System

Abstract

An object of a system according to an embodiment is to identify an unnecessary article in a home and support cleanup.SOLUTION: A system according to an embodiment includes a camera, a generation AI, and a suggestion unit. The camera photographs the inside of the home. The generation AI analyzes the image captured by the camera to identify the unwanted item. The suggestion unit suggests the unnecessary article specified by the generated AI to the user.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] Conventional technologies have had the problem of not being able to efficiently identify unnecessary items in the home and provide sufficient support for tidying up.

[0005] The system according to the embodiment aims to identify unnecessary items in the home and assist in tidying up. [Means for solving the problem]

[0006] The system according to the embodiment includes a camera, a generation AI, and a suggestion unit. The camera takes pictures of the inside of a home. The generation AI analyzes the images taken by the camera to identify unnecessary items. The suggestion unit suggests the unnecessary items identified by the generation AI to a user. [Effects of the Invention]

[0007] The system according to the embodiment can identify unnecessary items in the home and assist in tidying up. [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 tidying support system according to an embodiment of the present invention is a system that takes pictures of the inside of a home with a camera, analyzes them with a generating AI, identifies unnecessary items, and suggests them to the user. This allows the tidying support system to enable even people who are not good at tidying to tidy up their homes efficiently.

[0029] A tidying support system according to an embodiment includes a camera, a generation AI, and a suggestion unit. The camera captures images of a home. For example, it sequentially captures images of areas that need tidying, such as the living room, kitchen, and bedroom. The camera captures images at high resolution and generates detailed image data. The generation AI analyzes the images captured by the camera to identify unnecessary items. For example, the generation AI detects old magazines, unused home appliances, and broken furniture. The generation AI receives inputs containing prompts containing instructions from the user about what the user wants the generation AI to do, and the generation AI identifies unnecessary items based on the prompts. The generation AI analyzes the items in the images using a text generation AI (e.g., LLM) or a multimodal generation AI. The suggestion unit suggests unnecessary items identified by the generation AI to the user. For example, it makes specific suggestions such as, "I don't read this magazine anymore, so please throw it away" or "This appliance is broken, so please dispose of it." In this way, the tidying support system according to an embodiment efficiently promotes tidying by using AI to suggest what the user should throw away.

[0030] The camera is equipped with a 3D scanning function, which allows it to grasp the shape and size of an item in detail. For example, the camera can acquire the exact dimensions of furniture and home appliances, and the generative AI can use this information to identify unwanted items. This allows for a more accurate analysis by understanding the shape and size of the item in detail.

[0031] The camera is equipped with a temperature sensor, and can estimate the frequency of use of an item from temperature changes. For example, the camera is equipped with a temperature sensor and measures the surface temperature of the item. For example, frequently used items have a higher temperature, so the frequency of use can be estimated. In this way, unnecessary items can be identified more accurately by estimating the frequency of use of the item from temperature changes.

[0032] The camera can be used by a drone to take pictures of high places and hard-to-reach places, supporting overall tidying up. The camera can be used by a drone to take pictures of high places and hard-to-reach places, for example, by taking pictures of shelves near the ceiling or the top of a closet, supporting overall tidying up. This allows the camera to be used by a drone to take pictures of high places and hard-to-reach places, for example, by taking pictures of shelves near the ceiling or the top of a closet, supporting overall tidying up.

[0033] The camera is equipped with a voice recognition function, and the user can proceed with taking pictures while giving verbal instructions. For example, the camera may be equipped with a voice recognition function, and the user can proceed with taking pictures while giving verbal instructions. For example, the camera may issue an instruction such as "Take a picture of this shelf." This allows the user to proceed with taking pictures while giving verbal instructions.

[0034] The generation AI can input item usage history data and prioritize detecting items that are used less frequently. For example, the generation AI can input item usage history data and prioritize detecting items that are used less frequently. For example, it can identify items that have not been used even once in the past year. This allows for the priority detection of items that are used less frequently, making it possible to efficiently identify unnecessary items.

[0035] The generation AI can input market value data of items and prioritize detecting items with low value. For example, the generation AI can input market value data of items and prioritize detecting items with low value. For example, it can identify unnecessary items based on the market value of old home appliances or magazines. This allows it to prioritize detecting items with low value, making it possible to efficiently identify unnecessary items.

[0036] Generative AI can learn tidying standards from different cultures and identify unwanted items from an international perspective. For example, generative AI can compare Japanese tidying standards with American tidying standards. By learning tidying standards from different cultures, it can identify unwanted items from an international perspective.

[0037] The generative AI can evaluate the eco-friendliness of items and suggest eco-friendly tidying up methods. For example, the generative AI can evaluate the eco-friendliness of items and suggest eco-friendly tidying up methods. For example, it can prioritize disposing of recyclable items. In this way, by evaluating the eco-friendliness, it can suggest eco-friendly tidying up methods.

[0038] The generative AI can also suggest ways to reuse and recycle items. For example, it can suggest ways to separate recyclable materials. This makes it possible to clean up in an environmentally friendly way by suggesting ways to reuse and recycle items.

[0039] The generating AI can also suggest where to donate or sell the items. For example, the generating AI can introduce organizations to which unwanted clothing can be donated. This allows items to be used effectively by suggesting where to donate or sell them.

[0040] The generative AI can also provide the history and background information of the item, deepening the user's understanding. The generative AI can also provide the history and background information of the item, for example, providing information on the year of manufacture and manufacturer of old furniture. This allows the user to deepen their understanding by providing the history and background information of the item.

[0041] The generative AI can also suggest ways to store and organize items. For example, it can suggest ways to organize clothes by season. By suggesting storage and organization methods, it becomes easier to manage items.

[0042] The camera is equipped with a 3D scanning function, which allows it to grasp the shape and size of an item in detail. For example, the camera can acquire the exact dimensions of furniture and home appliances, and the generative AI can use this information to identify unwanted items. This allows for a more accurate analysis by understanding the shape and size of the item in detail.

[0043] The camera is equipped with a temperature sensor, and can estimate the frequency of use of an item from temperature changes. For example, the camera is equipped with a temperature sensor and measures the surface temperature of the item. For example, frequently used items have a higher temperature, so the frequency of use can be estimated. In this way, unnecessary items can be identified more accurately by estimating the frequency of use of the item from temperature changes.

[0044] The camera can be used by a drone to take pictures of high places and hard-to-reach places, supporting overall tidying up. The camera can be used by a drone to take pictures of high places and hard-to-reach places, for example, by taking pictures of shelves near the ceiling or the top of a closet, supporting overall tidying up. This allows the camera to be used by a drone to take pictures of high places and hard-to-reach places, for example, by taking pictures of shelves near the ceiling or the top of a closet, supporting overall tidying up.

[0045] The camera is equipped with a voice recognition function, and the user can proceed with taking pictures while giving verbal instructions. For example, the camera may be equipped with a voice recognition function, and the user can proceed with taking pictures while giving verbal instructions. For example, the camera may issue an instruction such as "Take a picture of this shelf." This allows the user to proceed with taking pictures while giving verbal instructions.

[0046] The generation AI can input item usage history data and prioritize detecting items that are used less frequently. For example, the generation AI can input item usage history data and prioritize detecting items that are used less frequently. For example, it can identify items that have not been used even once in the past year. This allows for the priority detection of items that are used less frequently, making it possible to efficiently identify unnecessary items.

[0047] The generation AI can input market value data of items and prioritize detecting items with low value. For example, the generation AI can input market value data of items and prioritize detecting items with low value. For example, it can identify unnecessary items based on the market value of old home appliances or magazines. This allows it to prioritize detecting items with low value, making it possible to efficiently identify unnecessary items.

[0048] Generative AI can learn tidying standards from different cultures and identify unwanted items from an international perspective. For example, generative AI can compare Japanese tidying standards with American tidying standards. By learning tidying standards from different cultures, it can identify unwanted items from an international perspective.

[0049] The generative AI can evaluate the eco-friendliness of items and suggest eco-friendly tidying up methods. For example, the generative AI can evaluate the eco-friendliness of items and suggest eco-friendly tidying up methods. For example, it can prioritize disposing of recyclable items. In this way, by evaluating the eco-friendliness, it can suggest eco-friendly tidying up methods.

[0050] The generative AI can also suggest ways to reuse and recycle items. For example, it can suggest ways to separate recyclable materials. This makes it possible to clean up in an environmentally friendly way by suggesting ways to reuse and recycle items.

[0051] The generating AI can also suggest where to donate or sell the items. For example, the generating AI can introduce organizations to which unwanted clothing can be donated. This allows items to be used effectively by suggesting where to donate or sell them.

[0052] The generative AI can also provide the history and background information of the item, deepening the user's understanding. The generative AI can also provide the history and background information of the item, for example, providing information on the year of manufacture and manufacturer of old furniture. This allows the user to deepen their understanding by providing the history and background information of the item.

[0053] The generative AI can also suggest ways to store and organize items. For example, it can suggest ways to organize clothes by season. By suggesting storage and organization methods, it becomes easier to manage items.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The tidying support system can also include a health management unit that monitors the user's health condition. For example, it can measure the user's heart rate and blood pressure and monitor the user's health condition in real time while tidying up. This can support the user so that they can tidy up without straining themselves. The health management unit can also suggest breaks based on the user's health condition. For example, if the user's heart rate rises, it can suggest, "Let's take a short break." The health management unit can also suggest ways to refresh the user after tidying up. For example, the health management unit can recommend stretching or light exercise to help maintain the user's health.

[0056] The tidying support system can further include a lifestyle rhythm learning unit that learns the user's lifestyle rhythm. For example, it can learn the user's wake-up time, bedtime, meal timings, etc., and suggest the optimal tidying time. This allows for efficient tidying that matches the user's lifestyle rhythm. The lifestyle rhythm learning unit can also set tidying priorities based on the user's lifestyle rhythm. For example, it can prioritize tidying the living room in the morning and tidying the bedroom in the evening. Furthermore, the lifestyle rhythm learning unit can send tidying reminders that match the user's lifestyle rhythm. For example, it can send a notification such as, "Now is the time to tidy up."

[0057] The tidying support system can further include a tidying style learning unit that learns the user's tidying style. For example, it can learn the order in which the user tidies up and what items they prioritize. This makes it possible to make suggestions that match the user's tidying style. The tidying style learning unit can also provide tidying advice based on the user's tidying style. For example, it can provide specific advice such as "Start by tidying up small items." Furthermore, the tidying style learning unit can set tidying goals that match the user's tidying style. For example, it can set a goal such as "Tidy up this shelf today."

[0058] The tidying support system can further include a history recording unit that records the user's tidying history. For example, it can record items that have been tidyed up in the past and the tidying methods used at that time. This allows the user to refer to their past tidying history. The history recording unit can also analyze tidying trends based on the user's tidying history. For example, if tidying is concentrated during a specific period, it can make tidying suggestions tailored to that period. Furthermore, the history recording unit can set tidying goals based on the user's tidying history. For example, it can set the next tidying goal by referring to the user's past tidying history.

[0059] The tidying support system can further include a knowledge provider that provides knowledge about tidying up to the user. For example, it can provide tips and efficient tidying methods. This allows the user to deepen their knowledge about tidying up. The knowledge provider can also answer questions about tidying up from the user. For example, it can provide specific advice in response to a question such as, "How should I put this item away?" Furthermore, the knowledge provider can periodically update the information about the user's tidying up. For example, it can provide information about new tidying methods and tools.

[0060] The tidying support system may further include a community section that provides community functions related to users' tidying up. For example, users may share their tidying up progress with other users and exchange advice. This allows users to interact with other users while tidying up. The community section may also encourage and encourage competition between users. For example, it may hold an event where users compete to see who can see who can see who can see who can tidy up. Furthermore, the community section may also share users' success stories regarding tidying up. For example, users may be motivated by learning from other users' success stories.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The camera takes pictures of your home, taking sequential pictures of areas that need cleaning, such as the living room, kitchen, and bedroom. The camera takes high-resolution pictures and generates detailed image data. Step 2: The generative AI analyzes the images captured by the camera to identify unwanted items. For example, the generative AI may detect old magazines, unused appliances, or broken furniture. The generative AI receives input from the user as prompts containing instructions on what the user wants the generative AI to do, and the generative AI uses the prompts to identify unwanted items. The generative AI uses text generation AI (e.g., LLM) or multimodal generative AI to analyze the items in the image. Step 3: The suggestion unit suggests unnecessary items identified by the generative AI to the user, such as "I don't read this magazine anymore, so please throw it away" or "This home appliance is broken, so please dispose of it."

[0063] (Example 2) The tidying support system according to an embodiment of the present invention is a system that takes pictures of the inside of a home with a camera, analyzes them with a generating AI, identifies unnecessary items, and suggests them to the user. This allows the tidying support system to enable even people who are not good at tidying to tidy up their homes efficiently.

[0064] A tidying support system according to an embodiment includes a camera, a generation AI, and a suggestion unit. The camera captures images of a home. For example, it sequentially captures images of areas that need tidying, such as the living room, kitchen, and bedroom. The camera captures images at high resolution and generates detailed image data. The generation AI analyzes the images captured by the camera to identify unnecessary items. For example, the generation AI detects old magazines, unused home appliances, and broken furniture. The generation AI receives inputs containing prompts containing instructions from the user about what the user wants the generation AI to do, and the generation AI identifies unnecessary items based on the prompts. The generation AI analyzes the items in the images using a text generation AI (e.g., LLM) or a multimodal generation AI. The suggestion unit suggests unnecessary items identified by the generation AI to the user. For example, it makes specific suggestions such as, "I don't read this magazine anymore, so please throw it away" or "This appliance is broken, so please dispose of it." In this way, the tidying support system according to an embodiment efficiently promotes tidying by using AI to suggest what the user should throw away.

[0065] The camera is equipped with a 3D scanning function, which allows it to grasp the shape and size of an item in detail. For example, the camera can acquire the exact dimensions of furniture and home appliances, and the generative AI can use this information to identify unwanted items. This allows for a more accurate analysis by understanding the shape and size of the item in detail.

[0066] The camera is equipped with a temperature sensor, and can estimate the frequency of use of an item from temperature changes. For example, the camera is equipped with a temperature sensor and measures the surface temperature of the item. For example, frequently used items have a higher temperature, so the frequency of use can be estimated. In this way, unnecessary items can be identified more accurately by estimating the frequency of use of the item from temperature changes.

[0067] The camera is equipped with an emotion estimation function, which measures the stress level felt by the user when taking a photo and can recommend taking a photo at a time when stress is low. For example, the camera is equipped with an emotion estimation function and measures the stress level by analyzing the user's facial expressions and voice. For example, it recommends taking a photo at a time when stress is low. In this way, by measuring the user's stress level and recommending taking a photo at a time when stress is low, efficient tidying up becomes possible.

[0068] The camera can be used by a drone to take pictures of high places and hard-to-reach places, supporting overall tidying up. The camera can be used by a drone to take pictures of high places and hard-to-reach places, for example, by taking pictures of shelves near the ceiling or the top of a closet, supporting overall tidying up. This allows the camera to be used by a drone to take pictures of high places and hard-to-reach places, for example, by taking pictures of shelves near the ceiling or the top of a closet, supporting overall tidying up.

[0069] The camera is equipped with a voice recognition function, and the user can proceed with taking pictures while giving verbal instructions. For example, the camera may be equipped with a voice recognition function, and the user can proceed with taking pictures while giving verbal instructions. For example, the camera may issue an instruction such as "Take a picture of this shelf." This allows the user to proceed with taking pictures while giving verbal instructions.

[0070] The camera can use its emotion estimation function to automatically adjust music and lighting to elicit positive emotions felt by the user when taking a photo. For example, the camera is equipped with an emotion estimation function and analyzes the user's emotional state. For example, it automatically plays relaxing music to elicit positive emotions. This automatically adjusts the music and lighting to make the user feel positive emotions, thereby increasing motivation to tidy up.

[0071] The generation AI can input item usage history data and prioritize detecting items that are used less frequently. For example, the generation AI can input item usage history data and prioritize detecting items that are used less frequently. For example, it can identify items that have not been used even once in the past year. This allows for the priority detection of items that are used less frequently, making it possible to efficiently identify unnecessary items.

[0072] The generation AI can input market value data of items and prioritize detecting items with low value. For example, the generation AI can input market value data of items and prioritize detecting items with low value. For example, it can identify unnecessary items based on the market value of old home appliances or magazines. This allows it to prioritize detecting items with low value, making it possible to efficiently identify unnecessary items.

[0073] The generation AI uses the emotion estimation function to analyze the degree of attachment a user feels toward an item and can prioritize detecting items with low attachment levels. The generation AI, for example, uses the emotion estimation function to analyze the degree of attachment a user feels toward an item. For example, it analyzes the user's facial expressions and voice and quantifies the degree of attachment. This allows for the prioritized detection of items with low attachment levels, making it possible to efficiently identify unnecessary items.

[0074] Generative AI can learn tidying standards from different cultures and identify unwanted items from an international perspective. For example, generative AI can compare Japanese tidying standards with American tidying standards. By learning tidying standards from different cultures, it can identify unwanted items from an international perspective.

[0075] The generative AI can evaluate the eco-friendliness of items and suggest eco-friendly tidying up methods. For example, the generative AI can evaluate the eco-friendliness of items and suggest eco-friendly tidying up methods. For example, it can prioritize disposing of recyclable items. In this way, by evaluating the eco-friendliness, it can suggest eco-friendly tidying up methods.

[0076] The generative AI can use its emotion estimation function to suggest tidying methods that will elicit positive emotions felt by the user toward their items. For example, the generative AI can use its emotion estimation function to suggest tidying methods that will elicit positive emotions felt by the user toward their items. For example, it can suggest ways to carefully store memorable items. In this way, suggesting tidying methods that will elicit positive emotions increases the user's motivation to tidy up.

[0077] The generative AI can also suggest ways to reuse and recycle items. For example, it can suggest ways to separate recyclable materials. This makes it possible to clean up in an environmentally friendly way by suggesting ways to reuse and recycle items.

[0078] The generating AI can also suggest where to donate or sell the items. For example, the generating AI can introduce organizations to which unwanted clothing can be donated. This allows items to be used effectively by suggesting where to donate or sell them.

[0079] The generation AI can use the emotion estimation function to analyze the timing when the user is likely to accept a proposal and make the proposal at that timing. For example, the generation AI can use the emotion estimation function to analyze the timing when the user is likely to accept a proposal. For example, it can make a proposal when the user is relaxed. This improves the acceptance rate of proposals by making proposals at times when the user is likely to accept them.

[0080] The generative AI can also provide the history and background information of the item, deepening the user's understanding. The generative AI can also provide the history and background information of the item, for example, providing information on the year of manufacture and manufacturer of old furniture. This allows the user to deepen their understanding by providing the history and background information of the item.

[0081] The generative AI can also suggest ways to store and organize items. For example, it can suggest ways to organize clothes by season. By suggesting storage and organization methods, it becomes easier to manage items.

[0082] The generation AI uses the emotion estimation function to analyze the language and tone that makes users more likely to accept suggestions, and can make suggestions based on the results. For example, the generation AI uses the emotion estimation function to analyze the language and tone that makes users more likely to accept suggestions. For example, it uses kind language and an encouraging tone. This increases the acceptance rate of suggestions by using language and tone that makes users more likely to accept suggestions.

[0083] The camera is equipped with a 3D scanning function, which allows it to grasp the shape and size of an item in detail. For example, the camera can acquire the exact dimensions of furniture and home appliances, and the generative AI can use this information to identify unwanted items. This allows for a more accurate analysis by understanding the shape and size of the item in detail.

[0084] The camera is equipped with a temperature sensor, and can estimate the frequency of use of an item from temperature changes. For example, the camera is equipped with a temperature sensor and measures the surface temperature of the item. For example, frequently used items have a higher temperature, so the frequency of use can be estimated. In this way, unnecessary items can be identified more accurately by estimating the frequency of use of the item from temperature changes.

[0085] The camera is equipped with an emotion estimation function, which measures the stress level felt by the user when taking a photo and can recommend taking a photo at a time when stress is low. For example, the camera is equipped with an emotion estimation function and measures the stress level by analyzing the user's facial expressions and voice. For example, it recommends taking a photo at a time when stress is low. In this way, by measuring the user's stress level and recommending taking a photo at a time when stress is low, efficient tidying up becomes possible.

[0086] The camera can be used by a drone to take pictures of high places and hard-to-reach places, supporting overall tidying up. The camera can be used by a drone to take pictures of high places and hard-to-reach places, for example, by taking pictures of shelves near the ceiling or the top of a closet, supporting overall tidying up. This allows the camera to be used by a drone to take pictures of high places and hard-to-reach places, for example, by taking pictures of shelves near the ceiling or the top of a closet, supporting overall tidying up.

[0087] The camera is equipped with a voice recognition function, and the user can proceed with taking pictures while giving verbal instructions. For example, the camera may be equipped with a voice recognition function, and the user can proceed with taking pictures while giving verbal instructions. For example, the camera may issue an instruction such as "Take a picture of this shelf." This allows the user to proceed with taking pictures while giving verbal instructions.

[0088] The camera can use its emotion estimation function to automatically adjust music and lighting to elicit positive emotions felt by the user when taking a photo. For example, the camera is equipped with an emotion estimation function and analyzes the user's emotional state. For example, it automatically plays relaxing music to elicit positive emotions. This automatically adjusts the music and lighting to make the user feel positive emotions, thereby increasing motivation to tidy up.

[0089] The generation AI can input item usage history data and prioritize detecting items that are used less frequently. For example, the generation AI can input item usage history data and prioritize detecting items that are used less frequently. For example, it can identify items that have not been used even once in the past year. This allows for the priority detection of items that are used less frequently, making it possible to efficiently identify unnecessary items.

[0090] The generation AI can input market value data of items and prioritize detecting items with low value. For example, the generation AI can input market value data of items and prioritize detecting items with low value. For example, it can identify unnecessary items based on the market value of old home appliances or magazines. This allows it to prioritize detecting items with low value, making it possible to efficiently identify unnecessary items.

[0091] The generation AI uses the emotion estimation function to analyze the degree of attachment a user feels toward an item and can prioritize detecting items with low attachment levels. The generation AI, for example, uses the emotion estimation function to analyze the degree of attachment a user feels toward an item. For example, it analyzes the user's facial expressions and voice and quantifies the degree of attachment. This allows for the prioritized detection of items with low attachment levels, making it possible to efficiently identify unnecessary items.

[0092] Generative AI can learn tidying standards from different cultures and identify unwanted items from an international perspective. For example, generative AI can compare Japanese tidying standards with American tidying standards. By learning tidying standards from different cultures, it can identify unwanted items from an international perspective.

[0093] The generative AI can evaluate the eco-friendliness of items and suggest eco-friendly tidying up methods. For example, the generative AI can evaluate the eco-friendliness of items and suggest eco-friendly tidying up methods. For example, it can prioritize disposing of recyclable items. In this way, by evaluating the eco-friendliness, it can suggest eco-friendly tidying up methods.

[0094] The generative AI can use its emotion estimation function to suggest tidying methods that will elicit positive emotions felt by the user toward their items. For example, the generative AI can use its emotion estimation function to suggest tidying methods that will elicit positive emotions felt by the user toward their items. For example, it can suggest ways to carefully store memorable items. In this way, suggesting tidying methods that will elicit positive emotions increases the user's motivation to tidy up.

[0095] The generative AI can also suggest ways to reuse and recycle items. For example, it can suggest ways to separate recyclable materials. This makes it possible to clean up in an environmentally friendly way by suggesting ways to reuse and recycle items.

[0096] The generating AI can also suggest where to donate or sell the items. For example, the generating AI can introduce organizations to which unwanted clothing can be donated. This allows items to be used effectively by suggesting where to donate or sell them.

[0097] The generation AI can use the emotion estimation function to analyze the timing when the user is likely to accept a proposal and make the proposal at that timing. For example, the generation AI can use the emotion estimation function to analyze the timing when the user is likely to accept a proposal. For example, it can make a proposal when the user is relaxed. This improves the acceptance rate of proposals by making proposals at times when the user is likely to accept them.

[0098] The generative AI can also provide the history and background information of the item, deepening the user's understanding. The generative AI can also provide the history and background information of the item, for example, providing information on the year of manufacture and manufacturer of old furniture. This allows the user to deepen their understanding by providing the history and background information of the item.

[0099] The generative AI can also suggest ways to store and organize items. For example, it can suggest ways to organize clothes by season. By suggesting storage and organization methods, it becomes easier to manage items.

[0100] The generation AI uses the emotion estimation function to analyze the language and tone that makes users more likely to accept suggestions, and can make suggestions based on the results. For example, the generation AI uses the emotion estimation function to analyze the language and tone that makes users more likely to accept suggestions. For example, it uses kind language and an encouraging tone. This increases the acceptance rate of suggestions by using language and tone that makes users more likely to accept suggestions.

[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 tidying support system can also include a health management unit that monitors the user's health condition. For example, it can measure the user's heart rate and blood pressure and monitor the user's health condition in real time while tidying up. This can support the user so that they can tidy up without straining themselves. The health management unit can also suggest breaks based on the user's health condition. For example, if the user's heart rate rises, it can suggest, "Let's take a short break." The health management unit can also suggest ways to refresh the user after tidying up. For example, the health management unit can recommend stretching or light exercise to help maintain the user's health.

[0103] The tidying support system can further include a lifestyle rhythm learning unit that learns the user's lifestyle rhythm. For example, it can learn the user's wake-up time, bedtime, meal timings, etc., and suggest the optimal tidying time. This allows for efficient tidying that matches the user's lifestyle rhythm. The lifestyle rhythm learning unit can also set tidying priorities based on the user's lifestyle rhythm. For example, it can prioritize tidying the living room in the morning and tidying the bedroom in the evening. Furthermore, the lifestyle rhythm learning unit can send tidying reminders that match the user's lifestyle rhythm. For example, it can send a notification such as, "Now is the time to tidy up."

[0104] The tidying support system can further include a tidying style learning unit that learns the user's tidying style. For example, it can learn the order in which the user tidies up and what items they prioritize. This makes it possible to make suggestions that match the user's tidying style. The tidying style learning unit can also provide tidying advice based on the user's tidying style. For example, it can provide specific advice such as "Start by tidying up small items." Furthermore, the tidying style learning unit can set tidying goals that match the user's tidying style. For example, it can set a goal such as "Tidy up this shelf today."

[0105] The tidying support system may further include a progress adjustment unit that estimates the user's emotions and adjusts the tidying progress based on the estimated emotions. For example, if it is estimated that the user is tired, it may suggest slowing down the pace of tidying. This allows the user to proceed with tidying without straining themselves. The progress adjustment unit may also change the tidying priority based on the user's emotions. For example, if the user is feeling stressed, it may prioritize tidying up areas where they can relax. Furthermore, the progress adjustment unit may adjust the tidying goal based on the user's emotions. For example, if the user is feeling positive emotions, it may set a slightly higher goal.

[0106] The tidying support system may further include a motivation improvement unit that estimates the user's emotions and increases the user's motivation to tidy up based on the estimated emotions. For example, if it is estimated that the user is tired, an encouraging message may be displayed. This increases the user's motivation to tidy up. The motivation improvement unit may also set a reward for tidying up based on the user's emotions. For example, it may play relaxing music after the tidying up is completed. Furthermore, the motivation improvement unit may visualize the progress of tidying up based on the user's emotions. For example, it may display the progress of tidying up in a graph to give the user a sense of accomplishment.

[0107] The tidying support system may further include an approach change unit that estimates the user's emotions and changes the tidying approach based on the estimated emotions. For example, if it is estimated that the user is feeling stressed, it may suggest simplifying the tidying method. This allows the user to proceed with tidying without straining themselves. The approach change unit may also change the tidying order based on the user's emotions. For example, if the user is relaxed, it may suggest starting with the larger items. Furthermore, the approach change unit may reset the tidying goal based on the user's emotions. For example, if the user is feeling positive, it may set a slightly higher goal.

[0108] The tidying support system can further include a feedback unit that estimates the user's emotions and provides feedback on the tidying up based on the estimated emotions. For example, if the user is feeling positive emotions, a praising message can be displayed. This can increase the user's motivation to tidy up. The feedback unit can also suggest improvements to the tidying up based on the user's emotions. For example, if the user is feeling stressed, the feedback unit can suggest reconsidering the tidying up method. Furthermore, the feedback unit can evaluate the progress of the tidying up based on the user's emotions. For example, the progress of the tidying up can be quantified to give the user a sense of accomplishment.

[0109] The tidying support system can further include a history recording unit that records the user's tidying history. For example, it can record items that have been tidyed up in the past and the tidying methods used at that time. This allows the user to refer to their past tidying history. The history recording unit can also analyze tidying trends based on the user's tidying history. For example, if tidying is concentrated during a specific period, it can make tidying suggestions tailored to that period. Furthermore, the history recording unit can set tidying goals based on the user's tidying history. For example, it can set the next tidying goal by referring to the user's past tidying history.

[0110] The tidying support system can further include a knowledge provider that provides knowledge about tidying up to the user. For example, it can provide tips and efficient tidying methods. This allows the user to deepen their knowledge about tidying up. The knowledge provider can also answer questions about tidying up from the user. For example, it can provide specific advice in response to a question such as, "How should I put this item away?" Furthermore, the knowledge provider can periodically update the information about the user's tidying up. For example, it can provide information about new tidying methods and tools.

[0111] The tidying support system may further include a community section that provides community functions related to users' tidying up. For example, users may share their tidying up progress with other users and exchange advice. This allows users to interact with other users while tidying up. The community section may also encourage and encourage competition between users. For example, it may hold an event where users compete to see who can see who can see who can see who can tidy up. Furthermore, the community section may also share users' success stories regarding tidying up. For example, users may be motivated by learning from other users' success stories.

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

[0113] Step 1: The camera takes pictures of your home, taking sequential pictures of areas that need cleaning, such as the living room, kitchen, and bedroom. The camera takes high-resolution pictures and generates detailed image data. Step 2: The generative AI analyzes the images captured by the camera to identify unwanted items. For example, the generative AI may detect old magazines, unused appliances, or broken furniture. The generative AI receives input from the user as prompts containing instructions on what the user wants the generative AI to do, and the generative AI uses the prompts to identify unwanted items. The generative AI uses text generation AI (e.g., LLM) or multimodal generative AI to analyze the items in the image. Step 3: The suggestion unit suggests unnecessary items identified by the generative AI to the user, such as "I don't read this magazine anymore, so please throw it away" or "This home appliance is broken, so please dispose of it."

[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 (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.

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

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0148] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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."

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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]

[0181] 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 camera and Generative AI and a proposal unit, The camera is Photographing the inside of the house, The generated AI is analyzing the images captured by the camera to identify unwanted items; The proposal unit Suggesting unnecessary items identified by the generative AI to the user A system characterized by:

2. The camera is Equipped with 3D scanning function, it can grasp the shape and size of the item in detail 2. The system of claim 1.

3. The camera is Drones are used to photograph high and hard-to-reach places, helping with overall cleanup.

2. The system of claim 1.

4. The generated AI is Input the usage history data of the items and prioritize detection of items that are used less frequently.

2. The system of claim 1.

5. The generated AI is We also propose ways to reuse and recycle the items.

2. The system of claim 1.

6. The camera is Equipped with an emotion estimation function, it measures the stress level felt by the user when taking a photo and recommends taking a photo at a time when stress is low.

2. The system of claim 1.

7. The generated AI is Analyzing the degree of attachment felt by the user to the item, and preferentially detecting items with low attachment levels 2. The system of claim 1.

8. The generated AI is Analyze the timing when the user is most likely to accept the proposal and make the proposal at that timing 2. The system of claim 1.

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