system

Abstract

An object of a system according to an embodiment is to efficiently perform user login management and processing of a usage fee for an AI service.SOLUTION: A system according to an embodiment includes a AI wallet, a generation AI, a user authenticator, and a usage fee manager. The AI wallet connects users, service providers, and WAI providers. The generating AI accesses the generating AI via the AI wallet. The user authenticating unit analyzes the authenticating information of the user who logs in to the AI wallet and confirms that the user is a valid user. The usage fee management unit calculates the usage fee of the generated AI via the AI wallet and completes the calculation between the user and the AI provider.SELECTED DRAWING: Figure 1

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 that managing user logins and processing fees for AI services are complicated and difficult to do efficiently.

[0005] The system of the embodiment aims to efficiently manage user logins and process usage fees for AI services. [Means for solving the problem]

[0006] The system according to the embodiment comprises an AI wallet, a generating AI, a user authentication unit, and a usage fee management unit. The AI ​​wallet connects users, service providers, and WAI providers. The generating AI accesses the generating AI via the AI ​​wallet. The user authentication unit analyzes the authentication information of users logging in to the AI ​​wallet and verifies that they are legitimate users. The usage fee management unit calculates the usage fee for the generating AI via the AI ​​wallet and completes the process between the user and the AI ​​provider. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently manage user logins and process fees for AI services. [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) The AI ​​wallet system according to an embodiment of the present invention connects users, service providers, and WAI providers. This system analyzes user authentication information via the generation AI and manages usage fees. This reduces the login effort required for users and makes it easier for AI service providers to manage usage fees.

[0029] An AI wallet system according to an embodiment includes an AI wallet, a generation AI, a user authentication unit, and a usage fee management unit. The AI ​​wallet connects users, service providers, and WAI providers. For example, a user can access multiple AI services by simply logging in to the AI ​​wallet once. The generation AI accesses the generation AI via the AI ​​wallet. For example, the generation AI uses a text generation AI (e.g., LLM) to analyze the user's authentication information. The user authentication unit analyzes the authentication information of a user logging in to the AI ​​wallet and verifies that the user is a legitimate user. For example, the user authentication unit uses the generation AI to perform facial recognition or fingerprint authentication of the user. The usage fee management unit calculates the usage fee for the generation AI via the AI ​​wallet and completes the process between the user and the AI ​​provider. For example, the usage fee management unit uses the generation AI to calculate the usage fee based on the user's usage time and data volume. This allows the AI ​​wallet system according to an embodiment to reduce the user's login effort and facilitate AI service provider usage fee management. For example, users can access multiple AI services with a single login, making it easier for AI service providers to manage usage fees.

[0030] The user authentication unit can use the generation AI to analyze user authentication information and confirm that the user is legitimate. For example, the user authentication unit adds a facial recognition function to the AI ​​wallet and performs facial authentication via a camera when the user logs in. The generation AI analyzes facial features and confirms that the user is legitimate. The user authentication unit can also integrate a fingerprint authentication function into the AI ​​wallet, allowing the user to log in using a fingerprint scanner. The generation AI analyzes the fingerprint data to enhance security. The user authentication unit also encrypts biometric authentication data, and the generation AI analyzes that data to authenticate the user. For example, facial recognition and fingerprint authentication can be combined to achieve double authentication. This improves the accuracy of user authentication.

[0031] The usage fee management unit can use the generation AI to calculate usage fees based on usage time and data volume. For example, the usage fee management unit adds a function where the AI ​​wallet learns the user's behavioral patterns and issues an alert if it detects an abnormal login attempt. For example, the AI ​​wallet learns the user's usual login times and locations and issues an alert if an abnormal login attempt is made. The generation AI analyzes the user's login history and issues an alert in real time when it detects an abnormal pattern. For example, if there are multiple login attempts in a short period of time. A function is added to automatically block abnormal login attempts based on the user's behavioral patterns. For example, if it detects a login from an unusual device. This makes usage fee calculations automatic and accurate.

[0032] The user authentication unit can use generation AI to analyze user authentication information and verify that the user is a legitimate user. For example, the user authentication unit uses an emotion estimation function to automatically generate an interface to reduce the stress and anxiety the user feels when logging in. For example, the emotion estimation function can be used to analyze the stress the user feels when logging in in real time and generate a relaxing interface. The user's facial expressions and voice can be analyzed to generate messages and guidance to reduce anxiety when logging in. For example, an encouraging message or simple operation guide can be displayed. Based on the emotion estimation data, a customized interface can be provided to reduce the stress the user feels when logging in. For example, a background or theme that matches the user's preferences can be displayed. This improves the accuracy of user authentication.

[0033] The usage fee management unit can use the generation AI to calculate usage fees based on usage time and data volume. For example, the usage fee management unit uses the AI ​​wallet to add a function that allows users to seamlessly share login information across multiple devices. For example, a cloud synchronization function can be added to the AI ​​wallet, allowing users to seamlessly share login information across different devices. The generation AI encrypts the user's login information and builds a system that securely shares it across multiple devices. For example, authentication between devices using QR codes. A function can be added that requests authentication from an existing device when a user logs in to the AI ​​wallet on a new device. For example, approval can be requested from a smartphone. This makes usage fee calculations automatic and accurate.

[0034] The user authentication unit can use the generation AI to analyze user authentication information and verify that the user is a legitimate user. For example, the user authentication unit adds a function to the AI ​​wallet that automatically converts data formats to facilitate data transfer between different AI services. For example, a data format conversion function can be added to the AI ​​wallet to automate data transfer between different AI services. A system can be built in which the generation AI analyzes the data formats of different AI services and automatically converts them into compatible formats. For example, format conversion of image data. When a user uses a different AI service, the AI ​​wallet automatically converts the data format, achieving seamless data transfer. For example, encoding conversion of text data. This improves the accuracy of user authentication.

[0035] The usage fee management unit can use the generation AI to calculate usage fees based on usage time and data volume. For example, the usage fee management unit uses an emotion estimation function to analyze the emotions felt by the user when logging in, and the generation AI provides a customized login screen to elicit positive emotions. For example, the emotion estimation function can be used to analyze the emotions felt by the user when logging in in real time, and a customized login screen to elicit positive emotions can be provided. The user's facial expressions and voice can be analyzed, and messages and guides can be generated to elicit positive emotions when logging in. For example, encouraging messages and simple operation guides can be displayed. Based on the emotion estimation data, the emotions felt by the user when logging in can be analyzed, and a customized interface can be provided to elicit positive emotions. For example, backgrounds and themes tailored to the user's preferences can be displayed. This allows for automated and accurate calculation of usage fees.

[0036] The user authentication unit can use the generation AI to analyze user authentication information and verify that the user is a legitimate user. The user authentication unit adds a function, for example, in which the generation AI analyzes the user's usage history and automatically proposes a plan to optimize usage fees. For example, the generation AI analyzes the user's usage history and automatically proposes the optimal usage plan. The generation AI analyzes the user's usage patterns and proposes a customized plan to optimize usage fees. For example, a discount plan for frequently used services. The generation AI proposes a plan in real time to optimize usage fees based on the user's usage history. For example, it presents the optimal plan based on usage time and data volume. This improves the accuracy of user authentication.

[0037] The usage fee management unit can use the generation AI to calculate usage fees based on usage time and data volume. The usage fee management unit adds a function that allows the AI ​​wallet to monitor usage fee fluctuations in real time and provide users with advice on how to reduce costs. For example, the AI ​​wallet monitors usage fee fluctuations in real time and provides users with advice on how to reduce costs. The generation AI analyzes usage fee fluctuations and provides users with specific advice on how to reduce costs. For example, it suggests the timing and method for reducing usage frequency. The AI ​​wallet monitors usage fee fluctuations in real time and issues alerts to users to reduce costs. For example, it notifies them when usage fees exceed a certain threshold. This makes usage fee calculations automated and accurate.

[0038] The user authentication unit can use the generation AI to analyze user authentication information and verify that the user is a legitimate user. For example, the user authentication unit uses an emotion estimation function to have the generation AI propose a customized pricing plan to alleviate the anxiety and dissatisfaction the user feels about usage fees. For example, the emotion estimation function is used to analyze the anxiety and dissatisfaction the user feels about usage fees in real time, and the generation AI proposes a customized pricing plan. The user's facial expressions and voice are analyzed to generate messages and guides to alleviate the anxiety and dissatisfaction about usage fees. For example, detailed explanations of pricing plans and discount information are provided. Based on the emotion estimation data, a customized pricing plan is provided to alleviate the anxiety and dissatisfaction the user feels about usage fees. For example, a plan tailored to the user's preferences is proposed. This improves the accuracy of user authentication.

[0039] The usage fee management unit can use the generation AI to calculate usage fees based on usage time and data volume. The usage fee management unit, for example, enables centralized management of usage fees across different AI services through an AI wallet, adding a function that allows users to check all fees on a single platform. For example, a function for centralized management of usage fees across different AI services can be added to the AI ​​wallet, allowing users to check all fees on a single platform. The generation AI analyzes the usage fees of different AI services and builds a system for centralized management. For example, a dashboard that displays the integrated usage fees of each service can be provided. Users can centrally manage usage fees across different AI services through the AI ​​wallet. For example, a function for centralized management of usage history and payment history can be added. This allows for automated and accurate calculation of usage fees.

[0040] The user authentication unit can use the generation AI to analyze user authentication information and verify that the user is a legitimate user. The user authentication unit adds a function, for example, where the generation AI analyzes the user's payment history and provides the user with money-saving advice based on their past payment patterns. For example, the generation AI analyzes the user's payment history and provides money-saving advice based on their past payment patterns. Based on the user's payment history, the generation AI provides customized advice for saving money. For example, it suggests the timing and method for reducing usage frequency. The generation AI analyzes the payment history of usage fees and provides the user with money-saving advice in real time based on their past payment patterns. For example, it suggests alternative services when fees rise. This improves the accuracy of user authentication.

[0041] The usage fee management unit can use the generation AI to calculate usage fees based on usage time and data volume. For example, the usage fee management unit uses an emotion estimation function to analyze the emotions felt by the user when paying the usage fee, and the generation AI provides a payment interface that elicits positive emotions. For example, the emotion estimation function can be used to analyze the emotions felt by the user when paying the usage fee in real time, and a payment interface that elicits positive emotions can be provided. The unit analyzes the user's facial expressions and voice, and generates messages and guides that elicit positive emotions when paying. For example, encouraging messages and simple operation guides can be displayed. Based on the emotion estimation data, the unit analyzes the emotions felt by the user when paying the usage fee, and provides a customized interface that elicits positive emotions. For example, backgrounds and themes that match the user's preferences can be displayed. This allows for automated and accurate calculation of usage fees.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The AI ​​wallet system can also include a health management unit that collects the user's health data and adjusts the usage fee based on the user's health condition. For example, the system collects the user's heart rate and sleep data and offers a discount on the usage fee if the user's health condition is good. The health management unit can also analyze the user's exercise data and offer a discount on the usage fee if the user exercises more than a certain amount. The health management unit can also collect the user's dietary data and offer a discount on the usage fee if the user eats a balanced diet. This allows users to use AI services while maintaining their health.

[0044] The AI ​​wallet system can further include a hobby estimation unit that provides customized services based on the user's hobbies and interests. For example, it can analyze the user's music playback history and recommend music that the user likes. The hobby estimation unit can also analyze the user's reading history and recommend books that interest the user. Furthermore, the hobby estimation unit can analyze the user's movie viewing history and recommend movies that the user likes. This allows the user to receive services that match their hobbies and interests.

[0045] The AI ​​wallet system may further include a savings advice unit that analyzes a user's purchasing history and provides savings advice based on purchasing patterns. For example, the system may analyze a user's purchasing history and provide advice on reducing wasteful spending. The savings advice unit may also analyze a user's purchasing patterns and suggest cheaper alternatives. Furthermore, the savings advice unit may provide sale information for specific products based on the user's purchasing history. This allows users to use AI services while spending money efficiently.

[0046] The AI ​​wallet system can further include a learning advice unit that analyzes the user's learning history and suggests effective learning methods based on the user's learning patterns. For example, the learning advice unit can analyze the user's learning history and suggest an optimal learning schedule. The learning advice unit can also analyze the user's learning patterns and suggest effective learning methods. Furthermore, the learning advice unit can manage the user's learning progress based on the user's learning history and provide advice for achieving goals. This allows the user to use AI services while efficiently progressing with their studies.

[0047] The AI ​​wallet system may further include a driving advice unit that analyzes the user's driving data and provides advice to promote safe driving. For example, the driving advice unit may analyze the user's driving data and provide advice for safe driving. The driving advice unit may also analyze the user's driving patterns and provide specific advice to reduce dangerous driving behaviors. Furthermore, the driving advice unit may suggest driving methods to improve fuel efficiency based on the user's driving data. This allows the user to use AI services while driving safely and efficiently.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The AI ​​wallet connects users, service providers, and WAI providers. For example, a user can access multiple AI services by logging in to the AI ​​wallet once. Step 2: The Generation AI accesses the Generation AI through the AI ​​wallet. For example, the Generation AI uses a text generation AI (e.g., LLM) to analyze the user's authentication information. Step 3: The user authentication unit analyzes the authentication information of the user logging in to the AI ​​wallet and verifies that the user is legitimate. For example, the user authentication unit uses the generated AI to perform facial recognition or fingerprint authentication of the user. Step 4: The usage fee management unit calculates the usage fee for the generated AI via the AI ​​wallet, and the transaction is concluded between the user and the AI ​​provider. For example, the usage fee management unit uses the generated AI to calculate the usage fee based on the user's usage time and data volume.

[0050] (Example 2) The AI ​​wallet system according to an embodiment of the present invention connects users, service providers, and WAI providers. This system analyzes user authentication information via the generation AI and manages usage fees. This reduces the login effort required for users and makes it easier for AI service providers to manage usage fees.

[0051] An AI wallet system according to an embodiment includes an AI wallet, a generation AI, a user authentication unit, and a usage fee management unit. The AI ​​wallet connects users, service providers, and WAI providers. For example, a user can access multiple AI services by simply logging in to the AI ​​wallet once. The generation AI accesses the generation AI via the AI ​​wallet. For example, the generation AI uses a text generation AI (e.g., LLM) to analyze the user's authentication information. The user authentication unit analyzes the authentication information of a user logging in to the AI ​​wallet and verifies that the user is a legitimate user. For example, the user authentication unit uses the generation AI to perform facial recognition or fingerprint authentication of the user. The usage fee management unit calculates the usage fee for the generation AI via the AI ​​wallet and completes the process between the user and the AI ​​provider. For example, the usage fee management unit uses the generation AI to calculate the usage fee based on the user's usage time and data volume. This allows the AI ​​wallet system according to an embodiment to reduce the user's login effort and facilitate AI service provider usage fee management. For example, users can access multiple AI services with a single login, making it easier for AI service providers to manage usage fees.

[0052] The user authentication unit can use the generation AI to analyze user authentication information and confirm that the user is legitimate. For example, the user authentication unit adds a facial recognition function to the AI ​​wallet and performs facial authentication via a camera when the user logs in. The generation AI analyzes facial features and confirms that the user is legitimate. The user authentication unit can also integrate a fingerprint authentication function into the AI ​​wallet, allowing the user to log in using a fingerprint scanner. The generation AI analyzes the fingerprint data to enhance security. The user authentication unit also encrypts biometric authentication data, and the generation AI analyzes that data to authenticate the user. For example, facial recognition and fingerprint authentication can be combined to achieve double authentication. This improves the accuracy of user authentication.

[0053] The usage fee management unit can use the generation AI to calculate usage fees based on usage time and data volume. For example, the usage fee management unit adds a function where the AI ​​wallet learns the user's behavioral patterns and issues an alert if it detects an abnormal login attempt. For example, the AI ​​wallet learns the user's usual login times and locations and issues an alert if an abnormal login attempt is made. The generation AI analyzes the user's login history and issues an alert in real time when it detects an abnormal pattern. For example, if there are multiple login attempts in a short period of time. A function is added to automatically block abnormal login attempts based on the user's behavioral patterns. For example, if it detects a login from an unusual device. This makes usage fee calculations automatic and accurate.

[0054] The user authentication unit can use generation AI to analyze user authentication information and verify that the user is a legitimate user. For example, the user authentication unit uses an emotion estimation function to automatically generate an interface to reduce the stress and anxiety the user feels when logging in. For example, the emotion estimation function can be used to analyze the stress the user feels when logging in in real time and generate a relaxing interface. The user's facial expressions and voice can be analyzed to generate messages and guidance to reduce anxiety when logging in. For example, an encouraging message or simple operation guide can be displayed. Based on the emotion estimation data, a customized interface can be provided to reduce the stress the user feels when logging in. For example, a background or theme that matches the user's preferences can be displayed. This improves the accuracy of user authentication.

[0055] The usage fee management unit can use the generation AI to calculate usage fees based on usage time and data volume. For example, the usage fee management unit uses the AI ​​wallet to add a function that allows users to seamlessly share login information across multiple devices. For example, a cloud synchronization function can be added to the AI ​​wallet, allowing users to seamlessly share login information across different devices. The generation AI encrypts the user's login information and builds a system that securely shares it across multiple devices. For example, authentication between devices using QR codes. A function can be added that requests authentication from an existing device when a user logs in to the AI ​​wallet on a new device. For example, approval can be requested from a smartphone. This makes usage fee calculations automatic and accurate.

[0056] The user authentication unit can use the generation AI to analyze user authentication information and verify that the user is a legitimate user. For example, the user authentication unit adds a function to the AI ​​wallet that automatically converts data formats to facilitate data transfer between different AI services. For example, a data format conversion function can be added to the AI ​​wallet to automate data transfer between different AI services. A system can be built in which the generation AI analyzes the data formats of different AI services and automatically converts them into compatible formats. For example, format conversion of image data. When a user uses a different AI service, the AI ​​wallet automatically converts the data format, achieving seamless data transfer. For example, encoding conversion of text data. This improves the accuracy of user authentication.

[0057] The usage fee management unit can use the generation AI to calculate usage fees based on usage time and data volume. For example, the usage fee management unit uses an emotion estimation function to analyze the emotions felt by the user when logging in, and the generation AI provides a customized login screen to elicit positive emotions. For example, the emotion estimation function can be used to analyze the emotions felt by the user when logging in in real time, and a customized login screen to elicit positive emotions can be provided. The user's facial expressions and voice can be analyzed, and messages and guides can be generated to elicit positive emotions when logging in. For example, encouraging messages and simple operation guides can be displayed. Based on the emotion estimation data, the emotions felt by the user when logging in can be analyzed, and a customized interface can be provided to elicit positive emotions. For example, backgrounds and themes tailored to the user's preferences can be displayed. This allows for automated and accurate calculation of usage fees.

[0058] The user authentication unit can use the generation AI to analyze user authentication information and verify that the user is a legitimate user. The user authentication unit adds a function, for example, in which the generation AI analyzes the user's usage history and automatically proposes a plan to optimize usage fees. For example, the generation AI analyzes the user's usage history and automatically proposes the optimal usage plan. The generation AI analyzes the user's usage patterns and proposes a customized plan to optimize usage fees. For example, a discount plan for frequently used services. The generation AI proposes a plan in real time to optimize usage fees based on the user's usage history. For example, it presents the optimal plan based on usage time and data volume. This improves the accuracy of user authentication.

[0059] The usage fee management unit can use the generation AI to calculate usage fees based on usage time and data volume. The usage fee management unit adds a function that allows the AI ​​wallet to monitor usage fee fluctuations in real time and provide users with advice on how to reduce costs. For example, the AI ​​wallet monitors usage fee fluctuations in real time and provides users with advice on how to reduce costs. The generation AI analyzes usage fee fluctuations and provides users with specific advice on how to reduce costs. For example, it suggests the timing and method for reducing usage frequency. The AI ​​wallet monitors usage fee fluctuations in real time and issues alerts to users to reduce costs. For example, it notifies them when usage fees exceed a certain threshold. This makes usage fee calculations automated and accurate.

[0060] The user authentication unit can use the generation AI to analyze user authentication information and verify that the user is a legitimate user. For example, the user authentication unit uses an emotion estimation function to have the generation AI propose a customized pricing plan to alleviate the anxiety and dissatisfaction the user feels about usage fees. For example, the emotion estimation function is used to analyze the anxiety and dissatisfaction the user feels about usage fees in real time, and the generation AI proposes a customized pricing plan. The user's facial expressions and voice are analyzed to generate messages and guides to alleviate the anxiety and dissatisfaction about usage fees. For example, detailed explanations of pricing plans and discount information are provided. Based on the emotion estimation data, a customized pricing plan is provided to alleviate the anxiety and dissatisfaction the user feels about usage fees. For example, a plan tailored to the user's preferences is proposed. This improves the accuracy of user authentication.

[0061] The usage fee management unit can use the generation AI to calculate usage fees based on usage time and data volume. The usage fee management unit, for example, enables centralized management of usage fees across different AI services through an AI wallet, adding a function that allows users to check all fees on a single platform. For example, a function for centralized management of usage fees across different AI services can be added to the AI ​​wallet, allowing users to check all fees on a single platform. The generation AI analyzes the usage fees of different AI services and builds a system for centralized management. For example, a dashboard that displays the integrated usage fees of each service can be provided. Users can centrally manage usage fees across different AI services through the AI ​​wallet. For example, a function for centralized management of usage history and payment history can be added. This allows for automated and accurate calculation of usage fees.

[0062] The user authentication unit can use the generation AI to analyze user authentication information and verify that the user is a legitimate user. The user authentication unit adds a function, for example, where the generation AI analyzes the user's payment history and provides the user with money-saving advice based on their past payment patterns. For example, the generation AI analyzes the user's payment history and provides money-saving advice based on their past payment patterns. Based on the user's payment history, the generation AI provides customized advice for saving money. For example, it suggests the timing and method for reducing usage frequency. The generation AI analyzes the payment history of usage fees and provides the user with money-saving advice in real time based on their past payment patterns. For example, it suggests alternative services when fees rise. This improves the accuracy of user authentication.

[0063] The usage fee management unit can use the generation AI to calculate usage fees based on usage time and data volume. For example, the usage fee management unit uses an emotion estimation function to analyze the emotions felt by the user when paying the usage fee, and the generation AI provides a payment interface that elicits positive emotions. For example, the emotion estimation function can be used to analyze the emotions felt by the user when paying the usage fee in real time, and a payment interface that elicits positive emotions can be provided. The unit analyzes the user's facial expressions and voice, and generates messages and guides that elicit positive emotions when paying. For example, encouraging messages and simple operation guides can be displayed. Based on the emotion estimation data, the unit analyzes the emotions felt by the user when paying the usage fee, and provides a customized interface that elicits positive emotions. For example, backgrounds and themes that match the user's preferences can be displayed. This allows for automated and accurate calculation of usage fees.

[0064] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0065] The AI ​​wallet system can also include a health management unit that collects the user's health data and adjusts the usage fee based on the user's health condition. For example, the system collects the user's heart rate and sleep data and offers a discount on the usage fee if the user's health condition is good. The health management unit can also analyze the user's exercise data and offer a discount on the usage fee if the user exercises more than a certain amount. The health management unit can also collect the user's dietary data and offer a discount on the usage fee if the user eats a balanced diet. This allows users to use AI services while maintaining their health.

[0066] The AI ​​wallet system can further include a hobby estimation unit that provides customized services based on the user's hobbies and interests. For example, it can analyze the user's music playback history and recommend music that the user likes. The hobby estimation unit can also analyze the user's reading history and recommend books that interest the user. Furthermore, the hobby estimation unit can analyze the user's movie viewing history and recommend movies that the user likes. This allows the user to receive services that match their hobbies and interests.

[0067] The AI ​​wallet system may further include a relaxation unit that estimates the user's emotions and provides relaxation content to reduce stress based on the estimated emotions. For example, if the user is feeling stressed, relaxation music may be provided. The relaxation unit may also analyze the user's emotions and provide relaxing video content. Furthermore, the relaxation unit may provide a guided meditation to help users relax based on the user's emotions. This allows users to use AI services while reducing stress.

[0068] The AI ​​wallet system may further include a savings advice unit that analyzes a user's purchasing history and provides savings advice based on purchasing patterns. For example, the system may analyze a user's purchasing history and provide advice on reducing wasteful spending. The savings advice unit may also analyze a user's purchasing patterns and suggest cheaper alternatives. Furthermore, the savings advice unit may provide sale information for specific products based on the user's purchasing history. This allows users to use AI services while spending money efficiently.

[0069] The AI ​​wallet system can further include an exercise unit that estimates the user's emotions and provides a customized exercise plan based on the estimated emotions. For example, if the user is tired, it can suggest light stretching. The exercise unit can also analyze the user's emotions and suggest more intense exercise if the user has high energy. Furthermore, the exercise unit can provide a relaxing yoga plan based on the user's emotions. This allows the user to use the AI ​​service while doing exercises that match their emotions.

[0070] The AI ​​wallet system can further include a learning advice unit that analyzes the user's learning history and suggests effective learning methods based on the user's learning patterns. For example, the learning advice unit can analyze the user's learning history and suggest an optimal learning schedule. The learning advice unit can also analyze the user's learning patterns and suggest effective learning methods. Furthermore, the learning advice unit can manage the user's learning progress based on the user's learning history and provide advice for achieving goals. This allows the user to use AI services while efficiently progressing with their studies.

[0071] The AI ​​wallet system can further include a travel planning unit that estimates the user's emotions and provides customized travel plans based on the estimated emotions. For example, if the user is looking for relaxation, the travel planning unit can suggest a quiet resort. The travel planning unit can also analyze the user's emotions and suggest an active travel plan if the user is looking for adventure. Furthermore, the travel planning unit can provide a family travel plan based on the user's emotions. This allows users to use AI services while planning a trip that suits their emotions.

[0072] The AI ​​wallet system can further include a meal planning unit that estimates the user's emotions and provides a customized meal plan based on the estimated emotions. For example, if the user is feeling stressed, it can suggest a relaxing meal. The meal planning unit can also analyze the user's emotions and suggest a nutritious meal if the user needs energy. Furthermore, the meal planning unit can provide a meal plan that supports dieting based on the user's emotions. This allows users to use AI services while enjoying meals that match their emotions.

[0073] The AI ​​wallet system may further include a driving advice unit that analyzes the user's driving data and provides advice to promote safe driving. For example, the driving advice unit may analyze the user's driving data and provide advice for safe driving. The driving advice unit may also analyze the user's driving patterns and provide specific advice to reduce dangerous driving behaviors. Furthermore, the driving advice unit may suggest driving methods to improve fuel efficiency based on the user's driving data. This allows the user to use AI services while driving safely and efficiently.

[0074] The AI ​​wallet system can further include a feedback unit that estimates the user's emotions and provides customized feedback based on the estimated emotions. For example, if the user is dissatisfied, the feedback unit can suggest areas for improvement. The feedback unit can also analyze the user's emotions and provide positive feedback if the user is highly satisfied. Furthermore, the feedback unit can suggest areas for improving the service based on the user's emotions. This allows users to use the AI ​​service while receiving feedback tailored to their emotions.

[0075] The processing flow of the second embodiment will be briefly explained below.

[0076] Step 1: The AI ​​wallet connects users, service providers, and WAI providers. For example, a user can access multiple AI services by logging in to the AI ​​wallet once. Step 2: The Generation AI accesses the Generation AI through the AI ​​wallet. For example, the Generation AI uses a text generation AI (e.g., LLM) to analyze the user's authentication information. Step 3: The user authentication unit analyzes the authentication information of the user logging in to the AI ​​wallet and verifies that the user is legitimate. For example, the user authentication unit uses the generated AI to perform facial recognition or fingerprint authentication of the user. Step 4: The usage fee management unit calculates the usage fee for the generated AI via the AI ​​wallet, and the transaction is concluded between the user and the AI ​​provider. For example, the usage fee management unit uses the generated AI to calculate the usage fee based on the user's usage time and data volume.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0081] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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).

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0096] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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).

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] In the robot 414, 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 robot 414 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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."

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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. [Explanation of symbols]

[0144] 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. An AI wallet that connects users, service providers, and WAI providers, A generation AI that accesses the generation AI via the AI ​​wallet; a user authentication unit that analyzes authentication information of a user logging in to the AI ​​wallet and verifies that the user is a legitimate user; A usage fee management unit that calculates the usage fee for the generated AI via the AI ​​wallet and completes the process between the user and the AI ​​provider. A system characterized by:

2. The user authentication unit The generated AI is used to analyze user authentication information and verify that the user is legitimate. The system of claim 1 .

3. The usage fee management unit The generation AI is used to calculate usage fees based on usage time and data volume. The system of claim 1 .

4. The user authentication unit The generated AI is used to analyze user authentication information and verify that the user is legitimate. The system of claim 1 .

5. The usage fee management unit The generation AI is used to calculate usage fees based on usage time and data volume. The system of claim 1 .

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