system

Abstract

The system according to the embodiment aims to automatically select and execute the optimal washing program according to the shape, material, and degree of dirt of the dishes. [Solution] A system according to an embodiment includes an image analysis unit, a type identification unit, a sensor unit, a program selection unit, and a washing unit. The image analysis unit analyzes the shape or material of the tableware. The type identification unit identifies the type of tableware based on information analyzed by the image analysis unit. The sensor unit detects dirt or debris on the tableware. The program selection unit selects a washing program based on information detected by the sensor unit. The washing unit executes the washing program selected by the program selection unit.

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 an instruction sentence related to a description of 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has the problem that it is difficult to automatically select the optimal washing program based on the shape, material, and degree of dirt of the dishes.

[0005] The system according to the embodiment aims to automatically select and execute the optimal washing program according to the shape, material, and degree of dirt of the dishes. [Means for solving the problem]

[0006] The system according to the embodiment includes an image analysis unit, a type identification unit, a sensor unit, a program selection unit, and a washing unit. The image analysis unit analyzes the shape or material of the tableware. The type identification unit identifies the type of tableware based on the information analyzed by the image analysis unit. The sensor unit detects dirt or debris on the tableware. The program selection unit selects a washing program based on the information detected by the sensor unit. The washing unit executes the washing program selected by the program selection unit. [Effects of the Invention]

[0007] The system according to this embodiment can automatically select and execute the optimal cleaning program according to the shape, material, and degree of soiling of the tableware. [Brief explanation 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. [Figure 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. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 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. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 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 INVENTION

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

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

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single 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 coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, 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 symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and 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. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "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, RAM 30, and 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 (for example, 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., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with 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 CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 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 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. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) An automatic analysis system according to an embodiment of the present invention is a system that fully automates a dishwasher. This automatic analysis system automatically identifies the type of dishes, automatically analyzes the dirt and debris on the dishes, and selects the optimal washing program. As a result, even dirty dishes can be placed in the dishwasher fully automatically and cleanly. For example, when a user places dirty dishes in the dishwasher, no special preparation is required; the user simply places the dishes as they are. When a user places plates, cups, etc. in the dishwasher after a meal, this information is input into the automatic analysis system. The automatic analysis system then analyzes the input information and automatically identifies the type of dishes. The automatic analysis system analyzes the shape and material of the dishes to identify the type (e.g., plate, cup, fork, knife, etc.). For example, the shape and material of a plate can be analyzed to identify that it is a ceramic plate. The automatic analysis system then automatically analyzes the dirt and debris on the dishes. The automatic analysis system analyzes the type and amount of dirt and debris on the dishes and selects the optimal washing program. For example, if the dishes are heavily soiled with oil, a powerful washing program can be selected, and if the dishes are lightly soiled, an eco mode can be selected. With this system, users can simply place their dirty dishes in the dishwasher and have them cleaned to a sparkling finish. For example, after a meal, users can simply place their dishes and cups in the dishwasher as is, and the automatic analysis system will automatically analyze them and select the optimal washing program to wash them. This allows users to keep their dishes clean without any effort. The automatic analysis system can then select the optimal washing program based on the shape and material of the dishes, as well as the type and amount of dirt, and wash the dishes fully automatically.

[0029] The automatic analysis system according to the embodiment includes an image analysis unit, a type identification unit, a sensor unit, a program selection unit, and a washing unit. The image analysis unit analyzes the shape or material of the tableware. For example, the image analysis unit analyzes the shape and material of the tableware using a camera. The camera acquires high-resolution images and can analyze the shape and material of the tableware in detail. For example, the image analysis unit classifies the shape of the tableware into circular, rectangular, complex shapes, etc., and the material into ceramic, glass, plastic, etc. The type identification unit identifies the type of tableware based on the information analyzed by the image analysis unit. For example, the type identification unit identifies the type of tableware, such as a plate, cup, fork, or knife, based on the shape and material information obtained from the image analysis unit. The sensor unit detects dirt or debris on the tableware. For example, the sensor unit detects the type and amount of dirt or debris attached to the tableware using an optical sensor, an infrared sensor, an ultrasonic sensor, etc. The program selection unit selects a washing program based on the information detected by the sensor unit. For example, the program selection unit selects the optimal washing program from among a powerful wash, an eco mode, a short-time wash, etc., depending on the type and amount of dirt. The washing unit executes the washing program selected by the program selection unit. For example, the washing unit adjusts the water pressure and amount of detergent based on the selected washing program to wash dishes efficiently. As a result, the automatic analysis system according to the embodiment can select the optimal washing program based on the shape and material of the dishes, and the type and amount of dirt, and wash dishes fully automatically.

[0030] The image analysis unit can analyze the shape and material of the tableware using a camera. The camera can acquire high-resolution images and analyze the shape and material of the tableware in detail. For example, the image analysis unit classifies the shape of the tableware into circular, rectangular, complex shapes, etc., and classifies the material into ceramic, glass, plastic, etc. This allows the shape and material of the tableware to be accurately analyzed using a camera. Some or all of the above-mentioned processing in the image analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the image analysis unit can input image data acquired by the camera into a generation AI and have the generation AI analyze the shape and material from the image data.

[0031] The sensor unit may include a sensor that detects the type or amount of dirt. The sensor unit detects the type and amount of dirt or debris attached to the dishes using an optical sensor, an infrared sensor, an ultrasonic sensor, or the like. For example, an optical sensor detects the type and amount of dirt using the reflection of light. An infrared sensor detects the temperature and type of dirt using infrared rays. An ultrasonic sensor detects the thickness and amount of dirt using ultrasonic waves. This allows the type and amount of dirt to be accurately detected. Some or all of the above-mentioned processing in the sensor unit may be performed using, for example, AI, or may be performed without using AI. For example, the sensor unit may input data on the detected dirt into a generation AI and have the generation AI analyze the type and amount of dirt.

[0032] The program selection unit may include an algorithm that selects the optimal washing program from multiple washing programs based on the analysis results. The program selection unit may include an algorithm that selects the optimal washing program from among powerful washing, eco mode, and short-time washing depending on the type and amount of dirt. For example, the program selection unit may use a machine learning algorithm to learn past washing data and select the optimal washing program. The program selection unit may also use a rule-based algorithm to select a washing program based on the type and amount of dirt. This allows the optimal washing program to be selected based on the analysis results. Some or all of the above-described processing in the program selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the program selection unit may input data on the type and amount of dirt into the generation AI and cause the generation AI to select the optimal washing program.

[0033] The washing unit can perform washing based on a selected washing program. The washing unit adjusts the appropriate water pressure and amount of detergent based on the selected washing program to wash dishes efficiently. For example, if a powerful washing program is selected, the washing unit performs washing at high water pressure, and if an eco mode is selected, the washing unit performs washing at low water pressure. The washing unit also adjusts the amount of detergent and uses an appropriate amount depending on the type and amount of dirt. This allows for efficient washing based on the selected washing program. Some or all of the above-mentioned processes in the washing unit may be performed using, for example, AI, or may be performed without using AI. For example, the washing unit can input data of the selected washing program into a generation AI and have the generation AI execute the washing.

[0034] The program selection unit can select a strong washing program when there is a large amount of oil stains, and select eco mode when there is only a small amount of stains. The program selection unit selects the optimal washing program from strong washing, eco mode, short-time washing, etc., depending on the type and amount of stains. For example, when there is a large amount of oil stains, a strong washing program is selected, and when there is only a small amount of stains, eco mode is selected. This makes it possible to select the optimal washing program depending on the type and amount of stains. Some or all of the above-mentioned processing in the program selection unit may be performed using AI, for example, or may be performed without using AI. For example, the program selection unit can input data on the type and amount of stains into the generation AI and have the generation AI select the optimal washing program.

[0035] The image analysis unit can improve the analysis accuracy based on the tableware usage history during image analysis. The image analysis unit improves the analysis accuracy by referring to the tableware usage history during image analysis. For example, the image analysis unit can improve the analysis accuracy by referring to data on the shape and material of tableware used in the past. The analysis speed can also be improved by preferentially referring to data on frequently used tableware. Furthermore, specific stain and dust patterns can be learned from the tableware usage history to improve the analysis accuracy. In this way, the analysis accuracy can be improved by referring to the tableware usage history. Some or all of the above-mentioned processing in the image analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the image analysis unit can input the usage history data into the generation AI and have the generation AI improve the analysis accuracy.

[0036] The image analysis unit can perform analysis based on the arrangement of tableware during image analysis. The image analysis unit takes the arrangement of tableware into consideration during image analysis. For example, if the tableware is stacked, the overlap is taken into account during the analysis. Also, if the tableware is placed at an angle, the angle can be corrected during the analysis. Furthermore, if the tableware is placed in multiple layers, each layer can be analyzed separately. This allows for more accurate analysis by considering the arrangement of the tableware. Some or all of the above processing in the image analysis unit may be performed using AI, for example, or without AI. For example, the image analysis unit can input arrangement data into a generating AI and have the generating AI perform analysis based on the arrangement.

[0037] The image analysis unit can perform analysis based on the color or design of the tableware during image analysis. The image analysis unit considers the color and design of the tableware during image analysis. For example, it can analyze the color of the tableware, group tableware of the same color, and perform analysis. It can also analyze the design patterns of the tableware and identify the type based on a specific design. Furthermore, it can combine the color and design of the tableware in its analysis to perform more accurate type identification. This allows for more accurate type identification by considering the color and design of the tableware. Some or all of the above processing in the image analysis unit may be performed using AI, for example, or without AI. For example, the image analysis unit can input color and design data into a generating AI and have the generating AI perform analysis based on color and design.

[0038] The image analysis unit can perform analysis by referring to the temperature information of the dishes when analyzing images. The image analysis unit performs analysis by referring to the temperature information of the dishes when analyzing images. For example, the temperature of the dishes can be analyzed and an analysis mode can be selected according to the temperature. It is also possible to prioritize analysis of high-temperature dishes to shorten the cooling time. It is also possible to analyze low-temperature dishes and suggest a washing program according to the temperature. In this way, by referring to the temperature information of the dishes, the accuracy of the analysis can be improved. Some or all of the above-mentioned processing in the image analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the image analysis unit can input temperature information data to the generation AI and cause the generation AI to perform an analysis based on the temperature.

[0039] The type identification unit can improve the identification accuracy by referring to the frequency of use of the tableware when identifying the type. The type identification unit improves the identification accuracy by referring to the frequency of use of the tableware when identifying the type. For example, it prioritizes identifying frequently used tableware to improve the identification accuracy. In addition, when identifying less frequently used tableware, it can also improve the accuracy by referring to past data. Furthermore, it can adjust the identification accuracy of specific tableware based on the frequency of use. In this way, it is possible to improve the identification accuracy by referring to the frequency of use of the tableware. Some or all of the above-mentioned processing in the type identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the type identification unit can input the use frequency data into the generation AI and cause the generation AI to improve the identification accuracy based on the use frequency.

[0040] The type identification unit can identify the type based on the manufacturer information of the tableware. The type identification unit takes the manufacturer information of the tableware into consideration when identifying the type. For example, the type identification unit analyzes the manufacturer information of the tableware and preferentially identifies tableware of a specific brand. Tableware made of a specific material or shape can also be identified based on the manufacturer information. Furthermore, tableware made by a specific manufacturer can be quickly identified by referring to the manufacturer information. This allows for more accurate type identification by taking the manufacturer information of the tableware into consideration. Some or all of the above-mentioned processing in the type identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the type identification unit can input manufacturer information data to the generation AI and cause the generation AI to perform identification based on the manufacturer information.

[0041] The type identification unit can identify the type based on changes in the shape of the tableware when identifying the type. The type identification unit takes into account changes in the shape of the tableware when identifying the type. For example, if the shape of the tableware has changed, the type identification unit can identify the type by taking that change into consideration. The type can also be identified by analyzing the changes in the shape of the tableware and based on the specific shape. Furthermore, the changes in the shape of the tableware can be corrected to identify the type accurately. This allows for more accurate type identification by taking the changes in the shape of the tableware into consideration. Some or all of the above-mentioned processing in the type identification unit may be performed using AI, for example, or may be performed without using AI. For example, the type identification unit can input shape change data to a generation AI and have the generation AI perform identification based on the shape change.

[0042] The type identification unit can identify the type by referring to the weight information of the tableware. The type identification unit can identify the type by referring to the weight information of the tableware. For example, the weight of the tableware can be analyzed and the type can be identified based on the weight. The type can also be identified based on a specific weight range by referring to the weight information. Furthermore, accurate type identification can be performed by taking the weight of the tableware into consideration. Thus, more accurate type identification can be performed by referring to the weight information of the tableware. Some or all of the above-mentioned processing in the type identification unit can be performed using, for example, AI, or without AI. For example, the type identification unit can input weight information data to the generation AI and have the generation AI perform identification based on weight.

[0043] The sensor unit can automatically select the type of detergent based on the type of dirt detected by the sensor unit. The sensor unit automatically selects the type of detergent based on the type of dirt detected by the sensor unit. For example, if oily dirt is detected, a strong detergent for oily dirt is automatically selected. Also, if light dirt is detected, an eco-mode detergent can be automatically selected. Furthermore, the optimal detergent can be automatically selected based on the specific dirt attached to the dishes. This makes it possible to automatically select the optimal detergent depending on the type of dirt. Some or all of the above-mentioned processing in the sensor unit may be performed using AI, for example, or may be performed without using AI. For example, the sensor unit can input dirt type data into the generation AI and have the generation AI select the detergent.

[0044] The sensor unit can automatically adjust the cleaning time based on the amount of dust detected by the sensor unit. The sensor unit automatically adjusts the cleaning time based on the amount of dust detected by the sensor unit. For example, if the amount of dust is large, the cleaning time can be extended to thoroughly clean the item. Alternatively, if the amount of dust is small, the cleaning time can be shortened to efficiently clean the item. Furthermore, the optimal cleaning time can be automatically adjusted based on the amount of dust. This enables efficient cleaning by automatically adjusting the cleaning time according to the amount of dust. Some or all of the above-described processing in the sensor unit may be performed using, for example, AI, or may be performed without using AI. For example, the sensor unit can input dust amount data into the generation AI and have the generation AI adjust the cleaning time.

[0045] The sensor unit can automatically adjust the wash temperature based on the type of dirt detected by the sensor unit. The sensor unit automatically adjusts the wash temperature based on the type of dirt detected by the sensor unit. For example, if oily dirt is detected, a high temperature wash temperature can be automatically selected. Also, if light dirt is detected, a low temperature wash temperature can be automatically selected. Furthermore, the optimal wash temperature can be automatically adjusted based on the type of dirt. This makes it possible to automatically adjust the optimal wash temperature according to the type of dirt. Some or all of the above-mentioned processing in the sensor unit may be performed using AI, for example, or may be performed without using AI. For example, the sensor unit can input dirt type data into the generation AI and have the generation AI adjust the wash temperature.

[0046] The sensor unit can automatically adjust the number of rinses based on the amount of debris detected by the sensor. For example, if there is a large amount of debris, the number of rinses can be increased for thorough rinsing. Conversely, if there is a small amount of debris, the number of rinses can be reduced for efficient rinsing. Furthermore, the sensor unit can automatically adjust the optimal number of rinses based on the amount of debris. This enables efficient rinsing by automatically adjusting the number of rinses according to the amount of debris. Some or all of the above processing in the sensor unit may be performed using AI, for example, or without AI. For example, the sensor unit can input debris amount data into a generating AI and have the generating AI perform the adjustment of the number of rinses.

[0047] The program selection unit can select the optimal program by referring to past cleaning history when selecting a program. For example, it can select the optimal program by referring to data on previously used cleaning programs. It can also prioritize the selection of frequently used cleaning programs. Furthermore, it can select the optimal program for specific types of dirt or debris based on past cleaning history. Thus, the optimal program can be selected by referring to past cleaning history. Some or all of the above-described processes in the program selection unit may be performed using AI, for example, or without AI. For example, the program selection unit can input cleaning history data into a generating AI and have the generating AI select the optimal program.

[0048] The program selection unit can adjust the washing intensity based on the material of the dishes when selecting a program. The program selection unit adjusts the washing intensity based on the material of the dishes when selecting a program. For example, for ceramic dishes, a strong washing program can be selected. For glass dishes, a gentle washing program can be selected. Furthermore, for plastic dishes, a moderate washing program can be selected. In this way, by adjusting the washing intensity according to the material of the dishes, appropriate washing can be performed. Some or all of the above-mentioned processing in the program selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the program selection unit can input material data into a generation AI and have the generation AI adjust the washing intensity.

[0049] When selecting a program, the program selection unit can select a program based on the intended use of the tableware. When selecting a program, the program selection unit selects a program based on the intended use of the tableware. For example, if the tableware is used daily, a normal washing program can be selected. Also, if the tableware is used at a special event, a powerful washing program can be selected. Furthermore, if the tableware is made of delicate material, a gentle washing program can be selected. This makes it possible to select the optimal program depending on the intended use of the tableware. Some or all of the above-mentioned processing in the program selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the program selection unit can input intended use data into the generation AI and cause the generation AI to select a program based on the intended use.

[0050] The program selection unit can select a program based on the storage state of the dishes when selecting a program. For example, if the dishes are stored close together, a powerful washing program may be selected. If the dishes are spread out, a normal washing program may be selected. Furthermore, if the dishes are stacked on top of each other, the optimal program may be selected based on a specific arrangement. This allows for the selection of the optimal program according to the storage state of the dishes. Some or all of the above processing in the program selection unit may be performed using AI, for example, or without AI. For example, the program selection unit can input storage state data into a generating AI and have the generating AI select a program based on the storage state.

[0051] The washing unit can adjust the water flow during washing, taking into account the arrangement of the dishes. For example, if the dishes are densely packed, the water flow can be increased to enhance the washing effect. If the dishes are spread out, the water flow can be set to normal mode. Furthermore, if the dishes are overlapping, the water flow can be adjusted based on a specific arrangement. This allows for efficient washing by adjusting the water flow according to the arrangement of the dishes. Some or all of the above processes in the washing unit may be performed using AI, for example, or without AI. For example, the washing unit can input arrangement data into a generating AI and have the generating AI perform water flow adjustments based on the arrangement.

[0052] The washing unit can adjust the amount of detergent used based on the material of the dishes when washing in the washing unit. The washing unit adjusts the amount of detergent used based on the material of the dishes when washing in the washing unit. For example, for ceramic dishes, an appropriate amount of detergent is used. Also, for glass dishes, a small amount of detergent can be used. Furthermore, for plastic dishes, an appropriate amount of detergent can be used. In this way, by adjusting the amount of detergent according to the material of the dishes, appropriate washing can be performed. Some or all of the above-mentioned processes in the washing unit may be performed using, for example, AI, or may be performed without using AI. For example, the washing unit can input material data into a generation AI and have the generation AI adjust the amount of detergent.

[0053] The washing unit can adjust the direction of the water flow based on the shape of the dishes when washing them in the washing unit. The washing unit adjusts the direction of the water flow based on the shape of the dishes when washing them in the washing unit. For example, if the dishes are deep, the water flow can be adjusted to be directed toward the deep part. Also, if the dishes are flat, the water flow can be adjusted to be directed over a wide area. Furthermore, if the dishes have a complex shape, the water flow direction can be adjusted based on the specific shape. This allows for efficient washing by adjusting the water flow direction according to the shape of the dishes. Some or all of the above-mentioned processing in the washing unit may be performed using, for example, AI, or may be performed without using AI. For example, the washing unit can input shape data into a generating AI and have the generating AI adjust the water flow direction based on the shape.

[0054] The washing unit can adjust the washing temperature based on the temperature of the dishes when washing in the washing unit. The washing unit adjusts the washing temperature based on the temperature of the dishes when washing in the washing unit. For example, if the dishes are hot, the washing temperature can be set lower. Also, if the dishes are cold, the washing temperature can be set higher. Furthermore, the optimal washing temperature can be adjusted based on the temperature of the dishes. This allows for efficient washing by adjusting the washing temperature according to the temperature of the dishes. Some or all of the above-mentioned processes in the washing unit may be performed using, for example, AI, or may be performed without using AI. For example, the washing unit can input temperature data into the generation AI and have the generation AI adjust the washing temperature based on the temperature.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The automatic analysis system can further include a voice recognition unit. The voice recognition unit can analyze the user's voice commands and use them as auxiliary information to identify the type of dish and the type of dirt. For example, if the user says by voice, "This dish is very oily," the voice recognition unit analyzes that information and transmits it to the program selection unit. Also, if the user says, "I'm in a hurry, please wash it quickly," the voice recognition unit can analyze that information and instruct the washing unit to wash quickly. This makes washing more flexible and efficient by using voice commands.

[0057] The automatic analysis system can further include a storage optimization unit that optimizes the storage state of the dishes. The storage optimization unit analyzes the arrangement of the dishes and proposes the optimal storage method. For example, if the dishes are stacked, it proposes an arrangement method to eliminate the overlap. If the dishes are arranged diagonally, it can also propose a method to arrange them horizontally. Furthermore, if the dishes are arranged in multiple layers, it can also propose the optimal arrangement method for each layer. This can improve the washing effect by optimizing the arrangement of the dishes.

[0058] The automatic analysis system can further include a learning unit that learns the usage history of dishes. The learning unit accumulates past washing data and provides it to the analysis unit. For example, if a particular type of dish is used frequently, the washing program for that dish can be optimized. Also, if a particular stain occurs frequently, the washing program for that stain can be strengthened. Furthermore, for dishes that are used less frequently, it can be recommended to wash them in eco mode. This makes it possible to provide an optimal washing program based on past usage history.

[0059] The automatic analysis system can further include a detergent selection unit that automatically selects the type of detergent based on the material of the tableware. The detergent selection unit analyzes the material of the tableware and selects the optimal detergent. For example, a strong detergent can be used for ceramic tableware, and a gentle detergent for glass tableware. A moderate detergent can also be used for plastic tableware. Furthermore, a dedicated detergent can be recommended for a specific material. This makes it possible to provide the optimal detergent according to the material of the tableware.

[0060] The automatic analysis system may further include a shape customization unit that customizes the washing program based on the shape of the dishes. The shape customization unit analyzes the shape of the dishes and provides an optimal washing program. For example, a specific water flow may be used for deep dishes, and a wide water flow may be used for flat dishes. A washing program based on the specific shape may also be provided for dishes with complex shapes. Furthermore, a dedicated washing program may be recommended for a specific shape. This makes it possible to provide an optimal washing program according to the shape of the dishes.

[0061] The automatic analysis system may further include a purpose selection unit that selects a washing program based on the intended use of the tableware. The purpose selection unit analyzes the intended use of the tableware and provides the optimal washing program. For example, a normal washing program may be selected for tableware used daily, and a powerful washing program may be selected for tableware used at special events. A gentle washing program may also be selected for tableware made of delicate materials. Furthermore, a dedicated washing program may be recommended for a specific intended use. This allows the optimal washing program to be provided according to the intended use of the tableware.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The image analysis unit analyzes the shape and material of the tableware. For example, it uses a camera to capture high-resolution images and classifies the shape of the tableware as round, rectangular, complex, etc., and the material as ceramic, glass, plastic, etc. Step 2: The type identification unit identifies the type of tableware based on the information analyzed by the image analysis unit. For example, the type is identified as a plate, cup, fork, knife, etc. based on information about the shape and material. Step 3: The sensor unit detects dirt or dust on the dishes. For example, an optical sensor, an infrared sensor, an ultrasonic sensor, or the like is used to detect the type and amount of dirt or dust on the dishes. Step 4: The program selection unit selects a wash program based on the information detected by the sensor unit. For example, depending on the type and amount of dirt, the program selector selects the most appropriate one from among powerful wash, eco mode, short wash, etc. Step 5: The washing unit executes the washing program selected by the program selection unit. For example, based on the selected washing program, the washing unit adjusts the water pressure and the amount of detergent to an appropriate level to wash the dishes efficiently.

[0064] (Example 2) An automatic analysis system according to an embodiment of the present invention is a system that fully automates a dishwasher. This automatic analysis system automatically identifies the type of dishes, automatically analyzes the dirt and debris on the dishes, and selects the optimal washing program. As a result, even dirty dishes can be placed in the dishwasher fully automatically and cleanly. For example, when a user places dirty dishes in the dishwasher, no special preparation is required; the user simply places the dishes as they are. When a user places plates, cups, etc. in the dishwasher after a meal, this information is input into the automatic analysis system. The automatic analysis system then analyzes the input information and automatically identifies the type of dishes. The automatic analysis system analyzes the shape and material of the dishes to identify the type (e.g., plate, cup, fork, knife, etc.). For example, the shape and material of a plate can be analyzed to identify that it is a ceramic plate. The automatic analysis system then automatically analyzes the dirt and debris on the dishes. The automatic analysis system analyzes the type and amount of dirt and debris on the dishes and selects the optimal washing program. For example, if the dishes are heavily soiled with oil, a powerful washing program can be selected, and if the dishes are lightly soiled, an eco mode can be selected. With this system, users can simply place their dirty dishes in the dishwasher and have them cleaned to a sparkling finish. For example, after a meal, users can simply place their dishes and cups in the dishwasher as is, and the automatic analysis system will automatically analyze them and select the optimal washing program to wash them. This allows users to keep their dishes clean without any effort. The automatic analysis system can then select the optimal washing program based on the shape and material of the dishes, as well as the type and amount of dirt, and wash the dishes fully automatically.

[0065] The automatic analysis system according to the embodiment includes an image analysis unit, a type identification unit, a sensor unit, a program selection unit, and a washing unit. The image analysis unit analyzes the shape or material of the tableware. For example, the image analysis unit analyzes the shape and material of the tableware using a camera. The camera acquires high-resolution images and can analyze the shape and material of the tableware in detail. For example, the image analysis unit classifies the shape of the tableware into circular, rectangular, complex shapes, etc., and the material into ceramic, glass, plastic, etc. The type identification unit identifies the type of tableware based on the information analyzed by the image analysis unit. For example, the type identification unit identifies the type of tableware, such as a plate, cup, fork, or knife, based on the shape and material information obtained from the image analysis unit. The sensor unit detects dirt or debris on the tableware. For example, the sensor unit detects the type and amount of dirt or debris attached to the tableware using an optical sensor, an infrared sensor, an ultrasonic sensor, etc. The program selection unit selects a washing program based on the information detected by the sensor unit. For example, the program selection unit selects the optimal washing program from among a powerful wash, an eco mode, a short-time wash, etc., depending on the type and amount of dirt. The washing unit executes the washing program selected by the program selection unit. For example, the washing unit adjusts the water pressure and amount of detergent based on the selected washing program to wash dishes efficiently. As a result, the automatic analysis system according to the embodiment can select the optimal washing program based on the shape and material of the dishes, and the type and amount of dirt, and wash dishes fully automatically.

[0066] The image analysis unit can analyze the shape and material of the tableware using a camera. The camera can acquire high-resolution images and analyze the shape and material of the tableware in detail. For example, the image analysis unit classifies the shape of the tableware into circular, rectangular, complex shapes, etc., and classifies the material into ceramic, glass, plastic, etc. This allows the shape and material of the tableware to be accurately analyzed using a camera. Some or all of the above-mentioned processing in the image analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the image analysis unit can input image data acquired by the camera into a generation AI and have the generation AI analyze the shape and material from the image data.

[0067] The sensor unit may include a sensor that detects the type or amount of dirt. The sensor unit detects the type and amount of dirt or debris attached to the dishes using an optical sensor, an infrared sensor, an ultrasonic sensor, or the like. For example, an optical sensor detects the type and amount of dirt using the reflection of light. An infrared sensor detects the temperature and type of dirt using infrared rays. An ultrasonic sensor detects the thickness and amount of dirt using ultrasonic waves. This allows the type and amount of dirt to be accurately detected. Some or all of the above-mentioned processing in the sensor unit may be performed using, for example, AI, or may be performed without using AI. For example, the sensor unit may input data on the detected dirt into a generation AI and have the generation AI analyze the type and amount of dirt.

[0068] The program selection unit may include an algorithm that selects the optimal washing program from multiple washing programs based on the analysis results. The program selection unit may include an algorithm that selects the optimal washing program from among powerful washing, eco mode, and short-time washing depending on the type and amount of dirt. For example, the program selection unit may use a machine learning algorithm to learn past washing data and select the optimal washing program. The program selection unit may also use a rule-based algorithm to select a washing program based on the type and amount of dirt. This allows the optimal washing program to be selected based on the analysis results. Some or all of the above-described processing in the program selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the program selection unit may input data on the type and amount of dirt into the generation AI and cause the generation AI to select the optimal washing program.

[0069] The washing unit can perform washing based on a selected washing program. The washing unit adjusts the appropriate water pressure and amount of detergent based on the selected washing program to wash dishes efficiently. For example, if a powerful washing program is selected, the washing unit performs washing at high water pressure, and if an eco mode is selected, the washing unit performs washing at low water pressure. The washing unit also adjusts the amount of detergent and uses an appropriate amount depending on the type and amount of dirt. This allows for efficient washing based on the selected washing program. Some or all of the above-mentioned processes in the washing unit may be performed using, for example, AI, or may be performed without using AI. For example, the washing unit can input data of the selected washing program into a generation AI and have the generation AI execute the washing.

[0070] The program selection unit can select a strong washing program when there is a large amount of oil stains, and select eco mode when there is only a small amount of stains. The program selection unit selects the optimal washing program from strong washing, eco mode, short-time washing, etc., depending on the type and amount of stains. For example, when there is a large amount of oil stains, a strong washing program is selected, and when there is only a small amount of stains, eco mode is selected. This makes it possible to select the optimal washing program depending on the type and amount of stains. Some or all of the above-mentioned processing in the program selection unit may be performed using AI, for example, or may be performed without using AI. For example, the program selection unit can input data on the type and amount of stains into the generation AI and have the generation AI select the optimal washing program.

[0071] The image analysis unit can estimate the user's emotions and adjust the accuracy of the image analysis based on the estimated user emotions. The image analysis unit can estimate the user's emotions and adjust the accuracy of the image analysis based on the estimated user emotions. For example, if the user is stressed, the image analysis accuracy can be increased to quickly and accurately identify the type of tableware. Also, if the user is relaxed, the image analysis accuracy can be set to normal mode and the analysis speed can be adjusted. Furthermore, if the user is in a hurry, the image analysis accuracy can be maximized to shorten the analysis time. This allows for more appropriate analysis results to be obtained by adjusting the image analysis accuracy according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the image analysis unit can be performed using, for example, an AI, or without an AI. For example, the image analysis unit can input the user's emotion data into the generative AI and cause the generative AI to adjust the analysis accuracy based on the emotion.

[0072] The image analysis unit can improve the analysis accuracy based on the tableware usage history during image analysis. The image analysis unit improves the analysis accuracy by referring to the tableware usage history during image analysis. For example, the image analysis unit can improve the analysis accuracy by referring to data on the shape and material of tableware used in the past. The analysis speed can also be improved by preferentially referring to data on frequently used tableware. Furthermore, specific stain and dust patterns can be learned from the tableware usage history to improve the analysis accuracy. In this way, the analysis accuracy can be improved by referring to the tableware usage history. Some or all of the above-mentioned processing in the image analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the image analysis unit can input the usage history data into the generation AI and have the generation AI improve the analysis accuracy.

[0073] The image analysis unit can perform analysis based on the arrangement of tableware during image analysis. The image analysis unit takes the arrangement of tableware into consideration during image analysis. For example, if the tableware is stacked, the overlap is taken into account during the analysis. Also, if the tableware is placed at an angle, the angle can be corrected during the analysis. Furthermore, if the tableware is placed in multiple layers, each layer can be analyzed separately. This allows for more accurate analysis by considering the arrangement of the tableware. Some or all of the above processing in the image analysis unit may be performed using AI, for example, or without AI. For example, the image analysis unit can input arrangement data into a generating AI and have the generating AI perform analysis based on the arrangement.

[0074] The image analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, it can provide a simple and highly visible display method. If the user is relaxed, it can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, it can provide a display method that gets straight to the point. In this way, by adjusting the display method of the analysis results according to the user's emotions, it is possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the image analysis unit may be performed using AI, for example, or without AI. For example, the image analysis unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display method based on the emotions.

[0075] The image analysis unit can perform analysis based on the color or design of the tableware during image analysis. The image analysis unit considers the color and design of the tableware during image analysis. For example, it can analyze the color of the tableware, group tableware of the same color, and perform analysis. It can also analyze the design patterns of the tableware and identify the type based on a specific design. Furthermore, it can combine the color and design of the tableware in its analysis to perform more accurate type identification. This allows for more accurate type identification by considering the color and design of the tableware. Some or all of the above processing in the image analysis unit may be performed using AI, for example, or without AI. For example, the image analysis unit can input color and design data into a generating AI and have the generating AI perform analysis based on color and design.

[0076] The image analysis unit can perform analysis by referring to the temperature information of the dishes when analyzing images. The image analysis unit performs analysis by referring to the temperature information of the dishes when analyzing images. For example, the temperature of the dishes can be analyzed and an analysis mode can be selected according to the temperature. It is also possible to prioritize analysis of high-temperature dishes to shorten the cooling time. It is also possible to analyze low-temperature dishes and suggest a washing program according to the temperature. In this way, by referring to the temperature information of the dishes, the accuracy of the analysis can be improved. Some or all of the above-mentioned processing in the image analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the image analysis unit can input temperature information data to the generation AI and cause the generation AI to perform an analysis based on the temperature.

[0077] The type identification unit can estimate the user's emotions and adjust the type identification criteria based on the estimated user emotions. The type identification unit can estimate the user's emotions and adjust the type identification criteria based on the estimated user emotions. For example, if the user is stressed, the type identification criteria can be tightened to enable accurate identification. Alternatively, if the user is relaxed, the type identification criteria can be set to normal mode. Furthermore, if the user is in a hurry, the type identification criteria can be relaxed to enable quick identification. This allows for more appropriate identification results by adjusting the type identification criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the type identification unit can be performed using, for example, an AI, or without an AI. For example, the type identification unit can input user emotion data into the generation AI and cause the generation AI to adjust the identification criteria based on the emotion.

[0078] The type identification unit can improve the identification accuracy by referring to the frequency of use of the tableware when identifying the type. The type identification unit improves the identification accuracy by referring to the frequency of use of the tableware when identifying the type. For example, it prioritizes identifying frequently used tableware to improve the identification accuracy. In addition, when identifying less frequently used tableware, it can also improve the accuracy by referring to past data. Furthermore, it can adjust the identification accuracy of specific tableware based on the frequency of use. In this way, it is possible to improve the identification accuracy by referring to the frequency of use of the tableware. Some or all of the above-mentioned processing in the type identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the type identification unit can input the use frequency data into the generation AI and cause the generation AI to improve the identification accuracy based on the use frequency.

[0079] The type identification unit can identify the type based on the manufacturer information of the tableware. The type identification unit takes the manufacturer information of the tableware into consideration when identifying the type. For example, the type identification unit analyzes the manufacturer information of the tableware and preferentially identifies tableware of a specific brand. Tableware made of a specific material or shape can also be identified based on the manufacturer information. Furthermore, tableware made by a specific manufacturer can be quickly identified by referring to the manufacturer information. This allows for more accurate type identification by taking the manufacturer information of the tableware into consideration. Some or all of the above-mentioned processing in the type identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the type identification unit can input manufacturer information data to the generation AI and cause the generation AI to perform identification based on the manufacturer information.

[0080] The user type identification unit can estimate the user's emotions and adjust the display method of the identification results based on the estimated emotions. For example, if the user is nervous, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that gets straight to the point can be provided. In this way, by adjusting the display method of the identification results according to the user's emotions, a display that is easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the user type identification unit may be performed using AI, for example, or not using AI. For example, the user type identification unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display method based on the emotions.

[0081] The type identification unit can identify the type based on changes in the shape of the tableware when identifying the type. The type identification unit takes into account changes in the shape of the tableware when identifying the type. For example, if the shape of the tableware has changed, the type identification unit can identify the type by taking that change into consideration. The type can also be identified by analyzing the changes in the shape of the tableware and based on the specific shape. Furthermore, the changes in the shape of the tableware can be corrected to identify the type accurately. This allows for more accurate type identification by taking the changes in the shape of the tableware into consideration. Some or all of the above-mentioned processing in the type identification unit may be performed using AI, for example, or may be performed without using AI. For example, the type identification unit can input shape change data to a generation AI and have the generation AI perform identification based on the shape change.

[0082] The type identification unit can identify the type by referring to the weight information of the tableware. The type identification unit can identify the type by referring to the weight information of the tableware. For example, the weight of the tableware can be analyzed and the type can be identified based on the weight. The type can also be identified based on a specific weight range by referring to the weight information. Furthermore, accurate type identification can be performed by taking the weight of the tableware into consideration. Thus, more accurate type identification can be performed by referring to the weight information of the tableware. Some or all of the above-mentioned processing in the type identification unit can be performed using, for example, AI, or without AI. For example, the type identification unit can input weight information data to the generation AI and have the generation AI perform identification based on weight.

[0083] The sensor unit can estimate the user's emotions and adjust the sensor's detection sensitivity based on the estimated user's emotions. The sensor unit can estimate the user's emotions and adjust the sensor's sensitivity based on the estimated user's emotions. For example, if the user is stressed, the sensor's sensitivity can be increased to quickly detect dirt and debris. Alternatively, if the user is relaxed, the sensor's sensitivity can be set to normal mode. Furthermore, if the user is in a hurry, the sensor's sensitivity can be maximized to shorten the detection time. This allows for more appropriate detection results by adjusting the sensor's sensitivity according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the sensor unit can be performed using AI, for example, or without AI. For example, the sensor unit can input user's emotion data into a generation AI and have the generation AI adjust the sensitivity based on the emotion.

[0084] The sensor unit can automatically select the type of detergent based on the type of dirt detected by the sensor unit. The sensor unit automatically selects the type of detergent based on the type of dirt detected by the sensor unit. For example, if oily dirt is detected, a strong detergent for oily dirt is automatically selected. Also, if light dirt is detected, an eco-mode detergent can be automatically selected. Furthermore, the optimal detergent can be automatically selected based on the specific dirt attached to the dishes. This makes it possible to automatically select the optimal detergent depending on the type of dirt. Some or all of the above-mentioned processing in the sensor unit may be performed using AI, for example, or may be performed without using AI. For example, the sensor unit can input dirt type data into the generation AI and have the generation AI select the detergent.

[0085] The sensor unit can automatically adjust the cleaning time based on the amount of dust detected by the sensor unit. The sensor unit automatically adjusts the cleaning time based on the amount of dust detected by the sensor unit. For example, if the amount of dust is large, the cleaning time can be extended to thoroughly clean the item. Alternatively, if the amount of dust is small, the cleaning time can be shortened to efficiently clean the item. Furthermore, the optimal cleaning time can be automatically adjusted based on the amount of dust. This enables efficient cleaning by automatically adjusting the cleaning time according to the amount of dust. Some or all of the above-described processing in the sensor unit may be performed using, for example, AI, or may be performed without using AI. For example, the sensor unit can input dust amount data into the generation AI and have the generation AI adjust the cleaning time.

[0086] The sensor unit can estimate the user's emotion and adjust the display method of the sensor's detection results based on the estimated user's emotion. The sensor unit can estimate the user's emotion and adjust the display method of the sensor's detection results based on the estimated user's emotion. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. This allows the display method of the detection results to be adjusted according to the user's emotion, thereby providing a display that is easy for the user to view. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the sensor unit can be performed using, for example, AI, or without AI. For example, the sensor unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method based on the emotion.

[0087] The sensor unit can automatically adjust the wash temperature based on the type of dirt detected by the sensor unit. The sensor unit automatically adjusts the wash temperature based on the type of dirt detected by the sensor unit. For example, if oily dirt is detected, a high temperature wash temperature can be automatically selected. Also, if light dirt is detected, a low temperature wash temperature can be automatically selected. Furthermore, the optimal wash temperature can be automatically adjusted based on the type of dirt. This makes it possible to automatically adjust the optimal wash temperature according to the type of dirt. Some or all of the above-mentioned processing in the sensor unit may be performed using AI, for example, or may be performed without using AI. For example, the sensor unit can input dirt type data into the generation AI and have the generation AI adjust the wash temperature.

[0088] The sensor unit can automatically adjust the number of rinses based on the amount of debris detected by the sensor. For example, if there is a large amount of debris, the number of rinses can be increased for thorough rinsing. Conversely, if there is a small amount of debris, the number of rinses can be reduced for efficient rinsing. Furthermore, the sensor unit can automatically adjust the optimal number of rinses based on the amount of debris. This enables efficient rinsing by automatically adjusting the number of rinses according to the amount of debris. Some or all of the above processing in the sensor unit may be performed using AI, for example, or without AI. For example, the sensor unit can input debris amount data into a generating AI and have the generating AI perform the adjustment of the number of rinses.

[0089] The program selection unit can estimate the user's emotions and adjust the program selection criteria based on the estimated user emotions. The program selection unit can estimate the user's emotions and adjust the program selection criteria based on the estimated user emotions. For example, if the user is stressed, the program selection criteria can be tightened to quickly select an optimal program. Alternatively, if the user is relaxed, the program selection criteria can be set to normal mode. Furthermore, if the user is in a hurry, the program selection criteria can be relaxed to quickly select a program. This allows for adjusting the program selection criteria according to the user's emotions to select a more appropriate program. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the program selection unit can be performed using, for example, an AI, or without an AI. For example, the program selection unit can input user emotion data into the generation AI and cause the generation AI to adjust the criteria based on the emotion.

[0090] The program selection unit can select the optimal program by referring to past cleaning history when selecting a program. For example, it can select the optimal program by referring to data on previously used cleaning programs. It can also prioritize the selection of frequently used cleaning programs. Furthermore, it can select the optimal program for specific types of dirt or debris based on past cleaning history. Thus, the optimal program can be selected by referring to past cleaning history. Some or all of the above-described processes in the program selection unit may be performed using AI, for example, or without AI. For example, the program selection unit can input cleaning history data into a generating AI and have the generating AI select the optimal program.

[0091] The program selection unit can adjust the washing intensity based on the material of the dishes when selecting a program. The program selection unit adjusts the washing intensity based on the material of the dishes when selecting a program. For example, for ceramic dishes, a strong washing program can be selected. For glass dishes, a gentle washing program can be selected. Furthermore, for plastic dishes, a moderate washing program can be selected. In this way, by adjusting the washing intensity according to the material of the dishes, appropriate washing can be performed. Some or all of the above-mentioned processing in the program selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the program selection unit can input material data into a generation AI and have the generation AI adjust the washing intensity.

[0092] The program selection unit can estimate the user's emotions and determine the priority of program selection based on the estimated user emotions. The program selection unit estimates the user's emotions and determines the priority of program selection based on the estimated user emotions. For example, if the user is stressed, a program that completes cleaning quickly can be selected preferentially. Also, if the user is relaxed, an energy-saving program such as eco mode can be selected preferentially. Furthermore, if the user is in a hurry, a program that completes cleaning in the shortest time can be selected preferentially. Thus, by determining the priority of program selection according to the user's emotions, a more appropriate program can be selected. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the program selection unit can be performed using, for example, an AI, or without an AI. For example, the program selection unit can input the user's emotion data into the generation AI and cause the generation AI to determine the priority based on emotions.

[0093] When selecting a program, the program selection unit can select a program based on the intended use of the tableware. When selecting a program, the program selection unit selects a program based on the intended use of the tableware. For example, if the tableware is used daily, a normal washing program can be selected. Also, if the tableware is used at a special event, a powerful washing program can be selected. Furthermore, if the tableware is made of delicate material, a gentle washing program can be selected. This makes it possible to select the optimal program depending on the intended use of the tableware. Some or all of the above-mentioned processing in the program selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the program selection unit can input intended use data into the generation AI and cause the generation AI to select a program based on the intended use.

[0094] The program selection unit can select a program based on the storage state of the dishes when selecting a program. For example, if the dishes are stored close together, a powerful washing program may be selected. If the dishes are spread out, a normal washing program may be selected. Furthermore, if the dishes are stacked on top of each other, the optimal program may be selected based on a specific arrangement. This allows for the selection of the optimal program according to the storage state of the dishes. Some or all of the above processing in the program selection unit may be performed using AI, for example, or without AI. For example, the program selection unit can input storage state data into a generating AI and have the generating AI select a program based on the storage state.

[0095] The washing unit can estimate the user's emotions and adjust the washing intensity based on the estimated emotions. For example, if the user is stressed, the washing intensity can be increased to complete the washing quickly. If the user is relaxed, the washing can be performed at normal intensity. Furthermore, if the user is in a hurry, the washing intensity can be maximized to shorten the washing time. In this way, by adjusting the washing intensity according to the user's emotions, a more appropriate washing can be performed. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the washing unit may be performed using AI, for example, or not using AI. For example, the washing unit can input user emotion data into a generative AI and have the generative AI perform emotion-based intensity adjustments.

[0096] The washing unit can adjust the water flow during washing, taking into account the arrangement of the dishes. For example, if the dishes are densely packed, the water flow can be increased to enhance the washing effect. If the dishes are spread out, the water flow can be set to normal mode. Furthermore, if the dishes are overlapping, the water flow can be adjusted based on a specific arrangement. This allows for efficient washing by adjusting the water flow according to the arrangement of the dishes. Some or all of the above processes in the washing unit may be performed using AI, for example, or without AI. For example, the washing unit can input arrangement data into a generating AI and have the generating AI perform water flow adjustments based on the arrangement.

[0097] The washing unit can adjust the amount of detergent used based on the material of the dishes when washing in the washing unit. The washing unit adjusts the amount of detergent used based on the material of the dishes when washing in the washing unit. For example, for ceramic dishes, an appropriate amount of detergent is used. Also, for glass dishes, a small amount of detergent can be used. Furthermore, for plastic dishes, an appropriate amount of detergent can be used. In this way, by adjusting the amount of detergent according to the material of the dishes, appropriate washing can be performed. Some or all of the above-mentioned processes in the washing unit may be performed using, for example, AI, or may be performed without using AI. For example, the washing unit can input material data into a generation AI and have the generation AI adjust the amount of detergent.

[0098] The cleaning unit can estimate the user's emotions and adjust the cleaning time based on the estimated user emotions. The cleaning unit can estimate the user's emotions and adjust the cleaning time based on the estimated user emotions. For example, if the user is stressed, the cleaning time can be shortened to quickly complete the cleaning. Also, if the user is relaxed, the cleaning can be performed in the normal cleaning time. Furthermore, if the user is in a hurry, the cleaning time can be minimized. This allows for more appropriate cleaning by adjusting the cleaning time according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the cleaning unit can be performed using, for example, AI, or without AI. For example, the cleaning unit can input the user's emotion data into the generation AI and have the generation AI adjust the time based on the emotion.

[0099] The washing unit can adjust the direction of the water flow based on the shape of the dishes when washing them in the washing unit. The washing unit adjusts the direction of the water flow based on the shape of the dishes when washing them in the washing unit. For example, if the dishes are deep, the water flow can be adjusted to be directed toward the deep part. Also, if the dishes are flat, the water flow can be adjusted to be directed over a wide area. Furthermore, if the dishes have a complex shape, the water flow direction can be adjusted based on the specific shape. This allows for efficient washing by adjusting the water flow direction according to the shape of the dishes. Some or all of the above-mentioned processing in the washing unit may be performed using, for example, AI, or may be performed without using AI. For example, the washing unit can input shape data into a generating AI and have the generating AI adjust the water flow direction based on the shape.

[0100] The washing unit can adjust the washing temperature based on the temperature of the dishes when washing in the washing unit. The washing unit adjusts the washing temperature based on the temperature of the dishes when washing in the washing unit. For example, if the dishes are hot, the washing temperature can be set lower. Also, if the dishes are cold, the washing temperature can be set higher. Furthermore, the optimal washing temperature can be adjusted based on the temperature of the dishes. This allows for efficient washing by adjusting the washing temperature according to the temperature of the dishes. Some or all of the above-mentioned processes in the washing unit may be performed using, for example, AI, or may be performed without using AI. For example, the washing unit can input temperature data into the generation AI and have the generation AI adjust the washing temperature based on the temperature. === Hard Collateral 1-1 === Each of the multiple elements described above, including the image analysis unit, type identification unit, sensor unit, program selection unit, and washing unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the image analysis unit analyzes the shape and material of the tableware using the camera 42 of the smart device 14. The type identification unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies the type of tableware based on the information obtained from the image analysis unit. The sensor unit detects dirt and debris on the tableware using the optical sensor and infrared sensor of the smart device 14. The program selection unit is implemented by the identification processing unit 290 of the data processing unit 12 and selects the optimal washing program based on the information obtained from the sensor unit. The washing unit is implemented by the control unit 46A of the smart device 14 and washes the tableware based on the selected washing program. === Hard Collateral 1-2 === Each of the multiple elements described above, including the image analysis unit, type identification unit, sensor unit, program selection unit, and washing unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the image analysis unit analyzes the shape and material of the tableware using the camera 42 of the smart glasses 214. The type identification unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies the type of tableware based on the information obtained from the image analysis unit. The sensor unit detects dirt and debris on the tableware using the optical sensor and infrared sensor of the smart glasses 214. The program selection unit is implemented by the identification processing unit 290 of the data processing unit 12 and selects the optimal washing program based on the information obtained from the sensor unit. The washing unit is implemented by the control unit 46A of the smart glasses 214 and washes the tableware based on the selected washing program. === Hard Collateral 1-3 === Each of the multiple elements described above, including the image analysis unit, type identification unit, sensor unit, program selection unit, and washing unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the image analysis unit analyzes the shape and material of the tableware using the camera 42 of the headset terminal 314. The type identification unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies the type of tableware based on the information obtained from the image analysis unit. The sensor unit detects dirt and debris on the tableware using the optical sensor and infrared sensor of the headset terminal 314. The program selection unit is implemented by the identification processing unit 290 of the data processing unit 12 and selects the optimal washing program based on the information obtained from the sensor unit. The washing unit is implemented by the control unit 46A of the headset terminal 314 and washes the tableware based on the selected washing program. === Hard Collateral 1-4 === Each of the multiple elements described above, including the image analysis unit, type identification unit, sensor unit, program selection unit, and washing unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the image analysis unit analyzes the shape and material of the tableware using the camera 42 of the robot 414. The type identification unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies the type of tableware based on the information obtained from the image analysis unit. The sensor unit detects dirt and debris on the tableware using the optical sensor and infrared sensor of the robot 414. The program selection unit is implemented by the identification processing unit 290 of the data processing unit 12 and selects the optimal washing program based on the information obtained from the sensor unit. The washing unit is implemented by the control unit 46A of the robot 414 and washes the tableware based on the selected washing program.

[0101] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0102] The automatic analysis system can further include a voice recognition unit. The voice recognition unit can analyze the user's voice commands and use them as auxiliary information to identify the type of dish and the type of dirt. For example, if the user says by voice, "This dish is very oily," the voice recognition unit analyzes that information and transmits it to the program selection unit. Also, if the user says, "I'm in a hurry, please wash it quickly," the voice recognition unit can analyze that information and instruct the washing unit to wash quickly. This makes washing more flexible and efficient by using voice commands.

[0103] The automated analysis system can also include a health monitoring unit that monitors the user's health status. The health monitoring unit measures the user's heart rate and stress level and provides this data to the analysis unit. For example, if the user's heart rate is high, the system determines that the user is stressed and adjusts the cleaning program to complete quickly. Conversely, if the user's stress level is low, it can recommend cleaning in eco mode. This allows the system to provide an optimal cleaning program tailored to the user's health condition.

[0104] The automatic analysis system can further include a storage optimization unit that optimizes the storage state of the dishes. The storage optimization unit analyzes the arrangement of the dishes and proposes the optimal storage method. For example, if the dishes are stacked, it proposes an arrangement method to eliminate the overlap. If the dishes are arranged diagonally, it can also propose a method to arrange them horizontally. Furthermore, if the dishes are arranged in multiple layers, it can also propose the optimal arrangement method for each layer. This can improve the washing effect by optimizing the arrangement of the dishes.

[0105] The automatic analysis system can further include a learning unit that learns the usage history of dishes. The learning unit accumulates past washing data and provides it to the analysis unit. For example, if a particular type of dish is used frequently, the washing program for that dish can be optimized. Also, if a particular stain occurs frequently, the washing program for that stain can be strengthened. Furthermore, for dishes that are used less frequently, it can be recommended to wash them in eco mode. This makes it possible to provide an optimal washing program based on past usage history.

[0106] The automated analysis system may further include an emotion estimation unit that estimates the user's emotions and selects a washing program based on those emotions. The emotion estimation unit estimates emotions from the user's facial expressions and voice and provides that data to the program selection unit. For example, if the user is tired, a fast washing program may be selected. If the user is relaxed, an eco-mode washing program may be recommended. Furthermore, if the user is in a hurry, a program that completes washing in the shortest possible time may be selected. This allows the system to provide the optimal washing program according to the user's emotions.

[0107] The automatic analysis system can further include a detergent selection unit that automatically selects the type of detergent based on the material of the tableware. The detergent selection unit analyzes the material of the tableware and selects the optimal detergent. For example, a strong detergent can be used for ceramic tableware, and a gentle detergent for glass tableware. A moderate detergent can also be used for plastic tableware. Furthermore, a dedicated detergent can be recommended for a specific material. This makes it possible to provide the optimal detergent according to the material of the tableware.

[0108] The automated analysis system may also include a notification unit that estimates the user's emotions and notifies them of the cleaning progress based on the estimated emotions. The notification unit analyzes the user's emotions and notifies them of the cleaning progress at the appropriate time. For example, if the user is feeling stressed, it will quickly notify them when the cleaning is complete. If the user is relaxed, it can also provide detailed information about the cleaning progress. Furthermore, if the user is in a hurry, it can provide concise information about the cleaning progress. This allows for the provision of appropriate notifications tailored to the user's emotions.

[0109] The automatic analysis system may further include a shape customization unit that customizes the washing program based on the shape of the dishes. The shape customization unit analyzes the shape of the dishes and provides an optimal washing program. For example, a specific water flow may be used for deep dishes, and a wide water flow may be used for flat dishes. A washing program based on the specific shape may also be provided for dishes with complex shapes. Furthermore, a dedicated washing program may be recommended for a specific shape. This makes it possible to provide an optimal washing program according to the shape of the dishes.

[0110] The automatic analysis system may further include a drying adjustment unit that estimates the user's emotions and adjusts the drying time after washing based on the estimated emotions. The drying adjustment unit analyzes the user's emotions and sets the optimal drying time. For example, if the user is in a hurry, the drying time can be shortened to complete the drying quickly. Alternatively, if the user is relaxed, the normal drying time can be set. Furthermore, if the user is feeling stressed, the drying time can be shortened to the maximum. This makes it possible to provide the optimal drying time according to the user's emotions.

[0111] The automatic analysis system may further include a purpose selection unit that selects a washing program based on the intended use of the tableware. The purpose selection unit analyzes the intended use of the tableware and provides the optimal washing program. For example, a normal washing program may be selected for tableware used daily, and a powerful washing program may be selected for tableware used at special events. A gentle washing program may also be selected for tableware made of delicate materials. Furthermore, a dedicated washing program may be recommended for a specific intended use. This allows the optimal washing program to be provided according to the intended use of the tableware.

[0112] The processing flow of the second embodiment will be briefly explained below.

[0113] Step 1: The image analysis unit analyzes the shape and material of the tableware. For example, it uses a camera to capture high-resolution images and classifies the shape of the tableware as round, rectangular, complex, etc., and the material as ceramic, glass, plastic, etc. Step 2: The type identification unit identifies the type of tableware based on the information analyzed by the image analysis unit. For example, the type is identified as a plate, cup, fork, knife, etc. based on information about the shape and material. Step 3: The sensor unit detects dirt or dust on the dishes. For example, an optical sensor, an infrared sensor, an ultrasonic sensor, or the like is used to detect the type and amount of dirt or dust on the dishes. Step 4: The program selection unit selects a wash program based on the information detected by the sensor unit. For example, depending on the type and amount of dirt, the program selector selects the most appropriate one from among powerful wash, eco mode, short wash, etc. Step 5: The washing unit executes the washing program selected by the program selection unit. For example, based on the selected washing program, the washing unit adjusts the water pressure and the amount of detergent to an appropriate level to wash the dishes efficiently.

[0114] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0115] 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 engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0116] Furthermore, the processing by the data processing system 10 described above 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 necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0117] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0118] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0119] 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.

[0120] 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, RAM 30, and 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.

[0121] 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.

[0122] 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 audio according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and 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 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0124] 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 carried out in a secure state.

[0125] Fig. 4 shows an example of the 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.

[0126] 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.

[0127] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0128] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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. The smart glasses 214 also 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 perform processing similar to that of the specific processing unit 290 using these models.

[0129] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0131] 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. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0132] 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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, 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, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0134] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0135] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0136] 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, RAM 30, and 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.

[0137] 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.

[0138] 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 audio according to instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and 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 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0140] 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 carried out in a secure state.

[0141] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0142] 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.

[0143] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0144] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification 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.

[0145] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0146] 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 audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0147] 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. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0148] 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 also 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. Furthermore, 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, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0149] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0150] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0151] 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.

[0152] 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, RAM 30, and 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.

[0153] 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.

[0154] 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 audio according to instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0156] 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 carried out in a secure state.

[0157] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the 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.

[0158] 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.

[0159] 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.

[0160] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0161] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also 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 perform the same process as the identification processing unit 290 using these models.

[0162] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0163] 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 voice indicating a user input regarding 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.

[0164] 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. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0165] 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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, 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.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0168] FIG. 9 illustrates 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. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0169] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically 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.

[0170] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0171] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0172] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0173] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of 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, "relieved," "calm," and "reassuring," have similar emotion values.

[0174] 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.

[0175] 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 USB (Universal Serial Bus) 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.

[0176] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0177] 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.

[0178] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0179] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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.

[0180] As an example of a system configured with a single 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 system that uses a processor that realizes the functions of an 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.

[0181] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0182] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0183] The above-described description and illustrations are a detailed explanation 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, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects 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 replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0184] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0185] [Explanation of symbols]

[0186] 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. an image analysis unit that analyzes the shape or material of the tableware; a type identification unit that identifies the type of tableware based on the information analyzed by the image analysis unit; a sensor unit for detecting dirt or dust on the tableware; a program selection unit that selects a cleaning program based on the information detected by the sensor unit; a cleaning unit that executes the cleaning program selected by the program selection unit. A system characterized by:

2. The image analysis unit We use cameras to analyze the shape and material of tableware.

2. The system of claim 1.

3. The sensor unit Includes sensors to detect the type or amount of dirt 2. The system of claim 1.

4. The program selection unit Includes an algorithm to select from multiple cleaning programs based on the analysis results 2. The system of claim 1.

5. The cleaning unit includes: Washing is performed according to the selected washing program 2. The system of claim 1.

6. The program selection unit Choose a powerful wash program for heavy oil stains and Eco mode for light stains 2. The system of claim 1.

7. The image analysis unit Estimate the user's emotions and adjust the accuracy of image analysis based on the estimated user emotions.

2. The system of claim 1.

8. The image analysis unit Improve the accuracy of image analysis based on the tableware usage history 2. The system of claim 1.

Images