System

Abstract

An object of a system according to an embodiment is to generate a message of encouragement in accordance with a goal or a theme of a user and notify the user of the message at an appropriate timing.SOLUTION: A system according to an embodiment includes a goal setting unit, a message generation unit, and a notification unit. The goal setting unit inputs a goal or a theme set by the user. The message generation unit generates an encouragement message on the basis of the goal and the theme input by the goal setting unit. The notification unit notifies the message generated by the message generation unit at every set time.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not automatically generate encouraging messages that are in line with the user's goals or themes and do not notify the user at an appropriate time, and there is room for improvement.

[0005] The system according to the embodiment aims to generate encouraging messages that are in line with the user's goals and themes, and notify the user at an appropriate time. [Means for solving the problem]

[0006] The system according to the embodiment includes a goal setting unit, a message generation unit, and a notification unit. The goal setting unit inputs a goal or theme set by a user. The message generation unit generates an encouraging message based on the goal or theme input by the goal setting unit. The notification unit notifies the user of the message generated by the message generation unit at set intervals. [Effects of the Invention]

[0007] The system according to the embodiment can generate encouraging messages that are in line with the user's goals and themes, and notify the user at an appropriate time. [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 encouraging message generation system according to an embodiment of the present invention uses a generation AI to generate encouraging messages based on goals and themes set by the user, and notifies the user at set times, like a daily calendar. This allows the encouraging message generation system to improve the user's motivation and encourage reminders.

[0029] An encouraging message generation system according to an embodiment includes a goal setting unit, a message generation unit, and a notification unit. The goal setting unit inputs a goal or theme set by a user. For example, the user can input a specific goal or theme such as "successfully lose weight" or "study English every day." The goal setting unit can also input a professional theme such as completing a project within a deadline. The message generation unit generates an encouraging message using a generation AI based on the goal or theme input by the goal setting unit. For example, the generation AI generates messages such as "Another step forward today! Good luck!" or "Great that you're continuing to study English!" The generation AI can also generate messages such as "The project is progressing well!" The generation AI generates messages using a text generation AI (e.g., LLM) or a multimodal generation AI. The notification unit notifies the user of the message generated by the message generation unit at a set time. For example, a message such as "Let's do our best today!" is notified at 8:00 a.m. every morning. If the user desires a reminder at a specific time, the notification unit can also notify the user of the message at that time. As a result, the encouraging message generation system according to the embodiment can improve the user's motivation and encourage reminders.

[0030] When a user sets a goal or theme, the goal setting unit allows the generation AI to suggest the most appropriate goal or theme based on past data. For example, when a user inputs a goal or theme, the goal setting unit allows the generation AI to analyze past successes and failures and suggest the most appropriate goal or theme. For example, when setting a diet goal, the generation AI suggests a specific goal based on past successes. The generation AI also analyzes the user's past behavioral data and progress and suggests the most appropriate goal or theme based on that. For example, it suggests an appropriate goal as the next step based on the progress of English learning. Furthermore, the generation AI automatically generates related sub-goals and steps for the goal or theme set by the user and suggests them to the user. For example, when setting a project goal, it suggests specific tasks and steps. This allows the user to set the most appropriate goal or theme.

[0031] The goal setting unit can have the generation AI automatically generate related sub-goals and steps for goals and themes set by the user and suggest them to the user. For example, the goal setting unit can have the generation AI automatically generate related sub-goals for goals set by the user and suggest them to the user. For example, for a diet goal, the generation AI can suggest sub-goals such as meal management and exercise plans. The generation AI can also automatically generate specific steps based on the user's goal and suggest them to the user. For example, for an English learning goal, the generation AI can suggest daily learning content and progress management steps. Furthermore, the generation AI can automatically generate related tasks and action plans for themes set by the user and suggest them to the user. For example, for a project theme, the generation AI can suggest specific tasks and schedules. This allows the user to set specific sub-goals and steps.

[0032] The goal setting unit can display success stories and statistical data of other users for reference when the user sets a goal or theme. The goal setting unit, for example, displays success stories of other users for reference when the user sets a goal or theme. For example, when setting a diet goal, specific examples of successful users are displayed. Furthermore, information related to the goal or theme set by the user is provided based on statistical data. For example, when setting a goal for learning English, highly effective learning methods and progress data are displayed. Furthermore, reference information is provided when the user sets a goal or theme based on success stories and statistical data of other users. For example, when setting a goal for a project, examples of successful projects and statistical data are displayed. This allows the user to set a goal by referring to other success stories and statistical data.

[0033] The goal setting unit can enable the setting of goals and themes using at least one of various interfaces, such as voice input or gesture input. The goal setting unit, for example, enables the user to use voice input when setting a goal or theme. For example, the user sets a goal by vocally inputting "succeed in dieting." The goal setting unit also enables the user to set a goal or theme using gesture input. For example, the user sets a goal by performing a specific gesture. Furthermore, various interfaces are provided to improve the convenience for the user when setting a goal or theme. For example, in addition to touch screen and keyboard input, voice input and gesture input are supported. This allows the user to set a goal using various interfaces.

[0034] The message generation unit can analyze the user's past behavioral data and progress status, and generate more personalized messages based on that. In the message generation unit, for example, the generation AI analyzes the user's past behavioral data and generates personalized messages based on that. For example, it generates an encouraging message based on goals the user has achieved in the past. The generation AI also analyzes the user's progress in real time and generates personalized messages based on that. For example, if the user is making good progress toward a goal, it generates a message praising that progress. Furthermore, the generation AI learns the user's past behavioral data and progress status, and generates optimal messages based on that. For example, it generates an encouraging message based on the user's past experiences of overcoming difficulties. This makes it possible to provide more personalized messages to the user.

[0035] The message generation unit can learn the user's preferences and interests and generate messages to increase motivation based on them. For example, the generation AI of the message generation unit learns the user's preferences and interests and generates messages to increase motivation based on them. For example, it generates messages related to the user's favorite sports. The generation AI also analyzes the user's interests and generates personalized messages based on them. For example, it generates messages related to topics that the user is interested in. Furthermore, the generation AI learns the user's preferences and interests and generates optimal messages based on them. For example, it generates messages related to the user's favorite music or movies. This makes it possible to provide messages to increase motivation based on the user's preferences and interests.

[0036] The message generation unit generates messages that correspond to different languages ​​and cultures, making it possible to cater to a global user base. For example, the generation AI generates messages that correspond to different languages, making it possible to cater to a global user base. For example, it generates messages in multiple languages, such as English, French, and Chinese. In addition, the generation AI learns cultural backgrounds and generates messages based on them to generate messages that correspond to different cultures. For example, it incorporates encouraging expressions from a specific culture. Furthermore, the generation AI generates messages that correspond to different languages ​​and cultures, building a system that caters to a global user base. For example, it customizes messages according to the user's language settings and cultural background. This makes it possible to provide messages that correspond to different languages ​​and cultures.

[0037] The message generation unit can analyze a user's social media posts and comments and generate messages based thereon. In the message generation unit, for example, the generation AI analyzes a user's social media posts and generates personalized messages based thereon. For example, it generates an encouraging message related to the content posted by the user. The generation AI also analyzes the user's social media comments and generates an optimal message based thereon. For example, it generates an encouraging message in response to a comment received by the user. Furthermore, the generation AI learns the user's social media posts and comments and generates messages based thereon. For example, it generates messages related to topics that interest the user. This makes it possible to provide messages based on the user's social media posts and comments.

[0038] The notification unit can optimize the timing of message notifications based on the user's lifestyle and behavioral patterns. The notification unit, for example, analyzes the user's lifestyle and sets the optimal message notification timing based on that. For example, if the user is a morning person, the notification unit notifies the user of a message in the morning hours. Furthermore, a system is constructed that optimizes the timing of message notifications based on behavioral patterns. For example, if the user exercises during a specific time period, the notification unit notifies the user of an encouraging message before and after the exercise. Furthermore, the system learns the user's lifestyle and behavioral patterns and dynamically adjusts the timing of message notifications based on that information. For example, if the user works late into the night, the notification unit notifies the user of a message encouraging them to relax in the evening hours. This allows the notification unit to notify the user of a message at the optimal timing based on the user's lifestyle and behavioral patterns.

[0039] The notification unit can customize the message notification method according to the user's device and environment. The notification unit customizes the message notification method according to the user's device, for example. For example, a user using a smart watch is notified of messages by vibration or voice. The message notification method is also adjusted according to the user's environment. For example, a user using a smart speaker is notified of messages by voice. Furthermore, a system is constructed that dynamically customizes the message notification method according to the device and environment. For example, if the user is out, a notification is sent to a smartphone, and if the user is at home, a notification is sent to a smart speaker. This allows messages to be notified in the optimal way according to the user's device and environment.

[0040] The notification unit can work in conjunction with the user's calendar and schedule to notify messages in accordance with important events and tasks. The notification unit, for example, works in conjunction with the user's calendar and schedule to notify messages in accordance with important events and tasks. For example, a reminder message may be sent before a meeting. A system can also be built that optimizes the timing of message notifications based on calendar and schedule data. For example, an encouraging message may be sent before a task deadline set by the user. Furthermore, the system can work in conjunction with the user's calendar and schedule to dynamically adjust messages in accordance with important events and tasks. For example, a message may be sent before or after an event the user has scheduled. This allows messages to be sent at the optimal timing based on the user's calendar and schedule.

[0041] The notification unit can customize the message notification method based on the user's location information and notify only at specific locations. The notification unit, for example, builds a system that notifies messages only at specific locations based on the user's location information. For example, if the user is at the gym, the notification unit notifies the user of a message related to exercise. The notification unit also analyzes the location information and notifies the user of the optimal message when the user is in a specific location. For example, if the user is in the office, the notification unit notifies the user of a message related to work. The notification unit also customizes the message notification method based on the user's location information. For example, if the user is at home, the notification unit notifies the user of a message encouraging relaxation. This allows messages to be notified in the optimal way based on the user's location information.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] In the goal setting unit, when a user sets a goal or theme, the generation AI can suggest the most appropriate goal or theme based on past data. For example, when a user inputs a goal or theme, the generation AI analyzes past successes and failures to suggest the most appropriate goal or theme. For example, when setting a diet goal, the generation AI suggests a specific goal based on past successes. The generation AI also analyzes the user's past behavioral data and progress, and suggests the most appropriate goal or theme based on that. For example, it suggests an appropriate goal as the next step based on the progress of English learning. Furthermore, the generation AI automatically generates related sub-goals and steps for the goal or theme set by the user and suggests them to the user. For example, when setting a project goal, it suggests specific tasks and steps. This allows the user to set the most appropriate goal or theme.

[0044] The goal setting unit allows the generation AI to automatically generate related sub-goals and steps for goals and themes set by the user and suggest them to the user. For example, the generation AI automatically generates related sub-goals for a goal set by the user and suggests them to the user. For example, for a diet goal, it suggests sub-goals such as meal management and exercise plans. The generation AI also automatically generates specific steps based on the user's goal and suggests them to the user. For example, for an English learning goal, it suggests daily learning content and progress management steps. Furthermore, the generation AI automatically generates related tasks and action plans for themes set by the user and suggests them to the user. For example, it suggests specific tasks and schedules for a project theme. This allows the user to set specific sub-goals and steps.

[0045] The goal setting unit can display success stories and statistical data of other users for reference when the user sets a goal or theme. For example, when the user sets a goal or theme, the unit can display success stories of other users for reference. For example, when setting a diet goal, the unit can display specific examples of successful users. Furthermore, the unit can provide information related to the goal or theme set by the user based on statistical data. For example, when setting a goal for learning English, the unit can display highly effective learning methods and progress data. Furthermore, the unit can provide reference information when the user sets a goal or theme based on success stories and statistical data of other users. For example, when setting a goal for a project, the unit can display examples of successful projects and statistical data. This allows the user to set a goal by referring to other success stories and statistical data.

[0046] The goal setting unit can enable the setting of goals and themes using at least one of various interfaces, such as voice input or gesture input. For example, the user can use voice input when setting a goal or theme. For example, the user can set a goal by vocally inputting "succeed in dieting." The user can also use gesture input to set a goal or theme. For example, the user can set a goal by performing a specific gesture. Furthermore, various interfaces are provided to improve the convenience for the user when setting goals and themes. For example, in addition to touch screen and keyboard input, voice input and gesture input are supported. This allows the user to set goals using various interfaces.

[0047] The message generation unit can analyze the user's past behavioral data and progress status, and generate more personalized messages based on that. For example, the generation AI analyzes the user's past behavioral data and generates personalized messages based on that. For example, it generates an encouraging message based on goals the user has achieved in the past. The generation AI also analyzes the user's progress in real time and generates personalized messages based on that. For example, if the user is making good progress toward a goal, it generates a message praising that progress. Furthermore, the generation AI learns the user's past behavioral data and progress status, and generates optimal messages based on that. For example, it generates an encouraging message based on the user's past experiences of overcoming difficulties. This makes it possible to provide more personalized messages to the user.

[0048] The message generation unit can learn the user's preferences and interests and generate messages to increase motivation based on them. For example, the generation AI learns the user's preferences and interests and generates messages to increase motivation based on them. For example, it generates messages related to the user's favorite sports. The generation AI also analyzes the user's interests and generates personalized messages based on them. For example, it generates messages related to topics that the user is interested in. Furthermore, the generation AI learns the user's preferences and interests and generates optimal messages based on them. For example, it generates messages related to the user's favorite music or movies. This makes it possible to provide messages to increase motivation based on the user's preferences and interests.

[0049] The message generation unit generates messages that correspond to different languages ​​and cultures, making it possible to cater to a global user base. For example, the generation AI generates messages that correspond to different languages ​​to cater to a global user base. For example, it generates messages in multiple languages, such as English, French, and Chinese. In addition, the generation AI learns cultural backgrounds and generates messages based on them to generate messages that correspond to different cultures. For example, it incorporates encouraging expressions from specific cultures. Furthermore, the generation AI generates messages that correspond to different languages ​​and cultures, building a system that caters to a global user base. For example, it customizes messages according to the user's language settings and cultural background. This makes it possible to provide messages that correspond to different languages ​​and cultures.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The goal setting unit inputs the goals and themes set by the user. For example, the user can input specific goals and themes such as "successfully lose weight" or "study English every day." The goal setting unit can also input professional themes such as completing a project within a deadline. Step 2: In the message generation section, the generation AI generates an encouraging message based on the goals and themes input by the goal setting section. For example, the generation AI generates messages such as "Another step forward today! Do your best!" or "It's great that you're continuing to study English!". The generation AI can also generate messages such as "The project is progressing well!". The generation AI generates messages using a text generation AI (e.g., LLM) or a multimodal generation AI. Step 3: The notification unit notifies the user of the message generated by the message generation unit at the set time. For example, a message saying "Let's do our best today!" is sent every morning at 8:00. If the user wishes to be reminded at a specific time, the notification unit can also send the message at that time.

[0052] (Example 2) The encouraging message generation system according to an embodiment of the present invention uses a generation AI to generate encouraging messages based on goals and themes set by the user, and notifies the user at set times, like a daily calendar. This allows the encouraging message generation system to improve the user's motivation and encourage reminders.

[0053] An encouraging message generation system according to an embodiment includes a goal setting unit, a message generation unit, and a notification unit. The goal setting unit inputs a goal or theme set by a user. For example, the user can input a specific goal or theme such as "successfully lose weight" or "study English every day." The goal setting unit can also input a professional theme such as completing a project within a deadline. The message generation unit generates an encouraging message using a generation AI based on the goal or theme input by the goal setting unit. For example, the generation AI generates messages such as "Another step forward today! Good luck!" or "Great that you're continuing to study English!" The generation AI can also generate messages such as "The project is progressing well!" The generation AI generates messages using a text generation AI (e.g., LLM) or a multimodal generation AI. The notification unit notifies the user of the message generated by the message generation unit at a set time. For example, a message such as "Let's do our best today!" is notified at 8:00 a.m. every morning. If the user desires a reminder at a specific time, the notification unit can also notify the user of the message at that time. As a result, the encouraging message generation system according to the embodiment can improve the user's motivation and encourage reminders.

[0054] When a user sets a goal or theme, the goal setting unit allows the generation AI to suggest the most appropriate goal or theme based on past data. For example, when a user inputs a goal or theme, the goal setting unit allows the generation AI to analyze past successes and failures and suggest the most appropriate goal or theme. For example, when setting a diet goal, the generation AI suggests a specific goal based on past successes. The generation AI also analyzes the user's past behavioral data and progress and suggests the most appropriate goal or theme based on that. For example, it suggests an appropriate goal as the next step based on the progress of English learning. Furthermore, the generation AI automatically generates related sub-goals and steps for the goal or theme set by the user and suggests them to the user. For example, when setting a project goal, it suggests specific tasks and steps. This allows the user to set the most appropriate goal or theme.

[0055] The goal setting unit can have the generation AI automatically generate related sub-goals and steps for goals and themes set by the user and suggest them to the user. For example, the goal setting unit can have the generation AI automatically generate related sub-goals for goals set by the user and suggest them to the user. For example, for a diet goal, the generation AI can suggest sub-goals such as meal management and exercise plans. The generation AI can also automatically generate specific steps based on the user's goal and suggest them to the user. For example, for an English learning goal, the generation AI can suggest daily learning content and progress management steps. Furthermore, the generation AI can automatically generate related tasks and action plans for themes set by the user and suggest them to the user. For example, for a project theme, the generation AI can suggest specific tasks and schedules. This allows the user to set specific sub-goals and steps.

[0056] The goal setting unit can use the emotion estimation function to analyze the emotions of the user when setting a goal or theme, and support goal setting that elicits positive emotions. For example, when the user sets a goal or theme, the goal setting unit uses the emotion estimation function to analyze the user's emotions in real time and support goal setting that elicits positive emotions. For example, if the user has negative emotions, the goal setting unit displays an encouraging message. Furthermore, the emotion estimation function is used to collect emotional data when the user sets a goal or theme, and provides advice for eliciting positive emotions. For example, if the user is feeling anxious, the goal setting unit suggests goal setting that gives a sense of security. Furthermore, when the user sets a goal or theme, the emotion estimation function is used to provide an interface for eliciting positive emotions. For example, when the user sets a goal, positive feedback and success stories are displayed. This allows the user to set goals with positive emotions.

[0057] The goal setting unit can display success stories and statistical data of other users for reference when the user sets a goal or theme. The goal setting unit, for example, displays success stories of other users for reference when the user sets a goal or theme. For example, when setting a diet goal, specific examples of successful users are displayed. Furthermore, information related to the goal or theme set by the user is provided based on statistical data. For example, when setting a goal for learning English, highly effective learning methods and progress data are displayed. Furthermore, reference information is provided when the user sets a goal or theme based on success stories and statistical data of other users. For example, when setting a goal for a project, examples of successful projects and statistical data are displayed. This allows the user to set a goal by referring to other success stories and statistical data.

[0058] The goal setting unit can enable the setting of goals and themes using at least one of various interfaces, such as voice input or gesture input. The goal setting unit, for example, enables the user to use voice input when setting a goal or theme. For example, the user sets a goal by vocally inputting "succeed in dieting." The goal setting unit also enables the user to set a goal or theme using gesture input. For example, the user sets a goal by performing a specific gesture. Furthermore, various interfaces are provided to improve the convenience for the user when setting a goal or theme. For example, in addition to touch screen and keyboard input, voice input and gesture input are supported. This allows the user to set a goal using various interfaces.

[0059] The goal setting unit uses the emotion estimation function to provide real-time feedback on the emotions of the user when setting a goal or theme, thereby encouraging optimal goal setting. For example, the goal setting unit uses the emotion estimation function to provide real-time feedback on emotions when the user sets a goal or theme. For example, if the user has positive emotions, goal setting that reinforces those emotions is suggested. The emotion estimation function also analyzes emotional data in real time when the user sets a goal or theme, encouraging optimal goal setting. For example, if the user is feeling anxious, goal setting that gives the user a sense of security is suggested. Furthermore, the emotion estimation function provides real-time feedback when the user sets a goal or theme, supporting goal setting that elicits positive emotions. For example, if the user has negative emotions, an encouraging message is displayed. This allows the user to set goals while receiving emotional feedback in real time.

[0060] The message generation unit can analyze the user's past behavioral data and progress status, and generate more personalized messages based on that. In the message generation unit, for example, the generation AI analyzes the user's past behavioral data and generates personalized messages based on that. For example, it generates an encouraging message based on goals the user has achieved in the past. The generation AI also analyzes the user's progress in real time and generates personalized messages based on that. For example, if the user is making good progress toward a goal, it generates a message praising that progress. Furthermore, the generation AI learns the user's past behavioral data and progress status, and generates optimal messages based on that. For example, it generates an encouraging message based on the user's past experiences of overcoming difficulties. This makes it possible to provide more personalized messages to the user.

[0061] The message generation unit can learn the user's preferences and interests and generate messages to increase motivation based on them. For example, the generation AI of the message generation unit learns the user's preferences and interests and generates messages to increase motivation based on them. For example, it generates messages related to the user's favorite sports. The generation AI also analyzes the user's interests and generates personalized messages based on them. For example, it generates messages related to topics that the user is interested in. Furthermore, the generation AI learns the user's preferences and interests and generates optimal messages based on them. For example, it generates messages related to the user's favorite music or movies. This makes it possible to provide messages to increase motivation based on the user's preferences and interests.

[0062] The message generation unit can use the emotion estimation function to analyze the user's current emotional state and generate a message accordingly. The message generation unit, for example, uses the emotion estimation function to analyze the user's current emotional state in real time and generate a message accordingly. For example, if the user is feeling stressed, it generates a message encouraging relaxation. The generation AI also analyzes the user's emotional state and generates an optimal message based on that. For example, if the user is feeling positive, it generates a message that reinforces that emotion. Furthermore, it uses the emotion estimation function to monitor the user's emotional state in real time and generate a message accordingly. For example, if the user is feeling anxious, it generates a message that gives a sense of security. This makes it possible to provide a message that corresponds to the user's emotional state.

[0063] The message generation unit generates messages that correspond to different languages ​​and cultures, making it possible to cater to a global user base. For example, the generation AI generates messages that correspond to different languages, making it possible to cater to a global user base. For example, it generates messages in multiple languages, such as English, French, and Chinese. In addition, the generation AI learns cultural backgrounds and generates messages based on them to generate messages that correspond to different cultures. For example, it incorporates encouraging expressions from a specific culture. Furthermore, the generation AI generates messages that correspond to different languages ​​and cultures, building a system that caters to a global user base. For example, it customizes messages according to the user's language settings and cultural background. This makes it possible to provide messages that correspond to different languages ​​and cultures.

[0064] The message generation unit can analyze a user's social media posts and comments and generate messages based thereon. In the message generation unit, for example, the generation AI analyzes a user's social media posts and generates personalized messages based thereon. For example, it generates an encouraging message related to the content posted by the user. The generation AI also analyzes the user's social media comments and generates an optimal message based thereon. For example, it generates an encouraging message in response to a comment received by the user. Furthermore, the generation AI learns the user's social media posts and comments and generates messages based thereon. For example, it generates messages related to topics that interest the user. This makes it possible to provide messages based on the user's social media posts and comments.

[0065] The message generation unit can use the emotion estimation function to generate a message in real time according to the user's emotions and immediately notify the user. The message generation unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time, generate a corresponding message, and immediately notify the user. For example, if the user is feeling stressed, a message encouraging relaxation is immediately notified. The generation AI also builds a system that generates optimal messages in real time based on the user's emotional data and immediately notifies the user. For example, if the user is feeling positive, a message that reinforces that emotion is immediately notified. Furthermore, the emotion estimation function is used to monitor the user's emotional state in real time, generate a corresponding message, and immediately notify the user. For example, if the user is feeling anxious, a message that provides a sense of security is immediately notified. This makes it possible to provide messages according to the user's emotions in real time.

[0066] The notification unit can optimize the timing of message notifications based on the user's lifestyle and behavioral patterns. The notification unit, for example, analyzes the user's lifestyle and sets the optimal message notification timing based on that. For example, if the user is a morning person, the notification unit notifies the user of a message in the morning hours. Furthermore, a system is constructed that optimizes the timing of message notifications based on behavioral patterns. For example, if the user exercises during a specific time period, the notification unit notifies the user of an encouraging message before and after the exercise. Furthermore, the system learns the user's lifestyle and behavioral patterns and dynamically adjusts the timing of message notifications based on that information. For example, if the user works late into the night, the notification unit notifies the user of a message encouraging them to relax in the evening hours. This allows the notification unit to notify the user of a message at the optimal timing based on the user's lifestyle and behavioral patterns.

[0067] The notification unit can customize the message notification method according to the user's device and environment. The notification unit customizes the message notification method according to the user's device, for example. For example, a user using a smart watch is notified of messages by vibration or voice. The message notification method is also adjusted according to the user's environment. For example, a user using a smart speaker is notified of messages by voice. Furthermore, a system is constructed that dynamically customizes the message notification method according to the device and environment. For example, if the user is out, a notification is sent to a smartphone, and if the user is at home, a notification is sent to a smart speaker. This allows messages to be notified in the optimal way according to the user's device and environment.

[0068] The notification unit can use the emotion estimation function to notify a message at the optimal timing according to the user's emotional state. For example, the notification unit uses the emotion estimation function to analyze the user's emotional state in real time and notify a message at the optimal timing according to that. For example, if the user is feeling stressed, the notification unit notifies the user of a message encouraging relaxation. Furthermore, a system is constructed that sets the optimal notification timing based on the user's emotional data. For example, if the user has positive emotions, the notification unit notifies the user of a message that reinforces those emotions. Furthermore, the emotion estimation function is used to monitor the user's emotional state in real time and notify the user of a message at the optimal timing according to that. For example, if the user is feeling anxious, the notification unit notifies the user of a message that gives a sense of security. This makes it possible to notify the user of a message at the optimal timing according to the user's emotional state.

[0069] The notification unit can work in conjunction with the user's calendar and schedule to notify messages in accordance with important events and tasks. The notification unit, for example, works in conjunction with the user's calendar and schedule to notify messages in accordance with important events and tasks. For example, a reminder message may be sent before a meeting. A system can also be built that optimizes the timing of message notifications based on calendar and schedule data. For example, an encouraging message may be sent before a task deadline set by the user. Furthermore, the system can work in conjunction with the user's calendar and schedule to dynamically adjust messages in accordance with important events and tasks. For example, a message may be sent before or after an event the user has scheduled. This allows messages to be sent at the optimal timing based on the user's calendar and schedule.

[0070] The notification unit can customize the message notification method based on the user's location information and notify only at specific locations. The notification unit, for example, builds a system that notifies messages only at specific locations based on the user's location information. For example, if the user is at the gym, the notification unit notifies the user of a message related to exercise. The notification unit also analyzes the location information and notifies the user of the optimal message when the user is in a specific location. For example, if the user is in the office, the notification unit notifies the user of a message related to work. The notification unit also customizes the message notification method based on the user's location information. For example, if the user is at home, the notification unit notifies the user of a message encouraging relaxation. This allows messages to be notified in the optimal way based on the user's location information.

[0071] The notification unit uses the emotion estimation function to notify the user of a message according to their emotional state, thereby eliciting positive emotions. The notification unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and notify the user of a message according to the analysis. For example, if the user is feeling stressed, the notification unit notifies the user of a message encouraging relaxation. Furthermore, a system is constructed that notifies the user of a message that elicits positive emotions based on the user's emotional data. For example, if the user has positive emotions, the notification unit notifies the user of a message that reinforces those emotions. Furthermore, the emotion estimation function is used to monitor the user's emotional state in real time and notify the user of a message according to the analysis. For example, if the user is feeling anxious, the notification unit notifies the user of a message that gives the user a sense of security. In this way, the notification unit can notify the user of a message according to their emotional state and elicit positive emotions.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] In the goal setting unit, when a user sets a goal or theme, the generation AI can suggest the most appropriate goal or theme based on past data. For example, when a user inputs a goal or theme, the generation AI analyzes past successes and failures to suggest the most appropriate goal or theme. For example, when setting a diet goal, the generation AI suggests a specific goal based on past successes. The generation AI also analyzes the user's past behavioral data and progress, and suggests the most appropriate goal or theme based on that. For example, it suggests an appropriate goal as the next step based on the progress of English learning. Furthermore, the generation AI automatically generates related sub-goals and steps for the goal or theme set by the user and suggests them to the user. For example, when setting a project goal, it suggests specific tasks and steps. This allows the user to set the most appropriate goal or theme.

[0074] The goal setting unit allows the generation AI to automatically generate related sub-goals and steps for goals and themes set by the user and suggest them to the user. For example, the generation AI automatically generates related sub-goals for a goal set by the user and suggests them to the user. For example, for a diet goal, it suggests sub-goals such as meal management and exercise plans. The generation AI also automatically generates specific steps based on the user's goal and suggests them to the user. For example, for an English learning goal, it suggests daily learning content and progress management steps. Furthermore, the generation AI automatically generates related tasks and action plans for themes set by the user and suggests them to the user. For example, it suggests specific tasks and schedules for a project theme. This allows the user to set specific sub-goals and steps.

[0075] The goal setting unit can use the emotion estimation function to analyze the emotions a user feels when setting a goal or theme, and support goal setting that elicits positive emotions. For example, when a user sets a goal or theme, the emotion estimation function is used to analyze the user's emotions in real time, and support goal setting that elicits positive emotions. For example, if the user has negative emotions, an encouraging message is displayed. Furthermore, the emotion estimation function is used to collect emotional data when the user sets a goal or theme, and provide advice to elicit positive emotions. For example, if the user is feeling anxious, goal setting that gives a sense of security is suggested. Furthermore, when the user sets a goal or theme, the emotion estimation function is used to provide an interface for eliciting positive emotions. For example, when the user sets a goal, positive feedback and success stories are displayed. This allows the user to set goals with positive emotions.

[0076] The goal setting unit can display success stories and statistical data of other users for reference when the user sets a goal or theme. For example, when the user sets a goal or theme, the unit can display success stories of other users for reference. For example, when setting a diet goal, the unit can display specific examples of successful users. Furthermore, the unit can provide information related to the goal or theme set by the user based on statistical data. For example, when setting a goal for learning English, the unit can display highly effective learning methods and progress data. Furthermore, the unit can provide reference information when the user sets a goal or theme based on success stories and statistical data of other users. For example, when setting a goal for a project, the unit can display examples of successful projects and statistical data. This allows the user to set a goal by referring to other success stories and statistical data.

[0077] The goal setting unit can enable the setting of goals and themes using at least one of various interfaces, such as voice input or gesture input. For example, the user can use voice input when setting a goal or theme. For example, the user can set a goal by vocally inputting "succeed in dieting." The user can also use gesture input to set a goal or theme. For example, the user can set a goal by performing a specific gesture. Furthermore, various interfaces are provided to improve the convenience for the user when setting goals and themes. For example, in addition to touch screen and keyboard input, voice input and gesture input are supported. This allows the user to set goals using various interfaces.

[0078] The goal setting unit uses the emotion estimation function to provide real-time feedback on the user's emotions when setting a goal or theme, thereby encouraging optimal goal setting. For example, when the user sets a goal or theme, the emotion estimation function is used to provide real-time feedback on emotions. For example, if the user has positive emotions, goal setting that reinforces those emotions is suggested. The emotion estimation function is also used to analyze emotional data when the user sets a goal or theme in real time, encouraging optimal goal setting. For example, if the user is feeling anxious, goal setting that gives a sense of security is suggested. Furthermore, when the user sets a goal or theme, the emotion estimation function is used to provide real-time feedback to support goal setting that elicits positive emotions. For example, if the user has negative emotions, an encouraging message is displayed. This allows the user to set goals while receiving emotional feedback in real time.

[0079] The message generation unit can analyze the user's past behavioral data and progress status, and generate more personalized messages based on that. For example, the generation AI analyzes the user's past behavioral data and generates personalized messages based on that. For example, it generates an encouraging message based on goals the user has achieved in the past. The generation AI also analyzes the user's progress in real time and generates personalized messages based on that. For example, if the user is making good progress toward a goal, it generates a message praising that progress. Furthermore, the generation AI learns the user's past behavioral data and progress status, and generates optimal messages based on that. For example, it generates an encouraging message based on the user's past experiences of overcoming difficulties. This makes it possible to provide more personalized messages to the user.

[0080] The message generation unit can learn the user's preferences and interests and generate messages to increase motivation based on them. For example, the generation AI learns the user's preferences and interests and generates messages to increase motivation based on them. For example, it generates messages related to the user's favorite sports. The generation AI also analyzes the user's interests and generates personalized messages based on them. For example, it generates messages related to topics that the user is interested in. Furthermore, the generation AI learns the user's preferences and interests and generates optimal messages based on them. For example, it generates messages related to the user's favorite music or movies. This makes it possible to provide messages to increase motivation based on the user's preferences and interests.

[0081] The message generation unit can use the emotion estimation function to analyze the user's current emotional state and generate a message accordingly. For example, the emotion estimation function can be used to analyze the user's current emotional state in real time and generate a message accordingly. For example, if the user is feeling stressed, a message encouraging relaxation can be generated. The generation AI can also analyze the user's emotional state and generate an optimal message based on that analysis. For example, if the user is feeling positive, a message that reinforces that emotion can be generated. Furthermore, the emotion estimation function can be used to monitor the user's emotional state in real time and generate a message accordingly. For example, if the user is feeling anxious, a message that provides a sense of security can be generated. This makes it possible to provide a message that corresponds to the user's emotional state.

[0082] The message generation unit generates messages that correspond to different languages ​​and cultures, making it possible to cater to a global user base. For example, the generation AI generates messages that correspond to different languages ​​to cater to a global user base. For example, it generates messages in multiple languages, such as English, French, and Chinese. In addition, the generation AI learns cultural backgrounds and generates messages based on them to generate messages that correspond to different cultures. For example, it incorporates encouraging expressions from specific cultures. Furthermore, the generation AI generates messages that correspond to different languages ​​and cultures, building a system that caters to a global user base. For example, it customizes messages according to the user's language settings and cultural background. This makes it possible to provide messages that correspond to different languages ​​and cultures.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The goal setting unit inputs the goals and themes set by the user. For example, the user can input specific goals and themes such as "successfully lose weight" or "study English every day." The goal setting unit can also input professional themes such as completing a project within a deadline. Step 2: In the message generation section, the generation AI generates an encouraging message based on the goals and themes input by the goal setting section. For example, the generation AI generates messages such as "Another step forward today! Do your best!" or "It's great that you're continuing to study English!". The generation AI can also generate messages such as "The project is progressing well!". The generation AI generates messages using a text generation AI (e.g., LLM) or a multimodal generation AI. Step 3: The notification unit notifies the user of the message generated by the message generation unit at the set time. For example, a message saying "Let's do our best today!" is sent every morning at 8:00. If the user wishes to be reminded at a specific time, the notification unit can also send the message at that time.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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."

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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]

[0152] 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 goal setting section for inputting goals and themes set by the user; a message generating unit that generates an encouraging message based on the goal or theme input by the goal setting unit; a notification unit that notifies the message generated by the message generation unit at set intervals. A system characterized by:

2. The goal setting unit When the user sets a goal or theme, the generation AI will suggest the most suitable goal or theme based on past data.

2. The system of claim 1.

3. The goal setting unit The generation AI automatically generates related sub-goals and steps for the goal or theme set by the user and proposes them to the user.

2. The system of claim 1.

4. The goal setting unit Analyze the emotions of the user when setting goals or themes, and support goal setting that elicits positive emotions 2. The system of claim 1.

5. The goal setting unit When the user sets goals or themes, the user can view other users' success stories and statistical data for reference.

2. The system of claim 1.

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