system

Abstract

The system according to this embodiment aims to recognize the material, color, and degree of soiling of each garment and to set the optimal washing course and detergent amount. [Solution] The system according to the embodiment comprises a recognition unit, a setting unit, an instruction unit, a suggestion unit, and a notification unit. The recognition unit recognizes the material, color, and degree of soiling of each garment. The setting unit sets the optimal washing course and detergent amount based on the information recognized by the recognition unit. The instruction unit instructs the user to classify the laundry based on the information set by the setting unit. The suggestion unit suggests the optimal washing timing considering the weather forecast and the amount of laundry. The notification unit sends a notification to the user when the washing is finished.

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, including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to set an appropriate washing course and classify laundry, and there is a possibility of damaging clothing.

[0005] The system according to the embodiment aims to recognize the material, color, and degree of dirt of each piece of clothing and set an optimal washing course and detergent amount.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a recognition unit, a setting unit, an instruction unit, a suggestion unit, and a notification unit. The recognition unit recognizes the material, color, and degree of soiling of each garment. The setting unit sets the optimal washing course and detergent amount based on the information recognized by the recognition unit. The instruction unit instructs the user to classify the laundry based on the information set by the setting unit. The suggestion unit suggests the optimal washing timing considering the weather forecast and the amount of laundry. The notification unit sends a notification to the user when the washing is finished. [Effects of the Invention]

[0007] The system according to this embodiment can recognize the material, color, and degree of soiling of each garment and set the optimal washing course and detergent amount. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The laundry management system according to an embodiment of the present invention is a system that uses AI to improve the efficiency of laundry. When the user puts laundry into the washing machine, the AI ​​uses cameras and sensors to analyze the material, color, and degree of soiling of each garment. Next, the AI ​​automatically sets the optimal washing course and detergent amount, and instructs the user on how to sort the laundry. Furthermore, it suggests the optimal washing time considering the weather forecast and the amount of laundry, and sends a notification to the user when the washing is finished. Finally, the AI ​​analyzes the user's daily schedule, creates an optimal laundry plan, and provides notifications. This makes it easier to manage laundry even with limited time, and enables more efficient laundry. For example, the user inputs the laundry into the laundry management system. For example, the laundry management system can scan handwritten laundry items and convert them into digital data, or input them directly in digital format. This data is converted into a format that is easy for the generating AI to analyze. Next, the laundry management system uses the generating AI to analyze the user's laundry and summarize its contents. The input to the generating AI is the user's laundry itself, and the generating AI generates a summary based on its contents. For example, the generative AI receives a prompt such as "Summarize the main points of this laundry," extracts the main points, and creates a summary. Next, the laundry management system calculates the similarity between the summary created by the generative AI and a pre-prepared model answer. Natural language processing techniques are used to calculate the similarity. For example, the generative AI analyzes the degree of word agreement and sentence structure similarity between the summary and the model answer to calculate a similarity score. Next, the laundry management system calculates a score for the laundry based on the similarity score. For example, it may be set so that a higher similarity score corresponds to a higher score. The final score is then fed back to the user. This allows the user to know how well their laundry matches the model answer. The laundry management system also applies a similar process to essay tests. The laundry management system reads the user's essay answer, the generative AI summarizes it, and calculates the similarity to the model answer. For example, the laundry management system summarizes the user's opinion and logical development on the essay topic and calculates a score by comparing it with the model answer.This enables the laundry management system to automatically grade long reading comprehension passages and essays from Japanese language tests. The system can then automatically grade the user's laundry and provide the graded results.

[0029] The laundry management system according to this embodiment comprises a recognition unit, a setting unit, an instruction unit, a suggestion unit, and a notification unit. The recognition unit recognizes the material, color, and degree of soiling of each garment. The recognition unit analyzes the material, color, and degree of soiling of each garment using, for example, a camera or sensors. The recognition unit recognizes the color of the garment using, for example, a camera, and analyzes the material of the garment using sensors. The recognition unit may also use image processing technology to analyze the degree of soiling. For example, the recognition unit uses image processing technology to analyze the degree of soiling of the garment and quantifies the degree of soiling. The setting unit sets the optimal washing course and detergent amount based on the information recognized by the recognition unit. The setting unit sets the optimal washing course based on the material, color, and degree of soiling of the garment recognized by the recognition unit. The setting unit can also automatically set the detergent amount. For example, the setting unit sets the optimal detergent amount based on the degree of soiling of the garment. The instruction unit instructs the user to classify the laundry based on the information set by the setting unit. The instruction unit, for example, instructs the user to sort laundry by color or material. The instruction unit can also instruct the user to sort laundry according to its degree of soiling. For example, the instruction unit can instruct the user to sort laundry into lightly soiled, moderately soiled, and heavily soiled categories according to the degree of soiling. The suggestion unit proposes the optimal washing timing considering the weather forecast and the amount of laundry. For example, the suggestion unit suggests a washing timing that avoids rainy days based on the weather forecast. The suggestion unit can also suggest the optimal washing timing based on the amount of laundry. For example, if there is a large amount of laundry, the suggestion unit suggests a washing timing that is appropriate for the capacity of the washing machine. The notification unit sends a notification to the user when the washing is finished. For example, the notification unit notifies the user that the washing is complete. The notification unit can also send a notification prompting the user to take in the laundry. For example, the notification unit notifies the user that the laundry is dry and prompts them to take it in. As a result, the laundry management system according to the embodiment can efficiently manage the user's laundry and improve the efficiency of the washing process.

[0030] The recognition unit recognizes the material, color, and degree of soiling of each garment. For example, it uses cameras and sensors to analyze the material, color, and degree of soiling of each garment. Specifically, the camera captures high-resolution images, and image processing technology is used to accurately recognize the color of the garment. Color recognition utilizes color spaces such as RGB and HSV values ​​to detect even subtle color differences. Sensors can use methods such as near-infrared spectroscopy and terahertz wave spectroscopy to analyze the material of the garment. This allows for high-precision identification of different materials such as cotton, polyester, and wool. Image processing technology can also be used to analyze the degree of soiling. For example, image processing algorithms are used to quantify the area and concentration of dirt attached to the surface of the garment, quantitatively evaluating the degree of soiling. Furthermore, the recognition unit can use AI to analyze this data and comprehensively evaluate the condition of the garment. Based on past data and learning models, the AI ​​accurately recognizes the material, color, and degree of soiling of the garment and provides basic information to suggest the optimal washing method. This allows the recognition unit to accurately grasp detailed information about each garment and provide the data necessary for the next processing step.

[0031] The settings unit sets the optimal washing course and detergent amount based on the information recognized by the recognition unit. Specifically, the washing machine's control system automatically selects the optimal washing course based on data on the material, color, and degree of soiling of the clothes provided by the recognition unit. For example, a low-temperature washing course is set for delicate materials, and a special course to prevent color transfer is selected for clothes that are prone to color bleeding. It can also adjust the amount of detergent and washing time according to the degree of soiling. For example, a small amount of detergent and a short wash time are set for lightly soiled items, and a larger amount of detergent and a longer wash time are set for heavily soiled items. Furthermore, the settings unit can use AI to learn past washing data and the user's washing habits to provide more personalized washing settings. Based on the user's preferences and past washing results, the AI ​​suggests the optimal washing course and detergent amount, maximizing the efficiency and effectiveness of the wash. In this way, the settings unit can improve the quality and efficiency of the wash by saving the user effort and automatically setting the optimal washing conditions.

[0032] The instruction unit instructs the user on how to sort laundry based on information set by the settings unit. Specifically, it instructs the user to sort laundry by color or material. For example, it displays specific instructions to the user through a smartphone app or the washing machine's display, such as "Wash white clothes together" or "Wash delicate items separately." It can also instruct the user to sort laundry according to the degree of soiling. For example, it can instruct the user to sort laundry into lightly soiled, moderately soiled, and heavily soiled categories, and suggest the appropriate washing course for each. Furthermore, the instruction unit can also provide voice instructions to the user using a voice assistant. This allows the user to sort laundry appropriately using not only visual information but also auditory information. The instruction unit can also collect user feedback and continuously improve the accuracy and effectiveness of its instructions. For example, it optimizes instructions for subsequent loads based on the user's results in sorting laundry according to the instructions. This allows the instruction unit to quickly provide the user with appropriate instructions, improving the efficiency and effectiveness of laundry.

[0033] The suggestion department proposes the optimal laundry timing, taking into account weather forecasts and the amount of laundry. Specifically, it suggests laundry times that avoid rainy days based on the weather forecast. For example, it analyzes the latest weather forecast data obtained from the internet and suggests doing laundry on sunny or windy days. The suggestion department can also suggest the optimal laundry timing based on the amount of laundry. For example, if there is a large amount of laundry, it will suggest a laundry timing that matches the capacity of the washing machine, allowing for more efficient laundry. Furthermore, the suggestion department can use AI to learn the user's lifestyle patterns and past laundry history, and propose the optimal laundry timing individually. For example, if a user often does laundry on weekends, it will suggest the optimal laundry timing considering the weekend weather forecast. In this way, the suggestion department can provide flexible suggestions tailored to the user's lifestyle, improving the efficiency and effectiveness of laundry.

[0034] The notification unit sends a notification to the user when the laundry is finished. Specifically, it notifies the user that the laundry is complete. For example, it notifies the user via a smartphone app, email, or SMS that the laundry is finished. The notification unit can also send notifications prompting the user to take in the laundry. For example, it notifies the user when the laundry is dry and prompts them to take it in. Furthermore, the notification unit can also notify the user about the status of the washing machine and maintenance information. For example, it notifies the user to perform appropriate maintenance if the washing machine filter is clogged or if the detergent level is low. In this way, the notification unit can quickly provide users with important information about laundry, improving the efficiency and effectiveness of the laundry. In addition, the notification unit can collect user feedback and continuously improve the accuracy and effectiveness of the notification content. For example, it can analyze the user's actions after receiving a notification and optimize the content of future notifications. In this way, the notification unit can quickly and reliably provide information to users, improving the efficiency and effectiveness of the laundry.

[0035] The schedule analysis unit analyzes the user's daily schedule and creates an optimal laundry plan. For example, the schedule analysis unit obtains data from the user's calendar app or schedule management app and analyzes the daily schedule. For example, the schedule analysis unit creates an optimal laundry plan considering the user's work schedule and household schedule. The schedule analysis unit can also suggest the timing of laundry considering the user's free time and available time. For example, the schedule analysis unit suggests the timing of laundry to match the user's free time. This makes laundry more efficient by creating a laundry plan based on the user's schedule. Some or all of the above processing in the schedule analysis unit may be performed using AI, for example, or without AI. For example, the schedule analysis unit can input the user's schedule data into a generating AI and have the generating AI create an optimal laundry plan.

[0036] The recognition unit can analyze the material, color, and degree of soiling of each garment using cameras and sensors. For example, the recognition unit can recognize the color of the garment using a camera and analyze the material of the garment using sensors. The recognition unit can also use image processing technology to analyze the degree of soiling. For example, the recognition unit can use image processing technology to analyze the degree of soiling of the garment and quantify the degree of soiling. This improves the accuracy of garment recognition by using cameras and sensors. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input data acquired by cameras and sensors into a generating AI and have the generating AI perform the analysis of the material, color, and degree of soiling of the garments.

[0037] The setting unit can automatically set the optimal washing course and detergent amount. For example, the setting unit sets the optimal washing course based on the material, color, and degree of soiling of the clothes recognized by the recognition unit. The setting unit can also automatically set the detergent amount. For example, the setting unit sets the optimal detergent amount based on the degree of soiling of the clothes. This improves the efficiency of washing by automatically making the optimal settings. Some or all of the above processes in the setting unit may be performed using AI, for example, or without AI. For example, the setting unit can input the information recognized by the recognition unit into a generating AI and cause the generating AI to set the optimal washing course and detergent amount.

[0038] The instruction unit can instruct the user to sort laundry. For example, the instruction unit can instruct the user to sort laundry by color or material. The instruction unit can also instruct the user to sort laundry according to its degree of soiling. For example, the instruction unit can instruct the user to sort laundry into lightly soiled, moderately soiled, and heavily soiled categories according to the degree of soiling. This makes laundry management easier by providing the user with appropriate sorting instructions. 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 information recognized by the recognition unit into a generating AI and have the generating AI execute laundry sorting instructions.

[0039] The suggestion unit can propose the optimal laundry timing by considering the weather forecast and the amount of laundry. For example, based on the weather forecast, the suggestion unit can suggest a laundry timing that avoids rainy days. The suggestion unit can also suggest the optimal laundry timing based on the amount of laundry. For example, if there is a large amount of laundry, the suggestion unit can suggest a laundry timing that is appropriate for the capacity of the washing machine. In this way, the optimal laundry timing can be proposed by considering the weather forecast and the amount of laundry. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without using AI. For example, the suggestion unit can input weather forecast data and laundry amount data into a generating AI and have the generating AI execute a suggestion for the optimal laundry timing.

[0040] The notification unit can send a notification to the user when the laundry is finished. For example, the notification unit can notify the user that the laundry is complete. The notification unit can also send a notification prompting the user to take in the laundry. For example, the notification unit can notify the user that the laundry is dry and prompt them to take it in. This allows the user to take in the laundry in a timely manner by notifying them when the laundry is finished. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the laundry completion data into a generating AI and have the generating AI generate the notification content.

[0041] The recognition unit can optimize its recognition algorithm by referring to past recognition data when recognizing clothing. For example, the recognition unit can prioritize referring to data of clothing that has frequently been misrecognized in the past to improve recognition accuracy. The recognition unit can also improve the recognition accuracy of clothing that is frequently used in a particular season by referring to past recognition data. Furthermore, the recognition unit can learn the recognition patterns of a specific user's clothing based on past recognition data to improve accuracy. Thus, recognition accuracy is improved by referring to past recognition data. Some or all of the above processes in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input past recognition data into a generating AI and have the generating AI perform the optimization of the recognition algorithm.

[0042] The recognition unit can improve recognition accuracy by considering the frequency of use and washing history of clothing during recognition. For example, the recognition unit can prioritize the recognition of frequently used clothing and determine the need for washing. The recognition unit can also refer to the washing history to improve the recognition accuracy of clothing that is prone to certain stains. Furthermore, the recognition unit can combine the frequency of use and the washing history to apply an optimal recognition algorithm. This improves recognition accuracy by considering the frequency of use and the washing history. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input clothing usage frequency and washing history data into a generating AI and have the generating AI perform the improvement of recognition accuracy.

[0043] The recognition unit can improve recognition accuracy by considering the user's geographical location information when recognizing clothing. For example, if the user lives in a cold region, the recognition unit can improve the recognition accuracy of winter clothing. Similarly, if the user lives in a warm region, the recognition unit can improve the recognition accuracy of summer clothing. Furthermore, the recognition unit can improve the recognition accuracy of region-specific clothing based on the user's geographical location information. Thus, recognition accuracy is improved by considering geographical location information. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input the user's geographical location information into a generating AI and have the generating AI perform the improvement of recognition accuracy.

[0044] The recognition unit can analyze the user's social media activity and acquire relevant recognition data when recognizing clothing. For example, the recognition unit can analyze images of clothing posted by the user on social media to improve recognition accuracy. The recognition unit can also identify trendy clothing from the user's social media activity to improve recognition accuracy. Furthermore, the recognition unit can improve the recognition accuracy of clothing from specific brands based on the user's social media activity. Thus, recognition accuracy is improved by analyzing social media activity. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input the user's social media activity data into a generating AI and have the generating AI perform the acquisition of recognition data.

[0045] The settings unit can optimize its setting algorithm by referring to past setting data when setting the washing course and detergent amount. For example, the settings unit can suggest the optimal settings based on the washing course and detergent amount used in the past. The settings unit can also learn and suggest the optimal settings for specific garments from past setting data. Furthermore, the settings unit can analyze past setting data and suggest the optimal settings for each season. This improves setting accuracy by referring to past setting data. Some or all of the above processes in the settings unit may be performed using AI, for example, or without AI. For example, the settings unit can input past setting data into a generating AI and have the generating AI perform the optimization of the setting algorithm.

[0046] The setting unit can improve the accuracy of settings by analyzing the material, color, and degree of soiling of clothing in detail when setting the washing course and detergent amount. For example, the setting unit can analyze the material of clothing in detail and set the optimal washing course. It can also take the color of the clothing into consideration and set the amount of detergent to prevent color fading. Furthermore, the setting unit can analyze the degree of soiling of the clothing in detail and set the amount of detergent according to the level of soiling. In this way, the accuracy of settings is improved by performing a detailed analysis of the clothing. Some or all of the above processes in the setting unit may be performed using AI, for example, or without using AI. For example, the setting unit can input data on the material, color, and degree of soiling of clothing into a generating AI and have the generating AI perform the improvement of setting accuracy.

[0047] The settings unit can improve setting accuracy by considering the user's geographical location information when setting the washing course and detergent amount. For example, if the user lives in a cold region, the settings unit can suggest settings suitable for winter clothing. Similarly, if the user lives in a warm region, the settings unit can suggest settings suitable for summer clothing. Furthermore, based on the user's geographical location information, the settings unit can suggest settings suitable for region-specific washing conditions. This improves setting accuracy by considering geographical location information. Some or all of the above processing in the settings unit may be performed using AI, for example, or without AI. For example, the settings unit can input the user's geographical location information into a generating AI and have the generating AI perform the improvement of setting accuracy.

[0048] The settings unit can analyze the user's social media activity and acquire relevant setting data when setting the washing course and detergent amount. For example, the settings unit can analyze images of clothing posted by the user on social media and suggest the optimal settings. The settings unit can also identify trending washing methods from the user's social media activity and reflect them in the settings. Furthermore, based on the user's social media activity, the settings unit can suggest settings suitable for clothing of a specific brand. In this way, the accuracy of the settings is improved by analyzing social media activity. Some or all of the above processing in the settings unit may be performed using AI, for example, or without AI. For example, the settings unit can input the user's social media activity data into a generating AI and have the generating AI acquire the setting data.

[0049] The instruction unit can optimize its instruction algorithm by referring to past classification data when giving instructions for classifying laundry. For example, the instruction unit can prioritize referring to data on clothing that was frequently misclassified in the past to improve classification accuracy. The instruction unit can also improve the classification accuracy of clothing that is frequently used in a particular season by referring to past classification data. Furthermore, the instruction unit can learn the classification patterns of a particular user's clothing based on past classification data to improve accuracy. In this way, classification accuracy is improved by referring to past classification data. 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 past classification data into a generating AI and have the generating AI perform the optimization of the instruction algorithm.

[0050] The instruction unit can improve classification accuracy by considering the user's geographical location information when giving instructions for laundry classification. For example, if the user lives in a cold region, the instruction unit can improve the classification accuracy of winter clothing. Similarly, if the user lives in a warm region, the instruction unit can improve the classification accuracy of summer clothing. Furthermore, based on the user's geographical location information, the instruction unit can improve the classification accuracy of region-specific clothing. In this way, classification accuracy is improved by considering geographical location information. 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 user's geographical location information into a generating AI and have the generating AI perform the classification accuracy improvement.

[0051] The suggestion unit can optimize its suggestion algorithm by referring to past suggestion data when suggesting laundry timing. For example, the suggestion unit can make the optimal suggestion based on laundry timing used in the past. The suggestion unit can also suggest laundry timing suitable for a specific season based on past suggestion data. Furthermore, the suggestion unit can analyze past suggestion data and suggest the optimal laundry timing for a specific user. This improves the accuracy of suggestions by referring to past suggestion data. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input past suggestion data into a generating AI and have the generating AI perform the optimization of the suggestion algorithm.

[0052] The suggestion unit can improve the accuracy of its suggestions by analyzing weather forecasts and laundry loads in detail when suggesting laundry timing. For example, the suggestion unit can suggest laundry timings that avoid rainy days based on the weather forecast. It can also analyze the laundry load in detail and suggest the optimal laundry timing. Furthermore, the suggestion unit can combine the weather forecast and laundry load to suggest the optimal laundry timing. This improves the accuracy of the suggestions by analyzing the weather forecast and laundry load in detail. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input weather forecast data and laundry load data into a generating AI and have the generating AI perform the improvement of the suggestion accuracy.

[0053] The suggestion unit can improve the accuracy of its laundry timing suggestions by considering the user's geographical location information. For example, if the user lives in a cold region, the suggestion unit can suggest a laundry timing suitable for winter clothes. Similarly, if the user lives in a warm region, the suggestion unit can suggest a laundry timing suitable for summer clothes. Furthermore, based on the user's geographical location information, the suggestion unit can suggest a laundry timing suitable for region-specific laundry conditions. This improves the accuracy of the suggestions by considering geographical location information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's geographical location information into a generating AI and have the generating AI perform the improvement of the suggestion accuracy.

[0054] The suggestion unit can analyze the user's social media activity and acquire relevant suggestion data when suggesting laundry timing. For example, the suggestion unit can analyze the content of the user's social media posts and suggest the optimal laundry timing. The suggestion unit can also identify trending laundry methods from the user's social media activity and reflect them in the suggestions. Furthermore, based on the user's social media activity, the suggestion unit can suggest laundry timing tailored to specific events. This improves the accuracy of suggestions by analyzing social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's social media activity data into a generating AI and have the generating AI acquire the suggestion data.

[0055] The notification unit can optimize its notification algorithm by referring to past notification data when it notifies the user that the laundry is finished. For example, the notification unit can provide the optimal notification based on previously used notification methods. It can also suggest a notification method suitable for a specific time period based on past notification data. Furthermore, the notification unit can analyze past notification data and suggest the optimal notification method for a specific user. This improves notification accuracy by referring to past notification data. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input past notification data into a generating AI and have the generating AI perform the optimization of the notification algorithm.

[0056] The notification unit can improve notification accuracy by considering the user's geographical location when notifying the user that the laundry is finished. For example, if the user is at home, the notification unit can provide an immediate notification. If the user is out, the notification unit can also provide a notification timed to coincide with the user's expected return time. Furthermore, the notification unit can suggest the optimal notification timing based on the user's geographical location. This improves notification accuracy by considering geographical location. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of improving notification accuracy.

[0057] The schedule analysis unit can optimize the planning algorithm by referring to past schedule data when formulating a laundry plan. For example, the schedule analysis unit can propose an optimal plan based on laundry plans used in the past. The schedule analysis unit can also propose a laundry plan suitable for a specific season from past schedule data. Furthermore, the schedule analysis unit can analyze past schedule data and propose an optimal laundry plan for a specific user. This improves planning accuracy by referring to past schedule data. Some or all of the above processing in the schedule analysis unit may be performed using AI, for example, or without AI. For example, the schedule analysis unit can input past schedule data into a generating AI and have the generating AI perform the optimization of the planning algorithm.

[0058] The schedule analysis unit can improve the accuracy of laundry planning by considering the user's geographical location information. For example, if the user lives in a cold region, the schedule analysis unit can propose a laundry plan suitable for winter clothes. Similarly, if the user lives in a warm region, the schedule analysis unit can propose a laundry plan suitable for summer clothes. Furthermore, based on the user's geographical location information, the schedule analysis unit can propose a laundry plan suitable for region-specific laundry conditions. This improves planning accuracy by considering geographical location information. Some or all of the above processing in the schedule analysis unit may be performed using AI, for example, or without AI. For example, the schedule analysis unit can input the user's geographical location information into a generating AI and have the generating AI perform the improvement of planning accuracy.

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

[0060] The laundry management system can also be equipped with a voice recognition unit. The voice recognition unit can analyze the user's voice commands and control each step of the laundry process by voice. For example, if the user says, "Start the wash," the voice recognition unit will analyze this command and start the washing machine. If the user says, "Add detergent," the voice recognition unit can instruct the system to add detergent. Furthermore, if the user says, "Tell me how to sort the laundry," the voice recognition unit can provide voice guidance on the appropriate sorting method. This allows the user to perform laundry operations hands-free, improving convenience.

[0061] The laundry management system can also include an energy management unit. This unit provides functions to optimize the washing machine's energy consumption. For example, it can set the washing machine's operating time to coincide with periods of lower electricity rates. It can also calculate the optimal energy consumption based on the amount and degree of soiling of the laundry, and adjust the washing machine's operating mode accordingly. Furthermore, the energy management unit can provide users with energy consumption reports and advice to help improve energy efficiency. This reduces energy consumption during laundry, enabling environmentally friendly washing.

[0062] The laundry management system can also include a health management section. This section adjusts laundry settings based on the user's health condition. For example, if a user has allergies, the health management section can suggest a special laundry cycle to remove allergens. It can also recommend the use of gentle detergents based on the user's skin condition. Furthermore, the health management section can monitor the user's exercise level and activity level, prioritizing the washing of sweaty clothes. This enables laundry that supports the user's health.

[0063] The laundry management system can also be equipped with a remote control unit. This unit allows users to operate the washing machine remotely using a smartphone or tablet. For example, users can start the washing machine or change the wash cycle while away from home. The remote control unit can also monitor the washing progress in real time and send notifications as needed. Furthermore, it can notify the user when the wash cycle is complete, prompting them to retrieve the laundry. This allows users to manage their laundry from anywhere, improving convenience.

[0064] The laundry management system can also include a learning unit. This unit learns the user's laundry habits and preferences and suggests optimal laundry settings. For example, it can record frequently used laundry cycles and detergent amounts and apply this information to future washes. Furthermore, the unit can fine-tune the laundry settings based on user feedback, providing a more satisfying laundry experience. In addition, the unit can automatically adjust laundry settings according to seasonal and weather changes. This enables personalized laundry tailored to the user's needs.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The recognition unit recognizes the material, color, and degree of soiling of each garment. The recognition unit uses cameras and sensors to analyze the material, color, and degree of soiling of each garment. For example, it uses a camera to recognize the color of the garment and sensors to analyze the material of the garment. It also uses image processing technology to analyze the degree of soiling of the garment and quantifies the degree of soiling. Step 2: The setting unit sets the optimal washing course and detergent amount based on the information recognized by the recognition unit. For example, it sets the optimal washing course and automatically sets the detergent amount based on the material, color, and degree of soiling of the clothes recognized by the recognition unit. Step 3: The instruction unit instructs the user to sort the laundry based on the information set by the settings unit. For example, it may instruct the user to sort the laundry by color or material, and to sort the laundry into lightly soiled, moderately soiled, and heavily soiled categories according to the degree of soiling. Step 4: The suggestion department proposes the optimal laundry timing, taking into account the weather forecast and the amount of laundry. For example, it suggests a laundry timing that avoids rainy days based on the weather forecast, and if there is a large amount of laundry, it suggests a laundry timing that is appropriate for the capacity of the washing machine. Step 5: The notification unit sends a notification to the user when the laundry is finished. For example, it notifies the user that the laundry is finished and prompts them to bring in the laundry.

[0067] (Example of form 2)The laundry management system according to an embodiment of the present invention is a system that uses AI to improve the efficiency of laundry. When the user puts laundry into the washing machine, the AI ​​uses cameras and sensors to analyze the material, color, and degree of soiling of each garment. Next, the AI ​​automatically sets the optimal washing course and detergent amount, and instructs the user on how to sort the laundry. Furthermore, it suggests the optimal washing time considering the weather forecast and the amount of laundry, and sends a notification to the user when the washing is finished. Finally, the AI ​​analyzes the user's daily schedule, creates an optimal laundry plan, and provides notifications. This makes it easier to manage laundry even with limited time, and enables more efficient laundry. For example, the user inputs the laundry into the laundry management system. For example, the laundry management system can scan handwritten laundry items and convert them into digital data, or input them directly in digital format. This data is converted into a format that is easy for the generating AI to analyze. Next, the laundry management system uses the generating AI to analyze the user's laundry and summarize its contents. The input to the generating AI is the user's laundry itself, and the generating AI generates a summary based on its contents. For example, the generative AI receives a prompt such as "Summarize the main points of this laundry," extracts the main points, and creates a summary. Next, the laundry management system calculates the similarity between the summary created by the generative AI and a pre-prepared model answer. Natural language processing techniques are used to calculate the similarity. For example, the generative AI analyzes the degree of word agreement and sentence structure similarity between the summary and the model answer to calculate a similarity score. Next, the laundry management system calculates a score for the laundry based on the similarity score. For example, it may be set so that a higher similarity score corresponds to a higher score. The final score is then fed back to the user. This allows the user to know how well their laundry matches the model answer. The laundry management system also applies a similar process to essay tests. The laundry management system reads the user's essay answer, the generative AI summarizes it, and calculates the similarity to the model answer. For example, the laundry management system summarizes the user's opinion and logical development on the essay topic and calculates a score by comparing it with the model answer.This enables the laundry management system to automatically grade long reading comprehension passages and essays from Japanese language tests. The system can then automatically grade the user's laundry and provide the graded results.

[0068] The laundry management system according to this embodiment comprises a recognition unit, a setting unit, an instruction unit, a suggestion unit, and a notification unit. The recognition unit recognizes the material, color, and degree of soiling of each garment. The recognition unit analyzes the material, color, and degree of soiling of each garment using, for example, a camera or sensors. The recognition unit recognizes the color of the garment using, for example, a camera, and analyzes the material of the garment using sensors. The recognition unit may also use image processing technology to analyze the degree of soiling. For example, the recognition unit uses image processing technology to analyze the degree of soiling of the garment and quantifies the degree of soiling. The setting unit sets the optimal washing course and detergent amount based on the information recognized by the recognition unit. The setting unit sets the optimal washing course based on the material, color, and degree of soiling of the garment recognized by the recognition unit. The setting unit can also automatically set the detergent amount. For example, the setting unit sets the optimal detergent amount based on the degree of soiling of the garment. The instruction unit instructs the user to classify the laundry based on the information set by the setting unit. The instruction unit, for example, instructs the user to sort laundry by color or material. The instruction unit can also instruct the user to sort laundry according to its degree of soiling. For example, the instruction unit can instruct the user to sort laundry into lightly soiled, moderately soiled, and heavily soiled categories according to the degree of soiling. The suggestion unit proposes the optimal washing timing considering the weather forecast and the amount of laundry. For example, the suggestion unit suggests a washing timing that avoids rainy days based on the weather forecast. The suggestion unit can also suggest the optimal washing timing based on the amount of laundry. For example, if there is a large amount of laundry, the suggestion unit suggests a washing timing that is appropriate for the capacity of the washing machine. The notification unit sends a notification to the user when the washing is finished. For example, the notification unit notifies the user that the washing is complete. The notification unit can also send a notification prompting the user to take in the laundry. For example, the notification unit notifies the user that the laundry is dry and prompts them to take it in. As a result, the laundry management system according to the embodiment can efficiently manage the user's laundry and improve the efficiency of the washing process.

[0069] The recognition unit recognizes the material, color, and degree of soiling of each garment. For example, it uses cameras and sensors to analyze the material, color, and degree of soiling of each garment. Specifically, the camera captures high-resolution images, and image processing technology is used to accurately recognize the color of the garment. Color recognition utilizes color spaces such as RGB and HSV values ​​to detect even subtle color differences. Sensors can use methods such as near-infrared spectroscopy and terahertz wave spectroscopy to analyze the material of the garment. This allows for high-precision identification of different materials such as cotton, polyester, and wool. Image processing technology can also be used to analyze the degree of soiling. For example, image processing algorithms are used to quantify the area and concentration of dirt attached to the surface of the garment, quantitatively evaluating the degree of soiling. Furthermore, the recognition unit can use AI to analyze this data and comprehensively evaluate the condition of the garment. Based on past data and learning models, the AI ​​accurately recognizes the material, color, and degree of soiling of the garment and provides basic information to suggest the optimal washing method. This allows the recognition unit to accurately grasp detailed information about each garment and provide the data necessary for the next processing step.

[0070] The settings unit sets the optimal washing course and detergent amount based on the information recognized by the recognition unit. Specifically, the washing machine's control system automatically selects the optimal washing course based on data on the material, color, and degree of soiling of the clothes provided by the recognition unit. For example, a low-temperature washing course is set for delicate materials, and a special course to prevent color transfer is selected for clothes that are prone to color bleeding. It can also adjust the amount of detergent and washing time according to the degree of soiling. For example, a small amount of detergent and a short wash time are set for lightly soiled items, and a larger amount of detergent and a longer wash time are set for heavily soiled items. Furthermore, the settings unit can use AI to learn past washing data and the user's washing habits to provide more personalized washing settings. Based on the user's preferences and past washing results, the AI ​​suggests the optimal washing course and detergent amount, maximizing the efficiency and effectiveness of the wash. In this way, the settings unit can improve the quality and efficiency of the wash by saving the user effort and automatically setting the optimal washing conditions.

[0071] The instruction unit instructs the user on how to sort laundry based on information set by the settings unit. Specifically, it instructs the user to sort laundry by color or material. For example, it displays specific instructions to the user through a smartphone app or the washing machine's display, such as "Wash white clothes together" or "Wash delicate items separately." It can also instruct the user to sort laundry according to the degree of soiling. For example, it can instruct the user to sort laundry into lightly soiled, moderately soiled, and heavily soiled categories, and suggest the appropriate washing course for each. Furthermore, the instruction unit can also provide voice instructions to the user using a voice assistant. This allows the user to sort laundry appropriately using not only visual information but also auditory information. The instruction unit can also collect user feedback and continuously improve the accuracy and effectiveness of its instructions. For example, it optimizes instructions for subsequent loads based on the user's results in sorting laundry according to the instructions. This allows the instruction unit to quickly provide the user with appropriate instructions, improving the efficiency and effectiveness of laundry.

[0072] The suggestion department proposes the optimal laundry timing, taking into account weather forecasts and the amount of laundry. Specifically, it suggests laundry times that avoid rainy days based on the weather forecast. For example, it analyzes the latest weather forecast data obtained from the internet and suggests doing laundry on sunny or windy days. The suggestion department can also suggest the optimal laundry timing based on the amount of laundry. For example, if there is a large amount of laundry, it will suggest a laundry timing that matches the capacity of the washing machine, allowing for more efficient laundry. Furthermore, the suggestion department can use AI to learn the user's lifestyle patterns and past laundry history, and propose the optimal laundry timing individually. For example, if a user often does laundry on weekends, it will suggest the optimal laundry timing considering the weekend weather forecast. In this way, the suggestion department can provide flexible suggestions tailored to the user's lifestyle, improving the efficiency and effectiveness of laundry.

[0073] The notification unit sends a notification to the user when the laundry is finished. Specifically, it notifies the user that the laundry is complete. For example, it notifies the user via a smartphone app, email, or SMS that the laundry is finished. The notification unit can also send notifications prompting the user to take in the laundry. For example, it notifies the user when the laundry is dry and prompts them to take it in. Furthermore, the notification unit can also notify the user about the status of the washing machine and maintenance information. For example, it notifies the user to perform appropriate maintenance if the washing machine filter is clogged or if the detergent level is low. In this way, the notification unit can quickly provide users with important information about laundry, improving the efficiency and effectiveness of the laundry. In addition, the notification unit can collect user feedback and continuously improve the accuracy and effectiveness of the notification content. For example, it can analyze the user's actions after receiving a notification and optimize the content of future notifications. In this way, the notification unit can quickly and reliably provide information to users, improving the efficiency and effectiveness of the laundry.

[0074] The schedule analysis unit analyzes the user's daily schedule and creates an optimal laundry plan. For example, the schedule analysis unit obtains data from the user's calendar app or schedule management app and analyzes the daily schedule. For example, the schedule analysis unit creates an optimal laundry plan considering the user's work schedule and household schedule. The schedule analysis unit can also suggest the timing of laundry considering the user's free time and available time. For example, the schedule analysis unit suggests the timing of laundry to match the user's free time. This makes laundry more efficient by creating a laundry plan based on the user's schedule. Some or all of the above processing in the schedule analysis unit may be performed using AI, for example, or without AI. For example, the schedule analysis unit can input the user's schedule data into a generating AI and have the generating AI create an optimal laundry plan.

[0075] The recognition unit can analyze the material, color, and degree of soiling of each garment using cameras and sensors. For example, the recognition unit can recognize the color of the garment using a camera and analyze the material of the garment using sensors. The recognition unit can also use image processing technology to analyze the degree of soiling. For example, the recognition unit can use image processing technology to analyze the degree of soiling of the garment and quantify the degree of soiling. This improves the accuracy of garment recognition by using cameras and sensors. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input data acquired by cameras and sensors into a generating AI and have the generating AI perform the analysis of the material, color, and degree of soiling of the garments.

[0076] The setting unit can automatically set the optimal washing course and detergent amount. For example, the setting unit sets the optimal washing course based on the material, color, and degree of soiling of the clothes recognized by the recognition unit. The setting unit can also automatically set the detergent amount. For example, the setting unit sets the optimal detergent amount based on the degree of soiling of the clothes. This improves the efficiency of washing by automatically making the optimal settings. Some or all of the above processes in the setting unit may be performed using AI, for example, or without AI. For example, the setting unit can input the information recognized by the recognition unit into a generating AI and cause the generating AI to set the optimal washing course and detergent amount.

[0077] The instruction unit can instruct the user to sort laundry. For example, the instruction unit can instruct the user to sort laundry by color or material. The instruction unit can also instruct the user to sort laundry according to its degree of soiling. For example, the instruction unit can instruct the user to sort laundry into lightly soiled, moderately soiled, and heavily soiled categories according to the degree of soiling. This makes laundry management easier by providing the user with appropriate sorting instructions. 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 information recognized by the recognition unit into a generating AI and have the generating AI execute laundry sorting instructions.

[0078] The suggestion unit can propose the optimal laundry timing by considering the weather forecast and the amount of laundry. For example, based on the weather forecast, the suggestion unit can suggest a laundry timing that avoids rainy days. The suggestion unit can also suggest the optimal laundry timing based on the amount of laundry. For example, if there is a large amount of laundry, the suggestion unit can suggest a laundry timing that is appropriate for the capacity of the washing machine. In this way, the optimal laundry timing can be proposed by considering the weather forecast and the amount of laundry. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without using AI. For example, the suggestion unit can input weather forecast data and laundry amount data into a generating AI and have the generating AI execute a suggestion for the optimal laundry timing.

[0079] The notification unit can send a notification to the user when the laundry is finished. For example, the notification unit can notify the user that the laundry is complete. The notification unit can also send a notification prompting the user to take in the laundry. For example, the notification unit can notify the user that the laundry is dry and prompt them to take it in. This allows the user to take in the laundry in a timely manner by notifying them when the laundry is finished. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the laundry completion data into a generating AI and have the generating AI generate the notification content.

[0080] The recognition unit can estimate the user's emotions and adjust the accuracy of clothing recognition based on the estimated emotions. For example, if the user is stressed, the recognition unit can increase recognition accuracy to reduce misrecognition. Conversely, if the user is relaxed, the recognition unit can maintain normal recognition accuracy and prioritize processing speed. Furthermore, if the user is in a hurry, the recognition unit can maximize processing speed even at the expense of slightly lowering recognition accuracy. This allows for a reduction in misrecognition by adjusting recognition accuracy according to 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-described processing in the recognition unit may be performed using AI, or not. For example, the recognition unit can input user emotion data into the generative AI and have the generative AI adjust the recognition accuracy.

[0081] The recognition unit can optimize its recognition algorithm by referring to past recognition data when recognizing clothing. For example, the recognition unit can prioritize referring to data of clothing that has frequently been misrecognized in the past to improve recognition accuracy. The recognition unit can also improve the recognition accuracy of clothing that is frequently used in a particular season by referring to past recognition data. Furthermore, the recognition unit can learn the recognition patterns of a specific user's clothing based on past recognition data to improve accuracy. Thus, recognition accuracy is improved by referring to past recognition data. Some or all of the above processes in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input past recognition data into a generating AI and have the generating AI perform the optimization of the recognition algorithm.

[0082] The recognition unit can improve recognition accuracy by considering the frequency of use and washing history of clothing during recognition. For example, the recognition unit can prioritize the recognition of frequently used clothing and determine the need for washing. The recognition unit can also refer to the washing history to improve the recognition accuracy of clothing that is prone to certain stains. Furthermore, the recognition unit can combine the frequency of use and the washing history to apply an optimal recognition algorithm. This improves recognition accuracy by considering the frequency of use and the washing history. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input clothing usage frequency and washing history data into a generating AI and have the generating AI perform the improvement of recognition accuracy.

[0083] The recognition unit can estimate the user's emotions and adjust the display method of the recognition results based on the estimated emotions. For example, if the user is nervous, the recognition unit can provide a simple and highly visible display method. If the user is relaxed, the recognition unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the recognition unit can provide a concise display method. By adjusting the display method according to the user's emotions, visibility is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display method.

[0084] The recognition unit can improve recognition accuracy by considering the user's geographical location information when recognizing clothing. For example, if the user lives in a cold region, the recognition unit can improve the recognition accuracy of winter clothing. Similarly, if the user lives in a warm region, the recognition unit can improve the recognition accuracy of summer clothing. Furthermore, the recognition unit can improve the recognition accuracy of region-specific clothing based on the user's geographical location information. Thus, recognition accuracy is improved by considering geographical location information. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input the user's geographical location information into a generating AI and have the generating AI perform the improvement of recognition accuracy.

[0085] The recognition unit can analyze the user's social media activity and acquire relevant recognition data when recognizing clothing. For example, the recognition unit can analyze images of clothing posted by the user on social media to improve recognition accuracy. The recognition unit can also identify trendy clothing from the user's social media activity to improve recognition accuracy. Furthermore, the recognition unit can improve the recognition accuracy of clothing from specific brands based on the user's social media activity. Thus, recognition accuracy is improved by analyzing social media activity. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input the user's social media activity data into a generating AI and have the generating AI perform the acquisition of recognition data.

[0086] The settings unit can estimate the user's emotions and adjust the washing course and detergent amount settings based on the estimated emotions. For example, if the user is stressed, the settings unit can provide simple setting options and automatically set the washing course and detergent amount. If the user is relaxed, the settings unit can also provide detailed setting options and suggest customizable settings. Furthermore, if the user is in a hurry, the settings unit can prioritize settings that complete the laundry in the shortest time. This improves laundry efficiency by adjusting settings according to 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 settings unit may be performed using AI or not using AI. For example, the settings unit can input user emotion data into a generative AI and have the generative AI perform the setting adjustments.

[0087] The settings unit can optimize its setting algorithm by referring to past setting data when setting the washing course and detergent amount. For example, the settings unit can suggest the optimal settings based on the washing course and detergent amount used in the past. The settings unit can also learn and suggest the optimal settings for specific garments from past setting data. Furthermore, the settings unit can analyze past setting data and suggest the optimal settings for each season. This improves setting accuracy by referring to past setting data. Some or all of the above processes in the settings unit may be performed using AI, for example, or without AI. For example, the settings unit can input past setting data into a generating AI and have the generating AI perform the optimization of the setting algorithm.

[0088] The setting unit can improve the accuracy of settings by analyzing the material, color, and degree of soiling of clothing in detail when setting the washing course and detergent amount. For example, the setting unit can analyze the material of clothing in detail and set the optimal washing course. It can also take the color of the clothing into consideration and set the amount of detergent to prevent color fading. Furthermore, the setting unit can analyze the degree of soiling of the clothing in detail and set the amount of detergent according to the level of soiling. In this way, the accuracy of settings is improved by performing a detailed analysis of the clothing. Some or all of the above processes in the setting unit may be performed using AI, for example, or without using AI. For example, the setting unit can input data on the material, color, and degree of soiling of clothing into a generating AI and have the generating AI perform the improvement of setting accuracy.

[0089] The settings unit can estimate the user's emotions and adjust the display method of the settings results based on the estimated emotions. For example, if the user is nervous, the settings unit can provide a simple and highly visible display method. If the user is relaxed, the settings unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the settings unit can provide a concise display method. By adjusting the display method according to the user's emotions, visibility is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the settings unit may be performed using AI, for example, or without AI. For example, the settings unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display method.

[0090] The settings unit can improve setting accuracy by considering the user's geographical location information when setting the washing course and detergent amount. For example, if the user lives in a cold region, the settings unit can suggest settings suitable for winter clothing. Similarly, if the user lives in a warm region, the settings unit can suggest settings suitable for summer clothing. Furthermore, based on the user's geographical location information, the settings unit can suggest settings suitable for region-specific washing conditions. This improves setting accuracy by considering geographical location information. Some or all of the above processing in the settings unit may be performed using AI, for example, or without AI. For example, the settings unit can input the user's geographical location information into a generating AI and have the generating AI perform the improvement of setting accuracy.

[0091] The settings unit can analyze the user's social media activity and acquire relevant setting data when setting the washing course and detergent amount. For example, the settings unit can analyze images of clothing posted by the user on social media and suggest the optimal settings. The settings unit can also identify trending washing methods from the user's social media activity and reflect them in the settings. Furthermore, based on the user's social media activity, the settings unit can suggest settings suitable for clothing of a specific brand. In this way, the accuracy of the settings is improved by analyzing social media activity. Some or all of the above processing in the settings unit may be performed using AI, for example, or without AI. For example, the settings unit can input the user's social media activity data into a generating AI and have the generating AI acquire the setting data.

[0092] The instruction unit can estimate the user's emotions and adjust the laundry sorting instructions based on the estimated emotions. For example, if the user is stressed, the instruction unit can provide simple sorting instructions. If the user is relaxed, it can also provide detailed sorting instructions. Furthermore, if the user is in a hurry, it can provide instructions that allow for quick sorting. This improves sorting efficiency by adjusting the sorting instructions according to 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 a generative AI and have the generative AI adjust the sorting instructions.

[0093] The instruction unit can optimize its instruction algorithm by referring to past classification data when giving instructions for classifying laundry. For example, the instruction unit can prioritize referring to data on clothing that was frequently misclassified in the past to improve classification accuracy. The instruction unit can also improve the classification accuracy of clothing that is frequently used in a particular season by referring to past classification data. Furthermore, the instruction unit can learn the classification patterns of a particular user's clothing based on past classification data to improve accuracy. In this way, classification accuracy is improved by referring to past classification data. 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 past classification data into a generating AI and have the generating AI perform the optimization of the instruction algorithm.

[0094] The instruction unit can estimate the user's emotions and adjust the display method of classification instructions based on the estimated user emotions. For example, if the user is tense, the instruction unit can provide a simple and highly visible display method. If the user is relaxed, the instruction unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the instruction unit can provide a concise display method. By adjusting the display method according to the user's emotions, visibility is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The 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 instruction unit may be performed using AI, for example, or not using AI. For example, the instruction unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display method.

[0095] The instruction unit can improve classification accuracy by considering the user's geographical location information when giving instructions for laundry classification. For example, if the user lives in a cold region, the instruction unit can improve the classification accuracy of winter clothing. Similarly, if the user lives in a warm region, the instruction unit can improve the classification accuracy of summer clothing. Furthermore, based on the user's geographical location information, the instruction unit can improve the classification accuracy of region-specific clothing. In this way, classification accuracy is improved by considering geographical location information. 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 user's geographical location information into a generating AI and have the generating AI perform the classification accuracy improvement.

[0096] The suggestion unit can estimate the user's emotions and adjust laundry timing suggestions based on those emotions. For example, if the user is stressed, the suggestion unit can provide simple suggestions and automatically set the laundry timing. If the user is relaxed, the suggestion unit can also provide detailed suggestions and propose customizable laundry timings. Furthermore, if the user is in a hurry, the suggestion unit can prioritize the timing that will complete the laundry in the shortest possible time. This improves laundry efficiency by adjusting suggestions according to 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the suggestions.

[0097] The suggestion unit can optimize its suggestion algorithm by referring to past suggestion data when suggesting laundry timing. For example, the suggestion unit can make the optimal suggestion based on laundry timing used in the past. The suggestion unit can also suggest laundry timing suitable for a specific season based on past suggestion data. Furthermore, the suggestion unit can analyze past suggestion data and suggest the optimal laundry timing for a specific user. This improves the accuracy of suggestions by referring to past suggestion data. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input past suggestion data into a generating AI and have the generating AI perform the optimization of the suggestion algorithm.

[0098] The suggestion unit can improve the accuracy of its suggestions by analyzing weather forecasts and laundry loads in detail when suggesting laundry timing. For example, the suggestion unit can suggest laundry timings that avoid rainy days based on the weather forecast. It can also analyze the laundry load in detail and suggest the optimal laundry timing. Furthermore, the suggestion unit can combine the weather forecast and laundry load to suggest the optimal laundry timing. This improves the accuracy of the suggestions by analyzing the weather forecast and laundry load in detail. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input weather forecast data and laundry load data into a generating AI and have the generating AI perform the improvement of the suggestion accuracy.

[0099] The suggestion unit can estimate the user's emotions and adjust the display method of the suggestion results based on the estimated user emotions. For example, if the user is nervous, the suggestion unit can provide a simple and highly visible display method. If the user is relaxed, the suggestion unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the suggestion unit can provide a concise display method. By adjusting the display method according to the user's emotions, visibility is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The 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 suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display method.

[0100] The suggestion unit can improve the accuracy of its laundry timing suggestions by considering the user's geographical location information. For example, if the user lives in a cold region, the suggestion unit can suggest a laundry timing suitable for winter clothes. Similarly, if the user lives in a warm region, the suggestion unit can suggest a laundry timing suitable for summer clothes. Furthermore, based on the user's geographical location information, the suggestion unit can suggest a laundry timing suitable for region-specific laundry conditions. This improves the accuracy of the suggestions by considering geographical location information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's geographical location information into a generating AI and have the generating AI perform the improvement of the suggestion accuracy.

[0101] The suggestion unit can analyze the user's social media activity and acquire relevant suggestion data when suggesting laundry timing. For example, the suggestion unit can analyze the content of the user's social media posts and suggest the optimal laundry timing. The suggestion unit can also identify trending laundry methods from the user's social media activity and reflect them in the suggestions. Furthermore, based on the user's social media activity, the suggestion unit can suggest laundry timing tailored to specific events. This improves the accuracy of suggestions by analyzing social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's social media activity data into a generating AI and have the generating AI acquire the suggestion data.

[0102] 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 concise and to-the-point notification. If the user is relaxed, the notification unit can also provide a notification with detailed information. Furthermore, if the user is in a hurry, the notification unit can provide a notification that allows for a quick response. This improves the effectiveness of notifications by adjusting the content according to 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 notification unit may be performed using AI, or not using AI. For example, the notification unit can input user emotion data into a generative AI and have the generative AI adjust the notification content.

[0103] The notification unit can optimize its notification algorithm by referring to past notification data when it notifies the user that the laundry is finished. For example, the notification unit can provide the optimal notification based on previously used notification methods. It can also suggest a notification method suitable for a specific time period based on past notification data. Furthermore, the notification unit can analyze past notification data and suggest the optimal notification method for a specific user. This improves notification accuracy by referring to past notification data. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input past notification data into a generating AI and have the generating AI perform the optimization of the notification algorithm.

[0104] The notification unit can estimate the user's emotions and adjust the notification display method based on the estimated emotions. For example, if the user is stressed, the notification unit can provide a simple and highly visible display method. If the user is relaxed, the notification unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the notification unit can provide a concise display method. By adjusting the display method according to the user's emotions, visibility is improved. 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 notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display method.

[0105] The notification unit can improve notification accuracy by considering the user's geographical location when notifying the user that the laundry is finished. For example, if the user is at home, the notification unit can provide an immediate notification. If the user is out, the notification unit can also provide a notification timed to coincide with the user's expected return time. Furthermore, the notification unit can suggest the optimal notification timing based on the user's geographical location. This improves notification accuracy by considering geographical location. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of improving notification accuracy.

[0106] The schedule analysis unit can estimate the user's emotions and adjust the laundry plan based on those emotions. For example, if the user is stressed, the schedule analysis unit can provide a simple laundry plan to reduce the workload. If the user is relaxed, the schedule analysis unit can provide a detailed laundry plan and suggest a customizable plan. Furthermore, if the user is in a hurry, the schedule analysis unit can prioritize a plan that completes the laundry in the shortest time. This improves laundry efficiency by adjusting the laundry plan according to 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 schedule analysis unit may be performed using AI or not. For example, the schedule analysis unit can input user emotion data into a generative AI and have the generative AI adjust the laundry plan.

[0107] The schedule analysis unit can optimize the planning algorithm by referring to past schedule data when formulating a laundry plan. For example, the schedule analysis unit can propose an optimal plan based on laundry plans used in the past. The schedule analysis unit can also propose a laundry plan suitable for a specific season from past schedule data. Furthermore, the schedule analysis unit can analyze past schedule data and propose an optimal laundry plan for a specific user. This improves planning accuracy by referring to past schedule data. Some or all of the above processing in the schedule analysis unit may be performed using AI, for example, or without AI. For example, the schedule analysis unit can input past schedule data into a generating AI and have the generating AI perform the optimization of the planning algorithm.

[0108] The schedule analysis unit can estimate the user's emotions and adjust the display method of the laundry plan based on the estimated emotions. For example, if the user is stressed, the schedule analysis unit can provide a simple and highly visible display method. If the user is relaxed, the schedule analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the schedule analysis unit can provide a concise display method. By adjusting the display method according to the user's emotions, visibility is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the schedule analysis unit may be performed using AI, for example, or without AI. For example, the schedule analysis unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the display method.

[0109] The schedule analysis unit can improve the accuracy of laundry planning by considering the user's geographical location information. For example, if the user lives in a cold region, the schedule analysis unit can propose a laundry plan suitable for winter clothes. Similarly, if the user lives in a warm region, the schedule analysis unit can propose a laundry plan suitable for summer clothes. Furthermore, based on the user's geographical location information, the schedule analysis unit can propose a laundry plan suitable for region-specific laundry conditions. This improves planning accuracy by considering geographical location information. Some or all of the above processing in the schedule analysis unit may be performed using AI, for example, or without AI. For example, the schedule analysis unit can input the user's geographical location information into a generating AI and have the generating AI perform the improvement of planning accuracy.

[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] The laundry management system can also be equipped with a voice recognition unit. The voice recognition unit can analyze the user's voice commands and control each step of the laundry process by voice. For example, if the user says, "Start the wash," the voice recognition unit will analyze this command and start the washing machine. If the user says, "Add detergent," the voice recognition unit can instruct the system to add detergent. Furthermore, if the user says, "Tell me how to sort the laundry," the voice recognition unit can provide voice guidance on the appropriate sorting method. This allows the user to perform laundry operations hands-free, improving convenience.

[0112] The laundry management system can also include an energy management unit. This unit provides functions to optimize the washing machine's energy consumption. For example, it can set the washing machine's operating time to coincide with periods of lower electricity rates. It can also calculate the optimal energy consumption based on the amount and degree of soiling of the laundry, and adjust the washing machine's operating mode accordingly. Furthermore, the energy management unit can provide users with energy consumption reports and advice to help improve energy efficiency. This reduces energy consumption during laundry, enabling environmentally friendly washing.

[0113] The laundry management system can also include a health management section. This section adjusts laundry settings based on the user's health condition. For example, if a user has allergies, the health management section can suggest a special laundry cycle to remove allergens. It can also recommend the use of gentle detergents based on the user's skin condition. Furthermore, the health management section can monitor the user's exercise level and activity level, prioritizing the washing of sweaty clothes. This enables laundry that supports the user's health.

[0114] The laundry management system can also be equipped with a remote control unit. This unit allows users to operate the washing machine remotely using a smartphone or tablet. For example, users can start the washing machine or change the wash cycle while away from home. The remote control unit can also monitor the washing progress in real time and send notifications as needed. Furthermore, it can notify the user when the wash cycle is complete, prompting them to retrieve the laundry. This allows users to manage their laundry from anywhere, improving convenience.

[0115] The laundry management system can also include a learning unit. This unit learns the user's laundry habits and preferences and suggests optimal laundry settings. For example, it can record frequently used laundry cycles and detergent amounts and apply this information to future washes. Furthermore, the unit can fine-tune the laundry settings based on user feedback, providing a more satisfying laundry experience. In addition, the unit can automatically adjust laundry settings according to seasonal and weather changes. This enables personalized laundry tailored to the user's needs.

[0116] The laundry management system can further utilize emotion estimation to adjust the laundry process based on the user's emotions. For example, if the user is stressed, the system can reduce the washing machine's noise level. Conversely, if the user is relaxed, the system can prioritize efficiency by running the laundry at normal noise levels. Furthermore, if the user is in a hurry, the system can shorten the washing time. By adjusting the laundry process according to the user's emotions, the system can provide a more comfortable laundry experience.

[0117] The laundry management system can further utilize emotion estimation to adjust the drying method based on the user's emotions. For example, if the user is stressed, the drying time can be shortened so that the laundry can be retrieved sooner. Conversely, if the user is relaxed, the system can prioritize energy efficiency by drying for the normal amount of time. Furthermore, if the user is in a hurry, the drying temperature can be increased to shorten the drying time. This allows for efficient drying by adjusting the drying method according to the user's emotions.

[0118] The laundry management system can further utilize emotion estimation to adjust the laundry finish based on the user's emotions. For example, if the user is stressed, it can suggest a special finishing course to prevent wrinkles. Conversely, if the user is relaxed, it can finish the laundry with a standard finishing course. Furthermore, if the user is in a hurry, it can suggest a fast finishing course to shorten the finishing time. By adjusting the laundry finish according to the user's emotions, it can provide a more satisfying laundry experience.

[0119] The laundry management system can further utilize emotion estimation to adjust the scent of laundry based on the user's emotions. For example, if the user is stressed, it can select a relaxing scent. Conversely, if the user is relaxed, it can finish the laundry with a standard scent. Furthermore, if the user is in a hurry, the scent intensity can be adjusted so that the fragrance spreads quickly. In this way, by adjusting the scent of laundry according to the user's emotions, it can provide a comfortable laundry experience.

[0120] The laundry management system can further utilize emotion estimation to adjust the timing of laundry collection based on the user's emotions. For example, if the user is stressed, it can send a notification immediately after the laundry is dry to encourage them to collect it quickly. If the user is relaxed, it can send a notification at the usual time. Furthermore, if the user is in a hurry, it can send a notification before the laundry is dry to encourage them to prepare to collect it. This allows for efficient laundry management by adjusting the timing of laundry collection according to the user's emotions.

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

[0122] Step 1: The recognition unit recognizes the material, color, and degree of soiling of each garment. The recognition unit uses cameras and sensors to analyze the material, color, and degree of soiling of each garment. For example, it uses a camera to recognize the color of the garment and sensors to analyze the material of the garment. It also uses image processing technology to analyze the degree of soiling of the garment and quantifies the degree of soiling. Step 2: The setting unit sets the optimal washing course and detergent amount based on the information recognized by the recognition unit. For example, it sets the optimal washing course and automatically sets the detergent amount based on the material, color, and degree of soiling of the clothes recognized by the recognition unit. Step 3: The instruction unit instructs the user to sort the laundry based on the information set by the settings unit. For example, it may instruct the user to sort the laundry by color or material, and to sort the laundry into lightly soiled, moderately soiled, and heavily soiled categories according to the degree of soiling. Step 4: The suggestion department proposes the optimal laundry timing, taking into account the weather forecast and the amount of laundry. For example, it suggests a laundry timing that avoids rainy days based on the weather forecast, and if there is a large amount of laundry, it suggests a laundry timing that is appropriate for the capacity of the washing machine. Step 5: The notification unit sends a notification to the user when the laundry is finished. For example, it notifies the user that the laundry is finished and prompts them to bring in the laundry.

[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 recognition unit, setting unit, instruction unit, suggestion unit, notification unit, and schedule analysis unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the recognition unit analyzes the material, color, and degree of soiling of clothing using the camera 42 and sensors of the smart device 14 and is implemented by the control unit 46A. The setting unit sets the optimal washing course and detergent amount based on the information from the recognition unit by the specific processing unit 290 of the data processing unit 12. The instruction unit instructs the user to classify the laundry by the control unit 46A of the smart device 14. The suggestion unit proposes the optimal washing timing considering the weather forecast and the amount of laundry by the specific processing unit 290 of the data processing unit 12. The notification unit notifies the user that the washing is complete by the control unit 46A of the smart device 14. The schedule analysis unit analyzes the user's daily schedule by the specific processing unit 290 of the data processing unit 12 and creates an optimal washing plan. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[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 recognition unit, setting unit, instruction unit, suggestion unit, notification unit, and schedule analysis unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the recognition unit analyzes the material, color, and degree of soiling of clothing using the camera 42 and sensors of the smart glasses 214 and is implemented by the control unit 46A. The setting unit sets the optimal washing course and detergent amount based on the information from the recognition unit by the specific processing unit 290 of the data processing unit 12. The instruction unit instructs the user to classify the laundry by the control unit 46A of the smart glasses 214. The suggestion unit proposes the optimal washing timing considering the weather forecast and the amount of laundry by the specific processing unit 290 of the data processing unit 12. The notification unit notifies the user that the washing is complete by the control unit 46A of the smart glasses 214. The schedule analysis unit analyzes the user's daily schedule by the specific processing unit 290 of the data processing unit 12 and creates an optimal washing plan. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications 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 recognition unit, setting unit, instruction unit, suggestion unit, notification unit, and schedule analysis unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the recognition unit analyzes the material, color, and degree of soiling of clothing using the camera 42 and sensors of the headset terminal 314 and is implemented by the control unit 46A. The setting unit sets the optimal washing course and detergent amount based on the information from the recognition unit by the specific processing unit 290 of the data processing unit 12. The instruction unit instructs the user to classify the laundry by the control unit 46A of the headset terminal 314. The suggestion unit proposes the optimal washing timing considering the weather forecast and the amount of laundry by the specific processing unit 290 of the data processing unit 12. The notification unit notifies the user that the washing is complete by the control unit 46A of the headset terminal 314. The schedule analysis unit analyzes the user's daily schedule by the specific processing unit 290 of the data processing unit 12 and creates an optimal washing plan. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[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 recognition unit, setting unit, instruction unit, suggestion unit, notification unit, and schedule analysis unit, is implemented by at least one of the robot 414 and the data processing unit 12. For example, the recognition unit analyzes the material, color, and degree of soiling of clothing using the camera 42 and sensors of the robot 414 and is implemented by the control unit 46A. The setting unit sets the optimal washing course and detergent amount based on the information from the recognition unit by the specific processing unit 290 of the data processing unit 12. The instruction unit instructs the user to sort the laundry by the control unit 46A of the robot 414. The suggestion unit proposes the optimal washing timing considering the weather forecast and the amount of laundry by the specific processing unit 290 of the data processing unit 12. The notification unit notifies the user that the washing is complete by the control unit 46A of the robot 414. The schedule analysis unit analyzes the user's daily schedule by the specific processing unit 290 of the data processing unit 12 and creates an optimal washing plan. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[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) A recognition unit that recognizes the material, color, and degree of soiling of each garment, A setting unit sets the optimal washing course and detergent amount based on the information recognized by the recognition unit, An instruction unit that instructs the user to classify laundry based on the information set by the setting unit, The proposal department suggests the optimal timing for doing laundry, taking into account the weather forecast and the amount of laundry. It includes a notification unit that sends a notification to the user when the washing is finished. A system characterized by the following features. (Note 2) It features a schedule analysis unit that analyzes the user's daily schedule and creates an optimal laundry plan. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned recognition unit, Cameras and sensors are used to analyze the material, color, and degree of soiling of each garment. The system described in Appendix 1, characterized by the features described herein. (Note 4) The setting unit is, Automatically sets the optimal wash cycle and detergent amount. The system described in Appendix 1, characterized by the features described herein. (Note 5) The indicator unit is, Instruct the user to sort the laundry. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, We suggest the optimal time to do laundry, taking into account the weather forecast and the amount of laundry. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned notification unit, Send a notification to the user when the laundry is finished. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned recognition unit, The system estimates the user's emotions and adjusts the accuracy of clothing recognition based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned recognition unit, When recognizing clothing, the recognition algorithm is optimized by referring to past recognition data. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned recognition unit, When recognizing clothing, the accuracy of the recognition is improved by considering the frequency of use and washing history of the clothing. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned recognition unit, It estimates the user's emotions and adjusts how the recognition results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned recognition unit, When recognizing clothing, the system improves recognition accuracy by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned recognition unit, When recognizing clothing, the system analyzes the user's social media activity and obtains relevant recognition data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The setting unit is, It estimates the user's emotions and adjusts the washing cycle and detergent amount settings based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The setting unit is, When setting the washing cycle or detergent amount, the setting algorithm is optimized by referring to past setting data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The setting unit is, When setting the washing cycle and detergent amount, the system analyzes the material, color, and degree of soiling of the clothes in detail to improve the accuracy of the settings. The system described in Appendix 1, characterized by the features described herein. (Note 17) The setting unit is, It estimates the user's emotions and adjusts how the settings are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The setting unit is, When setting the washing cycle and detergent amount, the system improves setting accuracy by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 19) The setting unit is, When setting the washing cycle and detergent amount, the system analyzes the user's social media activity and obtains relevant setting data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The indicator unit is, It estimates the user's emotions and adjusts the laundry sorting instructions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The indicator unit is, When giving instructions for sorting laundry, the instruction algorithm is optimized by referring to past sorting data. 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 adjusts how classification instructions are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The indicator unit is, When giving instructions for sorting laundry, the system improves sorting accuracy by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, It estimates the user's emotions and adjusts the laundry timing suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When suggesting laundry timing, the suggestion algorithm is optimized by referring to past suggestion data. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, To improve the accuracy of laundry timing suggestions, we analyze weather forecasts and laundry loads in detail. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, It estimates the user's emotions and adjusts how the suggested results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, When suggesting laundry timing, we improve the accuracy of the suggestion by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When suggesting laundry timing, we analyze the user's social media activity and obtain relevant suggestion data. The system described in Appendix 1, characterized by the features described herein. (Note 30) 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 31) The aforementioned notification unit, When a notification is sent after the laundry is finished, the notification algorithm is optimized by referring to past notification data. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned notification unit, It estimates the user's emotions and adjusts how notifications are displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned notification unit, When notifying users that their laundry is finished, we will improve the accuracy of the notification by taking into account their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned schedule analysis unit, It estimates the user's emotions and adjusts the laundry plan based on those emotions. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned schedule analysis unit, When creating a laundry schedule, refer to past schedule data to optimize the planning algorithm. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned schedule analysis unit, The system estimates the user's emotions and adjusts how the laundry plan is displayed based on those emotions. The system described in Appendix 2, characterized by the features described herein. (Note 37) The aforementioned schedule analysis unit, When creating a laundry schedule, consider the user's geographical location to improve the accuracy of the plan. The system described in Appendix 2, 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. A recognition unit that recognizes the material, color, and degree of soiling of each garment, A setting unit sets the optimal washing course and detergent amount based on the information recognized by the recognition unit, An instruction unit that instructs the user to classify laundry based on the information set by the setting unit, The proposal department suggests the optimal timing for doing laundry, taking into account the weather forecast and the amount of laundry. It includes a notification unit that sends a notification to the user when the washing is finished. A system characterized by the following features.

2. It features a schedule analysis unit that analyzes the user's daily schedule and creates an optimal laundry plan. The system according to feature 1.

3. The aforementioned recognition unit, Cameras and sensors are used to analyze the material, color, and degree of soiling of each garment. The system according to feature 1.

4. The setting unit is, Automatically sets the optimal wash cycle and detergent amount. The system according to feature 1.

5. The indicator unit is, Instruct the user to sort the laundry. The system according to feature 1.

6. The aforementioned proposal section is, We suggest the optimal time to do laundry, taking into account the weather forecast and the amount of laundry. The system according to feature 1.

7. The aforementioned notification unit, Send a notification to the user when the laundry is finished. The system according to feature 1.

8. The aforementioned recognition unit, The system estimates the user's emotions and adjusts the accuracy of clothing recognition based on those emotions. The system according to feature 1.

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