system

Abstract

The system according to this embodiment aims to provide appropriate medicinal teas based on individual nutritional status. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a communication unit, a blending unit, and a serving unit. The collection unit collects stool. The analysis unit analyzes the stool collected by the collection unit and analyzes the nutrients. The communication unit transmits the analysis results obtained by the analysis unit. The blending unit blends medicinal tea based on the analysis results transmitted by the communication unit. The serving unit provides the medicinal tea blended by the blending unit.

Description

Technical Field

[0006] , , ,

[0005] , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to provide an appropriate medicinal diet tea based on an individual nutritional status.

[0005] The system according to the embodiment aims to provide an appropriate medicinal diet tea based on an individual nutritional status. <00​​​​​The system according to this embodiment comprises a collection unit, an analysis unit, a communication unit, a blending unit, and a serving unit. The collection unit collects stool. The analysis unit analyzes the stool collected by the collection unit and analyzes its nutrients. The communication unit transmits the analysis results obtained by the analysis unit. The blending unit blends medicinal tea based on the analysis results transmitted by the communication unit. The serving unit provides the medicinal tea blended by the blending unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide appropriate medicinal teas based on an individual's nutritional status. [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 system according to an embodiment of the present invention uses a toilet equipped with a stool analysis device and a blending device to analyze the nutrients contained in a customer's stool, and based on the analysis results, blends and provides a medicinal herbal tea with the effects desired by the customer. In this system, first the customer uses the toilet, and the stool analysis device analyzes the stool. Next, the stool analysis device analyzes the nutrients contained in the stool and transmits the results to the blending device. Based on the analysis results, the blending device blends and provides a medicinal herbal tea with the effects desired by the customer. This system allows customers to easily manage their health in a way that suits them. For example, when a customer uses the toilet, the stool analysis device automatically collects the stool and starts the analysis. The stool analysis device analyzes the nutrients and other components contained in the stool in detail. For example, it identifies nutrients such as vitamins, minerals, and enzymes, and measures their content. These analysis results provide important information for understanding the customer's health condition and nutritional balance. Next, the analysis results obtained by the stool analysis device are transmitted to the blending device. Based on the analysis results, the blending device blends a medicinal herbal tea with the effects desired by the customer. For example, if the customer desires beauty effects, the blending device blends a medicinal herbal tea containing ingredients effective for beauty. Furthermore, if a customer desires a relaxing effect, the blending device will prepare a medicinal tea containing ingredients that promote relaxation. In this way, medicinal teas tailored to the customer's needs can be provided. The blending device then provides the blended medicinal tea to the customer. After using the toilet, the customer can immediately receive the medicinal tea that suits them. This allows customers to easily manage their health. In addition, since the medicinal tea is blended automatically, customers do not need to perform any special operations. This system allows customers who are highly interested in health and beauty to manage their health in a way that suits them without feeling the hassle of making it a habit. This system is particularly targeted at women in their 20s and 30s who attend yoga classes, and is ideal for people who are highly interested in health and beauty, willing to invest in themselves, but who find making it a habit "troublesome" or "uncertain about what suits them." By using toilets equipped with stool analysis equipment and blending devices, customers can easily manage their health in a way that suits them.This system, which uses a toilet equipped with a stool analysis device and a blending device, can analyze the nutrients contained in the customer's stool and, based on the analysis results, blend and provide herbal tea with the effects desired by the customer.

[0029] The stool analysis system according to this embodiment comprises a collection unit, an analysis unit, a communication unit, a mixing unit, and a supply unit. The collection unit collects stool. The collection unit includes, for example, a device for automatically collecting stool. When collecting stool, the collection unit can evaluate the condition of the stool (hardness, color, quantity, etc.) in real time and adjust the collection method. For example, if the stool is hard, collection can be carried out carefully. If the stool is soft, collection can be carried out quickly. Furthermore, if the amount of stool is large, collection can be carried out in parts. The analysis unit analyzes the stool collected by the collection unit and analyzes the nutrients. For example, the analysis unit analyzes nutrients such as vitamins, minerals, and enzymes contained in the stool. When analyzing stool, the analysis unit can optimize the analysis method considering the condition of the stool (hardness, color, quantity, etc.). For example, if the stool is hard, an analysis method appropriate to the hardness can be applied. If the stool is soft, an analysis method appropriate to the softness can also be applied. Furthermore, if the amount of stool is large, an analysis method appropriate to the quantity can also be applied. The communication unit transmits the analysis results obtained by the analysis unit. The communication unit transmits the analysis results to the blending device, for example. When transmitting the analysis results, the communication unit can determine the transmission priority based on the importance of the data. For example, it may transmit data on important nutrients first. It may also transmit data on general nutrients next. Furthermore, it may transmit supplementary data last. The blending unit blends the herbal tea based on the analysis results transmitted by the communication unit. The blending unit may, for example, blend a herbal tea with beauty benefits based on the analysis results. The blending unit may also blend a herbal tea with relaxation benefits based on the analysis results. Furthermore, the blending unit may also blend a herbal tea with health benefits based on the analysis results. The serving unit provides the herbal tea blended by the blending unit. The serving unit may, for example, provide the blended herbal tea to the customer. When providing the herbal tea, the serving unit can estimate the user's emotions and adjust the serving method based on the estimated emotions. For example, if the user is relaxed, a relaxed serving method may be adopted. If the user is tense, a quick serving method may be adopted. Furthermore, if the user is in a hurry, we can also offer a take-out option.As a result, the stool analysis system according to this embodiment can perform the entire process from stool collection and analysis to formulation and provision in a single, integrated manner.

[0030] The analysis unit can analyze the nutrients, such as vitamins, minerals, and enzymes, contained in stool. For example, the analysis unit can analyze vitamins in stool. It is necessary to clarify the specific types of vitamins and the analysis methods. For example, it can identify types such as vitamin A and vitamin C and measure their content. The analysis unit can also analyze minerals in stool. It is necessary to clarify the specific types of minerals and the analysis methods. For example, it can identify types such as calcium and magnesium and measure their content. Furthermore, the analysis unit can also analyze enzymes in stool. It is necessary to clarify the specific types of enzymes and the analysis methods. For example, it can identify types such as digestive enzymes and metabolic enzymes and measure their content. This allows the analysis unit to analyze the nutrients contained in stool in detail.

[0031] The blending unit can blend herbal teas with beauty benefits based on the analysis results. For example, the blending unit can blend herbal teas with beauty benefits based on the analysis results. It is necessary to clarify the specific criteria and evaluation methods for beauty benefits. For example, criteria such as skin improvement and hair health are set, and ingredients are selected based on these. The blending unit can also blend herbal teas that contain ingredients with beauty benefits. For example, it can blend herbal teas that contain ingredients such as collagen, vitamin C, and hyaluronic acid. Furthermore, the blending unit can optimize the blending method for herbal teas with beauty benefits. For example, it can adjust the ingredient ratios and blending procedures to blend herbal teas that achieve optimal beauty benefits. In this way, the blending unit can blend herbal teas with beauty benefits.

[0032] The blending unit can blend herbal teas with relaxing effects based on the analysis results. For example, the blending unit can blend herbal teas with relaxing effects based on the analysis results. It is necessary to clarify the specific criteria and evaluation methods for relaxation effects. For example, criteria such as stress reduction and improved sleep quality are set, and ingredients are selected based on these. The blending unit can also blend herbal teas that contain ingredients with relaxing effects. For example, it can blend herbal teas that contain ingredients such as chamomile, lavender, and valerian root. Furthermore, the blending unit can optimize the blending method for herbal teas with relaxing effects. For example, it can adjust the ingredient ratios and blending procedures to blend herbal teas that achieve the optimal relaxation effect. In this way, the blending unit can blend herbal teas with relaxing effects.

[0033] The service department can provide customers with blended herbal tea. For example, the service department can provide customers with blended herbal tea. When providing the herbal tea, the service department can estimate the user's emotions and adjust the method of providing the herbal tea based on the estimated emotions. For example, if the user is relaxed, a relaxed method of providing the tea can be adopted. If the user is tense, a quick method of providing the tea can be adopted. Furthermore, if the user is in a hurry, a method of providing the tea to take away can be adopted. In this way, the service department can provide customers with blended herbal tea.

[0034] The communication unit can transmit the analysis results to the compounding device. For example, the communication unit can transmit the analysis results to the compounding device. When transmitting the analysis results, the communication unit can determine the transmission priority based on the importance of the data. For example, it can prioritize the transmission of data for important nutrients. It can also transmit data for common nutrients next, and supplementary data last. This allows the communication unit to transmit the analysis results to the compounding device.

[0035] The collection unit can optimize the stool collection method by considering the user's dietary history. For example, if the user is consuming a high-fiber diet, the collection unit will collect the stool carefully. It can also collect the stool quickly if the user is consuming a high-fat diet. Furthermore, it can appropriately adjust the stool collection if the user is consuming a lot of fluids. It is necessary to clearly define the specific content and collection method of the dietary history. For example, consider the type of food, the amount consumed, and the frequency. This allows the collection unit to optimize the stool collection method based on the user's dietary history.

[0036] The collection unit can evaluate the condition of the stool (hardness, color, quantity, etc.) in real time during collection and adjust the collection method accordingly. For example, if the stool is hard, the collection unit can collect it carefully. If the stool is soft, it can be collected quickly. Furthermore, if the amount of stool is large, the collection can be divided into smaller portions. It is necessary to clarify the specific evaluation criteria and methods for the condition of the stool. For example, hardness, color, and quantity should be evaluated. This will allow the collection unit to adjust the collection method according to the condition of the stool.

[0037] The collection unit can optimize the stool collection method by considering the user's lifestyle (exercise level, sleep duration, etc.). For example, if the user exercises a lot, the collection unit will collect the stool quickly. Conversely, if the user is sleep-deprived, the collection unit can collect the stool carefully. Furthermore, if the user has a regular lifestyle, the collection unit can appropriately adjust the stool collection. It is necessary to clarify the specific details of lifestyle and the collection method. For example, exercise level and sleep duration should be considered. This allows the collection unit to optimize the stool collection method based on the user's lifestyle.

[0038] The collection unit can adjust the stool collection method considering the user's health condition (such as past medical history). For example, if the user has a history of digestive system illness, the collection unit will collect the stool carefully. It can also collect the stool quickly if the user has a history of constipation. Furthermore, if the user has a history of diarrhea, the collection can be appropriately adjusted. It is necessary to clearly define the specific content and evaluation methods of the health condition. For example, past medical history and current health condition should be considered. This allows the collection unit to adjust the stool collection method based on the user's health condition.

[0039] The analysis unit can improve the accuracy of nutrient analysis by considering the user's dietary history when analyzing stool samples. For example, if the user consumes a high-fiber diet, the analysis unit can improve the accuracy of fiber analysis. It can also improve the accuracy of fat analysis if the user consumes a high-fat diet. Furthermore, if the user consumes a lot of fluids, it can improve the accuracy of fluid analysis. It is necessary to clarify the specific content and collection method of the dietary history. For example, the type of food, amount consumed, and frequency should be considered. This allows the analysis unit to improve the accuracy of nutrient analysis based on the user's dietary history.

[0040] The analysis unit can optimize its analysis method based on the stool's condition (hardness, color, quantity, etc.) during stool analysis. For example, if the stool is hard, the analysis unit can apply an analysis method appropriate to its hardness. Similarly, if the stool is soft, it can apply an analysis method appropriate to its softness. Furthermore, if the stool is large, it can apply an analysis method appropriate to its quantity. It is necessary to clearly define specific evaluation criteria and methods for the stool's condition. For example, hardness, color, and quantity should be evaluated. This allows the analysis unit to optimize its analysis method according to the stool's condition.

[0041] The analysis unit can improve the accuracy of stool analysis by considering the user's lifestyle (exercise level, sleep duration, etc.). For example, if the user exercises a lot, the analysis unit can improve the accuracy according to the amount of exercise. It can also improve the accuracy according to the amount of sleep if the user is sleep-deprived. Furthermore, if the user lives a regular lifestyle, the analysis unit can improve the accuracy according to their lifestyle. It is necessary to clarify the specific details of lifestyle and how to collect this information. For example, exercise level and sleep duration should be considered. This allows the analysis unit to improve its accuracy based on the user's lifestyle.

[0042] The analysis unit can adjust the analysis method when analyzing stool samples, taking into account the user's health condition (such as past medical history). For example, if the user has a history of digestive system illness, the analysis unit will apply an analysis method appropriate to that history. It can also apply an analysis method appropriate for constipation if the user has a history of constipation. Furthermore, if the user has a history of diarrhea, it can apply an analysis method appropriate for diarrhea. It is necessary to clearly define the specific content and evaluation methods of the health condition. For example, past medical history and current health condition should be considered. This allows the analysis unit to adjust the analysis method based on the user's health condition.

[0043] The communications department can determine the transmission priority based on the importance of the data when sending analysis results. For example, the communications department may prioritize the transmission of data on important nutrients. It may also send data on general nutrients next, and supplementary data last. It is necessary to clarify the specific criteria and methods for evaluating data importance. For example, urgency and impact should be considered. This will allow the communications department to determine the transmission priority based on the importance of the data.

[0044] The communication unit can optimize the transmission method based on the type of data (vitamins, minerals, enzymes, etc.) when transmitting analysis results. For example, the communication unit can transmit vitamin data quickly, mineral data appropriately, and enzyme data carefully. It is necessary to clarify the specific content and classification method of the data types. For example, classifying vitamins, minerals, enzymes, etc. This allows the communication unit to optimize the transmission method according to the type of data.

[0045] The communication unit can optimize the transmission method based on the data destination (such as a compounding device or the user's smartphone) when transmitting analysis results. For example, when transmitting to a compounding device, the communication unit can transmit quickly. It can also transmit appropriately when transmitting to a user's smartphone. Furthermore, it can transmit carefully when transmitting to other devices. It is necessary to clearly define the specific types of data destinations and the selection criteria. For example, selecting a compounding device or a user's smartphone. This allows the communication unit to optimize the transmission method according to the data destination.

[0046] The communications unit can adjust the transmission order of analysis results based on the importance of the data. For example, it can prioritize the transmission of data on important nutrients, then send data on common nutrients next, and finally send supplementary data last. It is necessary to clarify specific criteria and methods for evaluating data importance, such as urgency and impact. This will allow the communications unit to adjust the transmission order based on the importance of the data.

[0047] The blending unit can optimize the blending components of medicinal teas based on the level of detail of the analysis results. For example, if the analysis results are detailed, the blending unit will use detailed components to blend the medicinal tea. If the analysis results are simple, it can also use simple components to blend the medicinal tea. Furthermore, if the analysis results are rapid, it can use components that allow for rapid blending. It is necessary to clarify specific criteria and evaluation methods for the level of detail of the analysis results. For example, the depth and accuracy of the analysis should be considered. This will allow the blending unit to optimize the blending components based on the level of detail of the analysis results.

[0048] The blending unit can select ingredients for herbal tea based on the user's past health data. For example, if the user previously desired beauty benefits, the blending unit can use ingredients with beauty benefits. Similarly, if the user previously desired relaxation benefits, it can use ingredients with relaxation benefits. Furthermore, if the user previously desired health benefits, it can use ingredients with health benefits. It is necessary to clearly define the specific content and collection methods of past health data. For example, medical history and diagnostic results should be considered. This allows the blending unit to select ingredients based on the user's past health data.

[0049] The blending unit can optimize the blending composition of herbal teas based on the user's lifestyle (exercise level, sleep duration, etc.). For example, if the user exercises frequently, the blending unit can blend a herbal tea containing ingredients that aid in post-exercise recovery. If the user suffers from sleep deprivation, it can blend a herbal tea containing ingredients that promote sleep. Furthermore, if the user maintains a regular lifestyle, it can blend a herbal tea containing ingredients that support overall health. It is necessary to clearly define the specific details of lifestyle habits and how this information is collected. For example, exercise level and sleep duration should be considered. This allows the blending unit to optimize the blending composition based on the user's lifestyle.

[0050] The blending unit can adjust the blending ingredients of herbal tea based on the user's health condition (such as past medical history) when blending the herbal tea. For example, if the user has a history of digestive system illness, the blending unit can blend a herbal tea containing ingredients that aid digestion. It can also blend a herbal tea containing ingredients that promote bowel movements if the user has a history of constipation. Furthermore, if the user has a history of diarrhea, it can blend a herbal tea containing ingredients that improve the intestinal environment. It is necessary to clearly define the specific details of the health condition and the evaluation method. For example, past medical history and current health condition should be considered. This allows the blending unit to adjust the blending ingredients based on the user's health condition.

[0051] The service provider can optimize the serving method of herbal tea based on the user's past preference data. For example, the service provider can serve herbal tea with a flavor the user has previously preferred. It can also serve herbal tea at a temperature the user has previously preferred. Furthermore, it can serve herbal tea in a quantity the user has previously preferred. It is necessary to clarify the specific content and collection method of the preference data. For example, past preferences and taste preferences should be considered. This will allow the service provider to optimize the serving method based on the user's past preference data.

[0052] The service provider can adjust the timing of serving herbal tea based on the user's current health condition. For example, if the user is tired, the service provider can serve the herbal tea immediately. If the user is relaxed, the service provider can serve the herbal tea slowly. Furthermore, if the user is in a hurry, the service provider can serve the herbal tea quickly. It is necessary to clarify the specific details and evaluation methods of the user's current health condition. For example, current illnesses and physical condition should be considered. This allows the service provider to adjust the timing of serving based on the user's current health condition.

[0053] The service provider can optimize the serving method of herbal tea based on the user's lifestyle (exercise level, sleep duration, etc.). For example, if a user exercises frequently, the service provider can adopt a serving method suitable for after exercise. If a user is sleep-deprived, the service provider can adopt a serving method that promotes sleep. Furthermore, if a user leads a regular lifestyle, the service provider can adopt a serving method that supports overall health. It is necessary to clearly define the specific details of lifestyle habits and how this information is collected. For example, exercise level and sleep duration should be considered. This allows the service provider to optimize the serving method based on the user's lifestyle.

[0054] The service provider can adjust the serving method of herbal tea based on the user's health condition (such as past medical history). For example, if the user has a history of digestive system illness, the service provider may adopt a serving method that aids digestion. Similarly, if the user has a history of constipation, they may adopt a serving method that promotes bowel movements. Furthermore, if the user has a history of diarrhea, they may adopt a serving method that improves the intestinal environment. It is necessary to clearly define the specific content and evaluation methods of health conditions. For example, past medical history and current health condition should be considered. This allows the service provider to adjust the serving method based on the user's health condition.

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

[0056] The stool analysis system may further include an exercise data acquisition unit that acquires the user's exercise data. The exercise data acquisition unit collects exercise data, for example, from the user's smartwatch or fitness tracker. The exercise data acquisition unit can analyze the user's exercise volume and type and transmit the results to the stool analysis system. For example, if the user performs high-intensity exercise, a medicinal tea containing ingredients that aid in post-exercise recovery can be formulated based on the stool analysis results. If the user performs light exercise, a medicinal tea containing ingredients that have a relaxing effect can be formulated. Furthermore, if the user does not exercise, a medicinal tea containing ingredients that support overall health can be formulated.

[0057] The stool analysis system may also include a sleep data acquisition unit that acquires the user's sleep data. The sleep data acquisition unit collects sleep data, for example, from the user's smartwatch or fitness tracker. The sleep data acquisition unit can analyze the user's sleep quality and sleep duration and transmit the results to the stool analysis system. For example, if the user has had enough sleep, a medicinal tea containing ingredients with refreshing effects can be formulated based on the stool analysis results. If the user has not had enough sleep, a medicinal tea containing ingredients with relaxing effects can be formulated. Furthermore, if the user has had irregular sleep patterns, a medicinal tea containing ingredients that support overall health can be formulated.

[0058] The stool analysis system may further include a food data acquisition unit that obtains the user's dietary data. The food data acquisition unit collects dietary data, for example, from the user's meal logging app or smart kitchen device. The food data acquisition unit can analyze the user's diet and nutrient intake and transmit the results to the stool analysis system. For example, if the user consumes a high-fiber diet, a medicinal tea containing ingredients that aid digestion can be formulated based on the stool analysis results. Also, if the user consumes a high-fat diet, a medicinal tea containing ingredients that promote fat burning can be formulated. Furthermore, if the user consumes a balanced diet, a medicinal tea containing ingredients that support overall health can be formulated.

[0059] The stool analysis system can further optimize the formulation of herbal tea by considering the user's past health data. For example, if the user has had digestive problems in the past, the system can formulate a herbal tea containing ingredients that aid digestion. Similarly, if the user has had stress-related problems in the past, the system can formulate a herbal tea containing ingredients that reduce stress. Furthermore, if the user has previously desired beauty benefits, the system can formulate a herbal tea containing ingredients that provide beauty benefits. This allows the system to provide the most suitable herbal tea based on the user's past health data.

[0060] The stool analysis system may also include a lifestyle data acquisition unit that obtains user lifestyle data. This unit collects lifestyle data, for example, from the user's smartwatch or fitness tracker. The unit analyzes the user's exercise level, sleep duration, and diet, and transmits the results to the stool analysis system. For example, if the user exercises frequently, the system can formulate a herbal tea containing ingredients that aid in post-exercise recovery. If the user is sleep-deprived, the system can formulate a herbal tea containing ingredients that promote relaxation. Furthermore, if the user leads a balanced lifestyle, the system can formulate a herbal tea containing ingredients that support overall health.

[0061] The stool analysis system can further optimize the way medicinal tea is served by considering the user's past preference data. For example, it can serve medicinal tea with a flavor the user has liked in the past. It can also serve medicinal tea at a temperature the user has preferred in the past. Furthermore, it can serve medicinal tea in a quantity the user has preferred in the past. It is necessary to clarify the specific content and collection method of the preference data. For example, past preferences and taste preferences should be considered. This will enable the provision of the optimal medicinal tea based on the user's past preference data.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The collection unit collects the stool. The collection unit is equipped with a device that automatically collects stool and can evaluate the condition of the stool (hardness, color, quantity, etc.) in real time and adjust the collection method. For example, if the stool is hard, it will be collected carefully, and if it is soft, it will be collected quickly. Also, if the amount of stool is large, it can be collected in portions. Step 2: The analysis unit analyzes the stool collected by the collection unit and analyzes its nutrients. The analysis unit analyzes nutrients such as vitamins, minerals, and enzymes contained in the stool and optimizes the analysis method considering the condition of the stool (hardness, color, volume, etc.). For example, if the stool is hard, an analysis method appropriate for its hardness is applied, and if the stool is soft, an analysis method appropriate for its softness is applied. If the volume of stool is large, an analysis method appropriate for its volume can also be applied. Step 3: The communication unit transmits the analysis results obtained by the analysis unit. The communication unit transmits the analysis results to the compounding device and determines the transmission priority based on the importance of the data. For example, it transmits data on important nutrients first, then data on general nutrients, and finally supplementary data. Step 4: The blending unit blends the medicinal tea based on the analysis results transmitted by the communication unit. Based on the analysis results, the blending unit can blend medicinal teas that have beauty, relaxation, and health benefits. Step 5: The serving department provides the herbal tea prepared by the blending department. The serving department provides the blended herbal tea to the customer and adjusts the serving method based on the user's mood. For example, if the user is relaxed, a relaxed serving method is used, and if the user is tense, a quick serving method is used. If the user is in a hurry, a takeaway serving method may also be used.

[0064] (Example of form 2) The system according to an embodiment of the present invention uses a toilet equipped with a stool analysis device and a blending device to analyze the nutrients contained in a customer's stool, and based on the analysis results, blends and provides a medicinal herbal tea with the effects desired by the customer. In this system, first the customer uses the toilet, and the stool analysis device analyzes the stool. Next, the stool analysis device analyzes the nutrients contained in the stool and transmits the results to the blending device. Based on the analysis results, the blending device blends and provides a medicinal herbal tea with the effects desired by the customer. This system allows customers to easily manage their health in a way that suits them. For example, when a customer uses the toilet, the stool analysis device automatically collects the stool and starts the analysis. The stool analysis device analyzes the nutrients and other components contained in the stool in detail. For example, it identifies nutrients such as vitamins, minerals, and enzymes, and measures their content. These analysis results provide important information for understanding the customer's health condition and nutritional balance. Next, the analysis results obtained by the stool analysis device are transmitted to the blending device. Based on the analysis results, the blending device blends a medicinal herbal tea with the effects desired by the customer. For example, if the customer desires beauty effects, the blending device blends a medicinal herbal tea containing ingredients effective for beauty. Furthermore, if a customer desires a relaxing effect, the blending device will prepare a medicinal tea containing ingredients that promote relaxation. In this way, medicinal teas tailored to the customer's needs can be provided. The blending device then provides the blended medicinal tea to the customer. After using the toilet, the customer can immediately receive the medicinal tea that suits them. This allows customers to easily manage their health. In addition, since the medicinal tea is blended automatically, customers do not need to perform any special operations. This system allows customers who are highly interested in health and beauty to manage their health in a way that suits them without feeling the hassle of making it a habit. This system is particularly targeted at women in their 20s and 30s who attend yoga classes, and is ideal for people who are highly interested in health and beauty, willing to invest in themselves, but who find making it a habit "troublesome" or "uncertain about what suits them." By using toilets equipped with stool analysis equipment and blending devices, customers can easily manage their health in a way that suits them.This system, which uses a toilet equipped with a stool analysis device and a blending device, can analyze the nutrients contained in the customer's stool and, based on the analysis results, blend and provide herbal tea with the effects desired by the customer.

[0065] The stool analysis system according to this embodiment comprises a collection unit, an analysis unit, a communication unit, a mixing unit, and a supply unit. The collection unit collects stool. The collection unit includes, for example, a device for automatically collecting stool. When collecting stool, the collection unit can evaluate the condition of the stool (hardness, color, quantity, etc.) in real time and adjust the collection method. For example, if the stool is hard, collection can be carried out carefully. If the stool is soft, collection can be carried out quickly. Furthermore, if the amount of stool is large, collection can be carried out in parts. The analysis unit analyzes the stool collected by the collection unit and analyzes the nutrients. For example, the analysis unit analyzes nutrients such as vitamins, minerals, and enzymes contained in the stool. When analyzing stool, the analysis unit can optimize the analysis method considering the condition of the stool (hardness, color, quantity, etc.). For example, if the stool is hard, an analysis method appropriate to the hardness can be applied. If the stool is soft, an analysis method appropriate to the softness can also be applied. Furthermore, if the amount of stool is large, an analysis method appropriate to the quantity can also be applied. The communication unit transmits the analysis results obtained by the analysis unit. The communication unit transmits the analysis results to the blending device, for example. When transmitting the analysis results, the communication unit can determine the transmission priority based on the importance of the data. For example, it may transmit data on important nutrients first. It may also transmit data on general nutrients next. Furthermore, it may transmit supplementary data last. The blending unit blends the herbal tea based on the analysis results transmitted by the communication unit. The blending unit may, for example, blend a herbal tea with beauty benefits based on the analysis results. The blending unit may also blend a herbal tea with relaxation benefits based on the analysis results. Furthermore, the blending unit may also blend a herbal tea with health benefits based on the analysis results. The serving unit provides the herbal tea blended by the blending unit. The serving unit may, for example, provide the blended herbal tea to the customer. When providing the herbal tea, the serving unit can estimate the user's emotions and adjust the serving method based on the estimated emotions. For example, if the user is relaxed, a relaxed serving method may be adopted. If the user is tense, a quick serving method may be adopted. Furthermore, if the user is in a hurry, we can also offer a take-out option.As a result, the stool analysis system according to this embodiment can perform the entire process from stool collection and analysis to formulation and provision in a single, integrated manner.

[0066] The analysis unit can analyze the nutrients, such as vitamins, minerals, and enzymes, contained in stool. For example, the analysis unit can analyze vitamins in stool. It is necessary to clarify the specific types of vitamins and the analysis methods. For example, it can identify types such as vitamin A and vitamin C and measure their content. The analysis unit can also analyze minerals in stool. It is necessary to clarify the specific types of minerals and the analysis methods. For example, it can identify types such as calcium and magnesium and measure their content. Furthermore, the analysis unit can also analyze enzymes in stool. It is necessary to clarify the specific types of enzymes and the analysis methods. For example, it can identify types such as digestive enzymes and metabolic enzymes and measure their content. This allows the analysis unit to analyze the nutrients contained in stool in detail.

[0067] The blending unit can blend herbal teas with beauty benefits based on the analysis results. For example, the blending unit can blend herbal teas with beauty benefits based on the analysis results. It is necessary to clarify the specific criteria and evaluation methods for beauty benefits. For example, criteria such as skin improvement and hair health are set, and ingredients are selected based on these. The blending unit can also blend herbal teas that contain ingredients with beauty benefits. For example, it can blend herbal teas that contain ingredients such as collagen, vitamin C, and hyaluronic acid. Furthermore, the blending unit can optimize the blending method for herbal teas with beauty benefits. For example, it can adjust the ingredient ratios and blending procedures to blend herbal teas that achieve optimal beauty benefits. In this way, the blending unit can blend herbal teas with beauty benefits.

[0068] The blending unit can blend herbal teas with relaxing effects based on the analysis results. For example, the blending unit can blend herbal teas with relaxing effects based on the analysis results. It is necessary to clarify the specific criteria and evaluation methods for relaxation effects. For example, criteria such as stress reduction and improved sleep quality are set, and ingredients are selected based on these. The blending unit can also blend herbal teas that contain ingredients with relaxing effects. For example, it can blend herbal teas that contain ingredients such as chamomile, lavender, and valerian root. Furthermore, the blending unit can optimize the blending method for herbal teas with relaxing effects. For example, it can adjust the ingredient ratios and blending procedures to blend herbal teas that achieve the optimal relaxation effect. In this way, the blending unit can blend herbal teas with relaxing effects.

[0069] The service department can provide customers with blended herbal tea. For example, the service department can provide customers with blended herbal tea. When providing the herbal tea, the service department can estimate the user's emotions and adjust the method of providing the herbal tea based on the estimated emotions. For example, if the user is relaxed, a relaxed method of providing the tea can be adopted. If the user is tense, a quick method of providing the tea can be adopted. Furthermore, if the user is in a hurry, a method of providing the tea to take away can be adopted. In this way, the service department can provide customers with blended herbal tea.

[0070] The communication unit can transmit the analysis results to the compounding device. For example, the communication unit can transmit the analysis results to the compounding device. When transmitting the analysis results, the communication unit can determine the transmission priority based on the importance of the data. For example, it can prioritize the transmission of data for important nutrients. It can also transmit data for common nutrients next, and supplementary data last. This allows the communication unit to transmit the analysis results to the compounding device.

[0071] The collection unit can estimate the user's emotions and adjust the timing of stool collection based on the estimated emotions. For example, if the user is relaxed, the collection unit can collect the stool immediately. If the user is stressed, it can slightly delay the collection. Furthermore, if the user is in a hurry, it can collect the stool quickly. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows the collection unit to adjust the timing of stool collection according to the user's emotions.

[0072] The collection unit can optimize the stool collection method by considering the user's dietary history. For example, if the user is consuming a high-fiber diet, the collection unit will collect the stool carefully. It can also collect the stool quickly if the user is consuming a high-fat diet. Furthermore, it can appropriately adjust the stool collection if the user is consuming a lot of fluids. It is necessary to clearly define the specific content and collection method of the dietary history. For example, consider the type of food, the amount consumed, and the frequency. This allows the collection unit to optimize the stool collection method based on the user's dietary history.

[0073] The collection unit can evaluate the condition of the stool (hardness, color, quantity, etc.) in real time during collection and adjust the collection method accordingly. For example, if the stool is hard, the collection unit can collect it carefully. If the stool is soft, it can be collected quickly. Furthermore, if the amount of stool is large, the collection can be divided into smaller portions. It is necessary to clarify the specific evaluation criteria and methods for the condition of the stool. For example, hardness, color, and quantity should be evaluated. This will allow the collection unit to adjust the collection method according to the condition of the stool.

[0074] The collection unit can estimate the user's emotions and determine the priority of stool collection based on the estimated emotions. For example, if the user is relaxed, the collection unit will prioritize stool collection. If the user is stressed, it may postpone stool collection. Furthermore, if the user is in a hurry, it may collect stool quickly. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows the collection unit to determine the priority of stool collection according to the user's emotions.

[0075] The collection unit can optimize the stool collection method by considering the user's lifestyle (exercise level, sleep duration, etc.). For example, if the user exercises a lot, the collection unit will collect the stool quickly. Conversely, if the user is sleep-deprived, the collection unit can collect the stool carefully. Furthermore, if the user has a regular lifestyle, the collection unit can appropriately adjust the stool collection. It is necessary to clarify the specific details of lifestyle and the collection method. For example, exercise level and sleep duration should be considered. This allows the collection unit to optimize the stool collection method based on the user's lifestyle.

[0076] The collection unit can adjust the stool collection method considering the user's health condition (such as past medical history). For example, if the user has a history of digestive system illness, the collection unit will collect the stool carefully. It can also collect the stool quickly if the user has a history of constipation. Furthermore, if the user has a history of diarrhea, the collection can be appropriately adjusted. It is necessary to clearly define the specific content and evaluation methods of the health condition. For example, past medical history and current health condition should be considered. This allows the collection unit to adjust the stool collection method based on the user's health condition.

[0077] The analysis unit can estimate the user's emotions and adjust the level of detail of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis. If the user is tense, it can perform a simplified analysis. Furthermore, if the user is in a hurry, it can perform a rapid analysis. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows the analysis unit to adjust the level of detail of the analysis according to the user's emotions.

[0078] The analysis unit can improve the accuracy of nutrient analysis by considering the user's dietary history when analyzing stool samples. For example, if the user consumes a high-fiber diet, the analysis unit can improve the accuracy of fiber analysis. It can also improve the accuracy of fat analysis if the user consumes a high-fat diet. Furthermore, if the user consumes a lot of fluids, it can improve the accuracy of fluid analysis. It is necessary to clarify the specific content and collection method of the dietary history. For example, the type of food, amount consumed, and frequency should be considered. This allows the analysis unit to improve the accuracy of nutrient analysis based on the user's dietary history.

[0079] The analysis unit can optimize its analysis method based on the stool's condition (hardness, color, quantity, etc.) during stool analysis. For example, if the stool is hard, the analysis unit can apply an analysis method appropriate to its hardness. Similarly, if the stool is soft, it can apply an analysis method appropriate to its softness. Furthermore, if the stool is large, it can apply an analysis method appropriate to its quantity. It is necessary to clearly define specific evaluation criteria and methods for the stool's condition. For example, hardness, color, and quantity should be evaluated. This allows the analysis unit to optimize its analysis method according to the stool's condition.

[0080] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is relaxed, the analysis unit can display detailed analysis results. If the user is stressed, it can display simplified analysis results. Furthermore, if the user is in a hurry, it can display rapid analysis results. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows the analysis unit to adjust the display method of the analysis results according to the user's emotions.

[0081] The analysis unit can improve the accuracy of stool analysis by considering the user's lifestyle (exercise level, sleep duration, etc.). For example, if the user exercises a lot, the analysis unit can improve the accuracy according to the amount of exercise. It can also improve the accuracy according to the amount of sleep if the user is sleep-deprived. Furthermore, if the user lives a regular lifestyle, the analysis unit can improve the accuracy according to their lifestyle. It is necessary to clarify the specific details of lifestyle and how to collect this information. For example, exercise level and sleep duration should be considered. This allows the analysis unit to improve its accuracy based on the user's lifestyle.

[0082] The analysis unit can adjust the analysis method when analyzing stool samples, taking into account the user's health condition (such as past medical history). For example, if the user has a history of digestive system illness, the analysis unit will apply an analysis method appropriate to that history. It can also apply an analysis method appropriate for constipation if the user has a history of constipation. Furthermore, if the user has a history of diarrhea, it can apply an analysis method appropriate for diarrhea. It is necessary to clearly define the specific content and evaluation methods of the health condition. For example, past medical history and current health condition should be considered. This allows the analysis unit to adjust the analysis method based on the user's health condition.

[0083] The communication unit can estimate the user's emotions and adjust the timing of sending the analysis results based on the estimated emotions. For example, if the user is relaxed, the communication unit can send the analysis results immediately. If the user is stressed, it can slightly delay sending the analysis results. Furthermore, if the user is in a hurry, it can send the analysis results quickly. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows the communication unit to adjust the timing of sending the analysis results according to the user's emotions.

[0084] The communications department can determine the transmission priority based on the importance of the data when sending analysis results. For example, the communications department may prioritize the transmission of data on important nutrients. It may also send data on general nutrients next, and supplementary data last. It is necessary to clarify the specific criteria and methods for evaluating data importance. For example, urgency and impact should be considered. This will allow the communications department to determine the transmission priority based on the importance of the data.

[0085] The communication unit can optimize the transmission method based on the type of data (vitamins, minerals, enzymes, etc.) when transmitting analysis results. For example, the communication unit can transmit vitamin data quickly, mineral data appropriately, and enzyme data carefully. It is necessary to clarify the specific content and classification method of the data types. For example, classifying vitamins, minerals, enzymes, etc. This allows the communication unit to optimize the transmission method according to the type of data.

[0086] The communication unit can estimate the user's emotions and adjust the frequency of sending analysis results based on the estimated emotions. For example, if the user is relaxed, the communication unit will send analysis results frequently. Conversely, if the user is stressed, it can reduce the frequency of sending analysis results. Furthermore, if the user is in a hurry, it can send analysis results quickly. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows the communication unit to adjust the frequency of sending analysis results according to the user's emotions.

[0087] The communication unit can optimize the transmission method based on the data destination (such as a compounding device or the user's smartphone) when transmitting analysis results. For example, when transmitting to a compounding device, the communication unit can transmit quickly. It can also transmit appropriately when transmitting to a user's smartphone. Furthermore, it can transmit carefully when transmitting to other devices. It is necessary to clearly define the specific types of data destinations and the selection criteria. For example, selecting a compounding device or a user's smartphone. This allows the communication unit to optimize the transmission method according to the data destination.

[0088] The communications unit can adjust the transmission order of analysis results based on the importance of the data. For example, it can prioritize the transmission of data on important nutrients, then send data on common nutrients next, and finally send supplementary data last. It is necessary to clarify specific criteria and methods for evaluating data importance, such as urgency and impact. This will allow the communications unit to adjust the transmission order based on the importance of the data.

[0089] The blending unit can estimate the user's emotions and adjust the blending method of the herbal tea based on the estimated emotions. For example, if the user is relaxed, the blending unit can blend a herbal tea containing many ingredients that have a relaxing effect. If the user is tense, it can also blend a herbal tea containing many ingredients that relieve tension. Furthermore, if the user is in a hurry, it can use ingredients that allow for quick blending. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows the blending unit to adjust the blending method of the herbal tea according to the user's emotions.

[0090] The blending unit can optimize the blending components of medicinal teas based on the level of detail of the analysis results. For example, if the analysis results are detailed, the blending unit will use detailed components to blend the medicinal tea. If the analysis results are simple, it can also use simple components to blend the medicinal tea. Furthermore, if the analysis results are rapid, it can use components that allow for rapid blending. It is necessary to clarify specific criteria and evaluation methods for the level of detail of the analysis results. For example, the depth and accuracy of the analysis should be considered. This will allow the blending unit to optimize the blending components based on the level of detail of the analysis results.

[0091] The blending unit can select ingredients for herbal tea based on the user's past health data. For example, if the user previously desired beauty benefits, the blending unit can use ingredients with beauty benefits. Similarly, if the user previously desired relaxation benefits, it can use ingredients with relaxation benefits. Furthermore, if the user previously desired health benefits, it can use ingredients with health benefits. It is necessary to clearly define the specific content and collection methods of past health data. For example, medical history and diagnostic results should be considered. This allows the blending unit to select ingredients based on the user's past health data.

[0092] The blending unit can estimate the user's emotions and adjust the amount of herbal tea blended based on the estimated emotions. For example, if the user is relaxed, the blending unit will blend an appropriate amount of herbal tea. If the user is stressed, it can blend a small amount. Furthermore, if the user is in a hurry, it can adjust the amount to allow for quick blending. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows the blending unit to adjust the amount of herbal tea blended according to the user's emotions.

[0093] The blending unit can optimize the blending composition of herbal teas based on the user's lifestyle (exercise level, sleep duration, etc.). For example, if the user exercises frequently, the blending unit can blend a herbal tea containing ingredients that aid in post-exercise recovery. If the user suffers from sleep deprivation, it can blend a herbal tea containing ingredients that promote sleep. Furthermore, if the user maintains a regular lifestyle, it can blend a herbal tea containing ingredients that support overall health. It is necessary to clearly define the specific details of lifestyle habits and how this information is collected. For example, exercise level and sleep duration should be considered. This allows the blending unit to optimize the blending composition based on the user's lifestyle.

[0094] The blending unit can adjust the blending ingredients of herbal tea based on the user's health condition (such as past medical history) when blending the herbal tea. For example, if the user has a history of digestive system illness, the blending unit can blend a herbal tea containing ingredients that aid digestion. It can also blend a herbal tea containing ingredients that promote bowel movements if the user has a history of constipation. Furthermore, if the user has a history of diarrhea, it can blend a herbal tea containing ingredients that improve the intestinal environment. It is necessary to clearly define the specific details of the health condition and the evaluation method. For example, past medical history and current health condition should be considered. This allows the blending unit to adjust the blending ingredients based on the user's health condition.

[0095] The service unit can estimate the user's emotions and adjust the method of serving the herbal tea based on those emotions. For example, if the user is relaxed, the service unit can adopt a relaxed serving method. If the user is tense, it can adopt a quick serving method. Furthermore, if the user is in a hurry, it can adopt a takeaway serving method. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows the service unit to adjust the method of serving the herbal tea according to the user's emotions.

[0096] The service provider can optimize the serving method of herbal tea based on the user's past preference data. For example, the service provider can serve herbal tea with a flavor the user has previously preferred. It can also serve herbal tea at a temperature the user has previously preferred. Furthermore, it can serve herbal tea in a quantity the user has previously preferred. It is necessary to clarify the specific content and collection method of the preference data. For example, past preferences and taste preferences should be considered. This will allow the service provider to optimize the serving method based on the user's past preference data.

[0097] The service provider can adjust the timing of serving herbal tea based on the user's current health condition. For example, if the user is tired, the service provider can serve the herbal tea immediately. If the user is relaxed, the service provider can serve the herbal tea slowly. Furthermore, if the user is in a hurry, the service provider can serve the herbal tea quickly. It is necessary to clarify the specific details and evaluation methods of the user's current health condition. For example, current illnesses and physical condition should be considered. This allows the service provider to adjust the timing of serving based on the user's current health condition.

[0098] The serving unit can estimate the user's emotions and adjust the amount of herbal tea served based on the estimated emotions. For example, if the user is relaxed, the serving unit will serve an appropriate amount of herbal tea. If the user is stressed, it may serve a smaller amount. Furthermore, if the user is in a hurry, it may adjust the amount to ensure quick service. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows the serving unit to adjust the amount of herbal tea served according to the user's emotions.

[0099] The service provider can optimize the serving method of herbal tea based on the user's lifestyle (exercise level, sleep duration, etc.). For example, if a user exercises frequently, the service provider can adopt a serving method suitable for after exercise. If a user is sleep-deprived, the service provider can adopt a serving method that promotes sleep. Furthermore, if a user leads a regular lifestyle, the service provider can adopt a serving method that supports overall health. It is necessary to clearly define the specific details of lifestyle habits and how this information is collected. For example, exercise level and sleep duration should be considered. This allows the service provider to optimize the serving method based on the user's lifestyle.

[0100] The service provider can adjust the serving method of herbal tea based on the user's health condition (such as past medical history). For example, if the user has a history of digestive system illness, the service provider may adopt a serving method that aids digestion. Similarly, if the user has a history of constipation, they may adopt a serving method that promotes bowel movements. Furthermore, if the user has a history of diarrhea, they may adopt a serving method that improves the intestinal environment. It is necessary to clearly define the specific content and evaluation methods of health conditions. For example, past medical history and current health condition should be considered. This allows the service provider to adjust the serving method based on the user's health condition. === Hard Collateral 1-1 === Each of the multiple elements described above, including the collection unit, analysis unit, communication unit, blending unit, and serving 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 automatically collects stool samples using the control unit 46A of the smart device 14 and evaluates the condition of the stool. The analysis unit analyzes the nutrients contained in the stool using, for example, the identification processing unit 290 of the data processing unit 12. The communication unit transmits the analysis results to the blending unit via, for example, the communication I / F 44 of the smart device 14. The blending unit blends the medicinal tea based on the analysis results using, for example, the identification processing unit 290 of the data processing unit 12. The serving unit provides the blended medicinal tea to the customer using, for example, the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements described above, including the collection unit, analysis unit, communication unit, blending unit, and serving unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit automatically collects stool samples using the control unit 46A of the smart glasses 214 and evaluates the condition of the stool. The analysis unit analyzes the nutrients contained in the stool using, for example, the identification processing unit 290 of the data processing unit 12. The communication unit transmits the analysis results to the blending unit, for example, via the communication I / F 44 of the smart glasses 214. The blending unit blends the medicinal tea based on the analysis results using, for example, the identification processing unit 290 of the data processing unit 12. The serving unit provides the blended medicinal tea to the customer using, for example, the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements described above, including the collection unit, analysis unit, communication unit, compounding unit, and serving unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit automatically collects stool samples using the control unit 46A of the headset terminal 314 and evaluates the condition of the stool. The analysis unit analyzes the nutrients contained in the stool using, for example, the identification processing unit 290 of the data processing unit 12. The communication unit transmits the analysis results to the compounding unit, for example, via the communication I / F 44 of the headset terminal 314. The compounding unit compounds the medicinal tea based on the analysis results using, for example, the identification processing unit 290 of the data processing unit 12. The serving unit provides the compounded medicinal tea to the customer using, for example, the control unit 46A of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements described above, including the collection unit, analysis unit, communication unit, blending unit, and serving unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit automatically collects stool samples using the control unit 46A of the robot 414 and evaluates the condition of the stool. The analysis unit analyzes the nutrients contained in the stool using, for example, the identification processing unit 290 of the data processing unit 12. The communication unit transmits the analysis results to the blending unit via, for example, the communication I / F 44 of the robot 414. The blending unit blends the medicinal tea based on the analysis results using, for example, the identification processing unit 290 of the data processing unit 12. The serving unit provides the blended medicinal tea to the customer using, for example, the control unit 46A of the robot 414.

[0101] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0102] The stool analysis system may further include an exercise data acquisition unit that acquires the user's exercise data. The exercise data acquisition unit collects exercise data, for example, from the user's smartwatch or fitness tracker. The exercise data acquisition unit can analyze the user's exercise volume and type and transmit the results to the stool analysis system. For example, if the user performs high-intensity exercise, a medicinal tea containing ingredients that aid in post-exercise recovery can be formulated based on the stool analysis results. If the user performs light exercise, a medicinal tea containing ingredients that have a relaxing effect can be formulated. Furthermore, if the user does not exercise, a medicinal tea containing ingredients that support overall health can be formulated.

[0103] The stool analysis system may further include a stress measurement unit that measures the user's stress level. The stress measurement unit estimates the stress level by, for example, measuring the user's heart rate and skin electrical activity. The stress measurement unit can adjust the stool analysis method based on the user's stress level. For example, if the user is in a high-stress state, a medicinal tea containing stress-reducing ingredients can be formulated. If the user is in a low-stress state, a medicinal tea containing relaxing ingredients can be formulated. Furthermore, if the user's stress level is moderate, a medicinal tea containing ingredients that support overall health can be formulated.

[0104] The stool analysis system may also include a sleep data acquisition unit that acquires the user's sleep data. The sleep data acquisition unit collects sleep data, for example, from the user's smartwatch or fitness tracker. The sleep data acquisition unit can analyze the user's sleep quality and sleep duration and transmit the results to the stool analysis system. For example, if the user has had enough sleep, a medicinal tea containing ingredients with refreshing effects can be formulated based on the stool analysis results. If the user has not had enough sleep, a medicinal tea containing ingredients with relaxing effects can be formulated. Furthermore, if the user has had irregular sleep patterns, a medicinal tea containing ingredients that support overall health can be formulated.

[0105] The stool analysis system may further include a food data acquisition unit that obtains the user's dietary data. The food data acquisition unit collects dietary data, for example, from the user's meal logging app or smart kitchen device. The food data acquisition unit can analyze the user's diet and nutrient intake and transmit the results to the stool analysis system. For example, if the user consumes a high-fiber diet, a medicinal tea containing ingredients that aid digestion can be formulated based on the stool analysis results. Also, if the user consumes a high-fat diet, a medicinal tea containing ingredients that promote fat burning can be formulated. Furthermore, if the user consumes a balanced diet, a medicinal tea containing ingredients that support overall health can be formulated.

[0106] The stool analysis system can further estimate the user's emotions and adjust the timing of herbal tea service based on those emotions. For example, if the user is relaxed, the herbal tea can be served slowly. If the user is stressed, the herbal tea can be served quickly. Furthermore, if the user is in a hurry, the herbal tea can be provided in a takeaway format. Emotion estimation is achieved, for example, using an emotion engine or generative AI. This allows the herbal tea to be served at the optimal time according to the user's emotions.

[0107] The stool analysis system can further optimize the formulation of herbal tea by considering the user's past health data. For example, if the user has had digestive problems in the past, the system can formulate a herbal tea containing ingredients that aid digestion. Similarly, if the user has had stress-related problems in the past, the system can formulate a herbal tea containing ingredients that reduce stress. Furthermore, if the user has previously desired beauty benefits, the system can formulate a herbal tea containing ingredients that provide beauty benefits. This allows the system to provide the most suitable herbal tea based on the user's past health data.

[0108] The stool analysis system can further estimate the user's emotions and adjust the ingredients of the herbal tea based on those emotions. For example, if the user is relaxed, the system can blend an herbal tea containing many ingredients that promote relaxation. If the user is stressed, the system can blend an herbal tea containing many ingredients that relieve stress. Furthermore, if the user is in a hurry, the system can use ingredients that allow for quick blending. Emotion estimation is achieved, for example, using an emotion engine or generative AI. This allows the system to provide an herbal tea with the optimal ingredients according to the user's emotions.

[0109] The stool analysis system may also include a lifestyle data acquisition unit that obtains user lifestyle data. This unit collects lifestyle data, for example, from the user's smartwatch or fitness tracker. The unit analyzes the user's exercise level, sleep duration, and diet, and transmits the results to the stool analysis system. For example, if the user exercises frequently, the system can formulate a herbal tea containing ingredients that aid in post-exercise recovery. If the user is sleep-deprived, the system can formulate a herbal tea containing ingredients that promote relaxation. Furthermore, if the user leads a balanced lifestyle, the system can formulate a herbal tea containing ingredients that support overall health.

[0110] The stool analysis system can further estimate the user's emotions and adjust the amount of herbal tea served based on those emotions. For example, if the user is relaxed, an appropriate amount of herbal tea will be served. If the user is stressed, a smaller amount may be served. Furthermore, if the user is in a hurry, the amount can be adjusted to allow for quick service. Emotion estimation is achieved, for example, using an emotion engine or generative AI. This allows the system to provide the optimal amount of herbal tea according to the user's emotions.

[0111] The stool analysis system can further optimize the way medicinal tea is served by considering the user's past preference data. For example, it can serve medicinal tea with a flavor the user has liked in the past. It can also serve medicinal tea at a temperature the user has preferred in the past. Furthermore, it can serve medicinal tea in a quantity the user has preferred in the past. It is necessary to clarify the specific content and collection method of the preference data. For example, past preferences and taste preferences should be considered. This will enable the provision of the optimal medicinal tea based on the user's past preference data.

[0112] The following briefly describes the processing flow for example form 2.

[0113] Step 1: The collection unit collects the stool. The collection unit is equipped with a device that automatically collects stool and can evaluate the condition of the stool (hardness, color, quantity, etc.) in real time and adjust the collection method. For example, if the stool is hard, it will be collected carefully, and if it is soft, it will be collected quickly. Also, if the amount of stool is large, it can be collected in portions. Step 2: The analysis unit analyzes the stool collected by the collection unit and analyzes its nutrients. The analysis unit analyzes nutrients such as vitamins, minerals, and enzymes contained in the stool and optimizes the analysis method considering the condition of the stool (hardness, color, volume, etc.). For example, if the stool is hard, an analysis method appropriate for its hardness is applied, and if the stool is soft, an analysis method appropriate for its softness is applied. If the volume of stool is large, an analysis method appropriate for its volume can also be applied. Step 3: The communication unit transmits the analysis results obtained by the analysis unit. The communication unit transmits the analysis results to the compounding device and determines the transmission priority based on the importance of the data. For example, it transmits data on important nutrients first, then data on general nutrients, and finally supplementary data. Step 4: The blending unit blends the medicinal tea based on the analysis results transmitted by the communication unit. Based on the analysis results, the blending unit can blend medicinal teas that have beauty, relaxation, and health benefits. Step 5: The serving department provides the herbal tea prepared by the blending department. The serving department provides the blended herbal tea to the customer and adjusts the serving method based on the user's mood. For example, if the user is relaxed, a relaxed serving method is used, and if the user is tense, a quick serving method is used. If the user is in a hurry, a takeaway serving method may also be used.

[0114] 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.

[0115] 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 the following. 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 (for example, 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. 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 a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0116] 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.

[0117] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0118] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.).

[0130] 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.

[0131] 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. 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.

[0132] 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.

[0133] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0134] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0139] 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).

[0140] 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.

[0141] 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.

[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 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.

[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 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.

[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. 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 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.

[0149] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0150] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.).

[0163] 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.

[0164] 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. 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.

[0165] 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.

[0166] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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."

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] [Explanation of Symbols]

[0186] 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 collection unit that collects feces, An analysis unit analyzes the stool collected by the collection unit and analyzes its nutrients, A communication unit that transmits the analysis results obtained by the analysis unit, A blending unit that prepares medicinal tea based on the analysis results transmitted by the aforementioned communication unit, The system includes a dispensing unit that provides the medicinal herbal tea prepared by the aforementioned blending unit. A system characterized by the following features.

2. The aforementioned analysis unit, Analyzing the vitamins, minerals, and enzymes contained in stool. The system according to feature 1.

3. The aforementioned mixing unit is Based on the analysis results, we will blend medicinal herbal teas that have beauty benefits. The system according to feature 1.

4. The aforementioned mixing unit is Based on the analysis results, a blend of medicinal herbal tea with relaxing effects is created. The system according to feature 1.

5. The aforementioned supply unit is, We offer our customers specially blended medicinal herbal teas. The system according to feature 1.

6. The aforementioned communications unit is Send the analysis results to the compounding device. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of stool collection based on those emotions. The system according to feature 1.

8. The aforementioned collection unit is When collecting stool samples, the collection method should be optimized by considering the user's dietary history. The system according to feature 1.

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