System

Abstract

A system according to an embodiment aims to provide a personalized treatment plan based on the user's hair condition and lifestyle.SOLUTION: A system includes a data collection part, a data analysis part, and a treatment plan creation part. The data collection unit collects data on a user's hair condition and lifestyle. The data analysis unit analyzes the data collected by the data collection unit. The treatment plan creation unit creates an individual treatment plan on the basis of the result analyzed by the data analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques do not adequately provide optimal treatment plans based on individual users' hair conditions and lifestyle habits, and there is room for improvement.

[0005] The system according to the embodiment aims to provide a personalized treatment plan based on the user's hair condition and lifestyle habits. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, a data analysis unit, and a treatment plan creation unit. The data collection unit collects data on the user's hair condition and lifestyle habits. The data analysis unit analyzes the data collected by the data collection unit. The treatment plan creation unit creates an individual treatment plan based on the results of the analysis by the data analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide a personalized treatment plan based on the user's hair condition and lifestyle habits. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The medical treatment support platform according to an embodiment of the present invention is a system that analyzes a user's hair condition and lifestyle habits and proposes optimal treatment and care methods, thereby enabling the medical treatment support platform to effectively improve the user's hair health and enhance their quality of life.

[0029] A treatment support platform according to an embodiment includes a data collection unit, a data analysis unit, and a treatment plan creation unit. The data collection unit collects data on a user's hair condition and lifestyle habits. For example, the user inputs information such as the amount of hair loss, hair quality, diet, and stress level. The data collection unit can also collect biometric information from a smart device. For example, it collects heart rate and sleep pattern data from a smartwatch or fitness tracker. The data analysis unit analyzes the collected data. For example, a generation AI analyzes the data using statistical analysis or machine learning algorithms and proposes optimal treatment and care methods based on the user's hair condition and lifestyle habits. The treatment plan creation unit creates an individual treatment plan based on the analysis results. For example, the generation AI creates a treatment plan customized for each user and provides specific treatment methods, care methods, and lifestyle improvement suggestions. This allows the treatment support platform according to an embodiment to provide an individual treatment plan based on the user's hair condition and lifestyle habits.

[0030] The data collection unit can collect biometric information from the smart device in addition to data on the user's hair condition and lifestyle habits. For example, the data collection unit can collect heart rate and sleep pattern data from a smartwatch or fitness tracker in addition to hair condition and lifestyle data provided by the user. This allows for a detailed understanding of the user's stress level and sleep quality, and identifies factors that affect the health of the user's hair. This allows for more detailed data analysis by collecting biometric information from the smart device.

[0031] The data collection unit can automatically evaluate the user's hair condition using image analysis technology. For example, the data collection unit involves the user taking a photo of their hair using a smartphone camera and uploading the image to the platform. The generative AI then uses image analysis technology to automatically evaluate hair density, thickness, and the amount of hair loss, collecting detailed data. This allows the image analysis technology to automatically evaluate hair condition.

[0032] The data collection unit adds family history and genetic information to the user's lifestyle data, allowing for analysis that takes genetic factors into account. For example, the data collection unit allows the user to input family history and genetic information into the platform, and the generation AI uses that data for analysis. For example, if there is a family history of hair loss, the system will suggest a treatment method that takes genetic factors into account. This makes it possible to take genetic factors into account in the analysis by taking family history and genetic information into account.

[0033] The data collection unit collects ingredient information from hair care products that users use on a daily basis, allowing the impact of the ingredients to be reflected in the analysis. For example, the data collection unit inputs ingredient information from shampoos and conditioners that users use on a daily basis into the platform, and the generation AI uses that data for analysis. For example, it evaluates the impact of specific ingredients on hair health. In this way, by collecting ingredient information from hair care products, the impact of the ingredients can be reflected in the analysis.

[0034] The data analysis unit uses the user's past treatment history and results in the analysis, allowing it to propose more accurate treatment methods. For example, the data analysis unit stores the user's past treatment history and results in a database, and the generating AI uses that data in the analysis. For example, it prioritizes the proposal of treatment methods that have been effective in the past. In this way, by using the past treatment history and results in the analysis, it is possible to propose more accurate treatment methods.

[0035] The data analysis unit can refer to the latest medical research and paper databases and reflect the latest treatment methods in its proposals. For example, the generative AI can automatically refer to the latest medical research and paper databases and propose treatment methods based on that information. For example, it can reflect information on new treatments and drugs. In this way, by referring to the latest medical research and paper databases, the latest treatment methods can be reflected in its proposals.

[0036] The data analysis unit uses the user's occupation and daily activity level in its analysis to suggest a treatment method suited to their lifestyle. For example, the data analysis unit registers the user's occupation and daily activity level in a database, and the generation AI suggests a treatment method based on that data. For example, for a user who does a lot of desk work, a treatment method that incorporates exercise is suggested. In this way, by taking occupation and daily activity level into consideration, a treatment method suited to a lifestyle can be suggested.

[0037] The data analysis unit uses regional climate and environmental factors in its analysis, and can propose treatment methods that take into account regionally specific influences. For example, the data analysis unit registers the climate and environmental factors of the user's residential area in a database, and the generation AI proposes treatment methods based on that data. For example, for a user living in a dry area, it proposes a care method with a high moisturizing effect. In this way, by taking into account regional climate and environmental factors, it is possible to propose treatment methods that reflect regionally specific influences.

[0038] The treatment plan creation unit can provide a plan that reflects the user's dietary and exercise habits in detail and aims to improve overall health. For example, the treatment plan creation unit records the user's dietary and exercise habits in detail, and the generation AI creates a treatment plan based on that data. For example, if a specific nutrient is lacking, it will suggest meals that contain that nutrient. This makes it possible to provide a plan that reflects the user's dietary and exercise habits in detail and aims to improve overall health.

[0039] The treatment plan creation unit can consider the user's lifestyle rhythm and sleep patterns and propose care methods at the optimal timing. For example, the treatment plan creation unit records the user's lifestyle rhythm and sleep patterns, and the generation AI creates a treatment plan based on that data. For example, for a user who is a night owl, it proposes care methods to be performed at night. This makes it possible to propose care methods at the optimal timing by considering the user's lifestyle rhythm and sleep patterns.

[0040] The treatment plan creation unit can provide a plan that makes it easy to receive support by taking into account the user's social support network (family and friends). For example, the treatment plan creation unit registers the user's social support network (family and friends) in a database, and the generation AI creates a treatment plan based on that data. For example, it can suggest a care method to be carried out together with family. In this way, by taking into account the social support network, it is possible to provide a plan that makes it easy to receive support.

[0041] The treatment plan creation unit can monitor the user's biometric information (e.g., blood pressure and weight) and evaluate the overall health condition. For example, the treatment plan creation unit registers the user's biometric information (e.g., blood pressure and weight) in a database, and the generation AI monitors the treatment effect based on that data. For example, it evaluates the impact of blood pressure fluctuations on hair health. In this way, the overall health condition can be evaluated by monitoring the biometric information.

[0042] The treatment plan creation unit can monitor the user's psychological state (e.g., stress level and happiness level) and evaluate the psychological effects. For example, the treatment plan creation unit registers the user's psychological state (e.g., stress level and happiness level) in a database, and the generation AI monitors the treatment effects based on that data. For example, it evaluates the impact that fluctuations in stress level have on hair health. In this way, by monitoring the psychological state, the psychological effects can also be evaluated.

[0043] The treatment plan creation unit can monitor changes in the user's workplace and home environment and provide feedback that takes environmental factors into account. For example, the treatment plan creation unit registers changes in the user's workplace and home environment in a database, and the generation AI monitors the treatment effect based on that data. For example, it evaluates the impact of workplace stress on hair health. This allows the system to provide feedback that takes environmental factors into account by monitoring changes in the workplace and home environment.

[0044] The treatment plan creation unit can monitor the user's hobbies and activity level and provide feedback to improve quality of life. For example, the treatment plan creation unit registers the user's hobbies and activity level in a database, and the generation AI monitors the treatment effect based on that data. For example, it evaluates the impact of hobbies and activities on hair health. In this way, by monitoring hobbies and activity level, feedback to improve quality of life can be provided.

[0045] The treatment plan creation unit can analyze posts within the community and extract and share common concerns and success stories among users. For example, the treatment plan creation unit uses a generation AI to analyze posts within the community and extract common concerns among users. For example, it can identify common concerns about thinning hair and hair loss and share that information. In this way, by analyzing posts within the community, common concerns and success stories can be extracted and shared.

[0046] The treatment plan creation unit can analyze conversations within the community and provide advice to promote mutual support between users. For example, the generation AI analyzes conversations within the community and provides advice to promote mutual support between users. For example, it can suggest encouraging messages and methods of support. In this way, by analyzing conversations within the community, advice to promote mutual support between users can be provided.

[0047] The treatment plan creation unit can analyze posts within the community and make suggestions to promote interaction between users from different regions and cultural spheres. For example, the generative AI analyzes posts within the community and makes suggestions to promote interaction between users from different regions and cultural spheres. For example, success stories from different cultural spheres are shared. In this way, by analyzing posts within the community, suggestions can be made to promote interaction between users from different regions and cultural spheres.

[0048] The treatment plan creation unit can analyze conversations within the community and suggest topics to stimulate discussions on specific themes. For example, the generative AI analyzes conversations within the community and suggests topics to stimulate discussions on specific themes. For example, it can suggest the latest treatment methods for thinning hair and hair loss. In this way, by analyzing conversations within the community, it is possible to suggest topics to stimulate discussions on specific themes.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The treatment support platform can also provide treatment plans that take into account the user's hobbies and interests. For example, if the user likes music, a treatment plan incorporating music therapy can be proposed. If the user likes outdoor activities, relaxation methods in nature can be suggested. Furthermore, if the user is interested in art, a treatment plan incorporating art therapy can be provided. In this way, the effectiveness of treatment can be improved by providing a treatment plan that takes into account the user's hobbies and interests.

[0051] The treatment support platform can also monitor the user's diet and nutritional status and provide a treatment plan that takes nutritional balance into consideration. For example, if the user is deficient in a particular nutrient, it can propose a meal plan to supplement that nutrient. Also, if the user is allergic to a particular ingredient, it can provide a meal plan that avoids that ingredient. Furthermore, if the user wishes to go on a diet, it can also propose a healthy diet plan. In this way, by providing a treatment plan that takes diet and nutritional status into consideration, it is possible to comprehensively support the user's health.

[0052] The treatment support platform can also monitor the user's lifestyle and sleep patterns to suggest optimally timed care methods. For example, if the user is a nocturnal person, it can suggest care methods to be performed at night. If the user has an irregular lifestyle, it can also provide care methods that are tailored to that rhythm. Furthermore, if the user is suffering from insufficient sleep, it can provide advice on how to improve the quality of their sleep. This allows for comprehensive support of the user's health by providing care methods that take into account the user's lifestyle and sleep patterns.

[0053] The treatment support platform can also analyze the user's occupation and daily activity level to suggest treatment methods suited to their lifestyle. For example, the user's occupation and daily activity level can be registered in a database, and the generation AI can suggest treatment methods based on that data. For example, for users who do a lot of desk work, treatment methods incorporating exercise can be suggested. Also, if the user does physical labor, care methods suited to that type of work can be provided. Furthermore, if the user works remotely, treatment methods suited to that environment can be suggested. In this way, by taking into account the user's occupation and daily activity level, treatment methods suited to their lifestyle can be suggested.

[0054] The treatment support platform can also analyze the climate and environmental factors of each user's region to suggest treatment methods that take into account regional influences. For example, the climate and environmental factors of the user's region can be registered in a database, and the generation AI can suggest treatment methods based on that data. For example, a care method with a high moisturizing effect can be suggested to a user living in a dry region. A treatment method that takes into account humidity control can also be offered to a user living in a humid region. Furthermore, it is possible to suggest a care method that takes into account environmental pollution control for users living in urban areas. In this way, by taking into account the climate and environmental factors of each region, treatment methods that reflect regional influences can be suggested.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The data collection unit collects data on the user's hair condition and lifestyle. For example, the user inputs information such as the amount and quality of hair loss, diet, and stress level. The data collection unit can also collect biometric information from smart devices. For example, it can collect heart rate and sleep pattern data from smartwatches and fitness trackers. Step 2: The data analysis unit analyzes the collected data. For example, the generative AI analyzes the data using statistical analysis and machine learning algorithms to suggest optimal treatment and care methods based on the user's hair condition and lifestyle habits. Step 3: The treatment plan creation unit creates an individual treatment plan based on the analysis results. For example, the generation AI creates a treatment plan customized for each user, providing specific treatment methods, care methods, and suggestions for improving lifestyle habits.

[0057] (Example 2) The medical treatment support platform according to an embodiment of the present invention is a system that analyzes a user's hair condition and lifestyle habits and proposes optimal treatment and care methods, thereby enabling the medical treatment support platform to effectively improve the user's hair health and enhance their quality of life.

[0058] A treatment support platform according to an embodiment includes a data collection unit, a data analysis unit, and a treatment plan creation unit. The data collection unit collects data on a user's hair condition and lifestyle habits. For example, the user inputs information such as the amount of hair loss, hair quality, diet, and stress level. The data collection unit can also collect biometric information from a smart device. For example, it collects heart rate and sleep pattern data from a smartwatch or fitness tracker. The data analysis unit analyzes the collected data. For example, a generation AI analyzes the data using statistical analysis or machine learning algorithms and proposes optimal treatment and care methods based on the user's hair condition and lifestyle habits. The treatment plan creation unit creates an individual treatment plan based on the analysis results. For example, the generation AI creates a treatment plan customized for each user and provides specific treatment methods, care methods, and lifestyle improvement suggestions. This allows the treatment support platform according to an embodiment to provide an individual treatment plan based on the user's hair condition and lifestyle habits.

[0059] The data collection unit can collect biometric information from the smart device in addition to data on the user's hair condition and lifestyle habits. For example, the data collection unit can collect heart rate and sleep pattern data from a smartwatch or fitness tracker in addition to hair condition and lifestyle data provided by the user. This allows for a detailed understanding of the user's stress level and sleep quality, and identifies factors that affect the health of the user's hair. This allows for more detailed data analysis by collecting biometric information from the smart device.

[0060] The data collection unit can automatically evaluate the user's hair condition using image analysis technology. For example, the data collection unit involves the user taking a photo of their hair using a smartphone camera and uploading the image to the platform. The generative AI then uses image analysis technology to automatically evaluate hair density, thickness, and the amount of hair loss, collecting detailed data. This allows the image analysis technology to automatically evaluate hair condition.

[0061] The data collection unit can analyze the user's emotional state and collect data that takes into account fluctuations in stress level and motivation. For example, when the user inputs data, the data collection unit uses a camera or microphone to analyze facial expressions and voice tone to estimate the emotional state. For example, if the user is under high stress, advice on how to reduce stress is provided. In this way, analyzing the emotional state makes it possible to collect data that takes into account fluctuations in stress level and motivation.

[0062] The data collection unit adds family history and genetic information to the user's lifestyle data, allowing for analysis that takes genetic factors into account. For example, the data collection unit allows the user to input family history and genetic information into the platform, and the generation AI uses that data for analysis. For example, if there is a family history of hair loss, the system will suggest a treatment method that takes genetic factors into account. This makes it possible to take genetic factors into account in the analysis by taking family history and genetic information into account.

[0063] The data collection unit collects ingredient information from hair care products that users use on a daily basis, allowing the impact of the ingredients to be reflected in the analysis. For example, the data collection unit inputs ingredient information from shampoos and conditioners that users use on a daily basis into the platform, and the generation AI uses that data for analysis. For example, it evaluates the impact of specific ingredients on hair health. In this way, by collecting ingredient information from hair care products, the impact of the ingredients can be reflected in the analysis.

[0064] The data collection unit can analyze the user's emotions in real time and provide an interface that elicits positive emotions. For example, when a user inputs data, the data collection unit uses an emotion estimation function to analyze emotions in real time and provide an interface that elicits positive emotions. For example, encouraging messages or positive feedback are displayed. This makes it possible to provide an interface that analyzes emotions in real time and elicits positive emotions.

[0065] The data analysis unit uses the user's past treatment history and results in the analysis, allowing it to propose more accurate treatment methods. For example, the data analysis unit stores the user's past treatment history and results in a database, and the generating AI uses that data in the analysis. For example, it prioritizes the proposal of treatment methods that have been effective in the past. In this way, by using the past treatment history and results in the analysis, it is possible to propose more accurate treatment methods.

[0066] The data analysis unit can refer to the latest medical research and paper databases and reflect the latest treatment methods in its proposals. For example, the generative AI can automatically refer to the latest medical research and paper databases and propose treatment methods based on that information. For example, it can reflect information on new treatments and drugs. In this way, by referring to the latest medical research and paper databases, the latest treatment methods can be reflected in its proposals.

[0067] The data analysis unit can propose a treatment method that takes into account the user's emotional state, thereby reducing stress and improving motivation. The data analysis unit, for example, uses an emotion estimation function to analyze the user's emotional state and proposes a treatment method based on the results. For example, if stress is high, a treatment method for reducing stress is proposed. In this way, by taking the emotional state into consideration, a treatment method can be proposed that reduces stress and improves motivation.

[0068] The data analysis unit uses the user's occupation and daily activity level in its analysis to suggest a treatment method suited to their lifestyle. For example, the data analysis unit registers the user's occupation and daily activity level in a database, and the generation AI suggests a treatment method based on that data. For example, for a user who does a lot of desk work, a treatment method that incorporates exercise is suggested. In this way, by taking occupation and daily activity level into consideration, a treatment method suited to a lifestyle can be suggested.

[0069] The data analysis unit uses regional climate and environmental factors in its analysis, and can propose treatment methods that take into account regionally specific influences. For example, the data analysis unit registers the climate and environmental factors of the user's residential area in a database, and the generation AI proposes treatment methods based on that data. For example, for a user living in a dry area, it proposes a care method with a high moisturizing effect. In this way, by taking into account regional climate and environmental factors, it is possible to propose treatment methods that reflect regionally specific influences.

[0070] The data analysis unit can identify the treatment method that evokes the most positive emotions in the user and preferentially suggest that method. The data analysis unit, for example, uses an emotion estimation function to analyze the emotions the user has toward the treatment method in real time and identify a treatment method that elicits positive emotions. For example, the data analysis unit preferentially suggests a treatment method that allows the user to relax. This allows the effectiveness of the treatment to be improved by identifying the treatment method that evokes the most positive emotions in the user and preferentially suggesting that method.

[0071] The treatment plan creation unit can provide a plan that reflects the user's dietary and exercise habits in detail and aims to improve overall health. For example, the treatment plan creation unit records the user's dietary and exercise habits in detail, and the generation AI creates a treatment plan based on that data. For example, if a specific nutrient is lacking, it will suggest meals that contain that nutrient. This makes it possible to provide a plan that reflects the user's dietary and exercise habits in detail and aims to improve overall health.

[0072] The treatment plan creation unit can consider the user's lifestyle rhythm and sleep patterns and propose care methods at the optimal timing. For example, the treatment plan creation unit records the user's lifestyle rhythm and sleep patterns, and the generation AI creates a treatment plan based on that data. For example, for a user who is a night owl, it proposes care methods to be performed at night. This makes it possible to propose care methods at the optimal timing by considering the user's lifestyle rhythm and sleep patterns.

[0073] The treatment plan creation unit creates a treatment plan according to the user's emotional state and can provide a plan that makes it easy to maintain motivation. The treatment plan creation unit, for example, uses an emotion estimation function to analyze the user's emotional state and creates a treatment plan based on the results. For example, if stress is high, the unit suggests relaxation methods to reduce stress. In this way, by creating a treatment plan according to the user's emotional state, it is possible to provide a plan that makes it easy to maintain motivation.

[0074] The treatment plan creation unit can provide a plan that makes it easy to receive support by taking into account the user's social support network (family and friends). For example, the treatment plan creation unit registers the user's social support network (family and friends) in a database, and the generation AI creates a treatment plan based on that data. For example, it can suggest a care method to be carried out together with family. In this way, by taking into account the social support network, it is possible to provide a plan that makes it easy to receive support.

[0075] The treatment plan creation unit can identify the treatment plan that evokes the most positive emotions in the user and provide that plan preferentially. The treatment plan creation unit, for example, uses an emotion estimation function to analyze the emotions the user has toward the treatment plan in real time and identify a treatment plan that elicits positive emotions. For example, it provides preferentially a plan that allows the user to relax. This allows the treatment effect to be improved by identifying the treatment plan that evokes the most positive emotions in the user and providing that plan preferentially.

[0076] The treatment plan creation unit can monitor the user's biometric information (e.g., blood pressure and weight) and evaluate the overall health condition. For example, the treatment plan creation unit registers the user's biometric information (e.g., blood pressure and weight) in a database, and the generation AI monitors the treatment effect based on that data. For example, it evaluates the impact of blood pressure fluctuations on hair health. In this way, the overall health condition can be evaluated by monitoring the biometric information.

[0077] The treatment plan creation unit can monitor the user's psychological state (e.g., stress level and happiness level) and evaluate the psychological effects. For example, the treatment plan creation unit registers the user's psychological state (e.g., stress level and happiness level) in a database, and the generation AI monitors the treatment effects based on that data. For example, it evaluates the impact that fluctuations in stress level have on hair health. In this way, by monitoring the psychological state, the psychological effects can also be evaluated.

[0078] The treatment plan creation unit can monitor the user's emotional state and provide feedback according to emotional fluctuations. The treatment plan creation unit, for example, uses an emotion estimation function to monitor the user's emotional state in real time and provides feedback based on the results. For example, if stress is high, advice on how to reduce stress is provided. In this way, by monitoring the emotional state, feedback according to emotional fluctuations can be provided.

[0079] The treatment plan creation unit can monitor changes in the user's workplace and home environment and provide feedback that takes environmental factors into account. For example, the treatment plan creation unit registers changes in the user's workplace and home environment in a database, and the generation AI monitors the treatment effect based on that data. For example, it evaluates the impact of workplace stress on hair health. This allows the system to provide feedback that takes environmental factors into account by monitoring changes in the workplace and home environment.

[0080] The treatment plan creation unit can monitor the user's hobbies and activity level and provide feedback to improve quality of life. For example, the treatment plan creation unit registers the user's hobbies and activity level in a database, and the generation AI monitors the treatment effect based on that data. For example, it evaluates the impact of hobbies and activities on hair health. In this way, by monitoring hobbies and activity level, feedback to improve quality of life can be provided.

[0081] The treatment plan creation unit can identify feedback that evokes the most positive emotions in the user and provide that feedback preferentially. The treatment plan creation unit, for example, uses an emotion estimation function to analyze the emotions the user feels in response to feedback in real time and identify feedback that elicits positive emotions. For example, it provides feedback that helps the user relax preferentially. This allows the effectiveness of treatment to be improved by identifying feedback that evokes the most positive emotions in the user and providing that feedback preferentially.

[0082] The treatment plan creation unit can analyze posts within the community and extract and share common concerns and success stories among users. For example, the treatment plan creation unit uses a generation AI to analyze posts within the community and extract common concerns among users. For example, it can identify common concerns about thinning hair and hair loss and share that information. In this way, by analyzing posts within the community, common concerns and success stories can be extracted and shared.

[0083] The treatment plan creation unit can analyze conversations within the community and provide advice to promote mutual support between users. For example, the generation AI analyzes conversations within the community and provides advice to promote mutual support between users. For example, it can suggest encouraging messages and methods of support. In this way, by analyzing conversations within the community, advice to promote mutual support between users can be provided.

[0084] The treatment plan creation unit can analyze the emotional state of users in the community and recommend posts that elicit positive emotions. For example, the treatment plan creation unit uses an emotion estimation function to analyze the emotional state of users in the community in real time and recommend posts that elicit positive emotions. For example, encouraging messages and sharing success stories. In this way, by analyzing the emotional state of users in the community, posts that elicit positive emotions can be recommended.

[0085] The treatment plan creation unit can analyze posts within the community and make suggestions to promote interaction between users from different regions and cultural spheres. For example, the generative AI analyzes posts within the community and makes suggestions to promote interaction between users from different regions and cultural spheres. For example, success stories from different cultural spheres are shared. In this way, by analyzing posts within the community, suggestions can be made to promote interaction between users from different regions and cultural spheres.

[0086] The treatment plan creation unit can analyze conversations within the community and suggest topics to stimulate discussions on specific themes. For example, the generative AI analyzes conversations within the community and suggests topics to stimulate discussions on specific themes. For example, it can suggest the latest treatment methods for thinning hair and hair loss. In this way, by analyzing conversations within the community, it is possible to suggest topics to stimulate discussions on specific themes.

[0087] The treatment plan creation unit can identify topics that users in the community feel the most positive about and provide those topics preferentially. For example, the treatment plan creation unit can use an emotion estimation function to analyze the emotions users in the community feel toward topics in real time and identify topics that elicit positive emotions. For example, it can provide topics that help users relax preferentially. This makes it possible to revitalize the community by identifying topics that users in the community feel the most positive about and providing those topics preferentially.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The treatment support platform can also provide treatment plans that take into account the user's hobbies and interests. For example, if the user likes music, a treatment plan incorporating music therapy can be proposed. If the user likes outdoor activities, relaxation methods in nature can be suggested. Furthermore, if the user is interested in art, a treatment plan incorporating art therapy can be provided. In this way, the effectiveness of treatment can be improved by providing a treatment plan that takes into account the user's hobbies and interests.

[0090] The therapeutic support platform can also monitor the user's emotional state in real time and provide feedback according to emotional fluctuations. For example, if the user is feeling stressed, advice on how to reduce stress can be provided. Also, if the user is feeling positive, feedback on how to maintain those emotions can be provided. Furthermore, if the user is losing motivation, an encouraging message can be provided to improve motivation. In this way, the therapeutic support platform can support the user's emotional state by providing feedback according to emotional fluctuations.

[0091] The treatment support platform can also monitor the user's diet and nutritional status and provide a treatment plan that takes nutritional balance into consideration. For example, if the user is deficient in a particular nutrient, it can propose a meal plan to supplement that nutrient. Also, if the user is allergic to a particular ingredient, it can provide a meal plan that avoids that ingredient. Furthermore, if the user wishes to go on a diet, it can also propose a healthy diet plan. In this way, by providing a treatment plan that takes diet and nutritional status into consideration, it is possible to comprehensively support the user's health.

[0092] The therapeutic support platform can further analyze the user's emotional state and provide an interface for eliciting positive emotions. For example, when a user enters data, an emotion estimation function can be used to analyze emotions in real time and provide an interface for eliciting positive emotions. For example, encouraging messages or positive feedback can be displayed. If the user is feeling stressed, relaxing music or videos can be provided. Furthermore, if the user is losing motivation, an interface for improving motivation can be provided. In this way, the user's emotional state can be supported by providing an interface for analyzing emotions in real time and eliciting positive emotions.

[0093] The treatment support platform can also monitor the user's lifestyle and sleep patterns to suggest optimally timed care methods. For example, if the user is a nocturnal person, it can suggest care methods to be performed at night. If the user has an irregular lifestyle, it can also provide care methods that are tailored to that rhythm. Furthermore, if the user is suffering from insufficient sleep, it can provide advice on how to improve the quality of their sleep. This allows for comprehensive support of the user's health by providing care methods that take into account the user's lifestyle and sleep patterns.

[0094] The treatment support platform can further propose treatment methods that take into account the user's emotional state, thereby reducing stress and improving motivation. For example, it can use the emotion estimation function to analyze the user's emotional state and propose treatment methods based on the results. For example, if the user is experiencing high stress, it can propose treatment methods to reduce stress. Also, if the user is experiencing positive emotions, it can provide treatment methods to help maintain those emotions. Furthermore, if the user has lost motivation, it can also propose treatment methods to improve motivation. In this way, it is possible to propose treatment methods that reduce stress and improve motivation by taking the user's emotional state into consideration.

[0095] The treatment support platform can also analyze the user's occupation and daily activity level to suggest treatment methods suited to their lifestyle. For example, the user's occupation and daily activity level can be registered in a database, and the generation AI can suggest treatment methods based on that data. For example, for users who do a lot of desk work, treatment methods incorporating exercise can be suggested. Also, if the user does physical labor, care methods suited to that type of work can be provided. Furthermore, if the user works remotely, treatment methods suited to that environment can be suggested. In this way, by taking into account the user's occupation and daily activity level, treatment methods suited to their lifestyle can be suggested.

[0096] The therapeutic support platform can further monitor the user's emotional state and provide feedback according to emotional fluctuations. For example, it can use an emotion estimation function to monitor the user's emotional state in real time and provide feedback based on the results. For example, if the user is under high stress, it can provide advice on how to reduce stress. Also, if the user is feeling positive, it can provide feedback to help maintain that emotion. Furthermore, if the user is losing motivation, it can provide an encouraging message to improve motivation. In this way, by monitoring the emotional state, it is possible to provide feedback according to emotional fluctuations.

[0097] The treatment support platform can also analyze the climate and environmental factors of each user's region to suggest treatment methods that take into account regional influences. For example, the climate and environmental factors of the user's region can be registered in a database, and the generation AI can suggest treatment methods based on that data. For example, a care method with a high moisturizing effect can be suggested to a user living in a dry region. A treatment method that takes into account humidity control can also be offered to a user living in a humid region. Furthermore, it is possible to suggest a care method that takes into account environmental pollution control for users living in urban areas. In this way, by taking into account the climate and environmental factors of each region, treatment methods that reflect regional influences can be suggested.

[0098] The treatment support platform can further analyze the user's emotional state and provide a treatment plan to elicit positive emotions. For example, the emotional estimation function can be used to analyze the user's emotional state in real time and provide a treatment plan to elicit positive emotions. For example, treatment methods that allow the user to relax can be preferentially suggested. Also, if the user is feeling stressed, treatment methods for stress reduction can be provided. Furthermore, if the user has lost motivation, it is also possible to suggest a treatment plan to improve motivation. This makes it possible to identify the treatment plan that will elicit the most positive emotions in the user and provide that plan preferentially, thereby improving the effectiveness of treatment.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The data collection unit collects data on the user's hair condition and lifestyle. For example, the user inputs information such as the amount and quality of hair loss, diet, and stress level. The data collection unit can also collect biometric information from smart devices. For example, it can collect heart rate and sleep pattern data from smartwatches and fitness trackers. Step 2: The data analysis unit analyzes the collected data. For example, the generative AI analyzes the data using statistical analysis and machine learning algorithms to suggest optimal treatment and care methods based on the user's hair condition and lifestyle habits. Step 3: The treatment plan creation unit creates an individual treatment plan based on the analysis results. For example, the generation AI creates a treatment plan customized for each user, providing specific treatment methods, care methods, and suggestions for improving lifestyle habits.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0114] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0115] 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.

[0116] 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.

[0117] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0118] 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.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0127] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0129] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] The data processing system 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.

[0134] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0145] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0146] 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.

[0147] 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.

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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]

[0168] 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 data collection unit that collects data on the user's hair condition and lifestyle habits; a data analysis unit that analyzes the data collected by the data collection unit; a treatment plan creation unit that creates an individual treatment plan based on the results of analysis by the data analysis unit. A system characterized by:

2. The data collection unit Analyze the user's emotional state and collect data that takes into account fluctuations in stress levels and motivation.

2. The system of claim 1.

3. The data analysis unit Analyzes the user's past treatment history and results to propose more accurate treatment methods 2. The system of claim 1.

4. The treatment plan creation unit It provides a plan that reflects the user's detailed diet and exercise habits and aims to improve overall health.

2. The system of claim 1.

5. The treatment plan creation unit Monitor the user's vital signs (e.g., blood pressure and weight) to assess overall health 2. The system of claim 1.

6. The treatment plan creation unit Analyzing the emotional state of users in a community and recommending posts that elicit positive emotions 2. The system of claim 1.

7. The data analysis unit Proposes treatment methods that take into account the user's emotional state, aiming to reduce stress and improve motivation 2. The system of claim 1.

8. The treatment plan creation unit Identify the treatment plan that elicits the most positive feelings from users and prioritize it 2. The system of claim 1.

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