system

Abstract

The system according to this embodiment aims to analyze the user's scalp condition in detail, identify the cause of hair loss, and propose an optimal treatment plan. [Solution] The system according to the embodiment comprises an analysis unit, a identification unit, and a proposal unit. The analysis unit analyzes the user's scalp condition in detail. The identification unit identifies the cause of hair loss based on the data analyzed by the analysis unit. The proposal unit proposes a treatment plan based on the cause identified by the identification unit.

Description

Technical Field

[0005] ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the scalp condition of a user has not been sufficiently analyzed in detail to identify the cause of hair loss, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze the scalp condition of a user in detail, identify the cause of hair loss, and propose an optimal treatment plan.

Means for Solving the Problems

[0006] The system according to the embodiment includes an analysis unit, an identification unit, and a proposal unit. The analysis unit analyzes the scalp condition of a user in detail. The identification unit identifies the cause of hair loss based on the data analyzed by the analysis unit. The proposal unit proposes a treatment plan based on the cause identified by the identification unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze the user's scalp condition in detail, identify the cause of hair loss, and propose an optimal treatment plan. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) An AI agent system according to an embodiment of the present invention is a system that analyzes the user's scalp condition in detail, identifies the cause of hair loss, and proposes a treatment plan. This AI agent system analyzes the user's scalp condition in detail and identifies the cause of hair loss. Next, based on the identified cause, it provides personalized care by suggesting an optimal treatment plan and lifestyle improvements. For example, the AI ​​agent system allows the user to upload images of their scalp taken with their smartphone, and analyzes these images to evaluate the scalp condition in detail. Next, the AI ​​agent system identifies factors that cause hair loss based on past user data and medical data. Subsequently, based on the identified cause, it suggests the use of specific medications and methods for improving diet and lifestyle. This allows the user to implement the treatment method best suited to them. This mechanism improves the success rate of hair loss treatment and increases user satisfaction. For example, treatment based on customer data improves the treatment success rate by an average of 30%, and personalized care results in customer satisfaction reaching over 85%. In addition, effective initial treatment reduces the return visit rate by 20%. Furthermore, the AI ​​agent system is also beneficial for dermatologists and hair loss treatment clinics. For example, dermatologists can provide patients with the most suitable treatment, and hair loss treatment clinics can improve treatment effectiveness. This leads to improved treatment effectiveness across all medical institutions and increased patient satisfaction. As a result, the AI ​​agent system can analyze the user's scalp condition in detail, identify the cause of hair loss, and propose a treatment plan.

[0029] The AI ​​agent system according to this embodiment comprises an analysis unit, a identification unit, and a proposal unit. The analysis unit analyzes the user's scalp condition in detail. For example, the analysis unit analyzes an image of the scalp taken by the user with a smartphone. The analysis unit evaluates the scalp condition in detail using image analysis technology. For example, the analysis unit evaluates the health of the scalp using an image processing algorithm. The analysis unit can also predict the scalp condition using machine learning technology. The identification unit identifies the cause of hair loss based on the data analyzed by the analysis unit. For example, the identification unit identifies the cause of hair loss based on past user data and medical data. The identification unit identifies the cause of hair loss from a large amount of data using machine learning technology. For example, the identification unit identifies the cause of hair loss based on the user's genetic information and lifestyle data. The identification unit can also identify the cause of hair loss based on medical data. The proposal unit proposes a treatment plan based on the cause identified by the identification unit. For example, the proposal unit proposes the use of specific medications or methods for improving diet and lifestyle. The proposal unit proposes the optimal treatment plan for each individual's condition using machine learning technology. For example, the proposal unit proposes the optimal treatment plan considering the user's health condition and lifestyle. The proposal unit can also propose the optimal treatment plan based on medical data. As a result, the AI ​​agent system according to this embodiment can analyze the user's scalp condition in detail, identify the cause of hair loss, and propose a treatment plan.

[0030] The analysis unit performs a detailed analysis of the user's scalp condition. For example, the analysis unit analyzes images of the scalp taken by the user with their smartphone. Specifically, the user takes an image of their scalp using their smartphone camera and sends the image to the analysis unit through a dedicated application. The analysis unit analyzes the received image at high resolution and applies image processing algorithms to evaluate the health of the scalp. For example, it performs pre-processing such as image noise reduction, contrast adjustment, and edge detection to extract detailed features of the scalp. Furthermore, the analysis unit can also predict the condition of the scalp using machine learning technology. Specifically, it uses a pre-trained model to evaluate the health of the scalp and identify conditions such as dryness, oiliness, inflammation, and the presence or absence of dandruff. This allows the analysis unit to gain a detailed understanding of the user's scalp condition and provide the data necessary for the next step of identifying the cause. The analysis unit stores this data on a cloud server, making it accessible to the identification and proposal units. In addition, the analysis unit performs regular image analysis to track changes in the user's scalp condition and support long-term health management. This allows the analysis unit to continuously monitor the user's scalp condition and intervene at the appropriate time.

[0031] The identification unit identifies the cause of hair loss based on data analyzed by the analysis unit. For example, the identification unit identifies the cause of hair loss based on past user data and medical data. Specifically, the identification unit compares the user's scalp condition data with past databases and identifies similar cases. This allows the identification unit to obtain reference information for identifying the cause of the user's hair loss. The identification unit uses machine learning technology to identify the cause of hair loss from large amounts of data. For example, the identification unit identifies the cause of hair loss based on the user's genetic information and lifestyle data. Specifically, it analyzes the user's genetic information and identifies gene mutations related to hair loss. It also analyzes the user's lifestyle data and evaluates the impact of factors such as diet, exercise, stress, and sleep on hair loss. Furthermore, the identification unit can also identify the cause of hair loss based on medical data. For example, it analyzes the user's medical history and drug use history to identify diseases and drug effects related to hair loss. This allows the identification unit to evaluate the cause of the user's hair loss from multiple angles and identify the most likely cause. The identification unit integrates this information with the data received from the analysis unit and provides the user with detailed cause analysis results. This allows the identification unit to accurately identify the cause of the user's hair loss and provide the information necessary to propose a treatment plan, which is the next step.

[0032] The Proposal Department proposes a treatment plan based on the causes identified by the Specific Department. For example, the Proposal Department might suggest the use of specific medications or methods for improving diet and lifestyle. Specifically, the Proposal Department selects the most suitable medication based on the user's scalp condition and the cause of hair loss, and proposes its usage. For instance, it might recommend the use of specific hair growth products or shampoos, and provide detailed explanations of their frequency and usage. The Proposal Department also proposes ways to improve the user's diet and lifestyle. For example, it might suggest meals containing nutrients effective for hair loss, or methods for improving lifestyle habits such as stress management, adequate sleep, and regular exercise. The Proposal Department uses machine learning technology to propose the most suitable treatment plan for each individual's condition. Specifically, it utilizes past data and medical data to propose the optimal treatment plan, taking into account the user's health condition and lifestyle. For example, it might refer to treatment plans that have been effective in similar cases in the past to propose the most suitable treatment plan for the user. The Proposal Department can also propose the optimal treatment plan based on medical data. For example, it might analyze medical data to identify effective treatments and medications for hair loss and propose them to the user. This allows the proposal department to suggest the optimal treatment plan tailored to the user's scalp condition and the cause of hair loss, thereby supporting the user's hair loss improvement. Furthermore, the proposal department can collect user feedback, evaluate the effectiveness of the treatment plan, and modify the treatment plan as needed. This enables the proposal department to continuously provide the user with the optimal treatment plan, maximizing the effectiveness of hair loss improvement.

[0033] The Improvement Department can offer suggestions for improving lifestyle habits. For example, it can suggest improvements to diet and recommend exercise. The Improvement Department uses machine learning technology to analyze the user's lifestyle habits and propose the most suitable improvement methods. For example, based on the user's dietary data, the Improvement Department can suggest improvements to nutritional balance. It can also suggest improvements to exercise habits based on the user's exercise data. In this way, by offering suggestions for improving lifestyle habits, the user's health can be improved.

[0034] The monitoring unit can monitor the effectiveness of treatment. For example, the monitoring unit performs regular examinations and records changes in symptoms. The monitoring unit uses machine learning technology to monitor treatment effectiveness. For instance, the monitoring unit evaluates treatment effectiveness based on the user's health data. Furthermore, the monitoring unit can also monitor treatment effectiveness based on the user's symptom data. This allows for monitoring treatment effectiveness and understanding the progress of treatment.

[0035] The analysis unit can analyze images of the scalp taken by the user with their smartphone. For example, the analysis unit can upload images of the scalp taken by the user with their smartphone and analyze these images to evaluate the condition of the scalp in detail. The analysis unit uses image processing algorithms to evaluate the health of the scalp. For example, the analysis unit adjusts the resolution and color tone of the images to evaluate the condition of the scalp in detail. The analysis unit can also predict the condition of the scalp using machine learning technology. As a result, users can easily understand the condition of their scalp by analyzing images of their scalp taken with their smartphone.

[0036] The identification unit can identify the cause of hair loss based on past user data and medical data. For example, the identification unit identifies the cause of hair loss based on past user data and medical data. The identification unit uses machine learning technology to identify the cause of hair loss from large amounts of data. For example, the identification unit identifies the cause of hair loss based on the user's genetic information and lifestyle data. Furthermore, the identification unit can also identify the cause of hair loss based on medical data. This allows for more accurate identification of the cause of hair loss by using past user data and medical data.

[0037] The recommendation system can suggest the use of specific medications or methods for improving diet and lifestyle. For example, it can suggest the use of specific medications or methods for improving diet and lifestyle. The recommendation system uses machine learning technology to propose the optimal treatment plan for each individual's condition. For example, it can propose the optimal treatment plan considering the user's health status and lifestyle. Furthermore, the recommendation system can propose the optimal treatment plan based on medical data. This allows the system to provide users with the most suitable treatment plan by suggesting the use of specific medications or methods for improving diet and lifestyle.

[0038] The analysis unit can improve the accuracy of scalp image analysis by referring to the user's past scalp condition data. For example, the analysis unit refers to the user's past scalp images and detects changes by comparing them with the current state. Based on the user's past scalp data, the analysis unit identifies specific patterns and improves the accuracy of the analysis. In addition, the analysis unit refers to the user's past treatment history and performs the analysis while considering the effects of treatment. In this way, the accuracy of the analysis can be improved by referring to the user's past scalp condition data.

[0039] The analysis unit can improve the reliability of scalp image analysis by using images taken under different lighting conditions. For example, the analysis unit analyzes multiple images taken under different lighting conditions to obtain reliable results. The analysis unit improves the accuracy of the analysis results by using an algorithm that corrects for the effects of the light source. In addition, the analysis unit combines and analyzes images taken under different lighting conditions to evaluate the detailed scalp condition. In this way, the reliability of the analysis can be improved by using images taken under different lighting conditions.

[0040] The analysis unit can customize the analysis results when analyzing scalp images, taking into account the user's geographical location information. For example, the analysis unit provides analysis results that take into account region-specific environmental factors based on the user's geographical location information. The analysis unit customizes the analysis results that take into account regional climate conditions based on the user's geographical location information. Furthermore, the analysis unit provides analysis results that take into account regional lifestyles based on the user's geographical location information. In this way, by taking into account the user's geographical location information, it is possible to provide analysis results that reflect region-specific environmental factors.

[0041] The analysis unit can improve the accuracy of scalp image analysis by using the user's lifestyle data in conjunction with the analysis. For example, the analysis unit can refer to the user's dietary data and perform analysis while considering nutritional status. The analysis unit can refer to the user's sleep data and perform analysis while considering sleep quality. Furthermore, the analysis unit can refer to the user's exercise habit data and perform analysis while considering the effects of exercise. In this way, the accuracy of the analysis can be improved by using the user's lifestyle data in conjunction with the analysis.

[0042] The identification unit can improve the accuracy of identifying the cause of hair loss by referring to the user's genetic information. For example, the identification unit identifies the cause by considering genetic factors based on the user's genetic information. The identification unit refers to the user's family history, assesses genetic risk, and identifies the cause. Furthermore, the identification unit analyzes the user's genetic data and identifies the cause by considering specific gene mutations. In this way, the accuracy of identification can be improved by referring to the user's genetic information.

[0043] The identification unit can improve the accuracy of identifying the cause of hair loss by monitoring the user's scalp health in real time. For example, the identification unit monitors the user's scalp health in real time, detects changes, and identifies the cause. The identification unit periodically monitors the user's scalp health and identifies the cause by considering long-term changes. In addition, the identification unit monitors the user's scalp health, detects abnormalities, and identifies the cause. As a result, the accuracy of identification can be improved by monitoring the user's scalp health in real time.

[0044] The identification unit can improve the accuracy of identifying the cause of hair loss by referring to the user's dietary data. For example, the identification unit refers to the user's dietary data and identifies the cause by considering nutritional status. Based on the user's dietary data, the identification unit identifies the cause by considering deficiencies in specific nutrients. Furthermore, the identification unit analyzes the user's dietary data and identifies the cause by considering eating patterns. In this way, the accuracy of identification can be improved by referring to the user's dietary data.

[0045] The identification unit can improve the accuracy of its hair loss cause identification by considering the user's stress level. For example, the identification unit evaluates the user's stress level and identifies stress-related causes. The identification unit monitors the user's stress level and identifies causes while considering changes. Furthermore, the identification unit identifies causes while suggesting stress management based on the user's stress level. In this way, the accuracy of identification can be improved by considering the user's stress level.

[0046] The proposal unit can suggest the optimal treatment plan by referring to the user's past treatment history. For example, the proposal unit can refer to the user's past treatment history and prioritize suggesting treatments that were effective. Based on the user's past treatment history, the proposal unit evaluates the effectiveness of treatments and proposes the optimal treatment plan. Furthermore, the proposal unit analyzes the user's past treatment history and proposes a treatment plan considering the success rate of the treatment. In this way, the optimal treatment plan can be suggested by referring to the user's past treatment history.

[0047] The proposal unit can improve the accuracy of its treatment plan suggestions by considering the user's lifestyle data. For example, the proposal unit can refer to the user's dietary data and propose a treatment plan that takes nutritional status into account. The proposal unit can also refer to the user's sleep data and propose a treatment plan that takes sleep quality into account. Furthermore, the proposal unit can refer to the user's exercise habit data and propose a treatment plan that takes the effects of exercise into account. In this way, the accuracy of the suggestions can be improved by considering the user's lifestyle data.

[0048] The proposal unit can propose the optimal treatment plan by considering the user's geographical location. For example, the proposal unit can propose a treatment plan based on the user's geographical location, taking into account local medical resources. The proposal unit can propose a treatment plan based on the user's geographical location, taking into account local climate conditions. Furthermore, the proposal unit can propose a treatment plan based on the user's geographical location, taking into account local lifestyles. In this way, by considering the user's geographical location, the optimal treatment plan can be proposed.

[0049] The proposal department can analyze a user's social media activity and propose a relevant treatment plan when suggesting treatment plans. For example, the proposal department can analyze a user's social media activity and propose a treatment plan based on their interests. The proposal department can also assess the user's stress level based on their social media activity and propose a treatment plan. Furthermore, the proposal department can refer to the user's social media activity and suggest ways to improve their lifestyle. In this way, by analyzing a user's social media activity, it is possible to propose a relevant treatment plan.

[0050] The improvement department can propose the most suitable improvement method by referring to the user's past lifestyle data when suggesting lifestyle improvements. For example, the improvement department can refer to the user's past lifestyle data and prioritize suggesting improvement methods that were effective in the past. Based on the user's past lifestyle data, the improvement department evaluates the effectiveness of improvements and proposes the most suitable improvement method. Furthermore, the improvement department analyzes the user's past lifestyle data and proposes improvement methods considering the success rate of the improvements. In this way, by referring to the user's past lifestyle data, it can propose the most suitable improvement method.

[0051] The improvement department can propose optimal improvement methods when suggesting lifestyle improvements, taking into account the user's geographical location. For example, the improvement department can propose improvement methods based on the user's geographical location, taking into account local lifestyle habits. The improvement department can propose improvement methods based on the user's geographical location, taking into account local climate conditions. Furthermore, the improvement department can propose improvement methods based on the user's geographical location, taking into account local medical resources. In this way, by considering the user's geographical location, the improvement department can propose the most optimal improvement methods.

[0052] The monitoring unit can improve the accuracy of monitoring by referring to the user's past treatment data during monitoring. For example, the monitoring unit refers to the user's past treatment data and performs monitoring while considering the effectiveness of the treatment. The monitoring unit identifies specific patterns based on the user's past treatment data and improves the accuracy of monitoring. In addition, the monitoring unit analyzes the user's past treatment data and performs monitoring while considering the success rate of the treatment. In this way, the accuracy of monitoring can be improved by referring to the user's past treatment data.

[0053] The monitoring unit can improve the accuracy of monitoring by considering the user's geographical location information during monitoring. For example, the monitoring unit performs monitoring based on the user's geographical location information, taking into account local environmental factors. The monitoring unit performs monitoring based on the user's geographical location information, taking into account local climate conditions. Furthermore, the monitoring unit performs monitoring based on the user's geographical location information, taking into account local lifestyles. In this way, the accuracy of monitoring can be improved by considering the user's geographical location information.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The analysis unit can not only analyze the user's scalp condition in detail, but also monitor the user's hair growth cycle. For example, the analysis unit tracks the user's hair growth rate and the frequency of hair loss, and evaluates the health of the hair based on this data. The analysis unit can also measure the density and thickness of the user's hair and evaluate the health of the hair based on this information. Furthermore, the analysis unit can track changes in the user's hair color and texture, and evaluate the health of the hair based on this data. This allows for a more detailed evaluation of the user's hair health by monitoring the hair growth cycle.

[0056] The specialized unit can not only analyze the user's scalp condition in detail but also evaluate the user's hormone balance. For example, it can evaluate hormone levels based on the user's blood test data and identify whether a hormonal imbalance is the cause of hair loss. It can also evaluate the user's stress level and identify the cause of hair loss by considering the impact of stress on hormone balance. Furthermore, it can evaluate the impact of diet and exercise on hormone balance based on the user's lifestyle data and identify whether these factors are causing hair loss. As a result, evaluating the user's hormone balance makes it possible to identify the cause of hair loss more accurately.

[0057] The consultation department can not only analyze the user's scalp condition in detail, but also propose treatment plans that take the user's mental health into consideration. For example, the consultation department can assess the user's stress level and suggest relaxation methods to reduce stress. It can also assess the user's sleep quality and suggest methods to improve sleep. Furthermore, the consultation department can suggest collaboration with counseling and mental health care professionals to support the user's mental health. This allows for more comprehensive care by proposing treatment plans that consider the user's mental health.

[0058] The Improvement Department can not only suggest improvements to users' lifestyles, but also evaluate users' nutritional status and suggest nutritional supplements. For example, based on users' dietary data, the Improvement Department can evaluate whether there is a deficiency in specific nutrients and suggest supplements to make up for the deficiencies. Furthermore, considering the user's health condition, the Improvement Department can also suggest nutritional supplements that are expected to have specific health benefits. In addition, based on users' lifestyle data, the Improvement Department can suggest the timing and method of taking nutritional supplements. This allows for more effective lifestyle improvements by evaluating users' nutritional status and suggesting nutritional supplements.

[0059] The monitoring unit not only monitors the effectiveness of the user's treatment but can also monitor the health of the user's scalp in real time. For example, the monitoring unit measures the temperature and humidity of the user's scalp in real time and evaluates the health of the scalp based on this data. The monitoring unit can also measure the blood flow of the user's scalp in real time and evaluate the impact of changes in blood flow on the health of the scalp. Furthermore, the monitoring unit can measure the amount of sebum secreted by the user's scalp in real time and evaluate the impact of changes in sebum secretion on the health of the scalp. This allows for more detailed monitoring of the effectiveness of the treatment by monitoring the health of the user's scalp in real time.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The analysis unit analyzes the user's scalp condition in detail. For example, the analysis unit analyzes images of the scalp taken by the user with their smartphone. The analysis unit uses image analysis technology to evaluate the scalp condition in detail. For example, the analysis unit uses image processing algorithms to evaluate the health of the scalp. The analysis unit can also use machine learning technology to predict the condition of the scalp. Step 2: The identification unit identifies the cause of hair loss based on the data analyzed by the analysis unit. The identification unit identifies the cause of hair loss based on, for example, past user data or medical data. The identification unit uses machine learning technology to identify the cause of hair loss from a large amount of data. For example, the identification unit identifies the cause of hair loss based on the user's genetic information and lifestyle data. The identification unit can also identify the cause of hair loss based on medical data. Step 3: The suggestion unit proposes a treatment plan based on the cause identified by the identification unit. The suggestion unit may, for example, suggest the use of specific medications or methods for improving diet and lifestyle. The suggestion unit uses machine learning technology to propose the optimal treatment plan for each individual's condition. For example, the suggestion unit may propose the optimal treatment plan considering the user's health status and lifestyle. The suggestion unit can also propose the optimal treatment plan based on medical data.

[0062] (Example of form 2) An AI agent system according to an embodiment of the present invention is a system that analyzes the user's scalp condition in detail, identifies the cause of hair loss, and proposes a treatment plan. This AI agent system analyzes the user's scalp condition in detail and identifies the cause of hair loss. Next, based on the identified cause, it provides personalized care by suggesting an optimal treatment plan and lifestyle improvements. For example, the AI ​​agent system allows the user to upload images of their scalp taken with their smartphone, and analyzes these images to evaluate the scalp condition in detail. Next, the AI ​​agent system identifies factors that cause hair loss based on past user data and medical data. Subsequently, based on the identified cause, it suggests the use of specific medications and methods for improving diet and lifestyle. This allows the user to implement the treatment method best suited to them. This mechanism improves the success rate of hair loss treatment and increases user satisfaction. For example, treatment based on customer data improves the treatment success rate by an average of 30%, and personalized care results in customer satisfaction reaching over 85%. In addition, effective initial treatment reduces the return visit rate by 20%. Furthermore, the AI ​​agent system is also beneficial for dermatologists and hair loss treatment clinics. For example, dermatologists can provide patients with the most suitable treatment, and hair loss treatment clinics can improve treatment effectiveness. This leads to improved treatment effectiveness across all medical institutions and increased patient satisfaction. As a result, the AI ​​agent system can analyze the user's scalp condition in detail, identify the cause of hair loss, and propose a treatment plan.

[0063] The AI ​​agent system according to this embodiment comprises an analysis unit, a identification unit, and a proposal unit. The analysis unit analyzes the user's scalp condition in detail. For example, the analysis unit analyzes an image of the scalp taken by the user with a smartphone. The analysis unit evaluates the scalp condition in detail using image analysis technology. For example, the analysis unit evaluates the health of the scalp using an image processing algorithm. The analysis unit can also predict the scalp condition using machine learning technology. The identification unit identifies the cause of hair loss based on the data analyzed by the analysis unit. For example, the identification unit identifies the cause of hair loss based on past user data and medical data. The identification unit identifies the cause of hair loss from a large amount of data using machine learning technology. For example, the identification unit identifies the cause of hair loss based on the user's genetic information and lifestyle data. The identification unit can also identify the cause of hair loss based on medical data. The proposal unit proposes a treatment plan based on the cause identified by the identification unit. For example, the proposal unit proposes the use of specific medications or methods for improving diet and lifestyle. The proposal unit proposes the optimal treatment plan for each individual's condition using machine learning technology. For example, the proposal unit proposes the optimal treatment plan considering the user's health condition and lifestyle. The proposal unit can also propose the optimal treatment plan based on medical data. As a result, the AI ​​agent system according to this embodiment can analyze the user's scalp condition in detail, identify the cause of hair loss, and propose a treatment plan.

[0064] The analysis unit performs a detailed analysis of the user's scalp condition. For example, the analysis unit analyzes images of the scalp taken by the user with their smartphone. Specifically, the user takes an image of their scalp using their smartphone camera and sends the image to the analysis unit through a dedicated application. The analysis unit analyzes the received image at high resolution and applies image processing algorithms to evaluate the health of the scalp. For example, it performs pre-processing such as image noise reduction, contrast adjustment, and edge detection to extract detailed features of the scalp. Furthermore, the analysis unit can also predict the condition of the scalp using machine learning technology. Specifically, it uses a pre-trained model to evaluate the health of the scalp and identify conditions such as dryness, oiliness, inflammation, and the presence or absence of dandruff. This allows the analysis unit to gain a detailed understanding of the user's scalp condition and provide the data necessary for the next step of identifying the cause. The analysis unit stores this data on a cloud server, making it accessible to the identification and proposal units. In addition, the analysis unit performs regular image analysis to track changes in the user's scalp condition and support long-term health management. This allows the analysis unit to continuously monitor the user's scalp condition and intervene at the appropriate time.

[0065] The identification unit identifies the cause of hair loss based on data analyzed by the analysis unit. For example, the identification unit identifies the cause of hair loss based on past user data and medical data. Specifically, the identification unit compares the user's scalp condition data with past databases and identifies similar cases. This allows the identification unit to obtain reference information for identifying the cause of the user's hair loss. The identification unit uses machine learning technology to identify the cause of hair loss from large amounts of data. For example, the identification unit identifies the cause of hair loss based on the user's genetic information and lifestyle data. Specifically, it analyzes the user's genetic information and identifies gene mutations related to hair loss. It also analyzes the user's lifestyle data and evaluates the impact of factors such as diet, exercise, stress, and sleep on hair loss. Furthermore, the identification unit can also identify the cause of hair loss based on medical data. For example, it analyzes the user's medical history and drug use history to identify diseases and drug effects related to hair loss. This allows the identification unit to evaluate the cause of the user's hair loss from multiple angles and identify the most likely cause. The identification unit integrates this information with the data received from the analysis unit and provides the user with detailed cause analysis results. This allows the identification unit to accurately identify the cause of the user's hair loss and provide the information necessary to propose a treatment plan, which is the next step.

[0066] The Proposal Department proposes a treatment plan based on the causes identified by the Specific Department. For example, the Proposal Department might suggest the use of specific medications or methods for improving diet and lifestyle. Specifically, the Proposal Department selects the most suitable medication based on the user's scalp condition and the cause of hair loss, and proposes its usage. For instance, it might recommend the use of specific hair growth products or shampoos, and provide detailed explanations of their frequency and usage. The Proposal Department also proposes ways to improve the user's diet and lifestyle. For example, it might suggest meals containing nutrients effective for hair loss, or methods for improving lifestyle habits such as stress management, adequate sleep, and regular exercise. The Proposal Department uses machine learning technology to propose the most suitable treatment plan for each individual's condition. Specifically, it utilizes past data and medical data to propose the optimal treatment plan, taking into account the user's health condition and lifestyle. For example, it might refer to treatment plans that have been effective in similar cases in the past to propose the most suitable treatment plan for the user. The Proposal Department can also propose the optimal treatment plan based on medical data. For example, it might analyze medical data to identify effective treatments and medications for hair loss and propose them to the user. This allows the proposal department to suggest the optimal treatment plan tailored to the user's scalp condition and the cause of hair loss, thereby supporting the user's hair loss improvement. Furthermore, the proposal department can collect user feedback, evaluate the effectiveness of the treatment plan, and modify the treatment plan as needed. This enables the proposal department to continuously provide the user with the optimal treatment plan, maximizing the effectiveness of hair loss improvement.

[0067] The Improvement Department can offer suggestions for improving lifestyle habits. For example, it can suggest improvements to diet and recommend exercise. The Improvement Department uses machine learning technology to analyze the user's lifestyle habits and propose the most suitable improvement methods. For example, based on the user's dietary data, the Improvement Department can suggest improvements to nutritional balance. It can also suggest improvements to exercise habits based on the user's exercise data. In this way, by offering suggestions for improving lifestyle habits, the user's health can be improved.

[0068] The monitoring unit can monitor the effectiveness of treatment. For example, the monitoring unit performs regular examinations and records changes in symptoms. The monitoring unit uses machine learning technology to monitor treatment effectiveness. For instance, the monitoring unit evaluates treatment effectiveness based on the user's health data. Furthermore, the monitoring unit can also monitor treatment effectiveness based on the user's symptom data. This allows for monitoring treatment effectiveness and understanding the progress of treatment.

[0069] The analysis unit can analyze images of the scalp taken by the user with their smartphone. For example, the analysis unit can upload images of the scalp taken by the user with their smartphone and analyze these images to evaluate the condition of the scalp in detail. The analysis unit uses image processing algorithms to evaluate the health of the scalp. For example, the analysis unit adjusts the resolution and color tone of the images to evaluate the condition of the scalp in detail. The analysis unit can also predict the condition of the scalp using machine learning technology. As a result, users can easily understand the condition of their scalp by analyzing images of their scalp taken with their smartphone.

[0070] The identification unit can identify the cause of hair loss based on past user data and medical data. For example, the identification unit identifies the cause of hair loss based on past user data and medical data. The identification unit uses machine learning technology to identify the cause of hair loss from large amounts of data. For example, the identification unit identifies the cause of hair loss based on the user's genetic information and lifestyle data. Furthermore, the identification unit can also identify the cause of hair loss based on medical data. This allows for more accurate identification of the cause of hair loss by using past user data and medical data.

[0071] The recommendation system can suggest the use of specific medications or methods for improving diet and lifestyle. For example, it can suggest the use of specific medications or methods for improving diet and lifestyle. The recommendation system uses machine learning technology to propose the optimal treatment plan for each individual's condition. For example, it can propose the optimal treatment plan considering the user's health status and lifestyle. Furthermore, the recommendation system can propose the optimal treatment plan based on medical data. This allows the system to provide users with the most suitable treatment plan by suggesting the use of specific medications or methods for improving diet and lifestyle.

[0072] The analysis unit can estimate the user's emotions and adjust the timing of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit will delay the timing of the analysis to perform it in a relaxed state. If the user is relaxed, the analysis unit will start the analysis immediately and provide quick results. Also, if the user is in a hurry, the analysis unit will perform it quickly and provide results in a short time. In this way, by adjusting the timing of the analysis based on the user's emotions, the analysis can be performed at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0073] The analysis unit can improve the accuracy of scalp image analysis by referring to the user's past scalp condition data. For example, the analysis unit refers to the user's past scalp images and detects changes by comparing them with the current state. Based on the user's past scalp data, the analysis unit identifies specific patterns and improves the accuracy of the analysis. In addition, the analysis unit refers to the user's past treatment history and performs the analysis while considering the effects of treatment. In this way, the accuracy of the analysis can be improved by referring to the user's past scalp condition data.

[0074] The analysis unit can improve the reliability of scalp image analysis by using images taken under different lighting conditions. For example, the analysis unit analyzes multiple images taken under different lighting conditions to obtain reliable results. The analysis unit improves the accuracy of the analysis results by using an algorithm that corrects for the effects of the light source. In addition, the analysis unit combines and analyzes images taken under different lighting conditions to evaluate the detailed scalp condition. In this way, the reliability of the analysis can be improved by using images taken under different lighting conditions.

[0075] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is tense, the analysis unit provides a simple and highly visible display method. If the user is relaxed, the analysis unit provides a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit provides a display method that gets straight to the point. In this way, by adjusting the display method of the analysis results based on the user's emotions, a display method that is easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0076] The analysis unit can customize the analysis results when analyzing scalp images, taking into account the user's geographical location information. For example, the analysis unit provides analysis results that take into account region-specific environmental factors based on the user's geographical location information. The analysis unit customizes the analysis results that take into account regional climate conditions based on the user's geographical location information. Furthermore, the analysis unit provides analysis results that take into account regional lifestyles based on the user's geographical location information. In this way, by taking into account the user's geographical location information, it is possible to provide analysis results that reflect region-specific environmental factors.

[0077] The analysis unit can improve the accuracy of scalp image analysis by using the user's lifestyle data in conjunction with the analysis. For example, the analysis unit can refer to the user's dietary data and perform analysis while considering nutritional status. The analysis unit can refer to the user's sleep data and perform analysis while considering sleep quality. Furthermore, the analysis unit can refer to the user's exercise habit data and perform analysis while considering the effects of exercise. In this way, the accuracy of the analysis can be improved by using the user's lifestyle data in conjunction with the analysis.

[0078] The identification unit can estimate the user's emotions and adjust the priority of identifying the cause of hair loss based on the estimated emotions. For example, if the user is stressed, the identification unit will prioritize identifying stress-related causes. If the user is relaxed, the identification unit will consider the user's overall health condition when identifying causes. Also, if the user is in a hurry, the identification unit will prioritize causes that can be identified quickly. By adjusting the priority of identifying the cause of hair loss based on the user's emotions, more appropriate cause identification becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0079] The identification unit can improve the accuracy of identifying the cause of hair loss by referring to the user's genetic information. For example, the identification unit identifies the cause by considering genetic factors based on the user's genetic information. The identification unit refers to the user's family history, assesses genetic risk, and identifies the cause. Furthermore, the identification unit analyzes the user's genetic data and identifies the cause by considering specific gene mutations. In this way, the accuracy of identification can be improved by referring to the user's genetic information.

[0080] The identification unit can improve the accuracy of identifying the cause of hair loss by monitoring the user's scalp health in real time. For example, the identification unit monitors the user's scalp health in real time, detects changes, and identifies the cause. The identification unit periodically monitors the user's scalp health and identifies the cause by considering long-term changes. In addition, the identification unit monitors the user's scalp health, detects abnormalities, and identifies the cause. As a result, the accuracy of identification can be improved by monitoring the user's scalp health in real time.

[0081] The identification unit can estimate the user's emotions and adjust the display method of the identification results based on the estimated user emotions. For example, if the user is tense, the identification unit provides a simple and highly visible display method. If the user is relaxed, the identification unit provides a display method that includes detailed information. Furthermore, if the user is in a hurry, the identification unit provides a display method that gets straight to the point. In this way, by adjusting the display method of the identification results based on the user's emotions, a display method that is easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The identification unit can improve the accuracy of identifying the cause of hair loss by referring to the user's dietary data. For example, the identification unit refers to the user's dietary data and identifies the cause by considering nutritional status. Based on the user's dietary data, the identification unit identifies the cause by considering deficiencies in specific nutrients. Furthermore, the identification unit analyzes the user's dietary data and identifies the cause by considering eating patterns. In this way, the accuracy of identification can be improved by referring to the user's dietary data.

[0083] The identification unit can improve the accuracy of its hair loss cause identification by considering the user's stress level. For example, the identification unit evaluates the user's stress level and identifies stress-related causes. The identification unit monitors the user's stress level and identifies causes while considering changes. Furthermore, the identification unit identifies causes while suggesting stress management based on the user's stress level. In this way, the accuracy of identification can be improved by considering the user's stress level.

[0084] The suggestion unit can estimate the user's emotions and adjust the treatment plan suggestion method based on the estimated emotions. For example, if the user is stressed, the suggestion unit will suggest a relaxing treatment plan. If the user is relaxed, the suggestion unit will suggest a detailed treatment plan. Also, if the user is in a hurry, the suggestion unit will suggest a treatment plan that can be implemented quickly. In this way, by adjusting the treatment plan suggestion method based on the user's emotions, the optimal suggestion method can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The proposal unit can suggest the optimal treatment plan by referring to the user's past treatment history. For example, the proposal unit can refer to the user's past treatment history and prioritize suggesting treatments that were effective. Based on the user's past treatment history, the proposal unit evaluates the effectiveness of treatments and proposes the optimal treatment plan. Furthermore, the proposal unit analyzes the user's past treatment history and proposes a treatment plan considering the success rate of the treatment. In this way, the optimal treatment plan can be suggested by referring to the user's past treatment history.

[0086] The proposal unit can improve the accuracy of its treatment plan suggestions by considering the user's lifestyle data. For example, the proposal unit can refer to the user's dietary data and propose a treatment plan that takes nutritional status into account. The proposal unit can also refer to the user's sleep data and propose a treatment plan that takes sleep quality into account. Furthermore, the proposal unit can refer to the user's exercise habit data and propose a treatment plan that takes the effects of exercise into account. In this way, the accuracy of the suggestions can be improved by considering the user's lifestyle data.

[0087] The suggestion unit can estimate the user's emotions and prioritize treatment plans based on those emotions. For example, if the user is stressed, the suggestion unit will suggest a treatment plan that prioritizes stress reduction. If the user is relaxed, the suggestion unit will suggest a treatment plan that considers their overall health. If the user is in a hurry, the suggestion unit will suggest a treatment plan that will produce quick results. In this way, by prioritizing treatment plans based on the user's emotions, the system can provide the user with the most optimal treatment plan. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The proposal unit can propose the optimal treatment plan by considering the user's geographical location. For example, the proposal unit can propose a treatment plan based on the user's geographical location, taking into account local medical resources. The proposal unit can propose a treatment plan based on the user's geographical location, taking into account local climate conditions. Furthermore, the proposal unit can propose a treatment plan based on the user's geographical location, taking into account local lifestyles. In this way, by considering the user's geographical location, the optimal treatment plan can be proposed.

[0089] The proposal department can analyze a user's social media activity and propose a relevant treatment plan when suggesting treatment plans. For example, the proposal department can analyze a user's social media activity and propose a treatment plan based on their interests. The proposal department can also assess the user's stress level based on their social media activity and propose a treatment plan. Furthermore, the proposal department can refer to the user's social media activity and suggest ways to improve their lifestyle. In this way, by analyzing a user's social media activity, it is possible to propose a relevant treatment plan.

[0090] The improvement unit can estimate the user's emotions and adjust the lifestyle improvement suggestions based on those emotions. For example, if the user is feeling stressed, the improvement unit will suggest lifestyle improvements that promote relaxation. If the user is relaxed, the improvement unit will suggest more detailed lifestyle improvements. If the user is in a hurry, the improvement unit will suggest lifestyle improvements that can be implemented quickly. By adjusting the lifestyle improvement suggestions based on the user's emotions, the system can provide the most suitable suggestions for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The improvement department can propose the most suitable improvement method by referring to the user's past lifestyle data when suggesting lifestyle improvements. For example, the improvement department can refer to the user's past lifestyle data and prioritize suggesting improvement methods that were effective in the past. Based on the user's past lifestyle data, the improvement department evaluates the effectiveness of improvements and proposes the most suitable improvement method. Furthermore, the improvement department analyzes the user's past lifestyle data and proposes improvement methods considering the success rate of the improvements. In this way, by referring to the user's past lifestyle data, it can propose the most suitable improvement method.

[0092] The improvement unit can estimate the user's emotions and determine the priority of lifestyle improvements based on those emotions. For example, if the user is stressed, the improvement unit will suggest lifestyle improvements that prioritize stress reduction. If the user is relaxed, the improvement unit will suggest lifestyle improvements that take into account their overall health. Furthermore, if the user is in a hurry, the improvement unit will suggest lifestyle improvements that produce quick results. In this way, by determining the priority of lifestyle improvements based on the user's emotions, the system can provide the user with the most optimal improvement method. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The improvement department can propose optimal improvement methods when suggesting lifestyle improvements, taking into account the user's geographical location. For example, the improvement department can propose improvement methods based on the user's geographical location, taking into account local lifestyle habits. The improvement department can propose improvement methods based on the user's geographical location, taking into account local climate conditions. Furthermore, the improvement department can propose improvement methods based on the user's geographical location, taking into account local medical resources. In this way, by considering the user's geographical location, the improvement department can propose the most optimal improvement methods.

[0094] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on the estimated emotions. For example, if the user is stressed, the monitoring unit will monitor frequently to quickly detect changes in their state. If the user is relaxed, the monitoring unit will reduce the monitoring frequency to alleviate the user's burden. If the user is in a hurry, the monitoring unit will perform monitoring for a short time to provide results quickly. In this way, by adjusting the monitoring frequency based on the user's emotions, the system can provide optimal monitoring for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0095] The monitoring unit can improve the accuracy of monitoring by referring to the user's past treatment data during monitoring. For example, the monitoring unit refers to the user's past treatment data and performs monitoring while considering the effectiveness of the treatment. The monitoring unit identifies specific patterns based on the user's past treatment data and improves the accuracy of monitoring. In addition, the monitoring unit analyzes the user's past treatment data and performs monitoring while considering the success rate of the treatment. In this way, the accuracy of monitoring can be improved by referring to the user's past treatment data.

[0096] The monitoring unit can estimate the user's emotions and adjust the display method of the monitoring results based on the estimated emotions. For example, if the user is tense, the monitoring unit provides a simple and highly visible display method. If the user is relaxed, the monitoring unit provides a display method that includes detailed information. If the user is in a hurry, the monitoring unit provides a display method that gets straight to the point. In this way, by adjusting the display method of the monitoring results based on the user's emotions, a display method that is easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The monitoring unit can improve the accuracy of monitoring by considering the user's geographical location information during monitoring. For example, the monitoring unit performs monitoring based on the user's geographical location information, taking into account local environmental factors. The monitoring unit performs monitoring based on the user's geographical location information, taking into account local climate conditions. Furthermore, the monitoring unit performs monitoring based on the user's geographical location information, taking into account local lifestyles. In this way, the accuracy of monitoring can be improved by considering the user's geographical location information.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The analysis unit can not only analyze the user's scalp condition in detail, but also monitor the user's hair growth cycle. For example, the analysis unit tracks the user's hair growth rate and the frequency of hair loss, and evaluates the health of the hair based on this data. The analysis unit can also measure the density and thickness of the user's hair and evaluate the health of the hair based on this information. Furthermore, the analysis unit can track changes in the user's hair color and texture, and evaluate the health of the hair based on this data. This allows for a more detailed evaluation of the user's hair health by monitoring the hair growth cycle.

[0100] The specialized unit can not only analyze the user's scalp condition in detail but also evaluate the user's hormone balance. For example, it can evaluate hormone levels based on the user's blood test data and identify whether a hormonal imbalance is the cause of hair loss. It can also evaluate the user's stress level and identify the cause of hair loss by considering the impact of stress on hormone balance. Furthermore, it can evaluate the impact of diet and exercise on hormone balance based on the user's lifestyle data and identify whether these factors are causing hair loss. As a result, evaluating the user's hormone balance makes it possible to identify the cause of hair loss more accurately.

[0101] The consultation department can not only analyze the user's scalp condition in detail, but also propose treatment plans that take the user's mental health into consideration. For example, the consultation department can assess the user's stress level and suggest relaxation methods to reduce stress. It can also assess the user's sleep quality and suggest methods to improve sleep. Furthermore, the consultation department can suggest collaboration with counseling and mental health care professionals to support the user's mental health. This allows for more comprehensive care by proposing treatment plans that consider the user's mental health.

[0102] The Improvement Department can not only suggest improvements to users' lifestyles, but also evaluate users' nutritional status and suggest nutritional supplements. For example, based on users' dietary data, the Improvement Department can evaluate whether there is a deficiency in specific nutrients and suggest supplements to make up for the deficiencies. Furthermore, considering the user's health condition, the Improvement Department can also suggest nutritional supplements that are expected to have specific health benefits. In addition, based on users' lifestyle data, the Improvement Department can suggest the timing and method of taking nutritional supplements. This allows for more effective lifestyle improvements by evaluating users' nutritional status and suggesting nutritional supplements.

[0103] The monitoring unit not only monitors the effectiveness of the user's treatment but can also monitor the health of the user's scalp in real time. For example, the monitoring unit measures the temperature and humidity of the user's scalp in real time and evaluates the health of the scalp based on this data. The monitoring unit can also measure the blood flow of the user's scalp in real time and evaluate the impact of changes in blood flow on the health of the scalp. Furthermore, the monitoring unit can measure the amount of sebum secreted by the user's scalp in real time and evaluate the impact of changes in sebum secretion on the health of the scalp. This allows for more detailed monitoring of the effectiveness of the treatment by monitoring the health of the user's scalp in real time.

[0104] The analysis unit can estimate the user's emotions and adjust the timing of the analysis based on those emotions. For example, if the user is stressed, the analysis can be delayed to allow the user to relax. If the user is relaxed, the analysis can start immediately to provide quick results. If the user is in a hurry, the analysis can be performed quickly to provide results in a short time. In this way, by adjusting the timing of the analysis based on the user's emotions, the analysis can be performed at a more appropriate time.

[0105] The identification unit can estimate the user's emotions and adjust the priority of identifying the cause of hair loss based on those emotions. For example, if the user is stressed, stress-related causes can be prioritized. If the user is relaxed, causes can be identified considering their overall health. Also, if the user is in a hurry, causes that can be identified quickly can be prioritized. By adjusting the priority of identifying the cause of hair loss based on the user's emotions, more accurate cause identification becomes possible.

[0106] The suggestion function can estimate the user's emotions and adjust the treatment plan suggestion method based on those emotions. For example, if the user is stressed, it can suggest a relaxing treatment plan. If the user is relaxed, it can suggest a detailed treatment plan. If the user is in a hurry, it can suggest a treatment plan that can be implemented quickly. In this way, by adjusting the treatment plan suggestion method based on the user's emotions, the system can provide the most suitable suggestion method for the user.

[0107] The improvement unit can estimate the user's emotions and adjust the lifestyle improvement suggestions based on those emotions. For example, if the user is stressed, it can suggest lifestyle improvements that promote relaxation. If the user is relaxed, it can suggest more detailed lifestyle improvements. If the user is in a hurry, it can suggest lifestyle improvements that can be implemented quickly. By adjusting the lifestyle improvement suggestions based on the user's emotions, the system can provide the most suitable suggestions for the user.

[0108] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on the estimated emotions. For example, if the user is stressed, frequent monitoring can be performed to quickly detect changes in their state. If the user is relaxed, the monitoring frequency can be reduced to lessen the user's burden. Also, if the user is in a hurry, monitoring can be performed for a short time to provide results quickly. In this way, by adjusting the monitoring frequency based on the user's emotions, the system can provide the optimal monitoring experience for the user.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The analysis unit analyzes the user's scalp condition in detail. For example, the analysis unit analyzes images of the scalp taken by the user with their smartphone. The analysis unit uses image analysis technology to evaluate the scalp condition in detail. For example, the analysis unit uses image processing algorithms to evaluate the health of the scalp. The analysis unit can also use machine learning technology to predict the condition of the scalp. Step 2: The identification unit identifies the cause of hair loss based on the data analyzed by the analysis unit. The identification unit identifies the cause of hair loss based on, for example, past user data or medical data. The identification unit uses machine learning technology to identify the cause of hair loss from a large amount of data. For example, the identification unit identifies the cause of hair loss based on the user's genetic information and lifestyle data. The identification unit can also identify the cause of hair loss based on medical data. Step 3: The suggestion unit proposes a treatment plan based on the cause identified by the identification unit. The suggestion unit may, for example, suggest the use of specific medications or methods for improving diet and lifestyle. The suggestion unit uses machine learning technology to propose the optimal treatment plan for each individual's condition. For example, the suggestion unit may propose the optimal treatment plan considering the user's health status and lifestyle. The suggestion unit can also propose the optimal treatment plan based on medical data.

[0111] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0112] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0113] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0114] Each of the multiple elements described above, including the analysis unit, identification unit, and proposal unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit uses the camera 42 of the smart device 14 to capture an image of the user's scalp and the control unit 46A performs image analysis. The identification unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and identifies the cause of hair loss based on past user data and medical data. The proposal unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and proposes an optimal treatment plan based on the identified cause. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] As shown in Figure 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.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements, including the analysis unit, identification unit, and proposal unit described above, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit uses the camera 42 of the smart glasses 214 to capture an image of the user's scalp and the control unit 46A performs image analysis. The identification unit is implemented in the identification processing unit 290 of the data processing unit 12 and identifies the cause of hair loss based on past user data and medical data. The proposal unit is implemented in the identification processing unit 290 of the data processing unit 12 and proposes an optimal treatment plan based on the identified cause. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0135] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements, including the analysis unit, identification unit, and proposal unit described above, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit uses the camera 42 of the headset terminal 314 to capture an image of the user's scalp and the control unit 46A performs image analysis. The identification unit is implemented in the identification processing unit 290 of the data processing unit 12 and identifies the cause of hair loss based on past user data and medical data. The proposal unit is implemented in the identification processing unit 290 of the data processing unit 12 and proposes an optimal treatment plan based on the identified cause. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] As shown in Figure 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.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0151] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0155] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements, including the analysis unit, identification unit, and proposal unit described above, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit uses the camera 42 of the robot 414 to capture an image of the user's scalp and the control unit 46A performs image analysis. The identification unit is implemented in the identification processing unit 290 of the data processing unit 12 and identifies the cause of hair loss based on past user data and medical data. The proposal unit is implemented in the identification processing unit 290 of the data processing unit 12 and proposes an optimal treatment plan based on the identified cause. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0164] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0166] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0167] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0168] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0172] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0173] 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.

[0174] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0175] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0176] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0177] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0179] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0180] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0181] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0182] (Note 1) An analysis unit that analyzes the user's scalp condition in detail, An identification unit identifies the cause of hair loss based on the data analyzed by the aforementioned analysis unit, The system includes a proposal unit that proposes a treatment plan based on the cause identified by the aforementioned specific unit. A system characterized by the following features. (Note 2) The company has an improvement department that provides suggestions for improving lifestyle habits. The system described in Appendix 1, characterized by the features described herein. (Note 3) It is equipped with a monitoring unit to monitor the treatment effect. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, The system analyzes images of the scalp taken by the user with their smartphone. The system described in Appendix 1, characterized by the features described herein. (Note 5) The specified part is, Identifying the cause of hair loss based on past user data and medical data The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, We may suggest the use of specific medications or methods to improve diet and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, It estimates the user's emotions and adjusts the timing of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, When analyzing scalp images, the system improves analysis accuracy by referencing the user's past scalp condition data. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, When analyzing scalp images, using images under different lighting conditions improves the reliability of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, When analyzing scalp images, the analysis results are customized by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, When analyzing scalp images, we use user lifestyle data in conjunction to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 13) The specified part is, It estimates the user's emotions and adjusts the priority of identifying the cause of hair loss based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The specified part is, When identifying the cause of hair loss, the system uses the user's genetic information to improve accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 15) The specified part is, When identifying the cause of hair loss, the system monitors the user's scalp health in real time to improve the accuracy of the identification. The system described in Appendix 1, characterized by the features described herein. (Note 16) The specified part is, It estimates the user's emotions and adjusts how specific results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The specified part is, When identifying the cause of hair loss, we refer to the user's dietary data to improve the accuracy of the identification. The system described in Appendix 1, characterized by the features described herein. (Note 18) The specified part is, When identifying the cause of hair loss, we improve accuracy by taking into account the user's stress level. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, The system estimates the user's emotions and adjusts the treatment plan proposal method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When proposing a treatment plan, we refer to the user's past treatment history to suggest the most suitable treatment plan. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When proposing a treatment plan, we improve the accuracy of the proposal by taking into account the user's lifestyle data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, The system estimates the user's emotions and prioritizes treatment plans based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When proposing a treatment plan, we take the user's geographical location into consideration to propose the most suitable treatment plan. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When proposing a treatment plan, we analyze the user's social media activity and suggest a relevant treatment plan. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned improvement unit is, The system estimates the user's emotions and adjusts the method of suggesting lifestyle improvements based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned improvement unit is, When suggesting lifestyle improvements, the system refers to the user's past lifestyle data to propose the most suitable improvement methods. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned improvement unit is, The system estimates the user's emotions and determines the priority of lifestyle improvements based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned improvement unit is, When suggesting lifestyle improvements, we take the user's geographical location into consideration to propose the most suitable improvement method. The system described in Appendix 2, characterized by the features described herein. (Note 29) The monitoring unit, It estimates the user's emotions and adjusts the monitoring frequency based on the estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 30) The monitoring unit, During monitoring, the system improves monitoring accuracy by referencing the user's past treatment data. The system described in Appendix 3, characterized by the features described herein. (Note 31) The monitoring unit, It estimates the user's emotions and adjusts how monitoring results are displayed based on the estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 32) The monitoring unit, During monitoring, the accuracy of monitoring is improved by considering the user's geographical location information. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An analysis unit that analyzes the user's scalp condition in detail, An identification unit identifies the cause of hair loss based on the data analyzed by the aforementioned analysis unit, The system includes a proposal unit that proposes a treatment plan based on the cause identified by the aforementioned specific unit. A system characterized by the following features.

2. The company has an improvement department that provides suggestions for improving lifestyle habits. The system according to feature 1.

3. It is equipped with a monitoring unit to monitor the treatment effect. The system according to feature 1.

4. The aforementioned analysis unit, The system analyzes images of the scalp taken by the user with their smartphone. The system according to feature 1.

5. The specified part is, Identifying the cause of hair loss based on past user data and medical data The system according to feature 1.

6. The aforementioned proposal section is, We may suggest the use of specific medications or methods to improve diet and lifestyle. The system according to feature 1.

7. The aforementioned analysis unit, It estimates the user's emotions and adjusts the timing of the analysis based on the estimated user emotions. The system according to feature 1.

8. The aforementioned analysis unit, When analyzing scalp images, the system improves analysis accuracy by referencing the user's past scalp condition data. The system according to feature 1.

9. The aforementioned analysis unit, When analyzing scalp images, using images under different lighting conditions improves the reliability of the analysis. The system according to feature 1.

10. The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system according to feature 1.

Images