System

Abstract

An object of a system according to an embodiment is to support target setting suitable for an individual.SOLUTION: A system according to an embodiment includes a psychological state analyzer, an objective setter, a task suggester, a feedback provider, a progress tracker, and a resource suggester. The psychological state analysis unit reads the psychological state and emotion from the text input of the user. The goal setting section sets a goal based on the psychological state and the emotion analyzed by the psychological state analyzing section. The task proposal unit proposes a task on the basis of the objective set by the objective setting unit. The feedback providing unit provides feedback on the task proposed by the task proposal unit. The progress tracker tracks the user's growth process and goals achieved. The resource proposal unit proposes a resource necessary for the user to work on the goal.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 techniques do not adequately support individuals in setting goals that are appropriate for them, and there is room for improvement.

[0005] The system according to the embodiment aims to support goal setting appropriate for each individual. [Means for solving the problem]

[0006] The system according to the embodiment includes a psychological state analysis unit, a goal setting unit, a task suggestion unit, a feedback providing unit, a progress tracking unit, and a resource suggestion unit. The psychological state analysis unit reads the user's psychological state and emotions from text input by the user. The goal setting unit sets goals based on the psychological state and emotions analyzed by the psychological state analysis unit. The task suggestion unit suggests tasks based on the goals set by the goal setting unit. The feedback providing unit provides feedback for the tasks suggested by the task suggestion unit. The progress tracking unit tracks the user's growth process and achieved goals. The resource suggestion unit suggests resources needed for the user to work on the goals. [Effects of the Invention]

[0007] The system according to the embodiment can support goal setting that is appropriate for each individual. [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 goal setting support system according to an embodiment of the present invention uses generative AI to support individual goal setting. This system reads the user's psychological state and emotions from text input and provides daily support toward the goal. This allows the goal setting support system to comprehensively support the user's growth and goal achievement.

[0029] A goal setting support system according to an embodiment includes a psychological state analysis unit, a goal setting unit, a task suggestion unit, a feedback provision unit, a progress tracking unit, and a resource suggestion unit. The psychological state analysis unit reads the user's psychological state and emotions from text input by the user. For example, if the user inputs, "I've been busy at work lately and I'm feeling stressed," the psychological state analysis unit understands the user's psychological state based on that information. The psychological state analysis unit can also analyze the user's emotions using a generation AI (e.g., a text generation AI or a multimodal generation AI). The goal setting unit sets goals based on the psychological state and emotions analyzed by the psychological state analysis unit. For example, if the user sets a goal of "I want to complete a marathon within one year," the goal setting unit creates a specific plan toward that goal. The task suggestion unit suggests tasks based on the goal set by the goal setting unit. For example, if the user sets a goal of "I want to complete a marathon within one year," the task suggestion unit suggests a training plan and daily exercise tasks toward that goal. The feedback provision unit provides feedback on the tasks suggested by the task suggestion unit. For example, a message such as "You worked hard on today's training! Let's do our best next time!" can motivate the user. The progress tracking unit tracks the user's progress and achieved goals. For example, it displays the user's achieved tasks and goals in graphs and charts, allowing the user to check their progress at a glance. The resource suggestion unit suggests resources the user needs to work on their goals. For example, if a user inputs "I want to study English," the resource suggestion unit suggests appropriate learning materials and online courses. This allows the goal setting support system according to the embodiment to support the user's growth and goal achievement. For example, by setting a long-term goal and completing short-term tasks toward that goal daily, the user can steadily progress toward that goal. Furthermore, positive feedback and encouraging messages can increase the user's motivation. Furthermore, the generation AI tracks the user's progress and visualizes their progress, making it easier for the user to realize their own growth. This allows the user to work toward their goals with confidence.

[0030] The psychological state analysis unit can analyze a user's past behavioral history or social media posts to estimate the user's psychological state with higher accuracy. The psychological state analysis unit, for example, stores the user's past behavioral history in a database and builds a system that analyzes the user's psychological state based on that data. For example, it analyzes the user's past task completion status and goal achievement history to estimate the user's current psychological state. The psychological state analysis unit can also analyze the user's social media posts to grasp the user's emotions and psychological state. For example, it analyzes the emotions expressed by the user in tweets or Facebook posts and estimates the user's psychological state based on that information. This makes it possible to estimate the user's psychological state with higher accuracy by analyzing the user's past behavioral history and social media posts.

[0031] The psychological state analysis unit can develop a psychological state analysis algorithm that can be applied to users from different cultural backgrounds or languages. The psychological state analysis unit, for example, develops a psychological state analysis algorithm that can be applied to users from different cultural backgrounds. For example, it constructs an emotion analysis model that takes cultural background into consideration. The psychological state analysis unit can also develop a psychological state analysis algorithm that can be applied to users from different languages. For example, it constructs an emotion analysis model that supports multiple languages ​​such as English, Japanese, and Spanish. This allows for global support by developing a psychological state analysis algorithm that can be applied to users from different cultural backgrounds or languages.

[0032] The goal setting unit can incorporate an algorithm that dynamically adjusts the difficulty or amount of tasks according to the user's level of goal achievement. The goal setting unit, for example, analyzes the user's level of goal achievement in real time and builds a system that dynamically adjusts the difficulty of tasks. For example, if the level of goal achievement is high, it suggests a task with a high level of difficulty. The goal setting unit can also dynamically adjust the amount of tasks according to the user's level of goal achievement. For example, it reduces the amount of tasks if the level of goal achievement is low. In this way, the difficulty and amount of tasks can be dynamically adjusted according to the user's level of goal achievement, making it easier to maintain the user's motivation.

[0033] The goal setting unit can make optimal task suggestions based on the user's lifestyle rhythm or schedule. The goal setting unit, for example, analyzes the user's lifestyle rhythm and builds a system that makes optimal task suggestions. For example, it determines whether the user is a morning person or a night person and suggests tasks for appropriate time periods. The goal setting unit can also make task suggestions based on the user's schedule. For example, it analyzes the user's calendar events and suggests tasks for times when the user is free. In this way, optimal task suggestions based on the user's lifestyle rhythm and schedule support the user's efficient goal achievement.

[0034] The goal setting unit can develop a goal setting algorithm that can be applied to users of different age groups or occupations. The goal setting unit develops a goal setting algorithm that can be applied to users of different age groups, for example. For example, it proposes academic goals to young people and health goals to middle-aged and elderly people. The goal setting unit can also develop a goal setting algorithm that can be applied to users of different occupations. For example, it proposes a goal to improve technical skills to engineers and a goal to improve teaching methods to teachers. In this way, by developing a goal setting algorithm that can be applied to users of different age groups and occupations, it is possible to accommodate a wide range of users.

[0035] The feedback providing unit can analyze the user's feedback history and identify the most effective feedback pattern. For example, the feedback providing unit stores the user's feedback history in a database and builds a system that identifies the most effective feedback pattern based on that data. For example, it analyzes past feedback and the user's reactions. The feedback providing unit can also analyze the user's feedback history and identify the most effective feedback pattern. For example, it identifies positive feedback patterns and negative feedback patterns. In this way, analyzing the user's feedback history and identifying the most effective feedback pattern increases the user's motivation.

[0036] The feedback providing unit can generate feedback messages that can be applied to users of different languages ​​or cultural backgrounds. The feedback providing unit, for example, builds a system that generates feedback messages that can be applied to users of different languages. For example, it automatically generates feedback messages that support multiple languages. The feedback providing unit can also generate feedback messages that can be applied to users of different cultural backgrounds. For example, it generates feedback messages that take cultural backgrounds into consideration. This allows for global support by generating feedback messages that can be applied to users of different languages ​​or cultural backgrounds.

[0037] The progress tracking unit can visualize the user's growth process with 3D animation to enhance the sense of accomplishment. The progress tracking unit, for example, builds a system that visualizes the user's growth process with 3D animation. For example, accomplished tasks and goals are displayed in the form of an evolving 3D character. The progress tracking unit can also enhance the sense of accomplishment by visualizing the user's growth process with 3D animation. For example, the progress tracking unit displays the goals achieved by the user with 3D animation to visually emphasize the sense of accomplishment. In this way, the user's sense of accomplishment is enhanced by visualizing the user's growth process with 3D animation.

[0038] The progress tracking unit can implement an algorithm that predicts future goal achievement based on the user's progress data. The progress tracking unit, for example, implements an algorithm that predicts future goal achievement based on the user's progress data. For example, it analyzes past data and predicts the probability of goal achievement. The progress tracking unit can also implement an algorithm that predicts future goal achievement based on the user's progress data. For example, it predicts the likelihood of goal achievement using a statistical model or a machine learning model. In this way, by predicting future goal achievement based on the user's progress data, it becomes easier to understand the likelihood of the user achieving their goal.

[0039] The progress tracking unit can introduce a ranking system that compares the user's progress data with other users and stimulates a competitive spirit. The progress tracking unit, for example, builds a ranking system that compares the user's progress data with other users. For example, it displays a ranking according to the degree of achievement. The progress tracking unit can also introduce a ranking system that compares the user's progress data with other users and stimulates a competitive spirit. For example, the user can compete with other users to encourage goal achievement. In this way, comparing the user's progress data with other users stimulates a competitive spirit and encourages goal achievement.

[0040] The resource suggestion unit can develop an algorithm that suggests optimal learning materials based on the user's learning style or preferences. The resource suggestion unit, for example, analyzes the user's learning style and develops an algorithm that suggests optimal learning materials. For example, it suggests visual learning materials for visual learners and audio learning materials for auditory learners. The resource suggestion unit can also suggest learning materials based on the user's preferences. For example, it suggests learning materials in areas that the user is interested in. This improves the user's learning efficiency by suggesting optimal learning materials based on the user's learning style and preferences.

[0041] The resource suggestion unit can dynamically adjust the difficulty of the learning materials according to the user's progress. The resource suggestion unit, for example, analyzes the user's progress in real time and builds a system that dynamically adjusts the difficulty of the learning materials. For example, if progress is good, it suggests learning materials with a high level of difficulty. The resource suggestion unit can also dynamically adjust the difficulty of the learning materials according to the user's progress. For example, if progress is slow, it suggests learning materials with a low level of difficulty. In this way, the difficulty of the learning materials is dynamically adjusted according to the user's progress, thereby improving the user's learning efficiency.

[0042] The resource suggestion unit can suggest learning materials according to different fields or specialized knowledge. The resource suggestion unit, for example, builds a system that suggests learning materials in different fields. For example, it provides learning materials according to fields such as science, technology, and art. The resource suggestion unit can also suggest learning materials according to the user's specialized knowledge. For example, for a user with medical knowledge, it suggests learning materials related to the latest medical research. In this way, by suggesting learning materials according to different fields or specialized knowledge, the user's learning efficiency is improved.

[0043] The resource suggestion unit can automatically generate a future study plan based on the user's study history. The resource suggestion unit, for example, stores the user's study history in a database and builds a system that automatically generates a future study plan based on that data. For example, it suggests the next step based on past study content. The resource suggestion unit can also automatically generate a future study plan based on the user's study history. For example, it suggests the next content to learn based on past study results. In this way, by automatically generating a future study plan based on the user's study history, the user's study efficiency is improved.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The goal setting support system can monitor the user's health condition and set goals based on their health. For example, it can acquire the user's heart rate and sleep data and analyze their health condition. If the user's heart rate is high, it can set a goal for relaxation. It can also suggest goals for improving sleep quality based on the sleep data. This allows the system to support the user's overall health by setting goals based on the user's health condition.

[0046] The goal setting support system can set goals based on the user's hobbies and interests. For example, if the user is interested in music, it can suggest goals such as practicing an instrument or learning music theory. If the user is interested in cooking, it can suggest trying a new recipe as a goal. Furthermore, if the user is interested in sports, it can suggest improving skills in a particular sport as a goal. In this way, setting goals based on the user's hobbies and interests can increase the user's motivation.

[0047] The goal setting support system can set goals to strengthen the user's social connections. For example, if the user wants to deepen relationships with friends and family, the system can set regular communication as a goal. If the user wants to make new friends, the system can suggest participating in social events as a goal. Furthermore, if the user is interested in volunteer activities, the system can set participation in local volunteer activities as a goal. Thus, by setting goals to strengthen the user's social connections, the system can improve the user's social well-being.

[0048] The goal setting support system can set goals to support the user's career goals. For example, if the user wants to acquire a specific skill, courses and training related to that skill can be suggested as goals. Also, if the user is aiming for promotion, the system can set goals to obtain necessary qualifications or complete projects. Furthermore, if the user wants to try a new career, the system can suggest related networking events and internships as goals. Thus, by setting goals to support the user's career goals, the system can promote the user's professional growth.

[0049] The goal-setting support system can set goals to increase the user's environmental awareness. For example, if the user wants to live an eco-friendly life, it can suggest that the goal be to develop recycling habits. If the user wants to reduce energy consumption, it can suggest that the user set a goal to use energy-efficient home appliances. Furthermore, if the user wants to live a sustainable diet, it can suggest that the user set a goal to purchase local agricultural products. In this way, by setting goals to increase the user's environmental awareness, it is possible to support the user's sustainable lifestyle.

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

[0051] Step 1: The psychological state analysis unit reads the user's psychological state and emotions from the text input. For example, if the user inputs, "I've been busy at work lately and feeling stressed," the psychological state analysis unit will understand the user's psychological state based on that information. It is also possible to analyze the user's emotions using generative AI (e.g., text generation AI or multimodal generation AI). Step 2: The goal setting unit sets a goal based on the psychological state and emotions analyzed by the psychological state analysis unit. For example, if the user sets a goal of "I want to complete a marathon within one year," the goal setting unit will create a specific plan to achieve that goal. Step 3: The task suggestion unit suggests tasks based on the goal set by the goal setting unit. For example, if the user sets a goal of "I want to complete a marathon within one year," the task suggestion unit will suggest training plans and daily exercise tasks toward that goal. Step 4: The feedback provider provides feedback on the tasks proposed by the task suggester. For example, the feedback provider may motivate the user by sending a message such as, "You did a great job on today's training! Let's try harder next time!" Step 5: The progress tracker tracks the user's progress and achievements, for example by displaying the tasks and goals achieved by the user in graphs and charts, allowing the user to see their progress at a glance. Step 6: The resource suggestion module suggests resources that the user needs to address their goal. For example, if the user enters "I want to study English," the resource suggestion module will suggest appropriate learning materials and online courses.

[0052] (Example 2) The goal setting support system according to an embodiment of the present invention uses generative AI to support individual goal setting. This system reads the user's psychological state and emotions from text input and provides daily support toward the goal. This allows the goal setting support system to comprehensively support the user's growth and goal achievement.

[0053] A goal setting support system according to an embodiment includes a psychological state analysis unit, a goal setting unit, a task suggestion unit, a feedback provision unit, a progress tracking unit, and a resource suggestion unit. The psychological state analysis unit reads the user's psychological state and emotions from text input by the user. For example, if the user inputs, "I've been busy at work lately and I'm feeling stressed," the psychological state analysis unit understands the user's psychological state based on that information. The psychological state analysis unit can also analyze the user's emotions using a generation AI (e.g., a text generation AI or a multimodal generation AI). The goal setting unit sets goals based on the psychological state and emotions analyzed by the psychological state analysis unit. For example, if the user sets a goal of "I want to complete a marathon within one year," the goal setting unit creates a specific plan toward that goal. The task suggestion unit suggests tasks based on the goal set by the goal setting unit. For example, if the user sets a goal of "I want to complete a marathon within one year," the task suggestion unit suggests a training plan and daily exercise tasks toward that goal. The feedback provision unit provides feedback on the tasks suggested by the task suggestion unit. For example, a message such as "You worked hard on today's training! Let's do our best next time!" can motivate the user. The progress tracking unit tracks the user's progress and achieved goals. For example, it displays the user's achieved tasks and goals in graphs and charts, allowing the user to check their progress at a glance. The resource suggestion unit suggests resources the user needs to work on their goals. For example, if a user inputs "I want to study English," the resource suggestion unit suggests appropriate learning materials and online courses. This allows the goal setting support system according to the embodiment to support the user's growth and goal achievement. For example, by setting a long-term goal and completing short-term tasks toward that goal daily, the user can steadily progress toward that goal. Furthermore, positive feedback and encouraging messages can increase the user's motivation. Furthermore, the generation AI tracks the user's progress and visualizes their progress, making it easier for the user to realize their own growth. This allows the user to work toward their goals with confidence.

[0054] The psychological state analysis unit can analyze the user's psychological state and emotions from multiple angles using voice input or facial expression recognition in addition to the user's text input. For example, the psychological state analysis unit builds a system that accepts voice input simultaneously when the user enters text. Using voice analysis technology, the unit estimates the user's emotions from the tone and speed of their voice and analyzes their psychological state in combination with the text input. The psychological state analysis unit can also capture the user's facial expressions with a camera and analyze their emotions using a facial expression recognition algorithm. For example, the unit can determine whether the user is smiling and grasp their psychological state based on that information. This allows for a multifaceted analysis of the user's psychological state and emotions, thereby achieving more accurate personalization.

[0055] The psychological state analysis unit can analyze a user's past behavioral history or social media posts to estimate the user's psychological state with higher accuracy. The psychological state analysis unit, for example, stores the user's past behavioral history in a database and builds a system that analyzes the user's psychological state based on that data. For example, it analyzes the user's past task completion status and goal achievement history to estimate the user's current psychological state. The psychological state analysis unit can also analyze the user's social media posts to grasp the user's emotions and psychological state. For example, it analyzes the emotions expressed by the user in tweets or Facebook posts and estimates the user's psychological state based on that information. This makes it possible to estimate the user's psychological state with higher accuracy by analyzing the user's past behavioral history and social media posts.

[0056] The psychological state analysis unit can use the emotion estimation function to monitor the user's emotional fluctuations in real time and provide support at the appropriate time. The psychological state analysis unit, for example, uses the emotion estimation function to develop a system that monitors the user's emotional fluctuations in real time. For example, it analyzes the user's input content and behavioral data and displays the emotional fluctuations in a graph. The psychological state analysis unit can also use the emotion estimation function to monitor the user's emotional fluctuations in real time and provide support at the appropriate time. For example, it can suggest relaxation when the user is feeling stressed. In this way, the user's emotional fluctuations can be monitored in real time and support can be provided at the appropriate time, thereby maintaining the user's motivation.

[0057] The psychological state analysis unit can suggest relaxation music or meditation guides based on the user's psychological state. The psychological state analysis unit, for example, analyzes the user's psychological state and builds a system that suggests relaxation music. For example, if it is determined that the user is under high stress, music with a relaxing effect is played. The psychological state analysis unit can also suggest meditation guides based on the user's psychological state. For example, when the user feels like relaxing, an audio guide for meditation is provided. In this way, relaxation music or meditation guides are suggested based on the user's psychological state, thereby improving the user's well-being.

[0058] The psychological state analysis unit can develop a psychological state analysis algorithm that can be applied to users from different cultural backgrounds or languages. The psychological state analysis unit, for example, develops a psychological state analysis algorithm that can be applied to users from different cultural backgrounds. For example, it constructs an emotion analysis model that takes cultural background into consideration. The psychological state analysis unit can also develop a psychological state analysis algorithm that can be applied to users from different languages. For example, it constructs an emotion analysis model that supports multiple languages ​​such as English, Japanese, and Spanish. This allows for global support by developing a psychological state analysis algorithm that can be applied to users from different cultural backgrounds or languages.

[0059] The psychological state analysis unit can use the emotion estimation function to suggest a customized exercise or fitness plan based on the user's emotions. For example, the psychological state analysis unit can use the emotion estimation function to build a system that suggests an exercise plan based on the user's emotions. For example, if it is determined that the user is under high stress, it can suggest yoga, which has a relaxing effect. The psychological state analysis unit can also suggest a fitness plan based on the user's emotions. For example, if the user is feeling energetic, it can suggest high-intensity training. In this way, by suggesting a customized exercise or fitness plan based on the user's emotions, the user's health and well-being can be improved.

[0060] The goal setting unit can incorporate an algorithm that dynamically adjusts the difficulty or amount of tasks according to the user's level of goal achievement. The goal setting unit, for example, analyzes the user's level of goal achievement in real time and builds a system that dynamically adjusts the difficulty of tasks. For example, if the level of goal achievement is high, it suggests a task with a high level of difficulty. The goal setting unit can also dynamically adjust the amount of tasks according to the user's level of goal achievement. For example, it reduces the amount of tasks if the level of goal achievement is low. In this way, the difficulty and amount of tasks can be dynamically adjusted according to the user's level of goal achievement, making it easier to maintain the user's motivation.

[0061] The goal setting unit can make optimal task suggestions based on the user's lifestyle rhythm or schedule. The goal setting unit, for example, analyzes the user's lifestyle rhythm and builds a system that makes optimal task suggestions. For example, it determines whether the user is a morning person or a night person and suggests tasks for appropriate time periods. The goal setting unit can also make task suggestions based on the user's schedule. For example, it analyzes the user's calendar events and suggests tasks for times when the user is free. In this way, optimal task suggestions based on the user's lifestyle rhythm and schedule support the user's efficient goal achievement.

[0062] The goal setting unit can use the emotion estimation function to suggest easier tasks when the user's motivation is low. The goal setting unit, for example, uses the emotion estimation function to build a system that suggests easier tasks when the user's motivation is low. For example, when the emotion score is low, it suggests tasks that can be completed in a short time. The goal setting unit can also suggest tasks that have a relaxing effect when the user's motivation is low. For example, when the emotion score is low, it suggests a relaxation task. In this way, by suggesting easier tasks when the user's motivation is low, it becomes easier to maintain the user's motivation.

[0063] The goal setting unit can develop a goal setting algorithm that can be applied to users of different age groups or occupations. The goal setting unit develops a goal setting algorithm that can be applied to users of different age groups, for example. For example, it proposes academic goals to young people and health goals to middle-aged and elderly people. The goal setting unit can also develop a goal setting algorithm that can be applied to users of different occupations. For example, it proposes a goal to improve technical skills to engineers and a goal to improve teaching methods to teachers. In this way, by developing a goal setting algorithm that can be applied to users of different age groups and occupations, it is possible to accommodate a wide range of users.

[0064] The goal setting unit can use the emotion estimation function to suggest hobbies or recreational activities according to the user's emotions. The goal setting unit, for example, uses the emotion estimation function to build a system that suggests hobbies according to the user's emotions. For example, if it is determined that stress is high, the goal setting unit can suggest hobbies that will help the user relax. The goal setting unit can also suggest recreational activities according to the user's emotions. For example, if the user feels like refreshing themselves, the goal setting unit can suggest outdoor activities such as hiking or camping. In this way, suggesting hobbies or recreational activities according to the user's emotions reduces the user's stress and improves their well-being.

[0065] The feedback providing unit can generate individually customized feedback messages based on the user's past successful experiences. The feedback providing unit, for example, stores the user's past successful experiences in a database and builds a system that generates customized feedback messages based on that data. For example, the feedback providing unit can send an encouraging message by citing a past success story. The feedback providing unit can also generate individually customized feedback messages based on the user's past successful experiences. For example, the feedback providing unit can send an encouraging message for achieving the next goal based on a goal that the user previously achieved. In this way, generating customized feedback messages based on the user's past successful experiences increases the user's motivation.

[0066] The feedback providing unit can collect feedback from the user's friends or family and provide an encouraging message based on that feedback. For example, the feedback providing unit can build a system that collects feedback from the user's friends and family and provide an encouraging message based on that data. For example, the feedback providing unit can encourage the user by quoting a message of encouragement from a friend. The feedback providing unit can also provide an encouraging message based on feedback from the user's friends and family. For example, the feedback providing unit can encourage the user by quoting a message of encouragement from a family member. In this way, providing an encouraging message based on feedback from the user's friends and family increases the user's motivation.

[0067] The feedback providing unit can use the emotion estimation function to provide feedback at a timing that corresponds to the user's emotion. The feedback providing unit, for example, uses the emotion estimation function to build a system that provides feedback at a timing that corresponds to the user's emotion. For example, an encouraging message is sent when the emotion score is low. The feedback providing unit can also provide feedback at a timing that corresponds to the user's emotion. For example, when the user is feeling stressed, the feedback providing unit suggests relaxation. In this way, providing feedback at a timing that corresponds to the user's emotion makes it easier to maintain the user's motivation.

[0068] The feedback providing unit can analyze the user's feedback history and identify the most effective feedback pattern. For example, the feedback providing unit stores the user's feedback history in a database and builds a system that identifies the most effective feedback pattern based on that data. For example, it analyzes past feedback and the user's reactions. The feedback providing unit can also analyze the user's feedback history and identify the most effective feedback pattern. For example, it identifies positive feedback patterns and negative feedback patterns. In this way, analyzing the user's feedback history and identifying the most effective feedback pattern increases the user's motivation.

[0069] The feedback providing unit can generate feedback messages that can be applied to users of different languages ​​or cultural backgrounds. The feedback providing unit, for example, builds a system that generates feedback messages that can be applied to users of different languages. For example, it automatically generates feedback messages that support multiple languages. The feedback providing unit can also generate feedback messages that can be applied to users of different cultural backgrounds. For example, it generates feedback messages that take cultural backgrounds into consideration. This allows for global support by generating feedback messages that can be applied to users of different languages ​​or cultural backgrounds.

[0070] The feedback providing unit can use the emotion estimation function to provide an audio message or a video message according to the user's emotion. The feedback providing unit, for example, uses the emotion estimation function to build a system that provides an audio message according to the user's emotion. For example, an encouraging audio message is sent when the emotion score is low. The feedback providing unit can also provide a video message according to the user's emotion. For example, a congratulatory video message is sent when the emotion score is high. In this way, providing an audio message or a video message according to the user's emotion increases the user's motivation.

[0071] The progress tracking unit can visualize the user's growth process with 3D animation to enhance the sense of accomplishment. The progress tracking unit, for example, builds a system that visualizes the user's growth process with 3D animation. For example, accomplished tasks and goals are displayed in the form of an evolving 3D character. The progress tracking unit can also enhance the sense of accomplishment by visualizing the user's growth process with 3D animation. For example, the progress tracking unit displays the goals achieved by the user with 3D animation to visually emphasize the sense of accomplishment. In this way, the user's sense of accomplishment is enhanced by visualizing the user's growth process with 3D animation.

[0072] The progress tracking unit can implement an algorithm that predicts future goal achievement based on the user's progress data. The progress tracking unit, for example, implements an algorithm that predicts future goal achievement based on the user's progress data. For example, it analyzes past data and predicts the probability of goal achievement. The progress tracking unit can also implement an algorithm that predicts future goal achievement based on the user's progress data. For example, it predicts the likelihood of goal achievement using a statistical model or a machine learning model. In this way, by predicting future goal achievement based on the user's progress data, it becomes easier to understand the likelihood of the user achieving their goal.

[0073] The progress tracking unit can use the emotion estimation function to analyze the relationship between the user's emotional fluctuations and goal achievement, and provide appropriate support. The progress tracking unit, for example, uses the emotion estimation function to build a system that analyzes the relationship between the user's emotional fluctuations and goal achievement. For example, it analyzes the correlation between the emotion score and the degree of goal achievement. The progress tracking unit can also analyze the relationship between the user's emotional fluctuations and goal achievement, and provide appropriate support. For example, it provides support to increase motivation when the emotion score is low. In this way, analyzing the relationship between the user's emotional fluctuations and goal achievement makes it easier to provide appropriate support.

[0074] The progress tracking unit can introduce a ranking system that compares the user's progress data with other users and stimulates a competitive spirit. The progress tracking unit, for example, builds a ranking system that compares the user's progress data with other users. For example, it displays a ranking according to the degree of achievement. The progress tracking unit can also introduce a ranking system that compares the user's progress data with other users and stimulates a competitive spirit. For example, the user can compete with other users to encourage goal achievement. In this way, comparing the user's progress data with other users stimulates a competitive spirit and encourages goal achievement.

[0075] The progress tracking unit can use the emotion estimation function to customize the format of the progress report (text, graph, video) according to the user's emotion. The progress tracking unit, for example, uses the emotion estimation function to build a system that customizes the format of the progress report according to the user's emotion. For example, if the emotion score is high, the report is made in video format. The progress tracking unit can also customize the format of the progress report according to the user's emotion. For example, if the emotion score is low, the report is made in text format. In this way, customizing the format of the progress report according to the user's emotion makes it easier for the user to understand the progress.

[0076] The resource suggestion unit can develop an algorithm that suggests optimal learning materials based on the user's learning style or preferences. The resource suggestion unit, for example, analyzes the user's learning style and develops an algorithm that suggests optimal learning materials. For example, it suggests visual learning materials for visual learners and audio learning materials for auditory learners. The resource suggestion unit can also suggest learning materials based on the user's preferences. For example, it suggests learning materials in areas that the user is interested in. This improves the user's learning efficiency by suggesting optimal learning materials based on the user's learning style and preferences.

[0077] The resource suggestion unit can dynamically adjust the difficulty of the learning materials according to the user's progress. The resource suggestion unit, for example, analyzes the user's progress in real time and builds a system that dynamically adjusts the difficulty of the learning materials. For example, if progress is good, it suggests learning materials with a high level of difficulty. The resource suggestion unit can also dynamically adjust the difficulty of the learning materials according to the user's progress. For example, if progress is slow, it suggests learning materials with a low level of difficulty. In this way, the difficulty of the learning materials is dynamically adjusted according to the user's progress, thereby improving the user's learning efficiency.

[0078] The resource suggestion unit can use the emotion estimation function to suggest a learning pace or break timing according to the user's emotions. The resource suggestion unit, for example, uses the emotion estimation function to build a system that suggests a learning pace according to the user's emotions. For example, the learning pace is slowed when the emotion score is low. The resource suggestion unit can also suggest break timing according to the user's emotions. For example, the break is shortened when the emotion score is high. In this way, by suggesting a learning pace or break timing according to the user's emotions, the user's learning efficiency and well-being are improved.

[0079] The resource suggestion unit can suggest learning materials according to different fields or specialized knowledge. The resource suggestion unit, for example, builds a system that suggests learning materials in different fields. For example, it provides learning materials according to fields such as science, technology, and art. The resource suggestion unit can also suggest learning materials according to the user's specialized knowledge. For example, for a user with medical knowledge, it suggests learning materials related to the latest medical research. In this way, by suggesting learning materials according to different fields or specialized knowledge, the user's learning efficiency is improved.

[0080] The resource suggestion unit can automatically generate a future study plan based on the user's study history. The resource suggestion unit, for example, stores the user's study history in a database and builds a system that automatically generates a future study plan based on that data. For example, it suggests the next step based on past study content. The resource suggestion unit can also automatically generate a future study plan based on the user's study history. For example, it suggests the next content to learn based on past study results. In this way, by automatically generating a future study plan based on the user's study history, the user's study efficiency is improved.

[0081] The resource suggestion unit can use the emotion estimation function to suggest interactive quizzes or games to increase the user's motivation to learn, according to the user's emotions. The resource suggestion unit, for example, uses the emotion estimation function to build a system that suggests interactive quizzes according to the user's emotions. For example, an easy quiz is provided when the emotion score is low. The resource suggestion unit can also suggest interactive games according to the user's emotions. For example, a challenging game is provided when the emotion score is high. In this way, suggesting interactive quizzes or games according to the user's emotions increases the user's motivation to learn.

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

[0083] The goal setting support system can monitor the user's health condition and set goals based on their health. For example, it can acquire the user's heart rate and sleep data and analyze their health condition. If the user's heart rate is high, it can set a goal for relaxation. It can also suggest goals for improving sleep quality based on the sleep data. This allows the system to support the user's overall health by setting goals based on the user's health condition.

[0084] The goal setting support system can set goals based on the user's hobbies and interests. For example, if the user is interested in music, it can suggest goals such as practicing an instrument or learning music theory. If the user is interested in cooking, it can suggest trying a new recipe as a goal. Furthermore, if the user is interested in sports, it can suggest improving skills in a particular sport as a goal. In this way, setting goals based on the user's hobbies and interests can increase the user's motivation.

[0085] The goal setting support system can set goals to strengthen the user's social connections. For example, if the user wants to deepen relationships with friends and family, the system can set regular communication as a goal. If the user wants to make new friends, the system can suggest participating in social events as a goal. Furthermore, if the user is interested in volunteer activities, the system can set participation in local volunteer activities as a goal. Thus, by setting goals to strengthen the user's social connections, the system can improve the user's social well-being.

[0086] The goal setting support system can set goals to support the user's career goals. For example, if the user wants to acquire a specific skill, courses and training related to that skill can be suggested as goals. Also, if the user is aiming for promotion, the system can set goals to obtain necessary qualifications or complete projects. Furthermore, if the user wants to try a new career, the system can suggest related networking events and internships as goals. Thus, by setting goals to support the user's career goals, the system can promote the user's professional growth.

[0087] The goal-setting support system can set goals to increase the user's environmental awareness. For example, if the user wants to live an eco-friendly life, it can suggest that the goal be to develop recycling habits. If the user wants to reduce energy consumption, it can suggest that the user set a goal to use energy-efficient home appliances. Furthermore, if the user wants to live a sustainable diet, it can suggest that the user set a goal to purchase local agricultural products. In this way, by setting goals to increase the user's environmental awareness, it is possible to support the user's sustainable lifestyle.

[0088] The goal setting support system can use its emotion estimation function to suggest relaxation methods according to the user's emotions. For example, if the user is feeling stressed, it can suggest deep breathing or meditation. If the user is feeling anxious, it can suggest relaxing music. Furthermore, if the user is tired, it can suggest a short break or light stretching. In this way, by suggesting relaxation methods according to the user's emotions, it is possible to reduce the user's stress and improve their well-being.

[0089] The goal setting support system can use the emotion estimation function to provide a message to improve motivation according to the user's emotions. For example, if the user is feeling down, an encouraging message can be sent. Also, if the user is losing motivation, a message reminding the user of past successes can be provided. Furthermore, if the user is feeling anxious, a message that gives a sense of security can be sent. In this way, by providing a message to improve motivation according to the user's emotions, it becomes easier to maintain the user's motivation.

[0090] The goal setting support system can use the emotion estimation function to provide feedback according to the user's emotions. For example, if the user feels a sense of accomplishment, a congratulatory message can be sent. If the user feels frustrated, an encouraging message can be provided. Furthermore, if the user feels anxious, feedback that gives a sense of security can be sent. In this way, by providing feedback according to the user's emotions, it becomes easier to maintain the user's motivation.

[0091] The goal setting support system can use the emotion estimation function to suggest a study method that corresponds to the user's emotions. For example, if the user lacks concentration, it can suggest a study task that can be completed in a short time. Also, if the user feels motivated, it can suggest a challenging study task. Furthermore, if the user is tired, it can suggest a method that allows the user to study while relaxing. In this way, by suggesting a study method that corresponds to the user's emotions, it is possible to improve the user's learning efficiency.

[0092] The goal setting support system can use its emotion estimation function to suggest an exercise plan that matches the user's emotions. For example, if the user is feeling stressed, it can suggest yoga, which has a relaxing effect. If the user is feeling energetic, it can suggest high-intensity training. Furthermore, if the user is tired, it can suggest light stretching or walking. In this way, by suggesting an exercise plan that matches the user's emotions, it is possible to improve the user's health and well-being.

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

[0094] Step 1: The psychological state analysis unit reads the user's psychological state and emotions from the text input. For example, if the user inputs, "I've been busy at work lately and feeling stressed," the psychological state analysis unit will understand the user's psychological state based on that information. It is also possible to analyze the user's emotions using generative AI (e.g., text generation AI or multimodal generation AI). Step 2: The goal setting unit sets a goal based on the psychological state and emotions analyzed by the psychological state analysis unit. For example, if the user sets a goal of "I want to complete a marathon within one year," the goal setting unit will create a specific plan to achieve that goal. Step 3: The task suggestion unit suggests tasks based on the goal set by the goal setting unit. For example, if the user sets a goal of "I want to complete a marathon within one year," the task suggestion unit will suggest training plans and daily exercise tasks toward that goal. Step 4: The feedback provider provides feedback on the tasks proposed by the task suggester. For example, the feedback provider may motivate the user by sending a message such as, "You did a great job on today's training! Let's try harder next time!" Step 5: The progress tracker tracks the user's progress and achievements, for example by displaying the tasks and goals achieved by the user in graphs and charts, allowing the user to see their progress at a glance. Step 6: The resource suggestion module suggests resources that the user needs to address their goal. For example, if the user enters "I want to study English," the resource suggestion module will suggest appropriate learning materials and online courses.

[0095] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0096] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0097] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

[0100] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0101] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0102] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0103] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0104] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0105] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0106] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0108] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0109] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

[0112] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0114] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0115] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0116] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0117] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

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

[0121] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0124] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0125] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

[0127] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0129] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0130] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0131] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

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

[0135] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0136] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0137] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0140] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0141] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

[0143] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0144] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0145] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0146] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0147] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0148] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the 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.

[0149] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0150] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0151] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0152] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0153] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0154] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0155] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0156] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0157] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0158] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0159] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0160] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0161] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0162] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A system that uses generative AI to support goal setting appropriate for each individual, a psychological state analysis unit that reads the user's psychological state and emotions from the user's text input; a goal setting unit that sets a goal based on the psychological state and the emotion analyzed by the psychological state analysis unit; a task suggestion unit that suggests a task based on the goal set by the goal setting unit; a feedback providing unit that provides feedback on the task proposed by the task proposing unit; a progress tracker for tracking the user's progress and goals achieved; a resource suggestion unit that suggests resources required for the user to work on the goal. A system characterized by:

2. The psychological state analysis unit In addition to the user's text input, voice input or facial expression recognition is used to analyze the user's mental state and emotions from multiple angles.

2. The system of claim 1.

3. The goal setting unit Introducing an algorithm that dynamically adjusts the difficulty or amount of the task depending on the user's level of goal achievement.

2. The system of claim 1.

4. The feedback providing unit: Generate personalized feedback messages based on the user's past successes 2. The system of claim 1.

5. The progress tracking unit Visualize the user's growth process with 3D animation to enhance the sense of accomplishment 2. The system of claim 1.

6. The resource suggestion unit Develop an algorithm that suggests optimal learning materials based on the user's learning style or preferences.

2. The system of claim 1.

7. The psychological state analysis unit Monitor the user's emotional fluctuations in real time and provide support at the appropriate time.

2. The system of claim 1.

8. The goal setting unit Suggesting easier tasks when the user's motivation is low 2. The system of claim 1.

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