system

Abstract

The system according to this embodiment aims to identify the cause of hair loss and provide an optimal treatment plan for each individual. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives input of scalp images and lifestyle data from the user. The analysis unit analyzes the data received by the reception unit and identifies the cause of hair loss. The generation unit generates a treatment plan based on the cause identified by the analysis unit. The provision unit provides the treatment plan generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including 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 prior art, there was a problem that it was difficult to identify the cause of hair loss and provide an optimal treatment plan individually.

[0005] The system according to the embodiment aims to identify the cause of hair loss and provide an optimal treatment plan individually.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives input of scalp images and lifestyle data from the user. The analysis unit analyzes the data received by the reception unit and identifies the cause of hair loss. The generation unit generates a treatment plan based on the cause identified by the analysis unit. The provision unit provides the treatment plan generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can identify the cause of hair loss and provide an optimal treatment plan tailored to each individual. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to the communication I / F 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. ️ shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. ️, 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also 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) The hair loss treatment system according to an embodiment of the present invention is a system that utilizes cutting-edge AI technology to identify the cause of individual hair loss and propose a personalized treatment method. This hair loss treatment system begins with the user taking an image of their scalp at home and inputting data on their lifestyle. This data is sent to the AI, which then performs analysis. The AI ​​combines deep learning models and natural language processing to analyze the user's scalp condition and lifestyle in detail. Next, based on the analysis results, the AI ​​generates an optimal treatment plan for each individual user. This treatment plan includes specific treatment methods and suggestions for improving lifestyle habits. For example, it may suggest the use of a specific shampoo or hair growth product, or improvements to diet and exercise. Furthermore, the user can monitor the progress of the treatment by regularly taking images of their scalp and sending them to the AI. The AI ​​updates the treatment plan based on the latest data and makes new suggestions as needed. This system allows users to easily treat their hair loss at home, eliminating the need for frequent visits to hospitals or clinics. In addition, because an optimal treatment method is provided for each individual user, effective treatment can be expected. This allows the hair loss treatment system to analyze the user's scalp condition and lifestyle in detail, and provide an optimal treatment plan for each individual user.

[0029] The hair loss treatment system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives input of scalp images and lifestyle data from the user. Scalp images and lifestyle data from the user include, for example, scalp images taken with a smartphone and lifestyle data such as diet, exercise, and sleep entered by the user, but are not limited to such examples. For example, the reception unit can take images of the scalp using a smartphone camera and input lifestyle data using a dedicated app. The reception unit can also send the data entered by the user to a cloud server and convert it into a format that is easy for AI to analyze. The analysis unit analyzes the data received by the reception unit by combining a deep learning model and natural language processing to identify the cause of hair loss. For example, the analysis unit can analyze scalp images using a deep learning model and evaluate the condition of the scalp. The analysis unit can also analyze lifestyle data using natural language processing and evaluate the impact of the user's lifestyle on hair loss. For example, the analysis unit uses a deep learning model to evaluate the health of the scalp using scalp images as input. The analysis unit can also use a natural language processing model to analyze diet and exercise patterns using lifestyle data as input. The generation unit generates a treatment plan based on the causes identified by the analysis unit. The generation unit generates treatment plans such as using a specific shampoo or hair growth product, or improving diet and exercise. For example, the generation unit suggests the optimal shampoo or hair growth product for the user based on the analysis results. The generation unit can also generate treatment plans that include suggestions for improving diet and exercise. For example, the generation unit suggests a shampoo containing a specific ingredient based on the user's scalp condition. The generation unit can also suggest improvements to diet and exercise based on the user's lifestyle. The delivery unit provides the treatment plan generated by the generation unit. For example, the delivery unit notifies the user of the generated treatment plan. The delivery unit can also check the progress of treatment and update the treatment plan by having the user periodically take images of their scalp and send them to the AI. For example, the delivery unit sends a reminder to the user to periodically take images of their scalp.Furthermore, the service provider can update the treatment plan based on the latest data and notify the user. This allows the hair loss treatment system according to the embodiment to analyze the user's scalp condition and lifestyle in detail and provide an optimal treatment plan for each individual user.

[0030] The reception desk accepts scalp images and lifestyle data from users. This data includes, but is not limited to, images of the scalp taken with a smartphone and lifestyle data such as diet, exercise, and sleep entered by the user. For example, the reception desk can take scalp images using a smartphone camera and input lifestyle data using a dedicated app. Specifically, users download the dedicated app and take scalp images following the in-app guide. The captured images are sent to a cloud server via the app. Similarly, lifestyle data is entered by entering information such as diet, exercise frequency, and sleep duration into a form within the app. This data is sent to the cloud server and converted into a format easily analyzed by AI. Furthermore, the reception desk can provide guidelines on data entry and shooting methods to improve the accuracy of the user-entered data. For example, it can display advice on lighting conditions and shooting angles when taking scalp images within the app. It can also facilitate accurate data entry by providing specific question formats and options for lifestyle data input. This allows the reception unit to efficiently and accurately collect data from users, providing a foundation for the analysis unit to perform highly accurate analysis.

[0031] The analysis unit combines deep learning models and natural language processing to analyze data received by the reception unit and identify the causes of hair loss. For example, the analysis unit uses a deep learning model to analyze scalp images and evaluate the condition of the scalp. Specifically, it uses a deep learning model that takes scalp images as input to evaluate factors such as clogged pores, excessive sebum secretion, and the presence or absence of inflammation. This model is trained using a large amount of scalp image data and can evaluate the condition of the scalp with high accuracy. The analysis unit can also use natural language processing to analyze lifestyle data and evaluate the impact of a user's lifestyle on hair loss. For example, it analyzes data such as diet, exercise frequency, and sleep duration entered by the user and evaluates how these factors affect hair loss. The natural language processing model can analyze text data and evaluate factors such as the nutritional balance of the diet, the type of exercise, and the quality of sleep. Furthermore, the analysis unit can utilize past data and statistical information to perform long-term risk assessments and trend analyses. For example, based on past user data, it can statistically evaluate the impact of specific lifestyle habits on hair loss and predict future risks. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The generation unit generates a treatment plan based on the causes identified by the analysis unit. The generation unit generates treatment plans such as the use of specific shampoos or hair growth products, or improvements to diet and exercise. Specifically, based on the analysis results, it suggests the most suitable shampoo or hair growth product for the user. For example, if the scalp is dry, it suggests a shampoo containing moisturizing ingredients; if there is excessive sebum secretion, it suggests a shampoo containing sebum-controlling ingredients. The generation unit can also generate treatment plans that include suggestions for diet and exercise improvements. For example, regarding diet, it suggests consuming foods containing nutrients necessary for hair health; and regarding exercise, it recommends moderate exercise to promote blood circulation. Furthermore, the generation unit can make specific improvement suggestions based on the user's lifestyle. For example, it can suggest relaxation methods to improve sleep quality or mental care methods to reduce stress. This allows the generation unit to provide an optimal treatment plan for each individual user based on their scalp condition and lifestyle. Additionally, the generation unit continuously evaluates the effectiveness of the treatment plan and can modify it as needed. For example, based on scalp images and lifestyle data regularly provided by the user, the effectiveness of the treatment plan is evaluated, and if no effect is seen, a new treatment plan is proposed. In this way, the system can always provide the user with the optimal treatment plan and support the improvement of hair loss.

[0033] The service provider provides the treatment plan generated by the generation unit. For example, the service provider notifies the user of the generated treatment plan. Specifically, it notifies the user of the treatment plan details via a dedicated app, explaining the implementation method and precautions. The service provider can also monitor the progress of the treatment and update the treatment plan by having the user periodically take images of their scalp and send them to the AI. For example, the service provider sends reminders to the user to periodically take images of their scalp. These reminders are sent to the user's smartphone to ensure they don't miss the timing for taking the images. Furthermore, the service provider can update the treatment plan based on the latest data and notify the user. For example, it evaluates the effectiveness of the treatment plan based on the latest scalp images and lifestyle data provided by the user and proposes a new treatment plan as needed. In addition, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the treatment plan. For example, by having the user report their impressions and the effects of the treatment plan within the app, the service provider can revise the treatment plan based on that information. The service provider can also reliably transmit information using multiple communication methods. For example, important information is reliably delivered not only through smartphone notifications, but also through voice calls, SMS, and email. This allows the service provider to quickly and reliably provide users with actionable instructions, maximizing the effectiveness of hair loss treatment.

[0034] The reception unit can receive scalp images and lifestyle data taken by users at home. For example, users can take scalp images using their smartphone cameras and input lifestyle data using a dedicated app. The reception unit can also send the data entered by the user to a cloud server and convert it into a format that is easy for AI to analyze. For example, the reception unit can save scalp images taken with a smartphone camera in high resolution and send them to the cloud server. The reception unit can also send lifestyle data such as diet, exercise, and sleep entered by the user to the cloud server and convert it into a format that is easy for AI to analyze. This makes it easy for users to input data at home. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input the data entered by the user into a generating AI and have the generating AI perform the data format conversion.

[0035] The analysis unit can combine deep learning models and natural language processing to analyze the user's scalp condition and lifestyle in detail. For example, the analysis unit can use a deep learning model to analyze scalp images and evaluate the scalp condition. The analysis unit can also use natural language processing to analyze lifestyle data and evaluate the impact of the user's lifestyle on hair loss. For example, the analysis unit uses a deep learning model to evaluate the health of the scalp, taking scalp images as input. The analysis unit can also use a natural language processing model to analyze patterns of diet and exercise, taking lifestyle data as input. This improves the accuracy of identifying the cause of hair loss by analyzing the user's scalp condition and lifestyle in detail. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input scalp image data into a generating AI and have the generating AI perform an evaluation of the scalp condition.

[0036] The generation unit can generate treatment plans based on analysis results, including the use of specific shampoos and hair growth products, and improvements to diet and exercise. For example, the generation unit can suggest the most suitable shampoo and hair growth product to the user based on the analysis results. The generation unit can also generate treatment plans that include suggestions for improvements to diet and exercise. For example, the generation unit can suggest a shampoo containing specific ingredients based on the user's scalp condition. The generation unit can also suggest improvements to diet and exercise based on the user's lifestyle. This allows the generation unit to provide an optimal treatment plan for each individual user based on the analysis results. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the analysis results into a generation AI and have the generation AI generate the treatment plan.

[0037] The service provider can provide the generated treatment plan to the user. For example, the service provider can notify the user of the generated treatment plan. The service provider can also monitor the progress of the treatment and update the treatment plan by having the user periodically take images of their scalp and send them to the AI. For example, the service provider can send the user a reminder to periodically take images of their scalp. The service provider can also update the treatment plan based on the latest data and notify the user. This allows the user to receive the generated treatment plan. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the generated treatment plan into a generating AI and have the generating AI execute the notification to the user.

[0038] The service provider can monitor treatment progress and update treatment plans by having users periodically take images of their scalp and send them to the AI. For example, the service provider can send reminders to users to periodically take images of their scalp. The service provider can also update treatment plans based on the latest data and notify the user. For example, the service provider can send images of the scalp taken by the user to the AI, and the AI ​​can update the treatment plan based on the analysis results. This allows the user to monitor treatment progress and update treatment plans based on the latest data. Some or all of the above processes in the service provider may be performed using AI, or not using AI. For example, the service provider can input the scalp image data taken by the user into a generating AI and have the generating AI update the treatment plan.

[0039] The reception desk can analyze the user's past data entry history and select the optimal input method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. The reception desk can also analyze patterns in the data the user has entered in the past and suggest the most efficient input method. Furthermore, the reception desk can suggest the optimal input method for a specific time period based on the user's past input history. In this way, the reception desk can suggest the optimal input method by analyzing the user's past data entry history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past data entry history into a generating AI and have the generating AI select the optimal input method.

[0040] The reception desk can filter data input based on the user's current lifestyle and areas of interest. For example, if the user is interested in health, the reception desk will prioritize prompting health-related data input. The reception desk can also prompt data input in the form of simple questions if the user is busy. Furthermore, if the user is relaxed, the reception desk can prompt detailed data input. This allows for the collection of more relevant data by prompting data input based on the user's current lifestyle and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's lifestyle data into a generating AI and have the generating AI perform the data input filtering.

[0041] The reception desk can prioritize the input of highly relevant data based on the user's geographical location information during data entry. For example, if the user is in a specific region, the reception desk can prioritize prompting the input of data related to that region. Furthermore, if the user is traveling, the reception desk can prioritize prompting the input of data related to their travel destination. Additionally, if the user is at home, the reception desk can prioritize prompting the input of data related to their home. This allows for the collection of more appropriate data by prioritizing the input of highly relevant data based on the user's geographical location information. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location information into a generating AI and have the generating AI prioritize the input of highly relevant data.

[0042] The reception desk can analyze a user's social media activity and input relevant data when data is entered. For example, if a user posts about health on social media, the reception desk can prioritize prompting the user to input health-related data. Similarly, if a user posts about stress on social media, the reception desk can prioritize prompting the user to input stress-related data. Furthermore, if a user posts about travel on social media, the reception desk can prioritize prompting the user to input travel-related data. This allows for the priority input of relevant data by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI input the relevant data.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on important data. It can also perform a simplified analysis on general data. Furthermore, the analysis unit can perform a detailed analysis on data of high user interest. In this way, by adjusting the level of detail of the analysis based on the importance of the data, a detailed analysis can be performed on important data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a health-specific analysis algorithm to health data. It can also apply a lifestyle-specific analysis algorithm to lifestyle data. Furthermore, it can apply an image analysis algorithm to scalp image data. By applying different analysis algorithms depending on the data category, it is possible to provide more appropriate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0045] The analysis unit can determine the priority of analysis based on the data submission timing during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent data. It can also prioritize the analysis of important data specified by the user. Furthermore, the analysis unit may prioritize the analysis of data submitted periodically. This allows for the prioritization of the analysis of the most recent data by determining the priority of analysis based on the data submission timing. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data submission timing into a generating AI and have the generating AI determine the analysis priority.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also prioritize the analysis of data of high user interest. Furthermore, it can prioritize the analysis of important data. By adjusting the order of analysis based on the relevance of the data, it is possible to prioritize the analysis of highly relevant data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0047] The generation unit can adjust the level of detail of a treatment plan based on the importance of the analysis results when generating the treatment plan. For example, the generation unit can provide a detailed treatment plan based on important analysis results. The generation unit can also provide a concise treatment plan based on general analysis results. Furthermore, the generation unit can provide a detailed treatment plan based on analysis results of high user interest. This allows for the provision of detailed treatment plans based on important analysis results by adjusting the level of detail of the plan based on the importance of the analysis results. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the importance of the analysis results into the generation AI and have the generation AI perform the adjustment of the level of detail of the plan.

[0048] The generation unit can apply different plan generation algorithms depending on the category of the analysis results when generating treatment plans. For example, the generation unit can generate health-specific treatment plans based on health data. It can also generate lifestyle-specific treatment plans based on lifestyle data. Furthermore, it can generate image analysis-specific treatment plans based on scalp image data. This allows for the provision of more appropriate treatment plans by applying different plan generation algorithms depending on the category of the analysis results. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the category of the analysis results into a generation AI and cause the generation AI to apply different plan generation algorithms.

[0049] The generation unit can determine the priority of treatment plans based on the timing of analysis result submissions when generating treatment plans. For example, the generation unit can provide treatment plans based on the latest analysis results. The generation unit can also provide treatment plans based on important analysis results specified by the user. Furthermore, the generation unit can provide treatment plans based on analysis results submitted periodically. This allows the generation unit to provide treatment plans based on the latest analysis results by determining the priority of plans based on the timing of analysis result submissions. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the timing of analysis result submissions into the generation AI and have the generation AI perform the determination of plan priorities.

[0050] The generation unit can adjust the order of treatment plans based on the relevance of the analysis results when generating treatment plans. For example, the generation unit can provide treatment plans based on highly relevant analysis results. The generation unit can also provide treatment plans based on analysis results of high user interest. Furthermore, the generation unit can provide treatment plans based on important analysis results. By adjusting the order of plans based on the relevance of the analysis results, it is possible to provide treatment plans based on highly relevant analysis results. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the relevance of the analysis results into a generation AI and have the generation AI perform the adjustment of the order of the plans.

[0051] The service provider can select the optimal delivery method by referring to the user's past treatment history when providing a treatment plan. For example, the service provider may prioritize providing treatment methods that have been effective for the user in the past. It can also avoid treatment methods the user has tried before and offer new methods. Furthermore, the service provider can analyze the user's past treatment history and provide the optimal treatment method. This allows the service provider to provide the optimal treatment method by referring to the user's past treatment history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past treatment history into a generating AI and have the generating AI select the optimal delivery method.

[0052] The service provider can customize the delivery method based on the user's current lifestyle when providing a treatment plan. For example, if the user is busy, the service provider can provide a treatment method that can be easily performed. Alternatively, if the user is relaxed, the service provider can provide a more detailed treatment method. Furthermore, if the user is traveling, the service provider can provide a portable treatment method. This allows for the provision of more appropriate treatment methods by customizing the delivery method based on the user's current lifestyle. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user lifestyle data into a generating AI and have the generating AI perform the customization of the delivery method.

[0053] The service provider can select the optimal delivery method based on the user's geographical location when providing a treatment plan. For example, if the user is in an urban area, the service provider can provide a treatment method that can be purchased at a nearby pharmacy. If the user is in a rural area, the service provider can also provide a treatment method that can be purchased online. Furthermore, if the user is traveling, the service provider can provide a treatment method that can be performed at their travel destination. This allows for the provision of more appropriate treatment methods by selecting the optimal delivery method based on the user's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI select the optimal delivery method.

[0054] The service provider can analyze the user's social media activity and propose means of providing a treatment plan. For example, if the user posts about health on social media, the service provider can provide health-related treatment methods. The service provider can also provide stress-reducing treatment methods if the user posts about stress on social media. Furthermore, if the service provider posts about travel on social media, the service provider can provide treatment methods that can be implemented during travel. This allows the service provider to provide relevant treatment methods by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI propose means of providing treatment.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The reception desk can analyze the user's past data entry history and select the optimal input method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. The reception desk can also analyze patterns in the data the user has entered in the past and suggest the most efficient input method. Furthermore, the reception desk can suggest the optimal input method for a specific time period based on the user's past input history. In this way, the reception desk can suggest the optimal input method by analyzing the user's past data entry history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past data entry history into a generating AI and have the generating AI select the optimal input method.

[0057] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on important data. It can also perform a simplified analysis on general data. Furthermore, the analysis unit can perform a detailed analysis on data of high user interest. In this way, by adjusting the level of detail of the analysis based on the importance of the data, a detailed analysis can be performed on important data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0058] The generation unit can adjust the level of detail of a treatment plan based on the importance of the analysis results when generating the treatment plan. For example, the generation unit can provide a detailed treatment plan based on important analysis results. The generation unit can also provide a concise treatment plan based on general analysis results. Furthermore, the generation unit can provide a detailed treatment plan based on analysis results of high user interest. This allows for the provision of detailed treatment plans based on important analysis results by adjusting the level of detail of the plan based on the importance of the analysis results. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the importance of the analysis results into the generation AI and have the generation AI perform the adjustment of the level of detail of the plan.

[0059] The service provider can select the optimal delivery method by referring to the user's past treatment history when providing a treatment plan. For example, the service provider may prioritize providing treatment methods that have been effective for the user in the past. It can also avoid treatment methods the user has tried before and offer new methods. Furthermore, the service provider can analyze the user's past treatment history and provide the optimal treatment method. This allows the service provider to provide the optimal treatment method by referring to the user's past treatment history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past treatment history into a generating AI and have the generating AI select the optimal delivery method.

[0060] The service provider can customize the delivery method based on the user's current lifestyle when providing a treatment plan. For example, if the user is busy, the service provider can provide a treatment method that can be easily performed. Alternatively, if the user is relaxed, the service provider can provide a more detailed treatment method. Furthermore, if the user is traveling, the service provider can provide a portable treatment method. This allows for the provision of more appropriate treatment methods by customizing the delivery method based on the user's current lifestyle. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user lifestyle data into a generating AI and have the generating AI perform the customization of the delivery method.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The reception desk receives scalp images and lifestyle data from users. For example, users can take a picture of their scalp using their smartphone camera and input lifestyle data using a dedicated app. The reception desk can also send the data entered by the user to a cloud server and convert it into a format that is easy for AI to analyze. Step 2: The analysis unit combines deep learning models and natural language processing to analyze the data received by the reception unit and identify the cause of hair loss. For example, it uses a deep learning model to analyze scalp images and evaluate the condition of the scalp. It also uses natural language processing to analyze lifestyle data and evaluate the impact of the user's lifestyle on hair loss. Step 3: The generation unit generates a treatment plan based on the causes identified by the analysis unit. For example, it may generate a treatment plan that includes the use of a specific shampoo or hair growth product, or improvements to diet and exercise. Based on the analysis results, the generation unit suggests the most suitable shampoo or hair growth product for the user. It can also generate a treatment plan that includes suggestions for improving diet and exercise. Step 4: The provider unit provides the treatment plan generated by the generator unit. For example, it notifies the user of the generated treatment plan. The user can also periodically take images of their scalp and send them to the AI ​​to monitor the progress of the treatment and update the treatment plan. The provider unit sends reminders to the user to periodically take images of their scalp. It can also update the treatment plan based on the latest data and notify the user.

[0063] (Example of form 2) The hair loss treatment system according to an embodiment of the present invention is a system that utilizes cutting-edge AI technology to identify the cause of individual hair loss and propose a personalized treatment method. This hair loss treatment system begins with the user taking an image of their scalp at home and inputting data on their lifestyle. This data is sent to the AI, which then performs analysis. The AI ​​combines deep learning models and natural language processing to analyze the user's scalp condition and lifestyle in detail. Next, based on the analysis results, the AI ​​generates an optimal treatment plan for each individual user. This treatment plan includes specific treatment methods and suggestions for improving lifestyle habits. For example, it may suggest the use of a specific shampoo or hair growth product, or improvements to diet and exercise. Furthermore, the user can monitor the progress of the treatment by regularly taking images of their scalp and sending them to the AI. The AI ​​updates the treatment plan based on the latest data and makes new suggestions as needed. This system allows users to easily treat their hair loss at home, eliminating the need for frequent visits to hospitals or clinics. In addition, because an optimal treatment method is provided for each individual user, effective treatment can be expected. This allows the hair loss treatment system to analyze the user's scalp condition and lifestyle in detail, and provide an optimal treatment plan for each individual user.

[0064] The hair loss treatment system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives input of scalp images and lifestyle data from the user. Scalp images and lifestyle data from the user include, for example, scalp images taken with a smartphone and lifestyle data such as diet, exercise, and sleep entered by the user, but are not limited to such examples. For example, the reception unit can take images of the scalp using a smartphone camera and input lifestyle data using a dedicated app. The reception unit can also send the data entered by the user to a cloud server and convert it into a format that is easy for AI to analyze. The analysis unit analyzes the data received by the reception unit by combining a deep learning model and natural language processing to identify the cause of hair loss. For example, the analysis unit can analyze scalp images using a deep learning model and evaluate the condition of the scalp. The analysis unit can also analyze lifestyle data using natural language processing and evaluate the impact of the user's lifestyle on hair loss. For example, the analysis unit uses a deep learning model to evaluate the health of the scalp using scalp images as input. The analysis unit can also use a natural language processing model to analyze diet and exercise patterns using lifestyle data as input. The generation unit generates a treatment plan based on the causes identified by the analysis unit. The generation unit generates treatment plans such as using a specific shampoo or hair growth product, or improving diet and exercise. For example, the generation unit suggests the optimal shampoo or hair growth product for the user based on the analysis results. The generation unit can also generate treatment plans that include suggestions for improving diet and exercise. For example, the generation unit suggests a shampoo containing a specific ingredient based on the user's scalp condition. The generation unit can also suggest improvements to diet and exercise based on the user's lifestyle. The delivery unit provides the treatment plan generated by the generation unit. For example, the delivery unit notifies the user of the generated treatment plan. The delivery unit can also check the progress of treatment and update the treatment plan by having the user periodically take images of their scalp and send them to the AI. For example, the delivery unit sends a reminder to the user to periodically take images of their scalp.Furthermore, the service provider can update the treatment plan based on the latest data and notify the user. This allows the hair loss treatment system according to the embodiment to analyze the user's scalp condition and lifestyle in detail and provide an optimal treatment plan for each individual user.

[0065] The reception desk accepts scalp images and lifestyle data from users. This data includes, but is not limited to, images of the scalp taken with a smartphone and lifestyle data such as diet, exercise, and sleep entered by the user. For example, the reception desk can take scalp images using a smartphone camera and input lifestyle data using a dedicated app. Specifically, users download the dedicated app and take scalp images following the in-app guide. The captured images are sent to a cloud server via the app. Similarly, lifestyle data is entered by entering information such as diet, exercise frequency, and sleep duration into a form within the app. This data is sent to the cloud server and converted into a format easily analyzed by AI. Furthermore, the reception desk can provide guidelines on data entry and shooting methods to improve the accuracy of the user-entered data. For example, it can display advice on lighting conditions and shooting angles when taking scalp images within the app. It can also facilitate accurate data entry by providing specific question formats and options for lifestyle data input. This allows the reception unit to efficiently and accurately collect data from users, providing a foundation for the analysis unit to perform highly accurate analysis.

[0066] The analysis unit combines deep learning models and natural language processing to analyze data received by the reception unit and identify the causes of hair loss. For example, the analysis unit uses a deep learning model to analyze scalp images and evaluate the condition of the scalp. Specifically, it uses a deep learning model that takes scalp images as input to evaluate factors such as clogged pores, excessive sebum secretion, and the presence or absence of inflammation. This model is trained using a large amount of scalp image data and can evaluate the condition of the scalp with high accuracy. The analysis unit can also use natural language processing to analyze lifestyle data and evaluate the impact of a user's lifestyle on hair loss. For example, it analyzes data such as diet, exercise frequency, and sleep duration entered by the user and evaluates how these factors affect hair loss. The natural language processing model can analyze text data and evaluate factors such as the nutritional balance of the diet, the type of exercise, and the quality of sleep. Furthermore, the analysis unit can utilize past data and statistical information to perform long-term risk assessments and trend analyses. For example, based on past user data, it can statistically evaluate the impact of specific lifestyle habits on hair loss and predict future risks. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0067] The generation unit generates a treatment plan based on the causes identified by the analysis unit. The generation unit generates treatment plans such as the use of specific shampoos or hair growth products, or improvements to diet and exercise. Specifically, based on the analysis results, it suggests the most suitable shampoo or hair growth product for the user. For example, if the scalp is dry, it suggests a shampoo containing moisturizing ingredients; if there is excessive sebum secretion, it suggests a shampoo containing sebum-controlling ingredients. The generation unit can also generate treatment plans that include suggestions for diet and exercise improvements. For example, regarding diet, it suggests consuming foods containing nutrients necessary for hair health; and regarding exercise, it recommends moderate exercise to promote blood circulation. Furthermore, the generation unit can make specific improvement suggestions based on the user's lifestyle. For example, it can suggest relaxation methods to improve sleep quality or mental care methods to reduce stress. This allows the generation unit to provide an optimal treatment plan for each individual user based on their scalp condition and lifestyle. Additionally, the generation unit continuously evaluates the effectiveness of the treatment plan and can modify it as needed. For example, based on scalp images and lifestyle data regularly provided by the user, the effectiveness of the treatment plan is evaluated, and if no effect is seen, a new treatment plan is proposed. In this way, the system can always provide the user with the optimal treatment plan and support the improvement of hair loss.

[0068] The service provider provides the treatment plan generated by the generation unit. For example, the service provider notifies the user of the generated treatment plan. Specifically, it notifies the user of the treatment plan details via a dedicated app, explaining the implementation method and precautions. The service provider can also monitor the progress of the treatment and update the treatment plan by having the user periodically take images of their scalp and send them to the AI. For example, the service provider sends reminders to the user to periodically take images of their scalp. These reminders are sent to the user's smartphone to ensure they don't miss the timing for taking the images. Furthermore, the service provider can update the treatment plan based on the latest data and notify the user. For example, it evaluates the effectiveness of the treatment plan based on the latest scalp images and lifestyle data provided by the user and proposes a new treatment plan as needed. In addition, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the treatment plan. For example, by having the user report their impressions and the effects of the treatment plan within the app, the service provider can revise the treatment plan based on that information. The service provider can also reliably transmit information using multiple communication methods. For example, important information is reliably delivered not only through smartphone notifications, but also through voice calls, SMS, and email. This allows the service provider to quickly and reliably provide users with actionable instructions, maximizing the effectiveness of hair loss treatment.

[0069] The reception unit can receive scalp images and lifestyle data taken by users at home. For example, users can take scalp images using their smartphone cameras and input lifestyle data using a dedicated app. The reception unit can also send the data entered by the user to a cloud server and convert it into a format that is easy for AI to analyze. For example, the reception unit can save scalp images taken with a smartphone camera in high resolution and send them to the cloud server. The reception unit can also send lifestyle data such as diet, exercise, and sleep entered by the user to the cloud server and convert it into a format that is easy for AI to analyze. This makes it easy for users to input data at home. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input the data entered by the user into a generating AI and have the generating AI perform the data format conversion.

[0070] The analysis unit can combine deep learning models and natural language processing to analyze the user's scalp condition and lifestyle in detail. For example, the analysis unit can use a deep learning model to analyze scalp images and evaluate the scalp condition. The analysis unit can also use natural language processing to analyze lifestyle data and evaluate the impact of the user's lifestyle on hair loss. For example, the analysis unit uses a deep learning model to evaluate the health of the scalp, taking scalp images as input. The analysis unit can also use a natural language processing model to analyze patterns of diet and exercise, taking lifestyle data as input. This improves the accuracy of identifying the cause of hair loss by analyzing the user's scalp condition and lifestyle in detail. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input scalp image data into a generating AI and have the generating AI perform an evaluation of the scalp condition.

[0071] The generation unit can generate treatment plans based on analysis results, including the use of specific shampoos and hair growth products, and improvements to diet and exercise. For example, the generation unit can suggest the most suitable shampoo and hair growth product to the user based on the analysis results. The generation unit can also generate treatment plans that include suggestions for improvements to diet and exercise. For example, the generation unit can suggest a shampoo containing specific ingredients based on the user's scalp condition. The generation unit can also suggest improvements to diet and exercise based on the user's lifestyle. This allows the generation unit to provide an optimal treatment plan for each individual user based on the analysis results. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the analysis results into a generation AI and have the generation AI generate the treatment plan.

[0072] The service provider can provide the generated treatment plan to the user. For example, the service provider can notify the user of the generated treatment plan. The service provider can also monitor the progress of the treatment and update the treatment plan by having the user periodically take images of their scalp and send them to the AI. For example, the service provider can send the user a reminder to periodically take images of their scalp. The service provider can also update the treatment plan based on the latest data and notify the user. This allows the user to receive the generated treatment plan. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the generated treatment plan into a generating AI and have the generating AI execute the notification to the user.

[0073] The service provider can monitor treatment progress and update treatment plans by having users periodically take images of their scalp and send them to the AI. For example, the service provider can send reminders to users to periodically take images of their scalp. The service provider can also update treatment plans based on the latest data and notify the user. For example, the service provider can send images of the scalp taken by the user to the AI, and the AI ​​can update the treatment plan based on the analysis results. This allows the user to monitor treatment progress and update treatment plans based on the latest data. Some or all of the above processes in the service provider may be performed using AI, or not using AI. For example, the service provider can input the scalp image data taken by the user into a generating AI and have the generating AI update the treatment plan.

[0074] The reception desk can estimate the user's emotions and adjust the timing of data entry based on the estimated emotions. For example, if the user is feeling stressed, the reception desk can send a notification prompting them to enter data during a time when they can relax. The reception desk can also send an immediate notification prompting data entry if the user is relaxed. Furthermore, if the user is busy, the reception desk can send a reminder to enter data according to their schedule. This allows for data entry at a more appropriate time by adjusting the timing of data entry according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0075] The reception desk can analyze the user's past data entry history and select the optimal input method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. The reception desk can also analyze patterns in the data the user has entered in the past and suggest the most efficient input method. Furthermore, the reception desk can suggest the optimal input method for a specific time period based on the user's past input history. In this way, the reception desk can suggest the optimal input method by analyzing the user's past data entry history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past data entry history into a generating AI and have the generating AI select the optimal input method.

[0076] The reception desk can filter data input based on the user's current lifestyle and areas of interest. For example, if the user is interested in health, the reception desk will prioritize prompting health-related data input. The reception desk can also prompt data input in the form of simple questions if the user is busy. Furthermore, if the user is relaxed, the reception desk can prompt detailed data input. This allows for the collection of more relevant data by prompting data input based on the user's current lifestyle and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's lifestyle data into a generating AI and have the generating AI perform the data input filtering.

[0077] The reception desk can estimate the user's emotions and prioritize input data based on the estimated emotions. For example, if the user is stressed, the reception desk may prompt for important data input first. It can also prompt for detailed data input first if the user is relaxed. Furthermore, if the user is busy, it can prompt for simple data input first. This allows for priority input of important data by prioritizing input data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0078] The reception desk can prioritize the input of highly relevant data based on the user's geographical location information during data entry. For example, if the user is in a specific region, the reception desk can prioritize prompting the input of data related to that region. Furthermore, if the user is traveling, the reception desk can prioritize prompting the input of data related to their travel destination. Additionally, if the user is at home, the reception desk can prioritize prompting the input of data related to their home. This allows for the collection of more appropriate data by prioritizing the input of highly relevant data based on the user's geographical location information. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location information into a generating AI and have the generating AI prioritize the input of highly relevant data.

[0079] The reception desk can analyze a user's social media activity and input relevant data when data is entered. For example, if a user posts about health on social media, the reception desk can prioritize prompting the user to input health-related data. Similarly, if a user posts about stress on social media, the reception desk can prioritize prompting the user to input stress-related data. Furthermore, if a user posts about travel on social media, the reception desk can prioritize prompting the user to input travel-related data. This allows for the priority input of relevant data by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI input the relevant data.

[0080] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is stressed, the analysis unit can also provide concise analysis results. Furthermore, if the user is excited, the analysis unit can provide visually stimulating analysis results. By adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on important data. It can also perform a simplified analysis on general data. Furthermore, the analysis unit can perform a detailed analysis on data of high user interest. In this way, by adjusting the level of detail of the analysis based on the importance of the data, a detailed analysis can be performed on important data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0082] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a health-specific analysis algorithm to health data. It can also apply a lifestyle-specific analysis algorithm to lifestyle data. Furthermore, it can apply an image analysis algorithm to scalp image data. By applying different analysis algorithms depending on the data category, it is possible to provide more appropriate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0083] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short analysis result. It can also provide a detailed analysis result if the user is relaxed. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis result. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0084] The analysis unit can determine the priority of analysis based on the data submission timing during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent data. It can also prioritize the analysis of important data specified by the user. Furthermore, the analysis unit may prioritize the analysis of data submitted periodically. This allows for the prioritization of the analysis of the most recent data by determining the priority of analysis based on the data submission timing. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data submission timing into a generating AI and have the generating AI determine the analysis priority.

[0085] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also prioritize the analysis of data of high user interest. Furthermore, it can prioritize the analysis of important data. By adjusting the order of analysis based on the relevance of the data, it is possible to prioritize the analysis of highly relevant data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0086] The generation unit can estimate the user's emotions and adjust the presentation of the treatment plan based on the estimated emotions. For example, if the user is relaxed, the generation unit can provide a detailed treatment plan. If the user is stressed, the generation unit can also provide a concise treatment plan. Furthermore, if the user is agitated, the generation unit can provide a visually stimulating treatment plan. This allows for the provision of a more appropriate treatment plan by adjusting the presentation of the treatment plan according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, or not using AI. For example, the generation unit can input user facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0087] The generation unit can adjust the level of detail of a treatment plan based on the importance of the analysis results when generating the treatment plan. For example, the generation unit can provide a detailed treatment plan based on important analysis results. The generation unit can also provide a concise treatment plan based on general analysis results. Furthermore, the generation unit can provide a detailed treatment plan based on analysis results of high user interest. This allows for the provision of detailed treatment plans based on important analysis results by adjusting the level of detail of the plan based on the importance of the analysis results. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the importance of the analysis results into the generation AI and have the generation AI perform the adjustment of the level of detail of the plan.

[0088] The generation unit can apply different plan generation algorithms depending on the category of the analysis results when generating treatment plans. For example, the generation unit can generate health-specific treatment plans based on health data. It can also generate lifestyle-specific treatment plans based on lifestyle data. Furthermore, it can generate image analysis-specific treatment plans based on scalp image data. This allows for the provision of more appropriate treatment plans by applying different plan generation algorithms depending on the category of the analysis results. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the category of the analysis results into a generation AI and cause the generation AI to apply different plan generation algorithms.

[0089] The generation unit can estimate the user's emotions and adjust the length of the treatment plan based on the estimated emotions. For example, if the user is in a hurry, the generation unit can provide a short treatment plan. It can also provide a detailed treatment plan if the user is relaxed. Furthermore, if the user is excited, the generation unit can provide a visually stimulating treatment plan. This allows for the provision of a more appropriate treatment plan by adjusting the length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, or not. For example, the generation unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0090] The generation unit can determine the priority of treatment plans based on the timing of analysis result submissions when generating treatment plans. For example, the generation unit can provide treatment plans based on the latest analysis results. The generation unit can also provide treatment plans based on important analysis results specified by the user. Furthermore, the generation unit can provide treatment plans based on analysis results submitted periodically. This allows the generation unit to provide treatment plans based on the latest analysis results by determining the priority of plans based on the timing of analysis result submissions. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the timing of analysis result submissions into the generation AI and have the generation AI perform the determination of plan priorities.

[0091] The generation unit can adjust the order of treatment plans based on the relevance of the analysis results when generating treatment plans. For example, the generation unit can provide treatment plans based on highly relevant analysis results. The generation unit can also provide treatment plans based on analysis results of high user interest. Furthermore, the generation unit can provide treatment plans based on important analysis results. By adjusting the order of plans based on the relevance of the analysis results, it is possible to provide treatment plans based on highly relevant analysis results. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the relevance of the analysis results into a generation AI and have the generation AI perform the adjustment of the order of the plans.

[0092] The service provider can estimate the user's emotions and adjust the method of delivering the treatment plan based on the estimated emotions. For example, if the user is relaxed, the service provider can provide a detailed treatment plan. If the user is stressed, the service provider can also provide a concise treatment plan. Furthermore, if the user is agitated, the service provider can provide a visually stimulating treatment plan. This allows for the delivery of a more appropriate treatment plan by adjusting the method of delivery according to the user's emotions. 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. Some or all of the above processing in the service provider may be performed using AI, or not using AI. For example, the service provider can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0093] The service provider can select the optimal delivery method by referring to the user's past treatment history when providing a treatment plan. For example, the service provider may prioritize providing treatment methods that have been effective for the user in the past. It can also avoid treatment methods the user has tried before and offer new methods. Furthermore, the service provider can analyze the user's past treatment history and provide the optimal treatment method. This allows the service provider to provide the optimal treatment method by referring to the user's past treatment history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past treatment history into a generating AI and have the generating AI select the optimal delivery method.

[0094] The service provider can customize the delivery method based on the user's current lifestyle when providing a treatment plan. For example, if the user is busy, the service provider can provide a treatment method that can be easily performed. Alternatively, if the user is relaxed, the service provider can provide a more detailed treatment method. Furthermore, if the user is traveling, the service provider can provide a portable treatment method. This allows for the provision of more appropriate treatment methods by customizing the delivery method based on the user's current lifestyle. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user lifestyle data into a generating AI and have the generating AI perform the customization of the delivery method.

[0095] The service provider can estimate the user's emotions and prioritize treatment plans based on those emotions. For example, if the user is stressed, the service provider will prioritize providing a treatment plan focused on stress reduction. If the user is relaxed, the service provider may also prioritize providing a long-term treatment plan. Furthermore, if the user is in a hurry, the service provider may prioritize providing a treatment plan with immediate effects. This allows for the provision of more appropriate treatment plans by prioritizing treatment plans according to the user's emotions. 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. Some or all of the above-described processes in the service provider may be performed using AI or not. For example, the service provider can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0096] The service provider can select the optimal delivery method based on the user's geographical location when providing a treatment plan. For example, if the user is in an urban area, the service provider can provide a treatment method that can be purchased at a nearby pharmacy. If the user is in a rural area, the service provider can also provide a treatment method that can be purchased online. Furthermore, if the user is traveling, the service provider can provide a treatment method that can be performed at their travel destination. This allows for the provision of more appropriate treatment methods by selecting the optimal delivery method based on the user's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI select the optimal delivery method.

[0097] The service provider can analyze the user's social media activity and propose means of providing a treatment plan. For example, if the user posts about health on social media, the service provider can provide health-related treatment methods. The service provider can also provide stress-reducing treatment methods if the user posts about stress on social media. Furthermore, if the service provider posts about travel on social media, the service provider can provide treatment methods that can be implemented during travel. This allows the service provider to provide relevant treatment methods by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI propose means of providing treatment.

[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 reception desk can estimate the user's emotions and adjust the timing of data entry based on the estimated emotions. For example, if the user is feeling stressed, the reception desk can send a notification prompting them to enter data during a time when they can relax. The reception desk can also send an immediate notification prompting data entry if the user is relaxed. Furthermore, if the user is busy, the reception desk can send a reminder to enter data according to their schedule. This allows for data entry at a more appropriate time by adjusting the timing of data entry according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0100] The reception desk can analyze the user's past data entry history and select the optimal input method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. The reception desk can also analyze patterns in the data the user has entered in the past and suggest the most efficient input method. Furthermore, the reception desk can suggest the optimal input method for a specific time period based on the user's past input history. In this way, the reception desk can suggest the optimal input method by analyzing the user's past data entry history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past data entry history into a generating AI and have the generating AI select the optimal input method.

[0101] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is stressed, the analysis unit can also provide concise analysis results. Furthermore, if the user is excited, the analysis unit can provide visually stimulating analysis results. By adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0102] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on important data. It can also perform a simplified analysis on general data. Furthermore, the analysis unit can perform a detailed analysis on data of high user interest. In this way, by adjusting the level of detail of the analysis based on the importance of the data, a detailed analysis can be performed on important data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0103] The generation unit can estimate the user's emotions and adjust the presentation of the treatment plan based on the estimated emotions. For example, if the user is relaxed, the generation unit can provide a detailed treatment plan. If the user is stressed, the generation unit can also provide a concise treatment plan. Furthermore, if the user is agitated, the generation unit can provide a visually stimulating treatment plan. This allows for the provision of a more appropriate treatment plan by adjusting the presentation of the treatment plan according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, or not using AI. For example, the generation unit can input user facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0104] The generation unit can adjust the level of detail of a treatment plan based on the importance of the analysis results when generating the treatment plan. For example, the generation unit can provide a detailed treatment plan based on important analysis results. The generation unit can also provide a concise treatment plan based on general analysis results. Furthermore, the generation unit can provide a detailed treatment plan based on analysis results of high user interest. This allows for the provision of detailed treatment plans based on important analysis results by adjusting the level of detail of the plan based on the importance of the analysis results. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the importance of the analysis results into the generation AI and have the generation AI perform the adjustment of the level of detail of the plan.

[0105] The service provider can estimate the user's emotions and adjust the method of delivering the treatment plan based on the estimated emotions. For example, if the user is relaxed, the service provider can provide a detailed treatment plan. If the user is stressed, the service provider can also provide a concise treatment plan. Furthermore, if the user is agitated, the service provider can provide a visually stimulating treatment plan. This allows for the delivery of a more appropriate treatment plan by adjusting the method of delivery according to the user's emotions. 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. Some or all of the above processing in the service provider may be performed using AI, or not using AI. For example, the service provider can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0106] The service provider can select the optimal delivery method by referring to the user's past treatment history when providing a treatment plan. For example, the service provider may prioritize providing treatment methods that have been effective for the user in the past. It can also avoid treatment methods the user has tried before and offer new methods. Furthermore, the service provider can analyze the user's past treatment history and provide the optimal treatment method. This allows the service provider to provide the optimal treatment method by referring to the user's past treatment history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past treatment history into a generating AI and have the generating AI select the optimal delivery method.

[0107] The service provider can customize the delivery method based on the user's current lifestyle when providing a treatment plan. For example, if the user is busy, the service provider can provide a treatment method that can be easily performed. Alternatively, if the user is relaxed, the service provider can provide a more detailed treatment method. Furthermore, if the user is traveling, the service provider can provide a portable treatment method. This allows for the provision of more appropriate treatment methods by customizing the delivery method based on the user's current lifestyle. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user lifestyle data into a generating AI and have the generating AI perform the customization of the delivery method.

[0108] The service provider can estimate the user's emotions and prioritize treatment plans based on those emotions. For example, if the user is stressed, the service provider will prioritize providing a treatment plan focused on stress reduction. If the user is relaxed, the service provider may also prioritize providing a long-term treatment plan. Furthermore, if the user is in a hurry, the service provider may prioritize providing a treatment plan with immediate effects. This allows for the provision of more appropriate treatment plans by prioritizing treatment plans according to the user's emotions. 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. Some or all of the above-described processes in the service provider may be performed using AI or not. For example, the service provider can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

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

[0110] Step 1: The reception desk receives scalp images and lifestyle data from users. For example, users can take a picture of their scalp using their smartphone camera and input lifestyle data using a dedicated app. The reception desk can also send the data entered by the user to a cloud server and convert it into a format that is easy for AI to analyze. Step 2: The analysis unit combines deep learning models and natural language processing to analyze the data received by the reception unit and identify the cause of hair loss. For example, it uses a deep learning model to analyze scalp images and evaluate the condition of the scalp. It also uses natural language processing to analyze lifestyle data and evaluate the impact of the user's lifestyle on hair loss. Step 3: The generation unit generates a treatment plan based on the causes identified by the analysis unit. For example, it may generate a treatment plan that includes the use of a specific shampoo or hair growth product, or improvements to diet and exercise. Based on the analysis results, the generation unit suggests the most suitable shampoo or hair growth product for the user. It can also generate a treatment plan that includes suggestions for improving diet and exercise. Step 4: The provider unit provides the treatment plan generated by the generator unit. For example, it notifies the user of the generated treatment plan. The user can also periodically take images of their scalp and send them to the AI ​​to monitor the progress of the treatment and update the treatment plan. The provider unit sends reminders to the user to periodically take images of their scalp. It can also update the treatment plan based on the latest data and notify the user.

[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 reception unit, analysis unit, generation unit, and provision unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit can take an image of the scalp using the camera 42 of the smart device 14 and input lifestyle data using a dedicated application. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the scalp image using a deep learning model and analyzes lifestyle data using natural language processing. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which generates a treatment plan based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the smart device 14, which notifies the user of the generated treatment plan and sends a reminder to take images of the scalp periodically. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed 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 signals from the user and accepts instructions from the user. 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 described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit can capture an image of the scalp using the camera 42 of the smart glasses 214 and input lifestyle data using a dedicated app. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the scalp image using a deep learning model and analyzes lifestyle data using natural language processing. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which generates a treatment plan based on the analysis results. The provision unit is implemented by the control unit 46A of the smart glasses 214, which notifies the user of the generated treatment plan and sends a reminder to periodically capture images of the scalp. 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 signals from the user and accepts instructions from the user. 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 described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit can capture an image of the scalp using the camera 42 of the headset terminal 314 and input lifestyle data using a dedicated application. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the scalp image using a deep learning model and analyzes lifestyle data using natural language processing. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which generates a treatment plan based on the analysis results. The provision unit is implemented by the control unit 46A of the headset terminal 314, which notifies the user of the generated treatment plan and sends reminders to periodically capture images of the scalp. 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 signals from the user and accepts instructions from the user. 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 described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit can take images of the scalp using the camera 42 of the robot 414 and input lifestyle data using a dedicated application. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the scalp images using a deep learning model and analyzes lifestyle data using natural language processing. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which generates a treatment plan based on the analysis results. The provision unit is implemented by, for example, the control unit 46A of the robot 414, which notifies the user of the generated treatment plan and sends reminders to take images of the scalp periodically. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[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) A reception unit that receives scalp images and lifestyle data from users, An analysis unit analyzes the data received by the reception unit and identifies the cause of hair loss, A generation unit that generates a treatment plan based on the cause identified by the analysis unit, The system comprises a providing unit that provides the treatment plan generated by the generating unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is The system accepts scalp images and lifestyle data taken by users at home. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, By combining deep learning models and natural language processing, we can analyze the user's scalp condition and lifestyle in detail. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Based on the analysis results, a treatment plan is generated that includes the use of specific shampoos and hair growth products, as well as improvements to diet and exercise. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Provide the generated treatment plan to the user. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Users regularly take images of their scalp and send them to the AI ​​to monitor treatment progress and update the treatment plan. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of data entry based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past data entry history and select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When entering data, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and prioritizes input data based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When entering data, the system prioritizes inputting data that is highly relevant based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When entering data, analyze the user's social media activity and input relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of analyses is determined based on the timing of data submission. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is The system estimates the user's emotions and adjusts how the treatment plan is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating a treatment plan, adjust the level of detail in the plan based on the importance of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating a treatment plan, different plan generation algorithms are applied depending on the category of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is The system estimates the user's emotions and adjusts the length of the treatment plan based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating a treatment plan, the priority of the plan is determined based on the timing of the submission of analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is When generating a treatment plan, adjust the order of the plan based on the relevance of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the delivery method of the treatment plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing a treatment plan, the system selects the optimal delivery method by referring to the user's past treatment history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing a treatment plan, the delivery method will be customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit 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 29) The aforementioned supply unit is, When providing a treatment plan, the optimal delivery method is selected based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing a treatment plan, we analyze the user's social media activity and propose delivery methods. The system described in Appendix 1, 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. A reception unit that receives scalp images and lifestyle data from users, An analysis unit analyzes the data received by the reception unit and identifies the cause of hair loss, A generation unit that generates a treatment plan based on the cause identified by the analysis unit, The system comprises a providing unit that provides the treatment plan generated by the generating unit. A system characterized by the following features.

2. The aforementioned reception unit is The system accepts scalp images and lifestyle data taken by users at home. The system according to feature 1.

3. The aforementioned analysis unit, By combining deep learning models and natural language processing, we can analyze the user's scalp condition and lifestyle in detail. The system according to feature 1.

4. The generating unit is Based on the analysis results, a treatment plan is generated that includes the use of specific shampoos and hair growth products, as well as improvements to diet and exercise. The system according to feature 1.

5. The aforementioned supply unit is, Provide the generated treatment plan to the user. The system according to feature 1.

6. The aforementioned supply unit is, Users periodically take images of their scalp and send them to the AI ​​to monitor treatment progress and update the treatment plan. The system according to feature 1.

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

8. The aforementioned reception unit is Analyze the user's past data entry history and select the optimal input method. The system according to feature 1.

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