System

Abstract

An object of the system according to the embodiment is to provide a personalized service by replacing a device in accordance with the growth of a user and taking over data.SOLUTION: A system according to an embodiment includes a device conversion unit, a data transfer unit, and a personalized service providing unit. The device conversion unit exchanges devices in accordance with the growth of the user. The data handover unit hands over the data to the device replaced by the device conversion unit. The personalized service providing section provides a service personalized based on the data taken over by the data taking-over section.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 do not adequately transfer data or personalize services when users change devices as they grow, so there is room for improvement.

[0005] The system according to the embodiment aims to provide personalized services by replacing devices as the user grows and taking over data. [Means for solving the problem]

[0006] The system according to the embodiment includes a device conversion unit, a data transfer unit, and a personalized service providing unit. The device conversion unit replaces devices as the user grows. The data transfer unit transfers data to the device replaced by the device conversion unit. The personalized service providing unit provides personalized services based on the data transferred by the data transfer unit. [Effects of the Invention]

[0007] The system according to the embodiment allows a user to exchange devices as the user grows, and data can be inherited to provide personalized services. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The personalized service providing system according to the embodiment of the present invention is a system that replaces devices as the user grows, and the generating AI takes over the data to provide personalized services. This allows the personalized service providing system to provide optimal devices and services as the user grows.

[0029] The personalized service providing system according to the embodiment includes a device conversion unit, a data transfer unit, and a personalized service providing unit. The device conversion unit replaces devices as a user grows. For example, when a child grows up and becomes a junior high school student, the device is replaced with a device designed for children. Also, when the user enters the workforce, the device is replaced with a device designed for students. The data transfer unit transfers data to the replaced device by the device conversion unit. For example, data is transferred using cloud storage. Local data can also be transferred to the new device. The personalized service providing unit provides personalized services based on the data transferred by the data transfer unit. For example, the personalized service providing system provides advice on daily outfits and job hunting based on the user's preferences and skills. It can also analyze the user's health data and suggest device settings according to the user's health condition. This allows the personalized service providing system according to the embodiment to provide optimal devices and services as the user grows. For example, users can always use devices and services that suit them throughout their growth process, from childhood to adulthood, improving their quality of life. Furthermore, receiving advice from the generation AI can help users make better choices in their job hunting and daily life.

[0030] The device conversion unit uses a generation AI to analyze a user's past behavioral patterns and automatically optimize the device's initial settings. For example, when a user switches to a new device, the generation AI analyzes the user's past usage history and behavioral patterns and automatically optimizes app placement and notification settings. For example, it places frequently used apps on the home screen and turns off unnecessary notifications. The generation AI also automatically carries over browser bookmarks and app login information to the next device based on the user's past browsing history and app usage history. For example, it adds frequently visited websites to bookmarks. The device conversion unit also analyzes the user's past behavioral patterns and automatically configures the next device to optimize battery consumption. For example, it limits background operation of frequently used apps to improve battery life. This allows the device's initial settings to be optimized based on the user's past behavioral patterns.

[0031] The device conversion unit can analyze the user's health data using generative AI and suggest device settings according to their health condition. For example, the device conversion unit analyzes the user's past health data and optimizes the settings of health management apps on a new device. For example, it automatically configures sleep tracking and exercise reminders. The generative AI also adjusts notification settings on the next device based on the user's health data. For example, if stress levels are high, it increases relaxation notifications, and if exercise is lacking, it strengthens exercise reminders. The device conversion unit also analyzes the user's health data and automatically adjusts the screen brightness and blue light cut settings on the new device. For example, it lowers screen brightness and cuts blue light at night. This allows it to suggest device settings according to the user's health condition.

[0032] The device conversion unit can use the generation AI to automatically install new apps and services based on the user's hobbies and interests. For example, the generation AI analyzes the user's past app usage history and search history and automatically installs the most suitable apps and services for the new device. For example, it automatically installs music apps and news apps that the user uses frequently. It also suggests recommended apps and services for the new device based on the user's hobbies and interests. For example, if the user is interested in sports, it installs sports-related apps. The generation AI also automatically configures the most suitable apps and services for the new device based on the user's past behavioral data. For example, if the user likes to travel, it installs travel-related apps. This makes it possible to automatically install new apps and services based on the user's hobbies and interests.

[0033] The device conversion unit can seamlessly transfer data between different devices via cloud storage. The device conversion unit, for example, uses cloud storage to build a system that seamlessly transfers data between different devices. For example, a user's photos and documents are stored in the cloud so that they can be instantly accessed on a new device. A system is also developed that automatically transfers app settings and user data via cloud storage. For example, a user's app settings and login information are stored in the cloud and automatically restored on a new device. Data transfer between different devices is also performed using cloud storage, minimizing the hassle when a user switches devices. For example, data stored in the cloud is automatically synchronized with a new device. This allows for seamless data transfer between different devices.

[0034] The personalization service providing unit can analyze a user's past purchasing history using the generation AI and suggest a personalized shopping list. For example, the personalization service providing unit uses the generation AI to analyze a user's past purchasing history and automatically generate a personalized shopping list. For example, it adds items that the user frequently purchases to the list. Furthermore, based on the user's purchasing history, the generation AI suggests items needed for the next shopping trip. For example, it adds daily necessities and food that the user purchases regularly to the list. Furthermore, the generation AI analyzes the user's purchasing history and suggests shopping lists tailored to specific events or seasons. For example, it adds items needed around Christmas or summer vacation to the list. In this way, a personalized shopping list can be suggested based on the user's past purchasing history.

[0035] The personalization service providing unit can analyze the user's learning history using the generation AI and automatically generate an optimal learning plan. For example, the personalization service providing unit uses the generation AI to analyze the user's past learning history and automatically generate a personalized learning plan. For example, it proposes a learning plan that focuses on areas in which the user is weak. Furthermore, based on the user's learning history, the generation AI suggests the next content and learning materials to be learned. For example, it indicates the next step the user should take based on what the user has learned in the past. Furthermore, the generation AI analyzes the user's learning history and proposes a learning plan aimed at a specific goal. For example, it automatically generates a learning plan for a qualification exam or university entrance exam. This makes it possible to automatically generate an optimal learning plan based on the user's learning history.

[0036] The personalization service providing unit can analyze a user's social media activity using the generation AI and suggest personalized content. For example, the generation AI analyzes a user's social media activity and suggests personalized content. For example, it suggests related articles and videos based on topics the user is interested in and accounts the user follows. The generation AI also suggests content that the user should watch next based on the user's social media activity. For example, it recommends related content based on genres and themes that the user frequently views. The generation AI also analyzes a user's social media activity and suggests content tailored to specific events or trends. For example, it provides content related to events and trends that the user is interested in. This makes it possible to suggest personalized content based on the user's social media activity.

[0037] The personalization service providing unit can analyze the user's fitness data using the generation AI and propose an optimal exercise plan. For example, the generation AI analyzes the user's past fitness data and proposes a personalized exercise plan. For example, the generation AI proposes an optimal exercise menu based on the user's exercise history and physical fitness level. The generation AI also proposes the next exercise or training to do based on the user's fitness data. For example, it provides an exercise plan aimed at the user's fitness goal. The generation AI also analyzes the user's fitness data and proposes an exercise plan aimed at a specific goal. For example, it provides a personalized exercise plan for a marathon or strength training. This makes it possible to propose an optimal exercise plan based on the user's fitness data.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The personalization service providing system can also analyze a user's past purchasing history and suggest a personalized shopping list. For example, it can add products that the user frequently purchases to the list. Based on the user's purchasing history, the generation AI can also suggest items needed for the next shopping trip. For example, it can add daily necessities and food items that the user purchases regularly to the list. The generation AI can also analyze the user's purchasing history and suggest shopping lists tailored to specific events or seasons. For example, it can add items needed around Christmas or summer vacation to the list. This makes it possible to suggest a personalized shopping list based on the user's past purchasing history.

[0040] The personalized service provision system can also analyze the user's learning history and automatically generate the optimal learning plan. For example, it can propose a learning plan that focuses on the areas in which the user is weak. The generation AI can also suggest the next content and learning materials to be studied based on the user's learning history. For example, it can show the next step the user should take based on what they have learned in the past. The generation AI can also analyze the user's learning history and propose a learning plan aimed at a specific goal. For example, it can automatically generate a learning plan for a qualification exam or university entrance exam. This makes it possible to automatically generate the optimal learning plan based on the user's learning history.

[0041] The personalized service providing system can also analyze the user's fitness data and propose optimal exercise plans. For example, it can propose an optimal exercise menu based on the user's exercise history and physical fitness level. The generation AI can also suggest the next exercise or training to do based on the user's fitness data. For example, it can provide an exercise plan aimed at the user's fitness goal. The generation AI can also analyze the user's fitness data and propose exercise plans aimed at specific goals. For example, it can provide a personalized exercise plan for a marathon or strength training. This allows it to propose optimal exercise plans based on the user's fitness data.

[0042] The personalization service providing system can also analyze a user's social media activity and suggest personalized content. For example, the generation AI analyzes a user's social media activity and suggests personalized content. For example, it may suggest related articles or videos based on the user's topics of interest or the accounts they follow. The generation AI may also suggest content that the user should watch next based on the user's social media activity. For example, it may recommend related content based on the genres or themes that the user frequently views. The generation AI may also analyze a user's social media activity and suggest content tailored to specific events or trends. For example, it may provide content related to events or trends that interest the user. This makes it possible to suggest personalized content based on the user's social media activity.

[0043] The processing flow of the first embodiment will be briefly explained below.

[0044] Step 1: The device conversion unit changes devices as the user grows. For example, when a child grows up and becomes a junior high school student, the device is changed from a device for children to a device for junior high school students. Also, when the user enters the workforce, the device is changed from a device for students to a device for working adults. Step 2: The data transfer unit transfers data to the replaced device by the device conversion unit. For example, data can be transferred using cloud storage. Local data can also be transferred to the new device. Step 3: The personalized service provider provides personalized services based on the data handed over by the data transfer unit. For example, it can provide daily coordination and job hunting advice based on the user's tastes and skills. It can also analyze the user's health data and suggest device settings based on their health condition.

[0045] (Example 2) The personalized service providing system according to the embodiment of the present invention is a system that replaces devices as the user grows, and the generating AI takes over the data to provide personalized services. This allows the personalized service providing system to provide optimal devices and services as the user grows.

[0046] The personalized service providing system according to the embodiment includes a device conversion unit, a data transfer unit, and a personalized service providing unit. The device conversion unit replaces devices as a user grows. For example, when a child grows up and becomes a junior high school student, the device is replaced with a device designed for children. Also, when the user enters the workforce, the device is replaced with a device designed for students. The data transfer unit transfers data to the replaced device by the device conversion unit. For example, data is transferred using cloud storage. Local data can also be transferred to the new device. The personalized service providing unit provides personalized services based on the data transferred by the data transfer unit. For example, the personalized service providing system provides advice on daily outfits and job hunting based on the user's preferences and skills. It can also analyze the user's health data and suggest device settings according to the user's health condition. This allows the personalized service providing system according to the embodiment to provide optimal devices and services as the user grows. For example, users can always use devices and services that suit them throughout their growth process, from childhood to adulthood, improving their quality of life. Furthermore, receiving advice from the generation AI can help users make better choices in their job hunting and daily life.

[0047] The device conversion unit uses a generation AI to analyze a user's past behavioral patterns and automatically optimize the device's initial settings. For example, when a user switches to a new device, the generation AI analyzes the user's past usage history and behavioral patterns and automatically optimizes app placement and notification settings. For example, it places frequently used apps on the home screen and turns off unnecessary notifications. The generation AI also automatically carries over browser bookmarks and app login information to the next device based on the user's past browsing history and app usage history. For example, it adds frequently visited websites to bookmarks. The device conversion unit also analyzes the user's past behavioral patterns and automatically configures the next device to optimize battery consumption. For example, it limits background operation of frequently used apps to improve battery life. This allows the device's initial settings to be optimized based on the user's past behavioral patterns.

[0048] The device conversion unit can analyze the user's health data using generative AI and suggest device settings according to their health condition. For example, the device conversion unit analyzes the user's past health data and optimizes the settings of health management apps on a new device. For example, it automatically configures sleep tracking and exercise reminders. The generative AI also adjusts notification settings on the next device based on the user's health data. For example, if stress levels are high, it increases relaxation notifications, and if exercise is lacking, it strengthens exercise reminders. The device conversion unit also analyzes the user's health data and automatically adjusts the screen brightness and blue light cut settings on the new device. For example, it lowers screen brightness and cuts blue light at night. This allows it to suggest device settings according to the user's health condition.

[0049] The device conversion unit uses generative AI to analyze the user's emotional state and can convert devices at a time when stress is low. The device conversion unit, for example, uses an emotion estimation function to analyze the user's emotional state in real time and suggest device conversion at a time when stress is low. For example, it sends a notification encouraging the user to change devices when the user is relaxed. It also optimizes the timing of device conversion based on the user's emotional data. For example, it avoids busy times or times of high stress and instead changes devices when the user is relaxed. It also uses the emotion estimation function to suggest the timing of device conversion based on the user's emotional state. For example, it changes devices when the user is feeling positive, supporting a smooth transition. This allows device conversion to be performed at a time when stress is low for the user.

[0050] The device conversion unit can use the generation AI to automatically install new apps and services based on the user's hobbies and interests. For example, the generation AI analyzes the user's past app usage history and search history and automatically installs the most suitable apps and services for the new device. For example, it automatically installs music apps and news apps that the user uses frequently. It also suggests recommended apps and services for the new device based on the user's hobbies and interests. For example, if the user is interested in sports, it installs sports-related apps. The generation AI also automatically configures the most suitable apps and services for the new device based on the user's past behavioral data. For example, if the user likes to travel, it installs travel-related apps. This makes it possible to automatically install new apps and services based on the user's hobbies and interests.

[0051] The device conversion unit can seamlessly transfer data between different devices via cloud storage. The device conversion unit, for example, uses cloud storage to build a system that seamlessly transfers data between different devices. For example, a user's photos and documents are stored in the cloud so that they can be instantly accessed on a new device. A system is also developed that automatically transfers app settings and user data via cloud storage. For example, a user's app settings and login information are stored in the cloud and automatically restored on a new device. Data transfer between different devices is also performed using cloud storage, minimizing the hassle when a user switches devices. For example, data stored in the cloud is automatically synchronized with a new device. This allows for seamless data transfer between different devices.

[0052] The device conversion unit can use the emotion estimation function to customize the interface so that the user feels positive about the new device. For example, the device conversion unit uses the emotion estimation function to analyze the user's emotional state and customize the interface of the new device to elicit positive emotions. For example, it can reflect the user's preferred colors and designs. The device conversion unit can also customize the interface of the new device based on the user's emotional data and set it to evoke positive emotions. For example, it can set a background image or theme that makes the user feel relaxed. The emotion estimation function can also be used to adjust the interface layout and functions so that the user feels positive about the new device. For example, it can place functions that the user uses frequently in prominent positions. In this way, the interface can be customized so that the user feels positive about the new device.

[0053] The personalization service providing unit can analyze a user's past purchasing history using the generation AI and suggest a personalized shopping list. For example, the personalization service providing unit uses the generation AI to analyze a user's past purchasing history and automatically generate a personalized shopping list. For example, it adds items that the user frequently purchases to the list. Furthermore, based on the user's purchasing history, the generation AI suggests items needed for the next shopping trip. For example, it adds daily necessities and food that the user purchases regularly to the list. Furthermore, the generation AI analyzes the user's purchasing history and suggests shopping lists tailored to specific events or seasons. For example, it adds items needed around Christmas or summer vacation to the list. In this way, a personalized shopping list can be suggested based on the user's past purchasing history.

[0054] The personalization service providing unit can analyze the user's learning history using the generation AI and automatically generate an optimal learning plan. For example, the personalization service providing unit uses the generation AI to analyze the user's past learning history and automatically generate a personalized learning plan. For example, it proposes a learning plan that focuses on areas in which the user is weak. Furthermore, based on the user's learning history, the generation AI suggests the next content and learning materials to be learned. For example, it indicates the next step the user should take based on what the user has learned in the past. Furthermore, the generation AI analyzes the user's learning history and proposes a learning plan aimed at a specific goal. For example, it automatically generates a learning plan for a qualification exam or university entrance exam. This makes it possible to automatically generate an optimal learning plan based on the user's learning history.

[0055] The personalization service providing unit can use the emotion estimation function to suggest relaxation methods according to the user's emotional state. For example, the personalization service providing unit uses the emotion estimation function to analyze the user's emotional state in real time and suggest relaxation methods. For example, if the user is feeling stressed, the unit can suggest relaxation music or a meditation app. Furthermore, the generation AI can suggest optimal relaxation methods based on the user's emotional data. For example, it can suggest activities or hobbies that will help the user relax. Furthermore, the emotion estimation function can be used to customize relaxation methods according to the user's emotional state. For example, personalized suggestions can be made based on the user's preferred relaxation methods. This makes it possible to suggest relaxation methods according to the user's emotional state.

[0056] The personalization service providing unit can analyze a user's social media activity using the generation AI and suggest personalized content. For example, the generation AI analyzes a user's social media activity and suggests personalized content. For example, it suggests related articles and videos based on topics the user is interested in and accounts the user follows. The generation AI also suggests content that the user should watch next based on the user's social media activity. For example, it recommends related content based on genres and themes that the user frequently views. The generation AI also analyzes a user's social media activity and suggests content tailored to specific events or trends. For example, it provides content related to events and trends that the user is interested in. This makes it possible to suggest personalized content based on the user's social media activity.

[0057] The personalization service providing unit can analyze the user's fitness data using the generation AI and propose an optimal exercise plan. For example, the generation AI analyzes the user's past fitness data and proposes a personalized exercise plan. For example, the generation AI proposes an optimal exercise menu based on the user's exercise history and physical fitness level. The generation AI also proposes the next exercise or training to do based on the user's fitness data. For example, it provides an exercise plan aimed at the user's fitness goal. The generation AI also analyzes the user's fitness data and proposes an exercise plan aimed at a specific goal. For example, it provides a personalized exercise plan for a marathon or strength training. This makes it possible to propose an optimal exercise plan based on the user's fitness data.

[0058] The personalization service providing unit can use the emotion estimation function to provide a news feed based on the topics that the user is most interested in. For example, the personalization service providing unit uses the emotion estimation function to analyze the user's emotional state and provide a news feed based on the topics that the user is most interested in. For example, news related to topics that the user has positive emotions about is preferentially displayed. In addition, the generation AI provides a personalized news feed based on the user's emotion data. For example, related news articles are recommended based on topics or themes that the user is interested in. In addition, the emotion estimation function is used to customize a news feed based on the topics that the user is most interested in. For example, news that the user has positive emotions about is preferentially displayed and negative news is filtered out. This makes it possible to provide a news feed based on the topics that the user is most interested in.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The personalized service providing system can also estimate the user's emotional state and, based on the estimated emotion, suggest music or podcasts that will help the user relax the most. For example, if the user is feeling stressed, it will suggest music with a relaxing effect, and if the user is relaxed, it will suggest an interesting podcast. Furthermore, based on the user's emotional data, the generative AI will suggest the most appropriate relaxation method. For example, it will suggest activities or hobbies that will help the user relax. This makes it possible to suggest relaxation methods that suit the user's emotional state.

[0061] The personalization service providing system can also analyze a user's past purchasing history and suggest a personalized shopping list. For example, it can add products that the user frequently purchases to the list. Based on the user's purchasing history, the generation AI can also suggest items needed for the next shopping trip. For example, it can add daily necessities and food items that the user purchases regularly to the list. The generation AI can also analyze the user's purchasing history and suggest shopping lists tailored to specific events or seasons. For example, it can add items needed around Christmas or summer vacation to the list. This makes it possible to suggest a personalized shopping list based on the user's past purchasing history.

[0062] The personalized service provision system can also analyze the user's learning history and automatically generate the optimal learning plan. For example, it can propose a learning plan that focuses on the areas in which the user is weak. The generation AI can also suggest the next content and learning materials to be studied based on the user's learning history. For example, it can show the next step the user should take based on what they have learned in the past. The generation AI can also analyze the user's learning history and propose a learning plan aimed at a specific goal. For example, it can automatically generate a learning plan for a qualification exam or university entrance exam. This makes it possible to automatically generate the optimal learning plan based on the user's learning history.

[0063] The personalized service providing system can also analyze the user's fitness data and propose optimal exercise plans. For example, it can propose an optimal exercise menu based on the user's exercise history and physical fitness level. The generation AI can also suggest the next exercise or training to do based on the user's fitness data. For example, it can provide an exercise plan aimed at the user's fitness goal. The generation AI can also analyze the user's fitness data and propose exercise plans aimed at specific goals. For example, it can provide a personalized exercise plan for a marathon or strength training. This allows it to propose optimal exercise plans based on the user's fitness data.

[0064] The personalization service providing system can also analyze a user's social media activity and suggest personalized content. For example, the generation AI analyzes a user's social media activity and suggests personalized content. For example, it may suggest related articles or videos based on the user's topics of interest or the accounts they follow. The generation AI may also suggest content that the user should watch next based on the user's social media activity. For example, it may recommend related content based on the genres or themes that the user frequently views. The generation AI may also analyze a user's social media activity and suggest content tailored to specific events or trends. For example, it may provide content related to events or trends that interest the user. This makes it possible to suggest personalized content based on the user's social media activity.

[0065] The personalized service providing system can also estimate the user's emotional state and, based on the estimated emotions, provide a news feed based on the topics that interest the user most. For example, news related to topics that the user has positive emotions about can be displayed preferentially. The generation AI also provides a personalized news feed based on the user's emotional data. For example, related news articles can be recommended based on topics or themes that interest the user. The emotion estimation function can also be used to customize the news feed based on the topics that interest the user most. For example, news that the user has positive emotions about can be displayed preferentially and negative news can be filtered out. This makes it possible to provide a news feed based on the topics that interest the user most.

[0066] The personalized service providing system can also estimate the user's emotional state and, based on the estimated emotion, suggest music or podcasts that will help the user relax the most. For example, if the user is feeling stressed, it will suggest music with a relaxing effect, and if the user is relaxed, it will suggest an interesting podcast. Furthermore, based on the user's emotional data, the generative AI will suggest the most appropriate relaxation method. For example, it will suggest activities or hobbies that will help the user relax. This makes it possible to suggest relaxation methods that suit the user's emotional state.

[0067] The personalized service providing system can also estimate the user's emotional state and, based on the estimated emotion, suggest music or podcasts that will help the user relax the most. For example, if the user is feeling stressed, it will suggest music with a relaxing effect, and if the user is relaxed, it will suggest an interesting podcast. Furthermore, based on the user's emotional data, the generative AI will suggest the most appropriate relaxation method. For example, it will suggest activities or hobbies that will help the user relax. This makes it possible to suggest relaxation methods that suit the user's emotional state.

[0068] The personalized service providing system can also estimate the user's emotional state and, based on the estimated emotion, suggest music or podcasts that will help the user relax the most. For example, if the user is feeling stressed, it will suggest music with a relaxing effect, and if the user is relaxed, it will suggest an interesting podcast. Furthermore, based on the user's emotional data, the generative AI will suggest the most appropriate relaxation method. For example, it will suggest activities or hobbies that will help the user relax. This makes it possible to suggest relaxation methods that suit the user's emotional state.

[0069] The personalized service providing system can also estimate the user's emotional state and, based on the estimated emotion, suggest music or podcasts that will help the user relax the most. For example, if the user is feeling stressed, it will suggest music with a relaxing effect, and if the user is relaxed, it will suggest an interesting podcast. Furthermore, based on the user's emotional data, the generative AI will suggest the most appropriate relaxation method. For example, it will suggest activities or hobbies that will help the user relax. This makes it possible to suggest relaxation methods that suit the user's emotional state.

[0070] The processing flow of the second embodiment will be briefly explained below.

[0071] Step 1: The device conversion unit changes devices as the user grows. For example, when a child grows up and becomes a junior high school student, the device is changed from a device for children to a device for junior high school students. Also, when the user enters the workforce, the device is changed from a device for students to a device for working adults. Step 2: The data transfer unit transfers data to the replaced device by the device conversion unit. For example, data can be transferred using cloud storage. Local data can also be transferred to the new device. Step 3: The personalized service provider provides personalized services based on the data handed over by the data transfer unit. For example, it can provide daily coordination and job hunting advice based on the user's tastes and skills. It can also analyze the user's health data and suggest device settings based on their health condition.

[0072] 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.

[0073] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0074] 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.

[0075] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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).

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0089] 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.

[0090] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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).

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0104] 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.

[0105] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0106] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0117] 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.

[0118] 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.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0126] 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."

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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]

[0139] 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 device conversion unit that changes devices according to the user's growth; a data transfer unit that transfers data to the device replaced by the device conversion unit; a personalized service providing unit that provides personalized services based on the data taken over by the data taking over unit. A system characterized by:

2. The device conversion unit Generative AI analyzes the user's past behavioral patterns and automatically optimizes the initial settings of the device.

2. The system of claim 1.

3. The device conversion unit Generative AI analyzes the user's health data and suggests device settings based on their health condition.

2. The system of claim 1.

4. The device conversion unit Generative AI analyzes the user's emotional state and converts the device at a time when stress is minimal.

2. The system of claim 1.

5. The device conversion unit Generative AI automatically installs new apps and services based on the user's hobbies and interests.

2. The system of claim 1.

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