System

Abstract

To provide a system capable of suggesting suitable dishes based on the health data of a user and cooking the same.SOLUTION: A system in an embodiment includes a collection unit, an analysis unit, a proposal unit, and an instruction unit. The collection unit collects the health data of a user. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes an appropriate food based on the analysis results obtained by the analysis unit. The instruction unit gives an instruction to a cooking robot to cook the dishes proposed by the proposal unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies do not adequately suggest and prepare appropriate dishes based on the user's health data, and there is room for improvement.

[0005] The system according to the embodiment aims to suggest and cook suitable dishes based on the user's health data. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a suggestion unit, and an instruction unit. The collection unit collects health data of a user. The analysis unit analyzes the data collected by the collection unit. The suggestion unit suggests a suitable dish based on the analysis result obtained by the analysis unit. The instruction unit instructs the cooking robot to cook the dish suggested by the suggestion unit. Effect of the Invention

[0007] The system according to the embodiment can suggest and cook suitable dishes based on the user's health data. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

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

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

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) The health management cooking system according to the embodiment of the present invention is a system that collects health data of a user, proposes appropriate dishes based on the data, and has a cooking robot cook the dishes. This system can accommodate a wide range of users, including those who need care, those with physical disabilities, and those who are not good at cooking. For example, health data is collected from a smart watch, a fitness tracker, a smart scale, a blood glucose monitor, etc., and an AI analyzes the data. Based on the analysis results, dishes are proposed according to the user's health condition, disease history, allergy information, and preferences. The proposed dishes are instructed to a cooking robot, which prepares ingredients based on a recipe and cooks the dishes. The cooked dishes are provided to the user. In addition, the collected data is shared around the world and can be used by other users. As a result, the health management cooking system can propose appropriate dishes based on the user's health data, and the cooking robot can cook the dishes.

[0029] The health care cooking system according to the embodiment includes a collection unit, an analysis unit, a suggestion unit, and an instruction unit. The collection unit collects health data of the user. The health data of the user includes, for example, heart rate, blood pressure, body temperature, etc., but is not limited to such examples. The collection unit collects data from, for example, a smart watch, a fitness tracker, a smart scale, or a blood glucose monitor. The smart watch can collect data such as heart rate and number of steps. The fitness tracker can collect data such as amount of exercise and calorie consumption. The smart scale can collect data such as weight and body fat percentage. The blood glucose monitor can collect blood glucose data. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, AI to grasp the health condition of the user. The AI ​​executes an algorithm to evaluate the health condition of the user based on the collected data. For example, the AI ​​can analyze data of heart rate and blood pressure to evaluate the cardiovascular health condition of the user. The suggestion unit suggests suitable dishes based on the analysis results obtained by the analysis unit. The suggestion unit suggests dishes using, for example, AI. The AI ​​executes an algorithm to select suitable dishes based on the user's health condition, disease history, allergy information, and preferences. For example, if the user has high blood pressure, the AI ​​can suggest dishes with low salt content. The instruction unit instructs the cooking robot to cook the dishes suggested by the suggestion unit. The instruction unit gives instructions to the cooking robot using, for example, AI. The AI ​​executes an algorithm to instruct the cooking robot on the preparation of ingredients and cooking procedures based on the recipe of the proposed dish. For example, the AI ​​can instruct the cooking robot on how to cut ingredients and the cooking time. As a result, the health management cooking system according to the embodiment suggests suitable dishes based on the user's health data, and the cooking robot can cook the dishes.

[0030] The collection unit collects health data of the user. The health data of the user includes, but is not limited to, for example, heart rate, blood pressure, and body temperature. The collection unit collects data from, for example, a smart watch, a fitness tracker, a smart scale, or a blood glucose monitor. The smart watch can collect data such as heart rate and number of steps. Specifically, the smart watch measures the heart rate using an optical heart rate sensor and counts the number of steps using an acceleration sensor. These data are transmitted to the collection unit via Bluetooth (registered trademark) or Wi-Fi. The fitness tracker can collect data such as the amount of exercise and the amount of calories burned. The fitness tracker uses a built-in acceleration sensor and gyro sensor to analyze the user's exercise pattern and calculate the calories burned. The smart scale can collect data such as weight and body fat percentage. The smart scale measures the body fat percentage using an electrical impedance method and measures the weight with a weight sensor. These data are also transmitted to the collection unit via wireless communication. The blood glucose monitor can collect blood glucose data. The blood glucose monitor collects a small amount of blood from the user's fingertip and measures the blood glucose level with a sensor. This allows the collection unit to collect a wide range of health data from various devices and grasp the user's health condition in real time. Furthermore, the collection unit can centrally manage these data and link them with other systems and departments as necessary. For example, the collected data is stored on a cloud server and made accessible to the analysis unit and proposal unit. In addition, by adjusting the frequency and accuracy of data collection, flexible responses according to specific situations and conditions are possible. This allows the collection unit to collect data efficiently and effectively and improve the performance of the entire system.

[0031] The analysis unit analyzes the data collected by the collection unit. The analysis unit, for example, uses AI to analyze the data and grasp the health condition of the user. The AI ​​executes an algorithm to evaluate the health condition of the user based on the collected data. Specifically, the AI ​​can analyze heart rate and blood pressure data to evaluate the cardiovascular health condition of the user. For example, the AI ​​can analyze heart rate fluctuation patterns and blood pressure trends to detect abnormal patterns. In addition, the AI ​​can analyze body temperature data to detect early signs of fever and hypothermia. Furthermore, the AI ​​can utilize past data and statistical information to analyze long-term health trends and predict the health risks of the user. For example, based on past blood glucose level data, it can evaluate the risk of diabetes and propose early preventive measures. Based on these analysis results, the analysis unit can comprehensively evaluate the health condition of the user and propose necessary measures. Furthermore, the analysis unit can use an anomaly detection algorithm to detect unusual patterns and abnormal data and issue an early warning. As a result, the analysis unit can not only grasp the situation in real time, but also handle long-term health management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The suggestion unit suggests suitable dishes based on the analysis results obtained by the analysis unit. The suggestion unit suggests dishes using, for example, AI. The AI ​​executes an algorithm to select suitable dishes based on the user's health condition, disease history, allergy information, and preferences. Specifically, the AI ​​can suggest nutritionally balanced dishes based on the user's health data. For example, if the user has high blood pressure, it can suggest low-salt dishes. Also, if the user has diabetes, it can suggest low-sugar dishes. Furthermore, the AI ​​can suggest dishes that do not contain allergens by taking into account the user's allergy information. For example, if the user has a nut allergy, it can suggest dishes that do not contain nuts. Also, the AI ​​can suggest dishes that the user likes based on the user's preferences and past meal history. As a result, the suggestion unit can suggest optimal dishes according to the user's health condition and preferences, and support the user's health management. Furthermore, the suggestion unit provides recipes and cooking procedures for the suggested dishes, and supports the user so that the user can easily cook the dishes. As a result, the suggestion unit can comprehensively support the user's health management and promote a healthy diet.

[0033] The instruction unit instructs the cooking robot to cook the dish proposed by the suggestion unit. The instruction unit instructs the cooking robot using, for example, AI. The AI ​​executes an algorithm that instructs the cooking robot on the preparation of ingredients and cooking procedures based on the recipe of the proposed dish. Specifically, the AI ​​can instruct the cooking robot on how to cut ingredients and the cooking time. For example, the AI ​​can instruct the cooking robot to cut vegetables into small pieces and grill meat at an appropriate temperature. The AI ​​can also instruct the cooking robot on the amount of seasonings and the timing of adding them. This allows the instruction unit to accurately reproduce the proposed dish and provide it to the user. Furthermore, the instruction unit can monitor the operation of the cooking robot in real time and make adjustments as necessary. For example, if an abnormality occurs during cooking, the AI ​​can respond immediately and issue new instructions to the cooking robot. Furthermore, the instruction unit can improve and optimize the cooking process by recording the operation log of the cooking robot and analyzing it later. This allows the instruction unit to realize efficient and high-quality cooking and provide healthy and delicious dishes to the user.

[0034] The collection unit can collect data from a smart watch, a fitness tracker, a smart scale, or a blood glucose monitor. The collection unit collects data on heart rate and steps using, for example, a smart watch. The smart watch is worn on the user's wrist and can collect data using a heart rate sensor and an acceleration sensor. The collection unit can also collect data on the amount of exercise and the amount of calories burned using a fitness tracker. The fitness tracker is worn on the user's wrist or waist and can collect data using an acceleration sensor and a gyro sensor. The collection unit can also collect data on weight and body fat percentage using a smart scale. The smart scale can measure weight and body fat percentage by having the user step on the scale and collect data. The collection unit can also collect blood glucose data using a blood glucose monitor. The blood glucose monitor can measure blood glucose levels using a blood sample of the user and collect data. This allows the collection unit to collect health data from various devices. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data obtained from a smartwatch into an AI and have the AI ​​analyze the data.

[0035] The analysis unit can analyze the collected data and grasp the health condition of the user. For example, the analysis unit analyzes the collected heart rate and blood pressure data and evaluates the cardiovascular health condition of the user. The analysis unit executes an algorithm that analyzes the data using AI and evaluates the health condition of the user. For example, the AI ​​can analyze the fluctuation of the heart rate and the range of blood pressure and evaluate the cardiovascular health condition of the user. The analysis unit can also analyze the collected data of weight and body fat percentage and evaluate the body composition of the user. For example, the AI ​​can analyze the fluctuation of weight and the change in the body fat percentage and evaluate the body composition of the user. Furthermore, the analysis unit can analyze the collected blood glucose level data and evaluate the blood glucose control state of the user. For example, the AI ​​can analyze the fluctuation and average value of the blood glucose level and evaluate the blood glucose control state of the user. This allows the analysis unit to accurately grasp the health condition of the user. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data to AI and have the AI ​​execute the analysis of the data.

[0036] The suggestion unit can suggest dishes based on the user's illness history and allergy information. The suggestion unit suggests suitable dishes based on, for example, the user's illness history and allergy information. The suggestion unit executes an algorithm that suggests dishes using AI. For example, if the user has high blood pressure, the AI ​​can suggest low-salt dishes. Also, if the user has diabetes, the AI ​​can suggest low-carb dishes. Furthermore, if the user has allergies, the AI ​​can suggest dishes that do not contain allergens. For example, if the user has a nut allergy, the AI ​​can suggest dishes that do not contain nuts. This allows the suggestion unit to suggest dishes according to the user's health condition. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's illness history and allergy information into the AI ​​and have the AI ​​execute the suggestion of suitable dishes.

[0037] The suggestion unit can suggest dishes according to the user's preferences. The suggestion unit, for example, suggests suitable dishes based on the user's preferences. The suggestion unit executes an algorithm that suggests dishes using AI. For example, if the user likes Japanese food, the AI ​​can suggest Japanese dishes. Also, if the user likes Western food, the AI ​​can suggest Western dishes. Furthermore, if the user likes a specific ingredient, the AI ​​can suggest dishes using that ingredient. For example, if the user likes chicken, the AI ​​can suggest dishes using chicken. This allows the suggestion unit to suggest dishes according to the user's preferences. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's preferences to the AI ​​and have the AI ​​execute the suggestion of suitable dishes.

[0038] The cooking unit can prepare ingredients and cook based on a recipe. The cooking unit, for example, prepares ingredients and cooks based on a recipe. The cooking unit executes an algorithm that uses AI to cook. For example, the AI ​​can instruct how to cut ingredients and the cooking time based on a recipe. The AI ​​can also instruct the cooking robot on the preparation of ingredients and the cooking procedure. Furthermore, the AI ​​can monitor the temperature and time during cooking and perform appropriate cooking. For example, if the temperature during cooking is not appropriate, the AI ​​can instruct the cooking robot to adjust the temperature. This allows the cooking unit to accurately cook based on the recipe. Some or all of the above-mentioned processing in the cooking unit may be performed using, for example, AI, or may be performed without using AI. For example, the cooking unit can input a recipe to the AI ​​and have the AI ​​execute cooking instructions.

[0039] The serving unit can serve the cooked food to the user. The serving unit, for example, serves the cooked food quickly. The serving unit executes an algorithm for serving using AI. For example, the AI ​​can instruct the timing for serving the cooked food to the user. The AI ​​can also monitor the temperature and presentation at the time of serving and provide the food appropriately. Furthermore, the AI ​​can select the optimal serving method by considering the user's situation at the time of serving. For example, the AI ​​can check whether the user is ready to eat and provide the food at an appropriate timing. This allows the serving unit to quickly serve the cooked food. Some or all of the above-mentioned processing in the serving unit may be performed, for example, using AI, or may be performed without using AI. For example, the serving unit can input the timing for serving the cooked food to the AI ​​and cause the AI ​​to execute the serving instruction.

[0040] The collection unit can share the collected data worldwide and make it useful to other users. The collection unit, for example, shares the collected data through a cloud service. The collection unit executes an algorithm for sharing data using AI. For example, the AI ​​can appropriately share the collected data based on privacy settings. The AI ​​can also set the data sharing range and share the data with a specific user or group. Furthermore, the AI ​​can select a data sharing method and provide an optimal sharing means. For example, the AI ​​can ensure security by encrypting and sharing the data. This allows the collection unit to share the collected data worldwide and make it useful to other users. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the collected data into AI and have the AI ​​execute the sharing of the data.

[0041] The collection unit can analyze the user's past health data and select the optimal data collection method. For example, the collection unit analyzes the user's past heart rate data and collects data during a time period when the heart rate is stable. The collection unit executes an algorithm that uses AI to analyze past data and selects the optimal data collection method. For example, the AI ​​can analyze past heart rate data and identify a time period when the heart rate is stable. The AI ​​can also analyze past blood glucose level data and collect data during a time period when the blood glucose level is stable. Furthermore, the AI ​​can analyze past sleep data and adjust to avoid data collection during sleep. For example, the AI ​​can analyze past sleep data and identify a time period when data collection during sleep is avoided. This allows the collection unit to select the optimal data collection method based on the past health data. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input past health data to AI and cause AI to select the optimal data collection method.

[0042] The collection unit can perform filtering based on the user's current activity status and environment when collecting data. For example, when the user is exercising, the collection unit collects only exercise data and filters other data. The collection unit executes an algorithm that filters data using AI. For example, the AI ​​can analyze the user's current activity status and preferentially collect data during exercise. In addition, when the user is resting, the AI ​​can preferentially collect heart rate and sleep data and filter other data. Furthermore, when the user is out, the AI ​​can collect related data based on location information and filter other data. For example, the AI ​​can analyze the user's location information and preferentially collect data while out. This allows the collection unit to filter data according to the user's activity status and environment. Part or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's activity status data to AI and have the AI ​​perform data filtering.

[0043] The collection unit can collect highly relevant data preferentially based on the geographical location information of the user when collecting data. For example, when the user is in a gym, the collection unit preferentially collects exercise data. The collection unit executes an algorithm that uses AI to analyze geographical location information and preferentially collects highly relevant data. For example, the AI ​​can analyze the user's location information and preferentially collect exercise data when the user is in a gym. In addition, the AI ​​can also preferentially collect relaxation-related data when the user is at home. Furthermore, the AI ​​can also preferentially collect data related to the user based on the location information when the user is out. For example, the AI ​​can analyze the user's location information and preferentially collect data when the user is out. This allows the collection unit to preferentially collect highly relevant data based on the geographical location information. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to AI and cause AI to collect highly relevant data.

[0044] The collection unit can analyze the social media activity of the user at the time of data collection and collect related data. For example, when the user posts on social media about exercise, the collection unit preferentially collects exercise data. The collection unit executes an algorithm that uses AI to analyze social media activity and collects related data. For example, the AI ​​can analyze the user's social media posts and preferentially collect exercise data when there are many posts on exercise. In addition, the AI ​​can also preferentially collect food-related data when the user posts on social media about food. In addition, the AI ​​can also preferentially collect stress-related data when the user posts on social media about stress. For example, the AI ​​can analyze the user's social media posts and preferentially collect stress-related data when there are many posts on stress. This allows the collection unit to collect related data based on social media activity. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity data into the AI ​​and cause the AI ​​to collect related data.

[0045] The analysis unit can adjust the level of detail of the analysis based on the importance of the health data during the analysis. The analysis unit performs a detailed analysis on important health data (e.g., heart rate, blood glucose level), for example. The analysis unit executes an algorithm that evaluates the importance of the health data using AI and adjusts the level of detail of the analysis. For example, the AI ​​can analyze data on heart rate and blood glucose level, and perform a detailed analysis on data with high importance. The AI ​​can also perform a simplified analysis on general health data (e.g., number of steps, calorie consumption). Furthermore, the AI ​​can also prioritize analysis of data with high importance according to the health condition of the user. For example, the AI ​​can evaluate the health condition of the user and prioritize analysis of data with high importance. This allows the analysis unit to adjust the level of detail of the analysis according to the importance of the health data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input health data to AI and cause AI to adjust the level of detail of the analysis.

[0046] The analysis unit can apply different analysis algorithms depending on the category of health data during analysis. For example, the analysis unit applies a heart rate variability analysis algorithm to heart rate data. The analysis unit uses AI to evaluate the category of health data and executes an algorithm that applies an appropriate analysis algorithm. For example, the AI ​​can apply a heart rate variability analysis algorithm to heart rate data and perform detailed analysis. The AI ​​can also apply a blood glucose variability analysis algorithm to blood glucose data. Furthermore, the AI ​​can apply a sleep stage analysis algorithm to sleep data. For example, the AI ​​can analyze sleep data and evaluate sleep stages. This allows the analysis unit to apply an optimal analysis algorithm depending on the category of health data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input health data to AI and cause AI to apply an appropriate analysis algorithm.

[0047] The analysis unit can determine the priority of the analysis based on the collection time of the data at the time of analysis. For example, the analysis unit preferentially analyzes the most recently collected data. The analysis unit executes an algorithm that evaluates the collection time of the data using AI and determines the priority of the analysis. For example, the AI ​​can preferentially analyze the most recently collected data and evaluate the latest health condition. In addition, the AI ​​can also analyze the current data while referring to the past data. Furthermore, the AI ​​can also preferentially analyze the data collected in a specific period. For example, the AI ​​can analyze the data collected in a specific period and evaluate the health condition in that period. This allows the analysis unit to determine the priority of the analysis based on the collection time of the data. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the collection time of the data to the AI ​​and cause the AI ​​to execute the determination of the priority of the analysis.

[0048] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit preferentially analyzes highly relevant data. The analysis unit executes an algorithm that evaluates the relevance of the data using AI and adjusts the order of analysis. For example, the AI ​​can preferentially analyze highly relevant data and evaluate important health conditions. The AI ​​can also analyze less relevant data later. Furthermore, the AI ​​can dynamically adjust the order of analysis based on the relevance of the data. For example, the AI ​​can analyze the relevance of the data and analyze the data in an optimal order. This allows the analysis unit to adjust the order of analysis based on the relevance of the data. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data to the AI ​​and cause the AI ​​to adjust the order of analysis.

[0049] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the dish when making a suggestion. For example, the suggestion unit makes a detailed suggestion for an important dish (e.g., a dish for a special event). The suggestion unit executes an algorithm that evaluates the importance of the dish using AI and adjusts the level of detail of the suggestion. For example, the AI ​​can make a detailed suggestion for a dish for a special event. Also, the AI ​​can make a simplified suggestion for a general dish (e.g., an everyday meal). Furthermore, the AI ​​can preferentially suggest dishes with high importance according to the health condition of the user. For example, the AI ​​can evaluate the health condition of the user and preferentially suggest dishes with high importance. This allows the suggestion unit to adjust the level of detail of the suggestion according to the importance of the dish. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the importance of the dish to the AI ​​and cause the AI ​​to adjust the level of detail of the suggestion.

[0050] The suggestion unit can apply different suggestion algorithms depending on the category of cuisine when making a suggestion. For example, the suggestion unit applies a suggestion algorithm dedicated to Japanese cuisine to Japanese cuisine. The suggestion unit executes an algorithm that evaluates the category of cuisine using AI and applies an appropriate suggestion algorithm. For example, the AI ​​can apply a suggestion algorithm dedicated to Japanese cuisine to Japanese cuisine and make detailed suggestions. The AI ​​can also apply a suggestion algorithm dedicated to Western cuisine to Western cuisine. Furthermore, the AI ​​can also apply a suggestion algorithm dedicated to Chinese cuisine to Chinese cuisine. For example, the AI ​​can apply a suggestion algorithm dedicated to Chinese cuisine to Chinese cuisine and make detailed suggestions. This allows the suggestion unit to apply an optimal suggestion algorithm depending on the category of cuisine. Part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input a cuisine category to the AI ​​and cause the AI ​​to apply an appropriate suggestion algorithm.

[0051] The suggestion unit can determine the priority of the suggestions based on the time of submission of the dishes at the time of suggestion. For example, the suggestion unit gives priority to suggestions for the most recent meal. The suggestion unit executes an algorithm that uses AI to evaluate the time of submission of the dishes and determines the priority of the suggestions. For example, the AI ​​can give priority to suggestions for the most recent meal and provide the latest meal plan. The AI ​​can also give priority to suggestions for dishes that match a specific event. Furthermore, the AI ​​can adjust the priority of the suggestions based on the user's schedule. For example, the AI ​​can analyze the user's schedule and suggest dishes at the optimal timing. This allows the suggestion unit to determine the priority of the suggestions based on the time of submission of the dishes. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the time of submission of the dishes to the AI ​​and cause the AI ​​to execute the determination of the priority of the suggestions.

[0052] The suggestion unit can adjust the order of suggestions based on the relevance of the dishes when making suggestions. The suggestion unit, for example, preferentially suggests dishes with high relevance. The suggestion unit executes an algorithm that uses AI to evaluate the relevance of dishes and adjusts the order of suggestions. For example, the AI ​​can preferentially suggest dishes with high relevance and provide an important meal plan. The AI ​​can also suggest dishes with low relevance later. Furthermore, the AI ​​can dynamically adjust the order of suggestions based on the relevance of the dishes. For example, the AI ​​can analyze the relevance of the dishes and suggest dishes in an optimal order. This allows the suggestion unit to adjust the order of suggestions based on the relevance of the dishes. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the relevance of the dishes to the AI ​​and cause the AI ​​to adjust the order of suggestions.

[0053] When instructing, the instruction unit can select the optimal instruction method by referring to the past operation history of the cooking robot. For example, the instruction unit gives instructions based on a cooking method that the cooking robot has been successful in the past. The instruction unit executes an algorithm that uses AI to analyze the past operation history of the cooking robot and selects the optimal instruction method. For example, the AI ​​can give instructions based on a cooking method that the cooking robot has been successful in the past, thereby improving the success rate of cooking. The AI ​​can also give instructions to avoid cooking methods that the cooking robot has failed in the past. Furthermore, the AI ​​can analyze the past operation history of the cooking robot and select the optimal instruction method. For example, the AI ​​can analyze past cooking data and give instructions for optimal cooking procedures. This allows the instruction unit to select the optimal instruction method based on the past operation history of the cooking robot. Part or all of the above-mentioned processing in the instruction unit may be performed, for example, using AI, or may be performed without using AI. For example, the instruction unit can input the past operation history of the cooking robot to the AI ​​and cause the AI ​​to select the optimal instruction method.

[0054] The instruction unit can customize the instruction means based on the current state of the cooking robot when giving an instruction. The instruction unit gives efficient instructions based on, for example, the remaining battery level of the cooking robot. The instruction unit executes an algorithm that evaluates the current state of the cooking robot using AI and customizes the instruction means. For example, the AI ​​can analyze the remaining battery level of the cooking robot and give efficient instructions. The AI ​​can also give optimal operation instructions based on the current position of the cooking robot. Furthermore, the AI ​​can also customize instructions in real time based on sensor information of the cooking robot. For example, the AI ​​can analyze sensor information of the cooking robot and give optimal operation instructions. This allows the instruction unit to customize the instruction means based on the current state of the cooking robot. Part or all of the above-mentioned processing in the instruction unit may be performed, for example, using AI, or may be performed without using AI. For example, the instruction unit can input the current state of the cooking robot to the AI ​​and cause the AI ​​to customize the instruction means.

[0055] The instruction unit can select an optimal instruction method based on the geographical position information of the cooking robot when instructing. For example, when the cooking robot is in the kitchen, the instruction unit performs an optimal operation instruction in the kitchen. The instruction unit executes an algorithm that evaluates the geographical position information of the cooking robot using AI and selects an optimal instruction method. For example, when the cooking robot is in the kitchen, the AI ​​can perform an optimal operation instruction in the kitchen. In addition, when the cooking robot is in an ingredient storage area, the AI ​​can also perform an instruction to take out ingredients. Furthermore, when the cooking robot is in a cooking area, the AI ​​can also perform instructions for cooking procedures. For example, the AI ​​can analyze the position information of the cooking robot and perform an optimal operation instruction. Thereby, the instruction unit can select an optimal instruction method based on the geographical position information of the cooking robot. Part or all of the above-mentioned processing in the instruction unit may be performed using, for example, AI, or may be performed without using AI. For example, the instruction unit can input the geographical position information of the cooking robot to AI and cause the AI ​​to select an optimal instruction method.

[0056] The instruction unit can analyze the social media activity of the cooking robot and suggest a means of instruction when giving instructions. For example, the instruction unit gives instructions based on a recipe shared by the cooking robot on social media. The instruction unit executes an algorithm that uses AI to analyze the social media activity of the cooking robot and suggest a means of instruction. For example, the AI ​​can give instructions to the cooking robot based on a recipe shared by the cooking robot on social media to improve the success rate of cooking. The AI ​​can also give instructions to the cooking robot based on a cooking method that has a high rating on social media. Furthermore, the AI ​​can also give instructions to the cooking robot based on trends on social media. For example, the AI ​​can analyze trends on social media and give instructions for optimal cooking procedures. This allows the instruction unit to suggest a means of instruction based on the social media activity of the cooking robot. A part or all of the above-mentioned processing in the instruction unit may be performed using, for example, AI, or may be performed without using AI. For example, the instruction unit can input social media activity data of the cooking robot to the AI ​​and cause the AI ​​to execute the suggestion of a means of instruction.

[0057] The cooking unit can select the optimal cooking method by analyzing the user's past meal history when cooking. The cooking unit, for example, cooks based on the cooking method of the user's favorite food in the past. The cooking unit executes an algorithm that analyzes the past meal history using AI and selects the optimal cooking method. For example, the AI ​​can cook based on the cooking method of the user's favorite food in the past, thereby improving the user's satisfaction. The AI ​​can also cook so as to avoid the cooking method of the food that the user avoided in the past. Furthermore, the AI ​​can analyze the user's past meal history and select the optimal cooking method. For example, the AI ​​can analyze past meal data and select the optimal cooking procedure. This allows the cooking unit to select the optimal cooking method based on the past meal history. Some or all of the above-mentioned processing in the cooking unit may be performed using, for example, AI, or may be performed without using AI. For example, the cooking unit can input the past meal history into the AI ​​and have the AI ​​select the optimal cooking method.

[0058] The cooking unit can customize the cooking means based on the user's current health condition when cooking. For example, if the user is tired, the cooking unit cooks a nutritious dish. The cooking unit executes an algorithm to evaluate the user's current health condition using AI and customize the cooking means. For example, the AI ​​can analyze the user's fatigue state and cook a nutritious dish. In addition, the AI ​​can also cook a balanced dish when the user is in a healthy state. Furthermore, the AI ​​can cook a dish that is easy to digest when the user is in poor health. For example, the AI ​​can analyze the user's physical condition and select a dish that is easy to digest. This allows the cooking unit to customize the cooking means based on the current health condition. Part or all of the above-mentioned processing in the cooking unit may be performed, for example, using AI, or may be performed without using AI. For example, the cooking unit can input the user's current health condition to the AI ​​and have the AI ​​execute the customization of the cooking means.

[0059] The cooking unit can select the optimal cooking method based on the geographical location information of the user when cooking. For example, when the user is at home, the cooking unit selects a cooking method that matches the kitchen facilities at home. The cooking unit executes an algorithm that evaluates the geographical location information of the user using AI and selects the optimal cooking method. For example, when the user is at home, the AI ​​can select a cooking method that matches the kitchen facilities at home. In addition, when the user is out, the AI ​​can also select a simple cooking method. Furthermore, when the user is in a specific location, the AI ​​can also select a cooking method that is suitable for that location. For example, the AI ​​can analyze the user's location information and select the optimal cooking procedure. This allows the cooking unit to select the optimal cooking method based on the user's geographical location information. Part or all of the above-mentioned processing in the cooking unit may be performed, for example, using AI, or may be performed without using AI. For example, the cooking unit can input the user's geographical location information to the AI ​​and cause the AI ​​to select the optimal cooking method.

[0060] The cooking unit can analyze the social media activity of the user and suggest cooking means when cooking. The cooking unit, for example, cooks based on a recipe shared by the user on social media. The cooking unit executes an algorithm that uses AI to analyze the social media activity of the user and suggests cooking means. For example, the AI ​​can cook based on a recipe shared by the user on social media to improve the success rate of cooking. The AI ​​can also cook based on a cooking method that the user has received high ratings on social media. Furthermore, the AI ​​can cook based on trends on social media by the user. For example, the AI ​​can analyze trends on social media and suggest optimal cooking procedures. This allows the cooking unit to suggest cooking means based on the social media activity of the user. Some or all of the above-mentioned processing in the cooking unit may be performed, for example, using AI, or may be performed without using AI. For example, the cooking unit can input the social media activity data of the user into the AI ​​and have the AI ​​execute the suggestion of cooking means.

[0061] The providing unit can select the optimal providing method by referring to the user's past meal history at the time of providing. The providing unit provides, for example, based on a providing method that the user has preferred in the past. The providing unit executes an algorithm that analyzes the past meal history using AI and selects the optimal providing method. For example, the AI ​​can provide based on a providing method that the user has preferred in the past, thereby improving the user's satisfaction. The AI ​​can also provide so as to avoid a providing method that the user has avoided in the past. Furthermore, the AI ​​can also analyze the user's past meal history and select the optimal providing method. For example, the AI ​​can analyze past meal data and select the optimal providing procedure. This allows the providing unit to select the optimal providing method based on the past meal history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the past meal history to the AI ​​and cause the AI ​​to select the optimal providing method.

[0062] The providing unit can customize the means of provision based on the current health condition of the user at the time of provision. For example, when the user is tired, the providing unit provides in a relaxing environment. The providing unit executes an algorithm to evaluate the current health condition of the user using AI and customize the means of provision. For example, the AI ​​can analyze the fatigue state of the user and provide in a relaxing environment. In addition, when the user is in a healthy state, the AI ​​can also select a balanced method of provision. Furthermore, when the user is in poor health, the AI ​​can also provide dishes that are easy to digest. For example, the AI ​​can analyze the user's physical condition and select dishes that are easy to digest. This allows the providing unit to customize the means of provision based on the current health condition. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the current health condition of the user to the AI ​​and cause the AI ​​to execute customization of the means of provision.

[0063] The providing unit can select the optimal providing method based on the geographical location information of the user at the time of providing. For example, when the user is at home, the providing unit selects a providing method that matches the home environment. The providing unit executes an algorithm that evaluates the geographical location information of the user using AI and selects the optimal providing method. For example, when the user is at home, the AI ​​can select a providing method that matches the home environment. In addition, when the user is out, the AI ​​can also select a simple providing method. Furthermore, when the user is in a specific location, the AI ​​can also select a providing method that is suitable for that location. For example, the AI ​​can analyze the user's location information and select the optimal providing procedure. Thereby, the providing unit can select the optimal providing method based on the user's geographical location information. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information to the AI ​​and cause the AI ​​to select the optimal providing method.

[0064] The providing unit can analyze the social media activity of the user and suggest a means of provision at the time of provision. The providing unit provides, for example, based on a provision method shared by the user on social media. The providing unit executes an algorithm that uses AI to analyze the social media activity of the user and suggests a means of provision. For example, the AI ​​can provide based on a provision method shared by the user on social media, thereby improving the success rate of the provision. The AI ​​can also provide based on a provision method that the user has highly rated on social media. Furthermore, the AI ​​can provide based on trends on social media by the user. For example, the AI ​​can analyze social media trends and suggest an optimal provision procedure. This allows the providing unit to suggest a means of provision based on the social media activity of the user. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the social media activity data of the user to the AI ​​and cause the AI ​​to execute a suggestion of a means of provision.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The health management cooking system may further include a feedback unit. The feedback unit collects feedback from the user on the provided food and transmits it to the analysis unit. For example, the user may provide feedback on the taste and texture of the food. The feedback unit may also collect data on the user's post-meal health condition (e.g., feeling full, digestive state). Furthermore, the feedback unit may provide data for the suggestion unit to improve the next dish suggestion based on the user's feedback. This allows the health management cooking system to utilize the user's feedback to make more personalized dish suggestions.

[0067] The health management cooking system may further include a notification unit. The notification unit notifies the user of the health condition and cooking progress. For example, if an abnormality is detected in the user's health data, the notification unit may send an alert to the user. The notification unit may also notify the user when cooking preparation is complete. Furthermore, the notification unit may also remind the user of meal timings based on the user's schedule. In this way, the health management cooking system may assist the user in efficiently managing health management and meal timings.

[0068] The health management cooking system may further include a learning unit. The learning unit learns to improve the performance of the entire system based on the user's health data and feedback. For example, the learning unit may optimize the proposed algorithm based on the user's changes in health condition and dietary preferences. The learning unit may also utilize data from other users to grasp broader health management trends and use them to improve the system. Furthermore, the learning unit may propose an individualized health management plan based on the user's long-term health goals. This allows the health management cooking system to provide optimal support according to the user's health condition.

[0069] The health management cooking system may further include an entertainment unit. The entertainment unit provides content for the user to enjoy cooking. For example, the entertainment unit may deliver the cooking process by live streaming, allowing the user to observe the progress of cooking in real time. The entertainment unit may also provide cooking-related quizzes and games, allowing the user to enjoy health management. Furthermore, the entertainment unit may provide music and video content according to the user's preferences, making mealtimes more enjoyable. In this way, the health management cooking system may not only support the user's health management, but also provide entertainment for enjoying mealtimes.

[0070] The health management cooking system may further include a communication unit. The communication unit provides a platform for users to share information with other users and experts and receive advice. For example, the communication unit allows users to post questions about their health conditions and diet and receive feedback from other users and nutritionists. The communication unit also allows users to participate in group discussions about health management and exchange information. Furthermore, the communication unit may provide a chat function for users to receive individual advice from experts. In this way, the health management cooking system can provide a communication means for users to share information with other people and receive support.

[0071] The process flow of the first embodiment will be briefly described below.

[0072] Step 1: The collection unit collects the user's health data. The user's health data includes heart rate, blood pressure, body temperature, etc. The collection unit collects data from a smart watch, a fitness tracker, a smart scale, or a blood glucose monitor. For example, a smart watch collects heart rate and number of steps, a fitness tracker collects exercise amount and calorie consumption, a smart scale collects weight and body fat percentage, and a blood glucose monitor collects blood glucose level data. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses AI to analyze the data and understand the user's health condition. The AI ​​executes an algorithm to evaluate the user's health condition based on the collected data. For example, it analyzes heart rate and blood pressure data to evaluate the user's cardiovascular health condition. Step 3: The suggestion unit suggests suitable dishes based on the analysis results obtained by the analysis unit. The suggestion unit uses AI to suggest dishes and executes algorithms to select suitable dishes based on the user's health condition, medical history, allergy information, and preferences. For example, if the user has high blood pressure, it will suggest low-salt dishes. Step 4: The instruction unit instructs the cooking robot to cook the dish suggested by the suggestion unit. The instruction unit uses AI to instruct the cooking robot and executes an algorithm to instruct the preparation of ingredients and cooking steps based on the recipe of the suggested dish. For example, it instructs how to cut ingredients and the cooking time.

[0073] (Example 2) The health management cooking system according to the embodiment of the present invention is a system that collects health data of a user, proposes appropriate dishes based on the data, and has a cooking robot cook the dishes. This system can accommodate a wide range of users, including those who need care, those with physical disabilities, and those who are not good at cooking. For example, health data is collected from a smart watch, a fitness tracker, a smart scale, a blood glucose monitor, etc., and an AI analyzes the data. Based on the analysis results, dishes are proposed according to the user's health condition, disease history, allergy information, and preferences. The proposed dishes are instructed to a cooking robot, which prepares ingredients based on a recipe and cooks the dishes. The cooked dishes are provided to the user. In addition, the collected data is shared around the world and can be used by other users. As a result, the health management cooking system can propose appropriate dishes based on the user's health data, and the cooking robot can cook the dishes.

[0074] The health care cooking system according to the embodiment includes a collection unit, an analysis unit, a suggestion unit, and an instruction unit. The collection unit collects health data of the user. The health data of the user includes, for example, heart rate, blood pressure, body temperature, etc., but is not limited to such examples. The collection unit collects data from, for example, a smart watch, a fitness tracker, a smart scale, or a blood glucose monitor. The smart watch can collect data such as heart rate and number of steps. The fitness tracker can collect data such as amount of exercise and calorie consumption. The smart scale can collect data such as weight and body fat percentage. The blood glucose monitor can collect blood glucose data. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, AI to grasp the health condition of the user. The AI ​​executes an algorithm to evaluate the health condition of the user based on the collected data. For example, the AI ​​can analyze data of heart rate and blood pressure to evaluate the cardiovascular health condition of the user. The suggestion unit suggests suitable dishes based on the analysis results obtained by the analysis unit. The suggestion unit suggests dishes using, for example, AI. The AI ​​executes an algorithm to select suitable dishes based on the user's health condition, disease history, allergy information, and preferences. For example, if the user has high blood pressure, the AI ​​can suggest dishes with low salt content. The instruction unit instructs the cooking robot to cook the dishes suggested by the suggestion unit. The instruction unit gives instructions to the cooking robot using, for example, AI. The AI ​​executes an algorithm to instruct the cooking robot on the preparation of ingredients and cooking procedures based on the recipe of the proposed dish. For example, the AI ​​can instruct the cooking robot on how to cut ingredients and the cooking time. As a result, the health management cooking system according to the embodiment suggests suitable dishes based on the user's health data, and the cooking robot can cook the dishes.

[0075] The collection unit collects health data of the user. The health data of the user includes, but is not limited to, for example, heart rate, blood pressure, and body temperature. The collection unit collects data from, for example, a smart watch, a fitness tracker, a smart scale, or a blood glucose monitor. The smart watch can collect data such as heart rate and number of steps. Specifically, the smart watch measures the heart rate using an optical heart rate sensor and counts the number of steps using an acceleration sensor. These data are transmitted to the collection unit via Bluetooth or Wi-Fi. The fitness tracker can collect data such as the amount of exercise and the amount of calories burned. The fitness tracker uses a built-in acceleration sensor and gyro sensor to analyze the user's exercise pattern and calculate the calories burned. The smart scale can collect data such as weight and body fat percentage. The smart scale measures the body fat percentage using an electrical impedance method and measures the weight with a weight sensor. These data are also transmitted to the collection unit via wireless communication. The blood glucose monitor can collect blood glucose data. The blood glucose monitor collects a small amount of blood from the user's fingertip and measures the blood glucose level with a sensor. This allows the collection unit to collect a wide range of health data from various devices and grasp the user's health condition in real time. Furthermore, the collection unit can centrally manage these data and link them with other systems and departments as necessary. For example, the collected data is stored on a cloud server and made accessible to the analysis unit and proposal unit. In addition, by adjusting the frequency and accuracy of data collection, flexible responses according to specific situations and conditions are possible. This allows the collection unit to collect data efficiently and effectively and improve the performance of the entire system.

[0076] The analysis unit analyzes the data collected by the collection unit. The analysis unit, for example, uses AI to analyze the data and grasp the health condition of the user. The AI ​​executes an algorithm to evaluate the health condition of the user based on the collected data. Specifically, the AI ​​can analyze heart rate and blood pressure data to evaluate the cardiovascular health condition of the user. For example, the AI ​​can analyze heart rate fluctuation patterns and blood pressure trends to detect abnormal patterns. In addition, the AI ​​can analyze body temperature data to detect early signs of fever and hypothermia. Furthermore, the AI ​​can utilize past data and statistical information to analyze long-term health trends and predict the health risks of the user. For example, based on past blood glucose level data, it can evaluate the risk of diabetes and propose early preventive measures. Based on these analysis results, the analysis unit can comprehensively evaluate the health condition of the user and propose necessary measures. Furthermore, the analysis unit can use an anomaly detection algorithm to detect unusual patterns and abnormal data and issue an early warning. As a result, the analysis unit can not only grasp the situation in real time, but also handle long-term health management and anomaly detection, improving the reliability and safety of the entire system.

[0077] The suggestion unit suggests suitable dishes based on the analysis results obtained by the analysis unit. The suggestion unit suggests dishes using, for example, AI. The AI ​​executes an algorithm to select suitable dishes based on the user's health condition, disease history, allergy information, and preferences. Specifically, the AI ​​can suggest nutritionally balanced dishes based on the user's health data. For example, if the user has high blood pressure, it can suggest low-salt dishes. Also, if the user has diabetes, it can suggest low-sugar dishes. Furthermore, the AI ​​can suggest dishes that do not contain allergens by taking into account the user's allergy information. For example, if the user has a nut allergy, it can suggest dishes that do not contain nuts. Also, the AI ​​can suggest dishes that the user likes based on the user's preferences and past meal history. As a result, the suggestion unit can suggest optimal dishes according to the user's health condition and preferences, and support the user's health management. Furthermore, the suggestion unit provides recipes and cooking procedures for the suggested dishes, and supports the user so that the user can easily cook the dishes. As a result, the suggestion unit can comprehensively support the user's health management and promote a healthy diet.

[0078] The instruction unit instructs the cooking robot to cook the dish proposed by the suggestion unit. The instruction unit instructs the cooking robot using, for example, AI. The AI ​​executes an algorithm that instructs the cooking robot on the preparation of ingredients and cooking procedures based on the recipe of the proposed dish. Specifically, the AI ​​can instruct the cooking robot on how to cut ingredients and the cooking time. For example, the AI ​​can instruct the cooking robot to cut vegetables into small pieces and grill meat at an appropriate temperature. The AI ​​can also instruct the cooking robot on the amount of seasonings and the timing of adding them. This allows the instruction unit to accurately reproduce the proposed dish and provide it to the user. Furthermore, the instruction unit can monitor the operation of the cooking robot in real time and make adjustments as necessary. For example, if an abnormality occurs during cooking, the AI ​​can respond immediately and issue new instructions to the cooking robot. Furthermore, the instruction unit can improve and optimize the cooking process by recording the operation log of the cooking robot and analyzing it later. This allows the instruction unit to realize efficient and high-quality cooking and provide healthy and delicious dishes to the user.

[0079] The collection unit can collect data from a smart watch, a fitness tracker, a smart scale, or a blood glucose monitor. The collection unit collects data on heart rate and steps using, for example, a smart watch. The smart watch is worn on the user's wrist and can collect data using a heart rate sensor and an acceleration sensor. The collection unit can also collect data on the amount of exercise and the amount of calories burned using a fitness tracker. The fitness tracker is worn on the user's wrist or waist and can collect data using an acceleration sensor and a gyro sensor. The collection unit can also collect data on weight and body fat percentage using a smart scale. The smart scale can measure weight and body fat percentage by having the user step on the scale and collect data. The collection unit can also collect blood glucose data using a blood glucose monitor. The blood glucose monitor can measure blood glucose levels using a blood sample of the user and collect data. This allows the collection unit to collect health data from various devices. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data obtained from a smartwatch into an AI and have the AI ​​analyze the data.

[0080] The analysis unit can analyze the collected data and grasp the health condition of the user. For example, the analysis unit analyzes the collected heart rate and blood pressure data and evaluates the cardiovascular health condition of the user. The analysis unit executes an algorithm that analyzes the data using AI and evaluates the health condition of the user. For example, the AI ​​can analyze the fluctuation of the heart rate and the range of blood pressure and evaluate the cardiovascular health condition of the user. The analysis unit can also analyze the collected data of weight and body fat percentage and evaluate the body composition of the user. For example, the AI ​​can analyze the fluctuation of weight and the change in the body fat percentage and evaluate the body composition of the user. Furthermore, the analysis unit can analyze the collected blood glucose level data and evaluate the blood glucose control state of the user. For example, the AI ​​can analyze the fluctuation and average value of the blood glucose level and evaluate the blood glucose control state of the user. This allows the analysis unit to accurately grasp the health condition of the user. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data to AI and have the AI ​​execute the analysis of the data.

[0081] The suggestion unit can suggest dishes based on the user's illness history and allergy information. The suggestion unit suggests suitable dishes based on, for example, the user's illness history and allergy information. The suggestion unit executes an algorithm that suggests dishes using AI. For example, if the user has high blood pressure, the AI ​​can suggest low-salt dishes. Also, if the user has diabetes, the AI ​​can suggest low-carb dishes. Furthermore, if the user has allergies, the AI ​​can suggest dishes that do not contain allergens. For example, if the user has a nut allergy, the AI ​​can suggest dishes that do not contain nuts. This allows the suggestion unit to suggest dishes according to the user's health condition. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's illness history and allergy information into the AI ​​and have the AI ​​execute the suggestion of suitable dishes.

[0082] The suggestion unit can suggest dishes according to the user's preferences. The suggestion unit, for example, suggests suitable dishes based on the user's preferences. The suggestion unit executes an algorithm that suggests dishes using AI. For example, if the user likes Japanese food, the AI ​​can suggest Japanese dishes. Also, if the user likes Western food, the AI ​​can suggest Western dishes. Furthermore, if the user likes a specific ingredient, the AI ​​can suggest dishes using that ingredient. For example, if the user likes chicken, the AI ​​can suggest dishes using chicken. This allows the suggestion unit to suggest dishes according to the user's preferences. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's preferences to the AI ​​and have the AI ​​execute the suggestion of suitable dishes.

[0083] The cooking unit can prepare ingredients and cook based on a recipe. The cooking unit, for example, prepares ingredients and cooks based on a recipe. The cooking unit executes an algorithm that uses AI to cook. For example, the AI ​​can instruct how to cut ingredients and the cooking time based on a recipe. The AI ​​can also instruct the cooking robot on the preparation of ingredients and the cooking procedure. Furthermore, the AI ​​can monitor the temperature and time during cooking and perform appropriate cooking. For example, if the temperature during cooking is not appropriate, the AI ​​can instruct the cooking robot to adjust the temperature. This allows the cooking unit to accurately cook based on the recipe. Some or all of the above-mentioned processing in the cooking unit may be performed using, for example, AI, or may be performed without using AI. For example, the cooking unit can input a recipe to the AI ​​and have the AI ​​execute cooking instructions.

[0084] The serving unit can serve the cooked food to the user. The serving unit, for example, serves the cooked food quickly. The serving unit executes an algorithm for serving using AI. For example, the AI ​​can instruct the timing for serving the cooked food to the user. The AI ​​can also monitor the temperature and presentation at the time of serving and provide the food appropriately. Furthermore, the AI ​​can select the optimal serving method by considering the user's situation at the time of serving. For example, the AI ​​can check whether the user is ready to eat and provide the food at an appropriate timing. This allows the serving unit to quickly serve the cooked food. Some or all of the above-mentioned processing in the serving unit may be performed, for example, using AI, or may be performed without using AI. For example, the serving unit can input the timing for serving the cooked food to the AI ​​and cause the AI ​​to execute the serving instruction.

[0085] The collection unit can share the collected data worldwide and make it useful to other users. The collection unit, for example, shares the collected data through a cloud service. The collection unit executes an algorithm for sharing data using AI. For example, the AI ​​can appropriately share the collected data based on privacy settings. The AI ​​can also set the data sharing range and share the data with a specific user or group. Furthermore, the AI ​​can select a data sharing method and provide an optimal sharing means. For example, the AI ​​can ensure security by encrypting and sharing the data. This allows the collection unit to share the collected data worldwide and make it useful to other users. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the collected data into AI and have the AI ​​execute the sharing of the data.

[0086] The collection unit can estimate the user's emotion and adjust the timing of data collection based on the estimated emotion of the user. For example, when the user is feeling stressed, the collection unit adjusts to collect data during a time period when the user is relaxed. The collection unit estimates the user's emotion using an emotion estimation algorithm. For example, the collection unit can analyze the user's facial expression using facial expression recognition technology to estimate the emotion. The collection unit can also analyze the tone and speed of the user's voice using voice analysis technology to estimate the emotion. Furthermore, the collection unit can collect biodata (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the collection unit can calculate an emotion score based on the fluctuation of the heart rate. This allows the collection unit to adjust the timing of data collection according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the user's emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0087] The collection unit can analyze the user's past health data and select the optimal data collection method. For example, the collection unit analyzes the user's past heart rate data and collects data during a time period when the heart rate is stable. The collection unit executes an algorithm that uses AI to analyze past data and selects the optimal data collection method. For example, the AI ​​can analyze past heart rate data and identify a time period when the heart rate is stable. The AI ​​can also analyze past blood glucose level data and collect data during a time period when the blood glucose level is stable. Furthermore, the AI ​​can analyze past sleep data and adjust to avoid data collection during sleep. For example, the AI ​​can analyze past sleep data and identify a time period when data collection during sleep is avoided. This allows the collection unit to select the optimal data collection method based on the past health data. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input past health data to AI and cause AI to select the optimal data collection method.

[0088] The collection unit can perform filtering based on the user's current activity status and environment when collecting data. For example, when the user is exercising, the collection unit collects only exercise data and filters other data. The collection unit executes an algorithm that filters data using AI. For example, the AI ​​can analyze the user's current activity status and preferentially collect data during exercise. In addition, when the user is resting, the AI ​​can preferentially collect heart rate and sleep data and filter other data. Furthermore, when the user is out, the AI ​​can collect related data based on location information and filter other data. For example, the AI ​​can analyze the user's location information and preferentially collect data while out. This allows the collection unit to filter data according to the user's activity status and environment. Part or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's activity status data to AI and have the AI ​​perform data filtering.

[0089] The collection unit can estimate the user's emotion and determine the priority of data to be collected based on the estimated emotion of the user. For example, when the user is feeling stressed, the collection unit preferentially collects stress-related data. The collection unit estimates the user's emotion using an emotion estimation algorithm. For example, the collection unit can analyze the user's facial expression using facial expression recognition technology to estimate the emotion. The collection unit can also analyze the tone and speed of the user's voice using voice analysis technology to estimate the emotion. Furthermore, the collection unit can collect biometric data (heart rate and electrodermal activity) using a sensor and estimate the emotion using an emotion estimation algorithm. For example, the collection unit can calculate an emotion score based on the fluctuation of the heart rate. This allows the collection unit to determine the priority of data to be collected according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the user's emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0090] The collection unit can collect highly relevant data preferentially based on the geographical location information of the user when collecting data. For example, when the user is in a gym, the collection unit preferentially collects exercise data. The collection unit executes an algorithm that uses AI to analyze geographical location information and preferentially collects highly relevant data. For example, the AI ​​can analyze the user's location information and preferentially collect exercise data when the user is in a gym. In addition, the AI ​​can also preferentially collect relaxation-related data when the user is at home. Furthermore, the AI ​​can also preferentially collect data related to the user based on the location information when the user is out. For example, the AI ​​can analyze the user's location information and preferentially collect data when the user is out. This allows the collection unit to preferentially collect highly relevant data based on the geographical location information. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to AI and cause AI to collect highly relevant data.

[0091] The collection unit can analyze the social media activity of the user at the time of data collection and collect related data. For example, when the user posts on social media about exercise, the collection unit preferentially collects exercise data. The collection unit executes an algorithm that uses AI to analyze social media activity and collects related data. For example, the AI ​​can analyze the user's social media posts and preferentially collect exercise data when there are many posts on exercise. In addition, the AI ​​can also preferentially collect food-related data when the user posts on social media about food. In addition, the AI ​​can also preferentially collect stress-related data when the user posts on social media about stress. For example, the AI ​​can analyze the user's social media posts and preferentially collect stress-related data when there are many posts on stress. This allows the collection unit to collect related data based on social media activity. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity data into the AI ​​and cause the AI ​​to collect related data.

[0092] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the estimated emotion of the user. For example, when the user is feeling stressed, the analysis unit provides a simple and highly visible analysis result. The analysis unit estimates the user's emotion using an emotion estimation algorithm. For example, the analysis unit can analyze the user's facial expression using facial expression recognition technology to estimate the emotion. The analysis unit can also analyze the tone and speed of the user's voice using voice analysis technology to estimate the emotion. Furthermore, the analysis unit can collect biodata (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on the fluctuation of the heart rate. This allows the analysis unit to adjust the expression method of the analysis according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the user's emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0093] The analysis unit can adjust the level of detail of the analysis based on the importance of the health data during the analysis. The analysis unit performs a detailed analysis on important health data (e.g., heart rate, blood glucose level), for example. The analysis unit executes an algorithm that evaluates the importance of the health data using AI and adjusts the level of detail of the analysis. For example, the AI ​​can analyze data on heart rate and blood glucose level, and perform a detailed analysis on data with high importance. The AI ​​can also perform a simplified analysis on general health data (e.g., number of steps, calorie consumption). Furthermore, the AI ​​can also prioritize analysis of data with high importance according to the health condition of the user. For example, the AI ​​can evaluate the health condition of the user and prioritize analysis of data with high importance. This allows the analysis unit to adjust the level of detail of the analysis according to the importance of the health data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input health data to AI and cause AI to adjust the level of detail of the analysis.

[0094] The analysis unit can apply different analysis algorithms depending on the category of health data during analysis. For example, the analysis unit applies a heart rate variability analysis algorithm to heart rate data. The analysis unit uses AI to evaluate the category of health data and executes an algorithm that applies an appropriate analysis algorithm. For example, the AI ​​can apply a heart rate variability analysis algorithm to heart rate data and perform detailed analysis. The AI ​​can also apply a blood glucose variability analysis algorithm to blood glucose data. Furthermore, the AI ​​can apply a sleep stage analysis algorithm to sleep data. For example, the AI ​​can analyze sleep data and evaluate sleep stages. This allows the analysis unit to apply an optimal analysis algorithm depending on the category of health data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input health data to AI and cause AI to apply an appropriate analysis algorithm.

[0095] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated emotion of the user. For example, when the user is in a hurry, the analysis unit provides a short and to the point analysis result. The analysis unit estimates the user's emotion using an emotion estimation algorithm. For example, the analysis unit can analyze the user's facial expression using facial expression recognition technology to estimate the emotion. The analysis unit can also analyze the tone and speed of the user's voice using voice analysis technology to estimate the emotion. Furthermore, the analysis unit can collect biodata (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on the fluctuation of the heart rate. This allows the analysis unit to adjust the length of the analysis according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the user's emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0096] The analysis unit can determine the priority of the analysis based on the collection time of the data at the time of analysis. For example, the analysis unit preferentially analyzes the most recently collected data. The analysis unit executes an algorithm that evaluates the collection time of the data using AI and determines the priority of the analysis. For example, the AI ​​can preferentially analyze the most recently collected data and evaluate the latest health condition. In addition, the AI ​​can also analyze the current data while referring to the past data. Furthermore, the AI ​​can also preferentially analyze the data collected in a specific period. For example, the AI ​​can analyze the data collected in a specific period and evaluate the health condition in that period. This allows the analysis unit to determine the priority of the analysis based on the collection time of the data. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the collection time of the data to the AI ​​and cause the AI ​​to execute the determination of the priority of the analysis.

[0097] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit preferentially analyzes highly relevant data. The analysis unit executes an algorithm that evaluates the relevance of the data using AI and adjusts the order of analysis. For example, the AI ​​can preferentially analyze highly relevant data and evaluate important health conditions. The AI ​​can also analyze less relevant data later. Furthermore, the AI ​​can dynamically adjust the order of analysis based on the relevance of the data. For example, the AI ​​can analyze the relevance of the data and analyze the data in an optimal order. This allows the analysis unit to adjust the order of analysis based on the relevance of the data. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data to the AI ​​and cause the AI ​​to adjust the order of analysis.

[0098] The suggestion unit can estimate the user's emotion and adjust the expression method of the suggestion based on the estimated emotion of the user. For example, when the user is feeling stressed, the suggestion unit makes a simple and highly visible suggestion. The suggestion unit estimates the user's emotion using an emotion estimation algorithm. For example, the suggestion unit can analyze the user's facial expression using facial expression recognition technology to estimate the emotion. The suggestion unit can also analyze the tone and speed of the user's voice using voice analysis technology to estimate the emotion. Furthermore, the suggestion unit can collect biodata (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the suggestion unit can calculate an emotion score based on the fluctuation of the heart rate. This allows the suggestion unit to adjust the expression method of the suggestion according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit may input emotion data of a user to a generation AI and cause the generation AI to estimate the emotion.

[0099] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the dish when making a suggestion. For example, the suggestion unit makes a detailed suggestion for an important dish (e.g., a dish for a special event). The suggestion unit executes an algorithm that evaluates the importance of the dish using AI and adjusts the level of detail of the suggestion. For example, the AI ​​can make a detailed suggestion for a dish for a special event. Also, the AI ​​can make a simplified suggestion for a general dish (e.g., an everyday meal). Furthermore, the AI ​​can preferentially suggest dishes with high importance according to the health condition of the user. For example, the AI ​​can evaluate the health condition of the user and preferentially suggest dishes with high importance. This allows the suggestion unit to adjust the level of detail of the suggestion according to the importance of the dish. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the importance of the dish to the AI ​​and cause the AI ​​to adjust the level of detail of the suggestion.

[0100] The suggestion unit can apply different suggestion algorithms depending on the category of cuisine when making a suggestion. For example, the suggestion unit applies a suggestion algorithm dedicated to Japanese cuisine to Japanese cuisine. The suggestion unit executes an algorithm that evaluates the category of cuisine using AI and applies an appropriate suggestion algorithm. For example, the AI ​​can apply a suggestion algorithm dedicated to Japanese cuisine to Japanese cuisine and make detailed suggestions. The AI ​​can also apply a suggestion algorithm dedicated to Western cuisine to Western cuisine. Furthermore, the AI ​​can also apply a suggestion algorithm dedicated to Chinese cuisine to Chinese cuisine. For example, the AI ​​can apply a suggestion algorithm dedicated to Chinese cuisine to Chinese cuisine and make detailed suggestions. This allows the suggestion unit to apply an optimal suggestion algorithm depending on the category of cuisine. Part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input a cuisine category to the AI ​​and cause the AI ​​to apply an appropriate suggestion algorithm.

[0101] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. For example, when the user is in a hurry, the suggestion unit makes a short and to the point suggestion. The suggestion unit estimates the user's emotion using an emotion estimation algorithm. For example, the suggestion unit can analyze the user's facial expression using facial expression recognition technology to estimate the emotion. The suggestion unit can also analyze the tone and speed of the user's voice using voice analysis technology to estimate the emotion. Furthermore, the suggestion unit can collect biodata (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the suggestion unit can calculate an emotion score based on the fluctuation of the heart rate. This allows the suggestion unit to adjust the length of the suggestion according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit may input emotion data of a user to a generation AI and cause the generation AI to estimate the emotion.

[0102] The suggestion unit can determine the priority of the suggestions based on the time of submission of the dishes at the time of suggestion. For example, the suggestion unit gives priority to suggestions for the most recent meal. The suggestion unit executes an algorithm that uses AI to evaluate the time of submission of the dishes and determines the priority of the suggestions. For example, the AI ​​can give priority to suggestions for the most recent meal and provide the latest meal plan. The AI ​​can also give priority to suggestions for dishes that match a specific event. Furthermore, the AI ​​can adjust the priority of the suggestions based on the user's schedule. For example, the AI ​​can analyze the user's schedule and suggest dishes at the optimal timing. This allows the suggestion unit to determine the priority of the suggestions based on the time of submission of the dishes. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the time of submission of the dishes to the AI ​​and cause the AI ​​to execute the determination of the priority of the suggestions.

[0103] The suggestion unit can adjust the order of suggestions based on the relevance of the dishes when making suggestions. The suggestion unit, for example, preferentially suggests dishes with high relevance. The suggestion unit executes an algorithm that uses AI to evaluate the relevance of dishes and adjusts the order of suggestions. For example, the AI ​​can preferentially suggest dishes with high relevance and provide an important meal plan. The AI ​​can also suggest dishes with low relevance later. Furthermore, the AI ​​can dynamically adjust the order of suggestions based on the relevance of the dishes. For example, the AI ​​can analyze the relevance of the dishes and suggest dishes in an optimal order. This allows the suggestion unit to adjust the order of suggestions based on the relevance of the dishes. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the relevance of the dishes to the AI ​​and cause the AI ​​to adjust the order of suggestions.

[0104] The instruction unit can estimate the user's emotion and adjust the instruction method based on the estimated user's emotion. For example, when the user is feeling stressed, the instruction unit gives a simple and highly visible instruction. The instruction unit estimates the user's emotion using an emotion estimation algorithm. For example, the instruction unit can analyze the user's facial expression using facial expression recognition technology to estimate the emotion. The instruction unit can also analyze the tone and speed of the user's voice using voice analysis technology to estimate the emotion. Furthermore, the instruction unit can collect biodata (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the instruction unit can calculate an emotion score based on the fluctuation of the heart rate. This allows the instruction unit to adjust the instruction method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the instruction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the instruction unit can input emotion data of a user to the generation AI and cause the generation AI to estimate the emotion.

[0105] When instructing, the instruction unit can select the optimal instruction method by referring to the past operation history of the cooking robot. For example, the instruction unit gives instructions based on a cooking method that the cooking robot has been successful in the past. The instruction unit executes an algorithm that uses AI to analyze the past operation history of the cooking robot and selects the optimal instruction method. For example, the AI ​​can give instructions based on a cooking method that the cooking robot has been successful in the past, thereby improving the success rate of cooking. The AI ​​can also give instructions to avoid cooking methods that the cooking robot has failed in the past. Furthermore, the AI ​​can analyze the past operation history of the cooking robot and select the optimal instruction method. For example, the AI ​​can analyze past cooking data and give instructions for optimal cooking procedures. This allows the instruction unit to select the optimal instruction method based on the past operation history of the cooking robot. Part or all of the above-mentioned processing in the instruction unit may be performed, for example, using AI, or may be performed without using AI. For example, the instruction unit can input the past operation history of the cooking robot to the AI ​​and cause the AI ​​to select the optimal instruction method.

[0106] The instruction unit can customize the instruction means based on the current state of the cooking robot when giving an instruction. The instruction unit gives efficient instructions based on, for example, the remaining battery level of the cooking robot. The instruction unit executes an algorithm that evaluates the current state of the cooking robot using AI and customizes the instruction means. For example, the AI ​​can analyze the remaining battery level of the cooking robot and give efficient instructions. The AI ​​can also give optimal operation instructions based on the current position of the cooking robot. Furthermore, the AI ​​can also customize instructions in real time based on sensor information of the cooking robot. For example, the AI ​​can analyze sensor information of the cooking robot and give optimal operation instructions. This allows the instruction unit to customize the instruction means based on the current state of the cooking robot. Part or all of the above-mentioned processing in the instruction unit may be performed, for example, using AI, or may be performed without using AI. For example, the instruction unit can input the current state of the cooking robot to the AI ​​and cause the AI ​​to customize the instruction means.

[0107] The instruction unit can estimate the user's emotion and determine the priority of the instruction based on the estimated emotion of the user. For example, when the user is stressed, the instruction unit prioritizes the instruction of a dish that is relaxing. The instruction unit estimates the user's emotion using an emotion estimation algorithm. For example, the instruction unit can analyze the user's facial expression using facial expression recognition technology to estimate the emotion. The instruction unit can also analyze the tone and speed of the user's voice using voice analysis technology to estimate the emotion. Furthermore, the instruction unit can collect biodata (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the instruction unit can calculate an emotion score based on the fluctuation of the heart rate. This allows the instruction unit to determine the priority of the instruction according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI. A part or all of the above-mentioned processing in the instruction unit may be performed using, for example, AI, or may be performed without using AI. For example, the instruction unit can input the user's emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0108] The instruction unit can select an optimal instruction method based on the geographical position information of the cooking robot when instructing. For example, when the cooking robot is in the kitchen, the instruction unit performs an optimal operation instruction in the kitchen. The instruction unit executes an algorithm that evaluates the geographical position information of the cooking robot using AI and selects an optimal instruction method. For example, when the cooking robot is in the kitchen, the AI ​​can perform an optimal operation instruction in the kitchen. In addition, when the cooking robot is in an ingredient storage area, the AI ​​can also perform an instruction to take out ingredients. Furthermore, when the cooking robot is in a cooking area, the AI ​​can also perform instructions for cooking procedures. For example, the AI ​​can analyze the position information of the cooking robot and perform an optimal operation instruction. Thereby, the instruction unit can select an optimal instruction method based on the geographical position information of the cooking robot. Part or all of the above-mentioned processing in the instruction unit may be performed using, for example, AI, or may be performed without using AI. For example, the instruction unit can input the geographical position information of the cooking robot to AI and cause the AI ​​to select an optimal instruction method.

[0109] The instruction unit can analyze the social media activity of the cooking robot and suggest a means of instruction when giving instructions. For example, the instruction unit gives instructions based on a recipe shared by the cooking robot on social media. The instruction unit executes an algorithm that uses AI to analyze the social media activity of the cooking robot and suggest a means of instruction. For example, the AI ​​can give instructions to the cooking robot based on a recipe shared by the cooking robot on social media to improve the success rate of cooking. The AI ​​can also give instructions to the cooking robot based on a cooking method that has a high rating on social media. Furthermore, the AI ​​can also give instructions to the cooking robot based on trends on social media. For example, the AI ​​can analyze trends on social media and give instructions for optimal cooking procedures. This allows the instruction unit to suggest a means of instruction based on the social media activity of the cooking robot. A part or all of the above-mentioned processing in the instruction unit may be performed using, for example, AI, or may be performed without using AI. For example, the instruction unit can input social media activity data of the cooking robot to the AI ​​and cause the AI ​​to execute the suggestion of a means of instruction.

[0110] The cooking unit can estimate the user's emotion and adjust the cooking method based on the estimated emotion of the user. For example, when the user is stressed, the cooking unit selects a cooking method that allows the user to relax. The cooking unit estimates the user's emotion using an emotion estimation algorithm. For example, the cooking unit can analyze the user's facial expression using facial expression recognition technology to estimate the emotion. The cooking unit can also analyze the tone and speed of the user's voice using voice analysis technology to estimate the emotion. Furthermore, the cooking unit can collect biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the cooking unit can calculate an emotion score based on the fluctuation of the heart rate. This allows the cooking unit to adjust the cooking method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the cooking unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the cooking department can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0111] The cooking unit can select the optimal cooking method by analyzing the user's past meal history when cooking. The cooking unit, for example, cooks based on the cooking method of the user's favorite food in the past. The cooking unit executes an algorithm that analyzes the past meal history using AI and selects the optimal cooking method. For example, the AI ​​can cook based on the cooking method of the user's favorite food in the past, thereby improving the user's satisfaction. The AI ​​can also cook so as to avoid the cooking method of the food that the user avoided in the past. Furthermore, the AI ​​can analyze the user's past meal history and select the optimal cooking method. For example, the AI ​​can analyze past meal data and select the optimal cooking procedure. This allows the cooking unit to select the optimal cooking method based on the past meal history. Some or all of the above-mentioned processing in the cooking unit may be performed using, for example, AI, or may be performed without using AI. For example, the cooking unit can input the past meal history into the AI ​​and have the AI ​​select the optimal cooking method.

[0112] The cooking unit can customize the cooking means based on the user's current health condition when cooking. For example, if the user is tired, the cooking unit cooks a nutritious dish. The cooking unit executes an algorithm to evaluate the user's current health condition using AI and customize the cooking means. For example, the AI ​​can analyze the user's fatigue state and cook a nutritious dish. In addition, the AI ​​can also cook a balanced dish when the user is in a healthy state. Furthermore, the AI ​​can cook a dish that is easy to digest when the user is in poor health. For example, the AI ​​can analyze the user's physical condition and select a dish that is easy to digest. This allows the cooking unit to customize the cooking means based on the current health condition. Part or all of the above-mentioned processing in the cooking unit may be performed, for example, using AI, or may be performed without using AI. For example, the cooking unit can input the user's current health condition to the AI ​​and have the AI ​​execute the customization of the cooking means.

[0113] The cooking unit can estimate the user's emotions and determine cooking priorities based on the estimated user's emotions. For example, when the user is feeling stressed, the cooking unit preferentially cooks dishes that are relaxing. The cooking unit estimates the user's emotions using an emotion estimation algorithm. For example, the cooking unit can analyze the user's facial expressions using facial expression recognition technology to estimate the emotions. The cooking unit can also analyze the tone and speed of the user's voice using voice analysis technology to estimate the emotions. Furthermore, the cooking unit can collect biometric data (heart rate and electrodermal activity) using a sensor and estimate the emotions using an emotion estimation algorithm. For example, the cooking unit can calculate an emotion score based on the fluctuation of the heart rate. This allows the cooking unit to determine cooking priorities according to the user's emotions. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI. Some or all of the above-mentioned processes in the cooking unit may be performed using AI, or may be performed without using AI. For example, the cooking unit may input the user's emotion data to the generation AI and have the generation AI estimate the emotion.

[0114] The cooking unit can select the optimal cooking method based on the geographical location information of the user when cooking. For example, when the user is at home, the cooking unit selects a cooking method that matches the kitchen facilities at home. The cooking unit executes an algorithm that evaluates the geographical location information of the user using AI and selects the optimal cooking method. For example, when the user is at home, the AI ​​can select a cooking method that matches the kitchen facilities at home. In addition, when the user is out, the AI ​​can also select a simple cooking method. Furthermore, when the user is in a specific location, the AI ​​can also select a cooking method that is suitable for that location. For example, the AI ​​can analyze the user's location information and select the optimal cooking procedure. This allows the cooking unit to select the optimal cooking method based on the user's geographical location information. Part or all of the above-mentioned processing in the cooking unit may be performed, for example, using AI, or may be performed without using AI. For example, the cooking unit can input the user's geographical location information to the AI ​​and cause the AI ​​to select the optimal cooking method.

[0115] The cooking unit can analyze the social media activity of the user and suggest cooking means when cooking. The cooking unit, for example, cooks based on a recipe shared by the user on social media. The cooking unit executes an algorithm that uses AI to analyze the social media activity of the user and suggests cooking means. For example, the AI ​​can cook based on a recipe shared by the user on social media to improve the success rate of cooking. The AI ​​can also cook based on a cooking method that the user has received high ratings on social media. Furthermore, the AI ​​can cook based on trends on social media by the user. For example, the AI ​​can analyze trends on social media and suggest optimal cooking procedures. This allows the cooking unit to suggest cooking means based on the social media activity of the user. Some or all of the above-mentioned processing in the cooking unit may be performed, for example, using AI, or may be performed without using AI. For example, the cooking unit can input the social media activity data of the user into the AI ​​and have the AI ​​execute the suggestion of cooking means.

[0116] The providing unit can estimate the user's emotion and adjust the method of providing based on the estimated emotion of the user. For example, when the user is feeling stressed, the providing unit provides the user in a relaxing environment. The providing unit estimates the user's emotion using an emotion estimation algorithm. For example, the providing unit can analyze the user's facial expression using facial expression recognition technology to estimate the emotion. The providing unit can also analyze the tone and speed of the user's voice using voice analysis technology to estimate the emotion. Furthermore, the providing unit can collect biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the providing unit can calculate an emotion score based on the fluctuation of the heart rate. This allows the providing unit to adjust the method of providing according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0117] The providing unit can select the optimal providing method by referring to the user's past meal history at the time of providing. The providing unit provides, for example, based on a providing method that the user has preferred in the past. The providing unit executes an algorithm that analyzes the past meal history using AI and selects the optimal providing method. For example, the AI ​​can provide based on a providing method that the user has preferred in the past, thereby improving the user's satisfaction. The AI ​​can also provide so as to avoid a providing method that the user has avoided in the past. Furthermore, the AI ​​can also analyze the user's past meal history and select the optimal providing method. For example, the AI ​​can analyze past meal data and select the optimal providing procedure. This allows the providing unit to select the optimal providing method based on the past meal history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the past meal history to the AI ​​and cause the AI ​​to select the optimal providing method.

[0118] The providing unit can customize the means of provision based on the current health condition of the user at the time of provision. For example, when the user is tired, the providing unit provides in a relaxing environment. The providing unit executes an algorithm to evaluate the current health condition of the user using AI and customize the means of provision. For example, the AI ​​can analyze the fatigue state of the user and provide in a relaxing environment. In addition, when the user is in a healthy state, the AI ​​can also select a balanced method of provision. Furthermore, when the user is in poor health, the AI ​​can also provide dishes that are easy to digest. For example, the AI ​​can analyze the user's physical condition and select dishes that are easy to digest. This allows the providing unit to customize the means of provision based on the current health condition. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the current health condition of the user to the AI ​​and cause the AI ​​to execute customization of the means of provision.

[0119] The providing unit can estimate the user's emotion and determine the priority of provision based on the estimated emotion of the user. For example, when the user is feeling stressed, the providing unit preferentially provides dishes that are relaxing. The providing unit estimates the user's emotion using an emotion estimation algorithm. For example, the providing unit can analyze the user's facial expression using facial expression recognition technology to estimate the emotion. The providing unit can also analyze the tone and speed of the user's voice using voice analysis technology to estimate the emotion. Furthermore, the providing unit can collect biodata (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the providing unit can calculate an emotion score based on the fluctuation of the heart rate. This allows the providing unit to determine the priority of provision according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input the user's emotion data to the generating AI and cause the generating AI to estimate the emotion.

[0120] The providing unit can select the optimal providing method based on the geographical location information of the user at the time of providing. For example, when the user is at home, the providing unit selects a providing method that matches the home environment. The providing unit executes an algorithm that evaluates the geographical location information of the user using AI and selects the optimal providing method. For example, when the user is at home, the AI ​​can select a providing method that matches the home environment. In addition, when the user is out, the AI ​​can also select a simple providing method. Furthermore, when the user is in a specific location, the AI ​​can also select a providing method that is suitable for that location. For example, the AI ​​can analyze the user's location information and select the optimal providing procedure. Thereby, the providing unit can select the optimal providing method based on the user's geographical location information. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information to the AI ​​and cause the AI ​​to select the optimal providing method.

[0121] The providing unit can analyze the social media activity of the user and suggest a means of provision at the time of provision. The providing unit provides, for example, based on a provision method shared by the user on social media. The providing unit executes an algorithm that uses AI to analyze the social media activity of the user and suggests a means of provision. For example, the AI ​​can provide based on a provision method shared by the user on social media, thereby improving the success rate of the provision. The AI ​​can also provide based on a provision method that the user has highly rated on social media. Furthermore, the AI ​​can provide based on trends on social media by the user. For example, the AI ​​can analyze social media trends and suggest an optimal provision procedure. This allows the providing unit to suggest a means of provision based on the social media activity of the user. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the social media activity data of the user to the AI ​​and cause the AI ​​to execute a suggestion of a means of provision.

[0122] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0123] The health management cooking system may further include a feedback unit. The feedback unit collects feedback from the user on the provided food and transmits it to the analysis unit. For example, the user may provide feedback on the taste and texture of the food. The feedback unit may also collect data on the user's post-meal health condition (e.g., feeling full, digestive state). Furthermore, the feedback unit may provide data for the suggestion unit to improve the next dish suggestion based on the user's feedback. This allows the health management cooking system to utilize the user's feedback to make more personalized dish suggestions.

[0124] The health management cooking system may further include a notification unit. The notification unit notifies the user of the health condition and cooking progress. For example, if an abnormality is detected in the user's health data, the notification unit may send an alert to the user. The notification unit may also notify the user when cooking preparation is complete. Furthermore, the notification unit may also remind the user of meal timings based on the user's schedule. In this way, the health management cooking system may assist the user in efficiently managing health management and meal timings.

[0125] The health management cooking system may further include a learning unit. The learning unit learns to improve the performance of the entire system based on the user's health data and feedback. For example, the learning unit may optimize the proposed algorithm based on the user's changes in health condition and dietary preferences. The learning unit may also utilize data from other users to grasp broader health management trends and use them to improve the system. Furthermore, the learning unit may propose an individualized health management plan based on the user's long-term health goals. This allows the health management cooking system to provide optimal support according to the user's health condition.

[0126] The health management cooking system may further include an entertainment unit. The entertainment unit provides content for the user to enjoy cooking. For example, the entertainment unit may deliver the cooking process by live streaming, allowing the user to observe the progress of cooking in real time. The entertainment unit may also provide cooking-related quizzes and games, allowing the user to enjoy health management. Furthermore, the entertainment unit may provide music and video content according to the user's preferences, making mealtimes more enjoyable. In this way, the health management cooking system may not only support the user's health management, but also provide entertainment for enjoying mealtimes.

[0127] The health management cooking system may further include a communication unit. The communication unit provides a platform for users to share information with other users and experts and receive advice. For example, the communication unit allows users to post questions about their health conditions and diet and receive feedback from other users and nutritionists. The communication unit also allows users to participate in group discussions about health management and exchange information. Furthermore, the communication unit may provide a chat function for users to receive individual advice from experts. In this way, the health management cooking system can provide a communication means for users to share information with other people and receive support.

[0128] In the health management cooking system, the cooking unit can estimate the user's emotions and adjust the cooking speed based on the estimated emotions. For example, if the user is in a hurry, the cooking unit can select a high-speed cooking mode to shorten the cooking time. If the user is relaxed, the cooking unit can select a slow cooking method to maximize the flavor of the ingredients. Furthermore, if the user is stressed, the cooking unit can select a dish using ingredients that have a relaxing effect and play relaxing music during the cooking process. In this way, the health management cooking system can provide an optimal cooking experience according to the user's emotions.

[0129] In the health management cooking system, the suggestion unit can estimate the user's emotions and adjust the method of suggesting dishes based on the estimated emotions. For example, if the user is tired, the suggestion unit can suggest dishes that are simple and easy to make. Also, if the user is energetic, the suggestion unit can suggest new recipes or challenging dishes. Furthermore, if the user is feeling stressed, the suggestion unit can suggest dishes that use ingredients that have a relaxing effect, and can provide relaxing music and images when suggesting dishes. In this way, the health management cooking system can make optimal dish suggestions according to the user's emotions.

[0130] In the health management cooking system, the serving unit can estimate the user's emotions and adjust the way food is served based on the estimated emotions. For example, if the user is feeling stressed, the serving unit can serve food in a relaxing environment. Also, if the user is happy, the serving unit can serve food with a special presentation. Furthermore, if the user is tired, the serving unit can serve food quickly and easily. In this way, the health management cooking system can provide optimal food according to the user's emotions.

[0131] In the health management cooking system, the analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide simple and highly visible analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the user is excited, the analysis unit can provide interactive analysis results so that the user can enjoy manipulating the data. In this way, the health management cooking system can display optimal analysis results according to the user's emotions.

[0132] In the health management cooking system, the collection unit can estimate the user's emotions and adjust the frequency of data collection based on the estimated emotions. For example, when the user is stressed, the collection unit can reduce the frequency of data collection to reduce the burden on the user. Also, when the user is relaxed, the collection unit can increase the frequency of data collection to collect more detailed data. Furthermore, when the user is excited, the collection unit can collect data in real time and immediately grasp changes in the user's emotions. This allows the health management cooking system to optimally collect data according to the user's emotions.

[0133] The process flow of the second embodiment will be briefly described below.

[0134] Step 1: The collection unit collects the user's health data. The user's health data includes heart rate, blood pressure, body temperature, etc. The collection unit collects data from a smart watch, a fitness tracker, a smart scale, or a blood glucose monitor. For example, a smart watch collects heart rate and number of steps, a fitness tracker collects exercise amount and calorie consumption, a smart scale collects weight and body fat percentage, and a blood glucose monitor collects blood glucose level data. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses AI to analyze the data and understand the user's health condition. The AI ​​executes an algorithm to evaluate the user's health condition based on the collected data. For example, it analyzes heart rate and blood pressure data to evaluate the user's cardiovascular health condition. Step 3: The suggestion unit suggests suitable dishes based on the analysis results obtained by the analysis unit. The suggestion unit uses AI to suggest dishes and executes algorithms to select suitable dishes based on the user's health condition, medical history, allergy information, and preferences. For example, if the user has high blood pressure, it will suggest low-salt dishes. Step 4: The instruction unit instructs the cooking robot to cook the dish suggested by the suggestion unit. The instruction unit uses AI to instruct the cooking robot and executes an algorithm to instruct the preparation of ingredients and cooking steps based on the recipe of the suggested dish. For example, it instructs how to cut ingredients and the cooking time.

[0135] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0136] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0137] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0138] For example, the collection unit can collect health data of the user using the camera 42 and microphone 38B of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests suitable dishes based on the analysis results. The instruction unit is realized by the specific processing unit 290 of the data processing device 12 and instructs the cooking robot to make the suggested dishes. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0139] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0140] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0141] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0146] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0149] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0151] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0153] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0154] For example, the collection unit can collect health data of the user using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests suitable dishes based on the analysis results. The instruction unit is realized by the specific processing unit 290 of the data processing device 12 and instructs the cooking robot to make the suggested dishes. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0155] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0156] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0157] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0158] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0159] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0161] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0162] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0163] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0164] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0165] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0166] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0167] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0168] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0169] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0170] For example, the collection unit can collect health data of the user using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and suggests suitable dishes based on the analysis results. The instruction unit is realized by the specific processing unit 290 of the data processing device 12, and instructs the cooking robot to make the suggested dishes. The correspondence between each unit and the device or control unit is not limited to the above example, and various modifications are possible.

[0171] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0172] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0173] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0174] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0175] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0176] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0177] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0178] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0179] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0180] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0181] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0182] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0183] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0184] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0185] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0186] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0187] For example, the collection unit can collect health data of the user using the camera 42 and microphone 238 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests suitable dishes based on the analysis results. The instruction unit is realized by the specific processing unit 290 of the data processing device 12 and instructs the cooking robot to make the suggested dishes. The correspondence between each unit and the device or control unit is not limited to the above example, and various modifications are possible.

[0188] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0189] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0190] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0191] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0192] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0193] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0194] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0195] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0196] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0197] In addition, 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 upon request from the data processing device 12.

[0198] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0199] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0200] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0201] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0202] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0203] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0204] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0205] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0206] (Appendix 1) A collection unit that collects health data of a user; an analysis unit that analyzes the data collected by the collection unit; A suggestion unit that suggests suitable dishes based on the analysis results obtained by the analysis unit; An instruction unit that instructs a cooking robot to cook the dish suggested by the suggestion unit; Equipped A system characterized by: (Appendix 2) The collecting unit includes: Collect data from your smartwatch, fitness tracker, smart scale, or blood glucose monitor 2. The system of claim 1, (Appendix 3) The analysis unit is Analyze the collected data to understand the user's health condition 2. The system of claim 1, (Appendix 4) The suggestion unit, Suggesting dishes based on the user's medical history and allergy information 2. The system of claim 1, (Appendix 5) The suggestion unit, Suggesting dishes based on the user's preferences 2. The system of claim 1, (Appendix 6) The cooking section includes: Prepare ingredients and cook according to recipes 2. The system of claim 1, (Appendix 7) The providing unit is Providing the completed cooked food to the user 2. The system of claim 1, (Appendix 8) The collecting unit includes: Provide specific means for sharing collected data and making it available to others 2. The system of claim 1, (Appendix 9) The collecting unit includes: The system estimates the user's emotions and adjusts the timing of data collection based on the estimated user emotions. 2. The system of claim 1, (Appendix 10) The collecting unit includes: Analyze users' past health data and select data collection methods based on specific criteria 2. The system of claim 1, (Appendix 11) The collecting unit includes: When collecting data, it is filtered based on the user's current activity and environment. 2. The system of claim 1, (Appendix 12) The collecting unit includes: Estimate user emotions and prioritize data to be collected based on the estimated user emotions 2. The system of claim 1, (Appendix 13) The collecting unit includes: When collecting data, prioritize collection of data that is more relevant based on your geographic location 2. The system of claim 1, (Appendix 14) The collecting unit includes: When collecting data, analyze your social media activity and collect relevant data 2. The system of claim 1, (Appendix 15) The analysis unit is Estimate the user's emotions and adjust the way the analysis is expressed based on the estimated user's emotions. 2. The system of claim 1, (Appendix 16) The analysis unit is During analysis, adjust the level of detail based on the importance of the health data 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, analytical algorithms are applied based on specific criteria depending on the category of health data. 2. The system of claim 1, (Appendix 18) The analysis unit is Estimate the user's emotions and adjust the length of the analysis based on the estimated user's emotions. 2. The system of claim 1, (Appendix 19) The analysis unit is During analysis, prioritize analysis based on when the data was collected 2. The system of claim 1, (Appendix 20) The analysis unit is During analysis, adjust the order of analysis based on data relevance 2. The system of claim 1, (Appendix 21) The suggestion unit, Estimate the user's emotions and adjust the way suggestions are presented based on the estimated user's emotions. 2. The system of claim 1, (Appendix 22) The suggestion unit, When making suggestions, adjust the detail of the suggestions based on the importance of the dish 2. The system of claim 1, (Appendix 23) The suggestion unit, When making suggestions, different suggestion algorithms are applied depending on the cuisine category. 2. The system of claim 1, (Appendix 24) The suggestion unit, Estimate the user's emotion and adjust the length of the suggestions based on the estimated user's emotion. 2. The system of claim 1, (Appendix 25) The suggestion unit, When making suggestions, prioritize suggestions based on when the dish was submitted 2. The system of claim 1, (Appendix 26) The suggestion unit, When making suggestions, adjust the order of suggestions based on dish relevance 2. The system of claim 1, (Appendix 27) The instruction unit is Estimate the user's emotions and adjust the method of instructions based on the estimated user's emotions. 2. The system of claim 1, (Appendix 28) The instruction unit is When giving instructions, the system refers to the cooking robot's past operation history to select the optimal instruction method. 2. The system of claim 1, (Appendix 29) The instruction unit is When instructing, customize the instruction method based on the current state of the cooking robot 2. The system of claim 1, (Appendix 30) The instruction unit is Estimate the user's emotions and determine the priority of instructions based on the estimated user's emotions. 2. The system of claim 1, (Appendix 31) The instruction unit is When giving instructions, the optimal instruction method is selected based on the cooking robot's geographical location information. 2. The system of claim 1, (Appendix 32) The instruction unit is When giving instructions, the system analyzes the cooking robot's social media activity to suggest ways to give instructions. 2. The system of claim 1, (Appendix 33) The cooking section includes: Estimate the user's emotions and adjust cooking methods based on the estimated user's emotions 2. The system of claim 1, (Appendix 34) The cooking section includes: When cooking, the system analyzes the user's past meal history to select the optimal cooking method. 2. The system of claim 1, (Appendix 35) The cooking section includes: When cooking, customize cooking methods based on the user's current health status 2. The system of claim 1, (Appendix 36) The cooking section includes: Estimating the user's emotions and determining cooking priorities based on the estimated user's emotions 2. The system of claim 1, (Appendix 37) The cooking section includes: When cooking, the best cooking method is selected based on the user's geographic location 2. The system of claim 1, (Appendix 38) The cooking section includes: When cooking, the app analyzes users' social media activity to suggest cooking methods 2. The system of claim 1, (Appendix 39) The providing unit is Estimate the user's emotions and adjust the method of delivery based on the estimated user's emotions 2. The system of claim 1, (Appendix 40) The providing unit is When providing food, the optimal method of providing the food is selected by referring to the user's past meal history. 2. The system of claim 1, (Appendix 41) The providing unit is At the time of delivery, customize the delivery method based on the user's current health status 2. The system of claim 1, (Appendix 42) The providing unit is Estimate user sentiment and determine priority of offers based on the estimated user sentiment 2. The system of claim 1, (Appendix 43) The providing unit is When providing information, the optimal method of providing the information is selected based on the user's geographic location information. 2. The system of claim 1, (Appendix 44) The providing unit is At the time of subscription, we analyze your social media activity to suggest subscription options 2. The system of claim 1, [Explanation of symbols]

[0207] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection unit that collects health data of a user; an analysis unit that analyzes the data collected by the collection unit; A suggestion unit that suggests suitable dishes based on the analysis results obtained by the analysis unit; An instruction unit that instructs a cooking robot to cook the dish suggested by the suggestion unit; Equipped A system characterized by:

2. The collecting unit includes: Collect data from your smartwatch, fitness tracker, smart scale, or blood glucose monitor 2. The system of claim 1 .

3. The analysis unit is Analyze the collected data to understand the user's health condition 2. The system of claim 1 .

4. The suggestion unit, Suggesting dishes based on the user's medical history and allergy information 2. The system of claim 1 .

5. The suggestion unit, Suggesting dishes based on the user's preferences 2. The system of claim 1 .

6. The collecting unit includes: Provide specific means for sharing collected data and making it available to others 2. The system of claim 1 .

7. The collecting unit includes: The system estimates the user's emotions and adjusts the timing of data collection based on the estimated user emotions.

2. The system of claim 1 .

8. The collecting unit includes: Analyze users' past health data and select data collection methods based on specific criteria 2. The system of claim 1 .

Images