System

Abstract

An object of a system according to an embodiment is to efficiently arrange clothes in a chest of drawers and select a storage method.SOLUTION: A system includes a storehouse arrangement part, a generation AI part, and a physical distribution system control part. A chest arrangement part arranges the clothes in the chest. The generation AI unit selects a storage method through a conversation with the user. The physical distribution system control unit controls the physical distribution system installed in the back of the house.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including 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] With conventional technology, organizing clothes in a closet and choosing a storage method was a time-consuming process, making it difficult to do so efficiently.

[0005] The system according to the embodiment aims to efficiently organize clothes in a dresser and select a storage method. [Means for solving the problem]

[0006] The system according to the embodiment includes a chest of drawers organizing unit, a generation AI unit, and a logistics system control unit. The chest of drawers organizing unit organizes the clothes in the chest of drawers. The generation AI unit selects a storage method through conversation with the user. The logistics system control unit controls the logistics system installed behind the house. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently organize clothes in a dresser and select a storage method. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The chest of drawers organizing system according to an embodiment of the present invention is a system in which AI organizes the clothes in the chest of drawers, and the generation AI selects a storage method through conversation with the user and controls the logistics system. As a result, the chest of drawers organizing system can efficiently organize the chest of drawers in the home and reduce the user's effort.

[0029] A chest of drawers organizing system according to an embodiment includes a chest of drawers organizing unit, a generation AI unit, and a logistics system control unit. The chest of drawers organizing unit organizes clothes in a chest of drawers. For example, the AI ​​grasps the location and type of clothes and organizes them efficiently. The chest of drawers organizing unit can also classify clothes by season and place frequently used clothes in an easily accessible location. For example, the AI ​​classifies clothes based on color, material, and frequency of use and places them in an easily accessible location. The generation AI unit selects a storage method through conversation with the user. For example, the generation AI asks questions such as, "Where should I store the shirt I wore today?" and suggests a storage method based on the user's answer. The generation AI unit can also analyze the user's preferences and past selection history and suggest a storage method based on that. For example, if the user has frequently sent shirts to the dry cleaners in the past, the generation AI suggests, "Should I also send this shirt to the dry cleaners?" The logistics system control unit controls a logistics system installed behind the house. For example, when the logistics system transports clothes to the chest of drawers, the AI ​​calculates the optimal route and transports them efficiently. The logistics system control unit can also monitor the condition of clothes and suggest cleaning or repair as necessary. For example, if clothes are stained or frayed, the AI ​​will suggest cleaning or repair to the user. This allows the dresser organization system according to the embodiment to efficiently organize dressers in the home and reduce the user's workload. For example, it makes it easier to change clothes for each season and to take out frequently used clothes. Furthermore, by constantly monitoring the condition of clothes, they can be kept clean and tidy.

[0030] The dresser organizer can classify clothes by season and place frequently used clothes in an easy-to-reach location. For example, AI can detect changes in the material or color of clothes, automatically list clothes that are showing signs of deterioration, and notify the user. For example, AI can periodically scan the material or color of clothes in the dresser and automatically list clothes that are showing signs of deterioration. The dresser organizer also uses sensors installed in the dresser to detect changes in the material or color of clothes. For example, it can use optical sensors or tactile sensors to monitor the condition of clothes. When listing clothes that are showing signs of deterioration, AI can also take into account the user's past usage and cleaning history. For example, it can prioritize the list of frequently used clothes. This makes it easier to change clothes for each season.

[0031] The generation AI unit can analyze the user's preferences and past selection history and suggest storage methods based on that. For example, the generation AI unit learns the user's lifestyle and schedule and automatically prepares clothes for specific events or seasons. For example, the AI ​​connects with the user's calendar or schedule app to automatically prepare clothes for specific events or seasons. The generation AI unit also analyzes the user's daily behavior patterns and preferences to learn the user's lifestyle. For example, a user who prefers casual clothing on weekends will be prepared with casual clothes. The generation AI unit also selects appropriate clothing based on seasonal temperature and weather data. For example, warm coats and sweaters will be prepared in winter, and light clothing in summer. This allows the system to suggest storage methods based on the user's preferences.

[0032] The dresser organizer can detect changes in the material or color of clothing, automatically list the clothing that is deteriorating, and notify the user. For example, AI can detect changes in the material or color of clothing, automatically list the clothing that is deteriorating, and notify the user. For example, AI can periodically scan the material or color of clothing in the dresser and automatically list the clothing that is deteriorating. The dresser organizer also uses sensors installed in the dresser to detect changes in the material or color of clothing. For example, it can monitor the condition of clothing using optical sensors or tactile sensors. In addition, when listing the clothing that is deteriorating, AI can also take into account the user's past usage history and cleaning history. For example, it can prioritize the listing of frequently used clothing. This allows the user to be notified of clothing that is deteriorating.

[0033] The generation AI unit can learn the user's past conversation history and suggest more personalized storage methods. For example, the generation AI unit learns the user's past conversation history and suggests personalized storage methods. For example, suggestions are made based on storage methods that the user has preferred in the past. In addition, to analyze the conversation history, the generation AI unit uses natural language processing technology to understand the user's intentions. For example, if the user frequently uses a specific phrase, suggestions are made based on that phrase. In addition, the generation AI unit learns storage method patterns based on the user's past conversation history and makes new suggestions based on that. For example, if the user prefers a specific storage method in a specific season, suggestions are made based on that pattern. This makes it possible to suggest storage methods based on the user's past conversation history.

[0034] The generation AI unit can also analyze conversations with other members of the household and propose a storage method that reflects the opinions of everyone. For example, the generation AI unit analyzes conversations with other members of the household and proposes a storage method that reflects the opinions of everyone. For example, it selects a storage method that all family members agree on. The generation AI unit also collects conversation data within the household and the generation AI proposes a storage method based on that data. For example, it aggregates the opinions of family members and proposes the optimal storage method. The generation AI unit also learns the conversation history with other members of the household and, based on that, proposes a storage method that everyone is satisfied with. For example, it selects a storage method that everyone agrees on based on past conversation history. This makes it possible to propose a storage method that reflects the opinions of everyone in the household.

[0035] The generation AI unit can also contribute to health management by suggesting clothing choices based on the user's health condition and mood. For example, the generation AI unit monitors the user's health condition and suggests clothing choices accordingly. For example, if the user has a cold, it will suggest warm clothing. In addition, to analyze the user's mood, the generation AI monitors the user's mood in real time using an emotion estimation function. For example, if the user is feeling stressed, it will suggest clothing that will help them relax. The generation AI unit also builds a system in which the generation AI selects the most suitable clothing for the user based on health condition and mood data. For example, it will suggest appropriate clothing based on the user's health data. This makes it possible to suggest clothing choices based on the user's health condition and mood.

[0036] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0037] The wardrobe organization system can further include a clothing recycling suggestion unit. The clothing recycling suggestion unit suggests ways to recycle clothes that the user has determined to be unnecessary. For example, it may analyze the material and condition of the clothes and list recyclable clothes. The recycling suggestion unit can also notify the user of local recycling centers and donation destinations. For example, it can provide information about nearby recycling centers to make recycling easier for the user. The recycling suggestion unit can also record the amount and frequency of clothes that the user recycles, and visualize the user's contribution to the environment. This allows the user to take environmentally conscious actions.

[0038] The wardrobe organization system can further include a clothing repair suggestion unit. The clothing repair suggestion unit suggests repair methods for deteriorated clothing. For example, it provides the user with easy repair methods for frayed or torn clothing. The repair suggestion unit can also introduce the user to nearby repair services. For example, it can provide information about nearby repair shops, allowing the user to easily request repairs. The repair suggestion unit can also record the history of clothing repairs the user has had done and notify the user when the next repair is due. This allows the user to use their clothing for a longer period of time.

[0039] The wardrobe organization system can further include a clothing coordination suggestion unit. The coordination suggestion unit suggests daily outfits based on the clothes the user owns. For example, AI can analyze the color and style of the user's clothes and suggest optimal outfits. The coordination suggestion unit can also suggest outfits that match the user's schedule or events. For example, it can suggest formal outfits for specific events. The coordination suggestion unit can also learn the user's past coordination history and make new suggestions based on that. This allows users to enjoy optimal outfits without having to worry about choosing their clothes every day.

[0040] The wardrobe organization system can further include a clothing purchase suggestion unit. The purchase suggestion unit suggests new clothing purchases based on the condition and trends of the user's clothes. For example, AI can analyze the deterioration status of the user's clothes and list the clothes that need replacing. The purchase suggestion unit can also analyze the latest fashion trends and suggest new clothes that suit the user. For example, it can suggest trendy items for each season. The purchase suggestion unit can also learn the user's past purchase history and make new suggestions based on that. This allows the user to always enjoy the latest fashion.

[0041] The wardrobe organization system can further include a clothing storage environment suggestion unit. The storage environment suggestion unit suggests the optimal storage environment to prevent clothing deterioration. For example, AI could monitor the temperature and humidity inside the wardrobe and make suggestions for maintaining the optimal storage environment. The storage environment suggestion unit can also suggest storage methods based on the material of the clothing. For example, it could suggest special storage methods for clothing made of delicate materials. The storage environment suggestion unit can also suggest storage methods based on the user's living environment. For example, it could suggest moisture-proofing measures for users living in humid areas. This allows users to store their clothing in good condition for a long period of time.

[0042] The processing flow of the first embodiment will be briefly explained below.

[0043] Step 1: The dresser organizer organizes the clothes in the dresser. For example, AI can identify the location and type of clothes and organize them efficiently. The dresser organizer can also classify clothes by season and place frequently used clothes in a location that makes them easy to access. For example, AI can classify clothes based on color, material, and frequency of use, and place them in a location that makes them easy to access. Step 2: The generation AI part selects a storage method through conversation with the user. For example, the generation AI may ask a question such as, "Where do you want to store the shirt you wore today?" and suggest a storage method based on the user's answer. The generation AI part can also analyze the user's preferences and past selection history and suggest a storage method based on that. For example, if the user has often sent their shirts to the dry cleaners in the past, the generation AI may suggest, "Do you want to send this shirt to the dry cleaners as well?" Step 3: The logistics system control unit controls the logistics system installed behind the house. For example, when the logistics system transports clothes to the closet, the AI ​​calculates the optimal route and transports them efficiently. The logistics system control unit can also monitor the condition of the clothes and suggest cleaning or repair as needed. For example, if the clothes are stained or frayed, the AI ​​will suggest cleaning or repair to the user.

[0044] (Example 2) The chest of drawers organizing system according to an embodiment of the present invention is a system in which AI organizes the clothes in the chest of drawers, and the generation AI selects a storage method through conversation with the user and controls the logistics system. As a result, the chest of drawers organizing system can efficiently organize the chest of drawers in the home and reduce the user's effort.

[0045] A chest of drawers organizing system according to an embodiment includes a chest of drawers organizing unit, a generation AI unit, and a logistics system control unit. The chest of drawers organizing unit organizes clothes in a chest of drawers. For example, the AI ​​grasps the location and type of clothes and organizes them efficiently. The chest of drawers organizing unit can also classify clothes by season and place frequently used clothes in an easily accessible location. For example, the AI ​​classifies clothes based on color, material, and frequency of use and places them in an easily accessible location. The generation AI unit selects a storage method through conversation with the user. For example, the generation AI asks questions such as, "Where should I store the shirt I wore today?" and suggests a storage method based on the user's answer. The generation AI unit can also analyze the user's preferences and past selection history and suggest a storage method based on that. For example, if the user has frequently sent shirts to the dry cleaners in the past, the generation AI suggests, "Should I also send this shirt to the dry cleaners?" The logistics system control unit controls a logistics system installed behind the house. For example, when the logistics system transports clothes to the chest of drawers, the AI ​​calculates the optimal route and transports them efficiently. The logistics system control unit can also monitor the condition of clothes and suggest cleaning or repair as necessary. For example, if clothes are stained or frayed, the AI ​​will suggest cleaning or repair to the user. This allows the dresser organization system according to the embodiment to efficiently organize dressers in the home and reduce the user's workload. For example, it makes it easier to change clothes for each season and to take out frequently used clothes. Furthermore, by constantly monitoring the condition of clothes, they can be kept clean and tidy.

[0046] The dresser organizer can classify clothes by season and place frequently used clothes in an easy-to-reach location. For example, AI can detect changes in the material or color of clothes, automatically list clothes that are showing signs of deterioration, and notify the user. For example, AI can periodically scan the material or color of clothes in the dresser and automatically list clothes that are showing signs of deterioration. The dresser organizer also uses sensors installed in the dresser to detect changes in the material or color of clothes. For example, it can use optical sensors or tactile sensors to monitor the condition of clothes. When listing clothes that are showing signs of deterioration, AI can also take into account the user's past usage and cleaning history. For example, it can prioritize the list of frequently used clothes. This makes it easier to change clothes for each season.

[0047] The generation AI unit can analyze the user's preferences and past selection history and suggest storage methods based on that. For example, the generation AI unit learns the user's lifestyle and schedule and automatically prepares clothes for specific events or seasons. For example, the AI ​​connects with the user's calendar or schedule app to automatically prepare clothes for specific events or seasons. The generation AI unit also analyzes the user's daily behavior patterns and preferences to learn the user's lifestyle. For example, a user who prefers casual clothing on weekends will be prepared with casual clothes. The generation AI unit also selects appropriate clothing based on seasonal temperature and weather data. For example, warm coats and sweaters will be prepared in winter, and light clothing in summer. This allows the system to suggest storage methods based on the user's preferences.

[0048] The dresser organizer can detect changes in the material or color of clothing, automatically list the clothing that is deteriorating, and notify the user. For example, AI can detect changes in the material or color of clothing, automatically list the clothing that is deteriorating, and notify the user. For example, AI can periodically scan the material or color of clothing in the dresser and automatically list the clothing that is deteriorating. The dresser organizer also uses sensors installed in the dresser to detect changes in the material or color of clothing. For example, it can monitor the condition of clothing using optical sensors or tactile sensors. In addition, when listing the clothing that is deteriorating, AI can also take into account the user's past usage history and cleaning history. For example, it can prioritize the listing of frequently used clothing. This allows the user to be notified of clothing that is deteriorating.

[0049] The generation AI unit can analyze the user's tone of voice and facial expression and suggest a storage method that suits the user's emotional state. For example, the generation AI unit analyzes the user's tone of voice and suggests a storage method that suits the user's emotional state. For example, if the user is tired, it will suggest a simple storage method. The generation AI unit also monitors the user's facial expression in real time using a camera to analyze the user's facial expression. For example, if the user is smiling, it will suggest a specific storage method. The generation AI unit also estimates the user's emotional state based on the tone of voice and facial expression data and suggests a storage method that suits the user. For example, if the user is feeling stressed, it will suggest a storage method that allows them to relax. This makes it possible to suggest storage methods that suit the user's emotional state.

[0050] The generation AI unit can learn the user's past conversation history and suggest more personalized storage methods. For example, the generation AI unit learns the user's past conversation history and suggests personalized storage methods. For example, suggestions are made based on storage methods that the user has preferred in the past. In addition, to analyze the conversation history, the generation AI unit uses natural language processing technology to understand the user's intentions. For example, if the user frequently uses a specific phrase, suggestions are made based on that phrase. In addition, the generation AI unit learns storage method patterns based on the user's past conversation history and makes new suggestions based on that. For example, if the user prefers a specific storage method in a specific season, suggestions are made based on that pattern. This makes it possible to suggest storage methods based on the user's past conversation history.

[0051] The generation AI unit uses the emotion estimation function to analyze the emotions a user has toward a specific storage method and can suggest a storage method that will provide high satisfaction. The generation AI unit, for example, uses the emotion estimation function to analyze the emotions a user has toward a specific storage method. For example, it analyzes the sense of security and satisfaction a user feels toward a specific storage method. The generation AI unit also suggests storage methods that provide high emotional satisfaction based on the user's emotion data. For example, it prioritizes suggesting storage methods that the user has found satisfactory in the past. The generation AI unit also uses the emotion estimation function to monitor the emotions a user has toward a specific storage method in real time and dynamically adjusts the storage method based on those emotions. For example, it suggests a storage method that will help the user relax in order to reduce the stress the user feels about a specific storage method. This makes it possible to suggest storage methods based on the user's emotions.

[0052] The generation AI unit can also analyze conversations with other members of the household and propose a storage method that reflects the opinions of everyone. For example, the generation AI unit analyzes conversations with other members of the household and proposes a storage method that reflects the opinions of everyone. For example, it selects a storage method that all family members agree on. The generation AI unit also collects conversation data within the household and the generation AI proposes a storage method based on that data. For example, it aggregates the opinions of family members and proposes the optimal storage method. The generation AI unit also learns the conversation history with other members of the household and, based on that, proposes a storage method that everyone is satisfied with. For example, it selects a storage method that everyone agrees on based on past conversation history. This makes it possible to propose a storage method that reflects the opinions of everyone in the household.

[0053] The generation AI unit can also contribute to health management by suggesting clothing choices based on the user's health condition and mood. For example, the generation AI unit monitors the user's health condition and suggests clothing choices accordingly. For example, if the user has a cold, it will suggest warm clothing. In addition, to analyze the user's mood, the generation AI monitors the user's mood in real time using an emotion estimation function. For example, if the user is feeling stressed, it will suggest clothing that will help them relax. The generation AI unit also builds a system in which the generation AI selects the most suitable clothing for the user based on health condition and mood data. For example, it will suggest appropriate clothing based on the user's health data. This makes it possible to suggest clothing choices based on the user's health condition and mood.

[0054] The generation AI unit uses the emotion estimation function to monitor the emotions a user has toward a specific storage method in real time and continuously suggest the optimal storage method. For example, the generation AI unit will develop a system that uses the emotion estimation function to monitor the emotions a user has toward a specific storage method in real time. For example, it will analyze the user's facial expressions and voice and calculate an emotion score. The generation AI unit will also build a system that continuously suggests the optimal storage method based on the user's emotional response data. For example, it will prioritize suggesting storage methods that receive a lot of positive emotional responses. The generation AI unit will also develop a system that collects emotion estimation data in real time and uses it to suggest storage methods. For example, it will dynamically adjust the storage method according to changes in the user's emotions. This will enable it to continuously suggest the optimal storage method based on the user's emotions.

[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 wardrobe organization system can further include a clothing recycling suggestion unit. The clothing recycling suggestion unit suggests ways to recycle clothes that the user has determined to be unnecessary. For example, it may analyze the material and condition of the clothes and list recyclable clothes. The recycling suggestion unit can also notify the user of local recycling centers and donation destinations. For example, it can provide information about nearby recycling centers to make recycling easier for the user. The recycling suggestion unit can also record the amount and frequency of clothes that the user recycles, and visualize the user's contribution to the environment. This allows the user to take environmentally conscious actions.

[0057] The wardrobe organization system can further include a clothing repair suggestion unit. The clothing repair suggestion unit suggests repair methods for deteriorated clothing. For example, it provides the user with easy repair methods for frayed or torn clothing. The repair suggestion unit can also introduce the user to nearby repair services. For example, it can provide information about nearby repair shops, allowing the user to easily request repairs. The repair suggestion unit can also record the history of clothing repairs the user has had done and notify the user when the next repair is due. This allows the user to use their clothing for a longer period of time.

[0058] The wardrobe organization system can further include a clothing coordination suggestion unit. The coordination suggestion unit suggests daily outfits based on the clothes the user owns. For example, AI can analyze the color and style of the user's clothes and suggest optimal outfits. The coordination suggestion unit can also suggest outfits that match the user's schedule or events. For example, it can suggest formal outfits for specific events. The coordination suggestion unit can also learn the user's past coordination history and make new suggestions based on that. This allows users to enjoy optimal outfits without having to worry about choosing their clothes every day.

[0059] The wardrobe organization system can further include a clothing purchase suggestion unit. The purchase suggestion unit suggests new clothing purchases based on the condition and trends of the user's clothes. For example, AI can analyze the deterioration status of the user's clothes and list the clothes that need replacing. The purchase suggestion unit can also analyze the latest fashion trends and suggest new clothes that suit the user. For example, it can suggest trendy items for each season. The purchase suggestion unit can also learn the user's past purchase history and make new suggestions based on that. This allows the user to always enjoy the latest fashion.

[0060] The wardrobe organization system can further include a clothing storage environment suggestion unit. The storage environment suggestion unit suggests the optimal storage environment to prevent clothing deterioration. For example, AI could monitor the temperature and humidity inside the wardrobe and make suggestions for maintaining the optimal storage environment. The storage environment suggestion unit can also suggest storage methods based on the material of the clothing. For example, it could suggest special storage methods for clothing made of delicate materials. The storage environment suggestion unit can also suggest storage methods based on the user's living environment. For example, it could suggest moisture-proofing measures for users living in humid areas. This allows users to store their clothing in good condition for a long period of time.

[0061] The generative AI unit can estimate the user's emotions and suggest clothing choices based on the estimated emotions. For example, if the user is feeling stressed, it will suggest relaxing clothing. The generative AI unit can also monitor the user's emotions in real time and dynamically adjust clothing choices according to those emotions. For example, if the user is tired, it will suggest comfortable clothing. The generative AI unit can also prioritize suggesting clothing choices that have given the user high satisfaction in the past based on the user's emotional data. This allows the user to always choose comfortable clothing.

[0062] The generative AI unit can estimate the user's emotions and suggest clothing storage methods based on the estimated emotions. For example, if the user feels busy, it will suggest a simple storage method. The generative AI unit can also monitor the user's emotions in real time and dynamically adjust the storage method according to the user's emotions. For example, if the user feels relaxed, it will suggest a detailed storage method. The generative AI unit can also prioritize suggesting storage methods that have given the user high satisfaction in the past based on the user's emotional data. This allows the user to always choose the optimal storage method.

[0063] The generative AI unit can estimate the user's emotions and suggest a cleaning method for clothes based on the estimated emotions. For example, if the user is tired, it will suggest an easy cleaning method. The generative AI unit can also monitor the user's emotions in real time and dynamically adjust the cleaning method according to the emotion. For example, if the user is feeling stressed, it will suggest a relaxing cleaning method. The generative AI unit can also prioritize suggesting cleaning methods that have given the user high satisfaction in the past based on the user's emotional data. This allows the user to always choose the optimal cleaning method.

[0064] The generative AI section can estimate the user's emotions and suggest clothing repair methods based on the estimated emotions. For example, if the user feels busy, it will suggest a simple repair method. The generative AI section can also monitor the user's emotions in real time and dynamically adjust the repair method according to the user's emotions. For example, if the user feels relaxed, it will suggest a detailed repair method. The generative AI section can also prioritize suggesting repair methods that have given the user high satisfaction in the past based on the user's emotional data. This allows the user to always choose the optimal repair method.

[0065] The generative AI unit can estimate the user's emotions and suggest clothing recycling methods based on the estimated emotions. For example, if the user is concerned about the environment, it will prioritize suggestions of recyclable clothing. The generative AI unit can also monitor the user's emotions in real time and dynamically adjust recycling methods according to those emotions. For example, if the user has positive feelings about recycling, it will suggest detailed recycling methods. The generative AI unit can also prioritize suggestions of recycling methods that have given users high satisfaction in the past based on the user's emotional data. This allows the user to always choose the optimal recycling method.

[0066] The processing flow of the second embodiment will be briefly explained below.

[0067] Step 1: The dresser organizer organizes the clothes in the dresser. For example, AI can identify the location and type of clothes and organize them efficiently. The dresser organizer can also classify clothes by season and place frequently used clothes in a location that makes them easy to access. For example, AI can classify clothes based on color, material, and frequency of use, and place them in a location that makes them easy to access. Step 2: The generation AI part selects a storage method through conversation with the user. For example, the generation AI may ask a question such as, "Where do you want to store the shirt you wore today?" and suggest a storage method based on the user's answer. The generation AI part can also analyze the user's preferences and past selection history and suggest a storage method based on that. For example, if the user has often sent their shirts to the dry cleaners in the past, the generation AI may suggest, "Do you want to send this shirt to the dry cleaners as well?" Step 3: The logistics system control unit controls the logistics system installed behind the house. For example, when the logistics system transports clothes to the closet, the AI ​​calculates the optimal route and transports them efficiently. The logistics system control unit can also monitor the condition of the clothes and suggest cleaning or repair as needed. For example, if the clothes are stained or frayed, the AI ​​will suggest cleaning or repair to the user.

[0068] 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.

[0069] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0070] 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.

[0071] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0072] 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.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] 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).

[0077] 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.

[0078] 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.

[0079] 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.

[0080] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0081] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0082] 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.

[0083] 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.

[0084] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] 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.

[0086] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0087] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0094] 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.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0096] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0097] 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.

[0098] 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.

[0099] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] 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.

[0101] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0112] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0113] 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.

[0114] 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.

[0115] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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."

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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. [Explanation of symbols]

[0135] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a dresser organizer for arranging clothes in the dresser; A generation AI part that selects the storage method through conversation with the user, and a logistics system control unit that controls the logistics system installed behind the house; A system characterized by:

2. The chest of drawers organizing section includes: The clothes are classified by season, and the clothes that are frequently used are placed in an easy-to-access location.

2. The system of claim 1.

3. The generation AI unit Analyzing the user's preferences and past selection history and proposing the storage method based on the results 2. The system of claim 1.

4. The generation AI unit It also analyzes conversations with other members of the household and proposes storage methods that reflect everyone's opinions.

2. The system of claim 1.

5. The generation AI unit Analyzing the tone of voice and facial expression of the user and proposing the storage method according to the emotional state of the user.

2. The system of claim 1.

Images