System

Abstract

An object of the system according to the embodiment is to consistently perform determination of a mental state, proposal of a specific action or diet, generation of an action plan, and feedback of a result.SOLUTION: A system includes a mental state determination part, an action suggestion part, an action plan generation part, and a feedback analysis part. The mental state determination unit determines a mental state using the generated AI. The action suggestion unit suggests an action or a meal based on the mental state determined by the mental state determination unit. The action plan generation unit generates a specific action plan based on the action or the meal proposed by the action proposal unit. The feedback analysis unit feeds back an execution result of the action plan.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology lacks a system that can consistently assess mental state, suggest specific actions and dietary recommendations, and provide feedback on the results, so there is room for improvement.

[0005] The system according to the embodiment aims to consistently carry out a range of processes, from assessing mental state to suggesting specific actions and diets, creating an action plan, and providing feedback on the results. [Means for solving the problem]

[0006] The system according to the embodiment includes a mental state assessment unit, an action suggestion unit, an action plan generation unit, and a feedback analysis unit. The mental state assessment unit assesses the mental state using a generation AI. The action suggestion unit suggests an action or meal based on the mental state assessed by the mental state assessment unit. The action plan generation unit generates a specific action plan based on the action or meal suggested by the action suggestion unit. The feedback analysis unit provides feedback on the results of executing the action plan. [Effects of the Invention]

[0007] The system according to the embodiment can consistently perform a range of tasks, from assessing mental state to suggesting specific actions and diets, creating action plans, and providing feedback on the results. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The mental health improvement system according to an embodiment of the present invention uses generative AI to assess a user's mental state, and based on the assessment results, suggests behaviors and diets, proposes specific action plans, and provides feedback on the results, providing a cycle of feedback. This allows the mental health improvement system to continuously improve the user's mental health.

[0029] A mental health improvement system according to an embodiment includes a mental state assessment unit, an action suggestion unit, an action plan generation unit, and a feedback analysis unit. The mental state assessment unit assesses a user's mental state using a generation AI. For example, the generation AI analyzes the user's mental state based on information provided by the user. The generation AI receives, for example, the user's self-report, diary, and sensor data (such as heart rate and sleep patterns) as input and assesses the user's mental state. The action suggestion unit suggests actions and meals based on the mental state assessed by the mental state assessment unit. For example, if the generation AI determines that the user is highly stressed, it suggests yoga or meditation to relax. Furthermore, if the generation AI determines that the user's nutritional balance is unbalanced, it suggests a balanced diet. The action plan generation unit generates a specific action plan based on the actions and meals suggested by the action suggestion unit. For example, the generation AI suggests specific action plans, such as incorporating yoga time into a daily schedule or adding healthy ingredients to a weekly shopping list. The feedback analysis unit provides feedback on the results of the action plan. For example, the generation AI reports the actions and meals the user has performed and analyzes the results. This allows the mental health improvement system to continuously improve the user's mental health.

[0030] The mental state assessment unit learns the user's past mental state data and can predict long-term mental health trends. In the mental state assessment unit, for example, the generation AI collects the user's past mental state data and analyzes long-term trends. For example, it predicts seasonal fluctuations in mental health based on data from the past year. The mental state assessment unit also learns the user's diary and self-reported data, and the generation AI predicts long-term mental health trends. For example, it analyzes the impact of specific events or situations on mental health. The mental state assessment unit also predicts long-term mental health trends based on data from sensors (heart rate, sleep patterns, etc.). For example, continued lack of sleep tends to increase stress. This makes it possible to predict long-term mental health trends.

[0031] The mental state assessment unit can analyze the user's exercise data and determine the correlation with the mental state. For example, the generation AI in the mental state assessment unit analyzes the user's step count data and determines the correlation with the mental state. For example, days with a low number of steps may indicate high stress. The mental state assessment unit also collects the user's exercise intensity data, and the generation AI analyzes the correlation with the mental state. For example, people tend to feel better on days with high exercise intensity. The mental state assessment unit also integrates the step count and exercise intensity data, and the generation AI comprehensively assesses the mental state. For example, an increase in the amount of exercise shows a tendency for the mental state to improve. This allows the user's exercise data to be analyzed and the correlation with the mental state to be determined.

[0032] The mental state assessment unit can collect feedback from family or friends and make a comprehensive assessment to determine the user's mental state. In the mental state assessment unit, for example, the generation AI collects feedback from family and friends and makes a comprehensive assessment of the user's mental state. For example, the mental state is assessed based on comments and observations from family. The mental state assessment unit also conducts a questionnaire survey of family and friends, and the generation AI analyzes the results. For example, feedback on the user's behavior and attitude is collected and the mental state is assessed. The mental state assessment unit also integrates feedback data from family and friends, and the generation AI makes a comprehensive assessment of the mental state. For example, the generation AI analyzes fluctuations in the mental state based on multiple pieces of feedback. This allows feedback from family and friends to be collected and the mental state to be assessed comprehensively.

[0033] The action suggestion unit can learn the user's past action history and suggest the most effective action or meal. In the action suggestion unit, for example, the generation AI analyzes the user's past action history and suggests the most effective action. For example, it re-suggests actions that have had a high relaxation effect in the past. The action suggestion unit also learns the user's diet history and the generation AI suggests the most effective meal. For example, it re-suggests meal menus that have had good nutritional balance in the past. The action suggestion unit also integrates the action history and diet history, and the generation AI suggests the most effective action or meal overall. For example, it shows that a combination of a specific action and meal is effective. In this way, the user's past action history can be learned and the most effective action or meal can be suggested.

[0034] The action suggestion unit can update action or meal suggestions in real time according to the user's current mental state. In the action suggestion unit, for example, the generation AI analyzes the user's current mental state and updates action suggestions in real time. For example, if stress is high, the generation AI suggests actions to relax. In addition, the action suggestion unit updates meal suggestions in real time according to the user's mental state. For example, if the user is feeling depressed, the generation AI suggests nutritious meals. In addition, the action suggestion unit dynamically adjusts action and meal suggestions according to fluctuations in the mental state. For example, if stress decreases, the generation AI suggests new actions or meals. This allows action and meal suggestions to be updated in real time according to the user's current mental state.

[0035] The action suggestion unit can suggest nearby relaxation spots or healthy restaurants based on the user's geographical location information. In the action suggestion unit, for example, the generation AI analyzes the user's geographical location information and suggests nearby relaxation spots. For example, it may suggest parks or nature walking courses. In addition, the action suggestion unit can suggest healthy restaurants based on the user's location information. For example, it may suggest restaurants that use organic ingredients. In addition, the action suggestion unit integrates the geographical location information and the mental state, and the generation AI comprehensively suggests relaxation spots and healthy restaurants. For example, it may suggest cafes that have a relaxing effect. In this way, it is possible to suggest nearby relaxation spots and healthy restaurants based on the user's geographical location information.

[0036] The action suggestion unit can suggest creative activities such as music or art depending on the user's mental state. In the action suggestion unit, for example, the generation AI analyzes the user's mental state and suggests music. For example, classical music with a relaxing effect is suggested. In addition, the action suggestion unit has the generation AI suggest art activities based on the user's emotional state. For example, it suggests painting or handicrafts to relieve stress. In addition, the action suggestion unit integrates data on the mental state and creative activities, and the generation AI makes comprehensive suggestions. For example, it suggests a relaxation method that combines music and art. This allows creative activities such as music or art to be suggested depending on the user's mental state.

[0037] The action plan generation unit can analyze the user's lifestyle rhythm and propose an action plan at the optimal timing. For example, the generation AI of the action plan generation unit analyzes the user's lifestyle rhythm and proposes the optimal time for yoga. For example, incorporating yoga into morning relaxation time. The action plan generation unit also proposes meal preparation at the optimal time based on the user's schedule. For example, suggesting a healthy snack before dinner. The action plan generation unit also integrates the user's lifestyle rhythm and mental state, and the generation AI proposes a comprehensive action plan. For example, suggesting activities to relax during times of high stress. This allows the user's lifestyle rhythm to be analyzed and an action plan to be proposed at the optimal time.

[0038] The action plan generation unit can dynamically adjust the action plan based on user feedback. In the action plan generation unit, for example, the generation AI analyzes the user's feedback and dynamically adjusts the action plan. For example, if yoga is not effective, the generation AI will suggest another relaxation method. In addition, the action plan generation unit adjusts the meal plan based on the user's feedback. For example, if the suggested meal does not suit the user's taste, the generation AI will suggest a different menu. In addition, the action plan generation unit integrates the feedback data, and the generation AI comprehensively adjusts the action plan. For example, the generation AI will re-suggest optimal actions and meals based on multiple pieces of feedback. This allows the action plan to be dynamically adjusted based on the user's feedback.

[0039] The action plan generation unit can propose a realistic action plan by taking into account the user's work or school schedule. In the action plan generation unit, for example, the generation AI analyzes the user's work schedule and proposes a realistic action plan. For example, it suggests ways to relax for a short time between work. In addition, the action plan generation unit proposes a realistic action plan by taking into account the user's school schedule. For example, it suggests activities to refresh yourself between classes. In addition, the action plan generation unit integrates the work or school schedule with the user's mental state, and the generation AI proposes a comprehensively realistic action plan. For example, it suggests actions that are easy to carry out during busy periods. In this way, it is possible to propose a realistic action plan by taking into account the user's work or school schedule.

[0040] The action plan generation unit can propose an action plan that combines short-term and long-term goals depending on the user's mental state. For example, the action plan generation unit uses a generation AI to analyze the user's mental state and propose an action plan that combines short-term and long-term goals. For example, it proposes activities for relaxation in the short term and healthy lifestyle habits in the long term. The action plan generation unit also proposes an action plan that combines short-term and long-term goals based on the user's emotional state. For example, it aims to relieve stress in the short term and maintain mental health in the long term. The action plan generation unit also integrates data on short-term and long-term goals, and the generation AI proposes a comprehensive action plan. For example, it proposes a plan to achieve long-term goals by accumulating short-term results. This makes it possible to propose an action plan that combines short-term and long-term goals depending on the user's mental state.

[0041] The feedback analysis unit can analyze user feedback and improve the algorithm to reflect it in the next proposal. For example, the feedback analysis unit allows the generation AI to analyze user feedback and improve the algorithm to reflect it in the next proposal. For example, the accuracy of the proposal is improved based on the feedback data. The feedback analysis unit also allows the generation AI to dynamically adjust the proposal algorithm based on user feedback. For example, it makes suggestions that match the user's preferences. The feedback analysis unit also integrates the feedback data, and the generation AI comprehensively improves the algorithm. For example, it makes the optimal proposal based on multiple pieces of feedback. This allows the generation AI to analyze user feedback and improve the algorithm to reflect it in the next proposal.

[0042] The feedback analysis unit can quantitatively evaluate the degree of improvement in mental health based on user feedback. In the feedback analysis unit, for example, the generation AI analyzes the user's feedback and quantitatively evaluates the degree of improvement in mental health. For example, it calculates the degree of improvement based on the feedback score. In addition, the feedback analysis unit has the generation AI evaluate the degree of improvement in mental health based on the user's feedback data. For example, it determines that the degree of improvement is high when there is a lot of positive feedback. In addition, the feedback analysis unit integrates the feedback data, and the generation AI comprehensively evaluates the degree of improvement in mental health. For example, it quantifies the degree of improvement based on multiple pieces of feedback. This allows the degree of improvement in mental health to be quantitatively evaluated based on the user's feedback.

[0043] The feedback analysis unit can compare the user's feedback with other users and extract best practices. In the feedback analysis unit, for example, the generation AI compares the user's feedback with other users and extracts best practices. For example, it makes optimal suggestions based on feedback from users who have taken the same action. The feedback analysis unit also analyzes the user's feedback data, and the generation AI extracts success stories from other users. For example, it suggests actions that have received a lot of positive feedback. The feedback analysis unit also integrates the feedback data, and the generation AI comprehensively extracts best practices. For example, it suggests optimal actions based on the feedback of multiple users. This makes it possible to compare the user's feedback with other users and extract best practices.

[0044] The feedback analysis unit allows the generation AI to generate new suggestions based on user feedback and have the user try them out. In the feedback analysis unit, for example, the generation AI analyzes user feedback and generates new suggestions. For example, it may suggest a new way to relax based on feedback data. In addition, the feedback analysis unit allows the generation AI to suggest a new meal menu based on user feedback data. For example, it may suggest a new recipe that suits preferences. In addition, the feedback analysis unit integrates the feedback data, and the generation AI comprehensively generates new suggestions. For example, it may suggest optimal actions or meals based on multiple pieces of feedback. In this way, the generation AI can generate new suggestions based on user feedback and have the user try them out.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The mental state assessment unit can learn from the user's past mental state data and predict long-term mental health trends. For example, the generation AI collects the user's past mental state data and analyzes long-term trends. For example, it predicts seasonal fluctuations in mental health based on data from the past year. The mental state assessment unit also learns from the user's diary and self-reported data, and the generation AI predicts long-term mental health trends. For example, it analyzes the impact of specific events or situations on mental health. The mental state assessment unit also predicts long-term mental health trends based on data from sensors (heart rate, sleep patterns, etc.). For example, continued lack of sleep tends to increase stress. This makes it possible to predict long-term mental health trends.

[0047] The mental state assessment unit can analyze the user's exercise data and determine the correlation with their mental state. For example, the generation AI analyzes the user's step count data and determines the correlation with their mental state. For example, days with a low number of steps may indicate high stress. The mental state assessment unit also collects the user's exercise intensity data, and the generation AI analyzes the correlation with their mental state. For example, people tend to feel better on days with high exercise intensity. The mental state assessment unit also integrates the step count and exercise intensity data, and the generation AI comprehensively assesses their mental state. For example, an increase in the amount of exercise shows a tendency for their mental state to improve. This allows the user's exercise data to be analyzed and the correlation with their mental state to be determined.

[0048] The mental state assessment unit can collect feedback from family or friends and make a comprehensive assessment to determine the user's mental state. For example, the generation AI collects feedback from family and friends and makes a comprehensive assessment of the user's mental state. For example, the mental state is assessed based on comments and observations from family members. The mental state assessment unit also conducts a survey of family and friends, and the generation AI analyzes the results. For example, feedback on the user's behavior and attitude is collected and the mental state is assessed. The mental state assessment unit also integrates feedback data from family and friends, and the generation AI makes a comprehensive assessment of the mental state. For example, the generation AI analyzes fluctuations in the mental state based on multiple pieces of feedback. This allows feedback from family and friends to be collected and the mental state to be assessed comprehensively.

[0049] The action suggestion unit can learn the user's past behavioral history and suggest the most effective actions or meals. For example, the generation AI analyzes the user's past behavioral history and suggests the most effective actions. For example, it may re-suggest actions that have had a high relaxing effect in the past. The action suggestion unit also learns the user's dietary history and the generation AI suggests the most effective meals. For example, it may re-suggest meal menus that have had a good nutritional balance in the past. The action suggestion unit also integrates the behavioral history and dietary history and the generation AI suggests the most effective actions or meals overall. For example, it may show that a combination of a specific action and meal is effective. This allows the generation AI to learn the user's past behavioral history and suggest the most effective actions and meals.

[0050] The action suggestion unit can update action or meal suggestions in real time according to the user's current mental state. For example, the generation AI analyzes the user's current mental state and updates action suggestions in real time. For example, if stress is high, it suggests actions to relax. The action suggestion unit also updates meal suggestions in real time according to the user's mental state. For example, if the user is feeling depressed, it suggests nutritious meals. The action suggestion unit also dynamically adjusts action and meal suggestions according to fluctuations in the user's mental state. For example, if stress decreases, it suggests new actions or meals. This allows action and meal suggestions to be updated in real time according to the user's current mental state.

[0051] The action suggestion unit can suggest nearby relaxation spots or healthy restaurants based on the user's geographical location information. For example, the generation AI analyzes the user's geographical location information and suggests nearby relaxation spots. For example, it can suggest parks or nature walking courses. The action suggestion unit also suggests healthy restaurants based on the user's location information. For example, it can suggest restaurants that use organic ingredients. The action suggestion unit also integrates the geographical location information and mental state, and the generation AI comprehensively suggests relaxation spots and healthy restaurants. For example, it can suggest cafes that have a relaxing effect. This makes it possible to suggest nearby relaxation spots and healthy restaurants based on the user's geographical location information.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The mental state assessment unit uses the generation AI to assess the user's mental state. The generation AI analyzes the user's mental state based on information provided by the user. For example, it receives input such as the user's self-report, diary entries, and data from sensors (such as heart rate and sleep patterns) to assess the user's mental state. Step 2: The behavior suggestion unit suggests actions and meals based on the mental state determined by the mental state determination unit. For example, if the generation AI determines that stress is high, it will suggest yoga or meditation to help relax. Also, if it determines that the nutritional balance is unbalanced, it will suggest a balanced diet. Step 3: The action plan generator generates a specific action plan based on the activities and meals suggested by the action suggester. For example, the generator might suggest specific actions such as incorporating yoga into a daily schedule or adding healthy ingredients to a weekly shopping list. Step 4: The feedback analysis unit provides feedback on the results of the action plan. The generation AI reports the actions and dietary details taken by the user and analyzes the results. This allows the mental health improvement system to continuously improve the user's mental health.

[0054] (Example 2) The mental health improvement system according to an embodiment of the present invention uses generative AI to assess a user's mental state, and based on the assessment results, suggests behaviors and diets, proposes specific action plans, and provides feedback on the results, providing a cycle of feedback. This allows the mental health improvement system to continuously improve the user's mental health.

[0055] A mental health improvement system according to an embodiment includes a mental state assessment unit, an action suggestion unit, an action plan generation unit, and a feedback analysis unit. The mental state assessment unit assesses a user's mental state using a generation AI. For example, the generation AI analyzes the user's mental state based on information provided by the user. The generation AI receives, for example, the user's self-report, diary, and sensor data (such as heart rate and sleep patterns) as input and assesses the user's mental state. The action suggestion unit suggests actions and meals based on the mental state assessed by the mental state assessment unit. For example, if the generation AI determines that the user is highly stressed, it suggests yoga or meditation to relax. Furthermore, if the generation AI determines that the user's nutritional balance is unbalanced, it suggests a balanced diet. The action plan generation unit generates a specific action plan based on the actions and meals suggested by the action suggestion unit. For example, the generation AI suggests specific action plans, such as incorporating yoga time into a daily schedule or adding healthy ingredients to a weekly shopping list. The feedback analysis unit provides feedback on the results of the action plan. For example, the generation AI reports the actions and meals the user has performed and analyzes the results. This allows the mental health improvement system to continuously improve the user's mental health.

[0056] The mental state determination unit can analyze the user's voice tone or facial expression and determine the emotional state in real time. For example, the generation AI in the mental state determination unit analyzes the user's voice tone and determines the emotional state in real time. For example, it analyzes the pitch, strength, and rhythm of the voice to determine whether the user is feeling stressed. The generation AI in the mental state determination unit also captures the user's facial expression with a camera and analyzes it. For example, it analyzes facial features such as smiling and furrowed brows to determine the user's emotional state. The mental state determination unit also integrates voice and facial expression data, and the generation AI comprehensively determines the emotional state. For example, if the voice tone is calm but the facial expression is tense, it suggests the possibility of hidden stress. This allows the user's emotional state to be determined in real time.

[0057] The mental state assessment unit learns the user's past mental state data and can predict long-term mental health trends. In the mental state assessment unit, for example, the generation AI collects the user's past mental state data and analyzes long-term trends. For example, it predicts seasonal fluctuations in mental health based on data from the past year. The mental state assessment unit also learns the user's diary and self-reported data, and the generation AI predicts long-term mental health trends. For example, it analyzes the impact of specific events or situations on mental health. The mental state assessment unit also predicts long-term mental health trends based on data from sensors (heart rate, sleep patterns, etc.). For example, continued lack of sleep tends to increase stress. This makes it possible to predict long-term mental health trends.

[0058] The mental state determination unit can use the emotion estimation function to estimate emotions from the user's diary or SNS posts and determine the mental state. In the mental state determination unit, for example, the generation AI analyzes the user's diary and determines the emotional state using the emotion estimation function. For example, a diary entry with many positive expressions indicates a good mental state. The mental state determination unit also collects the user's SNS posts, and the generation AI performs emotion estimation. For example, it analyzes keywords and context in the post content to determine the user's emotional state. The mental state determination unit also integrates diary and SNS post data, and the generation AI comprehensively determines the emotional state. For example, if the diary entry is positive but the SNS entry is full of negative posts, this indicates an unstable mental state. This makes it possible to estimate emotions from the user's diary and SNS posts and determine the mental state.

[0059] The mental state assessment unit can analyze the user's exercise data and determine the correlation with the mental state. For example, the generation AI in the mental state assessment unit analyzes the user's step count data and determines the correlation with the mental state. For example, days with a low number of steps may indicate high stress. The mental state assessment unit also collects the user's exercise intensity data, and the generation AI analyzes the correlation with the mental state. For example, people tend to feel better on days with high exercise intensity. The mental state assessment unit also integrates the step count and exercise intensity data, and the generation AI comprehensively assesses the mental state. For example, an increase in the amount of exercise shows a tendency for the mental state to improve. This allows the user's exercise data to be analyzed and the correlation with the mental state to be determined.

[0060] The mental state assessment unit can collect feedback from family or friends and make a comprehensive assessment to determine the user's mental state. In the mental state assessment unit, for example, the generation AI collects feedback from family and friends and makes a comprehensive assessment of the user's mental state. For example, the mental state is assessed based on comments and observations from family. The mental state assessment unit also conducts a questionnaire survey of family and friends, and the generation AI analyzes the results. For example, feedback on the user's behavior and attitude is collected and the mental state is assessed. The mental state assessment unit also integrates feedback data from family and friends, and the generation AI makes a comprehensive assessment of the mental state. For example, the generation AI analyzes fluctuations in the mental state based on multiple pieces of feedback. This allows feedback from family and friends to be collected and the mental state to be assessed comprehensively.

[0061] The mental state assessment unit can use the emotion estimation function to estimate emotions from the content the user watches and determine the mental state. For example, the mental state assessment unit uses a generation AI to analyze the content of the movie the user watches and determine the emotional state using the emotion estimation function. For example, after watching a sad movie, the mental state may decline. The mental state assessment unit also analyzes the genre and rhythm of the music the user listens to, and the generation AI estimates the emotional state. For example, listening to up-tempo music tends to lift one's mood. The mental state assessment unit also integrates movie and music viewing data, and the generation AI comprehensively assesses the emotional state. For example, it analyzes fluctuations in the mental state based on the viewing history. This allows the mental state to be assessed by estimating emotions from the content the user watches.

[0062] The action suggestion unit can learn the user's past action history and suggest the most effective action or meal. In the action suggestion unit, for example, the generation AI analyzes the user's past action history and suggests the most effective action. For example, it re-suggests actions that have had a high relaxation effect in the past. The action suggestion unit also learns the user's diet history and the generation AI suggests the most effective meal. For example, it re-suggests meal menus that have had good nutritional balance in the past. The action suggestion unit also integrates the action history and diet history, and the generation AI suggests the most effective action or meal overall. For example, it shows that a combination of a specific action and meal is effective. In this way, the user's past action history can be learned and the most effective action or meal can be suggested.

[0063] The action suggestion unit can update action or meal suggestions in real time according to the user's current mental state. In the action suggestion unit, for example, the generation AI analyzes the user's current mental state and updates action suggestions in real time. For example, if stress is high, the generation AI suggests actions to relax. In addition, the action suggestion unit updates meal suggestions in real time according to the user's mental state. For example, if the user is feeling depressed, the generation AI suggests nutritious meals. In addition, the action suggestion unit dynamically adjusts action and meal suggestions according to fluctuations in the mental state. For example, if stress decreases, the generation AI suggests new actions or meals. This allows action and meal suggestions to be updated in real time according to the user's current mental state.

[0064] The action suggestion unit can use the emotion estimation function to suggest relaxation methods or meal menus based on the user's emotions. For example, the generation AI in the action suggestion unit uses the emotion estimation function to suggest relaxation methods based on the user's emotions. For example, if stress is high, it may suggest meditation or deep breathing. The action suggestion unit also analyzes the user's emotional state, and the generation AI suggests meal menus based on the emotions. For example, if the user is feeling depressed, it may suggest ingredients that increase serotonin. The action suggestion unit also has the generation AI comprehensively suggest relaxation methods and meal menus based on the emotion estimation data. For example, it may suggest combining relaxing herbal tea with meditation. This makes it possible to suggest relaxation methods and meal menus based on the user's emotions.

[0065] The action suggestion unit can suggest nearby relaxation spots or healthy restaurants based on the user's geographical location information. In the action suggestion unit, for example, the generation AI analyzes the user's geographical location information and suggests nearby relaxation spots. For example, it may suggest parks or nature walking courses. In addition, the action suggestion unit can suggest healthy restaurants based on the user's location information. For example, it may suggest restaurants that use organic ingredients. In addition, the action suggestion unit integrates the geographical location information and the mental state, and the generation AI comprehensively suggests relaxation spots and healthy restaurants. For example, it may suggest cafes that have a relaxing effect. In this way, it is possible to suggest nearby relaxation spots and healthy restaurants based on the user's geographical location information.

[0066] The action suggestion unit can suggest creative activities such as music or art depending on the user's mental state. In the action suggestion unit, for example, the generation AI analyzes the user's mental state and suggests music. For example, classical music with a relaxing effect is suggested. In addition, the action suggestion unit has the generation AI suggest art activities based on the user's emotional state. For example, it suggests painting or handicrafts to relieve stress. In addition, the action suggestion unit integrates data on the mental state and creative activities, and the generation AI makes comprehensive suggestions. For example, it suggests a relaxation method that combines music and art. This allows creative activities such as music or art to be suggested depending on the user's mental state.

[0067] The action suggestion unit can use the emotion estimation function to suggest actions based on the user's favorite hobbies or activities. For example, the generation AI in the action suggestion unit uses the emotion estimation function to suggest actions based on the user's favorite hobbies. For example, the generation AI suggests gardening to a user who likes gardening. The action suggestion unit also analyzes the user's emotional state, and the generation AI suggests actions based on the user's favorite activities. For example, the generation AI suggests relaxing books to a user who likes reading. The action suggestion unit also integrates data on hobbies and activities, and the generation AI makes comprehensive action suggestions. For example, the generation AI suggests a relaxing playlist to a user who likes listening to music. This allows the generation AI to suggest actions based on the user's favorite hobbies and activities.

[0068] The action plan generation unit can analyze the user's lifestyle rhythm and propose an action plan at the optimal timing. For example, the generation AI of the action plan generation unit analyzes the user's lifestyle rhythm and proposes the optimal time for yoga. For example, incorporating yoga into morning relaxation time. The action plan generation unit also proposes meal preparation at the optimal time based on the user's schedule. For example, suggesting a healthy snack before dinner. The action plan generation unit also integrates the user's lifestyle rhythm and mental state, and the generation AI proposes a comprehensive action plan. For example, suggesting activities to relax during times of high stress. This allows the user's lifestyle rhythm to be analyzed and an action plan to be proposed at the optimal time.

[0069] The action plan generation unit can dynamically adjust the action plan based on user feedback. In the action plan generation unit, for example, the generation AI analyzes the user's feedback and dynamically adjusts the action plan. For example, if yoga is not effective, the generation AI will suggest another relaxation method. In addition, the action plan generation unit adjusts the meal plan based on the user's feedback. For example, if the suggested meal does not suit the user's taste, the generation AI will suggest a different menu. In addition, the action plan generation unit integrates the feedback data, and the generation AI comprehensively adjusts the action plan. For example, the generation AI will re-suggest optimal actions and meals based on multiple pieces of feedback. This allows the action plan to be dynamically adjusted based on the user's feedback.

[0070] The action plan generation unit uses the emotion estimation function to propose an action plan based on the user's emotions, thereby increasing motivation to carry out the plan. In the action plan generation unit, for example, the generation AI uses the emotion estimation function to propose an action plan based on the user's emotions. For example, when positive emotions are strong, a new challenge is proposed. The action plan generation unit also analyzes the user's emotional state, and the generation AI proposes an action plan based on the emotions. For example, when stress is high, an activity to relax is proposed. In addition, the action plan generation unit uses the emotion estimation data to propose a comprehensive action plan by the generation AI, thereby increasing motivation to carry out the plan. For example, positive feedback is provided to maintain motivation. This allows the action plan to be proposed based on the user's emotions, thereby increasing motivation to carry out the plan.

[0071] The action plan generation unit can propose a realistic action plan by taking into account the user's work or school schedule. In the action plan generation unit, for example, the generation AI analyzes the user's work schedule and proposes a realistic action plan. For example, it suggests ways to relax for a short time between work. In addition, the action plan generation unit proposes a realistic action plan by taking into account the user's school schedule. For example, it suggests activities to refresh yourself between classes. In addition, the action plan generation unit integrates the work or school schedule with the user's mental state, and the generation AI proposes a comprehensively realistic action plan. For example, it suggests actions that are easy to carry out during busy periods. In this way, it is possible to propose a realistic action plan by taking into account the user's work or school schedule.

[0072] The action plan generation unit can propose an action plan that combines short-term and long-term goals depending on the user's mental state. For example, the action plan generation unit uses a generation AI to analyze the user's mental state and propose an action plan that combines short-term and long-term goals. For example, it proposes activities for relaxation in the short term and healthy lifestyle habits in the long term. The action plan generation unit also proposes an action plan that combines short-term and long-term goals based on the user's emotional state. For example, it aims to relieve stress in the short term and maintain mental health in the long term. The action plan generation unit also integrates data on short-term and long-term goals, and the generation AI proposes a comprehensive action plan. For example, it proposes a plan to achieve long-term goals by accumulating short-term results. This makes it possible to propose an action plan that combines short-term and long-term goals depending on the user's mental state.

[0073] The action plan generation unit can use the emotion estimation function to propose an action plan that incorporates activities that the user can enjoy. For example, the action plan generation unit uses the emotion estimation function to propose an action plan that incorporates activities that the user can enjoy. For example, it proposes activities based on hobbies and interests. The action plan generation unit also analyzes the user's emotional state and proposes an action plan that incorporates activities that the generation AI can enjoy. For example, it suggests trying a new hobby when you're in a good mood. The action plan generation unit also proposes an action plan that incorporates activities that the generation AI can enjoy overall, based on the emotion estimation data. For example, it incorporates activities that elicit positive emotions into the plan. This makes it possible to propose an action plan that incorporates activities that the user can enjoy.

[0074] The feedback analysis unit can analyze user feedback and improve the algorithm to reflect it in the next proposal. For example, the feedback analysis unit allows the generation AI to analyze user feedback and improve the algorithm to reflect it in the next proposal. For example, the accuracy of the proposal is improved based on the feedback data. The feedback analysis unit also allows the generation AI to dynamically adjust the proposal algorithm based on user feedback. For example, it makes suggestions that match the user's preferences. The feedback analysis unit also integrates the feedback data, and the generation AI comprehensively improves the algorithm. For example, it makes the optimal proposal based on multiple pieces of feedback. This allows the generation AI to analyze user feedback and improve the algorithm to reflect it in the next proposal.

[0075] The feedback analysis unit can quantitatively evaluate the degree of improvement in mental health based on user feedback. In the feedback analysis unit, for example, the generation AI analyzes the user's feedback and quantitatively evaluates the degree of improvement in mental health. For example, it calculates the degree of improvement based on the feedback score. In addition, the feedback analysis unit has the generation AI evaluate the degree of improvement in mental health based on the user's feedback data. For example, it determines that the degree of improvement is high when there is a lot of positive feedback. In addition, the feedback analysis unit integrates the feedback data, and the generation AI comprehensively evaluates the degree of improvement in mental health. For example, it quantifies the degree of improvement based on multiple pieces of feedback. This allows the degree of improvement in mental health to be quantitatively evaluated based on the user's feedback.

[0076] The feedback analysis unit uses the emotion estimation function to analyze the user's emotional response and improve the quality of the feedback. For example, the generation AI uses the emotion estimation function to analyze the user's emotional response and improve the quality of the feedback. For example, the feedback content is adjusted based on the emotion score. The feedback analysis unit also analyzes the user's emotional state, and the generation AI provides feedback based on the emotion. For example, if the positive emotion is strong, encouraging feedback is provided. The feedback analysis unit also allows the generation AI to comprehensively improve the quality of the feedback based on the emotion estimation data. For example, the feedback content is optimized based on the emotional response. This allows the user's emotional response to be analyzed and the quality of the feedback to be improved.

[0077] The feedback analysis unit can compare the user's feedback with other users and extract best practices. In the feedback analysis unit, for example, the generation AI compares the user's feedback with other users and extracts best practices. For example, it makes optimal suggestions based on feedback from users who have taken the same action. The feedback analysis unit also analyzes the user's feedback data, and the generation AI extracts success stories from other users. For example, it suggests actions that have received a lot of positive feedback. The feedback analysis unit also integrates the feedback data, and the generation AI comprehensively extracts best practices. For example, it suggests optimal actions based on the feedback of multiple users. This makes it possible to compare the user's feedback with other users and extract best practices.

[0078] The feedback analysis unit allows the generation AI to generate new suggestions based on user feedback and have the user try them out. In the feedback analysis unit, for example, the generation AI analyzes user feedback and generates new suggestions. For example, it may suggest a new way to relax based on feedback data. In addition, the feedback analysis unit allows the generation AI to suggest a new meal menu based on user feedback data. For example, it may suggest a new recipe that suits preferences. In addition, the feedback analysis unit integrates the feedback data, and the generation AI comprehensively generates new suggestions. For example, it may suggest optimal actions or meals based on multiple pieces of feedback. In this way, the generation AI can generate new suggestions based on user feedback and have the user try them out.

[0079] The feedback analysis unit uses the emotion estimation function to monitor the user's emotional response to feedback in real time and reflect it in the next proposal. In the feedback analysis unit, for example, the generation AI uses the emotion estimation function to monitor the user's emotional response to feedback in real time. For example, it analyzes facial expressions and voice at the time of feedback. The feedback analysis unit also analyzes the user's emotional response, and the generation AI reflects this in the next proposal. For example, if there are many positive emotional responses, it will make a similar proposal. The feedback analysis unit also allows the generation AI to comprehensively optimize the next proposal based on the emotion estimation data. For example, it adjusts the content of the proposal based on the emotional response. This allows the user's emotional response to feedback to be monitored in real time and reflected in the next proposal.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The mental state assessment unit can analyze the user's voice tone or facial expression to determine their emotional state in real time. For example, the generation AI analyzes the user's voice tone to determine their emotional state in real time. For example, it analyzes the pitch, strength, and rhythm of the voice to determine whether the user is feeling stressed. The mental state assessment unit also uses the generation AI to capture the user's facial expression with a camera and analyze it. For example, it analyzes facial features such as smiling and furrowed brows to determine the user's emotional state. The mental state assessment unit also integrates voice and facial expression data, and the generation AI comprehensively assesses the emotional state. For example, if the voice tone is calm but the facial expression is tense, it suggests the possibility of hidden stress. This allows the user's emotional state to be determined in real time.

[0082] The mental state assessment unit can learn from the user's past mental state data and predict long-term mental health trends. For example, the generation AI collects the user's past mental state data and analyzes long-term trends. For example, it predicts seasonal fluctuations in mental health based on data from the past year. The mental state assessment unit also learns from the user's diary and self-reported data, and the generation AI predicts long-term mental health trends. For example, it analyzes the impact of specific events or situations on mental health. The mental state assessment unit also predicts long-term mental health trends based on data from sensors (heart rate, sleep patterns, etc.). For example, continued lack of sleep tends to increase stress. This makes it possible to predict long-term mental health trends.

[0083] The mental state determination unit can use the emotion estimation function to estimate emotions from a user's diary or SNS posts and determine their mental state. For example, the generation AI analyzes the user's diary and uses the emotion estimation function to determine their emotional state. For example, a diary entry with many positive expressions indicates a good mental state. The mental state determination unit also collects the user's SNS posts, and the generation AI performs emotion estimation. For example, it analyzes keywords and context in the post content to determine the user's emotional state. The mental state determination unit also integrates diary and SNS post data, and the generation AI comprehensively determines the emotional state. For example, if the diary entry is positive but the SNS entry is full of negative posts, this indicates an unstable mental state. This makes it possible to estimate emotions from the user's diary and SNS posts and determine their mental state.

[0084] The mental state assessment unit can analyze the user's exercise data and determine the correlation with their mental state. For example, the generation AI analyzes the user's step count data and determines the correlation with their mental state. For example, days with a low number of steps may indicate high stress. The mental state assessment unit also collects the user's exercise intensity data, and the generation AI analyzes the correlation with their mental state. For example, people tend to feel better on days with high exercise intensity. The mental state assessment unit also integrates the step count and exercise intensity data, and the generation AI comprehensively assesses their mental state. For example, an increase in the amount of exercise shows a tendency for their mental state to improve. This allows the user's exercise data to be analyzed and the correlation with their mental state to be determined.

[0085] The mental state assessment unit can collect feedback from family or friends and make a comprehensive assessment to determine the user's mental state. For example, the generation AI collects feedback from family and friends and makes a comprehensive assessment of the user's mental state. For example, the mental state is assessed based on comments and observations from family members. The mental state assessment unit also conducts a survey of family and friends, and the generation AI analyzes the results. For example, feedback on the user's behavior and attitude is collected and the mental state is assessed. The mental state assessment unit also integrates feedback data from family and friends, and the generation AI makes a comprehensive assessment of the mental state. For example, the generation AI analyzes fluctuations in the mental state based on multiple pieces of feedback. This allows feedback from family and friends to be collected and the mental state to be assessed comprehensively.

[0086] The mental state assessment unit can use the emotion estimation function to estimate emotions from the content the user watches and determine their mental state. For example, the generation AI analyzes the content of the movie the user watches and uses the emotion estimation function to determine their emotional state. For example, after watching a sad movie, there is a possibility that their mental state will decline. The mental state assessment unit also analyzes the genre and rhythm of the music the user listens to, and the generation AI estimates their emotional state. For example, listening to up-tempo music tends to lift one's mood. The mental state assessment unit also integrates movie and music viewing data, and the generation AI comprehensively assesses their emotional state. For example, it analyzes fluctuations in their mental state based on their viewing history. This allows the generation AI to estimate emotions from the content the user watches and determine their mental state.

[0087] The action suggestion unit can learn the user's past behavioral history and suggest the most effective actions or meals. For example, the generation AI analyzes the user's past behavioral history and suggests the most effective actions. For example, it may re-suggest actions that have had a high relaxing effect in the past. The action suggestion unit also learns the user's dietary history and the generation AI suggests the most effective meals. For example, it may re-suggest meal menus that have had a good nutritional balance in the past. The action suggestion unit also integrates the behavioral history and dietary history and the generation AI suggests the most effective actions or meals overall. For example, it may show that a combination of a specific action and meal is effective. This allows the generation AI to learn the user's past behavioral history and suggest the most effective actions and meals.

[0088] The action suggestion unit can update action or meal suggestions in real time according to the user's current mental state. For example, the generation AI analyzes the user's current mental state and updates action suggestions in real time. For example, if stress is high, it suggests actions to relax. The action suggestion unit also updates meal suggestions in real time according to the user's mental state. For example, if the user is feeling depressed, it suggests nutritious meals. The action suggestion unit also dynamically adjusts action and meal suggestions according to fluctuations in the user's mental state. For example, if stress decreases, it suggests new actions or meals. This allows action and meal suggestions to be updated in real time according to the user's current mental state.

[0089] The action suggestion unit can use the emotion estimation function to suggest relaxation methods or meal menus based on the user's emotions. For example, the generation AI uses the emotion estimation function to suggest relaxation methods based on the user's emotions. For example, if stress is high, it may suggest meditation or deep breathing. The action suggestion unit also analyzes the user's emotional state, and the generation AI suggests meal menus based on the emotions. For example, if the user is feeling depressed, it may suggest ingredients that increase serotonin. The action suggestion unit also uses the emotion estimation data to suggest comprehensive relaxation methods and meal menus. For example, it may suggest combining relaxing herbal tea with meditation. This makes it possible to suggest relaxation methods and meal menus based on the user's emotions.

[0090] The action suggestion unit can suggest nearby relaxation spots or healthy restaurants based on the user's geographical location information. For example, the generation AI analyzes the user's geographical location information and suggests nearby relaxation spots. For example, it can suggest parks or nature walking courses. The action suggestion unit also suggests healthy restaurants based on the user's location information. For example, it can suggest restaurants that use organic ingredients. The action suggestion unit also integrates the geographical location information and mental state, and the generation AI comprehensively suggests relaxation spots and healthy restaurants. For example, it can suggest cafes that have a relaxing effect. This makes it possible to suggest nearby relaxation spots and healthy restaurants based on the user's geographical location information.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The mental state assessment unit uses the generation AI to assess the user's mental state. The generation AI analyzes the user's mental state based on information provided by the user. For example, it receives input such as the user's self-report, diary entries, and data from sensors (such as heart rate and sleep patterns) to assess the user's mental state. Step 2: The behavior suggestion unit suggests actions and meals based on the mental state determined by the mental state determination unit. For example, if the generation AI determines that stress is high, it will suggest yoga or meditation to help relax. Also, if it determines that the nutritional balance is unbalanced, it will suggest a balanced diet. Step 3: The action plan generator generates a specific action plan based on the activities and meals suggested by the action suggester. For example, the generator might suggest specific actions such as incorporating yoga into a daily schedule or adding healthy ingredients to a weekly shopping list. Step 4: The feedback analysis unit provides feedback on the results of the action plan. The generation AI reports the actions and dietary details taken by the user and analyzes the results. This allows the mental health improvement system to continuously improve the user's mental health.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0106] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0107] 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.

[0108] 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.

[0109] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0110] 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.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0121] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0122] 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.

[0123] 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.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] 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.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0137] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] 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.

[0139] 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.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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]

[0160] 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 mental state determination unit that determines a mental state using a generation AI; an action suggestion unit that suggests an action or a meal based on the mental state determined by the mental state determination unit; an action plan generation unit that generates a specific action plan based on the action or the meal suggested by the action suggestion unit; a feedback analysis unit that feeds back the execution results of the action plan; A system characterized by:

2. The mental state determination unit Analyzes the user's tone of voice or facial expressions to determine their emotional state in real time 2. The system of claim 1.

3. The mental state determination unit Learn from users' past mental state data and predict long-term mental health trends 2. The system of claim 1.

4. The mental state determination unit Estimating emotions from the user's diary or SNS posts and determining the mental state 2. The system of claim 1.

5. The mental state determination unit Analyzing the user's exercise data and determining the correlation with the mental state 2. The system of claim 1.

6. The mental state determination unit Collecting and comprehensively evaluating the feedback from family or friends to determine the user's mental state 2. The system of claim 1.

7. The mental state determination unit Estimating emotions from content viewed by a user and determining the mental state 2. The system of claim 1.

8. The action suggestion unit Learn the user's past behavioral history and suggest the most effective behavior or meal.

2. The system of claim 1.

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