System

Abstract

An object of the system according to the embodiment is to efficiently perform abandonment and separation and reuse of property.SOLUTION: A system includes a property analysis part, an arrangement advice part, a reuse proposal part, and a rearrangement proposal part. The property analysis unit analyzes the property of the user. The organization advice unit provides advice on a method of organizing the unnecessary thing based on the property analyzed by the property analysis unit. The reuse proposal unit presents an idea of reuse or recycling after the abandonment based on the advice provided by the arrangement advice unit. Based on the idea presented by the reuse proposal unit, the rearrangement proposal unit proposes rearrangement and a new way of use according to the lifestyle.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have made the process of decluttering and reusing possessions complicated and difficult to carry out efficiently.

[0005] The system according to the embodiment aims to efficiently declutter and reuse possessions. [Means for solving the problem]

[0006] The system according to the embodiment includes a possessions analysis unit, an organizing advice unit, a reuse suggestion unit, and a rearrangement suggestion unit. The possessions analysis unit analyzes the user's possessions. The organizing advice unit provides advice on how to organize unnecessary items based on the possessions analyzed by the possessions analysis unit. The reuse suggestion unit presents ideas for reuse and recycling after decluttering based on the advice provided by the organizing advice unit. The rearrangement suggestion unit proposes rearrangements and new uses that suit a user's lifestyle based on the ideas presented by the reuse suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment allows users to efficiently declutter and reuse their possessions. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The ReNewAI system according to an embodiment of the present invention automatically analyzes a user's belongings, provides advice on how to organize them, and suggests ways to reuse or use them in new ways. By doing so, the ReNewAI system helps the user organize their belongings efficiently and suggests ways to reuse or use them in new ways, thereby creating a comfortable and organized living environment.

[0029] The ReNewAI system according to the embodiment includes an belongings analysis unit, an organizing advice unit, a reuse suggestion unit, and a rearrangement suggestion unit. The belongings analysis unit analyzes the user's belongings. For example, the belongings analysis unit receives a list and photos of the user's belongings as input information and performs analysis based on the input information. The belongings analysis unit also uses a generation AI to analyze the frequency of use and condition of items and identify unnecessary items. For example, when the generation AI inputs information about the user's clothing, furniture, books, etc., it analyzes the frequency of use and condition of these items and identifies unnecessary items. The organizing advice unit provides advice on how to organize unnecessary items based on the belongings analyzed by the belongings analysis unit. For example, the organizing advice unit generates specific advice such as, "This clothing is no longer worn, so I recommend donating or recycling it." The organizing advice unit can also use the generation AI to suggest optimal organizing methods. The reuse suggestion unit presents ideas for reuse and recycling after decluttering based on the advice provided by the organizing advice unit. For example, the reuse suggestion unit may suggest, "You can take this old furniture to a recycle shop or remodel it into new furniture with DIY." The reuse suggestion unit may also use a generative AI to suggest environmentally friendly reuse methods. The rearrangement suggestion unit may suggest rearrangements or new uses that suit a user's lifestyle based on the ideas suggested by the reuse suggestion unit. For example, the rearrangement suggestion unit may suggest, "To make effective use of this space, you can install a storage shelf and organize it." The rearrangement suggestion unit may also use a generative AI to suggest optimal layouts that suit a user's lifestyle. As a result, the ReNewAI system according to the embodiment efficiently organizes a user's belongings and suggests reuse and new uses, thereby realizing a comfortable and organized living environment. For example, the ReNewAI system may analyze a user's belongings, identify unnecessary items, and efficiently organize them. Furthermore, by suggesting ideas for reuse and recycling, environmentally friendly decluttering can be realized.Furthermore, by suggesting rearrangements and new uses that suit your lifestyle, you can create a more comfortable and organized living environment.

[0030] The possessions analysis unit can analyze the history and origin of possessions and evaluate their importance to the user. For example, the generation AI analyzes the purchase and usage history of possessions to identify the history and origin of the item. For example, it evaluates items purchased at specific events or items that have been used for a long time as important. The possessions analysis unit also inputs stories and memories related to the user's possessions, and the generation AI evaluates the importance of the item based on that information. For example, it identifies souvenirs purchased on trips or gifts from family. The possessions analysis unit also collects information from the internet and analyzes the history and origin of possessions. For example, it evaluates the value of antique furniture and collector's items and lists them as important items. In this way, the importance of possessions to the user can be evaluated by analyzing their history and origin.

[0031] The possessions analysis unit can create a digital twin of possessions and simulate their placement and usage in a virtual space. For example, the possessions analysis unit uses a generative AI to create a digital twin of possessions and simulate their placement and usage in a virtual space. For example, it tries out furniture placement in a virtual space and suggests the optimal placement. The possessions analysis unit also uses the digital twin to simulate how possessions are used and suggests efficient ways to use them. For example, it simulates the placement of kitchen utensils and suggests an easy-to-use placement. The possessions analysis unit also suggests rearrangements and new ways to use possessions through simulations in a virtual space. For example, it tries out rearranging a room in a virtual space and suggests the optimal layout. In this way, it is possible to suggest optimal placement and usage by simulating placement and usage in a virtual space using a digital twin.

[0032] The possessions analysis unit can evaluate the eco-footprint of possessions and suggest prioritizing keeping items with low environmental impact. For example, the generative AI evaluates the eco-footprint of possessions and identifies items with low environmental impact. For example, it suggests prioritizing keeping items made from recyclable materials and energy-efficient home appliances. The possessions analysis unit also analyzes the environmental impact of the manufacturing process and use of possessions to identify environmentally friendly items. For example, it suggests prioritizing keeping products with low carbon footprints or eco-labels. The possessions analysis unit also collects eco-footprint data of possessions and lists items with low environmental impact. For example, it identifies products that use renewable energy or recycled materials. This allows for environmentally conscious decluttering by prioritizing keeping items with low environmental impact.

[0033] The organizing advice unit can analyze the user's lifestyle and suggest the optimal time for organizing. For example, the generation AI in the organizing advice unit analyzes the user's lifestyle and suggests the optimal time for organizing. For example, it analyzes the user's activity patterns and identifies the best time of day for organizing. The organizing advice unit also collects the user's lifestyle data and builds a system in which the generation AI suggests the optimal time for organizing. For example, it suggests an organizing time taking into consideration the user's sleep patterns and work schedule. The organizing advice unit also analyzes the user's lifestyle in real time and dynamically suggests the optimal time for organizing. For example, it suggests an optimal time for organizing based on the user's current activity status. This enables efficient organizing by suggesting the optimal time for organizing based on the user's lifestyle.

[0034] The organizing advice unit can collect the opinions of the user's family and housemates and propose an organizing method that everyone can agree on. For example, the generation AI in the organizing advice unit collects the opinions of the user's family and housemates and proposes an organizing method that everyone can agree on. For example, opinions are collected in the form of a questionnaire and the optimal organizing method is proposed. The organizing advice unit also builds a system for collecting the opinions of family and housemates, and the generation AI proposes an organizing method based on that data. For example, the generation AI analyzes points of agreement and differences of opinion and proposes a method that everyone can agree on. The organizing advice unit also collects the opinions of family and housemates in real time and dynamically proposes an organizing method that everyone can agree on. For example, the organization advice unit adjusts the organizing method according to changes in opinion. In this way, by collecting the opinions of family and housemates, it is possible to propose an organizing method that everyone can agree on.

[0035] The organizing advice unit can take into account the user's hobbies and interests and suggest ways to organize while having fun. For example, the organizing advice unit uses a generation AI to analyze the user's hobbies and interests and suggest ways to organize while having fun. For example, it suggests ways to organize items related to a hobby while having fun. The organizing advice unit also builds a system in which the generation AI collects data on the user's hobbies and interests and suggests ways to organize while having fun based on that data. For example, it suggests an organizing method that incorporates activities related to a hobby. The organizing advice unit also uses a generation AI to analyze the user's hobbies and interests in real time and dynamically suggest ways to organize while having fun. For example, it adjusts the organizing method according to the user's current interests. In this way, it is possible to suggest ways to organize while having fun by taking hobbies and interests into consideration.

[0036] The reuse suggestion unit can collect information on local recycling facilities and reuse shops and propose the optimal reuse method. For example, the generation AI of the reuse suggestion unit collects information on local recycling facilities and reuse shops and proposes the optimal reuse method to the user. For example, it provides the locations and business hours of nearby recycling facilities. The reuse suggestion unit also builds a database of local recycling facilities and reuse shops, and the generation AI builds a system that proposes reuse methods based on that data. For example, it lists facilities that accept specific items. The reuse suggestion unit also collects information on local recycling facilities and reuse shops in real time and dynamically proposes the optimal reuse method. For example, it provides current business status and campaign information. In this way, by collecting information on local recycling facilities and reuse shops, the optimal reuse method can be proposed.

[0037] The reuse suggestion unit can evaluate the user's DIY skills and suggest easy-to-implement recycling projects. For example, the reuse suggestion unit uses a generation AI to evaluate the user's DIY skills and suggest easy-to-implement recycling projects. For example, it suggests DIY projects that can be made with simple tools and materials. The reuse suggestion unit also collects the user's DIY skill data and builds a system in which the generation AI uses that data to suggest recycling projects. For example, it makes suggestions based on past successful DIY projects. The reuse suggestion unit also uses a generation AI to evaluate the user's DIY skills in real time and dynamically suggest easy-to-implement recycling projects. For example, it adjusts the projects according to the user's current skill level. This makes it possible to suggest easy-to-implement recycling projects by evaluating the user's DIY skills.

[0038] The reuse suggestion unit can collect success stories of other users and propose useful recycling ideas. For example, the generation AI of the reuse suggestion unit collects success stories of other users and proposes useful recycling ideas. For example, it provides details of recycling projects carried out by other users. The reuse suggestion unit also builds a database of success stories of other users and constructs a system in which the generation AI proposes recycling ideas based on that data. For example, it analyzes commonalities between success stories and proposes optimal ideas. The reuse suggestion unit also collects success stories of other users in real time and dynamically proposes useful recycling ideas. For example, it proposes ideas based on the latest success stories. In this way, useful recycling ideas can be proposed by collecting success stories of other users.

[0039] The reuse proposal unit can collaborate with the user community to propose joint recycling projects. For example, the generation AI collaborates with the user community to propose joint recycling projects. For example, it proposes local recycling events and workshops. The reuse proposal unit also collects recycling project data from the community and builds a system in which the generation AI proposes joint projects based on that data. For example, it proposes projects that support local recycling activities. The reuse proposal unit also collaborates with the user community in real time to dynamically propose joint recycling projects. For example, it proposes projects that meet the needs of the community. This makes it possible to propose joint recycling projects by collaborating with the community.

[0040] The rearrangement suggestion unit can consider the user's health condition and suggest layouts and usages that promote health. For example, the generation AI in the rearrangement suggestion unit analyzes the user's health condition and suggests layouts and usages that promote health. For example, it suggests the placement of a standing desk to prevent excessive sitting. The rearrangement suggestion unit also collects the user's health data and builds a system in which the generation AI uses that data to suggest layouts and usages that promote health. For example, it suggests furniture layouts that encourage exercise. The rearrangement suggestion unit also analyzes the user's health condition in real time and dynamically suggests layouts and usages that promote health. For example, it adjusts the layout according to the user's current health condition. In this way, it is possible to suggest layouts and usages that promote health by taking the health condition into consideration.

[0041] The rearrangement suggestion unit can propose arrangement changes according to the season or event, providing a way to enjoy a sense of the season or event. For example, the rearrangement suggestion unit has a generation AI that proposes arrangement changes according to the season or event, providing a way to enjoy a sense of the season or event. For example, it proposes decoration arrangements for Christmas or Halloween. The rearrangement suggestion unit also collects seasonal and event data, and builds a system in which the generation AI proposes arrangement changes based on that data. For example, it proposes interior arrangements for each season. The rearrangement suggestion unit also dynamically provides a way to enjoy a sense of the season or event, by having the generation AI propose arrangement changes according to the season or event in real time. For example, it proposes an arrangement according to the current season or event. In this way, it is possible to provide a way to enjoy a sense of the season or event by suggesting arrangement changes according to the season or event.

[0042] The rearrangement suggestion unit can propose optimal placement and usage by taking into consideration the care of the user's pets and plants. For example, the generation AI of the rearrangement suggestion unit considers the care of the user's pets and plants and proposes optimal placement and usage. For example, it proposes spaces for pets and sunny places for plants. The rearrangement suggestion unit also collects pet and plant care data, and builds a system in which the generation AI proposes optimal placement and usage based on that data. For example, it proposes placement that takes into consideration the movement of pets and the growth conditions of plants. The rearrangement suggestion unit also analyzes the care of the user's pets and plants in real time by using the generation AI to dynamically propose optimal placement and usage. For example, it adjusts placement according to the current activity status of pets and the growth status of plants. This makes it possible to propose optimal placement and usage by taking into consideration the care of pets and plants.

[0043] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0044] The possessions analysis unit can evaluate the eco-footprint of possessions and suggest prioritizing keeping items with a low environmental impact. For example, the generation AI can evaluate the eco-footprint of possessions and identify items made from recyclable materials and energy-efficient home appliances. It can also analyze the environmental impact of the manufacturing process and use of possessions and suggest prioritizing keeping products with a low carbon footprint or eco-labels. Furthermore, the generation AI can collect eco-footprint data of possessions and identify products that use renewable energy or recycled materials. This allows for environmentally conscious decluttering by prioritizing keeping items with a low environmental impact.

[0045] The possessions analysis unit can create a digital twin of possessions and simulate their placement and usage in a virtual space. For example, the generative AI can create a digital twin of possessions, try out furniture placement in a virtual space, and suggest the optimal placement. It can also use the digital twin to simulate how possessions are used and suggest efficient ways to use them. Furthermore, it can suggest rearrangements and new ways to use possessions through simulations in a virtual space. This makes it possible to suggest optimal placement and usage by simulating placement and usage in a virtual space using the digital twin.

[0046] The possessions analysis unit can analyze the history and origin of possessions and evaluate their importance to the user. For example, the generation AI can analyze the purchase history and usage history of possessions and evaluate items purchased at specific events or items used for a long time as important. The user can also input stories and memories related to possessions, and the generation AI can evaluate the importance of the items based on that information. Furthermore, the generation AI can collect information on the Internet, evaluate the value of antique furniture and collector's items, and list them as important. In this way, the importance of possessions to the user can be evaluated by analyzing their history and origin.

[0047] The organizing advice unit can analyze the user's lifestyle and suggest the optimal time to organize. For example, the generation AI can analyze the user's lifestyle and identify the best time to organize based on their activity patterns. It is also possible to build a system that collects user lifestyle data and has the generation AI suggest the optimal time to organize. Furthermore, the generation AI can analyze the user's lifestyle in real time and suggest the best time to organize based on their current activity status. This allows for efficient organization by suggesting the optimal time to organize based on the user's lifestyle.

[0048] The organizing advice unit can collect the opinions of the user's family and housemates and propose an organizing method that everyone can agree on. For example, the generation AI can collect the opinions of family and housemates in the form of a questionnaire and propose the optimal organizing method. It is also possible to build a system to collect the opinions of family and housemates, and the generation AI can use that data to analyze the points of agreement and disagreement and propose a method that everyone can agree on. Furthermore, the generation AI can collect the opinions of family and housemates in real time and adjust the organizing method according to changes in opinion. In this way, by collecting the opinions of family and housemates, it is possible to propose an organizing method that everyone can agree on.

[0049] The organizing advice unit can consider the user's hobbies and interests and suggest ways to organize while having fun. For example, the generation AI can analyze the user's hobbies and interests and suggest ways to organize items related to the hobbies while having fun. It is also possible to build a system in which data on the user's hobbies and interests is collected and the generation AI uses that data to suggest organizing methods that incorporate activities related to the hobbies. Furthermore, the generation AI can analyze the user's hobbies and interests in real time and adjust the organizing method according to the user's current interests. This makes it possible to suggest ways to organize while having fun by taking hobbies and interests into consideration.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The possessions analysis unit analyzes the user's possessions. For example, the possessions analysis unit receives a list and photos of the items the user owns as input information and performs an analysis based on that. The possessions analysis unit also uses a generation AI to analyze the frequency of use and condition of items and identify unnecessary items. For example, when the generation AI inputs information about the user's clothing, furniture, books, etc., it analyzes the frequency of use and condition of these items and identifies unnecessary items. Step 2: The organizing advice unit provides advice on how to organize unnecessary items based on the belongings analyzed by the belongings analysis unit. For example, the organizing advice unit generates specific advice such as, "Since you no longer wear this clothing, we recommend donating or recycling it." The organizing advice unit can also use generation AI to suggest the optimal organizing method. Step 3: The reuse suggestion module presents ideas for reuse and recycling after decluttering based on the advice provided by the organizing advice module. For example, the reuse suggestion module may suggest, "You can take this old furniture to a recycle shop or remake it into new furniture with DIY." The reuse suggestion module can also use generative AI to suggest environmentally friendly reuse methods. Step 4: The rearrangement suggestion unit proposes rearrangements and new uses that suit the user's lifestyle based on the ideas presented by the reuse suggestion unit. For example, the rearrangement suggestion unit may suggest, "To make effective use of this space, you can install a storage shelf and organize it." The rearrangement suggestion unit can also use generative AI to suggest optimal layouts that suit the user's lifestyle.

[0052] (Example 2) The ReNewAI system according to an embodiment of the present invention automatically analyzes a user's belongings, provides advice on how to organize them, and suggests ways to reuse or use them in new ways. By doing so, the ReNewAI system helps the user organize their belongings efficiently and suggests ways to reuse or use them in new ways, thereby creating a comfortable and organized living environment.

[0053] The ReNewAI system according to the embodiment includes an belongings analysis unit, an organizing advice unit, a reuse suggestion unit, and a rearrangement suggestion unit. The belongings analysis unit analyzes the user's belongings. For example, the belongings analysis unit receives a list and photos of the user's belongings as input information and performs analysis based on the input information. The belongings analysis unit also uses a generation AI to analyze the frequency of use and condition of items and identify unnecessary items. For example, when the generation AI inputs information about the user's clothing, furniture, books, etc., it analyzes the frequency of use and condition of these items and identifies unnecessary items. The organizing advice unit provides advice on how to organize unnecessary items based on the belongings analyzed by the belongings analysis unit. For example, the organizing advice unit generates specific advice such as, "This clothing is no longer worn, so I recommend donating or recycling it." The organizing advice unit can also use the generation AI to suggest optimal organizing methods. The reuse suggestion unit presents ideas for reuse and recycling after decluttering based on the advice provided by the organizing advice unit. For example, the reuse suggestion unit may suggest, "You can take this old furniture to a recycle shop or remodel it into new furniture with DIY." The reuse suggestion unit may also use a generative AI to suggest environmentally friendly reuse methods. The rearrangement suggestion unit may suggest rearrangements or new uses that suit a user's lifestyle based on the ideas suggested by the reuse suggestion unit. For example, the rearrangement suggestion unit may suggest, "To make effective use of this space, you can install a storage shelf and organize it." The rearrangement suggestion unit may also use a generative AI to suggest optimal layouts that suit a user's lifestyle. As a result, the ReNewAI system according to the embodiment efficiently organizes a user's belongings and suggests reuse and new uses, thereby realizing a comfortable and organized living environment. For example, the ReNewAI system may analyze a user's belongings, identify unnecessary items, and efficiently organize them. Furthermore, by suggesting ideas for reuse and recycling, environmentally friendly decluttering can be realized.Furthermore, by suggesting rearrangements and new uses that suit your lifestyle, you can create a more comfortable and organized living environment.

[0054] The possessions analysis unit evaluates the emotional value of possessions and can identify items that are emotionally difficult for the user to let go of. For example, the generation AI performs an emotional analysis of the user's possessions and quantifies their emotional value. For example, it identifies items with high emotional value, such as photographs and diaries, and lists them as items that are difficult to let go of. The possessions analysis unit also collects the user's feelings toward possessions in the form of a questionnaire, and the generation AI evaluates their emotional value based on that data. For example, it identifies items that are emotionally important, such as keepsakes or gifts. The possessions analysis unit also analyzes the user's past behavioral data and identifies items with high emotional value. For example, it determines that frequently used items or items used at specific events are emotionally important. This helps reduce emotional burden by identifying items that are emotionally difficult for the user to let go of.

[0055] The possessions analysis unit can analyze the history and origin of possessions and evaluate their importance to the user. For example, the generation AI analyzes the purchase and usage history of possessions to identify the history and origin of the item. For example, it evaluates items purchased at specific events or items that have been used for a long time as important. The possessions analysis unit also inputs stories and memories related to the user's possessions, and the generation AI evaluates the importance of the item based on that information. For example, it identifies souvenirs purchased on trips or gifts from family. The possessions analysis unit also collects information from the internet and analyzes the history and origin of possessions. For example, it evaluates the value of antique furniture and collector's items and lists them as important items. In this way, the importance of possessions to the user can be evaluated by analyzing their history and origin.

[0056] The possessions analysis unit can use the emotion estimation function to analyze the emotions the user has toward their possessions and suggest that emotionally positive items be kept first. For example, the possessions analysis unit can use the emotion estimation function to analyze the emotions the user has toward their possessions in real time and identify items that evoke strong positive emotions. For example, it can suggest that items that evoke joy or satisfaction be kept first. The possessions analysis unit also records the emotions the user has toward their possessions in diary format, and the generation AI identifies emotionally positive items based on that data. For example, if there are many positive emotions toward a particular item, it can suggest that the item be kept. The possessions analysis unit also builds a system in which the generation AI analyzes the user's emotion data and suggests that emotionally positive items be kept first. For example, it can list items with high emotion scores and suggest them to the user. This can increase user satisfaction by prioritizing emotionally positive items.

[0057] The possessions analysis unit can create a digital twin of possessions and simulate their placement and usage in a virtual space. For example, the possessions analysis unit uses a generative AI to create a digital twin of possessions and simulate their placement and usage in a virtual space. For example, it tries out furniture placement in a virtual space and suggests the optimal placement. The possessions analysis unit also uses the digital twin to simulate how possessions are used and suggests efficient ways to use them. For example, it simulates the placement of kitchen utensils and suggests an easy-to-use placement. The possessions analysis unit also suggests rearrangements and new ways to use possessions through simulations in a virtual space. For example, it tries out rearranging a room in a virtual space and suggests the optimal layout. In this way, it is possible to suggest optimal placement and usage by simulating placement and usage in a virtual space using a digital twin.

[0058] The possessions analysis unit can evaluate the eco-footprint of possessions and suggest prioritizing keeping items with low environmental impact. For example, the generative AI evaluates the eco-footprint of possessions and identifies items with low environmental impact. For example, it suggests prioritizing keeping items made from recyclable materials and energy-efficient home appliances. The possessions analysis unit also analyzes the environmental impact of the manufacturing process and use of possessions to identify environmentally friendly items. For example, it suggests prioritizing keeping products with low carbon footprints or eco-labels. The possessions analysis unit also collects eco-footprint data of possessions and lists items with low environmental impact. For example, it identifies products that use renewable energy or recycled materials. This allows for environmentally conscious decluttering by prioritizing keeping items with low environmental impact.

[0059] The possessions analysis unit can use the emotion estimation function to provide a counseling function to reduce the emotional burden a user may feel when letting go of possessions. The possessions analysis unit, for example, uses the emotion estimation function to provide a counseling function to reduce the emotional burden a user may feel when letting go of possessions. For example, it may provide encouraging messages or support when letting go of emotionally significant items. The possessions analysis unit also analyzes the emotions a user feels when letting go of possessions in real time and provides advice to reduce the emotional burden. For example, it may suggest alternative items or ways to preserve memories when letting go of emotionally significant items. The possessions analysis unit also builds a system in which the generative AI analyzes the user's emotional data and provides a counseling function to reduce the emotional burden. For example, it may provide emotional support when letting go of emotionally significant items. This makes it easier for users to let go of their possessions by providing a counseling function that reduces the emotional burden.

[0060] The organizing advice unit can analyze the user's lifestyle and suggest the optimal time for organizing. For example, the generation AI in the organizing advice unit analyzes the user's lifestyle and suggests the optimal time for organizing. For example, it analyzes the user's activity patterns and identifies the best time of day for organizing. The organizing advice unit also collects the user's lifestyle data and builds a system in which the generation AI suggests the optimal time for organizing. For example, it suggests an organizing time taking into consideration the user's sleep patterns and work schedule. The organizing advice unit also analyzes the user's lifestyle in real time and dynamically suggests the optimal time for organizing. For example, it suggests an optimal time for organizing based on the user's current activity status. This enables efficient organizing by suggesting the optimal time for organizing based on the user's lifestyle.

[0061] The organizing advice unit can use the emotion estimation function to suggest relaxation methods to reduce stress felt by the user while organizing. For example, the organizing advice unit uses the emotion estimation function to analyze the stress felt by the user while organizing in real time and suggest relaxation methods. For example, it suggests relaxing music or deep breathing techniques. The organizing advice unit also builds a system that suggests relaxation methods to reduce stress while organizing based on the user's emotion data. For example, it suggests activities that can help relax when stress levels rise. The organizing advice unit also uses a generation AI to analyze the user's emotion data and dynamically suggest relaxation methods to reduce stress while organizing. For example, it suggests the optimal relaxation method based on the user's current emotional state. This reduces the burden on the user by suggesting relaxation methods to reduce stress while organizing.

[0062] The organizing advice unit can collect the opinions of the user's family and housemates and propose an organizing method that everyone can agree on. For example, the generation AI in the organizing advice unit collects the opinions of the user's family and housemates and proposes an organizing method that everyone can agree on. For example, opinions are collected in the form of a questionnaire and the optimal organizing method is proposed. The organizing advice unit also builds a system for collecting the opinions of family and housemates, and the generation AI proposes an organizing method based on that data. For example, the generation AI analyzes points of agreement and differences of opinion and proposes a method that everyone can agree on. The organizing advice unit also collects the opinions of family and housemates in real time and dynamically proposes an organizing method that everyone can agree on. For example, the organization advice unit adjusts the organizing method according to changes in opinion. In this way, by collecting the opinions of family and housemates, it is possible to propose an organizing method that everyone can agree on.

[0063] The organizing advice unit can take into account the user's hobbies and interests and suggest ways to organize while having fun. For example, the organizing advice unit uses a generation AI to analyze the user's hobbies and interests and suggest ways to organize while having fun. For example, it suggests ways to organize items related to a hobby while having fun. The organizing advice unit also builds a system in which the generation AI collects data on the user's hobbies and interests and suggests ways to organize while having fun based on that data. For example, it suggests an organizing method that incorporates activities related to a hobby. The organizing advice unit also uses a generation AI to analyze the user's hobbies and interests in real time and dynamically suggest ways to organize while having fun. For example, it adjusts the organizing method according to the user's current interests. In this way, it is possible to suggest ways to organize while having fun by taking hobbies and interests into consideration.

[0064] The organizing advice unit can use the emotion estimation function to provide feedback to maximize the sense of accomplishment the user feels after organizing. The organizing advice unit, for example, uses the emotion estimation function to analyze the sense of accomplishment the user feels after organizing in real time and provides feedback. For example, it provides a message emphasizing points that make the user feel a sense of accomplishment. The organizing advice unit also builds a system that provides feedback to maximize the sense of accomplishment after organizing based on the user's emotion data. For example, it provides feedback that emphasizes elements that make the user feel a sense of accomplishment. The organizing advice unit also uses a generation AI to analyze the user's emotion data and dynamically provide feedback to maximize the sense of accomplishment after organizing. For example, it provides optimal feedback based on the user's current emotional state. This makes it possible to increase user satisfaction by providing feedback that maximizes the sense of accomplishment after organizing.

[0065] The reuse suggestion unit can collect information on local recycling facilities and reuse shops and propose the optimal reuse method. For example, the generation AI of the reuse suggestion unit collects information on local recycling facilities and reuse shops and proposes the optimal reuse method to the user. For example, it provides the locations and business hours of nearby recycling facilities. The reuse suggestion unit also builds a database of local recycling facilities and reuse shops, and the generation AI builds a system that proposes reuse methods based on that data. For example, it lists facilities that accept specific items. The reuse suggestion unit also collects information on local recycling facilities and reuse shops in real time and dynamically proposes the optimal reuse method. For example, it provides current business status and campaign information. In this way, by collecting information on local recycling facilities and reuse shops, the optimal reuse method can be proposed.

[0066] The reuse suggestion unit can evaluate the user's DIY skills and suggest easy-to-implement recycling projects. For example, the reuse suggestion unit uses a generation AI to evaluate the user's DIY skills and suggest easy-to-implement recycling projects. For example, it suggests DIY projects that can be made with simple tools and materials. The reuse suggestion unit also collects the user's DIY skill data and builds a system in which the generation AI uses that data to suggest recycling projects. For example, it makes suggestions based on past successful DIY projects. The reuse suggestion unit also uses a generation AI to evaluate the user's DIY skills in real time and dynamically suggest easy-to-implement recycling projects. For example, it adjusts the projects according to the user's current skill level. This makes it possible to suggest easy-to-implement recycling projects by evaluating the user's DIY skills.

[0067] The reuse suggestion unit can use the emotion estimation function to suggest projects that will increase the user's satisfaction with reuse and recycling. For example, the reuse suggestion unit uses the emotion estimation function to analyze the user's satisfaction with reuse and recycling in real time and suggest projects. For example, it suggests projects that emphasize points that cause satisfaction. The reuse suggestion unit also builds a system that suggests projects that will increase the user's satisfaction with reuse and recycling based on the user's emotion data. For example, it suggests projects that incorporate elements that cause satisfaction. The reuse suggestion unit also uses a generation AI to analyze the user's emotion data and dynamically suggest projects that will increase the user's satisfaction with reuse and recycling. For example, it suggests optimal projects based on the user's current emotional state. This makes it possible to increase user satisfaction by suggesting projects that will increase the user's satisfaction with reuse and recycling.

[0068] The reuse suggestion unit can collect success stories of other users and propose useful recycling ideas. For example, the generation AI of the reuse suggestion unit collects success stories of other users and proposes useful recycling ideas. For example, it provides details of recycling projects carried out by other users. The reuse suggestion unit also builds a database of success stories of other users and constructs a system in which the generation AI proposes recycling ideas based on that data. For example, it analyzes commonalities between success stories and proposes optimal ideas. The reuse suggestion unit also collects success stories of other users in real time and dynamically proposes useful recycling ideas. For example, it proposes ideas based on the latest success stories. In this way, useful recycling ideas can be proposed by collecting success stories of other users.

[0069] The reuse proposal unit can collaborate with the user community to propose joint recycling projects. For example, the generation AI collaborates with the user community to propose joint recycling projects. For example, it proposes local recycling events and workshops. The reuse proposal unit also collects recycling project data from the community and builds a system in which the generation AI proposes joint projects based on that data. For example, it proposes projects that support local recycling activities. The reuse proposal unit also collaborates with the user community in real time to dynamically propose joint recycling projects. For example, it proposes projects that meet the needs of the community. This makes it possible to propose joint recycling projects by collaborating with the community.

[0070] The reuse suggestion unit can use the emotion estimation function to suggest events that will increase the sense of social contribution that the user feels through recycling activities. For example, the reuse suggestion unit uses the emotion estimation function to analyze in real time the sense of social contribution that the user feels through recycling activities and suggest events. For example, it suggests events that emphasize points that make the user feel a sense of social contribution. The reuse suggestion unit also builds a system that suggests events that will increase the sense of social contribution through recycling activities based on the user's emotion data. For example, it suggests events that incorporate elements that make the user feel a sense of social contribution. The reuse suggestion unit also uses a generation AI to analyze the user's emotion data and dynamically suggest events that will increase the sense of social contribution through recycling activities. For example, it suggests optimal events based on the user's current emotional state. This makes it possible to increase user satisfaction by suggesting events that will increase the sense of social contribution through recycling activities.

[0071] The rearrangement suggestion unit can consider the user's health condition and suggest layouts and usages that promote health. For example, the generation AI in the rearrangement suggestion unit analyzes the user's health condition and suggests layouts and usages that promote health. For example, it suggests the placement of a standing desk to prevent excessive sitting. The rearrangement suggestion unit also collects the user's health data and builds a system in which the generation AI uses that data to suggest layouts and usages that promote health. For example, it suggests furniture layouts that encourage exercise. The rearrangement suggestion unit also analyzes the user's health condition in real time and dynamically suggests layouts and usages that promote health. For example, it adjusts the layout according to the user's current health condition. In this way, it is possible to suggest layouts and usages that promote health by taking the health condition into consideration.

[0072] The rearrangement suggestion unit can use the emotion estimation function to make suggestions to maximize the satisfaction the user feels with a new layout or usage. The rearrangement suggestion unit, for example, uses the emotion estimation function to analyze in real time the satisfaction the user feels with a new layout or usage and makes suggestions. For example, it proposes a layout that emphasizes points that cause satisfaction. The rearrangement suggestion unit also builds a system that makes suggestions to maximize the satisfaction with a new layout or usage based on the user's emotion data. For example, it proposes a layout that incorporates elements that cause satisfaction. The rearrangement suggestion unit also uses a generation AI to analyze the user's emotion data and dynamically make suggestions to maximize the satisfaction with a new layout or usage. For example, it proposes an optimal layout based on the user's current emotional state. This makes it possible to increase user satisfaction by making suggestions that maximize the satisfaction with a new layout or usage.

[0073] The rearrangement suggestion unit can propose arrangement changes according to the season or event, providing a way to enjoy a sense of the season or event. For example, the rearrangement suggestion unit has a generation AI that proposes arrangement changes according to the season or event, providing a way to enjoy a sense of the season or event. For example, it proposes decoration arrangements for Christmas or Halloween. The rearrangement suggestion unit also collects seasonal and event data, and builds a system in which the generation AI proposes arrangement changes based on that data. For example, it proposes interior arrangements for each season. The rearrangement suggestion unit also dynamically provides a way to enjoy a sense of the season or event, by having the generation AI propose arrangement changes according to the season or event in real time. For example, it proposes an arrangement according to the current season or event. In this way, it is possible to provide a way to enjoy a sense of the season or event by suggesting arrangement changes according to the season or event.

[0074] The rearrangement suggestion unit can propose optimal placement and usage by taking into consideration the care of the user's pets and plants. For example, the generation AI of the rearrangement suggestion unit considers the care of the user's pets and plants and proposes optimal placement and usage. For example, it proposes spaces for pets and sunny places for plants. The rearrangement suggestion unit also collects pet and plant care data, and builds a system in which the generation AI proposes optimal placement and usage based on that data. For example, it proposes placement that takes into consideration the movement of pets and the growth conditions of plants. The rearrangement suggestion unit also analyzes the care of the user's pets and plants in real time by using the generation AI to dynamically propose optimal placement and usage. For example, it adjusts placement according to the current activity status of pets and the growth status of plants. This makes it possible to propose optimal placement and usage by taking into consideration the care of pets and plants.

[0075] The rearrangement suggestion unit can use the emotion estimation function to provide a visual simulation that increases the user's anticipation for a new layout or usage. The rearrangement suggestion unit, for example, uses the emotion estimation function to analyze the user's anticipation for a new layout or usage in real time and provides a visual simulation. For example, it provides a simulation that emphasizes points that create anticipation. The rearrangement suggestion unit also builds a system that provides a visual simulation that increases the user's anticipation for a new layout or usage based on the user's emotional data. For example, it provides a simulation that incorporates elements that create anticipation. The rearrangement suggestion unit also uses a generation AI to analyze the user's emotional data and dynamically provides a visual simulation that increases the user's anticipation for a new layout or usage. For example, it provides an optimal simulation based on the user's current emotional state. This makes it possible to increase user satisfaction by providing a visual simulation that increases the user's anticipation for a new layout or usage.

[0076] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0077] The possessions analysis unit can evaluate the eco-footprint of possessions and suggest prioritizing keeping items with a low environmental impact. For example, the generation AI can evaluate the eco-footprint of possessions and identify items made from recyclable materials and energy-efficient home appliances. It can also analyze the environmental impact of the manufacturing process and use of possessions and suggest prioritizing keeping products with a low carbon footprint or eco-labels. Furthermore, the generation AI can collect eco-footprint data of possessions and identify products that use renewable energy or recycled materials. This allows for environmentally conscious decluttering by prioritizing keeping items with a low environmental impact.

[0078] The possessions analysis unit can create a digital twin of possessions and simulate their placement and usage in a virtual space. For example, the generative AI can create a digital twin of possessions, try out furniture placement in a virtual space, and suggest the optimal placement. It can also use the digital twin to simulate how possessions are used and suggest efficient ways to use them. Furthermore, it can suggest rearrangements and new ways to use possessions through simulations in a virtual space. This makes it possible to suggest optimal placement and usage by simulating placement and usage in a virtual space using the digital twin.

[0079] The possessions analysis unit can use the emotion estimation function to analyze the emotions the user feels toward their possessions and suggest that emotionally positive items be kept first. For example, the emotion estimation function can be used to analyze the emotions the user feels toward their possessions in real time and identify items that evoke joy or satisfaction. The user can also record their emotions toward their possessions in diary format, and the generation AI can use that data to identify emotionally positive items. Furthermore, the generation AI can analyze the user's emotion data, list items with high emotion scores, and suggest them to the user. This can increase user satisfaction by prioritizing emotionally positive items.

[0080] The possessions analysis unit can analyze the history and origin of possessions and evaluate their importance to the user. For example, the generation AI can analyze the purchase history and usage history of possessions and evaluate items purchased at specific events or items used for a long time as important. The user can also input stories and memories related to possessions, and the generation AI can evaluate the importance of the items based on that information. Furthermore, the generation AI can collect information on the Internet, evaluate the value of antique furniture and collector's items, and list them as important. In this way, the importance of possessions to the user can be evaluated by analyzing their history and origin.

[0081] The possessions analysis unit can use the emotion estimation function to provide a counseling function to reduce the emotional burden when a user lets go of possessions. For example, the emotion estimation function can be used to analyze the emotions a user feels when letting go of a possession in real time and provide encouraging messages and support. It can also analyze the emotions a user feels when letting go of a possession and suggest alternative items or ways to preserve memories. Furthermore, it is possible to build a system in which the generative AI analyzes the user's emotional data and provides a counseling function to reduce the emotional burden. This makes it easier for users to let go of their possessions by providing a counseling function that reduces the emotional burden.

[0082] The organizing advice unit can analyze the user's lifestyle and suggest the optimal time to organize. For example, the generation AI can analyze the user's lifestyle and identify the best time to organize based on their activity patterns. It is also possible to build a system that collects user lifestyle data and has the generation AI suggest the optimal time to organize. Furthermore, the generation AI can analyze the user's lifestyle in real time and suggest the best time to organize based on their current activity status. This allows for efficient organization by suggesting the optimal time to organize based on the user's lifestyle.

[0083] The organizing advice unit can use the emotion estimation function to suggest relaxation methods to reduce the stress the user feels while organizing. For example, the emotion estimation function can be used to analyze the stress the user feels while organizing in real time and suggest relaxing music or deep breathing techniques. It can also suggest relaxing activities based on the user's emotion data when stress levels rise. Furthermore, the generation AI can analyze the user's emotion data and suggest optimal relaxation methods based on their current emotional state. This can reduce the burden on the user by suggesting relaxation methods to reduce stress while organizing.

[0084] The organizing advice unit can collect the opinions of the user's family and housemates and propose an organizing method that everyone can agree on. For example, the generation AI can collect the opinions of family and housemates in the form of a questionnaire and propose the optimal organizing method. It is also possible to build a system to collect the opinions of family and housemates, and the generation AI can use that data to analyze the points of agreement and disagreement and propose a method that everyone can agree on. Furthermore, the generation AI can collect the opinions of family and housemates in real time and adjust the organizing method according to changes in opinion. In this way, by collecting the opinions of family and housemates, it is possible to propose an organizing method that everyone can agree on.

[0085] The organizing advice unit can consider the user's hobbies and interests and suggest ways to organize while having fun. For example, the generation AI can analyze the user's hobbies and interests and suggest ways to organize items related to the hobbies while having fun. It is also possible to build a system in which data on the user's hobbies and interests is collected and the generation AI uses that data to suggest organizing methods that incorporate activities related to the hobbies. Furthermore, the generation AI can analyze the user's hobbies and interests in real time and adjust the organizing method according to the user's current interests. This makes it possible to suggest ways to organize while having fun by taking hobbies and interests into consideration.

[0086] The organizing advice unit can use the emotion estimation function to provide feedback to maximize the sense of accomplishment the user feels after organizing. For example, the emotion estimation function can be used to analyze in real time the sense of accomplishment the user feels after organizing, and provide a message emphasizing the points that make them feel a sense of accomplishment. It is also possible to build a system that provides feedback that emphasizes elements that make users feel a sense of accomplishment based on the user's emotion data. Furthermore, the generation AI can analyze the user's emotion data and provide optimal feedback based on their current emotional state. This can increase user satisfaction by providing feedback that maximizes the sense of accomplishment after organizing.

[0087] The processing flow of the second embodiment will be briefly explained below.

[0088] Step 1: The possessions analysis unit analyzes the user's possessions. For example, the possessions analysis unit receives a list and photos of the items the user owns as input information and performs an analysis based on that. The possessions analysis unit also uses a generation AI to analyze the frequency of use and condition of items and identify unnecessary items. For example, when the generation AI inputs information about the user's clothing, furniture, books, etc., it analyzes the frequency of use and condition of these items and identifies unnecessary items. Step 2: The organizing advice unit provides advice on how to organize unnecessary items based on the belongings analyzed by the belongings analysis unit. For example, the organizing advice unit generates specific advice such as, "Since you no longer wear this clothing, we recommend donating or recycling it." The organizing advice unit can also use generation AI to suggest the optimal organizing method. Step 3: The reuse suggestion module presents ideas for reuse and recycling after decluttering based on the advice provided by the organizing advice module. For example, the reuse suggestion module may suggest, "You can take this old furniture to a recycle shop or remake it into new furniture with DIY." The reuse suggestion module can also use generative AI to suggest environmentally friendly reuse methods. Step 4: The rearrangement suggestion unit proposes rearrangements and new uses that suit the user's lifestyle based on the ideas presented by the reuse suggestion unit. For example, the rearrangement suggestion unit may suggest, "To make effective use of this space, you can install a storage shelf and organize it." The rearrangement suggestion unit can also use generative AI to suggest optimal layouts that suit the user's lifestyle.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0093] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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).

[0098] 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.

[0099] 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.

[0100] 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.

[0101] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0102] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0108] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0123] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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."

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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]

[0156] 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 possessions analysis unit that analyzes possessions of a user; a sorting advice unit that provides advice on how to sort unnecessary items based on the belongings analyzed by the belongings analysis unit; A reuse suggestion unit that suggests ideas for reuse and recycling after decluttering based on the advice provided by the organizing advice unit; and a rearrangement suggestion unit that suggests rearrangement or new usage suited to a lifestyle based on the ideas presented by the reuse suggestion unit. A system characterized by:

2. The possession analysis unit Evaluating the emotional value of the possessions and identifying items that are emotionally difficult for the user to part with.

2. The system of claim 1.

3. The possession analysis unit Analyzing the history and provenance of the property and assessing its importance to the user 2. The system of claim 1.

4. The possession analysis unit Analyzing the emotions the user has toward the possessions and suggesting that emotionally positive items be kept first 2. The system of claim 1.

5. The possession analysis unit Create a digital twin of the property and simulate its placement and use in a virtual space.

2. The system of claim 1.

6. The possession analysis unit Evaluate the eco-footprint of said possessions and suggest prioritizing keeping items with a low environmental impact 2. The system of claim 1.

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