system

Abstract

The system according to this embodiment aims to provide efficient cleaning routes and methods for disposing of unwanted items. [Solution] The system according to the embodiment comprises a learning unit, a calculation unit, an instruction unit, a notification unit, and a guideline provision unit. The learning unit learns the layout and tendency to get dirty in each room. The calculation unit calculates the optimal cleaning route based on the information learned by the learning unit. The instruction unit issues instructions to the automatic cleaning device based on the cleaning route calculated by the calculation unit. The notification unit notifies the user of the cleaning frequency and the recommended locations of areas to be cleaned. The guideline provision unit learns how to dispose of unwanted items and provides guidance to the user.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the calculation of an efficient cleaning route and the provision of a method for disposing of unnecessary items have not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to calculate an efficient cleaning route and provide a method for disposing of unnecessary items.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a learning unit, a calculation unit, an instruction unit, a notification unit, and a guideline provision unit. The learning unit learns the layout and tendency to get dirty in each room. The calculation unit calculates the optimal cleaning route based on the information learned by the learning unit. The instruction unit issues instructions to the automatic cleaning device based on the cleaning route calculated by the calculation unit. The notification unit notifies the user of the cleaning frequency and the recommended locations of areas to be cleaned. The guideline provision unit learns how to dispose of unwanted items and provides guidance to the user. [Effects of the Invention]

[0007] The system according to this embodiment can calculate efficient cleaning routes and provide methods for disposing of unwanted items. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​cleaning assistant according to an embodiment of the present invention is a system designed for busy business people, parents, the elderly, and those who have difficulty cleaning and do not want to spend time on housework. The AI ​​cleaning assistant learns the layout and tendency to get dirty in each room and automatically instructs the most effective cleaning route and method. For example, it can give instructions to an automated cleaning device. Next, the AI ​​cleaning assistant notifies the user of the frequency of cleaning and the recommended locations of areas to be cleaned, saving the user the trouble of cleaning. Furthermore, the AI ​​cleaning assistant learns how to dispose of unwanted items and provides guidance to the user when discarding items. This makes it easier to choose whether an item is reusable, recyclable, or should be disposed of as garbage. For example, the AI ​​cleaning assistant learns the layout and tendency to get dirty in each room. In this process, it collects information on each room and calculates the optimal cleaning route. For example, it identifies areas that tend to get dirty, such as the living room and kitchen, and suggests an efficient cleaning route. Next, the AI ​​cleaning assistant notifies the user of the frequency of cleaning and the recommended locations of areas to be cleaned. For example, it may recommend that the living room should be cleaned daily, while the bedroom only needs to be cleaned once a week. This allows users to clean efficiently. Furthermore, the AI ​​cleaning assistant learns how to dispose of unwanted items and provides guidance to users when discarding items. For example, it can provide guidance such as plastic products being recyclable, while food waste should be disposed of as garbage. This allows users to choose the appropriate disposal method. This system is extremely useful for busy business people, parents, the elderly, and those who have difficulty cleaning, as they do not want to spend time on housework. By reducing cleaning time and providing efficient cleaning methods, it improves the quality of life for users. In this way, the AI ​​cleaning assistant can reduce users' cleaning time and provide efficient cleaning methods.

[0029] The AI ​​cleaning assistant according to this embodiment comprises a learning unit, a calculation unit, an instruction unit, a notification unit, and a guideline provision unit. The learning unit learns the layout and tendency to get dirty in each room. The learning unit, for example, collects information on each room and calculates the optimal cleaning route. For example, it identifies areas that get dirty easily, such as the living room and kitchen, and proposes an efficient cleaning route. The calculation unit calculates the optimal cleaning route based on the information learned by the learning unit. The calculation unit calculates the cleaning route based on criteria such as time efficiency, energy efficiency, and coverage rate. For example, the calculation unit calculates the cleaning route for the living room, and then the cleaning route for the kitchen. The instruction unit issues instructions to the automatic cleaning device based on the cleaning route calculated by the calculation unit. For example, the instruction unit instructs the automatic cleaning device to start cleaning the living room. The instruction unit can also instruct the device to start cleaning the kitchen. The notification unit notifies the user of the frequency of cleaning and the recommended locations of areas to be cleaned. For example, the notification unit recommends that the living room should be cleaned daily, but the bedroom only needs to be cleaned once a week. This allows users to clean efficiently. The guidance unit learns how to dispose of unwanted items and provides guidance to the user. For example, the guidance unit provides guidance such as plastic products are recyclable, but food waste should be disposed of as garbage. This allows users to choose the appropriate disposal method. As a result, the AI ​​cleaning assistant according to the embodiment can reduce the user's cleaning time and provide an efficient cleaning method.

[0030] The learning unit learns the layout and tendency to get dirty in each room. Specifically, the learning unit collects information such as the dimensions of each room, furniture placement, flooring type, and frequency of use, and uses this data to evaluate how easily each room gets dirty. For example, the living room is a place where the family often gathers, so food spills and dust tend to accumulate there. In the kitchen, there are many oil stains and food scraps generated during cooking, and the sink and stovetop areas are particularly prone to getting dirty. The learning unit inputs this information into an AI algorithm to quantify how easily each room gets dirty. Furthermore, based on past cleaning data and user feedback, the learning unit identifies areas and times that are prone to getting dirty and proposes an efficient cleaning route. For example, in the living room, it learns that the area under the sofa and around the television are particularly prone to getting dirty, and calculates a route that focuses on cleaning these areas. In this way, the learning unit gains a detailed understanding of the characteristics of each room and provides a foundation for proposing the optimal cleaning route.

[0031] The calculation unit calculates the optimal cleaning route based on information learned by the learning unit. Specifically, the calculation unit calculates the cleaning route based on criteria such as time efficiency, energy efficiency, and coverage rate. For example, when calculating the cleaning route for a living room, it considers the arrangement of furniture and areas prone to getting dirty to derive the route that achieves the highest coverage rate in the shortest time. The calculation unit uses an AI algorithm to simulate multiple routes and select the most efficient one. Furthermore, the calculation unit has the ability to detect obstacles and unexpected dirt that occur during cleaning in real time and dynamically correct the route. For example, if new dirt is discovered while cleaning the living room, the calculation unit immediately recalculates the route and continues cleaning efficiently. The calculation unit can also consider the frequency and time of day of cleaning and propose a flexible cleaning schedule that matches the user's lifestyle. In this way, the calculation unit always provides the optimal cleaning route and achieves efficient cleaning.

[0032] The instruction unit issues commands to the automatic cleaning device based on the cleaning route calculated by the calculation unit. Specifically, the instruction unit instructs the automatic cleaning device to start cleaning the living room. Furthermore, if the instruction unit detects obstacles or unexpected dirt during cleaning, it can issue new commands to the automatic cleaning device in real time. For example, if furniture is moved while cleaning the living room, the instruction unit immediately calculates a new route and issues commands to the automatic cleaning device. In addition, the instruction unit can monitor the progress of cleaning and adjust the cleaning intensity and speed as needed. For example, in particularly dirty areas, it can instruct the cleaning device to increase its cleaning intensity to efficiently remove dirt. The instruction unit also adjusts the timing of cleaning to match the user's schedule, ensuring that it does not disrupt the user's life. This allows the instruction unit to efficiently and flexibly control the automatic cleaning device, achieving optimal cleaning results.

[0033] The notification unit informs the user about the frequency of cleaning and recommended areas to clean. Specifically, it recommends that the living room should be cleaned daily, while the bedroom only needs to be cleaned once a week. The notification unit sends notifications to the user's smartphone or tablet, informing them of the timing and areas to clean. Furthermore, the notification unit also notifies the user in real time about the progress of cleaning and completion reports. For example, when cleaning the living room is complete, the notification unit sends a completion report to the user and suggests the next area to clean. The notification unit can also collect user feedback and continuously improve its cleaning frequency and area recommendations. For example, if a user wants to change the cleaning frequency of a particular area, the notification unit learns this information and reflects it in future notifications. In this way, the notification unit provides users with an efficient and flexible cleaning schedule, supporting their daily lives.

[0034] The guidance provision department learns about methods for disposing of unwanted items and provides guidance to users. Specifically, the guidance provision department provides guidance such as "plastic products are recyclable, but food waste should be disposed of as garbage." The guidance provision department learns about local recycling rules and waste sorting methods and proposes appropriate disposal methods to users. For example, when a user disposes of plastic products, it distinguishes between recyclable and non-recyclable plastics and instructs them on the appropriate disposal method. The guidance provision department can also propose an optimal garbage collection schedule according to the user's lifestyle and the amount of garbage. For example, if the amount of garbage in a household is large, it recommends taking out the garbage multiple times a week to provide an efficient way to dispose of garbage. Furthermore, based on user feedback, the guidance provision department can continuously improve the content of its guidelines and provide more appropriate disposal methods. In this way, the guidance provision department supports users in properly and efficiently disposing of unwanted items and contributes to environmental protection.

[0035] The learning unit can learn the layout and tendency to get dirty in each room. For example, the learning unit collects information about each room and calculates the optimal cleaning route. For example, it identifies areas that get dirty easily, such as the living room and kitchen, and proposes an efficient cleaning route. In this way, by learning the layout and tendency to get dirty in each room, it becomes possible to calculate the optimal cleaning route. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input information about each room into the AI ​​and have the AI ​​perform the calculation of the optimal cleaning route.

[0036] The calculation unit can calculate the optimal cleaning route based on the information learned by the learning unit. The calculation unit calculates the cleaning route based on criteria such as time efficiency, energy efficiency, and coverage rate. For example, the calculation unit calculates the cleaning route for the living room, and then the cleaning route for the kitchen. This enables efficient cleaning by calculating the optimal cleaning route based on the learned information. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input the information learned by the learning unit into the AI ​​and have the AI ​​perform the calculation of the optimal cleaning route.

[0037] The instruction unit can issue instructions to the automatic cleaning device based on the cleaning route calculated by the calculation unit. For example, the instruction unit can instruct the automatic cleaning device to start cleaning the living room. The instruction unit can also instruct the device to start cleaning the kitchen. This enables efficient cleaning by issuing instructions to the automatic cleaning device based on the calculated cleaning route. Some or all of the above processing in the instruction unit may be performed using AI, for example, or without AI. For example, the instruction unit can input the cleaning route calculated by the calculation unit into the AI ​​and have the AI ​​execute the instructions to the automatic cleaning device.

[0038] The notification unit can inform the user about the frequency of cleaning and the recommended locations of areas to clean. For example, the notification unit might recommend that the living room should be cleaned daily, while the bedroom only needs to be cleaned once a week. This allows the user to clean efficiently. Some or all of the above-described processes in the notification unit may be performed using AI, or not. For example, the notification unit can input the cleaning frequency and recommended locations of areas to clean into the AI ​​and have the AI ​​execute the notification to the user.

[0039] The guidance provision unit can learn how to dispose of unwanted items and provide guidance to the user. For example, the guidance provision unit may provide guidance such as "plastic products are recyclable, but food waste should be disposed of as garbage." This allows the user to choose the appropriate disposal method. Some or all of the above processing in the guidance provision unit may be performed using AI, for example, or not using AI. For example, the guidance provision unit can input methods for disposing of unwanted items into the AI ​​and have the AI ​​perform the task of providing guidance to the user.

[0040] The learning unit can improve the accuracy of learning how easily a room gets dirty by considering the frequency and time of use of each room. For example, if the living room is used frequently, the learning unit will focus on learning how easily that area gets dirty. The learning unit can also learn the dirt patterns during specific times if the kitchen is used during those times. Furthermore, if the bedroom is used infrequently, the learning unit can reduce the learning frequency for that area and concentrate resources on other areas. This improves the accuracy of learning how easily a room gets dirty by considering the frequency and time of use. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input data on the frequency and time of use of each room into the AI ​​and have the AI ​​perform the task of improving the accuracy of learning how easily a room gets dirty.

[0041] The learning unit can learn patterns of dirt accumulation based on the arrangement and materials of furniture. For example, in a room with many wooden furniture pieces, the learning unit can learn how easily wood gets dirty and suggest appropriate cleaning methods. The learning unit can also identify and learn areas where dirt tends to accumulate based on the arrangement of sofas and carpets. Furthermore, in a room with many glass furniture pieces, the learning unit can learn how easily fingerprints and dirt adhere to glass and suggest effective cleaning methods. This allows for more effective cleaning by learning patterns of dirt accumulation based on the arrangement and materials of furniture. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input data on furniture arrangement and materials into an AI and have the AI ​​learn patterns of dirt accumulation.

[0042] The learning unit can learn how easily a surface gets dirty by taking into account environmental data such as room temperature and humidity. For example, in a high-humidity room, the learning unit can learn patterns of dirt caused by mold and moisture. In a low-temperature room, the learning unit can also learn patterns of dirt caused by dust and static electricity. Furthermore, in a room with drastic temperature changes, the learning unit can learn seasonal dirt patterns. This improves the accuracy of learning how easily a surface gets dirty by taking environmental data into account. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input room temperature and humidity data into the AI ​​and have the AI ​​perform the learning of how easily a surface gets dirty.

[0043] The learning unit can learn how easily surfaces get dirty, taking into account family structure and whether or not pets are present. For example, in households with pets, the learning unit can learn the patterns of pet hair and dirt. In households with children, the learning unit can also learn the dirt patterns in areas where children frequently play. Furthermore, in households with elderly people, the learning unit can learn dirt patterns based on the frequency of use of specific areas. This improves the accuracy of learning how easily surfaces get dirty by considering family structure and whether or not pets are present. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input data on family structure and whether or not pets are present into the AI ​​and have the AI ​​perform the learning of how easily surfaces get dirty.

[0044] The calculation unit can improve the accuracy of cleaning route calculations by considering the shape of the room and the location of obstacles. For example, if the shape of the room is complex, the calculation unit can calculate the optimal route to avoid obstacles. The calculation unit can also calculate an efficient cleaning route by considering the arrangement of furniture. Furthermore, the calculation unit can detect the location of obstacles in real time and dynamically adjust the route. This improves the accuracy of cleaning route calculations by considering the shape of the room and the location of obstacles. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input data on the shape of the room and the location of obstacles into the AI ​​and have the AI ​​perform the task of improving the accuracy of cleaning route calculations.

[0045] The calculation unit can dynamically calculate the optimal cleaning route based on the time of day and frequency of cleaning. For example, the calculation unit can calculate a route that prioritizes cleaning the living room and kitchen in the morning. It can also calculate a route that prioritizes cleaning the bedroom and bathroom in the evening. Furthermore, it can calculate a route for overall cleaning on weekends. This enables efficient cleaning by dynamically calculating the cleaning route based on the time of day and frequency of cleaning. Some or all of the above processing in the calculation unit may be performed using AI, for example, or not. For example, the calculation unit can input data on the time of day and frequency of cleaning into the AI ​​and have the AI ​​perform the dynamic calculation of the optimal cleaning route.

[0046] The calculation unit can calculate the optimal cleaning route by considering the room's lighting conditions. For example, if the room is dark, the calculation unit will consider the lighting and calculate a safe route. Furthermore, if the room is bright, the calculation unit can also calculate an efficient cleaning route. In addition, the calculation unit can consider the position of the lighting to calculate a route that minimizes shadows. This improves the accuracy of the cleaning route calculation by considering the lighting conditions. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input room lighting condition data into the AI ​​and have the AI ​​calculate the optimal cleaning route.

[0047] The calculation unit can calculate the optimal cleaning route by considering the acoustic conditions of the room. For example, if the room is quiet, the calculation unit can calculate a route that allows for cleaning without making noise. Furthermore, if the room is noisy, the calculation unit can also calculate an efficient cleaning route. In addition, considering the acoustic conditions, the calculation unit can calculate a route that minimizes sound reflection. This improves the accuracy of the cleaning route calculation by considering the acoustic conditions. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input data on the room's acoustic conditions into the AI ​​and have the AI ​​calculate the optimal cleaning route.

[0048] The instruction unit can optimize the instructions given by considering the battery level and performance of the cleaning device. For example, if the battery level is low, the instruction unit will issue cleaning instructions that can be completed in a short time. Also, if the cleaning device has high performance, the instruction unit can issue detailed cleaning instructions to perform efficient cleaning. Furthermore, if the battery level is sufficient, the instruction unit can issue overall cleaning instructions. This makes efficient cleaning possible by considering the battery level and performance. Some or all of the above processing in the instruction unit may be performed using AI, for example, or without AI. For example, the instruction unit can input data on the cleaning device's battery level and performance into the AI ​​and have the AI ​​perform the optimization of the instructions.

[0049] The instruction unit can analyze the operation history of the cleaning device and improve the instructions for the next cleaning. For example, the instruction unit can analyze the most efficient cleaning route from past operation history and reflect this in the next instructions. The instruction unit can also issue instructions to optimize the performance of the cleaning device based on the operation history. Furthermore, the instruction unit can analyze the operation history and issue instructions to adjust the cleaning frequency of specific areas. As a result, by analyzing the operation history, the instructions for the next cleaning are improved, enabling more efficient cleaning. Some or all of the above processes in the instruction unit may be performed using AI, for example, or without AI. For example, the instruction unit can input data from the cleaning device's operation history into the AI ​​and have the AI ​​implement improvements to the instructions for the next cleaning.

[0050] The instruction unit can customize the instructions according to the type and model of the automated cleaning device. For example, the instruction unit can provide detailed cleaning instructions to high-performance cleaning devices, while providing simpler instructions to low-performance devices. Furthermore, the instruction unit can provide instructions optimized for specific models. This allows for efficient cleaning by customizing the instructions according to the type and model of the cleaning device. Some or all of the above processing in the instruction unit may be performed using AI, for example, or not. For example, the instruction unit can input data on the type and model of the automated cleaning device into the AI ​​and have the AI ​​perform the customization of the instructions.

[0051] The instruction unit can adjust its instructions based on the operating noise and vibration of the cleaning device. For example, if the operating noise is loud, the instruction unit can issue instructions to clean during quieter times. It can also issue instructions to minimize vibrations if they are strong. Furthermore, the instruction unit can issue efficient cleaning instructions based on the operating noise and vibration. This allows for more efficient cleaning by considering the operating noise and vibration. Some or all of the above processing in the instruction unit may be performed using AI, or not. For example, the instruction unit can input data on the operating noise and vibration of the cleaning device into the AI ​​and have the AI ​​adjust the instructions.

[0052] The notification unit can select the optimal notification timing by considering the user's schedule and lifestyle patterns. For example, the notification unit can refer to the user's calendar information and send notifications during free time. It can also analyze the user's lifestyle patterns and send notifications at the optimal time. Furthermore, the notification unit can send notifications while avoiding the user's busy times. In this way, the optimal notification timing is selected by considering the schedule and lifestyle patterns. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input data on the user's schedule and lifestyle patterns into AI and have the AI ​​select the optimal notification timing.

[0053] The notification unit can include cleaning progress and estimated completion time in its notifications. For example, the notification unit can notify the user of cleaning progress in real time, informing them of the current status. It can also notify the user of the estimated completion time, letting them know how long it will take until the cleaning is finished. Furthermore, the notification unit can combine cleaning progress and estimated completion time in its notifications, allowing the user to grasp the overall situation. This makes it easier for the user to understand the cleaning status by including cleaning progress and estimated completion time. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input data on cleaning progress and estimated completion time into the AI ​​and have the AI ​​generate the notification content.

[0054] The notification unit can select the optimal notification method by considering the user's device information when sending a notification. For example, if the user is using a smartphone, the notification unit will send a push notification. Furthermore, if the user is using a tablet, the notification unit can also send a notification optimized for a larger screen. Additionally, if the user is using a smartwatch, the notification unit can send a concise and highly visible notification. This ensures that the optimal notification method is selected by considering the device information. Some or all of the above processing in the notification unit may be performed using AI, or not. For example, the notification unit can input the user's device information into the AI ​​and have the AI ​​select the optimal notification method.

[0055] The notification unit can include cleaning tips and advice in its notifications. For example, it can notify users of cleaning tips based on their cleaning progress. It can also provide advice on how to improve cleaning efficiency. Furthermore, it can provide advice on cleaning frequency and methods. By including cleaning tips and advice, users can clean more efficiently. Some or all of the above processing in the notification unit may be performed using AI, for example, or not. For example, the notification unit can input data on cleaning tips and advice into an AI and have the AI ​​generate the notification content.

[0056] The guidance provision unit can propose the optimal disposal method by considering the material and condition of the unwanted items. For example, since plastic products are recyclable, the guidance provision unit can propose a recycling method. Also, since food waste should be disposed of as garbage, the guidance provision unit can propose an appropriate garbage disposal method. Furthermore, since metal products are reusable, the guidance provision unit can propose a reuse method. In this way, the optimal disposal method is proposed by considering the material and condition. Some or all of the above processing in the guidance provision unit may be performed using AI, for example, or not using AI. For example, the guidance provision unit can input data on the material and condition of the unwanted items into the AI ​​and have the AI ​​propose the optimal disposal method.

[0057] The guidance provision unit can propose disposal methods, taking into account local recycling rules and waste collection schedules. For example, the guidance provision unit can propose appropriate recycling methods based on local recycling rules. It can also propose the optimal timing for waste disposal, taking into account waste collection schedules. Furthermore, the guidance provision unit can provide information on local recycling facilities and waste collection points. This ensures that appropriate disposal methods are proposed, taking into account local rules and schedules. Some or all of the above processes in the guidance provision unit may be performed using AI, for example, or not. For example, the guidance provision unit can input data on local recycling rules and waste collection schedules into AI and have the AI ​​propose disposal methods.

[0058] The guidance provision unit can suggest the optimal disposal location when disposing of unwanted items, taking into account the user's geographical location. For example, the guidance provision unit can suggest the recycling facility closest to the user's current location. It can also suggest the optimal waste collection point based on the user's geographical location. Furthermore, the guidance provision unit can suggest the optimal disposal location, taking into account the user's geographical location. In this way, the optimal disposal location is suggested by considering geographical location. Some or all of the above processing in the guidance provision unit may be performed using AI, for example, or without AI. For example, the guidance provision unit can input the user's geographical location into AI and have AI suggest the optimal disposal location.

[0059] The guidance provision unit can provide information on relevant recycling companies and facilities when disposing of unwanted items. For example, when a user disposes of recyclable unwanted items, the guidance provision unit can provide information on the nearest recycling company. The guidance provision unit can also provide information on relevant recycling facilities when a user disposes of specific unwanted items. Furthermore, the guidance provision unit can provide information on relevant recycling companies and facilities when a user disposes of unwanted items. By providing relevant information, appropriate disposal becomes possible. Some or all of the above processing in the guidance provision unit may be performed using AI, for example, or not using AI. For example, the guidance provision unit can input information on recycling companies and facilities into AI and have the AI ​​perform the information provision.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] An AI cleaning assistant can monitor the user's health and adjust the frequency and method of cleaning based on that health status. For example, if the user has allergies, it can adjust the cleaning frequency to remove allergens. If the user has a cold, it can reduce the cleaning frequency and prioritize rest. Furthermore, if the user is healthy, it can maintain the normal cleaning frequency. This provides a healthier living environment by adjusting the cleaning frequency and method based on the user's health status. Health status monitoring can be done using, for example, wearable devices or health apps. Health data can be input into the AI, and the AI ​​can then adjust the cleaning frequency and method.

[0062] An AI cleaning assistant can learn the user's daily routine and suggest an optimal cleaning schedule. For example, if the user wakes up early, it can suggest cleaning in the morning. Similarly, if the user stays up late, it can suggest cleaning in the evening. Furthermore, if the user has an irregular sleep schedule, it can suggest a flexible cleaning schedule. This allows for efficient cleaning by suggesting an optimal cleaning schedule based on the user's daily rhythm. Learning of daily routines can be done using, for example, smart home devices or life-logging apps. By inputting this daily routine data into the AI, the AI ​​can then suggest and execute the optimal cleaning schedule.

[0063] An AI cleaning assistant can learn the user's family structure and lifestyle to suggest the optimal cleaning method. For example, in a household with pets, it can suggest a cleaning method that efficiently removes pet hair. In a household with children, it can suggest a method that focuses on cleaning areas where children frequently play. Furthermore, in a household with elderly people, it can suggest a method that prioritizes cleaning areas frequently used by the elderly. This allows for efficient cleaning by suggesting the optimal cleaning method based on family structure and lifestyle. Learning family structure and lifestyle can be done using, for example, smart home devices or life logging apps. By inputting family structure and lifestyle data into the AI, the AI ​​can suggest and execute the optimal cleaning method.

[0064] An AI cleaning assistant can learn the user's lifestyle patterns and optimize the frequency and timing of cleaning. For example, if the user is often home on weekends, it can suggest concentrating cleaning on weekends. If the user is busy during the week, it can suggest short cleaning sessions on weekday evenings. Furthermore, if the user has an irregular lifestyle, it can suggest a flexible cleaning schedule. This optimizes cleaning frequency and timing based on the user's lifestyle, enabling more efficient cleaning. Lifestyle patterns can be learned using, for example, smart home devices or life-logging apps. By inputting lifestyle pattern data into the AI, the AI ​​can optimize cleaning frequency and timing.

[0065] An AI cleaning assistant can learn the structure and interior design of a user's home and suggest the optimal cleaning method. For example, in a home with a complex structure, it can suggest an efficient cleaning route. It can also suggest cleaning methods that match the interior design, taking care not to damage furniture or decorations. Furthermore, it can suggest cleaning methods that focus on specific areas for more efficient cleaning. This means that the optimal cleaning method is suggested based on the home's structure and interior design, enabling efficient cleaning. Learning the home's structure and interior design can be done, for example, using smart home devices or interior design apps. By inputting the home's structure and interior design data into the AI, the AI ​​can suggest and execute the optimal cleaning method.

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

[0067] Step 1: The learning unit learns the layout and tendency to get dirty in each room. For example, it identifies areas that tend to get dirty, such as the living room and kitchen, and collects information on each room to propose an efficient cleaning route. Step 2: The calculation unit calculates the optimal cleaning route based on the information learned by the learning unit. For example, it calculates the cleaning route based on criteria such as time efficiency, energy efficiency, and coverage rate, and sequentially calculates the cleaning routes for the living room and kitchen. Step 3: The instruction unit issues instructions to the automatic cleaning device based on the cleaning route calculated by the calculation unit. For example, it might instruct the automatic cleaning device to start cleaning the living room, and then to start cleaning the kitchen. Step 4: The notification unit informs the user about the frequency of cleaning and the recommended areas to clean. For example, it might recommend that the living room should be cleaned daily, while the bedroom only needs to be cleaned once a week. Step 5: The guidance provision department learns how to dispose of unwanted items and provides guidance to users. For example, it provides guidance such as plastic products being recyclable, but food waste should be disposed of as garbage.

[0068] (Example of form 2) The AI ​​cleaning assistant according to an embodiment of the present invention is a system designed for busy business people, parents, the elderly, and those who have difficulty cleaning and do not want to spend time on housework. The AI ​​cleaning assistant learns the layout and tendency to get dirty in each room and automatically instructs the most effective cleaning route and method. For example, it can give instructions to an automated cleaning device. Next, the AI ​​cleaning assistant notifies the user of the frequency of cleaning and the recommended locations of areas to be cleaned, saving the user the trouble of cleaning. Furthermore, the AI ​​cleaning assistant learns how to dispose of unwanted items and provides guidance to the user when discarding items. This makes it easier to choose whether an item is reusable, recyclable, or should be disposed of as garbage. For example, the AI ​​cleaning assistant learns the layout and tendency to get dirty in each room. In this process, it collects information on each room and calculates the optimal cleaning route. For example, it identifies areas that tend to get dirty, such as the living room and kitchen, and suggests an efficient cleaning route. Next, the AI ​​cleaning assistant notifies the user of the frequency of cleaning and the recommended locations of areas to be cleaned. For example, it may recommend that the living room should be cleaned daily, while the bedroom only needs to be cleaned once a week. This allows users to clean efficiently. Furthermore, the AI ​​cleaning assistant learns how to dispose of unwanted items and provides guidance to users when discarding items. For example, it can provide guidance such as plastic products being recyclable, while food waste should be disposed of as garbage. This allows users to choose the appropriate disposal method. This system is extremely useful for busy business people, parents, the elderly, and those who have difficulty cleaning, as they do not want to spend time on housework. By reducing cleaning time and providing efficient cleaning methods, it improves the quality of life for users. In this way, the AI ​​cleaning assistant can reduce users' cleaning time and provide efficient cleaning methods.

[0069] The AI ​​cleaning assistant according to this embodiment comprises a learning unit, a calculation unit, an instruction unit, a notification unit, and a guideline provision unit. The learning unit learns the layout and tendency to get dirty in each room. The learning unit, for example, collects information on each room and calculates the optimal cleaning route. For example, it identifies areas that get dirty easily, such as the living room and kitchen, and proposes an efficient cleaning route. The calculation unit calculates the optimal cleaning route based on the information learned by the learning unit. The calculation unit calculates the cleaning route based on criteria such as time efficiency, energy efficiency, and coverage rate. For example, the calculation unit calculates the cleaning route for the living room, and then the cleaning route for the kitchen. The instruction unit issues instructions to the automatic cleaning device based on the cleaning route calculated by the calculation unit. For example, the instruction unit instructs the automatic cleaning device to start cleaning the living room. The instruction unit can also instruct the device to start cleaning the kitchen. The notification unit notifies the user of the frequency of cleaning and the recommended locations of areas to be cleaned. For example, the notification unit recommends that the living room should be cleaned daily, but the bedroom only needs to be cleaned once a week. This allows users to clean efficiently. The guidance unit learns how to dispose of unwanted items and provides guidance to the user. For example, the guidance unit provides guidance such as plastic products are recyclable, but food waste should be disposed of as garbage. This allows users to choose the appropriate disposal method. As a result, the AI ​​cleaning assistant according to the embodiment can reduce the user's cleaning time and provide an efficient cleaning method.

[0070] The learning unit learns the layout and tendency to get dirty in each room. Specifically, the learning unit collects information such as the dimensions of each room, furniture placement, flooring type, and frequency of use, and uses this data to evaluate how easily each room gets dirty. For example, the living room is a place where the family often gathers, so food spills and dust tend to accumulate there. In the kitchen, there are many oil stains and food scraps generated during cooking, and the sink and stovetop areas are particularly prone to getting dirty. The learning unit inputs this information into an AI algorithm to quantify how easily each room gets dirty. Furthermore, based on past cleaning data and user feedback, the learning unit identifies areas and times that are prone to getting dirty and proposes an efficient cleaning route. For example, in the living room, it learns that the area under the sofa and around the television are particularly prone to getting dirty, and calculates a route that focuses on cleaning these areas. In this way, the learning unit gains a detailed understanding of the characteristics of each room and provides a foundation for proposing the optimal cleaning route.

[0071] The calculation unit calculates the optimal cleaning route based on information learned by the learning unit. Specifically, the calculation unit calculates the cleaning route based on criteria such as time efficiency, energy efficiency, and coverage rate. For example, when calculating the cleaning route for a living room, it considers the arrangement of furniture and areas prone to getting dirty to derive the route that achieves the highest coverage rate in the shortest time. The calculation unit uses an AI algorithm to simulate multiple routes and select the most efficient one. Furthermore, the calculation unit has the ability to detect obstacles and unexpected dirt that occur during cleaning in real time and dynamically correct the route. For example, if new dirt is discovered while cleaning the living room, the calculation unit immediately recalculates the route and continues cleaning efficiently. The calculation unit can also consider the frequency and time of day of cleaning and propose a flexible cleaning schedule that matches the user's lifestyle. In this way, the calculation unit always provides the optimal cleaning route and achieves efficient cleaning.

[0072] The instruction unit issues commands to the automatic cleaning device based on the cleaning route calculated by the calculation unit. Specifically, the instruction unit instructs the automatic cleaning device to start cleaning the living room. Furthermore, if the instruction unit detects obstacles or unexpected dirt during cleaning, it can issue new commands to the automatic cleaning device in real time. For example, if furniture is moved while cleaning the living room, the instruction unit immediately calculates a new route and issues commands to the automatic cleaning device. In addition, the instruction unit can monitor the progress of cleaning and adjust the cleaning intensity and speed as needed. For example, in particularly dirty areas, it can instruct the cleaning device to increase its cleaning intensity to efficiently remove dirt. The instruction unit also adjusts the timing of cleaning to match the user's schedule, ensuring that it does not disrupt the user's life. This allows the instruction unit to efficiently and flexibly control the automatic cleaning device, achieving optimal cleaning results.

[0073] The notification unit informs the user about the frequency of cleaning and recommended areas to clean. Specifically, it recommends that the living room should be cleaned daily, while the bedroom only needs to be cleaned once a week. The notification unit sends notifications to the user's smartphone or tablet, informing them of the timing and areas to clean. Furthermore, the notification unit also notifies the user in real time about the progress of cleaning and completion reports. For example, when cleaning the living room is complete, the notification unit sends a completion report to the user and suggests the next area to clean. The notification unit can also collect user feedback and continuously improve its cleaning frequency and area recommendations. For example, if a user wants to change the cleaning frequency of a particular area, the notification unit learns this information and reflects it in future notifications. In this way, the notification unit provides users with an efficient and flexible cleaning schedule, supporting their daily lives.

[0074] The guidance provision department learns about methods for disposing of unwanted items and provides guidance to users. Specifically, the guidance provision department provides guidance such as "plastic products are recyclable, but food waste should be disposed of as garbage." The guidance provision department learns about local recycling rules and waste sorting methods and proposes appropriate disposal methods to users. For example, when a user disposes of plastic products, it distinguishes between recyclable and non-recyclable plastics and instructs them on the appropriate disposal method. The guidance provision department can also propose an optimal garbage collection schedule according to the user's lifestyle and the amount of garbage. For example, if the amount of garbage in a household is large, it recommends taking out the garbage multiple times a week to provide an efficient way to dispose of garbage. Furthermore, based on user feedback, the guidance provision department can continuously improve the content of its guidelines and provide more appropriate disposal methods. In this way, the guidance provision department supports users in properly and efficiently disposing of unwanted items and contributes to environmental protection.

[0075] The learning unit can learn the layout and tendency to get dirty in each room. For example, the learning unit collects information about each room and calculates the optimal cleaning route. For example, it identifies areas that get dirty easily, such as the living room and kitchen, and proposes an efficient cleaning route. In this way, by learning the layout and tendency to get dirty in each room, it becomes possible to calculate the optimal cleaning route. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input information about each room into the AI ​​and have the AI ​​perform the calculation of the optimal cleaning route.

[0076] The calculation unit can calculate the optimal cleaning route based on the information learned by the learning unit. The calculation unit calculates the cleaning route based on criteria such as time efficiency, energy efficiency, and coverage rate. For example, the calculation unit calculates the cleaning route for the living room, and then the cleaning route for the kitchen. This enables efficient cleaning by calculating the optimal cleaning route based on the learned information. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input the information learned by the learning unit into the AI ​​and have the AI ​​perform the calculation of the optimal cleaning route.

[0077] The instruction unit can issue instructions to the automatic cleaning device based on the cleaning route calculated by the calculation unit. For example, the instruction unit can instruct the automatic cleaning device to start cleaning the living room. The instruction unit can also instruct the device to start cleaning the kitchen. This enables efficient cleaning by issuing instructions to the automatic cleaning device based on the calculated cleaning route. Some or all of the above processing in the instruction unit may be performed using AI, for example, or without AI. For example, the instruction unit can input the cleaning route calculated by the calculation unit into the AI ​​and have the AI ​​execute the instructions to the automatic cleaning device.

[0078] The notification unit can inform the user about the frequency of cleaning and the recommended locations of areas to clean. For example, the notification unit might recommend that the living room should be cleaned daily, while the bedroom only needs to be cleaned once a week. This allows the user to clean efficiently. Some or all of the above-described processes in the notification unit may be performed using AI, or not. For example, the notification unit can input the cleaning frequency and recommended locations of areas to clean into the AI ​​and have the AI ​​execute the notification to the user.

[0079] The guidance provision unit can learn how to dispose of unwanted items and provide guidance to the user. For example, the guidance provision unit may provide guidance such as "plastic products are recyclable, but food waste should be disposed of as garbage." This allows the user to choose the appropriate disposal method. Some or all of the above processing in the guidance provision unit may be performed using AI, for example, or not using AI. For example, the guidance provision unit can input methods for disposing of unwanted items into the AI ​​and have the AI ​​perform the task of providing guidance to the user.

[0080] The learning unit can estimate the user's emotions and adjust the learning method for room layout and dirtiness based on the estimated user emotions. For example, if the user is stressed, the learning unit will prioritize learning simple layouts and postpone learning complex layouts. If the user is relaxed, the learning unit can also collect detailed layout information and precisely learn dirtiness patterns. Furthermore, if the user is in a hurry, the learning unit can focus on learning the main dirtiness areas. This allows for more appropriate learning by adjusting the learning method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI or not using AI. For example, the learning unit can input user emotion data into an AI and have the AI ​​perform the adjustment of the learning method.

[0081] The learning unit can improve the accuracy of learning how easily a room gets dirty by considering the frequency and time of use of each room. For example, if the living room is used frequently, the learning unit will focus on learning how easily that area gets dirty. The learning unit can also learn the dirt patterns during specific times if the kitchen is used during those times. Furthermore, if the bedroom is used infrequently, the learning unit can reduce the learning frequency for that area and concentrate resources on other areas. This improves the accuracy of learning how easily a room gets dirty by considering the frequency and time of use. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input data on the frequency and time of use of each room into the AI ​​and have the AI ​​perform the task of improving the accuracy of learning how easily a room gets dirty.

[0082] The learning unit can learn patterns of dirt accumulation based on the arrangement and materials of furniture. For example, in a room with many wooden furniture pieces, the learning unit can learn how easily wood gets dirty and suggest appropriate cleaning methods. The learning unit can also identify and learn areas where dirt tends to accumulate based on the arrangement of sofas and carpets. Furthermore, in a room with many glass furniture pieces, the learning unit can learn how easily fingerprints and dirt adhere to glass and suggest effective cleaning methods. This allows for more effective cleaning by learning patterns of dirt accumulation based on the arrangement and materials of furniture. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input data on furniture arrangement and materials into an AI and have the AI ​​learn patterns of dirt accumulation.

[0083] The learning unit can estimate the user's emotions and determine the priority of rooms to learn about based on the estimated emotions. For example, if the user is tired, the learning unit may prioritize cleaning the bedroom, where the user can relax. It may also prioritize cleaning the living room if the user is stressed, providing a more comfortable space. Furthermore, if the user is in a hurry, the learning unit may prioritize cleaning key areas such as the kitchen or bathroom. This allows for more appropriate cleaning by prioritizing rooms based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI or not. For example, the learning unit can input user emotion data into an AI and have the AI ​​determine the priority of rooms to learn about.

[0084] The learning unit can learn how easily a surface gets dirty by taking into account environmental data such as room temperature and humidity. For example, in a high-humidity room, the learning unit can learn patterns of dirt caused by mold and moisture. In a low-temperature room, the learning unit can also learn patterns of dirt caused by dust and static electricity. Furthermore, in a room with drastic temperature changes, the learning unit can learn seasonal dirt patterns. This improves the accuracy of learning how easily a surface gets dirty by taking environmental data into account. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input room temperature and humidity data into the AI ​​and have the AI ​​perform the learning of how easily a surface gets dirty.

[0085] The learning unit can learn how easily surfaces get dirty, taking into account family structure and whether or not pets are present. For example, in households with pets, the learning unit can learn the patterns of pet hair and dirt. In households with children, the learning unit can also learn the dirt patterns in areas where children frequently play. Furthermore, in households with elderly people, the learning unit can learn dirt patterns based on the frequency of use of specific areas. This improves the accuracy of learning how easily surfaces get dirty by considering family structure and whether or not pets are present. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input data on family structure and whether or not pets are present into the AI ​​and have the AI ​​perform the learning of how easily surfaces get dirty.

[0086] The calculation unit can estimate the user's emotions and adjust the calculation method for the optimal cleaning route based on the estimated emotions. For example, if the user is stressed, the calculation unit can calculate a cleaning route that can be completed in a short time. If the user is relaxed, the calculation unit can also calculate a detailed cleaning route that cleans every nook and cranny. Furthermore, if the user is in a hurry, the calculation unit can calculate a cleaning route that prioritizes the dirtiest areas. This allows for the calculation of a more appropriate cleaning route by adjusting the calculation method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the calculation unit may be performed using AI or not using AI. For example, the calculation unit can input user emotion data into AI and have the AI ​​adjust the calculation method for the optimal cleaning route.

[0087] The calculation unit can improve the accuracy of cleaning route calculations by considering the shape of the room and the location of obstacles. For example, if the shape of the room is complex, the calculation unit can calculate the optimal route to avoid obstacles. The calculation unit can also calculate an efficient cleaning route by considering the arrangement of furniture. Furthermore, the calculation unit can detect the location of obstacles in real time and dynamically adjust the route. This improves the accuracy of cleaning route calculations by considering the shape of the room and the location of obstacles. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input data on the shape of the room and the location of obstacles into the AI ​​and have the AI ​​perform the task of improving the accuracy of cleaning route calculations.

[0088] The calculation unit can dynamically calculate the optimal cleaning route based on the time of day and frequency of cleaning. For example, the calculation unit can calculate a route that prioritizes cleaning the living room and kitchen in the morning. It can also calculate a route that prioritizes cleaning the bedroom and bathroom in the evening. Furthermore, it can calculate a route for overall cleaning on weekends. This enables efficient cleaning by dynamically calculating the cleaning route based on the time of day and frequency of cleaning. Some or all of the above processing in the calculation unit may be performed using AI, for example, or not. For example, the calculation unit can input data on the time of day and frequency of cleaning into the AI ​​and have the AI ​​perform the dynamic calculation of the optimal cleaning route.

[0089] The calculation unit can estimate the user's emotions and determine the priority of the cleaning route based on the estimated emotions. For example, if the user is tired, the calculation unit may prioritize cleaning areas where the user can relax. It may also prioritize cleaning the living room to provide a comfortable space if the user is stressed. Furthermore, if the user is in a hurry, the calculation unit may prioritize cleaning areas that tend to get dirty easily. This allows for more appropriate cleaning by prioritizing the cleaning route based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the calculation unit may be performed using AI or not. For example, the calculation unit can input user emotion data into an AI and have the AI ​​determine the priority of the cleaning route.

[0090] The calculation unit can calculate the optimal cleaning route by considering the room's lighting conditions. For example, if the room is dark, the calculation unit will consider the lighting and calculate a safe route. Furthermore, if the room is bright, the calculation unit can also calculate an efficient cleaning route. In addition, the calculation unit can consider the position of the lighting to calculate a route that minimizes shadows. This improves the accuracy of the cleaning route calculation by considering the lighting conditions. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input room lighting condition data into the AI ​​and have the AI ​​calculate the optimal cleaning route.

[0091] The calculation unit can calculate the optimal cleaning route by considering the acoustic conditions of the room. For example, if the room is quiet, the calculation unit can calculate a route that allows for cleaning without making noise. Furthermore, if the room is noisy, the calculation unit can also calculate an efficient cleaning route. In addition, considering the acoustic conditions, the calculation unit can calculate a route that minimizes sound reflection. This improves the accuracy of the cleaning route calculation by considering the acoustic conditions. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input data on the room's acoustic conditions into the AI ​​and have the AI ​​calculate the optimal cleaning route.

[0092] The instruction unit can estimate the user's emotions and adjust the instructions given to the automatic cleaning device based on those emotions. For example, if the user is stressed, the instruction unit will prioritize simpler instructions. Conversely, if the user is relaxed, the instruction unit can provide more detailed instructions for more efficient cleaning. Furthermore, if the user is in a hurry, the instruction unit can focus instructions on the main areas prone to getting dirty. This allows for more appropriate cleaning by adjusting the instructions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the instruction unit may be performed using AI or not. For example, the instruction unit can input user emotion data into an AI and have the AI ​​adjust the instructions given to the automatic cleaning device.

[0093] The instruction unit can optimize the instructions given by considering the battery level and performance of the cleaning device. For example, if the battery level is low, the instruction unit will issue cleaning instructions that can be completed in a short time. Also, if the cleaning device has high performance, the instruction unit can issue detailed cleaning instructions to perform efficient cleaning. Furthermore, if the battery level is sufficient, the instruction unit can issue overall cleaning instructions. This makes efficient cleaning possible by considering the battery level and performance. Some or all of the above processing in the instruction unit may be performed using AI, for example, or without AI. For example, the instruction unit can input data on the cleaning device's battery level and performance into the AI ​​and have the AI ​​perform the optimization of the instructions.

[0094] The instruction unit can analyze the operation history of the cleaning device and improve the instructions for the next cleaning. For example, the instruction unit can analyze the most efficient cleaning route from past operation history and reflect this in the next instructions. The instruction unit can also issue instructions to optimize the performance of the cleaning device based on the operation history. Furthermore, the instruction unit can analyze the operation history and issue instructions to adjust the cleaning frequency of specific areas. As a result, by analyzing the operation history, the instructions for the next cleaning are improved, enabling more efficient cleaning. Some or all of the above processes in the instruction unit may be performed using AI, for example, or without AI. For example, the instruction unit can input data from the cleaning device's operation history into the AI ​​and have the AI ​​implement improvements to the instructions for the next cleaning.

[0095] The instruction unit can estimate the user's emotions and determine the priority of instructions based on the estimated emotions. For example, if the user is tired, the instruction unit may prioritize cleaning areas where the user can relax. If the user is stressed, the instruction unit may prioritize cleaning the living room to provide a more comfortable space. Furthermore, if the user is in a hurry, the instruction unit may prioritize cleaning areas prone to getting dirty. This allows for more appropriate cleaning by prioritizing instructions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the instruction unit may be performed using AI, or not. For example, the instruction unit can input user emotion data into an AI and have the AI ​​determine the priority of instructions.

[0096] The instruction unit can customize the instructions according to the type and model of the automated cleaning device. For example, the instruction unit can provide detailed cleaning instructions to high-performance cleaning devices, while providing simpler instructions to low-performance devices. Furthermore, the instruction unit can provide instructions optimized for specific models. This allows for efficient cleaning by customizing the instructions according to the type and model of the cleaning device. Some or all of the above processing in the instruction unit may be performed using AI, for example, or not. For example, the instruction unit can input data on the type and model of the automated cleaning device into the AI ​​and have the AI ​​perform the customization of the instructions.

[0097] The instruction unit can adjust its instructions based on the operating noise and vibration of the cleaning device. For example, if the operating noise is loud, the instruction unit can issue instructions to clean during quieter times. It can also issue instructions to minimize vibrations if they are strong. Furthermore, the instruction unit can issue efficient cleaning instructions based on the operating noise and vibration. This allows for more efficient cleaning by considering the operating noise and vibration. Some or all of the above processing in the instruction unit may be performed using AI, or not. For example, the instruction unit can input data on the operating noise and vibration of the cleaning device into the AI ​​and have the AI ​​adjust the instructions.

[0098] The notification unit can estimate the user's emotions and adjust the notification content based on the estimated emotions. For example, if the user is stressed, the notification unit can provide a simple and to-the-point notification. If the user is relaxed, the notification unit can provide a detailed notification, such as a detailed update on the cleaning progress. Furthermore, if the user is in a hurry, the notification unit can provide a concise notification to enable quick action. By adjusting the notification content based on the user's emotions, more appropriate notifications can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input user emotion data into an AI and have the AI ​​adjust the notification content.

[0099] The notification unit can select the optimal notification timing by considering the user's schedule and lifestyle patterns. For example, the notification unit can refer to the user's calendar information and send notifications during free time. It can also analyze the user's lifestyle patterns and send notifications at the optimal time. Furthermore, the notification unit can send notifications while avoiding the user's busy times. In this way, the optimal notification timing is selected by considering the schedule and lifestyle patterns. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input data on the user's schedule and lifestyle patterns into AI and have the AI ​​select the optimal notification timing.

[0100] The notification unit can include cleaning progress and estimated completion time in its notifications. For example, the notification unit can notify the user of cleaning progress in real time, informing them of the current status. It can also notify the user of the estimated completion time, letting them know how long it will take until the cleaning is finished. Furthermore, the notification unit can combine cleaning progress and estimated completion time in its notifications, allowing the user to grasp the overall situation. This makes it easier for the user to understand the cleaning status by including cleaning progress and estimated completion time. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input data on cleaning progress and estimated completion time into the AI ​​and have the AI ​​generate the notification content.

[0101] The notification unit can estimate the user's emotions and prioritize notifications based on those emotions. For example, if the user is tired, the notification unit will prioritize important notifications. If the user is relaxed, the notification unit can provide detailed notifications, such as a detailed update on cleaning progress. Furthermore, if the user is in a hurry, the notification unit can prioritize concise notifications to allow for quick action. This allows for more appropriate notifications by prioritizing them based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input user emotion data into an AI and have the AI ​​determine the priority of notifications.

[0102] The notification unit can select the optimal notification method by considering the user's device information when sending a notification. For example, if the user is using a smartphone, the notification unit will send a push notification. Furthermore, if the user is using a tablet, the notification unit can also send a notification optimized for a larger screen. Additionally, if the user is using a smartwatch, the notification unit can send a concise and highly visible notification. This ensures that the optimal notification method is selected by considering the device information. Some or all of the above processing in the notification unit may be performed using AI, or not. For example, the notification unit can input the user's device information into the AI ​​and have the AI ​​select the optimal notification method.

[0103] The notification unit can include cleaning tips and advice in its notifications. For example, it can notify users of cleaning tips based on their cleaning progress. It can also provide advice on how to improve cleaning efficiency. Furthermore, it can provide advice on cleaning frequency and methods. By including cleaning tips and advice, users can clean more efficiently. Some or all of the above processing in the notification unit may be performed using AI, for example, or not. For example, the notification unit can input data on cleaning tips and advice into an AI and have the AI ​​generate the notification content.

[0104] The guidance system can estimate the user's emotions and adjust the guidelines for disposing of unwanted items based on those emotions. For example, if the user is stressed, the guidance system can suggest a simple and easy-to-understand disposal method. If the user is relaxed, the guidance system can suggest a detailed disposal method and explain recycling and reuse methods in detail. Furthermore, if the user is in a hurry, the guidance system can suggest a method that allows for quick disposal. By adjusting the disposal guidelines based on the user's emotions, more appropriate disposal becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the guidance system may be performed using AI or not. For example, the guidance system can input user emotion data into an AI and have the AI ​​adjust the disposal guidelines.

[0105] The guidance provision unit can propose the optimal disposal method by considering the material and condition of the unwanted items. For example, since plastic products are recyclable, the guidance provision unit can propose a recycling method. Also, since food waste should be disposed of as garbage, the guidance provision unit can propose an appropriate garbage disposal method. Furthermore, since metal products are reusable, the guidance provision unit can propose a reuse method. In this way, the optimal disposal method is proposed by considering the material and condition. Some or all of the above processing in the guidance provision unit may be performed using AI, for example, or not using AI. For example, the guidance provision unit can input data on the material and condition of the unwanted items into the AI ​​and have the AI ​​propose the optimal disposal method.

[0106] The guidance provision unit can propose disposal methods, taking into account local recycling rules and waste collection schedules. For example, the guidance provision unit can propose appropriate recycling methods based on local recycling rules. It can also propose the optimal timing for waste disposal, taking into account waste collection schedules. Furthermore, the guidance provision unit can provide information on local recycling facilities and waste collection points. This ensures that appropriate disposal methods are proposed, taking into account local rules and schedules. Some or all of the above processes in the guidance provision unit may be performed using AI, for example, or not. For example, the guidance provision unit can input data on local recycling rules and waste collection schedules into AI and have the AI ​​propose disposal methods.

[0107] The guidance unit can estimate the user's emotions and determine the priority of disposal methods based on the estimated emotions. For example, if the user is tired, the guidance unit will prioritize suggesting easy disposal methods. If the user is relaxed, the guidance unit can suggest detailed disposal methods and explain recycling and reuse methods in detail. Furthermore, if the user is in a hurry, the guidance unit can prioritize suggesting methods that allow for quick disposal. This allows for more appropriate disposal by prioritizing disposal methods based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the guidance unit may be performed using AI or not. For example, the guidance unit can input user emotion data into AI and have the AI ​​determine the priority of disposal methods.

[0108] The guidance provision unit can suggest the optimal disposal location when disposing of unwanted items, taking into account the user's geographical location. For example, the guidance provision unit can suggest the recycling facility closest to the user's current location. It can also suggest the optimal waste collection point based on the user's geographical location. Furthermore, the guidance provision unit can suggest the optimal disposal location, taking into account the user's geographical location. In this way, the optimal disposal location is suggested by considering geographical location. Some or all of the above processing in the guidance provision unit may be performed using AI, for example, or without AI. For example, the guidance provision unit can input the user's geographical location into AI and have AI suggest the optimal disposal location.

[0109] The guidance provision unit can provide information on relevant recycling companies and facilities when disposing of unwanted items. For example, when a user disposes of recyclable unwanted items, the guidance provision unit can provide information on the nearest recycling company. The guidance provision unit can also provide information on relevant recycling facilities when a user disposes of specific unwanted items. Furthermore, the guidance provision unit can provide information on relevant recycling companies and facilities when a user disposes of unwanted items. By providing relevant information, appropriate disposal becomes possible. Some or all of the above processing in the guidance provision unit may be performed using AI, for example, or not using AI. For example, the guidance provision unit can input information on recycling companies and facilities into AI and have the AI ​​perform the information provision.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] An AI cleaning assistant can estimate the user's emotions and adjust the cleaning timing based on those emotions. For example, if the user is stressed, the cleaning can be scheduled for a time when they can relax. If the user is relaxed, they can be given the freedom to choose the cleaning time. Furthermore, if the user is in a hurry, cleaning tasks that can be completed quickly can be prioritized. This provides a more comfortable cleaning experience by adjusting the cleaning timing based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Adjusting the cleaning timing may be done using AI or not. For example, emotion data can be input into an AI, and the AI ​​can then perform the adjustment of the cleaning timing.

[0112] An AI cleaning assistant can monitor the user's health and adjust the frequency and method of cleaning based on that health status. For example, if the user has allergies, it can adjust the cleaning frequency to remove allergens. If the user has a cold, it can reduce the cleaning frequency and prioritize rest. Furthermore, if the user is healthy, it can maintain the normal cleaning frequency. This provides a healthier living environment by adjusting the cleaning frequency and method based on the user's health status. Health status monitoring can be done using, for example, wearable devices or health apps. Health data can be input into the AI, and the AI ​​can then adjust the cleaning frequency and method.

[0113] An AI cleaning assistant can estimate the user's emotions and adjust the cleaning volume based on those emotions. For example, if the user is stressed, it can select a quiet cleaning mode to minimize the cleaning noise. If the user is relaxed, it can clean at a normal volume. Furthermore, if the user is in a hurry, it can prioritize efficient cleaning and clean without worrying about the volume. This provides a more comfortable cleaning environment by adjusting the cleaning volume based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Adjusting the cleaning volume may be done using AI or not. For example, emotion data can be input into an AI, and the AI ​​can then adjust the cleaning volume.

[0114] An AI cleaning assistant can learn the user's daily routine and suggest an optimal cleaning schedule. For example, if the user wakes up early, it can suggest cleaning in the morning. Similarly, if the user stays up late, it can suggest cleaning in the evening. Furthermore, if the user has an irregular sleep schedule, it can suggest a flexible cleaning schedule. This allows for efficient cleaning by suggesting an optimal cleaning schedule based on the user's daily rhythm. Learning of daily routines can be done using, for example, smart home devices or life-logging apps. By inputting this daily routine data into the AI, the AI ​​can then suggest and execute the optimal cleaning schedule.

[0115] An AI cleaning assistant can estimate a user's emotions and prioritize cleaning based on those emotions. For example, if the user is stressed, it can prioritize cleaning areas that promote relaxation. If the user is relaxed, it can follow the normal cleaning schedule. Furthermore, if the user is in a hurry, it can prioritize cleaning areas that tend to get dirty easily. This allows for more appropriate cleaning by prioritizing cleaning based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Determining cleaning priorities may be done using AI or not. For example, emotion data can be input into an AI, and the AI ​​can then determine the cleaning priorities.

[0116] An AI cleaning assistant can learn the user's family structure and lifestyle to suggest the optimal cleaning method. For example, in a household with pets, it can suggest a cleaning method that efficiently removes pet hair. In a household with children, it can suggest a method that focuses on cleaning areas where children frequently play. Furthermore, in a household with elderly people, it can suggest a method that prioritizes cleaning areas frequently used by the elderly. This allows for efficient cleaning by suggesting the optimal cleaning method based on family structure and lifestyle. Learning family structure and lifestyle can be done using, for example, smart home devices or life logging apps. By inputting family structure and lifestyle data into the AI, the AI ​​can suggest and execute the optimal cleaning method.

[0117] An AI cleaning assistant can estimate the user's emotions and adjust cleaning notifications based on those emotions. For example, if the user is stressed, it can provide simple, to-the-point notifications. If the user is relaxed, it can provide detailed notifications and explain the cleaning progress in detail. Furthermore, if the user is in a hurry, it can provide concise notifications to allow for quick action. By adjusting notifications based on the user's emotions, more appropriate notifications can be provided. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. The adjustment of notification methods may be done using AI or not. For example, emotion data can be input into an AI, and the AI ​​can be used to adjust the notification methods.

[0118] An AI cleaning assistant can learn the user's lifestyle patterns and optimize the frequency and timing of cleaning. For example, if the user is often home on weekends, it can suggest concentrating cleaning on weekends. If the user is busy during the week, it can suggest short cleaning sessions on weekday evenings. Furthermore, if the user has an irregular lifestyle, it can suggest a flexible cleaning schedule. This optimizes cleaning frequency and timing based on the user's lifestyle, enabling more efficient cleaning. Lifestyle patterns can be learned using, for example, smart home devices or life-logging apps. By inputting lifestyle pattern data into the AI, the AI ​​can optimize cleaning frequency and timing.

[0119] An AI cleaning assistant can estimate the user's emotions and adjust how it notifies them of cleaning progress based on those emotions. For example, if the user is stressed, it can provide simple, to-the-point progress notifications. If the user is relaxed, it can provide detailed progress notifications, giving a more in-depth overview of the cleaning process. Furthermore, if the user is in a hurry, it can provide concise progress notifications to allow for quick action. This allows for more appropriate notifications by adjusting the progress notification method based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. The adjustment of the progress notification method may be done using AI or not. For example, emotion data can be input into an AI, and the AI ​​can be used to adjust the progress notification method.

[0120] An AI cleaning assistant can learn the structure and interior design of a user's home and suggest the optimal cleaning method. For example, in a home with a complex structure, it can suggest an efficient cleaning route. It can also suggest cleaning methods that match the interior design, taking care not to damage furniture or decorations. Furthermore, it can suggest cleaning methods that focus on specific areas for more efficient cleaning. This means that the optimal cleaning method is suggested based on the home's structure and interior design, enabling efficient cleaning. Learning the home's structure and interior design can be done, for example, using smart home devices or interior design apps. By inputting the home's structure and interior design data into the AI, the AI ​​can suggest and execute the optimal cleaning method.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The learning unit learns the layout and tendency to get dirty in each room. For example, it identifies areas that tend to get dirty, such as the living room and kitchen, and collects information on each room to propose an efficient cleaning route. Step 2: The calculation unit calculates the optimal cleaning route based on the information learned by the learning unit. For example, it calculates the cleaning route based on criteria such as time efficiency, energy efficiency, and coverage rate, and sequentially calculates the cleaning routes for the living room and kitchen. Step 3: The instruction unit issues instructions to the automatic cleaning device based on the cleaning route calculated by the calculation unit. For example, it might instruct the automatic cleaning device to start cleaning the living room, and then to start cleaning the kitchen. Step 4: The notification unit informs the user about the frequency of cleaning and the recommended areas to clean. For example, it might recommend that the living room should be cleaned daily, while the bedroom only needs to be cleaned once a week. Step 5: The guidance provision department learns how to dispose of unwanted items and provides guidance to users. For example, it provides guidance such as plastic products being recyclable, but food waste should be disposed of as garbage.

[0123] 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.

[0124] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0125] 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.

[0126] Each of the multiple elements described above, including the learning unit, calculation unit, instruction unit, notification unit, and guideline provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the learning unit collects information about each room using the camera 42 and sensors of the smart device 14, and the control unit 46A learns the layout and tendency to get dirty. The calculation unit calculates the optimal cleaning route using the specific processing unit 290 of the data processing unit 12, for example. The instruction unit issues instructions to the automatic cleaning device using the control unit 46A of the smart device 14, for example. The notification unit notifies the user of the cleaning frequency and recommended locations using the output device 40 of the smart device 14, for example. The guideline provision unit learns how to dispose of unwanted items using the specific processing unit 290 of the data processing unit 12, for example, and provides guidance to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] 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.

[0142] Each of the multiple elements described above, including the learning unit, calculation unit, instruction unit, notification unit, and guidance provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the learning unit collects information about each room using the camera 42 and sensors of the smart glasses 214, and the control unit 46A learns the layout and tendency to get dirty. The calculation unit calculates the optimal cleaning route, for example, using the specific processing unit 290 of the data processing unit 12. The instruction unit issues instructions to the automatic cleaning device, for example, using the control unit 46A of the smart glasses 214. The notification unit notifies the user of the cleaning frequency and recommended locations, for example, using the speaker 240 of the smart glasses 214. The guidance provision unit learns how to dispose of unwanted items, for example, using the specific processing unit 290 of the data processing unit 12, and provides guidance to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] 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.

[0158] Each of the multiple elements described above, including the learning unit, calculation unit, instruction unit, notification unit, and guideline provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the learning unit collects information about each room using the camera 42 and sensors of the headset terminal 314 and learns the layout and tendency to get dirty using the control unit 46A. The calculation unit calculates the optimal cleaning route using the specific processing unit 290 of the data processing unit 12. The instruction unit issues instructions to the automatic cleaning device using the control unit 46A of the headset terminal 314. The notification unit notifies the user of the cleaning frequency and recommended locations using the speaker 240 of the headset terminal 314. The guideline provision unit learns how to dispose of unwanted items using the specific processing unit 290 of the data processing unit 12 and provides guidance to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0174] 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.

[0175] Each of the multiple elements described above, including the learning unit, calculation unit, instruction unit, notification unit, and guideline provision unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the learning unit collects information about each room using the camera 42 and sensors of the robot 414, and the control unit 46A learns the layout and tendency to get dirty. The calculation unit calculates the optimal cleaning route, for example, using the specific processing unit 290 of the data processing unit 12. The instruction unit issues instructions to the automatic cleaning device, for example, using the control unit 46A of the robot 414. The notification unit notifies the user of the cleaning frequency and recommended locations, for example, using the speaker 240 of the robot 414. The guideline provision unit learns how to dispose of unwanted items, for example, using the specific processing unit 290 of the data processing unit 12, and provides guidance to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) The learning section allows you to learn about the layout of each room and how easily it gets dirty, A calculation unit that calculates the optimal cleaning route based on the information learned by the learning unit, An instruction unit that issues instructions to the automatic cleaning device based on the cleaning route calculated by the calculation unit, A notification unit that informs the user of the frequency of cleaning and the recommended locations of areas to be cleaned, It includes a guidance provision unit that learns how to dispose of unwanted items and provides guidance to the user. A system characterized by the following features. (Note 2) The aforementioned learning unit, Learn about the layout of each room and how easily they get dirty. The system described in Appendix 1, characterized by the features described herein. (Note 3) The calculation unit, The learning unit calculates the optimal cleaning route based on the information it has learned. The system described in Appendix 1, characterized by the features described herein. (Note 4) The indicator unit is, The calculation unit issues instructions to the automatic cleaning device based on the cleaning route it has calculated. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned notification unit, The system notifies the user of the frequency of cleaning and the recommended areas to clean. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned guideline provision unit, Learn how to dispose of unwanted items and provide users with guidance. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned learning unit, The system estimates the user's emotions and adjusts the room layout and learning method for dirtiness based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned learning unit, We will improve the accuracy of learning how easily each room gets dirty by considering the frequency and time of use of each room. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned learning unit, Learn patterns of how easily furniture gets dirty based on its arrangement and materials. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned learning unit, It estimates the user's emotions and prioritizes rooms based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned learning unit, The system learns how easily something gets dirty by taking into account environmental data such as room temperature and humidity. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned learning unit, The learning process takes into account family structure and whether or not pets are present. The system described in Appendix 1, characterized by the features described herein. (Note 13) The calculation unit, It estimates the user's emotions and adjusts the calculation method for the optimal cleaning route based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The calculation unit, Improve the accuracy of cleaning route calculations by taking into account the shape of the room and the location of obstacles. The system described in Appendix 1, characterized by the features described herein. (Note 15) The calculation unit, The optimal cleaning route is dynamically calculated based on the time of day and frequency of cleaning. The system described in Appendix 1, characterized by the features described herein. (Note 16) The calculation unit, It estimates the user's emotions and determines the priority of cleaning routes based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The calculation unit, When calculating the cleaning route, the optimal route is calculated by taking into account the lighting conditions of the room. The system described in Appendix 1, characterized by the features described herein. (Note 18) The calculation unit, When calculating the cleaning route, the optimal route is calculated by taking into account the acoustic conditions of the room. The system described in Appendix 1, characterized by the features described herein. (Note 19) The indicator unit is, The system estimates the user's emotions and adjusts the instructions given to the automatic cleaning device based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The indicator unit is, The instructions are optimized considering the battery level and performance of the cleaning device. The system described in Appendix 1, characterized by the features described herein. (Note 21) The indicator unit is, We analyze the operation history of the cleaning device to improve the next instruction. The system described in Appendix 1, characterized by the features described herein. (Note 22) The indicator unit is, It estimates the user's emotions and determines the priority of instructions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The indicator unit is, Customize the instructions according to the type and model of the automatic cleaning device. The system described in Appendix 1, characterized by the features described herein. (Note 24) The indicator unit is, The instructions are adjusted to take into account the operating noise and vibration of the cleaning device. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned notification unit, It estimates the user's emotions and adjusts the notification content based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned notification unit, The system selects the optimal notification timing, taking into account the user's schedule and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned notification unit, The notification should include cleaning progress and estimated completion time. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned notification unit, It estimates the user's emotions and prioritizes notifications based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned notification unit, When sending notifications, the system selects the most suitable notification method, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned notification unit, Include cleaning tips and advice in the notification content. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned guideline provision unit, We estimate the user's emotions and adjust the guidelines for disposing of unwanted items based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned guideline provision unit, We will propose the most suitable disposal method, taking into account the material and condition of the unwanted items. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned guideline provision unit, We will propose disposal methods that take into account local recycling rules and waste collection schedules. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned guideline provision unit, It estimates the user's emotions and determines the priority of disposal methods based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned guideline provision unit, When disposing of unwanted items, we suggest the optimal disposal location considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned guideline provision unit, When disposing of unwanted items, we provide information on relevant recycling companies and facilities. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0195] 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. The learning section allows you to learn about the layout of each room and how easily it gets dirty, A calculation unit that calculates the optimal cleaning route based on the information learned by the learning unit, An instruction unit that issues instructions to the automatic cleaning device based on the cleaning route calculated by the calculation unit, A notification unit that informs the user of the frequency of cleaning and the recommended locations of areas to be cleaned, It includes a guidance provision unit that learns how to dispose of unwanted items and provides guidance to the user. A system characterized by the following features.

2. The aforementioned learning unit, Learn about the layout of each room and how easily they get dirty. The system according to feature 1.

3. The calculation unit, The learning unit calculates the optimal cleaning route based on the information it has learned. The system according to feature 1.

4. The indicator unit is, The calculation unit issues instructions to the automatic cleaning device based on the cleaning route it has calculated. The system according to feature 1.

5. The aforementioned notification unit, The system notifies the user of the frequency of cleaning and the recommended areas to clean. The system according to feature 1.

6. The aforementioned guideline provision unit, Learn how to dispose of unwanted items and provide users with guidance. The system according to feature 1.

7. The aforementioned learning unit, The system estimates the user's emotions and adjusts the room layout and learning method for dirtiness based on the estimated user emotions. The system according to feature 1.

8. The aforementioned learning unit, We will improve the accuracy of learning how easily each room gets dirty by considering the frequency and time of use of each room. The system according to feature 1.

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