System

Abstract

An object of a system according to an embodiment is to clarify a cleanup goal and provide a specific cleanup procedure.SOLUTION: A system according to an embodiment includes a photograph upload unit, an image generation unit, a selection unit, and an instruction providing unit. The photograph upload unit uploads a photograph of a cluttered room. The image generation unit generates an image of the cleaned room on the basis of the photograph uploaded by the photograph upload unit. The selection unit selects an ideal room from the plurality of images generated by the image generation unit. The instruction providing unit provides a specific cleanup instruction based on the image of the ideal room selected by the selection unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it was difficult to see the goal of tidying up and the steps to tidying up, making it difficult to tidy up a room efficiently.

[0005] The system according to the embodiment aims to clarify the goal of tidying up and provide specific tidying up procedures. [Means for solving the problem]

[0006] The system according to the embodiment includes a photo uploading unit, an image generating unit, a selecting unit, and an instruction providing unit. The photo uploading unit uploads photos of a messy room. The image generating unit generates an image of a tidy room based on the photos uploaded by the photo uploading unit. The selecting unit selects an ideal room from the multiple images generated by the image generating unit. The instruction providing unit provides specific tidying instructions based on the image of the ideal room selected by the selecting unit. [Effects of the Invention]

[0007] The system according to the embodiment can clarify the goal of tidying up and provide specific tidying up procedures. [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 organizing and storage support system according to an embodiment of the present invention allows users to upload a photo of a messy room, have a generation AI generate an image of a tidy room, and then have the user select their ideal room, and receive specific instructions on how to turn the selected room into a tidy room. This allows the organizing and storage support system to help users efficiently tidy up their rooms.

[0029] The organizing and storage support system according to the embodiment includes a photo uploading unit, an image generating unit, a selection unit, and an instruction providing unit. The photo uploading unit uploads photos of a messy room. For example, the photos can be uploaded in image formats such as JPEG or PNG. The photo uploading unit also uses the uploaded photos as input prompts for the generation AI. The image generating unit generates an image of a tidy room based on the photos uploaded by the photo uploading unit. For example, the generation AI creates an image of a clean and tidy room by considering the room layout and furniture arrangement. The generation AI can generate the image using CG technology or an image processing algorithm. The selection unit selects an ideal room from multiple images generated by the image generating unit. For example, a user can select an image of a room that most closely matches their ideal from the multiple images generated by the generation AI. The instruction providing unit provides specific tidying instructions based on the ideal room image selected by the selection unit. For example, specific step-by-step instructions are provided, such as "First, pick up and sort all the items scattered on the floor," "Next, throw away unnecessary items," and "Finally, arrange the furniture like this." This allows the user to tidy up efficiently without waste. As a result, the organization and storage support system according to the embodiment allows the user to tidy up their room efficiently.

[0030] The image generation unit can generate an image of a clean and tidy room by taking into account the room layout and furniture arrangement. For example, the generation AI can analyze the room dimensions and recognize the furniture arrangement. The generation AI can generate an image using CG technology and image processing algorithms. This allows the user to have an image of their ideal room.

[0031] The instruction providing unit can provide instructions to pick up and sort items scattered on the floor. For example, the instruction providing unit provides instructions to pick up and sort items scattered on the floor. For example, the generation AI analyzes the lighting conditions and color tones in the room and suggests the optimal tidying method. The generation AI determines the tidying order taking into account the way natural light enters and the arrangement of lighting. This allows the user to tidy up efficiently.

[0032] The instruction providing unit can provide instructions for throwing away unnecessary items. The instruction providing unit provides, for example, instructions for throwing away unnecessary items. For example, the generation AI analyzes the user's emotions and provides advice for reducing stress. The generation AI suggests music for relaxation. This allows the user to efficiently dispose of unnecessary items.

[0033] The instruction providing unit can instruct the arrangement of furniture. The instruction providing unit, for example, instructs the arrangement of furniture. For example, the generation AI analyzes the dimensions of the room and the arrangement of furniture and proposes the optimal arrangement. The generation AI can generate a 3D model and visually show the arrangement of furniture. This allows the user to arrange furniture efficiently.

[0034] The photo upload unit can analyze the room dimensions and furniture layout when uploading a photo and generate a 3D model. For example, the photo upload unit can analyze the room dimensions and furniture layout when uploading a photo and generate a 3D model. For example, the generation AI can analyze the room dimensions and recognize the furniture layout. The generation AI can generate a 3D model using 3D scanning technology or modeling software. This allows the user to accurately grasp the room dimensions and furniture layout.

[0035] The photo upload unit can analyze the lighting conditions and color tones of a room when uploading photos and suggest the optimal tidying method. For example, the photo upload unit can analyze the lighting conditions and color tones of a room when uploading photos and suggest the optimal tidying method. For example, the generation AI can analyze the lighting conditions of a room and suggest the optimal tidying method. The generation AI determines the tidying order taking into account the way natural light enters and the placement of lighting. This allows the user to obtain the optimal tidying method based on the lighting conditions and color tones of the room.

[0036] The photo uploading unit can analyze audio data in the room when uploading a photo and suggest a tidying method that takes sound reverberation into consideration. For example, the photo uploading unit can analyze audio data in the room when uploading a photo and suggest a tidying method that takes sound reverberation into consideration. For example, the generation AI can analyze audio data in the room and suggest a tidying method that takes sound reverberation into consideration. The generation AI can recommend laying down a carpet to reduce sound reverberation. This allows the user to obtain a tidying method that takes sound reverberation into consideration.

[0037] The photo upload unit can analyze the room temperature and humidity when uploading photos and suggest the optimal tidying environment. For example, the photo upload unit can analyze the room temperature and humidity when uploading photos and suggest the optimal tidying environment. For example, the generation AI can analyze the room temperature and humidity and suggest the optimal tidying environment. If the humidity is high, the generation AI can recommend using a dehumidifier. This allows the user to obtain the optimal tidying environment based on the temperature and humidity.

[0038] When generating an image image of a room, the image generation unit can refer to the user's past tidying history and suggest the optimal tidying method. For example, when the generation AI generates an image image of a room, the image generation unit can refer to the user's past tidying history and suggest the optimal tidying method. For example, the generation AI makes suggestions based on tidying methods that have been successful in the past. This allows the user to obtain the optimal tidying method based on their past tidying history.

[0039] When generating an image image of a room, the image generation unit can suggest not only furniture arrangement but also interior design. For example, when the generation AI generates an image image of a room, the image generation unit can suggest not only furniture arrangement but also interior design. For example, the generation AI can suggest wall colors and curtain designs. This allows the user to tidy up their room while receiving interior design suggestions.

[0040] The image generation unit can incorporate interior styles from different cultures and regions when generating an image of a room. For example, when the generation AI generates an image of a room, the image generation unit incorporates interior styles from different cultures and regions. For example, the generation AI may suggest designs in the Nordic or Japanese style. This allows users to enjoy a room that incorporates the interior styles of different cultures and regions.

[0041] The image generation unit can suggest designs that correspond to the season or an event when generating an image image of a room. For example, when the generation AI generates an image image of a room, the image generation unit suggests designs that correspond to the season or an event. For example, the generation AI suggests decorations for Christmas or Halloween. This allows the user to enjoy designs that correspond to the season or an event.

[0042] The selection unit can automatically generate questions that the generation AI uses to narrow down the options when the user selects their ideal room. For example, the selection unit automatically generates questions that the generation AI uses to narrow down the options when the user selects their ideal room. For example, the generation AI presents a question such as, "What color furniture do you like?" This allows the user to efficiently select their ideal room.

[0043] When a user selects their ideal room, the selection unit allows the generation AI to present the advantages and disadvantages of each option. For example, when a user selects their ideal room, the selection unit allows the generation AI to present the advantages and disadvantages of each option. For example, the generation AI may provide information such as, "This room has a lot of storage space, but few windows." This allows the user to select their ideal room while understanding the advantages and disadvantages of each option.

[0044] The selection unit allows the generation AI to present recommendations by referring to the selection history of other users when the user selects their ideal room. For example, when the user selects their ideal room, the selection unit allows the generation AI to present recommendations by referring to the selection history of other users. For example, the generation AI recommends rooms selected by users with similar preferences. This allows the user to select their ideal room by referring to the selection history of other users.

[0045] When a user selects their ideal room, the selection unit allows the generation AI to display the options as a 3D model, providing a more concrete image. For example, when a user selects their ideal room, the selection unit allows the generation AI to display the options as a 3D model, providing a more concrete image. For example, the generation AI displays the entire room in 3D, allowing the user to check it in detail. This allows the user to select their ideal room while having a concrete image in mind.

[0046] When providing tidying up instructions, the instruction providing unit can provide step-by-step instructions taking into account the user's tidying up skill level. For example, when the generation AI provides tidying up instructions, the instruction providing unit provides step-by-step instructions taking into account the user's tidying up skill level. For example, the generation AI provides basic tidying procedures to beginners. This allows the user to proceed with tidying up while receiving step-by-step instructions according to their own skill level.

[0047] When providing tidying up instructions, the instruction providing unit can suggest a time schedule that matches the user's tidying up pace. For example, when the generation AI provides tidying up instructions, the instruction providing unit suggests a time schedule that matches the user's tidying up pace. For example, the generation AI instructs the user to take a break every hour. This allows the user to proceed with tidying up while receiving a time schedule that matches their own pace.

[0048] The instruction providing unit can provide tidying up instructions that take into consideration the user's health condition and physical strength. For example, when the generation AI provides tidying up instructions, the instruction providing unit provides instructions that take into consideration the user's health condition and physical strength. For example, the generation AI recommends that a user with low physical strength tidy up for a short period of time. This allows the user to proceed with tidying up while receiving instructions that are appropriate for their own health condition and physical strength.

[0049] The instruction providing unit can monitor the tidying up progress in real time and adjust the instructions as necessary. For example, when the generation AI provides tidying up instructions, the instruction providing unit can monitor the tidying up progress in real time and adjust the instructions as necessary. For example, the generation AI can add a simple task if progress is lagging behind. This allows the user to proceed with tidying up while receiving instructions according to the progress.

[0050] The instruction providing unit can provide tidying up instructions that take into consideration the user's health condition and physical strength. For example, when the generation AI provides tidying up instructions, the instruction providing unit provides instructions that take into consideration the user's health condition and physical strength. For example, the generation AI recommends that a user with low physical strength tidy up for a short period of time. This allows the user to proceed with tidying up while receiving instructions that are appropriate for their own health condition and physical strength.

[0051] The instruction providing unit can monitor the tidying up progress in real time and adjust the instructions as necessary. For example, when the generation AI provides tidying up instructions, the instruction providing unit can monitor the tidying up progress in real time and adjust the instructions as necessary. For example, the generation AI can add a simple task if progress is lagging behind. This allows the user to proceed with tidying up while receiving instructions according to the progress.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The organizing and storage support system further includes a voice recognition unit. The voice recognition unit enables the user to receive tidying instructions by voice. For example, when the user says, "Tell me the next instruction," the voice recognition unit provides the instruction by voice. The voice recognition unit also enables the user to receive instructions by voice even if their hands are full while tidying up. This allows the user to receive tidying instructions without using their hands, allowing for efficient tidying up.

[0054] The organizing and storage support system further includes a reward providing unit. The reward providing unit can provide a reward when the user completes tidying up. For example, when the user completes tidying up, the reward providing unit can award points and allow the user to use the points to obtain gift cards or discount coupons. The reward providing unit can also provide rewards in stages according to the progress of tidying up. This makes it easier for the user to maintain motivation to continue tidying up.

[0055] The organizing and storage support system also includes a community collaboration unit, which allows users to share their tidying progress and results with other users. For example, users can post before and after photos of their tidying up to the community and receive feedback from other users. The community collaboration unit also holds tidying challenges and contests, allowing users to compete against each other. This allows users to interact with other users while progressing with their tidying up.

[0056] The organizing and storage support system also includes a health management unit. The health management unit monitors the user's health status and can provide appropriate advice when tidying up. For example, it can monitor the user's heart rate and calorie consumption and instruct the user to tidy up within a reasonable range. The health management unit can also issue alerts to encourage the user to take appropriate breaks while tidying up. This allows the user to tidy up while maintaining their health.

[0057] The organizing and storage support system further includes an eco-advice unit. The eco-advice unit can provide environmentally conscious advice when tidying up. For example, it can suggest ways to separate recyclable items when throwing away unnecessary items. The eco-advice unit can also suggest energy-efficient placement and usage of home appliances. This allows the user to tidy up while being environmentally conscious.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The photo uploader uploads a photo of a messy room. For example, you can upload the photo in image formats such as JPEG or PNG. The photo uploader also uses the uploaded photo as a prompt to input into the generation AI. Step 2: The image generation unit generates an image of a tidy room based on the photos uploaded by the photo upload unit. For example, the generation AI takes into account the room layout and furniture placement to create an image of a clean and tidy room. The generation AI can generate the image using CG technology and image processing algorithms. Step 3: The selection unit selects the ideal room from the multiple image images generated by the image generation unit. For example, a user can select the image of the room that is closest to their ideal from the multiple image images generated by the generation AI. Step 4: The instruction providing unit provides specific tidying instructions based on the image of the ideal room selected by the selecting unit. For example, specific instructions are provided step by step, such as "First, pick up and sort all the items scattered on the floor," "Next, throw away unnecessary items," and "Finally, arrange the furniture like this."

[0060] (Example 2) The organizing and storage support system according to an embodiment of the present invention allows users to upload a photo of a messy room, have a generation AI generate an image of a tidy room, and then have the user select their ideal room, and receive specific instructions on how to turn the selected room into a tidy room. This allows the organizing and storage support system to help users efficiently tidy up their rooms.

[0061] The organizing and storage support system according to the embodiment includes a photo uploading unit, an image generating unit, a selection unit, and an instruction providing unit. The photo uploading unit uploads photos of a messy room. For example, the photos can be uploaded in image formats such as JPEG or PNG. The photo uploading unit also uses the uploaded photos as input prompts for the generation AI. The image generating unit generates an image of a tidy room based on the photos uploaded by the photo uploading unit. For example, the generation AI creates an image of a clean and tidy room by considering the room layout and furniture arrangement. The generation AI can generate the image using CG technology or an image processing algorithm. The selection unit selects an ideal room from multiple images generated by the image generating unit. For example, a user can select an image of a room that most closely matches their ideal from the multiple images generated by the generation AI. The instruction providing unit provides specific tidying instructions based on the ideal room image selected by the selection unit. For example, specific step-by-step instructions are provided, such as "First, pick up and sort all the items scattered on the floor," "Next, throw away unnecessary items," and "Finally, arrange the furniture like this." This allows the user to tidy up efficiently without waste. As a result, the organization and storage support system according to the embodiment allows the user to tidy up their room efficiently.

[0062] The image generation unit can generate an image of a clean and tidy room by taking into account the room layout and furniture arrangement. For example, the generation AI can analyze the room dimensions and recognize the furniture arrangement. The generation AI can generate an image using CG technology and image processing algorithms. This allows the user to have an image of their ideal room.

[0063] The instruction providing unit can provide instructions to pick up and sort items scattered on the floor. For example, the instruction providing unit provides instructions to pick up and sort items scattered on the floor. For example, the generation AI analyzes the lighting conditions and color tones in the room and suggests the optimal tidying method. The generation AI determines the tidying order taking into account the way natural light enters and the arrangement of lighting. This allows the user to tidy up efficiently.

[0064] The instruction providing unit can provide instructions for throwing away unnecessary items. The instruction providing unit provides, for example, instructions for throwing away unnecessary items. For example, the generation AI analyzes the user's emotions and provides advice for reducing stress. The generation AI suggests music for relaxation. This allows the user to efficiently dispose of unnecessary items.

[0065] The instruction providing unit can instruct the arrangement of furniture. The instruction providing unit, for example, instructs the arrangement of furniture. For example, the generation AI analyzes the dimensions of the room and the arrangement of furniture and proposes the optimal arrangement. The generation AI can generate a 3D model and visually show the arrangement of furniture. This allows the user to arrange furniture efficiently.

[0066] The photo upload unit can analyze the room dimensions and furniture layout when uploading a photo and generate a 3D model. For example, the photo upload unit can analyze the room dimensions and furniture layout when uploading a photo and generate a 3D model. For example, the generation AI can analyze the room dimensions and recognize the furniture layout. The generation AI can generate a 3D model using 3D scanning technology or modeling software. This allows the user to accurately grasp the room dimensions and furniture layout.

[0067] The photo upload unit can analyze the lighting conditions and color tones of a room when uploading photos and suggest the optimal tidying method. For example, the photo upload unit can analyze the lighting conditions and color tones of a room when uploading photos and suggest the optimal tidying method. For example, the generation AI can analyze the lighting conditions of a room and suggest the optimal tidying method. The generation AI determines the tidying order taking into account the way natural light enters and the placement of lighting. This allows the user to obtain the optimal tidying method based on the lighting conditions and color tones of the room.

[0068] The photo uploading unit can use the emotion estimation function to analyze the emotion a user has when uploading photos and provide advice to reduce stress. The photo uploading unit, for example, uses the emotion estimation function to analyze the emotion a user has when uploading photos and provide advice to reduce stress. For example, the generation AI analyzes the user's emotion and provides advice to reduce stress. The generation AI suggests relaxing music. This allows the user to proceed with tidying up while reducing stress.

[0069] The photo uploading unit can analyze audio data in the room when uploading a photo and suggest a tidying method that takes sound reverberation into consideration. For example, the photo uploading unit can analyze audio data in the room when uploading a photo and suggest a tidying method that takes sound reverberation into consideration. For example, the generation AI can analyze audio data in the room and suggest a tidying method that takes sound reverberation into consideration. The generation AI can recommend laying down a carpet to reduce sound reverberation. This allows the user to obtain a tidying method that takes sound reverberation into consideration.

[0070] The photo upload unit can analyze the room temperature and humidity when uploading photos and suggest the optimal tidying environment. For example, the photo upload unit can analyze the room temperature and humidity when uploading photos and suggest the optimal tidying environment. For example, the generation AI can analyze the room temperature and humidity and suggest the optimal tidying environment. If the humidity is high, the generation AI can recommend using a dehumidifier. This allows the user to obtain the optimal tidying environment based on the temperature and humidity.

[0071] The photo uploading unit can use the emotion estimation function to analyze the emotion of the user when uploading photos in real time and provide positive feedback. The photo uploading unit can, for example, use the emotion estimation function to analyze the emotion of the user when uploading photos in real time and provide positive feedback. For example, the generation AI analyzes the user's emotion in real time and provides positive feedback. The generation AI displays an encouraging message. This allows the user to continue tidying up while receiving positive feedback.

[0072] When generating an image image of a room, the image generation unit can refer to the user's past tidying history and suggest the optimal tidying method. For example, when the generation AI generates an image image of a room, the image generation unit can refer to the user's past tidying history and suggest the optimal tidying method. For example, the generation AI makes suggestions based on tidying methods that have been successful in the past. This allows the user to obtain the optimal tidying method based on their past tidying history.

[0073] When generating an image image of a room, the image generation unit can suggest not only furniture arrangement but also interior design. For example, when the generation AI generates an image image of a room, the image generation unit can suggest not only furniture arrangement but also interior design. For example, the generation AI can suggest wall colors and curtain designs. This allows the user to tidy up their room while receiving interior design suggestions.

[0074] The image generation unit can use the emotion estimation function to analyze the user's emotional response to the generated image and generate an image that elicits the most positive response. For example, when the generation AI generates an image, the image generation unit can use the emotion estimation function to analyze the user's emotional response and generate an image that elicits the most positive response. For example, the generation AI can reflect the user's preferred colors and designs. This allows the user to tidy up their room while feeling positive emotions.

[0075] The image generation unit can incorporate interior styles from different cultures and regions when generating an image of a room. For example, when the generation AI generates an image of a room, the image generation unit incorporates interior styles from different cultures and regions. For example, the generation AI may suggest designs in the Nordic or Japanese style. This allows users to enjoy a room that incorporates the interior styles of different cultures and regions.

[0076] The image generation unit can suggest designs that correspond to the season or an event when generating an image image of a room. For example, when the generation AI generates an image image of a room, the image generation unit suggests designs that correspond to the season or an event. For example, the generation AI suggests decorations for Christmas or Halloween. This allows the user to enjoy designs that correspond to the season or an event.

[0077] The image generation unit uses the emotion estimation function to analyze the user's emotional response to the generated image in real time, and can continuously generate optimal images. For example, when the generation AI generates an image, the image generation unit uses the emotion estimation function to analyze the user's emotional response in real time, and continuously generate optimal images. For example, the generation AI adjusts the image according to the user's emotions. This allows the user to continuously obtain optimal images.

[0078] The selection unit can automatically generate questions that the generation AI uses to narrow down the options when the user selects their ideal room. For example, the selection unit automatically generates questions that the generation AI uses to narrow down the options when the user selects their ideal room. For example, the generation AI presents a question such as, "What color furniture do you like?" This allows the user to efficiently select their ideal room.

[0079] When a user selects their ideal room, the selection unit allows the generation AI to present the advantages and disadvantages of each option. For example, when a user selects their ideal room, the selection unit allows the generation AI to present the advantages and disadvantages of each option. For example, the generation AI may provide information such as, "This room has a lot of storage space, but few windows." This allows the user to select their ideal room while understanding the advantages and disadvantages of each option.

[0080] The selection unit uses the emotion estimation function to analyze the emotions of the user when making a selection and can support the most positive selection. For example, the selection unit uses the emotion estimation function to analyze the emotions of the user when selecting their ideal room and can support the most positive selection. For example, if the user expresses positive emotions toward an option, the generation AI recommends that selection. This allows the user to select their ideal room while feeling positive emotions.

[0081] The selection unit allows the generation AI to present recommendations by referring to the selection history of other users when the user selects their ideal room. For example, when the user selects their ideal room, the selection unit allows the generation AI to present recommendations by referring to the selection history of other users. For example, the generation AI recommends rooms selected by users with similar preferences. This allows the user to select their ideal room by referring to the selection history of other users.

[0082] When a user selects their ideal room, the selection unit allows the generation AI to display the options as a 3D model, providing a more concrete image. For example, when a user selects their ideal room, the selection unit allows the generation AI to display the options as a 3D model, providing a more concrete image. For example, the generation AI displays the entire room in 3D, allowing the user to check it in detail. This allows the user to select their ideal room while having a concrete image in mind.

[0083] The selection unit uses the emotion estimation function to analyze the user's emotions in real time when making a selection, and can continuously support the user in making the optimal selection. For example, the selection unit uses the emotion estimation function to analyze the user's emotions in real time when selecting their ideal room, and can continuously support the user in making the optimal selection. For example, the generation AI adjusts the options according to the user's emotions. This allows the user to select their ideal room while receiving continuous support in making the optimal selection.

[0084] When providing tidying up instructions, the instruction providing unit can provide step-by-step instructions taking into account the user's tidying up skill level. For example, when the generation AI provides tidying up instructions, the instruction providing unit provides step-by-step instructions taking into account the user's tidying up skill level. For example, the generation AI provides basic tidying procedures to beginners. This allows the user to proceed with tidying up while receiving step-by-step instructions according to their own skill level.

[0085] When providing tidying up instructions, the instruction providing unit can suggest a time schedule that matches the user's tidying up pace. For example, when the generation AI provides tidying up instructions, the instruction providing unit suggests a time schedule that matches the user's tidying up pace. For example, the generation AI instructs the user to take a break every hour. This allows the user to proceed with tidying up while receiving a time schedule that matches their own pace.

[0086] The instruction providing unit can use the emotion estimation function to analyze the user's emotional response to tidying instructions and provide advice to reduce stress. The instruction providing unit, for example, uses the emotion estimation function to analyze the user's emotional response to tidying instructions and provide advice to reduce stress. For example, the generation AI analyzes the user's emotions and provides advice to reduce stress. The generation AI suggests music to play to relax. This allows the user to proceed with tidying up while reducing stress.

[0087] The instruction providing unit can provide tidying up instructions that take into consideration the user's health condition and physical strength. For example, when the generation AI provides tidying up instructions, the instruction providing unit provides instructions that take into consideration the user's health condition and physical strength. For example, the generation AI recommends that a user with low physical strength tidy up for a short period of time. This allows the user to proceed with tidying up while receiving instructions that are appropriate for their own health condition and physical strength.

[0088] The instruction providing unit can monitor the tidying up progress in real time and adjust the instructions as necessary. For example, when the generation AI provides tidying up instructions, the instruction providing unit can monitor the tidying up progress in real time and adjust the instructions as necessary. For example, the generation AI can add a simple task if progress is lagging behind. This allows the user to proceed with tidying up while receiving instructions according to the progress.

[0089] The instruction providing unit can use the emotion estimation function to analyze the user's emotional response to tidying instructions in real time and continuously provide optimal instructions. The instruction providing unit can, for example, use the emotion estimation function to analyze the user's emotional response to tidying instructions in real time and continuously provide optimal instructions. For example, the generation AI adjusts the instructions according to the user's emotions. This allows the user to proceed with tidying up while receiving optimal instructions according to their emotional response.

[0090] The instruction providing unit can provide tidying up instructions that take into consideration the user's health condition and physical strength. For example, when the generation AI provides tidying up instructions, the instruction providing unit provides instructions that take into consideration the user's health condition and physical strength. For example, the generation AI recommends that a user with low physical strength tidy up for a short period of time. This allows the user to proceed with tidying up while receiving instructions that are appropriate for their own health condition and physical strength.

[0091] The instruction providing unit can monitor the tidying up progress in real time and adjust the instructions as necessary. For example, when the generation AI provides tidying up instructions, the instruction providing unit can monitor the tidying up progress in real time and adjust the instructions as necessary. For example, the generation AI can add a simple task if progress is lagging behind. This allows the user to proceed with tidying up while receiving instructions according to the progress.

[0092] The instruction providing unit can use the emotion estimation function to analyze the user's emotional response to tidying instructions in real time and continuously provide optimal instructions. The instruction providing unit can, for example, use the emotion estimation function to analyze the user's emotional response to tidying instructions in real time and continuously provide optimal instructions. For example, the generation AI adjusts the instructions according to the user's emotions. This allows the user to proceed with tidying up while receiving optimal instructions according to their emotional response.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] The organizing and storage support system further includes a voice recognition unit. The voice recognition unit enables the user to receive tidying instructions by voice. For example, when the user says, "Tell me the next instruction," the voice recognition unit provides the instruction by voice. The voice recognition unit also enables the user to receive instructions by voice even if their hands are full while tidying up. This allows the user to receive tidying instructions without using their hands, allowing for efficient tidying up.

[0095] The organizing and storage support system further includes a reward providing unit. The reward providing unit can provide a reward when the user completes tidying up. For example, when the user completes tidying up, the reward providing unit can award points and allow the user to use the points to obtain gift cards or discount coupons. The reward providing unit can also provide rewards in stages according to the progress of tidying up. This makes it easier for the user to maintain motivation to continue tidying up.

[0096] The organizing and storage support system also includes a community collaboration unit, which allows users to share their tidying progress and results with other users. For example, users can post before and after photos of their tidying up to the community and receive feedback from other users. The community collaboration unit also holds tidying challenges and contests, allowing users to compete against each other. This allows users to interact with other users while progressing with their tidying up.

[0097] The organizing and storage support system also includes a health management unit. The health management unit monitors the user's health status and can provide appropriate advice when tidying up. For example, it can monitor the user's heart rate and calorie consumption and instruct the user to tidy up within a reasonable range. The health management unit can also issue alerts to encourage the user to take appropriate breaks while tidying up. This allows the user to tidy up while maintaining their health.

[0098] The organizing and storage support system further includes an eco-advice unit. The eco-advice unit can provide environmentally conscious advice when tidying up. For example, it can suggest ways to separate recyclable items when throwing away unnecessary items. The eco-advice unit can also suggest energy-efficient placement and usage of home appliances. This allows the user to tidy up while being environmentally conscious.

[0099] The organizing and storage support system can also use an emotion estimation function to manage the progress of tidying up based on the user's emotions. For example, if the user feels stressed about tidying up, the system can suggest relaxing music. If the user has positive emotions about tidying up, the system can display encouraging messages to help maintain those emotions. This allows the user to receive support according to their emotions while tidying up.

[0100] The organizing and storage support system can also use its emotion estimation function to suggest a tidying time schedule based on the user's emotions. For example, if the user is tired, the system will suggest alternating short tidying sessions with breaks. If the user is concentrating, the system will suggest long tidying sessions. This allows the user to proceed with tidying while receiving a time schedule that corresponds to their emotional state.

[0101] The organizing and storage support system can also use its emotion estimation function to suggest tidying up priorities based on the user's emotions. For example, if the user is feeling stressed, the system will suggest starting with easy tasks. If the user is highly motivated, the system will suggest prioritizing difficult tasks. This allows the user to proceed with tidying up while receiving priorities according to their emotions.

[0102] The organizing and storage support system can also use the emotion estimation function to provide feedback on tidying up based on the user's emotions. For example, if the user has positive emotions about tidying up, the system will provide positive feedback to reinforce those emotions. On the other hand, if the user has negative emotions, the system will provide advice to alleviate those emotions. This allows the user to proceed with tidying up while receiving feedback according to their emotions.

[0103] The organizing and storage support system can also use an emotion estimation function to provide progress reports on tidying up based on the user's emotions. For example, if the user has positive emotions about tidying up, the system will provide frequent progress reports to maintain those emotions. On the other hand, if the user has negative emotions, the system will provide less frequent progress reports. This allows the user to proceed with tidying up while receiving progress reports that correspond to their emotions.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The photo uploader uploads a photo of a messy room. For example, you can upload the photo in image formats such as JPEG or PNG. The photo uploader also uses the uploaded photo as a prompt to input into the generation AI. Step 2: The image generation unit generates an image of a tidy room based on the photos uploaded by the photo upload unit. For example, the generation AI takes into account the room layout and furniture placement to create an image of a clean and tidy room. The generation AI can generate the image using CG technology and image processing algorithms. Step 3: The selection unit selects the ideal room from the multiple image images generated by the image generation unit. For example, a user can select the image of the room that is closest to their ideal from the multiple image images generated by the generation AI. Step 4: The instruction providing unit provides specific tidying instructions based on the image of the ideal room selected by the selecting unit. For example, specific instructions are provided step by step, such as "First, pick up and sort all the items scattered on the floor," "Next, throw away unnecessary items," and "Finally, arrange the furniture like this."

[0106] 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.

[0107] 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.

[0108] 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.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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."

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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]

[0173] 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 photo upload section where you can upload photos of your messy room, an image generating unit that generates an image of a tidy room based on the photos uploaded by the photo uploading unit; a selection unit for selecting an ideal room from the plurality of image images generated by the image generation unit; an instruction providing unit that provides specific tidying instructions based on the image of the ideal room selected by the selecting unit. A system characterized by:

2. The photo upload unit When the photo is uploaded, the dimensions and furniture layout of the room are analyzed to generate the 3D model.

2. The system of claim 1.

3. The photo upload unit When the photo is uploaded, the temperature and humidity of the room are analyzed and the optimal tidying environment is suggested.

2. The system of claim 1.

4. The image generation unit When generating the image of the room, the tidying up history of the user is referred to, and the optimal tidying up method is suggested.

2. The system of claim 1.

5. The selection unit When a user selects their ideal room, questions are automatically generated to narrow down the options.

2. The system of claim 1.

6. The instruction providing unit When providing the cleaning instructions, the cleaning skill level of the user is taken into consideration and step-by-step instructions are provided.

2. The system of claim 1.

7. The photo upload unit Analyze the emotions users have when uploading the photos and provide advice to reduce stress 2. The system of claim 1.

8. The selection unit Analyzing the emotions of users when selecting their ideal room and supporting the most positive selection 2. The system of claim 1.

Images