system

Abstract

The system according to this embodiment aims to understand the state of a room and automatically generate and apply an optimal cleaning and tidying plan. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, an application unit, and a proposal unit. The collection unit collects data to understand the state of the room. The analysis unit analyzes the data collected by the collection unit. The generation unit generates a cleaning and tidying plan based on the data analyzed by the analysis unit. The application unit applies the plan generated by the generation unit to an IoT device. The proposal unit proposes the plan generated by the generation unit to the user.

Description

Technical Field

[0006] , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the state of the room is not sufficiently grasped, and an optimal cleaning and tidying plan cannot be automatically generated and applied.

[0005] The system according to the embodiment aims to grasp the state of the room and automatically generate and apply an optimal cleaning and tidying plan.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a generation unit, an application unit, and a proposal unit. The data collection unit collects data to understand the state of the room. The analysis unit analyzes the data collected by the data collection unit. The generation unit generates a cleaning and tidying plan based on the data analyzed by the analysis unit. The application unit applies the plan generated by the generation unit to an IoT device. The proposal unit proposes the plan generated by the generation unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment can grasp the state of a room and automatically generate and apply an optimal cleaning and tidying plan. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, 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. [[ID=??]]

[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] There seems to be a typo in the original text where "??" is used in line 12. This has been left as-is in the translation.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 home organization and cleaning system according to an embodiment of the present invention is a system that automates home organization and cleaning by combining AI and IoT. This system uses cameras and sensors to understand the state of a room, and a generating AI analyzes the collected data to generate an optimal cleaning and organization plan, which is then applied to IoT devices. The generating AI also generates an organization plan to optimize the placement of items and proposes it to the user. For example, it collects image data captured by cameras and dirt data acquired by sensors, and the generating AI learns from past data to generate an optimal plan. The generated plan is then applied by the generating AI to IoT devices such as robotic vacuum cleaners and proposed to the user. This system enables efficient home organization and cleaning, reducing the burden on the user. For example, the user can leave the cleaning to a robotic vacuum cleaner and receive suggestions to optimize the placement of items. This enables efficient home organization and cleaning, reducing the burden on the user. As a result, the home organization and cleaning system can efficiently organize and clean the home.

[0029] The home tidying and cleaning system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, an application unit, and a proposal unit. The collection unit collects data to understand the state of the room. The collection unit can, for example, collect image data captured by a camera and dirt data acquired by sensors. Image data captured by a camera includes, for example, the arrangement of objects in the room and the state of dirt. Dirt data acquired by sensors includes, for example, the amount of dirt on the floor and the amount of dust in the air. The analysis unit analyzes the collected data using a generation AI to understand the state of the room. The generation AI includes, for example, a process of learning past data to generate an optimal plan. The generation AI learns past cleaning and tidying data and generates an optimal plan. The generation unit generates an optimal cleaning and tidying plan based on the data analyzed using the generation AI. The generation AI analyzes the state of the room and generates an optimal cleaning and tidying plan. The application unit applies the plan generated by the generation AI to an IoT device such as an automatic vacuum cleaner. The application unit, for example, uses a generating AI to instruct an automatic vacuum cleaner to start cleaning. The proposal unit proposes to the user a tidying-up plan to optimize the placement of objects generated by the generating AI. The proposal unit, for example, uses a generating AI to generate a plan to optimize the placement of objects and proposes it to the user. As a result, the home tidying and cleaning system according to the embodiment can efficiently tidy up and clean the home.

[0030] The collection unit can collect image data captured by a camera or dirt data acquired by a sensor. Image data captured by a camera may include, for example, the arrangement of objects in a room and the state of dirt. The collection unit can, for example, take images of a room with a camera and collect that image data. Dirt data acquired by a sensor may include, for example, the amount of dirt on the floor and the amount of dust in the air. The collection unit can, for example, acquire floor dirt data using a sensor and collect that data. The collection unit can also, for example, acquire data using a sensor that measures the amount of dust in the air and collect that data. This allows for a detailed understanding of the room's condition using cameras and sensors.

[0031] The analysis unit can analyze the collected data using a generative AI to understand the state of the room. The generative AI includes, for example, a process of learning from past data to generate an optimal plan. The analysis unit can analyze the collected data using a generative AI to understand the state of the room. The generative AI learns from past cleaning and tidying data to generate an optimal plan. The generative AI analyzes the state of the room to generate an optimal cleaning and tidying plan. As a result, the state of the room can be analyzed with high accuracy by using a generative AI.

[0032] The generation unit can generate cleaning and tidying plans by learning from past data using a generation AI. The generation AI includes, for example, a process of learning from past data to generate an optimal plan. The generation unit can generate optimal cleaning and tidying plans by learning from past data using a generation AI. The generation AI, for example, learns from past cleaning and tidying data to generate an optimal plan. The generation AI, for example, analyzes the state of a room to generate an optimal cleaning and tidying plan. In this way, by learning from past data, it is possible to generate more effective plans.

[0033] The application unit can apply plans generated by the generation AI to IoT devices such as robotic vacuum cleaners. For example, the application unit can use the generation AI to instruct the robotic vacuum cleaner to start cleaning. For example, the application unit can apply the plan generated by the generation AI to the robotic vacuum cleaner to automate cleaning. Furthermore, the application unit can also apply the plan generated by the generation AI to IoT devices such as smart lights and smart thermostats to optimize the home environment. This allows for the automation of cleaning and tidying up by applying the generated plan to IoT devices such as robotic vacuum cleaners.

[0034] The suggestion unit can propose an organization and tidying plan to the user based on the arrangement of items generated by the generation AI. For example, the suggestion unit can have the generation AI generate a plan to optimize the arrangement of items and propose it to the user. For example, the suggestion unit can notify the user of the plan generated by the generation AI and make suggestions for optimizing the arrangement of items. In addition, the suggestion unit can display the plan generated by the generation AI to the user and suggest specific steps for optimizing the arrangement of items. In this way, by proposing a plan to optimize the arrangement of items to the user, the efficiency of organization and tidying can be improved.

[0035] The data collection unit can adjust the type and frequency of data collected based on how often a room is used. For example, in rooms with high usage, the unit will frequently collect data on cleanliness and the arrangement of objects. In rooms with low usage, the unit can reduce the frequency of data collection, collecting only the necessary data. Furthermore, in rooms with high usage during specific time periods, the unit can adjust data collection to match those times. This allows for efficient data collection by adjusting data collection according to the frequency of room use.

[0036] The data collection unit can track the placement of furniture and objects in real time and dynamically collect data. For example, the data collection unit can detect the movement of furniture in real time and collect its location information as data. For example, if the placement of objects changes, the data collection unit can track that change in real time and update the data. In addition, in rooms where the placement of furniture and objects changes frequently, the data collection unit can collect data in real time to understand the latest state. This allows for understanding the latest state by tracking the placement of furniture and objects in real time.

[0037] The data collection unit can apply different collection methods to each room in the home to collect optimal data. For example, in the living room, the unit can use a camera to collect data on the arrangement of objects and the degree of dirtiness. In the kitchen, for example, the unit can use sensors to collect data on dirtiness. Furthermore, in the bedroom, for example, the unit can combine a camera and sensors to collect data on the arrangement of objects and dirtiness. This allows for efficient data collection by applying the most suitable collection method for each room.

[0038] The data collection unit can simultaneously collect data on the condition of pets and plants in the home, thereby acquiring comprehensive environmental data. For example, the data collection unit can track the movements of pets with a camera and collect their location information as data. For example, the data collection unit can monitor the growth status of plants with sensors and collect that data. Furthermore, the data collection unit can track the condition of pets and plants in real time and acquire comprehensive environmental data. In this way, by simultaneously collecting data on the condition of pets and plants, comprehensive environmental data can be obtained.

[0039] The analysis unit can analyze the usage history of a room and predict the optimal timing for cleaning and tidying. For example, the analysis unit can analyze the frequency of room use and predict the optimal timing for cleaning. For example, the analysis unit can predict the optimal timing for tidying based on the room's usage history. Furthermore, for example, if a room is frequently used during a specific time period, the analysis unit can predict the timing for cleaning and tidying to match that time period. In this way, by analyzing the room's usage history, cleaning and tidying can be performed at the optimal time.

[0040] The analysis unit can improve accuracy by applying different analysis algorithms to different areas within the home. For example, in the living room, the analysis unit can apply an algorithm that analyzes the arrangement of objects. In the kitchen, for example, the analysis unit can apply an algorithm that analyzes the state of dirt. Furthermore, in the bedroom, for example, the analysis unit can combine algorithms that analyze the arrangement of objects and the state of dirt. This allows for improved analysis accuracy by applying the most suitable analysis algorithm for each area.

[0041] The analysis unit can provide different analysis results for each room in the home, offering the user the most relevant information. For example, in the living room, the analysis unit can provide analysis results regarding the arrangement of objects. In the kitchen, for example, the analysis unit can provide analysis results regarding the state of dirt. Furthermore, in the bedroom, for example, the analysis unit can provide analysis results regarding the arrangement of objects and the state of dirt. By providing optimal analysis results for each room, the system can offer the user valuable information.

[0042] The analysis unit can analyze the condition of pets and plants in the home and perform a comprehensive environmental analysis. For example, the analysis unit can analyze the movements of pets and perform an environmental analysis based on their location information. For example, the analysis unit can analyze the growth status of plants and perform an environmental analysis based on that data. Furthermore, the analysis unit can comprehensively analyze the condition of pets and plants and optimize the home environment. In this way, by analyzing the condition of pets and plants, a comprehensive environmental analysis can be performed.

[0043] The generation unit can learn from past cleaning and tidying data and generate an optimal plan. For example, the generation unit can learn from past cleaning data and generate an optimal cleaning plan. For example, the generation unit can learn from past tidying data and generate an optimal tidying plan. Furthermore, the generation unit can comprehensively learn from past data and generate an optimal cleaning and tidying plan. In this way, by learning from past data, it is possible to generate a more effective plan.

[0044] The generation unit can generate different plans for different areas within the home, enabling efficient cleaning and tidying. For example, in the living room, it can generate a plan that optimizes the placement of items. In the kitchen, for example, it can generate a plan that efficiently cleans dirt. Furthermore, in the bedroom, for example, it can generate a plan that combines the placement of items with cleaning. By generating the optimal plan for each area, efficient cleaning and tidying can be achieved.

[0045] The generation unit can generate different plans for each room in the home and provide the user with the most suitable suggestions. For example, in the living room, the generation unit can generate and suggest a plan that optimizes the placement of objects. In the kitchen, for example, the generation unit can generate and suggest a plan that efficiently cleans dirt. Furthermore, in the bedroom, for example, the generation unit can generate and suggest a plan that combines the placement of objects with cleaning. In this way, by generating the optimal plan for each room, it can provide the user with beneficial suggestions.

[0046] The generation unit can generate plans that take into account the condition of pets and plants in the home, enabling comprehensive environmental management. For example, the generation unit can generate a cleaning plan that takes into account the movements of pets. For example, the generation unit can generate a tidying and organizing plan that takes into account the growth stage of plants. Furthermore, the generation unit can generate an environmental management plan that comprehensively takes into account the condition of pets and plants. In this way, by generating plans that take into account the condition of pets and plants, comprehensive environmental management can be achieved.

[0047] The application unit can apply different IoT device settings to different areas within the home, enabling efficient cleaning and tidying. For example, in the living room, it can optimize the operation of an automatic vacuum cleaner for efficient cleaning. In the kitchen, for example, it can apply an IoT device that automatically starts cleaning when it detects dirt. Furthermore, in the bedroom, for example, it can apply an IoT device to optimize the placement of objects for tidying. By applying the most suitable IoT device settings to each area, efficient cleaning and tidying can be achieved.

[0048] The application unit can learn the operation history of IoT devices and apply the optimal operation pattern. For example, the application unit can learn the past operation history of an automatic vacuum cleaner and apply the optimal cleaning pattern. For example, the application unit can learn the operation history of a dirt-detecting sensor and start cleaning at the optimal timing. Furthermore, the application unit can learn the operation history of an IoT device that optimizes the placement of objects and achieve efficient organization and tidiness. In this way, by learning the operation history, the optimal operation pattern can be applied.

[0049] The application unit can apply different IoT device operations to different rooms in the home, providing optimal suggestions to the user. For example, in the living room, the application unit can optimize the operation of an automatic vacuum cleaner for efficient cleaning. In the kitchen, for example, the application unit can apply an IoT device that automatically starts cleaning when dirt is detected. Furthermore, in the bedroom, for example, the application unit can apply an IoT device to optimize the arrangement of objects for tidiness. By applying the optimal IoT device operation to each room, the application unit can provide beneficial suggestions to the user.

[0050] The application unit can perform comprehensive environmental management by applying IoT device operation that takes into account the condition of pets and plants in the home. For example, the application unit can generate a cleaning plan that takes into account the movements of pets and apply it to IoT devices. For example, the application unit can generate a tidying-up plan that takes into account the growth state of plants and apply it to IoT devices. Furthermore, the application unit can generate an environmental management plan that comprehensively takes into account the condition of pets and plants and apply it to IoT devices. This enables comprehensive environmental management by applying IoT device operation that takes into account the condition of pets and plants.

[0051] The suggestion function can make optimal suggestions by referring to the user's past behavioral history. For example, the suggestion function can refer to the user's past cleaning history and propose the optimal cleaning plan. For example, the suggestion function can refer to the user's past tidying history and propose the optimal tidying plan. Furthermore, the suggestion function can make optimal suggestions by comprehensively referring to the user's past behavioral history. In this way, by referring to past behavioral history, it can make suggestions that are best suited to the user.

[0052] The suggestion department can provide different suggestions for different areas within the home, enabling efficient cleaning and organization. For example, in the living room, it can suggest optimizing the placement of items. In the kitchen, for example, it can suggest efficient cleaning methods. Furthermore, in the bedroom, for example, it can suggest a combination of item placement and cleaning methods. By providing optimal suggestions for each area, efficient cleaning and organization can be achieved.

[0053] The suggestion function can provide users with optimal information by offering different suggestions for each room in the home. For example, in the living room, it can suggest optimizing the arrangement of items. In the kitchen, for example, it can suggest efficient cleaning methods. Furthermore, in the bedroom, for example, it can offer suggestions that combine item arrangement and cleaning methods. By providing optimal suggestions for each room, it can offer users useful information.

[0054] The proposal department can provide suggestions that take into account the condition of pets and plants in the home, enabling comprehensive environmental management. For example, the proposal department can propose a cleaning plan that takes into account the movements of pets. For example, the proposal department can propose a tidying and organizing plan that takes into account the growth stage of plants. Furthermore, the proposal department can propose an environmental management plan that comprehensively considers the condition of pets and plants. In this way, by providing suggestions that take into account the condition of pets and plants, comprehensive environmental management can be achieved.

[0055] The suggestion function can make optimal suggestions by referring to the user's past behavioral history. For example, the suggestion function can refer to the user's past cleaning history and propose the optimal cleaning plan. For example, the suggestion function can refer to the user's past tidying history and propose the optimal tidying plan. Furthermore, the suggestion function can make optimal suggestions by comprehensively referring to the user's past behavioral history. In this way, by referring to past behavioral history, it can make suggestions that are best suited to the user.

[0056] The suggestion department can provide different suggestions for different areas within the home, enabling efficient cleaning and organization. For example, in the living room, it can suggest optimizing the placement of items. In the kitchen, for example, it can suggest efficient cleaning methods. Furthermore, in the bedroom, for example, it can suggest a combination of item placement and cleaning methods. By providing optimal suggestions for each area, efficient cleaning and organization can be achieved.

[0057] The suggestion function can provide users with optimal information by offering different suggestions for each room in the home. For example, in the living room, it can suggest optimizing the arrangement of items. In the kitchen, for example, it can suggest efficient cleaning methods. Furthermore, in the bedroom, for example, it can offer suggestions that combine item arrangement and cleaning methods. By providing optimal suggestions for each room, it can offer users useful information.

[0058] The proposal department can provide suggestions that take into account the condition of pets and plants in the home, enabling comprehensive environmental management. For example, the proposal department can propose a cleaning plan that takes into account the movements of pets. For example, the proposal department can propose a tidying and organizing plan that takes into account the growth stage of plants. Furthermore, the proposal department can propose an environmental management plan that comprehensively considers the condition of pets and plants. In this way, by providing suggestions that take into account the condition of pets and plants, comprehensive environmental management can be achieved.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The home organization and cleaning system can also be equipped with a voice recognition unit. The voice recognition unit can recognize the user's voice commands and issue instructions to the data collection and analysis units. For example, if the user says, "Clean the living room," the voice recognition unit will analyze the instruction and have the data collection unit collect data about the living room. Also, if the user says, "Optimize the arrangement of items," the voice recognition unit can transmit that instruction to the analysis unit, which can then generate an optimal arrangement plan. This allows the user to operate the system using voice commands, resulting in a more intuitive and convenient user experience.

[0061] The home organization and cleaning system can also be equipped with an energy management unit. This unit can monitor household energy consumption and suggest efficient energy use. For example, based on data from the data collection unit, it can identify peak energy consumption times and suggest avoiding vacuum cleaner use during those times. Furthermore, the energy management unit can integrate with a solar power system, adjusting cleaning and organization timings based on power generation. This optimizes household energy consumption and contributes to an environmentally friendly lifestyle.

[0062] The home organization and cleaning system can also be equipped with a security unit. This security unit can monitor home security and notify the user if it detects any anomalies. For example, it can use cameras and sensors to detect suspicious activity and send an alert to the user. It can also monitor the opening and closing of doors and windows and sound an alarm if any abnormalities are detected. This enhances home security and allows for a safer living environment.

[0063] The home organization and cleaning system can also be equipped with a learning unit. This unit can learn the user's behavioral patterns and generate more effective cleaning and organization plans. For example, it can learn how often a user uses a particular room at a specific time of day and adjust the cleaning plan accordingly. It can also learn the user's preferred arrangement of items and suggest an optimal arrangement plan. This allows for the provision of an optimal plan based on the user's behavioral patterns.

[0064] The home organization and cleaning system can also be equipped with an environmental monitoring unit. This unit can collect environmental data such as temperature, humidity, and air quality within the home and make suggestions for maintaining an optimal environment. For example, it can suggest using an air conditioner if the temperature is too high, or a humidifier if the humidity is too low. It can also suggest using an air purifier if the air quality deteriorates. This helps optimize the home environment and supports a comfortable lifestyle.

[0065] The home organization and cleaning system can also be equipped with a remote control unit. The remote control unit provides functionality that allows the user to operate the system even when away from home. For example, the user can start the vacuum cleaner using their smartphone while away from home. Furthermore, the remote control unit allows the user to check the status of the rooms from outside and modify the organization plan as needed. This enables the user to manage home organization and cleaning from anywhere, resulting in a more flexible lifestyle.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The collection unit collects data to understand the condition of the room. The collection unit can collect, for example, image data captured by a camera and dirt data acquired by sensors. Image data captured by a camera includes, for example, the arrangement of objects in the room and the degree of dirt. Dirt data acquired by sensors includes, for example, the amount of dirt on the floor and the amount of dust in the air. Step 2: The analysis unit analyzes the collected data using generative AI to understand the state of the room. The generative AI includes, for example, a process of learning from past data to generate an optimal plan. Step 3: The generation unit generates an optimal cleaning and tidying plan based on the data analyzed using the generation AI. For example, the generation AI analyzes the condition of the room and generates an optimal cleaning and tidying plan. Step 4: The application unit applies the plan generated by the generating AI to an IoT device such as a robotic vacuum cleaner. For example, the application unit uses the generating AI to instruct the robotic vacuum cleaner to start cleaning. Step 5: The suggestion unit proposes to the user an organization plan to optimize the placement of objects generated by the generative AI. For example, the suggestion unit has the generative AI generate a plan to optimize the placement of objects and proposes it to the user.

[0068] (Example of form 2) The home organization and cleaning system according to an embodiment of the present invention is a system that automates home organization and cleaning by combining AI and IoT. This system uses cameras and sensors to understand the state of a room, and a generating AI analyzes the collected data to generate an optimal cleaning and organization plan, which is then applied to IoT devices. The generating AI also generates an organization plan to optimize the placement of items and proposes it to the user. For example, it collects image data captured by cameras and dirt data acquired by sensors, and the generating AI learns from past data to generate an optimal plan. The generated plan is then applied by the generating AI to IoT devices such as robotic vacuum cleaners and proposed to the user. This system enables efficient home organization and cleaning, reducing the burden on the user. For example, the user can leave the cleaning to a robotic vacuum cleaner and receive suggestions to optimize the placement of items. This enables efficient home organization and cleaning, reducing the burden on the user. As a result, the home organization and cleaning system can efficiently organize and clean the home.

[0069] The home tidying and cleaning system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, an application unit, and a proposal unit. The collection unit collects data to understand the state of the room. The collection unit can, for example, collect image data captured by a camera and dirt data acquired by sensors. Image data captured by a camera includes, for example, the arrangement of objects in the room and the state of dirt. Dirt data acquired by sensors includes, for example, the amount of dirt on the floor and the amount of dust in the air. The analysis unit analyzes the collected data using a generation AI to understand the state of the room. The generation AI includes, for example, a process of learning past data to generate an optimal plan. The generation AI learns past cleaning and tidying data and generates an optimal plan. The generation unit generates an optimal cleaning and tidying plan based on the data analyzed using the generation AI. The generation AI analyzes the state of the room and generates an optimal cleaning and tidying plan. The application unit applies the plan generated by the generation AI to an IoT device such as an automatic vacuum cleaner. The application unit, for example, uses a generating AI to instruct an automatic vacuum cleaner to start cleaning. The proposal unit proposes to the user a tidying-up plan to optimize the placement of objects generated by the generating AI. The proposal unit, for example, uses a generating AI to generate a plan to optimize the placement of objects and proposes it to the user. As a result, the home tidying and cleaning system according to the embodiment can efficiently tidy up and clean the home.

[0070] The collection unit can collect image data captured by a camera or dirt data acquired by a sensor. Image data captured by a camera may include, for example, the arrangement of objects in a room and the state of dirt. The collection unit can, for example, take images of a room with a camera and collect that image data. Dirt data acquired by a sensor may include, for example, the amount of dirt on the floor and the amount of dust in the air. The collection unit can, for example, acquire floor dirt data using a sensor and collect that data. The collection unit can also, for example, acquire data using a sensor that measures the amount of dust in the air and collect that data. This allows for a detailed understanding of the room's condition using cameras and sensors.

[0071] The analysis unit can analyze the collected data using a generative AI to understand the state of the room. The generative AI includes, for example, a process of learning from past data to generate an optimal plan. The analysis unit can analyze the collected data using a generative AI to understand the state of the room. The generative AI learns from past cleaning and tidying data to generate an optimal plan. The generative AI analyzes the state of the room to generate an optimal cleaning and tidying plan. As a result, the state of the room can be analyzed with high accuracy by using a generative AI.

[0072] The generation unit can generate cleaning and tidying plans by learning from past data using a generation AI. The generation AI includes, for example, a process of learning from past data to generate an optimal plan. The generation unit can generate optimal cleaning and tidying plans by learning from past data using a generation AI. The generation AI, for example, learns from past cleaning and tidying data to generate an optimal plan. The generation AI, for example, analyzes the state of a room to generate an optimal cleaning and tidying plan. In this way, by learning from past data, it is possible to generate more effective plans.

[0073] The application unit can apply plans generated by the generation AI to IoT devices such as robotic vacuum cleaners. For example, the application unit can use the generation AI to instruct the robotic vacuum cleaner to start cleaning. For example, the application unit can apply the plan generated by the generation AI to the robotic vacuum cleaner to automate cleaning. Furthermore, the application unit can also apply the plan generated by the generation AI to IoT devices such as smart lights and smart thermostats to optimize the home environment. This allows for the automation of cleaning and tidying up by applying the generated plan to IoT devices such as robotic vacuum cleaners.

[0074] The suggestion unit can propose an organization and tidying plan to the user based on the arrangement of items generated by the generation AI. For example, the suggestion unit can have the generation AI generate a plan to optimize the arrangement of items and propose it to the user. For example, the suggestion unit can notify the user of the plan generated by the generation AI and make suggestions for optimizing the arrangement of items. In addition, the suggestion unit can display the plan generated by the generation AI to the user and suggest specific steps for optimizing the arrangement of items. In this way, by proposing a plan to optimize the arrangement of items to the user, the efficiency of organization and tidying can be improved.

[0075] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on those emotions. For example, if the user is relaxed, the unit can reduce the frequency of data collection to avoid disturbing the user's life. If the user is stressed, the unit can adjust the timing of data collection to avoid causing discomfort. Furthermore, if the user is busy, the unit can collect data according to the user's schedule to ensure efficient data collection. This allows for data collection without disturbing the user's life by adjusting the timing of data collection according to their emotions.

[0076] The data collection unit can adjust the type and frequency of data collected based on how often a room is used. For example, in rooms with high usage, the unit will frequently collect data on cleanliness and the arrangement of objects. In rooms with low usage, the unit can reduce the frequency of data collection, collecting only the necessary data. Furthermore, in rooms with high usage during specific time periods, the unit can adjust data collection to match those times. This allows for efficient data collection by adjusting data collection according to the frequency of room use.

[0077] The data collection unit can track the placement of furniture and objects in real time and dynamically collect data. For example, the data collection unit can detect the movement of furniture in real time and collect its location information as data. For example, if the placement of objects changes, the data collection unit can track that change in real time and update the data. In addition, in rooms where the placement of furniture and objects changes frequently, the data collection unit can collect data in real time to understand the latest state. This allows for understanding the latest state by tracking the placement of furniture and objects in real time.

[0078] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on those emotions. For example, if the user is relaxed, the unit will prioritize collecting less important data. If the user is stressed, the unit will prioritize collecting more important data. Furthermore, if the user is busy, the unit can prioritize collecting only the essential data. This allows for the collection of important data by prioritizing it according to the user's emotions.

[0079] The data collection unit can apply different collection methods to each room in the home to collect optimal data. For example, in the living room, the unit can use a camera to collect data on the arrangement of objects and the degree of dirtiness. In the kitchen, for example, the unit can use sensors to collect data on dirtiness. Furthermore, in the bedroom, for example, the unit can combine a camera and sensors to collect data on the arrangement of objects and dirtiness. This allows for efficient data collection by applying the most suitable collection method for each room.

[0080] The data collection unit can simultaneously collect data on the condition of pets and plants in the home, thereby acquiring comprehensive environmental data. For example, the data collection unit can track the movements of pets with a camera and collect their location information as data. For example, the data collection unit can monitor the growth status of plants with sensors and collect that data. Furthermore, the data collection unit can track the condition of pets and plants in real time and acquire comprehensive environmental data. In this way, by simultaneously collecting data on the condition of pets and plants, comprehensive environmental data can be obtained.

[0081] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis to improve accuracy. If the user is stressed, for example, the analysis unit can perform a simplified analysis and provide results quickly. Also, if the user is busy, for example, the analysis unit can perform the minimum necessary analysis and provide results efficiently. In this way, by adjusting the accuracy of the analysis according to the user's emotions, it is possible to provide analysis results that are appropriate to the user's situation.

[0082] The analysis unit can analyze the usage history of a room and predict the optimal timing for cleaning and tidying. For example, the analysis unit can analyze the frequency of room use and predict the optimal timing for cleaning. For example, the analysis unit can predict the optimal timing for tidying based on the room's usage history. Furthermore, for example, if a room is frequently used during a specific time period, the analysis unit can predict the timing for cleaning and tidying to match that time period. In this way, by analyzing the room's usage history, cleaning and tidying can be performed at the optimal time.

[0083] The analysis unit can improve accuracy by applying different analysis algorithms to different areas within the home. For example, in the living room, the analysis unit can apply an algorithm that analyzes the arrangement of objects. In the kitchen, for example, the analysis unit can apply an algorithm that analyzes the state of dirt. Furthermore, in the bedroom, for example, the analysis unit can combine algorithms that analyze the arrangement of objects and the state of dirt. This allows for improved analysis accuracy by applying the most suitable analysis algorithm for each area.

[0084] The analysis unit can estimate the user's emotions and adjust how the analysis results are displayed based on those emotions. For example, if the user is relaxed, the analysis unit can display detailed analysis results. If the user is stressed, for example, the analysis unit can display simplified analysis results. Furthermore, if the user is busy, for example, the analysis unit can display concise analysis results. By adjusting how the analysis results are displayed according to the user's emotions, the system can provide the user with the most relevant information.

[0085] The analysis unit can provide different analysis results for each room in the home, offering the user the most relevant information. For example, in the living room, the analysis unit can provide analysis results regarding the arrangement of objects. In the kitchen, for example, the analysis unit can provide analysis results regarding the state of dirt. Furthermore, in the bedroom, for example, the analysis unit can provide analysis results regarding the arrangement of objects and the state of dirt. By providing optimal analysis results for each room, the system can offer the user valuable information.

[0086] The analysis unit can analyze the condition of pets and plants in the home and perform a comprehensive environmental analysis. For example, the analysis unit can analyze the movements of pets and perform an environmental analysis based on their location information. For example, the analysis unit can analyze the growth status of plants and perform an environmental analysis based on that data. Furthermore, the analysis unit can comprehensively analyze the condition of pets and plants and optimize the home environment. In this way, by analyzing the condition of pets and plants, a comprehensive environmental analysis can be performed.

[0087] The generation unit can estimate the user's emotions and adjust the level of detail in the generated plan based on those emotions. For example, if the user is relaxed, the generation unit can generate a detailed cleaning and tidying plan. If the user is stressed, for example, the generation unit can generate a simplified plan. Also, if the user is busy, for example, the generation unit can generate a plan with only the bare minimum. In this way, by adjusting the level of detail in the plan according to the user's emotions, it is possible to provide the user with the most suitable plan.

[0088] The generation unit can learn from past cleaning and tidying data and generate an optimal plan. For example, the generation unit can learn from past cleaning data and generate an optimal cleaning plan. For example, the generation unit can learn from past tidying data and generate an optimal tidying plan. Furthermore, the generation unit can comprehensively learn from past data and generate an optimal cleaning and tidying plan. In this way, by learning from past data, it is possible to generate a more effective plan.

[0089] The generation unit can generate different plans for different areas within the home, enabling efficient cleaning and tidying. For example, in the living room, it can generate a plan that optimizes the placement of items. In the kitchen, for example, it can generate a plan that efficiently cleans dirt. Furthermore, in the bedroom, for example, it can generate a plan that combines the placement of items with cleaning. By generating the optimal plan for each area, efficient cleaning and tidying can be achieved.

[0090] The generation unit can estimate the user's emotions and determine the priority of the plans to generate based on those emotions. For example, if the user is relaxed, the generation unit will prioritize generating plans of lower importance. If the user is stressed, for example, the generation unit can prioritize generating plans of higher importance. Also, if the user is busy, for example, the generation unit can prioritize generating only the essential plans. In this way, by prioritizing plans according to the user's emotions, important plans can be executed preferentially.

[0091] The generation unit can generate different plans for each room in the home and provide the user with the most suitable suggestions. For example, in the living room, the generation unit can generate and suggest a plan that optimizes the placement of objects. In the kitchen, for example, the generation unit can generate and suggest a plan that efficiently cleans dirt. Furthermore, in the bedroom, for example, the generation unit can generate and suggest a plan that combines the placement of objects with cleaning. In this way, by generating the optimal plan for each room, it can provide the user with beneficial suggestions.

[0092] The generation unit can generate plans that take into account the condition of pets and plants in the home, enabling comprehensive environmental management. For example, the generation unit can generate a cleaning plan that takes into account the movements of pets. For example, the generation unit can generate a tidying and organizing plan that takes into account the growth stage of plants. Furthermore, the generation unit can generate an environmental management plan that comprehensively takes into account the condition of pets and plants. In this way, by generating plans that take into account the condition of pets and plants, comprehensive environmental management can be achieved.

[0093] The application unit can estimate the user's emotions and adjust the operation of the IoT device based on the estimated emotions. For example, if the user is relaxed, the application unit can adjust the operation of the IoT device slowly. For example, if the user is stressed, the application unit can adjust the operation of the IoT device quickly. Furthermore, if the user is busy, the application unit can also adjust the operation of the IoT device efficiently. In this way, by adjusting the operation of the IoT device according to the user's emotions, the optimal operation for the user can be achieved.

[0094] The application unit can apply different IoT device settings to different areas within the home, enabling efficient cleaning and tidying. For example, in the living room, it can optimize the operation of an automatic vacuum cleaner for efficient cleaning. In the kitchen, for example, it can apply an IoT device that automatically starts cleaning when it detects dirt. Furthermore, in the bedroom, for example, it can apply an IoT device to optimize the placement of objects for tidying. By applying the most suitable IoT device settings to each area, efficient cleaning and tidying can be achieved.

[0095] The application unit can learn the operation history of IoT devices and apply the optimal operation pattern. For example, the application unit can learn the past operation history of an automatic vacuum cleaner and apply the optimal cleaning pattern. For example, the application unit can learn the operation history of a dirt-detecting sensor and start cleaning at the optimal timing. Furthermore, the application unit can learn the operation history of an IoT device that optimizes the placement of objects and achieve efficient organization and tidiness. In this way, by learning the operation history, the optimal operation pattern can be applied.

[0096] The application unit can estimate the user's emotions and adjust the operation order of IoT devices based on the estimated emotions. For example, if the user is relaxed, the application unit can slowly adjust the operation order of IoT devices. For example, if the user is stressed, the application unit can quickly adjust the operation order of IoT devices. Furthermore, if the user is busy, the application unit can efficiently adjust the operation order of IoT devices. In this way, by adjusting the operation order of IoT devices according to the user's emotions, the devices can be executed in the optimal order for the user.

[0097] The application unit can apply different IoT device operations to different rooms in the home, providing optimal suggestions to the user. For example, in the living room, the application unit can optimize the operation of an automatic vacuum cleaner for efficient cleaning. In the kitchen, for example, the application unit can apply an IoT device that automatically starts cleaning when dirt is detected. Furthermore, in the bedroom, for example, the application unit can apply an IoT device to optimize the arrangement of objects for tidiness. By applying the optimal IoT device operation to each room, the application unit can provide beneficial suggestions to the user.

[0098] The application unit can perform comprehensive environmental management by applying IoT device operation that takes into account the condition of pets and plants in the home. For example, the application unit can generate a cleaning plan that takes into account the movements of pets and apply it to IoT devices. For example, the application unit can generate a tidying-up plan that takes into account the growth state of plants and apply it to IoT devices. Furthermore, the application unit can generate an environmental management plan that comprehensively takes into account the condition of pets and plants and apply it to IoT devices. This enables comprehensive environmental management by applying IoT device operation that takes into account the condition of pets and plants.

[0099] The suggestion function can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is relaxed, the suggestion function can provide detailed suggestions. If the user is stressed, for example, the suggestion function can provide simplified suggestions. Furthermore, if the user is busy, for example, the suggestion function can provide concise suggestions. By adjusting the presentation of suggestions according to the user's emotions, the system can provide the most suitable suggestions for the user.

[0100] The suggestion function can make optimal suggestions by referring to the user's past behavioral history. For example, the suggestion function can refer to the user's past cleaning history and propose the optimal cleaning plan. For example, the suggestion function can refer to the user's past tidying history and propose the optimal tidying plan. Furthermore, the suggestion function can make optimal suggestions by comprehensively referring to the user's past behavioral history. In this way, by referring to past behavioral history, it can make suggestions that are best suited to the user.

[0101] The suggestion department can provide different suggestions for different areas within the home, enabling efficient cleaning and organization. For example, in the living room, it can suggest optimizing the placement of items. In the kitchen, for example, it can suggest efficient cleaning methods. Furthermore, in the bedroom, for example, it can suggest a combination of item placement and cleaning methods. By providing optimal suggestions for each area, efficient cleaning and organization can be achieved.

[0102] The suggestion function can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is relaxed, the suggestion function will prioritize less important suggestions. If the user is stressed, for example, the suggestion function will prioritize more important suggestions. Also, if the user is busy, for example, the suggestion function can prioritize only the essential suggestions. In this way, by prioritizing suggestions according to the user's emotions, important suggestions can be given priority.

[0103] The suggestion function can provide users with optimal information by offering different suggestions for each room in the home. For example, in the living room, it can suggest optimizing the arrangement of items. In the kitchen, for example, it can suggest efficient cleaning methods. Furthermore, in the bedroom, for example, it can offer suggestions that combine item arrangement and cleaning methods. By providing optimal suggestions for each room, it can offer users useful information.

[0104] The proposal department can provide suggestions that take into account the condition of pets and plants in the home, enabling comprehensive environmental management. For example, the proposal department can propose a cleaning plan that takes into account the movements of pets. For example, the proposal department can propose a tidying and organizing plan that takes into account the growth stage of plants. Furthermore, the proposal department can propose an environmental management plan that comprehensively considers the condition of pets and plants. In this way, by providing suggestions that take into account the condition of pets and plants, comprehensive environmental management can be achieved.

[0105] The suggestion function can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is relaxed, the suggestion function can provide detailed suggestions. If the user is stressed, for example, the suggestion function can provide simplified suggestions. Furthermore, if the user is busy, for example, the suggestion function can provide concise suggestions. By adjusting the presentation of suggestions according to the user's emotions, the system can provide the most suitable suggestions for the user.

[0106] The suggestion function can make optimal suggestions by referring to the user's past behavioral history. For example, the suggestion function can refer to the user's past cleaning history and propose the optimal cleaning plan. For example, the suggestion function can refer to the user's past tidying history and propose the optimal tidying plan. Furthermore, the suggestion function can make optimal suggestions by comprehensively referring to the user's past behavioral history. In this way, by referring to past behavioral history, it can make suggestions that are best suited to the user.

[0107] The suggestion department can provide different suggestions for different areas within the home, enabling efficient cleaning and organization. For example, in the living room, it can suggest optimizing the placement of items. In the kitchen, for example, it can suggest efficient cleaning methods. Furthermore, in the bedroom, for example, it can suggest a combination of item placement and cleaning methods. By providing optimal suggestions for each area, efficient cleaning and organization can be achieved.

[0108] The suggestion function can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is relaxed, the suggestion function will prioritize less important suggestions. If the user is stressed, for example, the suggestion function will prioritize more important suggestions. Also, if the user is busy, for example, the suggestion function can prioritize only the essential suggestions. In this way, by prioritizing suggestions according to the user's emotions, important suggestions can be given priority.

[0109] The suggestion function can provide users with optimal information by offering different suggestions for each room in the home. For example, in the living room, it can suggest optimizing the arrangement of items. In the kitchen, for example, it can suggest efficient cleaning methods. Furthermore, in the bedroom, for example, it can offer suggestions that combine item arrangement and cleaning methods. By providing optimal suggestions for each room, it can offer users useful information.

[0110] The proposal department can provide suggestions that take into account the condition of pets and plants in the home, enabling comprehensive environmental management. For example, the proposal department can propose a cleaning plan that takes into account the movements of pets. For example, the proposal department can propose a tidying and organizing plan that takes into account the growth stage of plants. Furthermore, the proposal department can propose an environmental management plan that comprehensively considers the condition of pets and plants. In this way, by providing suggestions that take into account the condition of pets and plants, comprehensive environmental management can be achieved. === Hard Collateral 1-1 === Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, application unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit uses the camera 42 and sensors of the smart device 14 to grasp the state of the room. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an optimal cleaning and tidying plan. The application unit is implemented by the control unit 46A of the smart device 14 and applies the generated plan to an IoT device such as an automatic vacuum cleaner. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes a plan to the user to optimize the placement of objects. === Hard Collateral 1-2 === Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, application unit, and proposal unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit uses the camera 42 and sensors of the smart glasses 214 to grasp the state of the room. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an optimal cleaning and tidying plan. The application unit is implemented by the control unit 46A of the smart glasses 214 and applies the generated plan to IoT devices such as an automatic vacuum cleaner. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes a plan to the user to optimize the placement of objects. === Hard Collateral 1-3 === Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, application unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit uses the camera 42 and sensors of the headset terminal 314 to grasp the state of the room. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an optimal cleaning and tidying plan. The application unit is implemented by the control unit 46A of the headset terminal 314 and applies the generated plan to IoT devices such as robotic vacuum cleaners. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes a plan to the user to optimize the placement of objects. === Hard Collateral 1-4 === Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, application unit, and proposal unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collection unit uses the camera 42 and sensors of the robot 414 to grasp the state of the room. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an optimal cleaning and tidying plan. The application unit is implemented by the control unit 46A of the robot 414 and applies the generated plan to IoT devices such as robotic vacuum cleaners. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes a plan to the user to optimize the placement of objects.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] The home organization and cleaning system can also be equipped with a voice recognition unit. The voice recognition unit can recognize the user's voice commands and issue instructions to the data collection and analysis units. For example, if the user says, "Clean the living room," the voice recognition unit will analyze the instruction and have the data collection unit collect data about the living room. Also, if the user says, "Optimize the arrangement of items," the voice recognition unit can transmit that instruction to the analysis unit, which can then generate an optimal arrangement plan. This allows the user to operate the system using voice commands, resulting in a more intuitive and convenient user experience.

[0113] The home organization and cleaning system can also be equipped with an energy management unit. This unit can monitor household energy consumption and suggest efficient energy use. For example, based on data from the data collection unit, it can identify peak energy consumption times and suggest avoiding vacuum cleaner use during those times. Furthermore, the energy management unit can integrate with a solar power system, adjusting cleaning and organization timings based on power generation. This optimizes household energy consumption and contributes to an environmentally friendly lifestyle.

[0114] The home organization and cleaning system can also include a health management unit. This unit can monitor the user's health status and suggest appropriate cleaning and organization practices. For example, it can monitor the user's heart rate and stress level, and suggest ways to create a relaxing environment if stress levels are high. It can also suggest cleaning plans to maintain an allergen-free environment for users with allergies. This allows for the provision of an optimal environment tailored to the user's health condition.

[0115] The home organization and cleaning system can also be equipped with an entertainment section. This entertainment section can estimate the user's emotions and provide appropriate entertainment based on those emotions. For example, if the user is relaxed, the entertainment section will play relaxing music. If the user is stressed, the entertainment section can provide content that helps relieve stress. Furthermore, if the user is busy, the entertainment section can suggest content that can be enjoyed in a short amount of time. In this way, by providing entertainment tailored to the user's emotions, it can support a comfortable lifestyle.

[0116] The home organization and cleaning system can also be equipped with a security unit. This security unit can monitor home security and notify the user if it detects any anomalies. For example, it can use cameras and sensors to detect suspicious activity and send an alert to the user. It can also monitor the opening and closing of doors and windows and sound an alarm if any abnormalities are detected. This enhances home security and allows for a safer living environment.

[0117] The home organization and cleaning system can also be equipped with a learning unit. This unit can learn the user's behavioral patterns and generate more effective cleaning and organization plans. For example, it can learn how often a user uses a particular room at a specific time of day and adjust the cleaning plan accordingly. It can also learn the user's preferred arrangement of items and suggest an optimal arrangement plan. This allows for the provision of an optimal plan based on the user's behavioral patterns.

[0118] The home organization and cleaning system can also be equipped with a communication unit. This unit can understand the user's emotions and needs through interaction with the system and respond appropriately. For example, if the user says, "I'm tired today," the communication unit can transmit this information to the analysis unit and adjust the cleaning and organization plan accordingly. Similarly, if the user says, "Please clean the living room more," the communication unit can transmit this instruction to the data collection unit to collect data on the living room. This allows for more personalized service through interaction with the user.

[0119] The home organization and cleaning system can also be equipped with an environmental monitoring unit. This unit can collect environmental data such as temperature, humidity, and air quality within the home and make suggestions for maintaining an optimal environment. For example, it can suggest using an air conditioner if the temperature is too high, or a humidifier if the humidity is too low. It can also suggest using an air purifier if the air quality deteriorates. This helps optimize the home environment and supports a comfortable lifestyle.

[0120] The home organization and cleaning system can also be equipped with a remote control unit. The remote control unit provides functionality that allows the user to operate the system even when away from home. For example, the user can start the vacuum cleaner using their smartphone while away from home. Furthermore, the remote control unit allows the user to check the status of the rooms from outside and modify the organization plan as needed. This enables the user to manage home organization and cleaning from anywhere, resulting in a more flexible lifestyle.

[0121] The home organization and cleaning system can also be equipped with a feedback unit. This feedback unit can collect user feedback and use it to improve the system. For example, if a user evaluates a cleaning plan as effective, the feedback unit can transmit this information to the learning unit, which will then be used to generate future plans. Similarly, if a user evaluates a layout as inconvenient, this information can be transmitted to the analysis unit, which will then be used to improve the layout plan. This allows for continuous improvement of the system based on user feedback, enabling the provision of more effective services.

[0122] The following briefly describes the processing flow for example form 2.

[0123] Step 1: The collection unit collects data to understand the condition of the room. The collection unit can collect, for example, image data captured by a camera and dirt data acquired by sensors. Image data captured by a camera includes, for example, the arrangement of objects in the room and the degree of dirt. Dirt data acquired by sensors includes, for example, the amount of dirt on the floor and the amount of dust in the air. Step 2: The analysis unit analyzes the collected data using generative AI to understand the state of the room. The generative AI includes, for example, a process of learning from past data to generate an optimal plan. Step 3: The generation unit generates an optimal cleaning and tidying plan based on the data analyzed using the generation AI. For example, the generation AI analyzes the condition of the room and generates an optimal cleaning and tidying plan. Step 4: The application unit applies the plan generated by the generating AI to an IoT device such as a robotic vacuum cleaner. For example, the application unit uses the generating AI to instruct the robotic vacuum cleaner to start cleaning. Step 5: The suggestion unit proposes to the user an organization plan to optimize the placement of objects generated by the generative AI. For example, the suggestion unit has the generative AI generate a plan to optimize the placement of objects and proposes it to the user.

[0124] 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.

[0125] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0126] 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.

[0127] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.).

[0140] 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.

[0141] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0142] 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.

[0143] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.).

[0156] 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.

[0157] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] 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.

[0159] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0160] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.).

[0173] 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.

[0174] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0175] 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.

[0176] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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."

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] [Explanation of symbols]

[0196] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data to understand the condition of the room, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit generates a cleaning and tidying plan based on the data analyzed by the aforementioned analysis unit, An application unit that applies the plan generated by the generation unit to an IoT device, The system includes a proposal unit that proposes a plan generated by the generation unit to the user. A system characterized by the following features.

2. The aforementioned collection unit is Collect image data captured by a camera or dirt data acquired by a sensor. The system according to feature 1.

3. The aforementioned analysis unit, The system uses generative AI to analyze collected data and understand the state of the room. The system according to feature 1.

4. The generating unit is Using generative AI, we learn from past data and generate cleaning and tidying plans. The system according to feature 1.

5. The aforementioned application unit is Applying plans generated by generative AI to IoT devices such as robotic vacuum cleaners. The system according to feature 1.

6. The aforementioned proposal section is, The system uses AI generation to create a layout and arrangement plan for objects, which the user then proposes. The system according to feature 1.

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

8. The aforementioned collection unit is Adjust the type and frequency of data collected based on how often the room is used. The system according to feature 1.

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