system

Abstract

The system according to this embodiment aims to reduce the effort involved in finding rare items and listing them for auction, thereby promoting auction use among casual users. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, an extraction unit, a display unit, and a processing unit. The collection unit collects data of the target object. The analysis unit analyzes the data collected by the collection unit. The extraction unit extracts shapes and contents that differ from the default product based on the data analyzed by the analysis unit. The display unit displays the estimated selling price based on the data extracted by the extraction unit. The processing unit processes the image based on the data displayed by the display unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there were problems that it was time-consuming to discover rare items and list them for auction, and it was difficult for light user groups to use auctions.

[0005] The system according to the embodiment aims to reduce the time and effort required to discover rare items and list them for auction, and to promote the use of auctions by light user groups.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, an extraction unit, a display unit, and a processing unit. The data collection unit collects data from the target object. The analysis unit analyzes the data collected by the data collection unit. The extraction unit extracts shapes and contents that differ from the default product based on the data analyzed by the analysis unit. The display unit displays the estimated selling price based on the data extracted by the extraction unit. The processing unit processes the image based on the data displayed by the display unit. [Effects of the Invention]

[0007] The system according to this embodiment can reduce the effort involved in finding rare items and listing them for auction, thereby promoting auction use among casual users. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The auction support system according to an embodiment of the present invention is an application aimed at expanding the user base of light users who do not regularly use auctions and contributing to sales by automating as much work as possible for users who do not regularly use auctions. The auction support system picks up items (such as coins and stamps) that have a different shape or content from the default item data available on the web. At this time, it describes the reason for the selection and, based on the content of that reason, automatically links with past auction results and price sites from around the world to display the estimated selling price (minimum price and average price). Next, the auction support system compares the item with the characteristic data (sales pitch) of auction exhibits and displays the estimated selling price (minimum price and average price) and the reason. If the user approves, the captured image can be processed and arranged by AI to make it easy to view, and then listed for auction as is. For example, the auction support system clarifies how the AI ​​determines the shape or content that differs from the default item. For example, it uses image analysis technology to analyze the shape and content of the item and compare it with the default item. Next, the auction support system also specifically describes how it links with auction results and price sites. For example, it uses an API to obtain data, and the AI ​​analyzes it to display the estimated selling price. Furthermore, the auction support system will also specify how the AI ​​processes images. For example, it will adjust the brightness and contrast of images to make them easier to view. The auction support system will have a collection unit, an analysis unit, an extraction unit, a display unit, and a processing unit, clearly defining the role of each element. The collection unit will collect data on the target object, and the analysis unit will analyze the collected data. The extraction unit will extract shapes and contents that differ from the default item based on the analysis results, and the display unit will display the estimated selling price. The processing unit will process the image to make it easier to view. The auction support system will also have an API integration unit and a database unit, clearly defining the relationships with each element. As a result, the auction support system will enable even inexperienced or beginner auction users to easily list items for auction, and is expected to expand the auction user base and increase sales.

[0029] The auction support system according to this embodiment comprises a collection unit, an analysis unit, an extraction unit, a display unit, and a processing unit. The collection unit collects data on the object. For example, the collection unit can collect image data of the object. The collection unit can also collect text data of the object. Furthermore, the collection unit can also collect sensor data of the object. For example, the collection unit can take an image of the object with a camera and save it as image data. The collection unit can also collect a description of the object as text data. Furthermore, the collection unit can also collect sensor data such as the temperature and humidity of the object. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can analyze the shape and contents of the object using image analysis technology. The analysis unit can also analyze the description of the object using text analysis technology. Furthermore, the analysis unit can also analyze sensor data. For example, the analysis unit can analyze the shape of the object using image analysis technology and compare it with a default item. The analysis unit can also analyze the description of the object using text analysis technology and compare it with a default item. Furthermore, the analysis unit can analyze sensor data and evaluate the state of the object. The extraction unit extracts shapes and contents that differ from the default product based on the data analyzed by the analysis unit. For example, the extraction unit can use image analysis technology to extract shapes that differ from the default product. The extraction unit can also use text analysis technology to extract contents that differ from the default product. Furthermore, the extraction unit can use sensor data to extract states that differ from the default product. For example, the extraction unit can use image analysis technology to extract that the shape of the object differs from the default product. The extraction unit can also use text analysis technology to extract that the description of the object differs from the default product. Furthermore, the extraction unit can use sensor data to extract that the state of the object differs from the default product. The display unit displays the estimated selling price based on the data extracted by the extraction unit. For example, the display unit can display the estimated selling price in conjunction with past auction results or price websites. The display unit can also display the minimum price and average price based on the extracted data.Furthermore, the display unit can also display sales pitches based on the extracted data. For example, the display unit can refer to past auction results and display the estimated selling price. The display unit can also refer to data from price comparison websites and display the minimum and average prices. Furthermore, the display unit can also display sales pitches based on the extracted data. The processing unit processes the image based on the data displayed by the display unit. The processing unit can, for example, adjust the brightness and contrast of the image. The processing unit can also apply filters to the image. Furthermore, the processing unit can adjust the size of the image. For example, the processing unit can adjust the brightness of the image to make it easier to view. The processing unit can also adjust the contrast of the image to make the details easier to understand. Furthermore, the processing unit can apply filters to the image to make it visually appealing. As a result, the auction support system according to this embodiment automates auction listings by collecting, analyzing, extracting, displaying, and processing data on the target items, thereby expanding the user base and contributing to sales.

[0030] The data collection unit collects data from the object. For example, the data collection unit can collect image data of the object. Specifically, it uses a camera to capture high-resolution images of the object and saves them as digital data. It is desirable that the image data be taken from multiple angles to clearly capture not only the overall image of the object, but also its detailed features and defects. The data collection unit can also collect text data from the object. For example, it can collect descriptions, specifications, and manufacturer information of the object as text data and save it in a database. Furthermore, the data collection unit can collect sensor data from the object. For example, it can acquire data in real time from temperature and humidity sensors attached to the object and use it for analysis. This allows the data collection unit to understand the physical state and environmental conditions of the object in detail. The collected data is centrally stored in a central database and made accessible to the analysis unit and other departments. The frequency and accuracy of data collection are adjusted according to the characteristics of the object and the requirements of the auction. For example, in the case of expensive works of art or antiques, higher-precision image data and detailed sensor data may be required. This allows the data collection unit to efficiently collect diverse data from the object and improve the overall performance of the system.

[0031] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can analyze the shape and content of an object using image analysis technology. Specifically, it uses image recognition algorithms to analyze the shape, color, and texture of an object and compare them with a default item. This allows for the automatic detection of the object's features and defects. The analysis unit can also analyze the description of an object using text analysis technology. It uses natural language processing technology to analyze the content of the description and extract important keywords and phrases. Furthermore, the analysis unit can analyze sensor data. For example, it can analyze fluctuations in temperature and humidity to evaluate the object's storage condition and environmental conditions. This allows the analysis unit to analyze the collected data from multiple angles and gain a detailed understanding of the object's state and characteristics. In addition, the analysis unit can utilize historical data and statistical information to analyze long-term trends and patterns. For example, it can predict the popularity and price trends of a specific category or brand based on past auction data. It can also use anomaly detection algorithms to detect unusual patterns and abnormal data early and issue warnings. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The extraction unit extracts shapes and contents that differ from the default product based on data analyzed by the analysis unit. Specifically, it uses image analysis technology to extract differences in the shape of the object compared to the default product. For example, it analyzes images of the object to identify differences in shape, color, and texture. The extraction unit can also use text analysis technology to extract differences in the description of the object compared to the default product. It uses natural language processing technology to analyze the content of the description and clarify the differences from the default product. Furthermore, the extraction unit can use sensor data to extract differences in the state of the object compared to the default product. For example, it analyzes temperature and humidity data to identify differences in storage conditions and environmental conditions. This allows the extraction unit to grasp the characteristics and state of the object in detail and clearly distinguish it from the default product. In addition, the extraction unit can evaluate the value and rarity of the object based on the extracted data. For example, if certain characteristics or conditions are highly valued in the market, it can calculate the value of the object based on that information. The extraction unit can also share the extracted data with other systems and departments and utilize it for auction strategies and pricing. This allows the extraction unit to provide detailed information about the object, contributing to the success of the auction.

[0033] The display unit shows the estimated selling price based on the data extracted by the extraction unit. Specifically, it can display the estimated selling price in conjunction with past auction results and price websites. For example, it can refer to past auction data and calculate the estimated selling price based on the selling prices of similar items. It can also refer to price website data and display the minimum and average prices. Furthermore, the display unit can also display sales pitches based on the extracted data. For example, it can automatically generate and display sales pitches that emphasize the features and rarity of the item. This allows the display unit to effectively communicate the value of the item to auction participants. In addition, the display unit can continuously revise the estimated selling price and sales pitches based on data that is updated in real time. For example, it can immediately update the display content when new auction data or market trends are reflected. The display unit also makes it easy for auction participants to check information through the user interface. For example, it can display price trends and selling price changes using visually easy-to-understand graphs and charts. This allows the display unit to provide auction participants with the latest information and support the success of the auction.

[0034] The processing unit processes images based on the data displayed by the display unit. Specifically, it can adjust the brightness and contrast of images. For example, it can adjust the brightness to make the image easier to view. It can also adjust the contrast to make the image more detailed. Furthermore, the processing unit can apply filters to images. For example, it can apply a specific filter to make the image visually appealing. The processing unit can also adjust the size of images. For example, it can optimize the image size to meet the display requirements of auction sites. This allows the processing unit to effectively process images of objects and provide attractive visuals to auction participants. In addition, the processing unit saves the image processing history and can revert to the original image if necessary. For example, if excessive processing has been performed, it can revert to the original image and reprocess it. The processing unit also has a function to process multiple images in batches, allowing it to efficiently process a large number of images. This allows the processing unit to quickly and efficiently prepare items for auction and improve the overall system performance.

[0035] The data collection unit can collect data from an object. For example, the data collection unit can collect image data from an object. The data collection unit can also collect text data from an object. Furthermore, the data collection unit can collect sensor data from an object. For example, the data collection unit can take an image of the object with a camera and save it as image data. The data collection unit can also collect a description of the object as text data. Furthermore, the data collection unit can collect sensor data such as the temperature and humidity of the object. This enables the automation of data collection by having the data collection unit collect data from the object. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input image data of an object into AI, and the AI ​​can analyze and collect the image data.

[0036] The analysis unit can analyze the collected data and determine shapes and contents that differ from the default product. For example, the analysis unit can use image analysis technology to analyze the shape and contents of the object. The analysis unit can also use text analysis technology to analyze the description of the object. Furthermore, the analysis unit can analyze sensor data. For example, the analysis unit can use image analysis technology to analyze the shape of the object and compare it with the default product. The analysis unit can also use text analysis technology to analyze the description of the object and compare it with the default product. Furthermore, the analysis unit can analyze sensor data and evaluate the state of the object. As a result, the analysis unit automates the identification of rare items by analyzing the data and determining shapes and contents that differ from the default product. Some or all of the above processing in the analysis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the analysis unit can input image data of the object into a generation AI, and the generation AI can analyze the image data to determine shapes and contents that differ from the default product.

[0037] The extraction unit can extract shapes and contents that differ from the default product based on the analysis results. For example, the extraction unit can use image analysis technology to extract shapes that differ from the default product. The extraction unit can also use text analysis technology to extract contents that differ from the default product. Furthermore, the extraction unit can use sensor data to extract states that differ from the default product. For example, the extraction unit can use image analysis technology to extract that the shape of the object differs from the default product. The extraction unit can also use text analysis technology to extract that the description of the object differs from the default product. Furthermore, the extraction unit can use sensor data to extract that the state of the object differs from the default product. In this way, the extraction unit realizes the automatic extraction of rare items by extracting shapes and contents that differ from the default product. Some or all of the above processing in the extraction unit may be performed using, for example, a generation AI, or without a generation AI. For example, the extraction unit can input image data of the object to a generation AI, and the generation AI can analyze the image data to extract shapes and contents that differ from the default product.

[0038] The display unit can display an estimated selling price based on the extracted data. For example, the display unit can display the estimated selling price in conjunction with past auction results or price comparison websites. The display unit can also display a minimum price and an average price based on the extracted data. Furthermore, the display unit can display a sales pitch based on the extracted data. For example, the display unit can refer to past auction results to display the estimated selling price. The display unit can also refer to price comparison website data to display the minimum price and an average price. Furthermore, the display unit can display a sales pitch based on the extracted data. In this way, the display unit provides the user with selling price information by displaying the estimated selling price. Some or all of the above processing in the display unit may be performed using, for example, a generating AI, or without a generating AI. For example, the display unit can input the extracted data into a generating AI, which can then calculate and display the estimated selling price.

[0039] The processing unit can process images based on the displayed data. For example, the processing unit can adjust the brightness and contrast of the image. It can also apply filters to the image. Furthermore, the processing unit can adjust the size of the image. For example, the processing unit can adjust the brightness of the image to make it easier to view. It can also adjust the contrast of the image to make the details easier to understand. Furthermore, the processing unit can apply filters to the image to make it visually appealing. In this way, the processing unit processes the image to automatically generate images for auction listings. Some or all of the above processing in the processing unit may be performed using, for example, a generation AI, or without a generation AI. For example, the processing unit can input image data into a generation AI, which can then process the image by adjusting its brightness and contrast.

[0040] The display unit can display estimated selling prices in conjunction with past auction results and price comparison websites. For example, the display unit can refer to past auction results and display estimated selling prices. The display unit can also refer to price comparison website data and display minimum and average prices. Furthermore, the display unit can display sales pitches based on extracted data. For example, the display unit can obtain past auction results via an API and display estimated selling prices. The display unit can also obtain price comparison website data via an API and display minimum and average prices. Furthermore, the display unit can display sales pitches based on extracted data. This allows the display unit to provide more accurate estimated selling prices by linking with past auction results and price comparison websites. Some or all of the above processing in the display unit may be performed using, for example, a generating AI, or without a generating AI. For example, the display unit can input past auction results and price comparison website data into a generating AI, which can then calculate and display estimated selling prices.

[0041] The processing unit can adjust the brightness and contrast of an image to make it easier to view. For example, the processing unit can adjust the brightness of an image to make it easier to view. It can also adjust the contrast of an image to make the details easier to understand. Furthermore, the processing unit can apply filters to an image to make it visually appealing. For example, the processing unit can adjust the brightness of an image to make it easier to view. It can also adjust the contrast of an image to make the details easier to understand. Furthermore, the processing unit can apply filters to an image to make it visually appealing. In this way, the processing unit provides an easy-to-view image by adjusting the brightness and contrast of the image. Some or all of the above processing in the processing unit may be performed using, for example, a generation AI, or without a generation AI. For example, the processing unit can input image data into a generation AI, and the generation AI can adjust the brightness and contrast of the image to process it.

[0042] The data collection unit can analyze the user's past data collection history and select the optimal data collection method. For example, the data collection unit can analyze patterns in data previously collected by the user and prioritize the collection of data with similar patterns. It can also select the most effective data collection method based on the success rate of data previously collected by the user. Furthermore, the data collection unit can identify periods in the user's past data collection history where data collection is more likely to succeed and perform data collection during those periods. In this way, the data collection unit provides the optimal data collection method by analyzing the user's past data collection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past data collection history into AI, which can then select the optimal data collection method.

[0043] The data collection unit can filter data based on the user's current areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to categories the user is currently interested in. The data collection unit can also filter and collect relevant data based on keywords the user has recently searched for. Furthermore, the data collection unit can collect data related to topics the user is following, providing information tailored to the user's interests. This allows the data collection unit to provide highly relevant data by filtering data based on the user's areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's search history data into AI, which can then filter and collect relevant data.

[0044] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of data related to the area where the user is currently located. The data collection unit can also collect data related to places the user has visited in the past. Furthermore, the data collection unit can also collect data related to places the user plans to visit in the future. In this way, the data collection unit provides highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information data into AI, which can then prioritize the collection of relevant data.

[0045] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect data related to posts that a user has "liked" on social media. The data collection unit can also collect data related to accounts that a user follows. Furthermore, the data collection unit can also collect data related to posts that a user has shared. In this way, the data collection unit provides highly relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into AI, and the AI ​​can collect relevant data.

[0046] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on highly important data. It can also perform a simplified analysis on less important data. Furthermore, it can perform an analysis with a moderate level of detail on data of moderate importance. This allows the analysis unit to achieve efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into the AI, and the AI ​​can adjust the level of detail of the analysis based on the importance.

[0047] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an image analysis algorithm to image data. It can also apply a natural language processing algorithm to text data. Furthermore, it can apply a statistical analysis algorithm to numerical data. This allows the analysis unit to provide appropriate analysis results by applying different analysis algorithms depending on the data category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into the AI, which can then apply a different analysis algorithm depending on the category.

[0048] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. It can also postpone the analysis of older data. Furthermore, the analysis unit may prioritize the analysis of data collected during a specific period. For example, the analysis unit may prioritize the analysis of the most recent data. It can also postpone the analysis of older data. Furthermore, the analysis unit may prioritize the analysis of data collected during a specific period. This allows the analysis unit to prioritize the analysis of the most recent data by determining the priority of analysis based on the data collection timing. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into the AI, and the AI ​​can determine the priority of analysis based on the collection timing.

[0049] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of data with high relevance. It can also postpone the analysis of data with low relevance. Furthermore, the analysis unit can prioritize the analysis of data related to a specific theme. For example, the analysis unit can prioritize the analysis of data with high relevance. It can also postpone the analysis of data with low relevance. Furthermore, the analysis unit can prioritize the analysis of data related to a specific theme. This enables efficient analysis by allowing the analysis unit to adjust the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into the AI, and the AI ​​can adjust the order of analysis based on the relevance.

[0050] The extraction unit can improve the accuracy of extraction by considering the interrelationships between data during the extraction process. For example, the extraction unit can analyze the correlations between data and extract highly relevant data. The extraction unit can also perform data clustering and extract data belonging to the same cluster. Furthermore, the extraction unit can perform data pattern recognition and extract data that matches a specific pattern. For example, the extraction unit can analyze the correlations between data and extract highly relevant data. The extraction unit can also perform data clustering and extract data belonging to the same cluster. Furthermore, the extraction unit can perform data pattern recognition and extract data that matches a specific pattern. By considering the interrelationships between data, the extraction unit provides highly accurate extraction results. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input the data correlations into AI, and the AI ​​can improve the accuracy of extraction based on the correlations.

[0051] The extraction unit can perform extraction while considering the attribute information of the data submitter. For example, the extraction unit can extract highly reliable data by considering the submitter's expertise. The extraction unit can also analyze the submitter's past submission history and extract highly reliable data. Furthermore, the extraction unit can also extract highly reliable data by considering the submitter's organization. For example, the extraction unit can extract highly reliable data by considering the submitter's expertise. Furthermore, the extraction unit can analyze the submitter's past submission history and extract highly reliable data. Furthermore, the extraction unit can also extract highly reliable data by considering the submitter's organization. In this way, the extraction unit provides highly reliable extraction results by considering the attribute information of the data submitter. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input the submitter's attribute information into AI, and the AI ​​can perform extraction based on the attribute information.

[0052] The extraction unit can perform extraction while considering the geographical distribution of the data. For example, the extraction unit can preferentially extract data related to a specific region. Furthermore, the extraction unit can extract data that is geographically distributed over a wide area. In addition, the extraction unit can extract data that is geographically concentrated. This allows the extraction unit to provide highly relevant extraction results by considering the geographical distribution of the data. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input the geographical distribution of the data into the AI, which can then perform extraction based on the geographical distribution.

[0053] The extraction unit can improve the accuracy of the extraction by referring to relevant literature during the extraction process. For example, the extraction unit can refer to relevant literature and extract reliable data. The extraction unit can also extract reliable data by considering the number of citations of the relevant literature. Furthermore, the extraction unit can extract reliable data by considering the reliability of the authors of the relevant literature. For example, the extraction unit can refer to relevant literature and extract reliable data. The extraction unit can also extract reliable data by considering the number of citations of the relevant literature. Furthermore, the extraction unit can extract reliable data by considering the reliability of the authors of the relevant literature. This allows the extraction unit to provide reliable extraction results by referring to relevant literature for the data. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input the relevant literature data into AI, and the AI ​​can improve the accuracy of the extraction based on the relevant literature.

[0054] The display unit can optimize the current display by referring to past display data when displaying content. For example, the display unit can optimize the current display based on data that the user has preferred to view in the past. It can also optimize the current display based on data that the user has skipped in the past. Furthermore, the display unit can optimize the current display based on data that the user has given high ratings in the past. For example, the display unit can optimize the current display based on data that the user has preferred to view in the past. It can also optimize the current display based on data that the user has skipped in the past. Furthermore, the display unit can optimize the current display based on data that the user has given high ratings in the past. In this way, the display unit provides the optimal display for the user by referring to past display data. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input past display data into AI, and the AI ​​can optimize the current display based on the past data.

[0055] The display unit can apply different display methods to each data category during display. For example, the display unit can apply thumbnail display to image data. It can also apply summary display to text data. Furthermore, the display unit can apply graph display to numerical data. In this way, the display unit provides an appropriate display by applying different display methods to each data category. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the data categories into the AI, and the AI ​​can apply different display methods to each category.

[0056] The display unit can adjust the display order based on the data collection time when displaying data. For example, the display unit can prioritize displaying the latest data. It can also prioritize displaying older data. Furthermore, the display unit can prioritize displaying data collected during a specific period. For example, the display unit can prioritize displaying the latest data. It can also prioritize displaying older data. Furthermore, the display unit can prioritize displaying data collected during a specific period. This allows the display unit to prioritize displaying the latest information by adjusting the display order based on the data collection time. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the data collection time into the AI, and the AI ​​can adjust the display order based on the collection time.

[0057] The display unit can optimize the display by referring to relevant market data when displaying data. For example, the display unit optimizes the current display based on relevant market data. The display unit can also adjust the display content considering market trends. Furthermore, the display unit can determine the display priority based on market demand. For example, the display unit optimizes the current display based on relevant market data. The display unit can also adjust the display content considering market trends. Furthermore, the display unit can determine the display priority based on market demand. This allows the display unit to provide the optimal display for the user by referring to relevant market data. Some or all of the above processing in the display unit may be performed using AI, for example, or not using AI. For example, the display unit can input relevant market data into AI, and the AI ​​can optimize the display based on the market data.

[0058] The processing unit can select the optimal processing method by referring to the user's past processing history when processing images. For example, the processing unit can prioritize applying processing methods that the user has preferred to use in the past. It can also exclude processing methods that the user has avoided in the past. Furthermore, the processing unit can analyze the user's past processing history and select the most effective processing method. For example, the processing unit can prioritize applying processing methods that the user has preferred to use in the past. It can also exclude processing methods that the user has avoided in the past. Furthermore, the processing unit can analyze the user's past processing history and select the most effective processing method. In this way, the processing unit provides the user with the optimal processing method by referring to the user's past processing history. Some or all of the above processing in the processing unit may be performed using AI, for example, or without AI. For example, the processing unit can input the user's past processing history data into AI, and the AI ​​can select the optimal processing method based on the past data.

[0059] The image processing unit can customize the processing methods based on the user's current lifestyle when processing images. For example, if the user is busy, the processing unit can provide a simple and quick processing method. Alternatively, if the user is relaxed, the processing unit can provide a detailed processing method. Furthermore, if the user is participating in a specific event, the processing unit can provide a processing method suitable for that event. This allows the processing unit to provide the user with the optimal image by customizing the processing methods based on the user's lifestyle. Some or all of the processing described above in the processing unit may be performed using AI, for example, or without AI. For example, the processing unit can input user lifestyle data into AI, which can then customize the processing methods based on the lifestyle.

[0060] The processing unit can select the optimal processing method when processing images, taking into account the user's geographical location information. For example, the processing unit can provide a processing method tailored to the characteristics of the area where the user is currently located. It can also provide processing methods related to places the user has visited in the past. Furthermore, it can provide processing methods related to places the user plans to visit in the future. In this way, the processing unit provides the optimal image for the user by taking into account the user's geographical location information. Some or all of the above processing in the processing unit may be performed using AI, for example, or without AI. For example, the processing unit can input the user's geographical location data into AI, and the AI ​​can select the optimal processing method based on the geographical location information.

[0061] The processing unit can analyze the user's social media activity and suggest processing methods when processing images. For example, the processing unit can suggest processing methods related to posts the user has "liked" on social media. It can also suggest processing methods related to accounts the user follows. Furthermore, the processing unit can suggest processing methods related to posts the user has shared. In this way, the processing unit provides the user with the most suitable processing method by analyzing the user's social media activity. Some or all of the above processing in the processing unit may be performed using AI, for example, or without AI. For example, the processing unit can input the user's social media activity data into AI, which can then suggest processing methods based on the social media activity.

[0062] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0063] Auction support systems can analyze a user's past listing history and select the optimal listing method. For example, they can prioritize listing methods that have been successful in the past. They can also avoid listing methods that have been unsuccessful in the past. Furthermore, they can identify times when listings are more likely to be successful based on the user's past listing history and list items during those times. In this way, auction support systems can improve success rates by providing optimal listing methods based on the user's past listing history.

[0064] The auction support system can filter the items listed for auction based on the user's current areas of interest. For example, it can prioritize listing items related to categories the user is currently interested in. It can also filter and list items based on keywords the user has recently searched for. Furthermore, it can list items related to topics the user is following, providing information tailored to the user's interests. In this way, the auction support system can provide highly relevant listings by filtering items based on the user's areas of interest.

[0065] The auction support system can prioritize listing items that are highly relevant to the user, taking into account their geographical location. For example, it can prioritize listing items related to the user's current location. It can also list items related to places the user has visited in the past. Furthermore, it can list items related to places the user plans to visit in the future. In this way, the auction support system can provide highly relevant listings by considering the user's geographical location.

[0066] The auction support system can analyze a user's social media activity and list relevant items for sale. For example, it can list items related to posts a user has "liked" on social media. It can also list items related to accounts a user follows. Furthermore, it can list items related to posts a user has shared. In this way, the auction support system can provide highly relevant listings by analyzing a user's social media activity.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The collection unit collects data from the object. The collection unit can collect, for example, image data, text data, and sensor data from the object. Specifically, the collection unit takes an image of the object with a camera and saves it as image data. It can also collect a description of the object as text data, and further collect sensor data such as the object's temperature and humidity. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the shape and content of the object using image analysis technology and analyze the description of the object using text analysis technology. Furthermore, it can also analyze sensor data and evaluate the state of the object. For example, the analysis unit can analyze the shape of the object using image analysis technology and compare it with a default product. It can also analyze the description of the object using text analysis technology and compare it with a default product. Furthermore, it can analyze sensor data and evaluate the state of the object. Step 3: The extraction unit extracts shapes and contents that differ from the default product based on the data analyzed by the analysis unit. The extraction unit can use image analysis technology to extract shapes that differ from the default product, and text analysis technology to extract contents that differ from the default product. Furthermore, it can also use sensor data to extract states that differ from the default product. For example, the extraction unit can use image analysis technology to extract that the shape of the object differs from the default product, and use text analysis technology to extract that the description of the object differs from the default product. Furthermore, it can also use sensor data to extract that the state of the object differs from the default product. Step 4: The display unit displays the estimated selling price based on the data extracted by the extraction unit. The display unit can display the estimated selling price in conjunction with past auction results and price comparison websites, and can also display the minimum price and average price. Furthermore, it can also display sales pitches based on the extracted data. For example, the display unit refers to past auction results and displays the estimated selling price. It can also refer to data from price comparison websites and display the minimum price and average price. Furthermore, it can also display sales pitches based on the extracted data. Step 5: The processing unit processes the image based on the data displayed by the display unit. The processing unit can adjust the brightness and contrast of the image, apply filters to the image, and adjust the image size. For example, the processing unit can adjust the brightness of the image to make it easier to view. It can also adjust the contrast of the image to make the details easier to see. Furthermore, it can apply filters to the image to make it visually appealing.

[0069] (Example of form 2) The auction support system according to an embodiment of the present invention is an application aimed at expanding the user base of light users who do not regularly use auctions and contributing to sales by automating as much work as possible for users who do not regularly use auctions. The auction support system picks up items (such as coins and stamps) that have a different shape or content from the default item data available on the web. At this time, it describes the reason for the selection and, based on the content of that reason, automatically links with past auction results and price sites from around the world to display the estimated selling price (minimum price and average price). Next, the auction support system compares the item with the characteristic data (sales pitch) of auction exhibits and displays the estimated selling price (minimum price and average price) and the reason. If the user approves, the captured image can be processed and arranged by AI to make it easy to view, and then listed for auction as is. For example, the auction support system clarifies how the AI ​​determines the shape or content that differs from the default item. For example, it uses image analysis technology to analyze the shape and content of the item and compare it with the default item. Next, the auction support system also specifically describes how it links with auction results and price sites. For example, it uses an API to obtain data, and the AI ​​analyzes it to display the estimated selling price. Furthermore, the auction support system will also specify how the AI ​​processes images. For example, it will adjust the brightness and contrast of images to make them easier to view. The auction support system will have a collection unit, an analysis unit, an extraction unit, a display unit, and a processing unit, clearly defining the role of each element. The collection unit will collect data on the target object, and the analysis unit will analyze the collected data. The extraction unit will extract shapes and contents that differ from the default item based on the analysis results, and the display unit will display the estimated selling price. The processing unit will process the image to make it easier to view. The auction support system will also have an API integration unit and a database unit, clearly defining the relationships with each element. As a result, the auction support system will enable even inexperienced or beginner auction users to easily list items for auction, and is expected to expand the auction user base and increase sales.

[0070] The auction support system according to this embodiment comprises a collection unit, an analysis unit, an extraction unit, a display unit, and a processing unit. The collection unit collects data on the object. For example, the collection unit can collect image data of the object. The collection unit can also collect text data of the object. Furthermore, the collection unit can also collect sensor data of the object. For example, the collection unit can take an image of the object with a camera and save it as image data. The collection unit can also collect a description of the object as text data. Furthermore, the collection unit can also collect sensor data such as the temperature and humidity of the object. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can analyze the shape and contents of the object using image analysis technology. The analysis unit can also analyze the description of the object using text analysis technology. Furthermore, the analysis unit can also analyze sensor data. For example, the analysis unit can analyze the shape of the object using image analysis technology and compare it with a default item. The analysis unit can also analyze the description of the object using text analysis technology and compare it with a default item. Furthermore, the analysis unit can analyze sensor data and evaluate the state of the object. The extraction unit extracts shapes and contents that differ from the default product based on the data analyzed by the analysis unit. For example, the extraction unit can use image analysis technology to extract shapes that differ from the default product. The extraction unit can also use text analysis technology to extract contents that differ from the default product. Furthermore, the extraction unit can use sensor data to extract states that differ from the default product. For example, the extraction unit can use image analysis technology to extract that the shape of the object differs from the default product. The extraction unit can also use text analysis technology to extract that the description of the object differs from the default product. Furthermore, the extraction unit can use sensor data to extract that the state of the object differs from the default product. The display unit displays the estimated selling price based on the data extracted by the extraction unit. For example, the display unit can display the estimated selling price in conjunction with past auction results or price websites. The display unit can also display the minimum price and average price based on the extracted data.Furthermore, the display unit can also display sales pitches based on the extracted data. For example, the display unit can refer to past auction results and display the estimated selling price. The display unit can also refer to data from price comparison websites and display the minimum and average prices. Furthermore, the display unit can also display sales pitches based on the extracted data. The processing unit processes the image based on the data displayed by the display unit. The processing unit can, for example, adjust the brightness and contrast of the image. The processing unit can also apply filters to the image. Furthermore, the processing unit can adjust the size of the image. For example, the processing unit can adjust the brightness of the image to make it easier to view. The processing unit can also adjust the contrast of the image to make the details easier to understand. Furthermore, the processing unit can apply filters to the image to make it visually appealing. As a result, the auction support system according to this embodiment automates auction listings by collecting, analyzing, extracting, displaying, and processing data on the target items, thereby expanding the user base and contributing to sales.

[0071] The data collection unit collects data from the object. For example, the data collection unit can collect image data of the object. Specifically, it uses a camera to capture high-resolution images of the object and saves them as digital data. It is desirable that the image data be taken from multiple angles to clearly capture not only the overall image of the object, but also its detailed features and defects. The data collection unit can also collect text data from the object. For example, it can collect descriptions, specifications, and manufacturer information of the object as text data and save it in a database. Furthermore, the data collection unit can collect sensor data from the object. For example, it can acquire data in real time from temperature and humidity sensors attached to the object and use it for analysis. This allows the data collection unit to understand the physical state and environmental conditions of the object in detail. The collected data is centrally stored in a central database and made accessible to the analysis unit and other departments. The frequency and accuracy of data collection are adjusted according to the characteristics of the object and the requirements of the auction. For example, in the case of expensive works of art or antiques, higher-precision image data and detailed sensor data may be required. This allows the data collection unit to efficiently collect diverse data from the object and improve the overall performance of the system.

[0072] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can analyze the shape and content of an object using image analysis technology. Specifically, it uses image recognition algorithms to analyze the shape, color, and texture of an object and compare them with a default item. This allows for the automatic detection of the object's features and defects. The analysis unit can also analyze the description of an object using text analysis technology. It uses natural language processing technology to analyze the content of the description and extract important keywords and phrases. Furthermore, the analysis unit can analyze sensor data. For example, it can analyze fluctuations in temperature and humidity to evaluate the object's storage condition and environmental conditions. This allows the analysis unit to analyze the collected data from multiple angles and gain a detailed understanding of the object's state and characteristics. In addition, the analysis unit can utilize historical data and statistical information to analyze long-term trends and patterns. For example, it can predict the popularity and price trends of a specific category or brand based on past auction data. It can also use anomaly detection algorithms to detect unusual patterns and abnormal data early and issue warnings. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0073] The extraction unit extracts shapes and contents that differ from the default product based on data analyzed by the analysis unit. Specifically, it uses image analysis technology to extract differences in the shape of the object compared to the default product. For example, it analyzes images of the object to identify differences in shape, color, and texture. The extraction unit can also use text analysis technology to extract differences in the description of the object compared to the default product. It uses natural language processing technology to analyze the content of the description and clarify the differences from the default product. Furthermore, the extraction unit can use sensor data to extract differences in the state of the object compared to the default product. For example, it analyzes temperature and humidity data to identify differences in storage conditions and environmental conditions. This allows the extraction unit to grasp the characteristics and state of the object in detail and clearly distinguish it from the default product. In addition, the extraction unit can evaluate the value and rarity of the object based on the extracted data. For example, if certain characteristics or conditions are highly valued in the market, it can calculate the value of the object based on that information. The extraction unit can also share the extracted data with other systems and departments and utilize it for auction strategies and pricing. This allows the extraction unit to provide detailed information about the object, contributing to the success of the auction.

[0074] The display unit shows the estimated selling price based on the data extracted by the extraction unit. Specifically, it can display the estimated selling price in conjunction with past auction results and price websites. For example, it can refer to past auction data and calculate the estimated selling price based on the selling prices of similar items. It can also refer to price website data and display the minimum and average prices. Furthermore, the display unit can also display sales pitches based on the extracted data. For example, it can automatically generate and display sales pitches that emphasize the features and rarity of the item. This allows the display unit to effectively communicate the value of the item to auction participants. In addition, the display unit can continuously revise the estimated selling price and sales pitches based on data that is updated in real time. For example, it can immediately update the display content when new auction data or market trends are reflected. The display unit also makes it easy for auction participants to check information through the user interface. For example, it can display price trends and selling price changes using visually easy-to-understand graphs and charts. This allows the display unit to provide auction participants with the latest information and support the success of the auction.

[0075] The processing unit processes images based on the data displayed by the display unit. Specifically, it can adjust the brightness and contrast of images. For example, it can adjust the brightness to make the image easier to view. It can also adjust the contrast to make the image more detailed. Furthermore, the processing unit can apply filters to images. For example, it can apply a specific filter to make the image visually appealing. The processing unit can also adjust the size of images. For example, it can optimize the image size to meet the display requirements of auction sites. This allows the processing unit to effectively process images of objects and provide attractive visuals to auction participants. In addition, the processing unit saves the image processing history and can revert to the original image if necessary. For example, if excessive processing has been performed, it can revert to the original image and reprocess it. The processing unit also has a function to process multiple images in batches, allowing it to efficiently process a large number of images. This allows the processing unit to quickly and efficiently prepare items for auction and improve the overall system performance.

[0076] The data collection unit can collect data from an object. For example, the data collection unit can collect image data from an object. The data collection unit can also collect text data from an object. Furthermore, the data collection unit can collect sensor data from an object. For example, the data collection unit can take an image of the object with a camera and save it as image data. The data collection unit can also collect a description of the object as text data. Furthermore, the data collection unit can collect sensor data such as the temperature and humidity of the object. This enables the automation of data collection by having the data collection unit collect data from the object. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input image data of an object into AI, and the AI ​​can analyze and collect the image data.

[0077] The analysis unit can analyze the collected data and determine shapes and contents that differ from the default product. For example, the analysis unit can use image analysis technology to analyze the shape and contents of the object. The analysis unit can also use text analysis technology to analyze the description of the object. Furthermore, the analysis unit can analyze sensor data. For example, the analysis unit can use image analysis technology to analyze the shape of the object and compare it with the default product. The analysis unit can also use text analysis technology to analyze the description of the object and compare it with the default product. Furthermore, the analysis unit can analyze sensor data and evaluate the state of the object. As a result, the analysis unit automates the identification of rare items by analyzing the data and determining shapes and contents that differ from the default product. Some or all of the above processing in the analysis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the analysis unit can input image data of the object into a generation AI, and the generation AI can analyze the image data to determine shapes and contents that differ from the default product.

[0078] The extraction unit can extract shapes and contents that differ from the default product based on the analysis results. For example, the extraction unit can use image analysis technology to extract shapes that differ from the default product. The extraction unit can also use text analysis technology to extract contents that differ from the default product. Furthermore, the extraction unit can use sensor data to extract states that differ from the default product. For example, the extraction unit can use image analysis technology to extract that the shape of the object differs from the default product. The extraction unit can also use text analysis technology to extract that the description of the object differs from the default product. Furthermore, the extraction unit can use sensor data to extract that the state of the object differs from the default product. In this way, the extraction unit realizes the automatic extraction of rare items by extracting shapes and contents that differ from the default product. Some or all of the above processing in the extraction unit may be performed using, for example, a generation AI, or without a generation AI. For example, the extraction unit can input image data of the object to a generation AI, and the generation AI can analyze the image data to extract shapes and contents that differ from the default product.

[0079] The display unit can display an estimated selling price based on the extracted data. For example, the display unit can display the estimated selling price in conjunction with past auction results or price comparison websites. The display unit can also display a minimum price and an average price based on the extracted data. Furthermore, the display unit can display a sales pitch based on the extracted data. For example, the display unit can refer to past auction results to display the estimated selling price. The display unit can also refer to price comparison website data to display the minimum price and an average price. Furthermore, the display unit can display a sales pitch based on the extracted data. In this way, the display unit provides the user with selling price information by displaying the estimated selling price. Some or all of the above processing in the display unit may be performed using, for example, a generating AI, or without a generating AI. For example, the display unit can input the extracted data into a generating AI, which can then calculate and display the estimated selling price.

[0080] The processing unit can process images based on the displayed data. For example, the processing unit can adjust the brightness and contrast of the image. It can also apply filters to the image. Furthermore, the processing unit can adjust the size of the image. For example, the processing unit can adjust the brightness of the image to make it easier to view. It can also adjust the contrast of the image to make the details easier to understand. Furthermore, the processing unit can apply filters to the image to make it visually appealing. In this way, the processing unit processes the image to automatically generate images for auction listings. Some or all of the above processing in the processing unit may be performed using, for example, a generation AI, or without a generation AI. For example, the processing unit can input image data into a generation AI, which can then process the image by adjusting its brightness and contrast.

[0081] The display unit can display estimated selling prices in conjunction with past auction results and price comparison websites. For example, the display unit can refer to past auction results and display estimated selling prices. The display unit can also refer to price comparison website data and display minimum and average prices. Furthermore, the display unit can display sales pitches based on extracted data. For example, the display unit can obtain past auction results via an API and display estimated selling prices. The display unit can also obtain price comparison website data via an API and display minimum and average prices. Furthermore, the display unit can display sales pitches based on extracted data. This allows the display unit to provide more accurate estimated selling prices by linking with past auction results and price comparison websites. Some or all of the above processing in the display unit may be performed using, for example, a generating AI, or without a generating AI. For example, the display unit can input past auction results and price comparison website data into a generating AI, which can then calculate and display estimated selling prices.

[0082] The processing unit can adjust the brightness and contrast of an image to make it easier to view. For example, the processing unit can adjust the brightness of an image to make it easier to view. It can also adjust the contrast of an image to make the details easier to understand. Furthermore, the processing unit can apply filters to an image to make it visually appealing. For example, the processing unit can adjust the brightness of an image to make it easier to view. It can also adjust the contrast of an image to make the details easier to understand. Furthermore, the processing unit can apply filters to an image to make it visually appealing. In this way, the processing unit provides an easy-to-view image by adjusting the brightness and contrast of the image. Some or all of the above processing in the processing unit may be performed using, for example, a generation AI, or without a generation AI. For example, the processing unit can input image data into a generation AI, and the generation AI can adjust the brightness and contrast of the image to process it.

[0083] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is excited, the data collection unit can immediately start collecting data and provide results quickly. Also, if the user is relaxed, the data collection unit can collect data slowly and gather more detailed information. Furthermore, if the user is stressed, the data collection unit can reduce the frequency of data collection to lessen the user's burden. For example, if the user is excited, the data collection unit can immediately start collecting data and provide results quickly. Also, if the user is relaxed, the data collection unit can collect data slowly and gather more detailed information. Furthermore, if the user is stressed, the data collection unit can reduce the frequency of data collection to lessen the user's burden. In this way, the data collection unit reduces the user's burden by adjusting the timing of data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's facial expression data into a generating AI, which can then estimate the user's emotions and adjust the timing of data collection.

[0084] The data collection unit can analyze the user's past data collection history and select the optimal data collection method. For example, the data collection unit can analyze patterns in data previously collected by the user and prioritize the collection of data with similar patterns. It can also select the most effective data collection method based on the success rate of data previously collected by the user. Furthermore, the data collection unit can identify periods in the user's past data collection history where data collection is more likely to succeed and perform data collection during those periods. In this way, the data collection unit provides the optimal data collection method by analyzing the user's past data collection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past data collection history into AI, which can then select the optimal data collection method.

[0085] The data collection unit can filter data based on the user's current areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to categories the user is currently interested in. The data collection unit can also filter and collect relevant data based on keywords the user has recently searched for. Furthermore, the data collection unit can collect data related to topics the user is following, providing information tailored to the user's interests. This allows the data collection unit to provide highly relevant data by filtering data based on the user's areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's search history data into AI, which can then filter and collect relevant data.

[0086] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is excited, the data collection unit will prioritize collecting the most recent data. It can also prioritize collecting detailed data if the user is relaxed. Furthermore, if the user is stressed, the data collection unit will prioritize collecting concise and important data. This allows the data collection unit to prioritize data that is important to the user by determining data priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user facial expression data into a generating AI, which can then estimate the user's emotions and determine the priority of the data to be collected.

[0087] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of data related to the area where the user is currently located. The data collection unit can also collect data related to places the user has visited in the past. Furthermore, the data collection unit can also collect data related to places the user plans to visit in the future. In this way, the data collection unit provides highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information data into AI, which can then prioritize the collection of relevant data.

[0088] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect data related to posts that a user has "liked" on social media. The data collection unit can also collect data related to accounts that a user follows. Furthermore, the data collection unit can also collect data related to posts that a user has shared. In this way, the data collection unit provides highly relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into AI, and the AI ​​can collect relevant data.

[0089] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results. Furthermore, if the user is excited, the analysis unit can also provide visually appealing analysis results. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results. Furthermore, if the user is excited, the analysis unit can also provide visually appealing analysis results. In this way, the analysis unit provides analysis results that are easy for the user to understand by adjusting the presentation of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into a generating AI, which can then estimate the user's emotions and adjust the method of expression in the analysis.

[0090] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on highly important data. It can also perform a simplified analysis on less important data. Furthermore, it can perform an analysis with a moderate level of detail on data of moderate importance. This allows the analysis unit to achieve efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into the AI, and the AI ​​can adjust the level of detail of the analysis based on the importance.

[0091] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an image analysis algorithm to image data. It can also apply a natural language processing algorithm to text data. Furthermore, it can apply a statistical analysis algorithm to numerical data. This allows the analysis unit to provide appropriate analysis results by applying different analysis algorithms depending on the data category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into the AI, which can then apply a different analysis algorithm depending on the category.

[0092] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can also provide a detailed analysis. Furthermore, if the user is excited, the analysis unit can also provide a visually appealing analysis. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can also provide a detailed analysis. Furthermore, if the user is excited, the analysis unit can also provide a visually appealing analysis. By adjusting the length of the analysis based on the user's emotions, the analysis unit provides an analysis result of an appropriate length for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into a generating AI, which can then estimate the user's emotions and adjust the length of the analysis.

[0093] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. It can also postpone the analysis of older data. Furthermore, the analysis unit may prioritize the analysis of data collected during a specific period. For example, the analysis unit may prioritize the analysis of the most recent data. It can also postpone the analysis of older data. Furthermore, the analysis unit may prioritize the analysis of data collected during a specific period. This allows the analysis unit to prioritize the analysis of the most recent data by determining the priority of analysis based on the data collection timing. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into the AI, and the AI ​​can determine the priority of analysis based on the collection timing.

[0094] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of data with high relevance. It can also postpone the analysis of data with low relevance. Furthermore, the analysis unit can prioritize the analysis of data related to a specific theme. For example, the analysis unit can prioritize the analysis of data with high relevance. It can also postpone the analysis of data with low relevance. Furthermore, the analysis unit can prioritize the analysis of data related to a specific theme. This enables efficient analysis by allowing the analysis unit to adjust the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into the AI, and the AI ​​can adjust the order of analysis based on the relevance.

[0095] The extraction unit can estimate the user's emotions and adjust the extraction criteria based on the estimated emotions. For example, if the user is relaxed, the extraction unit can apply detailed extraction criteria. It can also apply concise extraction criteria if the user is in a hurry. Furthermore, if the user is excited, the extraction unit can apply visually appealing extraction criteria. This allows the extraction unit to provide appropriate extraction results for the user by adjusting the extraction criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input user facial expression data into a generating AI, which can then estimate the user's emotions and adjust the extraction criteria.

[0096] The extraction unit can improve the accuracy of extraction by considering the interrelationships between data during the extraction process. For example, the extraction unit can analyze the correlations between data and extract highly relevant data. The extraction unit can also perform data clustering and extract data belonging to the same cluster. Furthermore, the extraction unit can perform data pattern recognition and extract data that matches a specific pattern. For example, the extraction unit can analyze the correlations between data and extract highly relevant data. The extraction unit can also perform data clustering and extract data belonging to the same cluster. Furthermore, the extraction unit can perform data pattern recognition and extract data that matches a specific pattern. By considering the interrelationships between data, the extraction unit provides highly accurate extraction results. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input the data correlations into AI, and the AI ​​can improve the accuracy of extraction based on the correlations.

[0097] The extraction unit can perform extraction while considering the attribute information of the data submitter. For example, the extraction unit can extract highly reliable data by considering the submitter's expertise. The extraction unit can also analyze the submitter's past submission history and extract highly reliable data. Furthermore, the extraction unit can also extract highly reliable data by considering the submitter's organization. For example, the extraction unit can extract highly reliable data by considering the submitter's expertise. Furthermore, the extraction unit can analyze the submitter's past submission history and extract highly reliable data. Furthermore, the extraction unit can also extract highly reliable data by considering the submitter's organization. In this way, the extraction unit provides highly reliable extraction results by considering the attribute information of the data submitter. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input the submitter's attribute information into AI, and the AI ​​can perform extraction based on the attribute information.

[0098] The extraction unit can estimate the user's emotions and adjust the order in which the extraction results are displayed based on the estimated emotions. For example, if the user is relaxed, the extraction unit may prioritize displaying detailed extraction results. It can also prioritize displaying concise extraction results if the user is in a hurry. Furthermore, if the user is excited, the extraction unit may prioritize displaying visually appealing extraction results. This allows the extraction unit to provide a user-friendly display by adjusting the order in which the extraction results are displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the extraction unit may be performed using AI, or not. For example, the extraction unit can input user facial expression data into a generating AI, which can then estimate the user's emotions and adjust the display order of the extraction results.

[0099] The extraction unit can perform extraction while considering the geographical distribution of the data. For example, the extraction unit can preferentially extract data related to a specific region. Furthermore, the extraction unit can extract data that is geographically distributed over a wide area. In addition, the extraction unit can extract data that is geographically concentrated. This allows the extraction unit to provide highly relevant extraction results by considering the geographical distribution of the data. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input the geographical distribution of the data into the AI, which can then perform extraction based on the geographical distribution.

[0100] The extraction unit can improve the accuracy of the extraction by referring to relevant literature during the extraction process. For example, the extraction unit can refer to relevant literature and extract reliable data. The extraction unit can also extract reliable data by considering the number of citations of the relevant literature. Furthermore, the extraction unit can extract reliable data by considering the reliability of the authors of the relevant literature. For example, the extraction unit can refer to relevant literature and extract reliable data. The extraction unit can also extract reliable data by considering the number of citations of the relevant literature. Furthermore, the extraction unit can extract reliable data by considering the reliability of the authors of the relevant literature. This allows the extraction unit to provide reliable extraction results by referring to relevant literature for the data. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input the relevant literature data into AI, and the AI ​​can improve the accuracy of the extraction based on the relevant literature.

[0101] The display unit can estimate the user's emotions and adjust the display method based on the estimated emotions. For example, if the user is relaxed, the display unit can display detailed information. If the user is in a hurry, the display unit can also display concise information. Furthermore, if the user is excited, the display unit can also display visually appealing information. For example, if the user is relaxed, the display unit can display detailed information. If the user is in a hurry, the display unit can also display concise information. Furthermore, if the user is excited, the display unit can also display visually appealing information. In this way, the display unit provides an easy-to-understand display for the user by adjusting the display method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user facial expression data into a generating AI, which can then estimate the user's emotions and adjust the display method accordingly.

[0102] The display unit can optimize the current display by referring to past display data when displaying content. For example, the display unit can optimize the current display based on data that the user has preferred to view in the past. It can also optimize the current display based on data that the user has skipped in the past. Furthermore, the display unit can optimize the current display based on data that the user has given high ratings in the past. For example, the display unit can optimize the current display based on data that the user has preferred to view in the past. It can also optimize the current display based on data that the user has skipped in the past. Furthermore, the display unit can optimize the current display based on data that the user has given high ratings in the past. In this way, the display unit provides the optimal display for the user by referring to past display data. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input past display data into AI, and the AI ​​can optimize the current display based on the past data.

[0103] The display unit can apply different display methods to each data category during display. For example, the display unit can apply thumbnail display to image data. It can also apply summary display to text data. Furthermore, the display unit can apply graph display to numerical data. In this way, the display unit provides an appropriate display by applying different display methods to each data category. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the data categories into the AI, and the AI ​​can apply different display methods to each category.

[0104] The display unit can estimate the user's emotions and determine the display priority based on the estimated emotions. For example, if the user is relaxed, the display unit may prioritize displaying detailed information. It can also prioritize displaying concise information if the user is in a hurry. Furthermore, if the user is excited, the display unit may prioritize displaying visually appealing information. This allows the display unit to prioritize information important to the user by determining the display priority based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user facial expression data into a generating AI, which can then estimate the user's emotions and determine the display priority.

[0105] The display unit can adjust the display order based on the data collection time when displaying data. For example, the display unit can prioritize displaying the latest data. It can also prioritize displaying older data. Furthermore, the display unit can prioritize displaying data collected during a specific period. For example, the display unit can prioritize displaying the latest data. It can also prioritize displaying older data. Furthermore, the display unit can prioritize displaying data collected during a specific period. This allows the display unit to prioritize displaying the latest information by adjusting the display order based on the data collection time. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the data collection time into the AI, and the AI ​​can adjust the display order based on the collection time.

[0106] The display unit can optimize the display by referring to relevant market data when displaying data. For example, the display unit optimizes the current display based on relevant market data. The display unit can also adjust the display content considering market trends. Furthermore, the display unit can determine the display priority based on market demand. For example, the display unit optimizes the current display based on relevant market data. The display unit can also adjust the display content considering market trends. Furthermore, the display unit can determine the display priority based on market demand. This allows the display unit to provide the optimal display for the user by referring to relevant market data. Some or all of the above processing in the display unit may be performed using AI, for example, or not using AI. For example, the display unit can input relevant market data into AI, and the AI ​​can optimize the display based on the market data.

[0107] The processing unit can estimate the user's emotions and adjust the image processing method based on the estimated emotions. For example, if the user is relaxed, the processing unit can process the image with soft tones. If the user is in a hurry, the processing unit can process the image with conciseness and clarity. Furthermore, if the user is excited, the processing unit can process the image with visual stimulation. In this way, the processing unit provides the user with the optimal image by adjusting the image processing method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the processing unit may be performed using AI, for example, or without AI. For example, the processing unit can input user facial expression data into a generating AI, which can then estimate the user's emotions and adjust the image processing method accordingly.

[0108] The processing unit can select the optimal processing method by referring to the user's past processing history when processing images. For example, the processing unit can prioritize applying processing methods that the user has preferred to use in the past. It can also exclude processing methods that the user has avoided in the past. Furthermore, the processing unit can analyze the user's past processing history and select the most effective processing method. For example, the processing unit can prioritize applying processing methods that the user has preferred to use in the past. It can also exclude processing methods that the user has avoided in the past. Furthermore, the processing unit can analyze the user's past processing history and select the most effective processing method. In this way, the processing unit provides the user with the optimal processing method by referring to the user's past processing history. Some or all of the above processing in the processing unit may be performed using AI, for example, or without AI. For example, the processing unit can input the user's past processing history data into AI, and the AI ​​can select the optimal processing method based on the past data.

[0109] The image processing unit can customize the processing methods based on the user's current lifestyle when processing images. For example, if the user is busy, the processing unit can provide a simple and quick processing method. Alternatively, if the user is relaxed, the processing unit can provide a detailed processing method. Furthermore, if the user is participating in a specific event, the processing unit can provide a processing method suitable for that event. This allows the processing unit to provide the user with the optimal image by customizing the processing methods based on the user's lifestyle. Some or all of the processing described above in the processing unit may be performed using AI, for example, or without AI. For example, the processing unit can input user lifestyle data into AI, which can then customize the processing methods based on the lifestyle.

[0110] The processing unit can estimate the user's emotions and determine the priority of image processing based on the estimated emotions. For example, if the user is relaxed, the processing unit will prioritize detailed processing. If the user is in a hurry, the processing unit can also prioritize concise processing. Furthermore, if the user is excited, the processing unit can also prioritize visually appealing processing. In this way, the processing unit prioritizes processing images that are important to the user by determining the priority of image processing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the processing unit may be performed using AI, for example, or without AI. For example, the processing unit can input user facial expression data into a generating AI, which can then estimate the user's emotions and determine the priority for image processing.

[0111] The processing unit can select the optimal processing method when processing images, taking into account the user's geographical location information. For example, the processing unit can provide a processing method tailored to the characteristics of the area where the user is currently located. It can also provide processing methods related to places the user has visited in the past. Furthermore, it can provide processing methods related to places the user plans to visit in the future. In this way, the processing unit provides the optimal image for the user by taking into account the user's geographical location information. Some or all of the above processing in the processing unit may be performed using AI, for example, or without AI. For example, the processing unit can input the user's geographical location data into AI, and the AI ​​can select the optimal processing method based on the geographical location information.

[0112] The processing unit can analyze the user's social media activity and suggest processing methods when processing images. For example, the processing unit can suggest processing methods related to posts the user has "liked" on social media. It can also suggest processing methods related to accounts the user follows. Furthermore, the processing unit can suggest processing methods related to posts the user has shared. In this way, the processing unit provides the user with the most suitable processing method by analyzing the user's social media activity. Some or all of the above processing in the processing unit may be performed using AI, for example, or without AI. For example, the processing unit can input the user's social media activity data into AI, which can then suggest processing methods based on the social media activity.

[0113] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0114] Auction support systems can estimate a user's emotions and adjust the timing of auction listings based on those emotions. For example, if a user is excited, the system can immediately start listing items and provide results quickly. If a user is relaxed, the system can list items slowly and provide detailed information. Furthermore, if a user is stressed, the system can reduce the frequency of listings to lessen the user's burden. In this way, by adjusting the timing of listings based on the user's emotions, auction support systems can reduce the user's burden and provide a more comfortable auction experience.

[0115] Auction support systems can analyze a user's past listing history and select the optimal listing method. For example, they can prioritize listing methods that have been successful in the past. They can also avoid listing methods that have been unsuccessful in the past. Furthermore, they can identify times when listings are more likely to be successful based on the user's past listing history and list items during those times. In this way, auction support systems can improve success rates by providing optimal listing methods based on the user's past listing history.

[0116] The auction support system can filter the items listed for auction based on the user's current areas of interest. For example, it can prioritize listing items related to categories the user is currently interested in. It can also filter and list items based on keywords the user has recently searched for. Furthermore, it can list items related to topics the user is following, providing information tailored to the user's interests. In this way, the auction support system can provide highly relevant listings by filtering items based on the user's areas of interest.

[0117] The auction support system can prioritize listing items that are highly relevant to the user, taking into account their geographical location. For example, it can prioritize listing items related to the user's current location. It can also list items related to places the user has visited in the past. Furthermore, it can list items related to places the user plans to visit in the future. In this way, the auction support system can provide highly relevant listings by considering the user's geographical location.

[0118] The auction support system can analyze a user's social media activity and list relevant items for sale. For example, it can list items related to posts a user has "liked" on social media. It can also list items related to accounts a user follows. Furthermore, it can list items related to posts a user has shared. In this way, the auction support system can provide highly relevant listings by analyzing a user's social media activity.

[0119] The auction support system can estimate the user's emotions and prioritize the items to be listed based on those emotions. For example, if the user is excited, it can prioritize listing the newest items. If the user is relaxed, it can prioritize listing items that provide detailed information. Furthermore, if the user is stressed, it can prioritize listing concise and important items. In this way, the auction support system can prioritize listing items that are important to the user by determining the priority of items to be listed based on the user's emotions.

[0120] The auction support system can estimate the user's emotions and adjust the way the listing is presented based on those emotions. For example, if the user is relaxed, it can provide a detailed description. If the user is in a hurry, it can provide a concise description. Furthermore, if the user is excited, it can provide a visually appealing description. In this way, the auction support system can provide a description that is easy for the user to understand by adjusting the way the listing is presented based on the user's emotions.

[0121] Auction support systems can estimate a user's emotions and adjust the length of their listings based on that estimation. For example, if a user is in a hurry, they can offer a short, to-the-point listing. If the user is relaxed, they can offer a more detailed listing. Furthermore, if the user is excited, they can offer a visually appealing listing. In this way, by adjusting the length of listings based on the user's emotions, auction support systems can provide users with listings of the appropriate length.

[0122] The auction support system can estimate the user's emotions and adjust the listing criteria based on those emotions. For example, if the user is relaxed, detailed listing criteria may be applied. If the user is in a hurry, concise listing criteria may be applied. Furthermore, if the user is excited, visually appealing listing criteria may be applied. In this way, the auction support system can provide users with appropriate auction results by adjusting the listing criteria based on their emotions.

[0123] The auction support system can estimate the user's emotions and adjust the order in which listing results are displayed based on those emotions. For example, if the user is relaxed, detailed listing results will be prioritized. If the user is in a hurry, concise listing results will be prioritized. Furthermore, if the user is excited, visually appealing listing results will be prioritized. In this way, the auction support system can provide a user-friendly display by adjusting the order in which listing results are displayed based on the user's emotions.

[0124] The following briefly describes the processing flow for example form 2.

[0125] Step 1: The collection unit collects data from the object. The collection unit can collect, for example, image data, text data, and sensor data from the object. Specifically, the collection unit takes an image of the object with a camera and saves it as image data. It can also collect a description of the object as text data, and further collect sensor data such as the object's temperature and humidity. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the shape and content of the object using image analysis technology and analyze the description of the object using text analysis technology. Furthermore, it can also analyze sensor data and evaluate the state of the object. For example, the analysis unit can analyze the shape of the object using image analysis technology and compare it with a default product. It can also analyze the description of the object using text analysis technology and compare it with a default product. Furthermore, it can analyze sensor data and evaluate the state of the object. Step 3: The extraction unit extracts shapes and contents that differ from the default product based on the data analyzed by the analysis unit. The extraction unit can use image analysis technology to extract shapes that differ from the default product, and text analysis technology to extract contents that differ from the default product. Furthermore, it can also use sensor data to extract states that differ from the default product. For example, the extraction unit can use image analysis technology to extract that the shape of the object differs from the default product, and use text analysis technology to extract that the description of the object differs from the default product. Furthermore, it can also use sensor data to extract that the state of the object differs from the default product. Step 4: The display unit displays the estimated selling price based on the data extracted by the extraction unit. The display unit can display the estimated selling price in conjunction with past auction results and price comparison websites, and can also display the minimum price and average price. Furthermore, it can also display sales pitches based on the extracted data. For example, the display unit refers to past auction results and displays the estimated selling price. It can also refer to data from price comparison websites and display the minimum price and average price. Furthermore, it can also display sales pitches based on the extracted data. Step 5: The processing unit processes the image based on the data displayed by the display unit. The processing unit can adjust the brightness and contrast of the image, apply filters to the image, and adjust the image size. For example, the processing unit can adjust the brightness of the image to make it easier to view. It can also adjust the contrast of the image to make the details easier to see. Furthermore, it can apply filters to the image to make it visually appealing.

[0126] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0127] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0128] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements described above, including the collection unit, analysis unit, extraction unit, display unit, and processing unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects images of objects using the camera 42 of the smart device 14 and analyzes them by the identification processing unit 290 of the data processing device 12. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the collected data. The extraction unit is implemented, for example, by the identification processing unit 290 of the data processing device 12 and extracts shapes and contents that differ from the default product based on the analysis results. The display unit is implemented, for example, by the display 40A of the smart device 14 and displays the expected selling price. The processing unit is implemented, for example, by the control unit 46A of the smart device 14 and processes the image to make it easier to view. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0130] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0131] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0133] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

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

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

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

[0137] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0138] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0140] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0142] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0144] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements described above, including the collection unit, analysis unit, extraction unit, display unit, and processing unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects images of objects using the camera 42 of the smart glasses 214 and analyzes them using the identification processing unit 290 of the data processing device 12. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the collected data. The extraction unit is implemented, for example, by the identification processing unit 290 of the data processing device 12 and extracts shapes and contents that differ from the default product based on the analysis results. The display unit is implemented, for example, by the display of the smart glasses 214 and displays the expected selling price. The processing unit is implemented, for example, by the control unit 46A of the smart glasses 214 and processes the image to make it easier to view. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0146] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0147] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0149] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

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

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

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

[0153] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the collection unit, analysis unit, extraction unit, display unit, and processing unit, is implemented in at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit collects images of objects using the camera 42 of the headset terminal 314 and analyzes them using the identification processing unit 290 of the data processing device 12. The analysis unit is implemented in the identification processing unit 290 of the data processing device 12 and analyzes the collected data. The extraction unit is implemented in the identification processing unit 290 of the data processing device 12 and extracts shapes and contents that differ from the default product based on the analysis results. The display unit is implemented in the display 343 of the headset terminal 314 and displays the expected selling price. The processing unit is implemented in the control unit 46A of the headset terminal 314 and processes the images to make them easier to view. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0162] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0163] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0164] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0165] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0166] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0167] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0168] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0169] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0170] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0171] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0172] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0173] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0174] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0175] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0176] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0177] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0178] Each of the multiple elements described above, including the collection unit, analysis unit, extraction unit, display unit, and processing unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects images of objects using the camera 42 of the robot 414 and analyzes them by the identification processing unit 290 of the data processing unit 12. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data. The extraction unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and extracts shapes and contents that differ from the default product based on the analysis results. The display unit is implemented, for example, by the display of the robot 414 and displays the expected selling price. The processing unit is implemented, for example, by the control unit 46A of the robot 414 and processes the images to make them easier to view. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0179] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0180] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0181] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0182] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0183] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0184] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0185] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0186] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0187] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0188] 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.

[0189] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0190] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0191] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0192] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0193] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0194] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0195] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0196] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0197] (Note 1) A data collection unit that collects data on the target object, An analysis unit analyzes the data collected by the aforementioned collection unit, An extraction unit extracts shapes and contents that differ from the default product based on the data analyzed by the aforementioned analysis unit, A display unit that displays an estimated selling price based on the data extracted by the extraction unit, The system includes a processing unit that processes an image based on the data displayed by the display unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect data on the target object. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data is analyzed to determine if the shape or contents differ from the default product. The system described in Appendix 1, characterized by the features described herein. (Note 4) The extraction unit is Based on the analysis results, extract shapes and contents that differ from the default product. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned display unit is The estimated selling price is displayed based on the extracted data. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned processing section is Process the image based on the displayed data. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned display unit is The estimated selling price is displayed in conjunction with past auction results and price comparison websites. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned processing section is Adjust the brightness and contrast of the image to make it easier to view. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is Analyze the user's past data collection history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, filtering is performed based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The extraction unit is We estimate the user's emotions and adjust the extraction criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The extraction unit is During extraction, consider the interrelationships between data to improve extraction accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 23) The extraction unit is During the extraction process, the attribute information of the data submitter will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The extraction unit is It estimates the user's sentiment and adjusts the order in which the extraction results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 25) The extraction unit is When extracting data, the geographical distribution of the data should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 26) The extraction unit is During extraction, we refer to relevant literature to improve the accuracy of the extraction. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned display unit is It estimates the user's emotions and adjusts the display method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned display unit is When displaying content, the system optimizes the current display by referencing past display data. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned display unit is When displaying data, different display methods are applied to each data category. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned display unit is It estimates the user's emotions and determines the display priority based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned display unit is When displaying data, adjust the display order based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned display unit is When displaying data, the system optimizes the display by referencing relevant market data. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned processing section is It estimates the user's emotions and adjusts the image processing method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned processing section is When processing images, the system selects the optimal processing method by referring to the user's past processing history. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned processing section is When processing images, the processing method is customized based on the user's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned processing section is It estimates the user's emotions and determines the priority of image processing based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned processing section is When processing images, the system selects the optimal processing method by considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned processing section is When processing images, we analyze the user's social media activity and suggest processing methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0198] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data on the target object, An analysis unit analyzes the data collected by the aforementioned collection unit, An extraction unit extracts shapes and contents that differ from the default product based on the data analyzed by the aforementioned analysis unit, A display unit that displays an estimated selling price based on the data extracted by the extraction unit, The system includes a processing unit that processes an image based on the data displayed by the display unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect data on the target object. The system according to feature 1.

3. The aforementioned analysis unit, The collected data is analyzed to determine if the shape or contents differ from the default product. The system according to feature 1.

4. The extraction unit is Based on the analysis results, extract shapes and contents that differ from the default product. The system according to feature 1.

5. The aforementioned display unit is The estimated selling price is displayed based on the extracted data. The system according to feature 1.

6. The aforementioned processing section is Process the image based on the displayed data. The system according to feature 1.

7. The aforementioned display unit is The estimated selling price is displayed in conjunction with past auction results and price comparison websites. The system according to feature 1.

8. The aforementioned processing section is Adjust the brightness and contrast of the image to make it easier to view. The system according to feature 1.

Images