system

Abstract

The system according to the embodiment aims to provide an optimal hair care program for each individual user. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects general-purpose information on the web. The collection unit collects personal information. The analysis unit analyzes the information collected by the collection unit. The generation unit generates a hair care program based on the information analyzed by the analysis unit. The provision unit provides the program generated by the generation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem of making it difficult to provide an optimal hair care program for each individual user.

[0005] The system according to the embodiment aims to provide an optimal hair care program for each individual user. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects general-purpose information on the web. The collection unit collects personal information. The analysis unit analyzes the information collected by the collection unit. The generation unit generates a hair care program based on the information analyzed by the analysis unit. The provision unit provides the program generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide an optimal hair care program for each individual user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The hair generation system according to an embodiment of the present invention aggregates general-purpose hair-related information from the web and personal information from daily life, such as photos posted to social media sites, health checkup results, and smartphone sensor data, to automatically generate a "hair generation AI program" tailored to each individual around the world. This hair generation system collects general-purpose hair-related information from the web and personal information from daily life, such as photos posted to social media sites, health checkup results, and smartphone sensor data. A generation AI analyzes this information. Based on the collected information, the generation AI automatically generates a hair generation program optimized for each user. For example, the program may recommend a diet rich in specific nutrients or a specific exercise. The generated hair generation program is provided to the user. By improving their lifestyle in accordance with the program, users can achieve a healthy, anti-aging lifestyle. For example, hair growth is promoted, improving hair quality and resulting in a more youthful appearance. This system allows users to receive optimal hair care, improving their quality of life. Furthermore, because the generation AI automatically generates the program, users can receive effective care without any effort. This allows the hair generation system to provide optimal hair care to the user, enabling them to achieve a healthy and anti-aging lifestyle.

[0029] The hair generation system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects general-purpose information about hair from the web. Examples of the information include, but are not limited to, general knowledge about hair growth and information about hair care. The collection unit may also have a function to automatically collect, for example, the latest research papers and patent information about hair growth. The collection unit also collects personal information from daily life, such as photos posted to social media, health checkup results, and smartphone sensors. Examples of the information include, but are not limited to, the user's diet, exercise habits, and stress level. The collection unit may also have a function to estimate the user's emotions and prioritize the information to be collected based on the estimated user emotions. The analysis unit uses a generation AI to analyze the collected information. Examples of the analysis include, but are not limited to, data mining technology and statistical analysis techniques. The analysis unit may also have a function to analyze the correlations of the collected information and generate a more accurate hair generation program. The generation unit uses the generation AI to generate the hair generation program based on the information analyzed by the analysis unit. For example, the system generates a program that recommends a diet rich in specific nutrients or a program that recommends specific exercise, but is not limited to these examples. The generation unit may also have a function of, for example, estimating a user's emotions and adjusting the content of the generated program based on the estimated user emotions. The provision unit provides the program generated by the generation unit to the user. For example, the program may be provided through an application or by email, but is not limited to these examples. The provision unit may also have a function of, for example, monitoring the execution status of the provided program and collecting feedback. As a result, the hair generation system according to the embodiment can provide optimal hair care to the user and achieve a healthy, anti-aging lifestyle.

[0030] The collection unit can collect information about hair care related to hair growth. The collection unit can collect, for example, general knowledge about hair growth and information about hair care. For example, it can collect information about the hair growth cycle and nutrients that promote hair growth. The collection unit can also have a function to automatically collect the latest research papers and patent information about hair growth. For example, the latest research papers about hair growth can be regularly collected and added to the database. It can also automatically collect patent information about new hair care products and provide it to users. It can also collect presentations from international conferences on hair and keep track of the latest technological trends. In this way, by collecting general knowledge about hair growth and information about hair care, the quality of information provided to users can be improved.

[0031] The collection unit can collect personal information from daily life, such as photo posts to social media, health checkup results, and smartphone sensor data. The collection unit collects personal information from daily life, such as photo posts to social media, health checkup results, and smartphone sensor data. Examples of such information include, but are not limited to, the user's diet, exercise habits, and stress level. The collection unit can also have a function for estimating the user's emotions and prioritizing the information to be collected based on the estimated user's emotions. For example, if the user is stressed, the collection unit can prioritize collecting hair care information that has a relaxing effect. Also, if the user is excited, the collection unit can prioritize collecting information on the latest hair growth techniques. Furthermore, if the user is tired, the collection unit can prioritize collecting easy-to-practice hair care methods. By collecting personal information, the collection unit can provide the user with an optimal hair growth program.

[0032] The analysis unit analyzes the collected information and can generate an effective hair growth program for each user. The analysis unit analyzes the collected information using, for example, data mining technology or statistical analysis methods. For example, the analysis unit can analyze correlations in the collected information and generate a more accurate hair growth program. For example, the analysis unit can analyze the correlation between diet and hair growth and suggest an optimal diet plan. It can also analyze the correlation between exercise habits and hair health and suggest an optimal exercise plan. It can also analyze the correlation between stress levels and hair quality and provide advice on stress management. In this way, by analyzing the collected information, it is possible to provide an optimal hair growth program for each user.

[0033] The generation unit can generate a program that recommends a diet rich in a specific nutrient. For example, the generation unit can generate a program that recommends a diet rich in a specific nutrient. For example, the generation unit can generate a program that recommends a diet rich in nutrients such as B vitamins, zinc, and iron. The generation unit can also generate a customized program tailored to the user's diet. For example, the generation unit can generate a program that recommends a diet rich in a specific nutrient tailored to the user's diet. Furthermore, the generation unit can have a function to periodically evaluate the effectiveness of the generated program and update the program as necessary. For example, the generation unit can periodically evaluate the user's hair growth status and update the program. In this way, the generation unit can promote the health of the user's hair by recommending a diet rich in a specific nutrient.

[0034] The generation unit can generate a program that recommends a specific type of exercise. The generation unit, for example, generates a program that recommends a specific exercise. For example, it can generate a program that recommends exercise such as aerobic exercise or stretching. The generation unit can also generate a customized program that matches the user's exercise habits. For example, it can generate a program that recommends a specific exercise that matches the user's exercise habits. Furthermore, the generation unit can also have a function to periodically evaluate the effectiveness of the generated program and update the program as necessary. For example, it can update the program in response to changes in the user's exercise habits. In this way, the recommendation of a specific exercise promotes the health of the user's hair.

[0035] The providing unit can provide the generated hair care program to the user. The providing unit, for example, provides the generated hair generation program to the user. For example, it can be provided through an application or by email. The providing unit can also have a function of monitoring the execution status of the provided program and collecting feedback. For example, it can monitor the progress when the user executes the program and collect feedback. Furthermore, the providing unit can also have a function of evaluating the effectiveness of the provided program and reporting it to the user. For example, it can evaluate and report the effectiveness after the user executes the program. In this way, by providing the generated hair generation program to the user, the user can receive effective care.

[0036] The collection unit can be added with a function to automatically collect the latest research papers and patent information related to hair. The collection unit, for example, has a function to automatically collect the latest research papers and patent information related to hair. For example, the latest research papers on hair growth are periodically collected and added to the database. Patent information on new hair care products can also be automatically collected and provided to the user. Furthermore, the contents of presentations at international conferences related to hair can be collected to grasp the latest technological trends. In this way, by collecting the latest research papers and patent information, the latest technological trends can be grasped.

[0037] The collection unit can add a function to evaluate the reliability of the information to be collected and select only highly reliable information. The collection unit, for example, has a function to evaluate the reliability of the information to be collected and select only highly reliable information. For example, an algorithm to evaluate the reliability of the information source can be introduced to collect only highly reliable information. Highly reliable information can also be selected based on user ratings and reviews. Furthermore, highly reliable information can also be selected with reference to expert opinions. This improves the quality of the information provided by collecting only highly reliable information.

[0038] The collection unit can be added with a function to collect not only information about hair but also information about beauty and health. The collection unit, for example, has a function to collect not only information about hair but also information about beauty and health. For example, skin care information related to hair care can be collected and provided to the user. Information about a healthy diet can also be collected and provided as information useful for hair growth. Furthermore, information about stress management can also be collected and provided as information useful for hair health. In this way, the range of information provided to the user can be expanded by collecting information about beauty and health as well.

[0039] The collection unit can add a function to collect information in multiple languages ​​by making the language of the collected information multilingual. The collection unit, for example, has a function to collect information from around the world by making the language of the collected information multilingual. For example, hair care information can be collected in multiple languages, such as English, Japanese, and Chinese. It can also collect hair care cultures and customs from each country and provide them to users. Furthermore, it can introduce a multilingual information collection algorithm to collect the latest information from around the world. This multilingual support makes it possible to collect information from around the world.

[0040] The collection unit can add a function for instantly monitoring the user's lifestyle habits and stress level. The collection unit has, for example, a function for monitoring the user's lifestyle habits and stress level in real time. For example, a smartphone sensor can be used to monitor the user's exercise amount in real time. Data can also be collected using a wearable device for measuring stress levels. Furthermore, an app can be provided that records the user's diet, and data can be collected in real time. This allows for more accurate information to be collected by monitoring the user's lifestyle habits and stress level in real time.

[0041] The collection unit can be added with a function to automatically record the user's meal contents. The collection unit, for example, has a function to automatically record the user's meal contents. For example, by simply taking a photo of the meal, the AI ​​can automatically analyze and record the meal contents. It can also automatically calculate the nutritional value of the meal and store it in a database. Furthermore, when the user inputs a meal, it can automatically make suggestions based on the user's past meal history. In this way, by automatically recording the user's meal contents, accurate information about the user's eating habits can be collected.

[0042] The collection unit can be added with a function to collect the user's sleep patterns and exercise amount. The collection unit has, for example, a function to collect the user's sleep patterns and exercise amount. For example, a smartphone sensor can be used to record the user's sleep patterns. A wearable device can also be used to measure the user's exercise amount and collect data. Furthermore, the sleep patterns and exercise amount can be automatically recorded without the user having to manually enter them. In this way, accurate information about lifestyle habits can be collected by collecting the user's sleep patterns and exercise amount.

[0043] The collection unit can be added with a function to collect the user's family history and genetic information. The collection unit has, for example, a function to collect the user's family history and genetic information. For example, it provides an interface that allows the user to easily input family history information. It can also provide a kit for analyzing genetic information and collect data. Furthermore, it can provide advice on hair care based on the user's family history and genetic information. In this way, by collecting the user's family history and genetic information, it is possible to provide a more personalized hair care program.

[0044] The analysis unit can add a function to analyze the correlation of collected information and generate a highly accurate hair generation program. The analysis unit, for example, has a function to analyze the correlation of collected information and generate a more accurate hair generation program. For example, it can analyze the correlation between diet and hair growth and suggest an optimal meal plan. It can also analyze the correlation between exercise habits and hair health and suggest an optimal exercise plan. It can also analyze the correlation between stress levels and hair quality and provide advice on stress management. In this way, by analyzing the correlation of information, a more accurate hair generation program can be provided.

[0045] The analysis unit can be added with a function to visualize the analysis results and present them clearly to the user. The analysis unit, for example, has a function to visualize the analysis results and present them to the user in an easy-to-understand manner. For example, the analysis results can be displayed in graphs or charts to present them to the user in an easy-to-understand manner. The analysis results can also be provided as infographics to make them visually easy to understand. Furthermore, the analysis results can be displayed in animation to make it easier for the user to understand. In this way, by visualizing the analysis results, information can be provided to the user in an easy-to-understand manner.

[0046] The analysis unit can add a function to integrate the analysis results with other health data and provide comprehensive health advice. The analysis unit, for example, has a function to integrate the analysis results with other health data and provide comprehensive health advice. For example, hair health data and dietary habit data can be integrated to provide comprehensive health advice. Hair health data and exercise habit data can also be integrated to provide comprehensive health advice. Furthermore, hair health data and stress level data can be integrated to provide comprehensive health advice. In this way, by integrating with other health data, comprehensive health advice can be provided.

[0047] The analysis unit can add a function to share the analysis results with a third party and perform a highly reliable analysis. The analysis unit, for example, has a function to share the analysis results with a third party and perform a more reliable analysis. For example, the analysis results can be shared with a medical institution to receive professional advice. The analysis results can also be shared with a research institution to reflect the latest research results. Furthermore, the analysis results can be shared with a hair care specialist to perform a more reliable analysis. As a result, by sharing with a third party, a more reliable analysis can be performed.

[0048] The generation unit can add a function to generate an individualized program tailored to the user's lifestyle habits. The generation unit has, for example, a function to generate a customized program tailored to the user's lifestyle habits. For example, a customized program tailored to the user's dietary habits can be provided. A customized program tailored to the user's exercise habits can also be provided. Furthermore, a customized program tailored to the user's sleep patterns can also be provided. In this way, by providing a customized program tailored to the user's lifestyle habits, more effective care can be provided.

[0049] The generation unit can add a function to periodically evaluate the effectiveness of the generated program and update the program as necessary. The generation unit, for example, has a function to periodically evaluate the effectiveness of the generated program and update the program as necessary. For example, the generation unit periodically evaluates the user's hair growth status and updates the program. The program can also be updated in response to changes in the user's lifestyle habits. Furthermore, the program can be improved based on user feedback. In this way, the program can always be provided as an optimal program by periodically evaluating the effectiveness of the program and updating it as necessary.

[0050] The generation unit can be added with a function to generate not only a hair generation program but also a skin care and whole body health management program. The generation unit, for example, has a function to generate not only a hair generation program but also a skin care and whole body health management program. For example, in addition to hair care, a skin care program can be provided. Also, in addition to hair care, a whole body health management program can be provided. Furthermore, in addition to hair care, a stress management program can be provided. In this way, by providing skin care and whole body health management programs, comprehensive health management becomes possible.

[0051] The generation unit can add a function for sharing the generated program with other users and creating a community. The generation unit, for example, has a function for sharing the generated program with other users and forming a community. For example, the generation unit provides a platform for users to share programs they have generated with other users. It can also provide a function for users to receive feedback from other users on programs they have generated. It can also provide a function for users to form a community and exchange information based on programs they have generated. This allows users to share programs and form communities, thereby promoting information exchange between users.

[0052] The providing unit can add a function to monitor the execution status of the provided program and collect feedback. The providing unit, for example, has a function to monitor the execution status of the provided program and collect feedback. For example, the providing unit monitors the progress when a user executes the program and collects feedback. It can also collect feedback after a user executes the program. It can also collect problems when a user executes the program and improve them. In this way, it becomes possible to improve the program by monitoring the execution status of the program and collecting feedback.

[0053] The providing unit can add a function to measure the effectiveness of the provided program and report it to the user. The providing unit has, for example, a function to evaluate the effectiveness of the provided program and report it to the user. For example, the providing unit evaluates and reports the effectiveness after the user executes the program. It can also evaluate and report the progress when the user executes the program. It can also evaluate and report any problems that the user encounters when executing the program. In this way, the effectiveness of the program can be evaluated and reported to the user, thereby improving the user's motivation.

[0054] The providing unit can add a function to link the provided program with other devices to support more efficient execution. The providing unit, for example, has a function to link the provided program with other devices to support more effective execution. For example, the providing unit can link with a smartphone to support program execution. It can also link with a wearable device to support program execution. It can also link with a smart home device to support program execution. This allows program execution to be more effectively supported by linking with other devices.

[0055] The providing unit can add a function for sharing the provided program with other users and creating a community. The providing unit, for example, has a function for sharing the provided program with other users and forming a community. For example, the providing unit provides a platform for a user to share a program created by the user with other users. It can also provide a function for receiving feedback from other users on a program created by the user. It can also provide a function for forming a community and exchanging information based on a program created by the user. This promotes information exchange between users by sharing programs and forming a community.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] The collection unit can also add a function to collect the user's family history and genetic information. For example, it can provide an interface that allows the user to easily input their family history. It can also provide a kit for analyzing genetic information and collect data. It can also provide advice on hair care based on the user's family history and genetic information. In this way, by collecting the user's family history and genetic information, it is possible to provide a more personalized hair care program.

[0058] The providing unit can also add a function for sharing the generated program with other users and forming a community. For example, it can provide a platform for users to share programs they have generated with other users. It can also provide a function for users to receive feedback from other users on programs they have generated. It can also provide a function for users to form a community based on programs they have generated and exchange information. This allows users to share programs and form communities, thereby promoting information exchange between users.

[0059] The generation unit can also add a function to generate not only a hair generation program but also skin care and whole body health management programs. For example, a skin care program can be provided in addition to hair care. Also, a whole body health management program can be provided in addition to hair care. Furthermore, a stress management program can be provided in addition to hair care. This allows comprehensive health management by providing skin care and whole body health management programs.

[0060] The collection unit can also add a function to evaluate the reliability of the information to be collected and select only highly reliable information. For example, an algorithm can be introduced to evaluate the reliability of information sources, and only highly reliable information can be collected. Highly reliable information can also be selected based on user ratings and reviews. Furthermore, highly reliable information can be selected with reference to expert opinions. This allows only highly reliable information to be collected, improving the quality of the information provided.

[0061] The analysis unit can also add a function to visualize the analysis results and present them to users in an easy-to-understand manner. For example, analysis results can be displayed in graphs or charts to make them easy to understand. Analysis results can also be provided as infographics to make them easier to understand visually. Furthermore, analysis results can be displayed as animations to make it easier for users to understand. In this way, by visualizing the analysis results, information can be provided to users in an easy-to-understand manner.

[0062] The provider can also add a function to monitor the execution status of the provided program and collect feedback. For example, the provider can monitor the progress when the user executes the program and collect feedback. It can also collect feedback after the user executes the program. It can also collect problems that the user encounters when executing the program and improve them. This makes it possible to improve the program by monitoring the execution status of the program and collecting feedback.

[0063] The processing flow of the first embodiment will be briefly explained below.

[0064] Step 1: The collection unit collects general-purpose information about hair from the web. For example, it has the function of automatically collecting general knowledge about hair growth, information about hair care, the latest research papers, and patent information. The collection unit also collects personal information from daily life, such as photos posted to social media, health checkup results, and smartphone sensors. This information includes the user's diet, exercise habits, and stress level. Furthermore, the collection unit can also have the function of inferring the user's emotions and prioritizing the information to be collected based on the inferred emotions. Step 2: The analysis unit analyzes the collected information. For example, it uses data mining technology and statistical analysis methods to analyze correlations in the collected information and generate a more accurate hair generation program. Step 3: The generation unit generates a hair growth program based on the information analyzed by the analysis unit. For example, the generation unit generates a program that recommends a diet rich in specific nutrients or a program that recommends specific exercise. Furthermore, the generation unit may have a function to estimate the user's emotions and adjust the content of the program to be generated based on the estimated emotions. Step 4: The providing unit provides the program generated by the generating unit to the user. For example, the providing unit provides the program through an application or by email. Furthermore, the providing unit may have a function of monitoring the execution status of the provided program and collecting feedback.

[0065] (Example 2) The hair generation system according to an embodiment of the present invention aggregates general-purpose hair-related information from the web and personal information from daily life, such as photos posted to social media sites, health checkup results, and smartphone sensor data, to automatically generate a "hair generation AI program" tailored to each individual around the world. This hair generation system collects general-purpose hair-related information from the web and personal information from daily life, such as photos posted to social media sites, health checkup results, and smartphone sensor data. A generation AI analyzes this information. Based on the collected information, the generation AI automatically generates a hair generation program optimized for each user. For example, the program may recommend a diet rich in specific nutrients or a specific exercise. The generated hair generation program is provided to the user. By improving their lifestyle in accordance with the program, users can achieve a healthy, anti-aging lifestyle. For example, hair growth is promoted, improving hair quality and resulting in a more youthful appearance. This system allows users to receive optimal hair care, improving their quality of life. Furthermore, because the generation AI automatically generates the program, users can receive effective care without any effort. This allows the hair generation system to provide optimal hair care to the user, enabling them to achieve a healthy and anti-aging lifestyle.

[0066] The hair generation system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects general-purpose information about hair from the web. Examples of the information include, but are not limited to, general knowledge about hair growth and information about hair care. The collection unit may also have a function to automatically collect, for example, the latest research papers and patent information about hair growth. The collection unit also collects personal information from daily life, such as photos posted to social media, health checkup results, and smartphone sensors. Examples of the information include, but are not limited to, the user's diet, exercise habits, and stress level. The collection unit may also have a function to estimate the user's emotions and prioritize the information to be collected based on the estimated user emotions. The analysis unit uses a generation AI to analyze the collected information. Examples of the analysis include, but are not limited to, data mining technology and statistical analysis techniques. The analysis unit may also have a function to analyze the correlations of the collected information and generate a more accurate hair generation program. The generation unit uses the generation AI to generate the hair generation program based on the information analyzed by the analysis unit. For example, the system generates a program that recommends a diet rich in specific nutrients or a program that recommends specific exercise, but is not limited to these examples. The generation unit may also have a function of, for example, estimating a user's emotions and adjusting the content of the generated program based on the estimated user emotions. The provision unit provides the program generated by the generation unit to the user. For example, the program may be provided through an application or by email, but is not limited to these examples. The provision unit may also have a function of, for example, monitoring the execution status of the provided program and collecting feedback. As a result, the hair generation system according to the embodiment can provide optimal hair care to the user and achieve a healthy, anti-aging lifestyle.

[0067] The collection unit can collect information about hair care related to hair growth. The collection unit, for example, collects general knowledge about hair growth and information about hair care. For example, it can collect information about the hair growth cycle and nutrients that promote hair growth. The collection unit can also have a function to automatically collect the latest research papers and patent information about hair growth. For example, the latest research papers about hair growth can be regularly collected and added to the database. It can also automatically collect patent information about new hair care products and provide it to users. It can also collect presentations from international conferences on hair and keep track of the latest technological trends. In this way, by collecting general knowledge about hair growth and information about hair care, the quality of information provided to users can be improved.

[0068] The collection unit can collect personal information from daily life, such as photo posts to social media, health checkup results, and smartphone sensor data. The collection unit collects personal information from daily life, such as photo posts to social media, health checkup results, and smartphone sensor data. Examples of such information include, but are not limited to, the user's diet, exercise habits, and stress level. The collection unit can also have a function for estimating the user's emotions and prioritizing the information to be collected based on the estimated user's emotions. For example, if the user is stressed, the collection unit can prioritize collecting hair care information that has a relaxing effect. Also, if the user is excited, the collection unit can prioritize collecting information on the latest hair growth techniques. Furthermore, if the user is tired, the collection unit can prioritize collecting easy-to-practice hair care methods. By collecting personal information, the collection unit can provide the user with an optimal hair growth program.

[0069] The analysis unit analyzes the collected information and can generate an effective hair growth program for each user. The analysis unit analyzes the collected information using, for example, data mining technology or statistical analysis methods. For example, the analysis unit can analyze correlations in the collected information and generate a more accurate hair growth program. For example, the analysis unit can analyze the correlation between diet and hair growth and suggest an optimal diet plan. It can also analyze the correlation between exercise habits and hair health and suggest an optimal exercise plan. It can also analyze the correlation between stress levels and hair quality and provide advice on stress management. In this way, by analyzing the collected information, it is possible to provide an optimal hair growth program for each user.

[0070] The generation unit can generate a program that recommends a diet rich in a specific nutrient. For example, the generation unit can generate a program that recommends a diet rich in a specific nutrient. For example, the generation unit can generate a program that recommends a diet rich in nutrients such as B vitamins, zinc, and iron. The generation unit can also generate a customized program tailored to the user's diet. For example, the generation unit can generate a program that recommends a diet rich in a specific nutrient tailored to the user's diet. Furthermore, the generation unit can have a function to periodically evaluate the effectiveness of the generated program and update the program as necessary. For example, the generation unit can periodically evaluate the user's hair growth status and update the program. In this way, the generation unit can promote the health of the user's hair by recommending a diet rich in a specific nutrient.

[0071] The generation unit can generate a program that recommends a specific type of exercise. The generation unit, for example, generates a program that recommends a specific exercise. For example, it can generate a program that recommends exercise such as aerobic exercise or stretching. The generation unit can also generate a customized program that matches the user's exercise habits. For example, it can generate a program that recommends a specific exercise that matches the user's exercise habits. Furthermore, the generation unit can also have a function to periodically evaluate the effectiveness of the generated program and update the program as necessary. For example, it can update the program in response to changes in the user's exercise habits. In this way, the recommendation of a specific exercise promotes the health of the user's hair.

[0072] The providing unit can provide the generated hair care program to the user. The providing unit, for example, provides the generated hair generation program to the user. For example, it can be provided through an application or by email. The providing unit can also have a function of monitoring the execution status of the provided program and collecting feedback. For example, it can monitor the progress when the user executes the program and collect feedback. Furthermore, the providing unit can also have a function of evaluating the effectiveness of the provided program and reporting it to the user. For example, it can evaluate and report the effectiveness after the user executes the program. In this way, by providing the generated hair generation program to the user, the user can receive effective care.

[0073] The collection unit can estimate the user's emotions and set a priority order for the information to be collected based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and determines the priority order for the information to be collected based on the estimated user emotions. For example, if the user is stressed, the collection unit can prioritize collecting hair care information with a relaxing effect. Also, if the user is excited, the collection unit can prioritize collecting information on the latest hair growth techniques. Furthermore, if the user is tired, the collection unit can prioritize collecting information on easy-to-follow hair care methods. This allows more appropriate information to be collected by prioritizing information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0074] The collection unit can be added with a function to automatically collect the latest research papers and patent information related to hair. The collection unit, for example, has a function to automatically collect the latest research papers and patent information related to hair. For example, the latest research papers on hair growth are periodically collected and added to the database. Patent information on new hair care products can also be automatically collected and provided to the user. Furthermore, the contents of presentations at international conferences related to hair can be collected to grasp the latest technological trends. In this way, by collecting the latest research papers and patent information, the latest technological trends can be grasped.

[0075] The collection unit can add a function to evaluate the reliability of the information to be collected and select only highly reliable information. The collection unit, for example, has a function to evaluate the reliability of the information to be collected and select only highly reliable information. For example, an algorithm to evaluate the reliability of the information source can be introduced to collect only highly reliable information. Highly reliable information can also be selected based on user ratings and reviews. Furthermore, highly reliable information can also be selected with reference to expert opinions. This improves the quality of the information provided by collecting only highly reliable information.

[0076] The collection unit can estimate the user's emotions and set categories of information to be collected based on the estimated user emotions. For example, the collection unit estimates the user's emotions and adjusts the categories of information to be collected based on the estimated user emotions. For example, if the user is relaxed, hair care information with a relaxation effect can be collected. If the user is excited, information on the latest hair growth techniques can be collected. Furthermore, if the user is tired, easy-to-practice hair care methods can be collected. By adjusting the categories of information based on the user's emotions, more appropriate information can be collected. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0077] The collection unit can be added with a function to collect not only information about hair but also information about beauty and health. The collection unit, for example, has a function to collect not only information about hair but also information about beauty and health. For example, skin care information related to hair care can be collected and provided to the user. Information about a healthy diet can also be collected and provided as information useful for hair growth. Furthermore, information about stress management can also be collected and provided as information useful for hair health. In this way, the range of information provided to the user can be expanded by collecting information about beauty and health as well.

[0078] The collection unit can add a function to collect information in multiple languages ​​by making the language of the collected information multilingual. The collection unit, for example, has a function to collect information from around the world by making the language of the collected information multilingual. For example, hair care information can be collected in multiple languages, such as English, Japanese, and Chinese. It can also collect hair care cultures and customs from each country and provide them to users. Furthermore, it can introduce a multilingual information collection algorithm to collect the latest information from around the world. This multilingual support makes it possible to collect information from around the world.

[0079] The collection unit can estimate the user's emotions and set a priority order for the personal information to be collected based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and determines the priority order for the personal information to be collected based on the estimated user emotions. For example, if the user is feeling stressed, information about the stress level can be collected preferentially. Also, if the user is relaxed, information about dietary habits and exercise habits can be collected preferentially. Furthermore, if the user is tired, information about sleep patterns can be collected preferentially. This allows more appropriate information to be collected by determining the priority order for the personal information based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0080] The collection unit can add a function for instantly monitoring the user's lifestyle habits and stress level. The collection unit has, for example, a function for monitoring the user's lifestyle habits and stress level in real time. For example, a smartphone sensor can be used to monitor the user's exercise amount in real time. Data can also be collected using a wearable device for measuring stress levels. Furthermore, an app can be provided that records the user's diet, and data can be collected in real time. This allows for more accurate information to be collected by monitoring the user's lifestyle habits and stress level in real time.

[0081] The collection unit can be added with a function to automatically record the user's meal contents. The collection unit, for example, has a function to automatically record the user's meal contents. For example, by simply taking a photo of the meal, the AI ​​can automatically analyze and record the meal contents. It can also automatically calculate the nutritional value of the meal and store it in a database. Furthermore, when the user inputs a meal, it can automatically make suggestions based on the user's past meal history. In this way, by automatically recording the user's meal contents, accurate information about the user's eating habits can be collected.

[0082] The collection unit can estimate the user's emotions and set categories of personal information to be collected based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and adjusts the categories of personal information to be collected based on the estimated user emotions. For example, when the user is relaxed, information about their eating habits and exercise habits can be collected. When the user is stressed, information about their stress level can be collected. Furthermore, when the user is tired, information about their sleep patterns can be collected. By adjusting the categories of personal information based on the user's emotions, more appropriate information can be collected. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0083] The collection unit can be added with a function to collect the user's sleep patterns and exercise amount. The collection unit has, for example, a function to collect the user's sleep patterns and exercise amount. For example, a smartphone sensor can be used to record the user's sleep patterns. A wearable device can also be used to measure the user's exercise amount and collect data. Furthermore, the sleep patterns and exercise amount can be automatically recorded without the user having to manually enter them. In this way, accurate information about lifestyle habits can be collected by collecting the user's sleep patterns and exercise amount.

[0084] The collection unit can be added with a function to collect the user's family history and genetic information. The collection unit has, for example, a function to collect the user's family history and genetic information. For example, it provides an interface that allows the user to easily input family history information. It can also provide a kit for analyzing genetic information and collect data. Furthermore, it can provide advice on hair care based on the user's family history and genetic information. In this way, by collecting the user's family history and genetic information, it is possible to provide a more personalized hair care program.

[0085] The analysis unit can estimate the user's emotions and set an analysis algorithm based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. For example, if the user is relaxed, a detailed analysis can be performed to provide highly accurate results. If the user is stressed, a simplified analysis can be performed to provide quick results. Furthermore, if the user is excited, a visually easy-to-understand analysis result can be provided. By adjusting the analysis algorithm based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0086] The analysis unit can add a function to analyze the correlation of collected information and generate a highly accurate hair generation program. The analysis unit, for example, has a function to analyze the correlation of collected information and generate a more accurate hair generation program. For example, it can analyze the correlation between diet and hair growth and suggest an optimal meal plan. It can also analyze the correlation between exercise habits and hair health and suggest an optimal exercise plan. It can also analyze the correlation between stress levels and hair quality and provide advice on stress management. In this way, by analyzing the correlation of information, a more accurate hair generation program can be provided.

[0087] The analysis unit can be added with a function to visualize the analysis results and present them clearly to the user. The analysis unit, for example, has a function to visualize the analysis results and present them to the user in an easy-to-understand manner. For example, the analysis results can be displayed in graphs or charts to present them to the user in an easy-to-understand manner. The analysis results can also be provided as infographics to make them visually easy to understand. Furthermore, the analysis results can be displayed in animation to make it easier for the user to understand. In this way, by visualizing the analysis results, information can be provided to the user in an easy-to-understand manner.

[0088] The analysis unit can estimate the user's emotion and set the display method of the analysis results based on the estimated user emotion. The analysis unit, for example, estimates the user's emotion and adjusts the display method of the analysis results based on the estimated user emotion. For example, if the user is relaxed, detailed analysis results can be displayed. Also, if the user is stressed, simplified analysis results can be displayed. Furthermore, if the user is excited, analysis results that are visually easy to understand can be displayed. In this way, by adjusting the display method of the analysis results based on the user's emotion, more appropriate information can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0089] The analysis unit can add a function to integrate the analysis results with other health data and provide comprehensive health advice. The analysis unit, for example, has a function to integrate the analysis results with other health data and provide comprehensive health advice. For example, hair health data and dietary habit data can be integrated to provide comprehensive health advice. Hair health data and exercise habit data can also be integrated to provide comprehensive health advice. Furthermore, hair health data and stress level data can be integrated to provide comprehensive health advice. In this way, by integrating with other health data, comprehensive health advice can be provided.

[0090] The analysis unit can add a function to share the analysis results with a third party and perform a highly reliable analysis. The analysis unit, for example, has a function to share the analysis results with a third party and perform a more reliable analysis. For example, the analysis results can be shared with a medical institution to receive professional advice. The analysis results can also be shared with a research institution to reflect the latest research results. Furthermore, the analysis results can be shared with a hair care specialist to perform a more reliable analysis. As a result, by sharing with a third party, a more reliable analysis can be performed.

[0091] The generation unit can estimate the user's emotion and set the content of the program to be generated based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and adjusts the content of the program to be generated based on the estimated user's emotion. For example, if the user is relaxed, a detailed hair generation program can be provided. Also, if the user is stressed, a simplified hair generation program can be provided. Furthermore, if the user is excited, a visually easy-to-understand hair generation program can be provided. In this way, by adjusting the program content based on the user's emotion, a more appropriate program can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0092] The generation unit can add a function to generate an individualized program tailored to the user's lifestyle habits. The generation unit has, for example, a function to generate a customized program tailored to the user's lifestyle habits. For example, a customized program tailored to the user's dietary habits can be provided. A customized program tailored to the user's exercise habits can also be provided. Furthermore, a customized program tailored to the user's sleep patterns can also be provided. In this way, by providing a customized program tailored to the user's lifestyle habits, more effective care can be provided.

[0093] The generation unit can add a function to periodically evaluate the effectiveness of the generated program and update the program as necessary. The generation unit, for example, has a function to periodically evaluate the effectiveness of the generated program and update the program as necessary. For example, the generation unit periodically evaluates the user's hair growth status and updates the program. The program can also be updated in response to changes in the user's lifestyle habits. Furthermore, the program can be improved based on user feedback. In this way, the program can always be provided as an optimal program by periodically evaluating the effectiveness of the program and updating it as necessary.

[0094] The generation unit can estimate the user's emotion and set a priority order for the programs to be generated based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and determines the priority order for the programs to be generated based on the estimated user's emotion. For example, if the user is relaxed, a detailed program can be provided preferentially. Also, if the user is stressed, a simplified program can be provided preferentially. Furthermore, if the user is excited, a program that is visually easy to understand can be provided preferentially. In this way, by determining the priority order of programs based on the user's emotion, more appropriate programs can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0095] The generation unit can be added with a function to generate not only a hair generation program but also a skin care and whole body health management program. The generation unit, for example, has a function to generate not only a hair generation program but also a skin care and whole body health management program. For example, in addition to hair care, a skin care program can be provided. Also, in addition to hair care, a whole body health management program can be provided. Furthermore, in addition to hair care, a stress management program can be provided. In this way, by providing skin care and whole body health management programs, comprehensive health management becomes possible.

[0096] The generation unit can add a function for sharing the generated program with other users and creating a community. The generation unit, for example, has a function for sharing the generated program with other users and forming a community. For example, the generation unit provides a platform for users to share programs they have generated with other users. It can also provide a function for users to receive feedback from other users on programs they have generated. It can also provide a function for users to form a community and exchange information based on programs they have generated. This allows users to share programs and form communities, thereby promoting information exchange between users.

[0097] The providing unit can estimate the user's emotion and set a display method for the program to be provided based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and adjusts the display method for the program to be provided based on the estimated user's emotion. For example, if the user is relaxed, a detailed program can be displayed. Also, if the user is stressed, a simplified program can be displayed. Furthermore, if the user is excited, a visually easy-to-understand program can be displayed. In this way, by adjusting the program display method based on the user's emotion, more appropriate information can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0098] The providing unit can add a function to monitor the execution status of the provided program and collect feedback. The providing unit, for example, has a function to monitor the execution status of the provided program and collect feedback. For example, the providing unit monitors the progress when a user executes the program and collects feedback. It can also collect feedback after a user executes the program. It can also collect problems when a user executes the program and improve them. In this way, it becomes possible to improve the program by monitoring the execution status of the program and collecting feedback.

[0099] The providing unit can add a function to measure the effectiveness of the provided program and report it to the user. The providing unit has, for example, a function to evaluate the effectiveness of the provided program and report it to the user. For example, the providing unit evaluates and reports the effectiveness after the user executes the program. It can also evaluate and report the progress when the user executes the program. It can also evaluate and report any problems that the user encounters when executing the program. In this way, the effectiveness of the program can be evaluated and reported to the user, thereby improving the user's motivation.

[0100] The providing unit can estimate the user's emotions and set a priority order for programs to be provided based on the estimated user emotions. The providing unit, for example, estimates the user's emotions and determines the priority order for programs to be provided based on the estimated user emotions. For example, if the user is relaxed, a detailed program can be provided preferentially. Also, if the user is stressed, a simplified program can be provided preferentially. Furthermore, if the user is excited, a program that is visually easy to understand can be provided preferentially. In this way, by determining the priority order for programs based on the user's emotions, more appropriate programs can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0101] The providing unit can add a function to link the provided program with other devices to support more efficient execution. The providing unit, for example, has a function to link the provided program with other devices to support more effective execution. For example, the providing unit can link with a smartphone to support program execution. It can also link with a wearable device to support program execution. It can also link with a smart home device to support program execution. This allows program execution to be more effectively supported by linking with other devices.

[0102] The providing unit can add a function for sharing the provided program with other users and creating a community. The providing unit, for example, has a function for sharing the provided program with other users and forming a community. For example, the providing unit provides a platform for a user to share a program created by the user with other users. It can also provide a function for receiving feedback from other users on a program created by the user. It can also provide a function for forming a community and exchanging information based on a program created by the user. This promotes information exchange between users by sharing programs and forming a community. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects personal information of the user using the camera 42 and microphone 38B of the smart device 14, and transmits the collected information to the data processing device 12 by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a hair generation program based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the smart device 14, and provides the generated program to the user. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects personal information of the user using the camera 42 and microphone 238 of the smart glasses 214, and transmits the collected information to the data processing device 12 by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a hair generation program based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides the generated program to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects personal information of the user using the camera 42 and microphone 238 of the headset type terminal 314, and transmits the collected information to the data processing device 12 by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a hair generation program based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314, and provides the generated program to the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects personal information of the user using the camera 42 and microphone 238 of the robot 414, and transmits the collected information to the data processing device 12 by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a hair generation program based on the analysis result. The provision unit is realized, for example, by the control unit 46A of the robot 414, and provides the generated program to the user.

[0103] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0104] The analysis unit can also estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is relaxed, a detailed analysis can be performed to provide highly accurate results. Alternatively, if the user is stressed, a simplified analysis can be performed to provide quick results. Furthermore, if the user is excited, analysis results that are easy to understand visually can be provided. In this way, by adjusting the analysis algorithm based on the user's emotions, more appropriate analysis results can be provided.

[0105] The collection unit can also add a function to collect the user's family history and genetic information. For example, it can provide an interface that allows the user to easily input their family history. It can also provide a kit for analyzing genetic information and collect data. It can also provide advice on hair care based on the user's family history and genetic information. In this way, by collecting the user's family history and genetic information, it is possible to provide a more personalized hair care program.

[0106] The providing unit can also add a function for sharing the generated program with other users and forming a community. For example, it can provide a platform for users to share programs they have generated with other users. It can also provide a function for users to receive feedback from other users on programs they have generated. It can also provide a function for users to form a community based on programs they have generated and exchange information. This allows users to share programs and form communities, thereby promoting information exchange between users.

[0107] The collection unit can also estimate the user's emotions and set categories of information to be collected based on the estimated emotions. For example, if the user is relaxed, hair care information with a relaxation effect can be collected. If the user is excited, information on the latest hair growth techniques can be collected. Furthermore, if the user is tired, easy-to-follow hair care methods can be collected. In this way, by adjusting the categories of information based on the user's emotions, more appropriate information can be collected.

[0108] The generation unit can also add a function to generate not only a hair generation program but also skin care and whole body health management programs. For example, a skin care program can be provided in addition to hair care. Also, a whole body health management program can be provided in addition to hair care. Furthermore, a stress management program can be provided in addition to hair care. This allows comprehensive health management by providing skin care and whole body health management programs.

[0109] The providing unit can also estimate the user's emotions and set the display method of the program to be provided based on the estimated emotions. For example, if the user is relaxed, a detailed program can be displayed. Alternatively, if the user is feeling stressed, a simplified program can be displayed. Furthermore, if the user is excited, a program that is visually easy to understand can be displayed. In this way, by adjusting the program display method based on the user's emotions, more appropriate information can be provided.

[0110] The collection unit can also add a function to evaluate the reliability of the information to be collected and select only highly reliable information. For example, an algorithm can be introduced to evaluate the reliability of information sources, and only highly reliable information can be collected. Highly reliable information can also be selected based on user ratings and reviews. Furthermore, highly reliable information can be selected with reference to expert opinions. This allows only highly reliable information to be collected, improving the quality of the information provided.

[0111] The analysis unit can also add a function to visualize the analysis results and present them to users in an easy-to-understand manner. For example, analysis results can be displayed in graphs or charts to make them easy to understand. Analysis results can also be provided as infographics to make them easier to understand visually. Furthermore, analysis results can be displayed as animations to make it easier for users to understand. In this way, by visualizing the analysis results, information can be provided to users in an easy-to-understand manner.

[0112] The generation unit can also estimate the user's emotions and set the content of the program to be generated based on the estimated emotions. For example, if the user is relaxed, a detailed hair generation program can be provided. If the user is stressed, a simplified hair generation program can be provided. Furthermore, if the user is excited, a visually easy-to-understand hair generation program can be provided. In this way, a more appropriate program can be provided by adjusting the content of the program based on the user's emotions.

[0113] The provider can also add a function to monitor the execution status of the provided program and collect feedback. For example, the provider can monitor the progress when the user executes the program and collect feedback. It can also collect feedback after the user executes the program. It can also collect problems that the user encounters when executing the program and improve them. This makes it possible to improve the program by monitoring the execution status of the program and collecting feedback.

[0114] The processing flow of the second embodiment will be briefly explained below.

[0115] Step 1: The collection unit collects general-purpose information about hair from the web. For example, it has the function of automatically collecting general knowledge about hair growth, information about hair care, the latest research papers, and patent information. The collection unit also collects personal information from daily life, such as photos posted to social media, health checkup results, and smartphone sensors. This information includes the user's diet, exercise habits, and stress level. Furthermore, the collection unit can also have the function of inferring the user's emotions and prioritizing the information to be collected based on the inferred emotions. Step 2: The analysis unit analyzes the collected information. For example, it uses data mining technology and statistical analysis methods to analyze correlations in the collected information and generate a more accurate hair generation program. Step 3: The generation unit generates a hair growth program based on the information analyzed by the analysis unit. For example, the generation unit generates a program that recommends a diet rich in specific nutrients or a program that recommends specific exercise. Furthermore, the generation unit may have a function to estimate the user's emotions and adjust the content of the program to be generated based on the estimated emotions. Step 4: The providing unit provides the program generated by the generating unit to the user. For example, the providing unit provides the program through an application or by email. Furthermore, the providing unit may have a function of monitoring the execution status of the provided program and collecting feedback.

[0116] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0118] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0119] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0120] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0121] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0122] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0123] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0124] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0126] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0127] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0128] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0129] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0130] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0131] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0132] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0134] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0135] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0136] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0137] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0138] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0144] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0145] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0146] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0147] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0148] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0150] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0151] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0152] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0153] 7, a 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.

[0154] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0155] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0156] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0157] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0158] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0159] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0160] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0161] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0162] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0163] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0164] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0165] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0166] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0167] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0170] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0171] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0172] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0173] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0174] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0175] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0176] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0177] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0178] 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.

[0179] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0180] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0181] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0182] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0183] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0184] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0185] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0186] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0187] [Explanation of symbols]

[0188] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection unit that collects general information on the web; a collection department that collects personal information; an analysis unit that analyzes the information collected by the collection unit; a generation unit that generates a hair care program based on the information analyzed by the analysis unit; a providing unit that provides the program generated by the generating unit. A system characterized by:

2. The collecting unit Collect information about hair care for hair growth 2. The system of claim 1.

3. The collecting unit Collecting personal information from daily life such as photos posted on SNS, health checkup results, smartphone sensors, etc.

2. The system of claim 1.

4. The analysis unit Analyze the collected information and create an effective hair growth program for each user.

2. The system of claim 1.

5. The generation unit Generate a program that recommends meals rich in specific nutrients 2. The system of claim 1.

6. The generation unit Generate a program that recommends specific types of exercise 2. The system of claim 1.

7. The providing unit Providing the generated hair care program to the user 2. The system of claim 1.

8. The collecting unit Estimate user emotions and prioritize information to be collected based on the estimated user emotions.

2. The system of claim 1.

Images