data and adjusts the monitoring standards. Furthermore, the monitoring unit can apply strict monitoring standards to technically mature data. Furthermore, the monitoring unit can apply flexible monitoring standards to technically immature data. In this way, the accuracy of monitoring is improved by taking into account the technical maturity.

[0054] The feedback unit can adjust the level of detail of the feedback based on the usage status of the data. For example, the feedback unit can provide detailed feedback when the frequency of data usage is high. The feedback unit can also provide brief feedback when the frequency of data usage is low. The feedback unit can also analyze the usage status of the data and determine the optimal level of detail of the feedback. This allows the data provider to deepen their understanding by providing feedback according to the usage status.

[0055] The feedback unit can improve the accuracy of the feedback by referring to the past feedback history of the data provider. The feedback unit provides optimal feedback, for example, based on the past feedback history of the data provider. The feedback unit can also analyze the past feedback history of the data provider to improve the accuracy of the feedback. The feedback unit can also customize the content of the feedback by referring to the past feedback history of the data provider. In this way, the accuracy of the feedback is improved by referring to the past feedback history.

[0056] The feedback unit can analyze the data user's evaluation and customize the content of the feedback. The feedback unit provides optimal feedback to the data provider based on, for example, the data user's evaluation. The feedback unit can also analyze the data user's evaluation and customize the content of the feedback. The feedback unit can also refer to the data user's evaluation and improve the accuracy of the feedback. In this way, the accuracy of the feedback is improved by customizing the feedback based on the user's evaluation.

[0057] The feedback unit can provide different feedback for each region by taking into account the geographical distribution of data. For example, the feedback unit can provide feedback related to a specific region to a data provider in that region. The feedback unit can also analyze the data usage status for each region and provide optimal feedback. The feedback unit can also customize the content of the feedback by taking into account the needs of the data provider for each region. In this way, optimal feedback can be provided for each region by taking into account the geographical distribution.

[0058] The feedback unit can improve the accuracy of the feedback by referring to literature related to the data. For example, the feedback unit can refer to the latest research and provide optimal feedback to the data provider. The feedback unit can also refer to related literature to strengthen the content of the feedback. The feedback unit can also refer to best practices regarding feedback to improve the accuracy of the feedback. In this way, the accuracy of the feedback is improved by referring to related literature.

[0059] The feedback unit can adjust the content of the feedback taking into account the market value of the data. For example, the feedback unit can provide detailed feedback to a data provider with high market value. The feedback unit can also provide additional feedback to a data provider with high market value. The feedback unit can also evaluate the market value of the data and provide appropriate feedback. In this way, feedback for important data can be strengthened by taking market value into account.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The reception unit can analyze the data provider's past registration history and suggest the optimal registration method. For example, it can automatically display as candidates the types of data that the data provider has frequently registered in the past. The reception unit can also preferentially suggest registration methods (voice, text, etc.) that the data provider has used in the past. The reception unit can also predict and suggest the registration method that will be used during a specific time period based on the data provider's past registration history. This improves the efficiency of registration by suggesting the optimal registration method based on the data provider's past registration history.

[0062] The security unit can apply different encryption algorithms depending on the type and importance of the data. For example, a powerful encryption algorithm is applied to highly confidential data. The security unit can also apply a standard encryption algorithm to general data. The security unit can also apply a lightweight encryption algorithm to data of low importance. In this way, the accuracy of security is improved by applying an encryption algorithm depending on the type and importance of the data.

[0063] The security unit can monitor data access history in real time and detect unauthorized access. For example, it can monitor data access history in real time and detect abnormal access patterns. The security unit can also analyze data access history and detect signs of unauthorized access. The security unit can also issue alerts in real time based on data access history. This allows unauthorized access to be detected quickly by monitoring access history in real time.

[0064] The monitoring unit can analyze the data usage history over the long term and predict trends. For example, the monitoring unit can analyze the data usage history over the long term and predict usage trends. The monitoring unit can also predict future usage patterns based on the data usage history. The monitoring unit can also predict usage trends by referring to the data usage history. This makes it possible to predict data usage trends through long-term analysis.

[0065] The feedback unit can adjust the level of detail of the feedback based on the usage status of the data. For example, if the frequency of data usage is high, detailed feedback is provided. On the other hand, if the frequency of data usage is low, the feedback unit can provide brief feedback. The feedback unit can also analyze the usage status of the data and determine the optimal level of detail of the feedback. In this way, providing feedback according to the usage status deepens the data provider's understanding.

[0066] The feedback unit can improve the accuracy of the feedback by referring to the past feedback history of the data provider. For example, the feedback unit can provide optimal feedback based on the past feedback history of the data provider. The feedback unit can also analyze the past feedback history of the data provider to improve the accuracy of the feedback. The feedback unit can also customize the content of the feedback by referring to the past feedback history of the data provider. In this way, the accuracy of the feedback can be improved by referring to the past feedback history.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The reception unit allows data providers to register their data in the system. Data providers can register data manually or automatically. Data formats include text data, image data, and audio data. Step 2: The security unit ensures the quality and security of the data received by the reception unit. For example, the security unit encrypts the data using algorithms such as AES or RSA. The security unit also controls access to the data using methods such as role-based access control (RBAC) or attribute-based access control (ABAC). Step 3: The monitoring unit monitors the usage of the data guaranteed by the security unit. For example, the monitoring unit collects the frequency of data usage, using the number of accesses and usage by time period. The monitoring unit also collects evaluations from data users, using questionnaires, feedback forms, evaluation scores, etc. Step 4: The feedback unit provides feedback based on the usage status of the data collected by the monitoring unit. For example, the feedback unit provides feedback to the data provider, such as the frequency of data use and user ratings. The feedback unit also provides feedback to the data user in the form of reports or real-time notifications.

[0069] (Example 2) A system according to an embodiment of the present invention is a system for creating a real data sharing market. This system manages the process by which data providers register their real data, data users search for the data they need through the system, and data users enter into a usage agreement. For example, the system implements security measures such as data encryption and access control to ensure data quality and security. The system also monitors data usage and provides appropriate feedback. For example, the system collects data usage frequency and user ratings and provides feedback to data providers. This allows data providers to understand the value of their data and revise the terms of data provision. Furthermore, data users can select reliable data by referring to other users' ratings. This creates a real data sharing market and promotes effective data utilization. This system allows data providers to effectively utilize their data and earn revenue. Meanwhile, data users can efficiently obtain the data they need and use it for business and research. Furthermore, the system ensures data quality and security, providing a reliable data sharing market.

[0070] A data sharing system according to an embodiment includes a reception unit, a security unit, a monitoring unit, and a feedback unit. The reception unit allows a data provider to register their own data in the system. The data provider can register data, for example, manually or automatically. Data formats include text data, image data, and audio data. The security unit ensures the quality and security of the data received by the reception unit. For example, the security unit encrypts the data. For encryption, algorithms such as the Advanced Encryption Standard (AES) and the Rivest-Shamir-Adleman (RSA) can be used. The security unit also performs access control to the data. For access control, methods such as role-based access control (RBAC) and attribute-based access control (ABAC) can be used. The monitoring unit monitors the usage status of the data guaranteed by the security unit. For example, the monitoring unit collects the frequency of data usage. For measuring the frequency of usage, for example, the number of accesses or usage status by time period can be used. The monitoring unit also collects evaluations from data users. The evaluations can be collected using, for example, questionnaires, feedback forms, evaluation scores, etc. The feedback unit provides feedback based on the data usage status collected by the monitoring unit. For example, the feedback unit provides feedback to the data provider. The content of the feedback includes, for example, the frequency of data usage and user evaluations. The feedback unit also provides feedback to the data user. Methods of providing feedback include, for example, report format and real-time notification. As a result, the data sharing system according to the embodiment can consistently register data, ensure quality and security, monitor usage status, and provide feedback.

[0071] The security unit can encrypt data. For encryption, algorithms such as AES (Advanced Encryption Standard) and RSA (Rivest-Shamir-Adleman) can be used. For example, the security unit encrypts data using AES. The security unit can also encrypt data using RSA. The security unit can also verify data integrity using SHA (Secure Hash Algorithm). This improves data security by encrypting the data.

[0072] The security unit can perform data access control. For access control, methods such as role-based access control (RBAC) and attribute-based access control (ABAC) can be used. For example, the security unit performs data access control using RBAC. The security unit can also perform data access control using ABAC. The security unit can also strengthen data access control using multi-factor authentication (MFA). This data access control prevents unauthorized use of data.

[0073] The monitoring unit can collect the frequency of data use. For example, the number of accesses or the usage status by time period can be used to measure the frequency of use. For example, the monitoring unit collects the number of accesses. The monitoring unit can also collect the usage status by time period. The monitoring unit can also monitor the frequency of data use in real time. In this way, by collecting the frequency of data use, the usage status of the data can be understood.

[0074] The monitoring unit can collect evaluations from data users. For example, a questionnaire, a feedback form, an evaluation score, or the like can be used to collect the evaluations. For example, the monitoring unit collects evaluations from data users using a questionnaire. The monitoring unit can also collect evaluations from data users using a feedback form. The monitoring unit can also collect evaluations from data users using an evaluation score. In this way, by collecting evaluations from data users, the reliability of data can be evaluated.

[0075] The feedback unit can provide feedback to the data provider. The content of the feedback includes, for example, the frequency of data use and user evaluations. For example, the feedback unit provides feedback regarding the frequency of data use. The feedback unit can also provide feedback regarding the evaluations of data users. The feedback unit can also suggest improvements to the data provider based on the data usage status. In this way, by providing feedback to the data provider, the value of the data can be understood.

[0076] The feedback unit can provide feedback to the data user. Methods of providing feedback include, for example, in the form of a report or in real-time notification. For example, the feedback unit can provide feedback to the data user in the form of a report. The feedback unit can also provide feedback to the data user in the form of a real-time notification. The feedback unit can also provide information useful for selecting data based on the data user's evaluation. Thus, providing feedback to the data user can help the data user select data.

[0077] The reception unit can estimate the emotion of the data provider and adjust the data registration interface based on the estimated emotion. For example, if the data provider is nervous, the reception unit can provide a simple and intuitive interface to simplify the registration procedure. Furthermore, if the data provider is relaxed, the reception unit can provide detailed input options and suggest a customizable registration method. Furthermore, if the data provider is in a hurry, the reception unit can prioritize voice input to enable quick data registration. This improves the convenience of registration by providing an interface that corresponds to the emotion of the data provider. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0078] The reception unit can analyze the data provider's past registration history and suggest the optimal registration method. For example, the reception unit can automatically display as candidates the types of data that the data provider has frequently registered in the past. The reception unit can also preferentially suggest registration methods (voice, text, etc.) that the data provider has used in the past. The reception unit can also predict and suggest the registration method to be used during a specific time period based on the data provider's past registration history. This improves registration efficiency by suggesting the optimal registration method based on the data provider's past registration history.

[0079] The reception unit can automatically generate metadata at the time of registration according to the type and content of data. For example, when a data provider registers image data, the reception unit automatically generates metadata such as image resolution and format. Furthermore, when a data provider registers text data, the reception unit can automatically generate metadata such as the language and number of characters of the text. Furthermore, when a data provider registers audio data, the reception unit can automatically generate metadata such as the length and format of the audio. In this way, automatic generation of metadata according to the type and content of data makes data management more efficient.

[0080] The reception unit can provide customized guidelines at the time of registration based on the industry or specialty of the data provider. For example, when a data provider registers data in the medical field, the reception unit can provide guidelines regarding the registration of medical data. Furthermore, when a data provider registers data in the financial field, the reception unit can also provide guidelines regarding the registration of financial data. Furthermore, when a data provider registers data in the education field, the reception unit can also provide guidelines regarding the registration of educational data. In this way, by providing guidelines according to the industry or specialty, the accuracy of data registration is improved.

[0081] The reception unit can estimate the emotion of the data provider and determine the priority of data registration based on the estimated emotion. For example, if the data provider is feeling stressed, the reception unit can prioritize registering data of high importance. Furthermore, if the data provider is relaxed, the reception unit can also prioritize registering detailed data. Furthermore, if the data provider is in a hurry, the reception unit can also prioritize registering simple data. This improves registration efficiency by setting priorities according to the emotion of the data provider. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0082] The reception unit can prioritize registering highly relevant data by taking into account the geographical location information of the data provider. For example, if the data provider is in a specific area, the reception unit can prioritize registering data related to that area. Furthermore, if the data provider is on the move, the reception unit can also prioritize registering data related to the current location. Furthermore, if the data provider is in a specific location, the reception unit can also prioritize registering data related to that location. In this way, highly relevant data can be efficiently registered by taking into account the geographical location information.

[0083] The reception unit can analyze the social media activity of the data provider and automatically suggest related data. The reception unit can suggest related data based on, for example, information shared by the data provider on social media. The reception unit can also analyze the content of posts on social media by the data provider and suggest related data. The reception unit can also suggest related data by taking into account the activities of the data provider's friends on social media. In this way, related data can be efficiently suggested by analyzing social media activity.

[0084] The reception unit can customize the registration method by reflecting the data provider's past feedback. For example, the reception unit can suggest the optimal registration method based on feedback provided by the data provider in the past. The reception unit can also preferentially suggest a specific registration method based on the data provider's past feedback. The reception unit can also analyze the data provider's past feedback and customize the registration method. In this way, the accuracy of the registration method is improved by reflecting past feedback.

[0085] The security unit can estimate the emotions of the data provider and adjust security settings based on the estimated emotions. For example, if the data provider feels anxious, the security unit can provide strong security settings. If the data provider feels relaxed, the security unit can also provide flexible security settings. If the data provider is in a hurry, the security unit can also provide security options that can be quickly set. This improves security convenience by providing security settings according to the emotions of the data provider. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0086] The security unit can apply different encryption algorithms depending on the type and importance of the data. For example, the security unit applies a powerful encryption algorithm to highly confidential data. The security unit can also apply a standard encryption algorithm to general data. The security unit can also apply a lightweight encryption algorithm to data of low importance. In this way, by applying an encryption algorithm depending on the type and importance of the data, the accuracy of security is improved.

[0087] The security unit can monitor data access history in real time and detect unauthorized access. For example, the security unit can monitor data access history in real time and detect abnormal access patterns. The security unit can also analyze data access history and detect signs of unauthorized access. The security unit can also issue alerts in real time based on data access history. In this way, unauthorized access can be quickly detected by monitoring access history in real time.

[0088] The security unit can automatically update the security policy based on the industry standards of the data provider. For example, the security unit automatically applies a medical data security policy to a data provider in the medical industry. The security unit can also automatically apply a financial data security policy to a data provider in the financial industry. The security unit can also automatically apply an educational data security policy to a data provider in the education industry. In this way, the accuracy of security is improved by automatically updating security policies based on industry standards.

[0089] The security unit can estimate the emotion of the data provider and adjust the level of access control based on the estimated emotion. For example, the security unit can provide strict access control if the data provider feels anxious. The security unit can also provide flexible access control if the data provider feels relaxed. The security unit can also provide access control options that can be quickly set if the data provider is in a hurry. This improves security convenience by providing access control according to the emotion of the data provider. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The security department can apply different security measures to each region by taking into account the geographic distribution of data. For example, the security department can apply measures based on the security regulations of a specific region to data in that region. The security department can also analyze the geographic distribution of data and apply the most appropriate security measures to each region. The security department can also evaluate the security risks of each region and take appropriate measures. In this way, by taking into account the geographic distribution, it is possible to provide the most appropriate security measures for each region.

[0091] The security department can improve the accuracy of security measures by referring to literature related to the data. For example, the security department can refer to the latest security research and update the security measures for the data. The security department can also refer to related security guidelines to strengthen the security measures for the data. The security department can also refer to security best practices to optimize the security measures for the data. In this way, the accuracy of security measures can be improved by referring to related literature.

[0092] The security department can strengthen security measures by taking into account the market value of the data. For example, the security department applies strong security measures to data with high value. The security department can also take additional security measures for data with high market value. The security department can also evaluate the market value of data and provide appropriate security measures. In this way, security measures for important data are strengthened by taking into account the market value.

[0093] The monitoring unit can estimate the emotions of the data user and adjust the monitoring standards based on the estimated emotions. For example, if the data user is feeling anxious, the monitoring unit can apply strict monitoring standards. Also, if the data user is relaxed, the monitoring unit can apply flexible monitoring standards. Also, if the data user is in a hurry, the monitoring unit can apply standards that allow for quick monitoring. This improves the accuracy of monitoring by providing monitoring standards that correspond to the emotions of the data user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0094] The monitoring unit can analyze the frequency and patterns of data use and detect abnormal usage. For example, the monitoring unit monitors the frequency of data use in real time and detects abnormal usage patterns. The monitoring unit can also analyze data usage patterns and detect abnormal usage. The monitoring unit can also predict abnormal usage based on the data usage history. This makes it possible to quickly detect abnormal usage by analyzing usage frequency and patterns.

[0095] The monitoring unit can improve the accuracy of monitoring by taking into account the attribute information of the data user. The monitoring unit improves the accuracy of monitoring, for example, based on the attribute information of the data user. The monitoring unit can also analyze the attribute information of the data user and propose an optimal monitoring method. The monitoring unit can also adjust the monitoring criteria by referring to the attribute information of the data user. In this way, the accuracy of monitoring is improved by taking into account the attribute information.

[0096] The monitoring unit can analyze the data usage history over the long term and predict trends. The monitoring unit, for example, analyzes the data usage history over the long term and predicts usage trends. The monitoring unit can also predict future usage patterns based on the data usage history. The monitoring unit can also predict usage trends by referring to the data usage history. This makes it possible to predict data usage trends through long-term analysis.

[0097] The monitoring unit can estimate the emotions of the data user and adjust the display method of the monitoring results based on the estimated emotions. For example, if the data user is feeling anxious, the monitoring unit can provide a simple, highly visible display method. Furthermore, if the data user is relaxed, the monitoring unit can also provide a display method that includes detailed information. Furthermore, if the data user is in a hurry, the monitoring unit can also provide a display method that focuses on the main points. This improves understanding of the monitoring results by providing a display method that corresponds to the emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0098] The monitoring unit can monitor the usage status for each region by taking into account the geographical distribution of data. For example, the monitoring unit monitors the data usage status in a specific region in real time. The monitoring unit can also analyze data usage patterns for each region and detect abnormal usage. The monitoring unit can also predict data usage trends for each region and improve the accuracy of monitoring. This allows for efficient monitoring of the usage status for each region by taking into account the geographical distribution.

[0099] The monitoring unit can analyze the usage status by referring to market data related to the data. The monitoring unit, for example, analyzes the usage status by referring to market data related to the data. The monitoring unit can also predict data usage trends based on the market data. The monitoring unit can also analyze data usage patterns by referring to market data. As a result, by referring to the related market data, the accuracy of the analysis of the usage status is improved.

[0100] The monitoring unit can adjust the monitoring standards taking into account the technical maturity of the data. For example, the monitoring unit evaluates the technical maturity of the data and adjusts the monitoring standards. Furthermore, the monitoring unit can apply strict monitoring standards to technically mature data. Furthermore, the monitoring unit can apply flexible monitoring standards to technically immature data. In this way, the accuracy of monitoring is improved by taking into account the technical maturity.

[0101] The feedback unit can estimate the emotion of the data provider and adjust the content of the feedback based on the estimated emotion. For example, if the data provider is feeling anxious, the feedback unit can prioritize providing positive feedback. The feedback unit can also provide detailed feedback if the data provider is relaxed. The feedback unit can also provide concise feedback if the data provider is in a hurry. This improves the satisfaction of the data provider by providing feedback according to the emotion. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0102] The feedback unit can adjust the level of detail of the feedback based on the usage status of the data. For example, the feedback unit can provide detailed feedback when the frequency of data usage is high. The feedback unit can also provide brief feedback when the frequency of data usage is low. The feedback unit can also analyze the usage status of the data and determine the optimal level of detail of the feedback. This allows the data provider to deepen their understanding by providing feedback according to the usage status.

[0103] The feedback unit can improve the accuracy of the feedback by referring to the past feedback history of the data provider. The feedback unit provides optimal feedback, for example, based on the past feedback history of the data provider. The feedback unit can also analyze the past feedback history of the data provider to improve the accuracy of the feedback. The feedback unit can also customize the content of the feedback by referring to the past feedback history of the data provider. In this way, the accuracy of the feedback is improved by referring to the past feedback history.

[0104] The feedback unit can analyze the data user's evaluation and customize the content of the feedback. The feedback unit provides optimal feedback to the data provider based on, for example, the data user's evaluation. The feedback unit can also analyze the data user's evaluation and customize the content of the feedback. The feedback unit can also refer to the data user's evaluation and improve the accuracy of the feedback. In this way, the accuracy of the feedback is improved by customizing the feedback based on the user's evaluation.

[0105] The feedback unit can estimate the emotion of the data provider and determine the priority of feedback based on the estimated emotion. For example, if the data provider is feeling anxious, the feedback unit can prioritize providing important feedback. Furthermore, if the data provider is relaxed, the feedback unit can prioritize providing detailed feedback. Furthermore, if the data provider is in a hurry, the feedback unit can prioritize providing concise feedback. In this way, by setting priorities according to emotions, important feedback can be provided quickly. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0106] The feedback unit can provide different feedback for each region by taking into account the geographical distribution of data. For example, the feedback unit can provide feedback related to a specific region to a data provider in that region. The feedback unit can also analyze the data usage status for each region and provide optimal feedback. The feedback unit can also customize the content of the feedback by taking into account the needs of the data provider for each region. In this way, optimal feedback can be provided for each region by taking into account the geographical distribution.

[0107] The feedback unit can improve the accuracy of the feedback by referring to literature related to the data. For example, the feedback unit can refer to the latest research and provide optimal feedback to the data provider. The feedback unit can also refer to related literature to strengthen the content of the feedback. The feedback unit can also refer to best practices regarding feedback to improve the accuracy of the feedback. In this way, the accuracy of the feedback is improved by referring to related literature.

[0108] The feedback unit can adjust the content of the feedback taking into account the market value of the data. For example, the feedback unit can provide detailed feedback to a data provider with high market value. The feedback unit can also provide additional feedback to a data provider with high market value. The feedback unit can also evaluate the market value of the data and provide appropriate feedback. In this way, feedback for important data can be strengthened by taking market value into account. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, security unit, monitoring unit, and feedback unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and provides an interface for a data provider to register data. The security unit is realized by the specific processing unit 290 of the data processing device 12 and performs data encryption and access control. The monitoring unit is realized by the specific processing unit 290 of the data processing device 12 and monitors the usage status of data. The feedback unit is realized by the specific processing unit 290 of the data processing device 12 and provides feedback to the data provider and data user. The reception unit has a function of, for example, estimating the emotion of the data provider and adjusting the interface based on the estimated emotion. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, security unit, monitoring unit, and feedback unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and provides an interface for the data provider to register data. The security unit is realized by the specific processing unit 290 of the data processing device 12 and performs data encryption and access control. The monitoring unit is realized by the specific processing unit 290 of the data processing device 12 and monitors the data usage status. The feedback unit is realized by the specific processing unit 290 of the data processing device 12 and provides feedback to the data provider and data user. The reception unit has a function, for example, of estimating the emotion of the data provider and adjusting the interface based on the estimated emotion. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, security unit, monitoring unit, and feedback unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and provides an interface when a data provider registers data. The security unit is realized by the specific processing unit 290 of the data processing device 12 and performs data encryption and access control. The monitoring unit is realized by the specific processing unit 290 of the data processing device 12 and monitors the usage status of data. The feedback unit is realized by the specific processing unit 290 of the data processing device 12 and provides feedback to the data provider and data user. The reception unit has a function, for example, of estimating the emotion of the data provider and adjusting the interface based on the estimated emotion. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, security unit, monitoring unit, and feedback unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and provides an interface for the data provider to register data. The security unit is realized by the specific processing unit 290 of the data processing device 12 and performs data encryption and access control. The monitoring unit is realized by the specific processing unit 290 of the data processing device 12 and monitors the data usage status. The feedback unit is realized by the specific processing unit 290 of the data processing device 12 and provides feedback to the data provider and data user. The reception unit has a function of, for example, estimating the emotion of the data provider and adjusting the interface based on the estimated emotion.

[0109] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0110] The reception unit can analyze the data provider's past registration history and suggest the optimal registration method. For example, it can automatically display as candidates the types of data that the data provider has frequently registered in the past. The reception unit can also preferentially suggest registration methods (voice, text, etc.) that the data provider has used in the past. The reception unit can also predict and suggest the registration method that will be used during a specific time period based on the data provider's past registration history. This improves the efficiency of registration by suggesting the optimal registration method based on the data provider's past registration history.

[0111] The security unit can apply different encryption algorithms depending on the type and importance of the data. For example, a powerful encryption algorithm is applied to highly confidential data. The security unit can also apply a standard encryption algorithm to general data. The security unit can also apply a lightweight encryption algorithm to data of low importance. In this way, the accuracy of security is improved by applying an encryption algorithm depending on the type and importance of the data.

[0112] The monitoring unit can estimate the emotions of the data user and adjust the monitoring standards based on the estimated emotions. For example, if the data user is feeling anxious, strict monitoring standards can be applied. The monitoring unit can also apply flexible monitoring standards if the data user is relaxed. The monitoring unit can also apply standards that allow for quick monitoring if the data user is in a hurry. This improves the accuracy of monitoring by providing monitoring standards that correspond to the emotions of the data user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0113] The feedback unit can estimate the emotion of the data provider and adjust the content of the feedback based on the estimated emotion. For example, if the data provider is feeling anxious, positive feedback can be given priority. The feedback unit can also provide detailed feedback if the data provider is relaxed. The feedback unit can also provide concise feedback if the data provider is in a hurry. This improves the satisfaction of the data provider by providing feedback according to the emotion. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0114] The reception unit can estimate the data provider's emotions and adjust the data registration interface based on the estimated emotions. For example, if the data provider is nervous, a simple and intuitive interface can be provided to simplify the registration procedure. Furthermore, if the data provider is relaxed, the reception unit can provide detailed input options and suggest a customizable registration method. Furthermore, if the data provider is in a hurry, the reception unit can prioritize voice input to enable quick data registration. This improves the convenience of registration by providing an interface that corresponds to the data provider's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0115] The security unit can monitor data access history in real time and detect unauthorized access. For example, it can monitor data access history in real time and detect abnormal access patterns. The security unit can also analyze data access history and detect signs of unauthorized access. The security unit can also issue alerts in real time based on data access history. This allows unauthorized access to be detected quickly by monitoring access history in real time.

[0116] The monitoring unit can analyze the data usage history over the long term and predict trends. For example, the monitoring unit can analyze the data usage history over the long term and predict usage trends. The monitoring unit can also predict future usage patterns based on the data usage history. The monitoring unit can also predict usage trends by referring to the data usage history. This makes it possible to predict data usage trends through long-term analysis.

[0117] The feedback unit can adjust the level of detail of the feedback based on the usage status of the data. For example, if the frequency of data usage is high, detailed feedback is provided. On the other hand, if the frequency of data usage is low, the feedback unit can provide brief feedback. The feedback unit can also analyze the usage status of the data and determine the optimal level of detail of the feedback. In this way, providing feedback according to the usage status deepens the data provider's understanding.

[0118] The security unit can estimate the emotions of the data provider and adjust security settings based on the estimated emotions. For example, if the data provider feels anxious, the security unit can provide strong security settings. The security unit can also provide flexible security settings if the data provider feels relaxed. The security unit can also provide security options that can be quickly set if the data provider is in a hurry. This improves security convenience by providing security settings according to the emotions of the data provider. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0119] The feedback unit can improve the accuracy of the feedback by referring to the past feedback history of the data provider. For example, the feedback unit can provide optimal feedback based on the past feedback history of the data provider. The feedback unit can also analyze the past feedback history of the data provider to improve the accuracy of the feedback. The feedback unit can also customize the content of the feedback by referring to the past feedback history of the data provider. In this way, the accuracy of the feedback can be improved by referring to the past feedback history.

[0120] The processing flow of the second embodiment will be briefly explained below.

[0121] Step 1: The reception unit allows data providers to register their data in the system. Data providers can register data manually or automatically. Data formats include text data, image data, and audio data. Step 2: The security unit ensures the quality and security of the data received by the reception unit. For example, the security unit encrypts the data using algorithms such as AES or RSA. The security unit also controls access to the data using methods such as role-based access control (RBAC) or attribute-based access control (ABAC). Step 3: The monitoring unit monitors the usage of the data guaranteed by the security unit. For example, the monitoring unit collects the frequency of data usage, using the number of accesses and usage by time period. The monitoring unit also collects evaluations from data users, using questionnaires, feedback forms, evaluation scores, etc. Step 4: The feedback unit provides feedback based on the usage status of the data collected by the monitoring unit. For example, the feedback unit provides feedback to the data provider, such as the frequency of data use and user ratings. The feedback unit also provides feedback to the data user in the form of reports or real-time notifications.

[0122] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0127] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0128] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0133] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0134] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0136] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0137] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0143] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0144] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0145] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0146] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0148] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0149] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0150] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0151] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0152] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0153] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0154] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0155] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0156] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0159] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0160] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0161] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0162] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0163] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0164] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0165] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0166] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0167] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0168] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0169] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0170] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0171] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0172] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0173] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0174] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0175] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0176] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0177] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0178] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0179] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0180] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0181] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0182] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0183] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0184] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0185] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0186] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0187] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0188] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0189] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0190] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0191] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0192] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0193] [Explanation of symbols]

[0194] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives data registration; a security unit that guarantees the quality and security of the data received by the reception unit; a monitoring unit that monitors the usage status of the data guaranteed by the security unit; a feedback unit that provides feedback based on the usage status of the data collected by the monitoring unit; Equipped with A system characterized by:

2. The security unit Encrypting data 2. The system of claim 1.

3. The security unit Controlling data access 2. The system of claim 1.

4. The monitoring unit Collect data usage frequency 2. The system of claim 1.

5. The monitoring unit Collect data user ratings 2. The system of claim 1.

6. The feedback unit Providing feedback to data contributors 2. The system of claim 1.

7. The feedback unit Providing feedback to data users 2. The system of claim 1.

8. The reception unit Estimate the sentiment of data providers and adjust the data registration interface based on the estimated sentiment.

2. The system of claim 1.

Images