System

Abstract

An object of a system according to an embodiment is to provide a specific action plan based on a goal and a habit of a user.SOLUTION: A system includes a target input part, a target analysis part, and an action guideline presentation part. The goal input unit inputs a goal or a habit of the user. The goal analysis unit analyzes the goal or the habit input by the goal input unit. The action agenda presenting unit presents a specific action agenda based on the result analyzed by the target analyzing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately provide specific guidelines for action based on a user's goals and habits, and there is room for improvement.

[0005] The system according to the embodiment aims to provide a specific guide to action based on the user's goals and habits. [Means for solving the problem]

[0006] The system according to the embodiment includes a goal input unit, a goal analysis unit, and a behavioral guideline presentation unit. The goal input unit inputs a user's goals and habits. The goal analysis unit analyzes the goals and habits input by the goal input unit. The behavioral guideline presentation unit presents a specific behavioral guideline based on the results of the analysis by the goal analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide specific guidelines for action based on the user's goals and habits. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The AI ​​personal coaching system according to an embodiment of the present invention is a system in which a user's goals and habits are input, and a generating AI analyzes them to present achievable short-term goals and specific guidelines for action. This allows the AI ​​personal coaching system to support the user in achieving their goals and motivate them to improve themselves.

[0029] The AI ​​personal coaching system according to the embodiment includes a goal input unit, a goal analysis unit, and a behavioral guideline presentation unit. The goal input unit inputs a user's goals and habits. For example, the user may input, "I want to continue exercising for 30 minutes every day." The goal input unit may also input, "I want to develop a reading habit." The goal input unit may also input, "I want to improve my work efficiency." The goal analysis unit uses a generation AI to analyze the goals and habits input by the goal input unit. For example, the generation AI may analyze the goals and habits using natural language processing technology. The generation AI may also generate an optimal action plan based on past data and success stories. The generation AI may also analyze the goals and habits using a machine learning algorithm. The behavioral guideline presentation unit presents specific behavioral guidelines based on the results of the analysis by the goal analysis unit. For example, the behavioral guideline presentation unit may suggest, "To continue exercising for 30 minutes every day, start by walking for 10 minutes three times a week." The action guideline presenting unit can also suggest, "To develop a reading habit, read for 10 minutes every day before going to bed." The action guideline presenting unit can also suggest, "To improve work efficiency, prioritize tasks." In this way, the AI ​​personal coaching system according to the embodiment can efficiently analyze the user's goals and habits and provide specific action guideline.

[0030] When a user inputs their goals and habits, the generation AI can provide real-time feedback and advice to increase the specificity and achievability of their goals. For example, if a user inputs, "I want to continue exercising for 30 minutes every day," the generation AI can provide real-time advice, such as, "Decide on the specific type of exercise, such as jogging or yoga." Similarly, if a user inputs, "I want to develop a reading habit," the generation AI can provide advice, such as, "Set a specific reading time, such as reading for 10 minutes every night." Similarly, if a user inputs, "I want to improve my work efficiency," the generation AI can provide advice, such as, "Prioritize your tasks." This allows for real-time feedback to increase the specificity and achievability of the user's goals.

[0031] The goal input unit allows the generation AI to automatically suggest goals and habits based on the user's past input history and behavioral data. For example, if a user previously input "I want to continue exercising 30 minutes every day," the generation AI could suggest, "Based on your previous goal, try exercising three times a week." Similarly, if a user previously input "I want to develop a reading habit," the generation AI could suggest, "Based on your previous goal, continue reading for 10 minutes every day before bed." Similarly, if a user previously input "I want to improve my work efficiency," the generation AI could suggest, "Based on your previous goal, continue to prioritize and work on your tasks." This allows the system to automatically suggest goals and habits based on the user's past input history and behavioral data.

[0032] The goal input unit supports voice input and image input when a user inputs goals and habits, enabling more diverse input methods. For example, when a user inputs by voice, "I want to continue exercising 30 minutes every day," the generation AI uses voice recognition technology to convert it into text and set a goal. In addition, when a user inputs by image, "I want to develop a reading habit," the generation AI can also use image analysis technology to convert it into text and set a goal. In addition, when a user inputs by voice, "I want to improve my work efficiency," the generation AI can also use voice recognition technology to convert it into text and set a goal. This allows the user to input goals and habits by voice input and image input, enabling more diverse input methods.

[0033] The goal input unit can integrate inputs from different devices to provide a seamless user experience. For example, the goal input unit allows a user to input "I want to continue exercising 30 minutes every day" on a smartphone and confirm that goal on a tablet. The goal input unit can also allow a user to input "I want to develop a reading habit" on a smartwatch and confirm that goal on a smartphone. The goal input unit can also allow a user to input "I want to improve my work efficiency" on a PC and confirm that goal on a tablet. This allows inputs from different devices to be integrated to provide a seamless user experience.

[0034] When the generation AI analyzes goals and habits, the goal analysis unit takes into account the user's lifestyle and daily schedule and can propose an optimal action plan. For example, when a user inputs, "I want to continue exercising for 30 minutes every day," the goal analysis unit can take into account the user's lifestyle and suggest, "It's effective to exercise in the morning." Similarly, when a user inputs, "I want to develop a reading habit," the goal analysis unit can take into account the user's daily schedule and suggest, "It's good to read before bed." Similarly, when a user inputs, "I want to improve my work efficiency," the goal analysis unit can take into account the user's lifestyle and suggest, "It's good to prioritize tasks." This allows the generation AI to propose an optimal action plan taking into account the user's lifestyle and daily schedule.

[0035] The goal analysis unit allows the generation AI to analyze not only past success stories but also failure stories and provide advice to avoid failure. For example, when a user inputs, "I want to continue exercising for 30 minutes every day," the generation AI analyzes past failure stories and provides advice such as, "Don't push yourself, start with a short period at first." Similarly, when a user inputs, "I want to develop a reading habit," the generation AI analyzes past failure stories and can provide advice such as, "Don't push yourself, start with a short period at first." Similarly, when a user inputs, "I want to improve my work efficiency," the generation AI analyzes past failure stories and can provide advice such as, "Don't push yourself, start with a short period at first." This makes it possible to analyze past success stories and failure stories and provide advice to avoid failure.

[0036] The goal analysis unit allows the generation AI to analyze the goals and habits of users from different cultures and regions and propose action plans from a global perspective. For example, when a user inputs, "I want to continue exercising for 30 minutes every day," the generation AI analyzes exercise habits from different cultures and suggests, "Try incorporating cross-cultural exercises such as yoga or tai chi." Similarly, when a user inputs, "I want to develop a reading habit," the generation AI can analyze reading habits from different regions and suggest, "Try reading literary works from different cultures." Similarly, when a user inputs, "I want to improve my work efficiency," the generation AI can analyze work habits from different cultures and suggest, "Try incorporating time management methods from different cultures." This allows the generation AI to analyze the goals and habits of users from different cultures and regions and propose action plans from a global perspective.

[0037] The goal analysis unit allows the generation AI to integrate the user's health data and suggest an action plan based on their health status. For example, if a user inputs, "I want to continue exercising for 30 minutes every day," the generation AI can integrate data from a fitness tracker and suggest, "Adjust your exercise plan based on your heart rate and calorie consumption." Similarly, if a user inputs, "I want to develop a reading habit," the generation AI can integrate data from a smartwatch and suggest, "Adjust your reading time based on your sleep data." Similarly, if a user inputs, "I want to improve my work efficiency," the generation AI can integrate health data and suggest, "Adjust your tasks based on your stress level." This allows the system to integrate the user's health data and suggest an action plan based on their health status.

[0038] The action guideline suggestion unit can use storytelling techniques to motivate users when presenting short-term goals and specific action guidelines. For example, if a user inputs, "I want to continue exercising for 30 minutes every day," the action guideline suggestion unit can use storytelling techniques to suggest, "Let's start your health story. The first chapter starts with walking three times a week." Similarly, if a user inputs, "I want to develop a reading habit," the action guideline suggestion unit can suggest, "Let's start your knowledge journey. The first chapter starts with reading for 10 minutes every day before bed." Similarly, if a user inputs, "I want to improve my work efficiency," the action guideline suggestion unit can suggest, "Let's start your career story. The first chapter starts with prioritizing and tackling tasks." This allows the use of storytelling techniques to motivate users.

[0039] The action guideline presentation unit allows the generation AI to monitor the user's progress in real time and dynamically adjust the action plan according to the level of achievement. For example, when a user inputs, "I want to continue exercising for 30 minutes every day," the generation AI monitors the progress and dynamically adjusts the action plan, saying, "You're making good progress. Next, aim to exercise four times a week." Similarly, when a user inputs, "I want to develop a reading habit," the generation AI can monitor the progress and dynamically adjust the action plan, saying, "You're making good progress. Next, aim to read for 15 minutes every day." Similarly, when a user inputs, "I want to improve my work efficiency," the generation AI can monitor the progress and dynamically adjust the action plan, saying, "You're making good progress. Next, try breaking down the task into smaller parts." This allows the user's progress to be monitored in real time and the action plan to be dynamically adjusted according to the level of achievement.

[0040] The action guideline presentation unit allows the generation AI to present an action plan to the user in a variety of formats using different media. For example, when the user inputs, "I want to continue exercising for 30 minutes every day," the generation AI presents an exercise plan in video format, making it visually easier to understand. In addition, when the user inputs, "I want to develop a reading habit," the action guideline presentation unit can present a reading plan in audio format, making it easier to understand auditorily. In addition, when the user inputs, "I want to improve my work efficiency," the generation AI can present a task plan in text format, making it visually easier to understand. This makes it possible to present action plans to the user in a variety of formats using different media.

[0041] The action guideline presentation unit allows the generation AI to utilize the user's social network to propose an action plan to gain support from friends and family. For example, when a user inputs, "I want to continue exercising for 30 minutes every day," the generation AI can suggest, "It's easier to continue if you exercise with friends," utilizing the user's social network. Furthermore, when a user inputs, "I want to develop a reading habit," the generation AI can suggest, "It's easier to continue if you read with your family." Furthermore, when a user inputs, "I want to improve my work efficiency," the generation AI can suggest, "Working on tasks together with colleagues will improve efficiency." This allows the user's social network to be utilized to propose an action plan to gain support from friends and family.

[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] The goal input unit supports voice and image input when the user inputs their goals and habits, enabling more diverse input methods. For example, when a user inputs by voice, "I want to continue exercising for 30 minutes every day," the generation AI uses voice recognition technology to convert it into text and set a goal. Also, when a user inputs by image, "I want to develop a reading habit," the generation AI can use image analysis technology to convert it into text and set a goal. Furthermore, when a user inputs by voice, "I want to improve my work efficiency," the generation AI can use voice recognition technology to convert it into text and set a goal. This allows the user to input their goals and habits by voice and image input, enabling more diverse input methods.

[0044] The goal input unit can integrate inputs from different devices to provide a seamless user experience. For example, a user can input "I want to continue exercising for 30 minutes every day" on a smartphone and confirm that goal on a tablet. Alternatively, a user can input "I want to develop a reading habit" on a smartwatch and confirm that goal on a smartphone. Furthermore, a user can input "I want to improve my work efficiency" on a computer and confirm that goal on a tablet. This allows inputs from different devices to be integrated to provide a seamless user experience.

[0045] When the generation AI analyzes goals and habits, the goal analysis unit takes into account the user's lifestyle and daily schedule and can propose the optimal action plan. For example, if a user inputs, "I want to continue exercising for 30 minutes every day," the generation AI will consider the user's lifestyle and suggest, "It's effective to exercise in the morning." Similarly, if a user inputs, "I want to develop a reading habit," the generation AI will consider the user's daily schedule and suggest, "It's good to read before bed." Furthermore, if a user inputs, "I want to improve my work efficiency," the generation AI will consider the user's lifestyle and suggest, "It's good to prioritize tasks." This allows the system to propose the optimal action plan, taking into account the user's lifestyle and daily schedule.

[0046] The goal analysis unit allows the generation AI to analyze not only past successes but also failures, and provide advice to avoid failure. For example, when a user inputs, "I want to continue exercising for 30 minutes every day," the generation AI analyzes past failures and provides the advice, "Don't push yourself, start with a short time at first." Similarly, when a user inputs, "I want to develop a reading habit," the generation AI analyzes past failures and can provide the advice, "Don't push yourself, start with a short time at first." Furthermore, when a user inputs, "I want to improve my work efficiency," the generation AI can analyze past failures and can provide the advice, "Don't push yourself, start with a short time at first." This makes it possible to analyze past successes and failures and provide advice to avoid failure.

[0047] The action guideline suggestion unit can use storytelling techniques to motivate users when the AI ​​proposes short-term goals and specific action guidelines. For example, if a user inputs, "I want to continue exercising for 30 minutes every day," the AI ​​can use storytelling techniques to suggest, "Let's start your health story. The first chapter starts with walking three times a week." Similarly, if a user inputs, "I want to develop a reading habit," the AI ​​can suggest, "Let's start your knowledge journey. The first chapter starts with reading for 10 minutes every day before bed." Furthermore, if a user inputs, "I want to improve my work efficiency," the AI ​​can suggest, "Let's start your career story. The first chapter starts with prioritizing and tackling tasks." This allows the AI ​​to use storytelling techniques to motivate users.

[0048] The action guideline presentation unit allows the generation AI to present an action plan to the user in a variety of formats using different media. For example, when a user inputs, "I want to continue exercising for 30 minutes every day," the generation AI can present an exercise plan in video format, making it visually easier to understand. Also, when a user inputs, "I want to develop a reading habit," the generation AI can present a reading plan in audio format, making it easier to understand auditorily. Furthermore, when a user inputs, "I want to improve my work efficiency," the generation AI can present a task plan in text format, making it visually easier to understand. This allows action plans to be presented to the user in a variety of formats using different media.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The goal input section inputs the user's goals and habits. For example, the user can input "I want to continue exercising for 30 minutes every day." The user can also input "I want to develop the habit of reading" or "I want to improve my work efficiency." Step 2: In the goal analysis section, the generation AI analyzes the goals and habits entered by the goal input section. For example, the generation AI can analyze the goals and habits using natural language processing technology and generate an optimal action plan based on past data and success stories. It can also analyze goals and habits using machine learning algorithms. Step 3: The action guideline suggestion unit presents specific action guidelines based on the results of the analysis by the goal analysis unit. For example, it might suggest, "To maintain 30 minutes of exercise every day, start by walking for 10 minutes three times a week," "To develop a reading habit, read for 10 minutes every day before going to bed," or "To improve work efficiency, prioritize your tasks."

[0051] (Example 2) The AI ​​personal coaching system according to an embodiment of the present invention is a system in which a user's goals and habits are input, and a generating AI analyzes them to present achievable short-term goals and specific guidelines for action. This allows the AI ​​personal coaching system to support the user in achieving their goals and motivate them to improve themselves.

[0052] The AI ​​personal coaching system according to the embodiment includes a goal input unit, a goal analysis unit, and a behavioral guideline presentation unit. The goal input unit inputs a user's goals and habits. For example, the user may input, "I want to continue exercising for 30 minutes every day." The goal input unit may also input, "I want to develop a reading habit." The goal input unit may also input, "I want to improve my work efficiency." The goal analysis unit uses a generation AI to analyze the goals and habits input by the goal input unit. For example, the generation AI may analyze the goals and habits using natural language processing technology. The generation AI may also generate an optimal action plan based on past data and success stories. The generation AI may also analyze the goals and habits using a machine learning algorithm. The behavioral guideline presentation unit presents specific behavioral guidelines based on the results of the analysis by the goal analysis unit. For example, the behavioral guideline presentation unit may suggest, "To continue exercising for 30 minutes every day, start by walking for 10 minutes three times a week." The action guideline presenting unit can also suggest, "To develop a reading habit, read for 10 minutes every day before going to bed." The action guideline presenting unit can also suggest, "To improve work efficiency, prioritize tasks." In this way, the AI ​​personal coaching system according to the embodiment can efficiently analyze the user's goals and habits and provide specific action guideline.

[0053] When a user inputs their goals and habits, the generation AI can provide real-time feedback and advice to increase the specificity and achievability of their goals. For example, if a user inputs, "I want to continue exercising for 30 minutes every day," the generation AI can provide real-time advice, such as, "Decide on the specific type of exercise, such as jogging or yoga." Similarly, if a user inputs, "I want to develop a reading habit," the generation AI can provide advice, such as, "Set a specific reading time, such as reading for 10 minutes every night." Similarly, if a user inputs, "I want to improve my work efficiency," the generation AI can provide advice, such as, "Prioritize your tasks." This allows for real-time feedback to increase the specificity and achievability of the user's goals.

[0054] The goal input unit allows the generation AI to automatically suggest goals and habits based on the user's past input history and behavioral data. For example, if a user previously input "I want to continue exercising 30 minutes every day," the generation AI could suggest, "Based on your previous goal, try exercising three times a week." Similarly, if a user previously input "I want to develop a reading habit," the generation AI could suggest, "Based on your previous goal, continue reading for 10 minutes every day before bed." Similarly, if a user previously input "I want to improve my work efficiency," the generation AI could suggest, "Based on your previous goal, continue to prioritize and work on your tasks." This allows the system to automatically suggest goals and habits based on the user's past input history and behavioral data.

[0055] The goal input unit can use the emotion estimation function to analyze the emotion of the user when inputting and make suggestions to elicit positive emotions. For example, when the user inputs "I want to continue exercising for 30 minutes every day," the emotion estimation function analyzes the user's emotion and makes a suggestion such as "Imagine the refreshing feeling after exercising." Furthermore, when the user inputs "I want to develop a reading habit," the emotion estimation function can analyze the user's emotion and make a suggestion such as "Imagine the sense of accomplishment after reading." Furthermore, when the user inputs "I want to improve my work efficiency," the emotion estimation function can analyze the user's emotion and make a suggestion such as "Imagine the satisfaction after completing a task." In this way, the user's emotion can be analyzed and suggestions can be made to elicit positive emotions.

[0056] The goal input unit supports voice input and image input when a user inputs goals and habits, enabling more diverse input methods. For example, when a user inputs by voice, "I want to continue exercising 30 minutes every day," the generation AI uses voice recognition technology to convert it into text and set a goal. In addition, when a user inputs by image, "I want to develop a reading habit," the generation AI can also use image analysis technology to convert it into text and set a goal. In addition, when a user inputs by voice, "I want to improve my work efficiency," the generation AI can also use voice recognition technology to convert it into text and set a goal. This allows the user to input goals and habits by voice input and image input, enabling more diverse input methods.

[0057] The goal input unit can integrate inputs from different devices to provide a seamless user experience. For example, the goal input unit allows a user to input "I want to continue exercising 30 minutes every day" on a smartphone and confirm that goal on a tablet. The goal input unit can also allow a user to input "I want to develop a reading habit" on a smartwatch and confirm that goal on a smartphone. The goal input unit can also allow a user to input "I want to improve my work efficiency" on a PC and confirm that goal on a tablet. This allows inputs from different devices to be integrated to provide a seamless user experience.

[0058] The goal input unit can use the emotion estimation function to monitor the user's emotions in real time when they input something and provide emotional support according to the input content. For example, when a user inputs, "I want to continue exercising for 30 minutes every day," the emotion estimation function monitors the user's emotions in real time and provides support by saying, "Imagine the feeling of exhilaration after exercising." In addition, when a user inputs, "I want to develop a reading habit," the emotion estimation function can monitor the user's emotions in real time and provide support by saying, "Imagine the sense of accomplishment after reading." In addition, when a user inputs, "I want to improve my work efficiency," the emotion estimation function can monitor the user's emotions in real time and provide support by saying, "Imagine the sense of satisfaction after completing a task." In this way, the user's emotions can be monitored in real time and emotional support can be provided according to the input content.

[0059] When the generation AI analyzes goals and habits, the goal analysis unit takes into account the user's lifestyle and daily schedule and can propose an optimal action plan. For example, when a user inputs, "I want to continue exercising for 30 minutes every day," the goal analysis unit can take into account the user's lifestyle and suggest, "It's effective to exercise in the morning." Similarly, when a user inputs, "I want to develop a reading habit," the goal analysis unit can take into account the user's daily schedule and suggest, "It's good to read before bed." Similarly, when a user inputs, "I want to improve my work efficiency," the goal analysis unit can take into account the user's lifestyle and suggest, "It's good to prioritize tasks." This allows the generation AI to propose an optimal action plan taking into account the user's lifestyle and daily schedule.

[0060] The goal analysis unit allows the generation AI to analyze not only past success stories but also failure stories and provide advice to avoid failure. For example, when a user inputs, "I want to continue exercising for 30 minutes every day," the generation AI analyzes past failure stories and provides advice such as, "Don't push yourself, start with a short period at first." Similarly, when a user inputs, "I want to develop a reading habit," the generation AI analyzes past failure stories and can provide advice such as, "Don't push yourself, start with a short period at first." Similarly, when a user inputs, "I want to improve my work efficiency," the generation AI analyzes past failure stories and can provide advice such as, "Don't push yourself, start with a short period at first." This makes it possible to analyze past success stories and failure stories and provide advice to avoid failure.

[0061] The goal analysis unit allows the generation AI to analyze the goals and habits of users from different cultures and regions and propose action plans from a global perspective. For example, when a user inputs, "I want to continue exercising for 30 minutes every day," the generation AI analyzes exercise habits from different cultures and suggests, "Try incorporating cross-cultural exercises such as yoga or tai chi." Similarly, when a user inputs, "I want to develop a reading habit," the generation AI can analyze reading habits from different regions and suggest, "Try reading literary works from different cultures." Similarly, when a user inputs, "I want to improve my work efficiency," the generation AI can analyze work habits from different cultures and suggest, "Try incorporating time management methods from different cultures." This allows the generation AI to analyze the goals and habits of users from different cultures and regions and propose action plans from a global perspective.

[0062] The goal analysis unit allows the generation AI to integrate the user's health data and suggest an action plan based on their health status. For example, if a user inputs, "I want to continue exercising for 30 minutes every day," the generation AI can integrate data from a fitness tracker and suggest, "Adjust your exercise plan based on your heart rate and calorie consumption." Similarly, if a user inputs, "I want to develop a reading habit," the generation AI can integrate data from a smartwatch and suggest, "Adjust your reading time based on your sleep data." Similarly, if a user inputs, "I want to improve my work efficiency," the generation AI can integrate health data and suggest, "Adjust your tasks based on your stress level." This allows the system to integrate the user's health data and suggest an action plan based on their health status.

[0063] The goal analysis unit can use the emotion estimation function to monitor the user's emotional state in real time and dynamically adjust the action plan according to the emotion. For example, when the user inputs, "I want to continue exercising for 30 minutes every day," the emotion estimation function monitors the user's emotions in real time and dynamically adjusts the action plan by saying, "Imagine the feeling of exhilaration after exercising." Furthermore, when the user inputs, "I want to develop a reading habit," the emotion estimation function can monitor the user's emotions in real time and dynamically adjust the action plan by saying, "Imagine the sense of accomplishment after reading." Furthermore, when the user inputs, "I want to improve my work efficiency," the emotion estimation function can monitor the user's emotions in real time and dynamically adjust the action plan by saying, "Imagine the feeling of satisfaction after completing a task." This allows the goal analysis unit to monitor the user's emotional state in real time and dynamically adjust the action plan according to the emotion.

[0064] The action guideline suggestion unit can use storytelling techniques to motivate users when presenting short-term goals and specific action guidelines. For example, if a user inputs, "I want to continue exercising for 30 minutes every day," the action guideline suggestion unit can use storytelling techniques to suggest, "Let's start your health story. The first chapter starts with walking three times a week." Similarly, if a user inputs, "I want to develop a reading habit," the action guideline suggestion unit can suggest, "Let's start your knowledge journey. The first chapter starts with reading for 10 minutes every day before bed." Similarly, if a user inputs, "I want to improve my work efficiency," the action guideline suggestion unit can suggest, "Let's start your career story. The first chapter starts with prioritizing and tackling tasks." This allows the use of storytelling techniques to motivate users.

[0065] The action guideline presentation unit allows the generation AI to monitor the user's progress in real time and dynamically adjust the action plan according to the level of achievement. For example, when a user inputs, "I want to continue exercising for 30 minutes every day," the generation AI monitors the progress and dynamically adjusts the action plan, saying, "You're making good progress. Next, aim to exercise four times a week." Similarly, when a user inputs, "I want to develop a reading habit," the generation AI can monitor the progress and dynamically adjust the action plan, saying, "You're making good progress. Next, aim to read for 15 minutes every day." Similarly, when a user inputs, "I want to improve my work efficiency," the generation AI can monitor the progress and dynamically adjust the action plan, saying, "You're making good progress. Next, try breaking down the task into smaller parts." This allows the user's progress to be monitored in real time and the action plan to be dynamically adjusted according to the level of achievement.

[0066] The action guideline presentation unit can use the emotion estimation function to analyze the user's emotional state and provide emotionally positive feedback. For example, when a user inputs, "I want to continue exercising for 30 minutes every day," the emotion estimation function analyzes the user's emotions and provides positive feedback, such as, "Imagine the feeling of exhilaration after exercising." Furthermore, when a user inputs, "I want to develop a reading habit," the emotion estimation function can analyze the user's emotions and provide positive feedback, such as, "Imagine the sense of accomplishment after reading." Furthermore, when a user inputs, "I want to improve my work efficiency," the emotion estimation function can analyze the user's emotions and provide positive feedback, such as, "Imagine the feeling of satisfaction after completing a task." In this way, the action guideline presentation unit can analyze the user's emotional state and provide emotionally positive feedback.

[0067] The action guideline presentation unit allows the generation AI to present an action plan to the user in a variety of formats using different media. For example, when the user inputs, "I want to continue exercising for 30 minutes every day," the generation AI presents an exercise plan in video format, making it visually easier to understand. In addition, when the user inputs, "I want to develop a reading habit," the action guideline presentation unit can present a reading plan in audio format, making it easier to understand auditorily. In addition, when the user inputs, "I want to improve my work efficiency," the generation AI can present a task plan in text format, making it visually easier to understand. This makes it possible to present action plans to the user in a variety of formats using different media.

[0068] The action guideline presentation unit allows the generation AI to utilize the user's social network to propose an action plan to gain support from friends and family. For example, when a user inputs, "I want to continue exercising for 30 minutes every day," the generation AI can suggest, "It's easier to continue if you exercise with friends," utilizing the user's social network. Furthermore, when a user inputs, "I want to develop a reading habit," the generation AI can suggest, "It's easier to continue if you read with your family." Furthermore, when a user inputs, "I want to improve my work efficiency," the generation AI can suggest, "Working on tasks together with colleagues will improve efficiency." This allows the user's social network to be utilized to propose an action plan to gain support from friends and family.

[0069] The action guideline presentation unit can use the emotion estimation function to monitor the user's emotional state in real time and provide feedback according to the emotion. For example, when the user inputs, "I want to continue exercising for 30 minutes every day," the emotion estimation function monitors the user's emotions in real time and provides feedback such as, "Imagine the feeling of exhilaration after exercising." Furthermore, when the user inputs, "I want to develop a reading habit," the emotion estimation function can monitor the user's emotions in real time and provide feedback such as, "Imagine the sense of accomplishment after reading." Furthermore, when the user inputs, "I want to improve my work efficiency," the emotion estimation function can monitor the user's emotions in real time and provide feedback such as, "Imagine the feeling of satisfaction after completing a task." In this way, the action guideline presentation unit can monitor the user's emotional state in real time and provide feedback according to the emotion.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The goal input unit supports voice and image input when the user inputs their goals and habits, enabling more diverse input methods. For example, when a user inputs by voice, "I want to continue exercising for 30 minutes every day," the generation AI uses voice recognition technology to convert it into text and set a goal. Also, when a user inputs by image, "I want to develop a reading habit," the generation AI can use image analysis technology to convert it into text and set a goal. Furthermore, when a user inputs by voice, "I want to improve my work efficiency," the generation AI can use voice recognition technology to convert it into text and set a goal. This allows the user to input their goals and habits by voice and image input, enabling more diverse input methods.

[0072] The goal input unit can use the emotion estimation function to analyze the emotion of the user when inputting a goal and make suggestions to elicit positive emotions. For example, when a user inputs "I want to continue exercising for 30 minutes every day," the emotion estimation function analyzes the user's emotion and makes a suggestion, "Imagine the refreshing feeling after exercising." When a user inputs "I want to develop a reading habit," the emotion estimation function can analyze the user's emotion and make a suggestion, "Imagine the sense of accomplishment after reading." When a user inputs "I want to improve my work efficiency," the emotion estimation function can analyze the user's emotion and make a suggestion, "Imagine the satisfaction after completing a task." In this way, the user's emotion can be analyzed and suggestions can be made to elicit positive emotions.

[0073] The goal input unit can integrate inputs from different devices to provide a seamless user experience. For example, a user can input "I want to continue exercising for 30 minutes every day" on a smartphone and confirm that goal on a tablet. Alternatively, a user can input "I want to develop a reading habit" on a smartwatch and confirm that goal on a smartphone. Furthermore, a user can input "I want to improve my work efficiency" on a computer and confirm that goal on a tablet. This allows inputs from different devices to be integrated to provide a seamless user experience.

[0074] When the generation AI analyzes goals and habits, the goal analysis unit takes into account the user's lifestyle and daily schedule and can propose the optimal action plan. For example, if a user inputs, "I want to continue exercising for 30 minutes every day," the generation AI will consider the user's lifestyle and suggest, "It's effective to exercise in the morning." Similarly, if a user inputs, "I want to develop a reading habit," the generation AI will consider the user's daily schedule and suggest, "It's good to read before bed." Furthermore, if a user inputs, "I want to improve my work efficiency," the generation AI will consider the user's lifestyle and suggest, "It's good to prioritize tasks." This allows the system to propose the optimal action plan, taking into account the user's lifestyle and daily schedule.

[0075] The goal analysis unit can use the emotion estimation function to monitor the user's emotional state in real time and dynamically adjust the action plan according to the emotion. For example, when a user inputs, "I want to continue exercising for 30 minutes every day," the emotion estimation function monitors the user's emotions in real time and dynamically adjusts the action plan by saying, "Imagine the sense of exhilaration after exercising." Similarly, when a user inputs, "I want to develop a reading habit," the emotion estimation function can monitor the user's emotions in real time and dynamically adjust the action plan by saying, "Imagine the sense of accomplishment after reading." Furthermore, when a user inputs, "I want to improve my work efficiency," the emotion estimation function can monitor the user's emotions in real time and dynamically adjust the action plan by saying, "Imagine the sense of satisfaction after completing a task." This allows the system to monitor the user's emotional state in real time and dynamically adjust the action plan according to the emotion.

[0076] The goal analysis unit allows the generation AI to analyze not only past successes but also failures, and provide advice to avoid failure. For example, when a user inputs, "I want to continue exercising for 30 minutes every day," the generation AI analyzes past failures and provides the advice, "Don't push yourself, start with a short time at first." Similarly, when a user inputs, "I want to develop a reading habit," the generation AI analyzes past failures and can provide the advice, "Don't push yourself, start with a short time at first." Furthermore, when a user inputs, "I want to improve my work efficiency," the generation AI can analyze past failures and can provide the advice, "Don't push yourself, start with a short time at first." This makes it possible to analyze past successes and failures and provide advice to avoid failure.

[0077] The action guideline suggestion unit can use storytelling techniques to motivate users when the AI ​​proposes short-term goals and specific action guidelines. For example, if a user inputs, "I want to continue exercising for 30 minutes every day," the AI ​​can use storytelling techniques to suggest, "Let's start your health story. The first chapter starts with walking three times a week." Similarly, if a user inputs, "I want to develop a reading habit," the AI ​​can suggest, "Let's start your knowledge journey. The first chapter starts with reading for 10 minutes every day before bed." Furthermore, if a user inputs, "I want to improve my work efficiency," the AI ​​can suggest, "Let's start your career story. The first chapter starts with prioritizing and tackling tasks." This allows the AI ​​to use storytelling techniques to motivate users.

[0078] The action guideline presentation unit can use the emotion estimation function to analyze the user's emotional state and provide emotionally positive feedback. For example, when a user inputs, "I want to continue exercising for 30 minutes every day," the emotion estimation function analyzes the user's emotions and provides positive feedback, such as, "Imagine the sense of exhilaration after exercising." When a user inputs, "I want to develop a reading habit," the emotion estimation function can analyze the user's emotions and provide positive feedback, such as, "Imagine the sense of accomplishment after reading." When a user inputs, "I want to improve my work efficiency," the emotion estimation function can analyze the user's emotions and provide positive feedback, such as, "Imagine the sense of satisfaction after completing a task." In this way, the user's emotional state can be analyzed and emotionally positive feedback can be provided.

[0079] The action guideline presentation unit allows the generation AI to present an action plan to the user in a variety of formats using different media. For example, when a user inputs, "I want to continue exercising for 30 minutes every day," the generation AI can present an exercise plan in video format, making it visually easier to understand. Also, when a user inputs, "I want to develop a reading habit," the generation AI can present a reading plan in audio format, making it easier to understand auditorily. Furthermore, when a user inputs, "I want to improve my work efficiency," the generation AI can present a task plan in text format, making it visually easier to understand. This allows action plans to be presented to the user in a variety of formats using different media.

[0080] The action guideline presentation unit can use the emotion estimation function to monitor the user's emotional state in real time and provide feedback according to the emotion. For example, when a user inputs, "I want to continue exercising for 30 minutes every day," the emotion estimation function can monitor the user's emotion in real time and provide feedback such as, "Imagine the feeling of exhilaration after exercising." When a user inputs, "I want to develop a reading habit," the emotion estimation function can monitor the user's emotion in real time and provide feedback such as, "Imagine the sense of accomplishment after reading." When a user inputs, "I want to improve my work efficiency," the emotion estimation function can monitor the user's emotion in real time and provide feedback such as, "Imagine the feeling of satisfaction after completing a task." In this way, the user's emotional state can be monitored in real time and feedback according to the emotion can be provided.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The goal input section inputs the user's goals and habits. For example, the user can input "I want to continue exercising for 30 minutes every day." The user can also input "I want to develop the habit of reading" or "I want to improve my work efficiency." Step 2: In the goal analysis section, the generation AI analyzes the goals and habits entered by the goal input section. For example, the generation AI can analyze the goals and habits using natural language processing technology and generate an optimal action plan based on past data and success stories. It can also analyze goals and habits using machine learning algorithms. Step 3: The action guideline suggestion unit presents specific action guidelines based on the results of the analysis by the goal analysis unit. For example, it might suggest, "To maintain 30 minutes of exercise every day, start by walking for 10 minutes three times a week," "To develop a reading habit, read for 10 minutes every day before going to bed," or "To improve work efficiency, prioritize your tasks."

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0111] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 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 input section for inputting the user's goals and habits; a goal analysis unit that analyzes the goals and habits input by the goal input unit; and an action guideline presentation unit that presents a specific action guideline based on the results of the analysis by the goal analysis unit. A system characterized by:

2. The goal input unit As users input their goals and habits, the generative AI provides real-time feedback and advice on how to make those goals more specific and achievable.

2. The system of claim 1.

3. The goal input unit When the user inputs the goals and habits, voice input and image input are supported, enabling the user to input the goals and habits in a wider variety of ways.

2. The system of claim 1.

4. The goal analysis unit When analyzing the goals and habits, the generative AI takes into account the user's lifestyle and daily schedule to propose an optimal action plan.

2. The system of claim 1.

5. The goal analysis unit Analyze the user's emotional state and suggest an emotionally positive action plan 2. The system of claim 1.

6. The action guideline presentation unit When the generative AI presents short-term goals and the course of action, it uses storytelling techniques to motivate the user.

2. The system of claim 1.

7. The action guideline presentation unit Analyzing the user's emotional state and providing emotionally positive feedback 2. The system of claim 1.

8. The action guideline presentation unit Monitor the user's emotional state in real time and provide emotional feedback.

2. The system of claim 1.

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