system

Abstract

The system according to the embodiment aims to quickly and accurately calculate the fair price of a reused product. [Solution] A system according to an embodiment includes a photo analysis unit, an information analysis unit, a market analysis unit, and a price calculation unit. The photo analysis unit analyzes photos of products to determine their condition. The information analysis unit analyzes detailed product information based on the product condition determined by the photo analysis unit to determine the original price and usage status. The market analysis unit analyzes past transaction data and current market trends based on the original price and usage status determined by the information analysis unit. The price calculation unit calculates a fair price based on the market trends analyzed by the market analysis unit.

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem of making it difficult to quickly and accurately calculate the fair price of reused products.

[0005] The system according to the embodiment aims to quickly and accurately calculate the fair price of a reused product. [Means for solving the problem]

[0006] The system according to the embodiment includes a photo analysis unit, an information analysis unit, a market analysis unit, and a price calculation unit. The photo analysis unit analyzes photos of products to determine their condition. The information analysis unit analyzes detailed product information based on the product condition determined by the photo analysis unit to determine the original price and usage status. The market analysis unit analyzes past transaction data and current market trends based on the original price and usage status determined by the information analysis unit. The price calculation unit calculates a fair price based on the market trends analyzed by the market analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can quickly and accurately calculate the fair price of a reused product. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The reuse product trading system according to an embodiment of the present invention is a system in which buyers and sellers of reuse products register the items they want, and the system automatically determines a fair price based on the original price, usage status, and market demand of the product from information such as photographs. When a user registers a reuse product, the system inputs photographs and detailed information about the product. Next, the AI ​​analyzes this information and determines the original price, usage status, and market demand for the product. Finally, the AI ​​automatically calculates a fair price based on these determination results. For example, when a user registers a reuse product, they input photographs and detailed information about the product. For example, they input information such as the product's brand name, model number, purchase date, and frequency of use. This information is input to the AI. Next, the AI ​​analyzes the input information. The AI ​​analyzes the product's photographs and determines the condition of the product. For example, it determines whether there are scratches or stains on the product's exterior. It also determines the product's original price and usage status based on the product's detailed information. For example, it estimates the original price from the product's brand name and model number, and determines the usage status from the purchase date and frequency of use. Furthermore, the AI ​​analyzes market demand. The AI ​​determines the demand for the product based on past transaction data and current market trends. For example, the system analyzes the prices at which similar branded and model-numbered products have been traded in the past, and the current market demand. Finally, the AI ​​automatically calculates a fair price based on these findings. For instance, it considers the original price, condition, and market needs of the product to determine a fair selling price. This system allows buyers and sellers to easily determine the fair price of a product, facilitating smooth transactions. This system streamlines the trading of used goods and improves satisfaction for both buyers and sellers. For example, buyers can purchase products at a fair price, and sellers can sell products at a fair price. Furthermore, because the AI ​​automatically determines the price, transaction transparency is improved, and unfair pricing can be prevented. In this way, the used goods trading system can streamline the trading of used goods and improve satisfaction for both buyers and sellers.

[0029] The reuse product trading system according to this embodiment comprises a photo analysis unit, an information analysis unit, a market analysis unit, and a price calculation unit. The photo analysis unit analyzes photographs of products to determine the condition of the products. For example, the photo analysis unit determines whether there are scratches or stains on the exterior of the product. The photo analysis unit can also determine the color and texture of the product and reflect this in the condition evaluation. For example, if the color of the product has faded, it detects the change in color and reflects this in the condition evaluation. Similarly, if the texture of the product has changed, it can detect the change in texture and reflect this in the condition evaluation. Furthermore, the photo analysis unit can analyze background information of the product to estimate the environment in which the product was used. For example, if the background of the product contains many outdoor elements, it can be estimated that it was used outdoors. Similarly, if the background of the product contains many indoor elements, it can be estimated that it was used indoors. The information analysis unit analyzes detailed product information to determine the original price and usage status. For example, the information analysis unit estimates the original price from the brand name and model number of the product and determines the usage status from the purchase date and frequency of use. The information analysis unit can also estimate the original price by considering the year and country of manufacture of the product. For example, if a product is old, the original price may be estimated lower. Conversely, if the country of manufacture is known for producing high-quality products, the original price may be estimated higher. Furthermore, the information analysis department can also analyze the product's usage history in detail and evaluate its usage status. For example, if a product is used frequently, the usage status may be evaluated as poor. Conversely, if a product is used infrequently, the usage status may be evaluated as good. The market analysis department analyzes market needs based on past transaction data and current market trends. For example, the market analysis department analyzes how much products of the same brand and model number have been traded for in the past and how much demand there is in the current market. The market analysis department can also evaluate demand by considering market trends in each region. For example, if demand is high in a particular region, the market trends of that region will be emphasized when evaluating demand. Conversely, if demand is low in a particular region, the market trends of that region will be emphasized when evaluating demand. Furthermore, the market analysis department can also predict fluctuations in demand based on seasons and events. For example, it can predict and evaluate seasonal fluctuations in demand. It can also predict and evaluate fluctuations in demand based on specific events.The pricing unit comprehensively considers the original price, usage status, and market needs of the product to determine an appropriate selling price. For example, the pricing unit calculates an appropriate price by considering the rarity of the product. It can also calculate an appropriate price by predicting the future value of the product. Furthermore, the pricing unit can also calculate an appropriate price by considering the insurable value of the product. For example, if the product has insurable value, the appropriate price is calculated to be higher. If the product does not have insurable value, the appropriate price can be calculated normally. As a result, the reuse product trading system according to this embodiment can streamline the trading of reuse products and improve the satisfaction of both buyers and sellers.

[0030] The photo analysis unit can analyze product photos to determine whether the product's appearance is damaged or dirty. For example, the photo analysis unit can analyze product photos at high resolution to determine the presence or absence of damage or dirt. For example, the photo analysis unit can detect whether there are small scratches on the surface of the product. The photo analysis unit can also detect whether there is dirt on the surface of the product. Furthermore, the photo analysis unit can analyze the entire appearance of the product to identify the extent of damage or dirt. For example, the photo analysis unit can scan the entire surface of the product to identify the location and extent of damage or dirt. This allows the photo analysis unit to evaluate the condition of the product's appearance in detail. Some or all of the above processing in the photo analysis unit may be performed using AI, for example, or without AI. For example, the photo analysis unit can input product photo data into a generating AI and have the generating AI perform the detection of damage or dirt.

[0031] The information analysis unit can estimate the original price from the product's brand name and model number, and determine the usage status from the purchase date and frequency of use. The information analysis unit estimates the original price based on the product's brand name and model number, for example. For example, if the product's brand name is a luxury brand, the information analysis unit can estimate the original price higher. Also, if the product's model number is the latest model, the information analysis unit can estimate the original price higher. Furthermore, the information analysis unit determines the usage status based on the product's purchase date and usage frequency. For example, if the product was recently purchased, the information analysis unit can determine that the usage status is good. Also, if the product is used infrequently, the information analysis unit can determine that the usage status is good. This allows the information analysis unit to accurately evaluate the product's original price and usage status. Some or all of the above-mentioned processing in the information analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the information analysis unit can input data such as the product's brand name, model number, purchase date, and usage frequency into the generation AI, and have the generation AI evaluate the original price and usage status.

[0032] The market analysis unit can analyze the past trade prices of products of the same brand and model number and the current market demand for the product based on past transaction data and current market trends. The market analysis unit, for example, analyzes the transaction prices of products based on past transaction data. For example, the market analysis unit analyzes the past trade prices of products of the same brand and model number. The market analysis unit can also analyze product demand based on current market trends. For example, the market analysis unit analyzes the current market demand for products of the same brand and model number. Furthermore, the market analysis unit can evaluate demand taking into account regional market trends. For example, if demand in a specific region is high, the market analysis unit can evaluate demand by focusing on the market trends of that region. Also, if demand in a specific region is low, the market analysis unit can evaluate demand by focusing on the market trends of that region. This allows the market analysis unit to accurately evaluate product demand based on market demand and past transaction data. Some or all of the above-described processing in the market analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the market analysis unit can input past transaction data and current market trend data into the generation AI and have the generation AI perform demand assessment.

[0033] The price calculation unit can present a fair selling price based on a comprehensive consideration of the original price, usage status, and market needs of the product. The price calculation unit, for example, calculates a fair selling price by comprehensively considering the original price, usage status, and market needs of the product. For example, if the original price of the product is high, the price calculation unit calculates a higher fair price. Also, if the usage status of the product is good, the price calculation unit can calculate a higher fair price. Furthermore, if market demand is high, the price calculation unit can calculate a higher fair price. This allows the price calculation unit to automatically calculate a fair price of the product. Some or all of the above-mentioned processing in the price calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the price calculation unit can input data on the original price, usage status, and market needs of the product into a generation AI and have the generation AI calculate a fair price.

[0034] The photo analysis unit can determine the color and texture of the product during photo analysis and reflect this in the condition evaluation. For example, if the color of the product has faded, the photo analysis unit detects the color change and reflects it in the condition evaluation. For example, if the color of the product is like new, the photo analysis unit reflects this information in the condition evaluation. The photo analysis unit can also detect the change in texture if the texture of the product has changed and reflect this in the condition evaluation. For example, if the texture of the product is like new, the photo analysis unit reflects this information in the condition evaluation. This allows the photo analysis unit to accurately evaluate the color and texture of the product. Some or all of the above-mentioned processing in the photo analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the photo analysis unit can input data on the color and texture of the product into the generation AI and have the generation AI perform an evaluation of the color and texture.

[0035] During photo analysis, the photo analysis unit can analyze the product's background information and estimate the product's usage environment. For example, if there are many outdoor elements in the product's background, the photo analysis unit estimates that the product was used outdoors. For example, if there are many indoor elements in the product's background, the photo analysis unit estimates that the product was used indoors. Furthermore, if the logo of a specific brand or store appears in the product's background, the photo analysis unit can use that information to estimate the usage environment. For example, if the logo of a specific brand appears in the product's background, the photo analysis unit estimates that the product was purchased at that brand's store. This allows the photo analysis unit to accurately estimate the product's usage environment. Some or all of the above-described processing in the photo analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the photo analysis unit can input product background information data into a generation AI and have the generation AI execute a usage environment estimation.

[0036] During photo analysis, the photo analysis unit can analyze a video of the product and evaluate its operating status. For example, the photo analysis unit analyzes a video of the product's operation and evaluates whether it is operating normally. For example, the photo analysis unit analyzes a video of the product's operation and evaluates whether there is any abnormal operation. The photo analysis unit can also analyze a video of the product's operation and evaluate whether there is any operating sound or vibration. For example, the photo analysis unit analyzes a video of the product's operation and evaluates whether there is any normal operating sound. This allows the photo analysis unit to accurately evaluate the operating status of the product. Some or all of the above-mentioned processing in the photo analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the photo analysis unit can input data of the video of the product's operation into a generation AI and have the generation AI evaluate the operating status.

[0037] The photo analysis unit can generate a 3D model of the product during photo analysis and perform a detailed condition evaluation. The photo analysis unit, for example, generates a 3D model from a photo of the product and evaluates the overall condition. For example, the photo analysis unit generates a 3D model of the product and evaluates the detailed condition. The photo analysis unit can also generate a 3D model of the product and identify abnormalities. For example, the photo analysis unit generates a 3D model of the product and identifies abnormalities. This allows the photo analysis unit to evaluate the detailed condition of the product using the 3D model. Some or all of the above-mentioned processing in the photo analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the photo analysis unit can input photo data of the product into a generation AI and cause the generation AI to generate a 3D model.

[0038] During information analysis, the information analysis unit can estimate the original price based on the year and country of manufacture of the product. For example, if the year of manufacture of the product is old, the information analysis unit estimates the original price lower. For example, if the country of manufacture of the product is a country that produces high-quality products, the information analysis unit estimates the original price higher. The information analysis unit can also estimate the original price by comprehensively considering information on the year and country of manufacture of the product. For example, the information analysis unit estimates the original price based on information on the year and country of manufacture of the product. This allows the information analysis unit to accurately estimate the original price by considering the year and country of manufacture of the product. Some or all of the above-mentioned processing in the information analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the information analysis unit can input data on the year and country of manufacture of the product into the generation AI and cause the generation AI to estimate the original price.

[0039] During information analysis, the information analysis unit can analyze the product usage history in detail and evaluate the usage status. For example, if the product is used frequently, the information analysis unit evaluates the usage status as bad. For example, if the product is used infrequently, the information analysis unit evaluates the usage status as good. The information analysis unit can also analyze the product usage history in detail and comprehensively evaluate the usage status. For example, the information analysis unit evaluates the usage status based on the product usage history. This allows the information analysis unit to analyze the product usage history in detail and accurately evaluate the usage status. Some or all of the above-mentioned processing in the information analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the information analysis unit can input data on the product usage history to the generation AI and have the generation AI evaluate the usage status.

[0040] The information analysis unit can estimate the original price based on the presence or absence of product accessories and original packaging during information analysis. For example, if the product accessories are all present, the information analysis unit will estimate a higher original price. For example, if the product has its original packaging, the information analysis unit will estimate a higher original price. The information analysis unit can also estimate the original price by comprehensively considering the presence or absence of product accessories and original packaging. For example, the information analysis unit will estimate the original price based on information about the product accessories and original packaging. This allows the information analysis unit to accurately estimate the original price by considering the presence or absence of product accessories and original packaging. Some or all of the above processing in the information analysis unit may be performed using AI, for example, or without AI. For example, the information analysis unit can input data on product accessories and original packaging into a generating AI and have the generating AI perform the estimation of the original price.

[0041] The information analysis unit can evaluate the usage status of a product based on its repair history during information analysis. For example, if a product has a repair history, the information analysis unit will evaluate the usage status as poor. For example, if a product has no repair history, the information analysis unit will evaluate the usage status as good. The information analysis unit can also analyze the product's repair history in detail and comprehensively evaluate the usage status. For example, the information analysis unit will evaluate the usage status based on the product's repair history. This allows the information analysis unit to accurately evaluate the usage status while taking the product's repair history into consideration. Some or all of the above processing in the information analysis unit may be performed using AI, for example, or without AI. For example, the information analysis unit can input the product's repair history data into a generating AI and have the generating AI perform the usage status evaluation.

[0042] The market analysis unit can evaluate demand based on regional market trends during market analysis. For example, if demand is high in a particular region, the market analysis unit will evaluate demand by emphasizing the market trends in that region. For example, if demand is low in a particular region, the market analysis unit will evaluate demand by emphasizing the market trends in that region. The market analysis unit can also evaluate demand by comprehensively considering regional market trends. For example, the market analysis unit will evaluate demand based on regional market trend data. This allows the market analysis unit to accurately evaluate demand by considering regional market trends. Some or all of the above processing in the market analysis unit may be performed using AI, for example, or without AI. For example, the market analysis unit can input regional market trend data into a generating AI and have the generating AI perform the demand evaluation.

[0043] The market analysis unit can predict demand fluctuations based on seasons and events during market analysis. For example, the market analysis unit can predict and evaluate demand fluctuations on a seasonal basis. For example, the market analysis unit can predict and evaluate demand fluctuations based on specific events. The market analysis unit can also predict demand fluctuations by comprehensively considering seasonal and event information. For example, the market analysis unit can predict demand fluctuations based on seasonal and event data. This allows the market analysis unit to accurately predict demand fluctuations based on seasons and events. Some or all of the above-described processes in the market analysis unit may be performed using AI, for example, or without AI. For example, the market analysis unit can input seasonal and event data into a generating AI and have the generating AI perform demand fluctuation predictions.

[0044] During market analysis, the market analysis unit can evaluate demand based on data from an online marketplace. The market analysis unit, for example, evaluates demand based on transaction data from an online marketplace. For example, the market analysis unit evaluates demand based on price trends in an online marketplace. The market analysis unit can also evaluate demand based on reviews and ratings from an online marketplace. For example, the market analysis unit evaluates demand based on reviews and ratings from an online marketplace. This allows the market analysis unit to accurately evaluate demand based on data from an online marketplace. Some or all of the above-described processing in the market analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the market analysis unit can input data from an online marketplace into a generation AI and cause the generation AI to evaluate demand.

[0045] The market analysis unit can predict demand by analyzing social media trends during market analysis. The market analysis unit predicts demand based on, for example, the number of social media posts and hashtag trends. For example, the market analysis unit predicts demand based on posts by social media influencers. The market analysis unit can also predict demand based on reactions from social media users. For example, the market analysis unit predicts demand based on reactions from social media users. This allows the market analysis unit to accurately predict demand based on social media trends. Some or all of the above-mentioned processing in the market analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the market analysis unit can input social media trend data into the generation AI and cause the generation AI to perform demand prediction.

[0046] The price calculation unit can calculate a fair price based on the rarity of the product when calculating the price. For example, if the product is rare, the price calculation unit calculates a higher fair price. For example, if the product is common, the price calculation unit calculates a normal fair price. The price calculation unit can also calculate a fair price by comprehensively considering the rarity of the product. For example, the price calculation unit calculates a fair price based on data on the rarity of the product. This allows the price calculation unit to accurately calculate a fair price by considering the rarity of the product. Some or all of the above-mentioned processing in the price calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the price calculation unit can input data on the rarity of the product into a generation AI and have the generation AI calculate the fair price.

[0047] When calculating the price, the price calculation unit can predict the future value of the product and calculate the fair price. For example, if the future value of the product is predicted to be high, the price calculation unit calculates a higher fair price. For example, if the future value of the product is predicted to be low, the price calculation unit calculates a lower fair price. The price calculation unit can also calculate the fair price by comprehensively predicting the future value of the product. For example, the price calculation unit calculates the fair price based on data on the future value of the product. This allows the price calculation unit to predict the future value of the product and accurately calculate the fair price. Some or all of the above-mentioned processing in the price calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the price calculation unit can input data on the future value of the product into a generation AI and have the generation AI calculate the fair price.

[0048] The pricing unit can calculate a fair price based on the insurable value of the product during price calculation. For example, if the product has insurable value, the pricing unit will calculate a higher fair price. For example, if the product has no insurable value, the pricing unit will calculate a fair price normally. The pricing unit can also calculate a fair price by comprehensively considering the insurable value of the product. For example, the pricing unit will calculate a fair price based on the insurable value data of the product. This allows the pricing unit to accurately calculate a fair price by considering the insurable value of the product. Some or all of the above processing in the pricing unit may be performed using AI, for example, or without AI. For example, the pricing unit can input the insurable value data of the product into a generating AI and have the generating AI perform the calculation of the fair price.

[0049] The price calculation unit can calculate an appropriate price based on the ecological footprint of the product when calculating the price. For example, if the ecological footprint of the product is low, the price calculation unit will calculate a higher appropriate price. For example, if the ecological footprint of the product is high, the price calculation unit will calculate a lower appropriate price. The price calculation unit can also calculate an appropriate price by comprehensively considering the ecological footprint of the product. For example, the price calculation unit will calculate an appropriate price based on the ecological footprint data of the product. This allows the price calculation unit to accurately calculate an appropriate price by considering the ecological footprint of the product. Some or all of the above processing in the price calculation unit may be performed using AI, for example, or without using AI. For example, the price calculation unit can input the ecological footprint data of the product into a generating AI and have the generating AI perform the calculation of the appropriate price.

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

[0051] The reused product trading system may also include a recommendation unit that analyzes a user's purchasing history and makes recommendations based on past purchasing trends. For example, the recommendation unit may analyze the brands and categories of products that the user has purchased in the past and recommend products from similar brands and categories. The recommendation unit may also analyze a user's purchasing frequency and purchase timing to make recommendations at appropriate times. Furthermore, the recommendation unit may recommend products similar to products that the user has given a high rating to, based on the user's ratings and reviews. This allows the reused product trading system to improve the user's purchasing experience and encourage repeat purchases.

[0052] The reused product trading system may also include a recommendation unit that analyzes a user's purchasing history and makes recommendations based on past purchasing trends. For example, the recommendation unit may analyze the brands and categories of products that the user has purchased in the past and recommend products from similar brands and categories. The recommendation unit may also analyze a user's purchasing frequency and purchase timing to make recommendations at appropriate times. Furthermore, the recommendation unit may recommend products similar to products that the user has given a high rating to, based on the user's ratings and reviews. This allows the reused product trading system to improve the user's purchasing experience and encourage repeat purchases.

[0053] The reused product trading system may also include a recommendation unit that analyzes a user's purchasing history and makes recommendations based on past purchasing trends. For example, the recommendation unit may analyze the brands and categories of products that the user has purchased in the past and recommend products from similar brands and categories. The recommendation unit may also analyze a user's purchasing frequency and purchase timing to make recommendations at appropriate times. Furthermore, the recommendation unit may recommend products similar to products that the user has given a high rating to, based on the user's ratings and reviews. This allows the reused product trading system to improve the user's purchasing experience and encourage repeat purchases.

[0054] The reused product trading system may also include a recommendation unit that analyzes a user's purchasing history and makes recommendations based on past purchasing trends. For example, the recommendation unit may analyze the brands and categories of products that the user has purchased in the past and recommend products from similar brands and categories. The recommendation unit may also analyze a user's purchasing frequency and purchase timing to make recommendations at appropriate times. Furthermore, the recommendation unit may recommend products similar to products that the user has given a high rating to, based on the user's ratings and reviews. This allows the reused product trading system to improve the user's purchasing experience and encourage repeat purchases.

[0055] The reused product trading system may also include a recommendation unit that analyzes a user's purchasing history and makes recommendations based on past purchasing trends. For example, the recommendation unit may analyze the brands and categories of products that the user has purchased in the past and recommend products from similar brands and categories. The recommendation unit may also analyze a user's purchasing frequency and purchase timing to make recommendations at appropriate times. Furthermore, the recommendation unit may recommend products similar to products that the user has given a high rating to, based on the user's ratings and reviews. This allows the reused product trading system to improve the user's purchasing experience and encourage repeat purchases.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The photo analysis unit analyzes the product photo to determine the product's condition. For example, it detects whether there are scratches or stains on the product's exterior, and detects changes in the product's color and texture, and reflects these in the condition assessment. It can also analyze the product's background information to estimate the environment in which the product was used. For example, if there are many outdoor elements in the product's background, it is assumed that the product was used outdoors. Step 2: The information analysis unit analyzes the product's detailed information based on the product's condition determined by the photo analysis unit, and determines the original price and usage status. For example, the original price is estimated from the product's brand name and model number, and usage status is determined from the purchase date and frequency of use. The original price is also estimated taking into account the product's year of manufacture and country of manufacture, and usage status is evaluated through a detailed analysis of the product's usage history. Step 3: The market analysis unit analyzes past transaction data and current market trends based on the original price and usage status determined by the information analysis unit. For example, it analyzes the price at which products of the same brand and model number have been traded in the past and the current demand in the market. It also predicts fluctuations in demand based on regional market trends, seasons, and events. Step 4: The pricing unit calculates a fair price based on market trends analyzed by the market analysis unit. For example, it calculates a fair price by considering the rarity, future value, and insurance value of the product. This streamlines the trading of reused goods and improves the satisfaction of both buyers and sellers.

[0058] (Example 2) The reuse product trading system according to an embodiment of the present invention is a system in which buyers and sellers of reuse products register the items they want, and the system automatically determines a fair price based on the original price, usage status, and market demand of the product from information such as photographs. When a user registers a reuse product, the system inputs photographs and detailed information about the product. Next, the AI ​​analyzes this information and determines the original price, usage status, and market demand for the product. Finally, the AI ​​automatically calculates a fair price based on these determination results. For example, when a user registers a reuse product, they input photographs and detailed information about the product. For example, they input information such as the product's brand name, model number, purchase date, and frequency of use. This information is input to the AI. Next, the AI ​​analyzes the input information. The AI ​​analyzes the product's photographs and determines the condition of the product. For example, it determines whether there are scratches or stains on the product's exterior. It also determines the product's original price and usage status based on the product's detailed information. For example, it estimates the original price from the product's brand name and model number, and determines the usage status from the purchase date and frequency of use. Furthermore, the AI ​​analyzes market demand. The AI ​​determines the demand for the product based on past transaction data and current market trends. For example, the system analyzes the prices at which similar branded and model-numbered products have been traded in the past, and the current market demand. Finally, the AI ​​automatically calculates a fair price based on these findings. For instance, it considers the original price, condition, and market needs of the product to determine a fair selling price. This system allows buyers and sellers to easily determine the fair price of a product, facilitating smooth transactions. This system streamlines the trading of used goods and improves satisfaction for both buyers and sellers. For example, buyers can purchase products at a fair price, and sellers can sell products at a fair price. Furthermore, because the AI ​​automatically determines the price, transaction transparency is improved, and unfair pricing can be prevented. In this way, the used goods trading system can streamline the trading of used goods and improve satisfaction for both buyers and sellers.

[0059] The reuse product trading system according to this embodiment comprises a photo analysis unit, an information analysis unit, a market analysis unit, and a price calculation unit. The photo analysis unit analyzes photographs of products to determine the condition of the products. For example, the photo analysis unit determines whether there are scratches or stains on the exterior of the product. The photo analysis unit can also determine the color and texture of the product and reflect this in the condition evaluation. For example, if the color of the product has faded, it detects the change in color and reflects this in the condition evaluation. Similarly, if the texture of the product has changed, it can detect the change in texture and reflect this in the condition evaluation. Furthermore, the photo analysis unit can analyze background information of the product to estimate the environment in which the product was used. For example, if the background of the product contains many outdoor elements, it can be estimated that it was used outdoors. Similarly, if the background of the product contains many indoor elements, it can be estimated that it was used indoors. The information analysis unit analyzes detailed product information to determine the original price and usage status. For example, the information analysis unit estimates the original price from the brand name and model number of the product and determines the usage status from the purchase date and frequency of use. The information analysis unit can also estimate the original price by considering the year and country of manufacture of the product. For example, if a product is old, the original price may be estimated lower. Conversely, if the country of manufacture is known for producing high-quality products, the original price may be estimated higher. Furthermore, the information analysis department can also analyze the product's usage history in detail and evaluate its usage status. For example, if a product is used frequently, the usage status may be evaluated as poor. Conversely, if a product is used infrequently, the usage status may be evaluated as good. The market analysis department analyzes market needs based on past transaction data and current market trends. For example, the market analysis department analyzes how much products of the same brand and model number have been traded for in the past and how much demand there is in the current market. The market analysis department can also evaluate demand by considering market trends in each region. For example, if demand is high in a particular region, the market trends of that region will be emphasized when evaluating demand. Conversely, if demand is low in a particular region, the market trends of that region will be emphasized when evaluating demand. Furthermore, the market analysis department can also predict fluctuations in demand based on seasons and events. For example, it can predict and evaluate seasonal fluctuations in demand. It can also predict and evaluate fluctuations in demand based on specific events.The pricing unit comprehensively considers the original price, usage status, and market needs of the product to determine an appropriate selling price. For example, the pricing unit calculates an appropriate price by considering the rarity of the product. It can also calculate an appropriate price by predicting the future value of the product. Furthermore, the pricing unit can also calculate an appropriate price by considering the insurable value of the product. For example, if the product has insurable value, the appropriate price is calculated to be higher. If the product does not have insurable value, the appropriate price can be calculated normally. As a result, the reuse product trading system according to this embodiment can streamline the trading of reuse products and improve the satisfaction of both buyers and sellers.

[0060] The photo analysis unit can analyze product photos to determine whether the product's appearance is damaged or dirty. For example, the photo analysis unit can analyze product photos at high resolution to determine the presence or absence of damage or dirt. For example, the photo analysis unit can detect whether there are small scratches on the surface of the product. The photo analysis unit can also detect whether there is dirt on the surface of the product. Furthermore, the photo analysis unit can analyze the entire appearance of the product to identify the extent of damage or dirt. For example, the photo analysis unit can scan the entire surface of the product to identify the location and extent of damage or dirt. This allows the photo analysis unit to evaluate the condition of the product's appearance in detail. Some or all of the above processing in the photo analysis unit may be performed using AI, for example, or without AI. For example, the photo analysis unit can input product photo data into a generating AI and have the generating AI perform the detection of damage or dirt.

[0061] The information analysis unit can estimate the original price from the product's brand name and model number, and determine the usage status from the purchase date and frequency of use. The information analysis unit estimates the original price based on the product's brand name and model number, for example. For example, if the product's brand name is a luxury brand, the information analysis unit can estimate the original price higher. Also, if the product's model number is the latest model, the information analysis unit can estimate the original price higher. Furthermore, the information analysis unit determines the usage status based on the product's purchase date and usage frequency. For example, if the product was recently purchased, the information analysis unit can determine that the usage status is good. Also, if the product is used infrequently, the information analysis unit can determine that the usage status is good. This allows the information analysis unit to accurately evaluate the product's original price and usage status. Some or all of the above-mentioned processing in the information analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the information analysis unit can input data such as the product's brand name, model number, purchase date, and usage frequency into the generation AI, and have the generation AI evaluate the original price and usage status.

[0062] The market analysis unit can analyze the past trade prices of products of the same brand and model number and the current market demand for the product based on past transaction data and current market trends. The market analysis unit, for example, analyzes the transaction prices of products based on past transaction data. For example, the market analysis unit analyzes the past trade prices of products of the same brand and model number. The market analysis unit can also analyze product demand based on current market trends. For example, the market analysis unit analyzes the current market demand for products of the same brand and model number. Furthermore, the market analysis unit can evaluate demand taking into account regional market trends. For example, if demand in a specific region is high, the market analysis unit can evaluate demand by focusing on the market trends of that region. Also, if demand in a specific region is low, the market analysis unit can evaluate demand by focusing on the market trends of that region. This allows the market analysis unit to accurately evaluate product demand based on market demand and past transaction data. Some or all of the above-described processing in the market analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the market analysis unit can input past transaction data and current market trend data into the generation AI and have the generation AI perform demand assessment.

[0063] The price calculation unit can present a fair selling price based on a comprehensive consideration of the original price, usage status, and market needs of the product. The price calculation unit, for example, calculates a fair selling price by comprehensively considering the original price, usage status, and market needs of the product. For example, if the original price of the product is high, the price calculation unit calculates a higher fair price. Also, if the usage status of the product is good, the price calculation unit can calculate a higher fair price. Furthermore, if market demand is high, the price calculation unit can calculate a higher fair price. This allows the price calculation unit to automatically calculate a fair price of the product. Some or all of the above-mentioned processing in the price calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the price calculation unit can input data on the original price, usage status, and market needs of the product into a generation AI and have the generation AI calculate a fair price.

[0064] The reused goods trading system includes a photo analysis unit that estimates a user's emotions and adjusts the accuracy of photo analysis based on the estimated user emotions. The photo analysis unit, for example, estimates the user's emotions and adjusts the accuracy of photo analysis based on the estimated user emotions. For example, if the user is stressed, the photo analysis unit increases the accuracy of the photo analysis and performs a detailed condition assessment. Alternatively, if the user is relaxed, the photo analysis unit may maintain the accuracy of the photo analysis at normal levels and perform a standard condition assessment. Furthermore, if the user is in a hurry, the photo analysis unit may decrease the accuracy of the photo analysis and perform a quick condition assessment. This allows the photo analysis unit to adjust the accuracy of the photo analysis according to the user's emotions. The estimation of the user's emotions is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the photo analysis unit may be performed using AI, or may be performed without AI. For example, the photo analysis unit may input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0065] The photo analysis unit can determine the color and texture of the product during photo analysis and reflect this in the condition evaluation. For example, if the color of the product has faded, the photo analysis unit detects the color change and reflects it in the condition evaluation. For example, if the color of the product is like new, the photo analysis unit reflects this information in the condition evaluation. The photo analysis unit can also detect the change in texture if the texture of the product has changed and reflect this in the condition evaluation. For example, if the texture of the product is like new, the photo analysis unit reflects this information in the condition evaluation. This allows the photo analysis unit to accurately evaluate the color and texture of the product. Some or all of the above-mentioned processing in the photo analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the photo analysis unit can input data on the color and texture of the product into the generation AI and have the generation AI perform an evaluation of the color and texture.

[0066] During photo analysis, the photo analysis unit can analyze the product's background information and estimate the product's usage environment. For example, if there are many outdoor elements in the product's background, the photo analysis unit estimates that the product was used outdoors. For example, if there are many indoor elements in the product's background, the photo analysis unit estimates that the product was used indoors. Furthermore, if the logo of a specific brand or store appears in the product's background, the photo analysis unit can use that information to estimate the usage environment. For example, if the logo of a specific brand appears in the product's background, the photo analysis unit estimates that the product was purchased at that brand's store. This allows the photo analysis unit to accurately estimate the product's usage environment. Some or all of the above-described processing in the photo analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the photo analysis unit can input product background information data into a generation AI and have the generation AI execute a usage environment estimation.

[0067] The photo analysis unit can estimate the user's emotions and determine the priority of photo analysis based on the estimated user's emotions. For example, if the user is in a hurry, the photo analysis unit prioritizes photo analysis of important parts. For example, if the user is relaxed, the photo analysis unit performs overall photo analysis. Furthermore, if the user is stressed, the photo analysis unit can prioritize detailed photo analysis. This allows the photo analysis unit to adjust the priority of photo analysis according to the user's emotions. The estimation of the user's emotions is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the photo analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the photo analysis unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0068] During photo analysis, the photo analysis unit can analyze a video of the product and evaluate its operating status. For example, the photo analysis unit analyzes a video of the product's operation and evaluates whether it is operating normally. For example, the photo analysis unit analyzes a video of the product's operation and evaluates whether there is any abnormal operation. The photo analysis unit can also analyze a video of the product's operation and evaluate whether there is any operating sound or vibration. For example, the photo analysis unit analyzes a video of the product's operation and evaluates whether there is any normal operating sound. This allows the photo analysis unit to accurately evaluate the operating status of the product. Some or all of the above-mentioned processing in the photo analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the photo analysis unit can input data of the video of the product's operation into a generation AI and have the generation AI evaluate the operating status.

[0069] The photo analysis unit can generate a 3D model of the product during photo analysis and perform a detailed condition evaluation. The photo analysis unit, for example, generates a 3D model from a photo of the product and evaluates the overall condition. For example, the photo analysis unit generates a 3D model of the product and evaluates the detailed condition. The photo analysis unit can also generate a 3D model of the product and identify abnormalities. For example, the photo analysis unit generates a 3D model of the product and identifies abnormalities. This allows the photo analysis unit to evaluate the detailed condition of the product using the 3D model. Some or all of the above-mentioned processing in the photo analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the photo analysis unit can input photo data of the product into a generation AI and cause the generation AI to generate a 3D model.

[0070] The information analysis unit can estimate the user's emotions and adjust the information analysis method based on the estimated user's emotions. For example, if the user is feeling stressed, the information analysis unit increases the accuracy of the information analysis and performs a detailed analysis. For example, if the user is relaxed, the information analysis unit maintains the accuracy of the information analysis at normal levels and performs a standard analysis. Furthermore, if the user is in a hurry, the information analysis unit can reduce the accuracy of the information analysis and perform a quick analysis. This allows the information analysis unit to adjust the information analysis method according to the user's emotions. The estimation of the user's emotions is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the information analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the information analysis unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0071] During information analysis, the information analysis unit can estimate the original price based on the year and country of manufacture of the product. For example, if the year of manufacture of the product is old, the information analysis unit estimates the original price lower. For example, if the country of manufacture of the product is a country that produces high-quality products, the information analysis unit estimates the original price higher. The information analysis unit can also estimate the original price by comprehensively considering information on the year and country of manufacture of the product. For example, the information analysis unit estimates the original price based on information on the year and country of manufacture of the product. This allows the information analysis unit to accurately estimate the original price by considering the year and country of manufacture of the product. Some or all of the above-mentioned processing in the information analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the information analysis unit can input data on the year and country of manufacture of the product into the generation AI and cause the generation AI to estimate the original price.

[0072] During information analysis, the information analysis unit can analyze the product usage history in detail and evaluate the usage status. For example, if the product is used frequently, the information analysis unit evaluates the usage status as bad. For example, if the product is used infrequently, the information analysis unit evaluates the usage status as good. The information analysis unit can also analyze the product usage history in detail and comprehensively evaluate the usage status. For example, the information analysis unit evaluates the usage status based on the product usage history. This allows the information analysis unit to analyze the product usage history in detail and accurately evaluate the usage status. Some or all of the above-mentioned processing in the information analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the information analysis unit can input data on the product usage history to the generation AI and have the generation AI evaluate the usage status.

[0073] The information analysis unit can estimate the user's emotions and determine the priority of information analysis based on the estimated user's emotions. For example, if the user is in a hurry, the information analysis unit prioritizes the analysis of important information. For example, if the user is relaxed, the information analysis unit can perform a comprehensive information analysis. Furthermore, if the user is feeling stressed, the information analysis unit can prioritize detailed information analysis. This allows the information analysis unit to adjust the priority of information analysis according to the user's emotions. The estimation of the user's emotions is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the information analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the information analysis unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0074] The information analysis unit can estimate the original price based on the presence or absence of product accessories and original packaging during information analysis. For example, if the product accessories are all present, the information analysis unit will estimate a higher original price. For example, if the product has its original packaging, the information analysis unit will estimate a higher original price. The information analysis unit can also estimate the original price by comprehensively considering the presence or absence of product accessories and original packaging. For example, the information analysis unit will estimate the original price based on information about the product accessories and original packaging. This allows the information analysis unit to accurately estimate the original price by considering the presence or absence of product accessories and original packaging. Some or all of the above processing in the information analysis unit may be performed using AI, for example, or without AI. For example, the information analysis unit can input data on product accessories and original packaging into a generating AI and have the generating AI perform the estimation of the original price.

[0075] The information analysis unit can evaluate the usage status of a product based on its repair history during information analysis. For example, if a product has a repair history, the information analysis unit will evaluate the usage status as poor. For example, if a product has no repair history, the information analysis unit will evaluate the usage status as good. The information analysis unit can also analyze the product's repair history in detail and comprehensively evaluate the usage status. For example, the information analysis unit will evaluate the usage status based on the product's repair history. This allows the information analysis unit to accurately evaluate the usage status while taking the product's repair history into consideration. Some or all of the above processing in the information analysis unit may be performed using AI, for example, or without AI. For example, the information analysis unit can input the product's repair history data into a generating AI and have the generating AI perform the usage status evaluation.

[0076] The market analysis unit can estimate user emotions and adjust its market analysis methods based on the estimated user emotions. For example, if a user is stressed, the market analysis unit can increase the accuracy of its market analysis and perform a more detailed analysis. For example, if a user is relaxed, the market analysis unit can maintain normal accuracy and perform a standard analysis. Furthermore, if a user is in a hurry, the market analysis unit can reduce the accuracy of its market analysis and perform a faster analysis. In this way, the market analysis unit can adjust its market analysis methods according to user emotions. User emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the market analysis unit may be performed using AI, for example, or not using AI. For example, the market analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0077] The market analysis unit can evaluate demand based on regional market trends during market analysis. For example, if demand is high in a particular region, the market analysis unit will evaluate demand by emphasizing the market trends in that region. For example, if demand is low in a particular region, the market analysis unit will evaluate demand by emphasizing the market trends in that region. The market analysis unit can also evaluate demand by comprehensively considering regional market trends. For example, the market analysis unit will evaluate demand based on regional market trend data. This allows the market analysis unit to accurately evaluate demand by considering regional market trends. Some or all of the above processing in the market analysis unit may be performed using AI, for example, or without AI. For example, the market analysis unit can input regional market trend data into a generating AI and have the generating AI perform the demand evaluation.

[0078] The market analysis unit can predict demand fluctuations based on seasons and events during market analysis. For example, the market analysis unit can predict and evaluate demand fluctuations on a seasonal basis. For example, the market analysis unit can predict and evaluate demand fluctuations based on specific events. The market analysis unit can also predict demand fluctuations by comprehensively considering seasonal and event information. For example, the market analysis unit can predict demand fluctuations based on seasonal and event data. This allows the market analysis unit to accurately predict demand fluctuations based on seasons and events. Some or all of the above-described processes in the market analysis unit may be performed using AI, for example, or without AI. For example, the market analysis unit can input seasonal and event data into a generating AI and have the generating AI perform demand fluctuation predictions.

[0079] The market analysis unit can estimate a user's emotions and determine the priority of market analysis based on the estimated user emotions. For example, if a user is in a hurry, the market analysis unit prioritizes the analysis of important market information. For example, if a user is relaxed, the market analysis unit can perform a comprehensive market analysis. Furthermore, if a user is stressed, the market analysis unit can prioritize a detailed market analysis. This allows the market analysis unit to adjust the priority of market analysis according to the user's emotions. The estimation of a user's emotions is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the market analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the market analysis unit can input user emotion data into the generation AI and have the generation AI perform emotion estimation.

[0080] During market analysis, the market analysis unit can evaluate demand based on data from an online marketplace. The market analysis unit, for example, evaluates demand based on transaction data from an online marketplace. For example, the market analysis unit evaluates demand based on price trends in an online marketplace. The market analysis unit can also evaluate demand based on reviews and ratings from an online marketplace. For example, the market analysis unit evaluates demand based on reviews and ratings from an online marketplace. This allows the market analysis unit to accurately evaluate demand based on data from an online marketplace. Some or all of the above-described processing in the market analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the market analysis unit can input data from an online marketplace into a generation AI and cause the generation AI to evaluate demand.

[0081] The market analysis unit can predict demand by analyzing social media trends during market analysis. The market analysis unit predicts demand based on, for example, the number of social media posts and hashtag trends. For example, the market analysis unit predicts demand based on posts by social media influencers. The market analysis unit can also predict demand based on reactions from social media users. For example, the market analysis unit predicts demand based on reactions from social media users. This allows the market analysis unit to accurately predict demand based on social media trends. Some or all of the above-mentioned processing in the market analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the market analysis unit can input social media trend data into the generation AI and cause the generation AI to perform demand prediction.

[0082] The price calculation unit can estimate the user's emotions and adjust the price calculation method based on the estimated user emotions. For example, if the user is feeling stressed, the price calculation unit increases the accuracy of the price calculation and performs a detailed calculation. For example, if the user is relaxed, the price calculation unit maintains the accuracy of the price calculation at normal levels and performs a standard calculation. Furthermore, if the user is in a hurry, the price calculation unit can reduce the accuracy of the price calculation and perform a quick calculation. This allows the price calculation unit to adjust the price calculation method according to the user's emotions. The estimation of the user's emotions is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the price calculation unit may be performed using an AI, for example, or without an AI. For example, the price calculation unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0083] The price calculation unit can calculate a fair price based on the rarity of the product when calculating the price. For example, if the product is rare, the price calculation unit calculates a higher fair price. For example, if the product is common, the price calculation unit calculates a normal fair price. The price calculation unit can also calculate a fair price by comprehensively considering the rarity of the product. For example, the price calculation unit calculates a fair price based on data on the rarity of the product. This allows the price calculation unit to accurately calculate a fair price by considering the rarity of the product. Some or all of the above-mentioned processing in the price calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the price calculation unit can input data on the rarity of the product into a generation AI and have the generation AI calculate the fair price.

[0084] When calculating the price, the price calculation unit can predict the future value of the product and calculate the fair price. For example, if the future value of the product is predicted to be high, the price calculation unit calculates a higher fair price. For example, if the future value of the product is predicted to be low, the price calculation unit calculates a lower fair price. The price calculation unit can also calculate the fair price by comprehensively predicting the future value of the product. For example, the price calculation unit calculates the fair price based on data on the future value of the product. This allows the price calculation unit to predict the future value of the product and accurately calculate the fair price. Some or all of the above-mentioned processing in the price calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the price calculation unit can input data on the future value of the product into a generation AI and have the generation AI calculate the fair price.

[0085] The price calculation unit can estimate the user's emotions and determine the priority of price calculation based on the estimated user's emotions. For example, if the user is in a hurry, the price calculation unit prioritizes the calculation of important price information. For example, if the user is relaxed, the price calculation unit performs overall price calculation. Furthermore, if the user is stressed, the price calculation unit can prioritize detailed price calculation. This allows the price calculation unit to adjust the priority of price calculation according to the user's emotions. The estimation of the user's emotions is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the price calculation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the price calculation unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0086] The pricing unit can calculate a fair price based on the insurable value of the product during price calculation. For example, if the product has insurable value, the pricing unit will calculate a higher fair price. For example, if the product has no insurable value, the pricing unit will calculate a fair price normally. The pricing unit can also calculate a fair price by comprehensively considering the insurable value of the product. For example, the pricing unit will calculate a fair price based on the insurable value data of the product. This allows the pricing unit to accurately calculate a fair price by considering the insurable value of the product. Some or all of the above processing in the pricing unit may be performed using AI, for example, or without AI. For example, the pricing unit can input the insurable value data of the product into a generating AI and have the generating AI perform the calculation of the fair price.

[0087] The price calculation unit can calculate an appropriate price based on the ecological footprint of the product when calculating the price. For example, if the ecological footprint of the product is low, the price calculation unit will calculate a higher appropriate price. For example, if the ecological footprint of the product is high, the price calculation unit will calculate a lower appropriate price. The price calculation unit can also calculate an appropriate price by comprehensively considering the ecological footprint of the product. For example, the price calculation unit will calculate an appropriate price based on the ecological footprint data of the product. This allows the price calculation unit to accurately calculate an appropriate price by considering the ecological footprint of the product. Some or all of the above processing in the price calculation unit may be performed using AI, for example, or without using AI. For example, the price calculation unit can input the ecological footprint data of the product into a generating AI and have the generating AI perform the calculation of the appropriate price. === Hard Collateral 1-1 === Each of the multiple elements including the photo analysis unit, information analysis unit, market analysis unit, and price calculation unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the photo analysis unit takes a photo of a product using the camera 42 of the smart device 14, and the photo is analyzed by the specific processing unit 290 of the data processing device 12. For example, the information analysis unit analyzes detailed product information by the specific processing unit 290 of the data processing device 12. For example, the market analysis unit analyzes past transaction data and market trends by the specific processing unit 290 of the data processing device 12. For example, the price calculation unit calculates a fair price by the specific processing unit 290 of the data processing device 12, comprehensively taking into consideration the original price, usage status, and market needs of the product. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned photo analysis unit, information analysis unit, market analysis unit, and price calculation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the photo analysis unit takes a photo of a product using the camera 42 of the smart glasses 214, and the photo is analyzed by the specific processing unit 290 of the data processing device 12. For example, the information analysis unit analyzes detailed product information by the specific processing unit 290 of the data processing device 12. For example, the market analysis unit analyzes past transaction data and market trends by the specific processing unit 290 of the data processing device 12. For example, the price calculation unit calculates a fair price by the specific processing unit 290 of the data processing device 12, comprehensively taking into consideration the original price, usage status, and market needs of the product. === Hard Collateral 1-3 === Each of the multiple elements including the photo analysis unit, information analysis unit, market analysis unit, and price calculation unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the photo analysis unit takes a photo of a product using the camera 42 of the headset terminal 314, and the photo is analyzed by the specific processing unit 290 of the data processing device 12. For example, the information analysis unit analyzes detailed product information by the specific processing unit 290 of the data processing device 12. For example, the market analysis unit analyzes past transaction data and market trends by the specific processing unit 290 of the data processing device 12. For example, the price calculation unit calculates a fair price by the specific processing unit 290 of the data processing device 12, comprehensively taking into consideration the original price, usage status, and market needs of the product. === Hard Collateral 1-4 === Each of the multiple elements including the photo analysis unit, information analysis unit, market analysis unit, and price calculation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the photo analysis unit takes a photo of a product using the camera 42 of the robot 414, and the photo is analyzed by the specific processing unit 290 of the data processing device 12. For example, the information analysis unit analyzes detailed product information by the specific processing unit 290 of the data processing device 12. For example, the market analysis unit analyzes past transaction data and market trends by the specific processing unit 290 of the data processing device 12. For example, the price calculation unit calculates a fair price by the specific processing unit 290 of the data processing device 12, taking into consideration the original price, usage status, and market needs of the product in a comprehensive manner.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The reused product trading system may also include a recommendation unit that analyzes a user's purchasing history and makes recommendations based on past purchasing trends. For example, the recommendation unit may analyze the brands and categories of products that the user has purchased in the past and recommend products from similar brands and categories. The recommendation unit may also analyze a user's purchasing frequency and purchase timing to make recommendations at appropriate times. Furthermore, the recommendation unit may recommend products similar to products that the user has given a high rating to, based on the user's ratings and reviews. This allows the reused product trading system to improve the user's purchasing experience and encourage repeat purchases.

[0090] The reused product trading system may also include a recommendation unit that estimates a user's emotions and adjusts the content of recommendations based on the estimated user emotions. For example, if a user is feeling stressed, the recommendation unit may recommend products or services that will help the user relax. Also, if the user is relaxed, the recommendation unit may recommend new products or trending products that pique the user's interest. Furthermore, if the user is in a hurry, the recommendation unit may recommend products that can be purchased quickly or products that are available for immediate delivery. This allows the reused product trading system to make recommendations based on the user's emotions and improve user satisfaction.

[0091] The reused product trading system may also include a recommendation unit that analyzes a user's purchasing history and makes recommendations based on past purchasing trends. For example, the recommendation unit may analyze the brands and categories of products that the user has purchased in the past and recommend products from similar brands and categories. The recommendation unit may also analyze a user's purchasing frequency and purchase timing to make recommendations at appropriate times. Furthermore, the recommendation unit may recommend products similar to products that the user has given a high rating to, based on the user's ratings and reviews. This allows the reused product trading system to improve the user's purchasing experience and encourage repeat purchases.

[0092] The reused product trading system may also include a recommendation unit that estimates a user's emotions and adjusts the content of recommendations based on the estimated user emotions. For example, if a user is feeling stressed, the recommendation unit may recommend products or services that will help the user relax. Also, if the user is relaxed, the recommendation unit may recommend new products or trending products that pique the user's interest. Furthermore, if the user is in a hurry, the recommendation unit may recommend products that can be purchased quickly or products that are available for immediate delivery. This allows the reused product trading system to make recommendations based on the user's emotions and improve user satisfaction.

[0093] The reused product trading system may also include a recommendation unit that analyzes a user's purchasing history and makes recommendations based on past purchasing trends. For example, the recommendation unit may analyze the brands and categories of products that the user has purchased in the past and recommend products from similar brands and categories. The recommendation unit may also analyze a user's purchasing frequency and purchase timing to make recommendations at appropriate times. Furthermore, the recommendation unit may recommend products similar to products that the user has given a high rating to, based on the user's ratings and reviews. This allows the reused product trading system to improve the user's purchasing experience and encourage repeat purchases.

[0094] The reused product trading system may also include a recommendation unit that estimates a user's emotions and adjusts the content of recommendations based on the estimated user emotions. For example, if a user is feeling stressed, the recommendation unit may recommend products or services that will help the user relax. Also, if the user is relaxed, the recommendation unit may recommend new products or trending products that pique the user's interest. Furthermore, if the user is in a hurry, the recommendation unit may recommend products that can be purchased quickly or products that are available for immediate delivery. This allows the reused product trading system to make recommendations based on the user's emotions and improve user satisfaction.

[0095] The reused product trading system may also include a recommendation unit that analyzes a user's purchasing history and makes recommendations based on past purchasing trends. For example, the recommendation unit may analyze the brands and categories of products that the user has purchased in the past and recommend products from similar brands and categories. The recommendation unit may also analyze a user's purchasing frequency and purchase timing to make recommendations at appropriate times. Furthermore, the recommendation unit may recommend products similar to products that the user has given a high rating to, based on the user's ratings and reviews. This allows the reused product trading system to improve the user's purchasing experience and encourage repeat purchases.

[0096] The reused product trading system may also include a recommendation unit that estimates a user's emotions and adjusts the content of recommendations based on the estimated user emotions. For example, if a user is feeling stressed, the recommendation unit may recommend products or services that will help the user relax. Also, if the user is relaxed, the recommendation unit may recommend new products or trending products that pique the user's interest. Furthermore, if the user is in a hurry, the recommendation unit may recommend products that can be purchased quickly or products that are available for immediate delivery. This allows the reused product trading system to make recommendations based on the user's emotions and improve user satisfaction.

[0097] The reused product trading system may also include a recommendation unit that analyzes a user's purchasing history and makes recommendations based on past purchasing trends. For example, the recommendation unit may analyze the brands and categories of products that the user has purchased in the past and recommend products from similar brands and categories. The recommendation unit may also analyze a user's purchasing frequency and purchase timing to make recommendations at appropriate times. Furthermore, the recommendation unit may recommend products similar to products that the user has given a high rating to, based on the user's ratings and reviews. This allows the reused product trading system to improve the user's purchasing experience and encourage repeat purchases.

[0098] The reused product trading system may also include a recommendation unit that estimates a user's emotions and adjusts the content of recommendations based on the estimated user emotions. For example, if a user is feeling stressed, the recommendation unit may recommend products or services that will help the user relax. Also, if the user is relaxed, the recommendation unit may recommend new products or trending products that pique the user's interest. Furthermore, if the user is in a hurry, the recommendation unit may recommend products that can be purchased quickly or products that are available for immediate delivery. This allows the reused product trading system to make recommendations based on the user's emotions and improve user satisfaction.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The photo analysis unit analyzes the product photo to determine the product's condition. For example, it detects whether there are scratches or stains on the product's exterior, and detects changes in the product's color and texture, and reflects these in the condition assessment. It can also analyze the product's background information to estimate the environment in which the product was used. For example, if there are many outdoor elements in the product's background, it is assumed that the product was used outdoors. Step 2: The information analysis unit analyzes the product's detailed information based on the product's condition determined by the photo analysis unit, and determines the original price and usage status. For example, the original price is estimated from the product's brand name and model number, and usage status is determined from the purchase date and frequency of use. The original price is also estimated taking into account the product's year of manufacture and country of manufacture, and usage status is evaluated through a detailed analysis of the product's usage history. Step 3: The market analysis unit analyzes past transaction data and current market trends based on the original price and usage status determined by the information analysis unit. For example, it analyzes the price at which products of the same brand and model number have been traded in the past and the current demand in the market. It also predicts fluctuations in demand based on regional market trends, seasons, and events. Step 4: The pricing unit calculates a fair price based on market trends analyzed by the market analysis unit. For example, it calculates a fair price by considering the rarity, future value, and insurance value of the product. This streamlines the trading of reused goods and improves the satisfaction of both buyers and sellers.

[0101] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0102] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0103] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0104] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0108] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0109] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0111] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0112] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0113] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0115] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0116] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0119] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0138] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

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

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

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

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

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

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

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

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

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

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

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

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

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

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0155] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0156] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0157] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0158] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0159] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0160] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0161] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0162] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0163] 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.

[0164] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0165] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0166] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0167] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0168] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0169] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0170] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0171] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0172] [Explanation of symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a photo analysis unit that analyzes photos of the products and determines the condition of the products; an information analysis unit that analyzes detailed information about the product based on the product condition determined by the photo analysis unit to determine the original price and usage status; a market analysis unit that analyzes past transaction data and current market trends based on the original price and usage status determined by the information analysis unit; a price calculation unit that calculates a fair price based on the market trend analyzed by the market analysis unit. A system characterized by:

2. The photo analysis unit Analyze product photos to determine if the product has any external scratches or blemishes 2. The system of claim 1.

3. The information analysis unit Estimate the original price from the product's brand name and model number, and determine usage status from the purchase date and frequency of use.

2. The system of claim 1.

4. The market analysis unit Based on past transaction data and current market trends, we analyze the prices at which products of the same brand and model number have traded in the past and the current demand in the market.

2. The system of claim 1.

5. The price calculation unit We offer a fair selling price based on the product's original price, usage status, and market needs.

2. The system of claim 1.

6. The photo analysis unit Estimate the user's emotions and adjust the accuracy of photo analysis based on the estimated user emotions.

2. The system of claim 1.

7. The photo analysis unit When analyzing photos, the color and texture of the product are determined and reflected in the condition evaluation.

2. The system of claim 1.

8. The photo analysis unit When analyzing photos, background information of the product is analyzed to estimate the product's usage environment.

2. The system of claim 1.

9. The photo analysis unit Estimate user emotions and prioritize photo analysis based on the estimated user emotions.

2. The system of claim 1.

10. The photo analysis unit During photo analysis, analyze the video of the product and evaluate its operating status 2. The system of claim 1.

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