System

Abstract

To provide a system for performing an appropriate washing setting by automatically reading a tag of laundry.SOLUTION: A system includes a reading section, an analysis section, a setting section, and a control section. The reading section reads a tag of laundry. The analysis section analyzes information on the tag read by the reading section. The setting section determines an appropriate washing setting on the basis of information analyzed by the analysis section. The control section controls a washing machine on the basis of the setting determined by the setting section.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

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

[0004] With conventional technology, it was time-consuming to check the tags on each piece of laundry, making it difficult to set the appropriate wash settings.

[0005] The system of the embodiment aims to automatically read the tags on the laundry and set appropriate washing settings. [Means for solving the problem]

[0006] The system according to the embodiment includes a reading unit, an analysis unit, a setting unit, and a control unit. The reading unit reads a tag on a laundry item. The analysis unit analyzes information on the tag read by the reading unit. The setting unit determines appropriate wash settings based on the information analyzed by the analysis unit. The control unit controls the washing machine based on the settings determined by the setting unit. Effect of the Invention

[0007] The system according to the embodiment can automatically read the laundry tag and set the appropriate washing settings. [Brief description of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The system for automatically setting the washing settings for laundry according to the embodiment of the present invention is a system that uses AI to read the tag of the laundry, determine the optimal washing settings, and control the washing machine. In this system, when a user puts laundry into a washing machine, AI reads the tag of the laundry. Next, AI analyzes the information on the tag and determines the optimal washing settings. For example, a low-temperature washing setting is set for clothes made of delicate materials, and a color-fading prevention setting is set for clothes that are prone to fading. Finally, the washing machine automatically washes based on the settings determined by the AI. This saves the user the trouble of checking each tag one by one, and even children can easily wash the clothes. For example, when a user puts laundry into a washing machine, AI reads the tag of the laundry. At this time, a camera or sensor built into the washing machine captures the tag information and sends it to the AI. For example, a camera is installed on the door of the washing machine, and the tag can be automatically read when the laundry is put in. Next, AI analyzes the tag information and determines the optimal washing settings. AI analyzes the tag information based on a database that has been learned in advance. For example, if the tag says "low-temperature washing", the AI ​​selects a low-temperature washing setting. Also, if the tag says "Caution: Color fading," the AI ​​will set the machine to prevent color fading. Finally, the washing machine will automatically wash the clothes based on the settings determined by the AI. The user does not need to make any special settings, and the washing machine will automatically set the optimal settings. This saves the user the trouble of checking each tag one by one, and even children can easily wash the clothes. For example, even if a child does the laundry, the AI ​​will automatically set the optimal settings, so you can leave the laundry to them with peace of mind. This system can protect your precious clothes without making mistakes in the washing settings of your laundry. It can also significantly reduce the user's efforts and improve the efficiency of washing. This can protect your precious clothes without making mistakes in the washing settings of your laundry. It can also significantly reduce the user's efforts and improve the efficiency of washing.

[0029] The system for automatically setting the washing settings for laundry according to the embodiment includes a reading unit, an analysis unit, a setting unit, and a control unit. The reading unit reads the tag of the laundry. The tag of the laundry includes, for example, information such as a washing method, temperature, and type of detergent, but is not limited to such examples. The reading unit reads the tag information using, for example, a camera or a sensor built into the washing machine. For example, a camera is installed on the door of the washing machine, and the tag can be automatically read when the laundry is put in. The reading unit can also identify the material and color of the laundry and compare it with the tag information to improve the reading accuracy. For example, the material of the laundry is identified and compared with the tag information to select an appropriate washing setting. The analysis unit analyzes the tag information read by the reading unit. The analysis unit analyzes the tag information based on a database learned in advance. For example, if the tag says "low temperature wash", the analysis unit selects a low temperature wash setting. If the tag says "Caution: Color fading", the analysis unit sets the color fading prevention setting. Furthermore, when the information on the tag is unclear, the analysis unit can estimate and analyze from the material and color of the laundry. For example, when the information on the tag is unclear, the optimal wash setting is estimated based on the material of the laundry. The setting unit determines the optimal wash setting based on the information analyzed by the analysis unit. The setting unit determines the optimal wash setting based on the information written on the tag. For example, a low temperature wash setting is set for clothes made of delicate materials, and a color fading prevention setting is set for clothes that are prone to fading. The setting unit can also automatically optimize the parameters of the wash setting according to the type and amount of laundry. For example, the optimal wash setting is automatically selected according to the type of laundry. The control unit controls the washing machine based on the setting determined by the setting unit. The control unit controls the amount of water in the washing machine and the timing of adding detergent according to the amount and type of laundry. For example, the optimal amount of water is automatically set according to the amount of laundry. The control unit can also adjust the settings in real time while the washing machine is operating based on the tag information of the laundry. For example, the optimal setting is adjusted in real time while the washing machine is operating based on the tag information. As a result, the system for automatically setting laundry washing settings according to the embodiment can reduce the user's efforts and improve the efficiency of washing.

[0030] The reading unit reads the tag of the laundry. The tag of the laundry includes, for example, information such as a washing method, temperature, and type of detergent, but is not limited to such examples. The reading unit reads the tag information, for example, using a camera or a sensor built into the washing machine. Specifically, a high-resolution camera installed on the door of the washing machine automatically scans the tag when the laundry is put in and converts the tag information into digital data using OCR (optical character recognition) technology. The sensor can also read RFID tags and NFC tags, which allows for contactless information acquisition. Furthermore, the reading unit can also identify the material and color of the laundry and compare it with the tag information to improve reading accuracy. For example, to identify the material of the laundry, near-infrared spectroscopy is used to identify the type of fiber, and a color matching algorithm is used to identify the color. This makes it possible to estimate appropriate wash settings from the material and color of the laundry even when the tag information is unclear. The reading unit transmits these pieces of information to a central database in real time, making them accessible to the analysis unit. This allows the reading unit to efficiently and accurately collect laundry information and improve the performance of the entire system.

[0031] The analysis unit analyzes the tag information read by the reading unit. The analysis unit analyzes the tag information based on a database that has been learned in advance. Specifically, the analysis unit uses natural language processing (NLP) technology to analyze the information written on the tag and extracts instructions such as the washing method, temperature, and type of detergent. For example, if the tag says "Wash at low temperature," the analysis unit selects a low-temperature wash setting. Also, if the tag says "Caution: Color fading," the analysis unit sets the setting to prevent color fading. Furthermore, if the tag information is unclear, the analysis unit can also perform analysis by estimating from the material and color of the laundry. For example, if the tag information is unclear, a machine learning algorithm is used to estimate the optimal wash setting based on the material of the laundry. This allows the analysis unit to quickly and accurately analyze the collected data and derive the optimal wash setting. Furthermore, the analysis unit can provide the optimal wash setting for each individual user by utilizing past wash data and user setting history. For example, if a user has preferred a specific setting in the past, the setting is selected preferentially. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings, allowing the analysis unit to not only grasp the situation in real time, but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The setting unit determines optimal wash settings based on the information analyzed by the analysis unit. The setting unit determines optimal wash settings based on the information written on the tag. Specifically, based on the information provided by the analysis unit, the setting unit automatically sets parameters such as the temperature of the washing machine, the type of detergent, the wash time, and the number of rinses. For example, a low-temperature wash setting is set for clothes made of delicate materials, and a color-fading prevention setting is set for clothes that are prone to fading. The setting unit can also automatically optimize parameters of the wash settings according to the type and amount of laundry. For example, when the amount of laundry is large, the wash time is extended and the amount of water is increased. Furthermore, the setting unit can provide individually customized wash settings taking into account the user's preferences and past setting history. For example, if a user has used a particular setting in the past, the setting is preferentially selected. The setting unit can also adjust the settings in real time while the washing machine is operating. For example, when the amount or type of laundry changes, the optimal settings are adjusted in real time during operation. This allows the setting unit to efficiently and effectively provide optimal wash settings and improve the quality and efficiency of laundry.

[0033] The control unit controls the washing machine based on the settings determined by the setting unit. The control unit controls the amount of water in the washing machine and the timing of detergent injection according to the amount and type of laundry. Specifically, the control unit automatically sets the optimal amount of water and adjusts the timing of detergent injection according to the amount of laundry. The control unit can also adjust the settings in real time while the washing machine is operating based on the tag information of the laundry. For example, the control unit adjusts the optimal settings in real time while the washing machine is operating based on the tag information. Furthermore, the control unit also has a function of monitoring the operating status based on data from various sensors of the washing machine and automatically stopping the operation if an abnormality is detected. For example, if a water leak or excessive vibration is detected, the control unit immediately stops the operation and issues a warning to the user. The control unit also transmits the operating status of the washing machine to the cloud, enabling remote monitoring and control. This allows the user to check the operating status of the washing machine in real time using a smartphone or tablet and change the settings as necessary. This allows the control unit to efficiently and safely control the operation of the washing machine and improve the quality and efficiency of washing.

[0034] The detergent adjustment unit can adjust the amount of detergent. The detergent adjustment unit, for example, automatically adjusts the amount of detergent. For example, the detergent adjustment unit automatically sets the optimal amount of detergent according to the amount of laundry and the degree of dirt. The detergent adjustment unit can also automatically select the type and amount of detergent according to the material and color of the laundry. For example, the optimal type of detergent is automatically selected according to the material of the laundry. Furthermore, the detergent adjustment unit can also adjust the concentration of detergent based on the tag information of the laundry. For example, the optimal concentration of detergent is automatically set based on the tag information. As a result, appropriate washing is possible by automatically adjusting the amount of detergent. Part or all of the above-mentioned processing in the detergent adjustment unit may be performed, for example, using AI, or may be performed without using AI. For example, the detergent adjustment unit can input data on the amount of laundry and the degree of dirt to the generation AI and cause the generation AI to execute the optimal amount of detergent.

[0035] The water temperature adjustment unit can adjust the water temperature. The water temperature adjustment unit, for example, automatically adjusts the water temperature. For example, the water temperature adjustment unit automatically sets the optimal water temperature according to the material and color of the laundry. The water temperature adjustment unit can also adjust the water temperature of the washing machine in real time based on the tag information of the laundry. For example, the water temperature of the washing machine is adjusted in real time based on the tag information. Furthermore, the water temperature adjustment unit can estimate the user's emotions and adjust the water temperature based on the estimated user's emotions. For example, when the user is in a hurry, the water temperature is quickly adjusted. This enables appropriate washing by automatically adjusting the water temperature. A part or all of the above-mentioned processing in the water temperature adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the water temperature adjustment unit can input data on the material and color of the laundry to the generation AI and cause the generation AI to execute the setting of the optimal water temperature.

[0036] The reading unit can read the tag information using a camera or sensor built into the washing machine. The reading unit reads the tag information using, for example, a camera or sensor built into the washing machine. For example, a camera is installed on the door of the washing machine, and the tag can be automatically read when the laundry is put in. The reading unit can also identify the material and color of the laundry and compare it with the tag information to improve the reading accuracy. For example, the material of the laundry is identified and compared with the tag information to select an appropriate wash setting. As a result, the tag information can be accurately read by using the camera or sensor built into the washing machine. Part or all of the above-mentioned processing in the reading unit may be performed using, for example, AI, or may be performed without using AI. For example, the reading unit can input image data acquired by the camera to the generation AI and cause the generation AI to read the tag information.

[0037] The analysis unit can analyze the tag information based on a database learned in advance. The analysis unit analyzes the tag information based on a database learned in advance, for example. For example, if the tag states "low temperature wash", the analysis unit selects a low temperature wash setting. Also, if the tag states "Caution: Color fading", the analysis unit sets the color fading prevention setting. Furthermore, if the tag information is unclear, the analysis unit can estimate the optimal wash setting based on the material and color of the laundry and perform analysis. For example, if the tag information is unclear, the optimal wash setting is estimated based on the material of the laundry. As a result, the tag information can be accurately analyzed by analyzing based on the database learned in advance. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the tag information to the generation AI and have the generation AI execute an analysis of the optimal wash setting.

[0038] The setting unit can determine appropriate wash settings based on the information written on the tag. The setting unit determines optimal wash settings based on, for example, the information written on the tag. For example, a low-temperature wash setting is set for clothes made of delicate materials, and a color-fading prevention setting is set for clothes that are prone to fading. The setting unit can also automatically optimize parameters of wash settings according to the type and amount of laundry. For example, the optimal wash settings are automatically selected according to the type of laundry. Furthermore, the setting unit can automatically select the type and amount of detergent based on the tag information of the laundry. For example, the optimal type of detergent is automatically selected based on the tag information. As a result, appropriate washing can be achieved by determining optimal wash settings based on the information written on the tag. Part or all of the above-mentioned processing in the setting unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the setting unit can input tag information to the generation AI and cause the generation AI to determine optimal wash settings.

[0039] The reading unit can improve the reading accuracy by identifying the material and color of the laundry and comparing it with the tag information. The reading unit can improve the reading accuracy by, for example, identifying the material and color of the laundry and comparing it with the tag information. For example, the material of the laundry is identified and the appropriate wash setting is selected by comparing it with the tag information. The reading unit can also identify the color of the laundry, evaluate the risk of color fading, and compare it with the tag information. Furthermore, the reading unit can simultaneously identify the material and color of the laundry and determine the optimal wash setting by comparing it with the tag information. In this way, the reading accuracy of the tag information can be improved by identifying the material and color of the laundry. Part or all of the above-mentioned processing in the reading unit may be performed, for example, using AI or may be performed without using AI. For example, the reading unit can input data on the material and color of the laundry to the generation AI and cause the generation AI to execute the comparison of the tag information.

[0040] The reading unit can add a function of simultaneously reading multiple tags according to the amount or type of laundry. The reading unit can add a function of simultaneously reading multiple tags according to, for example, the amount or type of laundry. For example, when the amount of laundry is large, efficiency is improved by reading multiple tags at once. In addition, when the types of laundry are diverse, the accuracy of the settings can be improved by simultaneously reading each tag. Furthermore, multiple tags can be simultaneously read at the optimal timing according to the amount and type of laundry. As a result, efficient washing is possible by simultaneously reading multiple tags according to the amount and type of laundry. Part or all of the above-mentioned processing in the reading unit may be performed, for example, using AI, or may be performed without using AI. For example, the reading unit can input data of multiple tags to the generation AI and cause the generation AI to execute the simultaneous reading process.

[0041] The reading unit can add a function to read the shape and size of the laundry in addition to the laundry tag. The reading unit adds a function to read the shape and size of the laundry, for example. For example, the reading unit reads the shape of the laundry and selects appropriate wash settings. It can also read the size of the laundry and set the settings according to the capacity of the washing machine. Furthermore, it can simultaneously read the shape and size of the laundry and determine the optimal wash settings. This makes it possible to set more appropriate wash settings by reading the shape and size of the laundry. Some or all of the above-mentioned processing in the reading unit may be performed, for example, using AI or may be performed without using AI. For example, the reading unit can input data on the shape and size of the laundry to the generation AI and cause the generation AI to determine the optimal wash settings.

[0042] The reading unit can detect the odor of the laundry when reading the laundry tag to complement the information. For example, the reading unit can detect the odor of the laundry when reading the laundry tag to complement the information. For example, the odor of the laundry can be detected to help select an appropriate detergent. The odor of the laundry can also be detected to use for adjusting the wash settings. Furthermore, the odor of the laundry can be detected and compared with the information on the tag to determine the optimal wash settings. As a result, more appropriate wash settings can be achieved by detecting the odor of the laundry. Some or all of the above-mentioned processing in the reading unit can be performed, for example, using AI or can be performed without using AI. For example, the reading unit can input the odor data of the laundry to the generation AI and cause the generation AI to determine the optimal wash settings.

[0043] The analysis unit can use the past washing history of the laundry in addition to the tag information for analysis. The analysis unit, for example, uses the past washing history of the laundry in addition to the tag information for analysis. For example, the optimal washing settings are proposed based on the past washing history of the laundry. The past washing history of the laundry can also be used to evaluate the risk of color fading and shrinkage. Furthermore, the past washing history of the laundry can be analyzed and used to select the optimal detergent. As a result, more appropriate washing settings can be achieved by using the past washing history for analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input past washing history data into the generation AI and cause the generation AI to perform an analysis of optimal washing settings.

[0044] The analysis unit can perform analysis by estimating from the material and color of the laundry when the tag information is unclear. For example, when the tag information is unclear, the analysis unit performs analysis by estimating from the material and color of the laundry. For example, when the tag information is unclear, the optimal wash setting is estimated based on the material of the laundry. In addition, when the tag information is unclear, the risk of color fading can be evaluated based on the color of the laundry. Furthermore, when the tag information is unclear, the optimal wash setting can be estimated based on the material and color of the laundry. As a result, even when the tag information is unclear, appropriate wash settings can be set by estimating from the material and color of the laundry and analyzing. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input data on the material and color of the laundry to the generation AI and cause the generation AI to execute estimation of the optimal wash setting.

[0045] The analysis unit can use the frequency of use and the degree of dirt of the laundry for analysis in addition to the tag information. The analysis unit, for example, uses the frequency of use and the degree of dirt of the laundry for analysis in addition to the tag information. For example, the optimal wash setting is suggested based on the frequency of use of the laundry. Also, the amount and type of detergent can be adjusted based on the degree of dirt of the laundry. Furthermore, the frequency of use and the degree of dirt of the laundry can be analyzed simultaneously to determine the optimal wash setting. As a result, more appropriate wash setting can be achieved by using the frequency of use and the degree of dirt of the laundry for analysis. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input data on the frequency of use and the degree of dirt of the laundry to the generation AI, and cause the generation AI to execute analysis of the optimal wash setting.

[0046] The analysis unit can refer to information on the brand and manufacturer of the laundry when analyzing the tag information. The analysis unit, for example, refers to information on the brand and manufacturer of the laundry when analyzing the tag information. For example, the analysis unit suggests optimal wash settings based on the brand information of the laundry. Also, the information on the manufacturer of the laundry can be used to help select a detergent. Furthermore, the optimal wash settings can be determined by simultaneously referring to the information on the brand and manufacturer of the laundry. As a result, more appropriate wash settings can be set by referring to the information on the brand and manufacturer of the laundry. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the brand and manufacturer information into the generation AI and cause the generation AI to perform an analysis of the optimal wash settings.

[0047] The setting unit can automatically optimize the parameters of the wash settings according to the type and amount of laundry. The setting unit automatically optimizes the parameters of the wash settings according to, for example, the type and amount of laundry. For example, the optimal wash settings are automatically selected according to the type of laundry. In addition, the amount of detergent and the operation time of the washing machine can be automatically adjusted according to the amount of laundry. Furthermore, the optimal wash settings can be automatically determined by simultaneously considering the type and amount of laundry. As a result, appropriate washing can be achieved by automatically optimizing the parameters of the wash settings according to the type and amount of laundry. Some or all of the above-mentioned processing in the setting unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the setting unit can input data on the type and amount of laundry to the generation AI and cause the generation AI to determine the optimal wash settings.

[0048] The setting unit can automatically select the type and amount of detergent based on the tag information of the laundry. The setting unit automatically selects the type and amount of detergent based on, for example, the tag information of the laundry. For example, the optimal type of detergent is automatically selected based on the tag information. The amount of detergent can also be automatically adjusted based on the tag information. Furthermore, the tag information can be analyzed and the optimal type and amount of detergent can be automatically determined. As a result, appropriate washing can be achieved by automatically selecting the type and amount of detergent based on the tag information of the laundry. A part or all of the above-mentioned processing in the setting unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the setting unit can input tag information to the generation AI and cause the generation AI to determine the optimal type and amount of detergent.

[0049] The setting unit can adjust the operation time and rotation speed of the washing machine based on the tag information of the laundry. The setting unit adjusts the operation time and rotation speed of the washing machine based on, for example, the tag information of the laundry. For example, the optimal operation time is automatically set based on the tag information. The rotation speed of the washing machine can also be automatically adjusted based on the tag information. Furthermore, the tag information can be analyzed and the optimal operation time and rotation speed can be automatically determined. As a result, appropriate washing can be achieved by adjusting the operation time and rotation speed based on the tag information of the laundry. A part or all of the above-mentioned processing in the setting unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the setting unit can input tag information to the generation AI and cause the generation AI to determine the optimal operation time and rotation speed.

[0050] The setting unit can automatically set the drying settings of the washing machine based on the tag information of the laundry. The setting unit automatically sets the drying settings of the washing machine based on, for example, the tag information of the laundry. For example, the optimal drying settings are automatically selected based on the tag information. The drying time can also be automatically adjusted based on the tag information. Furthermore, the tag information can be analyzed and the optimal drying settings and drying time can be automatically determined. As a result, appropriate drying can be achieved by automatically setting the drying settings based on the tag information of the laundry. A part or all of the above-mentioned processing in the setting unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the setting unit can input tag information to the generation AI and cause the generation AI to determine the optimal drying settings.

[0051] The control unit can control the amount of water in the washing machine and the timing of adding detergent according to the amount and type of laundry. The control unit controls the amount of water in the washing machine and the timing of adding detergent according to, for example, the amount and type of laundry. For example, the control unit automatically sets an optimal amount of water according to the amount of laundry. The control unit can also automatically adjust the timing of adding detergent according to the type of laundry. Furthermore, the control unit can automatically determine the optimal amount of water and the timing of adding detergent by simultaneously considering the amount and type of laundry. This makes it possible to perform appropriate washing by controlling the amount of water and the timing of adding detergent according to the amount and type of laundry. Part or all of the above-mentioned processing in the control unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the control unit can input data on the amount and type of laundry to the generation AI and cause the generation AI to determine the optimal amount of water and the timing of adding detergent.

[0052] The control unit can optimize the operation sound of the washing machine based on the tag information of the laundry. The control unit optimizes the operation sound of the washing machine based on, for example, the tag information of the laundry. For example, the control unit performs a setting to minimize the operation sound based on the tag information. Also, the control unit can adjust the operation sound and provide a quiet mode based on the tag information. Furthermore, the control unit can analyze the tag information and automatically determine an optimal operation sound setting. As a result, quiet operation is possible by optimizing the operation sound based on the tag information of the laundry. Part or all of the above-mentioned processing in the control unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the control unit can input tag information to the generation AI and cause the generation AI to determine the optimal operation sound setting.

[0053] The control unit can optimize the energy consumption of the washing machine based on the tag information of the laundry. The control unit optimizes the energy consumption of the washing machine based on, for example, the tag information of the laundry. For example, the control unit performs settings that minimize energy consumption based on the tag information. Also, the control unit can provide an energy-efficient operation mode based on the tag information. Furthermore, the control unit can analyze the tag information and automatically determine an optimal energy consumption setting. As a result, efficient washing is possible by optimizing energy consumption based on the tag information of the laundry. Some or all of the above-mentioned processing in the control unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the control unit can input the tag information to the generation AI and cause the generation AI to determine the optimal energy consumption setting.

[0054] The control unit can adjust the settings in real time during operation of the washing machine based on the tag information of the laundry. The control unit adjusts the settings in real time during operation of the washing machine based on, for example, the tag information of the laundry. For example, the control unit adjusts the optimal settings in real time during operation based on the tag information. Also, the amount of detergent and the amount of water can be adjusted in real time during operation based on the tag information. Furthermore, the tag information can be analyzed and the optimal settings can be determined in real time during operation. As a result, appropriate washing can be achieved by adjusting the settings in real time during operation based on the tag information of the laundry. Part or all of the above-mentioned processing in the control unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the control unit can input tag information to the generation AI and cause the generation AI to perform real-time setting adjustment during operation.

[0055] The detergent adjustment unit can automatically select the type and amount of detergent according to the material and dirtiness of the laundry. The detergent adjustment unit automatically selects the type and amount of detergent according to, for example, the material and dirtiness of the laundry. For example, the optimal type of detergent is automatically selected according to the material of the laundry. The amount of detergent can also be automatically adjusted according to the dirtiness of the laundry. Furthermore, the optimal type and amount of detergent can be automatically determined by simultaneously considering the material and dirtiness of the laundry. As a result, appropriate washing can be achieved by automatically selecting the type and amount of detergent according to the material and dirtiness of the laundry. Part or all of the above-mentioned processing in the detergent adjustment unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the detergent adjustment unit can input data on the material and dirtiness of the laundry to the generation AI and cause the generation AI to determine the optimal type and amount of detergent.

[0056] The detergent adjustment unit can adjust the detergent concentration based on the tag information of the laundry. The detergent adjustment unit adjusts the detergent concentration based on, for example, the tag information of the laundry. For example, the detergent adjustment unit automatically sets an optimal detergent concentration based on the tag information. Also, the detergent concentration can be adjusted based on the tag information to set a setting suitable for the material of the laundry. Furthermore, the tag information can be analyzed and an optimal detergent concentration setting can be automatically determined. As a result, appropriate washing can be achieved by adjusting the detergent concentration based on the tag information of the laundry. A part or all of the above-mentioned processing in the detergent adjustment unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the detergent adjustment unit can input tag information to the generation AI and cause the generation AI to determine an optimal detergent concentration setting.

[0057] The water temperature adjustment unit can automatically set the optimal water temperature according to the material and color of the laundry. The water temperature adjustment unit automatically sets the optimal water temperature according to, for example, the material and color of the laundry. For example, the optimal water temperature is automatically set according to the material of the laundry. In addition, the water temperature that minimizes the risk of color fading can be set according to the color of the laundry. Furthermore, the optimal water temperature can be automatically determined by simultaneously considering the material and color of the laundry. As a result, appropriate washing can be achieved by automatically setting the optimal water temperature according to the material and color of the laundry. Part or all of the above-mentioned processing in the water temperature adjustment unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the water temperature adjustment unit can input data on the material and color of the laundry to the generation AI and cause the generation AI to set the optimal water temperature.

[0058] The water temperature adjustment unit can adjust the water temperature of the washing machine in real time based on the tag information of the laundry. The water temperature adjustment unit adjusts the water temperature of the washing machine in real time based on, for example, the tag information of the laundry. For example, the water temperature of the washing machine is adjusted in real time based on the tag information. In addition, the water temperature suitable for the material of the laundry can be set in real time based on the tag information. Furthermore, the tag information can be analyzed and an optimal water temperature setting can be determined in real time. As a result, appropriate washing can be achieved by adjusting the water temperature in real time based on the tag information of the laundry. Part or all of the above-mentioned processing in the water temperature adjustment unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the water temperature adjustment unit can input tag information to the generation AI and cause the generation AI to perform water temperature adjustment in real time.

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

[0060] The drying settings of the laundry can be automatically set based on the tag information of the laundry. For example, the optimal drying settings are automatically selected based on the tag information. Also, the drying time can be automatically adjusted based on the tag information. Furthermore, the tag information can be analyzed and the optimal drying settings and drying time can be automatically determined. As a result, appropriate drying can be achieved by automatically setting the drying settings based on the tag information of the laundry. A part or all of the above-mentioned processing in the drying setting unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the drying setting unit can input the tag information to the generation AI and cause the generation AI to determine the optimal drying settings.

[0061] The operation sound of the washing machine can be optimized based on the tag information of the laundry. For example, the operation sound can be set to a minimum based on the tag information. Also, the operation sound can be adjusted based on the tag information to provide a quiet mode. Furthermore, the tag information can be analyzed and an optimal operation sound setting can be automatically determined. This allows quiet operation by optimizing the operation sound based on the tag information of the laundry. A part or all of the above-mentioned processing in the operation sound adjustment unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the operation sound adjustment unit can input tag information to the generation AI and cause the generation AI to determine the optimal operation sound setting.

[0062] The energy consumption of the washing machine can be optimized based on the tag information of the laundry. For example, a setting that minimizes energy consumption can be set based on the tag information. Also, an energy-efficient operation mode can be provided based on the tag information. Furthermore, the tag information can be analyzed and an optimal energy consumption setting can be automatically determined. As a result, efficient washing can be achieved by optimizing energy consumption based on the tag information of the laundry. Some or all of the above-mentioned processing in the energy consumption adjustment unit can be performed, for example, using the generation AI or can be performed without using the generation AI. For example, the energy consumption adjustment unit can input tag information to the generation AI and cause the generation AI to determine the optimal energy consumption setting.

[0063] Based on the tag information of the laundry, the settings can be adjusted in real time while the washing machine is running. For example, based on the tag information, the optimal settings are adjusted in real time while the washing machine is running. Also, based on the tag information, the amount of detergent and the amount of water can be adjusted in real time while the washing machine is running. Furthermore, the tag information can be analyzed and the optimal settings can be determined in real time while the washing machine is running. As a result, appropriate washing can be achieved by adjusting the settings in real time while the washing machine is running based on the tag information of the laundry. Part or all of the above-mentioned processing in the real-time setting adjustment unit may be performed, for example, using the generation AI or may be performed without using the generation AI. For example, the real-time setting adjustment unit can input the tag information to the generation AI and cause the generation AI to perform real-time setting adjustment during the operation.

[0064] The operation sequence of the washing machine can be determined based on the tag information of the laundry. For example, the operation sequence for washing important laundry with priority can be set based on the tag information. Also, the operation sequence for washing all laundry in order can be set based on the tag information. Furthermore, the tag information can be analyzed and the optimum operation sequence can be automatically determined. As a result, efficient washing can be achieved by determining the operation sequence based on the tag information of the laundry. A part or all of the above-mentioned processing in the operation sequence determination unit can be performed, for example, using the generation AI or can be performed without using the generation AI. For example, the operation sequence determination unit can input the tag information to the generation AI and cause the generation AI to determine the optimum operation sequence.

[0065] The process flow of the first embodiment will be briefly described below.

[0066] Step 1: The reading unit reads the laundry tag. The laundry tag contains information such as the washing method, temperature, and type of detergent. The reading unit reads the tag information using a camera and sensors built into the washing machine. For example, a camera can be installed on the washing machine door, which can automatically read the tag when the laundry is placed in the washing machine. The reading unit can also identify the material and color of the laundry and compare it with the tag information to improve reading accuracy. Step 2: The analysis unit analyzes the tag information read by the reading unit. The analysis unit analyzes the tag information based on a database that has been learned in advance. For example, if the tag says "Wash at low temperature," the analysis unit selects a low-temperature wash setting. If the tag says "Beware of color fading," the analysis unit sets the setting to prevent color fading. Furthermore, if the tag information is unclear, the analysis unit can also make an estimate based on the material and color of the laundry. Step 3: The setting unit determines optimal wash settings based on the information analyzed by the analysis unit. The setting unit determines optimal wash settings based on the information written on the tag. For example, a low temperature wash setting is used for clothes made of delicate materials, and a color fading prevention setting is used for clothes that are prone to fading. The setting unit can also automatically optimize wash setting parameters depending on the type and amount of laundry. Step 4: The control unit controls the washing machine based on the settings determined by the setting unit. The control unit controls the amount of water in the washing machine and the timing of adding detergent depending on the amount and type of laundry. For example, the control unit automatically sets the optimal amount of water depending on the amount of laundry. The control unit can also adjust the settings in real time while the washing machine is running based on the tag information of the laundry.

[0067] (Example 2) The system for automatically setting the washing settings for laundry according to the embodiment of the present invention is a system that uses AI to read the tag of the laundry, determine the optimal washing settings, and control the washing machine. In this system, when a user puts laundry into a washing machine, AI reads the tag of the laundry. Next, AI analyzes the information on the tag and determines the optimal washing settings. For example, a low-temperature washing setting is set for clothes made of delicate materials, and a color-fading prevention setting is set for clothes that are prone to fading. Finally, the washing machine automatically washes based on the settings determined by the AI. This saves the user the trouble of checking each tag one by one, and even children can easily wash the clothes. For example, when a user puts laundry into a washing machine, AI reads the tag of the laundry. At this time, a camera or sensor built into the washing machine captures the tag information and sends it to the AI. For example, a camera is installed on the door of the washing machine, and the tag can be automatically read when the laundry is put in. Next, AI analyzes the tag information and determines the optimal washing settings. AI analyzes the tag information based on a database that has been learned in advance. For example, if the tag says "low-temperature washing", the AI ​​selects a low-temperature washing setting. Also, if the tag says "Caution: Color fading," the AI ​​will set the machine to prevent color fading. Finally, the washing machine will automatically wash the clothes based on the settings determined by the AI. The user does not need to make any special settings, and the washing machine will automatically set the optimal settings. This saves the user the trouble of checking each tag one by one, and even children can easily wash the clothes. For example, even if a child does the laundry, the AI ​​will automatically set the optimal settings, so you can leave the laundry to them with peace of mind. This system can protect your precious clothes without making mistakes in the washing settings of your laundry. It can also significantly reduce the user's efforts and improve the efficiency of washing. This can protect your precious clothes without making mistakes in the washing settings of your laundry. It can also significantly reduce the user's efforts and improve the efficiency of washing.

[0068] The system for automatically setting the washing settings for laundry according to the embodiment includes a reading unit, an analysis unit, a setting unit, and a control unit. The reading unit reads the tag of the laundry. The tag of the laundry includes, for example, information such as a washing method, temperature, and type of detergent, but is not limited to such examples. The reading unit reads the tag information using, for example, a camera or a sensor built into the washing machine. For example, a camera is installed on the door of the washing machine, and the tag can be automatically read when the laundry is put in. The reading unit can also identify the material and color of the laundry and compare it with the tag information to improve the reading accuracy. For example, the material of the laundry is identified and compared with the tag information to select an appropriate washing setting. The analysis unit analyzes the tag information read by the reading unit. The analysis unit analyzes the tag information based on a database learned in advance. For example, if the tag says "low temperature wash", the analysis unit selects a low temperature wash setting. If the tag says "Caution: Color fading", the analysis unit sets the color fading prevention setting. Furthermore, when the information on the tag is unclear, the analysis unit can estimate and analyze from the material and color of the laundry. For example, when the information on the tag is unclear, the optimal wash setting is estimated based on the material of the laundry. The setting unit determines the optimal wash setting based on the information analyzed by the analysis unit. The setting unit determines the optimal wash setting based on the information written on the tag. For example, a low temperature wash setting is set for clothes made of delicate materials, and a color fading prevention setting is set for clothes that are prone to fading. The setting unit can also automatically optimize the parameters of the wash setting according to the type and amount of laundry. For example, the optimal wash setting is automatically selected according to the type of laundry. The control unit controls the washing machine based on the setting determined by the setting unit. The control unit controls the amount of water in the washing machine and the timing of adding detergent according to the amount and type of laundry. For example, the optimal amount of water is automatically set according to the amount of laundry. The control unit can also adjust the settings in real time while the washing machine is operating based on the tag information of the laundry. For example, the optimal setting is adjusted in real time while the washing machine is operating based on the tag information. As a result, the system for automatically setting laundry washing settings according to the embodiment can reduce the user's efforts and improve the efficiency of washing.

[0069] The reading unit reads the tag of the laundry. The tag of the laundry includes, for example, information such as a washing method, temperature, and type of detergent, but is not limited to such examples. The reading unit reads the tag information, for example, using a camera or a sensor built into the washing machine. Specifically, a high-resolution camera installed on the door of the washing machine automatically scans the tag when the laundry is put in and converts the tag information into digital data using OCR (optical character recognition) technology. The sensor can also read RFID tags and NFC tags, which allows for contactless information acquisition. Furthermore, the reading unit can also identify the material and color of the laundry and compare it with the tag information to improve reading accuracy. For example, to identify the material of the laundry, near-infrared spectroscopy is used to identify the type of fiber, and a color matching algorithm is used to identify the color. This makes it possible to estimate appropriate wash settings from the material and color of the laundry even when the tag information is unclear. The reading unit transmits these pieces of information to a central database in real time, making them accessible to the analysis unit. This allows the reading unit to efficiently and accurately collect laundry information and improve the performance of the entire system.

[0070] The analysis unit analyzes the tag information read by the reading unit. The analysis unit analyzes the tag information based on a database that has been learned in advance. Specifically, the analysis unit uses natural language processing (NLP) technology to analyze the information written on the tag and extracts instructions such as the washing method, temperature, and type of detergent. For example, if the tag says "Wash at low temperature," the analysis unit selects a low-temperature wash setting. Also, if the tag says "Caution: Color fading," the analysis unit sets the setting to prevent color fading. Furthermore, if the tag information is unclear, the analysis unit can also perform analysis by estimating from the material and color of the laundry. For example, if the tag information is unclear, a machine learning algorithm is used to estimate the optimal wash setting based on the material of the laundry. This allows the analysis unit to quickly and accurately analyze the collected data and derive the optimal wash setting. Furthermore, the analysis unit can provide the optimal wash setting for each individual user by utilizing past wash data and user setting history. For example, if a user has preferred a specific setting in the past, the setting is selected preferentially. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings, allowing the analysis unit to not only grasp the situation in real time, but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0071] The setting unit determines optimal wash settings based on the information analyzed by the analysis unit. The setting unit determines optimal wash settings based on the information written on the tag. Specifically, based on the information provided by the analysis unit, the setting unit automatically sets parameters such as the temperature of the washing machine, the type of detergent, the wash time, and the number of rinses. For example, a low-temperature wash setting is set for clothes made of delicate materials, and a color-fading prevention setting is set for clothes that are prone to fading. The setting unit can also automatically optimize parameters of the wash settings according to the type and amount of laundry. For example, when the amount of laundry is large, the wash time is extended and the amount of water is increased. Furthermore, the setting unit can provide individually customized wash settings taking into account the user's preferences and past setting history. For example, if a user has used a particular setting in the past, the setting is preferentially selected. The setting unit can also adjust the settings in real time while the washing machine is operating. For example, when the amount or type of laundry changes, the optimal settings are adjusted in real time during operation. This allows the setting unit to efficiently and effectively provide optimal wash settings and improve the quality and efficiency of laundry.

[0072] The control unit controls the washing machine based on the settings determined by the setting unit. The control unit controls the amount of water in the washing machine and the timing of detergent injection according to the amount and type of laundry. Specifically, the control unit automatically sets the optimal amount of water and adjusts the timing of detergent injection according to the amount of laundry. The control unit can also adjust the settings in real time while the washing machine is operating based on the tag information of the laundry. For example, the control unit adjusts the optimal settings in real time while the washing machine is operating based on the tag information. Furthermore, the control unit also has a function of monitoring the operating status based on data from various sensors of the washing machine and automatically stopping the operation if an abnormality is detected. For example, if a water leak or excessive vibration is detected, the control unit immediately stops the operation and issues a warning to the user. The control unit also transmits the operating status of the washing machine to the cloud, enabling remote monitoring and control. This allows the user to check the operating status of the washing machine in real time using a smartphone or tablet and change the settings as necessary. This allows the control unit to efficiently and safely control the operation of the washing machine and improve the quality and efficiency of washing.

[0073] The detergent adjustment unit can adjust the amount of detergent. The detergent adjustment unit, for example, automatically adjusts the amount of detergent. For example, the detergent adjustment unit automatically sets the optimal amount of detergent according to the amount of laundry and the degree of dirt. The detergent adjustment unit can also automatically select the type and amount of detergent according to the material and color of the laundry. For example, the optimal type of detergent is automatically selected according to the material of the laundry. Furthermore, the detergent adjustment unit can also adjust the concentration of detergent based on the tag information of the laundry. For example, the optimal concentration of detergent is automatically set based on the tag information. As a result, appropriate washing is possible by automatically adjusting the amount of detergent. Part or all of the above-mentioned processing in the detergent adjustment unit may be performed, for example, using AI, or may be performed without using AI. For example, the detergent adjustment unit can input data on the amount of laundry and the degree of dirt to the generation AI and cause the generation AI to execute the optimal amount of detergent.

[0074] The water temperature adjustment unit can adjust the water temperature. The water temperature adjustment unit, for example, automatically adjusts the water temperature. For example, the water temperature adjustment unit automatically sets the optimal water temperature according to the material and color of the laundry. The water temperature adjustment unit can also adjust the water temperature of the washing machine in real time based on the tag information of the laundry. For example, the water temperature of the washing machine is adjusted in real time based on the tag information. Furthermore, the water temperature adjustment unit can estimate the user's emotions and adjust the water temperature based on the estimated user's emotions. For example, when the user is in a hurry, the water temperature is quickly adjusted. This enables appropriate washing by automatically adjusting the water temperature. A part or all of the above-mentioned processing in the water temperature adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the water temperature adjustment unit can input data on the material and color of the laundry to the generation AI and cause the generation AI to execute the setting of the optimal water temperature.

[0075] The reading unit can read the tag information using a camera or sensor built into the washing machine. The reading unit reads the tag information using, for example, a camera or sensor built into the washing machine. For example, a camera is installed on the door of the washing machine, and the tag can be automatically read when the laundry is put in. The reading unit can also identify the material and color of the laundry and compare it with the tag information to improve the reading accuracy. For example, the material of the laundry is identified and compared with the tag information to select an appropriate wash setting. As a result, the tag information can be accurately read by using the camera or sensor built into the washing machine. Part or all of the above-mentioned processing in the reading unit may be performed using, for example, AI, or may be performed without using AI. For example, the reading unit can input image data acquired by the camera to the generation AI and cause the generation AI to read the tag information.

[0076] The analysis unit can analyze the tag information based on a database learned in advance. The analysis unit analyzes the tag information based on a database learned in advance, for example. For example, if the tag states "low temperature wash", the analysis unit selects a low temperature wash setting. Also, if the tag states "Caution: Color fading", the analysis unit sets the color fading prevention setting. Furthermore, if the tag information is unclear, the analysis unit can estimate the optimal wash setting based on the material and color of the laundry and perform analysis. For example, if the tag information is unclear, the optimal wash setting is estimated based on the material of the laundry. As a result, the tag information can be accurately analyzed by analyzing based on the database learned in advance. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the tag information to the generation AI and have the generation AI execute an analysis of the optimal wash setting.

[0077] The setting unit can determine appropriate wash settings based on the information written on the tag. The setting unit determines optimal wash settings based on, for example, the information written on the tag. For example, a low-temperature wash setting is set for clothes made of delicate materials, and a color-fading prevention setting is set for clothes that are prone to fading. The setting unit can also automatically optimize parameters of wash settings according to the type and amount of laundry. For example, the optimal wash settings are automatically selected according to the type of laundry. Furthermore, the setting unit can automatically select the type and amount of detergent based on the tag information of the laundry. For example, the optimal type of detergent is automatically selected based on the tag information. As a result, appropriate washing can be achieved by determining optimal wash settings based on the information written on the tag. Part or all of the above-mentioned processing in the setting unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the setting unit can input tag information to the generation AI and cause the generation AI to determine optimal wash settings.

[0078] The reading unit can estimate the user's emotion and adjust the timing of reading the tag based on the estimated user's emotion. The reading unit, for example, estimates the user's emotion and adjusts the timing of reading the tag based on the estimated user's emotion. For example, when the user is in a hurry, the reading is started immediately after the laundry is put in in order to quickly read the tag. Also, when the user is relaxed, the tag can be read after all the laundry is put in. Furthermore, when the user is stressed, the tag can be read automatically to minimize the user's operation. In this way, the timing of reading the tag is adjusted according to the user's emotion, thereby improving the user's convenience. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reading unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reading unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0079] The reading unit can improve the reading accuracy by identifying the material and color of the laundry and comparing it with the tag information. The reading unit can improve the reading accuracy by, for example, identifying the material and color of the laundry and comparing it with the tag information. For example, the material of the laundry is identified and the appropriate wash setting is selected by comparing it with the tag information. The reading unit can also identify the color of the laundry, evaluate the risk of color fading, and compare it with the tag information. Furthermore, the reading unit can simultaneously identify the material and color of the laundry and determine the optimal wash setting by comparing it with the tag information. In this way, the reading accuracy of the tag information can be improved by identifying the material and color of the laundry. Part or all of the above-mentioned processing in the reading unit may be performed, for example, using AI or may be performed without using AI. For example, the reading unit can input data on the material and color of the laundry to the generation AI and cause the generation AI to execute the comparison of the tag information.

[0080] The reading unit can add a function of simultaneously reading multiple tags according to the amount or type of laundry. The reading unit can add a function of simultaneously reading multiple tags according to, for example, the amount or type of laundry. For example, when the amount of laundry is large, efficiency is improved by reading multiple tags at once. In addition, when the types of laundry are diverse, the accuracy of the settings can be improved by simultaneously reading each tag. Furthermore, multiple tags can be simultaneously read at the optimal timing according to the amount and type of laundry. As a result, efficient washing is possible by simultaneously reading multiple tags according to the amount and type of laundry. Part or all of the above-mentioned processing in the reading unit may be performed, for example, using AI, or may be performed without using AI. For example, the reading unit can input data of multiple tags to the generation AI and cause the generation AI to execute the simultaneous reading process.

[0081] The reading unit can estimate the user's emotion and determine the tag reading priority based on the estimated user's emotion. The reading unit, for example, estimates the user's emotion and determines the tag reading priority based on the estimated user's emotion. For example, when the user is in a hurry, important tags are read preferentially. Also, when the user is relaxed, all tags can be read in order. Furthermore, when the user is stressed, only the most important tags can be read. This improves the user's convenience by determining the tag reading priority according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reading unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reading unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0082] The reading unit can add a function to read the shape and size of the laundry in addition to the laundry tag. The reading unit adds a function to read the shape and size of the laundry, for example. For example, the reading unit reads the shape of the laundry and selects appropriate wash settings. It can also read the size of the laundry and set the settings according to the capacity of the washing machine. Furthermore, it can simultaneously read the shape and size of the laundry and determine the optimal wash settings. This makes it possible to set more appropriate wash settings by reading the shape and size of the laundry. Some or all of the above-mentioned processing in the reading unit may be performed, for example, using AI or may be performed without using AI. For example, the reading unit can input data on the shape and size of the laundry to the generation AI and cause the generation AI to determine the optimal wash settings.

[0083] The reading unit can detect the odor of the laundry when reading the laundry tag to complement the information. For example, the reading unit can detect the odor of the laundry when reading the laundry tag to complement the information. For example, the odor of the laundry can be detected to help select an appropriate detergent. The odor of the laundry can also be detected to use for adjusting the wash settings. Furthermore, the odor of the laundry can be detected and compared with the information on the tag to determine the optimal wash settings. As a result, more appropriate wash settings can be achieved by detecting the odor of the laundry. Some or all of the above-mentioned processing in the reading unit can be performed, for example, using AI or can be performed without using AI. For example, the reading unit can input the odor data of the laundry to the generation AI and cause the generation AI to determine the optimal wash settings.

[0084] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the display method of the analysis result based on the estimated user's emotion. For example, when the user is nervous, a simple and highly visible display method is provided. Also, when the user is relaxed, a display method including detailed information can be provided. Furthermore, when the user is in a hurry, a display method that focuses on the main points can be provided. This improves the user's convenience by adjusting the display method of the analysis result according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0085] The analysis unit can use the past washing history of the laundry in addition to the tag information for analysis. The analysis unit, for example, uses the past washing history of the laundry in addition to the tag information for analysis. For example, the optimal washing settings are proposed based on the past washing history of the laundry. The past washing history of the laundry can also be used to evaluate the risk of color fading and shrinkage. Furthermore, the past washing history of the laundry can be analyzed and used to select the optimal detergent. As a result, more appropriate washing settings can be achieved by using the past washing history for analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input past washing history data into the generation AI and cause the generation AI to perform an analysis of optimal washing settings.

[0086] The analysis unit can perform analysis by estimating from the material and color of the laundry when the tag information is unclear. For example, when the tag information is unclear, the analysis unit performs analysis by estimating from the material and color of the laundry. For example, when the tag information is unclear, the optimal wash setting is estimated based on the material of the laundry. In addition, when the tag information is unclear, the risk of color fading can be evaluated based on the color of the laundry. Furthermore, when the tag information is unclear, the optimal wash setting can be estimated based on the material and color of the laundry. As a result, even when the tag information is unclear, appropriate wash settings can be set by estimating from the material and color of the laundry and analyzing. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input data on the material and color of the laundry to the generation AI and cause the generation AI to execute estimation of the optimal wash setting.

[0087] The analysis unit can estimate the user's emotion and determine the analysis priority based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and determines the analysis priority based on the estimated user's emotion. For example, when the user is in a hurry, important tag information is analyzed preferentially. Also, when the user is relaxed, all tag information can be analyzed in order. Furthermore, when the user is stressed, only the most important tag information can be analyzed. This improves the user's convenience by determining the analysis priority according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0088] The analysis unit can use the frequency of use and the degree of dirt of the laundry for analysis in addition to the tag information. The analysis unit, for example, uses the frequency of use and the degree of dirt of the laundry for analysis in addition to the tag information. For example, the optimal wash setting is suggested based on the frequency of use of the laundry. Also, the amount and type of detergent can be adjusted based on the degree of dirt of the laundry. Furthermore, the frequency of use and the degree of dirt of the laundry can be analyzed simultaneously to determine the optimal wash setting. As a result, more appropriate wash setting can be achieved by using the frequency of use and the degree of dirt of the laundry for analysis. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input data on the frequency of use and the degree of dirt of the laundry to the generation AI, and cause the generation AI to execute analysis of the optimal wash setting.

[0089] The analysis unit can refer to information on the brand and manufacturer of the laundry when analyzing the tag information. The analysis unit, for example, refers to information on the brand and manufacturer of the laundry when analyzing the tag information. For example, the analysis unit suggests optimal wash settings based on the brand information of the laundry. Also, the information on the manufacturer of the laundry can be used to help select a detergent. Furthermore, the optimal wash settings can be determined by simultaneously referring to the information on the brand and manufacturer of the laundry. As a result, more appropriate wash settings can be set by referring to the information on the brand and manufacturer of the laundry. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the brand and manufacturer information into the generation AI and cause the generation AI to perform an analysis of the optimal wash settings.

[0090] The setting unit can estimate the user's emotion and adjust the level of detail of the washing settings based on the estimated user's emotion. The setting unit, for example, estimates the user's emotion and adjusts the level of detail of the washing settings based on the estimated user's emotion. For example, when the user is nervous, a simple and highly visible setting is provided. Also, when the user is relaxed, detailed setting options can be provided. Furthermore, when the user is in a hurry, settings that focus on the main points can be provided. This improves the user's convenience by adjusting the level of detail of the washing settings according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the setting unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the setting unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0091] The setting unit can automatically optimize the parameters of the wash settings according to the type and amount of laundry. The setting unit automatically optimizes the parameters of the wash settings according to, for example, the type and amount of laundry. For example, the optimal wash settings are automatically selected according to the type of laundry. In addition, the amount of detergent and the operation time of the washing machine can be automatically adjusted according to the amount of laundry. Furthermore, the optimal wash settings can be automatically determined by simultaneously considering the type and amount of laundry. As a result, appropriate washing can be achieved by automatically optimizing the parameters of the wash settings according to the type and amount of laundry. Some or all of the above-mentioned processing in the setting unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the setting unit can input data on the type and amount of laundry to the generation AI and cause the generation AI to determine the optimal wash settings.

[0092] The setting unit can automatically select the type and amount of detergent based on the tag information of the laundry. The setting unit automatically selects the type and amount of detergent based on, for example, the tag information of the laundry. For example, the optimal type of detergent is automatically selected based on the tag information. The amount of detergent can also be automatically adjusted based on the tag information. Furthermore, the tag information can be analyzed and the optimal type and amount of detergent can be automatically determined. As a result, appropriate washing can be achieved by automatically selecting the type and amount of detergent based on the tag information of the laundry. A part or all of the above-mentioned processing in the setting unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the setting unit can input tag information to the generation AI and cause the generation AI to determine the optimal type and amount of detergent.

[0093] The setting unit can estimate the user's emotion and determine the priority of the washing settings based on the estimated user's emotion. The setting unit, for example, estimates the user's emotion and determines the priority of the washing settings based on the estimated user's emotion. For example, when the user is in a hurry, important washing settings are performed with priority. Also, when the user is relaxed, all washing settings can be performed in order. Furthermore, when the user is stressed, only the most important washing settings can be performed. This improves the user's convenience by determining the priority of the washing settings according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the setting unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the setting unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0094] The setting unit can adjust the operation time and rotation speed of the washing machine based on the tag information of the laundry. The setting unit adjusts the operation time and rotation speed of the washing machine based on, for example, the tag information of the laundry. For example, the optimal operation time is automatically set based on the tag information. The rotation speed of the washing machine can also be automatically adjusted based on the tag information. Furthermore, the tag information can be analyzed and the optimal operation time and rotation speed can be automatically determined. As a result, appropriate washing can be achieved by adjusting the operation time and rotation speed based on the tag information of the laundry. A part or all of the above-mentioned processing in the setting unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the setting unit can input tag information to the generation AI and cause the generation AI to determine the optimal operation time and rotation speed.

[0095] The setting unit can automatically set the drying settings of the washing machine based on the tag information of the laundry. The setting unit automatically sets the drying settings of the washing machine based on, for example, the tag information of the laundry. For example, the optimal drying settings are automatically selected based on the tag information. The drying time can also be automatically adjusted based on the tag information. Furthermore, the tag information can be analyzed and the optimal drying settings and drying time can be automatically determined. As a result, appropriate drying can be achieved by automatically setting the drying settings based on the tag information of the laundry. A part or all of the above-mentioned processing in the setting unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the setting unit can input tag information to the generation AI and cause the generation AI to determine the optimal drying settings.

[0096] The control unit can estimate the user's emotion and adjust the operation mode of the washing machine based on the estimated user's emotion. The control unit, for example, estimates the user's emotion and adjusts the operation mode of the washing machine based on the estimated user's emotion. For example, when the user is nervous, a simple and highly visible operation mode is provided. Also, when the user is relaxed, detailed setting options can be provided. Furthermore, when the user is in a hurry, an operation mode that focuses on the main points can be provided. This improves the user's convenience by adjusting the operation mode according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the control unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the control unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0097] The control unit can control the amount of water in the washing machine and the timing of adding detergent according to the amount and type of laundry. The control unit controls the amount of water in the washing machine and the timing of adding detergent according to, for example, the amount and type of laundry. For example, the control unit automatically sets an optimal amount of water according to the amount of laundry. The control unit can also automatically adjust the timing of adding detergent according to the type of laundry. Furthermore, the control unit can automatically determine the optimal amount of water and the timing of adding detergent by simultaneously considering the amount and type of laundry. This makes it possible to perform appropriate washing by controlling the amount of water and the timing of adding detergent according to the amount and type of laundry. Part or all of the above-mentioned processing in the control unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the control unit can input data on the amount and type of laundry to the generation AI and cause the generation AI to determine the optimal amount of water and the timing of adding detergent.

[0098] The control unit can optimize the operation sound of the washing machine based on the tag information of the laundry. The control unit optimizes the operation sound of the washing machine based on, for example, the tag information of the laundry. For example, the control unit performs a setting to minimize the operation sound based on the tag information. Also, the control unit can adjust the operation sound and provide a quiet mode based on the tag information. Furthermore, the control unit can analyze the tag information and automatically determine an optimal operation sound setting. As a result, quiet operation is possible by optimizing the operation sound based on the tag information of the laundry. Part or all of the above-mentioned processing in the control unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the control unit can input tag information to the generation AI and cause the generation AI to determine the optimal operation sound setting.

[0099] The control unit can estimate the user's emotion and determine the operation sequence of the washing machine based on the estimated user's emotion. The control unit, for example, estimates the user's emotion and determines the operation sequence of the washing machine based on the estimated user's emotion. For example, when the user is in a hurry, the operation sequence is set to wash important laundry with priority. In addition, when the user is relaxed, the operation sequence can be set to wash all laundry in order. Furthermore, when the user is stressed, the operation sequence can be set to wash only the most important laundry with priority. In this way, the operation sequence is determined according to the user's emotion, thereby improving the user's convenience. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the control unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the control unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0100] The control unit can optimize the energy consumption of the washing machine based on the tag information of the laundry. The control unit optimizes the energy consumption of the washing machine based on, for example, the tag information of the laundry. For example, the control unit performs settings that minimize energy consumption based on the tag information. Also, the control unit can provide an energy-efficient operation mode based on the tag information. Furthermore, the control unit can analyze the tag information and automatically determine an optimal energy consumption setting. As a result, efficient washing is possible by optimizing energy consumption based on the tag information of the laundry. Some or all of the above-mentioned processing in the control unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the control unit can input the tag information to the generation AI and cause the generation AI to determine the optimal energy consumption setting.

[0101] The control unit can adjust the settings in real time during operation of the washing machine based on the tag information of the laundry. The control unit adjusts the settings in real time during operation of the washing machine based on, for example, the tag information of the laundry. For example, the control unit adjusts the optimal settings in real time during operation based on the tag information. Also, the amount of detergent and the amount of water can be adjusted in real time during operation based on the tag information. Furthermore, the tag information can be analyzed and the optimal settings can be determined in real time during operation. As a result, appropriate washing can be achieved by adjusting the settings in real time during operation based on the tag information of the laundry. Part or all of the above-mentioned processing in the control unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the control unit can input tag information to the generation AI and cause the generation AI to perform real-time setting adjustment during operation.

[0102] The detergent adjustment unit can estimate the user's emotion and adjust the amount of detergent based on the estimated user's emotion. The detergent adjustment unit, for example, estimates the user's emotion and adjusts the amount of detergent based on the estimated user's emotion. For example, when the user is nervous, a simple and highly visible detergent amount setting is provided. Also, when the user is relaxed, a detailed detergent amount setting option can be provided. Furthermore, when the user is in a hurry, a detergent amount setting that covers the main points can be provided. This allows for appropriate washing by adjusting the amount of detergent according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the detergent adjustment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the detergent adjustment unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0103] The detergent adjustment unit can automatically select the type and amount of detergent according to the material and dirtiness of the laundry. The detergent adjustment unit automatically selects the type and amount of detergent according to, for example, the material and dirtiness of the laundry. For example, the optimal type of detergent is automatically selected according to the material of the laundry. The amount of detergent can also be automatically adjusted according to the dirtiness of the laundry. Furthermore, the optimal type and amount of detergent can be automatically determined by simultaneously considering the material and dirtiness of the laundry. As a result, appropriate washing can be achieved by automatically selecting the type and amount of detergent according to the material and dirtiness of the laundry. Part or all of the above-mentioned processing in the detergent adjustment unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the detergent adjustment unit can input data on the material and dirtiness of the laundry to the generation AI and cause the generation AI to determine the optimal type and amount of detergent.

[0104] The detergent adjustment unit can estimate the user's emotion and adjust the timing of detergent injection based on the estimated user's emotion. The detergent adjustment unit, for example, estimates the user's emotion and adjusts the timing of detergent injection based on the estimated user's emotion. For example, when the user is in a hurry, the timing of detergent injection is quickly performed. Also, when the user is relaxed, detergent can be injected after all the laundry has been put in. Furthermore, when the user is stressed, detergent injection can be performed automatically to minimize the user's operation. As a result, appropriate washing can be performed by adjusting the timing of detergent injection according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the detergent adjustment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the detergent adjustment unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0105] The detergent adjustment unit can adjust the detergent concentration based on the tag information of the laundry. The detergent adjustment unit adjusts the detergent concentration based on, for example, the tag information of the laundry. For example, the detergent adjustment unit automatically sets an optimal detergent concentration based on the tag information. Also, the detergent concentration can be adjusted based on the tag information to set a setting suitable for the material of the laundry. Furthermore, the tag information can be analyzed and an optimal detergent concentration setting can be automatically determined. As a result, appropriate washing can be achieved by adjusting the detergent concentration based on the tag information of the laundry. A part or all of the above-mentioned processing in the detergent adjustment unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the detergent adjustment unit can input tag information to the generation AI and cause the generation AI to determine an optimal detergent concentration setting.

[0106] The water temperature adjustment unit can estimate the user's emotion and adjust the water temperature based on the estimated user's emotion. The water temperature adjustment unit, for example, estimates the user's emotion and adjusts the water temperature based on the estimated user's emotion. For example, when the user is nervous, a simple and highly visible water temperature setting is provided. Also, when the user is relaxed, detailed water temperature setting options can be provided. Furthermore, when the user is in a hurry, a water temperature setting that covers the main points can be provided. This allows for appropriate washing by adjusting the water temperature according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the water temperature adjustment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the water temperature adjustment unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0107] The water temperature adjustment unit can automatically set the optimal water temperature according to the material and color of the laundry. The water temperature adjustment unit automatically sets the optimal water temperature according to, for example, the material and color of the laundry. For example, the optimal water temperature is automatically set according to the material of the laundry. In addition, the water temperature that minimizes the risk of color fading can be set according to the color of the laundry. Furthermore, the optimal water temperature can be automatically determined by simultaneously considering the material and color of the laundry. As a result, appropriate washing can be achieved by automatically setting the optimal water temperature according to the material and color of the laundry. Part or all of the above-mentioned processing in the water temperature adjustment unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the water temperature adjustment unit can input data on the material and color of the laundry to the generation AI and cause the generation AI to set the optimal water temperature.

[0108] The water temperature adjustment unit can estimate the user's emotion and adjust the change in water temperature based on the estimated user's emotion. The water temperature adjustment unit, for example, estimates the user's emotion and adjusts the change in water temperature based on the estimated user's emotion. For example, when the user is in a hurry, the water temperature is adjusted quickly. Also, when the user is relaxed, the water temperature can be adjusted slowly. Furthermore, when the user is stressed, the water temperature can be automatically adjusted to minimize the user's operation. This allows appropriate washing by adjusting the change in water temperature according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the water temperature adjustment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the water temperature adjustment unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0109] The water temperature adjustment unit can adjust the water temperature of the washing machine in real time based on the tag information of the laundry. The water temperature adjustment unit adjusts the water temperature of the washing machine in real time based on, for example, the tag information of the laundry. For example, the water temperature of the washing machine is adjusted in real time based on the tag information. In addition, the water temperature suitable for the material of the laundry can be set in real time based on the tag information. Furthermore, the tag information can be analyzed and an optimal water temperature setting can be determined in real time. As a result, appropriate washing can be achieved by adjusting the water temperature in real time based on the tag information of the laundry. Part or all of the above-mentioned processing in the water temperature adjustment unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the water temperature adjustment unit can input tag information to the generation AI and cause the generation AI to perform water temperature adjustment in real time.

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

[0111] The drying settings of the laundry can be automatically set based on the tag information of the laundry. For example, the optimal drying settings are automatically selected based on the tag information. Also, the drying time can be automatically adjusted based on the tag information. Furthermore, the tag information can be analyzed and the optimal drying settings and drying time can be automatically determined. As a result, appropriate drying can be achieved by automatically setting the drying settings based on the tag information of the laundry. A part or all of the above-mentioned processing in the drying setting unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the drying setting unit can input the tag information to the generation AI and cause the generation AI to determine the optimal drying settings.

[0112] The operation sound of the washing machine can be optimized based on the tag information of the laundry. For example, the operation sound can be set to a minimum based on the tag information. Also, the operation sound can be adjusted based on the tag information to provide a quiet mode. Furthermore, the tag information can be analyzed and an optimal operation sound setting can be automatically determined. This allows quiet operation by optimizing the operation sound based on the tag information of the laundry. A part or all of the above-mentioned processing in the operation sound adjustment unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the operation sound adjustment unit can input tag information to the generation AI and cause the generation AI to determine the optimal operation sound setting.

[0113] The energy consumption of the washing machine can be optimized based on the tag information of the laundry. For example, a setting that minimizes energy consumption can be set based on the tag information. Also, an energy-efficient operation mode can be provided based on the tag information. Furthermore, the tag information can be analyzed and an optimal energy consumption setting can be automatically determined. As a result, efficient washing can be achieved by optimizing energy consumption based on the tag information of the laundry. Some or all of the above-mentioned processing in the energy consumption adjustment unit can be performed, for example, using the generation AI or can be performed without using the generation AI. For example, the energy consumption adjustment unit can input tag information to the generation AI and cause the generation AI to determine the optimal energy consumption setting.

[0114] Based on the tag information of the laundry, the settings can be adjusted in real time while the washing machine is running. For example, based on the tag information, the optimal settings are adjusted in real time while the washing machine is running. Also, based on the tag information, the amount of detergent and the amount of water can be adjusted in real time while the washing machine is running. Furthermore, the tag information can be analyzed and the optimal settings can be determined in real time while the washing machine is running. As a result, appropriate washing can be achieved by adjusting the settings in real time while the washing machine is running based on the tag information of the laundry. Part or all of the above-mentioned processing in the real-time setting adjustment unit may be performed, for example, using the generation AI or may be performed without using the generation AI. For example, the real-time setting adjustment unit can input the tag information to the generation AI and cause the generation AI to perform real-time setting adjustment during the operation.

[0115] The operation sequence of the washing machine can be determined based on the tag information of the laundry. For example, the operation sequence for washing important laundry with priority can be set based on the tag information. Also, the operation sequence for washing all laundry in order can be set based on the tag information. Furthermore, the tag information can be analyzed and the optimum operation sequence can be automatically determined. As a result, efficient washing can be achieved by determining the operation sequence based on the tag information of the laundry. A part or all of the above-mentioned processing in the operation sequence determination unit can be performed, for example, using the generation AI or can be performed without using the generation AI. For example, the operation sequence determination unit can input the tag information to the generation AI and cause the generation AI to determine the optimum operation sequence.

[0116] The user's emotions can be estimated, and the operation mode of the washing machine can be adjusted based on the estimated user's emotions. For example, if the user is nervous, a simple and highly visible operation mode can be provided. Also, if the user is relaxed, detailed setting options can be provided. Furthermore, if the user is in a hurry, an operation mode that focuses on the main points can be provided. This improves user convenience by adjusting the operation mode according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the operation mode adjustment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the operation mode adjustment unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0117] The user's emotion can be estimated, and the amount of detergent can be adjusted based on the estimated user's emotion. For example, if the user is nervous, a simple and highly visible detergent amount setting can be provided. Also, if the user is relaxed, detailed detergent amount setting options can be provided. Furthermore, if the user is in a hurry, detergent amount settings that focus on the main points can be provided. This allows for appropriate washing by adjusting the amount of detergent according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the detergent amount adjustment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the detergent amount adjustment unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0118] The user's emotion can be estimated, and the timing of detergent injection can be adjusted based on the estimated user's emotion. For example, when the user is in a hurry, the timing of detergent injection can be quickly performed. Also, when the user is relaxed, detergent can be injected after all the laundry has been injected. Furthermore, when the user is stressed, detergent injection can be performed automatically to minimize the user's operation. As a result, appropriate washing can be performed by adjusting the timing of detergent injection according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the detergent injection timing adjustment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the detergent injection timing adjustment unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0119] The user's emotion can be estimated, and the water temperature can be adjusted based on the estimated user's emotion. For example, if the user is nervous, a simple and highly visible water temperature setting can be provided. Also, if the user is relaxed, detailed water temperature setting options can be provided. Furthermore, if the user is in a hurry, water temperature settings that focus on the main points can be provided. This allows for appropriate washing by adjusting the water temperature according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the water temperature adjustment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the water temperature adjustment unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0120] The user's emotion can be estimated, and the change in water temperature can be adjusted based on the estimated user's emotion. For example, if the user is in a hurry, the water temperature can be adjusted quickly. Also, if the user is relaxed, the water temperature can be adjusted slowly. Furthermore, if the user is stressed, the water temperature can be adjusted automatically to minimize the user's operation. This allows for appropriate washing by adjusting the change in water temperature according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the water temperature change adjustment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the water temperature change adjustment unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0121] The process flow of the second embodiment will be briefly described below.

[0122] Step 1: The reading unit reads the laundry tag. The laundry tag contains information such as the washing method, temperature, and type of detergent. The reading unit reads the tag information using a camera and sensors built into the washing machine. For example, a camera can be installed on the washing machine door, which can automatically read the tag when the laundry is placed in the washing machine. The reading unit can also identify the material and color of the laundry and compare it with the tag information to improve reading accuracy. Step 2: The analysis unit analyzes the tag information read by the reading unit. The analysis unit analyzes the tag information based on a database that has been learned in advance. For example, if the tag says "Wash at low temperature," the analysis unit selects a low-temperature wash setting. If the tag says "Beware of color fading," the analysis unit sets the setting to prevent color fading. Furthermore, if the tag information is unclear, the analysis unit can also make an estimate based on the material and color of the laundry. Step 3: The setting unit determines optimal wash settings based on the information analyzed by the analysis unit. The setting unit determines optimal wash settings based on the information written on the tag. For example, a low temperature wash setting is used for clothes made of delicate materials, and a color fading prevention setting is used for clothes that are prone to fading. The setting unit can also automatically optimize wash setting parameters depending on the type and amount of laundry. Step 4: The control unit controls the washing machine based on the settings determined by the setting unit. The control unit controls the amount of water in the washing machine and the timing of adding detergent depending on the amount and type of laundry. For example, the control unit automatically sets the optimal amount of water depending on the amount of laundry. The control unit can also adjust the settings in real time while the washing machine is running based on the tag information of 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0124] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0125] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements including the above-mentioned reading unit, analysis unit, setting unit, and control unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reading unit can read the tag of the laundry using the camera 42 or a sensor of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the information of the tag. The setting unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and determines the optimal washing setting. The control unit is realized, for example, by the control unit 46A of the smart device 14, and controls the washing machine. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

[0128] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0130] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0131] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0134] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0135] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0137] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0138] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

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

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements including the above-mentioned reading unit, analysis unit, setting unit, and control unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reading unit can read the tag of the laundry using the camera 42 or a sensor of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the information of the tag. The setting unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and determines the optimal washing setting. The control unit is realized, for example, by the control unit 46A of the smart glasses 214, and controls the washing machine. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

[0144] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0145] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0146] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0147] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0149] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0150] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0151] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0153] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0154] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements including the above-mentioned reading unit, analysis unit, setting unit, and control unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reading unit can read the tag of the laundry using the camera 42 or a sensor of the headset type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the information of the tag. The setting unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and determines the optimal washing setting. The control unit is realized, for example, by the control unit 46A of the headset type terminal 314, and controls the washing machine. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

[0160] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0161] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0162] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0163] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0165] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0166] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0167] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0168] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0169] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0170] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0171] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

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

[0173] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0175] Each of the multiple elements including the above-mentioned reading unit, analysis unit, setting unit, and control unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reading unit can read the tag of the laundry using the camera 42 or a sensor of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the information of the tag. The setting unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and determines the optimal washing setting. The control unit is realized, for example, by the control unit 46A of the robot 414, and controls the washing machine. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0176] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0177] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0178] These emotions are distributed in the three o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0179] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0180] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0181] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0183] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0184] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0185] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0186] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0187] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0188] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0189] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0190] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0191] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0192] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0193] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0194] (Appendix 1) A reading unit for reading a laundry tag; an analysis unit that analyzes the tag information read by the reading unit; a setting unit that determines appropriate wash settings based on the information analyzed by the analysis unit; A control unit that controls the washing machine based on the settings determined by the setting unit. A system characterized by: (Appendix 2) Equipped with a detergent adjustment unit that adjusts the amount of detergent 2. The system of claim 1, (Appendix 3) Equipped with a water temperature adjustment unit that adjusts the water temperature 2. The system of claim 1, (Appendix 4) The reading unit is The tag information is read using a camera or sensor built into the washing machine. 2. The system of claim 1, (Appendix 5) The analysis unit is Analyze tag information based on a pre-trained database 2. The system of claim 1, (Appendix 6) The setting unit is Determine the appropriate wash settings based on the information on the tag 2. The system of claim 1, (Appendix 7) The reading unit is Estimate user emotions and adjust tag reading timing based on the estimated user emotions 2. The system of claim 1, (Appendix 8) The reading unit is Identifying the material and color of laundry and comparing it with tag information to improve reading accuracy 2. The system of claim 1, (Appendix 9) The reading unit is Add the ability to read multiple tags simultaneously depending on the amount or type of laundry. 2. The system of claim 1, (Appendix 10) The reading unit is Estimate user emotions and determine tag reading priority based on the estimated user emotions 2. The system of claim 1, (Appendix 11) The reading unit is In addition to reading laundry tags, add a function to read the shape and size of laundry items. 2. The system of claim 1, (Appendix 12) The reading unit is When reading laundry tags, the system detects the laundry's odor to supplement the information. 2. The system of claim 1, (Appendix 13) The analysis unit is Infer user emotions and adjust the way analysis results are displayed based on the inferred user emotions 2. The system of claim 1, (Appendix 14) The analysis unit is In addition to tag information, the laundry's past washing history is also used for analysis. 2. The system of claim 1, (Appendix 15) The analysis unit is When the information on the tag is unclear, the system estimates it based on the material and color of the laundry and performs analysis. 2. The system of claim 1, (Appendix 16) The analysis unit is Inferring user emotions and determining analysis priorities based on the estimated user emotions 2. The system of claim 1, (Appendix 17) The analysis unit is In addition to the tag information, the frequency of use and degree of dirt of the laundry are also used for analysis. 2. The system of claim 1, (Appendix 18) The analysis unit is When analyzing tag information, look up information about the brand and manufacturer of the laundry. 2. The system of claim 1, (Appendix 19) The setting unit is Inferring a user's emotions and adjusting the level of detail of washing settings based on the estimated user's emotions 2. The system of claim 1, (Appendix 20) The setting unit is Automatically optimizes wash settings based on the type and amount of laundry 2. The system of claim 1, (Appendix 21) The setting unit is Automatically selects the type and amount of detergent based on the laundry tag information 2. The system of claim 1, (Appendix 22) The setting unit is Estimate the user's emotions and prioritize wash settings based on the estimated user's emotions. 2. The system of claim 1, (Appendix 23) The setting unit is Adjust the washing machine's operating time and spin speed based on laundry tag information 2. The system of claim 1, (Appendix 24) The setting unit is Automatically set the drying settings on your washing machine based on the laundry tag information 2. The system of claim 1, (Appendix 25) The control unit is To estimate a user's emotion and adjust an operation mode of a washing machine based on the estimated user's emotion. 2. The system of claim 1, (Appendix 26) The control unit is The amount of water used in the washing machine and the timing of detergent addition are controlled according to the amount and type of laundry. 2. The system of claim 1, (Appendix 27) The control unit is Optimize the sound of your washing machine based on laundry tag information 2. The system of claim 1, (Appendix 28) The control unit is Estimating a user's emotions and determining the operation sequence of washing machines based on the estimated user's emotions 2. The system of claim 1, (Appendix 29) The control unit is Optimize energy consumption of washing machines based on laundry tag information 2. The system of claim 1, (Appendix 30) The control unit is Based on laundry tag information, the washing machine adjusts settings in real time while it is running 2. The system of claim 1, (Appendix 31) The detergent preparation unit includes: Estimate the user's emotions and adjust the amount of detergent based on the estimated user's emotions 3. The system of claim 2, (Appendix 32) The detergent preparation unit includes: Automatically selects the type and amount of detergent depending on the material and degree of dirt of the laundry. 3. The system of claim 2, (Appendix 33) The detergent preparation unit includes: To estimate a user's emotion and adjust a timing of dispensing detergent based on the estimated emotion of the user. 3. The system of claim 2, (Appendix 34) The detergent preparation unit includes: Adjust detergent concentration based on laundry tag information 3. The system of claim 2, (Appendix 35) The water temperature adjustment unit is Estimate the user's emotions and adjust the water temperature based on the estimated user's emotions 4. The system of claim 3. (Appendix 36) The water temperature adjustment unit is Automatically sets the optimum water temperature according to the material and color of the laundry 4. The system of claim 3. (Appendix 37) The water temperature adjustment unit is Estimate the user's emotions and adjust the water temperature based on the estimated user's emotions 4. The system of claim 3. (Appendix 38) The water temperature adjustment unit is Adjust the washing machine water temperature in real time based on the laundry tag information 4. The system of claim 3. [Explanation of symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reading unit for reading a laundry tag; an analysis unit that analyzes the tag information read by the reading unit; a setting unit that determines appropriate wash settings based on the information analyzed by the analysis unit; A control unit that controls the washing machine based on the settings determined by the setting unit. A system characterized by:

2. Equipped with a detergent adjustment unit that adjusts the amount of detergent 2. The system of claim 1 .

3. Equipped with a water temperature adjustment unit that adjusts the water temperature 2. The system of claim 1 .

4. The reading unit is The tag information is read using a camera or sensor built into the washing machine.

2. The system of claim 1 .

5. The analysis unit is Analyze tag information based on a pre-trained database 2. The system of claim 1 .

6. The setting unit is Determine the appropriate wash settings based on the information on the tag 2. The system of claim 1 .

7. The reading unit is Estimate user emotions and adjust tag reading timing based on the estimated user emotions 2. The system of claim 1 .

8. The reading unit is Identifying the material and color of laundry and comparing it with tag information to improve reading accuracy 2. The system of claim 1 .

9. The reading unit is Add the ability to read multiple tags simultaneously depending on the amount or type of laundry.

2. The system of claim 1 .

10. The reading unit is Estimate user emotions and determine tag reading priority based on the estimated user emotions 2. The system of claim 1 .

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