System

Abstract

An object of the system according to the embodiment is to easily solve problems related to setting and operation of a mobile phone and to help understanding of a new function.SOLUTION: A system includes a problem solving part, a setting change support part, and a new function introduction part. The problem solving unit analyzes the user's problem and provides a solution. The setting change support unit guides a procedure of the setting change based on the solution provided by the problem solving unit. The new function introduction section explains how to use the new function.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that users who are not good at setting up and operating mobile phones have difficulty solving problems and understanding new functions.

[0005] The system according to the embodiment aims to easily solve problems related to the setting and operation of a mobile phone and to help users understand new functions. [Means for solving the problem]

[0006] The system according to the embodiment includes a problem solving unit, a setting change support unit, and a new function introduction unit. The problem solving unit analyzes a user's problem and provides a solution. The setting change support unit guides the user through the setting change procedure based on the solution provided by the problem solving unit. The new function introduction unit explains how to use the new function. [Effects of the Invention]

[0007] The system according to the embodiment can easily solve problems related to setting up and operating a mobile phone and can help users understand new features. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The mobile phone operation support system according to an embodiment of the present invention is a system that provides optimal solutions and advice by having a user talk to a generation AI about problems and requests related to their mobile phone. As a result, the mobile phone operation support system allows users to easily solve problems related to the settings and operation of their mobile phone and effectively utilize new functions of their mobile phone.

[0029] A mobile phone operation assistance system according to an embodiment includes a problem-solving unit, a setting change support unit, and a new function introduction unit. The problem-solving unit analyzes user problems and provides solutions. For example, if a user says, "My mobile phone battery runs out quickly," the generation AI suggests ways to save battery power or review settings. Also, if a user says, "My apps crash frequently," the generation AI suggests solutions such as reinstalling the app or resetting settings. The setting change support unit guides users through the steps to change settings based on the solutions provided by the problem-solving unit. For example, if a user says, "I want to change my Wi-Fi settings," the generation AI supports the user in changing the settings by providing specific steps. Also, if a user says, "I want to change my notification settings," the generation AI provides step-by-step instructions for changing the settings. The new function introduction unit explains how to use new features. For example, if a user says, "Please tell me how to use the new camera function," the generation AI provides a clear explanation of the details and usage of the function. Also, if a user says, "I want to know how to use the new messaging function," the generation AI provides a detailed explanation of how to use the function. As a result, the mobile phone operation support system according to the embodiment can consistently solve problems for the user, change settings, and introduce new functions.

[0030] The problem-solving unit can learn from the user's past problem-solving history and provide more personalized solutions. For example, the problem-solving unit stores the user's past problem-solving history in a database, and when a similar problem recurs, it refers to past solutions to make new suggestions. For example, if there is a history of past suggestions for battery saving methods, the system will provide the optimal solution based on that history when a similar problem occurs. The generative AI also learns the history of problems the user has solved in the past and provides personalized advice. For example, if there is a history of supporting changes to Wi-Fi settings in the past, the system will suggest the optimal procedure based on that history the next time the settings are changed. The generative AI also analyzes the user's past problem-solving history and provides personalized solutions. For example, if there is a history of solving app crashing issues in the past, the system will suggest the optimal solution based on that history when a similar problem occurs. This allows the system to provide more appropriate solutions based on the user's past history.

[0031] The problem-solving unit can analyze the user's voice tone and speaking patterns to determine their stress and urgency and adjust the response accordingly. In the problem-solving unit, for example, the generation AI analyzes the user's voice tone and determines their stress level. For example, if the user speaks in a high tone, it determines that the urgency is high and takes a prompt action. The generation AI also analyzes the user's speaking patterns and determines the urgency. For example, if the user speaks quickly, it determines that the urgency is high and provides an immediate solution. The generation AI also analyzes the user's voice tone and speaking patterns to determine their stress level and adjust the response accordingly. For example, if the user speaks in a calm tone, it provides a detailed explanation and responds in a way that is easy for the user to understand. This makes it possible to respond according to the user's stress and urgency.

[0032] The setting change support unit can learn the user's past setting change history and suggest the optimal procedure. For example, the generation AI stores the user's past setting change history in a database, and when a similar setting change is necessary, suggests the optimal procedure based on the past history. For example, if there is a history of Wi-Fi settings being changed in the past, the generation AI will guide the user through the procedure based on that history the next time the settings are changed. The generation AI also learns the user's past setting change history and suggests the optimal procedure. For example, if there is a history of Bluetooth settings being changed in the past, the generation AI will guide the user through the procedure based on that history the next time the settings are changed. The generation AI also analyzes the user's past setting change history and suggests the optimal procedure. For example, if there is a history of notification settings being changed in the past, the generation AI will guide the user through the procedure based on that history the next time the settings are changed. This makes it possible to suggest the optimal setting change procedure based on the user's past history.

[0033] The setting change support unit can detect user operation errors in real time and immediately suggest corrections. In the setting change support unit, for example, the generation AI detects user operation errors in real time and immediately suggests corrections. For example, if a user tries to change a setting by mistake, it will guide the user to the correct procedure. The generation AI also detects user operation errors in real time and suggests corrections. For example, if a user tries to disable Wi-Fi settings by mistake, it will guide the user to the correct procedure. The generation AI also detects user operation errors and immediately suggests corrections. For example, if a user tries to change notification settings by mistake, it will guide the user to the correct procedure. This allows user operation errors to be corrected immediately.

[0034] The new feature introduction unit can learn the user's past usage history and prioritize introducing the most useful new features. For example, the generation AI stores the user's past usage history in a database and prioritizes introducing the most useful new features. For example, it prioritizes introducing new features related to the camera function that the user uses frequently. The generation AI can also learn the user's past usage history and prioritize introducing the most useful new features. For example, it prioritizes introducing new features related to a messaging app that the user uses frequently. The generation AI can also analyze the user's past usage history and prioritize introducing the most useful new features. For example, it prioritizes introducing new features related to a music app that the user uses frequently. This makes it possible to prioritize introducing the most useful new features to the user.

[0035] The new function introduction unit can evaluate the user's level of understanding in real time and adjust the appropriate level of explanation. In the new function introduction unit, for example, the generation AI evaluates the user's level of understanding in real time and adjusts the appropriate level of explanation. For example, it explains new functions in simple terms so that the user can easily understand them. The generation AI also evaluates the user's level of understanding in real time and adjusts the appropriate level of explanation. For example, if the user can understand advanced technical terms, it provides a detailed technical explanation. The generation AI also evaluates the user's level of understanding and adjusts the appropriate level of explanation. For example, if the user is a beginner, it will carefully explain basic operating methods. This makes it possible to provide appropriate explanations according to the user's level of understanding.

[0036] The new feature introduction unit can collect feedback from other users and introduce the new features that are most highly rated. In the new feature introduction unit, for example, the generation AI collects feedback from other users and introduces the new features that are most highly rated. For example, it prioritizes introducing new features that many users have given high ratings to. The generation AI also collects feedback from other users and introduces the new features that are most highly rated. For example, it selects new features based on user reviews and ratings. The generation AI also analyzes feedback from other users and introduces the new features that are most highly rated. For example, it selects new features based on user comments and ratings. In this way, it is possible to introduce the new features that are most highly rated based on feedback from other users.

[0037] The new function introduction unit can suggest ways to use new functions based on the user's lifestyle and hobbies. In the new function introduction unit, for example, the generation AI learns the user's lifestyle and suggests ways to use new functions. For example, if the user likes outdoor activities, it suggests ways to use the camera function. The generation AI also learns the user's hobbies and suggests ways to use new functions. For example, if the user likes music, it suggests ways to use new functions in a music app. The generation AI also learns the user's lifestyle and hobbies and suggests ways to use new functions. For example, if the user likes cooking, it suggests ways to use new functions in a recipe app. In this way, it is possible to suggest ways to use new functions that suit the user's lifestyle and hobbies.

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

[0039] The mobile phone operation support system can also obtain the user's location information and provide location-based advice. For example, if the user is in a specific area, it can guide the user to available Wi-Fi spots and locations with strong signals. If the user is traveling, it can provide advice on local communication settings and roaming. Furthermore, if the user is at home, it can suggest the optimal communication environment within the home. This allows for more specific advice to be provided based on the user's location information.

[0040] The mobile phone operation assistance system can also obtain the user's schedule information and provide advice based on the schedule. For example, if the user is in a meeting, it can suggest ways to temporarily turn off notifications. If the user is planning a trip, it can provide advice on communication settings and roaming at the user's destination. Furthermore, if the user is exercising, it can suggest ways to set up a music app that is suitable for exercise. This allows the system to provide more specific advice based on the user's schedule.

[0041] The mobile phone operation support system can also obtain the user's health data and provide advice based on the user's health condition. For example, if the user's heart rate is high, it can suggest ways to relax. It can also encourage the user to take a break if they have been using their smartphone for a long time. It can also provide advice on improving the quality of their sleep based on the user's sleep data. This allows for more specific advice based on the user's health condition.

[0042] The mobile phone operation assistance system can also learn about the user's hobbies and interests and provide advice based on them. For example, if the user likes music, it can suggest new features for music apps or recommended playlists. If the user's hobby is photography, it can introduce camera app settings and photography techniques. Furthermore, if the user enjoys games, it can recommend optimal settings for game apps or new games. This allows the system to provide more specific advice based on the user's hobbies and interests.

[0043] The mobile phone operation support system can also analyze the user's usage history and suggest optimal apps. For example, it can suggest new apps related to apps the user frequently uses. Also, if the user frequently uses a specific function, it can introduce apps that enhance that function. Furthermore, if the user uses a specific app during a specific time period, it can suggest apps that are suitable for that time period. This allows for more specific app suggestions based on the user's usage history.

[0044] The processing flow of the first embodiment will be briefly explained below.

[0045] Step 1: The problem-solving part analyzes the user's problem and provides a solution. For example, if the user says, "My phone's battery runs out quickly," the generative AI will suggest ways to save battery or review settings. Similarly, if the user says, "My apps crash frequently," the generative AI will suggest solutions such as reinstalling the app or resetting settings. Step 2: The setting change support unit guides the user through the steps to change the settings based on the solution provided by the problem-solving unit. For example, if the generation AI says, "I want to change my Wi-Fi settings," it supports the user by showing specific steps. Similarly, if the generation AI says, "I want to change my notification settings," it provides step-by-step instructions on how to change the settings. Step 3: The new feature introduction section explains how to use the new feature. For example, if the user says, "Please tell me how to use the new camera function," the generation AI will clearly explain the details of the function and how to use it. Similarly, if the user says, "I want to know how to use the new messaging function," the generation AI will carefully explain how to use the function.

[0046] (Example 2) The mobile phone operation support system according to an embodiment of the present invention is a system that provides optimal solutions and advice by having a user talk to a generation AI about problems and requests related to their mobile phone. As a result, the mobile phone operation support system allows users to easily solve problems related to the settings and operation of their mobile phone and effectively utilize new functions of their mobile phone.

[0047] A mobile phone operation assistance system according to an embodiment includes a problem-solving unit, a setting change support unit, and a new function introduction unit. The problem-solving unit analyzes user problems and provides solutions. For example, if a user says, "My mobile phone battery runs out quickly," the generation AI suggests ways to save battery power or review settings. Also, if a user says, "My apps crash frequently," the generation AI suggests solutions such as reinstalling the app or resetting settings. The setting change support unit guides users through the steps to change settings based on the solutions provided by the problem-solving unit. For example, if a user says, "I want to change my Wi-Fi settings," the generation AI supports the user in changing the settings by providing specific steps. Also, if a user says, "I want to change my notification settings," the generation AI provides step-by-step instructions for changing the settings. The new function introduction unit explains how to use new features. For example, if a user says, "Please tell me how to use the new camera function," the generation AI provides a clear explanation of the details and usage of the function. Also, if a user says, "I want to know how to use the new messaging function," the generation AI provides a detailed explanation of how to use the function. As a result, the mobile phone operation support system according to the embodiment can consistently solve problems for the user, change settings, and introduce new functions.

[0048] The problem-solving unit can learn from the user's past problem-solving history and provide more personalized solutions. For example, the problem-solving unit stores the user's past problem-solving history in a database, and when a similar problem recurs, it refers to past solutions to make new suggestions. For example, if there is a history of past suggestions for battery saving methods, the system will provide the optimal solution based on that history when a similar problem occurs. The generative AI also learns the history of problems the user has solved in the past and provides personalized advice. For example, if there is a history of supporting changes to Wi-Fi settings in the past, the system will suggest the optimal procedure based on that history the next time the settings are changed. The generative AI also analyzes the user's past problem-solving history and provides personalized solutions. For example, if there is a history of solving app crashing issues in the past, the system will suggest the optimal solution based on that history when a similar problem occurs. This allows the system to provide more appropriate solutions based on the user's past history.

[0049] The problem-solving unit can analyze the user's voice tone and speaking patterns to determine their stress and urgency and adjust the response accordingly. In the problem-solving unit, for example, the generation AI analyzes the user's voice tone and determines their stress level. For example, if the user speaks in a high tone, it determines that the urgency is high and takes a prompt action. The generation AI also analyzes the user's speaking patterns and determines the urgency. For example, if the user speaks quickly, it determines that the urgency is high and provides an immediate solution. The generation AI also analyzes the user's voice tone and speaking patterns to determine their stress level and adjust the response accordingly. For example, if the user speaks in a calm tone, it provides a detailed explanation and responds in a way that is easy for the user to understand. This makes it possible to respond according to the user's stress and urgency.

[0050] The problem-solving unit can use the emotion estimation function to estimate the user's emotional state and provide advice and encouraging words to help them relax. For example, the problem-solving unit can use the emotion estimation function to analyze the user's emotional state in real time and provide advice to help them relax. For example, if the user is feeling stressed, the problem-solving unit can suggest deep breathing or relaxing music. The generation AI can also estimate the user's emotional state and provide encouraging words. For example, if the user is feeling anxious, the problem-solving unit can display a positive message to encourage the user. The problem-solving unit can also use the emotion estimation function to analyze the user's emotional state and provide advice to help them relax. For example, if the user is tense, the problem-solving unit can suggest stretching methods to help them relax. This makes it possible to provide appropriate advice and encouraging words according to the user's emotional state.

[0051] The setting change support unit can learn the user's past setting change history and suggest the optimal procedure. For example, the generation AI stores the user's past setting change history in a database, and when a similar setting change is necessary, suggests the optimal procedure based on the past history. For example, if there is a history of Wi-Fi settings being changed in the past, the generation AI will guide the user through the procedure based on that history the next time the settings are changed. The generation AI also learns the user's past setting change history and suggests the optimal procedure. For example, if there is a history of Bluetooth settings being changed in the past, the generation AI will guide the user through the procedure based on that history the next time the settings are changed. The generation AI also analyzes the user's past setting change history and suggests the optimal procedure. For example, if there is a history of notification settings being changed in the past, the generation AI will guide the user through the procedure based on that history the next time the settings are changed. This makes it possible to suggest the optimal setting change procedure based on the user's past history.

[0052] The setting change support unit can detect user operation errors in real time and immediately suggest corrections. In the setting change support unit, for example, the generation AI detects user operation errors in real time and immediately suggests corrections. For example, if a user tries to change a setting by mistake, it will guide the user to the correct procedure. The generation AI also detects user operation errors in real time and suggests corrections. For example, if a user tries to disable Wi-Fi settings by mistake, it will guide the user to the correct procedure. The generation AI also detects user operation errors and immediately suggests corrections. For example, if a user tries to change notification settings by mistake, it will guide the user to the correct procedure. This allows user operation errors to be corrected immediately.

[0053] The setting change support unit can use the emotion estimation function to provide advice to reduce stress felt by the user while changing settings. For example, the setting change support unit uses the emotion estimation function to analyze in real time the stress felt by the user while changing settings and provide advice to reduce stress. For example, if the user is feeling stressed, it suggests ways to relax. The generation AI also analyzes the user's emotions and provides advice to reduce stress felt while changing settings. For example, if the user is feeling anxious, it displays words of encouragement. The emotion estimation function can also be used to analyze the stress felt by the user while changing settings and provide advice to reduce stress. For example, if the user is feeling tense, it suggests ways to relax. This makes it possible to provide advice to reduce the user's stress.

[0054] The new feature introduction unit can learn the user's past usage history and prioritize introducing the most useful new features. For example, the generation AI stores the user's past usage history in a database and prioritizes introducing the most useful new features. For example, it prioritizes introducing new features related to the camera function that the user uses frequently. The generation AI can also learn the user's past usage history and prioritize introducing the most useful new features. For example, it prioritizes introducing new features related to a messaging app that the user uses frequently. The generation AI can also analyze the user's past usage history and prioritize introducing the most useful new features. For example, it prioritizes introducing new features related to a music app that the user uses frequently. This makes it possible to prioritize introducing the most useful new features to the user.

[0055] The new function introduction unit can evaluate the user's level of understanding in real time and adjust the appropriate level of explanation. In the new function introduction unit, for example, the generation AI evaluates the user's level of understanding in real time and adjusts the appropriate level of explanation. For example, it explains new functions in simple terms so that the user can easily understand them. The generation AI also evaluates the user's level of understanding in real time and adjusts the appropriate level of explanation. For example, if the user can understand advanced technical terms, it provides a detailed technical explanation. The generation AI also evaluates the user's level of understanding and adjusts the appropriate level of explanation. For example, if the user is a beginner, it will carefully explain basic operating methods. This makes it possible to provide appropriate explanations according to the user's level of understanding.

[0056] The new function introduction unit can use the emotion estimation function to provide an approach to increase the user's interest and concern when learning new functions. For example, the new function introduction unit uses the emotion estimation function to analyze the user's interest and concern when learning new functions in real time and provide an approach that will attract their interest. For example, it can introduce interesting cases related to functions in which the user has shown interest. The generative AI also analyzes the user's emotions and provides an approach to increase the user's interest and concern when learning new functions. For example, it can introduce new functions related to topics in which the user is interested. The emotion estimation function can also be used to analyze the user's interest and concern when learning new functions and provide an approach that will attract their interest. For example, it can provide a tutorial video related to functions in which the user has shown interest. This makes it possible to provide an approach to increase the user's interest and concern.

[0057] The new feature introduction unit can collect feedback from other users and introduce the new features that are most highly rated. In the new feature introduction unit, for example, the generation AI collects feedback from other users and introduces the new features that are most highly rated. For example, it prioritizes introducing new features that many users have given high ratings to. The generation AI also collects feedback from other users and introduces the new features that are most highly rated. For example, it selects new features based on user reviews and ratings. The generation AI also analyzes feedback from other users and introduces the new features that are most highly rated. For example, it selects new features based on user comments and ratings. In this way, it is possible to introduce the new features that are most highly rated based on feedback from other users.

[0058] The new function introduction unit can suggest ways to use new functions based on the user's lifestyle and hobbies. In the new function introduction unit, for example, the generation AI learns the user's lifestyle and suggests ways to use new functions. For example, if the user likes outdoor activities, it suggests ways to use the camera function. The generation AI also learns the user's hobbies and suggests ways to use new functions. For example, if the user likes music, it suggests ways to use new functions in a music app. The generation AI also learns the user's lifestyle and hobbies and suggests ways to use new functions. For example, if the user likes cooking, it suggests ways to use new functions in a recipe app. In this way, it is possible to suggest ways to use new functions that suit the user's lifestyle and hobbies.

[0059] The new feature introduction unit can use the emotion estimation function to analyze the emotions of the user when trying out a new feature and promote a positive experience. For example, the new feature introduction unit can use the emotion estimation function to analyze the emotions of the user when trying out a new feature in real time and promote a positive experience. For example, if the user is excited, it provides an approach to further enhance that emotion. In addition, the generative AI analyzes the user's emotions when trying out a new feature and promotes a positive experience. For example, if the user is enjoying it, it provides an approach to maintain that emotion. In addition, the emotion estimation function can be used to analyze the emotions of the user when trying out a new feature and promote a positive experience. For example, if the user is satisfied, it provides an approach to further enhance that emotion. This can promote a positive experience when the user tries out a new feature.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The mobile phone operation support system can also obtain the user's location information and provide location-based advice. For example, if the user is in a specific area, it can guide the user to available Wi-Fi spots and locations with strong signals. If the user is traveling, it can provide advice on local communication settings and roaming. Furthermore, if the user is at home, it can suggest the optimal communication environment within the home. This allows for more specific advice to be provided based on the user's location information.

[0062] The mobile phone operation assistance system can also obtain the user's schedule information and provide advice based on the schedule. For example, if the user is in a meeting, it can suggest ways to temporarily turn off notifications. If the user is planning a trip, it can provide advice on communication settings and roaming at the user's destination. Furthermore, if the user is exercising, it can suggest ways to set up a music app that is suitable for exercise. This allows the system to provide more specific advice based on the user's schedule.

[0063] The mobile phone operation support system can also obtain the user's health data and provide advice based on the user's health condition. For example, if the user's heart rate is high, it can suggest ways to relax. It can also encourage the user to take a break if they have been using their smartphone for a long time. It can also provide advice on improving the quality of their sleep based on the user's sleep data. This allows for more specific advice based on the user's health condition.

[0064] The mobile phone operation assistance system can also learn about the user's hobbies and interests and provide advice based on them. For example, if the user likes music, it can suggest new features for music apps or recommended playlists. If the user's hobby is photography, it can introduce camera app settings and photography techniques. Furthermore, if the user enjoys games, it can recommend optimal settings for game apps or new games. This allows the system to provide more specific advice based on the user's hobbies and interests.

[0065] The mobile phone operation support system can also analyze the user's usage history and suggest optimal apps. For example, it can suggest new apps related to apps the user frequently uses. Also, if the user frequently uses a specific function, it can introduce apps that enhance that function. Furthermore, if the user uses a specific app during a specific time period, it can suggest apps that are suitable for that time period. This allows for more specific app suggestions based on the user's usage history.

[0066] The mobile phone operation assistance system can also estimate the user's emotions and provide an environment in which the user can relax based on the estimated emotions. For example, if the user is feeling stressed, it can suggest relaxing music or a meditation app. If the user is tired, it can display a message encouraging the user to take a break. Furthermore, if the user is excited, it can suggest ways to calm down. This allows the system to provide more specific advice based on the user's emotions.

[0067] The mobile phone operation support system can also estimate the user's emotions and provide content that the user can enjoy based on the estimated emotions. For example, if the user is bored, it can suggest interesting videos or games. If the user is sad, it can provide positive messages or music to lift the user's spirits. Furthermore, if the user is excited, it can suggest entertainment content to further enhance the user's emotions. This allows the system to provide more specific content based on the user's emotions.

[0068] The mobile phone operation support system can also estimate the user's emotions and provide a learning environment that is easy for the user to use based on the estimated emotions. For example, if the user is concentrating, it can suggest a learning app to help the user maintain their concentration. If the user is tired, it can display a message encouraging the user to take a break. Furthermore, if the user is relaxed, it can suggest a relaxation app to help the user maintain that state. This makes it possible to provide a more specific learning environment based on the user's emotions.

[0069] The mobile phone operation support system can also estimate the user's emotions and provide an environment in which the user can work efficiently based on the estimated emotions. For example, if the user is concentrating, it can suggest a work app to help the user maintain their concentration. If the user is tired, it can also display a message encouraging the user to take a break. Furthermore, if the user is feeling stressed, it can also suggest a relaxation app to relieve that stress. This makes it possible to provide a more specific work environment based on the user's emotions.

[0070] The mobile phone operation support system can also estimate the user's emotions and provide an environment in which the user can enjoy communication based on the estimated emotions. For example, if the user feels lonely, it can display a message encouraging communication with friends and family. If the user feels excited, it can suggest a social networking app for sharing those emotions. Furthermore, if the user feels relaxed, it can suggest a relaxation app to help maintain that state. This makes it possible to provide a more specific communication environment based on the user's emotions.

[0071] The processing flow of the second embodiment will be briefly explained below.

[0072] Step 1: The problem-solving part analyzes the user's problem and provides a solution. For example, if the user says, "My phone's battery runs out quickly," the generative AI will suggest ways to save battery or review settings. Similarly, if the user says, "My apps crash frequently," the generative AI will suggest solutions such as reinstalling the app or resetting settings. Step 2: The setting change support unit guides the user through the steps to change the settings based on the solution provided by the problem-solving unit. For example, if the generation AI says, "I want to change my Wi-Fi settings," it supports the user by showing specific steps. Similarly, if the generation AI says, "I want to change my notification settings," it provides step-by-step instructions on how to change the settings. Step 3: The new feature introduction section explains how to use the new feature. For example, if the user says, "Please tell me how to use the new camera function," the generation AI will clearly explain the details of the function and how to use it. Similarly, if the user says, "I want to know how to use the new messaging function," the generation AI will carefully explain how to use the function.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0077] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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).

[0082] 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.

[0083] 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.

[0084] 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.

[0085] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0086] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0087] 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.

[0088] 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.

[0089] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0090] 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.

[0091] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0092] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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).

[0097] 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.

[0098] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0099] 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.

[0100] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0101] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0102] 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.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] 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.

[0106] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0107] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0117] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0119] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0121] The data processing system 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0127] 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."

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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]

[0140] 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 system equipped with a generative AI, a problem solving unit that analyzes user problems and provides solutions; a setting change support unit that guides the user through a setting change procedure based on the solution provided by the problem solving unit; A new function introduction section that explains how to use the new functions is provided. A system characterized by:

2. The problem solving unit Learn from the user's past problem-solving history to provide more personalized solutions 2. The system of claim 1.

3. The problem solving unit Analyze the user's tone of voice and speaking patterns to determine stress and urgency and adjust the response accordingly 2. The system of claim 1.

4. The problem solving unit Estimate the user's emotional state and provide advice and words of encouragement to help them relax 2. The system of claim 1.

5. The setting change support unit Learn the user's past setting change history and suggest the optimal procedure 2. The system of claim 1.

6. The setting change support unit Detecting the user's operational errors in real time and immediately suggesting corrections 2. The system of claim 1.

7. The setting change support unit Provide advice to reduce the stress felt by the user while changing settings 2. The system of claim 1.

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