system

Abstract

The system according to this embodiment aims to provide an optimal relaxation program tailored to individual needs. [Solution] The system according to the embodiment comprises a collection unit, a generation unit, and a provision unit. The collection unit collects the user's health data, mood, and requests. The generation unit analyzes the data collected by the collection unit and generates an optimal relaxation program. The provision unit provides food and beverage menus, spa and sauna lighting, fragrances, and other spatial elements based on the relaxation program generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that relaxation services corresponding to individual needs are not provided and the satisfaction is low.

[0005] The system according to the embodiment aims to provide an optimal relaxation program according to individual needs.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a generation unit, and a provision unit. The collection unit collects the user's health data, mood, and requests. The generation unit analyzes the data collected by the collection unit and generates an optimal relaxation program. The provision unit provides food and beverage menus, spa and sauna lighting, fragrances, and other spatial elements based on the relaxation program generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide an optimal relaxation program tailored to individual needs. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of 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), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The relaxation system according to an embodiment of the present invention is a system that uses generative AI to provide a relaxation experience tailored to individual needs. This relaxation system collects the user's health data, mood, and requests, and the generative AI analyzes this data to generate an optimal food and beverage menu, as well as a space with lighting and scents for the spa and sauna. As a result, the user can enjoy a relaxation experience that is individually optimized. For example, if the user inputs that they want to relax, the generative AI will suggest relaxing fruit juices, relaxing lighting, and scents. Based on the generated relaxation program, the user can enjoy a relaxation experience that is individually optimized. For example, the user can enjoy a spa and sauna in a relaxing environment with relaxing lighting and scents while drinking relaxing fruit juice. As a result, the user can reduce daily stress and fatigue. Furthermore, by using generative AI, real-time environmental adjustments are possible, maximizing user satisfaction. Thus, the relaxation system can provide a relaxation experience that is individually optimized based on the user's health data, mood, and requests.

[0029] The relaxation system according to the embodiment comprises a collection unit, a generation unit, and a provision unit. The collection unit collects the user's health data, mood, and requests. For example, the user can input their health status, mood, and requests into the collection unit. For example, the user can input information such as "I want to relax today" or "I want to drink fruit juice." The collection unit inputs this information into the generation AI. The generation unit uses the generation AI to analyze the data collected by the collection unit and generate an optimal relaxation program. For example, the generation unit suggests relaxing fruit juices, relaxing lighting, and fragrances based on the user's health data, mood, and requests. The generation unit can use the generation AI to generate an optimal relaxation program based on the user's health data, mood, and requests. For example, the generation unit receives a prompt from the generation AI saying, "Please suggest a relaxing fruit juice," and suggests the most suitable fruit juice to the user. The provision unit provides food and beverage menus, spa / sauna lighting, fragrances, and other spatial elements based on the relaxation program generated by the generation unit. The service unit, for example, provides fruit juices with relaxing effects and offers a space where users can enjoy spa and sauna treatments in relaxing lighting and aromas. Based on the generated relaxation program, the service unit can provide users with the optimal relaxation experience. Thus, the relaxation system according to the embodiment can provide individually optimized relaxation experiences based on the user's health data, mood, and requests.

[0030] The data collection unit collects user health data, mood, and requests. Specifically, it provides an interface for users to input their health status, mood, and requests. For example, through a smartphone app or web portal, users can input information such as "I want to relax today" or "I want to drink fruit juice." These interfaces are designed to allow users to easily input information and are intuitive to use. Furthermore, the data collection unit can automatically acquire health data from wearable devices and smartwatches. This allows for the collection of real-time data such as the user's heart rate, blood pressure, and stress level. The collected data is stored on a secure cloud server and made accessible to the data generation unit. To protect user privacy, the data collection unit encrypts the data and controls access to prevent unauthorized access by third parties. The data collection unit also refers to the user's past data and history and uses it as foundational data to provide more accurate relaxation programs. This allows the data collection unit to efficiently collect data to provide individually optimized relaxation experiences based on the user's health status, mood, and requests.

[0031] The generation unit uses a generation AI to analyze data collected by the collection unit and generate an optimal relaxation program. Specifically, the generation AI suggests relaxing fruit juices, lighting, and scents based on the user's health data, mood, and requests. For example, the generation AI analyzes the user's heart rate and stress level to generate a recipe for a highly relaxing fruit juice. It can also suggest the optimal lighting color and scent based on the user's mood and requests. The generation AI uses natural language processing technology to understand the user's input and generate an appropriate relaxation program. For example, if it receives a prompt such as "Please suggest a relaxing fruit juice," the generation AI will generate the optimal fruit juice recipe based on past data and scientific research results. Furthermore, the generation AI can refer to the user's past data and history to provide an individually optimized relaxation program. As a result, the generation unit can generate an individually optimized relaxation program based on the user's health condition, mood, and requests, and provide it to the delivery unit.

[0032] The service department provides food and beverage menus, spa and sauna lighting, and aromatherapy based on relaxation programs generated by the generation department. Specifically, it offers relaxing fruit juices and provides a space where users can enjoy the spa and sauna in relaxing lighting and aromatherapy. For example, the service department provides fresh fruit juices based on recipes suggested by the generation AI. The lighting in the spa and sauna is also adjusted to the optimal color and brightness suggested by the generation AI to maximize relaxation. Aromatherapy oils and essential oils suggested by the generation AI are also used to create a relaxing atmosphere. The service department can collect user feedback and continuously evaluate and improve the effectiveness of the relaxation programs. For example, by providing feedback on how users felt about the fruit juices, lighting, and aromatherapy provided, this can be reflected in the next relaxation program. Furthermore, the service department can offer multiple relaxation programs simultaneously, meeting the diverse needs of users. This allows the service department to provide users with the optimal relaxation experience and increase user satisfaction.

[0033] The data collection unit can collect user health data, mood, and requests in real time. For example, the data collection unit can collect user health data, mood, and requests in real time and input them into the generating AI. For example, the data collection unit allows users to input their health status, mood, and requests in real time. This enables the data collection unit to provide a more accurate relaxation program by collecting user health data, mood, and requests in real time. The specific time range and update frequency of "real time" include, but are not limited to, seconds, minutes, or data update intervals. Some or all of the processing described above in the data collection unit may be performed using AI, or not. For example, the data collection unit can provide a more accurate relaxation program by collecting user health data, mood, and requests in real time and inputting them into the generating AI.

[0034] The generation unit can generate an optimal relaxation program based on the user's health data, mood, and requests using generative AI. For example, the generation unit can use generative AI to suggest relaxing fruit juices, relaxing lighting, and relaxing scents based on the user's health data, mood, and requests. The generation unit can use generative AI to generate an optimal relaxation program based on the user's health data, mood, and requests. For example, the generation unit can have the generative AI receive a prompt such as "Please suggest a relaxing fruit juice" and then suggest the most suitable fruit juice to the user. In this way, the generation unit can generate an optimal relaxation program for the user using generative AI. Specific types and algorithms of generative AI include, but are not limited to, deep learning, reinforcement learning, and generative models. Some or all of the above-described processes in the generation unit are performed using generative AI.

[0035] The service provider can provide a space that includes food and beverage menus, spa and sauna lighting, and fragrances based on the generated relaxation program. For example, the service provider can offer relaxing fruit juices and provide a space where users can enjoy a spa or sauna in relaxing lighting and fragrances. The service provider can provide users with the optimal relaxation experience based on the generated relaxation program. This allows the service provider to provide users with the optimal space based on the generated relaxation program. Specific elements of the space and methods of provision include, but are not limited to, room layout, decoration, and sound effects. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can provide a space that includes food and beverage menus, spa and sauna lighting, and fragrances based on the generated relaxation program.

[0036] The service provider can perform real-time environmental adjustments. For example, the service provider can perform real-time environmental adjustments to maximize user satisfaction. The service provider can perform real-time adjustments such as temperature control, music changes, and lighting adjustments. This allows for real-time environmental adjustments to maximize user satisfaction. Specific methods and criteria for real-time environmental adjustments include, but are not limited to, temperature control, music changes, and lighting adjustments. Some or all of the above-described processes in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can perform real-time environmental adjustments to maximize user satisfaction.

[0037] The generation unit can suggest relaxing fruit juices, as well as relaxing lighting and scents. For example, the generation unit uses a generation AI to suggest relaxing fruit juices, as well as relaxing lighting and scents. The generation unit can use a generation AI to suggest relaxing fruit juices, as well as relaxing lighting and scents. For example, the generation AI receives a prompt such as "Please suggest a relaxing fruit juice" and suggests the most suitable fruit juice for the user. This improves the user's relaxation experience by providing relaxing suggestions. Specific types of relaxing fruit juices and the basis for their relaxing effects include, but are not limited to, orange juice and chamomile tea. Specific types of relaxing lighting and scents and the basis for their relaxing effects include, but are not limited to, warm-colored lighting and lavender scent. Some or all of the above processing in the generation unit is performed using a generation AI.

[0038] The data collection unit can analyze the user's past health data, mood, and requests to select the optimal data collection method. For example, the data collection unit analyzes the user's past health data, mood, and requests to select the optimal data collection method. For example, the data collection unit suggests the optimal data collection method based on the health data and mood frequently entered by the user in the past. For example, the data collection unit analyzes the user's past request history to customize the data collection method. For example, the data collection unit selects the optimal data collection method for a specific time period based on the user's past data. This allows for the selection of the optimal data collection method by analyzing past data. Specific criteria and methods for the optimal data collection method include, but are not limited to, the type of sensor and the frequency of data collection. Some or all of the above-described processes in the data collection unit may be performed using, for example, AI, or not. For example, the data collection unit can analyze the user's past health data, mood, and requests to select the optimal data collection method.

[0039] The data collection unit can filter health data, mood data, and requests based on the user's current lifestyle and areas of interest. For example, the data collection unit filters the data to be collected based on the user's current lifestyle and areas of interest. For example, the data collection unit filters the data to be collected according to the user's current lifestyle. For example, the data collection unit selects the data to be collected based on the user's areas of interest. For example, the data collection unit determines the priority of the data to be collected according to the user's lifestyle and areas of interest. This allows for the collection of more relevant data by filtering the data based on the user's lifestyle and areas of interest. Specific criteria and methods for filtering include, but are not limited to, data importance and methods for selecting highly relevant data. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not. For example, the data collection unit can filter health data, mood data, and requests based on the user's current lifestyle and areas of interest.

[0040] The data collection unit can prioritize the collection of highly relevant data, taking into account the user's geographical location, when collecting health data, mood data, and requests. For example, when collecting health data, mood data, and requests, the data collection unit prioritizes the collection of highly relevant data, taking into account the user's geographical location. For example, if the user is in a specific region, the data collection unit prioritizes the collection of health data related to that region. For example, the data collection unit collects data related to mood and requests based on the user's geographical location. For example, if the user is on the move, the data collection unit prioritizes the collection of data related to their destination. This allows for the priority collection of highly relevant data by considering the user's geographical location. Specific types of geographical location information and methods of collection include, but are not limited to, GPS data and location services. Some or all of the processing described above in the data collection unit may be performed using, for example, AI, or not. For example, when collecting health data, mood data, and requests, the data collection unit can prioritize the collection of highly relevant data, taking into account the user's geographical location.

[0041] The data collection unit can analyze a user's social media activity and collect relevant data when collecting health data, mood data, and requests. For example, the data collection unit can analyze a user's social media activity and collect relevant data when collecting health data, mood data, and requests. For example, the data collection unit can analyze a user's social media posts and collect information related to health data and mood data. For example, the data collection unit can collect data related to requests from a user's social media activity. For example, the data collection unit can analyze a user's social media friendships and collect relevant data. This allows for the collection of relevant data by analyzing social media activity. Specific types of social media activity and methods of analysis include, but are not limited to, posts, the number of likes, and comments. Some or all of the processing described above in the data collection unit may be performed using, for example, AI, or not. For example, the data collection unit can analyze a user's social media activity and collect relevant data when collecting health data, mood data, and requests.

[0042] The generation unit can adjust the level of detail of the relaxation program based on the user's health data, mood, and the importance of the request when generating the relaxation program. For example, the generation unit can adjust the level of detail of the relaxation program based on the user's health data. For example, the generation unit can generate a detailed relaxation program based on the user's health data. For example, the generation unit can adjust the level of detail of the relaxation program according to the user's mood. For example, the generation unit can determine the level of detail of the relaxation program based on the importance of the user's request. This allows for the provision of a more appropriate relaxation program by adjusting the level of detail of the generation based on the importance of the user's data. Specific criteria and methods for adjusting the level of detail of the generation include, but are not limited to, the length of the program and the depth of its content. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can adjust the level of detail of the relaxation program based on the user's health data, mood, and the importance of the request when generating the relaxation program.

[0043] The generation unit can apply different generation algorithms depending on the user's category when generating relaxation programs. For example, the generation unit applies different generation algorithms depending on the user's category when generating relaxation programs. For example, if the user is a business person, the generation unit generates a relaxation program for business. For example, if the user is a family person, the generation unit generates a relaxation program for family. For example, if the user is a student, the generation unit generates a relaxation program for students. By applying different generation algorithms depending on the user's category, it is possible to provide more appropriate relaxation programs. Specific types and classification methods for user categories include, but are not limited to, age, gender, and health status. Some or all of the above processing in the generation unit is performed using generation AI.

[0044] The generation unit can determine the generation priority based on the user's submission timing when generating relaxation programs. For example, the generation unit will prioritize the generation of relaxation programs if the user is in a hurry. If the user is relaxed, the generation unit will generate programs with the normal priority. If the user submits a request within a specific time period, the generation unit will determine the priority based on that time period. This allows for faster delivery of relaxation programs by determining the generation priority based on the user's submission timing. Specific criteria for submission timing and methods for determining priority include, but are not limited to, submission date and time, urgency, etc. Some or all of the above processing in the generation unit is performed using a generation AI.

[0045] The generation unit can adjust the order of generation of relaxation programs based on user relevance. For example, the generation unit adjusts the order of generation based on user relevance when generating relaxation programs. For example, the generation unit prioritizes generating programs that are highly relevant based on the user's health data. For example, the generation unit prioritizes generating programs that are highly relevant based on the user's mood. For example, the generation unit prioritizes generating programs that are highly relevant based on the importance of the user's request. By adjusting the order of generation based on user relevance, it is possible to provide more relevant relaxation programs. Specific criteria and evaluation methods for relevance include, but are not limited to, past data and the user's areas of interest. Some or all of the above processing in the generation unit is performed using generation AI.

[0046] The service provider can analyze the user's past relaxation experiences at the time of service to select the optimal service method. For example, the service provider can analyze the user's past relaxation experiences at the time of service to select the optimal service method. For example, the service provider can prioritize serving food and beverage items that the user has previously enjoyed. For example, the service provider can select the optimal lighting and scent based on the user's past relaxation experiences. For example, the service provider can customize the optimal service method based on the user's past experiences. This allows for the selection of a more appropriate service method by analyzing past relaxation experiences. Specific types of past relaxation experiences and methods of analysis include, but are not limited to, past program content and user feedback. Some or all of the above processing in the service provider may be performed using, for example, AI, or not. For example, the service provider can analyze the user's past relaxation experiences at the time of service to select the optimal service method.

[0047] The service provider can customize the means of delivery based on the user's current living situation at the time of delivery. For example, the service provider can customize the means of delivery based on the user's current living situation at the time of delivery. For example, if the user is busy, the service provider can select a means of delivery that can be delivered quickly. For example, if the user is relaxed, the service provider can select a leisurely means of delivery. For example, the service provider can customize the optimal means of delivery according to the user's living situation. By customizing the means of delivery based on the current living situation, a more appropriate relaxation experience can be provided. Specific types of current living situations and methods of collection include, but are not limited to, work situation, family situation, health status, etc. Some or all of the processing described above in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can customize the means of delivery based on the user's current living situation at the time of delivery.

[0048] The service provider can select the optimal service delivery method at the time of delivery, taking into account the user's geographical location information. For example, the service provider can select the optimal service delivery method at the time of delivery, taking into account the user's geographical location information. For example, if the user is in a specific region, the service provider can provide food and beverage menus related to that region. For example, the service provider can provide optimal lighting and aroma based on the user's geographical location information. For example, if the user is on the move, the service provider can select a service delivery method related to their destination. This allows for the selection of a more appropriate service delivery method by considering geographical location information. Specific types of geographical location information and methods of collection include, but are not limited to, GPS data and location-based services. Some or all of the processing described above in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can select the optimal service delivery method at the time of delivery, taking into account the user's geographical location information.

[0049] The service provider can analyze the user's social media activity at the time of service provision and propose a means of service provision. For example, the service provider can analyze the user's social media activity at the time of service provision and propose a means of service provision. For example, the service provider can analyze the user's social media posts and propose the optimal food and drink menu. For example, the service provider can propose the optimal lighting and scent based on the user's social media activity. For example, the service provider can analyze the user's social media friendships and propose the optimal means of service provision. In this way, by analyzing social media activity, a more appropriate means of service provision can be proposed. Specific types of social media activity and methods of analysis include, but are not limited to, posts, the content of posts, the number of likes, and comments. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can analyze the user's social media activity at the time of service provision and propose a means of service provision.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The relaxation system may also include a past experience analysis unit that analyzes the user's past relaxation experiences and generates an optimal relaxation program. For example, the past experience analysis unit analyzes the user's past preferred relaxation programs and their effects. It can prioritize suggesting programs that the user has previously found relaxing. For instance, if the user has previously relaxed with specific music or scents, the past experience analysis unit can suggest a relaxation program that includes those elements to the generation unit. Furthermore, based on the user's past feedback, the past experience analysis unit can identify areas for improvement in relaxation programs and suggest more effective programs. This allows for a more personalized relaxation experience by leveraging the user's past relaxation experiences.

[0052] The relaxation system may also include an activity level monitoring unit that monitors the user's current activity level and adjusts the relaxation program accordingly. The activity level monitoring unit, for example, monitors the user's steps and exercise level to assess their current activity level. If the user is at a high activity level, the unit can suggest a program with a high relaxation effect. Conversely, if the user is at a low activity level, it can suggest a lighter relaxation program. This allows the activity level monitoring unit to provide an optimal relaxation program considering the user's current activity level. For example, if the user wants to relax after exercise, the activity level monitoring unit can suggest a post-exercise recovery program. Furthermore, the activity level monitoring unit can monitor the user's activity level in real time and instruct the service provider to adjust the environment in real time. This allows for a more personalized relaxation experience by utilizing the user's activity level.

[0053] The relaxation system may also include a lifestyle filtering unit that filters data considering the user's lifestyle when collecting user health data, mood, and requests. The lifestyle filtering unit selects data to collect, for example, by considering the user's lifestyle habits such as diet, exercise, and sleep. If the user has healthy lifestyle habits, the lifestyle filtering unit can collect data based on those habits. Conversely, if the user has unhealthy lifestyle habits, it can collect data that can help improve them. This allows the lifestyle filtering unit to collect more relevant data by considering the user's lifestyle. For example, if the user exercises regularly, the lifestyle filtering unit can prioritize collecting exercise-related data. Similarly, if the user has irregular sleep habits, the lifestyle filtering unit can collect data related to sleep improvement. By filtering data while considering the user's lifestyle, a more effective relaxation program can be provided.

[0054] The following briefly describes the processing flow for example form 1.

[0055] Step 1: The data collection unit collects the user's health data, mood, and requests. For example, the user can input information such as "I want to relax today" or "I want to drink fruit juice." The data collection unit inputs this information into the generating AI. Step 2: The generation unit uses generation AI to analyze the data collected by the collection unit and generate an optimal relaxation program. For example, based on the user's health data, mood, and requests, it suggests relaxing fruit juices, relaxing lighting, and scents. Step 3: The service department provides food and beverage menus, spa and sauna lighting, aromas, and other elements of the space based on the relaxation program generated by the production department. For example, they might offer relaxing fruit juices and provide a space where customers can enjoy the spa and sauna in relaxing lighting and aromas.

[0056] (Example of form 2) The relaxation system according to an embodiment of the present invention is a system that uses generative AI to provide a relaxation experience tailored to individual needs. This relaxation system collects the user's health data, mood, and requests, and the generative AI analyzes this data to generate an optimal food and beverage menu, as well as a space with lighting and scents for the spa and sauna. As a result, the user can enjoy a relaxation experience that is individually optimized. For example, if the user inputs that they want to relax, the generative AI will suggest relaxing fruit juices, relaxing lighting, and scents. Based on the generated relaxation program, the user can enjoy a relaxation experience that is individually optimized. For example, the user can enjoy a spa and sauna in a relaxing environment with relaxing lighting and scents while drinking relaxing fruit juice. As a result, the user can reduce daily stress and fatigue. Furthermore, by using generative AI, real-time environmental adjustments are possible, maximizing user satisfaction. Thus, the relaxation system can provide a relaxation experience that is individually optimized based on the user's health data, mood, and requests.

[0057] The relaxation system according to the embodiment comprises a collection unit, a generation unit, and a provision unit. The collection unit collects the user's health data, mood, and requests. For example, the user can input their health status, mood, and requests into the collection unit. For example, the user can input information such as "I want to relax today" or "I want to drink fruit juice." The collection unit inputs this information into the generation AI. The generation unit uses the generation AI to analyze the data collected by the collection unit and generate an optimal relaxation program. For example, the generation unit suggests relaxing fruit juices, relaxing lighting, and fragrances based on the user's health data, mood, and requests. The generation unit can use the generation AI to generate an optimal relaxation program based on the user's health data, mood, and requests. For example, the generation unit receives a prompt from the generation AI saying, "Please suggest a relaxing fruit juice," and suggests the most suitable fruit juice to the user. The provision unit provides food and beverage menus, spa / sauna lighting, fragrances, and other spatial elements based on the relaxation program generated by the generation unit. The service unit, for example, provides fruit juices with relaxing effects and offers a space where users can enjoy spa and sauna treatments in relaxing lighting and aromas. Based on the generated relaxation program, the service unit can provide users with the optimal relaxation experience. Thus, the relaxation system according to the embodiment can provide individually optimized relaxation experiences based on the user's health data, mood, and requests.

[0058] The data collection unit collects user health data, mood, and requests. Specifically, it provides an interface for users to input their health status, mood, and requests. For example, through a smartphone app or web portal, users can input information such as "I want to relax today" or "I want to drink fruit juice." These interfaces are designed to allow users to easily input information and are intuitive to use. Furthermore, the data collection unit can automatically acquire health data from wearable devices and smartwatches. This allows for the collection of real-time data such as the user's heart rate, blood pressure, and stress level. The collected data is stored on a secure cloud server and made accessible to the data generation unit. To protect user privacy, the data collection unit encrypts the data and controls access to prevent unauthorized access by third parties. The data collection unit also refers to the user's past data and history and uses it as foundational data to provide more accurate relaxation programs. This allows the data collection unit to efficiently collect data to provide individually optimized relaxation experiences based on the user's health status, mood, and requests.

[0059] The generation unit uses a generation AI to analyze data collected by the collection unit and generate an optimal relaxation program. Specifically, the generation AI suggests relaxing fruit juices, lighting, and scents based on the user's health data, mood, and requests. For example, the generation AI analyzes the user's heart rate and stress level to generate a recipe for a highly relaxing fruit juice. It can also suggest the optimal lighting color and scent based on the user's mood and requests. The generation AI uses natural language processing technology to understand the user's input and generate an appropriate relaxation program. For example, if it receives a prompt such as "Please suggest a relaxing fruit juice," the generation AI will generate the optimal fruit juice recipe based on past data and scientific research results. Furthermore, the generation AI can refer to the user's past data and history to provide an individually optimized relaxation program. As a result, the generation unit can generate an individually optimized relaxation program based on the user's health condition, mood, and requests, and provide it to the delivery unit.

[0060] The service department provides food and beverage menus, spa and sauna lighting, and aromatherapy based on relaxation programs generated by the generation department. Specifically, it offers relaxing fruit juices and provides a space where users can enjoy the spa and sauna in relaxing lighting and aromatherapy. For example, the service department provides fresh fruit juices based on recipes suggested by the generation AI. The lighting in the spa and sauna is also adjusted to the optimal color and brightness suggested by the generation AI to maximize relaxation. Aromatherapy oils and essential oils suggested by the generation AI are also used to create a relaxing atmosphere. The service department can collect user feedback and continuously evaluate and improve the effectiveness of the relaxation programs. For example, by providing feedback on how users felt about the fruit juices, lighting, and aromatherapy provided, this can be reflected in the next relaxation program. Furthermore, the service department can offer multiple relaxation programs simultaneously, meeting the diverse needs of users. This allows the service department to provide users with the optimal relaxation experience and increase user satisfaction.

[0061] The data collection unit can collect user health data, mood, and requests in real time. For example, the data collection unit can collect user health data, mood, and requests in real time and input them into the generating AI. For example, the data collection unit allows users to input their health status, mood, and requests in real time. This enables the data collection unit to provide a more accurate relaxation program by collecting user health data, mood, and requests in real time. The specific time range and update frequency of "real time" include, but are not limited to, seconds, minutes, or data update intervals. Some or all of the processing described above in the data collection unit may be performed using AI, or not. For example, the data collection unit can provide a more accurate relaxation program by collecting user health data, mood, and requests in real time and inputting them into the generating AI.

[0062] The generation unit can generate an optimal relaxation program based on the user's health data, mood, and requests using generative AI. For example, the generation unit can use generative AI to suggest relaxing fruit juices, relaxing lighting, and relaxing scents based on the user's health data, mood, and requests. The generation unit can use generative AI to generate an optimal relaxation program based on the user's health data, mood, and requests. For example, the generation unit can have the generative AI receive a prompt such as "Please suggest a relaxing fruit juice" and then suggest the most suitable fruit juice to the user. In this way, the generation unit can generate an optimal relaxation program for the user using generative AI. Specific types and algorithms of generative AI include, but are not limited to, deep learning, reinforcement learning, and generative models. Some or all of the above-described processes in the generation unit are performed using generative AI.

[0063] The service provider can provide a space that includes food and beverage menus, spa and sauna lighting, and fragrances based on the generated relaxation program. For example, the service provider can offer relaxing fruit juices and provide a space where users can enjoy a spa or sauna in relaxing lighting and fragrances. The service provider can provide users with the optimal relaxation experience based on the generated relaxation program. This allows the service provider to provide users with the optimal space based on the generated relaxation program. Specific elements of the space and methods of provision include, but are not limited to, room layout, decoration, and sound effects. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can provide a space that includes food and beverage menus, spa and sauna lighting, and fragrances based on the generated relaxation program.

[0064] The service provider can perform real-time environmental adjustments. For example, the service provider can perform real-time environmental adjustments to maximize user satisfaction. The service provider can perform real-time adjustments such as temperature control, music changes, and lighting adjustments. This allows for real-time environmental adjustments to maximize user satisfaction. Specific methods and criteria for real-time environmental adjustments include, but are not limited to, temperature control, music changes, and lighting adjustments. Some or all of the above-described processes in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can perform real-time environmental adjustments to maximize user satisfaction.

[0065] The generation unit can suggest relaxing fruit juices, as well as relaxing lighting and scents. For example, the generation unit uses a generation AI to suggest relaxing fruit juices, as well as relaxing lighting and scents. The generation unit can use a generation AI to suggest relaxing fruit juices, as well as relaxing lighting and scents. For example, the generation AI receives a prompt such as "Please suggest a relaxing fruit juice" and suggests the most suitable fruit juice for the user. This improves the user's relaxation experience by providing relaxing suggestions. Specific types of relaxing fruit juices and the basis for their relaxing effects include, but are not limited to, orange juice and chamomile tea. Specific types of relaxing lighting and scents and the basis for their relaxing effects include, but are not limited to, warm-colored lighting and lavender scent. Some or all of the above processing in the generation unit is performed using a generation AI.

[0066] The data collection unit can estimate the user's emotions and adjust the timing of collecting health data, mood data, and requests based on the estimated emotions. For example, if the user is stressed, the data collection unit can increase the frequency of collection and collect data in real time. For example, if the user is relaxed, the data collection unit can decrease the frequency of collection and collect data periodically. For example, if the user is in a hurry, the data collection unit can shorten the frequency of collection and collect data quickly. By adjusting the collection timing according to the user's emotions, more appropriate data collection becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can estimate the user's emotions and adjust the timing of collecting health data, mood data, and requests based on the estimated emotions.

[0067] The data collection unit can analyze the user's past health data, mood, and requests to select the optimal data collection method. For example, the data collection unit analyzes the user's past health data, mood, and requests to select the optimal data collection method. For example, the data collection unit suggests the optimal data collection method based on the health data and mood frequently entered by the user in the past. For example, the data collection unit analyzes the user's past request history to customize the data collection method. For example, the data collection unit selects the optimal data collection method for a specific time period based on the user's past data. This allows for the selection of the optimal data collection method by analyzing past data. Specific criteria and methods for the optimal data collection method include, but are not limited to, the type of sensor and the frequency of data collection. Some or all of the above-described processes in the data collection unit may be performed using, for example, AI, or not. For example, the data collection unit can analyze the user's past health data, mood, and requests to select the optimal data collection method.

[0068] The data collection unit can filter health data, mood data, and requests based on the user's current lifestyle and areas of interest. For example, the data collection unit filters the data to be collected based on the user's current lifestyle and areas of interest. For example, the data collection unit filters the data to be collected according to the user's current lifestyle. For example, the data collection unit selects the data to be collected based on the user's areas of interest. For example, the data collection unit determines the priority of the data to be collected according to the user's lifestyle and areas of interest. This allows for the collection of more relevant data by filtering the data based on the user's lifestyle and areas of interest. Specific criteria and methods for filtering include, but are not limited to, data importance and methods for selecting highly relevant data. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not. For example, the data collection unit can filter health data, mood data, and requests based on the user's current lifestyle and areas of interest.

[0069] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated user emotions. For example, if the user is stressed, the data collection unit will prioritize collecting stress-related data. For example, if the user is relaxed, the data collection unit will prioritize collecting relaxation-related data. For example, if the user is in a hurry, the data collection unit will prioritize collecting data that can be collected quickly. This allows for the priority collection of more important data by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated user emotions.

[0070] The data collection unit can prioritize the collection of highly relevant data, taking into account the user's geographical location, when collecting health data, mood data, and requests. For example, when collecting health data, mood data, and requests, the data collection unit prioritizes the collection of highly relevant data, taking into account the user's geographical location. For example, if the user is in a specific region, the data collection unit prioritizes the collection of health data related to that region. For example, the data collection unit collects data related to mood and requests based on the user's geographical location. For example, if the user is on the move, the data collection unit prioritizes the collection of data related to their destination. This allows for the priority collection of highly relevant data by considering the user's geographical location. Specific types of geographical location information and methods of collection include, but are not limited to, GPS data and location services. Some or all of the processing described above in the data collection unit may be performed using, for example, AI, or not. For example, when collecting health data, mood data, and requests, the data collection unit can prioritize the collection of highly relevant data, taking into account the user's geographical location.

[0071] The data collection unit can analyze a user's social media activity and collect relevant data when collecting health data, mood data, and requests. For example, the data collection unit can analyze a user's social media activity and collect relevant data when collecting health data, mood data, and requests. For example, the data collection unit can analyze a user's social media posts and collect information related to health data and mood data. For example, the data collection unit can collect data related to requests from a user's social media activity. For example, the data collection unit can analyze a user's social media friendships and collect relevant data. This allows for the collection of relevant data by analyzing social media activity. Specific types of social media activity and methods of analysis include, but are not limited to, posts, the number of likes, and comments. Some or all of the processing described above in the data collection unit may be performed using, for example, AI, or not. For example, the data collection unit can analyze a user's social media activity and collect relevant data when collecting health data, mood data, and requests.

[0072] The generation unit can estimate the user's emotions and adjust the method of generating the relaxation program based on the estimated user emotions. For example, the generation unit estimates the user's emotions and adjusts the method of generating the relaxation program based on the estimated user emotions. For example, if the user is relaxed, the generation unit generates a relaxed relaxation program. For example, if the user is stressed, the generation unit generates a relaxation program specifically for stress relief. For example, if the user is in a hurry, the generation unit generates a short and effective relaxation program. In this way, by adjusting the generation method according to the user's emotions, a more appropriate relaxation program can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit is performed using a generation AI.

[0073] The generation unit can adjust the level of detail of the relaxation program based on the user's health data, mood, and the importance of the request when generating the relaxation program. For example, the generation unit can adjust the level of detail of the relaxation program based on the user's health data. For example, the generation unit can generate a detailed relaxation program based on the user's health data. For example, the generation unit can adjust the level of detail of the relaxation program according to the user's mood. For example, the generation unit can determine the level of detail of the relaxation program based on the importance of the user's request. This allows for the provision of a more appropriate relaxation program by adjusting the level of detail of the generation based on the importance of the user's data. Specific criteria and methods for adjusting the level of detail of the generation include, but are not limited to, the length of the program and the depth of its content. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can adjust the level of detail of the relaxation program based on the user's health data, mood, and the importance of the request when generating the relaxation program.

[0074] The generation unit can apply different generation algorithms depending on the user's category when generating relaxation programs. For example, the generation unit applies different generation algorithms depending on the user's category when generating relaxation programs. For example, if the user is a business person, the generation unit generates a relaxation program for business. For example, if the user is a family person, the generation unit generates a relaxation program for family. For example, if the user is a student, the generation unit generates a relaxation program for students. By applying different generation algorithms depending on the user's category, it is possible to provide more appropriate relaxation programs. Specific types and classification methods for user categories include, but are not limited to, age, gender, and health status. Some or all of the above processing in the generation unit is performed using generation AI.

[0075] The generation unit can estimate the user's emotions and adjust the length of the relaxation program based on the estimated emotions. For example, the generation unit estimates the user's emotions and adjusts the length of the relaxation program based on the estimated emotions. For example, if the user is relaxed, the generation unit generates a longer relaxation program. For example, if the user is in a hurry, the generation unit generates a shorter relaxation program. For example, if the user is stressed, the generation unit generates a relaxation program of appropriate length. By adjusting the program length according to the user's emotions, a more appropriate relaxation experience can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit is performed using a generation AI.

[0076] The generation unit can determine the generation priority based on the user's submission timing when generating relaxation programs. For example, the generation unit will prioritize the generation of relaxation programs if the user is in a hurry. If the user is relaxed, the generation unit will generate programs with the normal priority. If the user submits a request within a specific time period, the generation unit will determine the priority based on that time period. This allows for faster delivery of relaxation programs by determining the generation priority based on the user's submission timing. Specific criteria for submission timing and methods for determining priority include, but are not limited to, submission date and time, urgency, etc. Some or all of the above processing in the generation unit is performed using a generation AI.

[0077] The generation unit can adjust the order of generation of relaxation programs based on user relevance. For example, the generation unit adjusts the order of generation based on user relevance when generating relaxation programs. For example, the generation unit prioritizes generating programs that are highly relevant based on the user's health data. For example, the generation unit prioritizes generating programs that are highly relevant based on the user's mood. For example, the generation unit prioritizes generating programs that are highly relevant based on the importance of the user's request. By adjusting the order of generation based on user relevance, it is possible to provide more relevant relaxation programs. Specific criteria and evaluation methods for relevance include, but are not limited to, past data and the user's areas of interest. Some or all of the above processing in the generation unit is performed using generation AI.

[0078] The service provider can estimate the user's emotions and adjust the food and beverage menu, spa / sauna lighting, scents, and other spatial elements based on the estimated emotions. For example, if the user is relaxed, the service provider can provide relaxing lighting and scents. If the user is stressed, the service provider can provide food and beverage menus specifically designed for stress relief. If the user is in a hurry, the service provider can provide food and beverage menus and spaces that can be served quickly. By adjusting the service method according to the user's emotions, a more appropriate relaxation experience can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can estimate the user's emotions and adjust the food and beverage menu, as well as the lighting and scents of the spa and sauna, based on the estimated emotions of the user.

[0079] The service provider can analyze the user's past relaxation experiences at the time of service to select the optimal service method. For example, the service provider can analyze the user's past relaxation experiences at the time of service to select the optimal service method. For example, the service provider can prioritize serving food and beverage items that the user has previously enjoyed. For example, the service provider can select the optimal lighting and scent based on the user's past relaxation experiences. For example, the service provider can customize the optimal service method based on the user's past experiences. This allows for the selection of a more appropriate service method by analyzing past relaxation experiences. Specific types of past relaxation experiences and methods of analysis include, but are not limited to, past program content and user feedback. Some or all of the above processing in the service provider may be performed using, for example, AI, or not. For example, the service provider can analyze the user's past relaxation experiences at the time of service to select the optimal service method.

[0080] The service provider can customize the means of delivery based on the user's current living situation at the time of delivery. For example, the service provider can customize the means of delivery based on the user's current living situation at the time of delivery. For example, if the user is busy, the service provider can select a means of delivery that can be delivered quickly. For example, if the user is relaxed, the service provider can select a leisurely means of delivery. For example, the service provider can customize the optimal means of delivery according to the user's living situation. By customizing the means of delivery based on the current living situation, a more appropriate relaxation experience can be provided. Specific types of current living situations and methods of collection include, but are not limited to, work situation, family situation, health status, etc. Some or all of the processing described above in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can customize the means of delivery based on the user's current living situation at the time of delivery.

[0081] The service provider can estimate the user's emotions and, based on the estimated emotions, determine the priority of the food and beverage menu, spa / sauna lighting, scents, and other spatial elements. For example, if the user is relaxed, the service provider will prioritize providing food and beverages with relaxing effects. If the user is stressed, the service provider will prioritize providing lighting and scents specifically designed for stress relief. If the user is in a hurry, the service provider will prioritize providing spaces that can be served quickly. By prioritizing according to the user's emotions, a more appropriate relaxation experience can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can estimate the user's emotions and, based on those emotions, determine the priorities for the food and beverage menu, as well as the lighting and scents of the spa and sauna.

[0082] The service provider can select the optimal service delivery method at the time of delivery, taking into account the user's geographical location information. For example, the service provider can select the optimal service delivery method at the time of delivery, taking into account the user's geographical location information. For example, if the user is in a specific region, the service provider can provide food and beverage menus related to that region. For example, the service provider can provide optimal lighting and aroma based on the user's geographical location information. For example, if the user is on the move, the service provider can select a service delivery method related to their destination. This allows for the selection of a more appropriate service delivery method by considering geographical location information. Specific types of geographical location information and methods of collection include, but are not limited to, GPS data and location-based services. Some or all of the processing described above in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can select the optimal service delivery method at the time of delivery, taking into account the user's geographical location information.

[0083] The service provider can analyze the user's social media activity at the time of service provision and propose a means of service provision. For example, the service provider can analyze the user's social media activity at the time of service provision and propose a means of service provision. For example, the service provider can analyze the user's social media posts and propose the optimal food and drink menu. For example, the service provider can propose the optimal lighting and scent based on the user's social media activity. For example, the service provider can analyze the user's social media friendships and propose the optimal means of service provision. In this way, by analyzing social media activity, a more appropriate means of service provision can be proposed. Specific types of social media activity and methods of analysis include, but are not limited to, posts, the content of posts, the number of likes, and comments. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can analyze the user's social media activity at the time of service provision and propose a means of service provision.

[0084] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0085] The relaxation system can also include a voice analysis unit that collects and analyzes user voice data. The voice analysis unit analyzes, for example, the tone, speed, and volume of the user's voice to estimate the user's emotions and stress level. When a user is relaxed, the voice analysis unit can detect a calm voice tone and slow speed. Conversely, when a user is stressed, their voice tone may become higher and their speed faster. This allows the voice analysis unit to perform more accurate emotion estimations by analyzing the user's voice data, helping to optimize the relaxation program. For example, if a user is stressed, the voice analysis unit can suggest a relaxation program specifically designed for stress relief to the generation unit. Furthermore, the voice analysis unit can analyze the user's voice data in real time and instruct the provisioning unit to adjust the environment in real time. This allows for the provision of a more personalized relaxation experience by utilizing the user's voice data.

[0086] The relaxation system may also include a biometric data analysis unit that collects and analyzes the user's biometric data. This unit collects and analyzes biometric data such as the user's heart rate, blood pressure, and skin temperature. If the user's heart rate is high, the unit can estimate that the user is experiencing stress. Conversely, if the heart rate is low, it can estimate that the user is relaxed. This allows the biometric data analysis unit to perform more accurate emotion estimations by analyzing the user's biometric data, which can then be used to optimize the relaxation program. For example, if the user's heart rate is high, the biometric data analysis unit can propose a relaxation program specifically designed for stress relief to the generation unit. Furthermore, the biometric data analysis unit can analyze the user's biometric data in real time and instruct the provision unit to adjust the environment in real time. This allows for the provision of a more personalized relaxation experience by utilizing the user's biometric data.

[0087] The relaxation system may also include a past experience analysis unit that analyzes the user's past relaxation experiences and generates an optimal relaxation program. For example, the past experience analysis unit analyzes the user's past preferred relaxation programs and their effects. It can prioritize suggesting programs that the user has previously found relaxing. For instance, if the user has previously relaxed with specific music or scents, the past experience analysis unit can suggest a relaxation program that includes those elements to the generation unit. Furthermore, based on the user's past feedback, the past experience analysis unit can identify areas for improvement in relaxation programs and suggest more effective programs. This allows for a more personalized relaxation experience by leveraging the user's past relaxation experiences.

[0088] The relaxation system may also include an activity level monitoring unit that monitors the user's current activity level and adjusts the relaxation program accordingly. The activity level monitoring unit, for example, monitors the user's steps and exercise level to assess their current activity level. If the user is at a high activity level, the unit can suggest a program with a high relaxation effect. Conversely, if the user is at a low activity level, it can suggest a lighter relaxation program. This allows the activity level monitoring unit to provide an optimal relaxation program considering the user's current activity level. For example, if the user wants to relax after exercise, the activity level monitoring unit can suggest a post-exercise recovery program. Furthermore, the activity level monitoring unit can monitor the user's activity level in real time and instruct the service provider to adjust the environment in real time. This allows for a more personalized relaxation experience by utilizing the user's activity level.

[0089] The relaxation system may also include a music selection unit that estimates the user's emotions and selects music for the relaxation program based on those emotions. For example, the music selection unit might estimate the user's emotions and select calming music if they are relaxed, or music effective for stress relief if they are stressed. If the user is in a hurry, the music selection unit can also select music that allows for quick relaxation. This allows the music selection unit to enhance the relaxation effect by providing optimal music according to the user's emotions. For example, if the user is relaxed, the music selection unit might suggest classical music or nature sounds. If the user is stressed, the music selection unit might suggest rhythmic music or healing music. This allows for a more effective relaxation experience by selecting music based on the user's emotions.

[0090] The relaxation system may also include a fragrance selection unit that estimates the user's emotions and selects a fragrance for the relaxation program based on those emotions. For example, the fragrance selection unit estimates the user's emotions and selects a calming fragrance if the user is relaxed, or a fragrance effective for stress relief if the user is stressed. If the user is in a hurry, the fragrance selection unit can also select a fragrance that allows for quick relaxation. In this way, the fragrance selection unit can enhance the relaxation effect by providing the optimal fragrance according to the user's emotions. For example, if the user is relaxed, the fragrance selection unit can suggest lavender or chamomile. If the user is stressed, the fragrance selection unit can suggest mint or eucalyptus. In this way, by selecting a fragrance based on the user's emotions, a more effective relaxation experience can be provided.

[0091] The relaxation system may also include a lighting adjustment unit that estimates the user's emotions and adjusts the lighting of the relaxation program based on those emotions. For example, the lighting adjustment unit estimates the user's emotions and provides gentle lighting if the user is relaxed, and lighting that is effective for stress relief if the user is stressed. The lighting adjustment unit can also provide lighting that allows the user to relax quickly if the user is in a hurry. In this way, the lighting adjustment unit can enhance the relaxation effect by providing optimal lighting according to the user's emotions. For example, if the user is relaxed, the lighting adjustment unit can suggest warm-colored lighting. Conversely, if the user is stressed, the lighting adjustment unit can suggest cool-colored lighting. In this way, a more effective relaxation experience can be provided by adjusting the lighting based on the user's emotions.

[0092] The relaxation system may also include a content customization unit that estimates the user's emotions and customizes the content of the relaxation program based on those emotions. For example, the content customization unit might estimate the user's emotions and provide a calming program if they are relaxed, or a stress-relieving program if they are stressed. If the user is in a hurry, the content customization unit can also provide a program that allows for quick relaxation. This allows the content customization unit to enhance the relaxation effect by providing the optimal program according to the user's emotions. For example, if the user is relaxed, the content customization unit might suggest meditation or yoga programs. If the user is stressed, the content customization unit might suggest massage or aromatherapy programs. This allows for a more effective relaxation experience by customizing the program based on the user's emotions.

[0093] The relaxation system may also include a feedback collection unit that estimates the user's emotions and collects feedback on the relaxation program based on those emotions. For example, the feedback collection unit estimates the user's emotions and collects positive feedback if the user is relaxed, and negative feedback if the user is stressed. The feedback collection unit can also collect feedback quickly if the user is in a hurry. This allows the feedback collection unit to improve the relaxation program by collecting optimal feedback according to the user's emotions. For example, if the user is relaxed, the feedback collection unit can collect feedback on the program's effectiveness and satisfaction level. If the user is stressed, the feedback collection unit can collect feedback on areas for improvement and areas of dissatisfaction. This allows for the provision of a more effective relaxation program by collecting feedback based on the user's emotions.

[0094] The relaxation system may also include a lifestyle filtering unit that filters data considering the user's lifestyle when collecting user health data, mood, and requests. The lifestyle filtering unit selects data to collect, for example, by considering the user's lifestyle habits such as diet, exercise, and sleep. If the user has healthy lifestyle habits, the lifestyle filtering unit can collect data based on those habits. Conversely, if the user has unhealthy lifestyle habits, it can collect data that can help improve them. This allows the lifestyle filtering unit to collect more relevant data by considering the user's lifestyle. For example, if the user exercises regularly, the lifestyle filtering unit can prioritize collecting exercise-related data. Similarly, if the user has irregular sleep habits, the lifestyle filtering unit can collect data related to sleep improvement. By filtering data while considering the user's lifestyle, a more effective relaxation program can be provided.

[0095] The following briefly describes the processing flow for example form 2.

[0096] Step 1: The data collection unit collects the user's health data, mood, and requests. For example, the user can input information such as "I want to relax today" or "I want to drink fruit juice." The data collection unit inputs this information into the generating AI. Step 2: The generation unit uses generation AI to analyze the data collected by the collection unit and generate an optimal relaxation program. For example, based on the user's health data, mood, and requests, it suggests relaxing fruit juices, relaxing lighting, and scents. Step 3: The service department provides food and beverage menus, spa and sauna lighting, aromas, and other elements of the space based on the relaxation program generated by the production department. For example, they might offer relaxing fruit juices and provide a space where customers can enjoy the spa and sauna in relaxing lighting and aromas.

[0097] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0098] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0099] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0100] Each of the multiple elements described above, including the collection unit, generation unit, and provision unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects the user's health data, mood, and requests using the reception device 38 of the smart device 14. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the collected data using generation AI and generates an optimal relaxation program. The provision unit uses the output device 40 of the smart device 14 to provide food and beverage menus, spa / sauna lighting, fragrances, and other spatial elements based on the generated relaxation program. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0101] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0102] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0103] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0104] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0105] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0107] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0108] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0109] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0110] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0111] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0112] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0115] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] Each of the multiple elements described above, including the collection unit, generation unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects the user's health data, mood, and requests using the microphone 238 of the smart glasses 214. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the collected data using generation AI and generates an optimal relaxation program. The provision unit uses the speaker 240 of the smart glasses 214 to provide food and beverage menus, spa / sauna lighting, scents, and other spatial elements based on the generated relaxation program. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0117] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0118] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0119] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0120] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0121] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0123] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0124] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0125] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0126] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0127] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0129] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0131] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the collection unit, generation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects the user's health data, mood, and requests using the microphone 238 of the headset terminal 314. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data using a generation AI and generates an optimal relaxation program. The provision unit uses the display 343 of the headset terminal 314 to provide food and beverage menus, spa / sauna lighting, scents, and other spatial elements based on the generated relaxation program. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0133] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0134] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0137] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0141] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0142] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0143] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0144] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0146] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0148] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements described above, including the collection unit, generation unit, and provision unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects the user's health data, mood, and requests using the microphone 238 of the robot 414. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the collected data using a generation AI to generate an optimal relaxation program. The provision unit uses the speaker 240 of the robot 414 to provide food and beverage menus, spa / sauna lighting, scents, and other spatial elements based on the generated relaxation program. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0150] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0151] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0152] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0153] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0154] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0155] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0156] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0157] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0158] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0159] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0160] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0161] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0162] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0163] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0164] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0165] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0166] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0167] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0168] (Note 1) A data collection unit that collects user health data, mood, and requests, A generation unit analyzes the data collected by the collection unit and generates an optimal relaxation program, The system includes a provisioning unit that provides food and beverage menus, spa and sauna lighting, fragrances, and other spatial elements based on the relaxation program generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collects user health data, mood, and requests in real time. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is The AI ​​generates the optimal relaxation program based on the user's health data, mood, and requests. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Based on the generated relaxation program, we provide food and beverage menus, spa and sauna lighting, scents, and other elements to create a pleasant atmosphere. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Perform real-time environment adjustments. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is We suggest fruit juices with relaxing effects, as well as relaxing lighting and scents. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of collecting health data, mood data, and requests based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We analyze the user's past health data, mood, and requests to select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting health data, mood data, and requests, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting health data, mood data, and requests, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting health data, mood data, and requests, we analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is It estimates the user's emotions and adjusts the method of generating relaxation programs based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating relaxation programs, the level of detail is adjusted based on the user's health data, mood, and the importance of their requests. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating relaxation programs, different generation algorithms are applied depending on the user's category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and adjusts the length of the relaxation program based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating relaxation programs, the generation priority is determined based on when the user submitted the program. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating relaxation programs, the generation order is adjusted based on user relevance. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the food and beverage menu, as well as the lighting and scents of the spa and sauna, based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing the service, the system analyzes the user's past relaxation experiences to select the optimal delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing the service, the means of delivery will be customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, The system estimates the user's emotions and, based on those emotions, prioritizes the food and beverage menu, as well as the lighting and scents of the spa and sauna. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and propose a delivery method. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0169] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects user health data, mood, and requests, A generation unit analyzes the data collected by the collection unit and generates an optimal relaxation program, The system includes a provisioning unit that provides food and beverage menus, spa and sauna lighting, fragrances, and other spatial elements based on the relaxation program generated by the generation unit. A system characterized by the following features.

2. The aforementioned collection unit is Collects user health data, mood, and requests in real time. The system according to feature 1.

3. The generating unit is The AI ​​generates the optimal relaxation program based on the user's health data, mood, and requests. The system according to feature 1.

4. The aforementioned supply unit is, Based on the generated relaxation program, we provide food and beverage menus, spa and sauna lighting, scents, and other elements to create a pleasant atmosphere. The system according to feature 1.

5. The aforementioned supply unit is, Perform real-time environment adjustments. The system according to feature 1.

6. The generating unit is We suggest fruit juices with relaxing effects, as well as relaxing lighting and scents. The system according to feature 1.

7. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of collecting health data, mood data, and requests based on the estimated user emotions. The system according to feature 1.

8. The aforementioned collection unit is We analyze the user's past health data, mood, and requests to select the optimal data collection method. The system according to feature 1.

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