system

Abstract

The system according to this embodiment aims to visualize the frequency of use of items in a room, identify unnecessary items, and suggest decluttering. [Solution] The system according to the embodiment comprises a recognition unit, an analysis unit, a visualization unit, and a proposal unit. The recognition unit photographs items in a room. The analysis unit analyzes the images taken by the recognition unit and recognizes the items. The visualization unit visualizes the frequency of use of the items recognized by the analysis unit. The proposal unit proposes a decluttering method based on the unwanted items identified by the visualization unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to grasp the usage frequency of items in a room, identify unnecessary items, and perform decluttering.

[0005] The system according to the embodiment aims to visualize the usage frequency of items in a room, identify unnecessary items, and propose decluttering.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a recognition unit, an analysis unit, a visualization unit, and a proposal unit. The recognition unit photographs items in a room. The analysis unit analyzes the images captured by the recognition unit and recognizes the items. The visualization unit visualizes the frequency of use of the items recognized by the analysis unit. The proposal unit proposes a decluttering method based on the unwanted items identified by the visualization unit. [Effects of the Invention]

[0007] The system according to this embodiment can visualize the frequency of use of items in a room, identify unnecessary items, and suggest decluttering. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The decluttering service according to an embodiment of the present invention is a system that utilizes an AI camera and a generative AI to recognize items in a room, visualize the frequency of use of items from the user's daily life, and identify unwanted items. This system photographs items in a room, and the generative AI analyzes the images to recognize the items. Next, the generative AI visualizes the frequency of use of items from the user's daily life and identifies unwanted items. For example, the AI ​​camera photographs furniture, home appliances, books, etc. in the room, and the generative AI recognizes these items. Furthermore, the generative AI analyzes the user's behavioral data and calculates the frequency of use of each item. For example, based on data such as how many times an item is used per week or per month, it identifies items with high and low usage frequencies. Next, the decluttering AI mentor consults with the user on decluttering tailored to their individual needs, based on the status of unwanted items shown on the AI ​​camera and the user's requests. Specifically, this includes automatic listing on auction sites and flea markets, linking with donation recipients, requesting appraisals from recycling shops, and applying for municipal waste collection. For example, if an item with low usage frequency is to be listed on a flea market, the decluttering AI mentor automatically creates listing information and lists it on the flea market site. Furthermore, the service can also be used to connect users with donation recipients and to request appraisals from recycling shops. This system allows users to let go of their belongings and their worries. Moreover, it enables them to contribute to society through reduce, reuse, and recycle. For example, by donating items that are not used frequently, they can be put to good use by people who need them. Also, by requesting appraisals from recycling shops, items can be reused. This also contributes to environmental protection. As a result, the decluttering service can efficiently recognize, analyze, visualize, and suggest items in a room.

[0029] The decluttering system according to this embodiment comprises a recognition unit, an analysis unit, a visualization unit, and a suggestion unit. The recognition unit photographs items in a room. The recognition unit photographs furniture, home appliances, books, etc. in a room using, for example, an AI camera. The recognition unit can, for example, photograph items in a room at high resolution and acquire detailed image data. The recognition unit can also photograph items in a room from multiple angles and acquire three-dimensional image data. Furthermore, the recognition unit can periodically photograph items in a room and record changes over time. The analysis unit analyzes the images captured by the recognition unit and recognizes the items. The analysis unit analyzes the images using, for example, a generative AI and recognizes the type and characteristics of the items. The analysis unit can, for example, classify items such as furniture, home appliances, and books using image recognition technology. The analysis unit can also analyze the condition and usage status of items using a generative AI. Furthermore, the analysis unit can evaluate the value and importance of items using a generative AI. The visualization unit visualizes the frequency of use of the items recognized by the analysis unit. The visualization unit can, for example, use a generation AI to display the frequency of item usage in graphs and charts. The visualization unit can display the frequency of item usage in bar graphs or pie charts, making it easier to understand visually. It can also display the frequency of item usage over time to clarify usage patterns. Furthermore, the visualization unit can compare the frequency of item usage with other users to evaluate relative usage. The suggestion unit proposes decluttering methods based on the unwanted items identified by the visualization unit. The suggestion unit can, for example, use a generation AI to automatically create flea market listing information and list items on flea market websites. For example, the suggestion unit can automatically create information for listing infrequently used items on flea market websites and list them there. The suggestion unit can also connect with donation recipients. For example, the suggestion unit can automatically create information for donating infrequently used items and connect with donation recipients. Furthermore, the suggestion unit can request appraisals from recycling shops. The proposal department, for example, automatically creates information for requesting appraisals from recycling shops for items that are not used frequently, and then shares this information with the recycling shops.As a result, the decluttering system according to this embodiment can efficiently recognize, analyze, visualize, and suggest items in a room.

[0030] The recognition unit photographs items in the room. For example, it uses an AI camera to photograph furniture, appliances, books, and other items in the room. Specifically, the AI ​​camera is equipped with a high-resolution lens and advanced image processing technology, allowing it to photograph room items in detail. This enables it to accurately capture the subtle features and condition of the items. The recognition unit can also photograph room items from multiple angles to acquire three-dimensional image data. For example, the camera can automatically rotate to photograph the entire item from a 360-degree perspective, allowing for the collection of more accurate data. Furthermore, the recognition unit can periodically photograph room items and record changes over time. This allows it to track the deterioration and changes in usage of items, which can then be used as information for decluttering decisions. For example, by taking regular photographs once a month and recording changes in the condition of items over time, it is possible to understand the frequency of use and the degree of deterioration. As a result, the recognition unit can efficiently and accurately recognize room items and provide data to the analysis unit.

[0031] The analysis unit analyzes images captured by the recognition unit to recognize items. For example, the analysis unit uses generative AI to analyze images and recognize the type and characteristics of items. Specifically, the generative AI uses deep learning algorithms to analyze image data and automatically classify items such as furniture, home appliances, and books. For example, it can identify items such as sofas, tables, televisions, refrigerators, and books with high accuracy and register their respective characteristics in a database. The analysis unit can also use generative AI to analyze the condition and usage status of items. For example, it can analyze scratches and stains on the surface of furniture, and the frequency of use and operating status of home appliances to evaluate the condition of items. Furthermore, the analysis unit can use generative AI to evaluate the value and importance of items. For example, it can comprehensively evaluate the market value, frequency of use, and emotional value of items to determine the priority for decluttering. As a result, the analysis unit can analyze detailed information about items based on the data provided by the recognition unit and provide this data to the visualization unit and the suggestion unit.

[0032] The visualization unit visualizes the usage frequency of items recognized by the analysis unit. The visualization unit can, for example, use a generation AI to display item usage frequency in graphs and charts. Specifically, it can display item usage frequency in bar graphs or pie charts to make it easier to understand visually. For example, frequently used items will have higher bars in the bar graph, while less frequently used items will have lower bars. The visualization unit can also display item usage frequency on a time axis to clarify usage patterns. For example, monthly usage frequency can be displayed in a line graph to understand seasonal usage patterns. Furthermore, the visualization unit can compare item usage frequency with other users to evaluate relative usage. For example, users can compare their usage frequency with that of other users who own the same type of item to check if their usage is average. In this way, the visualization unit can visually display item usage in an easy-to-understand way and provide information to help users decide whether to declutter.

[0033] The proposal department proposes decluttering methods based on the unwanted items identified by the visualization department. For example, the proposal department can use generation AI to automatically create listing information for flea market apps and list them on flea market sites. Specifically, it automatically generates photos and descriptions of infrequently used items, sets appropriate prices, and lists them on flea market sites. The proposal department can also connect with donation organizations. For example, it can automatically create information for donating infrequently used items and connect it to donation organizations. It also automatically selects donation organizations and manages contact information, allowing users to donate without any hassle. Furthermore, the proposal department can request appraisals from recycling shops. For example, it can automatically create information for requesting appraisals from recycling shops for infrequently used items and connect it to recycling shops. After receiving the appraisal results, it proposes selling the items to the user, allowing the decluttering process to proceed smoothly. In this way, the proposal department can propose concrete and actionable decluttering methods to users and support efficient decluttering.

[0034] The suggestion function can automatically create listing information for flea market items and list them on flea market websites. For example, the suggestion function can automatically create information for listing infrequently used items on flea market websites and list them there. For example, the suggestion function can take photos of items and automatically create product descriptions. The suggestion function can also automatically set prices for items and list them on flea market websites. Furthermore, the suggestion function can automatically update listing information for items and manage sales status on flea market websites. As a result, decluttering becomes more efficient by automatically creating listing information for flea market items and listing them on flea market websites.

[0035] The proposal department can coordinate with recipient organizations. For example, it can automatically create information for donating infrequently used items and coordinate with recipient organizations. For example, it can take photos of items and automatically create information for sending to recipient organizations. Furthermore, the proposal department can select appropriate recipient organizations based on selection criteria and coordinate with them. In addition, the proposal department can manage the coordination status with recipient organizations and track the progress of donations. This allows unwanted items to be put to good use by coordinating with recipient organizations.

[0036] The proposal department can request appraisals from recycling shops. For example, the proposal department can automatically create information for requesting appraisals from recycling shops for items that are not used frequently, and then share that information with the recycling shops. For example, the proposal department can take photos of items and automatically create information for sending to recycling shops. The proposal department can also select and connect with appropriate recycling shops based on selection criteria. Furthermore, the proposal department can manage the status of appraisal requests to recycling shops and track the progress of the appraisals. This promotes the reuse of items by requesting appraisals from recycling shops.

[0037] The proposal department can apply for municipal waste collection services. For example, the proposal department can automatically create information for applying for municipal waste collection of infrequently used items and share it with the municipality. For example, the proposal department can take photos of items and automatically create information for sending to the municipality. The proposal department can also select and coordinate the appropriate collection method based on the municipality's waste collection procedures. Furthermore, the proposal department can manage the status of municipal waste collection applications and track the progress of collection. This makes it easier to dispose of unwanted items by applying for municipal waste collection services.

[0038] The recognition unit can automatically adjust the room's lighting conditions to provide an optimal shooting environment. For example, if the room is dark, the AI ​​in the recognition unit can automatically adjust the lighting to make it brighter. For example, if the room is too bright, the AI ​​in the recognition unit can automatically adjust the lighting to an appropriate brightness. Furthermore, if the room's lighting is uneven, the AI ​​in the recognition unit can automatically adjust the lighting to provide uniform brightness. In this way, an optimal shooting environment is provided by automatically adjusting the room's lighting conditions. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can detect the room's lighting conditions with a sensor and have the AI ​​perform the lighting adjustments.

[0039] The recognition unit can analyze the room layout and determine an efficient shooting order. For example, the recognition unit can analyze the room layout and determine an efficient order for photographing items. For example, the recognition unit can analyze the room layout and determine an order to avoid duplicate photography. The recognition unit can also analyze the room layout and determine an order to photograph all items in the shortest possible time. In this way, an efficient shooting order is determined by analyzing the room layout. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input room layout data into a generating AI and have the generating AI perform the determination of an efficient shooting order.

[0040] The recognition unit can evaluate the storage condition of an item by taking into account the room temperature and humidity. For example, if the room temperature is high, the recognition unit can evaluate the storage condition of a temperature-sensitive item. For example, if the room humidity is high, the recognition unit can evaluate the storage condition of a humidity-sensitive item. The recognition unit can also periodically evaluate the storage condition of an item if the room temperature and humidity fluctuate. This ensures that the storage condition of an item is appropriately evaluated by taking the room temperature and humidity into account. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can acquire room temperature and humidity data using sensors and have AI perform the evaluation of the storage condition.

[0041] The recognition unit can detect the noise level in a room and recommend shooting in a quiet environment. For example, if the noise level in the room is high, the recognition unit will recommend shooting in a quiet environment. For example, if the noise level in the room is low, the recognition unit can start shooting. The recognition unit can also adjust the optimal shooting timing if the noise level in the room fluctuates. In this way, by detecting the noise level in the room, shooting in a quiet environment is recommended. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can detect the noise level in the room with a sensor and have AI perform the adjustment of the shooting timing.

[0042] The analysis unit can analyze the material and condition of an item in detail and evaluate its degree of deterioration. For example, the analysis unit can analyze the material of an item and evaluate its degree of deterioration. For example, the analysis unit can analyze the condition of an item and evaluate its degree of deterioration. The analysis unit can also analyze the frequency of use of an item and evaluate its degree of deterioration. In this way, by analyzing the material and condition of an item in detail, the degree of deterioration is appropriately evaluated. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the material data of the item into a generating AI and have the generating AI perform the evaluation of the degree of deterioration.

[0043] The analysis unit can identify the brand and year of manufacture of an item and assess its value. For example, the analysis unit can identify the brand of an item and assess its value. For example, the analysis unit can identify the year of manufacture of an item and assess its value. The analysis unit can also investigate the market value of an item and assess its value. This ensures that the value is properly assessed by identifying the brand and year of manufacture of the item. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the brand data of an item into a generating AI and have the generating AI perform the value assessment.

[0044] The analysis unit can analyze the item's usage history and evaluate fluctuations in usage frequency. For example, the analysis unit can analyze the item's usage history and evaluate fluctuations in usage frequency. For example, the analysis unit can analyze the item's usage frequency on a monthly basis and evaluate fluctuations. The analysis unit can also analyze the item's usage frequency on a yearly basis and evaluate fluctuations. In this way, fluctuations in usage frequency can be appropriately evaluated by analyzing the item's usage history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input item usage history data into a generating AI and have the generating AI perform the evaluation of fluctuations in usage frequency.

[0045] The analysis unit can collect item-related information from the internet and reflect it in the analysis results. For example, the analysis unit can collect the market value of an item from the internet and reflect it in the analysis results. For example, the analysis unit can collect information on how to use an item from the internet and reflect it in the analysis results. The analysis unit can also collect maintenance information for an item from the internet and reflect it in the analysis results. By collecting item-related information from the internet, the analysis results become more accurate. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information collected from the internet into a generating AI and have the generating AI perform the task of reflecting it in the analysis results.

[0046] The visualization unit can display the frequency of item usage in graphs and charts, making it easier to understand visually. For example, the visualization unit can display the frequency of item usage in a bar graph, making it easier to understand visually. For example, the visualization unit can display the frequency of item usage in a pie chart, making it easier to understand visually. The visualization unit can also display the frequency of item usage in a line graph, making it easier to understand visually. In this way, displaying the frequency of item usage in graphs and charts makes it easier to understand visually. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input item usage frequency data into a generating AI and have the generating AI generate graphs and charts.

[0047] The visualization unit can display the frequency of item use on a time axis, making usage patterns clear. For example, the visualization unit can display the frequency of item use on a daily basis to make usage patterns clear. For example, the visualization unit can display the frequency of item use on a weekly basis to make usage patterns clear. The visualization unit can also display the frequency of item use on a monthly basis to make usage patterns clear. In this way, displaying the frequency of item use on a time axis makes usage patterns clear. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input item usage frequency data into a generating AI and have the generating AI perform the display on a time axis.

[0048] The visualization unit can compare the frequency of item use with other users and evaluate the relative usage status. For example, the visualization unit can compare the frequency of item use with other users and evaluate the relative usage status. For example, the visualization unit can compare the frequency of item use with users in the same region and evaluate the relative usage status. The visualization unit can also compare the frequency of item use with users in the same age group and evaluate the relative usage status. In this way, the relative usage status is evaluated by comparing the frequency of item use with other users. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input usage frequency data of other users into a generating AI and have the generating AI perform an evaluation of the relative usage status.

[0049] The visualization unit can classify the frequency of use of items by category and organize them visually. For example, the visualization unit can classify the frequency of use of items by category such as furniture, home appliances, and books and organize them visually. For example, the visualization unit can classify the frequency of use of items by category such as daily necessities, hobby items, and clothing and organize them visually. The visualization unit can also classify the frequency of use of items by high frequency, medium frequency, and low frequency and organize them visually. In this way, the frequency of use of items is classified by category and organized visually. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input the frequency of use of items into a generating AI and have the generating AI perform the classification by category.

[0050] The proposal unit can select the optimal disposal method by considering the market value of the item when making a proposal. For example, the proposal unit can evaluate the market value of the item and propose the optimal method for selling it on a flea market. For example, the proposal unit can evaluate the market value of the item and propose the optimal recipient for donation. The proposal unit can also evaluate the market value of the item and propose the optimal recycling shop. In this way, the optimal disposal method is selected by considering the market value of the item. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input market value data of the item into a generating AI and have the generating AI select the optimal disposal method.

[0051] The proposal unit can evaluate the environmental impact of an item and recommend eco-friendly disposal methods when making a proposal. For example, the proposal unit can evaluate the environmental impact of an item and propose a recyclable disposal method. For example, the proposal unit can evaluate the environmental impact of an item and propose a reusable donation destination. The proposal unit can also evaluate the environmental impact of an item and propose an environmentally friendly disposal method. In this way, eco-friendly disposal methods are recommended by evaluating the environmental impact of an item. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the item's environmental impact data into a generating AI and have the generating AI recommend an eco-friendly disposal method.

[0052] The proposal unit can select the optimal disposal method when making a proposal, taking into account the item's storage space. For example, the proposal unit can evaluate the item's storage space and propose the optimal method for selling it on a flea market. For example, the proposal unit can evaluate the item's storage space and propose the optimal recipient for donation. The proposal unit can also evaluate the item's storage space and propose the optimal recycling shop. In this way, the optimal disposal method is selected by considering the item's storage space. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input item storage space data into a generating AI and have the generating AI select the optimal disposal method.

[0053] The suggestion unit can propose the optimal disposal timing by considering the item's usage history when making a suggestion. For example, the suggestion unit can evaluate the item's usage history and propose the optimal timing for listing it on a flea market. For example, the suggestion unit can evaluate the item's usage history and propose the optimal timing for donation. The suggestion unit can also evaluate the item's usage history and propose the optimal timing for recycling. In this way, the optimal disposal timing is proposed by considering the item's usage history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input item usage history data into a generating AI and have the generating AI execute a suggestion for the optimal disposal timing.

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

[0055] The recognition unit can automatically adjust the room's lighting conditions to provide an optimal shooting environment. For example, if the room is dark, the AI ​​in the recognition unit can automatically adjust the lighting to make it brighter. For example, if the room is too bright, the AI ​​in the recognition unit can automatically adjust the lighting to an appropriate brightness. Furthermore, if the room's lighting is uneven, the AI ​​in the recognition unit can automatically adjust the lighting to provide uniform brightness. In this way, an optimal shooting environment is provided by automatically adjusting the room's lighting conditions. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can detect the room's lighting conditions with a sensor and have the AI ​​perform the lighting adjustments.

[0056] The recognition unit can analyze the room layout and determine an efficient shooting order. For example, the recognition unit can analyze the room layout and determine an efficient order for photographing items. For example, the recognition unit can analyze the room layout and determine an order to avoid duplicate photography. The recognition unit can also analyze the room layout and determine an order to photograph all items in the shortest possible time. In this way, an efficient shooting order is determined by analyzing the room layout. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input room layout data into a generating AI and have the generating AI perform the determination of an efficient shooting order.

[0057] The recognition unit can evaluate the storage condition of an item by taking into account the room temperature and humidity. For example, if the room temperature is high, the recognition unit can evaluate the storage condition of a temperature-sensitive item. For example, if the room humidity is high, the recognition unit can evaluate the storage condition of a humidity-sensitive item. The recognition unit can also periodically evaluate the storage condition of an item if the room temperature and humidity fluctuate. This ensures that the storage condition of an item is appropriately evaluated by taking the room temperature and humidity into account. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can acquire room temperature and humidity data using sensors and have AI perform the evaluation of the storage condition.

[0058] The analysis unit can analyze the material and condition of an item in detail and evaluate its degree of deterioration. For example, the analysis unit can analyze the material of an item and evaluate its degree of deterioration. For example, the analysis unit can analyze the condition of an item and evaluate its degree of deterioration. The analysis unit can also analyze the frequency of use of an item and evaluate its degree of deterioration. In this way, by analyzing the material and condition of an item in detail, the degree of deterioration is appropriately evaluated. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the material data of the item into a generating AI and have the generating AI perform the evaluation of the degree of deterioration.

[0059] The analysis unit can identify the brand and year of manufacture of an item and assess its value. For example, the analysis unit can identify the brand of an item and assess its value. For example, the analysis unit can identify the year of manufacture of an item and assess its value. The analysis unit can also investigate the market value of an item and assess its value. This ensures that the value is properly assessed by identifying the brand and year of manufacture of the item. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the brand data of an item into a generating AI and have the generating AI perform the value assessment.

[0060] The analysis unit can analyze the item's usage history and evaluate fluctuations in usage frequency. For example, the analysis unit can analyze the item's usage history and evaluate fluctuations in usage frequency. For example, the analysis unit can analyze the item's usage frequency on a monthly basis and evaluate fluctuations. The analysis unit can also analyze the item's usage frequency on a yearly basis and evaluate fluctuations. In this way, fluctuations in usage frequency can be appropriately evaluated by analyzing the item's usage history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input item usage history data into a generating AI and have the generating AI perform the evaluation of fluctuations in usage frequency.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The recognition unit photographs items in the room. The recognition unit uses an AI camera, for example, to photograph furniture, appliances, books, and other items in the room. The recognition unit can photograph items in the room at high resolution and acquire detailed image data. The recognition unit can also photograph items in the room from multiple angles and acquire three-dimensional image data. Furthermore, the recognition unit can periodically photograph items in the room and record changes over time. Step 2: The analysis unit analyzes the image captured by the recognition unit and recognizes the items. The analysis unit can, for example, use generative AI to analyze the image and recognize the type and characteristics of the items. The analysis unit can classify items such as furniture, home appliances, and books using image recognition technology. The analysis unit can also use generative AI to analyze the condition and usage status of the items. Furthermore, the analysis unit can use generative AI to evaluate the value and importance of the items. Step 3: The visualization unit visualizes the usage frequency of items recognized by the analysis unit. The visualization unit can, for example, use a generation AI to display the usage frequency of items in graphs or charts. The visualization unit displays the usage frequency of items in bar graphs or pie charts to make it easier to understand visually. The visualization unit can also display the usage frequency of items on a time axis to clarify usage patterns. Furthermore, the visualization unit can compare the usage frequency of items with that of other users to evaluate relative usage. Step 4: The suggestion unit proposes decluttering methods based on the unwanted items identified by the visualization unit. The suggestion unit can, for example, use a generation AI to automatically create listing information for flea market sales and list items on flea market websites. The suggestion unit automatically creates information for listing items that are not used frequently on flea market websites and lists them on flea market websites. The suggestion unit can also connect with donation recipients. The suggestion unit automatically creates information for donating items that are not used frequently and connects with donation recipients. Furthermore, the suggestion unit can also request appraisals from recycling shops. The suggestion unit automatically creates information for requesting appraisals from recycling shops for items that are not used frequently and connects with recycling shops.

[0063] (Example of form 2) The decluttering service according to an embodiment of the present invention is a system that utilizes an AI camera and a generative AI to recognize items in a room, visualize the frequency of use of items from the user's daily life, and identify unwanted items. This system photographs items in a room, and the generative AI analyzes the images to recognize the items. Next, the generative AI visualizes the frequency of use of items from the user's daily life and identifies unwanted items. For example, the AI ​​camera photographs furniture, home appliances, books, etc. in the room, and the generative AI recognizes these items. Furthermore, the generative AI analyzes the user's behavioral data and calculates the frequency of use of each item. For example, based on data such as how many times an item is used per week or per month, it identifies items with high and low usage frequencies. Next, the decluttering AI mentor consults with the user on decluttering tailored to their individual needs, based on the status of unwanted items shown on the AI ​​camera and the user's requests. Specifically, this includes automatic listing on auction sites and flea markets, linking with donation recipients, requesting appraisals from recycling shops, and applying for municipal waste collection. For example, if an item with low usage frequency is to be listed on a flea market, the decluttering AI mentor automatically creates listing information and lists it on the flea market site. Furthermore, the service can also be used to connect users with donation recipients and to request appraisals from recycling shops. This system allows users to let go of their belongings and their worries. Moreover, it enables them to contribute to society through reduce, reuse, and recycle. For example, by donating items that are not used frequently, they can be put to good use by people who need them. Also, by requesting appraisals from recycling shops, items can be reused. This also contributes to environmental protection. As a result, the decluttering service can efficiently recognize, analyze, visualize, and suggest items in a room.

[0064] The decluttering system according to this embodiment comprises a recognition unit, an analysis unit, a visualization unit, and a suggestion unit. The recognition unit photographs items in a room. The recognition unit photographs furniture, home appliances, books, etc. in a room using, for example, an AI camera. The recognition unit can, for example, photograph items in a room at high resolution and acquire detailed image data. The recognition unit can also photograph items in a room from multiple angles and acquire three-dimensional image data. Furthermore, the recognition unit can periodically photograph items in a room and record changes over time. The analysis unit analyzes the images captured by the recognition unit and recognizes the items. The analysis unit analyzes the images using, for example, a generative AI and recognizes the type and characteristics of the items. The analysis unit can, for example, classify items such as furniture, home appliances, and books using image recognition technology. The analysis unit can also analyze the condition and usage status of items using a generative AI. Furthermore, the analysis unit can evaluate the value and importance of items using a generative AI. The visualization unit visualizes the frequency of use of the items recognized by the analysis unit. The visualization unit can, for example, use a generation AI to display the frequency of item usage in graphs and charts. The visualization unit can display the frequency of item usage in bar graphs or pie charts, making it easier to understand visually. It can also display the frequency of item usage over time to clarify usage patterns. Furthermore, the visualization unit can compare the frequency of item usage with other users to evaluate relative usage. The suggestion unit proposes decluttering methods based on the unwanted items identified by the visualization unit. The suggestion unit can, for example, use a generation AI to automatically create flea market listing information and list items on flea market websites. For example, the suggestion unit can automatically create information for listing infrequently used items on flea market websites and list them there. The suggestion unit can also connect with donation recipients. For example, the suggestion unit can automatically create information for donating infrequently used items and connect with donation recipients. Furthermore, the suggestion unit can request appraisals from recycling shops. The proposal department, for example, automatically creates information for requesting appraisals from recycling shops for items that are not used frequently, and then shares this information with the recycling shops.As a result, the decluttering system according to this embodiment can efficiently recognize, analyze, visualize, and suggest items in a room.

[0065] The recognition unit photographs items in the room. For example, it uses an AI camera to photograph furniture, appliances, books, and other items in the room. Specifically, the AI ​​camera is equipped with a high-resolution lens and advanced image processing technology, allowing it to photograph room items in detail. This enables it to accurately capture the subtle features and condition of the items. The recognition unit can also photograph room items from multiple angles to acquire three-dimensional image data. For example, the camera can automatically rotate to photograph the entire item from a 360-degree perspective, allowing for the collection of more accurate data. Furthermore, the recognition unit can periodically photograph room items and record changes over time. This allows it to track the deterioration and changes in usage of items, which can then be used as information for decluttering decisions. For example, by taking regular photographs once a month and recording changes in the condition of items over time, it is possible to understand the frequency of use and the degree of deterioration. As a result, the recognition unit can efficiently and accurately recognize room items and provide data to the analysis unit.

[0066] The analysis unit analyzes images captured by the recognition unit to recognize items. For example, the analysis unit uses generative AI to analyze images and recognize the type and characteristics of items. Specifically, the generative AI uses deep learning algorithms to analyze image data and automatically classify items such as furniture, home appliances, and books. For example, it can identify items such as sofas, tables, televisions, refrigerators, and books with high accuracy and register their respective characteristics in a database. The analysis unit can also use generative AI to analyze the condition and usage status of items. For example, it can analyze scratches and stains on the surface of furniture, and the frequency of use and operating status of home appliances to evaluate the condition of items. Furthermore, the analysis unit can use generative AI to evaluate the value and importance of items. For example, it can comprehensively evaluate the market value, frequency of use, and emotional value of items to determine the priority for decluttering. As a result, the analysis unit can analyze detailed information about items based on the data provided by the recognition unit and provide this data to the visualization unit and the suggestion unit.

[0067] The visualization unit visualizes the usage frequency of items recognized by the analysis unit. The visualization unit can, for example, use a generation AI to display item usage frequency in graphs and charts. Specifically, it can display item usage frequency in bar graphs or pie charts to make it easier to understand visually. For example, frequently used items will have higher bars in the bar graph, while less frequently used items will have lower bars. The visualization unit can also display item usage frequency on a time axis to clarify usage patterns. For example, monthly usage frequency can be displayed in a line graph to understand seasonal usage patterns. Furthermore, the visualization unit can compare item usage frequency with other users to evaluate relative usage. For example, users can compare their usage frequency with that of other users who own the same type of item to check if their usage is average. In this way, the visualization unit can visually display item usage in an easy-to-understand way and provide information to help users decide whether to declutter.

[0068] The proposal department proposes decluttering methods based on the unwanted items identified by the visualization department. For example, the proposal department can use generation AI to automatically create listing information for flea market apps and list them on flea market sites. Specifically, it automatically generates photos and descriptions of infrequently used items, sets appropriate prices, and lists them on flea market sites. The proposal department can also connect with donation organizations. For example, it can automatically create information for donating infrequently used items and connect it to donation organizations. It also automatically selects donation organizations and manages contact information, allowing users to donate without any hassle. Furthermore, the proposal department can request appraisals from recycling shops. For example, it can automatically create information for requesting appraisals from recycling shops for infrequently used items and connect it to recycling shops. After receiving the appraisal results, it proposes selling the items to the user, allowing the decluttering process to proceed smoothly. In this way, the proposal department can propose concrete and actionable decluttering methods to users and support efficient decluttering.

[0069] The suggestion function can automatically create listing information for flea market items and list them on flea market websites. For example, the suggestion function can automatically create information for listing infrequently used items on flea market websites and list them there. For example, the suggestion function can take photos of items and automatically create product descriptions. The suggestion function can also automatically set prices for items and list them on flea market websites. Furthermore, the suggestion function can automatically update listing information for items and manage sales status on flea market websites. As a result, decluttering becomes more efficient by automatically creating listing information for flea market items and listing them on flea market websites.

[0070] The proposal department can coordinate with recipient organizations. For example, it can automatically create information for donating infrequently used items and coordinate with recipient organizations. For example, it can take photos of items and automatically create information for sending to recipient organizations. Furthermore, the proposal department can select appropriate recipient organizations based on selection criteria and coordinate with them. In addition, the proposal department can manage the coordination status with recipient organizations and track the progress of donations. This allows unwanted items to be put to good use by coordinating with recipient organizations.

[0071] The proposal department can request appraisals from recycling shops. For example, the proposal department can automatically create information for requesting appraisals from recycling shops for items that are not used frequently, and then share that information with the recycling shops. For example, the proposal department can take photos of items and automatically create information for sending to recycling shops. The proposal department can also select and connect with appropriate recycling shops based on selection criteria. Furthermore, the proposal department can manage the status of appraisal requests to recycling shops and track the progress of the appraisals. This promotes the reuse of items by requesting appraisals from recycling shops.

[0072] The proposal department can apply for municipal waste collection services. For example, the proposal department can automatically create information for applying for municipal waste collection of infrequently used items and share it with the municipality. For example, the proposal department can take photos of items and automatically create information for sending to the municipality. The proposal department can also select and coordinate the appropriate collection method based on the municipality's waste collection procedures. Furthermore, the proposal department can manage the status of municipal waste collection applications and track the progress of collection. This makes it easier to dispose of unwanted items by applying for municipal waste collection services.

[0073] The recognition unit can estimate the user's emotions and adjust the shooting timing based on the estimated emotions. For example, if the user is relaxed, the recognition unit can photograph room items at a natural timing. If the user is busy, the recognition unit can photograph room items efficiently in a short amount of time. Furthermore, if the user is stressed, the recognition unit can adjust the shooting timing to reduce the user's burden. By adjusting the shooting timing according to the user's emotions, more natural photography becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input user image data captured by the camera into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0074] The recognition unit can automatically adjust the room's lighting conditions to provide an optimal shooting environment. For example, if the room is dark, the AI ​​in the recognition unit can automatically adjust the lighting to make it brighter. For example, if the room is too bright, the AI ​​in the recognition unit can automatically adjust the lighting to an appropriate brightness. Furthermore, if the room's lighting is uneven, the AI ​​in the recognition unit can automatically adjust the lighting to provide uniform brightness. In this way, an optimal shooting environment is provided by automatically adjusting the room's lighting conditions. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can detect the room's lighting conditions with a sensor and have the AI ​​perform the lighting adjustments.

[0075] The recognition unit can analyze the room layout and determine an efficient shooting order. For example, the recognition unit can analyze the room layout and determine an efficient order for photographing items. For example, the recognition unit can analyze the room layout and determine an order to avoid duplicate photography. The recognition unit can also analyze the room layout and determine an order to photograph all items in the shortest possible time. In this way, an efficient shooting order is determined by analyzing the room layout. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input room layout data into a generating AI and have the generating AI perform the determination of an efficient shooting order.

[0076] The recognition unit can estimate the user's emotions and determine the priority of items to photograph based on the estimated emotions. For example, if the user is relaxed, the recognition unit may start photographing high-importance items first. If the user is busy, for example, the recognition unit may start photographing frequently used items first. The recognition unit may also adjust the priority to reduce the burden of photography if the user is stressed. This enables efficient photography by determining the priority of items to photograph according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input user image data captured by the camera into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0077] The recognition unit can evaluate the storage condition of an item by taking into account the room temperature and humidity. For example, if the room temperature is high, the recognition unit can evaluate the storage condition of a temperature-sensitive item. For example, if the room humidity is high, the recognition unit can evaluate the storage condition of a humidity-sensitive item. The recognition unit can also periodically evaluate the storage condition of an item if the room temperature and humidity fluctuate. This ensures that the storage condition of an item is appropriately evaluated by taking the room temperature and humidity into account. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can acquire room temperature and humidity data using sensors and have AI perform the evaluation of the storage condition.

[0078] The recognition unit can detect the noise level in a room and recommend shooting in a quiet environment. For example, if the noise level in the room is high, the recognition unit will recommend shooting in a quiet environment. For example, if the noise level in the room is low, the recognition unit can start shooting. The recognition unit can also adjust the optimal shooting timing if the noise level in the room fluctuates. In this way, by detecting the noise level in the room, shooting in a quiet environment is recommended. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can detect the noise level in the room with a sensor and have AI perform the adjustment of the shooting timing.

[0079] 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, if the user is relaxed, the analysis unit can display detailed analysis results. If the user is busy, for example, the analysis unit can display concise analysis results that get straight to the point. Furthermore, if the user is stressed, the analysis unit can display visually easy-to-understand analysis results. In this way, by adjusting the display method of the analysis results according to the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is 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 processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display method of the analysis results.

[0080] The analysis unit can analyze the material and condition of an item in detail and evaluate its degree of deterioration. For example, the analysis unit can analyze the material of an item and evaluate its degree of deterioration. For example, the analysis unit can analyze the condition of an item and evaluate its degree of deterioration. The analysis unit can also analyze the frequency of use of an item and evaluate its degree of deterioration. In this way, by analyzing the material and condition of an item in detail, the degree of deterioration is appropriately evaluated. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the material data of the item into a generating AI and have the generating AI perform the evaluation of the degree of deterioration.

[0081] The analysis unit can identify the brand and year of manufacture of an item and assess its value. For example, the analysis unit can identify the brand of an item and assess its value. For example, the analysis unit can identify the year of manufacture of an item and assess its value. The analysis unit can also investigate the market value of an item and assess its value. This ensures that the value is properly assessed by identifying the brand and year of manufacture of the item. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the brand data of an item into a generating AI and have the generating AI perform the value assessment.

[0082] The analysis unit can estimate the user's emotions and determine the priority of analysis results based on the estimated emotions. For example, if the user is relaxed, the analysis unit may prioritize displaying detailed analysis results. If the user is busy, the analysis unit may prioritize displaying important analysis results. Furthermore, if the user is stressed, the analysis unit may prioritize displaying visually easy-to-understand analysis results. This allows for more appropriate analysis results to be provided by prioritizing the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may 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 processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI determine the priority of analysis results.

[0083] The analysis unit can analyze the item's usage history and evaluate fluctuations in usage frequency. For example, the analysis unit can analyze the item's usage history and evaluate fluctuations in usage frequency. For example, the analysis unit can analyze the item's usage frequency on a monthly basis and evaluate fluctuations. The analysis unit can also analyze the item's usage frequency on a yearly basis and evaluate fluctuations. In this way, fluctuations in usage frequency can be appropriately evaluated by analyzing the item's usage history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input item usage history data into a generating AI and have the generating AI perform the evaluation of fluctuations in usage frequency.

[0084] The analysis unit can collect item-related information from the internet and reflect it in the analysis results. For example, the analysis unit can collect the market value of an item from the internet and reflect it in the analysis results. For example, the analysis unit can collect information on how to use an item from the internet and reflect it in the analysis results. The analysis unit can also collect maintenance information for an item from the internet and reflect it in the analysis results. By collecting item-related information from the internet, the analysis results become more accurate. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information collected from the internet into a generating AI and have the generating AI perform the task of reflecting it in the analysis results.

[0085] The visualization unit can estimate the user's emotions and adjust the visualization's presentation based on the estimated emotions. For example, if the user is relaxed, the visualization unit can display detailed graphs or charts. If the user is busy, for example, the visualization unit can display concise graphs or charts. Furthermore, if the user is stressed, the visualization unit can display visually easy-to-understand graphs or charts. This allows for more appropriate visualization by adjusting the visualization's presentation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the visualization unit may be performed using AI, or not using AI. For example, the visualization unit can input user emotion data into the generative AI and have the generative AI adjust the visualization's presentation.

[0086] The visualization unit can display the frequency of item usage in graphs and charts, making it easier to understand visually. For example, the visualization unit can display the frequency of item usage in a bar graph, making it easier to understand visually. For example, the visualization unit can display the frequency of item usage in a pie chart, making it easier to understand visually. The visualization unit can also display the frequency of item usage in a line graph, making it easier to understand visually. In this way, displaying the frequency of item usage in graphs and charts makes it easier to understand visually. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input item usage frequency data into a generating AI and have the generating AI generate graphs and charts.

[0087] The visualization unit can display the frequency of item use on a time axis, making usage patterns clear. For example, the visualization unit can display the frequency of item use on a daily basis to make usage patterns clear. For example, the visualization unit can display the frequency of item use on a weekly basis to make usage patterns clear. The visualization unit can also display the frequency of item use on a monthly basis to make usage patterns clear. In this way, displaying the frequency of item use on a time axis makes usage patterns clear. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input item usage frequency data into a generating AI and have the generating AI perform the display on a time axis.

[0088] The visualization unit can estimate the user's emotions and adjust the level of detail of the visualization based on the estimated emotions. For example, if the user is relaxed, the visualization unit can display detailed information. If the user is busy, the visualization unit can display concise information that gets straight to the point. Furthermore, if the user is stressed, the visualization unit can display visually easy-to-understand information. By adjusting the level of detail of the visualization according to the user's emotions, more appropriate visualization becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input user emotion data into the generative AI and have the generative AI adjust the level of detail of the visualization.

[0089] The visualization unit can compare the frequency of item use with other users and evaluate the relative usage status. For example, the visualization unit can compare the frequency of item use with other users and evaluate the relative usage status. For example, the visualization unit can compare the frequency of item use with users in the same region and evaluate the relative usage status. The visualization unit can also compare the frequency of item use with users in the same age group and evaluate the relative usage status. In this way, the relative usage status is evaluated by comparing the frequency of item use with other users. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input usage frequency data of other users into a generating AI and have the generating AI perform an evaluation of the relative usage status.

[0090] The visualization unit can classify the frequency of use of items by category and organize them visually. For example, the visualization unit can classify the frequency of use of items by category such as furniture, home appliances, and books and organize them visually. For example, the visualization unit can classify the frequency of use of items by category such as daily necessities, hobby items, and clothing and organize them visually. The visualization unit can also classify the frequency of use of items by high frequency, medium frequency, and low frequency and organize them visually. In this way, the frequency of use of items is classified by category and organized visually. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input the frequency of use of items into a generating AI and have the generating AI perform the classification by category.

[0091] The suggestion unit can estimate the user's emotions and adjust the suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is busy, the suggestion unit can provide concise suggestions that get straight to the point. Furthermore, if the user is stressed, the suggestion unit can provide visually easy-to-understand suggestions. This allows for more appropriate suggestions by adjusting the suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the suggestions.

[0092] The proposal unit can select the optimal disposal method by considering the market value of the item when making a proposal. For example, the proposal unit can evaluate the market value of the item and propose the optimal method for selling it on a flea market. For example, the proposal unit can evaluate the market value of the item and propose the optimal recipient for donation. The proposal unit can also evaluate the market value of the item and propose the optimal recycling shop. In this way, the optimal disposal method is selected by considering the market value of the item. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input market value data of the item into a generating AI and have the generating AI select the optimal disposal method.

[0093] The proposal unit can evaluate the environmental impact of an item and recommend eco-friendly disposal methods when making a proposal. For example, the proposal unit can evaluate the environmental impact of an item and propose a recyclable disposal method. For example, the proposal unit can evaluate the environmental impact of an item and propose a reusable donation destination. The proposal unit can also evaluate the environmental impact of an item and propose an environmentally friendly disposal method. In this way, eco-friendly disposal methods are recommended by evaluating the environmental impact of an item. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the item's environmental impact data into a generating AI and have the generating AI recommend an eco-friendly disposal method.

[0094] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit may prioritize detailed suggestions. If the user is busy, the suggestion unit may prioritize important suggestions. Furthermore, if the user is stressed, the suggestion unit may prioritize visually easy-to-understand suggestions. This allows for more appropriate suggestions by prioritizing suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI determine the priority of suggestions.

[0095] The proposal unit can select the optimal disposal method when making a proposal, taking into account the item's storage space. For example, the proposal unit can evaluate the item's storage space and propose the optimal method for selling it on a flea market. For example, the proposal unit can evaluate the item's storage space and propose the optimal recipient for donation. The proposal unit can also evaluate the item's storage space and propose the optimal recycling shop. In this way, the optimal disposal method is selected by considering the item's storage space. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input item storage space data into a generating AI and have the generating AI select the optimal disposal method.

[0096] The suggestion unit can propose the optimal disposal timing by considering the item's usage history when making a suggestion. For example, the suggestion unit can evaluate the item's usage history and propose the optimal timing for listing it on a flea market. For example, the suggestion unit can evaluate the item's usage history and propose the optimal timing for donation. The suggestion unit can also evaluate the item's usage history and propose the optimal timing for recycling. In this way, the optimal disposal timing is proposed by considering the item's usage history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input item usage history data into a generating AI and have the generating AI execute a suggestion for the optimal disposal timing.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The recognition unit can estimate the user's emotions and adjust the shooting timing based on the estimated emotions. For example, if the user is relaxed, the recognition unit can photograph room items at a natural timing. If the user is busy, the recognition unit can photograph room items efficiently in a short amount of time. Furthermore, if the user is stressed, the recognition unit can adjust the shooting timing to reduce the user's burden. By adjusting the shooting timing according to the user's emotions, more natural photography becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input user image data captured by the camera into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0099] The recognition unit can automatically adjust the room's lighting conditions to provide an optimal shooting environment. For example, if the room is dark, the AI ​​in the recognition unit can automatically adjust the lighting to make it brighter. For example, if the room is too bright, the AI ​​in the recognition unit can automatically adjust the lighting to an appropriate brightness. Furthermore, if the room's lighting is uneven, the AI ​​in the recognition unit can automatically adjust the lighting to provide uniform brightness. In this way, an optimal shooting environment is provided by automatically adjusting the room's lighting conditions. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can detect the room's lighting conditions with a sensor and have the AI ​​perform the lighting adjustments.

[0100] The recognition unit can analyze the room layout and determine an efficient shooting order. For example, the recognition unit can analyze the room layout and determine an efficient order for photographing items. For example, the recognition unit can analyze the room layout and determine an order to avoid duplicate photography. The recognition unit can also analyze the room layout and determine an order to photograph all items in the shortest possible time. In this way, an efficient shooting order is determined by analyzing the room layout. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input room layout data into a generating AI and have the generating AI perform the determination of an efficient shooting order.

[0101] The recognition unit can estimate the user's emotions and determine the priority of items to photograph based on the estimated emotions. For example, if the user is relaxed, the recognition unit may start photographing high-importance items first. If the user is busy, for example, the recognition unit may start photographing frequently used items first. The recognition unit may also adjust the priority to reduce the burden of photography if the user is stressed. This enables efficient photography by determining the priority of items to photograph according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input user image data captured by the camera into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0102] The recognition unit can evaluate the storage condition of an item by taking into account the room temperature and humidity. For example, if the room temperature is high, the recognition unit can evaluate the storage condition of a temperature-sensitive item. For example, if the room humidity is high, the recognition unit can evaluate the storage condition of a humidity-sensitive item. The recognition unit can also periodically evaluate the storage condition of an item if the room temperature and humidity fluctuate. This ensures that the storage condition of an item is appropriately evaluated by taking the room temperature and humidity into account. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can acquire room temperature and humidity data using sensors and have AI perform the evaluation of the storage condition.

[0103] 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, if the user is relaxed, the analysis unit can display detailed analysis results. If the user is busy, for example, the analysis unit can display concise analysis results that get straight to the point. Furthermore, if the user is stressed, the analysis unit can display visually easy-to-understand analysis results. In this way, by adjusting the display method of the analysis results according to the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is 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 processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display method of the analysis results.

[0104] The analysis unit can analyze the material and condition of an item in detail and evaluate its degree of deterioration. For example, the analysis unit can analyze the material of an item and evaluate its degree of deterioration. For example, the analysis unit can analyze the condition of an item and evaluate its degree of deterioration. The analysis unit can also analyze the frequency of use of an item and evaluate its degree of deterioration. In this way, by analyzing the material and condition of an item in detail, the degree of deterioration is appropriately evaluated. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the material data of the item into a generating AI and have the generating AI perform the evaluation of the degree of deterioration.

[0105] The analysis unit can identify the brand and year of manufacture of an item and assess its value. For example, the analysis unit can identify the brand of an item and assess its value. For example, the analysis unit can identify the year of manufacture of an item and assess its value. The analysis unit can also investigate the market value of an item and assess its value. This ensures that the value is properly assessed by identifying the brand and year of manufacture of the item. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the brand data of an item into a generating AI and have the generating AI perform the value assessment.

[0106] The analysis unit can estimate the user's emotions and determine the priority of analysis results based on the estimated emotions. For example, if the user is relaxed, the analysis unit may prioritize displaying detailed analysis results. If the user is busy, the analysis unit may prioritize displaying important analysis results. Furthermore, if the user is stressed, the analysis unit may prioritize displaying visually easy-to-understand analysis results. This allows for more appropriate analysis results to be provided by prioritizing the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may 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 processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI determine the priority of analysis results.

[0107] The analysis unit can analyze the item's usage history and evaluate fluctuations in usage frequency. For example, the analysis unit can analyze the item's usage history and evaluate fluctuations in usage frequency. For example, the analysis unit can analyze the item's usage frequency on a monthly basis and evaluate fluctuations. The analysis unit can also analyze the item's usage frequency on a yearly basis and evaluate fluctuations. In this way, fluctuations in usage frequency can be appropriately evaluated by analyzing the item's usage history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input item usage history data into a generating AI and have the generating AI perform the evaluation of fluctuations in usage frequency.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The recognition unit photographs items in the room. The recognition unit uses an AI camera, for example, to photograph furniture, appliances, books, and other items in the room. The recognition unit can photograph items in the room at high resolution and acquire detailed image data. The recognition unit can also photograph items in the room from multiple angles and acquire three-dimensional image data. Furthermore, the recognition unit can periodically photograph items in the room and record changes over time. Step 2: The analysis unit analyzes the image captured by the recognition unit and recognizes the items. The analysis unit can, for example, use generative AI to analyze the image and recognize the type and characteristics of the items. The analysis unit can classify items such as furniture, home appliances, and books using image recognition technology. The analysis unit can also use generative AI to analyze the condition and usage status of the items. Furthermore, the analysis unit can use generative AI to evaluate the value and importance of the items. Step 3: The visualization unit visualizes the usage frequency of items recognized by the analysis unit. The visualization unit can, for example, use a generation AI to display the usage frequency of items in graphs or charts. The visualization unit displays the usage frequency of items in bar graphs or pie charts to make it easier to understand visually. The visualization unit can also display the usage frequency of items on a time axis to clarify usage patterns. Furthermore, the visualization unit can compare the usage frequency of items with that of other users to evaluate relative usage. Step 4: The suggestion unit proposes decluttering methods based on the unwanted items identified by the visualization unit. The suggestion unit can, for example, use a generation AI to automatically create listing information for flea market sales and list items on flea market websites. The suggestion unit automatically creates information for listing items that are not used frequently on flea market websites and lists them on flea market websites. The suggestion unit can also connect with donation recipients. The suggestion unit automatically creates information for donating items that are not used frequently and connects with donation recipients. Furthermore, the suggestion unit can also request appraisals from recycling shops. The suggestion unit automatically creates information for requesting appraisals from recycling shops for items that are not used frequently and connects with recycling shops.

[0110] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0111] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0112] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0113] Each of the multiple elements described above, including the recognition unit, analysis unit, visualization unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the recognition unit uses the camera 42 of the smart device 14 to photograph items in a room, and the control unit 46A acquires image data. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and uses a generation AI to analyze the image and recognize the type and characteristics of the items. The visualization unit is implemented in the specific processing unit 290 of the data processing unit 12, and displays the frequency of item use in graphs and charts. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, and automatically creates flea market listing information and donation linkage information, and proposes them to the user. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0116] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0117] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0118] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0120] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0121] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0122] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0123] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0124] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0126] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0128] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements described above, including the recognition unit, analysis unit, visualization unit, and proposal unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the recognition unit uses the camera 42 of the smart glasses 214 to photograph items in a room, and the control unit 46A acquires image data. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and uses a generation AI to analyze the image and recognize the type and characteristics of the items. The visualization unit is implemented in the specific processing unit 290 of the data processing unit 12, and displays the frequency of item use in graphs and charts. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, and automatically creates flea market listing information and donation linkage information, and proposes them to the user. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0133] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0137] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0138] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements described above, including the recognition unit, analysis unit, visualization unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the recognition unit uses the camera 42 of the headset terminal 314 to photograph items in a room, and the control unit 46A acquires the image data. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and uses a generation AI to analyze the image and recognize the type and characteristics of the items. The visualization unit is implemented in the specific processing unit 290 of the data processing unit 12, and displays the frequency of item use in graphs and charts. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, and automatically creates flea market listing information and donation recipient linkage information, and proposes them to the user. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] As shown in Figure 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.

[0148] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0149] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0152] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0153] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0154] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0155] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0156] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0157] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0159] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0161] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0162] Each of the multiple elements described above, including the recognition unit, analysis unit, visualization unit, and proposal unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the recognition unit uses the camera 42 of the robot 414 to photograph items in a room, and the control unit 46A acquires the image data. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and uses a generating AI to analyze the image and recognize the type and characteristics of the items. The visualization unit is implemented in the specific processing unit 290 of the data processing unit 12, and displays the frequency of use of items in graphs and charts. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, and automatically creates flea market listing information and donation linkage information, and proposes them to the user. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0163] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0164] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0165] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0166] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0167] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0168] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0170] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0171] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0172] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0173] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0174] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0175] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0176] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0177] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0178] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0179] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0180] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0181] (Note 1) A recognition unit that photographs items in the room, An analysis unit analyzes the image captured by the recognition unit and recognizes the item, A visualization unit visualizes the frequency of use of items recognized by the analysis unit, The system includes a suggestion unit that proposes a decluttering method based on the unwanted items identified by the visualization unit. A system characterized by the following features. (Note 2) The aforementioned proposal section is, Automatically create listing information for flea market items and list them on flea market websites. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, Coordinate with the recipient of the donation. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Request an appraisal from a recycling shop. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, Apply for the municipal waste collection service. The system described in Appendix 1, characterized by the features described herein. (Note 6) The recognition unit, It estimates the user's emotions and adjusts the shooting timing based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The recognition unit, It automatically adjusts the room lighting conditions to provide the optimal shooting environment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The recognition unit, Analyze the room layout and determine the most efficient shooting order. The system described in Appendix 1, characterized by the features described herein. (Note 9) The recognition unit, It estimates the user's emotions and determines the priority of items to photograph based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The recognition unit, The condition of the items is evaluated by taking into account the room temperature and humidity. The system described in Appendix 1, characterized by the features described herein. (Note 11) The recognition unit, It detects the noise level in the room and recommends shooting in a quiet environment. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The material and condition of the item are analyzed in detail to evaluate the degree of deterioration. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, Identify the brand and year of manufacture of the item and assess its value. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, Analyze item usage history and evaluate changes in usage frequency. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, Collect item-related information from the internet and incorporate it into the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned visualization unit, It estimates the user's emotions and adjusts the visualization's presentation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned visualization unit, Display item usage frequency using graphs and charts for easier visual understanding. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned visualization unit, Display item usage frequency over time to clarify usage patterns. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned visualization unit, It estimates the user's emotions and adjusts the level of detail of the visualization based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned visualization unit, Compare your item usage frequency with other users to evaluate your relative usage. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned visualization unit, Classify and visually organize items by their frequency of use. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, It estimates the user's emotions and adjusts the suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When making a proposal, we will select the most suitable disposal method considering the market value of the item. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making a proposal, we will evaluate the environmental impact of the item and recommend eco-friendly disposal methods. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, When making a proposal, we will select the most suitable disposal method, taking into account the storage space available for the items. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When making a proposal, we will consider the item's usage history to suggest the optimal disposal timing. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A recognition unit that photographs items in the room, An analysis unit analyzes the image captured by the recognition unit and recognizes the item, A visualization unit visualizes the frequency of use of items recognized by the analysis unit, The system includes a suggestion unit that proposes a decluttering method based on the unwanted items identified by the visualization unit. A system characterized by the following features.

2. The aforementioned proposal section is, Automatically create listing information for flea market items and list them on flea market websites. The system according to feature 1.

3. The aforementioned proposal section is, Coordinate with the recipient of the donation. The system according to feature 1.

4. The aforementioned proposal section is, Request an appraisal from a recycling shop. The system according to feature 1.

5. The aforementioned proposal section is, Apply for the municipal waste collection service. The system according to feature 1.

6. The recognition unit, It estimates the user's emotions and adjusts the shooting timing based on the estimated user emotions. The system according to feature 1.

7. The recognition unit, It automatically adjusts the room lighting conditions to provide the optimal shooting environment. The system according to feature 1.

8. The recognition unit, Analyze the room layout and determine the most efficient shooting order. The system according to feature 1.

Images