System

Abstract

To provide a system that enables users to use a plurality of services with a single ID.SOLUTION: A system according to the embodiment includes a management unit, a recording unit, and a distribution unit. The management unit manages user IDs. The recording unit records the usage status of each service based on the IDs managed by the management unit. The distribution unit distributes income based on the usage status recorded by the recording unit.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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology requires users to sign separate contracts when using multiple services, which can be a hassle for users.

[0005] The system according to the embodiment aims to enable a user to use multiple services with one ID. [Means for solving the problem]

[0006] The system according to the embodiment includes a management unit, a recording unit, and a distribution unit. The management unit manages user IDs. The recording unit records the usage status of each service based on the IDs managed by the management unit. The distribution unit distributes income based on the usage status recorded by the recording unit. Effect of the Invention

[0007] The system according to the embodiment can enable a user to use a plurality of services with one ID. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The generative AI service platform according to the embodiment of the present invention is a system in which a user can use multiple generative AI services in a subscription format with one ID. This system allows a user to access multiple generative AI services by registering with the platform and obtaining one ID. For example, services such as text generation, image generation, voice recognition, and translation are included. When a user uses a specific service, the platform records the usage status and distributes income to each service provider according to the usage status. For example, if a user uses a text generation service a lot, a lot of income is distributed to the service provider. This mechanism allows the service provider to earn income according to the usage status. For example, a user registers with the platform and obtains one ID. Using this ID, the user can access multiple generative AI services provided on the platform. For example, services such as text generation, image generation, voice recognition, and translation are included. Next, when a user uses a specific service, the platform records the usage status. For example, if a user uses a text generation service, the usage time and the amount of generated text are recorded. Furthermore, the platform distributes income to each service provider according to the usage status. For example, if a user uses a text generation service a lot, a lot of income is distributed to the service provider. This mechanism allows service providers to earn revenue according to usage. This platform allows users to easily use multiple AI generation services, eliminating the need to sign individual contracts for each service. In addition, service providers can earn revenue according to usage, which creates an incentive to improve the quality of their services. Log data generated when each service is used is used to record usage. In addition, a revenue distribution algorithm is adopted that distributes revenue based on the time and frequency of use. To protect privacy, log data is anonymized and encrypted before storage. An API is provided as a procedure for service providers to join the platform, making it easy to integrate services.As a condition for service providers to join the platform, they are required to meet certain quality standards. This allows the generative AI service platform to make it easy for users to use multiple generative AI services, eliminating the need to enter into separate contracts for each service. In addition, service providers can earn revenue based on usage, creating an incentive to improve the quality of their services.

[0029] The generation AI service platform according to the embodiment includes a management unit, a recording unit, and a distribution unit. The management unit manages the ID of a user. The ID of a user includes, for example, a username, a password, an email address, and the like, but is not limited to such examples. For example, the management unit generates and manages an ID when a user registers to the platform. The management unit also handles when a user updates or deletes an ID. For example, the management unit registers a new password when a user changes a password, and deletes an old password. The recording unit records the usage status of each service based on the ID managed by the management unit. The usage status includes, for example, usage time, usage frequency, and the amount of generated content, but is not limited to such examples. For example, when a user uses a text generation service, the recording unit records the usage time and the amount of generated text. The recording unit also saves the usage status as log data. For example, the recording unit generates and saves log data every time a user uses a service. The distribution unit distributes income based on the usage status recorded by the recording unit. For example, an algorithm based on usage time and usage frequency is used for income distribution, but is not limited to such examples. For example, if a user uses a text generation service a lot, the distribution unit distributes a large amount of income to the service provider. The distribution unit also notifies the service provider of the result of the income distribution. For example, the distribution unit notifies the service provider of the result of the income distribution via email or a dashboard. This allows the generation AI service platform according to the embodiment to efficiently manage user IDs, record usage status, and distribute income.

[0030] The management unit manages the user's ID. The user's ID includes, but is not limited to, a user name, a password, an email address, and the like. For example, the management unit generates and manages an ID when a user registers with the platform. Specifically, when a user inputs necessary information into a new registration form, the management unit generates a unique user ID based on the information and stores it in a database. At this time, the password is hashed, and encryption technology is used to ensure security. The management unit also handles when a user updates or deletes an ID. For example, when a user changes a password, the management unit hashes the new password and stores it in the database, and deletes the hash of the old password. Furthermore, the management unit also handles when a user changes an email address or deletes an account, and ensures that these operations are performed appropriately. The management unit plays an important role in authenticating and authorizing users, and when a user accesses the platform, it checks whether the input information is correct and grants appropriate access rights. This allows the management unit to efficiently and safely manage users' IDs and maintain the security of the entire platform.

[0031] The recording unit records the usage status of each service based on the ID managed by the management unit. The usage status includes, for example, usage time, usage frequency, amount of generated content, etc., but is not limited to such examples. For example, when a user uses a text generation service, the recording unit records the usage time and the amount of generated text. Specifically, the time when the user starts the service and the time when the service ends are recorded as timestamps, and the usage time is calculated from the difference. The amount of generated content is measured using metrics such as the number of characters and file size. The recording unit collects these data in real time and stores them in a database. Furthermore, the recording unit stores the usage status as log data. For example, the recording unit generates and stores log data every time a user uses a service. The log data includes a user ID, a service ID, a usage start time, a usage end time, and an amount of generated content, and these data are used for later analysis and reporting. The recording unit regularly backs up these data to ensure the consistency and availability of the data. In addition, the recording unit provides a data visualization and report generation function, allowing an administrator or a service provider to easily grasp the usage status. This enables the recording unit to record users' usage in detail, which can be used to operate and improve the platform.

[0032] The distribution unit distributes the income based on the usage status recorded by the recording unit. For example, an algorithm based on the usage time and the usage frequency is used for the income distribution, but is not limited to such an example. Specifically, the distribution unit analyzes the usage status data of each user and calculates an index that is a standard for income distribution. For example, a user who uses the text generation service a lot is distributed income according to the usage time and the amount of generated content. The distribution unit calculates the income distribution amount for each service provider based on these indexes. A distribution rule or algorithm set in advance is applied to the calculation, thereby realizing a fair and transparent income distribution. In addition, the distribution unit notifies the service provider of the result of the income distribution. For example, the distribution unit notifies the service provider of the result of the income distribution via email or dashboard. The notification includes details of the distribution amount, the calculation method, an overview of the usage status, etc., so that the service provider can accurately grasp the content of the income distribution. Furthermore, the distribution unit saves the history of the income distribution so that it can be referred to later. As a result, the distribution unit can ensure the transparency and reliability of the income distribution and build a relationship of trust with the service provider. The Distribution Department automates the revenue distribution process and distributes revenue efficiently and accurately, thereby facilitating the smooth operation of the entire platform.

[0033] The recording unit can anonymize the log data, encrypt it, and store it. The anonymization of the log data includes, for example, but is not limited to, deletion of personal information and masking of data. The recording unit, for example, deletes the user's ID and personal information, and stores only the anonymized data. The recording unit also masks the data so that a specific individual cannot be identified. For example, the recording unit replaces the user's name and email address with a random character string. For example, encryption algorithms such as AES (Advanced Encryption Standard) and RSA (Rivest-Shamir-Adleman) are used for encrypting the log data, but are not limited to such examples. The recording unit encrypts and stores the log data using, for example, AES. The recording unit also performs key management for decrypting the encrypted data. For example, the recording unit safely stores the encryption key, and decrypts it as necessary. This allows the recording unit to strengthen privacy protection of the log data.

[0034] The distribution unit may include an algorithm for distributing income based on usage time or usage frequency. The measurement of usage time includes, but is not limited to, login time and active time. For example, the distribution unit measures the time that the user is logged in to the service, and distributes income based on the time. Also, the distribution unit measures the time that the user actively uses the service, and distributes income based on the time. The measurement of usage frequency includes, but is not limited to, the number of accesses and the number of usage sessions. For example, the distribution unit measures the number of times that the user accesses the service, and distributes income based on the number of accesses. Also, the distribution unit measures the number of sessions in which the user uses the service, and distributes income based on the number of sessions. This allows the distribution unit to distribute income fairly according to the usage situation.

[0035] The generation AI service platform includes an API providing unit that provides an API for a service provider to participate in the platform. The API providing unit provides an API for the service provider to participate in the platform. The API includes, for example, an endpoint and an authentication method, but is not limited to such examples. The API providing unit provides, for example, an endpoint for the service provider to integrate a service into the platform. In addition, the API providing unit provides an authentication method for the service provider to use the API. For example, the API providing unit authenticates the service provider using OAuth (Open Authorization). This allows the service provider to easily participate in the platform.

[0036] The generative AI service platform includes a quality standard checking unit that requires the service provider to meet certain quality standards. The quality standard checking unit requires the service provider to meet certain quality standards. The quality standards include, for example, service availability and response time, but are not limited to such examples. For example, the quality standard checking unit checks the availability of the service provided by the service provider. In addition, the quality standard checking unit checks the response time of the service provided by the service provider. For example, the quality standard checking unit requires the availability of the service to be 99.9% or more. In addition, the quality standard checking unit requires the response time of the service to be within 1 second. This ensures the quality of the service on the generative AI service platform.

[0037] The management unit can analyze the user's past usage history and select an appropriate ID management method. For example, the management unit automatically suggests an ID management method that the user has frequently used in the past. The management unit can also predict and suggest an optimal ID management method for a specific time period from the user's past usage history. Furthermore, the management unit can analyze the user's past usage patterns and select the most efficient ID management method. The collection of usage history includes, for example, access logs and usage patterns, but is not limited to such examples. For example, the management unit collects and analyzes the user's access logs. The management unit also analyzes the user's usage patterns and selects the optimal ID management method. This allows the management unit to perform optimal ID management based on the past usage history.

[0038] The management unit can perform filtering based on the user's current usage status or field of interest during ID management. The management unit preferentially displays related ID management options based on, for example, the service currently being used by the user. The management unit can also suggest an optimal ID management method based on the user's field of interest. Furthermore, the management unit can analyze the user's current usage status in real time and provide an appropriate ID management method. The collection of usage status includes, for example, current activity and frequency of use, but is not limited to such examples. The management unit monitors and analyzes, for example, the user's current activity. The management unit also analyzes the user's frequency of use and provides an optimal ID management method. The identification of the field of interest includes, for example, the user's search history and browsing history, but is not limited to such examples. The management unit, for example, analyzes the user's search history and identifies the field of interest. The management unit also analyzes the user's browsing history and identifies the field of interest. This allows the management unit to perform ID management according to the user's current situation and interests.

[0039] The management unit can prioritize management of highly relevant IDs based on the geographical location information of the user during ID management. For example, when the user is in a specific area, the management unit prioritizes management of IDs related to that area. The management unit can also propose an optimal ID management method based on the geographical location information of the user. Furthermore, when the user is moving, the management unit can also manage related IDs based on the current location. Collection of geographical location information includes, but is not limited to, GPS data and IP addresses, for example. The management unit collects and analyzes, for example, GPS data of the user. Furthermore, the management unit analyzes the IP address of the user and identifies the geographical location information. This allows the management unit to perform optimal ID management based on the geographical location information.

[0040] The management unit can analyze the user's social media activity and manage related IDs during ID management. For example, the management unit automatically suggests related IDs from the user's social media activity. The management unit can also analyze the user's social media activity and provide an optimal ID management method. Furthermore, the management unit can preferentially manage related IDs based on the user's social media activity. Collection of social media activity includes, for example, the content of posts and the number of likes, but is not limited to such examples. For example, the management unit analyzes the content of posts by the user and identifies related IDs. Also, the management unit analyzes the number of likes by the user and identifies related IDs. This allows the management unit to perform ID management based on social media activity.

[0041] The recording unit can adjust the level of detail of the recording based on the importance of the usage situation at the time of recording. For example, the recording unit records detailed log data for important usage situations. In addition, the recording unit can also record simplified log data for general usage situations. Furthermore, the recording unit can dynamically adjust the level of detail of the recording according to the importance of the usage situation. The evaluation of the importance includes, for example, the frequency of use and the priority of the user, but is not limited to such examples. For example, the recording unit records detailed log data for situations with high usage frequency. In addition, the recording unit adjusts the level of detail of the recording based on the priority of the user. Thereby, the recording unit can perform detailed recording according to the importance of the usage situation.

[0042] The recording unit can apply different recording algorithms depending on the category of the service used at the time of recording. For example, for a text generation service, the recording unit applies an algorithm that records the amount and quality of the generated text. Furthermore, for an image generation service, the recording unit can also apply an algorithm that records the resolution and size of the generated image. Furthermore, for a voice recognition service, the recording unit can also apply an algorithm that records the accuracy and speed of the recognized voice. The classification of categories includes, for example, classification of the type and function of the service, but is not limited to such examples. For example, the recording unit selects a recording algorithm based on the type of service. Furthermore, the recording unit selects a recording algorithm based on the function of the service. This allows the recording unit to perform optimal recording according to the service category.

[0043] The recording unit can determine the priority of recording based on the time of provision of the service used at the time of recording. For example, when a new service is provided, the recording unit preferentially records the usage status of the service. Furthermore, the recording unit can determine the priority of recording for existing services based on the frequency of use. Furthermore, the recording unit can dynamically adjust the priority of recording according to the time of provision of the service used. The evaluation of the time of provision includes, for example, the release date and update frequency of the service, but is not limited to such examples. For example, the recording unit determines the priority of recording based on the release date of the service. Furthermore, the recording unit adjusts the priority of recording based on the update frequency of the service. This allows the recording unit to perform optimal recording according to the time of provision.

[0044] The recording unit can adjust the order of recording based on the relevance of the services used at the time of recording. For example, the recording unit preferentially records the usage status of a highly relevant service. Also, the recording unit can record the usage status of a less relevant service later. Furthermore, the recording unit can dynamically adjust the order of recording according to the relevance of the services used. The evaluation of the relevance includes, for example, the interaction between services and the usage pattern of the user, but is not limited to such examples. For example, the recording unit analyzes the interaction between services and evaluates the relevance. Also, the recording unit analyzes the usage pattern of the user and evaluates the relevance. This allows the recording unit to perform optimal recording according to the relevance.

[0045] The distribution unit can adjust the level of detail of the distribution based on the importance of the usage situation at the time of distribution. For example, the distribution unit performs detailed income distribution for important usage situations. In addition, the distribution unit can also perform simplified income distribution for general usage situations. Furthermore, the distribution unit can dynamically adjust the level of detail of the distribution according to the importance of the usage situation. The evaluation of the importance includes, for example, the frequency of use and the priority of the user, but is not limited to such examples. For example, the distribution unit performs detailed income distribution for situations with high frequency of use. In addition, the distribution unit adjusts the level of detail of the distribution based on the priority of the user. In this way, the distribution unit can perform detailed distribution according to the importance of the usage situation.

[0046] The distribution unit can apply different distribution algorithms depending on the category of the service used at the time of distribution. For example, for a text generation service, the distribution unit applies an algorithm that distributes income based on the quantity and quality of generated text. Furthermore, for an image generation service, the distribution unit can also apply an algorithm that distributes income based on the resolution and size of a generated image. Furthermore, for a voice recognition service, the distribution unit can also apply an algorithm that distributes income based on the accuracy and speed of a recognized voice. The classification of categories includes, for example, classification of the type and function of a service, but is not limited to such examples. For example, the distribution unit selects a distribution algorithm based on the type of service. Furthermore, the distribution unit selects a distribution algorithm based on the function of the service. This allows the distribution unit to perform optimal distribution according to the service category.

[0047] The distribution unit can determine the priority of distribution based on the time of provision of the service used at the time of distribution. For example, when a new service is provided, the distribution unit preferentially distributes the income based on the usage status of the service. The distribution unit can also distribute income to existing services based on the frequency of use. Furthermore, the distribution unit can dynamically adjust the priority of income distribution according to the time of provision of the service used. The evaluation of the time of provision includes, for example, the release date and update frequency of the service, but is not limited to such examples. The distribution unit determines the priority of income distribution based on, for example, the release date of the service. The distribution unit also adjusts the priority of income distribution based on the update frequency of the service. This allows the distribution unit to perform optimal income distribution according to the time of provision.

[0048] The distribution unit can adjust the order of distribution based on the relevance of the services used at the time of distribution. For example, the distribution unit preferentially distributes income based on the usage status of highly related services. Also, the distribution unit can distribute income after the usage status of less related services. Furthermore, the distribution unit can dynamically adjust the order of income distribution according to the relevance of the services used. The evaluation of the relevance includes, for example, interactions between services and usage patterns of users, but is not limited to such examples. For example, the distribution unit analyzes interactions between services and evaluates the relevance. Also, the distribution unit analyzes usage patterns of users and evaluates the relevance. This allows the distribution unit to perform optimal income distribution according to the relevance.

[0049] When providing an API, the API providing unit can select an optimal API providing method by analyzing the past usage history of the service provider. For example, the API providing unit automatically suggests an API providing method that the service provider has frequently used in the past. The API providing unit can also predict and suggest an optimal API providing method for a specific time period from the past usage history of the service provider. Furthermore, the API providing unit can analyze the past usage patterns of the service provider and select the most efficient API providing method. The collection of usage history includes, for example, access logs and usage patterns, but is not limited to such examples. For example, the API providing unit collects and analyzes the access logs of the service provider. The API providing unit also analyzes the usage pattern of the service provider and selects the optimal API providing method. This allows the API providing unit to provide an optimal API based on the past usage history.

[0050] The API providing unit can select an appropriate API providing method based on the geographical location information of the service provider when providing the API. For example, when the service provider is in a specific area, the API providing unit preferentially provides an API providing method related to the area. The API providing unit can also suggest an optimal API providing method based on the geographical location information of the service provider. Furthermore, when the service provider is moving, the API providing unit can also provide a related API providing method based on the current location. The collection of geographical location information includes, for example, GPS data and IP addresses, but is not limited to such examples. For example, the API providing unit collects and analyzes GPS data of the service provider. Furthermore, the API providing unit analyzes the IP address of the service provider to identify the geographical location information. This allows the API providing unit to provide an optimal API based on the geographical location information.

[0051] The quality criterion checking unit can select an optimal checking method by analyzing the past quality history of the service provider when checking the quality criterion. For example, the quality criterion checking unit automatically suggests a quality criterion checking method that the service provider has frequently used in the past. The quality criterion checking unit can also predict and suggest an optimal quality criterion checking method for a specific time period from the past quality history of the service provider. Furthermore, the quality criterion checking unit can analyze the past quality patterns of the service provider and select the most efficient quality criterion checking method. The collection of the quality history includes, for example, past quality evaluations and feedback, but is not limited to such examples. For example, the quality criterion checking unit collects and analyzes the past quality evaluations of the service provider. The quality criterion checking unit can also analyze the feedback of the service provider and select an optimal quality criterion checking method. This allows the quality criterion checking unit to perform optimal quality criterion checking based on the past quality history.

[0052] The quality standard confirmation unit can select an appropriate confirmation method based on the geographical location information of the service provider when confirming the quality standard. For example, when the service provider is in a specific area, the quality standard confirmation unit preferentially provides a quality standard confirmation method related to the area. The quality standard confirmation unit can also propose an optimal quality standard confirmation method based on the geographical location information of the service provider. Furthermore, when the service provider is moving, the quality standard confirmation unit can also provide a related quality standard confirmation method based on the current location. The collection of geographical location information includes, for example, GPS data and IP addresses, but is not limited to such examples. For example, the quality standard confirmation unit collects and analyzes GPS data of the service provider. Furthermore, the quality standard confirmation unit analyzes the IP address of the service provider and identifies the geographical location information. This allows the quality standard confirmation unit to perform optimal quality standard confirmation based on the geographical location information.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The generative AI service platform can analyze a user's past usage history and select the optimal service delivery method. For example, it can automatically suggest services that the user has frequently used in the past. It can also predict and suggest the optimal service for a specific time period. Furthermore, it can analyze a user's past usage patterns and select the most efficient service delivery method. This makes it possible to provide optimal services based on the user's past usage history.

[0055] The generative AI service platform can filter services based on the user's current usage or areas of interest. For example, related services can be preferentially displayed based on the services the user is currently using. The platform can also suggest optimal services based on the user's areas of interest. Furthermore, the platform can analyze the user's current usage in real time and provide appropriate services. This makes it possible to provide services according to the user's current situation and interests.

[0056] The generative AI service platform can provide highly relevant services preferentially based on the user's geographic location information. For example, if the user is in a specific area, services related to that area are provided preferentially. In addition, the optimal service can be suggested based on the user's geographic location information. Furthermore, if the user is on the move, relevant services can be provided based on the user's current location. This makes it possible to provide optimal services based on geographic location information.

[0057] The generative AI service platform can analyze the social media activities of a user and provide related services. For example, related services can be automatically suggested based on the user's social media activities. It can also analyze the user's social media activities and provide an optimal service provision method. Furthermore, it can provide related services preferentially based on the user's social media activities. This makes it possible to provide services based on social media activities.

[0058] The process flow of the first embodiment will be briefly described below.

[0059] Step 1: The management unit manages the user's ID. The user's ID includes a username, password, email address, etc. The management unit generates and manages the ID when the user registers on the platform. It also handles when the user updates or deletes the ID. For example, when changing a password, the management unit registers a new password and deletes the old password. Step 2: The recording unit records the usage status of each service based on the ID managed by the management unit. The usage status includes the usage time, usage frequency, amount of generated content, etc. The recording unit generates and stores log data every time a user uses a service. Step 3: The distribution unit distributes the revenue based on the usage status recorded by the recording unit. An algorithm based on the usage time and frequency is used for the revenue distribution. The distribution unit notifies the service provider of the result of the revenue distribution. For example, the distribution unit notifies the service provider via email or a dashboard.

[0060] (Example 2) The generative AI service platform according to the embodiment of the present invention is a system in which a user can use multiple generative AI services in a subscription format with one ID. This system allows a user to access multiple generative AI services by registering with the platform and obtaining one ID. For example, services such as text generation, image generation, voice recognition, and translation are included. When a user uses a specific service, the platform records the usage status and distributes income to each service provider according to the usage status. For example, if a user uses a text generation service a lot, a lot of income is distributed to the service provider. This mechanism allows the service provider to earn income according to the usage status. For example, a user registers with the platform and obtains one ID. Using this ID, the user can access multiple generative AI services provided on the platform. For example, services such as text generation, image generation, voice recognition, and translation are included. Next, when a user uses a specific service, the platform records the usage status. For example, if a user uses a text generation service, the usage time and the amount of generated text are recorded. Furthermore, the platform distributes income to each service provider according to the usage status. For example, if a user uses a text generation service a lot, a lot of income is distributed to the service provider. This mechanism allows service providers to earn revenue according to usage. This platform allows users to easily use multiple AI generation services, eliminating the need to sign individual contracts for each service. In addition, service providers can earn revenue according to usage, which creates an incentive to improve the quality of their services. Log data generated when each service is used is used to record usage. In addition, a revenue distribution algorithm is adopted that distributes revenue based on the time and frequency of use. To protect privacy, log data is anonymized and encrypted before storage. An API is provided as a procedure for service providers to join the platform, making it easy to integrate services.As a condition for service providers to join the platform, they are required to meet certain quality standards. This allows the generative AI service platform to make it easy for users to use multiple generative AI services, eliminating the need to enter into separate contracts for each service. In addition, service providers can earn revenue based on usage, creating an incentive to improve the quality of their services.

[0061] The generation AI service platform according to the embodiment includes a management unit, a recording unit, and a distribution unit. The management unit manages the ID of a user. The ID of a user includes, for example, a username, a password, an email address, and the like, but is not limited to such examples. For example, the management unit generates and manages an ID when a user registers to the platform. The management unit also handles when a user updates or deletes an ID. For example, the management unit registers a new password when a user changes a password, and deletes an old password. The recording unit records the usage status of each service based on the ID managed by the management unit. The usage status includes, for example, usage time, usage frequency, and the amount of generated content, but is not limited to such examples. For example, when a user uses a text generation service, the recording unit records the usage time and the amount of generated text. The recording unit also saves the usage status as log data. For example, the recording unit generates and saves log data every time a user uses a service. The distribution unit distributes income based on the usage status recorded by the recording unit. For example, an algorithm based on usage time and usage frequency is used for income distribution, but is not limited to such examples. For example, if a user uses a text generation service a lot, the distribution unit distributes a large amount of income to the service provider. The distribution unit also notifies the service provider of the result of the income distribution. For example, the distribution unit notifies the service provider of the result of the income distribution via email or a dashboard. This allows the generation AI service platform according to the embodiment to efficiently manage user IDs, record usage status, and distribute income.

[0062] The management unit manages the user's ID. The user's ID includes, but is not limited to, a user name, a password, an email address, and the like. For example, the management unit generates and manages an ID when a user registers with the platform. Specifically, when a user inputs necessary information into a new registration form, the management unit generates a unique user ID based on the information and stores it in a database. At this time, the password is hashed, and encryption technology is used to ensure security. The management unit also handles when a user updates or deletes an ID. For example, when a user changes a password, the management unit hashes the new password and stores it in the database, and deletes the hash of the old password. Furthermore, the management unit also handles when a user changes an email address or deletes an account, and ensures that these operations are performed appropriately. The management unit plays an important role in authenticating and authorizing users, and when a user accesses the platform, it checks whether the input information is correct and grants appropriate access rights. This allows the management unit to efficiently and safely manage users' IDs and maintain the security of the entire platform.

[0063] The recording unit records the usage status of each service based on the ID managed by the management unit. The usage status includes, for example, usage time, usage frequency, amount of generated content, etc., but is not limited to such examples. For example, when a user uses a text generation service, the recording unit records the usage time and the amount of generated text. Specifically, the time when the user starts the service and the time when the service ends are recorded as timestamps, and the usage time is calculated from the difference. The amount of generated content is measured using metrics such as the number of characters and file size. The recording unit collects these data in real time and stores them in a database. Furthermore, the recording unit stores the usage status as log data. For example, the recording unit generates and stores log data every time a user uses a service. The log data includes a user ID, a service ID, a usage start time, a usage end time, and an amount of generated content, and these data are used for later analysis and reporting. The recording unit regularly backs up these data to ensure the consistency and availability of the data. In addition, the recording unit provides a data visualization and report generation function, allowing an administrator or a service provider to easily grasp the usage status. This enables the recording unit to record users' usage in detail, which can be used to operate and improve the platform.

[0064] The distribution unit distributes the income based on the usage status recorded by the recording unit. For example, an algorithm based on the usage time and the usage frequency is used for the income distribution, but is not limited to such an example. Specifically, the distribution unit analyzes the usage status data of each user and calculates an index that is a standard for income distribution. For example, a user who uses the text generation service a lot is distributed income according to the usage time and the amount of generated content. The distribution unit calculates the income distribution amount for each service provider based on these indexes. A distribution rule or algorithm set in advance is applied to the calculation, thereby realizing a fair and transparent income distribution. In addition, the distribution unit notifies the service provider of the result of the income distribution. For example, the distribution unit notifies the service provider of the result of the income distribution via email or dashboard. The notification includes details of the distribution amount, the calculation method, an overview of the usage status, etc., so that the service provider can accurately grasp the content of the income distribution. Furthermore, the distribution unit saves the history of the income distribution so that it can be referred to later. As a result, the distribution unit can ensure the transparency and reliability of the income distribution and build a relationship of trust with the service provider. The Distribution Department automates the revenue distribution process and distributes revenue efficiently and accurately, thereby facilitating the smooth operation of the entire platform.

[0065] The recording unit can anonymize the log data, encrypt it, and store it. The anonymization of the log data includes, for example, but is not limited to, deletion of personal information and masking of data. The recording unit, for example, deletes the user's ID and personal information, and stores only the anonymized data. The recording unit also masks the data so that a specific individual cannot be identified. For example, the recording unit replaces the user's name and email address with a random character string. For example, encryption algorithms such as AES (Advanced Encryption Standard) and RSA (Rivest-Shamir-Adleman) are used for encrypting the log data, but are not limited to such examples. The recording unit encrypts and stores the log data using, for example, AES. The recording unit also performs key management for decrypting the encrypted data. For example, the recording unit safely stores the encryption key, and decrypts it as necessary. This allows the recording unit to strengthen privacy protection of the log data.

[0066] The distribution unit may include an algorithm for distributing income based on usage time or usage frequency. The measurement of usage time includes, but is not limited to, login time and active time. For example, the distribution unit measures the time that the user is logged in to the service, and distributes income based on the time. Also, the distribution unit measures the time that the user actively uses the service, and distributes income based on the time. The measurement of usage frequency includes, but is not limited to, the number of accesses and the number of usage sessions. For example, the distribution unit measures the number of times that the user accesses the service, and distributes income based on the number of accesses. Also, the distribution unit measures the number of sessions in which the user uses the service, and distributes income based on the number of sessions. This allows the distribution unit to distribute income fairly according to the usage situation.

[0067] The generation AI service platform includes an API providing unit that provides an API for a service provider to participate in the platform. The API providing unit provides an API for the service provider to participate in the platform. The API includes, for example, an endpoint and an authentication method, but is not limited to such examples. The API providing unit provides, for example, an endpoint for the service provider to integrate a service into the platform. In addition, the API providing unit provides an authentication method for the service provider to use the API. For example, the API providing unit authenticates the service provider using OAuth (Open Authorization). This allows the service provider to easily participate in the platform.

[0068] The generative AI service platform includes a quality standard checking unit that requires the service provider to meet certain quality standards. The quality standard checking unit requires the service provider to meet certain quality standards. The quality standards include, for example, service availability and response time, but are not limited to such examples. For example, the quality standard checking unit checks the availability of the service provided by the service provider. In addition, the quality standard checking unit checks the response time of the service provided by the service provider. For example, the quality standard checking unit requires the availability of the service to be 99.9% or more. In addition, the quality standard checking unit requires the response time of the service to be within 1 second. This ensures the quality of the service on the generative AI service platform.

[0069] The management unit can estimate the user's emotion and adjust the ID management method based on the estimated user's emotion. For example, when the user is stressed, the management unit provides a simple ID management interface to simplify the operation. In addition, when the user is relaxed, the management unit can provide detailed ID management options and suggest customizable settings. Furthermore, when the user is in a hurry, the management unit can prioritize voice input to enable quick ID management. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. This allows the management unit to perform flexible ID management according to the user's emotion.

[0070] The management unit can analyze the user's past usage history and select an appropriate ID management method. For example, the management unit automatically suggests an ID management method that the user has frequently used in the past. The management unit can also predict and suggest an optimal ID management method for a specific time period from the user's past usage history. Furthermore, the management unit can analyze the user's past usage patterns and select the most efficient ID management method. The collection of usage history includes, for example, access logs and usage patterns, but is not limited to such examples. For example, the management unit collects and analyzes the user's access logs. The management unit also analyzes the user's usage patterns and selects the optimal ID management method. This allows the management unit to perform optimal ID management based on the past usage history.

[0071] The management unit can perform filtering based on the user's current usage status or field of interest during ID management. The management unit preferentially displays related ID management options based on, for example, the service currently being used by the user. The management unit can also suggest an optimal ID management method based on the user's field of interest. Furthermore, the management unit can analyze the user's current usage status in real time and provide an appropriate ID management method. The collection of usage status includes, for example, current activity and frequency of use, but is not limited to such examples. The management unit monitors and analyzes, for example, the user's current activity. The management unit also analyzes the user's frequency of use and provides an optimal ID management method. The identification of the field of interest includes, for example, the user's search history and browsing history, but is not limited to such examples. The management unit, for example, analyzes the user's search history and identifies the field of interest. The management unit also analyzes the user's browsing history and identifies the field of interest. This allows the management unit to perform ID management according to the user's current situation and interests.

[0072] The management unit can estimate the user's emotion and determine the priority of ID management based on the estimated user's emotion. For example, when the user is nervous, the management unit prioritizes important ID management tasks. Also, when the user is relaxed, the management unit can postpone detailed ID management tasks. Furthermore, when the user is in a hurry, the management unit can prioritize ID management tasks that need to be processed quickly. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. This allows the management unit to perform ID management with a priority according to the user's emotion.

[0073] The management unit can prioritize management of highly relevant IDs based on the geographical location information of the user during ID management. For example, when the user is in a specific area, the management unit prioritizes management of IDs related to that area. The management unit can also propose an optimal ID management method based on the geographical location information of the user. Furthermore, when the user is moving, the management unit can also manage related IDs based on the current location. Collection of geographical location information includes, but is not limited to, GPS data and IP addresses, for example. The management unit collects and analyzes, for example, GPS data of the user. Furthermore, the management unit analyzes the IP address of the user and identifies the geographical location information. This allows the management unit to perform optimal ID management based on the geographical location information.

[0074] The management unit can analyze the user's social media activity and manage related IDs during ID management. For example, the management unit automatically suggests related IDs from the user's social media activity. The management unit can also analyze the user's social media activity and provide an optimal ID management method. Furthermore, the management unit can preferentially manage related IDs based on the user's social media activity. Collection of social media activity includes, for example, the content of posts and the number of likes, but is not limited to such examples. For example, the management unit analyzes the content of posts by the user and identifies related IDs. Also, the management unit analyzes the number of likes by the user and identifies related IDs. This allows the management unit to perform ID management based on social media activity.

[0075] The recording unit can estimate the user's emotion and adjust the method of recording the log data based on the estimated user's emotion. For example, when the user is stressed, the recording unit provides a simple log data recording method and simplifies the operation. In addition, when the user is relaxed, the recording unit can provide detailed log data recording options and suggest customizable settings. Furthermore, when the user is in a hurry, the recording unit can prioritize voice input and enable quick recording of log data. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. This allows the recording unit to flexibly record log data according to the user's emotion.

[0076] The recording unit can adjust the level of detail of the recording based on the importance of the usage situation at the time of recording. For example, the recording unit records detailed log data for important usage situations. In addition, the recording unit can also record simplified log data for general usage situations. Furthermore, the recording unit can dynamically adjust the level of detail of the recording according to the importance of the usage situation. The evaluation of the importance includes, for example, the frequency of use and the priority of the user, but is not limited to such examples. For example, the recording unit records detailed log data for situations with high usage frequency. In addition, the recording unit adjusts the level of detail of the recording based on the priority of the user. Thereby, the recording unit can perform detailed recording according to the importance of the usage situation.

[0077] The recording unit can apply different recording algorithms depending on the category of the service used at the time of recording. For example, for a text generation service, the recording unit applies an algorithm that records the amount and quality of the generated text. Furthermore, for an image generation service, the recording unit can also apply an algorithm that records the resolution and size of the generated image. Furthermore, for a voice recognition service, the recording unit can also apply an algorithm that records the accuracy and speed of the recognized voice. The classification of categories includes, for example, classification of the type and function of the service, but is not limited to such examples. For example, the recording unit selects a recording algorithm based on the type of service. Furthermore, the recording unit selects a recording algorithm based on the function of the service. This allows the recording unit to perform optimal recording according to the service category.

[0078] The recording unit can estimate the user's emotion and adjust the length of the recording based on the estimated user's emotion. For example, when the user is nervous, the recording unit records short log data that is to the point. Also, when the user is relaxed, the recording unit can record longer log data that includes detailed explanations. Furthermore, when the user is in a hurry, the recording unit can adjust the length of the log data so that the recording is completed quickly. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. This allows the recording unit to perform flexible recording according to the user's emotion.

[0079] The recording unit can determine the priority of recording based on the time of provision of the service used at the time of recording. For example, when a new service is provided, the recording unit preferentially records the usage status of the service. Furthermore, the recording unit can determine the priority of recording for existing services based on the frequency of use. Furthermore, the recording unit can dynamically adjust the priority of recording according to the time of provision of the service used. The evaluation of the time of provision includes, for example, the release date and update frequency of the service, but is not limited to such examples. For example, the recording unit determines the priority of recording based on the release date of the service. Furthermore, the recording unit adjusts the priority of recording based on the update frequency of the service. This allows the recording unit to perform optimal recording according to the time of provision.

[0080] The recording unit can adjust the order of recording based on the relevance of the services used at the time of recording. For example, the recording unit preferentially records the usage status of a highly relevant service. Also, the recording unit can record the usage status of a less relevant service later. Furthermore, the recording unit can dynamically adjust the order of recording according to the relevance of the services used. The evaluation of the relevance includes, for example, the interaction between services and the usage pattern of the user, but is not limited to such examples. For example, the recording unit analyzes the interaction between services and evaluates the relevance. Also, the recording unit analyzes the usage pattern of the user and evaluates the relevance. This allows the recording unit to perform optimal recording according to the relevance.

[0081] The distribution unit can estimate the user's emotions and adjust the method of income distribution based on the estimated user's emotions. For example, when the user is stressed, the distribution unit provides a simple income distribution method to simplify the operation. In addition, when the user is relaxed, the distribution unit can provide detailed income distribution options and suggest customizable settings. Furthermore, when the user is in a hurry, the distribution unit can adjust so that income distribution is completed quickly. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This allows the distribution unit to perform flexible income distribution according to the user's emotions.

[0082] The distribution unit can adjust the level of detail of the distribution based on the importance of the usage situation at the time of distribution. For example, the distribution unit performs detailed income distribution for important usage situations. In addition, the distribution unit can also perform simplified income distribution for general usage situations. Furthermore, the distribution unit can dynamically adjust the level of detail of the distribution according to the importance of the usage situation. The evaluation of the importance includes, for example, the frequency of use and the priority of the user, but is not limited to such examples. For example, the distribution unit performs detailed income distribution for situations with high frequency of use. In addition, the distribution unit adjusts the level of detail of the distribution based on the priority of the user. In this way, the distribution unit can perform detailed distribution according to the importance of the usage situation.

[0083] The distribution unit can apply different distribution algorithms depending on the category of the service used at the time of distribution. For example, for a text generation service, the distribution unit applies an algorithm that distributes income based on the quantity and quality of generated text. Furthermore, for an image generation service, the distribution unit can also apply an algorithm that distributes income based on the resolution and size of a generated image. Furthermore, for a voice recognition service, the distribution unit can also apply an algorithm that distributes income based on the accuracy and speed of a recognized voice. The classification of categories includes, for example, classification of the type and function of a service, but is not limited to such examples. For example, the distribution unit selects a distribution algorithm based on the type of service. Furthermore, the distribution unit selects a distribution algorithm based on the function of the service. This allows the distribution unit to perform optimal distribution according to the service category.

[0084] The distribution unit can estimate the user's emotion and determine the priority of distribution based on the estimated user's emotion. For example, when the user is nervous, the distribution unit prioritizes important income distribution tasks. Also, when the user is relaxed, the distribution unit can postpone detailed income distribution tasks. Furthermore, when the user is in a hurry, the distribution unit can prioritize income distribution tasks that need to be processed quickly. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. This allows the distribution unit to perform income distribution in a priority order according to the user's emotion.

[0085] The distribution unit can determine the priority of distribution based on the time of provision of the service used at the time of distribution. For example, when a new service is provided, the distribution unit preferentially distributes the income based on the usage status of the service. The distribution unit can also distribute income to existing services based on the frequency of use. Furthermore, the distribution unit can dynamically adjust the priority of income distribution according to the time of provision of the service used. The evaluation of the time of provision includes, for example, the release date and update frequency of the service, but is not limited to such examples. The distribution unit determines the priority of income distribution based on, for example, the release date of the service. The distribution unit also adjusts the priority of income distribution based on the update frequency of the service. This allows the distribution unit to perform optimal income distribution according to the time of provision.

[0086] The distribution unit can adjust the order of distribution based on the relevance of the services used at the time of distribution. For example, the distribution unit preferentially distributes income based on the usage status of highly related services. Also, the distribution unit can distribute income after the usage status of less related services. Furthermore, the distribution unit can dynamically adjust the order of income distribution according to the relevance of the services used. The evaluation of the relevance includes, for example, interactions between services and usage patterns of users, but is not limited to such examples. For example, the distribution unit analyzes interactions between services and evaluates the relevance. Also, the distribution unit analyzes usage patterns of users and evaluates the relevance. This allows the distribution unit to perform optimal income distribution according to the relevance.

[0087] The API providing unit can estimate the user's emotion and adjust the API provision method based on the estimated user's emotion. For example, when the user is stressed, the API providing unit provides a simple API provision interface to simplify the operation. In addition, when the user is relaxed, the API providing unit can provide detailed API provision options and suggest customizable settings. Furthermore, when the user is in a hurry, the API providing unit can adjust so that the API provision is completed quickly. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. This allows the API providing unit to flexibly provide an API according to the user's emotion.

[0088] When providing an API, the API providing unit can select an optimal API providing method by analyzing the past usage history of the service provider. For example, the API providing unit automatically suggests an API providing method that the service provider has frequently used in the past. The API providing unit can also predict and suggest an optimal API providing method for a specific time period from the past usage history of the service provider. Furthermore, the API providing unit can analyze the past usage patterns of the service provider and select the most efficient API providing method. The collection of usage history includes, for example, access logs and usage patterns, but is not limited to such examples. For example, the API providing unit collects and analyzes the access logs of the service provider. The API providing unit also analyzes the usage pattern of the service provider and selects the optimal API providing method. This allows the API providing unit to provide an optimal API based on the past usage history.

[0089] The API providing unit can estimate the user's emotion and determine the priority of API provision based on the estimated user's emotion. For example, when the user is nervous, the API providing unit prioritizes important API provision tasks. Also, when the user is relaxed, the API providing unit can postpone detailed API provision tasks. Furthermore, when the user is in a hurry, the API providing unit can prioritize API provision tasks that need to be processed quickly. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. This allows the API providing unit to provide APIs in a priority order according to the user's emotion.

[0090] The API providing unit can select an appropriate API providing method based on the geographical location information of the service provider when providing the API. For example, when the service provider is in a specific area, the API providing unit preferentially provides an API providing method related to the area. The API providing unit can also suggest an optimal API providing method based on the geographical location information of the service provider. Furthermore, when the service provider is moving, the API providing unit can also provide a related API providing method based on the current location. The collection of geographical location information includes, for example, GPS data and IP addresses, but is not limited to such examples. For example, the API providing unit collects and analyzes GPS data of the service provider. Furthermore, the API providing unit analyzes the IP address of the service provider to identify the geographical location information. This allows the API providing unit to provide an optimal API based on the geographical location information.

[0091] The quality criterion checking unit can estimate the user's emotion and adjust the quality criterion checking method based on the estimated user's emotion. For example, when the user is stressed, the quality criterion checking unit provides a simple quality criterion checking method to simplify the operation. In addition, when the user is relaxed, the quality criterion checking unit can provide detailed quality criterion checking options and suggest customizable settings. Furthermore, when the user is in a hurry, the quality criterion checking unit can adjust so that the quality criterion checking is completed quickly. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. This allows the quality criterion checking unit to perform flexible quality criterion checking according to the user's emotion.

[0092] The quality criterion checking unit can select an optimal checking method by analyzing the past quality history of the service provider when checking the quality criterion. For example, the quality criterion checking unit automatically suggests a quality criterion checking method that the service provider has frequently used in the past. The quality criterion checking unit can also predict and suggest an optimal quality criterion checking method for a specific time period from the past quality history of the service provider. Furthermore, the quality criterion checking unit can analyze the past quality patterns of the service provider and select the most efficient quality criterion checking method. The collection of the quality history includes, for example, past quality evaluations and feedback, but is not limited to such examples. For example, the quality criterion checking unit collects and analyzes the past quality evaluations of the service provider. The quality criterion checking unit can also analyze the feedback of the service provider and select an optimal quality criterion checking method. This allows the quality criterion checking unit to perform optimal quality criterion checking based on the past quality history.

[0093] The quality standard checking unit can estimate the user's emotion and determine the priority order of the quality standard based on the estimated emotion of the user. For example, when the user is nervous, the quality standard checking unit prioritizes important quality standard checking tasks. Also, when the user is relaxed, the quality standard checking unit can postpone detailed quality standard checking tasks. Furthermore, when the user is in a hurry, the quality standard checking unit can prioritize quality standard checking tasks that need to be processed quickly. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. This allows the quality standard checking unit to perform quality standard checking in a priority order according to the user's emotion.

[0094] The quality standard confirmation unit can select an appropriate confirmation method based on the geographical location information of the service provider when confirming the quality standard. For example, when the service provider is in a specific area, the quality standard confirmation unit preferentially provides a quality standard confirmation method related to the area. The quality standard confirmation unit can also propose an optimal quality standard confirmation method based on the geographical location information of the service provider. Furthermore, when the service provider is moving, the quality standard confirmation unit can also provide a related quality standard confirmation method based on the current location. The collection of geographical location information includes, for example, GPS data and IP addresses, but is not limited to such examples. For example, the quality standard confirmation unit collects and analyzes GPS data of the service provider. Furthermore, the quality standard confirmation unit analyzes the IP address of the service provider and identifies the geographical location information. This allows the quality standard confirmation unit to perform optimal quality standard confirmation based on the geographical location information.

[0095] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0096] The generative AI service platform can estimate the user's emotions and adjust the way the service is provided based on the estimated emotions. For example, if the user is feeling stressed, a simple interface can be provided to simplify the operation. If the user is feeling relaxed, detailed options can be provided and customizable settings can be suggested. Furthermore, if the user is in a hurry, shortcuts can be provided to enable quick use of the service. This makes it possible to provide flexible services according to the user's emotions.

[0097] The generative AI service platform can analyze a user's past usage history and select the optimal service delivery method. For example, it can automatically suggest services that the user has frequently used in the past. It can also predict and suggest the optimal service for a specific time period. Furthermore, it can analyze a user's past usage patterns and select the most efficient service delivery method. This makes it possible to provide optimal services based on the user's past usage history.

[0098] The generative AI service platform can filter services based on the user's current usage or areas of interest. For example, related services can be preferentially displayed based on the services the user is currently using. The platform can also suggest optimal services based on the user's areas of interest. Furthermore, the platform can analyze the user's current usage in real time and provide appropriate services. This makes it possible to provide services according to the user's current situation and interests.

[0099] The generative AI service platform can provide highly relevant services preferentially based on the user's geographic location information. For example, if the user is in a specific area, services related to that area are provided preferentially. In addition, the optimal service can be suggested based on the user's geographic location information. Furthermore, if the user is on the move, relevant services can be provided based on the user's current location. This makes it possible to provide optimal services based on geographic location information.

[0100] The generative AI service platform can analyze the social media activities of a user and provide related services. For example, related services can be automatically suggested based on the user's social media activities. It can also analyze the user's social media activities and provide an optimal service provision method. Furthermore, it can provide related services preferentially based on the user's social media activities. This makes it possible to provide services based on social media activities.

[0101] The generative AI service platform can estimate the user's emotions and determine the priority of services based on the estimated emotions. For example, if the user is nervous, important services can be provided with priority. Also, if the user is relaxed, detailed services can be postponed. Furthermore, if the user is in a hurry, services that require quick processing can be prioritized. This makes it possible to provide services with priority according to the user's emotions.

[0102] The generative AI service platform can estimate the user's emotions and adjust the way the service is provided based on the estimated emotions. For example, if the user is feeling stressed, a simple interface can be provided to simplify the operation. If the user is feeling relaxed, detailed options can be provided and customizable settings can be suggested. Furthermore, if the user is in a hurry, shortcuts can be provided to enable quick use of the service. This makes it possible to provide flexible services according to the user's emotions.

[0103] The generative AI service platform can estimate the user's emotions and adjust the way the service is provided based on the estimated emotions. For example, if the user is feeling stressed, a simple interface can be provided to simplify the operation. If the user is feeling relaxed, detailed options can be provided and customizable settings can be suggested. Furthermore, if the user is in a hurry, shortcuts can be provided to enable quick use of the service. This makes it possible to provide flexible services according to the user's emotions.

[0104] The generative AI service platform can estimate the user's emotions and adjust the way the service is provided based on the estimated emotions. For example, if the user is feeling stressed, a simple interface can be provided to simplify the operation. If the user is feeling relaxed, detailed options can be provided and customizable settings can be suggested. Furthermore, if the user is in a hurry, shortcuts can be provided to enable quick use of the service. This makes it possible to provide flexible services according to the user's emotions.

[0105] The generative AI service platform can estimate the user's emotions and adjust the way the service is provided based on the estimated emotions. For example, if the user is feeling stressed, a simple interface can be provided to simplify the operation. If the user is feeling relaxed, detailed options can be provided and customizable settings can be suggested. Furthermore, if the user is in a hurry, shortcuts can be provided to enable quick use of the service. This makes it possible to provide flexible services according to the user's emotions.

[0106] The process flow of the second embodiment will be briefly described below.

[0107] Step 1: The management unit manages the user's ID. The user's ID includes a username, password, email address, etc. The management unit generates and manages the ID when the user registers on the platform. It also handles when the user updates or deletes the ID. For example, when changing a password, the management unit registers a new password and deletes the old password. Step 2: The recording unit records the usage status of each service based on the ID managed by the management unit. The usage status includes the usage time, usage frequency, amount of generated content, etc. The recording unit generates and stores log data every time a user uses a service. Step 3: The distribution unit distributes the revenue based on the usage status recorded by the recording unit. An algorithm based on the usage time and frequency is used for the revenue distribution. The distribution unit notifies the service provider of the result of the revenue distribution. For example, the distribution unit notifies the service provider via email or a dashboard.

[0108] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0109] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0110] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0111] Each of the multiple elements including the above-mentioned management unit, recording unit, distribution unit, API providing unit, and quality standard checking unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the management unit manages the user's ID by the control unit 46A of the smart device 14, and is also realized by the specific processing unit 290 of the data processing device 12. The recording unit records the usage status of each service by the control unit 46A of the smart device 14, and is also realized by the specific processing unit 290 of the data processing device 12. The distribution unit distributes income by the specific processing unit 290 of the data processing device 12, and is also realized by the control unit 46A of the smart device 14. The API providing unit provides an API to a service provider by the specific processing unit 290 of the data processing device 12, and is also realized by the control unit 46A of the smart device 14. The quality standard checking unit checks the quality standard of the service by the specific processing unit 290 of the data processing device 12, and is also realized by the control unit 46A of the smart device 14. The correspondence between each part and the device or control part is not limited to the above-mentioned example, and various modifications are possible.

[0112] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0113] 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.

[0114] 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, the RAM 30, and the 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.

[0115] 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.

[0116] 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 the voice according to instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0118] 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 performed in a secure state.

[0119] Fig. 4 shows an example of 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.

[0120] 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.

[0121] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0122] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0123] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0124] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0125] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0126] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, 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, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0127] Each of the multiple elements including the above-mentioned management unit, recording unit, distribution unit, API providing unit, and quality standard checking unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the management unit manages the user's ID by the control unit 46A of the smart glasses 214, and is also realized by the specific processing unit 290 of the data processing device 12. The recording unit records the usage status of each service by the control unit 46A of the smart glasses 214, and is also realized by the specific processing unit 290 of the data processing device 12. The distribution unit distributes income by the specific processing unit 290 of the data processing device 12, and is also realized by the control unit 46A of the smart glasses 214. The API providing unit provides an API to a service provider by the specific processing unit 290 of the data processing device 12, and is also realized by the control unit 46A of the smart glasses 214. The quality standard checking unit checks the quality standard of the service by the specific processing unit 290 of the data processing device 12, and is also realized by the control unit 46A of the smart glasses 214. The correspondence between each part and the device or control part is not limited to the above-mentioned example, and various modifications are possible.

[0128] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0129] 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.

[0130] 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, the RAM 30, and the 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.

[0131] 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.

[0132] 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 the voice according to instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0134] 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 performed in a secure state.

[0135] 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.

[0136] 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.

[0137] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0138] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific 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 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.

[0139] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0140] 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 voice indicating a user input for 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.

[0141] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0142] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0143] Each of the multiple elements including the above-mentioned management unit, recording unit, distribution unit, API providing unit, and quality standard checking unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the management unit manages the user's ID by the control unit 46A of the headset type terminal 314, and is also realized by the specific processing unit 290 of the data processing device 12. The recording unit records the usage status of each service by the control unit 46A of the headset type terminal 314, and is also realized by the specific processing unit 290 of the data processing device 12. The distribution unit distributes income by the specific processing unit 290 of the data processing device 12, and is also realized by the control unit 46A of the headset type terminal 314. The API providing unit provides API to the service provider by the specific processing unit 290 of the data processing device 12, and is also realized by the control unit 46A of the headset type terminal 314. The quality standard confirmation unit confirms the quality standard of the service by, for example, the specific processing unit 290 of the data processing device 12, and is also realized by the control unit 46A of the headset type terminal 314. The correspondence between each unit and the device or control unit is not limited to the above example, and various modifications are possible.

[0144] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0145] 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.

[0146] 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, the RAM 30, and the 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.

[0147] 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.

[0148] 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 the voice according to instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0150] 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 performed in a secure state.

[0151] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0152] 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.

[0153] 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.

[0154] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0155] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 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.

[0156] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0157] 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 a voice indicating a user input for 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.

[0158] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0159] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, 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.

[0160] Each of the multiple elements including the above-mentioned management unit, recording unit, distribution unit, API providing unit, and quality standard checking unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the management unit manages the ID of the user by the control unit 46A of the robot 414, and is also realized by the specific processing unit 290 of the data processing device 12. The recording unit records the usage status of each service by the control unit 46A of the robot 414, and is also realized by the specific processing unit 290 of the data processing device 12. The distribution unit distributes income by the specific processing unit 290 of the data processing device 12, and is also realized by the control unit 46A of the robot 414. The API providing unit provides an API to a service provider by the specific processing unit 290 of the data processing device 12, and is also realized by the control unit 46A of the robot 414. The quality standard checking unit checks the quality standard of the service by the specific processing unit 290 of the data processing device 12, and is also realized by the control unit 46A of the robot 414. The correspondence between each part and the device or control part is not limited to the above-mentioned example, and various modifications are possible.

[0161] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0162] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0163] These emotions are distributed in the three o'clock direction of emotion map 400 and usually 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.

[0164] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0165] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0166] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is 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 the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0167] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0168] 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.

[0169] 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 Universal Serial Bus (USB) 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.

[0170] In addition, 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 upon request from the data processing device 12.

[0171] 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.

[0172] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0173] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0174] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0175] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0176] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0177] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0178] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0179] (Appendix 1) A management unit that manages user IDs; a recording unit that records the usage status of each service based on the ID managed by the management unit; a distribution unit that distributes income based on the usage status recorded by the recording unit; Equipped A system characterized by: (Appendix 2) The recording unit is Log data is anonymized, encrypted and stored 2. The system of claim 1, (Appendix 3) The distribution unit includes: Contains algorithms that distribute revenue based on duration or frequency of use 2. The system of claim 1, (Appendix 4) Equipped with an API provider that provides APIs for service providers to participate in the platform 2. The system of claim 1, (Appendix 5) Equipped with a quality standard confirmation unit that requires service providers to meet certain quality standards 2. The system of claim 1, (Appendix 6) The management unit Inferring user emotions and adjusting ID management methods based on the inferred user emotions 2. The system of claim 1, (Appendix 7) The management unit Analyze users' past usage history and select the appropriate ID management method 2. The system of claim 1, (Appendix 8) The management unit During identity management, filtering is performed based on the user's current usage or interests. 2. The system of claim 1, (Appendix 9) The management unit Estimate user sentiment and determine ID management priorities based on the estimated user sentiment 2. The system of claim 1, (Appendix 10) The management unit When managing IDs, prioritize management of IDs that are highly relevant based on the user's geographic location information 2. The system of claim 1, (Appendix 11) The management unit When managing IDs, analyze users' social media activities and manage related IDs 2. The system of claim 1, (Appendix 12) The recording unit is To estimate a user's emotion and adjust a method of recording log data based on the estimated user's emotion. 2. The system of claim 1, (Appendix 13) The recording unit is When recording, adjust the detail level of the recording based on the importance of the usage 2. The system of claim 1, (Appendix 14) The recording unit is When recording, different recording algorithms are applied depending on the category of the service used 2. The system of claim 1, (Appendix 15) The recording unit is Inferring a user's emotion and adjusting the length of the recording based on the inferred user emotion 2. The system of claim 1, (Appendix 16) The recording unit is When recording, prioritize recording based on when the service is being provided 2. The system of claim 1, (Appendix 17) The recording unit is When recording, adjust the order of recordings based on the relevance of the service used 2. The system of claim 1, (Appendix 18) The distribution unit includes: Inferring user sentiment and adjusting revenue sharing method based on the estimated user sentiment 2. The system of claim 1, (Appendix 19) The distribution unit includes: During distribution, adjust distribution granularity based on the importance of usage 2. The system of claim 1, (Appendix 20) The distribution unit includes: During distribution, different distribution algorithms are applied depending on the category of the service used. 2. The system of claim 1, (Appendix 21) The distribution unit includes: Estimate the user's emotions and determine distribution priorities based on the estimated user emotions. 2. The system of claim 1, (Appendix 22) The distribution unit includes: At the time of distribution, the distribution priority is determined based on the timing of the provision of the service used. 2. The system of claim 1, (Appendix 23) The distribution unit includes: During distribution, adjust the distribution order based on the relevance of the services used 2. The system of claim 1, (Appendix 24) The API provider is Infer user sentiment and adjust API delivery based on the inferred user sentiment 2. The system of claim 1, (Appendix 25) The API providing unit is When providing an API, analyze the service provider's past usage history to select the optimal API provision method. 2. The system of claim 1, (Appendix 26) The API providing unit is Infer user sentiment and prioritize API provision based on the inferred user sentiment 2. The system of claim 1, (Appendix 27) The API providing unit is When providing APIs, select the appropriate API delivery method based on the geographic location information of the service provider. 2. The system of claim 1, (Appendix 28) The quality standard confirmation unit Inferring user sentiment and adjusting the method of checking quality standards based on the estimated user sentiment 2. The system of claim 1, (Appendix 29) The quality standard confirmation unit When checking quality standards, analyze the service provider's past quality history and select the most appropriate checking method. 2. The system of claim 1, (Appendix 30) The quality standard confirmation unit Estimating user sentiment and prioritizing quality criteria based on the estimated user sentiment 2. The system of claim 1, (Appendix 31) The quality standard confirmation unit When checking quality standards, select an appropriate checking method based on the geographic location information of the service provider. 2. The system of claim 1, [Explanation of symbols]

[0180] 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 management unit that manages user IDs; a recording unit that records the usage status of each service based on the ID managed by the management unit; a distribution unit that distributes income based on the usage status recorded by the recording unit; Equipped A system characterized by:

2. The recording unit is Anonymize and store encrypted log data 2. The system of claim 1 .

3. The distribution unit includes: Contains algorithms that distribute revenue based on duration or frequency of use 2. The system of claim 1 .

4. Equipped with an API provider that provides APIs for service providers to participate in the platform 2. The system of claim 1 .

5. Equipped with a quality standard confirmation unit that requires service providers to meet certain quality standards 2. The system of claim 1 .

6. The management unit Inferring user emotions and adjusting ID management methods based on the inferred user emotions 2. The system of claim 1 .

7. The management unit Analyze users' past usage history and select the appropriate ID management method 2. The system of claim 1 .

8. The management unit During identity management, filtering is performed based on the user's current usage or interests.

2. The system of claim 1 .

9. The management unit Estimate user sentiment and determine ID management priorities based on the estimated user sentiment 2. The system of claim 1 .

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