system

Abstract

The system according to this embodiment aims to determine the degree of deterioration of the material of the subject over time and to propose the optimal replacement material and replacement timing. [Solution] The system according to this embodiment comprises an analysis unit, an identification unit, and a suggestion unit. The analysis unit analyzes the captured image and determines the degree of deterioration of the material of the subject over time. The identification unit identifies the type of material, manufacturing date, and service life of the material being displayed based on the information analyzed by the analysis unit. The suggestion unit suggests the optimal replacement material and replacement timing based on the information identified by the identification unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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 the degree of aging deterioration of the material of the subject is not accurately determined, and an optimal replacement material and replacement timing are not sufficiently proposed.

[0005] [[ID=X8]] The system according to the embodiment aims to determine the degree of aging deterioration of the material of the subject and propose an optimal replacement material and replacement timing.

Means for Solving the Problems

[0006] Note: There seems to be a typo in the original text where "ID=1X" and "ID=X8" are used. I've translated them as is but they might need to be corrected in the original.The system according to this embodiment comprises an analysis unit, an identification unit, and a suggestion unit. The analysis unit analyzes the captured image and determines the degree of deterioration of the material of the subject over time. The identification unit identifies the type of material, manufacturing date, and service life of the material shown based on the information analyzed by the analysis unit. The suggestion unit suggests the optimal replacement material and replacement timing based on the information identified by the identification unit. [Effects of the Invention]

[0007] The system according to this embodiment can determine the degree of deterioration of the material of the subject over time and propose the optimal replacement material and replacement timing. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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 quality prediction system according to an embodiment of the present invention is a system that uses image analysis technology to predict the shelf life of materials such as wallpaper and car body paint, and suggests the optimal replacement material and replacement timing. The quality prediction system analyzes a captured image and determines the degree of deterioration over time of the material in the subject. Next, based on the analysis results, it identifies the type of material shown, the manufacturing date, the average service life at the shooting location, and the remaining service life. This allows it to suggest the optimal replacement material and replacement timing. For example, the quality prediction system analyzes a captured image and determines the degree of deterioration over time of the material in the subject. In this case, building materials, car paint, and works of art are assumed as subjects. For example, by analyzing images of wallpaper or car body paint, the degree of deterioration of the material can be determined. This allows it to predict the shelf life of the material. Next, based on the analysis results, the quality prediction system identifies the type of material shown, the manufacturing date, the average service life at the shooting location, and the remaining service life. For example, image analysis can identify that the material shown is wallpaper from a specific manufacturer and when that wallpaper was manufactured. Furthermore, the remaining lifespan can be calculated based on the average lifespan at the shooting location. For example, if the average lifespan at the shooting location is 5 years and 4 years have passed, it can be determined that the remaining lifespan is 1 year. In addition, based on this information, the system suggests the optimal replacement material and replacement timing. For example, if the remaining lifespan is 1 year, it can suggest replacing it in 1 year and propose new wallpaper from a specific manufacturer as the replacement material. This allows users to replace materials at the optimal time and maintain quality. This mechanism allows the degree of deterioration over time to be reflected in the transaction price when buying and selling used houses or used cars. For example, the degree of deterioration of the wallpaper in a used house or the paint on a used car can be determined, and the transaction price can be set based on the results. It can also be used as a reference for determining the value of works of art. For example, by analyzing images of works of art and determining the degree of deterioration over time, their value can be evaluated.This allows the quality prediction system to suggest the optimal replacement material and replacement timing to the user.

[0029] The quality prediction system according to this embodiment comprises an analysis unit, an identification unit, and a suggestion unit. The analysis unit analyzes a captured image and determines the degree of deterioration over time for the material of the subject. The analysis unit analyzes images of wallpaper, car paint, artwork, etc., using image analysis technology, for example, to determine the degree of deterioration of the material. For example, the analysis unit analyzes changes in color and the degree of surface damage in the image to determine the degree of deterioration over time. The analysis unit can also improve the accuracy of image analysis using AI. For example, the analysis unit uses an AI model to extract features from the image and determine the degree of deterioration. The identification unit identifies the type of material, manufacturing date, and service life of the material being shown based on the information analyzed by the analysis unit. For example, based on the image analysis results, the identification unit identifies that the material being shown is wallpaper from a specific manufacturer and when that wallpaper was made. The identification unit can also calculate the remaining service life based on the average service life at the shooting location. For example, the identification unit determines that if the average lifespan at the shooting location is 5 years and 4 years have passed, the remaining lifespan is 1 year. The suggestion unit suggests the optimal replacement material and replacement timing based on the information identified by the identification unit. For example, if the remaining lifespan is 1 year, the suggestion unit suggests replacing it in 1 year and proposes a new wallpaper from a specific manufacturer as the replacement material. The suggestion unit can also improve the accuracy of its suggestions using AI. For example, the suggestion unit can use an AI model to suggest the optimal replacement material and replacement timing. As a result, the quality prediction system according to this embodiment can suggest the optimal replacement material and replacement timing to the user.

[0030] The analysis unit analyzes captured images to determine the degree of deterioration over time for the subject's material. Specifically, it uses image analysis technology to analyze images of wallpaper, car paint, artwork, etc., to determine the degree of material deterioration. For example, it analyzes changes in image color and the degree of surface damage to determine the degree of deterioration over time. This includes detecting changes in the RGB values ​​of the image and subtle surface changes using texture analysis. Furthermore, the analysis unit can improve the accuracy of image analysis using AI. Specifically, it trains an image recognition model using deep learning to extract image features and determine the degree of deterioration. For example, it uses a convolutional neural network (CNN) to detect image edges and patterns and determine the progression of deterioration with high accuracy. The AI ​​model learns from past deterioration data and can predict the degree of deterioration with high accuracy even for new images. As a result, the analysis unit can detect subtle deterioration that was difficult with conventional methods and analyze deterioration of complex patterns, enabling an accurate understanding of the material's condition.

[0031] The identification unit identifies the type, manufacturing date, and lifespan of the material being displayed based on the information analyzed by the analysis unit. Specifically, based on the image analysis results, it identifies that the displayed material is wallpaper from a specific manufacturer and when that wallpaper was manufactured. For example, the identification unit compares the features obtained from the image analysis results with a database to identify the type and manufacturing date of the material. The database contains product information, manufacturing dates, and material characteristics for each manufacturer, and the identification unit uses this to derive accurate information. The identification unit can also calculate the remaining lifespan based on the average lifespan at the shooting location. For example, if the average lifespan at the shooting location is 5 years and 4 years have passed, the identification unit will determine that the remaining lifespan is 1 year. Furthermore, the identification unit can improve the accuracy of identification using AI. For example, it can use machine learning algorithms to learn material degradation patterns from past data and perform highly accurate identification on new data. As a result, the identification unit can accurately identify the type, manufacturing date, and lifespan of materials and provide reliable information to users.

[0032] The suggestion unit suggests the optimal replacement material and timing based on information identified by the specific unit. Specifically, if the remaining lifespan is one year, it will suggest replacing the material after one year and propose new wallpaper from a specific manufacturer as the replacement material. The suggestion unit selects the optimal replacement material based on information such as the type of material, manufacturing date, and lifespan provided by the specific unit. For example, it will suggest a new material with similar characteristics based on the characteristics and design of the currently used material. The suggestion unit can also improve the accuracy of its suggestions using AI. Specifically, it can use a recommendation system to learn from past replacement cases and user preferences to suggest the optimal replacement material and timing. For example, it can use collaborative filtering to refer to replacement materials selected by other users and make optimal suggestions. Furthermore, the suggestion unit can also suggest the optimal timing for replacement, taking into account seasonal and climatic conditions, as well as the usage environment. This allows the suggestion function to suggest the optimal replacement material and replacement timing to the user, minimizing costs and effort while maintaining material quality.

[0033] The analysis unit can analyze images of building materials, automobile paint, works of art, etc., and determine the degree of deterioration of the materials. For example, the analysis unit can analyze images of building materials and determine the degree of deterioration. For example, the analysis unit can analyze images of wallpaper and determine the degree of color change and surface damage. The analysis unit can also analyze images of automobile paint and determine the degree of paint deterioration. For example, the analysis unit can analyze images of car body paint and determine the degree of color change and surface damage. The analysis unit can also analyze images of works of art and determine the degree of material deterioration. For example, the analysis unit can analyze images of works of art and determine the degree of color change and surface damage. In this way, it is possible to analyze images of building materials, automobile paint, works of art, etc., and determine the degree of material deterioration. 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 image data into a generating AI, which can then determine the degree of material degradation.

[0034] The identification unit can identify the type and manufacturing date of the projected material, the average lifespan at the shooting location, and the remaining lifespan. For example, based on the image analysis results, the identification unit can identify that the projected material is wallpaper from a specific manufacturer and when that wallpaper was made. For example, based on the image analysis results, the identification unit can identify the manufacturer and manufacturing date of the wallpaper. The identification unit can also calculate the remaining lifespan based on the average lifespan at the shooting location. For example, if the average lifespan at the shooting location is 5 years and 4 years have passed, the identification unit will determine that the remaining lifespan is 1 year. The identification unit can also identify the type and manufacturing date of the projected material. For example, based on the image analysis results, the identification unit can identify the type and manufacturing date of the material. This allows the identification of the type and manufacturing date of the projected material, the average lifespan at the shooting location, and the remaining lifespan. Some or all of the above processing in the identification unit may be performed using AI, for example, or without using AI. For example, the specific unit inputs the image analysis results into a generating AI, which can then identify the type of material, manufacturing date, and service life.

[0035] The suggestion unit can suggest the optimal replacement material and replacement timing based on the identified information. For example, if the remaining useful life is one year, the suggestion unit will suggest replacing it after one year and propose new wallpaper from a specific manufacturer as the replacement material. The suggestion unit can also suggest the optimal replacement material and replacement timing based on the identified information. For example, the suggestion unit will suggest the optimal replacement material and replacement timing based on the identified information. This allows the suggestion unit to suggest the optimal replacement material and replacement timing based on the identified information. 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 the identified information into a generating AI, and the generating AI can suggest the optimal replacement material and replacement timing.

[0036] The suggestion function can make suggestions in the buying and selling of used houses and used cars to reflect the degree of deterioration over time in the transaction price. For example, the suggestion function can determine the degree of deterioration of the wallpaper in a used house or the paintwork in a used car and suggest setting the transaction price based on the result. The suggestion function can also determine the degree of deterioration of the paintwork in a used car and suggest setting the transaction price based on the result. This makes it possible to reflect the degree of deterioration over time in the transaction price in the buying and selling of used houses and used cars. Some or all of the above processing in the suggestion function may be performed using AI, for example, or without AI. For example, the suggestion function can input the result of the deterioration determination into a generating AI, and the generating AI can make suggestions for setting the transaction price.

[0037] The suggestion unit can suggest the degree of deterioration over time as a reference for determining the value of a work of art. For example, the suggestion unit can suggest evaluating the value of a work of art by analyzing an image of the work and determining the degree of deterioration over time. The suggestion unit can also evaluate the value of a work of art based on the degree of deterioration over time. For example, the suggestion unit can suggest evaluating the value of a work of art based on the degree of deterioration over time. This allows the suggestion unit to suggest the degree of deterioration over time as a reference for determining the value of a work of art. 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 the result of determining the degree of deterioration into a generating AI, and the generating AI can make suggestions for evaluating the value of the work of art.

[0038] The analysis unit can determine the degree of deterioration by considering environmental data such as the surface temperature and humidity of the object during analysis. For example, if the surface temperature of the object is high, the analysis unit will consider the possibility that deterioration is progressing. The analysis unit can also consider the risk of mold and corrosion if the humidity of the object is high. The analysis unit can also acquire environmental data of the object in real time and reflect it in the analysis results. This makes it possible to determine the degree of deterioration more accurately by considering the environmental data of the object. 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 environmental data into a generating AI, and the generating AI can determine the degree of deterioration.

[0039] The analysis unit can determine the degree of deterioration by referring to the subject's past maintenance history during the analysis. For example, the analysis unit can obtain the subject's past maintenance history from a database and reflect it in the analysis. The analysis unit can also increase the likelihood of deterioration progressing if there has been a long period without maintenance and perform the analysis accordingly. The analysis unit can also determine that deterioration is progressing slowly if maintenance has been performed regularly and perform the analysis accordingly. This improves the accuracy of determining the degree of deterioration by referring to past maintenance 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 the maintenance history into a generating AI, and the generating AI can determine the degree of deterioration.

[0040] The analysis unit can determine the degree of deterioration by considering the hue and gloss of the subject during analysis. For example, if the hue of the subject has changed, the analysis unit will consider the possibility that deterioration is progressing. The analysis unit can also consider the possibility that deterioration is progressing if the gloss of the subject has decreased. The analysis unit can also detect changes in hue and gloss in real time and reflect them in the analysis results. This makes it possible to determine the degree of deterioration more accurately by considering the hue and gloss of the subject. 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 hue and gloss data into a generating AI, and the generating AI can determine the degree of deterioration.

[0041] The analysis unit can determine the degree of deterioration by considering the surrounding environment of the subject (for example, whether it is in an urban or suburban area) during the analysis. For example, the analysis unit will analyze subjects in urban areas while considering the effects of exhaust fumes and air pollution. For example, the analysis unit can analyze subjects in suburban areas while considering the effects of the natural environment. For example, the analysis unit can analyze subjects in suburban areas while considering the effects of the natural environment. The analysis unit can also acquire environmental data around the subject in real time and reflect it in the analysis results. For example, the analysis unit can acquire environmental data around the subject in real time and reflect it in the analysis results. This makes it possible to determine the degree of deterioration more accurately by considering the surrounding environment of the subject. 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 surrounding environmental data into a generating AI, and the generating AI can determine the degree of deterioration.

[0042] The identification unit can perform identification by referring to detailed information about the manufacturing process and materials used for the subject. For example, the identification unit can obtain the manufacturing process of the subject from a database and reflect it in the identification. The identification unit can also improve the accuracy of identification by referring to detailed information about the materials used. The identification unit can also obtain information about the manufacturing process and materials in real time and reflect it in the identification results. For example, the identification unit can obtain information about the manufacturing process and materials in real time and reflect it in the identification results. This improves the accuracy of identification by referring to detailed information about the manufacturing process and materials. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input information about the manufacturing process and materials into a generating AI and perform identification using the generating AI.

[0043] The identification unit can perform identification by considering the subject's past usage history (e.g., frequency of use and usage environment) at the time of identification. For example, the identification unit can obtain the subject's past frequency of use from a database and reflect it in the identification. For example, the identification unit can obtain the subject's past frequency of use from a database and reflect it in the identification. The identification unit can also refer to information about the usage environment to improve the accuracy of identification. For example, the identification unit can refer to information about the usage environment to improve the accuracy of identification. The identification unit can also obtain usage data in real time and reflect it in the identification results. For example, the identification unit can obtain usage data in real time and reflect it in the identification results. This improves the accuracy of identification by considering past usage history. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input past usage data into a generating AI and perform identification using the generating AI.

[0044] The identification unit can perform identification while considering the manufacturer and brand information of the subject. For example, the identification unit can obtain the subject's manufacturer information from a database and reflect it in the identification. The identification unit can also refer to brand information to improve the accuracy of identification. The identification unit can also obtain manufacturer and brand information in real time and reflect it in the identification results. This improves the accuracy of identification by considering manufacturer and brand information. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input manufacturer and brand information into a generating AI and perform identification using the generating AI.

[0045] The identification unit can perform identification while considering the regional characteristics of the subject (e.g., climate and topography). For example, the identification unit can obtain climate data of the subject's region from a database and reflect it in the identification. The identification unit can also refer to regional topography information to improve the accuracy of identification. The identification unit can also obtain regional characteristics data in real time and reflect it in the identification results. This improves the accuracy of identification by considering regional characteristics. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input regional characteristics data into a generating AI and perform identification using the generating AI.

[0046] The suggestion unit can propose the optimal replacement material and timing by referring to the subject's past replacement history when making suggestions. For example, the suggestion unit can retrieve the subject's past replacement history from a database and reflect it in the suggestions. The suggestion unit can also propose an earlier replacement if there has been a long period without replacement. The suggestion unit can also predict and propose the next replacement timing if replacements are performed regularly. In this way, by referring to past replacement history, the optimal replacement material and timing can be proposed. 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 function can input past replacement history into a generating AI, which can then suggest the optimal replacement materials and timing.

[0047] The suggestion unit can propose the optimal replacement material and timing by considering the purpose and environment of use of the subject. For example, the suggestion unit can obtain the purpose of use of the subject from a database and reflect it in the suggestions. The suggestion unit can also refer to information about the environment and propose the optimal replacement material. The suggestion unit can also obtain data on the purpose of use and environment in real time and reflect it in the suggestion results. This allows the suggestion unit to propose the optimal replacement material and timing by considering the purpose of use and environment. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without using AI. For example, the suggestion function can input data on usage purpose and environment into a generating AI, which can then suggest the optimal replacement material and timing.

[0048] The suggestion unit can propose the optimal replacement material and timing, taking into account the cost-effectiveness of the subject during the suggestion process. For example, the suggestion unit can obtain the cost-effectiveness of the subject from a database and reflect it in the suggestions. The suggestion unit can also prioritize suggesting materials with high cost-effectiveness. The suggestion unit can also obtain cost-effectiveness data in real time and reflect it in the suggestion results. This allows the suggestion unit to propose the optimal replacement material and timing by considering cost-effectiveness. Some or all of the above processing in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input cost-effectiveness data into a generating AI, which can then propose the optimal replacement material and timing.

[0049] The suggestion unit can propose the optimal replacement material and timing, taking into account the eco-friendly materials of the subject during the suggestion process. For example, the suggestion unit can retrieve information on eco-friendly materials from a database and reflect it in the suggestions. The suggestion unit can also prioritize suggesting environmentally friendly materials. The suggestion unit can also acquire data on eco-friendly materials in real time and reflect it in the suggestion results. This allows the suggestion unit to propose environmentally friendly replacement materials and timing by considering eco-friendly materials. Some or all of the above processing in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input data on eco-friendly materials into a generating AI, which can then propose the optimal replacement material and timing.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The analysis unit can determine the degree of deterioration by considering environmental data such as the surface temperature and humidity of the object during analysis. For example, if the surface temperature of the object is high, the analysis can consider the possibility that deterioration is progressing. Also, if the humidity of the object is high, the analysis can consider the risk of mold and corrosion. Furthermore, the analysis unit can acquire environmental data of the object in real time and reflect it in the analysis results. This makes it possible to determine the degree of deterioration more accurately by considering the environmental data of the object. 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 environmental data into a generating AI, and the generating AI can determine the degree of deterioration.

[0052] The analysis unit can determine the degree of deterioration by referring to the subject's past maintenance history during analysis. For example, it can retrieve the subject's past maintenance history from a database and incorporate it into the analysis. Furthermore, if there has been a long period without maintenance, the analysis can be performed with a higher probability that deterioration is progressing. Moreover, if maintenance is performed regularly, the analysis can be performed assuming that the deterioration is progressing slowly. In this way, the accuracy of determining the degree of deterioration is improved by referring to past maintenance 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 the maintenance history into a generating AI, and the generating AI can determine the degree of deterioration.

[0053] The identification unit can perform identification by referring to detailed information about the manufacturing process and materials used of the subject during the identification process. For example, it can obtain the manufacturing process of the subject from a database and reflect it in the identification. It can also improve the accuracy of identification by referring to detailed information about the materials used. Furthermore, it can obtain information about the manufacturing process and materials in real time and reflect it in the identification results. This improves the accuracy of identification by referring to detailed information about the manufacturing process and materials. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input information about the manufacturing process and materials into a generating AI and perform identification using the generating AI.

[0054] The identification unit can perform identification by considering the subject's past usage history (e.g., frequency of use and usage environment) at the time of identification. For example, the subject's past usage frequency can be obtained from a database and reflected in the identification. It can also refer to information on the usage environment to improve the accuracy of identification. Furthermore, usage data can be obtained in real time and reflected in the identification results. This improves the accuracy of identification by considering past usage history. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input past usage data into a generating AI and perform identification using the generating AI.

[0055] The identification unit can perform identification while considering the manufacturer and brand information of the subject. For example, it can obtain the subject's manufacturer information from a database and reflect it in the identification process. It can also refer to brand information to improve the accuracy of identification. Furthermore, it can obtain manufacturer and brand information in real time and reflect it in the identification results. This improves the accuracy of identification by considering manufacturer and brand information. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input manufacturer and brand information into a generating AI and perform identification using the generating AI.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The analysis unit analyzes the captured image and determines the degree of deterioration of the subject's material over time. The analysis unit uses image analysis technology to analyze images of wallpaper, car paint, artwork, etc., and determines the degree of material deterioration. For example, it analyzes changes in image color and the degree of surface damage to determine the degree of deterioration over time. It is also possible to improve the accuracy of image analysis using AI. An AI model is used to extract image features and determine the degree of deterioration. Step 2: The identification unit identifies the type, manufacturing date, and lifespan of the material being displayed based on the information analyzed by the analysis unit. Based on the image analysis results, the identification unit identifies that the displayed material is wallpaper from a specific manufacturer and when that wallpaper was manufactured. It can also calculate the remaining lifespan based on the average lifespan at the shooting location. For example, if the average lifespan at the shooting location is 5 years and 4 years have passed, it will determine that the remaining lifespan is 1 year. Step 3: The suggestion unit suggests the optimal replacement material and replacement timing based on the information identified by the identification unit. For example, if the remaining lifespan is one year, it suggests replacing it in one year and proposes new wallpaper from a specific manufacturer as the replacement material. Furthermore, AI can be used to improve the accuracy of the suggestions. An AI model is used to suggest the optimal replacement material and replacement timing.

[0058] (Example of form 2) The quality prediction system according to an embodiment of the present invention is a system that uses image analysis technology to predict the shelf life of materials such as wallpaper and car body paint, and suggests the optimal replacement material and replacement timing. The quality prediction system analyzes a captured image and determines the degree of deterioration over time of the material in the subject. Next, based on the analysis results, it identifies the type of material shown, the manufacturing date, the average service life at the shooting location, and the remaining service life. This allows it to suggest the optimal replacement material and replacement timing. For example, the quality prediction system analyzes a captured image and determines the degree of deterioration over time of the material in the subject. In this case, building materials, car paint, and works of art are assumed as subjects. For example, by analyzing images of wallpaper or car body paint, the degree of deterioration of the material can be determined. This allows it to predict the shelf life of the material. Next, based on the analysis results, the quality prediction system identifies the type of material shown, the manufacturing date, the average service life at the shooting location, and the remaining service life. For example, image analysis can identify that the material shown is wallpaper from a specific manufacturer and when that wallpaper was manufactured. Furthermore, the remaining lifespan can be calculated based on the average lifespan at the shooting location. For example, if the average lifespan at the shooting location is 5 years and 4 years have passed, it can be determined that the remaining lifespan is 1 year. In addition, based on this information, the system suggests the optimal replacement material and replacement timing. For example, if the remaining lifespan is 1 year, it can suggest replacing it in 1 year and propose new wallpaper from a specific manufacturer as the replacement material. This allows users to replace materials at the optimal time and maintain quality. This mechanism allows the degree of deterioration over time to be reflected in the transaction price when buying and selling used houses or used cars. For example, the degree of deterioration of the wallpaper in a used house or the paint on a used car can be determined, and the transaction price can be set based on the results. It can also be used as a reference for determining the value of works of art. For example, by analyzing images of works of art and determining the degree of deterioration over time, their value can be evaluated.This allows the quality prediction system to suggest the optimal replacement material and replacement timing to the user.

[0059] The quality prediction system according to this embodiment comprises an analysis unit, an identification unit, and a suggestion unit. The analysis unit analyzes a captured image and determines the degree of deterioration over time for the material of the subject. The analysis unit analyzes images of wallpaper, car paint, artwork, etc., using image analysis technology, for example, to determine the degree of deterioration of the material. For example, the analysis unit analyzes changes in color and the degree of surface damage in the image to determine the degree of deterioration over time. The analysis unit can also improve the accuracy of image analysis using AI. For example, the analysis unit uses an AI model to extract features from the image and determine the degree of deterioration. The identification unit identifies the type of material, manufacturing date, and service life of the material being shown based on the information analyzed by the analysis unit. For example, based on the image analysis results, the identification unit identifies that the material being shown is wallpaper from a specific manufacturer and when that wallpaper was made. The identification unit can also calculate the remaining service life based on the average service life at the shooting location. For example, the identification unit determines that if the average lifespan at the shooting location is 5 years and 4 years have passed, the remaining lifespan is 1 year. The suggestion unit suggests the optimal replacement material and replacement timing based on the information identified by the identification unit. For example, if the remaining lifespan is 1 year, the suggestion unit suggests replacing it in 1 year and proposes a new wallpaper from a specific manufacturer as the replacement material. The suggestion unit can also improve the accuracy of its suggestions using AI. For example, the suggestion unit can use an AI model to suggest the optimal replacement material and replacement timing. As a result, the quality prediction system according to this embodiment can suggest the optimal replacement material and replacement timing to the user.

[0060] The analysis unit analyzes captured images to determine the degree of deterioration over time for the subject's material. Specifically, it uses image analysis technology to analyze images of wallpaper, car paint, artwork, etc., to determine the degree of material deterioration. For example, it analyzes changes in image color and the degree of surface damage to determine the degree of deterioration over time. This includes detecting changes in the RGB values ​​of the image and subtle surface changes using texture analysis. Furthermore, the analysis unit can improve the accuracy of image analysis using AI. Specifically, it trains an image recognition model using deep learning to extract image features and determine the degree of deterioration. For example, it uses a convolutional neural network (CNN) to detect image edges and patterns and determine the progression of deterioration with high accuracy. The AI ​​model learns from past deterioration data and can predict the degree of deterioration with high accuracy even for new images. As a result, the analysis unit can detect subtle deterioration that was difficult with conventional methods and analyze deterioration of complex patterns, enabling an accurate understanding of the material's condition.

[0061] The identification unit identifies the type, manufacturing date, and lifespan of the material being displayed based on the information analyzed by the analysis unit. Specifically, based on the image analysis results, it identifies that the displayed material is wallpaper from a specific manufacturer and when that wallpaper was manufactured. For example, the identification unit compares the features obtained from the image analysis results with a database to identify the type and manufacturing date of the material. The database contains product information, manufacturing dates, and material characteristics for each manufacturer, and the identification unit uses this to derive accurate information. The identification unit can also calculate the remaining lifespan based on the average lifespan at the shooting location. For example, if the average lifespan at the shooting location is 5 years and 4 years have passed, the identification unit will determine that the remaining lifespan is 1 year. Furthermore, the identification unit can improve the accuracy of identification using AI. For example, it can use machine learning algorithms to learn material degradation patterns from past data and perform highly accurate identification on new data. As a result, the identification unit can accurately identify the type, manufacturing date, and lifespan of materials and provide reliable information to users.

[0062] The suggestion unit suggests the optimal replacement material and timing based on information identified by the specific unit. Specifically, if the remaining lifespan is one year, it will suggest replacing the material after one year and propose new wallpaper from a specific manufacturer as the replacement material. The suggestion unit selects the optimal replacement material based on information such as the type of material, manufacturing date, and lifespan provided by the specific unit. For example, it will suggest a new material with similar characteristics based on the characteristics and design of the currently used material. The suggestion unit can also improve the accuracy of its suggestions using AI. Specifically, it can use a recommendation system to learn from past replacement cases and user preferences to suggest the optimal replacement material and timing. For example, it can use collaborative filtering to refer to replacement materials selected by other users and make optimal suggestions. Furthermore, the suggestion unit can also suggest the optimal timing for replacement, taking into account seasonal and climatic conditions, as well as the usage environment. This allows the suggestion function to suggest the optimal replacement material and replacement timing to the user, minimizing costs and effort while maintaining material quality.

[0063] The analysis unit can analyze images of building materials, automobile paint, works of art, etc., and determine the degree of deterioration of the materials. For example, the analysis unit can analyze images of building materials and determine the degree of deterioration. For example, the analysis unit can analyze images of wallpaper and determine the degree of color change and surface damage. The analysis unit can also analyze images of automobile paint and determine the degree of paint deterioration. For example, the analysis unit can analyze images of car body paint and determine the degree of color change and surface damage. The analysis unit can also analyze images of works of art and determine the degree of material deterioration. For example, the analysis unit can analyze images of works of art and determine the degree of color change and surface damage. In this way, it is possible to analyze images of building materials, automobile paint, works of art, etc., and determine the degree of material deterioration. 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 image data into a generating AI, which can then determine the degree of material degradation.

[0064] The identification unit can identify the type and manufacturing date of the projected material, the average lifespan at the shooting location, and the remaining lifespan. For example, based on the image analysis results, the identification unit can identify that the projected material is wallpaper from a specific manufacturer and when that wallpaper was made. For example, based on the image analysis results, the identification unit can identify the manufacturer and manufacturing date of the wallpaper. The identification unit can also calculate the remaining lifespan based on the average lifespan at the shooting location. For example, if the average lifespan at the shooting location is 5 years and 4 years have passed, the identification unit will determine that the remaining lifespan is 1 year. The identification unit can also identify the type and manufacturing date of the projected material. For example, based on the image analysis results, the identification unit can identify the type and manufacturing date of the material. This allows the identification of the type and manufacturing date of the projected material, the average lifespan at the shooting location, and the remaining lifespan. Some or all of the above processing in the identification unit may be performed using AI, for example, or without using AI. For example, the specific unit inputs the image analysis results into a generating AI, which can then identify the type of material, manufacturing date, and service life.

[0065] The suggestion unit can suggest the optimal replacement material and replacement timing based on the identified information. For example, if the remaining useful life is one year, the suggestion unit will suggest replacing it after one year and propose new wallpaper from a specific manufacturer as the replacement material. The suggestion unit can also suggest the optimal replacement material and replacement timing based on the identified information. For example, the suggestion unit will suggest the optimal replacement material and replacement timing based on the identified information. This allows the suggestion unit to suggest the optimal replacement material and replacement timing based on the identified information. 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 the identified information into a generating AI, and the generating AI can suggest the optimal replacement material and replacement timing.

[0066] The suggestion function can make suggestions in the buying and selling of used houses and used cars to reflect the degree of deterioration over time in the transaction price. For example, the suggestion function can determine the degree of deterioration of the wallpaper in a used house or the paintwork in a used car and suggest setting the transaction price based on the result. The suggestion function can also determine the degree of deterioration of the paintwork in a used car and suggest setting the transaction price based on the result. This makes it possible to reflect the degree of deterioration over time in the transaction price in the buying and selling of used houses and used cars. Some or all of the above processing in the suggestion function may be performed using AI, for example, or without AI. For example, the suggestion function can input the result of the deterioration determination into a generating AI, and the generating AI can make suggestions for setting the transaction price.

[0067] The suggestion unit can suggest the degree of deterioration over time as a reference for determining the value of a work of art. For example, the suggestion unit can suggest evaluating the value of a work of art by analyzing an image of the work and determining the degree of deterioration over time. The suggestion unit can also evaluate the value of a work of art based on the degree of deterioration over time. For example, the suggestion unit can suggest evaluating the value of a work of art based on the degree of deterioration over time. This allows the suggestion unit to suggest the degree of deterioration over time as a reference for determining the value of a work of art. 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 the result of determining the degree of deterioration into a generating AI, and the generating AI can make suggestions for evaluating the value of the work of art.

[0068] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can increase the accuracy of the analysis to provide more detailed results. For example, if the user is stressed, the analysis unit can increase the accuracy of the analysis to provide more detailed results. The analysis unit can also adjust the accuracy of the analysis to provide more concise results if the user is relaxed. For example, if the user is relaxed, the analysis unit can adjust the accuracy of the analysis to provide more concise results. The analysis unit can also prioritize the speed of the analysis and provide results quickly if the user is in a hurry. For example, if the analysis unit prioritizes the speed of the analysis and provides results quickly if the user is in a hurry. In this way, by adjusting the accuracy of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, 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 AI. For example, the analysis unit can input user emotion data into a generating AI, which can then adjust the accuracy of the analysis.

[0069] The analysis unit can determine the degree of deterioration by considering environmental data such as the surface temperature and humidity of the object during analysis. For example, if the surface temperature of the object is high, the analysis unit will consider the possibility that deterioration is progressing. The analysis unit can also consider the risk of mold and corrosion if the humidity of the object is high. The analysis unit can also acquire environmental data of the object in real time and reflect it in the analysis results. This makes it possible to determine the degree of deterioration more accurately by considering the environmental data of the object. 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 environmental data into a generating AI, and the generating AI can determine the degree of deterioration.

[0070] The analysis unit can determine the degree of deterioration by referring to the subject's past maintenance history during the analysis. For example, the analysis unit can obtain the subject's past maintenance history from a database and reflect it in the analysis. The analysis unit can also increase the likelihood of deterioration progressing if there has been a long period without maintenance and perform the analysis accordingly. The analysis unit can also determine that deterioration is progressing slowly if maintenance has been performed regularly and perform the analysis accordingly. This improves the accuracy of determining the degree of deterioration by referring to past maintenance 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 the maintenance history into a generating AI, and the generating AI can determine the degree of deterioration.

[0071] 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 nervous, the analysis unit can provide a simple and highly visible display method. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. The analysis unit can also provide a display method that includes detailed information if the user is relaxed. For example, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. The analysis unit can also provide a display method that gets to the point if the user is in a hurry. For example, if the user is in a hurry, the analysis unit can provide a display method that gets to the point. By adjusting the display method of the analysis results based on the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, 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-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generating AI, which can then adjust how the analysis results are displayed.

[0072] The analysis unit can determine the degree of deterioration by considering the hue and gloss of the subject during analysis. For example, if the hue of the subject has changed, the analysis unit will consider the possibility that deterioration is progressing. The analysis unit can also consider the possibility that deterioration is progressing if the gloss of the subject has decreased. The analysis unit can also detect changes in hue and gloss in real time and reflect them in the analysis results. This makes it possible to determine the degree of deterioration more accurately by considering the hue and gloss of the subject. 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 hue and gloss data into a generating AI, and the generating AI can determine the degree of deterioration.

[0073] The analysis unit can determine the degree of deterioration by considering the surrounding environment of the subject (for example, whether it is in an urban or suburban area) during the analysis. For example, the analysis unit will analyze subjects in urban areas while considering the effects of exhaust fumes and air pollution. For example, the analysis unit can analyze subjects in suburban areas while considering the effects of the natural environment. For example, the analysis unit can analyze subjects in suburban areas while considering the effects of the natural environment. The analysis unit can also acquire environmental data around the subject in real time and reflect it in the analysis results. For example, the analysis unit can acquire environmental data around the subject in real time and reflect it in the analysis results. This makes it possible to determine the degree of deterioration more accurately by considering the surrounding environment of the subject. 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 surrounding environmental data into a generating AI, and the generating AI can determine the degree of deterioration.

[0074] The identification unit can estimate the user's emotions and adjust specific accuracy based on the estimated user emotions. For example, if the user is stressed, the identification unit can increase specific accuracy to provide detailed results. For example, if the user is stressed, the identification unit can increase specific accuracy to provide detailed results. The identification unit can also adjust specific accuracy to provide concise results if the user is relaxed. For example, if the user is relaxed, the identification unit can adjust specific accuracy to provide concise results. The identification unit can also prioritize a specific speed and provide results quickly if the user is in a hurry. For example, if the identification unit prioritizes a specific speed and provides results quickly if the user is in a hurry. This allows for the provision of more appropriate identification results by adjusting specific accuracy based on the user's emotions. Emotion estimation is implemented using an emotion estimation function, for example, an emotion engine or a 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 identification unit may be performed using AI, for example, or without AI. For example, the specific unit can input user emotion data into a generating AI, which can then adjust the specific accuracy.

[0075] The identification unit can perform identification by referring to detailed information about the manufacturing process and materials used for the subject. For example, the identification unit can obtain the manufacturing process of the subject from a database and reflect it in the identification. The identification unit can also improve the accuracy of identification by referring to detailed information about the materials used. The identification unit can also obtain information about the manufacturing process and materials in real time and reflect it in the identification results. For example, the identification unit can obtain information about the manufacturing process and materials in real time and reflect it in the identification results. This improves the accuracy of identification by referring to detailed information about the manufacturing process and materials. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input information about the manufacturing process and materials into a generating AI and perform identification using the generating AI.

[0076] The identification unit can perform identification by considering the subject's past usage history (e.g., frequency of use and usage environment) at the time of identification. For example, the identification unit can obtain the subject's past frequency of use from a database and reflect it in the identification. For example, the identification unit can obtain the subject's past frequency of use from a database and reflect it in the identification. The identification unit can also refer to information about the usage environment to improve the accuracy of identification. For example, the identification unit can refer to information about the usage environment to improve the accuracy of identification. The identification unit can also obtain usage data in real time and reflect it in the identification results. For example, the identification unit can obtain usage data in real time and reflect it in the identification results. This improves the accuracy of identification by considering past usage history. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input past usage data into a generating AI and perform identification using the generating AI.

[0077] The identification unit can estimate the user's emotions and adjust the display method of the identification results based on the estimated user emotions. For example, if the user is nervous, the identification unit can provide a simple and highly visible display method. For example, if the user is nervous, the identification unit can provide a simple and highly visible display method. The identification unit can also provide a display method that includes detailed information if the user is relaxed. For example, if the user is relaxed, the identification unit can provide a display method that includes detailed information. The identification unit can also provide a display method that gets to the point if the user is in a hurry. For example, if the user is in a hurry, the identification unit can provide a display method that gets to the point. By adjusting the display method of the identification results based on the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, 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 identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input user emotion data into a generating AI, which can then adjust how the identification results are displayed.

[0078] The identification unit can perform identification while considering the manufacturer and brand information of the subject. For example, the identification unit can obtain the subject's manufacturer information from a database and reflect it in the identification. The identification unit can also refer to brand information to improve the accuracy of identification. The identification unit can also obtain manufacturer and brand information in real time and reflect it in the identification results. This improves the accuracy of identification by considering manufacturer and brand information. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input manufacturer and brand information into a generating AI and perform identification using the generating AI.

[0079] The identification unit can perform identification while considering the regional characteristics of the subject (e.g., climate and topography). For example, the identification unit can obtain climate data of the subject's region from a database and reflect it in the identification. The identification unit can also refer to regional topography information to improve the accuracy of identification. The identification unit can also obtain regional characteristics data in real time and reflect it in the identification results. This improves the accuracy of identification by considering regional characteristics. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input regional characteristics data into a generating AI and perform identification using the generating AI.

[0080] The suggestion section can estimate the user's emotions and adjust the content of its suggestions based on those emotions. For example, if the user is stressed, the suggestion section can provide simple and easy-to-understand suggestions. For example, if the user is stressed, the suggestion section can provide simple and easy-to-understand suggestions. For example, if the user is relaxed, the suggestion section can provide suggestions that include detailed information. For example, if the user is relaxed, the suggestion section can provide suggestions that include detailed information. For example, if the user is in a hurry, the suggestion section can provide suggestions quickly. For example, if the user is in a hurry, the suggestion section can provide suggestions quickly. By adjusting the content of suggestions based on the user's emotions, more appropriate suggestions can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. 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 suggestion section may be performed using AI, for example, or without AI. For example, the suggestion section can input user sentiment data into a generating AI, which can then adjust the content of the suggestions.

[0081] The suggestion unit can propose the optimal replacement material and timing by referring to the subject's past replacement history when making suggestions. For example, the suggestion unit can retrieve the subject's past replacement history from a database and reflect it in the suggestions. The suggestion unit can also propose an earlier replacement if there has been a long period without replacement. The suggestion unit can also predict and propose the next replacement timing if replacements are performed regularly. In this way, by referring to past replacement history, the optimal replacement material and timing can be proposed. 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 function can input past replacement history into a generating AI, which can then suggest the optimal replacement materials and timing.

[0082] The suggestion unit can propose the optimal replacement material and timing by considering the purpose and environment of use of the subject. For example, the suggestion unit can obtain the purpose of use of the subject from a database and reflect it in the suggestions. The suggestion unit can also refer to information about the environment and propose the optimal replacement material. The suggestion unit can also obtain data on the purpose of use and environment in real time and reflect it in the suggestion results. This allows the suggestion unit to propose the optimal replacement material and timing by considering the purpose of use and environment. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without using AI. For example, the suggestion function can input data on usage purpose and environment into a generating AI, which can then suggest the optimal replacement material and timing.

[0083] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated emotions. For example, if the user is stressed, the suggestion unit will prioritize providing important suggestions. For example, if the user is stressed, the suggestion unit will prioritize providing important suggestions. For example, if the user is relaxed, the suggestion unit will provide detailed suggestions. For example, if the user is relaxed, the suggestion unit will provide detailed suggestions. For example, if the user is in a hurry, the suggestion unit will quickly provide important suggestions. For example, if the user is in a hurry, the suggestion unit will quickly provide important suggestions. In this way, more appropriate suggestions can be provided by determining the priority of suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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 suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion section can input user sentiment data into a generating AI, which can then determine the priority of the suggestions.

[0084] The suggestion unit can propose the optimal replacement material and timing, taking into account the cost-effectiveness of the subject during the suggestion process. For example, the suggestion unit can obtain the cost-effectiveness of the subject from a database and reflect it in the suggestions. The suggestion unit can also prioritize suggesting materials with high cost-effectiveness. The suggestion unit can also obtain cost-effectiveness data in real time and reflect it in the suggestion results. This allows the suggestion unit to propose the optimal replacement material and timing by considering cost-effectiveness. Some or all of the above processing in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input cost-effectiveness data into a generating AI, which can then propose the optimal replacement material and timing.

[0085] The suggestion unit can propose the optimal replacement material and timing, taking into account the eco-friendly materials of the subject during the suggestion process. For example, the suggestion unit can retrieve information on eco-friendly materials from a database and reflect it in the suggestions. The suggestion unit can also prioritize suggesting environmentally friendly materials. The suggestion unit can also acquire data on eco-friendly materials in real time and reflect it in the suggestion results. This allows the suggestion unit to propose environmentally friendly replacement materials and timing by considering eco-friendly materials. Some or all of the above processing in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input data on eco-friendly materials into a generating AI, which can then propose the optimal replacement material and timing.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is stressed, the accuracy of the analysis can be increased to provide more detailed results. If the user is relaxed, the accuracy of the analysis can be adjusted to provide concise results. Furthermore, if the user is in a hurry, the speed of the analysis can be prioritized to provide results quickly. In this way, by adjusting the accuracy of the analysis based on the user's emotions, more appropriate analysis results can be provided. 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 analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI, and the generative AI can adjust the accuracy of the analysis.

[0088] The analysis unit can determine the degree of deterioration by considering environmental data such as the surface temperature and humidity of the object during analysis. For example, if the surface temperature of the object is high, the analysis can consider the possibility that deterioration is progressing. Also, if the humidity of the object is high, the analysis can consider the risk of mold and corrosion. Furthermore, the analysis unit can acquire environmental data of the object in real time and reflect it in the analysis results. This makes it possible to determine the degree of deterioration more accurately by considering the environmental data of the object. 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 environmental data into a generating AI, and the generating AI can determine the degree of deterioration.

[0089] The analysis unit can determine the degree of deterioration by referring to the subject's past maintenance history during analysis. For example, it can retrieve the subject's past maintenance history from a database and incorporate it into the analysis. Furthermore, if there has been a long period without maintenance, the analysis can be performed with a higher probability that deterioration is progressing. Moreover, if maintenance is performed regularly, the analysis can be performed assuming that the deterioration is progressing slowly. In this way, the accuracy of determining the degree of deterioration is improved by referring to past maintenance 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 the maintenance history into a generating AI, and the generating AI can determine the degree of deterioration.

[0090] 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 nervous, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that gets straight to the point can be provided. In this way, adjusting the display method of the analysis results based on the user's emotions enables a more appropriate display. 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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI, and the generative AI can adjust the display method of the analysis results.

[0091] The identification unit can estimate the user's emotions and adjust specific accuracy based on the estimated emotions. For example, if the user is stressed, specific accuracy can be increased to provide more detailed results. If the user is relaxed, specific accuracy can be adjusted to provide concise results. Furthermore, if the user is in a hurry, specific speed can be prioritized to provide results quickly. This allows for more appropriate identification results by adjusting specific accuracy based on 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 identification unit may be performed using AI or not using AI. For example, the identification unit can input user emotion data into a generative AI, and the generative AI can adjust specific accuracy.

[0092] The identification unit can perform identification by referring to detailed information about the manufacturing process and materials used of the subject during the identification process. For example, it can obtain the manufacturing process of the subject from a database and reflect it in the identification. It can also improve the accuracy of identification by referring to detailed information about the materials used. Furthermore, it can obtain information about the manufacturing process and materials in real time and reflect it in the identification results. This improves the accuracy of identification by referring to detailed information about the manufacturing process and materials. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input information about the manufacturing process and materials into a generating AI and perform identification using the generating AI.

[0093] The identification unit can perform identification by considering the subject's past usage history (e.g., frequency of use and usage environment) at the time of identification. For example, the subject's past usage frequency can be obtained from a database and reflected in the identification. It can also refer to information on the usage environment to improve the accuracy of identification. Furthermore, usage data can be obtained in real time and reflected in the identification results. This improves the accuracy of identification by considering past usage history. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input past usage data into a generating AI and perform identification using the generating AI.

[0094] The identification unit can estimate the user's emotions and adjust the display method of the identification results based on the estimated user emotions. For example, if the user is nervous, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that gets straight to the point can be provided. In this way, by adjusting the display method of the identification results based on 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, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the identification unit may be performed using AI, for example, or not using AI. For example, the identification unit can input user emotion data into a generative AI, and the generative AI can adjust the display method of the identification results.

[0095] The identification unit can perform identification while considering the manufacturer and brand information of the subject. For example, it can obtain the subject's manufacturer information from a database and reflect it in the identification process. It can also refer to brand information to improve the accuracy of identification. Furthermore, it can obtain manufacturer and brand information in real time and reflect it in the identification results. This improves the accuracy of identification by considering manufacturer and brand information. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input manufacturer and brand information into a generating AI and perform identification using the generating AI.

[0096] The suggestion unit can estimate the user's emotions and adjust the content of its suggestions based on those emotions. For example, if the user is stressed, it can provide simple and easy-to-understand suggestions. If the user is relaxed, it can provide suggestions that include more detailed information. Furthermore, if the user is in a hurry, it can provide suggestions quickly. By adjusting the content of suggestions based on the user's emotions, more appropriate suggestions can be provided. 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 processing 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 the generative AI can adjust the content of the suggestions.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The analysis unit analyzes the captured image and determines the degree of deterioration of the subject's material over time. The analysis unit uses image analysis technology to analyze images of wallpaper, car paint, artwork, etc., and determines the degree of material deterioration. For example, it analyzes changes in image color and the degree of surface damage to determine the degree of deterioration over time. It is also possible to improve the accuracy of image analysis using AI. An AI model is used to extract image features and determine the degree of deterioration. Step 2: The identification unit identifies the type, manufacturing date, and lifespan of the material being displayed based on the information analyzed by the analysis unit. Based on the image analysis results, the identification unit identifies that the displayed material is wallpaper from a specific manufacturer and when that wallpaper was manufactured. It can also calculate the remaining lifespan based on the average lifespan at the shooting location. For example, if the average lifespan at the shooting location is 5 years and 4 years have passed, it will determine that the remaining lifespan is 1 year. Step 3: The suggestion unit suggests the optimal replacement material and replacement timing based on the information identified by the identification unit. For example, if the remaining lifespan is one year, it suggests replacing it in one year and proposes new wallpaper from a specific manufacturer as the replacement material. Furthermore, AI can be used to improve the accuracy of the suggestions. An AI model is used to suggest the optimal replacement material and replacement timing.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] Each of the multiple elements described above, including the analysis unit, identification unit, and suggestion unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit analyzes an image captured using the camera 42 of the smart device 14 using the identification unit 290 of the data processing unit 12 to determine the degree of deterioration of the material of the subject over time. The identification unit identifies the type of material, manufacturing date, and service life based on the analysis results using the identification unit 290 of the data processing unit 12. The suggestion unit suggests the optimal replacement material and replacement timing using the identification unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] Each of the multiple elements described above, including the analysis unit, identification unit, and suggestion unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit analyzes an image captured using the camera 42 of the smart glasses 214 using the identification unit 290 of the data processing unit 12 to determine the degree of deterioration of the material of the subject over time. The identification unit identifies the type of material, manufacturing date, and service life based on the analysis results, for example, using the identification unit 290 of the data processing unit 12. The suggestion unit suggests the optimal replacement material and replacement timing, for example, using the identification unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] Each of the multiple elements described above, including the analysis unit, identification unit, and suggestion unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit analyzes an image captured using the camera 42 of the headset terminal 314 using the identification unit 290 of the data processing unit 12 to determine the degree of deterioration of the material of the subject over time. The identification unit identifies the type of material, manufacturing date, and service life based on the analysis results using the identification unit 290 of the data processing unit 12. The suggestion unit suggests the optimal replacement material and replacement timing using the identification unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] Each of the multiple elements described above, including the analysis unit, identification unit, and suggestion unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the analysis unit analyzes images captured using the camera 42 of the robot 414 using the identification unit 290 of the data processing unit 12 to determine the degree of deterioration of the material of the subject over time. The identification unit identifies the type of material, manufacturing date, and service life based on the analysis results using, for example, the identification unit 290 of the data processing unit 12. The suggestion unit suggests the optimal replacement material and replacement timing using, for example, the identification unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] (Note 1) An analysis unit analyzes the captured image and determines the degree of deterioration over time of the subject's material, Based on the information analyzed by the aforementioned analysis unit, an identification unit identifies the type of material, manufacturing date, and service life of the displayed material. The system includes a suggestion unit that suggests the optimal replacement material and replacement timing based on the information identified by the aforementioned specific unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, The system analyzes images of building materials, car paint, and works of art to determine the degree of material deterioration. The system described in Appendix 1, characterized by the features described herein. (Note 3) The specified part is, Identify the type and manufacturing date of the materials being shown, the average lifespan at the filming location, and the remaining lifespan. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned suggestion section is, Based on the identified information, the system suggests the optimal replacement material and replacement timing. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned suggestion section is, In the buying and selling of used houses and cars, suggestions are made to reflect the degree of deterioration due to age in the transaction price. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned suggestion section is, To help assess the value of artwork, the degree of deterioration over time will be suggested. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, During analysis, the degree of deterioration is determined by considering environmental data such as the surface temperature and humidity of the subject. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, During analysis, the degree of deterioration is determined by referring to the subject's past maintenance history. The system described in Appendix 1, characterized by the features described herein. (Note 10) 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 11) The aforementioned analysis unit, During analysis, the degree of deterioration is determined by considering the hue and glossiness of the subject. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, the degree of deterioration is determined by considering the surrounding environment of the subject. The system described in Appendix 1, characterized by the features described herein. (Note 13) The specified part is, It estimates the user's emotions and adjusts specific accuracy based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The specified part is, During identification, the manufacturing process and detailed information about the materials used in the subject are referenced. The system described in Appendix 1, characterized by the features described herein. (Note 15) The specified part is, When identifying a subject, the past usage history of that subject is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 16) The specified part is, It estimates the user's emotions and adjusts how specific results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The specified part is, When identifying an item, the manufacturer and brand information of the subject are taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 18) The specified part is, When identifying a subject, the regional characteristics of the subject should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned suggestion section is, It estimates the user's emotions and adjusts the suggestions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned suggestion section is, When suggesting images, the system refers to the subject's past replacement history to propose the most suitable replacement material and timing. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned suggestion section is, When suggesting, the system proposes the most suitable replacement material and timing, taking into account the subject's intended use and environment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned suggestion 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 23) The aforementioned suggestion section is, When suggesting images, the system proposes the most suitable replacement material and timing, taking into account the cost-effectiveness of the subject. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned suggestion section is, When suggesting, the system takes into account the eco-friendly materials of the subject and proposes the most suitable replacement materials and timing. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0171] 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. An analysis unit analyzes the captured image and determines the degree of deterioration of the subject's material over time, Based on the information analyzed by the aforementioned analysis unit, an identification unit identifies the type of material, manufacturing date, and service life of the displayed material. The system includes a suggestion unit that suggests the optimal replacement material and replacement timing based on the information identified by the aforementioned specific unit. A system characterized by the following features.

2. The aforementioned analysis unit, The system analyzes images of building materials, car paint, and works of art to determine the degree of material deterioration. The system according to feature 1.

3. The specified part is, Identify the type and manufacturing date of the materials being shown, the average lifespan at the filming location, and the remaining lifespan. The system according to feature 1.

4. The aforementioned suggestion section is, Based on the identified information, the system suggests the optimal replacement material and replacement timing. The system according to feature 1.

5. The aforementioned suggestion section is, In the buying and selling of used houses and cars, suggestions are made to reflect the degree of deterioration due to age in the transaction price. The system according to feature 1.

6. The aforementioned suggestion section is, To help assess the value of artwork, the degree of deterioration over time is suggested. The system according to feature 1.

7. The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. The system according to feature 1.

8. The aforementioned analysis unit, During analysis, the degree of deterioration is determined by considering environmental data such as the surface temperature and humidity of the subject. The system according to feature 1.

9. The aforementioned analysis unit, During analysis, the degree of deterioration is determined by referring to the subject's past maintenance history. The system according to feature 1.

10. 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 according to feature 1.

Images