System

Abstract

An object of the system according to the embodiment is to support a user so that the user can use a smartphone alone.SOLUTION: A system according to an embodiment includes a personal information storage unit, a support providing unit, a recommendation unit, and a proposal unit. The personal information storage unit stores personal information of a user. The support providing unit supports the usage based on the information stored by the personal information storage unit. The recommendation unit recommends a recommended app to the user using deep learning. The suggestion unit suggests a convenient way of using the application recommended by the recommendation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including 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] With conventional technology, there was a problem that users who are unfamiliar with smartphone operation, such as elderly people, found it difficult to use smartphones on their own.

[0005] The system according to the embodiment aims to support users so that they can master using their smartphones even when they are alone. [Means for solving the problem]

[0006] The system according to the embodiment includes a personal information storage unit, a support providing unit, a recommendation unit, and a suggestion unit. The personal information storage unit stores personal information of a user. The support providing unit supports usage based on the information stored by the personal information storage unit. The recommendation unit recommends recommended apps to the user using deep learning. The suggestion unit suggests convenient ways to use the apps recommended by the recommendation unit. [Effects of the Invention]

[0007] The system according to the embodiment can support users to master using their smartphones even when they are alone. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 Buddy App of the present embodiment is a system that helps elderly people to use smartphones more easily. This system remembers personal details of the user and supports their usage. It also uses deep learning to suggest recommended apps and convenient usage methods to the user one after another. This allows the Buddy App to help elderly people to use smartphones more easily.

[0029] A buddy app according to an embodiment includes a personal information storage unit, a support providing unit, a recommendation unit, and a suggestion unit. The personal information storage unit stores personal information of a user. For example, the personal information storage unit stores the user's name, age, hobbies, usage history, etc. The personal information storage unit also stores the user's operation history and provides support based on the history. The support providing unit supports usage based on the information stored by the personal information storage unit. For example, the support providing unit provides operation guides, tutorials, and hints. The support providing unit also provides appropriate support based on the user's operation history. The recommendation unit recommends recommended apps to the user using deep learning. For example, the recommendation unit analyzes the user's interests and suggests optimal apps based on the analysis. The recommendation unit also recommends apps based on the user's past usage history. The suggestion unit suggests convenient ways to use the apps recommended by the recommendation unit. For example, the suggestion unit suggests how to use a camera app or convenient functions of a messaging app. The suggestion unit also suggests optimal usage based on the user's operation history. This allows the buddy app according to the embodiment to help elderly people use smartphones more easily. For example, the app can memorize frequently used apps and operation methods and provide appropriate support based on them, making smartphone operation easier. Furthermore, by recommending recommended apps and suggesting convenient usage methods, users can make more effective use of their smartphones.

[0030] The support providing unit can analyze the user's lifestyle rhythm and daily activity patterns and automatically suggest optimal support timing based on the lifestyle rhythm and daily activity patterns. The support providing unit, for example, analyzes the user's smartphone usage history and location information to understand the lifestyle rhythm and daily activity patterns. For example, it identifies the time for a morning walk and the time for relaxation in the evening. The support providing unit also automatically suggests optimal support timing based on the lifestyle rhythm and daily activity patterns. For example, it suggests a health management app for when the user takes their morning walk. This makes it possible to provide support tailored to the user's lifestyle rhythm.

[0031] The support providing unit can analyze the user's voice input and provide more intuitive support using natural language processing. The support providing unit, for example, analyzes the user's voice input in real time and provides intuitive support using natural language processing technology. For example, it explains how to operate an app using voice commands. The support providing unit also provides appropriate support based on the user's voice input. For example, in response to a question such as "What do I do next?", it explains the next operating procedure by voice. This makes it possible to provide intuitive support based on the user's voice input.

[0032] The personal information storage unit can customize support content, including information about the user's family and friends. The personal information storage unit, for example, stores information about the user's family and friends and customizes support content based on that information. For example, it suggests ways to contact family members and sharing functions with friends. The personal information storage unit also provides support content taking into account information about the user's family and friends. For example, it can set reminders for family members' birthdays. This makes it possible to provide support that takes into account information about the user's family and friends.

[0033] The personal information storage unit can integrate the user's past medical history and health data to enhance support for health management. The personal information storage unit, for example, stores the user's past medical history and health data and provides health management support based on that. For example, it can set reminders for regular health checks. The personal information storage unit also analyzes the user's health data and provides health management advice. For example, it can provide health advice based on records of exercise volume and diet. This enables support to enhance the user's health management.

[0034] The recommendation unit can analyze the user's past app usage history in detail and suggest the most suitable app based on the frequency of use and the duration of use. The recommendation unit, for example, analyzes the user's past app usage history in detail and suggests the most suitable app based on the frequency of use and the duration of use. For example, it recommends related apps to frequently used apps. The recommendation unit also suggests appropriate apps based on the user's usage history. For example, it suggests related apps to apps that the user frequently uses. This makes it possible to suggest more appropriate apps by suggesting the most suitable app based on the user's past app usage history.

[0035] The recommendation unit can monitor the user's interests and concerns in real time and recommend apps based on the latest trends. The recommendation unit, for example, monitors the user's interests and concerns in real time and recommends apps based on the latest trends. For example, it suggests apps based on keywords recently searched by the user. The recommendation unit also suggests appropriate apps based on the user's interests and concerns. For example, it suggests apps related to topics in which the user is interested. This makes it possible to suggest more appropriate apps by suggesting apps that are in line with the latest trends based on the user's interests and concerns.

[0036] The recommendation unit can recommend apps that share common interests by referring to apps used by the user's family and friends. For example, the recommendation unit analyzes apps used by the user's family and friends and recommends apps that share common interests. For example, it can suggest a health management app that is used by all family members. The recommendation unit can also suggest appropriate apps based on the usage history of the user's family and friends. For example, it can suggest apps that friends often use. This makes it possible to suggest apps that share common interests by referring to apps used by the user's family and friends.

[0037] The recommendation unit can utilize the user's geographical location information to suggest area-specific apps and services. The recommendation unit, for example, analyzes the user's geographical location information to suggest area-specific apps and services. For example, it suggests an app for making reservations at nearby restaurants. The recommendation unit also suggests appropriate apps based on the user's location information. For example, it suggests an app that provides information about local events. This makes it possible to suggest area-specific apps and services based on the user's geographical location information.

[0038] The suggestion unit can analyze the user's operation history in detail and automatically set frequently used functions as shortcuts. The suggestion unit, for example, analyzes the user's operation history in detail and automatically sets frequently used functions as shortcuts. For example, it places frequently used apps on the home screen. The suggestion unit also sets appropriate shortcuts based on the user's operation history. For example, it makes frequently used functions accessible with one tap. This improves convenience by setting frequently used functions as shortcuts based on the user's operation history.

[0039] The suggestion unit can analyze the user's voice input and suggest convenient uses via voice commands. For example, the suggestion unit analyzes the user's voice input in real time and suggests convenient uses via voice commands. For example, when the user says "take a photo," the suggestion unit launches a camera app. The suggestion unit also suggests appropriate uses based on the user's voice input. For example, when the user says "send a message," the suggestion unit launches a message app. In this way, convenient uses are suggested based on the user's voice input, thereby improving convenience.

[0040] The suggestion unit can suggest common, convenient ways of using the function by taking into account the functions that are frequently used by the user's family and friends. For example, the suggestion unit analyzes the functions that are frequently used by the user's family and friends and suggests common, convenient ways of using the function. For example, it suggests how to use a shared calendar that is used by all family members. The suggestion unit also suggests appropriate ways of using the function based on the usage history of the user's family and friends. For example, it suggests functions that are frequently used by friends. This makes it possible to suggest common, convenient ways of using the function by taking into account the functions that are frequently used by the user's family and friends.

[0041] The suggestion unit can utilize the user's geographical location information to suggest convenient, area-specific uses. The suggestion unit, for example, analyzes the user's geographical location information and suggests convenient, area-specific uses. For example, it suggests how to make a reservation at a nearby restaurant. The suggestion unit also suggests appropriate uses based on the user's location information. For example, it provides information about local events. This makes it possible to suggest convenient, area-specific uses based on the user's geographical location information.

[0042] The suggestion unit can store the content of the smartphone class so that the user can review it even after returning home. The suggestion unit, for example, stores the content of the smartphone class so that the user can review it even after returning home. For example, the operation methods learned in the smartphone class can be provided in the form of video or text so that the user can check it at any time. The suggestion unit also analyzes the content of the smartphone class and provides review content according to the user's level of understanding. For example, the suggestion unit provides review content in a format that is easy for the user to understand. In this way, the content of the smartphone class can be stored so that the user can review it even after returning home, making it possible to remember and utilize the content that has been learned.

[0043] The suggestion unit can monitor the user's level of understanding in real time and automatically adjust the review content according to the level of understanding. The suggestion unit, for example, monitors the user's level of understanding in real time and automatically adjusts the review content according to the level of understanding. For example, if the level of understanding is low, a detailed explanation is provided. The suggestion unit also provides appropriate review content based on the user's level of understanding. For example, if the level of understanding is high, applied content is provided. This makes it possible to adjust the review content according to the user's level of understanding.

[0044] The suggestion unit can analyze the user's voice input and provide a review using voice guidance. For example, the suggestion unit analyzes the user's voice input in real time and provides a review using voice guidance. For example, when the user asks, "What do I do next?", the suggestion unit explains the next operation procedure by voice. The suggestion unit also provides appropriate review content based on the user's voice input. For example, the suggestion unit provides voice guidance in a format that is easy for the user to understand. This makes it possible to review using voice guidance based on the user's voice input.

[0045] The suggestion unit can also integrate the content of smartphone classes attended by the user's family and friends to provide common review content. The suggestion unit, for example, integrates the content of smartphone classes attended by the user's family and friends to provide common review content. For example, all family members can share the operation methods they have learned. The suggestion unit also provides appropriate review content based on the learning content of the user's family and friends. For example, it provides review content based on a common level of understanding. This makes it possible to provide common review content by integrating the content of smartphone classes attended by the user's family and friends.

[0046] The suggestion unit can utilize the user's geographical location information to provide region-specific smartphone class content. The suggestion unit, for example, analyzes the user's geographical location information and provides region-specific smartphone class content. For example, it provides content for nearby smartphone classes. The suggestion unit also provides appropriate review content based on the user's location information. For example, it provides information about local events. This makes it possible to provide region-specific smartphone class content based on the user's geographical location information.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The Buddy app can also suggest relevant events and activities based on the user's hobbies and interests. For example, if a user is interested in gardening, it can provide information about nearby gardening workshops and plant markets. If a user likes music, it can provide information about nearby concerts and music events. Furthermore, if a user is interested in cooking, it can suggest cooking classes and recipes. This makes it possible to provide information based on a user's hobbies and interests, improving the quality of life.

[0049] The Buddy app can provide reminders to support healthy lifestyle habits based on the user's lifestyle rhythm. For example, a walking reminder can be set to coincide with the time for a morning walk. It can also provide reminders to meditate or stretch to coincide with evening relaxation. It can also provide healthy meal suggestions and hydration reminders to coincide with mealtimes. This makes it possible to support healthy lifestyle habits that are tailored to the user's lifestyle rhythm.

[0050] Buddy apps can analyze users' voice inputs and support the operation of home appliances through voice commands. For example, you can change the TV channel or adjust the air conditioner temperature with a voice command. You can also turn lights on and off with a voice command. You can also use voice commands to operate a smart speaker, play music, or check the weather forecast. This makes it possible to operate home appliances based on the user's voice input, improving convenience.

[0051] The Buddy app can utilize information about a user's family and friends to help them connect with people who share common hobbies and interests. For example, it can suggest online events or hobby groups that the whole family can join. It can also suggest activities that can be enjoyed together based on shared hobbies with friends. It can also remind users of their family and friends' birthdays and anniversaries and suggest special messages and gifts. This helps users deepen their interactions with their family and friends.

[0052] Buddy apps can integrate users' past medical history and health data to provide enhanced support for health management. For example, they can set reminders for regular health checks. They can also analyze users' health data and provide health management advice. For example, they can provide health advice based on exercise and diet records. This can provide support for strengthening users' health management.

[0053] The Buddy app can perform a detailed analysis of a user's past app usage history and suggest the most suitable app based on the frequency and duration of use. For example, it can recommend related apps to frequently used apps. It can also suggest appropriate apps based on the user's usage history. For example, it can suggest related apps to apps that the user uses frequently. This makes it possible to suggest more appropriate apps by suggesting the most suitable app based on the user's past app usage history.

[0054] The Buddy app monitors users' interests in real time and can recommend apps based on the latest trends. For example, it can suggest apps based on keywords the user has recently searched for. It can also suggest appropriate apps based on the user's interests. For example, it can suggest apps related to topics the user is interested in. This allows it to suggest more appropriate apps by suggesting apps that are in line with the latest trends based on the user's interests.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The personal information storage unit stores personal information about the user, such as the user's name, age, hobbies, usage history, and operation history. Step 2: The support provider provides support for usage based on the information stored in the personal information storage unit. For example, it provides operation guides, tutorials, and hints, and provides appropriate support based on the user's operation history. Step 3: The recommendation unit uses deep learning to recommend apps to the user. For example, it analyzes the user's interests and concerns and suggests the most suitable apps based on those, and recommends apps based on past usage history. Step 4: The suggestion unit suggests convenient ways to use the apps recommended by the recommendation unit. For example, it suggests ways to use the camera app or convenient functions of a messaging app, and suggests optimal ways to use the apps based on the user's operation history.

[0057] (Example 2) The Buddy App of the present embodiment is a system that helps elderly people to use smartphones more easily. This system remembers personal details of the user and supports their usage. It also uses deep learning to suggest recommended apps and convenient usage methods to the user one after another. This allows the Buddy App to help elderly people to use smartphones more easily.

[0058] A buddy app according to an embodiment includes a personal information storage unit, a support providing unit, a recommendation unit, and a suggestion unit. The personal information storage unit stores personal information of a user. For example, the personal information storage unit stores the user's name, age, hobbies, usage history, etc. The personal information storage unit also stores the user's operation history and provides support based on the history. The support providing unit supports usage based on the information stored by the personal information storage unit. For example, the support providing unit provides operation guides, tutorials, and hints. The support providing unit also provides appropriate support based on the user's operation history. The recommendation unit recommends recommended apps to the user using deep learning. For example, the recommendation unit analyzes the user's interests and suggests optimal apps based on the analysis. The recommendation unit also recommends apps based on the user's past usage history. The suggestion unit suggests convenient ways to use the apps recommended by the recommendation unit. For example, the suggestion unit suggests how to use a camera app or convenient functions of a messaging app. The suggestion unit also suggests optimal usage based on the user's operation history. This allows the buddy app according to the embodiment to help elderly people use smartphones more easily. For example, the app can memorize frequently used apps and operation methods and provide appropriate support based on them, making smartphone operation easier. Furthermore, by recommending recommended apps and suggesting convenient usage methods, users can make more effective use of their smartphones.

[0059] The support providing unit can estimate the user's emotional state in real time and dynamically change the support content according to the emotional state. The support providing unit, for example, analyzes the user's facial expressions and voice tone to estimate the emotional state in real time. For example, it analyzes the user's emotions using a camera or microphone and suggests relaxing activities if the user is feeling stressed. The support providing unit also dynamically changes the support content according to the user's emotional state. For example, if the user is happy, it displays a praising message. This allows for more appropriate support by providing support according to the user's emotions.

[0060] The support providing unit can analyze the user's lifestyle rhythm and daily activity patterns and automatically suggest optimal support timing based on the lifestyle rhythm and daily activity patterns. The support providing unit, for example, analyzes the user's smartphone usage history and location information to understand the lifestyle rhythm and daily activity patterns. For example, it identifies the time for a morning walk and the time for relaxation in the evening. The support providing unit also automatically suggests optimal support timing based on the lifestyle rhythm and daily activity patterns. For example, it suggests a health management app for when the user takes their morning walk. This makes it possible to provide support tailored to the user's lifestyle rhythm.

[0061] The support providing unit can analyze the user's voice input and provide more intuitive support using natural language processing. The support providing unit, for example, analyzes the user's voice input in real time and provides intuitive support using natural language processing technology. For example, it explains how to operate an app using voice commands. The support providing unit also provides appropriate support based on the user's voice input. For example, in response to a question such as "What do I do next?", it explains the next operating procedure by voice. This makes it possible to provide intuitive support based on the user's voice input.

[0062] The personal information storage unit can customize support content, including information about the user's family and friends. The personal information storage unit, for example, stores information about the user's family and friends and customizes support content based on that information. For example, it suggests ways to contact family members and sharing functions with friends. The personal information storage unit also provides support content taking into account information about the user's family and friends. For example, it can set reminders for family members' birthdays. This makes it possible to provide support that takes into account information about the user's family and friends.

[0063] The personal information storage unit can integrate the user's past medical history and health data to enhance support for health management. The personal information storage unit, for example, stores the user's past medical history and health data and provides health management support based on that. For example, it can set reminders for regular health checks. The personal information storage unit also analyzes the user's health data and provides health management advice. For example, it can provide health advice based on records of exercise volume and diet. This enables support to enhance the user's health management.

[0064] The personal information storage unit can use the emotion estimation function to suggest a relaxing activity when the user is feeling stressed. The personal information storage unit, for example, analyzes the user's emotional state in real time and suggests a relaxing activity when the user is feeling stressed. For example, it suggests relaxing music or a meditation app. The personal information storage unit also suggests an appropriate activity based on the user's emotional state. For example, if the user is feeling stressed, it suggests a relaxing activity. In this way, by suggesting a relaxing activity when the user is feeling stressed, it is possible to reduce the user's stress.

[0065] The recommendation unit can analyze the emotional state of the user and recommend apps according to the emotional state. For example, the recommendation unit analyzes the emotional state of the user in real time and recommends apps according to the emotional state. For example, if the user is feeling stressed, the recommendation unit suggests an app that helps the user relax. The recommendation unit also suggests appropriate apps based on the user's emotional state. For example, if the user is happy, the recommendation unit suggests an entertainment app. This makes it possible to recommend apps according to the user's emotions and suggest more appropriate apps.

[0066] The recommendation unit can analyze the user's past app usage history in detail and suggest the most suitable app based on the frequency of use and the duration of use. The recommendation unit, for example, analyzes the user's past app usage history in detail and suggests the most suitable app based on the frequency of use and the duration of use. For example, it recommends related apps to frequently used apps. The recommendation unit also suggests appropriate apps based on the user's usage history. For example, it suggests related apps to apps that the user frequently uses. This makes it possible to suggest more appropriate apps by suggesting the most suitable app based on the user's past app usage history.

[0067] The recommendation unit can monitor the user's interests and concerns in real time and recommend apps based on the latest trends. The recommendation unit, for example, monitors the user's interests and concerns in real time and recommends apps based on the latest trends. For example, it suggests apps based on keywords recently searched by the user. The recommendation unit also suggests appropriate apps based on the user's interests and concerns. For example, it suggests apps related to topics in which the user is interested. This makes it possible to suggest more appropriate apps by suggesting apps that are in line with the latest trends based on the user's interests and concerns.

[0068] The recommendation unit can recommend apps that share common interests by referring to apps used by the user's family and friends. For example, the recommendation unit analyzes apps used by the user's family and friends and recommends apps that share common interests. For example, it can suggest a health management app that is used by all family members. The recommendation unit can also suggest appropriate apps based on the usage history of the user's family and friends. For example, it can suggest apps that friends often use. This makes it possible to suggest apps that share common interests by referring to apps used by the user's family and friends.

[0069] The recommendation unit can utilize the user's geographical location information to suggest area-specific apps and services. The recommendation unit, for example, analyzes the user's geographical location information to suggest area-specific apps and services. For example, it suggests an app for making reservations at nearby restaurants. The recommendation unit also suggests appropriate apps based on the user's location information. For example, it suggests an app that provides information about local events. This makes it possible to suggest area-specific apps and services based on the user's geographical location information.

[0070] The suggestion unit can analyze the emotional state of the user and suggest usage methods according to the emotional state. For example, the suggestion unit analyzes the emotional state of the user in real time and suggests usage methods according to the emotional state. For example, if the user is feeling stressed, the suggestion unit suggests usage methods that will help the user relax. The suggestion unit also suggests appropriate usage methods based on the user's emotional state. For example, if the user is happy, the suggestion unit suggests how to use an entertainment app. This makes it possible to suggest usage methods according to the user's emotions.

[0071] The suggestion unit can analyze the user's operation history in detail and automatically set frequently used functions as shortcuts. The suggestion unit, for example, analyzes the user's operation history in detail and automatically sets frequently used functions as shortcuts. For example, it places frequently used apps on the home screen. The suggestion unit also sets appropriate shortcuts based on the user's operation history. For example, it makes frequently used functions accessible with one tap. This improves convenience by setting frequently used functions as shortcuts based on the user's operation history.

[0072] The suggestion unit can analyze the user's voice input and suggest convenient uses via voice commands. For example, the suggestion unit analyzes the user's voice input in real time and suggests convenient uses via voice commands. For example, when the user says "take a photo," the suggestion unit launches a camera app. The suggestion unit also suggests appropriate uses based on the user's voice input. For example, when the user says "send a message," the suggestion unit launches a message app. In this way, convenient uses are suggested based on the user's voice input, thereby improving convenience.

[0073] The suggestion unit can suggest common, convenient ways of using the function by taking into account the functions that are frequently used by the user's family and friends. For example, the suggestion unit analyzes the functions that are frequently used by the user's family and friends and suggests common, convenient ways of using the function. For example, it suggests how to use a shared calendar that is used by all family members. The suggestion unit also suggests appropriate ways of using the function based on the usage history of the user's family and friends. For example, it suggests functions that are frequently used by friends. This makes it possible to suggest common, convenient ways of using the function by taking into account the functions that are frequently used by the user's family and friends.

[0074] The suggestion unit can utilize the user's geographical location information to suggest convenient, area-specific uses. The suggestion unit, for example, analyzes the user's geographical location information and suggests convenient, area-specific uses. For example, it suggests how to make a reservation at a nearby restaurant. The suggestion unit also suggests appropriate uses based on the user's location information. For example, it provides information about local events. This makes it possible to suggest convenient, area-specific uses based on the user's geographical location information.

[0075] The suggestion unit can use the emotion estimation function to suggest ways of using the app that will prevent the user from feeling stressed. For example, the suggestion unit uses the emotion estimation function to suggest ways of using the app that will prevent the user from feeling stressed. For example, it suggests ways of using an app that will help the user relax. The suggestion unit also suggests appropriate ways of using the app based on the emotional state of the user. For example, if the user is feeling stressed, it suggests a simple operation method. This makes it possible to suggest ways of using the app that will prevent the user from feeling stressed.

[0076] The suggestion unit can store the content of the smartphone class so that the user can review it even after returning home. The suggestion unit, for example, stores the content of the smartphone class so that the user can review it even after returning home. For example, the operation methods learned in the smartphone class can be provided in the form of video or text so that the user can check it at any time. The suggestion unit also analyzes the content of the smartphone class and provides review content according to the user's level of understanding. For example, the suggestion unit provides review content in a format that is easy for the user to understand. In this way, the content of the smartphone class can be stored so that the user can review it even after returning home, making it possible to remember and utilize the content that has been learned.

[0077] The suggestion unit can monitor the user's level of understanding in real time and automatically adjust the review content according to the level of understanding. The suggestion unit, for example, monitors the user's level of understanding in real time and automatically adjusts the review content according to the level of understanding. For example, if the level of understanding is low, a detailed explanation is provided. The suggestion unit also provides appropriate review content based on the user's level of understanding. For example, if the level of understanding is high, applied content is provided. This makes it possible to adjust the review content according to the user's level of understanding.

[0078] The suggestion unit can analyze the user's voice input and provide a review using voice guidance. For example, the suggestion unit analyzes the user's voice input in real time and provides a review using voice guidance. For example, when the user asks, "What do I do next?", the suggestion unit explains the next operation procedure by voice. The suggestion unit also provides appropriate review content based on the user's voice input. For example, the suggestion unit provides voice guidance in a format that is easy for the user to understand. This makes it possible to review using voice guidance based on the user's voice input.

[0079] The suggestion unit can also integrate the content of smartphone classes attended by the user's family and friends to provide common review content. The suggestion unit, for example, integrates the content of smartphone classes attended by the user's family and friends to provide common review content. For example, all family members can share the operation methods they have learned. The suggestion unit also provides appropriate review content based on the learning content of the user's family and friends. For example, it provides review content based on a common level of understanding. This makes it possible to provide common review content by integrating the content of smartphone classes attended by the user's family and friends.

[0080] The suggestion unit can utilize the user's geographical location information to provide region-specific smartphone class content. The suggestion unit, for example, analyzes the user's geographical location information and provides region-specific smartphone class content. For example, it provides content for nearby smartphone classes. The suggestion unit also provides appropriate review content based on the user's location information. For example, it provides information about local events. This makes it possible to provide region-specific smartphone class content based on the user's geographical location information.

[0081] The suggestion unit can use the emotion estimation function to provide review content that will make the user feel positive emotions. The suggestion unit, for example, uses the emotion estimation function to provide review content that will make the user feel positive emotions. For example, it provides review content that will make the user feel joy. The suggestion unit also provides appropriate review content based on the emotional state of the user. For example, it provides review content that will make the user feel relaxed. This makes it possible to provide review content that will make the user feel positive emotions.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] The Buddy app can also suggest relevant events and activities based on the user's hobbies and interests. For example, if a user is interested in gardening, it can provide information about nearby gardening workshops and plant markets. If a user likes music, it can provide information about nearby concerts and music events. Furthermore, if a user is interested in cooking, it can suggest cooking classes and recipes. This makes it possible to provide information based on a user's hobbies and interests, improving the quality of life.

[0084] The Buddy app can estimate a user's emotional state and suggest relaxing music or podcasts based on the estimated emotion. For example, if a user is feeling stressed, it can suggest relaxing classical music or podcasts with nature sounds. If a user is feeling down, it can suggest uplifting, positive music or podcasts with encouraging messages. Furthermore, if a user is happy, it can suggest upbeat music to further enhance the joyful mood. This makes it possible to suggest music and podcasts that correspond to the user's emotions, thereby supporting emotional stability.

[0085] The Buddy app can provide reminders to support healthy lifestyle habits based on the user's lifestyle rhythm. For example, a walking reminder can be set to coincide with the time for a morning walk. It can also provide reminders to meditate or stretch to coincide with evening relaxation. It can also provide healthy meal suggestions and hydration reminders to coincide with mealtimes. This makes it possible to support healthy lifestyle habits that are tailored to the user's lifestyle rhythm.

[0086] Buddy apps can analyze users' voice inputs and support the operation of home appliances through voice commands. For example, you can change the TV channel or adjust the air conditioner temperature with a voice command. You can also turn lights on and off with a voice command. You can also use voice commands to operate a smart speaker, play music, or check the weather forecast. This makes it possible to operate home appliances based on the user's voice input, improving convenience.

[0087] The Buddy app can utilize information about a user's family and friends to help them connect with people who share common hobbies and interests. For example, it can suggest online events or hobby groups that the whole family can join. It can also suggest activities that can be enjoyed together based on shared hobbies with friends. It can also remind users of their family and friends' birthdays and anniversaries and suggest special messages and gifts. This helps users deepen their interactions with their family and friends.

[0088] Buddy apps can integrate users' past medical history and health data to provide enhanced support for health management. For example, they can set reminders for regular health checks. They can also analyze users' health data and provide health management advice. For example, they can provide health advice based on exercise and diet records. This can provide support for strengthening users' health management.

[0089] The Buddy app can estimate the user's emotional state and suggest activities that will help the user relax based on the estimated emotion. For example, it can suggest relaxing music or a meditation app. It can also suggest appropriate activities based on the user's emotional state. For example, if the user is feeling stressed, it can suggest relaxing activities. This allows the user to reduce stress by suggesting relaxing activities when they are feeling stressed.

[0090] The Buddy app can analyze a user's emotional state and recommend apps that correspond to that state. For example, if a user is feeling stressed, it can suggest apps that help them relax. It can also suggest appropriate apps based on the user's emotional state. For example, if a user is happy, it can suggest entertainment apps. This makes it possible to recommend apps that correspond to the user's emotions, making it possible to suggest more appropriate apps.

[0091] The Buddy app can perform a detailed analysis of a user's past app usage history and suggest the most suitable app based on the frequency and duration of use. For example, it can recommend related apps to frequently used apps. It can also suggest appropriate apps based on the user's usage history. For example, it can suggest related apps to apps that the user uses frequently. This makes it possible to suggest more appropriate apps by suggesting the most suitable app based on the user's past app usage history.

[0092] The Buddy app monitors users' interests in real time and can recommend apps based on the latest trends. For example, it can suggest apps based on keywords the user has recently searched for. It can also suggest appropriate apps based on the user's interests. For example, it can suggest apps related to topics the user is interested in. This allows it to suggest more appropriate apps by suggesting apps that are in line with the latest trends based on the user's interests.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The personal information storage unit stores personal information about the user, such as the user's name, age, hobbies, usage history, and operation history. Step 2: The support provider provides support for usage based on the information stored in the personal information storage unit. For example, it provides operation guides, tutorials, and hints, and provides appropriate support based on the user's operation history. Step 3: The recommendation unit uses deep learning to recommend apps to the user. For example, it analyzes the user's interests and concerns and suggests the most suitable apps based on those, and recommends apps based on past usage history. Step 4: The suggestion unit suggests convenient ways to use the apps recommended by the recommendation unit. For example, it suggests ways to use the camera app or convenient functions of a messaging app, and suggests optimal ways to use the apps based on the user's operation history.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0108] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0121] 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.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0123] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 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.

[0130] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, 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.

[0131] 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.

[0132] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0133] 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).

[0134] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0139] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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."

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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]

[0162] 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 personal information storage unit that stores personal information of a user; a support providing unit that provides support for usage based on the information stored in the personal information storage unit; A recommendation unit that uses deep learning to recommend apps to users; a suggestion unit that suggests a convenient way to use the application recommended by the recommendation unit. A system characterized by:

2. The support providing unit The emotional state of the user is estimated in real time, and the support content is dynamically changed according to the emotional state.

2. The system of claim 1.

3. The personal information storage unit Customize support by including information about the user's family and friends 2. The system of claim 1.

4. The recommendation unit Analyze the user's emotional state and recommend apps according to the emotional state 2. The system of claim 1.

5. The proposal unit Analyze the user's emotional state and suggest ways to use the device according to that emotional state 2. The system of claim 1.

6. The personal information storage unit Suggest relaxing activities if the user is feeling stressed 2. The system of claim 1.

7. The recommendation unit Prioritize recommendations for apps that users have positive feelings about 2. The system of claim 1.

8. The proposal unit Propose ways to use the product that users will not feel stressed about.

2. The system of claim 1.

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