system

Abstract

The system according to this embodiment aims to provide capsule-type meals tailored to the user's nutritional needs and preferences. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a generation unit, and a supply unit. The data collection unit collects the user's health data. The analysis unit analyzes the data collected by the data collection unit and estimates the user's nutritional status. Based on the nutritional status estimated by the analysis unit, the generation unit compresses ingredients to match the user's preferences and requests and generates capsule-type meals. The supply unit provides the capsule-type meals generated by the generation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, it is difficult to provide a diet that fully meets the nutritional needs and preferences of individual users, and there is room for improvement.

[0005] The system according to the embodiment aims to provide a capsule-type diet that suits the nutritional needs and preferences of users.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a generation unit, and a supply unit. The data collection unit collects the user's health data. The analysis unit analyzes the data collected by the data collection unit and estimates the user's nutritional status. Based on the nutritional status estimated by the analysis unit, the generation unit compresses ingredients tailored to the user's preferences and needs to produce capsule-type meals. The supply unit provides the capsule-type meals produced by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide capsule-type meals tailored to the user's nutritional needs and preferences. [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 controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

[0016] [First Embodiment] FIG. 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 capsule-type meal generation system according to an embodiment of the present invention is a system that generates capsule-type meals that satisfy the unique nutritional needs and preferences of each individual. This system takes in the user's health data, and a multimodal generative AI analyzes this data to estimate the user's nutritional status. Furthermore, the generative AI compresses ingredients that reproduce the taste tailored to the user's preferences and requests, and generates capsule-type meals. These capsule-type meals provide the necessary nutrients and a sense of satisfaction in a short amount of time. For example, the system takes in the user's health data. At this time, detailed data such as fitness data, biological rhythms, and health checkup results are collected. For example, this includes the user's exercise level, sleep patterns, and blood test results. This allows the system to understand the user's health status. Next, the multimodal generative AI analyzes the taken data. The generative AI estimates the nutritional status based on the user's health data. For example, it can estimate a high-calorie meal for a user with a high exercise level, and a meal rich in B vitamins for a user who is sleep-deprived. This allows the system to provide meals tailored to the user's nutritional needs. Furthermore, the generative AI compresses ingredients that reproduce the taste tailored to the user's preferences and requests, and generates capsule-type meals. For example, if a user has a sweet tooth, the generation AI selects ingredients that replicate that sweet taste and compresses them into a capsule. This allows the user to obtain the necessary nutrients and feel satisfied in a short amount of time. This mechanism can provide a future of meals recommended for people in today's fast-paced world who don't want to spend a lot of time on meals but still want to get proper nutrition. It can also eliminate nutritional deficiencies and food waste, and provide fully personalized meals tailored to lifestyle and health conditions. For example, it can cater to a variety of users, such as busy business people, seniors, and athletes. In this way, the capsule meal generation system can provide personalized capsule meals based on the user's health data.

[0029] The capsule-type meal generation system according to the embodiment comprises a data collection unit, an analysis unit, a generation unit, and a supply unit. The data collection unit collects the user's health data. The user's health data includes, but is not limited to, fitness data, circadian rhythms, and health checkup results. For example, the data collection unit can use a wearable device to collect fitness data. The data collection unit can also use a sleep tracker to understand circadian rhythms. Furthermore, the data collection unit can obtain data from medical institutions to obtain health checkup results. For example, the data collection unit collects fitness data such as heart rate and steps from a wearable device. A sleep tracker records the user's sleep patterns and provides them to the data collection unit. Data acquisition from medical institutions is performed through an electronic medical record system with the user's consent. The analysis unit uses a generation AI to analyze the data collected by the data collection unit and estimate the user's nutritional status. The analysis is performed using, but is not limited to, statistical analysis or machine learning algorithms. For example, the analysis unit uses statistical analysis to estimate the nutritional status from the user's health data. Furthermore, the analysis unit can use machine learning algorithms to analyze the user's health data and estimate their nutritional status. The analysis unit can also use generative AI to estimate nutritional status based on the user's health data. For example, the analysis unit performs regression analysis based on the user's health data as a statistical analysis to estimate nutritional status. The machine learning algorithm uses a model that takes the user's health data as input and outputs nutritional status for estimation. The generative AI receives prompts to estimate nutritional status based on the user's health data and outputs the estimation results. Based on the nutritional status estimated by the analysis unit, the generative unit compresses ingredients tailored to the user's preferences and needs to produce capsule-type meals. Generation is performed by methods such as vacuum compression or freeze-drying, but is not limited to these examples. For example, the generative unit uses vacuum compression to compress ingredients and produce capsule-type meals. The generative unit can also use freeze-drying to compress ingredients and produce capsule-type meals.Furthermore, the production unit can adjust the flavor to reproduce a taste that matches the user's preferences and requests. For example, the production unit can produce capsule-shaped meals by vacuum compression, which compresses ingredients in a vacuum state. Freeze-drying produces capsule-shaped meals by freezing and drying ingredients. Flavor adjustment adjusts sweetness, sourness, etc., to match the user's preferences. The supply unit provides the capsule-shaped meals produced by the production unit. Supply is carried out by methods such as delivery or vending machines, but is not limited to these examples. For example, the supply unit delivers the produced capsule-shaped meals to the user. The supply unit can also provide capsule-shaped meals using vending machines. The supply unit can also enable users to pick up capsule-shaped meals at a location specified by the user. For example, the supply unit delivers capsule-shaped meals to the user's address as a delivery. Vending machines are installed so that users can purchase capsule-shaped meals. Pickup at a specified location allows users to pick up capsule-shaped meals at a location specified by the user. As a result, the capsule-shaped meal production system according to the embodiment can provide personalized capsule-shaped meals based on the user's health data.

[0030] The data collection unit collects user health data. This data includes, but is not limited to, fitness data, circadian rhythms, and health checkup results. For example, the data collection unit can use wearable devices to collect fitness data. Wearable devices collect data such as heart rate, steps, calories burned, and exercise intensity in real time and transmit it to the data collection unit. This allows for an accurate understanding of the user's daily exercise and activity levels. The data collection unit can also use sleep trackers to understand circadian rhythms. Sleep trackers record the user's sleep patterns, sleep quality, and sleep duration and provide this information to the data collection unit. This allows for a detailed analysis of the user's sleep state and rhythms. Furthermore, the data collection unit can obtain data from medical institutions to acquire health checkup results. Data acquisition from medical institutions is done through electronic medical record systems with the user's consent. This allows for the collection of detailed health data, such as the user's blood test results and diagnostic information. For example, the data collection unit collects fitness data such as heart rate and steps from wearable devices. The sleep tracker records the user's sleep patterns and provides them to the data collection unit. Data acquisition from medical institutions is done through the electronic medical record system with the user's consent. This allows the data collection unit to collect a wide range of health data from various devices and systems, enabling a comprehensive understanding of the user's health status. Furthermore, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server and made accessible to the analysis and generation units. In addition, the frequency and accuracy of data collection can be adjusted, allowing for flexible responses to specific situations and conditions. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit uses generative AI to analyze data collected by the data collection unit and estimate the user's nutritional status. The analysis is performed using, for example, statistical analysis or machine learning algorithms, but is not limited to these examples. Specifically, as a statistical analysis, regression analysis is performed based on the user's health data to estimate the nutritional status. Regression analysis uses a model that takes the user's fitness data and health checkup results as input and outputs the nutritional status. For example, it can estimate the user's calorie consumption and nutrient intake based on data such as heart rate, steps taken, and blood test results. Machine learning algorithms also use a model that takes the user's health data as input and outputs the nutritional status. Machine learning algorithms can learn from large amounts of data and estimate the user's health and nutritional status with high accuracy. For example, it can learn from past health data and dietary history to build a model that predicts the user's nutritional status. Furthermore, the analysis unit can also estimate the nutritional status based on the user's health data using generative AI. The generative AI receives a prompt to estimate the nutritional status based on the user's health data and outputs the estimation result. For example, the generating AI takes user fitness data and health checkup results as input, receives prompts to output nutritional status, and outputs estimated results. This allows the analysis unit to quickly and accurately analyze the collected data and understand the user's nutritional status in real time. Furthermore, the analysis unit can also utilize historical data and statistical information to perform long-term nutritional status evaluations and trend analysis. For example, it can predict fluctuations in nutritional status over a specific period based on past health data and formulate future countermeasures. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. As a result, the analysis unit can not only understand nutritional status in real time but also handle long-term nutritional management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The generation unit compresses ingredients tailored to the user's preferences and needs, based on the nutritional status estimated by the analysis unit, to produce capsule-shaped meals. Generation is carried out by methods such as vacuum compression and freeze-drying, but is not limited to these examples. Specifically, vacuum compression is used to compress ingredients in a vacuum state to produce capsule-shaped meals. Vacuum compression can significantly reduce the volume while preserving the flavor and nutrients of the ingredients. Freeze-drying can also be used to freeze and dry ingredients to produce capsule-shaped meals. Freeze-drying removes moisture from ingredients, making them suitable for long-term storage. Furthermore, the generation unit can adjust the flavor to reproduce the taste tailored to the user's preferences and needs. For example, the generation unit can adjust flavors such as sweetness, sourness, and saltiness to produce capsule-shaped meals tailored to the user's preferences. This allows the generation unit to provide personalized capsule-shaped meals based on the user's nutritional status. In addition, the generation unit can also devise innovative methods for selecting ingredients and cooking methods to provide nutritionally balanced meals. For example, the food processor can select highly nutritious ingredients and use appropriate cooking methods to maximize the extraction of nutrients. Furthermore, by devising combinations of ingredients and cooking methods, the food processor can provide meals optimized for the user's health and nutritional needs. In this way, the food processor can support the user's health and provide nutritionally balanced meals.

[0033] The supply unit provides capsule-type meals produced by the generation unit. Delivery is carried out by methods such as delivery or vending machines, but is not limited to these examples. Specifically, the supply unit delivers the produced capsule-type meals to users. Delivery involves directly delivering the capsule-type meals to the user's address, allowing users to easily receive meals at home. The supply unit can also provide capsule-type meals using vending machines. These vending machines are installed to allow users to purchase capsule-type meals and are available 24 hours a day. Furthermore, the supply unit can also enable users to receive capsule-type meals at a designated location. For example, users can receive capsule-type meals at a designated location such as their workplace or school. This allows the supply unit to implement flexible delivery methods tailored to users' lifestyles. Additionally, the supply unit can collect user feedback and continuously improve the accuracy and effectiveness of its delivery methods. For example, based on user feedback, delivery schedules may be reviewed and vending machine locations optimized. The supply unit can also reliably transmit information using multiple communication methods. For example, important information can be reliably delivered using not only smartphone notifications but also voice calls, SMS, and email. This allows the service provider to deliver capsule-type meals to users quickly and reliably, thereby improving user satisfaction.

[0034] The data collection unit can collect detailed data such as fitness data, circadian rhythms, and health checkup results. For example, the data collection unit can use wearable devices to collect fitness data. For instance, the data collection unit collects fitness data such as heart rate and steps from wearable devices. The data collection unit can also use sleep trackers to understand circadian rhythms. For example, the data collection unit uses a sleep tracker to record the user's sleep patterns and provides this data to the data collection unit. Furthermore, the data collection unit can obtain data from medical institutions to acquire health checkup results. For example, the data collection unit obtains data from medical institutions through an electronic medical record system with the user's consent. This allows for a more accurate estimation of nutritional status by collecting detailed health data. Detailed data includes, but is not limited to, fitness data, circadian rhythms, and health checkup results. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input fitness data acquired from wearable devices into a generating AI and have the generating AI perform data analysis.

[0035] The analysis unit can estimate high-calorie meals for users with high levels of physical activity, and meals rich in B vitamins for users who are sleep-deprived. For example, the analysis unit estimates high-calorie meals for users with high levels of physical activity. For example, the analysis unit estimates high-calorie meals for users with high levels of physical activity to increase their calorie intake. The analysis unit can also estimate meals rich in B vitamins for users who are sleep-deprived. For example, the analysis unit promotes fatigue recovery for sleep-deprived users by estimating meals rich in B vitamins. This makes it possible to provide meals tailored to the user's specific health condition. High-calorie meals include, for example, the number of calories and the nutrients they contain, but are not limited to such examples. Meals rich in B vitamins include, for example, the amount of vitamins B1, B2, B6, and B12 they contain, but are not limited to such examples. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input the user's health data into a generating AI and have the generating AI perform the estimation of nutritional status.

[0036] The generating unit can select ingredients that reproduce a taste tailored to the user's preferences and requests, and compress them into capsules. For example, the generating unit adjusts the flavor to reproduce a taste tailored to the user's preferences and requests. For example, if the user prefers sweet things, the generating unit selects ingredients that reproduce a sweet taste and compresses them into capsules. Also, if the user prefers sour things, the generating unit can select ingredients that reproduce a sour taste and compress them into capsules. Also, if the user prefers spicy things, the generating unit can select ingredients that reproduce a spicy taste and compress them into capsules. For example, to reproduce a sweet taste, the generating unit adjusts the flavor and compresses it into capsules. To reproduce a sour taste, the flavor adjusts and compresses it into capsules. To reproduce a spicy taste, the flavor adjusts and compresses it into capsules. This makes it possible to provide meals tailored to the user's preferences. Tastes tailored to preferences and requests include, but are not limited to, the use of taste sensors and flavor adjustments. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without using a generation AI. For example, the generation unit can input user preference data into the generation AI and cause the generation AI to reproduce flavors that match the user's preferences.

[0037] The supply unit can provide the generated capsule-shaped meals to the user. For example, the supply unit can deliver the generated capsule-shaped meals to the user. For example, the supply unit can deliver the capsule-shaped meals to the user's address. The supply unit can also provide the capsule-shaped meals using vending machines. For example, the supply unit can install vending machines so that users can purchase capsule-shaped meals. The supply unit can also allow users to pick up capsule-shaped meals at a location of their choosing. For example, the supply unit can allow users to pick up capsule-shaped meals at a location of their choosing. This allows for the rapid provision of the generated capsule-shaped meals. Rapid provision includes, but is not limited to, delivery time and delivery method. Some or all of the above processes in the supply unit may be performed using, for example, AI, or not using AI. For example, the supply unit can input a delivery schedule into a generation AI and have the generation AI calculate the optimal delivery route.

[0038] The data collection unit can analyze the user's past health data and select the optimal data collection method. For example, the data collection unit can recreate the data collection method used when the user was in their healthiest state, based on past health data. The data collection unit can also avoid data collection methods used when the user's health deteriorated, based on past health data. The data collection unit can also analyze past health data and select a data collection method used when the user's health was stable. By selecting the optimal data collection method based on past health data, more accurate data can be collected. The optimal data collection method includes, but is not limited to, the selection of data types and collection methods. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past health data into a generating AI and have the AI ​​select the optimal data collection method.

[0039] The data collection unit can filter data based on the user's current lifestyle and areas of interest during data collection. For example, if the user is on a diet, the data collection unit can prioritize collecting data related to calories and nutrients. The data collection unit can also prioritize collecting data related to a specific illness if the user has one. The data collection unit can also prioritize collecting data related to a new fitness program if the user has started one. This allows for the collection of more relevant data by filtering the data based on the user's lifestyle and areas of interest. Filtering includes, but is not limited to, methods for selecting data based on lifestyle and areas of interest. Some or all of the processing described above in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input data on the user's lifestyle and areas of interest into a generating AI, and have the generating AI perform filtering.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is at high altitude, the data collection unit can prioritize the collection of data related to health status at high altitude. For example, if the user is at high altitude, the data collection unit can prioritize the collection of data related to health status in urban areas. For example, if the user is in urban areas, the data collection unit can prioritize the collection of data related to health status in urban areas. For example, if the user is traveling, the data collection unit can prioritize the collection of data related to the environment of the travel destination. For example, if the user is traveling, the data collection unit can prioritize the collection of data related to the environment of the travel destination. This allows for the collection of more relevant data by considering the user's geographical location information. Geographical location information includes, but is not limited to, the use of GPS data or data selection based on location information. Some or all of the processing described above in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the user's geographical location information into the generating AI, causing the AI ​​to prioritize the collection of highly relevant data.

[0041] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, if a user posts about health on social media, the data collection unit can collect data related to the content of those posts. The data collection unit can also collect data related to a specific fitness challenge if the user is participating in that challenge. The data collection unit can also collect data related to a user's posts about food if the user is posting about food. By analyzing social media activity, data related to the user's interests can be collected. Social media activity includes, but is not limited to, analyzing post content and extracting topics of interest. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect the relevant data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the health data during the analysis. For example, the analysis unit can perform a detailed analysis on important health data. The analysis unit can also perform a standard analysis on general health data. The analysis unit can also perform a simplified analysis on less important health data. By adjusting the level of detail of the analysis based on the importance of the health data, more appropriate analysis results can be provided. The importance of health data includes, but is not limited to, the evaluation of medical professionals and the reliability of the data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input the importance of the health data into the generative AI and have the generative AI adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the category of health data during analysis. For example, the analysis unit can apply an exercise analysis algorithm to fitness data. For example, the analysis unit can apply an exercise analysis algorithm to fitness data. The analysis unit can also apply a sleep analysis algorithm to circadian rhythm data. For example, the analysis unit can apply a medical analysis algorithm to health checkup results. For example, the analysis unit can apply a medical analysis algorithm to health checkup results. By applying the appropriate analysis algorithm according to the category of health data, more accurate analysis results can be provided. Analysis algorithms include, but are not limited to, regression analysis and clustering. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the categories of health data into a generative AI and have the generative AI execute the application of an appropriate analysis algorithm.

[0044] The analysis unit can determine the priority of analysis based on the timing of health data collection during the analysis. For example, the analysis unit may prioritize the analysis of recently collected health data. The analysis unit can also analyze current data while referring to past health data. The analysis unit can also prioritize the analysis of data collected during a specific period. By determining the priority of analysis based on the timing of health data collection, more important data can be analyzed preferentially. The timing of collection includes, but is not limited to, data freshness and collection frequency. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input the timing of health data collection into a generative AI and have the generative AI determine the priority of analysis.

[0045] The analysis unit can adjust the order of analysis based on the relevance of health data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. The analysis unit can also postpone the analysis of less relevant data. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the data. This allows for more efficient analysis by adjusting the order of analysis based on the relevance of health data. The relevance of health data includes, but is not limited to, correlation analysis and causal relationship evaluation. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input the relevance of health data into a generative AI and have the generative AI adjust the order of analysis.

[0046] The generation unit can analyze the user's past preference data during generation to select the most suitable ingredients. For example, the generation unit can select the most suitable ingredients based on ingredients the user has enjoyed eating in the past. The generation unit can also analyze past preference data to select ingredients that match the user's preferences. The generation unit can also suggest new ingredients based on past preference data. By selecting the most suitable ingredients based on past preference data, the generation unit can provide meals that match the user's preferences. Preference data includes, but is not limited to, past meal history and survey results. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the user's past preference data into a generation AI and have the generation AI select the most suitable ingredients.

[0047] The generation unit can customize the nutrients in the capsules based on the user's current health condition during generation. For example, if the user is tired, the generation unit can generate capsules containing nutrients suitable for energy replenishment. The generation unit can also generate capsules containing balanced nutrients if the user is in good health. The generation unit can also generate capsules containing a high amount of a specific nutrient if the user requires that nutrient. This allows for more appropriate nutritional supplementation by customizing the capsule nutrients according to the user's health condition. Nutrient customization includes, but is not limited to, adjusting the type and amount of nutrients. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can input the user's health data into the generation AI and have the generation AI perform the customization of the capsule nutrients.

[0048] The generation unit can select the most suitable ingredients during generation, taking into account the user's geographical location information. For example, if the user is at high altitude, the generation unit can select ingredients suitable for nutritional supplementation at high altitude. The generation unit can also select ingredients suitable for nutritional supplementation in urban areas if the user is in an urban area. The generation unit can also select ingredients suitable for the environment of the travel destination if the user is traveling. By considering the user's geographical location information, more appropriate ingredients can be selected. Geographical location information includes, but is not limited to, the use of local ingredients and efficient delivery. Some or all of the above-described processing in the generation unit may be performed using, for example, generation AI, or without generation AI. For example, the generation unit can input the user's geographical location information into the generation AI and have the AI ​​select the optimal ingredients.

[0049] The generation unit can analyze the user's social media activity during generation and suggest capsule content. For example, if the user has made health-related posts on social media, the generation unit can suggest capsules related to those posts. The generation unit can also suggest capsules related to specific fitness challenges if the user is participating in them. The generation unit can also suggest capsules related to food if the user has made food-related posts. In this way, by analyzing social media activity, capsules related to the user's interests can be suggested. Social media activity includes, but is not limited to, analyzing post content and extracting topics of interest. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can input the user's social media activity data into a generation AI and have the generation AI suggest capsule content.

[0050] The service provider can analyze the user's past consumption behavior and select the optimal service method at the time of service delivery. For example, the service provider can select the optimal service method based on the user's preferred service method in the past. The service provider can also analyze past consumption behavior and select a service method that suits the user's preferences. The service provider can also propose a new service method based on past consumption behavior. By selecting the optimal service method based on past consumption behavior, it becomes possible to provide services that suit the user's preferences. Consumption behavior includes, but is not limited to, analysis of purchase history and consumption patterns. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the user's past consumption behavior data into a generating AI and have the generating AI select the optimal service method.

[0051] The service provider can customize the delivery method based on the user's current lifestyle at the time of delivery. For example, if the user is busy, the service provider can select a delivery method that allows for quick delivery. For example, if the user is relaxed, the service provider can select a delivery method that allows for leisurely enjoyment. For example, if the user is traveling, the service provider can select a delivery method that allows for delivery at the travel destination. By customizing the delivery method according to the user's lifestyle, more appropriate delivery becomes possible. Lifestyle includes, but is not limited to, lifestyle rhythms and activity levels. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the user's lifestyle data into a generating AI and have the generating AI perform the customization of the delivery method.

[0052] The delivery unit can select the optimal delivery method at the time of delivery, taking into account the user's geographical location information. For example, if the user is at a high altitude, the delivery unit can select a method suitable for delivery at a high altitude. For example, if the user is at a high altitude, the delivery unit can select a method suitable for delivery in an urban area. For example, if the user is at an urban area, the delivery unit can select a method suitable for delivery in an urban area. For example, if the user is traveling, the delivery unit can select a method that allows the user to receive the delivery at their travel destination. For example, if the user is traveling, the delivery unit can select a method that allows the user to receive the delivery at their travel destination. This allows for the selection of a more appropriate delivery method by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, the use of local ingredients and the efficiency of delivery. Some or all of the above processing in the delivery unit may be performed using, for example, AI, or not using AI. For example, the delivery unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal delivery method.

[0053] The service provider can analyze the user's social media activity and propose means of provision at the time of provision. For example, if the user has made health-related posts on social media, the service provider can propose means of provision related to the content of those posts. The service provider can also propose means of provision related to a specific fitness challenge if the user is participating in that challenge. The service provider can also propose means of provision related to a food-related post if the user has made food-related posts. By analyzing social media activity, the service provider can propose means of provision related to the user's interests. Social media activity includes, but is not limited to, analyzing post content and extracting topics of interest. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI propose means of provision.

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

[0055] The capsule-type meal generation system can further collect user allergy data, and the analysis unit can select ingredients that address those allergies. For example, the data collection unit can obtain user allergy data from medical institutions. The analysis unit can then exclude ingredients that may cause allergies based on the allergy data. Furthermore, the generation unit can select ingredients that address the allergies and generate capsule-type meals. This allows for the provision of safe meals to users with allergies.

[0056] The analysis unit can estimate meals that take into account seasonal nutritional needs based on the user's health data. For example, it can estimate meals rich in vitamin D in winter and meals that emphasize hydration in summer. It can also estimate meals rich in antioxidants to help with allergies in spring and meals that boost immunity in autumn. This allows the system to provide meals tailored to seasonal nutritional needs.

[0057] The data collection unit collects data on the user's living environment, and the analysis unit can estimate nutritional needs based on that environment. For example, for users living in urban areas, it can estimate a diet rich in antioxidants as a countermeasure against air pollution, and for users living in rural areas, it can estimate a diet with detoxifying effects as a countermeasure against pesticides. Furthermore, for users living in cold regions, it can estimate a high-calorie diet to maintain body temperature, and for users living in warm regions, it can estimate a diet that emphasizes hydration. This allows for the provision of meals tailored to nutritional needs based on the user's living environment.

[0058] The generation unit can analyze the user's eating history and suggest new recipes based on ingredients they have previously enjoyed. For example, it can generate new recipes by combining ingredients the user has enjoyed in the past and provide them as capsule-type meals. It can also suggest ingredients the user has not yet tried based on their past eating history. Furthermore, it can analyze the user's eating history and suggest new recipes that take nutritional balance into consideration. This allows for the provision of new dining experiences tailored to the user's preferences.

[0059] The data collection unit can collect user health data while considering the user's family history. For example, if a family member has a specific disease, data related to that disease can be prioritized for collection. Furthermore, based on family history, preventative data for diseases that may develop in the future can be collected. In addition, health data based on genetic factors can be collected while considering family history. This enables the collection of more accurate health data that takes family history into account.

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

[0061] Step 1: The data collection unit collects the user's health data. This data includes fitness data, circadian rhythms, and health checkup results. For example, the data collection unit collects fitness data such as heart rate and steps using wearable devices, records the user's sleep patterns using sleep trackers, and obtains health checkup results from medical institutions through electronic medical record systems. Step 2: The analysis unit analyzes the data collected by the data collection unit and estimates the user's nutritional status. The analysis is performed using statistical analysis, machine learning algorithms, and generative AI. For example, regression analysis is performed as statistical analysis, the nutritional status is estimated using machine learning algorithms, and the generative AI receives prompts and outputs the estimation results. Step 3: The production unit compresses ingredients tailored to the user's preferences and needs, based on the nutritional status estimated by the analysis unit, to create capsule-shaped meals. Production is carried out by methods such as vacuum compression and freeze-drying. For example, ingredients are compressed using vacuum compression, and dried using freeze-drying. In addition, the flavor is adjusted to reproduce a taste that matches the user's preferences. Step 4: The supply unit provides the capsule-shaped meals produced by the generation unit. Delivery is carried out by methods such as delivery or vending machines. For example, the capsule-shaped meals may be delivered to the user's address, dispensed using a vending machine, or picked up at a location specified by the user.

[0062] (Example of form 2) The capsule-type meal generation system according to an embodiment of the present invention is a system that generates capsule-type meals that satisfy the unique nutritional needs and preferences of each individual. This system takes in the user's health data, and a multimodal generative AI analyzes this data to estimate the user's nutritional status. Furthermore, the generative AI compresses ingredients that reproduce the taste tailored to the user's preferences and requests, and generates capsule-type meals. These capsule-type meals provide the necessary nutrients and a sense of satisfaction in a short amount of time. For example, the system takes in the user's health data. At this time, detailed data such as fitness data, biological rhythms, and health checkup results are collected. For example, this includes the user's exercise level, sleep patterns, and blood test results. This allows the system to understand the user's health status. Next, the multimodal generative AI analyzes the taken data. The generative AI estimates the nutritional status based on the user's health data. For example, it can estimate a high-calorie meal for a user with a high exercise level, and a meal rich in B vitamins for a user who is sleep-deprived. This allows the system to provide meals tailored to the user's nutritional needs. Furthermore, the generative AI compresses ingredients that reproduce the taste tailored to the user's preferences and requests, and generates capsule-type meals. For example, if a user has a sweet tooth, the generation AI selects ingredients that replicate that sweet taste and compresses them into a capsule. This allows the user to obtain the necessary nutrients and feel satisfied in a short amount of time. This mechanism can provide a future of meals recommended for people in today's fast-paced world who don't want to spend a lot of time on meals but still want to get proper nutrition. It can also eliminate nutritional deficiencies and food waste, and provide fully personalized meals tailored to lifestyle and health conditions. For example, it can cater to a variety of users, such as busy business people, seniors, and athletes. In this way, the capsule meal generation system can provide personalized capsule meals based on the user's health data.

[0063] The capsule-type meal generation system according to the embodiment comprises a data collection unit, an analysis unit, a generation unit, and a supply unit. The data collection unit collects the user's health data. The user's health data includes, but is not limited to, fitness data, circadian rhythms, and health checkup results. For example, the data collection unit can use a wearable device to collect fitness data. The data collection unit can also use a sleep tracker to understand circadian rhythms. Furthermore, the data collection unit can obtain data from medical institutions to obtain health checkup results. For example, the data collection unit collects fitness data such as heart rate and steps from a wearable device. A sleep tracker records the user's sleep patterns and provides them to the data collection unit. Data acquisition from medical institutions is performed through an electronic medical record system with the user's consent. The analysis unit uses a generation AI to analyze the data collected by the data collection unit and estimate the user's nutritional status. The analysis is performed using, but is not limited to, statistical analysis or machine learning algorithms. For example, the analysis unit uses statistical analysis to estimate the nutritional status from the user's health data. Furthermore, the analysis unit can use machine learning algorithms to analyze the user's health data and estimate their nutritional status. The analysis unit can also use generative AI to estimate nutritional status based on the user's health data. For example, the analysis unit performs regression analysis based on the user's health data as a statistical analysis to estimate nutritional status. The machine learning algorithm uses a model that takes the user's health data as input and outputs nutritional status for estimation. The generative AI receives prompts to estimate nutritional status based on the user's health data and outputs the estimation results. Based on the nutritional status estimated by the analysis unit, the generative unit compresses ingredients tailored to the user's preferences and needs to produce capsule-type meals. Generation is performed by methods such as vacuum compression or freeze-drying, but is not limited to these examples. For example, the generative unit uses vacuum compression to compress ingredients and produce capsule-type meals. The generative unit can also use freeze-drying to compress ingredients and produce capsule-type meals.Furthermore, the production unit can adjust the flavor to reproduce a taste that matches the user's preferences and requests. For example, the production unit can produce capsule-shaped meals by vacuum compression, which compresses ingredients in a vacuum state. Freeze-drying produces capsule-shaped meals by freezing and drying ingredients. Flavor adjustment adjusts sweetness, sourness, etc., to match the user's preferences. The supply unit provides the capsule-shaped meals produced by the production unit. Supply is carried out by methods such as delivery or vending machines, but is not limited to these examples. For example, the supply unit delivers the produced capsule-shaped meals to the user. The supply unit can also provide capsule-shaped meals using vending machines. The supply unit can also enable users to pick up capsule-shaped meals at a location specified by the user. For example, the supply unit delivers capsule-shaped meals to the user's address as a delivery. Vending machines are installed so that users can purchase capsule-shaped meals. Pickup at a specified location allows users to pick up capsule-shaped meals at a location specified by the user. As a result, the capsule-shaped meal production system according to the embodiment can provide personalized capsule-shaped meals based on the user's health data.

[0064] The data collection unit collects user health data. This data includes, but is not limited to, fitness data, circadian rhythms, and health checkup results. For example, the data collection unit can use wearable devices to collect fitness data. Wearable devices collect data such as heart rate, steps, calories burned, and exercise intensity in real time and transmit it to the data collection unit. This allows for an accurate understanding of the user's daily exercise and activity levels. The data collection unit can also use sleep trackers to understand circadian rhythms. Sleep trackers record the user's sleep patterns, sleep quality, and sleep duration and provide this information to the data collection unit. This allows for a detailed analysis of the user's sleep state and rhythms. Furthermore, the data collection unit can obtain data from medical institutions to acquire health checkup results. Data acquisition from medical institutions is done through electronic medical record systems with the user's consent. This allows for the collection of detailed health data, such as the user's blood test results and diagnostic information. For example, the data collection unit collects fitness data such as heart rate and steps from wearable devices. The sleep tracker records the user's sleep patterns and provides them to the data collection unit. Data acquisition from medical institutions is done through the electronic medical record system with the user's consent. This allows the data collection unit to collect a wide range of health data from various devices and systems, enabling a comprehensive understanding of the user's health status. Furthermore, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server and made accessible to the analysis and generation units. In addition, the frequency and accuracy of data collection can be adjusted, allowing for flexible responses to specific situations and conditions. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0065] The analysis unit uses generative AI to analyze data collected by the data collection unit and estimate the user's nutritional status. The analysis is performed using, for example, statistical analysis or machine learning algorithms, but is not limited to these examples. Specifically, as a statistical analysis, regression analysis is performed based on the user's health data to estimate the nutritional status. Regression analysis uses a model that takes the user's fitness data and health checkup results as input and outputs the nutritional status. For example, it can estimate the user's calorie consumption and nutrient intake based on data such as heart rate, steps taken, and blood test results. Machine learning algorithms also use a model that takes the user's health data as input and outputs the nutritional status. Machine learning algorithms can learn from large amounts of data and estimate the user's health and nutritional status with high accuracy. For example, it can learn from past health data and dietary history to build a model that predicts the user's nutritional status. Furthermore, the analysis unit can also estimate the nutritional status based on the user's health data using generative AI. The generative AI receives a prompt to estimate the nutritional status based on the user's health data and outputs the estimation result. For example, the generating AI takes user fitness data and health checkup results as input, receives prompts to output nutritional status, and outputs estimated results. This allows the analysis unit to quickly and accurately analyze the collected data and understand the user's nutritional status in real time. Furthermore, the analysis unit can also utilize historical data and statistical information to perform long-term nutritional status evaluations and trend analysis. For example, it can predict fluctuations in nutritional status over a specific period based on past health data and formulate future countermeasures. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. As a result, the analysis unit can not only understand nutritional status in real time but also handle long-term nutritional management and anomaly detection, improving the reliability and safety of the entire system.

[0066] The generation unit compresses ingredients tailored to the user's preferences and needs, based on the nutritional status estimated by the analysis unit, to produce capsule-shaped meals. Generation is carried out by methods such as vacuum compression and freeze-drying, but is not limited to these examples. Specifically, vacuum compression is used to compress ingredients in a vacuum state to produce capsule-shaped meals. Vacuum compression can significantly reduce the volume while preserving the flavor and nutrients of the ingredients. Freeze-drying can also be used to freeze and dry ingredients to produce capsule-shaped meals. Freeze-drying removes moisture from ingredients, making them suitable for long-term storage. Furthermore, the generation unit can adjust the flavor to reproduce the taste tailored to the user's preferences and needs. For example, the generation unit can adjust flavors such as sweetness, sourness, and saltiness to produce capsule-shaped meals tailored to the user's preferences. This allows the generation unit to provide personalized capsule-shaped meals based on the user's nutritional status. In addition, the generation unit can also devise innovative methods for selecting ingredients and cooking methods to provide nutritionally balanced meals. For example, the food processor can select highly nutritious ingredients and use appropriate cooking methods to maximize the extraction of nutrients. Furthermore, by devising combinations of ingredients and cooking methods, the food processor can provide meals optimized for the user's health and nutritional needs. In this way, the food processor can support the user's health and provide nutritionally balanced meals.

[0067] The supply unit provides capsule-type meals produced by the generation unit. Delivery is carried out by methods such as delivery or vending machines, but is not limited to these examples. Specifically, the supply unit delivers the produced capsule-type meals to users. Delivery involves directly delivering the capsule-type meals to the user's address, allowing users to easily receive meals at home. The supply unit can also provide capsule-type meals using vending machines. These vending machines are installed to allow users to purchase capsule-type meals and are available 24 hours a day. Furthermore, the supply unit can also enable users to receive capsule-type meals at a designated location. For example, users can receive capsule-type meals at a designated location such as their workplace or school. This allows the supply unit to implement flexible delivery methods tailored to users' lifestyles. Additionally, the supply unit can collect user feedback and continuously improve the accuracy and effectiveness of its delivery methods. For example, based on user feedback, delivery schedules may be reviewed and vending machine locations optimized. The supply unit can also reliably transmit information using multiple communication methods. For example, important information can be reliably delivered using not only smartphone notifications but also voice calls, SMS, and email. This allows the service provider to deliver capsule-type meals to users quickly and reliably, thereby improving user satisfaction.

[0068] The data collection unit can collect detailed data such as fitness data, circadian rhythms, and health checkup results. For example, the data collection unit can use wearable devices to collect fitness data. For instance, the data collection unit collects fitness data such as heart rate and steps from wearable devices. The data collection unit can also use sleep trackers to understand circadian rhythms. For example, the data collection unit uses a sleep tracker to record the user's sleep patterns and provides this data to the data collection unit. Furthermore, the data collection unit can obtain data from medical institutions to acquire health checkup results. For example, the data collection unit obtains data from medical institutions through an electronic medical record system with the user's consent. This allows for a more accurate estimation of nutritional status by collecting detailed health data. Detailed data includes, but is not limited to, fitness data, circadian rhythms, and health checkup results. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input fitness data acquired from wearable devices into a generating AI and have the generating AI perform data analysis.

[0069] The analysis unit can estimate high-calorie meals for users with high levels of physical activity, and meals rich in B vitamins for users who are sleep-deprived. For example, the analysis unit estimates high-calorie meals for users with high levels of physical activity. For example, the analysis unit estimates high-calorie meals for users with high levels of physical activity to increase their calorie intake. The analysis unit can also estimate meals rich in B vitamins for users who are sleep-deprived. For example, the analysis unit promotes fatigue recovery for sleep-deprived users by estimating meals rich in B vitamins. This makes it possible to provide meals tailored to the user's specific health condition. High-calorie meals include, for example, the number of calories and the nutrients they contain, but are not limited to such examples. Meals rich in B vitamins include, for example, the amount of vitamins B1, B2, B6, and B12 they contain, but are not limited to such examples. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input the user's health data into a generating AI and have the generating AI perform the estimation of nutritional status.

[0070] The generating unit can select ingredients that reproduce a taste tailored to the user's preferences and requests, and compress them into capsules. For example, the generating unit adjusts the flavor to reproduce a taste tailored to the user's preferences and requests. For example, if the user prefers sweet things, the generating unit selects ingredients that reproduce a sweet taste and compresses them into capsules. Also, if the user prefers sour things, the generating unit can select ingredients that reproduce a sour taste and compress them into capsules. Also, if the user prefers spicy things, the generating unit can select ingredients that reproduce a spicy taste and compress them into capsules. For example, to reproduce a sweet taste, the generating unit adjusts the flavor and compresses it into capsules. To reproduce a sour taste, the flavor adjusts and compresses it into capsules. To reproduce a spicy taste, the flavor adjusts and compresses it into capsules. This makes it possible to provide meals tailored to the user's preferences. Tastes tailored to preferences and requests include, but are not limited to, the use of taste sensors and flavor adjustments. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without using a generation AI. For example, the generation unit can input user preference data into the generation AI and cause the generation AI to reproduce flavors that match the user's preferences.

[0071] The supply unit can provide the generated capsule-shaped meals to the user. For example, the supply unit can deliver the generated capsule-shaped meals to the user. For example, the supply unit can deliver the capsule-shaped meals to the user's address. The supply unit can also provide the capsule-shaped meals using vending machines. For example, the supply unit can install vending machines so that users can purchase capsule-shaped meals. The supply unit can also allow users to pick up capsule-shaped meals at a location of their choosing. For example, the supply unit can allow users to pick up capsule-shaped meals at a location of their choosing. This allows for the rapid provision of the generated capsule-shaped meals. Rapid provision includes, but is not limited to, delivery time and delivery method. Some or all of the above processes in the supply unit may be performed using, for example, AI, or not using AI. For example, the supply unit can input a delivery schedule into a generation AI and have the generation AI calculate the optimal delivery route.

[0072] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit will collect data during times when the user is relaxed. The data collection unit can also collect data more frequently and in detail when the user is relaxed. The data collection unit can also collect data quickly and in a short amount of time when the user is in a hurry, collecting only the minimum necessary data. By adjusting the timing of data collection according to the user's emotions, more appropriate data can be collected. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0073] The data collection unit can analyze the user's past health data and select the optimal data collection method. For example, the data collection unit can recreate the data collection method used when the user was in their healthiest state, based on past health data. The data collection unit can also avoid data collection methods used when the user's health deteriorated, based on past health data. The data collection unit can also analyze past health data and select a data collection method used when the user's health was stable. By selecting the optimal data collection method based on past health data, more accurate data can be collected. The optimal data collection method includes, but is not limited to, the selection of data types and collection methods. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past health data into a generating AI and have the AI ​​select the optimal data collection method.

[0074] The data collection unit can filter data based on the user's current lifestyle and areas of interest during data collection. For example, if the user is on a diet, the data collection unit can prioritize collecting data related to calories and nutrients. The data collection unit can also prioritize collecting data related to a specific illness if the user has one. The data collection unit can also prioritize collecting data related to a new fitness program if the user has started one. This allows for the collection of more relevant data by filtering the data based on the user's lifestyle and areas of interest. Filtering includes, but is not limited to, methods for selecting data based on lifestyle and areas of interest. Some or all of the processing described above in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input data on the user's lifestyle and areas of interest into a generating AI, and have the generating AI perform filtering.

[0075] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting stress-related data. For example, if the user is relaxed, the data collection unit will prioritize collecting stress-related data. For example, if the user is relaxed, the data collection unit will prioritize collecting overall health data in a balanced manner. For example, if the user is in a hurry, the data collection unit will prioritize collecting only the most important data. For example, if the user is in a hurry, the data collection unit will prioritize collecting only the most important data. In this way, by determining the priority of data according to the user's emotions, more important data can be collected preferentially. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Data prioritization includes, but is not limited to, emotion intensity or data importance. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI and have the generating AI determine the priority of the data.

[0076] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is at high altitude, the data collection unit can prioritize the collection of data related to health status at high altitude. For example, if the user is at high altitude, the data collection unit can prioritize the collection of data related to health status in urban areas. For example, if the user is in urban areas, the data collection unit can prioritize the collection of data related to health status in urban areas. For example, if the user is traveling, the data collection unit can prioritize the collection of data related to the environment of the travel destination. For example, if the user is traveling, the data collection unit can prioritize the collection of data related to the environment of the travel destination. This allows for the collection of more relevant data by considering the user's geographical location information. Geographical location information includes, but is not limited to, the use of GPS data or data selection based on location information. Some or all of the processing described above in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the user's geographical location information into the generating AI, causing the AI ​​to prioritize the collection of highly relevant data.

[0077] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, if a user posts about health on social media, the data collection unit can collect data related to the content of those posts. The data collection unit can also collect data related to a specific fitness challenge if the user is participating in that challenge. The data collection unit can also collect data related to a user's posts about food if the user is posting about food. By analyzing social media activity, data related to the user's interests can be collected. Social media activity includes, but is not limited to, analyzing post content and extracting topics of interest. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect the relevant data.

[0078] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide simple and visually easy-to-understand analysis results. For example, if the user is stressed, the analysis unit can provide simple and visually easy-to-understand analysis results. The analysis unit can also provide detailed analysis results if the user is relaxed. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. The analysis unit can also provide concise analysis results that get straight to the point if the user is in a hurry. For example, if the user is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. By adjusting the presentation of the analysis according to the user's emotions, it is possible to provide analysis results that are easier to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Presentation methods of the analysis include, for example, graph displays and text summaries, but is not limited to such examples. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the method of expressing the analysis.

[0079] The analysis unit can adjust the level of detail of the analysis based on the importance of the health data during the analysis. For example, the analysis unit can perform a detailed analysis on important health data. The analysis unit can also perform a standard analysis on general health data. The analysis unit can also perform a simplified analysis on less important health data. By adjusting the level of detail of the analysis based on the importance of the health data, more appropriate analysis results can be provided. The importance of health data includes, but is not limited to, the evaluation of medical professionals and the reliability of the data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input the importance of the health data into the generative AI and have the generative AI adjust the level of detail of the analysis.

[0080] The analysis unit can apply different analysis algorithms depending on the category of health data during analysis. For example, the analysis unit can apply an exercise analysis algorithm to fitness data. For example, the analysis unit can apply an exercise analysis algorithm to fitness data. The analysis unit can also apply a sleep analysis algorithm to circadian rhythm data. For example, the analysis unit can apply a medical analysis algorithm to health checkup results. For example, the analysis unit can apply a medical analysis algorithm to health checkup results. By applying the appropriate analysis algorithm according to the category of health data, more accurate analysis results can be provided. Analysis algorithms include, but are not limited to, regression analysis and clustering. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the categories of health data into a generative AI and have the generative AI execute the application of an appropriate analysis algorithm.

[0081] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. The analysis unit can also provide a detailed analysis if the user is relaxed. For example, if the user is relaxed, the analysis unit can provide a detailed analysis. The analysis unit can also provide a visually stimulating analysis if the user is excited. For example, if the user is excited, the analysis unit can provide a visually stimulating analysis. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. The length of the analysis includes, but is not limited to, the amount of data and the level of detail of the analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the length of the analysis.

[0082] The analysis unit can determine the priority of analysis based on the timing of health data collection during the analysis. For example, the analysis unit may prioritize the analysis of recently collected health data. The analysis unit can also analyze current data while referring to past health data. The analysis unit can also prioritize the analysis of data collected during a specific period. By determining the priority of analysis based on the timing of health data collection, more important data can be analyzed preferentially. The timing of collection includes, but is not limited to, data freshness and collection frequency. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input the timing of health data collection into a generative AI and have the generative AI determine the priority of analysis.

[0083] The analysis unit can adjust the order of analysis based on the relevance of health data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. The analysis unit can also postpone the analysis of less relevant data. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the data. This allows for more efficient analysis by adjusting the order of analysis based on the relevance of health data. The relevance of health data includes, but is not limited to, correlation analysis and causal relationship evaluation. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input the relevance of health data into a generative AI and have the generative AI adjust the order of analysis.

[0084] The generation unit can estimate the user's emotions and adjust the flavor of the capsule it generates based on those emotions. For example, if the user is stressed, the generation unit can reproduce a relaxing flavor. For example, if the user is relaxed, the generation unit can reproduce a preferred flavor. For example, if the user is relaxed, the generation unit can reproduce a preferred flavor. For example, if the user is in a hurry, the generation unit can reproduce a flavor suitable for energy replenishment. For example, if the user is in a hurry, the generation unit can reproduce a flavor suitable for energy replenishment. This allows for a more satisfying meal by adjusting the capsule flavor according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. The capsule flavor is, but is not limited to, the addition of flavors or the use of a taste sensor. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without using a generation AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the flavor of the capsule.

[0085] The generation unit can analyze the user's past preference data during generation to select the most suitable ingredients. For example, the generation unit can select the most suitable ingredients based on ingredients the user has enjoyed eating in the past. The generation unit can also analyze past preference data to select ingredients that match the user's preferences. The generation unit can also suggest new ingredients based on past preference data. By selecting the most suitable ingredients based on past preference data, the generation unit can provide meals that match the user's preferences. Preference data includes, but is not limited to, past meal history and survey results. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the user's past preference data into a generation AI and have the generation AI select the most suitable ingredients.

[0086] The generation unit can customize the nutrients in the capsules based on the user's current health condition during generation. For example, if the user is tired, the generation unit can generate capsules containing nutrients suitable for energy replenishment. The generation unit can also generate capsules containing balanced nutrients if the user is in good health. The generation unit can also generate capsules containing a high amount of a specific nutrient if the user requires that nutrient. This allows for more appropriate nutritional supplementation by customizing the capsule nutrients according to the user's health condition. Nutrient customization includes, but is not limited to, adjusting the type and amount of nutrients. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can input the user's health data into the generation AI and have the generation AI perform the customization of the capsule nutrients.

[0087] The generation unit can estimate the user's emotions and determine the priority of capsules to generate based on the estimated emotions. For example, if the user is stressed, the generation unit will prioritize generating capsules with relaxing effects. The generation unit can also prioritize generating capsules that the user likes if they are relaxed. The generation unit can also prioritize generating capsules suitable for energy replenishment if the user is in a hurry. This allows for the provision of more appropriate capsules by prioritizing capsules according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Capsule prioritization includes, but is not limited to, the intensity of the emotion or the urgency of the nutritional status. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI determine the priority of capsules.

[0088] The generation unit can select the most suitable ingredients during generation, taking into account the user's geographical location information. For example, if the user is at high altitude, the generation unit can select ingredients suitable for nutritional supplementation at high altitude. The generation unit can also select ingredients suitable for nutritional supplementation in urban areas if the user is in an urban area. The generation unit can also select ingredients suitable for the environment of the travel destination if the user is traveling. By considering the user's geographical location information, more appropriate ingredients can be selected. Geographical location information includes, but is not limited to, the use of local ingredients and efficient delivery. Some or all of the above-described processing in the generation unit may be performed using, for example, generation AI, or without generation AI. For example, the generation unit can input the user's geographical location information into the generation AI and have the AI ​​select the optimal ingredients.

[0089] The generation unit can analyze the user's social media activity during generation and suggest capsule content. For example, if the user has made health-related posts on social media, the generation unit can suggest capsules related to those posts. The generation unit can also suggest capsules related to specific fitness challenges if the user is participating in them. The generation unit can also suggest capsules related to food if the user has made food-related posts. In this way, by analyzing social media activity, capsules related to the user's interests can be suggested. Social media activity includes, but is not limited to, analyzing post content and extracting topics of interest. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can input the user's social media activity data into a generation AI and have the generation AI suggest capsule content.

[0090] The delivery unit can estimate the user's emotions and adjust the capsule delivery method based on the estimated emotions. For example, if the user is stressed, the delivery unit can select a delivery method that promotes relaxation. The delivery unit can also select a preferred delivery method if the user is relaxed. The delivery unit can also select a method that allows for quick delivery if the user is in a hurry. By adjusting the delivery method according to the user's emotions, a more satisfying delivery can be achieved. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Delivery methods include, but are not limited to, the selection of the timing and means of delivery. Some or all of the above-described processes in the delivery unit may be performed using, for example, AI, or not using AI. For example, the service provider can input user emotion data into a generating AI and have the AI ​​adjust the delivery method.

[0091] The service provider can analyze the user's past consumption behavior and select the optimal service method at the time of service delivery. For example, the service provider can select the optimal service method based on the user's preferred service method in the past. The service provider can also analyze past consumption behavior and select a service method that suits the user's preferences. The service provider can also propose a new service method based on past consumption behavior. By selecting the optimal service method based on past consumption behavior, it becomes possible to provide services that suit the user's preferences. Consumption behavior includes, but is not limited to, analysis of purchase history and consumption patterns. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the user's past consumption behavior data into a generating AI and have the generating AI select the optimal service method.

[0092] The service provider can customize the delivery method based on the user's current lifestyle at the time of delivery. For example, if the user is busy, the service provider can select a delivery method that allows for quick delivery. For example, if the user is relaxed, the service provider can select a delivery method that allows for leisurely enjoyment. For example, if the user is traveling, the service provider can select a delivery method that allows for delivery at the travel destination. By customizing the delivery method according to the user's lifestyle, more appropriate delivery becomes possible. Lifestyle includes, but is not limited to, lifestyle rhythms and activity levels. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the user's lifestyle data into a generating AI and have the generating AI perform the customization of the delivery method.

[0093] The delivery unit can estimate the user's emotions and determine the priority of capsule delivery based on the estimated emotions. For example, if the user is stressed, the delivery unit will prioritize providing capsules with relaxing effects. For example, if the user is relaxed, the delivery unit will prioritize providing capsules of the user's preference. For example, if the user is relaxed, the delivery unit will prioritize providing capsules of the user's preference. For example, if the user is in a hurry, the delivery unit will prioritize providing capsules suitable for energy replenishment. For example, if the user is in a hurry, the delivery unit will prioritize providing capsules suitable for energy replenishment. In this way, by determining the priority of delivery according to the user's emotions, more appropriate capsules can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Prioritization of delivery includes, but is not limited to, the intensity of emotions or the urgency of nutritional status. Some or all of the processing described above in the delivery unit may be performed using, for example, a generative AI, or without a generative AI. For example, the delivery unit can input user emotion data into a generative AI and have the generative AI determine the priority of deliveries.

[0094] The delivery unit can select the optimal delivery method at the time of delivery, taking into account the user's geographical location information. For example, if the user is at a high altitude, the delivery unit can select a method suitable for delivery at a high altitude. For example, if the user is at a high altitude, the delivery unit can select a method suitable for delivery in an urban area. For example, if the user is at an urban area, the delivery unit can select a method suitable for delivery in an urban area. For example, if the user is traveling, the delivery unit can select a method that allows the user to receive the delivery at their travel destination. For example, if the user is traveling, the delivery unit can select a method that allows the user to receive the delivery at their travel destination. This allows for the selection of a more appropriate delivery method by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, the use of local ingredients and the efficiency of delivery. Some or all of the above processing in the delivery unit may be performed using, for example, AI, or not using AI. For example, the delivery unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal delivery method.

[0095] The service provider can analyze the user's social media activity and propose means of provision at the time of provision. For example, if the user has made health-related posts on social media, the service provider can propose means of provision related to the content of those posts. The service provider can also propose means of provision related to a specific fitness challenge if the user is participating in that challenge. The service provider can also propose means of provision related to a food-related post if the user has made food-related posts. By analyzing social media activity, the service provider can propose means of provision related to the user's interests. Social media activity includes, but is not limited to, analyzing post content and extracting topics of interest. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI propose means of provision.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The capsule-type meal generation system can further collect user allergy data, and the analysis unit can select ingredients that address those allergies. For example, the data collection unit can obtain user allergy data from medical institutions. The analysis unit can then exclude ingredients that may cause allergies based on the allergy data. Furthermore, the generation unit can select ingredients that address the allergies and generate capsule-type meals. This allows for the provision of safe meals to users with allergies.

[0098] The data collection unit can estimate the user's emotions and adjust the frequency of data collection based on the estimated emotions. For example, if the user is stressed, the frequency of data collection can be reduced, and if the user is relaxed, the frequency can be increased. Also, if the user is in a hurry, data collection can be performed for a short time, and only the minimum necessary data can be collected. In this way, by adjusting the frequency of data collection according to the user's emotions, more appropriate data can be collected.

[0099] The analysis unit can estimate meals that take into account seasonal nutritional needs based on the user's health data. For example, it can estimate meals rich in vitamin D in winter and meals that emphasize hydration in summer. It can also estimate meals rich in antioxidants to help with allergies in spring and meals that boost immunity in autumn. This allows the system to provide meals tailored to seasonal nutritional needs.

[0100] The generation unit can estimate the user's emotions and adjust the capsule's texture based on those emotions. For example, if the user is stressed, it can generate a soft-textured capsule; if the user is relaxed, it can generate a chewy capsule. Furthermore, if the user is in a hurry, it can generate a capsule that is easy to chew. By adjusting the capsule's texture according to the user's emotions, a more satisfying meal can be provided.

[0101] The delivery unit can estimate the user's emotions and adjust the timing of capsule delivery based on those estimates. For example, if the user is stressed, a capsule can be delivered during a time when they can relax; if the user is relaxed, a capsule can be delivered at any time. Furthermore, if the user is in a hurry, a capsule can be delivered quickly. By adjusting the delivery timing according to the user's emotions, a more satisfying service can be provided.

[0102] The data collection unit collects data on the user's living environment, and the analysis unit can estimate nutritional needs based on that environment. For example, for users living in urban areas, it can estimate a diet rich in antioxidants as a countermeasure against air pollution, and for users living in rural areas, it can estimate a diet with detoxifying effects as a countermeasure against pesticides. Furthermore, for users living in cold regions, it can estimate a high-calorie diet to maintain body temperature, and for users living in warm regions, it can estimate a diet that emphasizes hydration. This allows for the provision of meals tailored to nutritional needs based on the user's living environment.

[0103] The analysis unit can estimate the user's emotions and adjust the notification method of the analysis results based on the estimated emotions. For example, if the user is stressed, it can choose a simple and visually easy-to-understand notification method, while if the user is relaxed, it can provide detailed analysis results. Furthermore, if the user is in a hurry, it can choose a concise notification method that gets straight to the point. By adjusting the notification method of the analysis results according to the user's emotions, it can provide analysis results that are easier to understand.

[0104] The generation unit can analyze the user's eating history and suggest new recipes based on ingredients they have previously enjoyed. For example, it can generate new recipes by combining ingredients the user has enjoyed in the past and provide them as capsule-type meals. It can also suggest ingredients the user has not yet tried based on their past eating history. Furthermore, it can analyze the user's eating history and suggest new recipes that take nutritional balance into consideration. This allows for the provision of new dining experiences tailored to the user's preferences.

[0105] The delivery system can estimate the user's emotions and customize the capsule delivery method based on those estimates. For example, if a user is stressed, the capsule can be delivered with relaxing music; if the user is relaxed, it can be delivered with music of their choice. Furthermore, if a user is in a hurry, a method that allows for quick delivery can be selected. This allows for a more satisfying service experience by customizing the delivery method according to the user's emotions.

[0106] The data collection unit can collect user health data while considering the user's family history. For example, if a family member has a specific disease, data related to that disease can be prioritized for collection. Furthermore, based on family history, preventative data for diseases that may develop in the future can be collected. In addition, health data based on genetic factors can be collected while considering family history. This enables the collection of more accurate health data that takes family history into account.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The data collection unit collects the user's health data. This data includes fitness data, circadian rhythms, and health checkup results. For example, the data collection unit collects fitness data such as heart rate and steps using wearable devices, records the user's sleep patterns using sleep trackers, and obtains health checkup results from medical institutions through electronic medical record systems. Step 2: The analysis unit analyzes the data collected by the data collection unit and estimates the user's nutritional status. The analysis is performed using statistical analysis, machine learning algorithms, and generative AI. For example, regression analysis is performed as statistical analysis, the nutritional status is estimated using machine learning algorithms, and the generative AI receives prompts and outputs the estimation results. Step 3: The production unit compresses ingredients tailored to the user's preferences and needs, based on the nutritional status estimated by the analysis unit, to create capsule-shaped meals. Production is carried out by methods such as vacuum compression and freeze-drying. For example, ingredients are compressed using vacuum compression, and dried using freeze-drying. In addition, the flavor is adjusted to reproduce a taste that matches the user's preferences. Step 4: The supply unit provides the capsule-shaped meals produced by the generation unit. Delivery is carried out by methods such as delivery or vending machines. For example, the capsule-shaped meals may be delivered to the user's address, dispensed using a vending machine, or picked up at a location specified by the user.

[0109] 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.

[0110] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0111] 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.

[0112] Each of the multiple elements described above, including the data collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit is implemented using a wearable device or sleep tracker of the smart device 14 to collect fitness data and sleep patterns. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 to analyze the collected data using generation AI and estimate the user's nutritional status. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 to compress ingredients according to the user's preferences and requests to produce capsule-type meals. The provision unit is implemented in the control unit 46A of the smart device 14 to provide the generated capsule-type meals to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] 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.

[0128] Each of the multiple elements described above, including the data collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit is implemented using the wearable device and sleep tracker of the smart glasses 214 to collect fitness data and sleep patterns. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 to analyze the collected data using generation AI and estimate the user's nutritional status. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 to compress ingredients according to the user's preferences and requests to produce capsule-type meals. The provision unit is implemented in the control unit 46A of the smart glasses 214 to provide the generated capsule-type meals to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] 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.

[0144] Each of the multiple elements described above, including the data collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit is implemented using the wearable device and sleep tracker of the headset terminal 314 to collect fitness data and sleep patterns. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 to analyze the collected data using generation AI and estimate the user's nutritional status. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 to compress ingredients according to the user's preferences and requests to produce capsule-type meals. The provision unit is implemented in the control unit 46A of the headset terminal 314 to provide the generated capsule-type meals to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] 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.

[0161] Each of the multiple elements described above, including the data collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit is implemented using the robot 414's wearable device and sleep tracker to collect fitness data and sleep patterns. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data using a generation AI to estimate the user's nutritional status. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which compresses ingredients according to the user's preferences and requests to produce capsule-type meals. The provision unit is implemented, for example, by the control unit 46A of the robot 414, which provides the generated capsule-type meals to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) A data collection unit that collects user health data, An analysis unit analyzes the data collected by the aforementioned data collection unit and estimates the user's nutritional status. Based on the nutritional status estimated by the analysis unit, a production unit compresses ingredients tailored to the user's preferences and requests to produce capsule-type meals. The system includes a dispensing unit that provides capsule-shaped meals produced by the generating unit. A system characterized by the following features. (Note 2) The aforementioned data acquisition unit is Collect detailed data such as fitness data, circadian rhythms, and health checkup results. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, For users with high activity levels, a high-calorie diet is recommended, while for users with sleep deprivation, a diet rich in B vitamins is recommended. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Ingredients are selected to recreate flavors tailored to the user's preferences and requests, and then compressed into capsule form. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, The generated capsule-shaped meals are provided to the user. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned data acquisition unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned data acquisition unit is Analyze the user's past health data and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned data acquisition unit is During data collection, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned data acquisition unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned data acquisition unit is During data collection, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned data acquisition unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the health data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of health data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the health data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the health data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is It estimates the user's emotions and adjusts the flavor of the capsules it generates based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is During generation, the system analyzes the user's past preference data to select the most suitable ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During generation, the nutrients in the capsule are customized based on the user's current health condition. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is It estimates the user's emotions and determines the priority of capsules to generate based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is During generation, the system selects the most suitable ingredients by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is During generation, the content of the capsule is suggested by analyzing the user's social media activity. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the capsule delivery method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing the service, we analyze the user's past consumption behavior to select the optimal delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing the service, the means of delivery will be customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, The system estimates the user's emotions and determines the priority of capsule delivery based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and propose a delivery method. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects user health data, An analysis unit analyzes the data collected by the aforementioned data collection unit and estimates the user's nutritional status. Based on the nutritional status estimated by the analysis unit, a production unit compresses ingredients tailored to the user's preferences and requests to produce capsule-type meals. The system includes a dispensing unit that provides capsule-shaped meals produced by the generating unit. A system characterized by the following features.

2. The aforementioned data acquisition unit is Collect detailed data such as fitness data, circadian rhythms, and health checkup results. The system according to feature 1.

3. The aforementioned analysis unit, For users with high activity levels, a high-calorie diet is recommended, while for users with sleep deprivation, a diet rich in B vitamins is recommended. The system according to feature 1.

4. The generating unit is Ingredients are selected to recreate flavors tailored to the user's preferences and requests, and then compressed into capsule form. The system according to feature 1.

5. The aforementioned supply unit is, The generated capsule-shaped meals are provided to the user. The system according to feature 1.

6. The aforementioned data acquisition unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

7. The aforementioned data acquisition unit is Analyze the user's past health data and select the optimal data collection method. The system according to feature 1.

8. The aforementioned data acquisition unit is During data collection, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

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