System

Abstract

An object of a system according to an embodiment is to efficiently integrate a plurality of services and provide an optimum service to a user.SOLUTION: A system according to an embodiment includes a service aggregation unit, a ranking unit, a recommendation unit, a translation unit, a summarization unit, a classification unit, and a feedback unit. The service aggregating unit aggregates services. The ranking unit displays a ranking of the services aggregated by the service aggregating unit. The recommendation unit recommends a service based on the ranking displayed by the ranking unit. The translation unit automatically translates the service. The summarizer automatically summarizes the service. The classification unit automatically classifies the service. The feedback unit collects feedback from the user and reflects the feedback on improvement of the platform.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to efficiently aggregate multiple services and provide optimal services to users.

[0005] The system according to the embodiment aims to efficiently aggregate a plurality of services and provide the most suitable service to the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a service aggregation unit, a ranking unit, a recommendation unit, a translation unit, a summarization unit, a classification unit, and a feedback unit. The service aggregation unit aggregates services. The ranking unit displays a ranking of the services aggregated by the service aggregation unit. The recommendation unit recommends services based on the ranking displayed by the ranking unit. The translation unit automatically translates services. The summarization unit automatically summarizes services. The classification unit automatically classifies services. The feedback unit collects feedback from users and reflects it in improving the platform. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently aggregate a plurality of services and provide the most suitable service to the user. [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-Compass system according to an embodiment of the present invention aggregates services that utilize generative AI and provides an environment in which users can freely select and use services that best suit their challenges. This allows users to quickly find and use the service that best suits their challenges. Furthermore, the system also allows users to overcome language barriers and use overseas services, enabling problem-solving from a global perspective. Furthermore, by incorporating user feedback, the system realizes a platform that is constantly evolving.

[0029] The AI-Compass system according to the embodiment includes a service aggregation unit, a ranking unit, a recommendation unit, a translation unit, a summarization unit, a classification unit, and a feedback unit. The service aggregation unit aggregates services. For example, various generative AI services, such as automatic text generation, image analysis, and data analysis, are aggregated. The ranking unit displays rankings of the services aggregated by the service aggregation unit. For example, rankings based on frequency of use and ratings are displayed. The recommendation unit recommends services based on the rankings displayed by the ranking unit. For example, if a user requests "automatic text generation," related services are recommended. The translation unit automatically translates services. For example, services provided in English are automatically translated into Japanese. The summarization unit automatically summarizes services. For example, the summarization unit briefly summarizes the content of the service. The classification unit automatically classifies services. For example, services are classified into categories such as text generation services, image analysis services, and data analysis services. The feedback unit collects user feedback and reflects it in improving the platform. For example, if a user sends feedback such as "I would like the usability of this service to be improved," the generation AI analyzes that opinion and reflects it in improving the platform. As a result, the AI-Compass system according to the embodiment provides an environment in which users can freely select and use services that suit their needs.

[0030] The service aggregation unit analyzes the usage history, learns the user's usage patterns, and can automatically suggest the most suitable service for the next use. The service aggregation unit, for example, analyzes the usage history of each service and learns the user's usage patterns. For example, it identifies services that the user uses frequently and trends in use during specific time periods, and automatically suggests the most suitable service for the next use. The service aggregation unit also learns usage patterns based on the user's past usage history and develops an algorithm that suggests the most suitable service for the next use. For example, it makes suggestions taking into account the frequency and time periods of use of specific services. The service aggregation unit also analyzes the usage history and learns the user's usage patterns, thereby building a system that automatically suggests the most suitable service for the next use. For example, it suggests the most suitable service for the next use based on the history of services the user has used in the past. In this way, it is possible to learn the user's usage patterns and suggest the most suitable service for the next use.

[0031] The service aggregation unit can develop an algorithm that evaluates service performance based on past user feedback and selects the optimal service. The service aggregation unit, for example, analyzes past user feedback and develops an algorithm that evaluates service performance. For example, it evaluates service quality and usability based on user ratings and comments. The service aggregation unit also constructs an algorithm that evaluates service performance based on feedback data and selects the optimal service. For example, it analyzes user rating scores and comments and suggests the optimal service. The service aggregation unit also develops a system that evaluates service performance based on past user feedback. For example, it analyzes feedback data, evaluates service quality and usability, and selects the optimal service. This makes it possible to evaluate service performance based on user feedback and select the optimal service.

[0032] The service aggregation unit can make services available seamlessly across different devices. For example, the service aggregation unit builds a system that makes services available seamlessly across different devices. For example, it makes it possible to continue using a service started on a smartphone on a PC. The service aggregation unit also develops data synchronization and sharing functions to achieve seamless service use across devices. For example, it synchronizes data between devices using cloud storage. The service aggregation unit also unifies the user interface to make services available seamlessly across different devices. For example, it provides the same operating feel on smartphones, tablets, and PCs. This allows services to be used seamlessly across different devices.

[0033] The service aggregation unit can provide a dashboard that visualizes service usage status and allows users to understand at a glance which services they are using and to what extent. The service aggregation unit, for example, builds a system that provides a dashboard that visualizes service usage status. For example, it displays the frequency and usage time of each service in graphs and charts. The service aggregation unit also develops a dashboard that allows users to understand at a glance which services they are using and to what extent. For example, it updates the usage status in real time and displays it visually. The service aggregation unit also builds a system that collects and analyzes data to visualize service usage status. For example, it collects usage data for each service and displays it on a dashboard. This visualizes service usage status and allows users to understand it at a glance.

[0034] The ranking unit can generate individually customized rankings based on the user's past usage history and feedback. The ranking unit, for example, analyzes the user's past usage history and feedback and builds a system that generates individually customized rankings. For example, it creates rankings based on usage frequency and evaluation scores. The ranking unit also develops an algorithm that generates individually customized rankings based on the usage history and feedback data. For example, it creates rankings that reflect the user's preferences and evaluations. The ranking unit also develops a system that generates individually customized rankings based on the user's past usage history and feedback. For example, it analyzes the usage history and feedback in real time and updates the rankings. In this way, it is possible to generate individually customized rankings based on the user's past usage history and feedback.

[0035] The recommendation unit can utilize social media data to reflect the user's interests and concerns. The recommendation unit, for example, utilizes social media data in a recommendation algorithm to build a system that reflects the user's interests and concerns. For example, it analyzes data on user posts and likes. The recommendation unit also develops a recommendation algorithm that reflects the user's interests and concerns based on social media data. For example, it analyzes data on users' following and hashtags. The recommendation unit also utilizes social media data in a recommendation algorithm to develop a system that reflects the user's interests and concerns. For example, it analyzes data on users' social media activity and reflects it in recommendations. In this way, social media data can be utilized to reflect the user's interests and concerns.

[0036] The recommendation unit can customize the recommendation function based on the user's geographical location information and suggest region-specific services. The recommendation unit, for example, builds a system that customizes the recommendation function based on the user's geographical location information. For example, it suggests region-specific services based on the user's current location. The recommendation unit also develops a recommendation algorithm that suggests the most suitable services to the user based on the geographical location information. For example, it obtains the user's location information in real time and suggests region-specific services. The recommendation unit also develops a system that customizes the recommendation function based on the user's geographical location information. For example, it suggests local events and services based on the user's location information. This makes it possible to suggest region-specific services based on the user's geographical location information.

[0037] The recommendation unit can link with the user's schedule and calendar and suggest services at the optimal timing. The recommendation unit, for example, builds a system that links recommendation results with the user's schedule and calendar. For example, it suggests services at the optimal timing based on the user's schedule. The recommendation unit also develops a recommendation algorithm that suggests services to the user at the optimal timing based on schedule and calendar data. For example, it suggests services based on the user's schedule. The recommendation unit also develops a system that links recommendation results with the user's schedule and calendar. For example, it suggests services at the optimal timing based on the user's schedule. This makes it possible to link with the user's schedule and calendar and suggest services at the optimal timing.

[0038] The usage tips providing unit can analyze usage data of each service and automatically extract the most effective usage to provide to the user. The usage tips providing unit, for example, analyzes usage data of each service and builds a system that automatically extracts the most effective usage. For example, it identifies effective usage based on usage frequency and success rate and provides it to the user. The usage tips providing unit also develops an algorithm that extracts the most effective usage based on usage data. For example, it analyzes user usage history and feedback to identify effective usage. The usage tips providing unit also develops a system that analyzes usage data of each service and automatically extracts the most effective usage. For example, it analyzes usage data in real time and provides effective usage to the user. This makes it possible to analyze usage data of each service and automatically extract the most effective usage to provide to the user.

[0039] The usage tips providing unit can customize and provide usage tips according to the user's skill level. For example, the usage tips providing unit builds a system that customizes and provides usage tips according to the user's skill level. For example, it provides simple usage tips for beginners and advanced usage tips for advanced users. The usage tips providing unit also develops an algorithm that customizes usage tips according to the skill level. For example, it determines the skill level based on the user's usage history and feedback and provides appropriate usage tips. The usage tips providing unit also develops a system that customizes and provides usage tips according to the user's skill level. For example, it provides tutorials and guides according to the skill level. This makes it possible to customize and provide usage tips according to the user's skill level.

[0040] The usage tips providing unit can provide usage tips in the form of videos or interactive tutorials to make them visually easier to understand. The usage tips providing unit, for example, builds a system that provides usage tips in the form of videos. For example, it creates videos that explain how to use each service and provides them to users. The usage tips providing unit also develops a system that provides usage tips in the form of interactive tutorials. For example, it provides interactive tutorials that allow users to learn while actually operating the service. The usage tips providing unit also builds a system that provides videos or interactive tutorials to make usage tips visually easier to understand. For example, it provides visual guides or demos. This allows usage tips to be provided in the form of videos or interactive tutorials to make them visually easier to understand.

[0041] The usage tips providing unit can add a community function for sharing success stories and best practices of other users. The usage tips providing unit, for example, builds a system for adding a community function for sharing success stories and best practices of other users. For example, it provides a forum where users can post their own success stories. The usage tips providing unit also develops a system for sharing success stories and best practices of other users through the community function. For example, it provides a function where users can rate and comment on success stories. The usage tips providing unit also develops a system for adding a community function for sharing success stories and best practices of other users. For example, it provides a function whereby success stories can be searched for and viewed. This makes it possible to add a community function for sharing success stories and best practices of other users.

[0042] The translation department may develop an algorithm that reflects user feedback in real time to improve the accuracy of translations and summaries. The translation department may, for example, develop an algorithm that reflects user feedback in real time to improve the accuracy of translations and summaries. For example, the accuracy of translations and summaries may be adjusted based on user ratings and comments. The translation department may also build a system that improves the accuracy of translations and summaries based on user feedback. For example, the feedback data may be analyzed in real time to improve the accuracy of translations and summaries. The translation department may also develop a system that reflects user feedback in real time to improve the accuracy of translations and summaries. For example, the accuracy of translations and summaries may be adjusted based on user ratings and comments. This may allow the development of an algorithm that reflects user feedback in real time to improve the accuracy of translations and summaries.

[0043] The translation department can customize the translation and summary results according to the user's expertise and background. For example, the translation department builds a system that customizes the translation and summary results according to the user's expertise and background. For example, it provides translations and summaries that take technical terminology and industry jargon into consideration. The translation department also develops an algorithm that customizes the translation and summary results according to the user's expertise and background. For example, it adjusts the translation and summary based on the user's profile information. The translation department also develops a system that customizes the translation and summary results according to the user's expertise and background. For example, it provides translations and summaries that take technical terminology and industry jargon into consideration. This allows the translation and summary results to be customized according to the user's expertise and background.

[0044] The translation department can provide the results of translation and summarization in different formats (text, audio, video) to improve user convenience. The translation department, for example, builds a system that provides the results of translation and summarization in different formats. For example, it provides translations and summaries in text, audio, and video formats. The translation department also develops algorithms for providing the results of translation and summarization in different formats. For example, it utilizes technology for converting text to audio and technology for generating video. The translation department also develops a system that provides the results of translation and summarization in different formats. For example, it provides translations and summaries in text, audio, and video formats according to the user's preferences. This allows the results of translation and summarization to be provided in different formats to improve user convenience.

[0045] The translation department may add a function that allows the results of translation and summarization to be shared with other users, thereby promoting collaboration. For example, the translation department may build a system that adds a function that allows the results of translation and summarization to be shared with other users. For example, the translation department may generate a link for sharing the results of translation and summarization. The translation department may also develop a function for sharing the results of translation and summarization with other users. For example, the translation department may provide a function that allows collaboration with other users through a shared link. The translation department may also develop a system that adds a function that allows the results of translation and summarization to be shared with other users. For example, the translation department may provide a function that allows collaboration with other users through a shared link. This may add a function that allows the results of translation and summarization to be shared with other users, thereby promoting collaboration.

[0046] The classification unit reflects user usage history and feedback in the classification algorithm of the service, thereby achieving more accurate classification. The classification unit, for example, builds a system that reflects user usage history and feedback in the classification algorithm of the service. For example, it classifies services based on usage frequency and rating scores. The classification unit also develops an algorithm that achieves more accurate classification based on usage history and feedback data. For example, it analyzes user ratings and comments and adjusts service categories. The classification unit also develops a system that reflects user usage history and feedback in the classification algorithm of the service. For example, it analyzes usage history and feedback in real time and updates the classification. In this way, it is possible to reflect user usage history and feedback in the classification algorithm of the service, thereby achieving more accurate classification.

[0047] The classification unit can customize the classification results of the service according to the user's expertise and background. The classification unit, for example, builds a system that customizes the classification results of the service according to the user's expertise and background. For example, it provides classifications that take technical terms and industry terminology into consideration. The classification unit also develops an algorithm that customizes the classification results of the service according to the user's expertise and background. For example, it adjusts the classification based on the user's profile information. The classification unit also develops a system that customizes the classification results of the service according to the user's expertise and background. For example, it provides classifications that take technical terms and industry terminology into consideration. This allows the classification results of the service to be customized according to the user's expertise and background.

[0048] The classification unit can display the service classification results in different visual formats (graphs, charts, mind maps) to allow the user to intuitively understand. The classification unit, for example, builds a system for displaying the service classification results in different visual formats. For example, it displays the classification results in the format of a graph, chart, or mind map. The classification unit also develops an algorithm for displaying the service classification results in different visual formats. For example, it provides a tool for visually displaying the classification results. The classification unit also develops a system for displaying the service classification results in different visual formats. For example, it displays the classification results in a graph or chart to allow the user to intuitively understand. This allows the service classification results to be displayed in different visual formats to allow the user to intuitively understand.

[0049] The classification unit can integrate the service classification results with other datasets (e.g., patent data or market data) to gain new insights. For example, the classification unit builds a system that integrates the service classification results with other datasets. For example, it integrates with patent data or market data to gain new insights. The classification unit also develops an algorithm for integrating the service classification results with other datasets. For example, it analyzes patent data or market data and reflects the results in the service classification. The classification unit also develops a system that integrates the service classification results with other datasets. For example, it integrates with patent data or market data to gain new insights. This allows the service classification results to be integrated with other datasets to gain new insights.

[0050] The feedback unit can analyze the content of the feedback and automatically extract user needs and issues, and reflect this in improvements to the platform. The feedback unit, for example, builds a system that analyzes the content of the feedback and automatically extracts user needs and issues. For example, it analyzes feedback data and identifies areas for improvement. The feedback unit also develops an algorithm that extracts needs and issues based on user feedback. For example, it analyzes feedback data and identifies areas for improvement in the platform. The feedback unit also develops a system that analyzes the content of the feedback and automatically extracts user needs and issues. For example, it analyzes feedback data in real time and identifies areas for improvement. This makes it possible to analyze the content of the feedback and automatically extract user needs and issues, and reflect this in improvements to the platform.

[0051] The feedback unit can automate the feedback collection process, allowing users to easily provide their opinions. The feedback unit, for example, builds a system that automates the feedback collection process. For example, it automatically generates a feedback form, allowing users to easily provide their opinions. The feedback unit also develops an algorithm for automating the feedback collection process. For example, it displays a feedback form in response to a user's operation. The feedback unit also develops a system that automates the feedback collection process. For example, it automatically generates a feedback form, allowing users to easily provide their opinions. This automates the feedback collection process, allowing users to easily provide their opinions.

[0052] The feedback unit can share the feedback results with other users and promote improvement suggestions throughout the community. The feedback unit, for example, builds a system for sharing the feedback results with other users. For example, it provides a forum for sharing the feedback results. The feedback unit also develops a function for sharing the feedback results with other users. For example, it generates a link for sharing the feedback results. The feedback unit also develops a system for sharing the feedback results with other users. For example, it provides a forum for sharing the feedback results. This allows the feedback results to be shared with other users and promote improvement suggestions throughout the community.

[0053] The feedback unit can display the feedback content in different visual formats (graphs, charts, mind maps) to allow the user to intuitively understand. The feedback unit, for example, builds a system for displaying the feedback content in different visual formats. For example, the feedback content is displayed in the format of a graph, chart, or mind map. The feedback unit also develops an algorithm for displaying the feedback content in different visual formats. For example, the feedback unit provides a tool for visually displaying the feedback content. The feedback unit also develops a system for displaying the feedback content in different visual formats. For example, the feedback content is displayed in a graph or chart to allow the user to intuitively understand. This allows the feedback content to be displayed in different visual formats to allow the user to intuitively understand.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The AI-Compass system can also include a health monitoring unit that monitors the user's health status and suggests optimal services based on that status. For example, it can analyze the user's heart rate and sleep patterns to suggest services that help reduce stress and promote relaxation. The health monitoring unit can also suggest fitness-related services based on the user's exercise data. Furthermore, the health monitoring unit can analyze the user's dietary data to suggest services that are useful for nutritional management and dieting. This makes it possible to suggest optimal services based on the user's health status.

[0056] The AI-Compass system can further include a learning support unit that analyzes the user's learning history and proposes services to maximize learning effectiveness. For example, it analyzes the content the user has studied in the past and their progress and proposes what they should study next. The learning support unit can also propose optimal learning methods and learning materials based on the user's learning style. Furthermore, the learning support unit can propose a schedule to maximize learning effectiveness based on the user's learning history. This makes it possible to propose services to maximize the user's learning effectiveness.

[0057] The AI-Compass system can further include a hobby support unit that analyzes the user's hobbies and interests and proposes services to support the user's hobby activities. For example, the system can analyze the user's areas of interest and events the user has attended in the past and propose related events and activities. The hobby support unit can also provide information and communities related to the user's hobbies. Furthermore, the hobby support unit can propose tools and resources to support the user's hobby activities. This makes it possible to propose services to support the user's hobby activities.

[0058] The AI-Compass system can also analyze the user's geographic location information and suggest optimal services based on that location information. For example, it can suggest nearby restaurants and cafes based on the user's current location. It can also suggest events and activities that the user can participate in based on the geographic location information. It can also analyze the user's movement patterns based on the location information and suggest optimal transportation methods and routes. This makes it possible to suggest optimal services based on the user's geographic location information.

[0059] The AI-Compass system can also analyze a user's social media data to suggest optimal services based on their interests. For example, it can analyze a user's posts and likes to suggest related services. It can also suggest topics and events that the user is interested in based on social media data. It can also suggest services that are popular within the user's network based on social media data. This allows it to suggest optimal services based on a user's social media data.

[0060] The AI-Compass system can also analyze the user's schedule and calendar data to suggest services at the optimal time. For example, it can send reminders and notifications based on the user's schedule. It can also suggest services that are optimal for the time periods the user is available based on the schedule and calendar data. It can also suggest services and events related to the user's schedule based on the schedule and calendar data. This allows it to suggest services at the optimal time based on the user's schedule and calendar data.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The service aggregation unit aggregates services. For example, various generative AI services such as automatic text generation, image analysis, and data analysis are aggregated. Step 2: The ranking unit displays the ranking of the services aggregated by the service aggregation unit. For example, the ranking is displayed based on frequency of use or rating. Step 3: The recommendation unit recommends services based on the rankings displayed by the ranking unit. For example, if a user requests "automatic text generation," related services are recommended. Step 4: The translation department automatically translates the service. For example, a service provided in English is automatically translated into Japanese. Step 5: The summarizing unit automatically summarizes the service, for example, briefly summarizing the content of the service. Step 6: The classification unit automatically classifies the services. For example, they are classified into categories such as text generation services, image analysis services, and data analysis services. Step 7: The feedback section collects user feedback and reflects it in improving the platform. For example, if a user sends feedback such as "I would like the usability of this service to be improved," the generation AI analyzes that opinion and reflects it in improving the platform.

[0063] (Example 2) The AI-Compass system according to an embodiment of the present invention aggregates services that utilize generative AI and provides an environment in which users can freely select and use services that best suit their challenges. This allows users to quickly find and use the service that best suits their challenges. Furthermore, the system also allows users to overcome language barriers and use overseas services, enabling problem-solving from a global perspective. Furthermore, by incorporating user feedback, the system realizes a platform that is constantly evolving.

[0064] The AI-Compass system according to the embodiment includes a service aggregation unit, a ranking unit, a recommendation unit, a translation unit, a summarization unit, a classification unit, and a feedback unit. The service aggregation unit aggregates services. For example, various generative AI services, such as automatic text generation, image analysis, and data analysis, are aggregated. The ranking unit displays rankings of the services aggregated by the service aggregation unit. For example, rankings based on frequency of use and ratings are displayed. The recommendation unit recommends services based on the rankings displayed by the ranking unit. For example, if a user requests "automatic text generation," related services are recommended. The translation unit automatically translates services. For example, services provided in English are automatically translated into Japanese. The summarization unit automatically summarizes services. For example, the summarization unit briefly summarizes the content of the service. The classification unit automatically classifies services. For example, services are classified into categories such as text generation services, image analysis services, and data analysis services. The feedback unit collects user feedback and reflects it in improving the platform. For example, if a user sends feedback such as "I would like the usability of this service to be improved," the generation AI analyzes that opinion and reflects it in improving the platform. As a result, the AI-Compass system according to the embodiment provides an environment in which users can freely select and use services that suit their needs.

[0065] The service aggregation unit analyzes the usage history, learns the user's usage patterns, and can automatically suggest the most suitable service for the next use. The service aggregation unit, for example, analyzes the usage history of each service and learns the user's usage patterns. For example, it identifies services that the user uses frequently and trends in use during specific time periods, and automatically suggests the most suitable service for the next use. The service aggregation unit also learns usage patterns based on the user's past usage history and develops an algorithm that suggests the most suitable service for the next use. For example, it makes suggestions taking into account the frequency and time periods of use of specific services. The service aggregation unit also analyzes the usage history and learns the user's usage patterns, thereby building a system that automatically suggests the most suitable service for the next use. For example, it suggests the most suitable service for the next use based on the history of services the user has used in the past. In this way, it is possible to learn the user's usage patterns and suggest the most suitable service for the next use.

[0066] The service aggregation unit can develop an algorithm that evaluates service performance based on past user feedback and selects the optimal service. The service aggregation unit, for example, analyzes past user feedback and develops an algorithm that evaluates service performance. For example, it evaluates service quality and usability based on user ratings and comments. The service aggregation unit also constructs an algorithm that evaluates service performance based on feedback data and selects the optimal service. For example, it analyzes user rating scores and comments and suggests the optimal service. The service aggregation unit also develops a system that evaluates service performance based on past user feedback. For example, it analyzes feedback data, evaluates service quality and usability, and selects the optimal service. This makes it possible to evaluate service performance based on user feedback and select the optimal service.

[0067] The service aggregation unit uses the emotion estimation function to monitor the emotions of a user when using a service in real time, and can preferentially suggest services that elicit positive emotions. The service aggregation unit, for example, uses the emotion estimation function to monitor the emotions of a user when using a service in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The service aggregation unit also builds a system that monitors the user's emotions in real time and preferentially suggests services that elicit positive emotions. For example, it preferentially suggests services with a high emotion score. The service aggregation unit also uses the emotion estimation function to monitor the user's emotions in real time and develops an algorithm that suggests services that elicit positive emotions. For example, it suggests optimal services based on the user's emotion score. This makes it possible to monitor the user's emotions in real time and suggest services that elicit positive emotions.

[0068] The service aggregation unit can make services available seamlessly across different devices. For example, the service aggregation unit builds a system that makes services available seamlessly across different devices. For example, it makes it possible to continue using a service started on a smartphone on a PC. The service aggregation unit also develops data synchronization and sharing functions to achieve seamless service use across devices. For example, it synchronizes data between devices using cloud storage. The service aggregation unit also unifies the user interface to make services available seamlessly across different devices. For example, it provides the same operating feel on smartphones, tablets, and PCs. This allows services to be used seamlessly across different devices.

[0069] The service aggregation unit can provide a dashboard that visualizes service usage status and allows users to understand at a glance which services they are using and to what extent. The service aggregation unit, for example, builds a system that provides a dashboard that visualizes service usage status. For example, it displays the frequency and usage time of each service in graphs and charts. The service aggregation unit also develops a dashboard that allows users to understand at a glance which services they are using and to what extent. For example, it updates the usage status in real time and displays it visually. The service aggregation unit also builds a system that collects and analyzes data to visualize service usage status. For example, it collects usage data for each service and displays it on a dashboard. This visualizes service usage status and allows users to understand it at a glance.

[0070] The service aggregation unit uses the emotion estimation function to analyze the emotions a user feels when using a service and customize the service based on the emotions. The service aggregation unit, for example, uses the emotion estimation function to build a system that analyzes the emotions a user feels when using a service. For example, the service aggregation unit analyzes the user's facial expressions and voice and calculates an emotion score. The service aggregation unit also analyzes the user's emotions and develops an algorithm that customizes services based on the emotions. For example, the service aggregation unit adjusts the settings and content of the service according to the emotion score. The service aggregation unit also uses the emotion estimation function to analyze the user's emotions and build a system that customizes services based on the emotions. For example, the service aggregation unit performs customization to elicit positive emotions. This makes it possible to customize services based on the user's emotions.

[0071] The ranking unit can generate individually customized rankings based on the user's past usage history and feedback. The ranking unit, for example, analyzes the user's past usage history and feedback and builds a system that generates individually customized rankings. For example, it creates rankings based on usage frequency and evaluation scores. The ranking unit also develops an algorithm that generates individually customized rankings based on the usage history and feedback data. For example, it creates rankings that reflect the user's preferences and evaluations. The ranking unit also develops a system that generates individually customized rankings based on the user's past usage history and feedback. For example, it analyzes the usage history and feedback in real time and updates the rankings. In this way, it is possible to generate individually customized rankings based on the user's past usage history and feedback.

[0072] The recommendation unit can utilize social media data to reflect the user's interests and concerns. The recommendation unit, for example, utilizes social media data in a recommendation algorithm to build a system that reflects the user's interests and concerns. For example, it analyzes data on user posts and likes. The recommendation unit also develops a recommendation algorithm that reflects the user's interests and concerns based on social media data. For example, it analyzes data on users' following and hashtags. The recommendation unit also utilizes social media data in a recommendation algorithm to develop a system that reflects the user's interests and concerns. For example, it analyzes data on users' social media activity and reflects it in recommendations. In this way, social media data can be utilized to reflect the user's interests and concerns.

[0073] The recommendation unit uses the emotion estimation function to make recommendations according to the user's emotional state and can prioritize suggest services that elicit positive emotions. The recommendation unit, for example, uses the emotion estimation function to build a system that makes recommendations according to the user's emotional state. For example, the recommendation unit analyzes the user's facial expressions and voice and calculates an emotion score. The recommendation unit also develops an algorithm that makes recommendations according to the user's emotional state and prioritizes suggesting services that elicit positive emotions. For example, the recommendation unit suggests services according to the emotion score. The recommendation unit also uses the emotion estimation function to develop a system that makes recommendations according to the user's emotional state. For example, the recommendation unit prioritizes suggesting services that elicit positive emotions. This makes it possible to make recommendations according to the user's emotional state and suggest services that elicit positive emotions.

[0074] The recommendation unit can customize the recommendation function based on the user's geographical location information and suggest region-specific services. The recommendation unit, for example, builds a system that customizes the recommendation function based on the user's geographical location information. For example, it suggests region-specific services based on the user's current location. The recommendation unit also develops a recommendation algorithm that suggests the most suitable services to the user based on the geographical location information. For example, it obtains the user's location information in real time and suggests region-specific services. The recommendation unit also develops a system that customizes the recommendation function based on the user's geographical location information. For example, it suggests local events and services based on the user's location information. This makes it possible to suggest region-specific services based on the user's geographical location information.

[0075] The recommendation unit can link with the user's schedule and calendar and suggest services at the optimal timing. The recommendation unit, for example, builds a system that links recommendation results with the user's schedule and calendar. For example, it suggests services at the optimal timing based on the user's schedule. The recommendation unit also develops a recommendation algorithm that suggests services to the user at the optimal timing based on schedule and calendar data. For example, it suggests services based on the user's schedule. The recommendation unit also develops a system that links recommendation results with the user's schedule and calendar. For example, it suggests services at the optimal timing based on the user's schedule. This makes it possible to link with the user's schedule and calendar and suggest services at the optimal timing.

[0076] The ranking unit uses the emotion estimation function to generate rankings based on the user's emotions, and can display services that are likely to resonate emotionally at the top. The ranking unit, for example, uses the emotion estimation function to build a system that generates rankings based on the user's emotions. For example, the ranking unit analyzes the user's facial expressions and voice and calculates an emotion score. The ranking unit also develops an algorithm that generates rankings based on the user's emotions and displays services that are likely to resonate emotionally at the top. For example, the ranking unit creates rankings according to the emotion score. The ranking unit also uses the emotion estimation function to develop a system that generates rankings based on the user's emotions. For example, services that elicit positive emotions are displayed at the top. This allows rankings to be generated based on the user's emotions, and services that are likely to resonate emotionally at the top.

[0077] The usage tips providing unit can analyze usage data of each service and automatically extract the most effective usage to provide to the user. The usage tips providing unit, for example, analyzes usage data of each service and builds a system that automatically extracts the most effective usage. For example, it identifies effective usage based on usage frequency and success rate and provides it to the user. The usage tips providing unit also develops an algorithm that extracts the most effective usage based on usage data. For example, it analyzes user usage history and feedback to identify effective usage. The usage tips providing unit also develops a system that analyzes usage data of each service and automatically extracts the most effective usage. For example, it analyzes usage data in real time and provides effective usage to the user. This makes it possible to analyze usage data of each service and automatically extract the most effective usage to provide to the user.

[0078] The usage tips providing unit can customize and provide usage tips according to the user's skill level. For example, the usage tips providing unit builds a system that customizes and provides usage tips according to the user's skill level. For example, it provides simple usage tips for beginners and advanced usage tips for advanced users. The usage tips providing unit also develops an algorithm that customizes usage tips according to the skill level. For example, it determines the skill level based on the user's usage history and feedback and provides appropriate usage tips. The usage tips providing unit also develops a system that customizes and provides usage tips according to the user's skill level. For example, it provides tutorials and guides according to the skill level. This makes it possible to customize and provide usage tips according to the user's skill level.

[0079] The usage tips providing unit uses the emotion estimation function to analyze the emotions of the user when learning usage tips and can suggest a method for eliciting positive emotions. The usage tips providing unit, for example, uses the emotion estimation function to build a system that analyzes the emotions of the user when learning usage tips. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The usage tips providing unit also analyzes the user's emotions and develops an algorithm that suggests a method for eliciting positive emotions. For example, it adjusts the learning method according to the emotion score. The usage tips providing unit also uses the emotion estimation function to develop a system that analyzes the user's emotions and suggests a method for eliciting positive emotions. For example, it provides a learning method for eliciting positive emotions. This makes it possible to analyze the emotions of the user when learning usage tips and suggest a method for eliciting positive emotions.

[0080] The usage tips providing unit can provide usage tips in the form of videos or interactive tutorials to make them visually easier to understand. The usage tips providing unit, for example, builds a system that provides usage tips in the form of videos. For example, it creates videos that explain how to use each service and provides them to users. The usage tips providing unit also develops a system that provides usage tips in the form of interactive tutorials. For example, it provides interactive tutorials that allow users to learn while actually operating the service. The usage tips providing unit also builds a system that provides videos or interactive tutorials to make usage tips visually easier to understand. For example, it provides visual guides or demos. This allows usage tips to be provided in the form of videos or interactive tutorials to make them visually easier to understand.

[0081] The usage tips providing unit can add a community function for sharing success stories and best practices of other users. The usage tips providing unit, for example, builds a system for adding a community function for sharing success stories and best practices of other users. For example, it provides a forum where users can post their own success stories. The usage tips providing unit also develops a system for sharing success stories and best practices of other users through the community function. For example, it provides a function where users can rate and comment on success stories. The usage tips providing unit also develops a system for adding a community function for sharing success stories and best practices of other users. For example, it provides a function whereby success stories can be searched for and viewed. This makes it possible to add a community function for sharing success stories and best practices of other users.

[0082] The usage tips providing unit uses the emotion estimation function to monitor the emotions of the user when learning usage tips in real time, thereby maximizing the learning effect. The usage tips providing unit, for example, uses the emotion estimation function to build a system that monitors the emotions of the user when learning usage tips in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The usage tips providing unit also monitors the user's emotions in real time and develops a system for maximizing the learning effect. For example, it adjusts the learning method according to the emotion score. The usage tips providing unit also uses the emotion estimation function to build a system that monitors the user's emotions in real time and maximizes the learning effect. For example, it provides a learning method for eliciting positive emotions. This makes it possible to monitor the emotions of the user when learning usage tips in real time and maximize the learning effect.

[0083] The translation department may develop an algorithm that reflects user feedback in real time to improve the accuracy of translations and summaries. The translation department may, for example, develop an algorithm that reflects user feedback in real time to improve the accuracy of translations and summaries. For example, the accuracy of translations and summaries may be adjusted based on user ratings and comments. The translation department may also build a system that improves the accuracy of translations and summaries based on user feedback. For example, the feedback data may be analyzed in real time to improve the accuracy of translations and summaries. The translation department may also develop a system that reflects user feedback in real time to improve the accuracy of translations and summaries. For example, the accuracy of translations and summaries may be adjusted based on user ratings and comments. This may allow the development of an algorithm that reflects user feedback in real time to improve the accuracy of translations and summaries.

[0084] The translation department can customize the translation and summary results according to the user's expertise and background. For example, the translation department builds a system that customizes the translation and summary results according to the user's expertise and background. For example, it provides translations and summaries that take technical terminology and industry jargon into consideration. The translation department also develops an algorithm that customizes the translation and summary results according to the user's expertise and background. For example, it adjusts the translation and summary based on the user's profile information. The translation department also develops a system that customizes the translation and summary results according to the user's expertise and background. For example, it provides translations and summaries that take technical terminology and industry jargon into consideration. This allows the translation and summary results to be customized according to the user's expertise and background.

[0085] The translation unit uses the emotion estimation function to analyze the user's emotions regarding the results of the translation or summary, and can provide translations or summaries that elicit positive emotions. The translation unit, for example, uses the emotion estimation function to build a system that analyzes the user's emotions regarding the results of the translation or summary. For example, the translation unit analyzes the user's facial expressions and voice and calculates an emotion score. The translation unit also analyzes the user's emotions and develops an algorithm that provides translations or summaries that elicit positive emotions. For example, the translation unit adjusts the content of the translation or summary according to the emotion score. The translation unit also uses the emotion estimation function to analyze the user's emotions regarding the results of the translation or summary, and develops a system that provides translations or summaries that elicit positive emotions. For example, the translation unit provides translations or summaries that elicit positive emotions. This makes it possible to analyze the user's emotions regarding the results of the translation or summary, and provide translations or summaries that elicit positive emotions.

[0086] The translation department can provide the results of translation and summarization in different formats (text, audio, video) to improve user convenience. The translation department, for example, builds a system that provides the results of translation and summarization in different formats. For example, it provides translations and summaries in text, audio, and video formats. The translation department also develops algorithms for providing the results of translation and summarization in different formats. For example, it utilizes technology for converting text to audio and technology for generating video. The translation department also develops a system that provides the results of translation and summarization in different formats. For example, it provides translations and summaries in text, audio, and video formats according to the user's preferences. This allows the results of translation and summarization to be provided in different formats to improve user convenience.

[0087] The translation department may add a function that allows the results of translation and summarization to be shared with other users, thereby promoting collaboration. For example, the translation department may build a system that adds a function that allows the results of translation and summarization to be shared with other users. For example, the translation department may generate a link for sharing the results of translation and summarization. The translation department may also develop a function for sharing the results of translation and summarization with other users. For example, the translation department may provide a function that allows collaboration with other users through a shared link. The translation department may also develop a system that adds a function that allows the results of translation and summarization to be shared with other users. For example, the translation department may provide a function that allows collaboration with other users through a shared link. This may add a function that allows the results of translation and summarization to be shared with other users, thereby promoting collaboration.

[0088] The translation unit uses the emotion estimation function to monitor the user's emotions regarding the translation and summarization results in real time, and can provide optimal results. For example, the translation unit uses the emotion estimation function to build a system that monitors the user's emotions regarding the translation and summarization results in real time. For example, the translation unit analyzes the user's facial expressions and voice and calculates an emotion score. The translation unit also monitors the user's emotions in real time and develops an algorithm that provides optimal translation and summarization results. For example, the translation unit adjusts the content of the translation and summary according to the emotion score. The translation unit also uses the emotion estimation function to develop a system that monitors the user's emotions regarding the translation and summarization results in real time and provides optimal results. For example, the translation unit provides translations and summarizations that elicit positive emotions. This makes it possible to monitor the user's emotions regarding the translation and summarization results in real time and provide optimal results.

[0089] The classification unit reflects user usage history and feedback in the classification algorithm of the service, thereby achieving more accurate classification. The classification unit, for example, builds a system that reflects user usage history and feedback in the classification algorithm of the service. For example, it classifies services based on usage frequency and rating scores. The classification unit also develops an algorithm that achieves more accurate classification based on usage history and feedback data. For example, it analyzes user ratings and comments and adjusts service categories. The classification unit also develops a system that reflects user usage history and feedback in the classification algorithm of the service. For example, it analyzes usage history and feedback in real time and updates the classification. In this way, it is possible to reflect user usage history and feedback in the classification algorithm of the service, thereby achieving more accurate classification.

[0090] The classification unit can customize the classification results of the service according to the user's expertise and background. The classification unit, for example, builds a system that customizes the classification results of the service according to the user's expertise and background. For example, it provides classifications that take technical terms and industry terminology into consideration. The classification unit also develops an algorithm that customizes the classification results of the service according to the user's expertise and background. For example, it adjusts the classification based on the user's profile information. The classification unit also develops a system that customizes the classification results of the service according to the user's expertise and background. For example, it provides classifications that take technical terms and industry terminology into consideration. This allows the classification results of the service to be customized according to the user's expertise and background.

[0091] The classification unit uses the emotion estimation function to analyze a user's emotions toward the categorized services and provide classifications that elicit positive emotions. The classification unit, for example, uses the emotion estimation function to build a system that analyzes a user's emotions toward the categorized services. For example, the classification unit analyzes a user's facial expressions and voice and calculates an emotion score. The classification unit also analyzes the user's emotions and develops an algorithm that provides classifications that elicit positive emotions. For example, the classification unit adjusts the service classification according to the emotion score. The classification unit also uses the emotion estimation function to analyze a user's emotions toward the categorized services and develops a system that provides classifications that elicit positive emotions. For example, the classifications are provided to elicit positive emotions. This makes it possible to analyze a user's emotions toward the categorized services and provide classifications that elicit positive emotions.

[0092] The classification unit can display the service classification results in different visual formats (graphs, charts, mind maps) to allow the user to intuitively understand. The classification unit, for example, builds a system for displaying the service classification results in different visual formats. For example, it displays the classification results in the format of a graph, chart, or mind map. The classification unit also develops an algorithm for displaying the service classification results in different visual formats. For example, it provides a tool for visually displaying the classification results. The classification unit also develops a system for displaying the service classification results in different visual formats. For example, it displays the classification results in a graph or chart to allow the user to intuitively understand. This allows the service classification results to be displayed in different visual formats to allow the user to intuitively understand.

[0093] The classification unit can integrate the service classification results with other datasets (e.g., patent data or market data) to gain new insights. For example, the classification unit builds a system that integrates the service classification results with other datasets. For example, it integrates with patent data or market data to gain new insights. The classification unit also develops an algorithm for integrating the service classification results with other datasets. For example, it analyzes patent data or market data and reflects the results in the service classification. The classification unit also develops a system that integrates the service classification results with other datasets. For example, it integrates with patent data or market data to gain new insights. This allows the service classification results to be integrated with other datasets to gain new insights.

[0094] The classification unit can use the emotion estimation function to monitor the user's emotion toward the categorized services in real time and provide an optimal classification. The classification unit, for example, uses the emotion estimation function to build a system that monitors the user's emotion toward the categorized services in real time. For example, the classification unit analyzes the user's facial expressions and voice and calculates an emotion score. The classification unit also monitors the user's emotion in real time and develops an algorithm that provides an optimal classification. For example, the classification unit adjusts the service classification according to the emotion score. The classification unit also uses the emotion estimation function to monitor the user's emotion toward the categorized services in real time and develops a system that provides an optimal classification. For example, the classification unit provides a classification that elicits positive emotions. This makes it possible to monitor the user's emotion toward the categorized services in real time and provide an optimal classification.

[0095] The feedback unit can analyze the content of the feedback and automatically extract user needs and issues, and reflect this in improvements to the platform. The feedback unit, for example, builds a system that analyzes the content of the feedback and automatically extracts user needs and issues. For example, it analyzes feedback data and identifies areas for improvement. The feedback unit also develops an algorithm that extracts needs and issues based on user feedback. For example, it analyzes feedback data and identifies areas for improvement in the platform. The feedback unit also develops a system that analyzes the content of the feedback and automatically extracts user needs and issues. For example, it analyzes feedback data in real time and identifies areas for improvement. This makes it possible to analyze the content of the feedback and automatically extract user needs and issues, and reflect this in improvements to the platform.

[0096] The feedback unit can automate the feedback collection process, allowing users to easily provide their opinions. The feedback unit, for example, builds a system that automates the feedback collection process. For example, it automatically generates a feedback form, allowing users to easily provide their opinions. The feedback unit also develops an algorithm for automating the feedback collection process. For example, it displays a feedback form in response to a user's operation. The feedback unit also develops a system that automates the feedback collection process. For example, it automatically generates a feedback form, allowing users to easily provide their opinions. This automates the feedback collection process, allowing users to easily provide their opinions.

[0097] The feedback unit uses the emotion estimation function to analyze the user's emotions regarding the feedback content and can make improvement suggestions that elicit positive emotions. The feedback unit, for example, uses the emotion estimation function to build a system that analyzes the user's emotions regarding the feedback content. For example, the feedback unit analyzes the user's facial expressions and voice and calculates an emotion score. The feedback unit also develops an algorithm that analyzes the user's emotions and makes improvement suggestions that elicit positive emotions. For example, the feedback unit adjusts the improvement suggestions according to the emotion score. The feedback unit also uses the emotion estimation function to develop a system that analyzes the user's emotions regarding the feedback content and makes improvement suggestions that elicit positive emotions. For example, the feedback unit provides improvement suggestions that elicit positive emotions. This makes it possible to analyze the user's emotions regarding the feedback content and make improvement suggestions that elicit positive emotions.

[0098] The feedback unit can share the feedback results with other users and promote improvement suggestions throughout the community. The feedback unit, for example, builds a system for sharing the feedback results with other users. For example, it provides a forum for sharing the feedback results. The feedback unit also develops a function for sharing the feedback results with other users. For example, it generates a link for sharing the feedback results. The feedback unit also develops a system for sharing the feedback results with other users. For example, it provides a forum for sharing the feedback results. This allows the feedback results to be shared with other users and promote improvement suggestions throughout the community.

[0099] The feedback unit can display the feedback content in different visual formats (graphs, charts, mind maps) to allow the user to intuitively understand. The feedback unit, for example, builds a system for displaying the feedback content in different visual formats. For example, the feedback content is displayed in the format of a graph, chart, or mind map. The feedback unit also develops an algorithm for displaying the feedback content in different visual formats. For example, the feedback unit provides a tool for visually displaying the feedback content. The feedback unit also develops a system for displaying the feedback content in different visual formats. For example, the feedback content is displayed in a graph or chart to allow the user to intuitively understand. This allows the feedback content to be displayed in different visual formats to allow the user to intuitively understand.

[0100] The feedback unit can use the emotion estimation function to monitor the user's emotions regarding the feedback content in real time and provide optimal improvement suggestions. The feedback unit, for example, uses the emotion estimation function to build a system that monitors the user's emotions regarding the feedback content in real time. For example, the feedback unit analyzes the user's facial expressions and voice and calculates an emotion score. The feedback unit also develops an algorithm that monitors the user's emotions in real time and provides optimal improvement suggestions. For example, the improvement suggestions are adjusted according to the emotion score. The feedback unit also uses the emotion estimation function to develop a system that monitors the user's emotions regarding the feedback content in real time and provides optimal improvement suggestions. For example, improvement suggestions are provided to elicit positive emotions. This makes it possible to monitor the user's emotions regarding the feedback content in real time and provide optimal improvement suggestions.

[0101] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0102] The AI-Compass system can also include a health monitoring unit that monitors the user's health status and suggests optimal services based on that status. For example, it can analyze the user's heart rate and sleep patterns to suggest services that help reduce stress and promote relaxation. The health monitoring unit can also suggest fitness-related services based on the user's exercise data. Furthermore, the health monitoring unit can analyze the user's dietary data to suggest services that are useful for nutritional management and dieting. This makes it possible to suggest optimal services based on the user's health status.

[0103] The AI-Compass system can further include a learning support unit that analyzes the user's learning history and proposes services to maximize learning effectiveness. For example, it analyzes the content the user has studied in the past and their progress and proposes what they should study next. The learning support unit can also propose optimal learning methods and learning materials based on the user's learning style. Furthermore, the learning support unit can propose a schedule to maximize learning effectiveness based on the user's learning history. This makes it possible to propose services to maximize the user's learning effectiveness.

[0104] The AI-Compass system can further include a hobby support unit that analyzes the user's hobbies and interests and proposes services to support the user's hobby activities. For example, the system can analyze the user's areas of interest and events the user has attended in the past and propose related events and activities. The hobby support unit can also provide information and communities related to the user's hobbies. Furthermore, the hobby support unit can propose tools and resources to support the user's hobby activities. This makes it possible to propose services to support the user's hobby activities.

[0105] The AI-Compass system can also estimate the user's emotions and suggest services for relaxation and stress reduction based on the estimated emotions. For example, it can analyze the user's facial expressions and voice to estimate their stress level. The emotion estimation function can also be used to suggest music or meditation services that will help the user relax. Furthermore, the emotion estimation function can also be used to suggest activities for relaxation and stress reduction based on the user's emotions. This makes it possible to suggest services for relaxation and stress reduction based on the user's emotions.

[0106] The AI-Compass system can also estimate the user's emotions and suggest entertainment services based on the estimated emotions. For example, it can analyze the user's facial expressions and voice to estimate their current emotional state. It can also use the emotion estimation function to suggest movies and games that the user can enjoy. It can also use the emotion estimation function to suggest entertainment events and activities based on the user's emotions. This makes it possible to suggest entertainment services based on the user's emotions.

[0107] The AI-Compass system can also estimate the user's emotions and suggest services to maximize learning effectiveness based on the estimated emotions. For example, it can analyze the user's facial expressions and voice to estimate their emotional state during learning. The emotion estimation function can also be used to suggest learning environments and learning materials that help the user concentrate. Furthermore, the emotion estimation function can also be used to suggest schedules and break methods to maximize learning effectiveness based on the user's emotions. This makes it possible to suggest services to maximize learning effectiveness based on the user's emotions.

[0108] The AI-Compass system can also estimate the user's emotions and provide feedback based on the estimated emotions. For example, it can analyze the user's facial expressions and voice to estimate their emotional state in response to the feedback. The emotion estimation function can also be used to provide feedback that encourages the user to have positive emotions. Furthermore, the emotion estimation function can also be used to adjust the content and method of feedback based on the user's emotions. This allows the system to provide feedback based on the user's emotions.

[0109] The AI-Compass system can also analyze the user's geographic location information and suggest optimal services based on that location information. For example, it can suggest nearby restaurants and cafes based on the user's current location. It can also suggest events and activities that the user can participate in based on the geographic location information. It can also analyze the user's movement patterns based on the location information and suggest optimal transportation methods and routes. This makes it possible to suggest optimal services based on the user's geographic location information.

[0110] The AI-Compass system can also analyze a user's social media data to suggest optimal services based on their interests. For example, it can analyze a user's posts and likes to suggest related services. It can also suggest topics and events that the user is interested in based on social media data. It can also suggest services that are popular within the user's network based on social media data. This allows it to suggest optimal services based on a user's social media data.

[0111] The AI-Compass system can also analyze the user's schedule and calendar data to suggest services at the optimal time. For example, it can send reminders and notifications based on the user's schedule. It can also suggest services that are optimal for the time periods the user is available based on the schedule and calendar data. It can also suggest services and events related to the user's schedule based on the schedule and calendar data. This allows it to suggest services at the optimal time based on the user's schedule and calendar data.

[0112] The processing flow of the second embodiment will be briefly explained below.

[0113] Step 1: The service aggregation unit aggregates services. For example, various generative AI services such as automatic text generation, image analysis, and data analysis are aggregated. Step 2: The ranking unit displays the ranking of the services aggregated by the service aggregation unit. For example, the ranking is displayed based on frequency of use or rating. Step 3: The recommendation unit recommends services based on the rankings displayed by the ranking unit. For example, if a user requests "automatic text generation," related services are recommended. Step 4: The translation department automatically translates the service. For example, a service provided in English is automatically translated into Japanese. Step 5: The summarizing unit automatically summarizes the service, for example, briefly summarizing the content of the service. Step 6: The classification unit automatically classifies the services. For example, they are classified into categories such as text generation services, image analysis services, and data analysis services. Step 7: The feedback section collects user feedback and reflects it in improving the platform. For example, if a user sends feedback such as "I would like the usability of this service to be improved," the generation AI analyzes that opinion and reflects it in improving the platform.

[0114] 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.

[0115] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] 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.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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 AI 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.

[0131] 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.

[0132] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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 AI 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.

[0146] 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.

[0147] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0148] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] 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.

[0160] 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.

[0161] 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 AI 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0168] 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."

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a service aggregation unit that aggregates services; a ranking unit that displays a ranking of the services aggregated by the service aggregation unit; a recommendation unit that recommends a service based on the ranking displayed by the ranking unit; A translation department that automatically translates the service; a summarization unit that automatically summarizes the service; a classification unit that automatically classifies services; A feedback unit that collects feedback from users and reflects it in improving the platform. A system characterized by:

2. The service aggregation unit Analyzes usage history, learns the user's usage patterns, and automatically suggests the most suitable service for the next use. The system of claim 1 .

3. The service aggregation unit Develop an algorithm to evaluate the performance of the services and select the most suitable service based on the users' past feedback. The system of claim 1 .

4. The service aggregation unit The emotions of the user when using the service are monitored in real time, and the service that elicits positive emotions is preferentially suggested. The system of claim 1 .

5. The service aggregation unit Enable seamless use of the service across different devices The system of claim 1 .

6. The service aggregation unit To provide a dashboard that visualizes the usage status of the service and allows the user to understand at a glance which service the user is using and to what extent. The system of claim 1 .

7. The service aggregation unit Analyzing the emotions of the user when using the service and customizing the service based on the emotions The system of claim 1 .

8. The ranking unit The ranking is generated based on the user's past usage history and the feedback. The system of claim 1 .

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