system

Abstract

The system according to the embodiment aims to efficiently suggest methods for disposing of or recycling unwanted items and to support people in taking action. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a suggestion unit, and a listing support unit. The collection unit collects information about items. The analysis unit analyzes the information collected by the collection unit. The suggestion unit suggests disposal or recycling methods based on the analysis results obtained by the analysis unit. The listing support unit supports listing of items suggested by the suggestion unit in cooperation with a flea market app.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to efficiently suggest ways to dispose of or recycle unwanted items, making it difficult to put such ideas into action.

[0005] The system according to the embodiment aims to efficiently suggest methods for disposing of or recycling unwanted items and to support people in taking action. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a suggestion unit, and a listing support unit. The collection unit collects information about items. The analysis unit analyzes the information collected by the collection unit. The suggestion unit suggests disposal or recycling methods based on the analysis results obtained by the analysis unit. The listing support unit supports listing of items suggested by the suggestion unit in cooperation with a flea market app. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently suggest methods for disposing of or recycling unwanted items and support the user in taking action. [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 decluttering support system according to an embodiment of the present invention collects, analyzes, and recommends items and assists users in listing them for sale. In the decluttering support system, users photograph items to be discarded and register simple information such as frequency of use and degree of dirt. AI then analyzes the information and offers suggestions on how to dispose of the items (depending on the region), how to recycle them, and how to sell them at a flea market. Furthermore, for items that can be sold at a flea market, the system works in conjunction with a flea market app to smoothly assist users in listing the items. For example, the decluttering support system photographs items to be discarded. It is important to capture the entire image of the item. This applies to a variety of items, including old furniture, clothing, and home appliances. This information is then input into the AI. Next, the user registers simple information about the items, such as frequency of use and degree of dirt. For example, specific information can be entered, such as when furniture is rarely used or when clothing is rarely worn. This information is also input into the AI. The decluttering support system then analyzes the input information and offers suggestions on how to dispose of the items, how to recycle them, and how to sell them at a flea market. For example, the system suggests how to dispose of items based on local waste sorting rules. It also suggests recycling methods for recyclable items. Furthermore, for items that can be sold at flea markets, it works with flea market apps to assist with listing. For example, it can automatically generate photos and descriptions of items, simplifying the listing process. This allows users to sell items without hassle. This allows the decluttering support system to make decluttering easy for users. By simply taking a photo of the item to be discarded and registering basic information, AI can suggest the optimal way to discard, recycle, or sell it at a flea market. This allows for smooth disposal of items and efficient decluttering. This allows the decluttering support system to efficiently declutter. For example, by collecting, analyzing, and suggesting information about items and providing support for listing, users can dispose of items without hassle. Furthermore, by promoting the recycling and sale of items, it can also contribute to environmental protection.

[0029] The decluttering support system according to the embodiment includes a collection unit, an analysis unit, a suggestion unit, and a listing support unit. The collection unit collects information about items. The collection unit, for example, takes photos of the items. The collection unit can also register information about the frequency of use of the items and their degree of dirtiness. For example, the collection unit provides an interface for a user to input the frequency of use and degree of dirtiness of the items. The analysis unit analyzes the information collected by the collection unit. The analysis unit, for example, refers to regional waste sorting rules. The analysis unit can also analyze the material and condition of the items to identify recyclable items. For example, the analysis unit analyzes the material of the items to identify recyclable items. The suggestion unit suggests disposal methods and recycling methods based on the analysis results obtained by the analysis unit. The suggestion unit suggests how to dispose of items based on regional waste sorting rules, for example. The suggestion unit can also suggest recycling methods for recyclable items. For example, the suggestion unit suggests recycling methods for recyclable items. The listing support unit cooperates with the flea market app to support the listing of the items suggested by the suggestion unit. The listing support unit, for example, automatically generates photos and descriptions of the items and supports the listing procedure. For example, the listing support unit analyzes photos of the items and automatically generates optimal descriptions. This allows the decluttering support system according to the embodiment to enable users to declutter efficiently. For example, by collecting, analyzing, and suggesting information about items and providing listing support, users can dispose of items without hassle. Furthermore, promoting the recycling and sale of items can contribute to environmental protection.

[0030] The collection unit can take photos of items. It is important to take photos of items in a way that allows the entire image of the item to be seen. For example, when taking photos of items, the collection unit can adjust the shooting angle, resolution, lighting conditions, etc. For example, when taking photos of furniture, the collection unit can use a wide-angle lens and take photos using natural light. When taking photos of clothing, the collection unit can automatically adjust lighting to make the color and texture more visible. Furthermore, when taking photos of home appliances, the collection unit can take photos from multiple angles and select the optimal photo so that the function and condition of the appliance can be seen. This allows accurate collection of information about the appliance by taking photos. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can take photos of the appliance, input the photos into a generation AI, and have the generation AI select the optimal photo.

[0031] The collection unit can register information on the frequency of use or degree of dirt of an item. Information on the frequency of use or degree of dirt of an item includes, for example, the number of times of use, the duration of use, the type and degree of dirt, etc. For example, the collection unit provides an interface through which a user can input the frequency of use or degree of dirt of an item. The collection unit can also automatically detect the frequency of use or degree of dirt of an item. For example, the collection unit can perform image analysis of dirt on the surface of an item and automatically evaluate the degree of dirt. The collection unit can also estimate and register the frequency of use of an item from past usage history or sensor information. Furthermore, the collection unit can perform image analysis of the condition of an item and automatically detect whether or not there are scratches or damage. In this way, by registering information such as the frequency of use and degree of dirt of an item, the condition of the item can be accurately understood. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the frequency of use and degree of dirt of an item into a generation AI, and the generation AI can register the information.

[0032] The analysis unit can refer to regional waste sorting rules. Regional waste sorting rules include, for example, municipal guidelines and online databases. For example, the analysis unit can refer to regional waste sorting rules and suggest how to dispose of the items. The analysis unit can also analyze the material and condition of an item to identify recyclable items. For example, the analysis unit can analyze the material of an item to identify recyclable items. Furthermore, the analysis unit can refer to the usage history of the item to suggest the optimal recycling method. For example, the analysis unit can analyze the usage history of an item to identify recyclable items and suggest the optimal recycling method. In this way, by referring to regional waste sorting rules, an appropriate disposal method can be suggested. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input regional waste sorting rules into a generation AI, which can then suggest the optimal disposal method.

[0033] The suggestion unit can suggest recycling methods for recyclable items. Recyclable items include, for example, the type of material and recycling conditions. For example, the suggestion unit suggests recycling methods for recyclable items. The suggestion unit can also analyze the material and condition of an item to identify recyclable items. For example, the suggestion unit can analyze the material of an item to identify recyclable items. Furthermore, the suggestion unit can refer to the usage history of the item to suggest an optimal recycling method. For example, the suggestion unit can analyze the usage history of an item to identify recyclable items and suggest an optimal recycling method. In this way, by suggesting recycling methods for recyclable items, reuse of the items can be promoted. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input information about recyclable items into a generation AI, which can suggest an optimal recycling method.

[0034] The listing support unit can automatically generate photos or descriptions of items to support the listing process. The automatic generation of item photos and descriptions includes, for example, the algorithms used and the criteria for the content to be generated. For example, the listing support unit can analyze photos of items and automatically generate optimal descriptions. The listing support unit can also analyze photos of items and select optimal photos. Furthermore, the listing support unit can automatically generate photos and descriptions of items to simplify the listing process. For example, the listing support unit can analyze photos of items and automatically generate optimal descriptions to support the listing process. By automatically generating photos and descriptions of items and supporting the listing process, users can sell items without hassle. Some or all of the above-described processing in the listing support unit may be performed using, for example, AI, or may be performed without AI. For example, the listing support unit can input the generation of item photos and descriptions into a generation AI, which can then generate optimal photos and descriptions.

[0035] When photographing an item, the collection unit can automatically adjust the appropriate shooting angle or lighting depending on the type of item. For example, in the case of furniture, the collection unit uses a wide-angle lens and natural light to capture the entire image. In addition, in the case of clothing, the collection unit can automatically adjust the appropriate lighting to capture the image so that the color and texture are easily seen. Furthermore, in the case of home appliances, the collection unit can also photograph the item from multiple angles and select the optimal photo so that the function and condition can be seen. In this way, by automatically adjusting the optimal shooting angle and lighting depending on the type of item, the item can be photographed in the optimal state. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can photograph the item, input the photograph to a generation AI, and the generation AI can adjust the optimal shooting angle and lighting.

[0036] The collection unit can automatically detect the frequency of use and degree of dirt of an item, eliminating the need for the user to input such information. For example, the collection unit uses AI to perform image analysis of dirt on the surface of an item and automatically evaluate the degree of dirt. The collection unit can also use AI to estimate and register the frequency of use of an item based on past usage history and sensor information. Furthermore, the collection unit can also use AI to perform image analysis of the condition of the item and automatically detect whether or not there are scratches or damage. This automatically detects the frequency of use and degree of dirt of an item, eliminating the need for the user to input such information. Some or all of the above-described processing in the collection unit may be performed using AI, or may be performed without AI. For example, the collection unit can input the frequency of use and degree of dirt of an item into a generation AI, which then registers the information.

[0037] The collection unit can automatically estimate the size or weight of an item when a photograph of the item is taken and add it to the registration information. For example, the collection unit can use AI to estimate the size from a photograph of the item and add it to the registration information. The collection unit can also use AI to estimate the weight from a photograph of the item and add it to the registration information. Furthermore, the collection unit can also use AI to simultaneously estimate the size and weight from a photograph of the item and add it to the registration information. In this way, by automatically estimating the size and weight of an item and adding it to the registration information, it is possible to accurately grasp the information about the item. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input a photograph of the item into a generation AI and have the generation AI estimate the size and weight.

[0038] When photographing an item, the collection unit can prioritize photographing highly relevant items by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes photographing items that are frequently used in that area. Furthermore, when the user is traveling, the collection unit can prioritize photographing items that are easy to carry. Furthermore, when the user is at home, the collection unit can prioritize photographing items used at home. This allows for efficient collection of item information by prioritizing photographing highly relevant items by taking into account the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into a generation AI, and the generation AI can prioritize photographing highly relevant items.

[0039] When photographing an item, the collection unit can analyze the user's social media activity and photograph related items. For example, the collection unit prioritizes photographing items that the user frequently posts on social media. The collection unit can also analyze the content of the user's social media posts and photograph related items. Furthermore, the collection unit can also photograph related items with reference to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity and photographing related items, information on items based on the user's interests can be collected. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input the user's social media activity data into a generation AI and have the generation AI photograph related items.

[0040] The collection unit can customize the photography method by reflecting the user's past feedback when taking a photograph of an item. The collection unit can, for example, suggest an optimal photography method based on the user's preferred photography methods in the past. The collection unit can also analyze the user's past feedback and customize the photography method. Furthermore, the collection unit can also suggest an optimal photography method based on the quality of photos the user has taken in the past. In this way, by customizing the photography method by reflecting the user's past feedback, it is possible to provide an optimal photography method that meets the user's preferences. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into a generation AI, and the generation AI can customize the optimal photography method.

[0041] During the analysis, the analysis unit can perform a detailed analysis of the material or condition of the item and propose an appropriate disposal method. For example, the analysis unit can use AI to analyze the material of the item and identify recyclable items. The analysis unit can also use AI to analyze the condition of the item and identify repairable items. Furthermore, the analysis unit can use AI to comprehensively analyze the material and condition of the item and propose an optimal disposal method. This makes it possible to propose an optimal disposal method by analyzing the material and condition of the item in detail. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or can be performed without using AI. For example, the analysis unit can input data on the material and condition of the item into a generation AI, which can then propose an optimal disposal method.

[0042] During the analysis, the analysis unit can automatically evaluate the market value of the item and determine the possibility of selling. For example, the analysis unit uses AI to analyze the market value of the item and identify items that can be sold. The analysis unit can also use AI to analyze the market value of the item and propose an optimal selling price. Furthermore, the analysis unit can also use AI to analyze the market value of the item and determine the possibility of selling. In this way, the possibility of selling can be determined by automatically evaluating the market value of the item. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input data on the market value of the item into a generation AI, and the generation AI can determine the possibility of selling.

[0043] During analysis, the analysis unit can refer to the usage history of the item and suggest an appropriate recycling method. For example, the analysis unit uses AI to analyze the usage history of the item and identify recyclable items. The analysis unit can also use AI to analyze the usage history of the item and suggest an optimal recycling method. Furthermore, the analysis unit can also use AI to analyze the usage history of the item, identify recyclable items, and suggest an optimal recycling method. In this way, the optimal recycling method can be suggested by referring to the usage history of the item. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input data on the usage history of the item into a generation AI, and the generation AI can suggest an optimal recycling method.

[0044] During analysis, the analysis unit can perform analysis based on the geographical distribution of items. For example, the analysis unit can use AI to analyze the geographical distribution of items and propose the optimal disposal method. The analysis unit can also use AI to analyze the geographical distribution of items and identify recyclable items. The analysis unit can also use AI to analyze the geographical distribution of items and propose the optimal recycling method. In this way, by performing analysis taking into account the geographical distribution of items, it is possible to propose the optimal disposal method according to the region. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input data on the geographical distribution of items into a generation AI, and the generation AI can propose the optimal disposal method.

[0045] The analysis unit can improve the accuracy of the analysis by referring to literature related to the item during analysis. For example, the analysis unit can improve the accuracy of the analysis by having AI refer to literature related to the item. The analysis unit can also improve the accuracy of the analysis by having AI refer to literature related to the item. Furthermore, the analysis unit can also improve the accuracy of the analysis by having AI refer to literature related to the item and identify recyclable items. In this way, by referring to literature related to the item, the accuracy of the analysis can be improved. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or can be performed without using AI. For example, the analysis unit can input data from literature related to the item into a generation AI, and the generation AI can improve the accuracy of the analysis.

[0046] During the analysis, the analysis unit can perform the analysis based on the market trends of the item. For example, the analysis unit can use AI to analyze the market trends of the item and propose an optimal selling price. The analysis unit can also use AI to analyze the market trends of the item and identify items that can be sold. Furthermore, the analysis unit can use AI to analyze the market trends of the item and propose an optimal selling method. In this way, by performing an analysis taking into account the market trends of the item, it is possible to propose an optimal selling method and price. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input data on the market trends of the item into the generation AI, and the generation AI can propose an optimal selling method and price.

[0047] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the item when making a suggestion. For example, the suggestion unit makes a detailed suggestion for an item with a high level of importance. The suggestion unit can also make a concise suggestion for an item with a low level of importance. Furthermore, the suggestion unit can automatically adjust the level of detail of the suggestion according to the importance. As a result, by adjusting the level of detail of the suggestion based on the importance of the item, it is possible to provide an optimal suggestion for the user. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data on the importance of the item to a generation AI, and the generation AI can adjust the level of detail of the optimal suggestion.

[0048] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of the item. For example, in the case of furniture, the suggestion unit can suggest recycling methods or selling methods. In addition, in the case of clothing, the suggestion unit can also suggest donation destinations or recycling methods. Furthermore, in the case of home appliances, the suggestion unit can also suggest repair methods or selling methods. In this way, by applying different suggestion algorithms depending on the category of the item, optimal suggestions can be provided. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data on the category of the item into a generation AI, and the generation AI can apply the optimal suggestion algorithm.

[0049] When making a proposal, the suggestion unit can improve the accuracy of the proposal based on the user's past proposal results. The suggestion unit makes an optimal proposal based on, for example, proposals that the user has accepted in the past. The suggestion unit can also analyze the user's past proposal results and improve the accuracy of the proposal. Furthermore, the suggestion unit can avoid proposals that the user has rejected in the past and make an optimal proposal. In this way, the accuracy of the proposal can be improved by referring to the user's past proposal results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data of the user's past proposal results into a generation AI, which can then make an optimal proposal.

[0050] When making a proposal, the proposal unit can determine the priority of the proposal based on the submission date of the item. For example, if the submission date of the item is close, the proposal unit can prioritize the proposal. Also, if the submission date of the item is far away, the proposal unit can postpone the proposal. Furthermore, the proposal unit can automatically adjust the priority of the proposal according to the submission date. In this way, by determining the priority of the proposal based on the submission date of the item, the proposals can be made in an optimal order. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input data on the submission date of the item into a generation AI, and the generation AI can determine the optimal priority of the proposals.

[0051] The suggestion unit can adjust the order of suggestions based on the relevance of the items when making suggestions. For example, the suggestion unit prioritizes suggestions for highly relevant items. The suggestion unit can also postpone suggestions for less relevant items. Furthermore, the suggestion unit can automatically adjust the order of suggestions based on the relevance of the items. As a result, suggestions can be made in an optimal order by adjusting the order of suggestions based on the relevance of the items. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data on the relevance of items to a generation AI, and the generation AI can adjust the optimal order of suggestions.

[0052] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal based on the user's level of expertise. For example, if the user has technical expertise, the suggestion unit can make a proposal that uses a lot of technical terminology. Also, if the user does not have technical expertise, the suggestion unit can make a proposal in simple language. Furthermore, the suggestion unit can automatically adjust the use of technical terminology in the proposal according to the user's level of expertise. This allows the proposal to be provided in a way that is easy for the user to understand by adjusting the use of technical terminology in the proposal according to the user's level of expertise. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data on the user's level of expertise into a generation AI, and the generation AI can adjust the use of technical terminology in the proposal to be optimal.

[0053] The listing support unit can automatically evaluate the market value of an item and propose an appropriate pricing when supporting the listing. For example, the listing support unit can use AI to analyze the market value of an item and propose an optimal price. The listing support unit can also use AI to analyze the market value of an item and propose a sellable price. Furthermore, the listing support unit can use AI to analyze the market value of an item and propose an optimal pricing. In this way, optimal pricing can be proposed by automatically evaluating the market value of an item. Some or all of the above-mentioned processing in the listing support unit may be performed using AI, for example, or may be performed without using AI. For example, the listing support unit can input data on the market value of an item into a generation AI, which can then propose an optimal pricing.

[0054] The listing support unit automatically generates photos and descriptions of items during listing support, simplifying the listing process. For example, the listing support unit uses AI to analyze photos of items and automatically generate optimal descriptions. The listing support unit can also analyze photos of items and select optimal photos. Furthermore, the listing support unit can automatically generate photos and descriptions of items using AI, simplifying the listing process. This simplifies the listing process by automatically generating photos and descriptions of items. Some or all of the above-described processing in the listing support unit may be performed using AI, for example, or may be performed without using AI. For example, the listing support unit can input the generation of photos and descriptions of items into a generation AI, which can then generate optimal photos and descriptions.

[0055] When supporting a listing, the listing support unit can suggest an appropriate listing method based on the item's past sales history. The listing support unit, for example, uses AI to analyze the item's past sales history and suggest the optimal listing method. The listing support unit can also use AI to analyze the item's past sales history and suggest the optimal selling price. The listing support unit can also use AI to analyze the item's past sales history and suggest the optimal listing method. In this way, the optimal listing method can be suggested by referring to the item's past sales history. Some or all of the above-mentioned processing in the listing support unit may be performed, for example, using AI, or may be performed without using AI. For example, the listing support unit can input data on the item's past sales history into a generation AI, and the generation AI can suggest the optimal listing method.

[0056] When supporting listing, the listing support unit can suggest an appropriate listing method based on the geographical distribution of the item. The listing support unit, for example, uses AI to analyze the geographical distribution of the item and suggest an optimal listing method. The listing support unit can also use AI to analyze the geographical distribution of the item and suggest an optimal selling price. The listing support unit can also use AI to analyze the geographical distribution of the item and suggest an optimal listing method. In this way, the optimal listing method can be suggested by taking the geographical distribution of the item into consideration. Some or all of the above-mentioned processing in the listing support unit may be performed, for example, using AI, or may be performed without using AI. For example, the listing support unit can input data on the geographical distribution of the item into a generation AI, and the generation AI can suggest an optimal listing method.

[0057] The listing support unit can improve the accuracy of the listing procedure by referring to literature related to the item when supporting the listing. For example, the listing support unit can improve the accuracy of the listing procedure by using AI to refer to literature related to the item. The listing support unit can also use AI to refer to literature related to the item and propose the optimal listing method. Furthermore, the listing support unit can also use AI to refer to literature related to the item and propose the optimal selling price. In this way, by referring to literature related to the item, the accuracy of the listing procedure can be improved. Some or all of the above-mentioned processing in the listing support unit may be performed using AI, for example, or may be performed without using AI. For example, the listing support unit can input data from literature related to the item into a generation AI, which can then propose the optimal listing method.

[0058] When supporting listing, the listing support unit can propose an appropriate listing method based on market trends for the item. For example, the listing support unit uses AI to analyze market trends for the item and propose an optimal listing method. The listing support unit can also use AI to analyze market trends for the item and propose an optimal selling price. Furthermore, the listing support unit can also use AI to analyze market trends for the item and propose an optimal listing method. This makes it possible to propose an optimal listing method by taking market trends for the item into consideration. Some or all of the above-mentioned processing in the listing support unit may be performed using AI, for example, or may be performed without using AI. For example, the listing support unit can input data on market trends for the item into a generation AI, and the generation AI can propose an optimal listing method.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] When taking a photo of an item, the collection unit can refer to the user's past photography history and automatically apply optimal photography settings. For example, the collection unit automatically selects optimal settings based on the user's previously preferred photography angles and lighting conditions. The collection unit can also evaluate the quality of photos taken by the user in the past and suggest optimal photography settings. Furthermore, the collection unit can analyze the user's past feedback and customize photography settings. In this way, by referring to the user's past photography history, optimal photography settings can be automatically applied and high-quality photos of items can be taken.

[0061] When proposing a recycling method for an item, the suggestion unit can refer to the user's past recycling history and suggest the optimal recycling method. For example, the suggestion unit can suggest the optimal recycling method based on recycling methods that the user has used in the past. The suggestion unit can also analyze the user's past recycling history and customize the recycling method. Furthermore, the suggestion unit can avoid recycling methods that the user has rejected in the past and suggest the optimal recycling method. In this way, the optimal recycling method can be suggested by referring to the user's past recycling history.

[0062] The collection unit can automatically apply optimal shooting settings in consideration of the user's geographical location information when taking a photo of an item. For example, when the user is in a specific area, the collection unit selects optimal shooting settings in consideration of the lighting conditions and background of the area. In addition, when the user is traveling, the collection unit can prioritize photographing items that are easy to carry. Furthermore, when the user is at home, the collection unit can photograph items used at home under optimal conditions. In this way, optimal shooting settings can be automatically applied in consideration of the user's geographical location information, and high-quality photos of items can be taken.

[0063] When proposing a recycling method for an item, the suggestion unit can refer to the usage history of the item and suggest the optimal recycling method. For example, the suggestion unit analyzes the usage history of the item and identifies recyclable items. The suggestion unit can also suggest the optimal recycling method based on the usage history of the item. Furthermore, the suggestion unit can also customize the recycling method by referring to the usage history of the item. In this way, the optimal recycling method can be suggested by referring to the usage history of the item.

[0064] The listing support unit can analyze a user's social media activity and reflect related content when automatically generating photos and descriptions of items. For example, the listing support unit generates descriptions related to items that the user frequently posts on social media. The listing support unit can also analyze the content of the user's social media posts and select related photos. Furthermore, the listing support unit can generate descriptions of related items by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity and reflecting related content, it is possible to automatically generate photos and descriptions of items in a form that is optimal for the user.

[0065] When assessing the market value of an item, the analysis unit can refer to literature related to the item to improve the accuracy of the assessment. For example, the analysis unit can refer to literature related to the item to perform an optimal market value assessment. The analysis unit can also propose an optimal selling price based on the literature related to the item. Furthermore, the analysis unit can also improve the accuracy of the assessment by referring to literature related to the item. In this way, by referring to literature related to the item, the accuracy of the assessment can be improved and an optimal market value assessment can be provided.

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

[0067] Step 1: The collection unit collects information about the item. For example, it can take a photo of the item and register information such as how often the item is used and how dirty it is. It provides an interface for the user to input information about how often the item is used and how dirty it is. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, it refers to local waste sorting rules, analyzes the material and condition of the items, and identifies recyclable items. Step 3: The proposal unit proposes disposal and recycling methods based on the analysis results obtained by the analysis unit. For example, it proposes disposal methods based on local waste sorting rules, and proposes recycling methods for recyclable items. Step 4: The listing support unit assists in listing the items proposed by the proposal unit in cooperation with the flea market app. For example, it automatically generates photos and descriptions of the items and assists in the listing process.

[0068] (Example 2) The decluttering support system according to an embodiment of the present invention collects, analyzes, and recommends items and assists users in listing them for sale. In the decluttering support system, users photograph items to be discarded and register simple information such as frequency of use and degree of dirt. AI then analyzes the information and offers suggestions on how to dispose of the items (depending on the region), how to recycle them, and how to sell them at a flea market. Furthermore, for items that can be sold at a flea market, the system works in conjunction with a flea market app to smoothly assist users in listing the items. For example, the decluttering support system photographs items to be discarded. It is important to capture the entire image of the item. This applies to a variety of items, including old furniture, clothing, and home appliances. This information is then input into the AI. Next, the user registers simple information about the items, such as frequency of use and degree of dirt. For example, specific information can be entered, such as when furniture is rarely used or when clothing is rarely worn. This information is also input into the AI. The decluttering support system then analyzes the input information and offers suggestions on how to dispose of the items, how to recycle them, and how to sell them at a flea market. For example, the system suggests how to dispose of items based on local waste sorting rules. It also suggests recycling methods for recyclable items. Furthermore, for items that can be sold at flea markets, it works with flea market apps to assist with listing. For example, it can automatically generate photos and descriptions of items, simplifying the listing process. This allows users to sell items without hassle. This allows the decluttering support system to make decluttering easy for users. By simply taking a photo of the item to be discarded and registering basic information, AI can suggest the optimal way to discard, recycle, or sell it at a flea market. This allows for smooth disposal of items and efficient decluttering. This allows the decluttering support system to efficiently declutter. For example, by collecting, analyzing, and suggesting information about items and providing support for listing, users can dispose of items without hassle. Furthermore, by promoting the recycling and sale of items, it can also contribute to environmental protection.

[0069] The decluttering support system according to the embodiment includes a collection unit, an analysis unit, a suggestion unit, and a listing support unit. The collection unit collects information about items. The collection unit, for example, takes photos of the items. The collection unit can also register information about the frequency of use of the items and their degree of dirtiness. For example, the collection unit provides an interface for a user to input the frequency of use and degree of dirtiness of the items. The analysis unit analyzes the information collected by the collection unit. The analysis unit, for example, refers to regional waste sorting rules. The analysis unit can also analyze the material and condition of the items to identify recyclable items. For example, the analysis unit analyzes the material of the items to identify recyclable items. The suggestion unit suggests disposal methods and recycling methods based on the analysis results obtained by the analysis unit. The suggestion unit suggests how to dispose of items based on regional waste sorting rules, for example. The suggestion unit can also suggest recycling methods for recyclable items. For example, the suggestion unit suggests recycling methods for recyclable items. The listing support unit cooperates with the flea market app to support the listing of the items suggested by the suggestion unit. The listing support unit, for example, automatically generates photos and descriptions of the items and supports the listing procedure. For example, the listing support unit analyzes photos of the items and automatically generates optimal descriptions. This allows the decluttering support system according to the embodiment to enable users to declutter efficiently. For example, by collecting, analyzing, and suggesting information about items and providing listing support, users can dispose of items without hassle. Furthermore, promoting the recycling and sale of items can contribute to environmental protection.

[0070] The collection unit can take photos of items. It is important to take photos of items in a way that allows the entire image of the item to be seen. For example, when taking photos of items, the collection unit can adjust the shooting angle, resolution, lighting conditions, etc. For example, when taking photos of furniture, the collection unit can use a wide-angle lens and take photos using natural light. When taking photos of clothing, the collection unit can automatically adjust lighting to make the color and texture more visible. Furthermore, when taking photos of home appliances, the collection unit can take photos from multiple angles and select the optimal photo so that the function and condition of the appliance can be seen. This allows accurate collection of information about the appliance by taking photos. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can take photos of the appliance, input the photos into a generation AI, and have the generation AI select the optimal photo.

[0071] The collection unit can register information on the frequency of use or degree of dirt of an item. Information on the frequency of use or degree of dirt of an item includes, for example, the number of times of use, the duration of use, the type and degree of dirt, etc. For example, the collection unit provides an interface through which a user can input the frequency of use or degree of dirt of an item. The collection unit can also automatically detect the frequency of use or degree of dirt of an item. For example, the collection unit can perform image analysis of dirt on the surface of an item and automatically evaluate the degree of dirt. The collection unit can also estimate and register the frequency of use of an item from past usage history or sensor information. Furthermore, the collection unit can perform image analysis of the condition of an item and automatically detect whether or not there are scratches or damage. In this way, by registering information such as the frequency of use and degree of dirt of an item, the condition of the item can be accurately understood. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the frequency of use and degree of dirt of an item into a generation AI, and the generation AI can register the information.

[0072] The analysis unit can refer to regional waste sorting rules. Regional waste sorting rules include, for example, municipal guidelines and online databases. For example, the analysis unit can refer to regional waste sorting rules and suggest how to dispose of the items. The analysis unit can also analyze the material and condition of an item to identify recyclable items. For example, the analysis unit can analyze the material of an item to identify recyclable items. Furthermore, the analysis unit can refer to the usage history of the item to suggest the optimal recycling method. For example, the analysis unit can analyze the usage history of an item to identify recyclable items and suggest the optimal recycling method. In this way, by referring to regional waste sorting rules, an appropriate disposal method can be suggested. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input regional waste sorting rules into a generation AI, which can then suggest the optimal disposal method.

[0073] The suggestion unit can suggest recycling methods for recyclable items. Recyclable items include, for example, the type of material and recycling conditions. For example, the suggestion unit suggests recycling methods for recyclable items. The suggestion unit can also analyze the material and condition of an item to identify recyclable items. For example, the suggestion unit can analyze the material of an item to identify recyclable items. Furthermore, the suggestion unit can refer to the usage history of the item to suggest an optimal recycling method. For example, the suggestion unit can analyze the usage history of an item to identify recyclable items and suggest an optimal recycling method. In this way, by suggesting recycling methods for recyclable items, reuse of the items can be promoted. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input information about recyclable items into a generation AI, which can suggest an optimal recycling method.

[0074] The listing support unit can automatically generate photos or descriptions of items to support the listing process. The automatic generation of item photos and descriptions includes, for example, the algorithms used and the criteria for the content to be generated. For example, the listing support unit can analyze photos of items and automatically generate optimal descriptions. The listing support unit can also analyze photos of items and select optimal photos. Furthermore, the listing support unit can automatically generate photos and descriptions of items to simplify the listing process. For example, the listing support unit can analyze photos of items and automatically generate optimal descriptions to support the listing process. By automatically generating photos and descriptions of items and supporting the listing process, users can sell items without hassle. Some or all of the above-described processing in the listing support unit may be performed using, for example, AI, or may be performed without AI. For example, the listing support unit can input the generation of item photos and descriptions into a generation AI, which can then generate optimal photos and descriptions.

[0075] The collection unit can estimate the user's emotions and adjust the timing of photographing an item based on the estimated user's emotions. For example, if the user is relaxed, the collection unit can prompt the user to photograph the item at a time when natural light is best. Furthermore, if the user is feeling stressed, the collection unit can automatically use AI to select the optimal timing and notify the user. Furthermore, if the user is busy, the collection unit can use AI to refer to the user's schedule and suggest a time when the user is free. This allows the photo to be taken at the optimal timing by adjusting the timing of photographing the item based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit can be performed using AI, or without AI. For example, the collection unit can input the user's emotion data into the generation AI, which can then adjust the optimal timing for photographing.

[0076] When photographing an item, the collection unit can automatically adjust the appropriate shooting angle or lighting depending on the type of item. For example, in the case of furniture, the collection unit uses a wide-angle lens and natural light to capture the entire image. In addition, in the case of clothing, the collection unit can automatically adjust the appropriate lighting to capture the image so that the color and texture are easily seen. Furthermore, in the case of home appliances, the collection unit can also photograph the item from multiple angles and select the optimal photo so that the function and condition can be seen. In this way, by automatically adjusting the optimal shooting angle and lighting depending on the type of item, the item can be photographed in the optimal state. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can photograph the item, input the photograph to a generation AI, and the generation AI can adjust the optimal shooting angle and lighting.

[0077] The collection unit can automatically detect the frequency of use and degree of dirt of an item, eliminating the need for the user to input such information. For example, the collection unit uses AI to perform image analysis of dirt on the surface of an item and automatically evaluate the degree of dirt. The collection unit can also use AI to estimate and register the frequency of use of an item based on past usage history and sensor information. Furthermore, the collection unit can also use AI to perform image analysis of the condition of the item and automatically detect whether or not there are scratches or damage. This automatically detects the frequency of use and degree of dirt of an item, eliminating the need for the user to input such information. Some or all of the above-described processing in the collection unit may be performed using AI, or may be performed without AI. For example, the collection unit can input the frequency of use and degree of dirt of an item into a generation AI, which then registers the information.

[0078] The collection unit can automatically estimate the size or weight of an item when a photograph of the item is taken and add it to the registration information. For example, the collection unit can use AI to estimate the size from a photograph of the item and add it to the registration information. The collection unit can also use AI to estimate the weight from a photograph of the item and add it to the registration information. Furthermore, the collection unit can also use AI to simultaneously estimate the size and weight from a photograph of the item and add it to the registration information. In this way, by automatically estimating the size and weight of an item and adding it to the registration information, it is possible to accurately grasp the information about the item. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input a photograph of the item into a generation AI and have the generation AI estimate the size and weight.

[0079] The collection unit can estimate the user's emotions and determine the priority of photographing items based on the estimated user emotions. For example, when the user is relaxed, the collection unit can prioritize photographing items that are more important. Furthermore, when the user is stressed, the collection unit can prioritize photographing items that are easier to photograph. Furthermore, when the user is busy, the collection unit can automatically determine the priority and notify the user. By determining the priority of photographing items based on the user's emotions, photos can be taken in an optimal order. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's emotion data into a generation AI, which can then determine the optimal photo-taking priority.

[0080] When photographing an item, the collection unit can prioritize photographing highly relevant items by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes photographing items that are frequently used in that area. Furthermore, when the user is traveling, the collection unit can prioritize photographing items that are easy to carry. Furthermore, when the user is at home, the collection unit can prioritize photographing items used at home. This allows for efficient collection of item information by prioritizing photographing highly relevant items by taking into account the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into a generation AI, and the generation AI can prioritize photographing highly relevant items.

[0081] When photographing an item, the collection unit can analyze the user's social media activity and photograph related items. For example, the collection unit prioritizes photographing items that the user frequently posts on social media. The collection unit can also analyze the content of the user's social media posts and photograph related items. Furthermore, the collection unit can also photograph related items with reference to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity and photographing related items, information on items based on the user's interests can be collected. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input the user's social media activity data into a generation AI and have the generation AI photograph related items.

[0082] The collection unit can customize the photography method by reflecting the user's past feedback when taking a photograph of an item. The collection unit can, for example, suggest an optimal photography method based on the user's preferred photography methods in the past. The collection unit can also analyze the user's past feedback and customize the photography method. Furthermore, the collection unit can also suggest an optimal photography method based on the quality of photos the user has taken in the past. In this way, by customizing the photography method by reflecting the user's past feedback, it is possible to provide an optimal photography method that meets the user's preferences. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into a generation AI, and the generation AI can customize the optimal photography method.

[0083] The analysis unit can estimate the user's emotions and adjust the analysis priority based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit can prioritize analyzing items that are more important. Furthermore, if the user is stressed, the analysis unit can prioritize analyzing items that are easier to analyze. Furthermore, if the user is busy, the analysis unit can automatically determine the priority and notify the user. By adjusting the analysis priority based on the user's emotions, analysis can be performed in an optimal order. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI, and the generation AI can adjust the optimal analysis priority.

[0084] During the analysis, the analysis unit can perform a detailed analysis of the material or condition of the item and propose an appropriate disposal method. For example, the analysis unit can use AI to analyze the material of the item and identify recyclable items. The analysis unit can also use AI to analyze the condition of the item and identify repairable items. Furthermore, the analysis unit can use AI to comprehensively analyze the material and condition of the item and propose an optimal disposal method. This makes it possible to propose an optimal disposal method by analyzing the material and condition of the item in detail. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or can be performed without using AI. For example, the analysis unit can input data on the material and condition of the item into a generation AI, which can then propose an optimal disposal method.

[0085] During the analysis, the analysis unit can automatically evaluate the market value of the item and determine the possibility of selling. For example, the analysis unit uses AI to analyze the market value of the item and identify items that can be sold. The analysis unit can also use AI to analyze the market value of the item and propose an optimal selling price. Furthermore, the analysis unit can also use AI to analyze the market value of the item and determine the possibility of selling. In this way, the possibility of selling can be determined by automatically evaluating the market value of the item. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input data on the market value of the item into a generation AI, and the generation AI can determine the possibility of selling.

[0086] During analysis, the analysis unit can refer to the usage history of the item and suggest an appropriate recycling method. For example, the analysis unit uses AI to analyze the usage history of the item and identify recyclable items. The analysis unit can also use AI to analyze the usage history of the item and suggest an optimal recycling method. Furthermore, the analysis unit can also use AI to analyze the usage history of the item, identify recyclable items, and suggest an optimal recycling method. In this way, the optimal recycling method can be suggested by referring to the usage history of the item. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input data on the usage history of the item into a generation AI, and the generation AI can suggest an optimal recycling method.

[0087] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, the analysis unit can display detailed analysis results when the user is relaxed. The analysis unit can also display concise analysis results when the user is stressed. Furthermore, when the user is busy, the analysis unit can automatically select the optimal display method using AI and notify the user. By adjusting the display method of the analysis results based on the user's emotions, the analysis unit can provide the analysis results in a form that is optimal for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI, and the generation AI can adjust the display method of the analysis results to be optimal.

[0088] During analysis, the analysis unit can perform analysis based on the geographical distribution of items. For example, the analysis unit can use AI to analyze the geographical distribution of items and propose the optimal disposal method. The analysis unit can also use AI to analyze the geographical distribution of items and identify recyclable items. The analysis unit can also use AI to analyze the geographical distribution of items and propose the optimal recycling method. In this way, by performing analysis taking into account the geographical distribution of items, it is possible to propose the optimal disposal method according to the region. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input data on the geographical distribution of items into a generation AI, and the generation AI can propose the optimal disposal method.

[0089] The analysis unit can improve the accuracy of the analysis by referring to literature related to the item during analysis. For example, the analysis unit can improve the accuracy of the analysis by having AI refer to literature related to the item. The analysis unit can also improve the accuracy of the analysis by having AI refer to literature related to the item. Furthermore, the analysis unit can also improve the accuracy of the analysis by having AI refer to literature related to the item and identify recyclable items. In this way, by referring to literature related to the item, the accuracy of the analysis can be improved. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or can be performed without using AI. For example, the analysis unit can input data from literature related to the item into a generation AI, and the generation AI can improve the accuracy of the analysis.

[0090] During the analysis, the analysis unit can perform the analysis based on the market trends of the item. For example, the analysis unit can use AI to analyze the market trends of the item and propose an optimal selling price. The analysis unit can also use AI to analyze the market trends of the item and identify items that can be sold. Furthermore, the analysis unit can use AI to analyze the market trends of the item and propose an optimal selling method. In this way, by performing an analysis taking into account the market trends of the item, it is possible to propose an optimal selling method and price. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input data on the market trends of the item into the generation AI, and the generation AI can propose an optimal selling method and price.

[0091] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user emotions. For example, the suggestion unit can provide detailed suggestions when the user is relaxed. The suggestion unit can also provide concise suggestions when the user is stressed. Furthermore, when the user is busy, the suggestion unit can automatically select the optimal suggestion method and notify the user. By adjusting the way suggestions are expressed based on the user's emotions, suggestions can be provided in an optimal form for the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI, or without an AI. For example, the suggestion unit can input the user's emotion data into the generation AI, and the generation AI can adjust the optimal way suggestions are expressed.

[0092] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the item when making a suggestion. For example, the suggestion unit makes a detailed suggestion for an item with a high level of importance. The suggestion unit can also make a concise suggestion for an item with a low level of importance. Furthermore, the suggestion unit can automatically adjust the level of detail of the suggestion according to the importance. As a result, by adjusting the level of detail of the suggestion based on the importance of the item, it is possible to provide an optimal suggestion for the user. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data on the importance of the item to a generation AI, and the generation AI can adjust the level of detail of the optimal suggestion.

[0093] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of the item. For example, in the case of furniture, the suggestion unit can suggest recycling methods or selling methods. In addition, in the case of clothing, the suggestion unit can also suggest donation destinations or recycling methods. Furthermore, in the case of home appliances, the suggestion unit can also suggest repair methods or selling methods. In this way, by applying different suggestion algorithms depending on the category of the item, optimal suggestions can be provided. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data on the category of the item into a generation AI, and the generation AI can apply the optimal suggestion algorithm.

[0094] When making a proposal, the suggestion unit can improve the accuracy of the proposal based on the user's past proposal results. The suggestion unit makes an optimal proposal based on, for example, proposals that the user has accepted in the past. The suggestion unit can also analyze the user's past proposal results and improve the accuracy of the proposal. Furthermore, the suggestion unit can avoid proposals that the user has rejected in the past and make an optimal proposal. In this way, the accuracy of the proposal can be improved by referring to the user's past proposal results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data of the user's past proposal results into a generation AI, which can then make an optimal proposal.

[0095] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, the suggestion unit can provide detailed suggestions when the user is relaxed. The suggestion unit can also provide concise suggestions when the user is stressed. Furthermore, when the user is busy, the suggestion unit can automatically select the optimal length of the suggestions and notify the user. By adjusting the length of the suggestions based on the user's emotions, the suggestion unit can provide suggestions in an optimal form for the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI, or without an AI. For example, the suggestion unit can input the user's emotion data into the generation AI, and the generation AI can adjust the optimal length of the suggestions.

[0096] When making a proposal, the proposal unit can determine the priority of the proposal based on the submission date of the item. For example, if the submission date of the item is close, the proposal unit can prioritize the proposal. Also, if the submission date of the item is far away, the proposal unit can postpone the proposal. Furthermore, the proposal unit can automatically adjust the priority of the proposal according to the submission date. In this way, by determining the priority of the proposal based on the submission date of the item, the proposals can be made in an optimal order. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input data on the submission date of the item into a generation AI, and the generation AI can determine the optimal priority of the proposals.

[0097] The suggestion unit can adjust the order of suggestions based on the relevance of the items when making suggestions. For example, the suggestion unit prioritizes suggestions for highly relevant items. The suggestion unit can also postpone suggestions for less relevant items. Furthermore, the suggestion unit can automatically adjust the order of suggestions based on the relevance of the items. As a result, suggestions can be made in an optimal order by adjusting the order of suggestions based on the relevance of the items. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data on the relevance of items to a generation AI, and the generation AI can adjust the optimal order of suggestions.

[0098] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal based on the user's level of expertise. For example, if the user has technical expertise, the suggestion unit can make a proposal that uses a lot of technical terminology. Also, if the user does not have technical expertise, the suggestion unit can make a proposal in simple language. Furthermore, the suggestion unit can automatically adjust the use of technical terminology in the proposal according to the user's level of expertise. This allows the proposal to be provided in a way that is easy for the user to understand by adjusting the use of technical terminology in the proposal according to the user's level of expertise. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data on the user's level of expertise into a generation AI, and the generation AI can adjust the use of technical terminology in the proposal to be optimal.

[0099] The listing support unit can estimate the user's emotions and adjust the listing procedure based on the estimated user emotions. For example, if the user is relaxed, the listing support unit can suggest a detailed listing procedure. Furthermore, if the user is stressed, the listing support unit can also suggest a simple listing procedure. Furthermore, if the user is busy, the listing support unit can automatically select the optimal listing procedure and notify the user. By adjusting the listing procedure based on the user's emotions, the listing procedure can be optimized for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the listing support unit may be performed using, for example, AI, or without AI. For example, the listing support unit can input the user's emotion data into the generation AI, which can then adjust the optimal listing procedure.

[0100] The listing support unit can automatically evaluate the market value of an item and propose an appropriate pricing when supporting the listing. For example, the listing support unit can use AI to analyze the market value of an item and propose an optimal price. The listing support unit can also use AI to analyze the market value of an item and propose a sellable price. Furthermore, the listing support unit can use AI to analyze the market value of an item and propose an optimal pricing. In this way, optimal pricing can be proposed by automatically evaluating the market value of an item. Some or all of the above-mentioned processing in the listing support unit may be performed using AI, for example, or may be performed without using AI. For example, the listing support unit can input data on the market value of an item into a generation AI, which can then propose an optimal pricing.

[0101] The listing support unit automatically generates photos and descriptions of items during listing support, simplifying the listing process. For example, the listing support unit uses AI to analyze photos of items and automatically generate optimal descriptions. The listing support unit can also analyze photos of items and select optimal photos. Furthermore, the listing support unit can automatically generate photos and descriptions of items using AI, simplifying the listing process. This simplifies the listing process by automatically generating photos and descriptions of items. Some or all of the above-described processing in the listing support unit may be performed using AI, for example, or may be performed without using AI. For example, the listing support unit can input the generation of photos and descriptions of items into a generation AI, which can then generate optimal photos and descriptions.

[0102] When supporting a listing, the listing support unit can suggest an appropriate listing method based on the item's past sales history. The listing support unit, for example, uses AI to analyze the item's past sales history and suggest the optimal listing method. The listing support unit can also use AI to analyze the item's past sales history and suggest the optimal selling price. The listing support unit can also use AI to analyze the item's past sales history and suggest the optimal listing method. In this way, the optimal listing method can be suggested by referring to the item's past sales history. Some or all of the above-mentioned processing in the listing support unit may be performed, for example, using AI, or may be performed without using AI. For example, the listing support unit can input data on the item's past sales history into a generation AI, and the generation AI can suggest the optimal listing method.

[0103] The listing support unit can estimate the user's emotions and determine the priority of the listing process based on the estimated user emotions. For example, when the user is relaxed, the listing support unit can prioritize listing items that are more important. Furthermore, when the user is stressed, the listing support unit can prioritize listing items that are easier to list. Furthermore, when the user is busy, the listing support unit can automatically determine the priority and notify the user. By determining the priority of the listing process based on the user's emotions, the listing process can be performed in an optimal order. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the listing support unit may be performed using, for example, an AI, or without an AI. For example, the listing support unit can input the user's emotion data into the generation AI, which can then determine the optimal listing process priority.

[0104] When supporting listing, the listing support unit can suggest an appropriate listing method based on the geographical distribution of the item. The listing support unit, for example, uses AI to analyze the geographical distribution of the item and suggest an optimal listing method. The listing support unit can also use AI to analyze the geographical distribution of the item and suggest an optimal selling price. The listing support unit can also use AI to analyze the geographical distribution of the item and suggest an optimal listing method. In this way, the optimal listing method can be suggested by taking the geographical distribution of the item into consideration. Some or all of the above-mentioned processing in the listing support unit may be performed, for example, using AI, or may be performed without using AI. For example, the listing support unit can input data on the geographical distribution of the item into a generation AI, and the generation AI can suggest an optimal listing method.

[0105] The listing support unit can improve the accuracy of the listing procedure by referring to literature related to the item when supporting the listing. For example, the listing support unit can improve the accuracy of the listing procedure by using AI to refer to literature related to the item. The listing support unit can also use AI to refer to literature related to the item and propose the optimal listing method. Furthermore, the listing support unit can also use AI to refer to literature related to the item and propose the optimal selling price. In this way, by referring to literature related to the item, the accuracy of the listing procedure can be improved. Some or all of the above-mentioned processing in the listing support unit may be performed using AI, for example, or may be performed without using AI. For example, the listing support unit can input data from literature related to the item into a generation AI, which can then propose the optimal listing method.

[0106] When supporting listing, the listing support unit can propose an appropriate listing method based on market trends for the item. For example, the listing support unit uses AI to analyze market trends for the item and propose an optimal listing method. The listing support unit can also use AI to analyze market trends for the item and propose an optimal selling price. Furthermore, the listing support unit can also use AI to analyze market trends for the item and propose an optimal listing method. This makes it possible to propose an optimal listing method by taking market trends for the item into consideration. Some or all of the above-mentioned processing in the listing support unit may be performed using AI, for example, or may be performed without using AI. For example, the listing support unit can input data on market trends for the item into a generation AI, and the generation AI can propose an optimal listing method. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, and listing support unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit takes a photo of an item using the camera 42 of the smart device 14, and the control unit 46A registers information such as the frequency of use and degree of dirt of the item. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests disposal and recycling methods based on the analysis results. The listing support unit is realized, for example, by the control unit 46A of the smart device 14 and automatically generates photos and descriptions of items and supports the listing process in cooperation with a flea market app. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, and listing support unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit takes a photo of an item using the camera 42 of the smart glasses 214, and the control unit 46A registers information such as the frequency of use and degree of dirt of the item. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests disposal and recycling methods based on the analysis results. The listing support unit is realized, for example, by the control unit 46A of the smart glasses 214, and automatically generates photos and descriptions of items and supports the listing process in cooperation with a flea market app. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, suggestion unit, and listing support unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit takes a photo of an item using the camera 42 of the headset terminal 314, and the control unit 46A registers information such as the frequency of use and degree of dirt of the item. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests disposal and recycling methods based on the analysis results. The listing support unit is realized, for example, by the control unit 46A of the headset terminal 314, and automatically generates photos and descriptions of items and supports the listing process in cooperation with a flea market app. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, suggestion unit, and listing support unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit takes photos of items using the camera 42 of the robot 414, and the control unit 46A registers information such as the frequency of use and degree of dirt of the items. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests disposal and recycling methods based on the analysis results. The listing support unit is realized, for example, by the control unit 46A of the robot 414, and automatically generates photos and descriptions of items and supports the listing process in cooperation with a flea market app.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] When taking a photo of an item, the collection unit can refer to the user's past photography history and automatically apply optimal photography settings. For example, the collection unit automatically selects optimal settings based on the user's previously preferred photography angles and lighting conditions. The collection unit can also evaluate the quality of photos taken by the user in the past and suggest optimal photography settings. Furthermore, the collection unit can analyze the user's past feedback and customize photography settings. In this way, by referring to the user's past photography history, optimal photography settings can be automatically applied and high-quality photos of items can be taken.

[0109] When analyzing the material and condition of an item, the analysis unit can estimate the user's emotions and adjust the level of analysis detail based on the estimated emotions. For example, if the user is relaxed, detailed analysis results can be provided. Alternatively, if the user is feeling stressed, concise analysis results can be provided. Furthermore, if the user is busy, the AI ​​can automatically select the optimal level of analysis detail and notify the user. This allows the analysis results to be provided in the most appropriate form for the user by adjusting the level of analysis detail based on the user's emotions.

[0110] When proposing a recycling method for an item, the suggestion unit can refer to the user's past recycling history and suggest the optimal recycling method. For example, the suggestion unit can suggest the optimal recycling method based on recycling methods that the user has used in the past. The suggestion unit can also analyze the user's past recycling history and customize the recycling method. Furthermore, the suggestion unit can avoid recycling methods that the user has rejected in the past and suggest the optimal recycling method. In this way, the optimal recycling method can be suggested by referring to the user's past recycling history.

[0111] When automatically generating photos and descriptions of items, the listing support unit can estimate the user's emotions and adjust the generated content based on the estimated emotions. For example, if the user is relaxed, a detailed description can be generated. On the other hand, if the user is stressed, a concise description can be generated. Furthermore, if the user is busy, the AI ​​can automatically select the optimal content to generate and notify the user. In this way, by adjusting the generated content based on the user's emotions, it is possible to automatically generate photos and descriptions of items in a way that is optimal for the user.

[0112] The collection unit can automatically apply optimal shooting settings in consideration of the user's geographical location information when taking a photo of an item. For example, when the user is in a specific area, the collection unit selects optimal shooting settings in consideration of the lighting conditions and background of the area. In addition, when the user is traveling, the collection unit can prioritize photographing items that are easy to carry. Furthermore, when the user is at home, the collection unit can photograph items used at home under optimal conditions. In this way, optimal shooting settings can be automatically applied in consideration of the user's geographical location information, and high-quality photos of items can be taken.

[0113] When assessing the market value of an item, the analysis unit can estimate the user's emotions and adjust the level of detail of the assessment based on the estimated emotions. For example, if the user is relaxed, a detailed market value assessment can be provided. Alternatively, if the user is stressed, a concise market value assessment can be provided. Furthermore, if the user is busy, the AI ​​can automatically select the optimal level of detail of the assessment and notify the user. This allows the market value assessment to be provided in a form that is optimal for the user by adjusting the level of detail of the assessment based on the user's emotions.

[0114] When proposing a recycling method for an item, the suggestion unit can refer to the usage history of the item and suggest the optimal recycling method. For example, the suggestion unit analyzes the usage history of the item and identifies recyclable items. The suggestion unit can also suggest the optimal recycling method based on the usage history of the item. Furthermore, the suggestion unit can also customize the recycling method by referring to the usage history of the item. In this way, the optimal recycling method can be suggested by referring to the usage history of the item.

[0115] The listing support unit can analyze a user's social media activity and reflect related content when automatically generating photos and descriptions of items. For example, the listing support unit generates descriptions related to items that the user frequently posts on social media. The listing support unit can also analyze the content of the user's social media posts and select related photos. Furthermore, the listing support unit can generate descriptions of related items by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity and reflecting related content, it is possible to automatically generate photos and descriptions of items in a form that is optimal for the user.

[0116] The collection unit can estimate the user's emotions when taking photos of items and determine the priority of photos based on the estimated emotions. For example, if the user is relaxed, it can prioritize photos of items that are more important. Also, if the user is feeling stressed, it can prioritize photos of items that are easier to photograph. Furthermore, if the user is busy, the AI ​​can automatically determine the priority and notify the user. This allows photos of items to be taken in the optimal order by determining the priority of photos based on the user's emotions.

[0117] When assessing the market value of an item, the analysis unit can refer to literature related to the item to improve the accuracy of the assessment. For example, the analysis unit can refer to literature related to the item to perform an optimal market value assessment. The analysis unit can also propose an optimal selling price based on the literature related to the item. Furthermore, the analysis unit can also improve the accuracy of the assessment by referring to literature related to the item. In this way, by referring to literature related to the item, the accuracy of the assessment can be improved and an optimal market value assessment can be provided.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The collection unit collects information about the item. For example, it can take a photo of the item and register information such as how often the item is used and how dirty it is. It provides an interface for the user to input information about how often the item is used and how dirty it is. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, it refers to local waste sorting rules, analyzes the material and condition of the items, and identifies recyclable items. Step 3: The proposal unit proposes disposal and recycling methods based on the analysis results obtained by the analysis unit. For example, it proposes disposal methods based on local waste sorting rules, and proposes recycling methods for recyclable items. Step 4: The listing support unit assists in listing the items proposed by the proposal unit in cooperation with the flea market app. For example, it automatically generates photos and descriptions of the items and assists in the listing process.

[0120] 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.

[0121] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of 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.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0125] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0126] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0127] The 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.

[0128] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0131] Fig. 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.

[0132] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0134] 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.

[0135] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] The data processing system 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.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0150] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0151] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] The data processing system 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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."

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] [Explanation of symbols]

[0192] 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 collection unit that collects information about the items; an analysis unit that analyzes the information collected by the collection unit; a suggestion unit that suggests disposal or recycling methods based on the analysis results obtained by the analysis unit; a listing support unit that supports listing of the items proposed by the suggestion unit in cooperation with a flea market app; Equipped with A system characterized by:

2. The collecting unit Take a photo of the item 2. The system of claim 1.

3. The collecting unit Register information about how often an item is used or how dirty it is 2. The system of claim 1.

4. The analysis unit Refer to local garbage sorting rules 2. The system of claim 1.

5. The proposal unit Propose recycling methods for recyclable items 2. The system of claim 1.

6. The listing support department Automatically generate photos or descriptions of items to assist with the listing process 2. The system of claim 1.

7. The collecting unit To estimate a user's emotion and adjust the timing of photographing an item based on the estimated user's emotion.

2. The system of claim 1.

8. The collecting unit When taking a photo of an item, automatically adjust the shooting angle or lighting to suit the type of item.

2. The system of claim 1.

Images