System

Abstract

An object of the system according to the embodiment is to enable a general user to easily appraise an antique, a brand product, a painting, or the like.SOLUTION: A system includes an image input unit, a preprocessing unit, an analysis unit, a result generation unit, and a display unit. The image input unit receives an image from a user. The preprocessing unit preprocesses the image received by the image input unit. The analysis unit analyzes the image preprocessed by the preprocessing unit. The result generation unit generates an appraisal result based on the result analyzed by the analysis unit. The display unit displays the appraisal result generated by the result generation unit to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that specialized knowledge is required to appraise antiques, brand-name goods, paintings, etc., making it difficult for general users to easily perform appraisals.

[0005] The system according to the embodiment aims to enable general users to easily appraise antiques, branded goods, paintings, and the like. [Means for solving the problem]

[0006] The system according to the embodiment includes an image input unit, a preprocessing unit, an analysis unit, a result generation unit, and a display unit. The image input unit receives an image from a user. The preprocessing unit preprocesses the image received by the image input unit. The analysis unit analyzes the image preprocessed by the preprocessing unit. The result generation unit generates an appraisal result based on the result of the analysis by the analysis unit. The display unit displays the appraisal result generated by the result generation unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment allows general users to easily appraise antiques, branded goods, paintings, and the like. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The appraisal app according to an embodiment of the present invention is a system that automatically analyzes images taken by users and uses a generation AI to provide appraisal results. This allows the appraisal app to quickly and accurately appraise antiques, brand-name goods, paintings, and other items based on images taken by users.

[0029] The appraisal app according to the embodiment includes an image input unit, a preprocessing unit, an analysis unit, a result generation unit, and a display unit. The image input unit receives an image from a user. For example, the image may be captured using a smartphone camera and uploaded to the app. The image input unit may also select and upload an existing image file. The preprocessing unit preprocesses the received image. For example, the preprocessing unit may adjust the image resolution and remove noise. The preprocessing unit may also extract the image contours. The analysis unit analyzes the preprocessed image. For example, the analysis unit may extract features within the image using a generation AI. The analysis unit may also recognize patterns within the image. The result generation unit generates an appraisal result based on the analysis result. For example, the result generation unit may determine the authenticity of an object based on the extracted features. The result generation unit may also evaluate the value and condition of the object. The display unit displays the generated appraisal result to the user. For example, the display unit may display the appraisal result in text format. The display unit may also display the appraisal result graphically. As a result, the appraisal app according to the embodiment can quickly and accurately appraise antiques, brand-name goods, paintings, and the like based on images taken by the user.

[0030] The preprocessing unit can suggest optimal shooting angles and lighting conditions to the user in real time when capturing an image. For example, the preprocessing unit uses a generation AI to analyze the camera's preview screen and suggest optimal shooting angles and lighting conditions to the user in real time. For example, when photographing an antique, the preprocessing unit suggests an angle to avoid light reflections and a lighting position to reduce shadows. The preprocessing unit also monitors the user's camera operation and automatically adjusts optimal settings before capturing. For example, when photographing a designer bag, the preprocessing unit automatically adjusts the camera's exposure and white balance. The preprocessing unit also analyzes the user's shooting environment and suggests optimal shooting conditions. For example, when photographing a painting, the preprocessing unit suggests the time of day to utilize natural light and the placement of artificial lighting. This allows the user to capture images under optimal conditions.

[0031] The preprocessing unit analyzes the material and surface texture of an object and can perform noise removal and resolution adjustment based on that. For example, the generation AI analyzes the material of the object in the image and performs noise removal based on that. For example, a specific filter is applied to suppress reflections on the metal parts of an antique. The preprocessing unit also analyzes the surface texture of the object and adjusts the resolution based on that. For example, a specific resolution setting is applied to emphasize the leather texture of a designer bag. The preprocessing unit also analyzes the material and texture of the object in the image and performs preprocessing based on that. For example, a filter is applied to remove noise while preserving the texture of the canvas of a painting. This allows optimal preprocessing to be performed according to the material and texture of the object.

[0032] The image input unit can use voice instructions or gesture recognition to enable the user to operate the unit without using their hands. For example, the generation AI analyzes voice instructions to enable the user to take an image without using their hands. For example, the camera will automatically release the shutter just by saying "start shooting." The generation AI can also use gesture recognition to enable the user to operate the unit without using their hands. For example, the camera settings can be changed just by waving your hand. The generation AI can also combine voice instructions and gesture recognition to enable the user to input an image without using their hands. For example, the user can say "zoom in" by voice and adjust the zoom with hand movements. This allows the user to operate the unit without using their hands.

[0033] The preprocessing section develops preprocessing algorithms specialized for different types of objects, allowing optimal preprocessing for each object. For example, the generation AI analyzes an image of a jewel and applies a specialized preprocessing algorithm. For example, it applies a filter to emphasize the brilliance of the jewel. The generation AI also analyzes an image of a watch and applies a specialized preprocessing algorithm. For example, it adjusts the resolution to emphasize the details of the watch face. The generation AI also develops preprocessing algorithms specialized for different types of objects, allowing optimal preprocessing for each object. For example, it applies the optimal filter and resolution adjustment for each object, such as antiques, brand-name goods, and paintings. This allows optimal preprocessing for each object.

[0034] The analysis unit can extract fine features within an image with high accuracy, improving the accuracy of appraisal. For example, the analysis unit allows the generation AI to extract handwriting within an image with high accuracy, improving the accuracy of appraisal. For example, analyzing the handwriting on a painting can help identify the artist. The analysis unit also allows the generation AI to extract weave within an image with high accuracy, improving the accuracy of appraisal. For example, analyzing the weave of an antique fabric can help identify its age and place of origin. The analysis unit also allows the generation AI to extract fine features within an image with high accuracy, improving the accuracy of appraisal. For example, analyzing the stitching on a brand-name bag can determine whether it is genuine or fake. This allows the generation AI to extract fine features with high accuracy, improving the accuracy of appraisal.

[0035] The analysis unit can perform more detailed feature extraction by referring to the historical background of the object and information about the creator. In the analysis unit, for example, the generation AI performs image analysis by referring to the historical background of the object. For example, when analyzing an image of an antique, features are extracted taking into account the characteristics of the era. In addition, the analysis unit performs image analysis by the generation AI by referring to information about the creator. For example, when analyzing an image of a painting, features are extracted taking into account the artist's distinctive techniques. In addition, the analysis unit performs more detailed feature extraction by referring to the historical background of the object and information about the creator. For example, when analyzing an image of a brand-name bag, features are extracted taking into account the brand's history and design changes. In this way, by referring to the historical background and information about the creator, more detailed feature extraction is possible.

[0036] The analysis unit can also use 3D scanning technology to extract the three-dimensional features of an object. For example, the generation AI in the analysis unit uses 3D scanning technology to extract the three-dimensional features of an object. For example, it analyzes the three-dimensional shape of an antique and extracts detailed features. The generation AI in the analysis unit also extracts the three-dimensional features of an object based on 3D scan data. For example, it analyzes the three-dimensional shape of a brand-name bag and determines whether it is genuine or fake. The generation AI in the analysis unit also uses 3D scanning technology to extract the three-dimensional features of an object with high accuracy. For example, it analyzes the three-dimensional handwriting of a painting to help identify the artist. This makes it possible to extract three-dimensional features by using 3D scanning technology in combination.

[0037] The analysis unit can integrate multiple images taken from different viewpoints to extract more detailed features. For example, the generation AI in the analysis unit integrates multiple images taken from different viewpoints to extract detailed features. For example, images of an antique are taken from multiple angles and integrated for analysis. The generation AI also integrates multiple images in the analysis unit to extract detailed features of the object. For example, a brand-name bag is photographed from different angles and integrated to determine whether it is genuine or fake. The generation AI also integrates images taken from different viewpoints to extract more detailed features. For example, images of a painting are photographed from multiple angles and integrated to analyze handwriting and color features. In this way, detailed feature extraction becomes possible by integrating images from different viewpoints.

[0038] The result generation unit can quantify the reliability of the appraisal result and provide it to the user. For example, the generation AI quantifies the reliability of the appraisal result and provides it to the user. For example, the reliability is displayed as a percentage for the appraisal result of an antique. The result generation unit also quantifies the reliability of the appraisal result and provides it to the user. For example, the reliability is displayed as a number for the appraisal result of a brand-name bag. The result generation unit also quantifies the reliability of the appraisal result and provides it to the user. For example, the reliability is displayed as a graph for the appraisal result of a painting. This allows the reliability of the appraisal result to be quantified and provided to the user.

[0039] The result generation unit can provide more accurate appraisal results by referring to past appraisal data and market trends. In the result generation unit, for example, the generation AI refers to past appraisal data to generate appraisal results. For example, the accuracy of the current appraisal results is improved based on past appraisal data for antiques. In addition, the result generation unit can generate appraisal results by referring to market trends. For example, the accuracy of the current appraisal results is improved based on market trends for brand-name goods. In addition, the result generation unit can provide more accurate appraisal results by referring to past appraisal data and market trends. For example, the accuracy of the current appraisal results is improved based on past appraisal data and market trends for paintings. In this way, by referring to past data and market trends, more accurate appraisal results can be provided.

[0040] When generating appraisal results, the result generation unit can add a function to explain the results using audio or video. For example, the result generation unit adds a function whereby the generation AI explains the appraisal results using audio. For example, the appraisal results for antiques are explained in detail using audio. The result generation unit also adds a function whereby the generation AI explains the appraisal results using video. For example, the appraisal results for a brand-name bag are explained visually using video. The result generation unit also adds a function whereby the generation AI explains the appraisal results using audio or video. For example, the appraisal results for a painting are explained in detail using video. In this way, by explaining the results using audio or video, appraisal results that are easy for users to understand can be provided.

[0041] The result generation unit can simultaneously provide multiple appraisal results based on different appraisal standards. For example, the generation AI in the result generation unit provides appraisal results based on artistic value. For example, the generation AI evaluates the artistic value of a painting and displays the result. The result generation unit also provides appraisal results based on historical value. For example, the generation AI evaluates the historical value of an antique and displays the result. The result generation unit also provides multiple appraisal results based on different appraisal standards simultaneously. For example, the generation AI simultaneously evaluates the artistic value and historical value of a brand-name bag and displays the result. This allows multiple appraisal results based on different appraisal standards to be provided simultaneously.

[0042] When displaying the appraisal results, the display unit can use interactive graphs and charts to make them visually easy to understand. For example, the generation AI displays the appraisal results in an interactive graph on the display unit. For example, the change in value of an antique is shown in a graph. The generation AI also displays the appraisal results in an interactive chart on the display unit. For example, the characteristics of a brand-name bag are visually shown in a chart. The generation AI also displays the appraisal results in an interactive graph or chart on the display unit, allowing the user to access more information. For example, the conservation state of a painting is shown in a chart, and more information is displayed by clicking on it. This makes the appraisal results visually easy to understand.

[0043] When storing appraisal results, the display unit can use blockchain technology to ensure data tamper-proofing and reliability. For example, the generation AI stores the appraisal results on the blockchain in the display unit, ensuring data tamper-proofing and reliability. For example, the appraisal results of antiques are recorded on the blockchain. The generation AI also uses blockchain technology to ensure the reliability of the appraisal results. For example, the appraisal results of a brand-name bag are stored on the blockchain. The generation AI also stores the appraisal results on the blockchain in the display unit, ensuring data tamper-proofing and reliability. For example, the appraisal results of a painting are recorded on the blockchain. In this way, by using blockchain technology, data tamper-proofing and reliability can be ensured.

[0044] The display unit can synchronize appraisal results across multiple devices, allowing them to be viewed anywhere. For example, the display unit allows the generation AI to save appraisal results in the cloud and synchronize them across multiple devices. For example, the appraisal results of an antique photographed with a smartphone can be viewed on a PC. The display unit also allows the generation AI to synchronize appraisal results in real time, allowing them to be viewed on multiple devices. For example, the appraisal results of a brand-name bag photographed with a tablet can be viewed on a smartphone. The display unit also allows the generation AI to save appraisal results in the cloud and synchronize them across multiple devices. For example, the appraisal results of a painting photographed with a PC can be viewed on a tablet. This allows appraisal results to be synchronized across multiple devices, allowing them to be viewed anywhere.

[0045] The display unit can add a function that allows the appraisal results to be easily shared on social media or by email. For example, the display unit adds a function that allows the generation AI to share the appraisal results on social media. For example, the appraisal results for antiques are shared on Facebook or Twitter. The display unit also adds a function that allows the generation AI to share the appraisal results by email. For example, the appraisal results for a brand-name bag are sent by email. The display unit also adds a function that allows the generation AI to easily share the appraisal results on social media or by email. For example, the appraisal results for a painting are shared on Instagram or by email. This makes it easy to share the appraisal results on social media or by email.

[0046] The display unit can display the appraisal history in chronological order, allowing changes in past appraisal results to be tracked. The display unit, for example, allows the generation AI to display the appraisal history in chronological order, allowing changes in past appraisal results to be tracked. For example, the appraisal results of antiques can be displayed in chronological order, allowing changes in value to be tracked. The display unit also allows the generation AI to display the appraisal history in chronological order, allowing changes in past appraisal results to be tracked. For example, the appraisal results of a brand-name bag can be displayed in chronological order, allowing changes in condition to be tracked. The display unit also allows the generation AI to display the appraisal history in chronological order, allowing changes in past appraisal results to be tracked. For example, the appraisal results of a painting can be displayed in chronological order, allowing changes in preservation condition to be tracked. This allows the appraisal history to be displayed in chronological order, allowing changes in past appraisal results to be tracked.

[0047] The display unit can organize the appraisal history by category, allowing the generation AI to easily search for appraisal results that belong to a specific category. For example, the display unit displays the appraisal history by category, allowing the generation AI to easily search for appraisal results that belong to a specific category. For example, the display unit displays the appraisal history by category, such as antiques, brand-name goods, and paintings. The display unit also organizes the appraisal history by category, allowing the generation AI to easily search for appraisal results that belong to a specific category. For example, the display unit displays antiques by category, such as pottery, metal products, and wood products. The display unit also organizes the appraisal history by category, allowing the generation AI to easily search for appraisal results that belong to a specific category. For example, the display unit displays brand-name goods by category, such as bags, watches, and accessories. This allows the generation AI to organize the appraisal history by category, allowing the generation AI to easily search for appraisal results that belong to a specific category.

[0048] The display unit can store the appraisal history on the cloud and synchronize data between devices. For example, the generation AI on the display unit stores the appraisal history on the cloud and synchronizes data between devices. For example, the appraisal history of an antique photographed with a smartphone can be checked on a PC. The display unit also stores the appraisal history on the cloud in real time and synchronizes data between devices. For example, the appraisal history of a brand-name bag photographed with a tablet can be checked on a smartphone. The display unit also stores the appraisal history on the cloud and synchronizes data between devices. For example, the appraisal history of a painting photographed with a PC can be checked on a tablet. This allows the appraisal history to be stored on the cloud and synchronizes data between devices.

[0049] The display unit can add a function that enables the appraisal history to be exported in PDF or Excel format. For example, the display unit adds a function that enables the generation AI to export the appraisal history in PDF format. For example, the appraisal history of an antique is saved as a PDF file. The display unit also adds a function that enables the generation AI to export the appraisal history in Excel format. For example, the appraisal history of a brand-name bag is saved as an Excel file. The display unit also adds a function that enables the generation AI to export the appraisal history in PDF or Excel format. For example, the appraisal history of a painting is saved as a PDF or Excel file. This allows the appraisal history to be exported in PDF or Excel format.

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

[0051] The image input unit can recognize the user's voice instructions and gestures, allowing hands-free operation. For example, the camera can automatically release the shutter just by the user saying "start shooting." It can also change camera settings just by waving a hand. Furthermore, voice instructions and gesture recognition can be combined to allow the user to input images without using their hands. This allows the user to operate the device without using their hands.

[0052] The preprocessing unit develops preprocessing algorithms specialized for different types of objects, enabling optimal preprocessing for each object. For example, it can analyze an image of a jewel and apply a filter to enhance its brilliance. It can also analyze an image of a watch and adjust the resolution to emphasize the details of the dial. It can also apply the optimal filters and resolution adjustments for antiques, luxury goods, paintings, etc. This allows optimal preprocessing for each object.

[0053] The analysis unit can extract fine features within an image with high precision, improving the accuracy of appraisals. For example, it can extract handwriting from a painting with high precision, which can be used to identify the artist. It can also extract the weave of an antique's fabric with high precision, which can identify its age and place of origin. It can also analyze the stitching on a brand-name bag to determine whether it is genuine or fake. This allows for the accurate extraction of fine features, improving the accuracy of appraisals.

[0054] The analysis unit can extract features in more detail by referring to the historical background of the object and information about the creator. For example, when analyzing an image of an antique, it can extract features by taking into account the characteristics of the era. Also, when analyzing an image of a painting, it can extract features by taking into account the artist's distinctive techniques. Furthermore, when analyzing an image of a brand-name bag, it can extract features by taking into account the brand's history and design changes. This makes it possible to extract features in more detail by referring to the historical background and information about the creator.

[0055] The result generation unit can quantify the reliability of the appraisal result and provide it to the user. For example, the reliability of the appraisal result of an antique can be displayed as a percentage. The reliability of the appraisal result of a brand-name bag can also be displayed as a number. Furthermore, the reliability of the appraisal result of a painting can also be displayed as a graph. In this way, the reliability of the appraisal result can be quantified and provided to the user.

[0056] When displaying appraisal results, the display unit can use interactive graphs and charts to make them visually easy to understand. For example, the changes in the value of an antique can be shown in a graph. Or the characteristics of a brand-name bag can be visually shown in a chart. Furthermore, the state of conservation of a painting can be shown in a chart, and detailed information can be displayed by clicking on it. This makes it possible to display appraisal results visually and easily.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The image input unit receives an image from the user. For example, the user can take an image using the camera on their smartphone and upload it to the app. Alternatively, the user can select an existing image file and upload it. Step 2: The preprocessing section preprocesses the received image, for example, adjusting the image resolution and removing noise. It can also extract the image contours. Step 3: The analysis unit analyzes the preprocessed image. For example, generative AI can be used to extract features in the image and recognize patterns within the image. Step 4: The result generation unit generates an appraisal result based on the analyzed results. For example, based on the extracted features, it can determine the authenticity of the object and evaluate its value and condition. Step 5: The display unit displays the generated appraisal result to the user. For example, the appraisal result may be displayed in text format or graphically.

[0059] (Example 2) The appraisal app according to an embodiment of the present invention is a system that automatically analyzes images taken by users and uses a generation AI to provide appraisal results. This allows the appraisal app to quickly and accurately appraise antiques, brand-name goods, paintings, and other items based on images taken by users.

[0060] The appraisal app according to the embodiment includes an image input unit, a preprocessing unit, an analysis unit, a result generation unit, and a display unit. The image input unit receives an image from a user. For example, the image may be captured using a smartphone camera and uploaded to the app. The image input unit may also select and upload an existing image file. The preprocessing unit preprocesses the received image. For example, the preprocessing unit may adjust the image resolution and remove noise. The preprocessing unit may also extract the image contours. The analysis unit analyzes the preprocessed image. For example, the analysis unit may extract features within the image using a generation AI. The analysis unit may also recognize patterns within the image. The result generation unit generates an appraisal result based on the analysis result. For example, the result generation unit may determine the authenticity of an object based on the extracted features. The result generation unit may also evaluate the value and condition of the object. The display unit displays the generated appraisal result to the user. For example, the display unit may display the appraisal result in text format. The display unit may also display the appraisal result graphically. As a result, the appraisal app according to the embodiment can quickly and accurately appraise antiques, brand-name goods, paintings, and the like based on images taken by the user.

[0061] The preprocessing unit can suggest optimal shooting angles and lighting conditions to the user in real time when capturing an image. For example, the preprocessing unit uses a generation AI to analyze the camera's preview screen and suggest optimal shooting angles and lighting conditions to the user in real time. For example, when photographing an antique, the preprocessing unit suggests an angle to avoid light reflections and a lighting position to reduce shadows. The preprocessing unit also monitors the user's camera operation and automatically adjusts optimal settings before capturing. For example, when photographing a designer bag, the preprocessing unit automatically adjusts the camera's exposure and white balance. The preprocessing unit also analyzes the user's shooting environment and suggests optimal shooting conditions. For example, when photographing a painting, the preprocessing unit suggests the time of day to utilize natural light and the placement of artificial lighting. This allows the user to capture images under optimal conditions.

[0062] The preprocessing unit analyzes the material and surface texture of an object and can perform noise removal and resolution adjustment based on that. For example, the generation AI analyzes the material of the object in the image and performs noise removal based on that. For example, a specific filter is applied to suppress reflections on the metal parts of an antique. The preprocessing unit also analyzes the surface texture of the object and adjusts the resolution based on that. For example, a specific resolution setting is applied to emphasize the leather texture of a designer bag. The preprocessing unit also analyzes the material and texture of the object in the image and performs preprocessing based on that. For example, a filter is applied to remove noise while preserving the texture of the canvas of a painting. This allows optimal preprocessing to be performed according to the material and texture of the object.

[0063] The preprocessing unit can analyze the user's emotions and provide a shooting guide to elicit positive emotions. In the preprocessing unit, for example, the generation AI analyzes the user's facial expressions and infers the user's emotions at the time of shooting. For example, if the user is nervous, the generation AI displays a guide to help them relax. In the preprocessing unit, the generation AI analyzes the user's tone of voice and infers the user's emotions. For example, if the user is anxious, the generation AI provides advice to help them calm down. In the preprocessing unit, the generation AI analyzes the user's emotions in real time and provides a shooting guide to elicit positive emotions. For example, the generation AI displays an encouraging message to help the user enjoy shooting. This allows the user to shoot with positive emotions.

[0064] The image input unit can use voice instructions or gesture recognition to enable the user to operate the unit without using their hands. For example, the generation AI analyzes voice instructions to enable the user to take an image without using their hands. For example, the camera will automatically release the shutter just by saying "start shooting." The generation AI can also use gesture recognition to enable the user to operate the unit without using their hands. For example, the camera settings can be changed just by waving your hand. The generation AI can also combine voice instructions and gesture recognition to enable the user to input an image without using their hands. For example, the user can say "zoom in" by voice and adjust the zoom with hand movements. This allows the user to operate the unit without using their hands.

[0065] The preprocessing section develops preprocessing algorithms specialized for different types of objects, allowing optimal preprocessing for each object. For example, the generation AI analyzes an image of a jewel and applies a specialized preprocessing algorithm. For example, it applies a filter to emphasize the brilliance of the jewel. The generation AI also analyzes an image of a watch and applies a specialized preprocessing algorithm. For example, it adjusts the resolution to emphasize the details of the watch face. The generation AI also develops preprocessing algorithms specialized for different types of objects, allowing optimal preprocessing for each object. For example, it applies the optimal filter and resolution adjustment for each object, such as antiques, brand-name goods, and paintings. This allows optimal preprocessing for each object.

[0066] The preprocessing unit can provide feedback on the user's emotional response in real time and suggest optimal shooting conditions. In the preprocessing unit, for example, the generation AI analyzes the user's emotional response in real time and suggests optimal shooting conditions. For example, if the user is dissatisfied, it suggests different angles or lighting conditions. The preprocessing unit also allows the generation AI to automatically adjust shooting conditions based on the user's emotional response. For example, if the user is dissatisfied, it encourages the user to change the camera settings and take the photo again. The preprocessing unit also allows the generation AI to provide feedback on the user's emotional response in real time and suggest optimal shooting conditions. For example, if the user is happy, it instructs the user to continue shooting with the current settings. This makes it possible to suggest optimal shooting conditions according to the user's emotions.

[0067] The analysis unit can extract fine features within an image with high accuracy, improving the accuracy of appraisal. For example, the analysis unit allows the generation AI to extract handwriting within an image with high accuracy, improving the accuracy of appraisal. For example, analyzing the handwriting on a painting can help identify the artist. The analysis unit also allows the generation AI to extract weave within an image with high accuracy, improving the accuracy of appraisal. For example, analyzing the weave of an antique fabric can help identify its age and place of origin. The analysis unit also allows the generation AI to extract fine features within an image with high accuracy, improving the accuracy of appraisal. For example, analyzing the stitching on a brand-name bag can determine whether it is genuine or fake. This allows the generation AI to extract fine features with high accuracy, improving the accuracy of appraisal.

[0068] The analysis unit can perform more detailed feature extraction by referring to the historical background of the object and information about the creator. In the analysis unit, for example, the generation AI performs image analysis by referring to the historical background of the object. For example, when analyzing an image of an antique, features are extracted taking into account the characteristics of the era. In addition, the analysis unit performs image analysis by the generation AI by referring to information about the creator. For example, when analyzing an image of a painting, features are extracted taking into account the artist's distinctive techniques. In addition, the analysis unit performs more detailed feature extraction by referring to the historical background of the object and information about the creator. For example, when analyzing an image of a brand-name bag, features are extracted taking into account the brand's history and design changes. In this way, by referring to the historical background and information about the creator, more detailed feature extraction is possible.

[0069] The analysis unit can analyze the user's emotions and adjust the feature extraction algorithm based on those emotions. For example, the generation AI in the analysis unit analyzes the user's emotions and adjusts the feature extraction algorithm based on those emotions. For example, if the user is feeling anxious, more detailed feature extraction is performed. The analysis unit also allows the generation AI to set priority for feature extraction based on the user's emotional response. For example, if the user is interested in a particular feature, that feature is preferentially extracted. The analysis unit also allows the generation AI to analyze the user's emotions in real time and dynamically adjust the feature extraction algorithm based on those emotions. For example, if the user is satisfied, standard feature extraction is performed. This makes it possible to adjust the feature extraction algorithm based on the user's emotions.

[0070] The analysis unit can also use 3D scanning technology to extract the three-dimensional features of an object. For example, the generation AI in the analysis unit uses 3D scanning technology to extract the three-dimensional features of an object. For example, it analyzes the three-dimensional shape of an antique and extracts detailed features. The generation AI in the analysis unit also extracts the three-dimensional features of an object based on 3D scan data. For example, it analyzes the three-dimensional shape of a brand-name bag and determines whether it is genuine or fake. The generation AI in the analysis unit also uses 3D scanning technology to extract the three-dimensional features of an object with high accuracy. For example, it analyzes the three-dimensional handwriting of a painting to help identify the artist. This makes it possible to extract three-dimensional features by using 3D scanning technology in combination.

[0071] The analysis unit can integrate multiple images taken from different viewpoints to extract more detailed features. For example, the generation AI in the analysis unit integrates multiple images taken from different viewpoints to extract detailed features. For example, images of an antique are taken from multiple angles and integrated for analysis. The generation AI also integrates multiple images in the analysis unit to extract detailed features of the object. For example, a brand-name bag is photographed from different angles and integrated to determine whether it is genuine or fake. The generation AI also integrates images taken from different viewpoints to extract more detailed features. For example, images of a painting are photographed from multiple angles and integrated to analyze handwriting and color features. In this way, detailed feature extraction becomes possible by integrating images from different viewpoints.

[0072] The analysis unit can analyze the user's emotions and set priority for feature extraction based on those emotions. For example, the generation AI analyzes the user's emotions and sets priority for feature extraction based on those emotions. For example, if the user has a strong interest in a particular feature, that feature will be preferentially extracted. The analysis unit also allows the generation AI to adjust the feature extraction algorithm based on the user's emotional response. For example, if the user is feeling anxious, more detailed feature extraction will be performed. The analysis unit also allows the generation AI to analyze the user's emotions in real time and dynamically set priority for feature extraction based on those emotions. For example, if the user is satisfied, standard feature extraction will be performed. This makes it possible to set priority for feature extraction based on the user's emotions.

[0073] The result generation unit can quantify the reliability of the appraisal result and provide it to the user. For example, the generation AI quantifies the reliability of the appraisal result and provides it to the user. For example, the reliability is displayed as a percentage for the appraisal result of an antique. The result generation unit also quantifies the reliability of the appraisal result and provides it to the user. For example, the reliability is displayed as a number for the appraisal result of a brand-name bag. The result generation unit also quantifies the reliability of the appraisal result and provides it to the user. For example, the reliability is displayed as a graph for the appraisal result of a painting. This allows the reliability of the appraisal result to be quantified and provided to the user.

[0074] The result generation unit can provide more accurate appraisal results by referring to past appraisal data and market trends. In the result generation unit, for example, the generation AI refers to past appraisal data to generate appraisal results. For example, the accuracy of the current appraisal results is improved based on past appraisal data for antiques. In addition, the result generation unit can generate appraisal results by referring to market trends. For example, the accuracy of the current appraisal results is improved based on market trends for brand-name goods. In addition, the result generation unit can provide more accurate appraisal results by referring to past appraisal data and market trends. For example, the accuracy of the current appraisal results is improved based on past appraisal data and market trends for paintings. In this way, by referring to past data and market trends, more accurate appraisal results can be provided.

[0075] The result generation unit can analyze the user's emotions and adjust the way the results are presented based on those emotions. For example, the generation AI in the result generation unit analyzes the user's emotions and adjusts the way the appraisal results are presented based on those emotions. For example, if the user is feeling anxious, a detailed explanation is added. The result generation unit also adjusts the way the appraisal results are displayed based on the user's emotional response. For example, if the user is satisfied, a concise result is displayed. The result generation unit also analyzes the user's emotions in real time and dynamically adjusts the way the appraisal results are presented based on those emotions. For example, if the user is excited, a visually appealing display is displayed. This makes it possible to adjust the way the results are presented based on the user's emotions.

[0076] When generating appraisal results, the result generation unit can add a function to explain the results using audio or video. For example, the result generation unit adds a function whereby the generation AI explains the appraisal results using audio. For example, the appraisal results for antiques are explained in detail using audio. The result generation unit also adds a function whereby the generation AI explains the appraisal results using video. For example, the appraisal results for a brand-name bag are explained visually using video. The result generation unit also adds a function whereby the generation AI explains the appraisal results using audio or video. For example, the appraisal results for a painting are explained in detail using video. In this way, by explaining the results using audio or video, appraisal results that are easy for users to understand can be provided.

[0077] The result generation unit can simultaneously provide multiple appraisal results based on different appraisal standards. For example, the generation AI in the result generation unit provides appraisal results based on artistic value. For example, the generation AI evaluates the artistic value of a painting and displays the result. The result generation unit also provides appraisal results based on historical value. For example, the generation AI evaluates the historical value of an antique and displays the result. The result generation unit also provides multiple appraisal results based on different appraisal standards simultaneously. For example, the generation AI simultaneously evaluates the artistic value and historical value of a brand-name bag and displays the result. This allows multiple appraisal results based on different appraisal standards to be provided simultaneously.

[0078] The result generation unit can monitor the user's emotions in real time and adjust the display method of the results based on those emotions. In the result generation unit, for example, the generation AI monitors the user's emotions in real time and adjusts the display method of the appraisal results based on those emotions. For example, if the user is feeling anxious, a detailed explanation is added. In addition, the result generation unit dynamically adjusts the display method of the appraisal results based on the user's emotional response. For example, if the user is satisfied, a concise result is displayed. In addition, the result generation unit analyzes the user's emotions in real time and adjusts the display method of the appraisal results based on those emotions. For example, if the user is excited, a visually appealing display is displayed. This allows the display method of the results to be adjusted in real time based on the user's emotions.

[0079] When displaying the appraisal results, the display unit can use interactive graphs and charts to make them visually easy to understand. For example, the generation AI displays the appraisal results in an interactive graph on the display unit. For example, the change in value of an antique is shown in a graph. The generation AI also displays the appraisal results in an interactive chart on the display unit. For example, the characteristics of a brand-name bag are visually shown in a chart. The generation AI also displays the appraisal results in an interactive graph or chart on the display unit, allowing the user to access more information. For example, the conservation state of a painting is shown in a chart, and more information is displayed by clicking on it. This makes the appraisal results visually easy to understand.

[0080] When storing appraisal results, the display unit can use blockchain technology to ensure data tamper-proofing and reliability. For example, the generation AI stores the appraisal results on the blockchain in the display unit, ensuring data tamper-proofing and reliability. For example, the appraisal results of antiques are recorded on the blockchain. The generation AI also uses blockchain technology to ensure the reliability of the appraisal results. For example, the appraisal results of a brand-name bag are stored on the blockchain. The generation AI also stores the appraisal results on the blockchain in the display unit, ensuring data tamper-proofing and reliability. For example, the appraisal results of a painting are recorded on the blockchain. In this way, by using blockchain technology, data tamper-proofing and reliability can be ensured.

[0081] The display unit can analyze the user's emotions and customize the display method of the appraisal results based on those emotions. For example, the generation AI in the display unit analyzes the user's emotions and customizes the display method of the appraisal results based on those emotions. For example, if the user is feeling anxious, a detailed explanation is added. The display unit also customizes the display method of the appraisal results based on the user's emotional response. For example, if the user is satisfied, a concise result is displayed. The display unit also analyzes the user's emotions in real time and customizes the display method of the appraisal results based on those emotions. For example, if the user is excited, a visually appealing display is displayed. This makes it possible to customize the display method of the appraisal results based on the user's emotions.

[0082] The display unit can synchronize appraisal results across multiple devices, allowing them to be viewed anywhere. For example, the display unit allows the generation AI to save appraisal results in the cloud and synchronize them across multiple devices. For example, the appraisal results of an antique photographed with a smartphone can be viewed on a PC. The display unit also allows the generation AI to synchronize appraisal results in real time, allowing them to be viewed on multiple devices. For example, the appraisal results of a brand-name bag photographed with a tablet can be viewed on a smartphone. The display unit also allows the generation AI to save appraisal results in the cloud and synchronize them across multiple devices. For example, the appraisal results of a painting photographed with a PC can be viewed on a tablet. This allows appraisal results to be synchronized across multiple devices, allowing them to be viewed anywhere.

[0083] The display unit can add a function that allows the appraisal results to be easily shared on social media or by email. For example, the display unit adds a function that allows the generation AI to share the appraisal results on social media. For example, the appraisal results for antiques are shared on Facebook or Twitter. The display unit also adds a function that allows the generation AI to share the appraisal results by email. For example, the appraisal results for a brand-name bag are sent by email. The display unit also adds a function that allows the generation AI to easily share the appraisal results on social media or by email. For example, the appraisal results for a painting are shared on Instagram or by email. This makes it easy to share the appraisal results on social media or by email.

[0084] The display unit can analyze the user's emotions and suggest a method for sharing the appraisal results based on those emotions. For example, the generation AI in the display unit analyzes the user's emotions and suggests a method for sharing the appraisal results based on those emotions. For example, if the user is happy, it suggests sharing on social media. The display unit also suggests a method for sharing the appraisal results based on the user's emotional response. For example, if the user is feeling anxious, it suggests sharing by email. The display unit also suggests a method for sharing the appraisal results based on the user's emotions when the generation AI analyzes the user's emotions in real time and suggests a method for sharing the appraisal results based on those emotions. For example, if the user is excited, it suggests sharing on social media. In this way, it is possible to suggest a method for sharing the appraisal results based on the user's emotions.

[0085] The display unit can display the appraisal history in chronological order, allowing changes in past appraisal results to be tracked. The display unit, for example, allows the generation AI to display the appraisal history in chronological order, allowing changes in past appraisal results to be tracked. For example, the appraisal results of antiques can be displayed in chronological order, allowing changes in value to be tracked. The display unit also allows the generation AI to display the appraisal history in chronological order, allowing changes in past appraisal results to be tracked. For example, the appraisal results of a brand-name bag can be displayed in chronological order, allowing changes in condition to be tracked. The display unit also allows the generation AI to display the appraisal history in chronological order, allowing changes in past appraisal results to be tracked. For example, the appraisal results of a painting can be displayed in chronological order, allowing changes in preservation condition to be tracked. This allows the appraisal history to be displayed in chronological order, allowing changes in past appraisal results to be tracked.

[0086] The display unit can organize the appraisal history by category, allowing the generation AI to easily search for appraisal results that belong to a specific category. For example, the display unit displays the appraisal history by category, allowing the generation AI to easily search for appraisal results that belong to a specific category. For example, the display unit displays the appraisal history by category, such as antiques, brand-name goods, and paintings. The display unit also organizes the appraisal history by category, allowing the generation AI to easily search for appraisal results that belong to a specific category. For example, the display unit displays antiques by category, such as pottery, metal products, and wood products. The display unit also organizes the appraisal history by category, allowing the generation AI to easily search for appraisal results that belong to a specific category. For example, the display unit displays brand-name goods by category, such as bags, watches, and accessories. This allows the generation AI to organize the appraisal history by category, allowing the generation AI to easily search for appraisal results that belong to a specific category.

[0087] The display unit can analyze the user's emotions and customize the display method of the appraisal history based on those emotions. For example, the generation AI in the display unit analyzes the user's emotions and customizes the display method of the appraisal history based on those emotions. For example, if the user is feeling anxious, a detailed explanation is added. The display unit also customizes the display method of the appraisal history based on the user's emotional response. For example, if the user is satisfied, a concise history is displayed. The display unit also analyzes the user's emotions in real time and customizes the display method of the appraisal history based on those emotions. For example, if the user is excited, a visually appealing display is displayed. This makes it possible to customize the display method of the appraisal history based on the user's emotions.

[0088] The display unit can store the appraisal history on the cloud and synchronize data between devices. For example, the generation AI on the display unit stores the appraisal history on the cloud and synchronizes data between devices. For example, the appraisal history of an antique photographed with a smartphone can be checked on a PC. The display unit also stores the appraisal history on the cloud in real time and synchronizes data between devices. For example, the appraisal history of a brand-name bag photographed with a tablet can be checked on a smartphone. The display unit also stores the appraisal history on the cloud and synchronizes data between devices. For example, the appraisal history of a painting photographed with a PC can be checked on a tablet. This allows the appraisal history to be stored on the cloud and synchronizes data between devices.

[0089] The display unit can add a function that enables the appraisal history to be exported in PDF or Excel format. For example, the display unit adds a function that enables the generation AI to export the appraisal history in PDF format. For example, the appraisal history of an antique is saved as a PDF file. The display unit also adds a function that enables the generation AI to export the appraisal history in Excel format. For example, the appraisal history of a brand-name bag is saved as an Excel file. The display unit also adds a function that enables the generation AI to export the appraisal history in PDF or Excel format. For example, the appraisal history of a painting is saved as a PDF or Excel file. This allows the appraisal history to be exported in PDF or Excel format.

[0090] The display unit can analyze the user's emotions and suggest a method for sharing the appraisal history based on those emotions. For example, the generation AI in the display unit analyzes the user's emotions and suggests a method for sharing the appraisal history based on those emotions. For example, if the user is happy, it suggests sharing on social media. The display unit also suggests a method for sharing the appraisal history based on the user's emotional response. For example, if the user is feeling anxious, it suggests sharing by email. The display unit also suggests a method for sharing the appraisal history based on the user's emotions in real time, and it suggests a method for sharing the appraisal history based on those emotions. For example, if the user is excited, it suggests sharing on social media. In this way, it is possible to suggest a method for sharing the appraisal history based on the user's emotions.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The image input unit can recognize the user's voice instructions and gestures, allowing hands-free operation. For example, the camera can automatically release the shutter just by the user saying "start shooting." It can also change camera settings just by waving a hand. Furthermore, voice instructions and gesture recognition can be combined to allow the user to input images without using their hands. This allows the user to operate the device without using their hands.

[0093] The preprocessing unit develops preprocessing algorithms specialized for different types of objects, enabling optimal preprocessing for each object. For example, it can analyze an image of a jewel and apply a filter to enhance its brilliance. It can also analyze an image of a watch and adjust the resolution to emphasize the details of the dial. It can also apply the optimal filters and resolution adjustments for antiques, luxury goods, paintings, etc. This allows optimal preprocessing for each object.

[0094] The preprocessing unit can analyze the user's emotions and provide a shooting guide to bring out positive emotions. For example, it can analyze the user's facial expression and estimate the emotions at the time of shooting. If the user is nervous, it can display a guide to help them relax. It can also analyze the tone of their voice and estimate their emotions. If the user is anxious, it can provide advice to help them calm down. Furthermore, it can analyze the user's emotions in real time and provide a shooting guide to bring out positive emotions. This allows the user to shoot with positive emotions.

[0095] The analysis unit can extract fine features within an image with high precision, improving the accuracy of appraisals. For example, it can extract handwriting from a painting with high precision, which can be used to identify the artist. It can also extract the weave of an antique's fabric with high precision, which can identify its age and place of origin. It can also analyze the stitching on a brand-name bag to determine whether it is genuine or fake. This allows for the accurate extraction of fine features, improving the accuracy of appraisals.

[0096] The analysis unit can extract features in more detail by referring to the historical background of the object and information about the creator. For example, when analyzing an image of an antique, it can extract features by taking into account the characteristics of the era. Also, when analyzing an image of a painting, it can extract features by taking into account the artist's distinctive techniques. Furthermore, when analyzing an image of a brand-name bag, it can extract features by taking into account the brand's history and design changes. This makes it possible to extract features in more detail by referring to the historical background and information about the creator.

[0097] The analysis unit can analyze the user's emotions and adjust the feature extraction algorithm based on those emotions. For example, if the user is feeling anxious, more detailed feature extraction can be performed. In addition, the priority of feature extraction can be set based on the user's emotional response. If the user is interested in a particular feature, that feature can be preferentially extracted. Furthermore, the user's emotions can be analyzed in real time and the feature extraction algorithm can be dynamically adjusted based on those emotions. This allows the feature extraction algorithm to be adjusted based on the user's emotions.

[0098] The result generation unit can quantify the reliability of the appraisal result and provide it to the user. For example, the reliability of the appraisal result of an antique can be displayed as a percentage. The reliability of the appraisal result of a brand-name bag can also be displayed as a number. Furthermore, the reliability of the appraisal result of a painting can also be displayed as a graph. In this way, the reliability of the appraisal result can be quantified and provided to the user.

[0099] The result generation unit can analyze the user's emotions and adjust the way the results are presented based on those emotions. For example, if the user is feeling anxious, a detailed explanation can be added. The unit can also adjust the way the appraisal results are displayed based on the user's emotional response. If the user is satisfied, a concise result can be displayed. Furthermore, the unit can analyze the user's emotions in real time and dynamically adjust the way the appraisal results are presented based on those emotions. This allows the way the results are presented to be adjusted based on the user's emotions.

[0100] When displaying appraisal results, the display unit can use interactive graphs and charts to make them visually easy to understand. For example, the changes in the value of an antique can be shown in a graph. Or the characteristics of a brand-name bag can be visually shown in a chart. Furthermore, the state of conservation of a painting can be shown in a chart, and detailed information can be displayed by clicking on it. This makes it possible to display appraisal results visually and easily.

[0101] The display unit can analyze the user's emotions and customize the way the appraisal results are displayed based on those emotions. For example, if the user is feeling anxious, a detailed explanation can be added. The way the appraisal results are displayed can also be customized based on the user's emotional response. If the user is satisfied, a concise result can be displayed. Furthermore, the user's emotions can be analyzed in real time and the way the appraisal results are displayed can be customized based on those emotions. This makes it possible to customize the way the appraisal results are displayed based on the user's emotions.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The image input unit receives an image from the user. For example, the user can take an image using the camera on their smartphone and upload it to the app. Alternatively, the user can select an existing image file and upload it. Step 2: The preprocessing section preprocesses the received image, for example, adjusting the image resolution and removing noise. It can also extract the image contours. Step 3: The analysis unit analyzes the preprocessed image. For example, generative AI can be used to extract features in the image and recognize patterns within the image. Step 4: The result generation unit generates an appraisal result based on the analyzed results. For example, based on the extracted features, it can determine the authenticity of the object and evaluate its value and condition. Step 5: The display unit displays the generated appraisal result to the user. For example, the appraisal result may be displayed in text format or graphically.

[0104] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0145] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0154] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0155] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0156] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0157] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0158] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0159] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0160] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0161] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0162] 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.

[0163] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0164] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0165] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0166] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0167] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0168] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0169] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0170] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an image input unit that receives an image from a user; a preprocessing unit that preprocesses the image received by the image input unit; an analysis unit that analyzes the image preprocessed by the preprocessing unit; a result generation unit that generates an appraisal result based on the result of the analysis by the analysis unit; a display unit that displays the verification result generated by the result generation unit to a user. A system characterized by:

2. The pre-treatment unit Providing the user with optimal shooting angles and lighting conditions in real time when taking the image 2. The system of claim 1.

3. The pre-treatment unit Analyzes the material and surface texture of the object, and performs noise removal and resolution adjustment based on that.

2. The system of claim 1.

4. The pre-treatment unit Analyzing the user's emotions and providing a shooting guide to elicit positive emotions 2. The system of claim 1.

5. The image input unit Using voice commands or gesture recognition to enable hands-free operation for the user 2. The system of claim 1.

6. The pre-treatment unit Develop pre-processing algorithms specialized for different types of objects and perform the optimal pre-processing for each object.

2. The system of claim 1.

7. The pre-treatment unit Feedback the user's emotional response in real time and suggest optimal shooting conditions 2. The system of claim 1.

8. The analysis unit Extracting minute features from the image with high accuracy improves the accuracy of the appraisal.

2. The system of claim 1.

Images