System

Abstract

An object of a system according to an embodiment is to integrate and efficiently perform processes from provision of an AI application to operation support, data-linking, and provision of a prompt.SOLUTION: A system according to an embodiment includes an application market providing unit, a generated-data platform unit, a AI linkage unit, and a prompt providing unit. The application market providing unit provides an application. The generation AI base unit supports the operation of the application provided by the application market providing unit. The data-linking unit links AI to the application supported by the generated-data platform unit. The prompt provider provides a prompt from the technician based on the data coordinated by the data coordinator.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that the provision of AI apps, their operational support, data integration, and prompt provision are not integrated, making it difficult to use them efficiently.

[0005] The system of the embodiment aims to efficiently integrate and perform everything from providing AI apps to operational support, data integration, and prompt provision. [Means for solving the problem]

[0006] The system according to the embodiment includes an app market providing unit, a generation AI platform unit, a data linking unit, and a prompt providing unit. The app market providing unit provides apps. The generation AI platform unit supports the operation of apps provided by the app market providing unit. The data linking unit links data to apps supported by the generation AI platform unit. The prompt providing unit provides prompts from engineers based on the data linked by the data linking unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently integrate and perform everything from providing AI applications to operational support, data integration, and prompt provision. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The AI-dedicated app market system according to an embodiment of the present invention utilizes generative AI and in-house AI platforms in the background, with data integration and prompts provided by engineers around the world. This allows users to easily use AI apps and dramatically improves AI learning opportunities.

[0029] An AI-dedicated app market system according to an embodiment includes an app market provider, a generation AI platform unit, a data linking unit, and a prompt providing unit. The app market provider provides apps to users. For example, if a user is looking for a specific AI app, they can search for and download it within the app market. The generation AI platform unit supports the operation of the app. For example, the generation AI analyzes user input data and provides optimal results. An in-house AI platform supports the operation of the app and achieves stable performance. The data linking unit links data to the app. For example, based on prompts provided by an engineer, the generation AI analyzes user input data and suggests appropriate actions. The prompt providing unit provides prompts from the engineer. For example, based on prompts provided by the engineer, the generation AI analyzes user input data and suggests appropriate actions. This allows the AI-dedicated app market system according to an embodiment to easily use AI apps and dramatically improve AI learning opportunities.

[0030] The app market providing unit allows the generation AI to individually recommend the most suitable app based on the user's past download history. For example, the app market providing unit analyzes the user's past download history, and the generation AI recommends the most suitable app based on that history. For example, it suggests new apps taking into account the categories and usage frequency of previously downloaded apps. The app market providing unit also allows the generation AI to recommend similar apps based on the user's download history. For example, it prioritizes displaying apps with the same category or functions as previously downloaded apps. The app market providing unit also analyzes the user's past download history, and the generation AI predicts and recommends new app trends based on that history. For example, it suggests new popular apps in the same category. This improves convenience by recommending the most suitable apps to users.

[0031] The app market providing unit can collect user feedback in real time, and the generation AI can dynamically update app rankings based on that feedback. For example, the app market providing unit adds a function that allows users to provide feedback after using an app, and the generation AI analyzes that feedback to update the app rankings. For example, apps with a lot of positive feedback are displayed at the top. The app market providing unit also collects user feedback in real time, and the generation AI dynamically updates the app rankings based on that data. For example, the content of the feedback and evaluation scores are analyzed to adjust the rankings. The app market providing unit also builds a system in which the generation AI updates app rankings in real time based on user feedback. For example, the feedback trends are analyzed to adjust the rankings. In this way, the reliability of the rankings is improved by dynamically updating app rankings based on user feedback.

[0032] The app market providing unit allows users to upload AI apps they have created and share them with other users. For example, the app market providing unit adds a function that allows users to upload AI apps they have created to the app market and share them with other users. For example, users can publish their own AI apps and make them available for other users to download. The app market providing unit also adds a function that allows users to upload their own AI apps to the app market and builds a system for sharing them with other users. For example, it creates a community where users can evaluate their own AI apps. The app market providing unit also adds a function that allows users to upload their own AI apps to the app market and share them with other users. For example, it provides a platform where users can sell their own AI apps. This is expected to revitalize the community as users share their own AI apps.

[0033] The app market providing unit can provide submarkets specialized for different industries or uses, allowing users to easily find more specialized apps. For example, the app market providing unit provides submarkets specialized for different industries or uses within the app market, allowing users to easily find specialized apps. For example, categories such as medical, education, and entertainment are provided. The app market providing unit also provides submarkets, building a system that allows users to easily find apps specialized for specific industries or uses. For example, specialized apps are collected in each submarket. The app market providing unit also provides submarkets specialized for different industries or uses within the app market, allowing users to easily find specialized apps. For example, a function for recommending apps related to each submarket is added. This makes it easier for users to find specialized apps.

[0034] The generation AI platform unit can refer to similar data from the past when analyzing user input data and provide more accurate results. For example, when the generation AI analyzes user input data, the generation AI platform unit refers to similar data from the past and provides more accurate results. For example, it improves the predictive model based on past data. The generation AI platform unit also builds a system in which the generation AI refers to similar data from the past when analyzing user input data. For example, it provides optimal results based on past data. The generation AI platform unit also refers to similar data from the past when analyzing user input data and provides more accurate results. For example, it corrects errors based on past data. In this way, by referring to similar data from the past, the accuracy of the analysis results is improved.

[0035] The generative AI platform department can monitor the operation of an app and automatically make corrections if an abnormality is detected. For example, the generative AI platform department adds a function where an in-house AI platform monitors the operation of an app and automatically makes corrections if an abnormality is detected. For example, it builds a system that automatically makes corrections when an error occurs. The generative AI platform department also monitors the operation of an app and adds a function where an in-house AI platform automatically makes corrections if an abnormality is detected. For example, it develops an algorithm that automatically makes corrections when an abnormality is detected. The generative AI platform department also monitors the operation of an app and adds a function where an in-house AI platform monitors the operation of an app and automatically makes corrections if an abnormality is detected. For example, it builds a system that automatically makes corrections when an abnormality is detected. This automatically corrects abnormalities in the app's operation, improving the stability of the system.

[0036] The generative AI platform unit can integrate information from different data sources when analyzing user input data to provide more multifaceted results. For example, when the generative AI analyzes user input data, the generative AI platform unit integrates information from different data sources to provide more multifaceted results. For example, it performs analysis based on multiple data sources. The generative AI platform unit also builds a system in which the generative AI integrates information from different data sources when analyzing user input data. For example, it provides optimal results based on different data sources. The generative AI platform unit also integrates information from different data sources when the generative AI analyzes user input data to provide more multifaceted results. For example, it corrects errors based on different data sources. This allows for a multifaceted perspective on the analysis results by integrating information from different data sources.

[0037] The generative AI platform unit can dynamically allocate cloud resources to optimize the operation of an app. For example, the generative AI platform unit adds a function to dynamically allocate cloud resources so that the in-house AI platform can optimize the operation of an app. For example, it builds a system that dynamically allocates resources according to resource usage. The generative AI platform unit also adds a function to dynamically allocate cloud resources so that the in-house AI platform can optimize the operation of an app. For example, it develops an algorithm that dynamically allocates resources according to resource usage. The generative AI platform unit also adds a function to dynamically allocate cloud resources so that the in-house AI platform can optimize the operation of an app. For example, it builds a system that dynamically allocates resources according to resource usage. In this way, the operation of the app is optimized by dynamically allocating cloud resources.

[0038] The prompt providing unit enables the generation AI to automatically evaluate prompts provided by engineers and select the most appropriate prompt. The prompt providing unit adds, for example, a function whereby the generation AI automatically evaluates prompts provided by engineers and selects the most appropriate prompt. For example, the evaluation is based on the quality and relevance of the prompt. The prompt providing unit also builds a system whereby the generation AI automatically evaluates prompts provided by engineers and selects the most appropriate prompt. For example, the evaluation is based on the effectiveness and scope of application of the prompt. The prompt providing unit also adds a function whereby the generation AI automatically evaluates prompts provided by engineers and selects the most appropriate prompt. For example, the evaluation is based on the accuracy and reliability of the prompt. In this way, the efficiency of the system is improved by automatically evaluating prompts provided by engineers and selecting the most appropriate prompt.

[0039] The prompt providing unit enables the generation AI to evaluate data quality and filter low-quality data when data is integrated. For example, the prompt providing unit adds a function that allows the generation AI to evaluate data quality and filter low-quality data when data is integrated. For example, the prompt providing unit evaluates based on the accuracy and consistency of the data. The prompt providing unit also builds a system that allows the generation AI to evaluate data quality when data is integrated and filter low-quality data. For example, the prompt providing unit evaluates based on missing data and outliers. The prompt providing unit also adds a function that allows the generation AI to evaluate data quality when data is integrated and filter low-quality data. For example, the prompt providing unit evaluates based on the reliability and recency of the data. In this way, by evaluating data quality and filtering low-quality data, the reliability of the system is improved.

[0040] The data integration unit can improve data compatibility by adding a function to automatically convert different data formats when data is integrated. The data integration unit, for example, adds a function to automatically convert different data formats when data is integrated, improving data compatibility. For example, it converts data in CSV format to JSON format. The data integration unit also builds a system in which the generation AI automatically converts different data formats when data is integrated. For example, it converts data in XML format to SQL format. The data integration unit also adds a function to automatically convert different data formats when data is integrated, improving data compatibility. For example, it converts data in Excel format to API format. This automatically converts different data formats, improving data compatibility.

[0041] The prompt providing unit can enable the generation AI to automatically translate prompts provided by the engineer, thereby enabling support for users of different languages. For example, the prompt providing unit enables the generation AI to automatically translate prompts provided by the engineer, thereby enabling support for users of different languages. For example, translating an English prompt into Japanese. The prompt providing unit also builds a system in which the generation AI automatically translates prompts provided by the engineer, thereby enabling support for users of different languages. For example, translating a French prompt into Spanish. The prompt providing unit also enables the generation AI to automatically translate prompts provided by the engineer, thereby enabling support for users of different languages. For example, translating a Chinese prompt into German. In this way, automatic translation of prompts provided by the engineer can enable support for users of different languages.

[0042] The app market providing unit allows the generation AI to automatically recommend the optimal dataset when a user downloads an app. The app market providing unit adds a function whereby the generation AI automatically recommends the optimal dataset when a user downloads an app. For example, the application market providing unit suggests the optimal dataset based on the user's past usage history. The app market providing unit also builds a system whereby the generation AI analyzes the user's download history and recommends the optimal dataset. For example, the application market providing unit suggests datasets similar to datasets the user has used in the past. The app market providing unit also adds a function whereby the generation AI automatically recommends the optimal dataset when a user downloads an app. For example, the application market providing unit suggests the optimal dataset based on the user's current needs. This improves convenience by recommending the optimal dataset when a user downloads an app.

[0043] The app market providing unit can have the generation AI guide the user through operation procedures in real time when using an app. For example, the app market providing unit adds a function whereby the generation AI guides the user through operation procedures in real time when using an app. For example, appropriate guidance is provided when the user is unsure how to operate the app. The app market providing unit also builds a system whereby the generation AI monitors user operations in real time and provides guidance on operation procedures as necessary. For example, appropriate guidance is provided when the user makes an operation error. The app market providing unit also adds a function whereby the generation AI guides the user through operation procedures in real time when using an app. For example, appropriate guidance is provided when the user uses a new function. This improves ease of operation by providing real-time guidance on operation procedures when the user uses the app.

[0044] The app market providing unit can introduce a voice assistant when a user uses an app, enabling voice operation. The app market providing unit, for example, introduces a voice assistant when a user uses an app, enabling voice operation. For example, it allows the user to operate the app's functions with voice commands. The app market providing unit also introduces a voice assistant to build a system that allows the user to operate the app with voice. For example, it allows the user to input data or search by voice. The app market providing unit also introduces a voice assistant when a user uses an app, enabling voice operation. For example, it allows the user to change app settings by voice. This allows the user to operate the app with voice, improving ease of operation.

[0045] The app market providing unit can introduce gesture recognition to enable intuitive operation when a user uses an app. The app market providing unit, for example, introduces gesture recognition to enable intuitive operation when a user uses an app. For example, the app market providing unit allows a user to operate app functions with hand movements. The app market providing unit also introduces gesture recognition to build a system that allows a user to intuitively operate an app. For example, the app market providing unit allows a user to scroll or select data with finger movements. The app market providing unit also introduces gesture recognition to enable intuitive operation when a user uses an app. For example, the app market providing unit allows a user to change app settings with hand movements. This allows a user to operate an app with gestures, thereby improving ease of operation.

[0046] The generation AI platform unit can automatically expand the learning data as the number of users increases, thereby improving learning efficiency. The generation AI platform unit, for example, adds a function that allows the generation AI to automatically expand the learning data as the number of users increases, thereby improving learning efficiency. For example, new user data is automatically added to the learning data. The generation AI platform unit also builds a system that allows the generation AI to automatically expand the learning data as the number of users increases. For example, user behavior data is added to the learning data in real time. The generation AI platform unit also adds a function that allows the generation AI to automatically expand the learning data as the number of users increases, thereby improving learning efficiency. For example, data from a new data source is automatically added to the learning data. This automatically expands the learning data as the number of users increases, thereby improving learning efficiency.

[0047] As the number of users increases, the generation AI platform unit can compare and analyze data from different user groups and extract common patterns. For example, as the number of users increases, the generation AI platform unit adds a function that allows the generation AI to compare and analyze data from different user groups and extract common patterns. For example, it compares and analyzes user data from different age groups. The generation AI platform unit also builds a system in which the generation AI compares and analyzes data from different user groups as the number of users increases. For example, it compares and analyzes user data from different regions. The generation AI platform unit also adds a function that allows the generation AI to compare and analyze data from different user groups and extract common patterns as the number of users increases. For example, it compares and analyzes user data from different occupations. This allows the generation AI to compare and analyze data from different user groups and extract common patterns, thereby improving the accuracy of the system.

[0048] As the number of users increases, the generation AI platform unit can learn data from different regions and cultural spheres and perform analysis from a global perspective. For example, as the number of users increases, the generation AI platform unit adds a function that allows the generation AI to learn data from different regions and cultural spheres and perform analysis from a global perspective. For example, it learns user data from different countries. The generation AI platform unit also builds a system in which the generation AI learns data from different regions and cultural spheres as the number of users increases. For example, it learns user data in different languages. The generation AI platform unit also adds a function that allows the generation AI to learn data from different regions and cultural spheres and perform analysis from a global perspective as the number of users increases. For example, it learns user data from different cultural backgrounds. This improves the accuracy of the system by learning data from different regions and cultural spheres and performing analysis from a global perspective.

[0049] As the number of users increases, the generation AI platform unit can learn data from different industries and perform industry-specific analysis. For example, as the number of users increases, the generation AI platform unit adds a function that allows the generation AI to learn data from different industries and perform industry-specific analysis. For example, it learns data from the medical industry. The generation AI platform unit also builds a system in which the generation AI learns data from different industries as the number of users increases. For example, it learns data from the education industry. The generation AI platform unit also builds a function that allows the generation AI to learn data from different industries and perform industry-specific analysis as the number of users increases. For example, it learns data from the entertainment industry. This allows the generation AI to learn data from different industries and perform industry-specific analysis, improving the accuracy of the system.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The app market provider can collect user feedback in real time and have the generation AI dynamically update app rankings based on that feedback. For example, a feature could be added that allows users to provide feedback after using an app, and the generation AI could analyze that feedback and update the app rankings. Apps with a lot of positive feedback could also be displayed at the top. The app market provider can also collect user feedback in real time and have the generation AI dynamically update app rankings based on that data. It is also possible to adjust rankings by analyzing the content of the feedback and evaluation scores. Furthermore, the app market provider builds a system in which the generation AI updates app rankings in real time based on user feedback. It can also adjust rankings by analyzing feedback trends. This dynamically updates app rankings based on user feedback, improving the reliability of the rankings.

[0052] The app market provider can enable users to upload AI apps they have created and share them with other users. For example, a function can be added that allows users to upload AI apps they have created to the app market and share them with other users. Users can also publish their own AI apps and make them available for other users to download. The app market provider can also add a function that allows users to upload their own AI apps to the app market and build a system for sharing them with other users. Users can also create a community where users can evaluate their own AI apps. Furthermore, the app market provider can add a function that allows users to upload their own AI apps to the app market and share them with other users. It is also possible to provide a platform where users can sell their own AI apps. This is expected to revitalize the community as users share their own AI apps.

[0053] The app market provider can create submarkets specialized for different industries or uses, allowing users to easily find more specialized apps. For example, submarkets specialized for different industries or uses can be created within the app market, allowing users to easily find specialized apps. Categories such as medical, education, and entertainment can also be created. The app market provider can also create a system that allows users to easily find apps specialized for specific industries or uses by creating submarkets. Specialized apps can also be collected in each submarket. Furthermore, the app market provider can create submarkets specialized for different industries or uses within the app market, allowing users to easily find specialized apps. A function for recommending apps related to each submarket can also be added, making it easier for users to find specialized apps.

[0054] When analyzing user input data, the generative AI platform can refer to similar data from the past to provide more accurate results. For example, when the generative AI analyzes user input data, it can refer to similar data from the past to provide more accurate results. It is also possible to improve the predictive model based on past data. The generative AI platform also builds a system in which the generative AI refers to similar data from the past when analyzing user input data. It is also possible to provide optimal results based on past data. Furthermore, when the generative AI analyzes user input data, the generative AI platform can refer to similar data from the past to provide more accurate results. It is also possible to correct errors based on past data. In this way, by referring to similar data from the past, the accuracy of the analysis results is improved.

[0055] The generative AI platform unit can monitor the operation of an app and automatically make corrections if an abnormality is detected. For example, an in-house AI platform can monitor the operation of an app and add a function that automatically makes corrections if an abnormality is detected. It is also possible to build a system that automatically makes corrections when an error occurs. The generative AI platform unit can also monitor the operation of an app and add a function that automatically makes corrections if an abnormality is detected. It is also possible to develop an algorithm that automatically makes corrections when an abnormality is detected. The generative AI platform unit can also monitor the operation of an app and add a function that automatically makes corrections if an abnormality is detected. It is also possible to build a system that automatically makes corrections when an abnormality is detected. This automatically corrects abnormalities in app operation, improving the stability of the system.

[0056] When analyzing user input data, the generative AI platform can integrate information from different data sources to provide more multifaceted results. For example, when the generative AI analyzes user input data, it can integrate information from different data sources to provide more multifaceted results. It is also possible to perform analysis based on multiple data sources. The generative AI platform also builds a system in which the generative AI integrates information from different data sources when analyzing user input data. It is also possible to provide optimal results based on different data sources. Furthermore, when the generative AI analyzes user input data, the generative AI platform can integrate information from different data sources to provide more multifaceted results. It is also possible to correct errors based on different data sources. This allows for a multifaceted perspective of the analysis results by integrating information from different data sources.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The app market provider provides apps to users. For example, if a user is looking for a specific AI app, they can search for it in the app market and download it. Step 2: The generative AI platform supports the operation of the app. For example, the generative AI analyzes the user's input data and provides optimal results. In addition, an in-house AI platform supports the operation of the app, ensuring stable performance. Step 3: The data integration unit integrates the data with the app. For example, based on prompts provided by the engineer, the generation AI analyzes the user's input data and suggests appropriate actions. Step 4: The prompt provider provides prompts from the technician. For example, based on the prompts provided by the technician, the generation AI analyzes the user's input data and suggests appropriate actions.

[0059] (Example 2) The AI-dedicated app market system according to an embodiment of the present invention utilizes generative AI and in-house AI platforms in the background, with data integration and prompts provided by engineers around the world. This allows users to easily use AI apps and dramatically improves AI learning opportunities.

[0060] An AI-dedicated app market system according to an embodiment includes an app market provider, a generation AI platform unit, a data linking unit, and a prompt providing unit. The app market provider provides apps to users. For example, if a user is looking for a specific AI app, they can search for and download it within the app market. The generation AI platform unit supports the operation of the app. For example, the generation AI analyzes user input data and provides optimal results. An in-house AI platform supports the operation of the app and achieves stable performance. The data linking unit links data to the app. For example, based on prompts provided by an engineer, the generation AI analyzes user input data and suggests appropriate actions. The prompt providing unit provides prompts from the engineer. For example, based on prompts provided by the engineer, the generation AI analyzes user input data and suggests appropriate actions. This allows the AI-dedicated app market system according to an embodiment to easily use AI apps and dramatically improve AI learning opportunities.

[0061] The app market providing unit allows the generation AI to individually recommend the most suitable app based on the user's past download history. For example, the app market providing unit analyzes the user's past download history, and the generation AI recommends the most suitable app based on that history. For example, it suggests new apps taking into account the categories and usage frequency of previously downloaded apps. The app market providing unit also allows the generation AI to recommend similar apps based on the user's download history. For example, it prioritizes displaying apps with the same category or functions as previously downloaded apps. The app market providing unit also analyzes the user's past download history, and the generation AI predicts and recommends new app trends based on that history. For example, it suggests new popular apps in the same category. This improves convenience by recommending the most suitable apps to users.

[0062] The app market providing unit can collect user feedback in real time, and the generation AI can dynamically update app rankings based on that feedback. For example, the app market providing unit adds a function that allows users to provide feedback after using an app, and the generation AI analyzes that feedback to update the app rankings. For example, apps with a lot of positive feedback are displayed at the top. The app market providing unit also collects user feedback in real time, and the generation AI dynamically updates the app rankings based on that data. For example, the content of the feedback and evaluation scores are analyzed to adjust the rankings. The app market providing unit also builds a system in which the generation AI updates app rankings in real time based on user feedback. For example, the feedback trends are analyzed to adjust the rankings. In this way, the reliability of the rankings is improved by dynamically updating app rankings based on user feedback.

[0063] The app market providing unit can use the emotion estimation function to analyze the emotions of a user when searching for an app and recommend apps that elicit positive emotions. For example, the app market providing unit uses the emotion estimation function to analyze the emotions of a user when searching for an app in real time and recommend apps that elicit positive emotions. For example, apps that are likely to interest the user are preferentially displayed. The app market providing unit also builds a system that analyzes the emotional state of a user and recommends apps that elicit positive emotions. For example, when the user is relaxing, it recommends relaxation-related apps. The app market providing unit also uses the emotion estimation function to analyze the emotions of a user when searching for an app and recommends apps that elicit positive emotions. For example, when the user is having fun, it recommends entertainment-related apps. In this way, by analyzing the user's emotions and recommending apps that elicit positive emotions, the user experience is improved.

[0064] The app market providing unit allows users to upload AI apps they have created and share them with other users. For example, the app market providing unit adds a function that allows users to upload AI apps they have created to the app market and share them with other users. For example, users can publish their own AI apps and make them available for other users to download. The app market providing unit also adds a function that allows users to upload their own AI apps to the app market and builds a system for sharing them with other users. For example, it creates a community where users can evaluate their own AI apps. The app market providing unit also adds a function that allows users to upload their own AI apps to the app market and share them with other users. For example, it provides a platform where users can sell their own AI apps. This is expected to revitalize the community as users share their own AI apps.

[0065] The app market providing unit can provide submarkets specialized for different industries or uses, allowing users to easily find more specialized apps. For example, the app market providing unit provides submarkets specialized for different industries or uses within the app market, allowing users to easily find specialized apps. For example, categories such as medical, education, and entertainment are provided. The app market providing unit also provides submarkets, building a system that allows users to easily find apps specialized for specific industries or uses. For example, specialized apps are collected in each submarket. The app market providing unit also provides submarkets specialized for different industries or uses within the app market, allowing users to easily find specialized apps. For example, a function for recommending apps related to each submarket is added. This makes it easier for users to find specialized apps.

[0066] The app market providing unit can use the emotion estimation function to analyze the emotion of a user when downloading an app and predict the level of satisfaction after the download. For example, the app market providing unit uses the emotion estimation function to analyze the emotion of a user when downloading an app in real time and predict the level of satisfaction after the download. For example, the satisfaction level of an app downloaded when the user is excited is predicted. The app market providing unit also builds a system that analyzes the user's emotional state and predicts the level of satisfaction after the download. For example, the satisfaction level of an app downloaded when the user is relaxed is predicted. The app market providing unit also adds a function that uses the emotion estimation function to analyze the emotion of a user when downloading an app and predict the level of satisfaction after the download. For example, the satisfaction level of an app downloaded when the user is enjoying the app is predicted. In this way, the user experience is improved by analyzing the user's emotion and predicting the level of satisfaction after the download.

[0067] The generation AI platform unit can refer to similar data from the past when analyzing user input data and provide more accurate results. For example, when the generation AI analyzes user input data, the generation AI platform unit refers to similar data from the past and provides more accurate results. For example, it improves the predictive model based on past data. The generation AI platform unit also builds a system in which the generation AI refers to similar data from the past when analyzing user input data. For example, it provides optimal results based on past data. The generation AI platform unit also refers to similar data from the past when analyzing user input data and provides more accurate results. For example, it corrects errors based on past data. In this way, by referring to similar data from the past, the accuracy of the analysis results is improved.

[0068] The generative AI platform department can monitor the operation of an app and automatically make corrections if an abnormality is detected. For example, the generative AI platform department adds a function where an in-house AI platform monitors the operation of an app and automatically makes corrections if an abnormality is detected. For example, it builds a system that automatically makes corrections when an error occurs. The generative AI platform department also monitors the operation of an app and adds a function where an in-house AI platform automatically makes corrections if an abnormality is detected. For example, it develops an algorithm that automatically makes corrections when an abnormality is detected. The generative AI platform department also monitors the operation of an app and adds a function where an in-house AI platform monitors the operation of an app and automatically makes corrections if an abnormality is detected. For example, it builds a system that automatically makes corrections when an abnormality is detected. This automatically corrects abnormalities in the app's operation, improving the stability of the system.

[0069] The generation AI platform unit can use the emotion estimation function to analyze the user's emotion regarding input data and provide the optimal result according to the emotion. The generation AI platform unit, for example, uses the emotion estimation function to analyze the user's emotion regarding input data in real time and provide the optimal result according to the emotion. For example, it provides the optimal result when the user is excited. The generation AI platform unit also builds a system that analyzes the user's emotional state and provides the optimal result according to the emotion. For example, it provides the optimal result when the user is relaxed. The generation AI platform unit also uses the emotion estimation function to analyze the user's emotion regarding input data and provide the optimal result according to the emotion. For example, it provides the optimal result when the user is having fun. This improves the user experience by providing the optimal result according to the user's emotion.

[0070] The generative AI platform unit can integrate information from different data sources when analyzing user input data to provide more multifaceted results. For example, when the generative AI analyzes user input data, the generative AI platform unit integrates information from different data sources to provide more multifaceted results. For example, it performs analysis based on multiple data sources. The generative AI platform unit also builds a system in which the generative AI integrates information from different data sources when analyzing user input data. For example, it provides optimal results based on different data sources. The generative AI platform unit also integrates information from different data sources when the generative AI analyzes user input data to provide more multifaceted results. For example, it corrects errors based on different data sources. This allows for a multifaceted perspective on the analysis results by integrating information from different data sources.

[0071] The generative AI platform unit can dynamically allocate cloud resources to optimize the operation of an app. For example, the generative AI platform unit adds a function to dynamically allocate cloud resources so that the in-house AI platform can optimize the operation of an app. For example, it builds a system that dynamically allocates resources according to resource usage. The generative AI platform unit also adds a function to dynamically allocate cloud resources so that the in-house AI platform can optimize the operation of an app. For example, it develops an algorithm that dynamically allocates resources according to resource usage. The generative AI platform unit also adds a function to dynamically allocate cloud resources so that the in-house AI platform can optimize the operation of an app. For example, it builds a system that dynamically allocates resources according to resource usage. In this way, the operation of the app is optimized by dynamically allocating cloud resources.

[0072] The generation AI platform unit can use the emotion estimation function to monitor the user's emotions regarding input data in real time and provide feedback according to the emotions. The generation AI platform unit, for example, uses the emotion estimation function to monitor the user's emotions regarding input data in real time and provide feedback according to the emotions. For example, it provides appropriate feedback when the user is excited. The generation AI platform unit also builds a system that monitors the user's emotional state in real time and provides feedback according to the emotions. For example, it provides appropriate feedback when the user is relaxed. The generation AI platform unit also uses the emotion estimation function to monitor the user's emotions regarding input data in real time and provide feedback according to the emotions. For example, it provides appropriate feedback when the user is enjoying themselves. In this way, the user experience is improved by monitoring the user's emotions in real time and providing feedback according to the emotions.

[0073] The prompt providing unit enables the generation AI to automatically evaluate prompts provided by engineers and select the most appropriate prompt. The prompt providing unit adds, for example, a function whereby the generation AI automatically evaluates prompts provided by engineers and selects the most appropriate prompt. For example, the evaluation is based on the quality and relevance of the prompt. The prompt providing unit also builds a system whereby the generation AI automatically evaluates prompts provided by engineers and selects the most appropriate prompt. For example, the evaluation is based on the effectiveness and scope of application of the prompt. The prompt providing unit also adds a function whereby the generation AI automatically evaluates prompts provided by engineers and selects the most appropriate prompt. For example, the evaluation is based on the accuracy and reliability of the prompt. In this way, the efficiency of the system is improved by automatically evaluating prompts provided by engineers and selecting the most appropriate prompt.

[0074] The prompt providing unit enables the generation AI to evaluate data quality and filter low-quality data when data is integrated. For example, the prompt providing unit adds a function that allows the generation AI to evaluate data quality and filter low-quality data when data is integrated. For example, the prompt providing unit evaluates based on the accuracy and consistency of the data. The prompt providing unit also builds a system that allows the generation AI to evaluate data quality when data is integrated and filter low-quality data. For example, the prompt providing unit evaluates based on missing data and outliers. The prompt providing unit also adds a function that allows the generation AI to evaluate data quality when data is integrated and filter low-quality data. For example, the prompt providing unit evaluates based on the reliability and recency of the data. In this way, by evaluating data quality and filtering low-quality data, the reliability of the system is improved.

[0075] The prompt providing unit can use the emotion estimation function to analyze the user's emotion toward the prompts provided by the technician and optimize the prompts based on the emotion. For example, the prompt providing unit uses the emotion estimation function to analyze the user's emotion toward the prompts provided by the technician in real time and optimizes the prompts based on the emotion. For example, prompts that the user has positive emotion about are preferentially used. The prompt providing unit also analyzes the user's emotion toward the prompts provided by the technician and builds a system that optimizes prompts based on the emotion. For example, prompts that the user has negative emotion about are improved. The prompt providing unit also uses the emotion estimation function to analyze the user's emotion toward the prompts provided by the technician and optimizes the prompts based on the emotion. For example, prompts that the user enjoys are preferentially used. In this way, the user experience is improved by analyzing the user's emotion and optimizing the prompts based on the emotion.

[0076] The data integration unit can improve data compatibility by adding a function to automatically convert different data formats when data is integrated. The data integration unit, for example, adds a function to automatically convert different data formats when data is integrated, improving data compatibility. For example, it converts data in CSV format to JSON format. The data integration unit also builds a system in which the generation AI automatically converts different data formats when data is integrated. For example, it converts data in XML format to SQL format. The data integration unit also adds a function to automatically convert different data formats when data is integrated, improving data compatibility. For example, it converts data in Excel format to API format. This automatically converts different data formats, improving data compatibility.

[0077] The prompt providing unit can enable the generation AI to automatically translate prompts provided by the engineer, thereby enabling support for users of different languages. For example, the prompt providing unit enables the generation AI to automatically translate prompts provided by the engineer, thereby enabling support for users of different languages. For example, translating an English prompt into Japanese. The prompt providing unit also builds a system in which the generation AI automatically translates prompts provided by the engineer, thereby enabling support for users of different languages. For example, translating a French prompt into Spanish. The prompt providing unit also enables the generation AI to automatically translate prompts provided by the engineer, thereby enabling support for users of different languages. For example, translating a Chinese prompt into German. In this way, automatic translation of prompts provided by the engineer can enable support for users of different languages.

[0078] The prompt providing unit can use the emotion estimation function to monitor the user's emotion toward the prompts provided by the technician in real time and improve the prompts according to the emotion. For example, the prompt providing unit uses the emotion estimation function to monitor the user's emotion toward the prompts provided by the technician in real time and improve the prompts according to the emotion. For example, it prioritizes the use of prompts that the user has positive emotion about. Furthermore, the prompt providing unit builds a system that monitors the user's emotion toward the prompts provided by the technician in real time and improves the prompts according to the emotion. For example, it improves prompts that the user has negative emotion about. Furthermore, the prompt providing unit uses the emotion estimation function to monitor the user's emotion toward the prompts provided by the technician in real time and improves the prompts according to the emotion. For example, it prioritizes the use of prompts that the user enjoys. In this way, the user experience is improved by monitoring the user's emotion in real time and improving the prompts according to the emotion.

[0079] The app market providing unit allows the generation AI to automatically recommend the optimal dataset when a user downloads an app. The app market providing unit adds a function whereby the generation AI automatically recommends the optimal dataset when a user downloads an app. For example, the application market providing unit suggests the optimal dataset based on the user's past usage history. The app market providing unit also builds a system whereby the generation AI analyzes the user's download history and recommends the optimal dataset. For example, the application market providing unit suggests datasets similar to datasets the user has used in the past. The app market providing unit also adds a function whereby the generation AI automatically recommends the optimal dataset when a user downloads an app. For example, the application market providing unit suggests the optimal dataset based on the user's current needs. This improves convenience by recommending the optimal dataset when a user downloads an app.

[0080] The app market providing unit can have the generation AI guide the user through operation procedures in real time when using an app. For example, the app market providing unit adds a function whereby the generation AI guides the user through operation procedures in real time when using an app. For example, appropriate guidance is provided when the user is unsure how to operate the app. The app market providing unit also builds a system whereby the generation AI monitors user operations in real time and provides guidance on operation procedures as necessary. For example, appropriate guidance is provided when the user makes an operation error. The app market providing unit also adds a function whereby the generation AI guides the user through operation procedures in real time when using an app. For example, appropriate guidance is provided when the user uses a new function. This improves ease of operation by providing real-time guidance on operation procedures when the user uses the app.

[0081] The app market providing unit can use the emotion estimation function to analyze the stress felt by the user during operation and make suggestions to reduce the stress. For example, the app market providing unit uses the emotion estimation function to analyze the stress felt by the user during operation in real time and make suggestions to reduce the stress. For example, the app market providing unit simplifies operations when the user is feeling stressed. The app market providing unit also builds a system that analyzes the user's emotional state in real time and makes suggestions to reduce stress. For example, it provides appropriate advice when the user is feeling stressed. The app market providing unit also uses the emotion estimation function to analyze the stress felt by the user during operation and make suggestions to reduce stress. For example, it provides an operation guide that helps the user relax. This improves the user experience by making suggestions to reduce the stress felt by the user during operation.

[0082] The app market providing unit can introduce a voice assistant when a user uses an app, enabling voice operation. The app market providing unit, for example, introduces a voice assistant when a user uses an app, enabling voice operation. For example, it allows the user to operate the app's functions with voice commands. The app market providing unit also introduces a voice assistant to build a system that allows the user to operate the app with voice. For example, it allows the user to input data or search by voice. The app market providing unit also introduces a voice assistant when a user uses an app, enabling voice operation. For example, it allows the user to change app settings by voice. This allows the user to operate the app with voice, improving ease of operation.

[0083] The app market providing unit can introduce gesture recognition to enable intuitive operation when a user uses an app. The app market providing unit, for example, introduces gesture recognition to enable intuitive operation when a user uses an app. For example, the app market providing unit allows a user to operate app functions with hand movements. The app market providing unit also introduces gesture recognition to build a system that allows a user to intuitively operate an app. For example, the app market providing unit allows a user to scroll or select data with finger movements. The app market providing unit also introduces gesture recognition to enable intuitive operation when a user uses an app. For example, the app market providing unit allows a user to change app settings with hand movements. This allows a user to operate an app with gestures, thereby improving ease of operation.

[0084] The app market providing unit can use the emotion estimation function to monitor the emotions felt by the user during operation in real time and provide operation guides according to the emotions. For example, the app market providing unit uses the emotion estimation function to monitor the emotions felt by the user during operation in real time and provide operation guides according to the emotions. For example, a guide that simplifies operation is provided when the user is feeling stressed. The app market providing unit also builds a system that analyzes the user's emotional state in real time and provides operation guides according to the emotions. For example, a detailed operation guide is provided when the user is relaxed. The app market providing unit also uses the emotion estimation function to monitor the emotions felt by the user during operation in real time and provide operation guides according to the emotions. For example, an operation guide for a new function is provided when the user is enjoying themselves. In this way, the user experience is improved by monitoring the emotions felt by the user during operation in real time and providing operation guides according to the emotions.

[0085] The generation AI platform unit can automatically expand the learning data as the number of users increases, thereby improving learning efficiency. The generation AI platform unit, for example, adds a function that allows the generation AI to automatically expand the learning data as the number of users increases, thereby improving learning efficiency. For example, new user data is automatically added to the learning data. The generation AI platform unit also builds a system that allows the generation AI to automatically expand the learning data as the number of users increases. For example, user behavior data is added to the learning data in real time. The generation AI platform unit also adds a function that allows the generation AI to automatically expand the learning data as the number of users increases, thereby improving learning efficiency. For example, data from a new data source is automatically added to the learning data. This automatically expands the learning data as the number of users increases, thereby improving learning efficiency.

[0086] As the number of users increases, the generation AI platform unit can compare and analyze data from different user groups and extract common patterns. For example, as the number of users increases, the generation AI platform unit adds a function that allows the generation AI to compare and analyze data from different user groups and extract common patterns. For example, it compares and analyzes user data from different age groups. The generation AI platform unit also builds a system in which the generation AI compares and analyzes data from different user groups as the number of users increases. For example, it compares and analyzes user data from different regions. The generation AI platform unit also adds a function that allows the generation AI to compare and analyze data from different user groups and extract common patterns as the number of users increases. For example, it compares and analyzes user data from different occupations. This allows the generation AI to compare and analyze data from different user groups and extract common patterns, thereby improving the accuracy of the system.

[0087] The generation AI platform unit can use the emotion estimation function to analyze user emotions as the number of users increases and develop a learning algorithm for eliciting positive emotions. The generation AI platform unit, for example, uses the emotion estimation function to analyze user emotions in real time as the number of users increases and develop a learning algorithm for eliciting positive emotions. For example, it provides an optimal learning algorithm when the user is having fun. The generation AI platform unit also builds a system that analyzes user emotions as the number of users increases and develops a learning algorithm for eliciting positive emotions. For example, it provides an optimal learning algorithm when the user is relaxed. The generation AI platform unit also uses the emotion estimation function to analyze user emotions as the number of users increases and develops a learning algorithm for eliciting positive emotions. For example, it provides an optimal learning algorithm when the user is excited. This improves the user experience by analyzing user emotions as the number of users increases and developing a learning algorithm for eliciting positive emotions.

[0088] As the number of users increases, the generation AI platform unit can learn data from different regions and cultural spheres and perform analysis from a global perspective. For example, as the number of users increases, the generation AI platform unit adds a function that allows the generation AI to learn data from different regions and cultural spheres and perform analysis from a global perspective. For example, it learns user data from different countries. The generation AI platform unit also builds a system in which the generation AI learns data from different regions and cultural spheres as the number of users increases. For example, it learns user data in different languages. The generation AI platform unit also adds a function that allows the generation AI to learn data from different regions and cultural spheres and perform analysis from a global perspective as the number of users increases. For example, it learns user data from different cultural backgrounds. This improves the accuracy of the system by learning data from different regions and cultural spheres and performing analysis from a global perspective.

[0089] As the number of users increases, the generation AI platform unit can learn data from different industries and perform industry-specific analysis. For example, as the number of users increases, the generation AI platform unit adds a function that allows the generation AI to learn data from different industries and perform industry-specific analysis. For example, it learns data from the medical industry. The generation AI platform unit also builds a system in which the generation AI learns data from different industries as the number of users increases. For example, it learns data from the education industry. The generation AI platform unit also builds a function that allows the generation AI to learn data from different industries and perform industry-specific analysis as the number of users increases. For example, it learns data from the entertainment industry. This allows the generation AI to learn data from different industries and perform industry-specific analysis, improving the accuracy of the system.

[0090] The generation AI platform unit can use the emotion estimation function to monitor user emotions in real time as the number of users increases, and optimize the learning algorithm according to the emotions. The generation AI platform unit, for example, uses the emotion estimation function to monitor user emotions in real time as the number of users increases, and optimize the learning algorithm according to the emotions. For example, it provides an optimal learning algorithm when the user has positive emotions. The generation AI platform unit also builds a system that monitors user emotions in real time as the number of users increases, and optimizes the learning algorithm according to the emotions. For example, it provides an optimal learning algorithm when the user has negative emotions. The generation AI platform unit also uses the emotion estimation function to monitor user emotions in real time as the number of users increases, and optimizes the learning algorithm according to the emotions. For example, it provides an optimal learning algorithm when the user is having fun. This improves the user experience by monitoring user emotions in real time as the number of users increases, and optimizing the learning algorithm according to the emotions.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The app market provider can collect user feedback in real time and have the generation AI dynamically update app rankings based on that feedback. For example, a feature could be added that allows users to provide feedback after using an app, and the generation AI could analyze that feedback and update the app rankings. Apps with a lot of positive feedback could also be displayed at the top. The app market provider can also collect user feedback in real time and have the generation AI dynamically update app rankings based on that data. It is also possible to adjust rankings by analyzing the content of the feedback and evaluation scores. Furthermore, the app market provider builds a system in which the generation AI updates app rankings in real time based on user feedback. It can also adjust rankings by analyzing feedback trends. This dynamically updates app rankings based on user feedback, improving the reliability of the rankings.

[0093] The app market providing unit can use the emotion estimation function to analyze the emotions of a user when searching for an app and recommend apps that elicit positive emotions. For example, the emotion estimation function can be used to analyze the emotions of a user when searching for an app in real time and recommend apps that elicit positive emotions. It can also prioritize the display of apps that are likely to interest the user. The app market providing unit also builds a system that analyzes the user's emotional state and recommends apps that elicit positive emotions. It is also possible to recommend relaxation apps when the user is relaxing. Furthermore, the app market providing unit uses the emotion estimation function to analyze the emotions of a user when searching for an app and recommend apps that elicit positive emotions. It can also recommend entertainment apps when the user is having fun. This improves the user experience by analyzing the user's emotions and recommending apps that elicit positive emotions.

[0094] The app market provider can enable users to upload AI apps they have created and share them with other users. For example, a function can be added that allows users to upload AI apps they have created to the app market and share them with other users. Users can also publish their own AI apps and make them available for other users to download. The app market provider can also add a function that allows users to upload their own AI apps to the app market and build a system for sharing them with other users. Users can also create a community where users can evaluate their own AI apps. Furthermore, the app market provider can add a function that allows users to upload their own AI apps to the app market and share them with other users. It is also possible to provide a platform where users can sell their own AI apps. This is expected to revitalize the community as users share their own AI apps.

[0095] The app market provider can create submarkets specialized for different industries or uses, allowing users to easily find more specialized apps. For example, submarkets specialized for different industries or uses can be created within the app market, allowing users to easily find specialized apps. Categories such as medical, education, and entertainment can also be created. The app market provider can also create a system that allows users to easily find apps specialized for specific industries or uses by creating submarkets. Specialized apps can also be collected in each submarket. Furthermore, the app market provider can create submarkets specialized for different industries or uses within the app market, allowing users to easily find specialized apps. A function for recommending apps related to each submarket can also be added, making it easier for users to find specialized apps.

[0096] The app market providing unit can use the emotion estimation function to analyze the emotion a user has when downloading an app and predict the level of satisfaction after the download. For example, the emotion estimation function can be used to analyze the emotion a user has when downloading an app in real time and predict the level of satisfaction after the download. It is also possible to predict the level of satisfaction for an app downloaded when the user is excited. The app market providing unit also builds a system that analyzes the user's emotional state and predicts the level of satisfaction after the download. It is also possible to predict the level of satisfaction for an app downloaded when the user is relaxed. Furthermore, the app market providing unit adds a function that uses the emotion estimation function to analyze the emotion a user has when downloading an app and predict the level of satisfaction after the download. It is also possible to predict the level of satisfaction for an app downloaded when the user is enjoying the app. In this way, the user experience is improved by analyzing the user's emotion and predicting the level of satisfaction after the download.

[0097] When analyzing user input data, the generative AI platform can refer to similar data from the past to provide more accurate results. For example, when the generative AI analyzes user input data, it can refer to similar data from the past to provide more accurate results. It is also possible to improve the predictive model based on past data. The generative AI platform also builds a system in which the generative AI refers to similar data from the past when analyzing user input data. It is also possible to provide optimal results based on past data. Furthermore, when the generative AI analyzes user input data, the generative AI platform can refer to similar data from the past to provide more accurate results. It is also possible to correct errors based on past data. In this way, by referring to similar data from the past, the accuracy of the analysis results is improved.

[0098] The generative AI platform unit can monitor the operation of an app and automatically make corrections if an abnormality is detected. For example, an in-house AI platform can monitor the operation of an app and add a function that automatically makes corrections if an abnormality is detected. It is also possible to build a system that automatically makes corrections when an error occurs. The generative AI platform unit can also monitor the operation of an app and add a function that automatically makes corrections if an abnormality is detected. It is also possible to develop an algorithm that automatically makes corrections when an abnormality is detected. The generative AI platform unit can also monitor the operation of an app and add a function that automatically makes corrections if an abnormality is detected. It is also possible to build a system that automatically makes corrections when an abnormality is detected. This automatically corrects abnormalities in app operation, improving the stability of the system.

[0099] The generative AI platform unit can use the emotion estimation function to analyze the user's emotions regarding input data and provide the optimal result according to the emotion. For example, the emotion estimation function can be used to analyze the user's emotions regarding input data in real time and provide the optimal result according to the emotion. It is also possible to provide the optimal result when the user is excited. The generative AI platform unit can also build a system that analyzes the user's emotional state and provides the optimal result according to the emotion. It is also possible to provide the optimal result when the user is relaxed. Furthermore, the generative AI platform unit can use the emotion estimation function to analyze the user's emotions regarding input data and provide the optimal result according to the emotion. It is also possible to provide the optimal result when the user is enjoying themselves. This improves the user experience by providing the optimal result according to the user's emotions.

[0100] When analyzing user input data, the generative AI platform can integrate information from different data sources to provide more multifaceted results. For example, when the generative AI analyzes user input data, it can integrate information from different data sources to provide more multifaceted results. It is also possible to perform analysis based on multiple data sources. The generative AI platform also builds a system in which the generative AI integrates information from different data sources when analyzing user input data. It is also possible to provide optimal results based on different data sources. Furthermore, when the generative AI analyzes user input data, the generative AI platform can integrate information from different data sources to provide more multifaceted results. It is also possible to correct errors based on different data sources. This allows for a multifaceted perspective of the analysis results by integrating information from different data sources.

[0101] The generative AI platform unit can use the emotion estimation function to monitor the user's emotions in response to input data in real time and provide feedback according to the emotions. For example, the emotion estimation function can be used to monitor the user's emotions in response to input data in real time and provide feedback according to the emotions. It is also possible to provide appropriate feedback when the user is excited. The generative AI platform unit can also build a system that monitors the user's emotional state in real time and provides feedback according to the emotions. It is also possible to provide appropriate feedback when the user is relaxed. Furthermore, the generative AI platform unit can use the emotion estimation function to monitor the user's emotions in response to input data in real time and provide feedback according to the emotions. It is also possible to provide appropriate feedback when the user is enjoying themselves. This improves the user experience by monitoring the user's emotions in real time and providing feedback according to the emotions.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The app market provider provides apps to users. For example, if a user is looking for a specific AI app, they can search for it in the app market and download it. Step 2: The generative AI platform supports the operation of the app. For example, the generative AI analyzes the user's input data and provides optimal results. In addition, an in-house AI platform supports the operation of the app, ensuring stable performance. Step 3: The data integration unit integrates the data with the app. For example, based on prompts provided by the engineer, the generation AI analyzes the user's input data and suggests appropriate actions. Step 4: The prompt provider provides prompts from the technician. For example, based on the prompts provided by the technician, the generation AI analyzes the user's input data and suggests appropriate actions.

[0104] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] 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.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] 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.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] 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.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] 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.

[0130] 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.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 7, the 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.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] 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.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0145] 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.

[0146] 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.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0154] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0155] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0156] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0157] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0158] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0159] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0160] 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.

[0161] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0162] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0163] 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.

[0164] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0165] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0166] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0167] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0168] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0169] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0170] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an app market provider that provides apps; a generation AI infrastructure unit that supports the operation of the apps provided by the app market provider; a data linking unit that links data to an application supported by the generation AI infrastructure unit; a prompt providing unit that provides prompts from a technician based on the data linked by the data linking unit; A system characterized by:

2. The application market providing unit The AI ​​will recommend the best apps for each user based on their download history.

2. The system of claim 1.

3. The application market providing unit Collecting user feedback in real time, the generating AI dynamically updates the ranking of the app based on that feedback.

2. The system of claim 1.

4. The application market providing unit Analyzing users' emotions when searching for apps and recommending apps that evoke positive emotions 2. The system of claim 1.

5. The application market providing unit Users can upload the AI ​​apps they create and share them with other users.

2. The system of claim 1.

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