system

Abstract

The system according to this embodiment aims to efficiently manage household chores and schedules, thereby reducing the burden on the user. [Solution] The system according to the embodiment comprises an analysis unit, a cooking unit, a room analysis unit, a cleaning unit, a management unit, and an execution unit. The analysis unit analyzes the stored ingredients. The cooking unit prepares dishes based on the ingredients analyzed by the analysis unit. The room analysis unit analyzes the room. The cleaning unit cleans based on the information analyzed by the room analysis unit. The management unit manages household chores and schedules. The execution unit performs household chores based on the schedule managed by the management unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to efficiently perform housework and schedule management, and the burden on the user is large.

[0005] The system according to the embodiment aims to efficiently perform housework and schedule management and reduce the burden on the user.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a cooking unit, a room analysis unit, a cleaning unit, a management unit, and an execution unit. The analysis unit analyzes stored food ingredients. The cooking unit prepares food based on the food ingredients analyzed by the analysis unit. The room analysis unit analyzes the room. The cleaning unit cleans based on the information analyzed by the room analysis unit. The management unit manages household chores and schedules. The execution unit performs household chores based on the schedule managed by the management unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently manage household chores and schedules, thereby reducing the burden on the user. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The household assistance robot according to an embodiment of the present invention is a robot that learns the user's lifestyle patterns and automatically schedules and performs household chores such as cleaning, laundry, and cooking. This household assistance robot provides the following specific services. First, the AI ​​analyzes stored ingredients and automatically prepares meals. This reduces food waste and provides well-balanced meals. For example, based on the stored ingredients, the AI ​​selects the optimal recipe and cooks. This allows the user to enjoy healthy meals without wasting food. Next, the AI ​​analyzes the room and automatically cleans it in the most optimal way. Because it can efficiently handle multiple cleaning tasks, it can maintain the cleanliness of the room. For example, the AI ​​understands the room layout, calculates the optimal cleaning route, and cleans. This reduces the user's cleaning effort. Furthermore, the AI ​​manages household chores and schedules. Since the user can give instructions by voice, it also functions as a personal assistant robot. For example, if the user instructs, "Do the laundry at 8 AM tomorrow," the AI ​​will receive the instruction and perform the laundry. This allows the user to efficiently manage their household chore schedule. This robot is extremely useful for busy dual-income households, elderly households, and operators of care facilities—people who find it difficult to dedicate time and energy to housework. The generating AI learns the user's lifestyle and proposes an optimal housework schedule and tasks, freeing users from the burden of chores and enabling them to live more efficiently and safely. For example, dual-income households often struggle to balance work and housework, leading to increased mental and physical strain. This robot can reduce the burden of housework, allowing them to concentrate on their work. Elderly households may also find it physically difficult to perform household chores or require continuous support. This robot can improve the quality of life for the elderly by performing these tasks. Furthermore, operators of care facilities face challenges such as labor shortages and cost reduction. Introducing this robot allows for efficient housework and reduced operating costs. Thus, this house-cleaning robot equipped with generating AI is a groundbreaking solution for enriching users' lives and reducing the burden of housework.This allows the household robot to learn the user's lifestyle patterns and automatically schedule and perform household chores.

[0029] The household assistance robot according to this embodiment comprises an analysis unit, a cooking unit, a room analysis unit, a cleaning unit, a management unit, and an execution unit. The analysis unit analyzes the stored food ingredients. For example, the analysis unit analyzes the components of the stored food ingredients and evaluates their nutritional value. The analysis unit can also evaluate the freshness of the stored food ingredients. Furthermore, the analysis unit can also evaluate the expiration date of the stored food ingredients. For example, the analysis unit analyzes the components of the stored food ingredients using a chemical analyzer and evaluates their nutritional value. The freshness of the stored food ingredients can be evaluated using sensors. The expiration date of the stored food ingredients can be calculated based on the storage conditions. The cooking unit prepares a meal based on the ingredients analyzed by the analysis unit. For example, the cooking unit selects the optimal recipe based on the stored food ingredients and performs the cooking. Furthermore, the cooking unit can provide a well-balanced meal considering the nutritional value of the stored food ingredients. Furthermore, the cooking unit can select the optimal cooking method considering the freshness of the stored food ingredients. For example, the cooking unit selects the optimal recipe based on the stored food ingredients and performs the cooking. It is also possible to provide well-balanced meals by considering the nutritional value of the stored ingredients. It is also possible to select the optimal cooking method by considering the freshness of the stored ingredients. The room analysis unit analyzes the room. For example, the room analysis unit can understand the layout of the room and calculate the optimal cleaning route. The room analysis unit can also evaluate the degree of dirtiness in the room. Furthermore, the room analysis unit can also evaluate the temperature and humidity of the room. For example, the room analysis unit can understand the layout of the room using a 3D scanning device and calculate the optimal cleaning route. The degree of dirtiness in the room can be evaluated using sensors. The temperature and humidity of the room can be evaluated using temperature sensors and humidity sensors. The cleaning unit performs cleaning based on the information analyzed by the room analysis unit. For example, the cleaning unit sets the optimal cleaning route based on the layout of the room and performs cleaning. Furthermore, the cleaning unit can adjust the cleaning frequency based on the degree of dirtiness in the room. Furthermore, the cleaning unit can adjust the cleaning method based on the temperature and humidity of the room. For example, the cleaning unit sets the optimal cleaning route based on the layout of the room and performs cleaning. It can also adjust the cleaning frequency based on the degree of dirtiness in the room.The cleaning method can also be adjusted based on the room temperature and humidity. The management unit manages household chores and schedules. For example, the management unit learns the user's lifestyle and suggests an optimal household chore schedule. The management unit can also receive voice commands from the user and adjust the household chore schedule. Furthermore, the management unit can monitor the progress of household chores and send reminders as needed. For example, the management unit learns the user's lifestyle and suggests an optimal household chore schedule. It can also receive voice commands from the user and adjust the household chore schedule. It can also monitor the progress of household chores and send reminders as needed. The execution unit performs household chores based on the schedule managed by the management unit. For example, the execution unit cleans based on the schedule managed by the management unit. It can also cook based on the schedule managed by the management unit. Furthermore, the execution unit can also do laundry based on the schedule managed by the management unit. For example, the execution unit cleans based on the schedule managed by the management unit. It can also cook based on the schedule managed by the management unit. It can also do laundry based on the schedule managed by the management unit. As a result, the household assistance robot according to this embodiment can learn the user's lifestyle patterns and automatically schedule and execute household chores.

[0030] The Analysis Department analyzes stored food ingredients. Specifically, it uses chemical analyzers to analyze the components of stored food ingredients in detail and measures the content of each component. This allows for an accurate assessment of the nutritional value of the food ingredients. For example, it measures the content of major nutrients such as vitamins, minerals, proteins, lipids, and carbohydrates to collect basic data for providing balanced meals. The Analysis Department also utilizes sensor technology to assess the freshness of stored food ingredients. For example, it uses gas sensors to detect volatile organic compounds emitted from food ingredients, enabling early detection of spoilage. Furthermore, to assess the shelf life of stored food ingredients, it uses algorithms based on storage conditions (temperature, humidity, light intensity, etc.) to predict the rate of deterioration. This allows for the suggestion of the optimal timing for using food ingredients, minimizing food waste. The Analysis Department centrally manages this data and collaborates with other departments to build a foundation for providing optimal housekeeping services.

[0031] The cooking department prepares dishes based on ingredients analyzed by the analysis department. Specifically, it selects the optimal recipe and automates the cooking process based on the nutritional information and freshness information of the stored ingredients. For example, it uses AI to generate recipes to provide well-balanced meals, taking into account the nutritional value of the ingredients. The AI ​​can learn the user's eating history and health status and suggest recipes tailored to individual needs. The cooking department also selects the optimal cooking method considering the freshness of the stored ingredients. For example, fresh ingredients are served raw or lightly cooked, while ingredients that have lost their freshness are cooked using methods such as stewing or grilling. Furthermore, the cooking department uses robotic arms and automated cooking equipment to automate the cooking process. This allows for accurate and efficient execution of cooking steps such as cutting, mixing, and heating ingredients. The cooking department can also keep the cooked dishes warm at the appropriate temperature and serve them to users at the right time. In this way, the cooking department can provide users with high-quality, balanced meals and support a healthy lifestyle.

[0032] The Room Analysis Department analyzes rooms. Specifically, it uses a 3D scanning device to understand the room layout in detail and calculate the optimal cleaning route. The 3D scanning device scans the room's shape and furniture arrangement with high precision and generates a digital map. This allows the cleaning department to optimize the cleaning route for efficient cleaning. The Room Analysis Department also uses multiple sensors to evaluate the degree of dirtiness in the room. For example, optical sensors and infrared sensors are used to detect dirt on the floor and furniture surfaces and quantify the degree of dirtiness. Furthermore, temperature and humidity sensors are used to evaluate the room's temperature and humidity. This allows for real-time monitoring of the room's environmental conditions and provides data to select the optimal cleaning method. Based on this data, the Room Analysis Department proposes an optimal cleaning schedule to maintain the room's cleanliness and works in cooperation with the cleaning department to achieve efficient cleaning.

[0033] The cleaning department performs cleaning based on information analyzed by the room analysis department. Specifically, it sets the optimal cleaning route based on the room layout and then performs the cleaning. The cleaning department uses cleaning equipment such as robotic vacuum cleaners and automatic mops to efficiently clean the entire room. For example, the robotic vacuum cleaner cleans the shortest route while avoiding obstacles based on a digital map of the room. The cleaning department can also adjust the cleaning frequency based on the degree of dirtiness in the room. For example, it can achieve efficient cleaning by cleaning heavily soiled areas more frequently and relatively clean areas less frequently. Furthermore, the cleaning department can adjust the cleaning method based on the room temperature and humidity. For example, it can maintain an optimal environment in the room by using the drying function when humidity is high and the humidifying function when humidity is low. By making full use of these functions, the cleaning department can keep the room clean and comfortable at all times.

[0034] The management department manages household chores and schedules. Specifically, it uses AI to learn the user's lifestyle and propose the optimal chore schedule. The AI ​​analyzes the user's past behavioral data and chore history to automatically generate a schedule tailored to the user's lifestyle. The management department can also receive voice commands from the user and adjust the chore schedule in real time. For example, if the user says, "I want the cleaning done tomorrow morning," the management department receives the command, updates the schedule, and sends instructions to the execution department. Furthermore, the management department can monitor the progress of chores and send reminders as needed. For example, it can send a notification to the user when the laundry is finished, letting them know when to take out the laundry. Through these functions, the management department can efficiently support the user's life and reduce the burden of household chores.

[0035] The execution unit performs household chores based on a schedule managed by the management unit. Specifically, it automatically performs chores such as cleaning, cooking, and laundry according to the schedule set by the management unit. For example, in the case of cleaning, the execution unit sends instructions to the cleaning unit to start cleaning the room. In the case of cooking, the execution unit sends instructions to the cooking unit to start cooking based on the stored ingredients. In the case of laundry, the execution unit operates the washing machine to wash and dry the laundry. To perform these chores efficiently, the execution unit cooperates with each department to support the smooth progress of the chores. The execution unit can also monitor the progress of the chores in real time and make adjustments as needed. For example, if cleaning is completed earlier than scheduled, it will optimize the overall schedule by starting the next chore earlier. In this way, the execution unit can efficiently support the user's life and reduce the burden of household chores.

[0036] The system includes a voice receiver that accepts voice commands. The voice receiver accepts voice commands from the user. For example, if the user says, "Start cleaning," the voice receiver accepts that voice command. The voice receiver can also accept if the user says, "Start cooking." Furthermore, the voice receiver can also accept if the user says, "Start washing." For example, the voice receiver receives the user's voice command via a microphone and analyzes it using speech recognition technology. The speech recognition technology can convert the voice command into text data using, for example, a generative AI. This allows the user to give commands by voice. Some or all of the above processing in the voice receiver may be performed using, for example, AI, or not using AI. For example, the voice receiver can input the user's voice command into a generative AI and have the generative AI perform the analysis of the voice command.

[0037] The voice reception unit receives voice commands from the user and transmits them to the management unit. For example, if the user says, "Start cleaning," the voice reception unit receives the voice command and transmits it to the management unit. The voice reception unit can also receive voice commands from the user, such as "Start cooking," and transmit them to the management unit. Furthermore, the voice reception unit can also receive voice commands from the user, such as "Start washing," and transmit them to the management unit. For example, the voice reception unit receives the user's voice command via a microphone, analyzes it using voice recognition technology, and transmits the results to the management unit. The voice recognition technology can, for example, use a generative AI to convert the voice command into text data and transmit that text data to the management unit. This makes it possible to manage household chore schedules by transmitting voice commands to the management unit. Some or all of the above processing in the voice reception unit may be performed using AI, for example, or without AI. For example, the voice reception unit can input the user's voice command into a generative AI, have the generative AI analyze the voice command, and transmit the results to the management unit.

[0038] The cleaning unit sets the optimal cleaning route based on the room layout and performs the cleaning. For example, the cleaning unit can determine the room layout using a 3D scanning device and calculate the optimal cleaning route. The cleaning unit can also evaluate the degree of dirtiness in the room using sensors and set the optimal cleaning route. Furthermore, the cleaning unit can evaluate the temperature and humidity of the room using sensors and set the optimal cleaning route. For example, the cleaning unit can determine the room layout using a 3D scanning device and calculate the optimal cleaning route. It can also evaluate the degree of dirtiness in the room using sensors and set the optimal cleaning route. It can also evaluate the temperature and humidity of the room using sensors and set the optimal cleaning route. This makes efficient cleaning possible by setting the optimal cleaning route based on the room layout. Some or all of the above processes in the cleaning unit may be performed using AI, for example, or without AI. For example, the cleaning unit can input room layout data into a generating AI and have the generating AI calculate the optimal cleaning route.

[0039] The cooking department selects the optimal recipe based on the stored ingredients and prepares the meal. For example, the cooking department provides a well-balanced meal considering the nutritional value of the stored ingredients. The cooking department can also select the optimal cooking method considering the freshness of the stored ingredients. Furthermore, the cooking department can select the optimal recipe considering the expiration date of the stored ingredients. For example, the cooking department provides a well-balanced meal considering the nutritional value of the stored ingredients. It can also select the optimal cooking method considering the freshness of the stored ingredients. It can also select the optimal recipe considering the expiration date of the stored ingredients. This reduces food waste and allows for the provision of well-balanced meals by selecting the optimal recipe based on the stored ingredients and preparing the meal. Some or all of the above processes in the cooking department may be performed using AI, for example, or not. For example, the cooking department can input data on stored ingredients into a generating AI and have the generating AI select the optimal recipe.

[0040] The management department learns the user's lifestyle habits and proposes an optimal household chore schedule. For example, the management department collects and analyzes the user's lifestyle habits as data. It can also propose an optimal household chore schedule based on the user's past household chore history. Furthermore, the management department can learn the user's lifestyle patterns and determine the priority of household chores. For example, the management department collects and analyzes the user's lifestyle habits as data. It can also propose an optimal household chore schedule based on the user's past household chore history. It can also learn the user's lifestyle patterns and determine the priority of household chores. As a result, by learning the user's lifestyle habits and proposing an optimal household chore schedule, the efficiency of household chores is improved. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input the user's lifestyle habit data into a generating AI and have the generating AI propose an optimal household chore schedule.

[0041] The execution unit performs household chores based on a schedule managed by the management unit. For example, the execution unit can clean based on a schedule managed by the management unit. The execution unit can also cook based on a schedule managed by the management unit. Furthermore, the execution unit can also do laundry based on a schedule managed by the management unit. For example, the execution unit can clean based on a schedule managed by the management unit. It can also cook based on a schedule managed by the management unit. It can also do laundry based on a schedule managed by the management unit. This improves the efficiency of household chores by performing them based on a schedule managed by the management unit. Some or all of the above processes in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input schedule data managed by the management unit into a generating AI and have the generating AI perform the household chores.

[0042] The analysis unit monitors the freshness of stored ingredients in real time and suggests the optimal timing for use. For example, the analysis unit monitors the freshness of ingredients with sensors and suggests using them before they spoil. The analysis unit can also record the storage period of ingredients and notify the optimal timing for use. Furthermore, the analysis unit can suggest the optimal recipe based on the freshness information of the ingredients. For example, the analysis unit monitors the freshness of ingredients with sensors and suggests using them before they spoil. It can also record the storage period of ingredients and notify the optimal timing for use. It can also suggest the optimal recipe based on the freshness information of the ingredients. In this way, by monitoring the freshness of stored ingredients in real time, the optimal timing for use can be suggested. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input freshness data of ingredients into a generating AI and have the generating AI suggest the optimal timing for use.

[0043] The analysis department analyzes the nutritional value of ingredients and selects ingredients according to the user's health condition. For example, the analysis department selects ingredients containing necessary nutrients based on the user's health data. The analysis department can also select appropriate ingredients considering the user's allergy information. Furthermore, the analysis department can select ingredients that match the user's weight loss goals. For example, the analysis department selects ingredients containing necessary nutrients based on the user's health data. It can also select appropriate ingredients considering the user's allergy information. It can also select ingredients that match the user's weight loss goals. In this way, by analyzing the nutritional value of ingredients and selecting ingredients according to the user's health condition, a healthy meal can be provided. Some or all of the above processing in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input the user's health data into a generating AI and have the generating AI perform the ingredient selection.

[0044] The analysis department analyzes the origin information of ingredients and prioritizes the use of locally produced ingredients. For example, the analysis department obtains the origin information of ingredients and prioritizes the selection of locally produced ingredients. Furthermore, by using locally produced ingredients, the analysis department can reduce transportation costs. In addition, by using locally produced ingredients, the analysis department can support the local economy. For example, the analysis department obtains the origin information of ingredients and prioritizes the selection of locally produced ingredients. By using locally produced ingredients, transportation costs can be reduced. By using locally produced ingredients, the local economy can be supported. Thus, by analyzing the origin information of ingredients and prioritizing the use of locally produced ingredients, transportation costs can be reduced and the local economy can be supported. Some or all of the above processing in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input the origin information of ingredients into a generating AI and have the generating AI select locally produced ingredients.

[0045] The analysis unit analyzes allergen information of ingredients and selects ingredients suitable for users with allergies. For example, the analysis unit selects appropriate ingredients based on the user's allergy information. The analysis unit can also acquire allergen information of ingredients and suggest recipes suitable for users with allergies. Furthermore, the analysis unit can suggest alternative ingredients based on allergen information. For example, the analysis unit selects appropriate ingredients based on the user's allergy information. It can also acquire allergen information of ingredients and suggest recipes suitable for users with allergies. It can also suggest alternative ingredients based on allergen information. By analyzing allergen information of ingredients and selecting ingredients suitable for users with allergies, allergic reactions can be prevented. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's allergy information into a generating AI and have the generating AI select appropriate ingredients.

[0046] The cooking department optimizes cooking times and prepares meals according to the user's schedule. For example, the cooking department sets the optimal cooking time based on the user's schedule. It can also select recipes that can be prepared quickly if the user is in a hurry. Furthermore, it can select recipes that require more time if the user has ample time. For example, the cooking department sets the optimal cooking time based on the user's schedule. It can also select recipes that can be prepared quickly if the user is in a hurry. It can also select recipes that require more time if the user has ample time. This optimizes cooking times and allows for efficient cooking according to the user's schedule. Some or all of the above processes in the cooking department may be performed using AI, for example, or not. For example, the cooking department can input the user's schedule data into a generating AI and have the generating AI set the optimal cooking time.

[0047] The cooking department analyzes the appearance of the dishes and arranges them to suit the user's preferences. For example, the cooking department arranges the dishes based on the user's preferred colors and shapes. The cooking department can also analyze the user's past preferences and suggest the optimal arrangement. Furthermore, the cooking department can consider the user's cultural background and arrange the dishes in a traditional style. For example, the cooking department arranges the dishes based on the user's preferred colors and shapes. It can also analyze the user's past preferences and suggest the optimal arrangement. It can also consider the user's cultural background and arrange the dishes in a traditional style. By analyzing the appearance of the dishes and arranging them to suit the user's preferences, it is possible to provide more satisfying meals. Some or all of the above processes in the cooking department may be performed using AI, for example, or not. For example, the cooking department can input user preference data into a generating AI and have the generating AI suggest arrangements.

[0048] The cooking department calculates the calories in dishes and provides menus tailored to the user's weight loss goals. For example, the cooking department calculates calories and provides menus based on the user's weight loss goals. It can also analyze the user's past eating history and suggest an optimal calorie menu. Furthermore, the cooking department can provide menus with adjusted calories considering the user's exercise level. For example, the cooking department calculates calories and provides menus based on the user's weight loss goals. It can also analyze the user's past eating history and suggest an optimal calorie menu. It can also provide menus with adjusted calories considering the user's exercise level. In this way, by calculating the calories in dishes and providing menus tailored to the user's weight loss goals, healthy meals can be provided. Some or all of the above processes in the cooking department may be performed using AI, for example, or not using AI. For example, the cooking department can input the user's weight loss goal data into a generating AI and have the generating AI perform calorie calculations and menu suggestions.

[0049] The cooking department considers the cultural background of the cuisine and proposes international dishes tailored to the user's preferences. For example, the cooking department may suggest traditional dishes based on the user's cultural background. It can also analyze the user's past preferences and propose international dishes. Furthermore, the cooking department may consider the user's travel history and propose dishes from countries they have visited. For example, the cooking department may suggest traditional dishes based on the user's cultural background. It can also analyze the user's past preferences and propose international dishes. It can also consider the user's travel history and propose dishes from countries they have visited. This allows for the provision of a wider variety of dishes by considering the cultural background of the cuisine and proposing international dishes tailored to the user's preferences. Some or all of the above processing in the cooking department may be performed using AI, for example, or not. For example, the cooking department can input the user's cultural background data into a generating AI and have the generating AI propose international dishes.

[0050] The room analysis unit monitors the room temperature and humidity and makes suggestions for maintaining an optimal environment. For example, the room analysis unit can monitor the room temperature with a sensor and suggest an optimal temperature. It can also monitor the room humidity with a sensor and suggest an optimal humidity. Furthermore, the room analysis unit can comprehensively analyze the room temperature and humidity to provide a comfortable environment. For example, the room analysis unit can monitor the room temperature with a sensor and suggest an optimal temperature. It can also monitor the room humidity with a sensor and suggest an optimal humidity. It can also comprehensively analyze the room temperature and humidity to provide a comfortable environment. This makes it possible to make suggestions for maintaining an optimal environment by monitoring the room temperature and humidity. Some or all of the above processing in the room analysis unit may be performed using AI, for example, or without AI. For example, the room analysis unit can input room temperature and humidity data into a generating AI and have the generating AI execute a suggestion for an optimal environment.

[0051] The room analysis unit analyzes the furniture arrangement in a room and proposes the optimal layout. For example, the room analysis unit can 3D scan the furniture arrangement in a room and propose the optimal layout. The room analysis unit can also propose the optimal furniture arrangement based on the user's lifestyle patterns. Furthermore, the room analysis unit can propose furniture arrangements that make effective use of the room space. For example, the room analysis unit can 3D scan the furniture arrangement in a room and propose the optimal layout. It can also propose the optimal furniture arrangement based on the user's lifestyle patterns. It can also propose furniture arrangements that make effective use of the room space. In this way, by analyzing the furniture arrangement in a room, the optimal layout can be proposed. Some or all of the above processing in the room analysis unit may be performed using AI, for example, or without AI. For example, the room analysis unit can input the room's furniture arrangement data into a generating AI and have the generating AI propose the optimal layout.

[0052] The room analysis unit analyzes the lighting conditions in a room and proposes the optimal lighting settings. For example, the room analysis unit monitors the lighting conditions in a room using sensors and proposes the optimal lighting settings. The room analysis unit can also propose the optimal lighting settings based on the user's lifestyle patterns. Furthermore, the room analysis unit can automatically adjust the room lighting to provide a comfortable environment. For example, the room analysis unit monitors the lighting conditions in a room using sensors and proposes the optimal lighting settings. It can also propose the optimal lighting settings based on the user's lifestyle patterns. It can also automatically adjust the room lighting to provide a comfortable environment. In this way, by analyzing the lighting conditions in a room, it is possible to propose the optimal lighting settings. Some or all of the above processing in the room analysis unit may be performed using AI, for example, or without AI. For example, the room analysis unit can input room lighting data into a generating AI and have the generating AI execute a proposal for the optimal lighting settings.

[0053] The room analysis unit analyzes the air quality in a room and proposes the optimal way to use the air purifier. For example, the room analysis unit monitors the air quality in a room with sensors and proposes the optimal way to use the air purifier. The room analysis unit can also analyze the air quality in a room and propose the optimal time to replace the filter. Furthermore, the room analysis unit can comprehensively analyze the air quality in a room and provide a comfortable environment. For example, the room analysis unit monitors the air quality in a room with sensors and proposes the optimal way to use the air purifier. It can also analyze the air quality in a room and propose the optimal time to replace the filter. It can also comprehensively analyze the air quality in a room and provide a comfortable environment. In this way, by analyzing the air quality in a room, it is possible to propose the optimal way to use the air purifier. Some or all of the above processing in the room analysis unit may be performed using AI, for example, or without AI. For example, the room analysis unit can input room air quality data into a generating AI and have the generating AI propose the optimal way to use the air purifier.

[0054] The cleaning department optimizes the cleaning tools used during cleaning to perform efficient cleaning. For example, the cleaning department analyzes the degree of dirtiness in a room and selects the most suitable cleaning tools. It can also record the frequency of use of cleaning tools and perform replacement or maintenance at the optimal time. Furthermore, the cleaning department can analyze the types of cleaning tools and suggest the optimal combination for efficient cleaning. For example, the cleaning department analyzes the degree of dirtiness in a room and selects the most suitable cleaning tools. It can also record the frequency of use of cleaning tools and perform replacement or maintenance at the optimal time. It can also analyze the types of cleaning tools and suggest the optimal combination for efficient cleaning. By optimizing the cleaning tools used during cleaning, efficient cleaning becomes possible. Some or all of the above processes in the cleaning department may be performed using AI, for example, or not. For example, the cleaning department can input room dirtiness data into a generating AI and have the generating AI select the most suitable cleaning tools.

[0055] The cleaning unit monitors the progress of cleaning in real time and makes adjustments as needed. For example, the cleaning unit monitors the progress of cleaning with sensors and checks the progress in real time. The cleaning unit can also analyze the progress of cleaning and adjust the cleaning route as needed. Furthermore, the cleaning unit can notify the user of the progress of cleaning and receive instructions as needed. For example, the cleaning unit monitors the progress of cleaning with sensors and checks the progress in real time. It can also analyze the progress of cleaning and adjust the cleaning route as needed. It can also notify the user of the progress of cleaning and receive instructions as needed. This allows for adjustments to be made as needed by monitoring the progress of cleaning in real time. Some or all of the above processes in the cleaning unit may be performed using AI, for example, or not using AI. For example, the cleaning unit can input cleaning progress data into a generating AI and have the generating AI perform monitoring and adjustment of the progress.

[0056] The cleaning department optimizes the types of detergents used during cleaning to perform environmentally friendly cleaning. For example, the cleaning department analyzes the degree of dirtiness in a room and selects the most suitable detergent. It can also optimize the amount of detergent used to perform environmentally friendly cleaning. Furthermore, the cleaning department can analyze the ingredients of detergents and select detergents suitable for users with allergies. For example, the cleaning department analyzes the degree of dirtiness in a room and selects the most suitable detergent. It can also optimize the amount of detergent used to perform environmentally friendly cleaning. It can also analyze the ingredients of detergents and select detergents suitable for users with allergies. This makes environmentally friendly cleaning possible by optimizing the types of detergents used during cleaning. Some or all of the above processes in the cleaning department may be performed using AI, for example, or not. For example, the cleaning department can input room dirtiness data into a generating AI and have the generating AI select the most suitable detergent.

[0057] The cleaning unit automatically sorts the waste generated during cleaning, promoting recycling. For example, the cleaning unit identifies the type of waste using sensors and sorts it automatically. The cleaning unit can also record the sorting status of the waste and suggest ways to promote recycling. Furthermore, the cleaning unit can notify the user of how to sort the waste, promoting appropriate recycling. For example, the cleaning unit identifies the type of waste using sensors and sorts it automatically. It can also record the sorting status of the waste and suggest ways to promote recycling. It can also notify the user of how to sort the waste, promoting appropriate recycling. In this way, recycling can be promoted by automatically sorting the waste generated during cleaning. Some or all of the above processes in the cleaning unit may be performed using AI, for example, or without AI. For example, the cleaning unit can input waste type data into a generating AI and have the generating AI perform the waste sorting.

[0058] The management department re-evaluates the priority of household chores in real time and proposes an optimal schedule. For example, the management department monitors the progress of household chores in real time and re-evaluates the priority. It can also analyze the importance of household chores and propose an optimal schedule. Furthermore, the management department can notify the user of the progress of household chores and adjust the schedule as needed. For example, the management department monitors the progress of household chores in real time and re-evaluates the priority. It can also analyze the importance of household chores and propose an optimal schedule. It can also notify the user of the progress of household chores and adjust the schedule as needed. This allows the management department to propose an optimal schedule by re-evaluating the priority of household chores in real time. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input household chore progress data into a generating AI and have the generating AI perform the re-evaluation of priorities and schedule proposals.

[0059] The management unit monitors the progress of household chores and sends reminders as needed. For example, the management unit monitors the progress of household chores in real time and sends reminders. The management unit can also analyze the progress of household chores and adjust reminders as needed. Furthermore, the management unit can notify users of the progress of household chores and receive instructions as needed. For example, the management unit monitors the progress of household chores in real time and sends reminders. It can also analyze the progress of household chores and adjust reminders as needed. It can also notify users of the progress of household chores and receive instructions as needed. This allows the management unit to send reminders as needed by monitoring the progress of household chores. Some or all of the above processes in the management unit may be performed using AI, for example, or not using AI. For example, the management unit can input household chore progress data into a generating AI and have the generating AI send reminders.

[0060] The management department visualizes the progress of household chores and reports the progress to the user. For example, the management department visualizes the progress of household chores using graphs and charts and reports it to the user. The management department can also display the progress of household chores in real time and notify the user. Furthermore, the management department can analyze the progress of household chores and provide progress reports as needed. For example, the management department visualizes the progress of household chores using graphs and charts and reports it to the user. It can also display the progress of household chores in real time and notify the user. It can also analyze the progress of household chores and provide progress reports as needed. In this way, progress reports can be provided to the user by visualizing the progress of household chores. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input household chore progress data into a generating AI and have the generating AI perform visualization and progress reporting.

[0061] The management department shares the household chore schedule with other family members and works together to perform household chores. For example, the management department can share the household chore schedule with family members and work together to perform household chores. The management department can also notify family members of the progress of household chores and work together to perform them. Furthermore, the management department can adjust the household chore schedule and work together with family members to perform household chores efficiently. For example, the management department can share the household chore schedule with family members and work together to perform household chores. It can also notify family members of the progress of household chores and work together to perform them. It can also adjust the household chore schedule and work together with family members to perform household chores efficiently. In this way, by sharing the household chore schedule with other family members, household chores can be performed collaboratively. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input household chore schedule data into a generating AI and have the generating AI perform sharing and adjustment.

[0062] The execution unit customizes household chores to suit the user's preferences. For example, the execution unit can perform customized cleaning based on the user's preferred cleaning method. It can also perform customized cooking based on the user's preferred cooking method. Furthermore, it can perform customized laundry based on the user's preferred laundry method. For example, the execution unit can perform customized cleaning based on the user's preferred cleaning method. It can also perform customized cooking based on the user's preferred cooking method. It can also perform customized laundry based on the user's preferred laundry method. This allows for more satisfying household chore execution by customizing the process to the user's preferences. Some or all of the above-described processes in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input user preference data into a generating AI and have the generating AI make customization suggestions.

[0063] The execution unit monitors the status of household chores in real time and makes adjustments as needed. For example, the execution unit monitors the status of household chores using sensors and checks the progress in real time. The execution unit can also analyze the status of household chores and adjust the execution method as needed. Furthermore, the execution unit can notify the user of the status of household chores and receive instructions as needed. For example, the execution unit monitors the status of household chores using sensors and checks the progress in real time. It can also analyze the status of household chores and adjust the execution method as needed. It can also notify the user of the status of household chores and receive instructions as needed. This allows for adjustments to be made as needed by monitoring the status of household chores in real time. Some or all of the above processes in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input household chore execution data into a generating AI and have the generating AI monitor and adjust the progress.

[0064] The execution unit optimizes energy consumption and performs household chores in an environmentally friendly manner. For example, the execution unit proposes methods for optimizing energy consumption when performing household chores. The execution unit can also use environmentally friendly detergents and cleaning tools when performing household chores. Furthermore, the execution unit can propose schedules to minimize energy consumption when performing household chores. For example, the execution unit proposes methods for optimizing energy consumption when performing household chores. It can also use environmentally friendly detergents and cleaning tools when performing household chores. It can also propose schedules to minimize energy consumption when performing household chores. This makes it possible to perform household chores in an environmentally friendly way by optimizing energy consumption. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input energy consumption data into a generating AI and have the generating AI execute optimization suggestions.

[0065] The execution unit works in conjunction with other home appliances to perform household chores efficiently. For example, the execution unit can work in conjunction with other home appliances to clean efficiently. The execution unit can also work in conjunction with other home appliances to cook efficiently. Furthermore, the execution unit can work in conjunction with other home appliances to do laundry efficiently. For example, the execution unit can work in conjunction with other home appliances to clean efficiently. It can also work in conjunction with other home appliances to cook efficiently. It can also work in conjunction with other home appliances to do laundry efficiently. This makes it possible to perform household chores efficiently by coordinating with other home appliances. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input data from other home appliances into a generating AI and have the generating AI execute suggestions for coordination.

[0066] The voice reception unit learns the user's speech patterns to improve the accuracy of voice instruction recognition. For example, the voice reception unit analyzes the user's speech patterns to improve the accuracy of voice instruction recognition. The voice reception unit can also record the user's speech patterns to improve the accuracy of voice instruction recognition. Furthermore, the voice reception unit can suggest the optimal method for receiving voice instructions based on the user's speech patterns. For example, the voice reception unit analyzes the user's speech patterns to improve the accuracy of voice instruction recognition. It can also record the user's speech patterns to improve the accuracy of voice instruction recognition. It can also suggest the optimal method for receiving voice instructions based on the user's speech patterns. In this way, the accuracy of voice instruction recognition can be improved by learning the user's speech patterns. Some or all of the above processing in the voice reception unit may be performed using AI, for example, or without AI. For example, the voice reception unit can input the user's speech pattern data into a generating AI and have the generating AI perform speech pattern learning.

[0067] The voice reception unit analyzes the history of voice commands and makes suggestions tailored to the user's preferences. For example, the voice reception unit analyzes the user's past voice command history and makes optimal suggestions. It can also propose a household chore schedule tailored to the user's preferences based on the user's voice command history. Furthermore, the voice reception unit can analyze the user's voice command history and propose the most suitable household chore tasks. For example, the voice reception unit analyzes the user's past voice command history and makes optimal suggestions. It can also propose a household chore schedule tailored to the user's preferences based on the user's voice command history. It can also analyze the user's voice command history and propose the most suitable household chore tasks. In this way, by analyzing the history of voice commands, it is possible to make suggestions tailored to the user's preferences. Some or all of the above processing in the voice reception unit may be performed using AI, for example, or not using AI. For example, the voice reception unit can input voice command history data into a generating AI and have the generating AI perform history analysis and suggestions.

[0068] The voice reception unit improves recognition accuracy by removing background noise when it receives voice commands. For example, the voice reception unit removes background noise in real time when it receives voice commands. The voice reception unit can also improve recognition accuracy using noise cancellation technology. Furthermore, the voice reception unit can analyze the background noise and suggest the optimal noise reduction method. For example, the voice reception unit removes background noise in real time when it receives voice commands. It can also improve recognition accuracy using noise cancellation technology. It can also analyze the background noise and suggest the optimal noise reduction method. This improves the recognition accuracy of voice commands by removing background noise. Some or all of the above processing in the voice reception unit may be performed using AI, for example, or without AI. For example, the voice reception unit can input background noise data into a generating AI and have the generating AI perform noise reduction.

[0069] The voice reception unit provides the optimal response when it receives a voice command, taking into account the user's device information. For example, the voice reception unit provides the optimal voice response based on the user's device information. The voice reception unit can also analyze the user's device information and propose the optimal method for receiving voice commands. Furthermore, the voice reception unit can configure the optimal settings for receiving voice commands based on the user's device information. For example, the voice reception unit provides the optimal voice response based on the user's device information. It can also analyze the user's device information and propose the optimal method for receiving voice commands. It can also configure the optimal settings for receiving voice commands based on the user's device information. This allows the voice reception unit to provide the optimal voice response by taking the user's device information into consideration. Some or all of the above processing in the voice reception unit may be performed using AI, for example, or without AI. For example, the voice reception unit can input the user's device information into a generating AI and have the generating AI perform the task of providing the optimal response.

[0070] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0071] Housekeeping robots can also be equipped with a health management unit that monitors the user's health. This unit can, for example, measure the user's heart rate and blood pressure using sensors, and monitor their health in real time. Furthermore, it can suggest appropriate exercise and rest based on the user's health data. In addition, the health management unit can adjust the housework schedule according to the user's health condition. For example, if the user is tired, the health management unit can prioritize less strenuous tasks. This allows for housework to be performed while considering the user's health.

[0072] Housekeeping robots can also be equipped with a lifestyle pattern learning unit that learns the user's lifestyle patterns and proposes an optimal chore schedule. For example, the lifestyle pattern learning unit can analyze the user's past chore history and suggest an optimal schedule. Furthermore, the lifestyle pattern learning unit can collect and analyze the user's lifestyle habits as data. In addition, the lifestyle pattern learning unit can learn the user's lifestyle patterns and determine the priority of chores. This improves the efficiency of household chores by suggesting an optimal chore schedule based on the user's lifestyle patterns.

[0073] Housekeeping robots can also be equipped with a health management unit that takes the user's health condition into consideration and proposes an optimal housework schedule. For example, the health management unit proposes an appropriate housework schedule based on the user's health data. Furthermore, the health management unit can adjust the priority of housework according to the user's health condition. In addition, the health management unit can monitor the user's health condition in real time and adjust the housework schedule as needed. This makes it possible to perform housework while taking the user's health condition into consideration.

[0074] Housekeeping robots can also be equipped with a lifestyle pattern learning unit that learns the user's lifestyle patterns and proposes an optimal chore schedule. For example, the lifestyle pattern learning unit can analyze the user's past chore history and suggest an optimal schedule. Furthermore, the lifestyle pattern learning unit can collect and analyze the user's lifestyle habits as data. In addition, the lifestyle pattern learning unit can learn the user's lifestyle patterns and determine the priority of chores. This improves the efficiency of household chores by suggesting an optimal chore schedule based on the user's lifestyle patterns.

[0075] Housekeeping robots can also be equipped with a health management unit that takes the user's health condition into consideration and proposes an optimal housework schedule. For example, the health management unit proposes an appropriate housework schedule based on the user's health data. Furthermore, the health management unit can adjust the priority of housework according to the user's health condition. In addition, the health management unit can monitor the user's health condition in real time and adjust the housework schedule as needed. This makes it possible to perform housework while taking the user's health condition into consideration.

[0076] The following briefly describes the processing flow for example form 1.

[0077] Step 1: The analysis department analyzes the stored food ingredients. For example, they analyze the components of the stored food ingredients using a chemical analyzer and evaluate their nutritional value. The freshness of the stored food ingredients is evaluated using sensors, and the expiration date is calculated based on the storage conditions. Step 2: The cooking department prepares dishes based on the ingredients analyzed by the analysis department. For example, they select the optimal recipe based on the preserved ingredients and then cook them. They provide well-balanced meals and optimal cooking methods, taking into account the nutritional value and freshness of the preserved ingredients. Step 3: The room analysis unit analyzes the room. For example, it uses a 3D scanning device to understand the room layout and calculate the optimal cleaning route. The degree of dirtiness in the room is evaluated using sensors, and temperature and humidity are evaluated using temperature and humidity sensors. Step 4: The cleaning department performs cleaning based on the information analyzed by the room analysis department. For example, they set the optimal cleaning route based on the room layout and then perform the cleaning. They adjust the cleaning frequency based on the degree of dirtiness in the room and adjust the cleaning method based on temperature and humidity. Step 5: The management department manages household chores and schedules. For example, it learns the user's lifestyle and suggests an optimal chore schedule. It receives voice commands from the user, adjusts the chore schedule, monitors the progress of chores, and sends reminders. Step 6: The execution department performs household chores according to the schedule managed by the management department. For example, they perform cleaning, cooking, and laundry according to the schedule managed by the management department.

[0078] (Example of form 2) The household assistance robot according to an embodiment of the present invention is a robot that learns the user's lifestyle patterns and automatically schedules and performs household chores such as cleaning, laundry, and cooking. This household assistance robot provides the following specific services. First, the AI ​​analyzes stored ingredients and automatically prepares meals. This reduces food waste and provides well-balanced meals. For example, based on the stored ingredients, the AI ​​selects the optimal recipe and cooks. This allows the user to enjoy healthy meals without wasting food. Next, the AI ​​analyzes the room and automatically cleans it in the most optimal way. Because it can efficiently handle multiple cleaning tasks, it can maintain the cleanliness of the room. For example, the AI ​​understands the room layout, calculates the optimal cleaning route, and cleans. This reduces the user's cleaning effort. Furthermore, the AI ​​manages household chores and schedules. Since the user can give instructions by voice, it also functions as a personal assistant robot. For example, if the user instructs, "Do the laundry at 8 AM tomorrow," the AI ​​will receive the instruction and perform the laundry. This allows the user to efficiently manage their household chore schedule. This robot is extremely useful for busy dual-income households, elderly households, and operators of care facilities—people who find it difficult to dedicate time and energy to housework. The generating AI learns the user's lifestyle and proposes an optimal housework schedule and tasks, freeing users from the burden of chores and enabling them to live more efficiently and safely. For example, dual-income households often struggle to balance work and housework, leading to increased mental and physical strain. This robot can reduce the burden of housework, allowing them to concentrate on their work. Elderly households may also find it physically difficult to perform household chores or require continuous support. This robot can improve the quality of life for the elderly by performing these tasks. Furthermore, operators of care facilities face challenges such as labor shortages and cost reduction. Introducing this robot allows for efficient housework and reduced operating costs. Thus, this house-cleaning robot equipped with generating AI is a groundbreaking solution for enriching users' lives and reducing the burden of housework.This allows the household robot to learn the user's lifestyle patterns and automatically schedule and perform household chores.

[0079] The household assistance robot according to this embodiment comprises an analysis unit, a cooking unit, a room analysis unit, a cleaning unit, a management unit, and an execution unit. The analysis unit analyzes the stored food ingredients. For example, the analysis unit analyzes the components of the stored food ingredients and evaluates their nutritional value. The analysis unit can also evaluate the freshness of the stored food ingredients. Furthermore, the analysis unit can also evaluate the expiration date of the stored food ingredients. For example, the analysis unit analyzes the components of the stored food ingredients using a chemical analyzer and evaluates their nutritional value. The freshness of the stored food ingredients can be evaluated using sensors. The expiration date of the stored food ingredients can be calculated based on the storage conditions. The cooking unit prepares a meal based on the ingredients analyzed by the analysis unit. For example, the cooking unit selects the optimal recipe based on the stored food ingredients and performs the cooking. Furthermore, the cooking unit can provide a well-balanced meal considering the nutritional value of the stored food ingredients. Furthermore, the cooking unit can select the optimal cooking method considering the freshness of the stored food ingredients. For example, the cooking unit selects the optimal recipe based on the stored food ingredients and performs the cooking. It is also possible to provide well-balanced meals by considering the nutritional value of the stored ingredients. It is also possible to select the optimal cooking method by considering the freshness of the stored ingredients. The room analysis unit analyzes the room. For example, the room analysis unit can understand the layout of the room and calculate the optimal cleaning route. The room analysis unit can also evaluate the degree of dirtiness in the room. Furthermore, the room analysis unit can also evaluate the temperature and humidity of the room. For example, the room analysis unit can understand the layout of the room using a 3D scanning device and calculate the optimal cleaning route. The degree of dirtiness in the room can be evaluated using sensors. The temperature and humidity of the room can be evaluated using temperature sensors and humidity sensors. The cleaning unit performs cleaning based on the information analyzed by the room analysis unit. For example, the cleaning unit sets the optimal cleaning route based on the layout of the room and performs cleaning. Furthermore, the cleaning unit can adjust the cleaning frequency based on the degree of dirtiness in the room. Furthermore, the cleaning unit can adjust the cleaning method based on the temperature and humidity of the room. For example, the cleaning unit sets the optimal cleaning route based on the layout of the room and performs cleaning. It can also adjust the cleaning frequency based on the degree of dirtiness in the room.The cleaning method can also be adjusted based on the room temperature and humidity. The management unit manages household chores and schedules. For example, the management unit learns the user's lifestyle and suggests an optimal household chore schedule. The management unit can also receive voice commands from the user and adjust the household chore schedule. Furthermore, the management unit can monitor the progress of household chores and send reminders as needed. For example, the management unit learns the user's lifestyle and suggests an optimal household chore schedule. It can also receive voice commands from the user and adjust the household chore schedule. It can also monitor the progress of household chores and send reminders as needed. The execution unit performs household chores based on the schedule managed by the management unit. For example, the execution unit cleans based on the schedule managed by the management unit. It can also cook based on the schedule managed by the management unit. Furthermore, the execution unit can also do laundry based on the schedule managed by the management unit. For example, the execution unit cleans based on the schedule managed by the management unit. It can also cook based on the schedule managed by the management unit. It can also do laundry based on the schedule managed by the management unit. As a result, the household assistance robot according to this embodiment can learn the user's lifestyle patterns and automatically schedule and execute household chores.

[0080] The Analysis Department analyzes stored food ingredients. Specifically, it uses chemical analyzers to analyze the components of stored food ingredients in detail and measures the content of each component. This allows for an accurate assessment of the nutritional value of the food ingredients. For example, it measures the content of major nutrients such as vitamins, minerals, proteins, lipids, and carbohydrates to collect basic data for providing balanced meals. The Analysis Department also utilizes sensor technology to assess the freshness of stored food ingredients. For example, it uses gas sensors to detect volatile organic compounds emitted from food ingredients, enabling early detection of spoilage. Furthermore, to assess the shelf life of stored food ingredients, it uses algorithms based on storage conditions (temperature, humidity, light intensity, etc.) to predict the rate of deterioration. This allows for the suggestion of the optimal timing for using food ingredients, minimizing food waste. The Analysis Department centrally manages this data and collaborates with other departments to build a foundation for providing optimal housekeeping services.

[0081] The cooking department prepares dishes based on ingredients analyzed by the analysis department. Specifically, it selects the optimal recipe and automates the cooking process based on the nutritional information and freshness information of the stored ingredients. For example, it uses AI to generate recipes to provide well-balanced meals, taking into account the nutritional value of the ingredients. The AI ​​can learn the user's eating history and health status and suggest recipes tailored to individual needs. The cooking department also selects the optimal cooking method considering the freshness of the stored ingredients. For example, fresh ingredients are served raw or lightly cooked, while ingredients that have lost their freshness are cooked using methods such as stewing or grilling. Furthermore, the cooking department uses robotic arms and automated cooking equipment to automate the cooking process. This allows for accurate and efficient execution of cooking steps such as cutting, mixing, and heating ingredients. The cooking department can also keep the cooked dishes warm at the appropriate temperature and serve them to users at the right time. In this way, the cooking department can provide users with high-quality, balanced meals and support a healthy lifestyle.

[0082] The Room Analysis Department analyzes rooms. Specifically, it uses a 3D scanning device to understand the room layout in detail and calculate the optimal cleaning route. The 3D scanning device scans the room's shape and furniture arrangement with high precision and generates a digital map. This allows the cleaning department to optimize the cleaning route for efficient cleaning. The Room Analysis Department also uses multiple sensors to evaluate the degree of dirtiness in the room. For example, optical sensors and infrared sensors are used to detect dirt on the floor and furniture surfaces and quantify the degree of dirtiness. Furthermore, temperature and humidity sensors are used to evaluate the room's temperature and humidity. This allows for real-time monitoring of the room's environmental conditions and provides data to select the optimal cleaning method. Based on this data, the Room Analysis Department proposes an optimal cleaning schedule to maintain the room's cleanliness and works in cooperation with the cleaning department to achieve efficient cleaning.

[0083] The cleaning department performs cleaning based on information analyzed by the room analysis department. Specifically, it sets the optimal cleaning route based on the room layout and then performs the cleaning. The cleaning department uses cleaning equipment such as robotic vacuum cleaners and automatic mops to efficiently clean the entire room. For example, the robotic vacuum cleaner cleans the shortest route while avoiding obstacles based on a digital map of the room. The cleaning department can also adjust the cleaning frequency based on the degree of dirtiness in the room. For example, it can achieve efficient cleaning by cleaning heavily soiled areas more frequently and relatively clean areas less frequently. Furthermore, the cleaning department can adjust the cleaning method based on the room temperature and humidity. For example, it can maintain an optimal environment in the room by using the drying function when humidity is high and the humidifying function when humidity is low. By making full use of these functions, the cleaning department can keep the room clean and comfortable at all times.

[0084] The management department manages household chores and schedules. Specifically, it uses AI to learn the user's lifestyle and propose the optimal chore schedule. The AI ​​analyzes the user's past behavioral data and chore history to automatically generate a schedule tailored to the user's lifestyle. The management department can also receive voice commands from the user and adjust the chore schedule in real time. For example, if the user says, "I want the cleaning done tomorrow morning," the management department receives the command, updates the schedule, and sends instructions to the execution department. Furthermore, the management department can monitor the progress of chores and send reminders as needed. For example, it can send a notification to the user when the laundry is finished, letting them know when to take out the laundry. Through these functions, the management department can efficiently support the user's life and reduce the burden of household chores.

[0085] The execution unit performs household chores based on a schedule managed by the management unit. Specifically, it automatically performs chores such as cleaning, cooking, and laundry according to the schedule set by the management unit. For example, in the case of cleaning, the execution unit sends instructions to the cleaning unit to start cleaning the room. In the case of cooking, the execution unit sends instructions to the cooking unit to start cooking based on the stored ingredients. In the case of laundry, the execution unit operates the washing machine to wash and dry the laundry. To perform these chores efficiently, the execution unit cooperates with each department to support the smooth progress of the chores. The execution unit can also monitor the progress of the chores in real time and make adjustments as needed. For example, if cleaning is completed earlier than scheduled, it will optimize the overall schedule by starting the next chore earlier. In this way, the execution unit can efficiently support the user's life and reduce the burden of household chores.

[0086] The system includes a voice receiver that accepts voice commands. The voice receiver accepts voice commands from the user. For example, if the user says, "Start cleaning," the voice receiver accepts that voice command. The voice receiver can also accept if the user says, "Start cooking." Furthermore, the voice receiver can also accept if the user says, "Start washing." For example, the voice receiver receives the user's voice command via a microphone and analyzes it using speech recognition technology. The speech recognition technology can convert the voice command into text data using, for example, a generative AI. This allows the user to give commands by voice. Some or all of the above processing in the voice receiver may be performed using, for example, AI, or not using AI. For example, the voice receiver can input the user's voice command into a generative AI and have the generative AI perform the analysis of the voice command.

[0087] The voice reception unit receives voice commands from the user and transmits them to the management unit. For example, if the user says, "Start cleaning," the voice reception unit receives the voice command and transmits it to the management unit. The voice reception unit can also receive voice commands from the user, such as "Start cooking," and transmit them to the management unit. Furthermore, the voice reception unit can also receive voice commands from the user, such as "Start washing," and transmit them to the management unit. For example, the voice reception unit receives the user's voice command via a microphone, analyzes it using voice recognition technology, and transmits the results to the management unit. The voice recognition technology can, for example, use a generative AI to convert the voice command into text data and transmit that text data to the management unit. This makes it possible to manage household chore schedules by transmitting voice commands to the management unit. Some or all of the above processing in the voice reception unit may be performed using AI, for example, or without AI. For example, the voice reception unit can input the user's voice command into a generative AI, have the generative AI analyze the voice command, and transmit the results to the management unit.

[0088] The cleaning unit sets the optimal cleaning route based on the room layout and performs the cleaning. For example, the cleaning unit can determine the room layout using a 3D scanning device and calculate the optimal cleaning route. The cleaning unit can also evaluate the degree of dirtiness in the room using sensors and set the optimal cleaning route. Furthermore, the cleaning unit can evaluate the temperature and humidity of the room using sensors and set the optimal cleaning route. For example, the cleaning unit can determine the room layout using a 3D scanning device and calculate the optimal cleaning route. It can also evaluate the degree of dirtiness in the room using sensors and set the optimal cleaning route. It can also evaluate the temperature and humidity of the room using sensors and set the optimal cleaning route. This makes efficient cleaning possible by setting the optimal cleaning route based on the room layout. Some or all of the above processes in the cleaning unit may be performed using AI, for example, or without AI. For example, the cleaning unit can input room layout data into a generating AI and have the generating AI calculate the optimal cleaning route.

[0089] The cooking department selects the optimal recipe based on the stored ingredients and prepares the meal. For example, the cooking department provides a well-balanced meal considering the nutritional value of the stored ingredients. The cooking department can also select the optimal cooking method considering the freshness of the stored ingredients. Furthermore, the cooking department can select the optimal recipe considering the expiration date of the stored ingredients. For example, the cooking department provides a well-balanced meal considering the nutritional value of the stored ingredients. It can also select the optimal cooking method considering the freshness of the stored ingredients. It can also select the optimal recipe considering the expiration date of the stored ingredients. This reduces food waste and allows for the provision of well-balanced meals by selecting the optimal recipe based on the stored ingredients and preparing the meal. Some or all of the above processes in the cooking department may be performed using AI, for example, or not. For example, the cooking department can input data on stored ingredients into a generating AI and have the generating AI select the optimal recipe.

[0090] The management department learns the user's lifestyle habits and proposes an optimal household chore schedule. For example, the management department collects and analyzes the user's lifestyle habits as data. It can also propose an optimal household chore schedule based on the user's past household chore history. Furthermore, the management department can learn the user's lifestyle patterns and determine the priority of household chores. For example, the management department collects and analyzes the user's lifestyle habits as data. It can also propose an optimal household chore schedule based on the user's past household chore history. It can also learn the user's lifestyle patterns and determine the priority of household chores. As a result, by learning the user's lifestyle habits and proposing an optimal household chore schedule, the efficiency of household chores is improved. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input the user's lifestyle habit data into a generating AI and have the generating AI propose an optimal household chore schedule.

[0091] The execution unit performs household chores based on a schedule managed by the management unit. For example, the execution unit can clean based on a schedule managed by the management unit. The execution unit can also cook based on a schedule managed by the management unit. Furthermore, the execution unit can also do laundry based on a schedule managed by the management unit. For example, the execution unit can clean based on a schedule managed by the management unit. It can also cook based on a schedule managed by the management unit. It can also do laundry based on a schedule managed by the management unit. This improves the efficiency of household chores by performing them based on a schedule managed by the management unit. Some or all of the above processes in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input schedule data managed by the management unit into a generating AI and have the generating AI perform the household chores.

[0092] The analysis unit estimates the user's emotions and adjusts the method of analyzing ingredients based on the estimated emotions. For example, if the user is stressed, the analysis unit will prioritize analyzing ingredients with relaxing effects. It can also prioritize analyzing highly nutritious ingredients if the user is tired. Furthermore, if the user is energetic, the analysis unit can analyze ingredients to help them try new recipes. This allows for more appropriate ingredient analysis by adjusting the method of analyzing ingredients based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0093] The analysis unit monitors the freshness of stored ingredients in real time and suggests the optimal timing for use. For example, the analysis unit monitors the freshness of ingredients with sensors and suggests using them before they spoil. The analysis unit can also record the storage period of ingredients and notify the optimal timing for use. Furthermore, the analysis unit can suggest the optimal recipe based on the freshness information of the ingredients. For example, the analysis unit monitors the freshness of ingredients with sensors and suggests using them before they spoil. It can also record the storage period of ingredients and notify the optimal timing for use. It can also suggest the optimal recipe based on the freshness information of the ingredients. In this way, by monitoring the freshness of stored ingredients in real time, the optimal timing for use can be suggested. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input freshness data of ingredients into a generating AI and have the generating AI suggest the optimal timing for use.

[0094] The analysis department analyzes the nutritional value of ingredients and selects ingredients according to the user's health condition. For example, the analysis department selects ingredients containing necessary nutrients based on the user's health data. The analysis department can also select appropriate ingredients considering the user's allergy information. Furthermore, the analysis department can select ingredients that match the user's weight loss goals. For example, the analysis department selects ingredients containing necessary nutrients based on the user's health data. It can also select appropriate ingredients considering the user's allergy information. It can also select ingredients that match the user's weight loss goals. In this way, by analyzing the nutritional value of ingredients and selecting ingredients according to the user's health condition, a healthy meal can be provided. Some or all of the above processing in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input the user's health data into a generating AI and have the generating AI perform the ingredient selection.

[0095] The analysis unit estimates the user's emotions and determines the priority of ingredients based on the estimated emotions. For example, if the user is stressed, the analysis unit will prioritize ingredients with relaxing effects. It can also prioritize highly nutritious ingredients if the user is tired. Furthermore, if the user is energetic, the analysis unit can prioritize ingredients for trying new recipes. This allows for the selection of more appropriate ingredients by prioritizing them based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can input user emotion data into a generating AI, have the AI ​​perform emotion estimation, and then determine the priority of ingredients based on the results.

[0096] The analysis department analyzes the origin information of ingredients and prioritizes the use of locally produced ingredients. For example, the analysis department obtains the origin information of ingredients and prioritizes the selection of locally produced ingredients. Furthermore, by using locally produced ingredients, the analysis department can reduce transportation costs. In addition, by using locally produced ingredients, the analysis department can support the local economy. For example, the analysis department obtains the origin information of ingredients and prioritizes the selection of locally produced ingredients. By using locally produced ingredients, transportation costs can be reduced. By using locally produced ingredients, the local economy can be supported. Thus, by analyzing the origin information of ingredients and prioritizing the use of locally produced ingredients, transportation costs can be reduced and the local economy can be supported. Some or all of the above processing in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input the origin information of ingredients into a generating AI and have the generating AI select locally produced ingredients.

[0097] The analysis unit analyzes allergen information of ingredients and selects ingredients suitable for users with allergies. For example, the analysis unit selects appropriate ingredients based on the user's allergy information. The analysis unit can also acquire allergen information of ingredients and suggest recipes suitable for users with allergies. Furthermore, the analysis unit can suggest alternative ingredients based on allergen information. For example, the analysis unit selects appropriate ingredients based on the user's allergy information. It can also acquire allergen information of ingredients and suggest recipes suitable for users with allergies. It can also suggest alternative ingredients based on allergen information. By analyzing allergen information of ingredients and selecting ingredients suitable for users with allergies, allergic reactions can be prevented. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's allergy information into a generating AI and have the generating AI select appropriate ingredients.

[0098] The cooking department estimates the user's emotions and adjusts the seasoning of the dish based on those emotions. For example, if the user is stressed, the cooking department will use relaxing seasonings. If the user is tired, the cooking department can also use highly nutritious seasonings. Furthermore, if the user is energetic, the cooking department can try new seasonings. For example, if the user is stressed, the cooking department will use relaxing seasonings. If the user is tired, the cooking department can also use highly nutritious seasonings. If the user is energetic, the cooking department can also try new seasonings. This allows for the provision of more appropriate dishes by adjusting the seasoning of the dish based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the cooking department may be performed using AI, for example, or without AI. For example, the cooking department can input user emotion data into a generative AI, have the AI ​​estimate the emotion, and then adjust the seasoning of the dish based on the result.

[0099] The cooking department optimizes cooking times and prepares meals according to the user's schedule. For example, the cooking department sets the optimal cooking time based on the user's schedule. It can also select recipes that can be prepared quickly if the user is in a hurry. Furthermore, it can select recipes that require more time if the user has ample time. For example, the cooking department sets the optimal cooking time based on the user's schedule. It can also select recipes that can be prepared quickly if the user is in a hurry. It can also select recipes that require more time if the user has ample time. This optimizes cooking times and allows for efficient cooking according to the user's schedule. Some or all of the above processes in the cooking department may be performed using AI, for example, or not. For example, the cooking department can input the user's schedule data into a generating AI and have the generating AI set the optimal cooking time.

[0100] The cooking department analyzes the appearance of the dishes and arranges them to suit the user's preferences. For example, the cooking department arranges the dishes based on the user's preferred colors and shapes. The cooking department can also analyze the user's past preferences and suggest the optimal arrangement. Furthermore, the cooking department can consider the user's cultural background and arrange the dishes in a traditional style. For example, the cooking department arranges the dishes based on the user's preferred colors and shapes. It can also analyze the user's past preferences and suggest the optimal arrangement. It can also consider the user's cultural background and arrange the dishes in a traditional style. By analyzing the appearance of the dishes and arranging them to suit the user's preferences, it is possible to provide more satisfying meals. Some or all of the above processes in the cooking department may be performed using AI, for example, or not. For example, the cooking department can input user preference data into a generating AI and have the generating AI suggest arrangements.

[0101] The cooking department estimates the user's emotions and determines the order in which dishes are served based on the estimated emotions. For example, if the user is stressed, the cooking department will serve relaxing dishes first. If the user is tired, the cooking department may also serve nutritious dishes first. Furthermore, if the user is energetic, the cooking department may also serve new dishes first. For example, if the user is stressed, the cooking department will serve relaxing dishes first. If the user is tired, the cooking department may also serve nutritious dishes first. If the user is energetic, the cooking department may also serve new dishes first. This allows for the provision of more appropriate dishes by determining the order in which dishes are served based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the cooking department may be performed using AI, for example, or without AI. For example, the cooking department can input user emotion data into a generative AI, have the AI ​​perform emotion estimation, and then determine the order in which dishes are served based on the results.

[0102] The cooking department calculates the calories in dishes and provides menus tailored to the user's weight loss goals. For example, the cooking department calculates calories and provides menus based on the user's weight loss goals. It can also analyze the user's past eating history and suggest an optimal calorie menu. Furthermore, the cooking department can provide menus with adjusted calories considering the user's exercise level. For example, the cooking department calculates calories and provides menus based on the user's weight loss goals. It can also analyze the user's past eating history and suggest an optimal calorie menu. It can also provide menus with adjusted calories considering the user's exercise level. In this way, by calculating the calories in dishes and providing menus tailored to the user's weight loss goals, healthy meals can be provided. Some or all of the above processes in the cooking department may be performed using AI, for example, or not using AI. For example, the cooking department can input the user's weight loss goal data into a generating AI and have the generating AI perform calorie calculations and menu suggestions.

[0103] The cooking department considers the cultural background of the cuisine and proposes international dishes tailored to the user's preferences. For example, the cooking department may suggest traditional dishes based on the user's cultural background. It can also analyze the user's past preferences and propose international dishes. Furthermore, the cooking department may consider the user's travel history and propose dishes from countries they have visited. For example, the cooking department may suggest traditional dishes based on the user's cultural background. It can also analyze the user's past preferences and propose international dishes. It can also consider the user's travel history and propose dishes from countries they have visited. This allows for the provision of a wider variety of dishes by considering the cultural background of the cuisine and proposing international dishes tailored to the user's preferences. Some or all of the above processing in the cooking department may be performed using AI, for example, or not. For example, the cooking department can input the user's cultural background data into a generating AI and have the generating AI propose international dishes.

[0104] The room analysis unit estimates the user's emotions and adjusts the room analysis method based on the estimated emotions. For example, if the user is stressed, the room analysis unit will analyze the room to create a relaxing environment. If the user is tired, the room analysis unit can also analyze the room to provide a comfortable environment. Furthermore, if the user is energetic, the room analysis unit can analyze the room to suggest new interior designs. For example, if the user is stressed, the room analysis unit will analyze the room to create a relaxing environment. If the user is tired, it can also analyze the room to provide a comfortable environment. If the user is energetic, it can also analyze the room to suggest new interior designs. By adjusting the room analysis method based on the user's emotions, a more appropriate room analysis becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the room analysis unit may be performed using AI, for example, or without AI. For example, the room analysis unit can input user emotion data into a generating AI, have the AI ​​perform emotion estimation, and adjust the room analysis method based on the results.

[0105] The room analysis unit monitors the room temperature and humidity and makes suggestions for maintaining an optimal environment. For example, the room analysis unit can monitor the room temperature with a sensor and suggest an optimal temperature. It can also monitor the room humidity with a sensor and suggest an optimal humidity. Furthermore, the room analysis unit can comprehensively analyze the room temperature and humidity to provide a comfortable environment. For example, the room analysis unit can monitor the room temperature with a sensor and suggest an optimal temperature. It can also monitor the room humidity with a sensor and suggest an optimal humidity. It can also comprehensively analyze the room temperature and humidity to provide a comfortable environment. This makes it possible to make suggestions for maintaining an optimal environment by monitoring the room temperature and humidity. Some or all of the above processing in the room analysis unit may be performed using AI, for example, or without AI. For example, the room analysis unit can input room temperature and humidity data into a generating AI and have the generating AI execute a suggestion for an optimal environment.

[0106] The room analysis unit analyzes the furniture arrangement in a room and proposes the optimal layout. For example, the room analysis unit can 3D scan the furniture arrangement in a room and propose the optimal layout. The room analysis unit can also propose the optimal furniture arrangement based on the user's lifestyle patterns. Furthermore, the room analysis unit can propose furniture arrangements that make effective use of the room space. For example, the room analysis unit can 3D scan the furniture arrangement in a room and propose the optimal layout. It can also propose the optimal furniture arrangement based on the user's lifestyle patterns. It can also propose furniture arrangements that make effective use of the room space. In this way, by analyzing the furniture arrangement in a room, the optimal layout can be proposed. Some or all of the above processing in the room analysis unit may be performed using AI, for example, or without AI. For example, the room analysis unit can input the room's furniture arrangement data into a generating AI and have the generating AI propose the optimal layout.

[0107] The room analysis unit estimates the user's emotions and determines the cleaning priority of rooms based on the estimated emotions. For example, if the user is stressed, the room analysis unit will prioritize cleaning rooms with a relaxing effect. It can also prioritize cleaning rooms that provide a comfortable environment if the user is tired. Furthermore, if the user is energetic, the room analysis unit can prioritize cleaning rooms that offer new interior design suggestions. This allows for more appropriate cleaning by determining cleaning priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the room analysis unit may be performed using AI, or not. For example, the room analysis unit can input user emotion data into a generating AI, have the AI ​​estimate the emotion, and then determine the cleaning priority of the rooms based on the results.

[0108] The room analysis unit analyzes the lighting conditions in a room and proposes the optimal lighting settings. For example, the room analysis unit monitors the lighting conditions in a room using sensors and proposes the optimal lighting settings. The room analysis unit can also propose the optimal lighting settings based on the user's lifestyle patterns. Furthermore, the room analysis unit can automatically adjust the room lighting to provide a comfortable environment. For example, the room analysis unit monitors the lighting conditions in a room using sensors and proposes the optimal lighting settings. It can also propose the optimal lighting settings based on the user's lifestyle patterns. It can also automatically adjust the room lighting to provide a comfortable environment. In this way, by analyzing the lighting conditions in a room, it is possible to propose the optimal lighting settings. Some or all of the above processing in the room analysis unit may be performed using AI, for example, or without AI. For example, the room analysis unit can input room lighting data into a generating AI and have the generating AI execute a proposal for the optimal lighting settings.

[0109] The room analysis unit analyzes the air quality in a room and proposes the optimal way to use the air purifier. For example, the room analysis unit monitors the air quality in a room with sensors and proposes the optimal way to use the air purifier. The room analysis unit can also analyze the air quality in a room and propose the optimal time to replace the filter. Furthermore, the room analysis unit can comprehensively analyze the air quality in a room and provide a comfortable environment. For example, the room analysis unit monitors the air quality in a room with sensors and proposes the optimal way to use the air purifier. It can also analyze the air quality in a room and propose the optimal time to replace the filter. It can also comprehensively analyze the air quality in a room and provide a comfortable environment. In this way, by analyzing the air quality in a room, it is possible to propose the optimal way to use the air purifier. Some or all of the above processing in the room analysis unit may be performed using AI, for example, or without AI. For example, the room analysis unit can input room air quality data into a generating AI and have the generating AI propose the optimal way to use the air purifier.

[0110] The cleaning unit estimates the user's emotions and adjusts the cleaning frequency based on the estimated emotions. For example, if the user is stressed, the cleaning unit will frequently perform relaxing cleaning. If the user is tired, the cleaning unit may also frequently perform cleaning to provide a comfortable environment. Furthermore, if the user is energetic, the cleaning unit may also frequently perform cleaning to suggest new interior designs. For example, if the user is stressed, the cleaning unit will frequently perform relaxing cleaning. If the user is tired, the cleaning unit may also frequently perform cleaning to provide a comfortable environment. If the user is energetic, the cleaning unit may also frequently perform cleaning to suggest new interior designs. By adjusting the cleaning frequency based on the user's emotions, more appropriate cleaning becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the cleaning unit may be performed using AI, for example, or without AI. For example, the cleaning unit can input user emotion data into a generating AI, have the AI ​​estimate the emotion, and adjust the cleaning frequency based on the results.

[0111] The cleaning department optimizes the cleaning tools used during cleaning to perform efficient cleaning. For example, the cleaning department analyzes the degree of dirtiness in a room and selects the most suitable cleaning tools. It can also record the frequency of use of cleaning tools and perform replacement or maintenance at the optimal time. Furthermore, the cleaning department can analyze the types of cleaning tools and suggest the optimal combination for efficient cleaning. For example, the cleaning department analyzes the degree of dirtiness in a room and selects the most suitable cleaning tools. It can also record the frequency of use of cleaning tools and perform replacement or maintenance at the optimal time. It can also analyze the types of cleaning tools and suggest the optimal combination for efficient cleaning. By optimizing the cleaning tools used during cleaning, efficient cleaning becomes possible. Some or all of the above processes in the cleaning department may be performed using AI, for example, or not. For example, the cleaning department can input room dirtiness data into a generating AI and have the generating AI select the most suitable cleaning tools.

[0112] The cleaning unit monitors the progress of cleaning in real time and makes adjustments as needed. For example, the cleaning unit monitors the progress of cleaning with sensors and checks the progress in real time. The cleaning unit can also analyze the progress of cleaning and adjust the cleaning route as needed. Furthermore, the cleaning unit can notify the user of the progress of cleaning and receive instructions as needed. For example, the cleaning unit monitors the progress of cleaning with sensors and checks the progress in real time. It can also analyze the progress of cleaning and adjust the cleaning route as needed. It can also notify the user of the progress of cleaning and receive instructions as needed. This allows for adjustments to be made as needed by monitoring the progress of cleaning in real time. Some or all of the above processes in the cleaning unit may be performed using AI, for example, or not using AI. For example, the cleaning unit can input cleaning progress data into a generating AI and have the generating AI perform monitoring and adjustment of the progress.

[0113] The cleaning unit estimates the user's emotions and determines the cleaning order based on the estimated emotions. For example, if the user is stressed, the cleaning unit will prioritize cleaning rooms that have a relaxing effect. If the user is tired, the cleaning unit can also prioritize cleaning rooms that provide a comfortable environment. Furthermore, if the user is energetic, the cleaning unit can also prioritize cleaning rooms that offer new interior design suggestions. For example, if the user is stressed, the cleaning unit will prioritize cleaning rooms that have a relaxing effect. If the user is tired, the cleaning unit can also prioritize cleaning rooms that provide a comfortable environment. If the user is energetic, the cleaning unit can also prioritize cleaning rooms that offer new interior design suggestions. This allows for more appropriate cleaning by determining the cleaning order based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the cleaning unit may be performed using AI, for example, or without AI. For example, the cleaning unit can input user emotion data into a generative AI, have the AI ​​perform emotion estimation, and then determine the cleaning order based on the results.

[0114] The cleaning department optimizes the types of detergents used during cleaning to perform environmentally friendly cleaning. For example, the cleaning department analyzes the degree of dirtiness in a room and selects the most suitable detergent. It can also optimize the amount of detergent used to perform environmentally friendly cleaning. Furthermore, the cleaning department can analyze the ingredients of detergents and select detergents suitable for users with allergies. For example, the cleaning department analyzes the degree of dirtiness in a room and selects the most suitable detergent. It can also optimize the amount of detergent used to perform environmentally friendly cleaning. It can also analyze the ingredients of detergents and select detergents suitable for users with allergies. This makes environmentally friendly cleaning possible by optimizing the types of detergents used during cleaning. Some or all of the above processes in the cleaning department may be performed using AI, for example, or not. For example, the cleaning department can input room dirtiness data into a generating AI and have the generating AI select the most suitable detergent.

[0115] The cleaning unit automatically sorts the waste generated during cleaning, promoting recycling. For example, the cleaning unit identifies the type of waste using sensors and sorts it automatically. The cleaning unit can also record the sorting status of the waste and suggest ways to promote recycling. Furthermore, the cleaning unit can notify the user of how to sort the waste, promoting appropriate recycling. For example, the cleaning unit identifies the type of waste using sensors and sorts it automatically. It can also record the sorting status of the waste and suggest ways to promote recycling. It can also notify the user of how to sort the waste, promoting appropriate recycling. In this way, recycling can be promoted by automatically sorting the waste generated during cleaning. Some or all of the above processes in the cleaning unit may be performed using AI, for example, or without AI. For example, the cleaning unit can input waste type data into a generating AI and have the generating AI perform the waste sorting.

[0116] The management department estimates the user's emotions and adjusts the household chore schedule based on the estimated emotions. For example, if the user is feeling stressed, the management department suggests a relaxing household chore schedule. If the user is tired, the management department can also suggest a less burdensome household chore schedule. Furthermore, if the user is feeling energetic, the management department can also suggest a schedule that includes new household chore tasks. For example, if the user is feeling stressed, the management department suggests a relaxing household chore schedule. If the user is tired, it can also suggest a less burdensome household chore schedule. If the user is feeling energetic, it can also suggest a schedule that includes new household chore tasks. This allows for a more appropriate household chore schedule by adjusting it based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management department may be performed using AI, for example, or without AI. For example, the management department can input user emotion data into a generative AI, have the AI ​​perform emotion estimation, and then adjust the household chore schedule based on the results.

[0117] The management department re-evaluates the priority of household chores in real time and proposes an optimal schedule. For example, the management department monitors the progress of household chores in real time and re-evaluates the priority. It can also analyze the importance of household chores and propose an optimal schedule. Furthermore, the management department can notify the user of the progress of household chores and adjust the schedule as needed. For example, the management department monitors the progress of household chores in real time and re-evaluates the priority. It can also analyze the importance of household chores and propose an optimal schedule. It can also notify the user of the progress of household chores and adjust the schedule as needed. This allows the management department to propose an optimal schedule by re-evaluating the priority of household chores in real time. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input household chore progress data into a generating AI and have the generating AI perform the re-evaluation of priorities and schedule proposals.

[0118] The management unit monitors the progress of household chores and sends reminders as needed. For example, the management unit monitors the progress of household chores in real time and sends reminders. The management unit can also analyze the progress of household chores and adjust reminders as needed. Furthermore, the management unit can notify users of the progress of household chores and receive instructions as needed. For example, the management unit monitors the progress of household chores in real time and sends reminders. It can also analyze the progress of household chores and adjust reminders as needed. It can also notify users of the progress of household chores and receive instructions as needed. This allows the management unit to send reminders as needed by monitoring the progress of household chores. Some or all of the above processes in the management unit may be performed using AI, for example, or not using AI. For example, the management unit can input household chore progress data into a generating AI and have the generating AI send reminders.

[0119] The management unit estimates the user's emotions and determines the order in which household chores are performed based on the estimated emotions. For example, if the user is stressed, the management unit will prioritize performing chores that have a relaxing effect. If the user is tired, the management unit can also prioritize performing chores that are less burdensome. Furthermore, if the user is energetic, the management unit can also prioritize performing new chore tasks. For example, if the user is stressed, the management unit will prioritize performing chores that have a relaxing effect. If the user is tired, it can also prioritize performing chores that are less burdensome. If the user is energetic, it can also prioritize performing new chore tasks. This allows for more appropriate execution of chores by determining the order in which chores are performed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using AI, for example, or not using AI. For example, the management department can input user emotion data into a generative AI, have the AI ​​perform emotion estimation, and then determine the order in which household chores are performed based on the results.

[0120] The management department visualizes the progress of household chores and reports the progress to the user. For example, the management department visualizes the progress of household chores using graphs and charts and reports it to the user. The management department can also display the progress of household chores in real time and notify the user. Furthermore, the management department can analyze the progress of household chores and provide progress reports as needed. For example, the management department visualizes the progress of household chores using graphs and charts and reports it to the user. It can also display the progress of household chores in real time and notify the user. It can also analyze the progress of household chores and provide progress reports as needed. In this way, progress reports can be provided to the user by visualizing the progress of household chores. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input household chore progress data into a generating AI and have the generating AI perform visualization and progress reporting.

[0121] The management department shares the household chore schedule with other family members and works together to perform household chores. For example, the management department can share the household chore schedule with family members and work together to perform household chores. The management department can also notify family members of the progress of household chores and work together to perform them. Furthermore, the management department can adjust the household chore schedule and work together with family members to perform household chores efficiently. For example, the management department can share the household chore schedule with family members and work together to perform household chores. It can also notify family members of the progress of household chores and work together to perform them. It can also adjust the household chore schedule and work together with family members to perform household chores efficiently. In this way, by sharing the household chore schedule with other family members, household chores can be performed collaboratively. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input household chore schedule data into a generating AI and have the generating AI perform sharing and adjustment.

[0122] The execution unit estimates the user's emotions and adjusts the way household chores are performed based on the estimated emotions. For example, if the user is feeling stressed, the execution unit suggests a way to perform household chores that has a relaxing effect. The execution unit can also suggest a way to perform household chores that is less burdensome if the user is tired. Furthermore, if the user is feeling energetic, the execution unit can suggest a way to perform a new household chore task. For example, if the user is feeling stressed, the execution unit suggests a way to perform household chores that has a relaxing effect. If the user is tired, it can also suggest a way to perform household chores that is less burdensome. If the user is feeling energetic, it can also suggest a way to perform a new household chore task. This allows for more appropriate household chore performance by adjusting the way household chores are performed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input user emotion data into a generating AI, have the generating AI perform emotion estimation, and adjust the method of performing household chores based on the results.

[0123] The execution unit customizes household chores to suit the user's preferences. For example, the execution unit can perform customized cleaning based on the user's preferred cleaning method. It can also perform customized cooking based on the user's preferred cooking method. Furthermore, it can perform customized laundry based on the user's preferred laundry method. For example, the execution unit can perform customized cleaning based on the user's preferred cleaning method. It can also perform customized cooking based on the user's preferred cooking method. It can also perform customized laundry based on the user's preferred laundry method. This allows for more satisfying household chore execution by customizing the process to the user's preferences. Some or all of the above-described processes in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input user preference data into a generating AI and have the generating AI make customization suggestions.

[0124] The execution unit monitors the status of household chores in real time and makes adjustments as needed. For example, the execution unit monitors the status of household chores using sensors and checks the progress in real time. The execution unit can also analyze the status of household chores and adjust the execution method as needed. Furthermore, the execution unit can notify the user of the status of household chores and receive instructions as needed. For example, the execution unit monitors the status of household chores using sensors and checks the progress in real time. It can also analyze the status of household chores and adjust the execution method as needed. It can also notify the user of the status of household chores and receive instructions as needed. This allows for adjustments to be made as needed by monitoring the status of household chores in real time. Some or all of the above processes in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input household chore execution data into a generating AI and have the generating AI monitor and adjust the progress.

[0125] The execution unit estimates the user's emotions and determines the order in which household chores are performed based on the estimated emotions. For example, if the user is stressed, the execution unit will prioritize performing chores that have a relaxing effect. If the user is tired, the execution unit can also prioritize performing chores that are less burdensome. Furthermore, if the user is energetic, the execution unit can also prioritize performing new chore tasks. For example, if the user is stressed, the execution unit will prioritize performing chores that have a relaxing effect. If the user is tired, it can also prioritize performing chores that are less burdensome. If the user is energetic, it can also prioritize performing new chore tasks. This allows for more appropriate execution of chores by determining the order in which chores are performed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input user emotion data into a generating AI, have the generating AI perform emotion estimation, and then determine the order in which household chores are performed based on the results.

[0126] The execution unit optimizes energy consumption and performs household chores in an environmentally friendly manner. For example, the execution unit proposes methods for optimizing energy consumption when performing household chores. The execution unit can also use environmentally friendly detergents and cleaning tools when performing household chores. Furthermore, the execution unit can propose schedules to minimize energy consumption when performing household chores. For example, the execution unit proposes methods for optimizing energy consumption when performing household chores. It can also use environmentally friendly detergents and cleaning tools when performing household chores. It can also propose schedules to minimize energy consumption when performing household chores. This makes it possible to perform household chores in an environmentally friendly way by optimizing energy consumption. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input energy consumption data into a generating AI and have the generating AI execute optimization suggestions.

[0127] The execution unit works in conjunction with other home appliances to perform household chores efficiently. For example, the execution unit can work in conjunction with other home appliances to clean efficiently. The execution unit can also work in conjunction with other home appliances to cook efficiently. Furthermore, the execution unit can work in conjunction with other home appliances to do laundry efficiently. For example, the execution unit can work in conjunction with other home appliances to clean efficiently. It can also work in conjunction with other home appliances to cook efficiently. It can also work in conjunction with other home appliances to do laundry efficiently. This makes it possible to perform household chores efficiently by coordinating with other home appliances. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input data from other home appliances into a generating AI and have the generating AI execute suggestions for coordination.

[0128] The voice reception unit estimates the user's emotions and adjusts the method of receiving voice instructions based on the estimated emotions. For example, if the user is stressed, the voice reception unit provides a simple method of receiving voice instructions. If the user is relaxed, the voice reception unit can also provide a detailed method of receiving voice instructions. Furthermore, if the user is in a hurry, the voice reception unit can also provide a method of receiving voice instructions quickly. By adjusting the method of receiving voice instructions based on the user's emotions, more appropriate voice instruction reception becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the voice reception unit may be performed using AI, for example, or without AI. For example, the voice reception unit can input user emotion data into a generating AI, have the AI ​​perform emotion estimation, and adjust the method of receiving voice instructions based on the results.

[0129] The voice reception unit learns the user's speech patterns to improve the accuracy of voice instruction recognition. For example, the voice reception unit analyzes the user's speech patterns to improve the accuracy of voice instruction recognition. The voice reception unit can also record the user's speech patterns to improve the accuracy of voice instruction recognition. Furthermore, the voice reception unit can suggest the optimal method for receiving voice instructions based on the user's speech patterns. For example, the voice reception unit analyzes the user's speech patterns to improve the accuracy of voice instruction recognition. It can also record the user's speech patterns to improve the accuracy of voice instruction recognition. It can also suggest the optimal method for receiving voice instructions based on the user's speech patterns. In this way, the accuracy of voice instruction recognition can be improved by learning the user's speech patterns. Some or all of the above processing in the voice reception unit may be performed using AI, for example, or without AI. For example, the voice reception unit can input the user's speech pattern data into a generating AI and have the generating AI perform speech pattern learning.

[0130] The voice reception unit analyzes the history of voice commands and makes suggestions tailored to the user's preferences. For example, the voice reception unit analyzes the user's past voice command history and makes optimal suggestions. It can also propose a household chore schedule tailored to the user's preferences based on the user's voice command history. Furthermore, the voice reception unit can analyze the user's voice command history and propose the most suitable household chore tasks. For example, the voice reception unit analyzes the user's past voice command history and makes optimal suggestions. It can also propose a household chore schedule tailored to the user's preferences based on the user's voice command history. It can also analyze the user's voice command history and propose the most suitable household chore tasks. In this way, by analyzing the history of voice commands, it is possible to make suggestions tailored to the user's preferences. Some or all of the above processing in the voice reception unit may be performed using AI, for example, or not using AI. For example, the voice reception unit can input voice command history data into a generating AI and have the generating AI perform history analysis and suggestions.

[0131] The voice reception unit estimates the user's emotions and prioritizes voice instructions based on those emotions. For example, if the user is stressed, the voice reception unit will prioritize voice instructions that have a relaxing effect. It can also prioritize voice instructions that are less burdensome if the user is tired. Furthermore, if the user is energetic, the voice reception unit can prioritize voice instructions for new household tasks. This allows for more appropriate voice instruction to be received by prioritizing voice instructions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the voice reception unit may be performed using AI, for example, or without AI. For example, the voice reception unit can input user emotion data into a generating AI, have the generating AI perform emotion estimation, and determine the priority of voice instructions based on the results.

[0132] The voice reception unit improves recognition accuracy by removing background noise when it receives voice commands. For example, the voice reception unit removes background noise in real time when it receives voice commands. The voice reception unit can also improve recognition accuracy using noise cancellation technology. Furthermore, the voice reception unit can analyze the background noise and suggest the optimal noise reduction method. For example, the voice reception unit removes background noise in real time when it receives voice commands. It can also improve recognition accuracy using noise cancellation technology. It can also analyze the background noise and suggest the optimal noise reduction method. This improves the recognition accuracy of voice commands by removing background noise. Some or all of the above processing in the voice reception unit may be performed using AI, for example, or without AI. For example, the voice reception unit can input background noise data into a generating AI and have the generating AI perform noise reduction.

[0133] The voice reception unit provides the optimal response when it receives a voice command, taking into account the user's device information. For example, the voice reception unit provides the optimal voice response based on the user's device information. The voice reception unit can also analyze the user's device information and propose the optimal method for receiving voice commands. Furthermore, the voice reception unit can configure the optimal settings for receiving voice commands based on the user's device information. For example, the voice reception unit provides the optimal voice response based on the user's device information. It can also analyze the user's device information and propose the optimal method for receiving voice commands. It can also configure the optimal settings for receiving voice commands based on the user's device information. This allows the voice reception unit to provide the optimal voice response by taking the user's device information into consideration. Some or all of the above processing in the voice reception unit may be performed using AI, for example, or without AI. For example, the voice reception unit can input the user's device information into a generating AI and have the generating AI perform the task of providing the optimal response.

[0134] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0135] Housekeeping robots can also be equipped with a health management unit that monitors the user's health. This unit can, for example, measure the user's heart rate and blood pressure using sensors, and monitor their health in real time. Furthermore, it can suggest appropriate exercise and rest based on the user's health data. In addition, the health management unit can adjust the housework schedule according to the user's health condition. For example, if the user is tired, the health management unit can prioritize less strenuous tasks. This allows for housework to be performed while considering the user's health.

[0136] Housekeeping robots can also be equipped with an emotion management unit that estimates the user's emotions and adjusts the priority of household chores based on those emotions. For example, if the user is feeling stressed, the emotion management unit will prioritize chores that have a relaxing effect. It can also prioritize less burdensome chores if the user is tired. Furthermore, if the user is feeling energetic, the emotion management unit can prioritize new chore tasks. This allows for more appropriate chore execution by adjusting the priority of chores based on the user's emotions.

[0137] Housekeeping robots can also be equipped with a lifestyle pattern learning unit that learns the user's lifestyle patterns and proposes an optimal chore schedule. For example, the lifestyle pattern learning unit can analyze the user's past chore history and suggest an optimal schedule. Furthermore, the lifestyle pattern learning unit can collect and analyze the user's lifestyle habits as data. In addition, the lifestyle pattern learning unit can learn the user's lifestyle patterns and determine the priority of chores. This improves the efficiency of household chores by suggesting an optimal chore schedule based on the user's lifestyle patterns.

[0138] The household assistance robot could also be equipped with a cooking unit that estimates the user's emotions and adjusts the seasoning of the food based on those emotions. For example, if the user is stressed, the cooking unit could season the food in a way that promotes relaxation. If the user is tired, the cooking unit could season the food in a way that promotes nutrition. Furthermore, if the user is feeling energetic, the cooking unit could try new seasonings. This would allow the robot to provide more appropriate meals by adjusting the seasoning based on the user's emotions.

[0139] Housekeeping robots can also be equipped with a cleaning unit that estimates the user's emotions and adjusts the cleaning frequency based on those emotions. For example, if the user is feeling stressed, the cleaning unit may frequently perform relaxing cleaning. It can also frequently perform cleaning to provide a comfortable environment if the user is tired. Furthermore, if the user is feeling energetic, the cleaning unit may frequently perform cleaning to suggest new interior design ideas. This allows for more appropriate cleaning by adjusting the cleaning frequency based on the user's emotions.

[0140] The household chore robot could also be equipped with a management unit that estimates the user's emotions and adjusts the chore schedule based on those emotions. For example, if the user is feeling stressed, the management unit could suggest a relaxing chore schedule. It could also suggest a less burdensome chore schedule if the user is tired. Furthermore, if the user is feeling energetic, the management unit could suggest a schedule that includes new chore tasks. This allows for a more appropriate chore schedule by adjusting it based on the user's emotions.

[0141] Housekeeping robots can also be equipped with a health management unit that takes the user's health condition into consideration and proposes an optimal housework schedule. For example, the health management unit proposes an appropriate housework schedule based on the user's health data. Furthermore, the health management unit can adjust the priority of housework according to the user's health condition. In addition, the health management unit can monitor the user's health condition in real time and adjust the housework schedule as needed. This makes it possible to perform housework while taking the user's health condition into consideration.

[0142] Housekeeping robots can also be equipped with a lifestyle pattern learning unit that learns the user's lifestyle patterns and proposes an optimal chore schedule. For example, the lifestyle pattern learning unit can analyze the user's past chore history and suggest an optimal schedule. Furthermore, the lifestyle pattern learning unit can collect and analyze the user's lifestyle habits as data. In addition, the lifestyle pattern learning unit can learn the user's lifestyle patterns and determine the priority of chores. This improves the efficiency of household chores by suggesting an optimal chore schedule based on the user's lifestyle patterns.

[0143] The household chore robot may also be equipped with an execution unit that estimates the user's emotions and determines the order in which household chores are performed based on those emotions. For example, if the user is feeling stressed, the execution unit may prioritize performing chores that have a relaxing effect. It may also prioritize less burdensome chores if the user is tired. Furthermore, if the user is feeling energetic, the execution unit may prioritize performing new chore tasks. This allows for more appropriate chore execution by determining the order of chores based on the user's emotions.

[0144] Housekeeping robots can also be equipped with a health management unit that takes the user's health condition into consideration and proposes an optimal housework schedule. For example, the health management unit proposes an appropriate housework schedule based on the user's health data. Furthermore, the health management unit can adjust the priority of housework according to the user's health condition. In addition, the health management unit can monitor the user's health condition in real time and adjust the housework schedule as needed. This makes it possible to perform housework while taking the user's health condition into consideration.

[0145] The following briefly describes the processing flow for example form 2.

[0146] Step 1: The analysis department analyzes the stored food ingredients. For example, they analyze the components of the stored food ingredients using a chemical analyzer and evaluate their nutritional value. The freshness of the stored food ingredients is evaluated using sensors, and the expiration date is calculated based on the storage conditions. Step 2: The cooking department prepares dishes based on the ingredients analyzed by the analysis department. For example, they select the optimal recipe based on the preserved ingredients and then cook them. They provide well-balanced meals and optimal cooking methods, taking into account the nutritional value and freshness of the preserved ingredients. Step 3: The room analysis unit analyzes the room. For example, it uses a 3D scanning device to understand the room layout and calculate the optimal cleaning route. The degree of dirtiness in the room is evaluated using sensors, and temperature and humidity are evaluated using temperature and humidity sensors. Step 4: The cleaning department performs cleaning based on the information analyzed by the room analysis department. For example, they set the optimal cleaning route based on the room layout and then perform the cleaning. They adjust the cleaning frequency based on the degree of dirtiness in the room and adjust the cleaning method based on temperature and humidity. Step 5: The management department manages household chores and schedules. For example, it learns the user's lifestyle and suggests an optimal chore schedule. It receives voice commands from the user, adjusts the chore schedule, monitors the progress of chores, and sends reminders. Step 6: The execution department performs household chores according to the schedule managed by the management department. For example, they perform cleaning, cooking, and laundry according to the schedule managed by the management department.

[0147] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0148] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0149] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] Each of the multiple elements described above, including the analysis unit, cooking unit, room analysis unit, cleaning unit, management unit, execution unit, and voice reception unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit analyzes ingredients using the camera 42 and sensors of the smart device 14 and evaluates their nutritional value and freshness using the identification processing unit 290 of the data processing unit 12. The cooking unit selects the optimal recipe using the identification processing unit 290 of the data processing unit 12 and performs cooking using the control unit 46A of the smart device 14. The room analysis unit understands the room layout and level of dirtiness using the camera 42 and sensors of the smart device 14 and calculates the optimal cleaning route using the identification processing unit 290 of the data processing unit 12. The cleaning unit performs cleaning using the control unit 46A of the smart device 14. The management unit learns the user's lifestyle habits using the identification processing unit 290 of the data processing unit 12 and proposes an optimal household chore schedule. The execution unit performs household chores using the control unit 46A of the smart device 14. The voice reception unit receives user voice commands using the microphone 38B of the smart device 14 and analyzes them using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device and control unit is not limited to the example described above and can be modified in various ways.

[0151] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0152] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0153] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0154] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0155] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0157] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0158] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0159] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0160] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0161] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0162] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0163] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0164] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0165] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0166] Each of the multiple elements described above, including the analysis unit, cooking unit, room analysis unit, cleaning unit, management unit, execution unit, and voice reception unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit analyzes ingredients using the camera 42 and sensors of the smart glasses 214 and evaluates their nutritional value and freshness using the identification processing unit 290 of the data processing unit 12. The cooking unit selects the optimal recipe using the identification processing unit 290 of the data processing unit 12 and performs cooking using the control unit 46A of the smart glasses 214. The room analysis unit understands the room layout and level of dirtiness using the camera 42 and sensors of the smart glasses 214 and calculates the optimal cleaning route using the identification processing unit 290 of the data processing unit 12. The cleaning unit performs cleaning using the control unit 46A of the smart glasses 214. The management unit learns the user's lifestyle habits using the identification processing unit 290 of the data processing unit 12 and proposes an optimal household chore schedule. The execution unit performs household chores using the control unit 46A of the smart glasses 214. The voice reception unit receives user voice commands using the microphone 238 of the smart glasses 214 and analyzes them using the identification processing unit 290 of the data processing unit 12. The correspondence between each part and the device and control unit is not limited to the example described above and can be modified in various ways.

[0167] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0168] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0169] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0170] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0171] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0172] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0173] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0174] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0175] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0176] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0177] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0178] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0179] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0180] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0181] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0182] Each of the multiple elements described above, including the analysis unit, cooking unit, room analysis unit, cleaning unit, management unit, execution unit, and voice reception unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit analyzes ingredients using the camera 42 and sensors of the headset terminal 314 and evaluates their nutritional value and freshness using the identification processing unit 290 of the data processing unit 12. The cooking unit selects the optimal recipe using the identification processing unit 290 of the data processing unit 12 and performs cooking using the control unit 46A of the headset terminal 314. The room analysis unit grasps the room layout and level of dirtiness using the camera 42 and sensors of the headset terminal 314 and calculates the optimal cleaning route using the identification processing unit 290 of the data processing unit 12. The cleaning unit performs cleaning using the control unit 46A of the headset terminal 314. The management unit learns the user's lifestyle habits using the identification processing unit 290 of the data processing unit 12 and proposes an optimal household chore schedule. The execution unit performs household chores using the control unit 46A of the headset terminal 314. The voice reception unit receives user voice commands using the microphone 238 of the headset terminal 314 and analyzes them using the specific processing unit 290 of the data processing device 12. The correspondence between each part and the device and control unit is not limited to the example described above and can be modified in various ways.

[0183] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0184] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0185] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0186] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0187] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0188] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0189] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0190] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0191] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0192] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0193] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0194] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0195] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0196] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0197] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0198] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0199] Each of the multiple elements described above, including the analysis unit, cooking unit, room analysis unit, cleaning unit, management unit, execution unit, and voice reception unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the analysis unit analyzes ingredients using the camera 42 and sensors of the robot 414 and evaluates their nutritional value and freshness using the specific processing unit 290 of the data processing unit 12. The cooking unit selects the optimal recipe using the specific processing unit 290 of the data processing unit 12 and performs cooking using the control unit 46A of the robot 414. The room analysis unit understands the room layout and level of dirtiness using the camera 42 and sensors of the robot 414 and calculates the optimal cleaning route using the specific processing unit 290 of the data processing unit 12. The cleaning unit performs cleaning using the control unit 46A of the robot 414. The management unit learns the user's lifestyle habits using the specific processing unit 290 of the data processing unit 12 and proposes an optimal household chore schedule. The execution unit performs household chores using the control unit 46A of the robot 414. The voice reception unit receives user voice commands using the microphone 238 of the robot 414, and the specific processing unit 290 of the data processing unit 12 analyzes them. The correspondence between each part and the device and control unit is not limited to the example described above, and various modifications are possible.

[0200] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0201] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0202] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0203] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0204] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0205] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0206] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0207] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0208] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0209] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0210] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0211] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0212] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0213] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0214] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0215] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0216] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0217] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0218] (Note 1) The analysis department analyzes the preserved food ingredients, The cooking department prepares dishes based on the ingredients analyzed by the aforementioned analysis department, The Room Analysis Department analyzes rooms, A cleaning unit that performs cleaning based on the information analyzed by the aforementioned room analysis unit, The management department manages household chores and schedules, The system includes an execution unit that performs household chores based on a schedule managed by the aforementioned management unit. A system characterized by the following features. (Note 2) Equipped with a voice reception unit that accepts voice commands. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned voice reception unit is It receives voice commands from the user and transmits them to the management unit. The system described in Appendix 2, characterized by the features described herein. (Note 4) The aforementioned cleaning department, The system will set the optimal cleaning route based on the room layout and then perform the cleaning. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned cooking department, Select the best recipe based on the stored ingredients and then cook. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned management department, It learns the user's lifestyle habits and suggests the optimal household chore schedule. The system described in Appendix 1, characterized by the features described herein. (Note 7) The execution unit is, Household chores are performed according to a schedule managed by the aforementioned management department. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit is The system estimates the user's emotions and adjusts the food analysis method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit is It monitors the freshness of stored food in real time and suggests the optimal time for use. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit is The nutritional value of ingredients is analyzed, and ingredients are selected according to the user's health condition. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit is The system estimates the user's emotions and determines the priority of ingredients based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is We analyze the origin information of our ingredients and prioritize using locally sourced ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is We analyze allergen information in food ingredients and select ingredients suitable for users with allergies. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned cooking department, It estimates the user's emotions and adjusts the seasoning of the food based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned cooking department, Optimize cooking times and cook according to the user's schedule. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned cooking department, The system analyzes the appearance of the dishes and arranges them to suit the user's preferences. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned cooking department, The system estimates the user's emotions and determines the order in which dishes are served based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned cooking department, It calculates the calories in dishes and provides menus tailored to the user's weight loss goals. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned cooking department, We take into account the cultural background of the cuisine and suggest international dishes tailored to the user's preferences. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned room analysis unit is It estimates the user's emotions and adjusts the room analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned room analysis unit is We monitor the room temperature and humidity and provide suggestions for maintaining an optimal environment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned room analysis unit is We analyze the furniture arrangement in the room and propose the optimal layout. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned room analysis unit is It estimates the user's emotions and determines the cleaning priority of the rooms based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned room analysis unit is We analyze the room's lighting conditions and suggest the optimal lighting settings. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned room analysis unit is We analyze the air quality in your room and suggest the optimal way to use your air purifier. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned cleaning department, It estimates the user's emotions and adjusts the cleaning frequency based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned cleaning department, Optimize the cleaning tools used during cleaning to perform efficient cleaning. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned cleaning department, The cleaning progress is monitored in real time, and adjustments are made as needed. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned cleaning department, It estimates the user's emotions and determines the cleaning order based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned cleaning department, Optimize the types of detergents used during cleaning to perform environmentally friendly cleaning. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned cleaning department, The system automatically sorts the waste generated during cleaning, promoting recycling. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned management department, It estimates the user's emotions and adjusts the household chore schedule based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned management department, It re-evaluates household chore priorities in real time and suggests the optimal schedule. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned management department, Monitor the progress of household chores and send reminders as needed. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned management department, It estimates the user's emotions and determines the order in which household chores should be performed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned management department, Visualize the progress of household chores and provide progress reports to the user. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned management department, Share your household chore schedule with other family members and cooperate in doing household tasks. The system described in Appendix 1, characterized by the features described herein. (Note 38) The execution unit is, It estimates the user's emotions and adjusts how household chores are performed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The execution unit is, When performing household chores, customize the process to suit the user's preferences. The system described in Appendix 1, characterized by the features described herein. (Note 40) The execution unit is, The system monitors the progress of household chores in real time and makes adjustments as needed. The system described in Appendix 1, characterized by the features described herein. (Note 41) The execution unit is, It estimates the user's emotions and determines the order in which household chores should be performed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 42) The execution unit is, When performing household chores, optimize energy consumption and perform them in an environmentally friendly way. The system according to Appendix 1, characterized in that... (Appendix 43) The execution unit When performing housework, it cooperates with other household appliances to efficiently perform housework The system according to Appendix 1, characterized in that... (Appendix 44) The voice reception unit Estimates the user's emotion and adjusts the method of receiving voice instructions based on the estimated user's emotion The system according to Appendix 2, characterized in that... (Appendix 45) The voice reception unit Learns the user's speech pattern in order to improve the recognition accuracy of voice instructions The system according to Appendix 2, characterized in that... (Appendix 46) The voice reception unit Analyzes the history of voice instructions and makes proposals according to the user's preferences The system according to Appendix 2, characterized in that... (Appendix 47) The voice reception unit Estimates the user's emotion and determines the priority of voice instructions based on the estimated user's emotion The system according to Appendix 2, characterized in that... (Appendix 48) The voice reception unit Removes background noise when receiving voice instructions to improve recognition accuracy The system according to Appendix 2, characterized in that... (Appendix 49) The voice reception unit Provides an optimal response considering the user's device information when receiving voice instructions The system according to Appendix 2, characterized in that...

Explanation of Reference Numerals

[0219] 10, 210, 310, 410 Data processing system 12 Data processing device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The analysis department analyzes the preserved food ingredients, The cooking department prepares dishes based on the ingredients analyzed by the aforementioned analysis department, The Room Analysis Department analyzes rooms, A cleaning unit that performs cleaning based on the information analyzed by the aforementioned room analysis unit, The management department manages household chores and schedules, The system includes an execution unit that performs household chores based on a schedule managed by the aforementioned management unit. A system characterized by the following features.

2. Equipped with a voice reception unit that accepts voice commands. The system according to feature 1.

3. The aforementioned voice reception unit is It receives voice commands from the user and transmits them to the management unit. The system according to feature 2.

4. The aforementioned cleaning department, The system will set the optimal cleaning route based on the room layout and then perform the cleaning. The system according to feature 1.

5. The aforementioned cooking department, Select the best recipe based on the stored ingredients and then cook. The system according to feature 1.

6. The aforementioned management department, It learns the user's lifestyle habits and suggests the optimal household chore schedule. The system according to feature 1.

7. The execution unit is, Household chores are performed according to a schedule managed by the aforementioned management department. The system according to feature 1.

8. The aforementioned analysis unit is The system estimates the user's emotions and adjusts the food analysis method based on those estimated emotions. The system according to feature 1.

9. The aforementioned analysis unit is It monitors the freshness of stored food in real time and suggests the optimal time for use. The system according to feature 1.

10. The aforementioned analysis unit is The nutritional value of ingredients is analyzed, and ingredients are selected according to the user's health condition. The system according to feature 1.

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